diff --git a/.github/workflows/build_installer.yml b/.github/workflows/build_installer.yml new file mode 100644 index 000000000..ca8b0fd52 --- /dev/null +++ b/.github/workflows/build_installer.yml @@ -0,0 +1,63 @@ +name: Build installer +on: + workflow_dispatch: + inputs: + version: + description: 'Build installer for version (e.g. "1.0.4")' + required: true + type: string + release: + description: 'Upload as release artifact' + required: true + type: boolean + +jobs: + build: + name: ActivitySim-${{ matrix.OS_NAME }}-${{ matrix.ARCH }} + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + include: + - os: windows-latest + ARCH: x86_64 + TARGET_PLATFORM: win-64 + OS_NAME: "Windows" + + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - uses: conda-incubator/setup-miniconda@35d1405e78aa3f784fe3ce9a2eb378d5eeb62169 + with: + miniconda-version: "latest" + if: contains(matrix.OS_NAME, 'Windows') + + - name: Build installer + env: + ARCH: ${{ matrix.ARCH }} + OS_NAME: ${{ matrix.OS_NAME }} + TARGET_PLATFORM: ${{ matrix.TARGET_PLATFORM }} + run: | + export ACTIVITYSIM_VERSION=${{ inputs.version }} + export EXT=exe + source /c/Miniconda3/Scripts/activate; + source other_resources/installer/build_win.sh; + ls -alh build + shell: bash + + - name: Upload installer to Github artifact + uses: actions/upload-artifact@v3 + with: + path: build/Activitysim-${{ inputs.version }}-${{ matrix.OS_NAME }}-${{ matrix.ARCH }}* + name: Activitysim-${{ inputs.version }}-${{ matrix.OS_NAME }}-${{ matrix.ARCH }} + + - name: Upload installer to release + uses: svenstaro/upload-release-action@v2 + with: + repo_token: ${{ secrets.GITHUB_TOKEN }} + file: build/Activitysim* + tag: v${{ inputs.version }} + overwrite: true + file_glob: true + if: inputs.release diff --git a/.github/workflows/core_tests.yml b/.github/workflows/core_tests.yml index ef4b4acaf..6215c66da 100644 --- a/.github/workflows/core_tests.yml +++ b/.github/workflows/core_tests.yml @@ -81,6 +81,7 @@ jobs: run: | python -m pytest --pyargs activitysim.cli + cross-platform: # also test foundation cross platforms, but do not require a successful # completion before starting regional model tests @@ -162,6 +163,7 @@ jobs: run: | python -m pytest --pyargs activitysim.cli + regional_models: needs: foundation env: @@ -251,6 +253,59 @@ jobs: run: | python -m pytest activitysim/examples/${{ matrix.region }}/test --durations=0 + random_seed_generation: + needs: foundation + env: + mamba-env-prefix: /usr/share/miniconda3/envs/asim-test + python-version: 3.9 + label: linux-64 + defaults: + run: + shell: bash -l {0} + name: random_seed_generation_test + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + + - name: Setup Mambaforge + uses: conda-incubator/setup-miniconda@v2 + with: + miniforge-variant: Mambaforge + miniforge-version: latest + activate-environment: asim-test + use-mamba: true + python-version: ${{ env.python-version }} + + - name: Set cache date for year and month + run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV + + - uses: actions/cache@v2 + with: + path: ${{ env.mamba-env-prefix }} + key: ${{ env.label }}-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} + id: cache + + - name: Update environment + run: mamba env update -n asim-test -f conda-environments/github-actions-tests.yml + if: steps.cache.outputs.cache-hit != 'true' + + - name: Install activitysim + # installing without dependencies is faster, we trust that all needed dependencies + # are in the conda environment defined above. Also, this avoids pip getting + # confused and reinstalling tables (pytables). + run: | + python -m pip install -e . --no-deps + + - name: Conda checkup + run: | + mamba info -a + mamba list + + - name: Test Random Seed Generation + run: | + python -m pytest test/random_seed/test_random_seed.py --durations=0 + estimation_mode: needs: foundation env: diff --git a/.gitignore b/.gitignore index f89439651..b1c67af87 100644 --- a/.gitignore +++ b/.gitignore @@ -72,6 +72,8 @@ _test_est *_local/ *_local.* +**/__sharrowcache__ +**/skims.zarr **/output/ **/_generated_version.py docs/**/_generated diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4556a45a8..3a59ed152 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,8 +3,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.3.0 hooks: - - id: check-yaml - exclude: logging.yaml # TODO don't exclude, will require fixing logging - id: end-of-file-fixer exclude: .*\.ipynb - id: trailing-whitespace diff --git a/HOW_TO_RELEASE.md b/HOW_TO_RELEASE.md index edbfee7c2..fdfc60719 100644 --- a/HOW_TO_RELEASE.md +++ b/HOW_TO_RELEASE.md @@ -40,18 +40,17 @@ and development. If they are not updated, these environments will end up with dependencies loaded from *pip* instead of *conda-forge*. -00. Run `black` to ensure that the codebase passes minimal style checks. +00. Run black to ensure that the codebase passes all style checks. This check should only take a few seconds. These checks are also done on - GitHub Actions and are platform independent, so they should not be necessary to + Travis and are platform independent, so they should not be necessary to replicate locally, but are listed here for completeness. ```sh black --check --diff . ``` -00. Run the regular test suite on Windows. Most GitHub Actions tests are done on Linux, - Linux (it's faster to start up and run a new clean VM for testing) but most - users are on Windows, and the test suite should also be run on Windows to - ensure that it works on that platform as well. If you +00. Run the regular test suite on Windows. Travis tests are done on Linux, + but most users are on Windows, and the test suite should also be run + on Windows to ensure that it works on that platform as well. If you are not preparing this release on Windows, you should be sure to run at least through this step on a Windows machine before finalizing a release. @@ -67,7 +66,7 @@ ``` 00. Test the full-scale regional examples. These examples are big, too - large to run on GitHub Actions, and will take a lot of time (many hours) to + large to run on Travis, and will take a lot of time (many hours) to download and run. ```sh mkdir tmp-asim @@ -88,6 +87,37 @@ There are also demo notebooks for estimation, but their functionality is completely tested in the unit tests run previously. +00. Use bump2version to tag the release commit and update the + version number. The following code will generate a "patch" release, + incrementing the third value in the version number (i.e. "1.2.3" + becomes "1.2.4"). Alternatively, make a "minor" or "major" release. + The `--list` command will generate output to your console to confirm + that the old and new version numbers are what you expect, before you + push the commit (with the changed version in the code) and tags to + GitHub. + ```sh + bump2version patch --list + ``` + + It is also possible to make a development pre-release. To do so, + explicitly set the version number to the next patch plus a ".devN" + suffix: + + ```sh + bump2version patch --new-version 1.2.3.dev0 --list + ``` + + Then, when ready to make a "final" release, set the version by + explicitly removing the suffix: + ```sh + bump2version patch --new-version 1.2.3 --list + ``` + +00. Push the tagged commit to GitHub. + ```sh + git push --tags + ``` + 00. For non-development releases, open a pull request to merge the proposed release into main. The following command will open a web browser for you to create the pull request. @@ -105,10 +135,6 @@ ```sh gh release create v1.2.3 ``` - The process of creating and tagging a release will automatically - trigger various GitHub Actions scripts to build, test, and publish the - new release to PyPI and conda forge, assuming there are no errors. - For a development pre-release, include the `--prerelease` argument. As the project's policy is that only formally released code is merged to the main branch, any pre-release should also be built against a @@ -129,11 +155,3 @@ conda deactivate conda env remove -n TEMP-ASIM-DEV ``` - -00. Change the default redirect page for the ActivitySim documentation to point - to the newly released documentation. The redirect page can be edited - [here](https://github.com/ActivitySim/activitysim/blob/gh-pages/index.html). - -00. Add the new release to the `switch.json` file. Don't delete the references - for existing old documentation. The switcher can be edited - [here](https://github.com/ActivitySim/activitysim/blob/gh-pages/switcher.json). diff --git a/activitysim/abm/models/__init__.py b/activitysim/abm/models/__init__.py index 76e685563..91b802d76 100644 --- a/activitysim/abm/models/__init__.py +++ b/activitysim/abm/models/__init__.py @@ -1,5 +1,7 @@ +# flake8: noqa # ActivitySim # See full license in LICENSE.txt. + from . import ( accessibility, atwork_subtour_destination, @@ -8,6 +10,7 @@ atwork_subtour_scheduling, auto_ownership, cdap, + disaggregate_accessibility, free_parking, initialize, initialize_los, @@ -24,11 +27,15 @@ non_mandatory_scheduling, non_mandatory_tour_frequency, parking_location_choice, + school_escorting, stop_frequency, summarize, + telecommute_frequency, tour_mode_choice, tour_od_choice, tour_scheduling_probabilistic, + transit_pass_ownership, + transit_pass_subsidy, trip_departure_choice, trip_destination, trip_matrices, @@ -39,4 +46,5 @@ trip_scheduling_choice, vehicle_allocation, vehicle_type_choice, + work_from_home, ) diff --git a/activitysim/abm/models/atwork_subtour_scheduling.py b/activitysim/abm/models/atwork_subtour_scheduling.py index 188cfaeaa..041d899a2 100644 --- a/activitysim/abm/models/atwork_subtour_scheduling.py +++ b/activitysim/abm/models/atwork_subtour_scheduling.py @@ -41,6 +41,7 @@ def atwork_subtour_scheduling( estimator = estimation.manager.begin_estimation("atwork_subtour_scheduling") model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) + sharrow_skip = model_settings.get("sharrow_skip") coefficients_df = simulate.read_model_coefficients(model_settings) model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) @@ -79,6 +80,7 @@ def atwork_subtour_scheduling( estimator=estimator, chunk_size=chunk_size, trace_label=trace_label, + sharrow_skip=sharrow_skip, ) if estimator: diff --git a/activitysim/abm/models/disaggregate_accessibility.py b/activitysim/abm/models/disaggregate_accessibility.py new file mode 100644 index 000000000..fe79d3fcd --- /dev/null +++ b/activitysim/abm/models/disaggregate_accessibility.py @@ -0,0 +1,763 @@ +import logging +import random +from functools import reduce + +import numpy as np +import pandas as pd +from orca import orca +from sklearn.cluster import KMeans + +from activitysim.abm.models import initialize, location_choice +from activitysim.abm.models.util import estimation, tour_destination +from activitysim.abm.tables import shadow_pricing +from activitysim.core import config, inject, los, pipeline, tracing, util +from activitysim.core.expressions import assign_columns + +logger = logging.getLogger(__name__) + + +def read_disaggregate_accessibility_yaml(file_name): + """ + Adds in default table suffixes 'proto_' if not defined in the settings file + """ + model_settings = config.read_model_settings(file_name) + if not model_settings.get("suffixes"): + model_settings["suffixes"] = { + "SUFFIX": "proto_", + "ROOTS": [ + "persons", + "households", + "tours", + "persons_merged", + "person_id", + "household_id", + "tour_id", + ], + } + # Convert decimal sample rate to integer sample size + for sample in ["ORIGIN_SAMPLE_SIZE", "DESTINATION_SAMPLE_SIZE"]: + size = model_settings.get(sample, 0) + if size > 0 and size < 1: + model_settings[sample] = round( + size * len(pipeline.get_table("land_use").index) + ) + + return model_settings + + +class ProtoPop: + def __init__(self, network_los, chunk_size): + # Run necessary inits for later + initialize.initialize_landuse() + + # Initialization + self.proto_pop = {} + self.zone_list = [] + self.land_use = pipeline.get_table("land_use") + self.network_los = network_los + self.chunk_size = chunk_size + self.model_settings = read_disaggregate_accessibility_yaml( + "disaggregate_accessibility.yaml" + ) + + # Random seed + self.seed = self.model_settings.get("BASE_RANDOM_SEED", 0) + len( + self.land_use.index + ) + + # Generation + self.params = self.read_table_settings() + self.create_proto_pop() + logger.info( + "Created a proto-population with %s households across %s origin zones to %s possible destination zones" + % ( + len(self.proto_pop["proto_households"]), + len(self.proto_pop["proto_households"].home_zone_id.unique()), + self.model_settings["DESTINATION_SAMPLE_SIZE"], + ) + ) + self.inject_tables() + self.annotate_tables() + self.merge_persons() + + # - initialize shadow_pricing size tables after annotating household and person tables + # since these are scaled to model size, they have to be created while single-process + # this can now be called as a standalone model step instead, add_size_tables + add_size_tables = self.model_settings.get("add_size_tables", True) + if add_size_tables: + # warnings.warn(f"Calling add_size_tables from initialize will be removed in the future.", FutureWarning) + shadow_pricing._add_size_tables( + self.model_settings.get("suffixes"), scale=False + ) + + def zone_sampler(self): + """ + This is a "pre"-sampling method, which selects a sample from the total zones and generates a proto-pop on it. + This is particularly useful for multi-zone models where there are many MAZs. + Otherwise it could cause memory usage and computation time to explode. + + Important to distinguish this zone sampling from the core destination_sample method. The destination method + is destination oriented, and essentially weights samples by their size terms in order to sample important + destinations. This is irrelevant for accessibility, which is concerned with the accessibility FROM origins + to destinations. + + Thus, this sampling approach will weight the zones by their relative population. + + method: + None/Default - Sample zones weighted by population, ensuring at least one TAZ is sampled per MAZ + If n-samples > n-tazs then sample 1 MAZ from each TAZ until n-remaining-samples < n-tazs, + then sample n-remaining-samples TAZs and sample an MAZ within each of those TAZs. + If n-samples < n-tazs, then it proceeds to the above 'then' condition. + + uniform - Unweighted sample of N zones independent of each other. + + uniform-taz - Unweighted sample of 1 zone per taz up to the N samples specified. + + k-means - K-Means clustering is performed on the zone centroids (must be provided as maz_centroids.csv), + weighted by population. The clustering yields k XY coordinates weighted by zone population + for n-samples = k-clusters specified. Once k new cluster centroids are found, these are then + approximated into the nearest available zone centroid and used to calculate accessibilities on. + + By default, the k-means method is run on 10 different initial cluster seeds (n_init) using using + "k-means++" seeding algorithm (https://en.wikipedia.org/wiki/K-means%2B%2B). The k-means method + runs for max_iter iterations (default=300). + + """ + + # default_zone_col = 'TAZ' if not (self.network_los.zone_system == los.ONE_ZONE) else 'zone_id' + # zone_cols = self.model_settings["zone_id_names"].get("zone_group_cols", default_zone_col) + id_col = self.model_settings["zone_id_names"].get("index_col", "zone_id") + method = self.model_settings.get("ORIGIN_SAMPLE_METHOD") + n_samples = self.model_settings.get("ORIGIN_SAMPLE_SIZE", 0) + + # Get weights, need to get households first to get persons merged. + # Note: This will cause empty zones to be excluded. Which is intended, but just know that. + zone_weights = self.land_use[ + self.model_settings["ORIGIN_WEIGHTING_COLUMN"] + ].to_frame("weight") + zone_weights = zone_weights[zone_weights.weight != 0] + + # If more samples than zones, just default to all zones + if n_samples == 0 or n_samples > len(zone_weights.index): + n_samples = len(zone_weights.index) + print("WARNING: ORIGIN_SAMPLE_SIZE >= n-zones. Using all zones.") + method = "full" # If it's a full sample, no need to sample + + if method and method == "full": + sample_idx = self.land_use.index + elif method and method.lower() == "uniform": + sample_idx = sorted(random.sample(sorted(self.land_use.index), n_samples)) + elif method and method.lower() == "uniform-taz": + # Randomly select one MAZ per TAZ by randomizing the index and then select the first MAZ in each TAZ + # Then truncate the sampled indices by N samples and sort it + sample_idx = ( + self.land_use.sample(frac=1) + .reset_index() + .groupby("TAZ")[id_col] + .first() + ) + sample_idx = sorted(sample_idx) + elif method and method.lower() == "kmeans": + # Only implemented for 2-zone system for now + assert ( + self.network_los.zone_system == los.TWO_ZONE + ), "K-Means only implemented for 2-zone systems for now" + + # Performs a simple k-means clustering using centroid XY coordinates + centroids_df = pipeline.get_table("maz_centroids") + + # Assert that land_use zone ids is subset of centroid zone ids + assert set(self.land_use.index).issubset(set(centroids_df.index)) + + # Join the land_use pop on centroids, + # this also filter only zones we need (relevant if running scaled model) + centroids_df = centroids_df.join( + self.land_use[self.model_settings["ORIGIN_WEIGHTING_COLUMN"]], + how="inner", + ) + xy_list = list(centroids_df[["X", "Y"]].itertuples(index=False, name=None)) + xy_weights = np.array( + centroids_df[self.model_settings["ORIGIN_WEIGHTING_COLUMN"]] + ) + + # Initializer k-means class + """ + init: (default='k-means++') + ‘k-means++’ : selects initial cluster centroids using sampling based on an + empirical probability distribution of the points’ contribution to the overall inertia. + This technique speeds up convergence, and is theoretically proven to be O(log k) -optimal. + See the description of n_init for more details. + + ‘random’: choose n_clusters observations (rows) at random from data for the initial centroids. + + n_init: (default=10) + Number of time the k-means algorithm will be run with different centroid seeds. + The final results will be the best output of n_init consecutive runs in terms of inertia. + + max_iter: (default=300) + Maximum number of iterations of the k-means algorithm for a single run. + + n_clusters (pass n-samples): + The number of clusters to form as well as the number of centroids to generate. + This is the n-samples we are choosing. + """ + + kmeans = KMeans( + init="k-means++", + n_clusters=n_samples, + n_init=10, + max_iter=300, + random_state=self.seed, + ) + + # Calculate the k-means cluster points + # Find the nearest MAZ for each cluster + kmeans_res = kmeans.fit(X=xy_list, sample_weight=xy_weights) + sample_idx = [ + util.nearest_node_index(_xy, xy_list) + for _xy in kmeans_res.cluster_centers_ + ] + else: + # Default method. + # First sample the TAZ then select subzones weighted by the population size + if self.network_los.zone_system == los.TWO_ZONE: + # Join on TAZ and aggregate + maz_candidates = zone_weights.merge( + self.network_los.maz_taz_df, left_index=True, right_on="MAZ" + ) + taz_candidates = maz_candidates.groupby("TAZ").sum().drop(columns="MAZ") + + # Sample TAZs then sample sample 1 MAZ per TAZ for all TAZs, repeat MAZ sampling until no samples left + n_samples_remaining = n_samples + maz_sample_idx = [] + + while len(maz_candidates.index) > 0 and n_samples_remaining > 0: + # To ensure that each TAZ gets selected at least once when n > n-TAZs + if n_samples_remaining >= len(maz_candidates.groupby("TAZ").size()): + # Sample 1 MAZ per TAZ based on weight + maz_sample_idx += list( + maz_candidates.groupby("TAZ") + .sample( + n=1, + weights="weight", + replace=False, + random_state=self.seed, + ) + .MAZ + ) + else: + # If there are more TAZs than samples remaining, then sample from TAZs first, then MAZs + # Otherwise we would end up with more samples than we want + taz_sample_idx = list( + taz_candidates.sample( + n=n_samples_remaining, + weights="weight", + replace=False, + random_state=self.seed, + ).index + ) + # Now keep only those TAZs and sample MAZs from them + maz_candidates = maz_candidates[ + maz_candidates.TAZ.isin(taz_sample_idx) + ] + maz_sample_idx += list( + maz_candidates.groupby("TAZ") + .sample( + n=1, + weights="weight", + replace=False, + random_state=self.seed, + ) + .MAZ + ) + + # Remove selected candidates from weight list + maz_candidates = maz_candidates[ + ~maz_candidates.MAZ.isin(maz_sample_idx) + ] + # Calculate the remaining samples to collect + n_samples_remaining = n_samples - len(maz_sample_idx) + n_samples_remaining = ( + 0 if n_samples_remaining < 0 else n_samples_remaining + ) + + # The final MAZ list + sample_idx = maz_sample_idx + else: + sample_idx = list( + zone_weights.sample( + n=n_samples, + weights="weight", + replace=True, + random_state=self.seed, + ).index + ) + + return {id_col: sorted(sample_idx)} + + def read_table_settings(self): + # Check if setup properly + + assert "CREATE_TABLES" in self.model_settings.keys() + + # Set zone_id name if not already specified + self.model_settings["zone_id_names"] = self.model_settings.get( + "zone_id_names", {"index_col": "zone_id"} + ) + create_tables = self.model_settings.get("CREATE_TABLES") + from_templates = self.model_settings.get("FROM_TEMPLATES", False) + zone_list = self.zone_sampler() + params = {} + + assert all( + [ + True + for x in ["PERSONS", "HOUSEHOLDS", "TOURS"] + if x in create_tables.keys() + ] + ) + + if from_templates: + params = { + k.lower(): {"file": v, "index_col": k.lower()[:-1] + "_id"} + for k, v in create_tables.items() + } + params = {**params, **zone_list} + params["proto_households"]["zone_col"] = "home_zone_id" + else: + assert all( + [True for k, v in create_tables.items() if "VARIABLES" in v.keys()] + ) + for name, table in create_tables.items(): + # Ensure table variables are all lists + params[name.lower()] = { + "variables": { + k: (v if isinstance(v, list) else [v]) + for k, v in table["VARIABLES"].items() + }, + "mapped": table.get("mapped_fields", []), + "filter": table.get("filter_rows", []), + "join_on": table.get("JOIN_ON", []), + "index_col": table.get("index_col", []), + "zone_col": table.get("zone_col", []), + "rename_columns": table.get("rename_columns", []), + } + + # Add zones to households dicts as vary_on variable + params["proto_households"]["variables"] = { + **params["proto_households"]["variables"], + **zone_list, + } + + return params + + def generate_replicates(self, table_name): + """ + Generates replicates finding the cartesian product of the non-mapped field variables. + The mapped fields are then annotated after replication + """ + # Generate replicates + df = pd.DataFrame(util.named_product(**self.params[table_name]["variables"])) + + # Applying mapped variables + if len(self.params[table_name]["mapped"]) > 0: + for mapped_from, mapped_to_pair in self.params[table_name][ + "mapped" + ].items(): + for name, mapped_to in mapped_to_pair.items(): + df[name] = df[mapped_from].map(mapped_to) + + # Perform filter step + if (len(self.params[table_name]["filter"])) > 0: + for filt in self.params[table_name]["filter"]: + df[eval(filt)].reset_index(drop=True) + + return df + + def expand_template_zones(self, tables): + assert ( + len( + set(tables["proto_households"].proto_household_id).difference( + tables["proto_persons"].proto_household_id + ) + ) + == 0 + ), "Unmatched template_household_id in households/persons templates" + + assert ( + len( + set(tables["proto_persons"].proto_person_id).difference( + tables["proto_tours"].proto_person_id + ) + ) + == 0 + ), "Unmatched template_household_id in persons/tours templates" + + # Create one master template + master_template = ( + tables["proto_households"] + .merge(tables["proto_persons"], on="proto_household_id", how="left") + .merge( + tables["proto_tours"], + on=["proto_household_id", "proto_person_id"], + how="left", + ) + .reset_index(drop=True) + ) + + # Run cartesian product on the index vs zones + index_params = { + "index": master_template.index, + "home_zone_id": self.params.get("zone_id"), + } + + # Create cartesian product on the index and zone id + _expanded = pd.DataFrame(util.named_product(**index_params)).set_index("index") + + # Use result to join template onto expanded table of zones + ex_table = _expanded.join(master_template).reset_index() + + # Concatenate a new unique set of ids + cols = ["home_zone_id", "proto_household_id", "proto_person_id"] + col_filler = { + x: len(ex_table[x].unique().max().astype(str)) + for x in ["proto_household_id", "proto_person_id"] + } + + # Convert IDs to string and pad zeroes + df_ids = ex_table[cols].astype(str) + for col, fill in col_filler.items(): + df_ids[col] = df_ids[col].str.zfill(fill) + + ex_table["proto_person_id"] = df_ids[cols].apply("".join, axis=1).astype(int) + ex_table["proto_household_id"] = ( + df_ids[cols[:-1]].apply("".join, axis=1).astype(int) + ) + + # Separate out into households, persons, tours + col_keys = {k: list(v.columns) for k, v in tables.items()} + col_keys["proto_households"].append("home_zone_id") + + proto_tables = {k: ex_table[v].drop_duplicates() for k, v in col_keys.items()} + proto_tables["proto_tours"] = ( + proto_tables["proto_tours"] + .reset_index() + .rename(columns={"index": "proto_tour_id"}) + ) + proto_tables["proto_tours"].index += 1 + + return [x for x in proto_tables.values()] + + def create_proto_pop(self): + # Separate out the mapped data from the varying data and create base replicate tables + klist = ["proto_households", "proto_persons", "proto_tours"] + + # Create ID columns, defaults to "%tablename%_id" + hhid, perid, tourid = [ + self.params[x]["index_col"] + if len(self.params[x]["index_col"]) > 0 + else x + "_id" + for x in klist + ] + + if self.model_settings.get("FROM_TEMPLATES"): + table_params = {k: self.params.get(k) for k in klist} + tables = { + k: pd.read_csv(config.config_file_path(v.get("file"))) + for k, v in table_params.items() + } + households, persons, tours = self.expand_template_zones(tables) + households["household_serial_no"] = households[hhid] + else: + households, persons, tours = [self.generate_replicates(k) for k in klist] + + # Names + households.name, persons.name, tours.name = klist + + # Create hhid + households[hhid] = households.index + 1 + households["household_serial_no"] = households[hhid] + + # Assign persons to households + rep = ( + pd.DataFrame( + util.named_product(hhid=households[hhid], index=persons.index) + ) + .set_index("index") + .rename(columns={"hhid": hhid}) + ) + persons = rep.join(persons).sort_values(hhid).reset_index(drop=True) + persons[perid] = persons.index + 1 + + # Assign persons to tours + tkey, pkey = list(self.params["proto_tours"]["join_on"].items())[0] + tours = tours.merge( + persons[[pkey, hhid, perid]], left_on=tkey, right_on=pkey + ) + tours.index = tours.index.set_names([tourid]) + tours.index += 1 + tours = tours.reset_index().drop(columns=[pkey]) + + # Set index + households.set_index(hhid, inplace=True, drop=False) + persons.set_index(perid, inplace=True, drop=False) + tours.set_index(tourid, inplace=True, drop=False) + + # Store tables + self.proto_pop = { + "proto_households": households, + "proto_persons": persons, + "proto_tours": tours, + } + + # Rename any columns. Do this first before any annotating + for tablename, df in self.proto_pop.items(): + colnames = self.params[tablename].get("rename_columns", []) + if len(colnames) > 0: + df.rename(columns=colnames, inplace=True) + + def inject_tables(self): + # Update canonical tables lists + inject.add_injectable( + "traceable_tables", + inject.get_injectable("traceable_tables") + list(self.proto_pop.keys()), + ) + for tablename, df in self.proto_pop.items(): + inject.add_table(tablename, df) + pipeline.get_rn_generator().add_channel(tablename, df) + tracing.register_traceable_table(tablename, df) + # pipeline.get_rn_generator().drop_channel(tablename) + + def annotate_tables(self): + # Extract annotations + for annotations in self.model_settings["annotate_proto_tables"]: + tablename = annotations["tablename"] + df = pipeline.get_table(tablename) + assert df is not None + assert annotations is not None + assign_columns( + df=df, + model_settings={ + **annotations["annotate"], + **self.model_settings["suffixes"], + }, + trace_label=tracing.extend_trace_label("ProtoPop.annotate", tablename), + ) + pipeline.replace_table(tablename, df) + + def merge_persons(self): + persons = pipeline.get_table("proto_persons") + households = pipeline.get_table("proto_households") + + # For dropping any extra columns created during merge + cols_to_use = households.columns.difference(persons.columns) + + # persons_merged to emulate the persons_merged table in the pipeline + persons_merged = persons.join( + households[cols_to_use], on=self.params["proto_households"]["index_col"] + ).merge( + self.land_use, + left_on=self.params["proto_households"]["zone_col"], + right_on=self.model_settings["zone_id_names"]["index_col"], + ) + + perid = self.params["proto_persons"]["index_col"] + persons_merged.set_index(perid, inplace=True, drop=True) + self.proto_pop["proto_persons_merged"] = persons_merged + + # Store in pipeline + inject.add_table("proto_persons_merged", persons_merged) + + +def get_disaggregate_logsums(network_los, chunk_size, trace_hh_id): + logsums = {} + persons_merged = pipeline.get_table("proto_persons_merged").sort_index( + inplace=False + ) + disagg_model_settings = read_disaggregate_accessibility_yaml( + "disaggregate_accessibility.yaml" + ) + + for model_name in [ + "workplace_location", + "school_location", + "non_mandatory_tour_destination", + ]: + trace_label = tracing.extend_trace_label(model_name, "accessibilities") + print("Running model {}".format(trace_label)) + model_settings = config.read_model_settings(model_name + ".yaml") + model_settings["SAMPLE_SIZE"] = disagg_model_settings.get( + "DESTINATION_SAMPLE_SIZE" + ) + estimator = estimation.manager.begin_estimation(trace_label) + if estimator: + location_choice.write_estimation_specs( + estimator, model_settings, model_name + ".yaml" + ) + + # Append table references in settings with "proto_" + # This avoids having to make duplicate copies of config files for disagg accessibilities + model_settings = util.suffix_tables_in_settings(model_settings) + model_settings["CHOOSER_ID_COLUMN"] = "proto_person_id" + + # Include the suffix tags to pass onto downstream logsum models (e.g., tour mode choice) + if model_settings.get("LOGSUM_SETTINGS", None): + suffixes = util.concat_suffix_dict(disagg_model_settings.get("suffixes")) + suffixes.insert(0, model_settings.get("LOGSUM_SETTINGS")) + model_settings["LOGSUM_SETTINGS"] = " ".join(suffixes) + + if model_name != "non_mandatory_tour_destination": + spc = shadow_pricing.load_shadow_price_calculator(model_settings) + # explicitly turning off shadow pricing for disaggregate accessibilities + spc.use_shadow_pricing = False + # filter to only workers or students + chooser_filter_column = model_settings["CHOOSER_FILTER_COLUMN_NAME"] + choosers = persons_merged[persons_merged[chooser_filter_column]] + + # run location choice and return logsums + _logsums, _ = location_choice.run_location_choice( + choosers, + network_los, + shadow_price_calculator=spc, + want_logsums=True, + want_sample_table=True, + estimator=estimator, + model_settings=model_settings, + chunk_size=chunk_size, + chunk_tag=trace_label, + trace_hh_id=trace_hh_id, + trace_label=trace_label, + skip_choice=True, + ) + + # Merge onto persons + if _logsums is not None and len(_logsums.index) > 0: + keep_cols = list(set(_logsums.columns).difference(choosers.columns)) + logsums[model_name] = persons_merged.merge( + _logsums[keep_cols], on="proto_person_id" + ) + + else: + tours = pipeline.get_table("proto_tours") + tours = tours[tours.tour_category == "non_mandatory"] + + _logsums, _ = tour_destination.run_tour_destination( + tours, + persons_merged, + want_logsums=True, + want_sample_table=True, + model_settings=model_settings, + network_los=network_los, + estimator=estimator, + chunk_size=chunk_size, + trace_hh_id=trace_hh_id, + trace_label=trace_label, + skip_choice=True, + ) + + # Merge onto persons & tours + if _logsums is not None and len(_logsums.index) > 0: + tour_logsums = tours.merge( + _logsums["logsums"].to_frame(), left_index=True, right_index=True + ) + keep_cols = list( + set(tour_logsums.columns).difference(persons_merged.columns) + ) + logsums[model_name] = persons_merged.merge( + tour_logsums[keep_cols], on="proto_person_id" + ) + + return logsums + + +@inject.step() +def initialize_proto_population(network_los, chunk_size): + # Synthesize the proto-population + ProtoPop(network_los, chunk_size) + return + + +@inject.step() +def compute_disaggregate_accessibility(network_los, chunk_size, trace_hh_id): + """ + Compute enhanced disaggregate accessibility for user specified population segments, + as well as each zone in land use file using expressions from accessibility_spec. + + """ + + # Re-Register tables in this step, necessary for multiprocessing + for tablename in ["proto_households", "proto_persons", "proto_tours"]: + df = inject.get_table(tablename).to_frame() + traceables = inject.get_injectable("traceable_tables") + if tablename not in pipeline.get_rn_generator().channels: + pipeline.get_rn_generator().add_channel(tablename, df) + if tablename not in traceables: + inject.add_injectable("traceable_tables", traceables + [tablename]) + tracing.register_traceable_table(tablename, df) + del df + + # Run location choice + logsums = get_disaggregate_logsums(network_los, chunk_size, trace_hh_id) + logsums = {k + "_accessibility": v for k, v in logsums.items()} + + # Combined accessibility table + # Setup dict for fixed location accessibilities + access_list = [] + for k, df in logsums.items(): + if "non_mandatory_tour_destination" in k: + # cast non-mandatory purposes to wide + df = pd.pivot( + df, + index=["proto_household_id", "proto_person_id"], + columns="tour_type", + values="logsums", + ) + df.columns = ["_".join([str(x), "accessibility"]) for x in df.columns] + access_list.append(df) + else: + access_list.append( + df[["proto_household_id", "logsums"]].rename(columns={"logsums": k}) + ) + # Merge to wide data frame. Merged on household_id, logsums are at household level + access_df = reduce( + lambda x, y: pd.merge(x, y, on="proto_household_id", how="outer"), access_list + ) + + # Merge in the proto pop data and inject it + access_df = ( + access_df.merge( + pipeline.get_table("proto_persons_merged").reset_index(), + on="proto_household_id", + ) + .set_index("proto_person_id") + .sort_index() + ) + + logsums["proto_disaggregate_accessibility"] = access_df + + # Drop any tables prematurely created + for tablename in [ + "school_destination_size", + "workplace_destination_size", + ]: + pipeline.drop_table(tablename) + + for ch in list(pipeline.get_rn_generator().channels.keys()): + pipeline.get_rn_generator().drop_channel(ch) + + # Drop any prematurely added traceables + for trace in [ + x for x in inject.get_injectable("traceable_tables") if "proto_" not in x + ]: + tracing.deregister_traceable_table(trace) + + # need to clear any premature tables that were added during the previous run + orca._TABLES.clear() + for name, func in inject._DECORATED_TABLES.items(): + logger.debug("reinject decorated table %s" % name) + orca.add_table(name, func) + + # Inject accessibility results into pipeline + [inject.add_table(k, df) for k, df in logsums.items()] + + return diff --git a/activitysim/abm/models/initialize.py b/activitysim/abm/models/initialize.py index 14419dfde..fd9f47bb8 100644 --- a/activitysim/abm/models/initialize.py +++ b/activitysim/abm/models/initialize.py @@ -6,7 +6,7 @@ import pandas as pd -from activitysim.abm.tables import shadow_pricing +from activitysim.abm.tables import shadow_pricing, disaggregate_accessibility from activitysim.core import chunk, config, expressions, inject, mem, pipeline, tracing from activitysim.core.steps.output import ( track_skim_usage, @@ -141,7 +141,8 @@ def initialize_households(): add_size_tables = model_settings.get("add_size_tables", True) if add_size_tables: # warnings.warn(f"Calling add_size_tables from initialize will be removed in the future.", FutureWarning) - shadow_pricing.add_size_tables() + suffixes = inject.get_injectable("disaggregate_suffixes") + shadow_pricing.add_size_tables(suffixes) # - preload person_windows person_windows = inject.get_table("person_windows").to_frame() diff --git a/activitysim/abm/models/initialize_los.py b/activitysim/abm/models/initialize_los.py index 63e2f415f..2649c09a3 100644 --- a/activitysim/abm/models/initialize_los.py +++ b/activitysim/abm/models/initialize_los.py @@ -154,8 +154,16 @@ def compute_utilities_for_attribute_tuple( choosers_df, chunk_size, trace_label, chunk_tag=chunk_tag ): # we should count choosers_df as chunk overhead since its pretty big and was custom made for compute_utilities - assert chooser_chunk._is_view # otherwise copying it is wasteful - chooser_chunk = chooser_chunk.copy() + if chooser_chunk._is_view: + chooser_chunk = ( + chooser_chunk.copy() + ) # copy is needed when we start with a view + else: + # copying this is wasteful, but the code below edits the dataframe, + # which could have undesirable side effects. + # TODO: convert to Dataset or otherwise stop this copying, without + # harming anything else. + chooser_chunk = chooser_chunk.copy() chunk.log_df(trace_label, "attribute_chooser_chunk", chooser_chunk) # add any attribute columns specified as column attributes in settings (the rest will be scalars in locals_dict) diff --git a/activitysim/abm/models/joint_tour_participation.py b/activitysim/abm/models/joint_tour_participation.py index 7ef4d43af..939d9fedd 100644 --- a/activitysim/abm/models/joint_tour_participation.py +++ b/activitysim/abm/models/joint_tour_participation.py @@ -133,6 +133,8 @@ def participants_chooser(probs, choosers, spec, trace_label): In principal, this shold always occur eventually, but we fail after MAX_ITERATIONS, just in case there is some failure in program logic (haven't seen this occur.) + The return values are the same as logit.make_choices + Parameters ---------- probs : pandas.DataFrame diff --git a/activitysim/abm/models/joint_tour_scheduling.py b/activitysim/abm/models/joint_tour_scheduling.py index 32c444c76..ca56327d0 100644 --- a/activitysim/abm/models/joint_tour_scheduling.py +++ b/activitysim/abm/models/joint_tour_scheduling.py @@ -71,6 +71,7 @@ def joint_tour_scheduling(tours, persons_merged, tdd_alts, chunk_size, trace_hh_ estimator = estimation.manager.begin_estimation("joint_tour_scheduling") model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) + sharrow_skip = model_settings.get("sharrow_skip", False) coefficients_df = simulate.read_model_coefficients(model_settings) model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) @@ -91,6 +92,7 @@ def joint_tour_scheduling(tours, persons_merged, tdd_alts, chunk_size, trace_hh_ estimator=estimator, chunk_size=chunk_size, trace_label=trace_label, + sharrow_skip=sharrow_skip, ) if estimator: diff --git a/activitysim/abm/models/location_choice.py b/activitysim/abm/models/location_choice.py index f35d3eb15..1a7564942 100644 --- a/activitysim/abm/models/location_choice.py +++ b/activitysim/abm/models/location_choice.py @@ -10,9 +10,7 @@ config, expressions, inject, - logit, los, - mem, pipeline, simulate, tracing, @@ -116,6 +114,7 @@ def _location_sample( chunk_size, chunk_tag, trace_label, + zone_layer=None, ): """ select a sample of alternative locations. @@ -149,7 +148,13 @@ def _location_sample( ) sample_size = 0 - locals_d = {"skims": skims, "segment_size": segment_name} + locals_d = { + "skims": skims, + "segment_size": segment_name, + "orig_col_name": skims.orig_key, # added for sharrow flows + "dest_col_name": skims.dest_key, # added for sharrow flows + "timeframe": "timeless", + } constants = config.get_model_constants(model_settings) locals_d.update(constants) @@ -175,6 +180,7 @@ def _location_sample( chunk_size=chunk_size, chunk_tag=chunk_tag, trace_label=trace_label, + zone_layer=zone_layer, ) return choices @@ -227,7 +233,7 @@ def location_sample( DEST_MAZ = "dest_MAZ" -def aggregate_size_terms(dest_size_terms, network_los): +def aggregate_size_terms(dest_size_terms, network_los, model_settings): # # aggregate MAZ_size_terms to TAZ_size_terms # @@ -235,13 +241,14 @@ def aggregate_size_terms(dest_size_terms, network_los): MAZ_size_terms = dest_size_terms.copy() # add crosswalk DEST_TAZ column to MAZ_size_terms - maz_to_taz = ( - network_los.maz_taz_df[["MAZ", "TAZ"]] - .set_index("MAZ") - .sort_values(by="TAZ") - .TAZ + MAZ_size_terms[DEST_TAZ] = network_los.map_maz_to_taz(MAZ_size_terms.index) + + MAZ_size_terms["avail_MAZ"] = np.where( + (MAZ_size_terms.size_term > 0) + & (MAZ_size_terms.shadow_price_utility_adjustment > -999), + 1, + 0, ) - MAZ_size_terms[DEST_TAZ] = MAZ_size_terms.index.map(maz_to_taz) weighted_average_cols = [ "shadow_price_size_term_adjustment", @@ -255,12 +262,31 @@ def aggregate_size_terms(dest_size_terms, network_los): "size_term": "sum", "shadow_price_size_term_adjustment": "sum", "shadow_price_utility_adjustment": "sum", + "avail_MAZ": "sum", } ) for c in weighted_average_cols: TAZ_size_terms[c] /= TAZ_size_terms["size_term"] # weighted average + spc = shadow_pricing.load_shadow_price_calculator(model_settings) + if spc.use_shadow_pricing and ( + spc.shadow_settings["SHADOW_PRICE_METHOD"] == "simulation" + ): + # allow TAZs with at least one underassigned MAZ in them, therefore with a shadowprice larger than -999, to be selected again + TAZ_size_terms["shadow_price_utility_adjustment"] = np.where( + (TAZ_size_terms["shadow_price_utility_adjustment"] > -999) + & (TAZ_size_terms["avail_MAZ"] > 0), + 0, + -999, + ) + # now, negative size term means shadow price is -999. Setting size_term to 0 so the prob of that MAZ being selected becomes 0 + MAZ_size_terms["size_term"] = np.where( + MAZ_size_terms["shadow_price_utility_adjustment"] < 0, + 0, + MAZ_size_terms["size_term"], + ) + if TAZ_size_terms.isna().any(axis=None): logger.warning( f"TAZ_size_terms with NAN values\n{TAZ_size_terms[TAZ_size_terms.isna().any(axis=1)]}" @@ -308,7 +334,9 @@ def location_presample( alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"] assert DEST_TAZ != alt_dest_col_name - MAZ_size_terms, TAZ_size_terms = aggregate_size_terms(dest_size_terms, network_los) + MAZ_size_terms, TAZ_size_terms = aggregate_size_terms( + dest_size_terms, network_los, model_settings + ) # convert MAZ zone_id to 'TAZ' in choosers (persons_merged) # persons_merged[HOME_TAZ] = persons_merged[HOME_MAZ].map(maz_to_taz) @@ -341,6 +369,7 @@ def location_presample( chunk_size, chunk_tag, trace_label, + zone_layer="taz", ) # print(f"taz_sample\n{taz_sample}") @@ -524,6 +553,7 @@ def run_location_simulate( chunk_size, chunk_tag, trace_label, + skip_choice=False, ): """ run location model on location_sample annotated with mode_choice logsum @@ -563,7 +593,13 @@ def run_location_simulate( skim_dict = network_los.get_default_skim_dict() skims = skim_dict.wrap("home_zone_id", alt_dest_col_name) - locals_d = {"skims": skims, "segment_size": segment_name} + locals_d = { + "skims": skims, + "segment_size": segment_name, + "orig_col_name": skims.orig_key, # added for sharrow flows + "dest_col_name": skims.dest_key, # added for sharrow flows + "timeframe": "timeless", + } constants = config.get_model_constants(model_settings) if constants is not None: locals_d.update(constants) @@ -594,6 +630,7 @@ def run_location_simulate( trace_label=trace_label, trace_choice_name=model_settings["DEST_CHOICE_COLUMN_NAME"], estimator=estimator, + skip_choice=skip_choice, ) if not want_logsums: @@ -618,6 +655,7 @@ def run_location_choice( chunk_tag, trace_hh_id, trace_label, + skip_choice=False, ): """ Run the three-part location choice algorithm to generate a location choice for each chooser @@ -716,6 +754,7 @@ def run_location_choice( trace_label=tracing.extend_trace_label( trace_label, "simulate.%s" % segment_name ), + skip_choice=skip_choice, ) if estimator: @@ -856,6 +895,7 @@ def iterate_location_choice( # chooser segmentation allows different sets coefficients for e.g. different income_segments or tour_types chooser_segment_column = model_settings["CHOOSER_SEGMENT_COLUMN_NAME"] + segment_ids = model_settings["SEGMENT_IDS"] assert ( chooser_segment_column in persons_merged_df @@ -867,13 +907,24 @@ def iterate_location_choice( logger.debug("%s max_iterations: %s" % (trace_label, max_iterations)) + choices_df = None # initialize to None, will be populated in first iteration + for iteration in range(1, max_iterations + 1): + persons_merged_df_ = persons_merged_df.copy() + if spc.use_shadow_pricing and iteration > 1: spc.update_shadow_prices() - choices_df, save_sample_df = run_location_choice( - persons_merged_df, + if spc.shadow_settings["SHADOW_PRICE_METHOD"] == "simulation": + # filter from the sampled persons + persons_merged_df_ = persons_merged_df_[ + persons_merged_df_.index.isin(spc.sampled_persons.index) + ] + persons_merged_df_ = persons_merged_df_.sort_index() + + choices_df_, save_sample_df = run_location_choice( + persons_merged_df_, network_los, shadow_price_calculator=spc, want_logsums=logsum_column_name is not None, @@ -886,10 +937,33 @@ def iterate_location_choice( trace_label=tracing.extend_trace_label(trace_label, "i%s" % iteration), ) - # choices_df is a pandas DataFrame with columns 'choice' and (optionally) 'logsum' - if choices_df is None: + # choices_df is a pandas DataFrame with columns "choice" and (optionally) "logsum" + if choices_df_ is None: break + if spc.use_shadow_pricing: + # handle simulation method + if ( + spc.shadow_settings["SHADOW_PRICE_METHOD"] == "simulation" + and iteration > 1 + ): + # if a process ends up with no sampled workers in it, hence an empty choice_df_, then choice_df wil be what it was previously + if len(choices_df_) != 0: + choices_df = pd.concat([choices_df, choices_df_], axis=0) + choices_df_index = choices_df_.index.name + choices_df = choices_df.reset_index() + # update choices of workers/students + choices_df = choices_df.drop_duplicates( + subset=[choices_df_index], keep="last" + ) + choices_df = choices_df.set_index(choices_df_index) + choices_df = choices_df.sort_index() + else: + choices_df = choices_df_.copy() + + else: + choices_df = choices_df_ + spc.set_choices( choices=choices_df["choice"], segment_ids=persons_merged_df[chooser_segment_column].reindex( diff --git a/activitysim/abm/models/non_mandatory_destination.py b/activitysim/abm/models/non_mandatory_destination.py index 322d0ed32..adfd4a098 100644 --- a/activitysim/abm/models/non_mandatory_destination.py +++ b/activitysim/abm/models/non_mandatory_destination.py @@ -7,7 +7,8 @@ from activitysim.core import config, inject, pipeline, simulate, tracing from activitysim.core.util import assign_in_place -from .util import estimation, tour_destination +from .util import estimation, tour_destination, annotate + logger = logging.getLogger(__name__) @@ -43,7 +44,18 @@ def non_mandatory_tour_destination( # choosers are tours - in a sense tours are choosing their destination non_mandatory_tours = tours[tours.tour_category == "non_mandatory"] - # - if no mandatory_tours + # separating out pure escort school tours + # they already have their destination set + if pipeline.is_table("school_escort_tours"): + nm_tour_index = non_mandatory_tours.index + pure_school_escort_tours = non_mandatory_tours[ + (non_mandatory_tours["school_esc_outbound"] == "pure_escort") + | (non_mandatory_tours["school_esc_inbound"] == "pure_escort") + ] + non_mandatory_tours = non_mandatory_tours[ + ~non_mandatory_tours.index.isin(pure_school_escort_tours.index) + ] + if non_mandatory_tours.shape[0] == 0: tracing.no_results(trace_label) return @@ -85,14 +97,27 @@ def non_mandatory_tour_destination( non_mandatory_tours["destination"] = choices_df.choice + # merging back in school escort tours and preserving index + if pipeline.is_table("school_escort_tours"): + non_mandatory_tours = pd.concat( + [pure_school_escort_tours, non_mandatory_tours] + ).set_index(nm_tour_index) + assign_in_place(tours, non_mandatory_tours[["destination"]]) if want_logsums: non_mandatory_tours[logsum_column_name] = choices_df["logsum"] assign_in_place(tours, non_mandatory_tours[[logsum_column_name]]) + assert all( + ~tours["destination"].isna() + ), f"Tours are missing destination: {tours[tours['destination'].isna()]}" + pipeline.replace_table("tours", tours) + if model_settings.get("annotate_tours"): + annotate.annotate_tours(model_settings, trace_label) + if want_sample_table: assert len(save_sample_df.index.get_level_values(0).unique()) == len(choices_df) # save_sample_df.set_index(model_settings['ALT_DEST_COL_NAME'], append=True, inplace=True) diff --git a/activitysim/abm/models/non_mandatory_scheduling.py b/activitysim/abm/models/non_mandatory_scheduling.py index b827f1853..5b32c9550 100644 --- a/activitysim/abm/models/non_mandatory_scheduling.py +++ b/activitysim/abm/models/non_mandatory_scheduling.py @@ -8,10 +8,7 @@ from activitysim.core import timetable as tt from activitysim.core import tracing from activitysim.core.util import assign_in_place - -from .util import estimation from .util.tour_scheduling import run_tour_scheduling -from .util.vectorize_tour_scheduling import vectorize_tour_scheduling logger = logging.getLogger(__name__) DUMP = False diff --git a/activitysim/abm/models/non_mandatory_tour_frequency.py b/activitysim/abm/models/non_mandatory_tour_frequency.py index 813bc7de6..93b36b2f4 100644 --- a/activitysim/abm/models/non_mandatory_tour_frequency.py +++ b/activitysim/abm/models/non_mandatory_tour_frequency.py @@ -17,6 +17,9 @@ from activitysim.core.interaction_simulate import interaction_simulate from .util import estimation +from .util import annotate +from .util.school_escort_tours_trips import recompute_tour_count_statistics + from .util.overlap import person_max_window from .util.tour_frequency import process_non_mandatory_tours @@ -376,6 +379,13 @@ def non_mandatory_tour_frequency(persons, persons_merged, chunk_size, trace_hh_i tracing.register_traceable_table("tours", non_mandatory_tours) pipeline.get_rn_generator().add_channel("tours", non_mandatory_tours) + if pipeline.is_table("school_escort_tours"): + # need to re-compute tour frequency statistics to account for school escort tours + recompute_tour_count_statistics() + + if model_settings.get("annotate_tours"): + annotate.annotate_tours(model_settings, trace_label) + expressions.assign_columns( df=persons, model_settings=model_settings.get("annotate_persons"), diff --git a/activitysim/abm/models/parking_location_choice.py b/activitysim/abm/models/parking_location_choice.py index bddbc6519..3b10f32b4 100644 --- a/activitysim/abm/models/parking_location_choice.py +++ b/activitysim/abm/models/parking_location_choice.py @@ -116,12 +116,17 @@ def parking_destination_simulate( spec = get_spec_for_segment(model_settings, "SPECIFICATION", segment_name) + coefficients_df = simulate.read_model_coefficients(model_settings) + spec = simulate.eval_coefficients(spec, coefficients_df, None) + alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"] logger.info("Running trip_destination_simulate with %d trips", len(trips)) locals_dict = config.get_model_constants(model_settings).copy() locals_dict.update(skims) + locals_dict["timeframe"] = "trip" + locals_dict["PARKING"] = skims["op_skims"].dest_key parking_locations = interaction_sample_simulate( choosers=trips, @@ -171,7 +176,6 @@ def choose_parking_location( ) destination_sample.index = np.repeat(trips.index.values, len(alternatives)) destination_sample.index.name = trips.index.name - destination_sample = destination_sample[[alt_dest_col_name]].copy() # # - trip_destination_simulate destinations = parking_destination_simulate( @@ -229,8 +233,6 @@ def run_parking_destination( alt_column_filter_name = model_settings.get("ALTERNATIVE_FILTER_COLUMN_NAME") alternatives = land_use[land_use[alt_column_filter_name]] - # don't need size terms in alternatives, just TAZ index - alternatives = alternatives.drop(alternatives.columns, axis=1) alternatives.index.name = parking_location_column_name choices_list = [] @@ -296,8 +298,27 @@ def parking_location( trips_merged_df = trips_merged.to_frame() land_use_df = land_use.to_frame() + proposed_trip_departure_period = model_settings["TRIP_DEPARTURE_PERIOD"] + # TODO: the number of skim time periods should be more readily available than this + n_skim_time_periods = np.unique( + network_los.los_settings["skim_time_periods"]["labels"] + ).size + if trips_merged_df[proposed_trip_departure_period].max() > n_skim_time_periods: + # max proposed_trip_departure_period is out of range, + # it is most likely the high-resolution time period, we need the skim-level time period + if "trip_period" not in trips_merged_df: + # TODO: resolve this to the skim time period index not the label, it will be faster + trips_merged_df["trip_period"] = network_los.skim_time_period_label( + trips_merged_df[proposed_trip_departure_period] + ) + model_settings["TRIP_DEPARTURE_PERIOD"] = "trip_period" + locals_dict = {"network_los": network_los} - locals_dict.update(config.get_model_constants(model_settings)) + + constants = config.get_model_constants(model_settings) + + if constants is not None: + locals_dict.update(constants) if preprocessor_settings: expressions.assign_columns( diff --git a/activitysim/abm/models/school_escorting.py b/activitysim/abm/models/school_escorting.py new file mode 100644 index 000000000..1b5a97fc9 --- /dev/null +++ b/activitysim/abm/models/school_escorting.py @@ -0,0 +1,541 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import config, expressions, inject, pipeline, simulate, tracing +from activitysim.core.interaction_simulate import interaction_simulate +from activitysim.core.util import reindex + +from .util import estimation, school_escort_tours_trips + +logger = logging.getLogger(__name__) + +# setting global defaults for max number of escortees and escortees in model +NUM_ESCORTEES = 3 +NUM_CHAPERONES = 2 + + +def determine_escorting_participants(choosers, persons, model_settings): + """ + Determining which persons correspond to chauffer 1..n and escortee 1..n. + Chauffers are those with the highest weight given by: + weight = 100 * person type + 10 * gender + 1*(age > 25) + and escortees are selected youngest to oldest. + """ + global NUM_ESCORTEES + global NUM_CHAPERONES + NUM_ESCORTEES = model_settings.get("NUM_ESCORTEES", NUM_ESCORTEES) + NUM_CHAPERONES = model_settings.get("NUM_CHAPERONES", NUM_CHAPERONES) + + ptype_col = model_settings.get("PERSONTYPE_COLUMN", "ptype") + sex_col = model_settings.get("GENDER_COLUMN", "sex") + age_col = model_settings.get("AGE_COLUMN", "age") + + escortee_age_cutoff = model_settings.get("ESCORTEE_AGE_CUTOFF", 16) + chaperone_age_cutoff = model_settings.get("CHAPERONE_AGE_CUTOFF", 18) + + escortees = persons[ + persons.is_student + & (persons[age_col] < escortee_age_cutoff) + & (persons.cdap_activity == "M") + ] + households_with_escortees = escortees["household_id"] + + # can specify different weights to determine chaperones + persontype_weight = model_settings.get("PERSON_WEIGHT", 100) + gender_weight = model_settings.get("PERSON_WEIGHT", 10) + age_weight = model_settings.get("AGE_WEIGHT", 1) + + # can we move all of these to a config file? + chaperones = persons[ + (persons[age_col] > chaperone_age_cutoff) + & persons.household_id.isin(households_with_escortees) + ] + + chaperones["chaperone_weight"] = ( + (persontype_weight * chaperones[ptype_col]) + + (gender_weight * np.where(chaperones[sex_col] == 1, 1, 2)) + + (age_weight * np.where(chaperones[age_col] > 25, 1, 0)) + ) + + chaperones["chaperone_num"] = ( + chaperones.sort_values("chaperone_weight", ascending=False) + .groupby("household_id") + .cumcount() + + 1 + ) + escortees["escortee_num"] = ( + escortees.sort_values("age", ascending=True).groupby("household_id").cumcount() + + 1 + ) + + participant_columns = [] + for i in range(1, NUM_CHAPERONES + 1): + choosers["chauf_id" + str(i)] = ( + chaperones[chaperones["chaperone_num"] == i] + .reset_index() + .set_index("household_id") + .reindex(choosers.index)["person_id"] + ) + participant_columns.append("chauf_id" + str(i)) + for i in range(1, NUM_ESCORTEES + 1): + choosers["child_id" + str(i)] = ( + escortees[escortees["escortee_num"] == i] + .reset_index() + .set_index("household_id") + .reindex(choosers.index)["person_id"] + ) + participant_columns.append("child_id" + str(i)) + + return choosers, participant_columns + + +def check_alts_consistency(alts): + """ + Checking to ensure that the alternatives file is consistent with + the number of chaperones and escortees set in the model settings. + """ + for i in range(1, NUM_ESCORTEES + 1): + chauf_col = f"chauf{i}" + # The number of chauf columns should equal the number of escortees + assert chauf_col in alts.columns, f"Missing {chauf_col} in alternatives file" + + # Each escortee should be able to be escorted by each chaperone with ride hail or pure escort + assert alts[chauf_col].max() == (NUM_CHAPERONES * 2) + return + + +def add_prev_choices_to_choosers(choosers, choices, alts, stage): + # adding choice details to chooser table + escorting_choice = "school_escorting_" + stage + choosers[escorting_choice] = choices + + stage_alts = alts.copy() + stage_alts.columns = stage_alts.columns + "_" + stage + + choosers = ( + choosers.reset_index() + .merge( + stage_alts, + how="left", + left_on=escorting_choice, + right_on=stage_alts.index.name, + ) + .set_index("household_id") + ) + + return choosers + + +def create_bundle_attributes(row): + """ + Parse a bundle to determine escortee numbers and tour info. + """ + escortee_str = "" + escortee_num_str = "" + school_dests_str = "" + school_starts_str = "" + school_ends_str = "" + school_tour_ids_str = "" + num_escortees = 0 + + for child_num in row["child_order"]: + child_num = str(child_num) + child_id = int(row["bundle_child" + child_num]) + + if child_id > 0: + num_escortees += 1 + school_dest = str(int(row["school_destination_child" + child_num])) + school_start = str(int(row["school_start_child" + child_num])) + school_end = str(int(row["school_end_child" + child_num])) + school_tour_id = str(int(row["school_tour_id_child" + child_num])) + + if escortee_str == "": + escortee_str = str(child_id) + escortee_num_str = str(child_num) + school_dests_str = school_dest + school_starts_str = school_start + school_ends_str = school_end + school_tour_ids_str = school_tour_id + else: + escortee_str = escortee_str + "_" + str(child_id) + escortee_num_str = escortee_num_str + "_" + str(child_num) + school_dests_str = school_dests_str + "_" + school_dest + school_starts_str = school_starts_str + "_" + school_start + school_ends_str = school_ends_str + "_" + school_end + school_tour_ids_str = school_tour_ids_str + "_" + school_tour_id + + row["escortees"] = escortee_str + row["escortee_nums"] = escortee_num_str + row["num_escortees"] = num_escortees + row["school_destinations"] = school_dests_str + row["school_starts"] = school_starts_str + row["school_ends"] = school_ends_str + row["school_tour_ids"] = school_tour_ids_str + return row + + +def create_school_escorting_bundles_table(choosers, tours, stage): + """ + Creates a table that has one row for every school escorting bundle. + Additional calculations are performed to help facilitate tour and + trip creation including escortee order, times, etc. + + Parameters + ---------- + choosers : pd.DataFrame + households pre-processed for the school escorting model + tours : pd.Dataframe + mandatory tours + stage : str + inbound or outbound_cond + + Returns + ------- + bundles : pd.DataFrame + one school escorting bundle per row + """ + # making a table of bundles + choosers = choosers.reset_index() + choosers = choosers.loc[choosers.index.repeat(choosers["nbundles"])] + + bundles = pd.DataFrame() + # bundles.index = choosers.index + bundles["household_id"] = choosers["household_id"] + bundles["home_zone_id"] = choosers["home_zone_id"] + bundles["school_escort_direction"] = ( + "outbound" if "outbound" in stage else "inbound" + ) + bundles["bundle_num"] = bundles.groupby("household_id").cumcount() + 1 + + # initialize values + bundles["chauf_type_num"] = 0 + + # getting bundle school start times and locations + school_tours = tours[(tours.tour_type == "school") & (tours.tour_num == 1)] + + school_starts = school_tours.set_index("person_id").start + school_ends = school_tours.set_index("person_id").end + school_destinations = school_tours.set_index("person_id").destination + school_origins = school_tours.set_index("person_id").origin + school_tour_ids = school_tours.reset_index().set_index("person_id").tour_id + + for child_num in range(1, NUM_ESCORTEES + 1): + i = str(child_num) + bundles["bundle_child" + i] = np.where( + choosers["bundle" + i] == bundles["bundle_num"], + choosers["child_id" + i], + -1, + ) + bundles["chauf_type_num"] = np.where( + (choosers["bundle" + i] == bundles["bundle_num"]), + choosers["chauf" + i], + bundles["chauf_type_num"], + ) + bundles["time_home_to_school" + i] = np.where( + (choosers["bundle" + i] == bundles["bundle_num"]), + choosers["time_home_to_school" + i], + np.NaN, + ) + + bundles["school_destination_child" + i] = reindex( + school_destinations, bundles["bundle_child" + i] + ) + bundles["school_origin_child" + i] = reindex( + school_origins, bundles["bundle_child" + i] + ) + bundles["school_start_child" + i] = reindex( + school_starts, bundles["bundle_child" + i] + ) + bundles["school_end_child" + i] = reindex( + school_ends, bundles["bundle_child" + i] + ) + bundles["school_tour_id_child" + i] = reindex( + school_tour_ids, bundles["bundle_child" + i] + ) + + # each chauffeur option has ride share or pure escort + bundles["chauf_num"] = np.ceil(bundles["chauf_type_num"].div(2)).astype(int) + + # getting bundle chauffeur id based on the chauffeur num + bundles["chauf_id"] = -1 + for i in range(1, NUM_CHAPERONES + 1): + bundles["chauf_id"] = np.where( + bundles["chauf_num"] == i, + choosers["chauf_id" + str(i)], + bundles["chauf_id"], + ) + bundles["chauf_id"] = bundles["chauf_id"].astype(int) + assert ( + bundles["chauf_id"] > 0 + ).all(), "Invalid chauf_id's for school escort bundles!" + + # odd chauf_type_num means ride share, even means pure escort + # this comes from the way the alternatives file is constructed where chauf_id is + # incremented for each possible chauffeur and for each tour type + bundles["escort_type"] = np.where( + bundles["chauf_type_num"].mod(2) == 1, "ride_share", "pure_escort" + ) + + # This is just pulled from the pre-processor. Will break if removed or renamed in pre-processor + # I think this is still a better implmentation than re-calculating here... + school_time_cols = [ + "time_home_to_school" + str(i) for i in range(1, NUM_ESCORTEES + 1) + ] + bundles["outbound_order"] = list(bundles[school_time_cols].values.argsort() + 1) + bundles["inbound_order"] = list( + (-1 * bundles[school_time_cols]).values.argsort() + 1 + ) # inbound gets reverse order + bundles["child_order"] = np.where( + bundles["school_escort_direction"] == "outbound", + bundles["outbound_order"], + bundles["inbound_order"], + ) + + bundles = bundles.apply(lambda row: create_bundle_attributes(row), axis=1) + + # getting chauffer mandatory times + mandatory_escort_tours = tours[ + (tours.tour_category == "mandatory") & (tours.tour_num == 1) + ] + # bundles["first_mand_tour_start_time"] = reindex( + # mandatory_escort_tours.set_index("person_id").start, bundles["chauf_id"] + # ) + # bundles["first_mand_tour_end_time"] = reindex( + # mandatory_escort_tours.set_index("person_id").end, bundles["chauf_id"] + # ) + bundles["first_mand_tour_id"] = reindex( + mandatory_escort_tours.reset_index().set_index("person_id").tour_id, + bundles["chauf_id"], + ) + bundles["first_mand_tour_dest"] = reindex( + mandatory_escort_tours.reset_index().set_index("person_id").destination, + bundles["chauf_id"], + ) + bundles["first_mand_tour_purpose"] = reindex( + mandatory_escort_tours.reset_index().set_index("person_id").tour_type, + bundles["chauf_id"], + ) + + bundles["Alt"] = choosers["Alt"] + bundles["Description"] = choosers["Description"] + + return bundles + + +@inject.step() +def school_escorting( + households, households_merged, persons, tours, chunk_size, trace_hh_id +): + """ + school escorting model + + The school escorting model determines whether children are dropped-off at or + picked-up from school, simultaneously with the driver responsible for + chauffeuring the children, which children are bundled together on half-tours, + and the type of tour (pure escort versus rideshare). + + Run iteratively for an outbound choice, an inbound choice, and an outbound choice + conditional on the inbound choice. The choices for inbound and outbound conditional + are used to create school escort tours and trips. + + Updates / adds the following tables to the pipeline: + + :: + + - households with school escorting choice + - tours including pure school escorting + - school_escort_tours which contains only pure school escort tours + - school_escort_trips + - timetable to avoid joint tours scheduled over school escort tours + + """ + trace_label = "school_escorting_simulate" + model_settings_file_name = "school_escorting.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + + persons = persons.to_frame() + households = households.to_frame() + households_merged = households_merged.to_frame() + tours = tours.to_frame() + + alts = simulate.read_model_alts(model_settings["ALTS"], set_index="Alt") + + households_merged, participant_columns = determine_escorting_participants( + households_merged, persons, model_settings + ) + + check_alts_consistency(alts) + + constants = config.get_model_constants(model_settings) + locals_dict = {} + locals_dict.update(constants) + + school_escorting_stages = ["outbound", "inbound", "outbound_cond"] + escort_bundles = [] + choices = None + for stage_num, stage in enumerate(school_escorting_stages): + stage_trace_label = trace_label + "_" + stage + estimator = estimation.manager.begin_estimation("school_escorting_" + stage) + + model_spec_raw = simulate.read_model_spec( + file_name=model_settings[stage.upper() + "_SPEC"] + ) + coefficients_df = simulate.read_model_coefficients( + file_name=model_settings[stage.upper() + "_COEFFICIENTS"] + ) + model_spec = simulate.eval_coefficients( + model_spec_raw, coefficients_df, estimator + ) + + # allow for skipping sharrow entirely in this model with `sharrow_skip: true` + # or skipping stages selectively with a mapping of the stages to skip + sharrow_skip = model_settings.get("sharrow_skip", False) + stage_sharrow_skip = False # default is false unless set below + if sharrow_skip: + if isinstance(sharrow_skip, dict): + stage_sharrow_skip = sharrow_skip.get(stage.upper(), False) + else: + stage_sharrow_skip = True + if stage_sharrow_skip: + locals_dict["_sharrow_skip"] = True + else: + locals_dict.pop("_sharrow_skip", None) + + # reduce memory by limiting columns if selected columns are supplied + chooser_columns = model_settings.get("SIMULATE_CHOOSER_COLUMNS", None) + if chooser_columns is not None: + chooser_columns = chooser_columns + participant_columns + choosers = households_merged[chooser_columns] + else: + choosers = households_merged + + # add previous data to stage + if stage_num >= 1: + choosers = add_prev_choices_to_choosers( + choosers, choices, alts, school_escorting_stages[stage_num - 1] + ) + + locals_dict.update(coefficients_df) + + logger.info("Running %s with %d households", stage_trace_label, len(choosers)) + + preprocessor_settings = model_settings.get("preprocessor_" + stage, None) + if preprocessor_settings: + expressions.assign_columns( + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_dict, + trace_label=stage_trace_label, + ) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df, model_settings) + estimator.write_choosers(choosers) + + log_alt_losers = config.setting("log_alt_losers", False) + + choices = interaction_simulate( + choosers=choosers, + alternatives=alts, + spec=model_spec, + log_alt_losers=log_alt_losers, + locals_d=locals_dict, + chunk_size=chunk_size, + trace_label=stage_trace_label, + trace_choice_name="school_escorting_" + "stage", + estimator=estimator, + ) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values( + choices, "households", "school_escorting_" + stage + ) + estimator.write_override_choices(choices) + estimator.end_estimation() + + # no need to reindex as we used all households + escorting_choice = "school_escorting_" + stage + households[escorting_choice] = choices + + # tracing each step -- outbound, inbound, outbound_cond + tracing.print_summary( + escorting_choice, households[escorting_choice], value_counts=True + ) + + if trace_hh_id: + tracing.trace_df(households, label=escorting_choice, warn_if_empty=True) + + if stage_num >= 1: + choosers["Alt"] = choices + choosers = choosers.join(alts, how="left", on="Alt") + bundles = create_school_escorting_bundles_table( + choosers[choosers["Alt"] > 1], tours, stage + ) + escort_bundles.append(bundles) + + escort_bundles = pd.concat(escort_bundles) + escort_bundles["bundle_id"] = ( + escort_bundles["household_id"] * 10 + + escort_bundles.groupby("household_id").cumcount() + + 1 + ) + escort_bundles.sort_values( + by=["household_id", "school_escort_direction"], + ascending=[True, False], + inplace=True, + ) + + school_escort_tours = school_escort_tours_trips.create_pure_school_escort_tours( + escort_bundles + ) + chauf_tour_id_map = { + v: k for k, v in school_escort_tours["bundle_id"].to_dict().items() + } + escort_bundles["chauf_tour_id"] = np.where( + escort_bundles["escort_type"] == "ride_share", + escort_bundles["first_mand_tour_id"], + escort_bundles["bundle_id"].map(chauf_tour_id_map), + ) + + tours = school_escort_tours_trips.add_pure_escort_tours(tours, school_escort_tours) + tours = school_escort_tours_trips.process_tours_after_escorting_model( + escort_bundles, tours + ) + + school_escort_trips = school_escort_tours_trips.create_school_escort_trips( + escort_bundles + ) + + # update pipeline + pipeline.replace_table("households", households) + pipeline.replace_table("tours", tours) + pipeline.get_rn_generator().drop_channel("tours") + pipeline.get_rn_generator().add_channel("tours", tours) + pipeline.replace_table("escort_bundles", escort_bundles) + # save school escorting tours and trips in pipeline so we can overwrite results from downstream models + pipeline.replace_table("school_escort_tours", school_escort_tours) + pipeline.replace_table("school_escort_trips", school_escort_trips) + + # updating timetable object with pure escort tours so joint tours do not schedule ontop + timetable = inject.get_injectable("timetable") + + # Need to do this such that only one person is in nth_tours + # thus, looping through tour_category and tour_num + # including mandatory tours because their start / end times may have + # changed to match the school escort times + for tour_category in tours.tour_category.unique(): + for tour_num, nth_tours in tours[tours.tour_category == tour_category].groupby( + "tour_num", sort=True + ): + timetable.assign( + window_row_ids=nth_tours["person_id"], tdds=nth_tours["tdd"] + ) + + timetable.replace_table() diff --git a/activitysim/abm/models/stop_frequency.py b/activitysim/abm/models/stop_frequency.py index dd52c0b7a..94a208075 100644 --- a/activitysim/abm/models/stop_frequency.py +++ b/activitysim/abm/models/stop_frequency.py @@ -4,6 +4,7 @@ import numpy as np import pandas as pd +from activitysim.abm.models.util import school_escort_tours_trips from activitysim.core import config, expressions, inject, pipeline, simulate, tracing from activitysim.core.util import assign_in_place, reindex @@ -234,3 +235,6 @@ def stop_frequency( slicer="person_id", columns=None, ) + + if pipeline.is_table("school_escort_trips"): + school_escort_tours_trips.merge_school_escort_trips_into_pipeline() diff --git a/activitysim/examples/prototype_semcog/extensions/telecommute_frequency.py b/activitysim/abm/models/telecommute_frequency.py similarity index 100% rename from activitysim/examples/prototype_semcog/extensions/telecommute_frequency.py rename to activitysim/abm/models/telecommute_frequency.py diff --git a/activitysim/abm/models/tour_mode_choice.py b/activitysim/abm/models/tour_mode_choice.py index 0dd2a64f5..1e826ae91 100644 --- a/activitysim/abm/models/tour_mode_choice.py +++ b/activitysim/abm/models/tour_mode_choice.py @@ -19,7 +19,7 @@ from activitysim.core.pathbuilder import TransitVirtualPathBuilder from activitysim.core.util import assign_in_place, reindex -from .util import estimation, trip +from .util import estimation, trip, annotate, school_escort_tours_trips from .util.mode import run_tour_mode_choice_simulate logger = logging.getLogger(__name__) @@ -403,17 +403,20 @@ def tour_mode_choice_simulate( all_tours = tours.to_frame() assign_in_place(all_tours, choices_df) + if pipeline.is_table("school_escort_tours") & model_settings.get( + "FORCE_ESCORTEE_CHAUFFEUR_MODE_MATCH", True + ): + all_tours = ( + school_escort_tours_trips.force_escortee_tour_modes_to_match_chauffeur( + all_tours + ) + ) + pipeline.replace_table("tours", all_tours) # - annotate tours table if model_settings.get("annotate_tours"): - tours = inject.get_table("tours").to_frame() - expressions.assign_columns( - df=tours, - model_settings=model_settings.get("annotate_tours"), - trace_label=tracing.extend_trace_label(trace_label, "annotate_tours"), - ) - pipeline.replace_table("tours", tours) + annotate.annotate_tours(model_settings, trace_label) if trace_hh_id: tracing.trace_df( diff --git a/activitysim/examples/prototype_semcog/extensions/transit_pass_ownership.py b/activitysim/abm/models/transit_pass_ownership.py similarity index 100% rename from activitysim/examples/prototype_semcog/extensions/transit_pass_ownership.py rename to activitysim/abm/models/transit_pass_ownership.py diff --git a/activitysim/examples/prototype_semcog/extensions/transit_pass_subsidy.py b/activitysim/abm/models/transit_pass_subsidy.py similarity index 100% rename from activitysim/examples/prototype_semcog/extensions/transit_pass_subsidy.py rename to activitysim/abm/models/transit_pass_subsidy.py diff --git a/activitysim/abm/models/trip_destination.py b/activitysim/abm/models/trip_destination.py index 133e5c5b0..545cfee29 100644 --- a/activitysim/abm/models/trip_destination.py +++ b/activitysim/abm/models/trip_destination.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import logging from builtins import range +from pathlib import Path import numpy as np import pandas as pd @@ -12,7 +13,6 @@ ) from activitysim.abm.tables.size_terms import tour_destination_size_terms from activitysim.core import ( - assign, chunk, config, expressions, @@ -24,11 +24,12 @@ ) from activitysim.core.interaction_sample import interaction_sample from activitysim.core.interaction_sample_simulate import interaction_sample_simulate -from activitysim.core.pathbuilder import TransitVirtualPathBuilder from activitysim.core.skim_dictionary import DataFrameMatrix from activitysim.core.tracing import print_elapsed_time from activitysim.core.util import assign_in_place, reindex +from ...core.configuration.base import Any, PydanticBase +from .util.school_escort_tours_trips import split_out_school_escorting_trips from .util import estimation logger = logging.getLogger(__name__) @@ -41,6 +42,38 @@ # DEST_MAZ = 'dest_maz' +class TripDestinationSettings(PydanticBase): + """Settings for the trip_destination component. + + .. versionadded:: 1.2 + + Note that this implementation is presently used only for generating + documentation, but future work may migrate the settings implementation to + actually use this pydantic code to validate the settings before running + the model. + """ + + SAMPLE_SPEC: Path + SPEC: Path + COEFFICIENTS: Path + SAMPLE_SIZE: int + """This many candidate stop locations will be sampled for each choice.""" + DESTINATION_SAMPLE_SPEC: Path + DESTINATION_SPEC: Path + LOGSUM_SETTINGS: Path + DEST_CHOICE_LOGSUM_COLUMN_NAME: str = None + DEST_CHOICE_SAMPLE_TABLE_NAME: str = None + TRIP_ORIGIN: str = "origin" + ALT_DEST_COL_NAME: str = "dest_taz" + PRIMARY_DEST: str = "tour_leg_dest" # must be created in preprocessor + REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS: list[str] = None + CONSTANTS: dict[str, Any] = None + preprocessor: Any + CLEANUP: bool + fail_some_trips_for_testing: bool = False + """This setting is used by testing code to force failed trip_destination.""" + + def _destination_sample( primary_purpose, trips, @@ -53,6 +86,7 @@ def _destination_sample( chunk_size, chunk_tag, trace_label, + zone_layer=None, ): """ @@ -95,7 +129,13 @@ def _destination_sample( # cannot be determined until after choosers are joined with alternatives # (unless we iterate over trip.purpose - which we could, though we are already iterating over trip_num) # so, instead, expressions determine row-specific size_term by a call to: size_terms.get(df.alt_dest, df.purpose) - locals_dict.update({"size_terms": size_term_matrix}) + locals_dict.update( + { + "size_terms": size_term_matrix, + "size_terms_array": size_term_matrix.df.to_numpy(), + "timeframe": "trip", + } + ) locals_dict.update(skims) log_alt_losers = config.setting("log_alt_losers", False) @@ -113,6 +153,7 @@ def _destination_sample( chunk_size=chunk_size, chunk_tag=chunk_tag, trace_label=trace_label, + zone_layer=zone_layer, ) return choices @@ -152,12 +193,12 @@ def destination_sample( return choices -def aggregate_size_term_matrix(maz_size_term_matrix, maz_taz): +def aggregate_size_term_matrix(maz_size_term_matrix, network_los): df = maz_size_term_matrix.df assert ALT_DEST_TAZ not in df - dest_taz = df.index.map(maz_taz) + dest_taz = network_los.map_maz_to_taz(df.index) taz_size_term_matrix = df.groupby(dest_taz).sum() taz_size_term_matrix = DataFrameMatrix(taz_size_term_matrix) @@ -256,9 +297,13 @@ def choose_MAZ_for_TAZ( # there will be a different set (and number) of candidate MAZs for each TAZ # (preserve index, which will have duplicates as result of join) - maz_taz = network_los.maz_taz_df[["MAZ", "TAZ"]].rename( - columns={"TAZ": DEST_TAZ, "MAZ": DEST_MAZ} + maz_taz = ( + network_los.get_maz_to_taz_series.rename(DEST_TAZ) + .rename_axis(index=DEST_MAZ) + .to_frame() + .reset_index() ) + maz_sizes = pd.merge( taz_choices[[chooser_id_col, DEST_TAZ]].reset_index(), maz_taz, @@ -426,20 +471,21 @@ def destination_presample( chunk_tag = "trip_destination.presample" # distinguish from trip_destination.sample alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"] - maz_taz = network_los.maz_taz_df[["MAZ", "TAZ"]].set_index("MAZ").TAZ - TAZ_size_term_matrix = aggregate_size_term_matrix(size_term_matrix, maz_taz) + TAZ_size_term_matrix = aggregate_size_term_matrix(size_term_matrix, network_los) TRIP_ORIGIN = model_settings["TRIP_ORIGIN"] PRIMARY_DEST = model_settings["PRIMARY_DEST"] trips_taz = trips.copy() - trips_taz[TRIP_ORIGIN] = trips_taz[TRIP_ORIGIN].map(maz_taz) - trips_taz[PRIMARY_DEST] = trips_taz[PRIMARY_DEST].map(maz_taz) + trips_taz[TRIP_ORIGIN] = network_los.map_maz_to_taz(trips_taz[TRIP_ORIGIN]) + trips_taz[PRIMARY_DEST] = network_los.map_maz_to_taz(trips_taz[PRIMARY_DEST]) # alternatives is just an empty dataframe indexed by maz with index name # but logically, we are aggregating so lets do it, as there is no particular gain in being clever - alternatives = alternatives.groupby(alternatives.index.map(maz_taz)).sum() + alternatives = alternatives.groupby( + network_los.map_maz_to_taz(alternatives.index) + ).sum() # # i did this but after changing alt_dest_col_name to 'trip_dest' it # # shouldn't be needed anymore @@ -459,6 +505,7 @@ def destination_presample( chunk_size, chunk_tag=chunk_tag, trace_label=trace_label, + zone_layer="taz", ) # choose a MAZ for each DEST_TAZ choice, choice probability based on MAZ size_term fraction of TAZ total @@ -677,6 +724,7 @@ def compute_logsums( "odt_skims": skims["odt_skims"], "dot_skims": skims["dot_skims"], "od_skims": skims["od_skims"], + "timeframe": "trip", } if network_los.zone_system == los.THREE_ZONE: od_skims.update( @@ -769,8 +817,20 @@ def trip_destination_simulate( skims = skim_hotel.sample_skims(presample=False) + if not np.issubdtype(trips["trip_period"].dtype, np.integer): + if hasattr(skims["odt_skims"], "map_time_periods"): + trip_period_idx = skims["odt_skims"].map_time_periods(trips) + if trip_period_idx is not None: + trips["trip_period"] = trip_period_idx + locals_dict = config.get_model_constants(model_settings).copy() - locals_dict.update({"size_terms": size_term_matrix}) + locals_dict.update( + { + "size_terms": size_term_matrix, + "size_terms_array": size_term_matrix.df.to_numpy(), + "timeframe": "trip", + } + ) locals_dict.update(skims) log_alt_losers = config.setting("log_alt_losers", False) @@ -924,6 +984,7 @@ def sample_skims(self, presample): o = self.model_settings["TRIP_ORIGIN"] d = self.model_settings["ALT_DEST_COL_NAME"] + n = self.model_settings.get("PRIMARY_ORIGIN", "origin") p = self.model_settings["PRIMARY_DEST"] if presample: @@ -935,6 +996,8 @@ def sample_skims(self, presample): skims = { "od_skims": skim_dict.wrap(o, d), "dp_skims": skim_dict.wrap(d, p), + "op_skims": skim_dict.wrap(o, p), + "nd_skims": skim_dict.wrap(n, d), "odt_skims": skim_dict.wrap_3d( orig_key=o, dest_key=d, dim3_key="trip_period" ), @@ -947,6 +1010,18 @@ def sample_skims(self, presample): "pdt_skims": skim_dict.wrap_3d( orig_key=p, dest_key=d, dim3_key="trip_period" ), + "opt_skims": skim_dict.wrap_3d( + orig_key=o, dest_key=p, dim3_key="trip_period" + ), + "pot_skims": skim_dict.wrap_3d( + orig_key=p, dest_key=o, dim3_key="trip_period" + ), + "ndt_skims": skim_dict.wrap_3d( + orig_key=n, dest_key=d, dim3_key="trip_period" + ), + "dnt_skims": skim_dict.wrap_3d( + orig_key=d, dest_key=n, dim3_key="trip_period" + ), } return skims @@ -1168,7 +1243,10 @@ def run_trip_destination( trace_label, "trip_num_%s" % trip_num ) - locals_dict = {"network_los": network_los} + locals_dict = { + "network_los": network_los, + "size_terms": size_term_matrix, + } locals_dict.update(config.get_model_constants(model_settings)) # - annotate nth_trips @@ -1180,6 +1258,15 @@ def run_trip_destination( trace_label=nth_trace_label, ) + if not np.issubdtype(nth_trips["trip_period"].dtype, np.integer): + skims = network_los.get_default_skim_dict() + if hasattr(skims, "map_time_periods_from_series"): + trip_period_idx = skims.map_time_periods_from_series( + nth_trips["trip_period"] + ) + if trip_period_idx is not None: + nth_trips["trip_period"] = trip_period_idx + logger.info("Running %s with %d trips", nth_trace_label, nth_trips.shape[0]) # - choose destination for nth_trips, segmented by primary_purpose @@ -1264,11 +1351,28 @@ def run_trip_destination( @inject.step() def trip_destination(trips, tours_merged, chunk_size, trace_hh_id): """ - Choose a destination for all 'intermediate' trips based on trip purpose. + Choose a destination for all intermediate trips based on trip purpose. - Final trips already have a destination (the primary tour destination for outbound trips, - and home for inbound trips.) + The trip (or stop) location choice model predicts the location of trips + (or stops) along the tour other than the primary destination. This model is + structured as a multinomial logit model using a zone attraction size + variable and route deviation measure as impedance. The alternatives are + sampled from the full set of zones, subject to availability of a zonal + attraction size term (i.e., it is non-zero). The sampling mechanism is also + usually based on accessibility between tour origin and primary destination, + and can be subject to certain rules based on tour mode. + Parameters + ---------- + trips : orca.DataFrameWrapper + The trips table. This table is edited in-place to add the trip + destinations. + tours_merged : orca.DataFrameWrapper + The tours table, with columns merge from persons and households as well. + chunk_size : int + If non-zero, iterate over trips using this chunk size. + trace_hh_id : int or list[int] + Generate trace output for these households. """ trace_label = "trip_destination" @@ -1284,6 +1388,13 @@ def trip_destination(trips, tours_merged, chunk_size, trace_hh_id): trips_df = trips.to_frame() tours_merged_df = tours_merged.to_frame() + if pipeline.is_table("school_escort_trips"): + school_escort_trips = pipeline.get_table("school_escort_trips") + # separate out school escorting trips to exclude them from the model and estimation data bundle + trips_df, se_trips_df, full_trips_index = split_out_school_escorting_trips( + trips_df, school_escort_trips + ) + estimator = estimation.manager.begin_estimation("trip_destination") if estimator: @@ -1318,7 +1429,7 @@ def trip_destination(trips, tours_merged, chunk_size, trace_hh_id): ): if (trips_df.trip_num < trips_df.trip_count).sum() == 0: raise RuntimeError( - f"can't honor 'testing_fail_trip_destination' setting because no intermediate trips" + "can't honor 'testing_fail_trip_destination' setting because no intermediate trips" ) fail_o = trips_df[trips_df.trip_num < trips_df.trip_count].origin.max() @@ -1356,6 +1467,23 @@ def trip_destination(trips, tours_merged, chunk_size, trace_hh_id): trips_df.drop(columns="failed", inplace=True, errors="ignore") + if pipeline.is_table("school_escort_trips"): + # setting destination for school escort trips + se_trips_df["destination"] = reindex( + school_escort_trips.destination, se_trips_df.index + ) + # merge trips back together preserving index order + trips_df = pd.concat([trips_df, se_trips_df]) + trips_df["destination"] = trips_df["destination"].astype(int) + trips_df = trips_df.reindex(full_trips_index) + # Origin is previous destination + # (leaving first origin alone as it's already set correctly) + trips_df["origin"] = np.where( + (trips_df["trip_num"] == 1) & (trips_df["outbound"] == True), + trips_df["origin"], + trips_df.groupby("tour_id")["destination"].shift(), + ).astype(int) + pipeline.replace_table("trips", trips_df) if trace_hh_id: diff --git a/activitysim/abm/models/trip_matrices.py b/activitysim/abm/models/trip_matrices.py index 0f33e69e6..0c9e1f447 100644 --- a/activitysim/abm/models/trip_matrices.py +++ b/activitysim/abm/models/trip_matrices.py @@ -38,7 +38,7 @@ def write_trip_matrices(network_los): # this might legitimately happen if they comment out some steps to debug but still want write_tables # this saves them the hassle of remembering to comment out this step logger.warning( - f"write_trip_matrices returning empty-handed because there is no trips table" + "write_trip_matrices returning empty-handed because there is no trips table" ) return @@ -52,6 +52,7 @@ def write_trip_matrices(network_los): parking_settings = config.read_model_settings("parking_location_choice.yaml") parking_taz_col_name = parking_settings["ALT_DEST_COL_NAME"] if parking_taz_col_name in trips_df: + # TODO make parking zone negative, not zero, if not used trips_df.loc[trips_df[parking_taz_col_name] > 0, "destination"] = trips_df[ parking_taz_col_name ] @@ -60,7 +61,9 @@ def write_trip_matrices(network_los): # write matrices by zone system type if network_los.zone_system == los.ONE_ZONE: # taz trips written to taz matrices logger.info("aggregating trips one zone...") - aggregate_trips = trips_df.groupby(["origin", "destination"], sort=False).sum() + aggregate_trips = trips_df.groupby(["origin", "destination"], sort=False).sum( + numeric_only=True + ) # use the average household weight for all trips in the origin destination pair hh_weight_col = model_settings.get("HH_EXPANSION_WEIGHT_COL") @@ -75,15 +78,21 @@ def write_trip_matrices(network_los): dest_vals = aggregate_trips.index.get_level_values("destination") # use the land use table for the set of possible tazs - zone_index = pipeline.get_table("land_use").index + land_use = pipeline.get_table("land_use") + zone_index = land_use.index assert all(zone in zone_index for zone in orig_vals) assert all(zone in zone_index for zone in dest_vals) _, orig_index = zone_index.reindex(orig_vals) _, dest_index = zone_index.reindex(dest_vals) + try: + zone_labels = land_use[f"_original_{land_use.index.name}"] + except KeyError: + zone_labels = land_use.index + write_matrices( - aggregate_trips, zone_index, orig_index, dest_index, model_settings + aggregate_trips, zone_labels, orig_index, dest_index, model_settings ) elif network_los.zone_system == los.TWO_ZONE: # maz trips written to taz matrices @@ -94,7 +103,9 @@ def write_trip_matrices(network_los): trips_df["dtaz"] = ( pipeline.get_table("land_use").reindex(trips_df["destination"]).TAZ.tolist() ) - aggregate_trips = trips_df.groupby(["otaz", "dtaz"], sort=False).sum() + aggregate_trips = trips_df.groupby(["otaz", "dtaz"], sort=False).sum( + numeric_only=True + ) # use the average household weight for all trips in the origin destination pair hh_weight_col = model_settings.get("HH_EXPANSION_WEIGHT_COL") @@ -108,6 +119,15 @@ def write_trip_matrices(network_los): orig_vals = aggregate_trips.index.get_level_values("otaz") dest_vals = aggregate_trips.index.get_level_values("dtaz") + try: + land_use_taz = pipeline.get_table("land_use_taz") + except (KeyError, RuntimeError): + pass # table missing, ignore + else: + if "_original_TAZ" in land_use_taz.columns: + orig_vals = orig_vals.map(land_use_taz["_original_TAZ"]) + dest_vals = dest_vals.map(land_use_taz["_original_TAZ"]) + zone_index = pd.Index(network_los.get_tazs(), name="TAZ") assert all(zone in zone_index for zone in orig_vals) assert all(zone in zone_index for zone in dest_vals) @@ -130,7 +150,9 @@ def write_trip_matrices(network_los): trips_df["dtaz"] = ( pipeline.get_table("land_use").reindex(trips_df["destination"]).TAZ.tolist() ) - aggregate_trips = trips_df.groupby(["otaz", "dtaz"], sort=False).sum() + aggregate_trips = trips_df.groupby(["otaz", "dtaz"], sort=False).sum( + numeric_only=True + ) # use the average household weight for all trips in the origin destination pair hh_weight_col = model_settings.get("HH_EXPANSION_WEIGHT_COL") @@ -144,6 +166,15 @@ def write_trip_matrices(network_los): orig_vals = aggregate_trips.index.get_level_values("otaz") dest_vals = aggregate_trips.index.get_level_values("dtaz") + try: + land_use_taz = pipeline.get_table("land_use_taz") + except (KeyError, RuntimeError): + pass # table missing, ignore + else: + if "_original_TAZ" in land_use_taz.columns: + orig_vals = orig_vals.map(land_use_taz["_original_TAZ"]) + dest_vals = dest_vals.map(land_use_taz["_original_TAZ"]) + zone_index = pd.Index(network_los.get_tazs(), name="TAZ") assert all(zone in zone_index for zone in orig_vals) assert all(zone in zone_index for zone in dest_vals) @@ -156,7 +187,9 @@ def write_trip_matrices(network_los): ) logger.info("aggregating trips three zone tap...") - aggregate_trips = trips_df.groupby(["btap", "atap"], sort=False).sum() + aggregate_trips = trips_df.groupby(["btap", "atap"], sort=False).sum( + numeric_only=True + ) # use the average household weight for all trips in the origin destination pair hh_weight_col = model_settings.get("HH_EXPANSION_WEIGHT_COL") @@ -216,6 +249,14 @@ def annotate_trips(trips, network_los, model_settings): trips_df, locals_dict, skims, model_settings, trace_label ) + if not np.issubdtype(trips_df["trip_period"].dtype, np.integer): + if hasattr(skim_dict, "map_time_periods_from_series"): + trip_period_idx = skim_dict.map_time_periods_from_series( + trips_df["trip_period"] + ) + if trip_period_idx is not None: + trips_df["trip_period"] = trip_period_idx + # Data will be expanded by an expansion weight column from # the households pipeline table, if specified in the model settings. hh_weight_col = model_settings.get("HH_EXPANSION_WEIGHT_COL") diff --git a/activitysim/abm/models/trip_mode_choice.py b/activitysim/abm/models/trip_mode_choice.py index 59bc8433f..54586a448 100644 --- a/activitysim/abm/models/trip_mode_choice.py +++ b/activitysim/abm/models/trip_mode_choice.py @@ -20,7 +20,7 @@ from activitysim.core.pathbuilder import TransitVirtualPathBuilder from activitysim.core.util import assign_in_place -from .util import estimation +from .util import estimation, annotate, school_escort_tours_trips from .util.mode import mode_choice_simulate logger = logging.getLogger(__name__) @@ -102,6 +102,13 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id): ) od_skim_wrapper = skim_dict.wrap("origin", "destination") + if hasattr(skim_dict, "map_time_periods_from_series"): + trip_period_idx = skim_dict.map_time_periods_from_series( + trips_merged["trip_period"] + ) + if trip_period_idx is not None: + trips_merged["trip_period"] = trip_period_idx + skims = { "odt_skims": odt_skim_stack_wrapper, "dot_skims": dot_skim_stack_wrapper, @@ -204,6 +211,7 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id): estimator.write_choosers(trips_segment) locals_dict.update(skims) + locals_dict["timeframe"] = "trip" choices = mode_choice_simulate( choosers=trips_segment, @@ -273,6 +281,15 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id): trips_df = trips.to_frame() assign_in_place(trips_df, choices_df) + if pipeline.is_table("school_escort_tours") & model_settings.get( + "FORCE_ESCORTEE_CHAUFFEUR_MODE_MATCH", True + ): + trips_df = ( + school_escort_tours_trips.force_escortee_trip_modes_to_match_chauffeur( + trips_df + ) + ) + tracing.print_summary("trip_modes", trips_merged.tour_mode, value_counts=True) tracing.print_summary( @@ -283,6 +300,9 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id): pipeline.replace_table("trips", trips_df) + if model_settings.get("annotate_trips"): + annotate.annotate_trips(model_settings, trace_label) + if trace_hh_id: tracing.trace_df( trips_df, diff --git a/activitysim/abm/models/trip_purpose.py b/activitysim/abm/models/trip_purpose.py index fe3103135..1e48444e7 100644 --- a/activitysim/abm/models/trip_purpose.py +++ b/activitysim/abm/models/trip_purpose.py @@ -1,7 +1,6 @@ # ActivitySim # See full license in LICENSE.txt. import logging - import numpy as np import pandas as pd @@ -17,6 +16,8 @@ ) from .util import estimation +from activitysim.core.util import reindex +from .util.school_escort_tours_trips import split_out_school_escorting_trips logger = logging.getLogger(__name__) @@ -71,9 +72,7 @@ def choose_intermediate_trip_purpose( # probs should sum to 1 across rows sum_probs = probs_spec[purpose_cols].sum(axis=1) - probs_spec.loc[:, purpose_cols] = probs_spec.loc[:, purpose_cols].div( - sum_probs, axis=0 - ) + probs_spec[purpose_cols] = probs_spec[purpose_cols].div(sum_probs, axis=0) # left join trips to probs (there may be multiple rows per trip for multiple depart ranges) choosers = pd.merge( @@ -265,6 +264,13 @@ def trip_purpose(trips, chunk_size, trace_hh_id): trips_df = trips.to_frame() + if pipeline.is_table("school_escort_trips"): + school_escort_trips = pipeline.get_table("school_escort_trips") + # separate out school escorting trips to exclude them from the model and estimation data bundle + trips_df, se_trips_df, full_trips_index = split_out_school_escorting_trips( + trips_df, school_escort_trips + ) + estimator = estimation.manager.begin_estimation("trip_purpose") if estimator: chooser_cols_for_estimation = [ @@ -293,6 +299,13 @@ def trip_purpose(trips, chunk_size, trace_hh_id): trips_df["purpose"] = choices + if pipeline.is_table("school_escort_trips"): + # setting purpose for school escort trips + se_trips_df["purpose"] = reindex(school_escort_trips.purpose, se_trips_df.index) + # merge trips back together preserving index order + trips_df = pd.concat([trips_df, se_trips_df]) + trips_df = trips_df.reindex(full_trips_index) + # we should have assigned a purpose to all trips assert not trips_df.purpose.isnull().any() diff --git a/activitysim/abm/models/trip_scheduling.py b/activitysim/abm/models/trip_scheduling.py index a594f5e0b..f8345650d 100644 --- a/activitysim/abm/models/trip_scheduling.py +++ b/activitysim/abm/models/trip_scheduling.py @@ -11,6 +11,7 @@ from activitysim.core import chunk, config, inject, logit, pipeline, tracing from activitysim.core.util import reindex +from .util.school_escort_tours_trips import split_out_school_escorting_trips from .util import probabilistic_scheduling as ps logger = logging.getLogger(__name__) @@ -405,6 +406,14 @@ def trip_scheduling(trips, tours, chunk_size, trace_hh_id): trips_df = trips.to_frame() tours = tours.to_frame() + if pipeline.is_table("school_escort_trips"): + school_escort_trips = pipeline.get_table("school_escort_trips") + # separate out school escorting trips to exclude them from the model and estimation data bundle + trips_df, se_trips_df, full_trips_index = split_out_school_escorting_trips( + trips_df, school_escort_trips + ) + non_se_trips_df = trips_df + # add columns 'tour_hour', 'earliest', 'latest' to trips set_tour_hour(trips_df, tours) @@ -500,6 +509,13 @@ def trip_scheduling(trips, tours, chunk_size, trace_hh_id): trips_df = trips.to_frame() + if pipeline.is_table("school_escort_trips"): + # separate out school escorting trips to exclude them from the model and estimation data bundle + trips_df, se_trips_df, full_trips_index = split_out_school_escorting_trips( + trips_df, school_escort_trips + ) + non_se_trips_df = trips_df + choices = pd.concat(choices_list) choices = choices.reindex(trips_df.index) @@ -530,6 +546,20 @@ def trip_scheduling(trips, tours, chunk_size, trace_hh_id): trips_df["depart"] = choices + if pipeline.is_table("school_escort_trips"): + # setting destination for school escort trips + se_trips_df["depart"] = reindex(school_escort_trips.depart, se_trips_df.index) + non_se_trips_df["depart"] = reindex(trips_df.depart, non_se_trips_df.index) + # merge trips back together + full_trips_df = pd.concat([non_se_trips_df, se_trips_df]) + full_trips_df["depart"] = full_trips_df["depart"].astype(int) + # want to preserve the original order, but first need to remove trips that were dropped + new_full_trips_index = full_trips_index[ + full_trips_index.isin(trips_df.index) + | full_trips_index.isin(se_trips_df.index) + ] + trips_df = full_trips_df.reindex(new_full_trips_index) + assert not trips_df.depart.isnull().any() pipeline.replace_table("trips", trips_df) diff --git a/activitysim/abm/models/trip_scheduling_choice.py b/activitysim/abm/models/trip_scheduling_choice.py index 463a00ee7..5a7abe1f5 100644 --- a/activitysim/abm/models/trip_scheduling_choice.py +++ b/activitysim/abm/models/trip_scheduling_choice.py @@ -370,6 +370,9 @@ def trip_scheduling_choice(trips, tours, skim_dict, chunk_size, trace_hh_id): "od_skims": od_skim_stack_wrapper, "do_skims": do_skim_stack_wrapper, "obib_skims": obib_skim_stack_wrapper, + "orig_col_name": "origin", + "dest_col_name": "destination", + "timeframe": "timeless_directional", } simulate.set_skim_wrapper_targets(tours_df, skims) diff --git a/activitysim/abm/models/util/annotate.py b/activitysim/abm/models/util/annotate.py new file mode 100644 index 000000000..a7ef4f54c --- /dev/null +++ b/activitysim/abm/models/util/annotate.py @@ -0,0 +1,51 @@ +# ActivitySim +# See full license in LICENSE.txt. +import pandas as pd +import logging + +from activitysim.core import expressions +from activitysim.core import tracing +from activitysim.core import inject +from activitysim.core import pipeline + +""" +Code for annotating tables +""" + +logger = logging.getLogger(__name__) + + +def annotate_tours(model_settings, trace_label): + """ + Add columns to the tours table in the pipeline according to spec. + + Parameters + ---------- + model_settings : dict + trace_label : str + """ + tours = inject.get_table("tours").to_frame() + expressions.assign_columns( + df=tours, + model_settings=model_settings.get("annotate_tours"), + trace_label=tracing.extend_trace_label(trace_label, "annotate_tours"), + ) + pipeline.replace_table("tours", tours) + + +def annotate_trips(model_settings, trace_label): + """ + Add columns to the trips table in the pipeline according to spec. + + Parameters + ---------- + model_settings : dict + trace_label : str + """ + trips = inject.get_table("trips").to_frame() + expressions.assign_columns( + df=trips, + model_settings=model_settings.get("annotate_trips"), + trace_label=tracing.extend_trace_label(trace_label, "annotate_trips"), + ) + pipeline.replace_table("trips", trips) diff --git a/activitysim/abm/models/util/canonical_ids.py b/activitysim/abm/models/util/canonical_ids.py index 0720340fc..4e46e26aa 100644 --- a/activitysim/abm/models/util/canonical_ids.py +++ b/activitysim/abm/models/util/canonical_ids.py @@ -1,11 +1,15 @@ # ActivitySim # See full license in LICENSE.txt. import logging +import re import numpy as np import pandas as pd +import re -from activitysim.core.util import reindex +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import simulate logger = logging.getLogger(__name__) @@ -54,6 +58,158 @@ def enumerate_tour_types(tour_flavors): return channels +def read_alts_file(file_name, set_index=None): + try: + alts = simulate.read_model_alts(file_name, set_index=set_index) + except (RuntimeError, FileNotFoundError): + logger.warning(f"Could not find file {file_name} to determine tour flavors.") + return pd.DataFrame() + return alts + + +def parse_tour_flavor_from_columns(columns, tour_flavor): + """ + determines the max number from columns if column name contains tour flavor + example: columns={'work1', 'work2'} -> 2 + + Parameters + ---------- + columns : list of str + tour_flavor : str + string subset that you want to find in columns + + Returns + ------- + int + max int found in columns with tour_flavor + """ + # below produces a list of numbers present in each column containing the tour flavor string + tour_numbers = [(re.findall(r"\d+", col)) for col in columns if tour_flavor in col] + + # flatten list + tour_numbers = [int(item) for sublist in tour_numbers for item in sublist] + + # find max + try: + max_tour_flavor = max(tour_numbers) + return max_tour_flavor + except ValueError: + # could not find a maximum integer for this flavor in the columns + return -1 + + +def determine_mandatory_tour_flavors(mtf_settings, model_spec, default_flavors): + provided_flavors = mtf_settings.get("MANDATORY_TOUR_FLAVORS", None) + + mandatory_tour_flavors = { + # hard code work and school tours + "work": parse_tour_flavor_from_columns(model_spec.columns, "work"), + "school": parse_tour_flavor_from_columns(model_spec.columns, "school"), + } + + valid_flavors = (mandatory_tour_flavors["work"] >= 1) & ( + mandatory_tour_flavors["school"] >= 1 + ) + + if provided_flavors is not None: + if mandatory_tour_flavors != provided_flavors: + logger.warning( + "Specified tour flavors do not match alternative file flavors" + ) + logger.warning( + f"{provided_flavors} does not equal {mandatory_tour_flavors}" + ) + # use provided flavors if provided + return provided_flavors + + if not valid_flavors: + # if flavors could not be parsed correctly and no flavors provided, return the default + logger.warning( + "Could not determine alts from alt file and no flavors were provided." + ) + logger.warning(f"Using defaults: {default_flavors}") + return default_flavors + + return mandatory_tour_flavors + + +def determine_non_mandatory_tour_max_extension( + model_settings, extension_probs, default_max_extension=2 +): + provided_max_extension = model_settings.get("MAX_EXTENSION", None) + + max_extension = parse_tour_flavor_from_columns(extension_probs.columns, "tour") + + if provided_max_extension is not None: + if provided_max_extension != max_extension: + logger.warning( + "Specified non mandatory tour extension does not match extension probabilities file" + ) + return provided_max_extension + + if (max_extension >= 0) & isinstance(max_extension, int): + return max_extension + + return default_max_extension + + +def determine_flavors_from_alts_file( + alts, provided_flavors, default_flavors, max_extension=0 +): + """ + determines the max number from alts for each column containing numbers + example: alts={'index': ['alt1', 'alt2'], 'escort': [1, 2], 'othdisc': [3, 4]} + yelds -> {'escort': 2, 'othdisc': 4} + + will return provided flavors if available + else, return default flavors if alts can't be groked + + Parameters + ---------- + alts : pd.DataFrame + provided_flavors : dict, optional + tour flavors provided by user in the model yaml + default_flavors : dict + default tour flavors to fall back on + max_extension : int + scale to increase number of tours accross all alternatives + + Returns + ------- + dict + tour flavors + """ + try: + flavors = { + c: int(alts[c].max() + max_extension) + for c in alts.columns + if all(alts[c].astype(str).str.isnumeric()) + } + valid_flavors = all( + [(isinstance(flavor, str) & (num >= 0)) for flavor, num in flavors.items()] + ) & (len(flavors) > 0) + except (ValueError, AttributeError): + valid_flavors = False + + if provided_flavors is not None: + if flavors != provided_flavors: + logger.warning( + f"Specified tour flavors {provided_flavors} do not match alternative file flavors {flavors}" + ) + # use provided flavors if provided + return provided_flavors + + if not valid_flavors: + # if flavors could not be parsed correctly and no flavors provided, return the default + logger.warning( + "Could not determine alts from alt file and no flavors were provided." + ) + logger.warning(f"Using defaults: {default_flavors}") + return default_flavors + + return flavors + + def canonical_tours(): """ create labels for every the possible tour by combining tour_type/tour_num. @@ -63,32 +219,74 @@ def canonical_tours(): list of canonical tour labels in alphabetical order """ - # FIXME we pathalogically know what the possible tour_types and their max tour_nums are - # FIXME instead, should get flavors from alts tables (but we would have to know their names...) - # alts = pipeline.get_table('non_mandatory_tour_frequency_alts') - # non_mandatory_tour_flavors = {c : alts[c].max() for c in alts.columns} - - # - non_mandatory_channels - MAX_EXTENSION = 2 - non_mandatory_tour_flavors = { - "escort": 2 + MAX_EXTENSION, - "shopping": 1 + MAX_EXTENSION, - "othmaint": 1 + MAX_EXTENSION, - "othdiscr": 1 + MAX_EXTENSION, - "eatout": 1 + MAX_EXTENSION, - "social": 1 + MAX_EXTENSION, + # ---- non_mandatory_channels + nm_model_settings_file_name = "non_mandatory_tour_frequency.yaml" + nm_model_settings = config.read_model_settings(nm_model_settings_file_name) + nm_alts = read_alts_file("non_mandatory_tour_frequency_alternatives.csv") + + # first need to determine max extension + try: + ext_probs_f = config.config_file_path( + "non_mandatory_tour_frequency_extension_probs.csv" + ) + extension_probs = pd.read_csv(ext_probs_f, comment="#") + except (RuntimeError, FileNotFoundError): + logger.warning( + f"non_mandatory_tour_frequency_extension_probs.csv file not found" + ) + extension_probs = pd.DataFrame() + max_extension = determine_non_mandatory_tour_max_extension( + nm_model_settings, extension_probs, default_max_extension=2 + ) + + provided_nm_tour_flavors = nm_model_settings.get("NON_MANDATORY_TOUR_FLAVORS", None) + default_nm_tour_flavors = { + "escort": 2 + max_extension, + "shopping": 1 + max_extension, + "othmaint": 1 + max_extension, + "othdiscr": 1 + max_extension, + "eatout": 1 + max_extension, + "social": 1 + max_extension, } + + non_mandatory_tour_flavors = determine_flavors_from_alts_file( + nm_alts, provided_nm_tour_flavors, default_nm_tour_flavors, max_extension + ) non_mandatory_channels = enumerate_tour_types(non_mandatory_tour_flavors) - # - mandatory_channels - mandatory_tour_flavors = {"work": 2, "school": 2} + logger.info(f"Non-Mandatory tour flavors used are {non_mandatory_tour_flavors}") + + # ---- mandatory_channels + mtf_model_settings_file_name = "mandatory_tour_frequency.yaml" + mtf_model_settings = config.read_model_settings(mtf_model_settings_file_name) + mtf_spec = mtf_model_settings.get("SPEC", "mandatory_tour_frequency.csv") + mtf_model_spec = read_alts_file(file_name=mtf_spec) + default_mandatory_tour_flavors = {"work": 2, "school": 2} + + mandatory_tour_flavors = determine_mandatory_tour_flavors( + mtf_model_settings, mtf_model_spec, default_mandatory_tour_flavors + ) mandatory_channels = enumerate_tour_types(mandatory_tour_flavors) - # - atwork_subtour_channels + logger.info(f"Mandatory tour flavors used are {mandatory_tour_flavors}") + + # ---- atwork_subtour_channels + atwork_model_settings_file_name = "atwork_subtour_frequency.yaml" + atwork_model_settings = config.read_model_settings(atwork_model_settings_file_name) + atwork_alts = read_alts_file("atwork_subtour_frequency_alternatives.csv") + + provided_atwork_flavors = atwork_model_settings.get("ATWORK_SUBTOUR_FLAVORS", None) + default_atwork_flavors = {"eat": 1, "business": 2, "maint": 1} + + atwork_subtour_flavors = determine_flavors_from_alts_file( + atwork_alts, provided_atwork_flavors, default_atwork_flavors + ) + atwork_subtour_channels = enumerate_tour_types(atwork_subtour_flavors) + + logger.info(f"Atwork subtour flavors used are {atwork_subtour_flavors}") + # we need to distinguish between subtours of different work tours # (e.g. eat1_1 is eat subtour for parent work tour 1 and eat1_2 is for work tour 2) - atwork_subtour_flavors = {"eat": 1, "business": 2, "maint": 1} - atwork_subtour_channels = enumerate_tour_types(atwork_subtour_flavors) max_work_tours = mandatory_tour_flavors["work"] atwork_subtour_channels = [ "%s_%s" % (c, i + 1) @@ -96,14 +294,24 @@ def canonical_tours(): for i in range(max_work_tours) ] - # - joint_tour_channels - joint_tour_flavors = { + # ---- joint_tour_channels + jtf_model_settings_file_name = "joint_tour_frequency.yaml" + jtf_model_settings = config.read_model_settings(jtf_model_settings_file_name) + jtf_alts = read_alts_file("joint_tour_frequency_alternatives.csv") + provided_joint_flavors = jtf_model_settings.get("JOINT_TOUR_FLAVORS", None) + + default_joint_flavors = { "shopping": 2, "othmaint": 2, "othdiscr": 2, "eatout": 2, "social": 2, } + joint_tour_flavors = determine_flavors_from_alts_file( + jtf_alts, provided_joint_flavors, default_joint_flavors + ) + logger.info(f"Joint tour flavors used are {joint_tour_flavors}") + joint_tour_channels = enumerate_tour_types(joint_tour_flavors) joint_tour_channels = ["j_%s" % c for c in joint_tour_channels] @@ -114,12 +322,29 @@ def canonical_tours(): + joint_tour_channels ) + # ---- school escort channels + # only include if model is run + if pipeline.is_table("school_escort_tours") | ( + "school_escorting" in config.setting("models", default=[]) + ): + se_model_settings_file_name = "school_escorting.yaml" + se_model_settings = config.read_model_settings(se_model_settings_file_name) + num_escortees = se_model_settings.get("NUM_ESCORTEES", 3) + school_escort_flavors = {"escort": 2 * num_escortees} + school_escort_channels = enumerate_tour_types(school_escort_flavors) + school_escort_channels = ["se_%s" % c for c in school_escort_channels] + logger.info(f"School escort tour flavors used are {school_escort_flavors}") + + sub_channels = sub_channels + school_escort_channels + sub_channels.sort() return sub_channels -def set_tour_index(tours, parent_tour_num_col=None, is_joint=False): +def set_tour_index( + tours, parent_tour_num_col=None, is_joint=False, is_school_escorting=False +): """ The new index values are stable based on the person_id, tour_type, and tour_num. The existing index is ignored and replaced. @@ -161,6 +386,9 @@ def set_tour_index(tours, parent_tour_num_col=None, is_joint=False): if is_joint: tours["tour_id"] = "j_" + tours["tour_id"] + if is_school_escorting: + tours["tour_id"] = "se_" + tours["tour_id"] + # map recognized strings to ints tours.tour_id = tours.tour_id.replace( to_replace=possible_tours, value=list(range(possible_tours_count)) @@ -171,9 +399,16 @@ def set_tour_index(tours, parent_tour_num_col=None, is_joint=False): tours.tour_id = (tours.person_id * possible_tours_count) + tours.tour_id - # if tours.tour_id.duplicated().any(): - # print("\ntours.tour_id not unique\n%s" % tours[tours.tour_id.duplicated(keep=False)]) - # print(tours[tours.tour_id.duplicated(keep=False)][['survey_tour_id', 'tour_type', 'tour_category']]) + if tours.tour_id.duplicated().any(): + print( + "\ntours.tour_id not unique\n%s" + % tours[tours.tour_id.duplicated(keep=False)] + ) + print( + tours[tours.tour_id.duplicated(keep=False)][ + ["survey_tour_id", "tour_type", "tour_category"] + ] + ) assert not tours.tour_id.duplicated().any() tours.set_index("tour_id", inplace=True, verify_integrity=True) @@ -182,14 +417,50 @@ def set_tour_index(tours, parent_tour_num_col=None, is_joint=False): return tours -def set_trip_index(trips, tour_id_column="tour_id"): +def determine_max_trips_per_leg(default_max_trips_per_leg=4): + model_settings_file_name = "stop_frequency.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + + # first see if flavors given explicitly + provided_max_trips_per_leg = model_settings.get("MAX_TRIPS_PER_LEG", None) + + # determine flavors from alternative file + try: + alts = read_alts_file("stop_frequency_alternatives.csv") + trips_per_leg = [ + int(alts[c].max()) + for c in alts.columns + if all(alts[c].astype(str).str.isnumeric()) + ] + # adding one for additional trip home or to primary dest + max_trips_per_leg = max(trips_per_leg) + 1 + if max_trips_per_leg > 1: + valid_max_trips = True + except (ValueError, RuntimeError): + valid_max_trips = False + + if provided_max_trips_per_leg is not None: + if provided_max_trips_per_leg != max_trips_per_leg: + logger.warning( + "Provided max number of stops on tour does not match with stop frequency alternatives file" + ) + return provided_max_trips_per_leg - MAX_TRIPS_PER_LEG = 4 # max number of trips per leg (inbound or outbound) of tour + if valid_max_trips: + return max_trips_per_leg + + return default_max_trips_per_leg + + +def set_trip_index(trips, tour_id_column="tour_id"): + # max number of trips per leg (inbound or outbound) of tour + # = stops + 1 for primary half-tour destination + max_trips_per_leg = determine_max_trips_per_leg() # canonical_trip_num: 1st trip out = 1, 2nd trip out = 2, 1st in = 5, etc. - canonical_trip_num = (~trips.outbound * MAX_TRIPS_PER_LEG) + trips.trip_num + canonical_trip_num = (~trips.outbound * max_trips_per_leg) + trips.trip_num trips["trip_id"] = ( - trips[tour_id_column] * (2 * MAX_TRIPS_PER_LEG) + canonical_trip_num + trips[tour_id_column] * (2 * max_trips_per_leg) + canonical_trip_num ) trips.set_index("trip_id", inplace=True, verify_integrity=True) diff --git a/activitysim/abm/models/util/school_escort_tours_trips.py b/activitysim/abm/models/util/school_escort_tours_trips.py new file mode 100644 index 000000000..778fb8645 --- /dev/null +++ b/activitysim/abm/models/util/school_escort_tours_trips.py @@ -0,0 +1,689 @@ +import logging +import pandas as pd +import numpy as np +import warnings + +from activitysim.abm.models.util import canonical_ids +from activitysim.core import pipeline +from activitysim.core import inject +from activitysim.core.util import reindex + +from ..school_escorting import NUM_ESCORTEES + +logger = logging.getLogger(__name__) + + +def determine_chauf_outbound_flag(row, i): + if row["school_escort_direction"] == "outbound": + outbound = True + elif ( + (row["school_escort_direction"] == "inbound") + & (i == 0) + & (row["escort_type"] == "pure_escort") + ): + # chauf is going to pick up the first child + outbound = True + else: + # chauf is inbound and has already picked up a child or taken their mandatory tour + outbound = False + return outbound + + +def create_chauf_trip_table(row): + dropoff = True if row["school_escort_direction"] == "outbound" else False + + row["person_id"] = row["chauf_id"] + row["destination"] = row["school_destinations"].split("_") + + participants = [] + school_escort_trip_num = [] + outbound = [] + purposes = [] + + for i, child_id in enumerate(row["escortees"].split("_")): + if dropoff: + # have the remaining children in car + participants.append("_".join(row["escortees"].split("_")[i:])) + else: + # remaining children not yet in car + participants.append("_".join(row["escortees"].split("_")[: i + 1])) + school_escort_trip_num.append(i + 1) + outbound.append(determine_chauf_outbound_flag(row, i)) + purposes.append("escort") + + if not dropoff: + # adding trip home + outbound.append(False) + school_escort_trip_num.append(i + 2) + purposes.append("home") + row["destination"].append(row["home_zone_id"]) + # kids aren't in car until after they are picked up, inserting empty car for first trip + participants = [""] + participants + + if dropoff & (row["escort_type"] == "ride_share"): + # adding trip to work + outbound.append(True) + school_escort_trip_num.append(i + 2) + purposes.append(row["first_mand_tour_purpose"]) + row["destination"].append(row["first_mand_tour_dest"]) + # kids have already been dropped off + participants = participants + [""] + + row["escort_participants"] = participants + row["school_escort_trip_num"] = school_escort_trip_num + row["outbound"] = outbound + row["purpose"] = purposes + return row + + +def create_chauf_escort_trips(bundles): + + chauf_trip_bundles = bundles.apply(lambda row: create_chauf_trip_table(row), axis=1) + chauf_trip_bundles["tour_id"] = bundles["chauf_tour_id"].astype(int) + + # departure time is the first school start in the outbound school_escort_direction and the last school end in the inbound school_escort_direction + starts = ( + chauf_trip_bundles["school_starts"].str.split("_", expand=True).astype(float) + ) + ends = chauf_trip_bundles["school_ends"].str.split("_", expand=True).astype(float) + chauf_trip_bundles["depart"] = np.where( + chauf_trip_bundles["school_escort_direction"] == "outbound", + starts.min(axis=1), + ends.max(axis=1), + ) + + # create a new trip for each escortee destination + chauf_trips = chauf_trip_bundles.explode( + [ + "destination", + "escort_participants", + "school_escort_trip_num", + "outbound", + "purpose", + ] + ).reset_index() + + # numbering trips such that outbound escorting trips must come first and inbound trips must come last + outbound_trip_num = -1 * ( + chauf_trips.groupby(["tour_id", "outbound"]).cumcount(ascending=False) + 1 + ) + inbound_trip_num = 100 + chauf_trips.groupby(["tour_id", "outbound"]).cumcount( + ascending=True + ) + chauf_trips["trip_num"] = np.where( + chauf_trips.outbound == True, outbound_trip_num, inbound_trip_num + ) + + # --- determining trip origin + # origin is previous destination + chauf_trips["origin"] = chauf_trips.groupby("tour_id")["destination"].shift() + # outbound trips start at home + first_outbound_trips = (chauf_trips["outbound"] == True) & ( + chauf_trips["school_escort_trip_num"] == 1 + ) + chauf_trips.loc[first_outbound_trips, "origin"] = chauf_trips.loc[ + first_outbound_trips, "home_zone_id" + ] + # inbound school escort ride sharing trips start at work + first_rs_inb = ( + (chauf_trips["outbound"] == False) + & (chauf_trips["school_escort_trip_num"] == 1) + & (chauf_trips["escort_type"] == "ride_share") + ) + chauf_trips.loc[first_rs_inb, "origin"] = chauf_trips.loc[ + first_rs_inb, "first_mand_tour_dest" + ] + + assert all( + ~chauf_trips["origin"].isna() + ), f"Missing trip origins for {chauf_trips[chauf_trips['origin'].isna()]}" + + chauf_trips["primary_purpose"] = np.where( + chauf_trips["escort_type"] == "pure_escort", + "escort", + chauf_trips["first_mand_tour_purpose"], + ) + assert all( + ~chauf_trips["primary_purpose"].isna() + ), f"Missing tour purpose for {chauf_trips[chauf_trips['primary_purpose'].isna()]}" + + chauf_trips.loc[ + chauf_trips["purpose"] == "home", "trip_num" + ] = 999 # trips home are always last + chauf_trips.sort_values( + by=["household_id", "tour_id", "outbound", "trip_num"], + ascending=[True, True, False, True], + inplace=True, + ) + + return chauf_trips + + +def create_child_escorting_stops(row, escortee_num): + escortees = row["escortees"].split("_") + if escortee_num > (len(escortees) - 1): + # this bundle does not have this many escortees + return row + dropoff = True if row["school_escort_direction"] == "outbound" else False + + row["person_id"] = int(escortees[escortee_num]) + row["tour_id"] = row["school_tour_ids"].split("_")[escortee_num] + school_dests = row["school_destinations"].split("_") + + destinations = [] + purposes = [] + participants = [] + school_escort_trip_num = [] + + escortee_order = ( + escortees[: escortee_num + 1] if dropoff else escortees[escortee_num:] + ) + + # for i, child_id in enumerate(escortees[:escortee_num+1]): + for i, child_id in enumerate(escortee_order): + is_last_stop = i == len(escortee_order) - 1 + + if dropoff: + # dropping childen off + # children in car are the child and the children after + participants.append("_".join(escortees[i:])) + dest = school_dests[i] + purpose = "school" if row["person_id"] == int(child_id) else "escort" + + else: + # picking children up + # children in car are the child and those already picked up + participants.append("_".join(escortees[: escortee_num + i + 1])) + # going home if last stop, otherwise to next school destination + dest = ( + row["home_zone_id"] + if is_last_stop + else school_dests[escortee_num + i + 1] + ) + purpose = "home" if is_last_stop else "escort" + + # filling arrays + destinations.append(dest) + school_escort_trip_num.append(i + 1) + purposes.append(purpose) + + row["escort_participants"] = participants + row["school_escort_trip_num"] = school_escort_trip_num + row["purpose"] = purposes + row["destination"] = destinations + return row + + +def create_escortee_trips(bundles): + + escortee_trips = [] + for escortee_num in range(0, int(bundles.num_escortees.max()) + 1): + escortee_bundles = bundles.apply( + lambda row: create_child_escorting_stops(row, escortee_num), axis=1 + ) + escortee_trips.append(escortee_bundles) + + escortee_trips = pd.concat(escortee_trips) + escortee_trips = escortee_trips[~escortee_trips.person_id.isna()] + + # departure time is the first school start in the outbound direction and the last school end in the inbound direction + starts = escortee_trips["school_starts"].str.split("_", expand=True).astype(float) + ends = escortee_trips["school_ends"].str.split("_", expand=True).astype(float) + escortee_trips["outbound"] = np.where( + escortee_trips["school_escort_direction"] == "outbound", True, False + ) + escortee_trips["depart"] = np.where( + escortee_trips["school_escort_direction"] == "outbound", + starts.min(axis=1), + ends.max(axis=1), + ).astype(int) + escortee_trips["primary_purpose"] = "school" + + # create a new trip for each escortee destination + escortee_trips = escortee_trips.explode( + ["destination", "escort_participants", "school_escort_trip_num", "purpose"] + ).reset_index() + + # numbering trips such that outbound escorting trips must come first and inbound trips must come last + # this comes in handy when merging trips to others in the tour decided downstream + outbound_trip_num = -1 * ( + escortee_trips.groupby(["tour_id", "outbound"]).cumcount(ascending=False) + 1 + ) + inbound_trip_num = 100 + escortee_trips.groupby(["tour_id", "outbound"]).cumcount( + ascending=True + ) + escortee_trips["trip_num"] = np.where( + escortee_trips.outbound == True, outbound_trip_num, inbound_trip_num + ) + escortee_trips["trip_count"] = escortee_trips["trip_num"] + escortee_trips.groupby( + ["tour_id", "outbound"] + ).trip_num.transform("count") + + id_cols = ["household_id", "person_id", "tour_id"] + escortee_trips[id_cols] = escortee_trips[id_cols].astype("int64") + + escortee_trips.loc[ + escortee_trips["purpose"] == "home", "trip_num" + ] = 999 # trips home are always last + escortee_trips.sort_values( + by=["household_id", "tour_id", "outbound", "trip_num"], + ascending=[True, True, False, True], + inplace=True, + ) + escortee_trips["origin"] = escortee_trips.groupby("tour_id")["destination"].shift() + # first trips on tour start from home (except for atwork subtours, but school escorting doesn't happen on those tours) + escortee_trips["origin"] = np.where( + escortee_trips["origin"].isna(), + escortee_trips["home_zone_id"], + escortee_trips["origin"], + ) + + return escortee_trips + + +def create_school_escort_trips(escort_bundles): + chauf_trips = create_chauf_escort_trips(escort_bundles) + escortee_trips = create_escortee_trips(escort_bundles) + school_escort_trips = pd.concat([chauf_trips, escortee_trips], axis=0) + + # Can't assign a true trip id yet because they are numbered based on the number of stops in each direction. + # This isn't decided until after the stop frequency model runs. + # Creating this temporary school_escort_trip_id column to match them downstream + school_escort_trips["school_escort_trip_id"] = ( + school_escort_trips["tour_id"].astype("int64") * 10 + + school_escort_trips.groupby("tour_id")["trip_num"].cumcount() + ) + + return school_escort_trips + + +def add_pure_escort_tours(tours, school_escort_tours): + missing_cols = [ + col for col in tours.columns if col not in school_escort_tours.columns + ] + assert ( + len(missing_cols) == 0 + ), f"missing columns {missing_cols} in school_escort_tours" + if len(missing_cols) > 0: + logger.warning(f"Columns {missing_cols} are missing from school escort tours") + school_escort_tours[missing_cols] = pd.NA + + tours_to_add = school_escort_tours[~school_escort_tours.index.isin(tours.index)] + tours = pd.concat([tours, tours_to_add[tours.columns]]) + + return tours + + +def add_school_escorting_type_to_tours_table(escort_bundles, tours): + school_tour = (tours.tour_type == "school") & (tours.tour_num == 1) + + for school_escort_direction in ["outbound", "inbound"]: + for escort_type in ["ride_share", "pure_escort"]: + bundles = escort_bundles[ + (escort_bundles.school_escort_direction == school_escort_direction) + & (escort_bundles.escort_type == escort_type) + ] + # Setting for child school tours + for child_num in range(1, NUM_ESCORTEES + 1): + i = str(child_num) + filter = school_tour & tours["person_id"].isin( + bundles["bundle_child" + i] + ) + tours.loc[filter, "school_esc_" + school_escort_direction] = escort_type + + tours.loc[ + bundles.chauf_tour_id, "school_esc_" + school_escort_direction + ] = escort_type + + return tours + + +def process_tours_after_escorting_model(escort_bundles, tours): + # adding indicators to tours that include school escorting + tours = add_school_escorting_type_to_tours_table(escort_bundles, tours) + + # setting number of escortees on tour + num_escortees = escort_bundles.drop_duplicates("chauf_tour_id").set_index( + "chauf_tour_id" + )["num_escortees"] + tours.loc[num_escortees.index, "num_escortees"] = num_escortees + + # set same start / end time for tours if they are bundled together + tour_segment_id_cols = [ + "school_tour_id_child" + str(i) for i in range(1, NUM_ESCORTEES + 1) + ] + ["chauf_tour_id"] + + for id_col in tour_segment_id_cols: + out_segment_bundles = escort_bundles[ + (escort_bundles[id_col] > 1) + & (escort_bundles.school_escort_direction == "outbound") + ].set_index(id_col) + starts = ( + out_segment_bundles["school_starts"].str.split("_").str[0].astype(int) + ) # first start + tours.loc[starts.index, "start"] = starts + + inb_segment_bundles = escort_bundles[ + (escort_bundles[id_col] > 1) + & (escort_bundles.school_escort_direction == "inbound") + ].set_index(id_col) + ends = ( + inb_segment_bundles["school_ends"].str.split("_").str[-1].astype(int) + ) # last end + tours.loc[ends.index, "end"] = ends + + bad_end_times = tours["start"] > tours["end"] + tours.loc[bad_end_times, "end"] = tours.loc[bad_end_times, "start"] + + # updating tdd to match start and end times + tdd_alts = inject.get_injectable("tdd_alts") + tdd_alts["tdd"] = tdd_alts.index + tours.drop(columns="tdd", inplace=True) + + tours["tdd"] = pd.merge( + tours.reset_index(), tdd_alts, how="left", on=["start", "end"] + ).set_index("tour_id")["tdd"] + # since this is an injectable, we want to leave it how we found it + # not removing tdd created here will caues problems downstream + tdd_alts.drop(columns="tdd", inplace=True) + + assert all( + ~tours.tdd.isna() + ), f"Tours have missing tdd values: {tours[tours.tdd.isna()][['tour_type', 'start', 'end', 'tdd']]}" + + return tours + + +def merge_school_escort_trips_into_pipeline(): + school_escort_trips = pipeline.get_table("school_escort_trips") + tours = pipeline.get_table("tours") + trips = pipeline.get_table("trips") + + # want to remove stops if school escorting takes place on that half tour so we can replace them with the actual stops + out_se_tours = tours[ + tours["school_esc_outbound"].isin(["pure_escort", "ride_share"]) + ] + inb_se_tours = tours[ + tours["school_esc_inbound"].isin(["pure_escort", "ride_share"]) + ] + # removing outbound stops + trips = trips[ + ~(trips.tour_id.isin(out_se_tours.index) & (trips["outbound"] == True)) + ] + # removing inbound stops + trips = trips[ + ~(trips.tour_id.isin(inb_se_tours.index) & (trips["outbound"] == False)) + ] + + # don't want to double count the non-escort half-tour of chauffeurs doing pure escort + inb_chauf_pe_tours = tours[ + (tours["school_esc_inbound"] == "pure_escort") + & (tours.primary_purpose == "escort") + ] + out_chauf_pe_tours = tours[ + (tours["school_esc_outbound"] == "pure_escort") + & (tours.primary_purpose == "escort") + ] + school_escort_trips = school_escort_trips[ + ~( + school_escort_trips.tour_id.isin(inb_chauf_pe_tours.index) + & (school_escort_trips["outbound"] == True) + ) + ] + school_escort_trips = school_escort_trips[ + ~( + school_escort_trips.tour_id.isin(out_chauf_pe_tours.index) + & (school_escort_trips["outbound"] == False) + ) + ] + + # for better merge with trips created in stop frequency + school_escort_trips["failed"] = False + + trips = pd.concat( + [ + trips, + school_escort_trips[ + list(trips.columns) + + [ + "escort_participants", + "school_escort_direction", + "school_escort_trip_id", + ] + ], + ] + ) + # sorting by escorting order as determined when creating the school escort trips + trips.sort_values( + by=["household_id", "tour_id", "outbound", "trip_num"], + ascending=[True, True, False, True], + inplace=True, + ) + grouped = trips.groupby(["tour_id", "outbound"]) + trips["trip_num"] = trips.groupby(["tour_id", "outbound"]).cumcount() + 1 + trips["trip_count"] = trips["trip_num"] + grouped.cumcount(ascending=False) + + # ensuring data types + trips["outbound"] = trips["outbound"].astype(bool) + trips["origin"] = trips["origin"].astype(int) + trips["destination"] = trips["destination"].astype(int) + + # updating trip_id now that we have all trips + trips = canonical_ids.set_trip_index(trips) + school_escort_trip_id_map = { + v: k + for k, v in trips.loc[ + ~trips["school_escort_trip_id"].isna(), "school_escort_trip_id" + ] + .to_dict() + .items() + } + + school_escort_trips["trip_id"] = np.where( + school_escort_trips["school_escort_trip_id"].isin( + school_escort_trip_id_map.keys() + ), + school_escort_trips["school_escort_trip_id"].map(school_escort_trip_id_map), + school_escort_trips["school_escort_trip_id"], + ) + school_escort_trips.set_index("trip_id", inplace=True) + + # can drop school_escort_trip_id column now since it has been replaced + trips.drop(columns="school_escort_trip_id", inplace=True) + + # replace trip table and pipeline and register with the random number generator + pipeline.replace_table("trips", trips) + pipeline.get_rn_generator().drop_channel("trips") + pipeline.get_rn_generator().add_channel("trips", trips) + pipeline.replace_table("school_escort_trips", school_escort_trips) + + # updating stop frequency in tours tabel to be consistent + num_outbound_stops = ( + trips[trips.outbound == True].groupby("tour_id")["trip_num"].count() - 1 + ) + num_inbound_stops = ( + trips[trips.outbound == False].groupby("tour_id")["trip_num"].count() - 1 + ) + stop_freq = ( + num_outbound_stops.astype(str) + "out_" + num_inbound_stops.astype(str) + "in" + ) + tours.loc[stop_freq.index, "stop_frequency"] = stop_freq + + # no need to reset random number generator since no tours added + pipeline.replace_table("tours", tours) + + return trips + + +def recompute_tour_count_statistics(): + tours = pipeline.get_table("tours") + + grouped = tours.groupby(["person_id", "tour_type"]) + tours["tour_type_num"] = grouped.cumcount() + 1 + tours["tour_type_count"] = tours["tour_type_num"] + grouped.cumcount( + ascending=False + ) + + grouped = tours.groupby("person_id") + tours["tour_num"] = grouped.cumcount() + 1 + tours["tour_count"] = tours["tour_num"] + grouped.cumcount(ascending=False) + + pipeline.replace_table("tours", tours) + + +def create_pure_school_escort_tours(bundles): + # creating home to school tour for chauffers making pure escort tours + # ride share tours are already created since they go off the mandatory tour + pe_tours = bundles[bundles["escort_type"] == "pure_escort"] + + pe_tours["origin"] = pe_tours["home_zone_id"] + # desination is the last dropoff / pickup location + pe_tours["destination"] = ( + pe_tours["school_destinations"].str.split("_").str[-1].astype(int) + ) + # start is the first start time for outbound trips or the last school end time for inbound trips + starts = pe_tours["school_starts"].str.split("_").str[0].astype(int) + ends = pe_tours["school_ends"].str.split("_").str[-1].astype(int) + pe_tours["start"] = np.where( + pe_tours["school_escort_direction"] == "outbound", starts, ends + ) + + # just set end equal to start time -- non-escort half of tour is determined downstream + pe_tours["end"] = pe_tours["start"] + pe_tours["duration"] = pe_tours["end"] - pe_tours["start"] + pe_tours["tdd"] = pd.NA # updated with full tours table + + pe_tours["person_id"] = pe_tours["chauf_id"] + + pe_tours["tour_category"] = "non_mandatory" + pe_tours["number_of_participants"] = 1 + pe_tours["tour_type"] = "escort" + pe_tours["school_esc_outbound"] = np.where( + pe_tours["school_escort_direction"] == "outbound", "pure_escort", pd.NA + ) + pe_tours["school_esc_inbound"] = np.where( + pe_tours["school_escort_direction"] == "inbound", "pure_escort", pd.NA + ) + + pe_tours = pe_tours.sort_values(by=["household_id", "person_id", "start"]) + + # finding what the next start time for that person for scheduling + pe_tours["next_pure_escort_start"] = ( + pe_tours.groupby("person_id")["start"].shift(-1).fillna(0) + ) + + grouped = pe_tours.groupby(["person_id", "tour_type"]) + pe_tours["tour_type_num"] = grouped.cumcount() + 1 + pe_tours["tour_type_count"] = pe_tours["tour_type_num"] + grouped.cumcount( + ascending=False + ) + + grouped = pe_tours.groupby("person_id") + pe_tours["tour_num"] = grouped.cumcount() + 1 + pe_tours["tour_count"] = pe_tours["tour_num"] + grouped.cumcount(ascending=False) + + pe_tours = canonical_ids.set_tour_index(pe_tours, is_school_escorting=True) + + return pe_tours + + +def split_out_school_escorting_trips(trips, school_escort_trips): + # separate out school escorting trips to exclude them from the model + full_trips_index = trips.index + se_trips_mask = trips.index.isin(school_escort_trips.index) + se_trips = trips[se_trips_mask] + trips = trips[~se_trips_mask] + + return trips, se_trips, full_trips_index + + +def force_escortee_tour_modes_to_match_chauffeur(tours): + # FIXME: escortee tour can have different chauffeur in outbound vs inbound direction + # which tour mode should it be set to? Currently it's whatever comes last. + # Does it even matter if trip modes are getting matched later? + escort_bundles = inject.get_table("escort_bundles").to_frame() + + # grabbing the school tour ids for each school escort bundle + se_tours = escort_bundles[["school_tour_ids", "chauf_tour_id"]].copy() + # merging in chauffeur tour mode + se_tours["tour_mode"] = reindex(tours.tour_mode, se_tours.chauf_tour_id) + # creating entry for each escort school tour + se_tours["school_tour_ids"] = se_tours.school_tour_ids.str.split("_") + se_tours = se_tours.explode(["school_tour_ids"]) + # create mapping between school tour id and chauffeur tour mode + se_tours["school_tour_ids"] = se_tours["school_tour_ids"].astype("int64") + mode_mapping = se_tours.set_index("school_tour_ids")["tour_mode"] + + # setting escortee tours to have the same tour mode as chauffeur + original_modes = tours["tour_mode"].copy() + tours.loc[mode_mapping.index, "tour_mode"] = mode_mapping + diff = tours["tour_mode"] != original_modes + logger.info( + f"Changed {diff.sum()} tour modes of school escortees to match their chauffeur" + ) + + assert ( + ~tours.tour_mode.isna() + ).all(), f"Missing tour mode for {tours[tours.tour_mode.isna()]}" + return tours + + +def force_escortee_trip_modes_to_match_chauffeur(trips): + school_escort_trips = inject.get_table("school_escort_trips").to_frame() + + # starting with only trips that are created as part of the school escorting model + se_trips = trips[trips.index.isin(school_escort_trips.index)].copy() + + # getting chauffeur tour id + se_trips["chauf_tour_id"] = reindex( + school_escort_trips.chauf_tour_id, se_trips.index + ) + # merging chauffeur trips onto escortee trips + se_trips = ( + se_trips.reset_index() + .merge( + se_trips[ + [ + "origin", + "destination", + "depart", + "escort_participants", + "chauf_tour_id", + "trip_mode", + ] + ], + how="left", + left_on=[ + "origin", + "destination", + "depart", + "escort_participants", + "tour_id", + ], + right_on=[ + "origin", + "destination", + "depart", + "escort_participants", + "chauf_tour_id", + ], + suffixes=("", "_chauf"), + ) + .set_index("trip_id") + ) + # trip_mode_chauf is na if the trip belongs to a chauffeur instead of an escortee + # only want to change mode for escortees + mode_mapping = se_trips[~se_trips["trip_mode_chauf"].isna()]["trip_mode_chauf"] + + # setting escortee trips to have the same trip mode as chauffeur + original_modes = trips["trip_mode"].copy() + trips.loc[mode_mapping.index, "trip_mode"] = mode_mapping + diff = trips["trip_mode"] != original_modes + logger.info( + f"Changed {diff.sum()} trip modes of school escortees to match their chauffeur" + ) + + assert ( + ~trips.trip_mode.isna() + ).all(), f"Missing trip mode for {trips[trips.trip_mode.isna()]}" + return trips diff --git a/activitysim/abm/models/util/test/configs/atwork_subtour_frequency_alternatives.csv b/activitysim/abm/models/util/test/configs/atwork_subtour_frequency_alternatives.csv new file mode 100644 index 000000000..ba9941919 --- /dev/null +++ b/activitysim/abm/models/util/test/configs/atwork_subtour_frequency_alternatives.csv @@ -0,0 +1,8 @@ +#,,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,eat,business,maint +no_subtours,0,0,0 +eat,1,0,0 +business1,0,1,0 +maint,0,0,1 +business2,0,2,0 +eat_business,1,1,0 diff --git a/activitysim/abm/models/util/test/configs/joint_tour_frequency_alternatives.csv b/activitysim/abm/models/util/test/configs/joint_tour_frequency_alternatives.csv new file mode 100644 index 000000000..7bf93731f --- /dev/null +++ b/activitysim/abm/models/util/test/configs/joint_tour_frequency_alternatives.csv @@ -0,0 +1,23 @@ +#,,,,,alt file for building joint tours +alt,shopping,othmaint,eatout,social,othdiscr +0_tours,0,0,0,0,0 +1_Shop,1,0,0,0,0 +1_Main,0,1,0,0,0 +1_Eat,0,0,1,0,0 +1_Visit,0,0,0,1,0 +1_Disc,0,0,0,0,1 +2_SS,2,0,0,0,0 +2_SM,1,1,0,0,0 +2_SE,1,0,1,0,0 +2_SV,1,0,0,1,0 +2_SD,1,0,0,0,1 +2_MM,0,2,0,0,0 +2_ME,0,1,1,0,0 +2_MV,0,1,0,1,0 +2_MD,0,1,0,0,1 +2_EE,0,0,2,0,0 +2_EV,0,0,1,1,0 +2_ED,0,0,1,0,1 +2_VV,0,0,0,2,0 +2_VD,0,0,0,1,1 +2_DD,0,0,0,0,2 diff --git a/activitysim/abm/models/util/test/configs/mandatory_tour_frequency.csv b/activitysim/abm/models/util/test/configs/mandatory_tour_frequency.csv new file mode 100644 index 000000000..848bbf77a --- /dev/null +++ b/activitysim/abm/models/util/test/configs/mandatory_tour_frequency.csv @@ -0,0 +1,101 @@ +Label,Description,Expression,work1,work2,school1,school2,work_and_school +util_ft_worker,Full-time worker alternative-specific constants,ptype == 1,0,coef_ft_worker_work2_asc,,, +util_pt_worker,Part-time worker alternative-specific constants,ptype == 2,0,coef_pt_worker_work2_asc,,, +util_univ,University student alternative-specific constants,ptype == 3,coef_univ_work1_asc,coef_univ_work2_asc,0,coef_univ_school2_asc,coef_univ_work_and_school_asc +util_non_working_adult,Non-working adult alternative-specific constants,ptype == 4,,,,, +util_retired,Retired alternative-specific constants,ptype == 5,,,,, +util_driving_age_child,Driving-age child alternative-specific constants,ptype == 6,,,0,coef_driving_age_child_school2_asc,coef_driving_age_child_work_and_school_asc +util_pre_driving_age_child,Pre-driving age child who is in school alternative-specific constants,ptype == 7,,,0,coef_pre_driving_age_child_school2_asc, +util_female_ft_worker,Female - Full-time worker interaction,(ptype == 1) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school +util_female_pt_worker,Female - Part-time worker interaction,(ptype == 2) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school +util_female_univ,Female - University student interaction,(ptype == 3) & female,coef_female_work1,coef_female_work2,coef_female_school1,coef_female_school2,coef_female_work_and_school +util_female_non_working_adult,Female - Non-working adult interaction,(ptype == 4) & female,0,coef_female_work2,coef_female_school1,, +util_female_retired,Female - Retired interaction,(ptype == 5) & female,0,coef_female_work2,coef_female_school1,, +util_female_driving_age_child,Female - Driving-age child interaction,(ptype == 6) & female,coef_female_work1,,0,coef_female_school2,coef_female_work_and_school +util_female_pre_driving,Female - Pre-driving age child who is in school interaction,(ptype == 7) & female,coef_female_work1,,0,coef_female_school2, +util_under_35_ft,Under 35 - Full-time worker interaction,(ptype == 1) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school +util_under_35_pt,Under 35 - Part-time worker interaction,(ptype == 2) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school +util_under_35_univ,Under 35 - University student interaction,(ptype == 3) & (age <= 35),coef_under_35_work1,coef_under_35_work2,0,coef_under_35_school2,coef_under_35_work_and_school +util_under_35_non_working,Under 35 - Non-working adult interaction,(ptype == 4) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,, +util_can_walk_to_work_ft,Can walk to work - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_pt,Can walk to work - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_univ,Can walk to work - University student interaction,(ptype == 3) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_non_working_adult,Can walk to work - Non-working adult interaction,(ptype == 4) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, +util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, +util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, +util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 +util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 +util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 +util_round_trip_auto_time_to_work_non_working_adult,Round trip auto time to work - Non-working adult interaction,(ptype == 4) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, +util_round_trip_auto_time_to_work_retired,Round trip auto time to work - Retired,(ptype == 5) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, +util_round_trip_auto_time_to_school_univ,Round trip auto time to school - University student interaction,(ptype == 3) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school +util_round_trip_auto_time_to_school_driving_age_child,Round trip auto time to school - Driving-age child interaction,(ptype == 6) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school +util_round_trip_auto_time_to_school_pre_driving_age_child,Round trip auto time to school - Pre-driving age child who is in school interaction,(ptype == 7) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2, +util_student_employted_univ,Student is employed - University student interaction,(ptype == 3) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed +util_student_employted_driving_age_child,Student is employed - Driving-age child interaction,(ptype == 6) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed +util_non_student_goes_to_school_ft,Non-student goes to school - Full-time worker interaction,(ptype == 1) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school +util_non_student_goes_to_school_pt,Non-student goes to school - Part-time worker interaction,(ptype == 2) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school +util_non_student_goes_to_school_non_working_adult,Non-student goes to school - Non-working adult interaction,(ptype == 4) & nonstudent_to_school,,,coef_non_student_goes_to_school,, +util_non_student_goes_to_school_retired,Non-student goes to school - Retired interaction,(ptype == 5) & nonstudent_to_school,,,coef_non_student_goes_to_school,, +util_no_cars_in_hh_ft,No cars in household - Full-time worker interaction,(ptype == 1) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_pt,No cars in household - Part-time worker interaction,(ptype == 2) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_unif,No cars in household - University student interaction,(ptype == 3) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_non_working_adult,No cars in household - Non-working adult interaction,(ptype == 4) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, +util_no_cars_in_hh_retired,No cars in household - Retired interaction,(ptype == 5) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, +util_no_cars_in_hh_driving_age_student,No cars in household - Driving-age student interaction,(ptype == 6) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_pre_driving_age,No cars in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2, +util_fewer_cars_than_drivers_univ,Fewer cars than drivers in household - University student interaction,(ptype == 3) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, +util_fewer_cars_than_drivers_driving_age_student,Fewer cars than drivers in household - Driving-age student interaction,(ptype == 6) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, +util_fewer_cars_than_drivers_pre_driving_age,Fewer cars than drivers in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, +util_num_preschool_in_hh_ft,Number of preschool children in household - Full-time worker interaction,(ptype == 1) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_pt,Number of preschool children in household - Part-time worker interaction,(ptype == 2) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_univ,Number of preschool children in household - University student interaction,(ptype == 3) * (num_young_children),coef_num_preschool_in_hh_work1,coef_num_preschool_in_hh_work2,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_non_working_adult,Number of preschool children in household - Non-working adult interaction,(ptype == 4) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, +util_num_preschool_in_hh_retired,Number of preschool children in household - Retired interaction,(ptype == 5) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, +util_num_preschool_in_hh_driving_age_student,Number of preschool children in household - Driving-age student interaction,(ptype == 6) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_pre_driving_age_in_school,Number of preschool children in household - Pre-driving age child who is in school interaction,(ptype == 7) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2, +util_num_nonworkers_in_hh_ft,Number of non-workers in the household - Full-time worker interaction,(ptype == 1) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, +util_num_nonworkers_in_hh_pt,Number of non-workers in the household - Part-time worker interaction,(ptype == 2) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, +util_hh_income_gt_50k_ft,Household income higher than $50k - Full-time worker interaction,(ptype == 1) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school +util_hh_income_gt_50k_pt,Household income higher than $50k - Part-time worker interaction,(ptype == 2) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school +util_hh_income_gt_50k_univ,Household income higher than $50k - University student interaction,(ptype == 3) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,coef_hh_income_gt_50k_work,0,,coef_hh_income_gt_50k_student_work_and_school +util_hh_income_gt_50k_non_working_adult,Household income higher than $50k - Non-working adult interaction,(ptype == 4) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, +util_hh_income_gt_50k_retired,Household income higher than $50k - Retired interaction,(ptype == 5) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, +util_hh_income_gt_50k_driving_age_student,Household income higher than $50k - Driving-age student interaction,(ptype == 6) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,,coef_hh_income_gt_50k_student_work_and_school +util_hh_income_gt_50k_pre_driving_age_student,Household income higher than $50k - Pre-driving age child who is in school interaction,(ptype == 7) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,, +util_non_family_hh_ft,Non-family household - Full-time worker interaction,(ptype == 1) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 +util_non_family_hh_pt,Non-family household - Part-time worker interaction,(ptype == 2) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 +util_non_family_hh_univ,Non-family household - University student interaction,(ptype == 3) & non_family,coef_non_family_hh_category2,coef_non_family_hh_category2,0,,coef_non_family_hh_category2 +util_non_family_hh_non_working_adult,Non-family household - Non-working adult interaction,(ptype == 4) & non_family,0,,coef_non_family_hh_category1,, +util_non_family_hh_retired,Non-family household - Retired interaction,(ptype == 5) & non_family,0,,coef_non_family_hh_category1,, +util_non_family_hh_driving_age_student,Non-family household - Driving-age student interaction,(ptype == 6) & non_family,coef_non_family_hh_category2,,0,,coef_non_family_hh_category2 +util_non_family_hh_pre_driving_age_student,Non-family household - Pre-driving age child who is in school interaction,(ptype == 7) & non_family,coef_non_family_hh_category2,,0,, +util_num_under_16_not_at_school_ft,Number of children under 16 not at school - Full-time worker interaction,(ptype == 1) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_pt,Number of children under 16 not at school - Part-time worker interaction,(ptype == 2) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_univ,Number of children under 16 not at school - University student interaction,(ptype == 3) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_non_working_adult,Number of children under 16 not at school - Non-working adult interaction,(ptype == 4) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, +util_num_under_16_not_at_school_retired,Number of children under 16 not at school - Retired,(ptype == 5) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, +util_num_under_16_not_at_school_driving_age_student,Number of children under 16 not at school - Driving-age student interaction,(ptype == 6) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_pre_driving_age,Number of children under 16 not at school - Pre-driving age child who is in school interaction,(ptype == 7) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2, +util_nome_urban_ft,Home is in urban area - Full-time worker interaction,(ptype == 1) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school +util_nome_urban_pt,Home is in urban area - Part-time worker interaction,(ptype == 2) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school +util_nome_urban_univ,Home is in urban area - University student interaction,(ptype == 3) & home_is_urban,coef_home_urban_work1,coef_home_urban_work2,0,coef_home_urban_school2,coef_home_urban_work_and_school +util_nome_urban_non_working_adult,Home is in urban area - Non-working adult interaction,(ptype == 4) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, +util_nome_urban_retired,Home is in urban area - Retired interaction,(ptype == 5) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, +util_nome_urban_driving_age_student,Home is in urban area - Driving-age student interaction,(ptype == 6) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2,coef_home_urban_work_and_school +util_nome_urban_pre_driving_age_student,Home is in urban area - Pre-driving age child who is in school interaction,(ptype == 7) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2, +util_availability_ft,Unavailable: Full-time worker,ptype == 1,,,,coef_unavailable, +util_availability_pt,Unavailable: Part-time worker,ptype == 2,,,,coef_unavailable, +util_availability_non_working_adult,Unavailable: Non-working adult,ptype == 4,,,,coef_unavailable,coef_unavailable +util_availability_retired,Unavailable: Retired,ptype == 5,,,,coef_unavailable,coef_unavailable +util_availability_driving_age_child,Unavailable: Driving-age child,ptype == 6,coef_unavailable,coef_unavailable,,, +util_availability_pre_driving_age_student,Unavailable: Pre-driving age child who is in school,ptype == 7,,coef_unavailable,,,coef_unavailable +util_availability_pre_driving_age_not_in_school,Unavailable: Pre-driving age child who is not in school,ptype == 8,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable +util_availability_work_tours_no_usual_work_location,Unavailable: Work tours for those with no usual work location,~(workplace_zone_id > -1),coef_unavailable,coef_unavailable,,,coef_unavailable +util_availability_school_tours_no_usual_school_location,Unavailable: School tours for those with no usual school location,~(school_zone_id > -1),,,coef_unavailable,coef_unavailable,coef_unavailable diff --git a/activitysim/abm/models/util/test/configs/mandatory_tour_frequency.yaml b/activitysim/abm/models/util/test/configs/mandatory_tour_frequency.yaml new file mode 100644 index 000000000..ae7c323c0 --- /dev/null +++ b/activitysim/abm/models/util/test/configs/mandatory_tour_frequency.yaml @@ -0,0 +1,10 @@ + +SPEC: mandatory_tour_frequency.csv +COEFFICIENTS: mandatory_tour_frequency_coefficients.csv + +annotate_persons: + SPEC: annotate_persons_mtf + DF: persons + TABLES: + - tours + diff --git a/activitysim/abm/models/util/test/configs/non_mandatory_tour_frequency_alternatives.csv b/activitysim/abm/models/util/test/configs/non_mandatory_tour_frequency_alternatives.csv new file mode 100644 index 000000000..b9765aa75 --- /dev/null +++ b/activitysim/abm/models/util/test/configs/non_mandatory_tour_frequency_alternatives.csv @@ -0,0 +1,97 @@ +escort,shopping,othmaint,othdiscr,eatout,social +0,0,0,0,0,0 +0,0,0,1,0,0 +0,0,0,0,0,1 +0,0,0,1,0,1 +0,0,0,0,1,0 +0,0,0,1,1,0 +0,0,0,0,1,1 +0,0,0,1,1,1 +0,0,1,0,0,0 +0,0,1,1,0,0 +0,0,1,0,0,1 +0,0,1,1,0,1 +0,0,1,0,1,0 +0,0,1,1,1,0 +0,0,1,0,1,1 +0,0,1,1,1,1 +0,1,0,0,0,0 +0,1,0,1,0,0 +0,1,0,0,0,1 +0,1,0,1,0,1 +0,1,0,0,1,0 +0,1,0,1,1,0 +0,1,0,0,1,1 +0,1,0,1,1,1 +0,1,1,0,0,0 +0,1,1,1,0,0 +0,1,1,0,0,1 +0,1,1,1,0,1 +0,1,1,0,1,0 +0,1,1,1,1,0 +0,1,1,0,1,1 +0,1,1,1,1,1 +1,0,0,0,0,0 +1,0,0,1,0,0 +1,0,0,0,0,1 +1,0,0,1,0,1 +1,0,0,0,1,0 +1,0,0,1,1,0 +1,0,0,0,1,1 +1,0,0,1,1,1 +1,0,1,0,0,0 +1,0,1,1,0,0 +1,0,1,0,0,1 +1,0,1,1,0,1 +1,0,1,0,1,0 +1,0,1,1,1,0 +1,0,1,0,1,1 +1,0,1,1,1,1 +1,1,0,0,0,0 +1,1,0,1,0,0 +1,1,0,0,0,1 +1,1,0,1,0,1 +1,1,0,0,1,0 +1,1,0,1,1,0 +1,1,0,0,1,1 +1,1,0,1,1,1 +1,1,1,0,0,0 +1,1,1,1,0,0 +1,1,1,0,0,1 +1,1,1,1,0,1 +1,1,1,0,1,0 +1,1,1,1,1,0 +1,1,1,0,1,1 +1,1,1,1,1,1 +2,0,0,0,0,0 +2,0,0,1,0,0 +2,0,0,0,0,1 +2,0,0,1,0,1 +2,0,0,0,1,0 +2,0,0,1,1,0 +2,0,0,0,1,1 +2,0,0,1,1,1 +2,0,1,0,0,0 +2,0,1,1,0,0 +2,0,1,0,0,1 +2,0,1,1,0,1 +2,0,1,0,1,0 +2,0,1,1,1,0 +2,0,1,0,1,1 +2,0,1,1,1,1 +2,1,0,0,0,0 +2,1,0,1,0,0 +2,1,0,0,0,1 +2,1,0,1,0,1 +2,1,0,0,1,0 +2,1,0,1,1,0 +2,1,0,0,1,1 +2,1,0,1,1,1 +2,1,1,0,0,0 +2,1,1,1,0,0 +2,1,1,0,0,1 +2,1,1,1,0,1 +2,1,1,0,1,0 +2,1,1,1,1,0 +2,1,1,0,1,1 +2,1,1,1,1,1 diff --git a/activitysim/abm/models/util/test/configs/non_mandatory_tour_frequency_extension_probs.csv b/activitysim/abm/models/util/test/configs/non_mandatory_tour_frequency_extension_probs.csv new file mode 100644 index 000000000..ec78c4c8e --- /dev/null +++ b/activitysim/abm/models/util/test/configs/non_mandatory_tour_frequency_extension_probs.csv @@ -0,0 +1,193 @@ +ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours +1,0,0,1,0.829545455,1,1 +2,0,0,1,0.769230769,1,1 +3,0,0,1,0.893939394,1,1 +4,0,0,1,0.75,1,1 +5,0,0,1,0.842105263,1,1 +6,0,0,1,0.714285714,1,1 +7,0,0,1,0.814814815,1,1 +8,0,0,1,0.75,1,1 +1,1,0,1,0.789473684,1,1 +2,1,0,1,0.6,1,1 +3,1,0,1,1,1,1 +4,1,0,1,1,1,1 +5,1,0,1,0.825910931,1,1 +6,1,0,1,0.837209302,1,1 +7,1,0,1,0.6,1,1 +8,1,0,1,1,1,1 +1,0,1,1,0.842105263,1,1 +2,0,1,1,1,1,1 +3,0,1,1,1,1,1 +4,0,1,1,1,1,1 +5,0,1,1,1,1,1 +6,0,1,1,1,1,1 +7,0,1,1,1,1,1 +8,0,1,1,1,1,1 +1,1,1,1,1,1,1 +2,1,1,1,1,1,1 +3,1,1,1,1,1,1 +4,1,1,1,1,1,1 +5,1,1,1,0.777777778,1,1 +6,1,1,1,1,1,1 +7,1,1,1,1,1,1 +8,1,1,1,1,1,1 +1,0,0,2,0.892694064,0.99086758,1 +2,0,0,2,0.84057971,0.992753623,1 +3,0,0,2,0.971014493,1,1 +4,0,0,2,0.96969697,1,1 +5,0,0,2,0.870056497,0.994350282,1 +6,0,0,2,0.866666667,1,1 +7,0,0,2,0.971014493,1,1 +8,0,0,2,0.931034483,1,1 +1,1,0,2,0.885057471,1,1 +2,1,0,2,0.727272727,1,1 +3,1,0,2,0.971428571,1,1 +4,1,0,2,1,1,1 +5,1,0,2,0.895977809,0.993065187,1 +6,1,0,2,0.885185185,1,1 +7,1,0,2,1,1,1 +8,1,0,2,1,1,1 +1,0,1,2,0.910087719,0.993421053,1 +2,0,1,2,0.88,1,1 +3,0,1,2,0.8,1,1 +4,0,1,2,1,1,1 +5,0,1,2,1,1,1 +6,0,1,2,1,1,1 +7,0,1,2,1,1,1 +8,0,1,2,1,1,1 +1,1,1,2,1,1,1 +2,1,1,2,1,1,1 +3,1,1,2,1,1,1 +4,1,1,2,1,1,1 +5,1,1,2,1,1,1 +6,1,1,2,0.964912281,1,1 +7,1,1,2,1,1,1 +8,1,1,2,0.888888889,1,1 +1,0,0,3,0.935643564,0.997524752,1 +2,0,0,3,0.905660377,1,1 +3,0,0,3,0.978813559,1,1 +4,0,0,3,0.928571429,1,1 +5,0,0,3,0.901515152,0.992424242,1 +6,0,0,3,0.863636364,1,1 +7,0,0,3,0.947368421,1,1 +8,0,0,3,0.913043478,1,1 +1,1,0,3,0.893333333,0.986666667,1 +2,1,0,3,1,1,1 +3,1,0,3,1,1,1 +4,1,0,3,0.857142857,1,1 +5,1,0,3,0.916071429,0.996428571,1 +6,1,0,3,0.856382979,0.984042553,1 +7,1,0,3,1,1,1 +8,1,0,3,1,1,1 +1,0,1,3,0.916201117,0.991620112,1 +2,0,1,3,0.912280702,0.98245614,1 +3,0,1,3,1,1,1 +4,0,1,3,1,1,1 +5,0,1,3,1,1,1 +6,0,1,3,0.833333333,1,1 +7,0,1,3,0.961538462,1,1 +8,0,1,3,1,1,1 +1,1,1,3,0.97826087,0.989130435,1 +2,1,1,3,0.97260274,1,1 +3,1,1,3,1,1,1 +4,1,1,3,1,1,1 +5,1,1,3,0.995762712,1,1 +6,1,1,3,0.921568627,0.980392157,1 +7,1,1,3,1,1,1 +8,1,1,3,1,1,1 +1,0,0,4,0.9218107,0.995884774,1 +2,0,0,4,0.900900901,1,1 +3,0,0,4,0.997354497,1,1 +4,0,0,4,0.991176471,1,1 +5,0,0,4,0.921568627,0.980392157,1 +6,0,0,4,0.954545455,1,1 +7,0,0,4,1,1,1 +8,0,0,4,0.954545455,1,1 +1,1,0,4,0.941176471,0.970588235,1 +2,1,0,4,0.925925926,1,1 +3,1,0,4,1,1,1 +4,1,0,4,0.875,1,1 +5,1,0,4,0.915322581,1,1 +6,1,0,4,0.947674419,0.994186047,1 +7,1,0,4,0.666666667,1,1 +8,1,0,4,1,1,1 +1,0,1,4,0.925925926,0.987654321,1 +2,0,1,4,0.903703704,1,1 +3,0,1,4,1,1,1 +4,0,1,4,1,1,1 +5,0,1,4,1,1,1 +6,0,1,4,1,1,1 +7,0,1,4,1,1,1 +8,0,1,4,1,1,1 +1,1,1,4,1,1,1 +2,1,1,4,0.911111111,1,1 +3,1,1,4,1,1,1 +4,1,1,4,1,1,1 +5,1,1,4,1,1,1 +6,1,1,4,0.962962963,1,1 +7,1,1,4,1,1,1 +8,1,1,4,1,1,1 +1,0,0,5,0.976744186,1,1 +2,0,0,5,0.981818182,1,1 +3,0,0,5,0.985915493,1,1 +4,0,0,5,1,1,1 +5,0,0,5,1,1,1 +6,0,0,5,1,1,1 +7,0,0,5,1,1,1 +8,0,0,5,0.875,1,1 +1,1,0,5,1,1,1 +2,1,0,5,1,1,1 +3,1,0,5,0.964285714,1,1 +4,1,0,5,1,1,1 +5,1,0,5,0.985714286,1,1 +6,1,0,5,0.951807229,1,1 +7,1,0,5,1,1,1 +8,1,0,5,1,1,1 +1,0,1,5,0.926605505,1,1 +2,0,1,5,0.941176471,1,1 +3,0,1,5,1,1,1 +4,0,1,5,1,1,1 +5,0,1,5,1,1,1 +6,0,1,5,1,1,1 +7,0,1,5,1,1,1 +8,0,1,5,1,1,1 +1,1,1,5,1,1,1 +2,1,1,5,1,1,1 +3,1,1,5,0.972972973,1,1 +4,1,1,5,1,1,1 +5,1,1,5,1,1,1 +6,1,1,5,0.933333333,1,1 +7,1,1,5,1,1,1 +8,1,1,5,1,1,1 +1,0,0,6,0.93837535,0.988795518,1 +2,0,0,6,0.888888889,1,1 +3,0,0,6,0.966832504,0.998341625,1 +4,0,0,6,0.942028986,1,1 +5,0,0,6,0.88034188,1,1 +6,0,0,6,0.925925926,1,1 +7,0,0,6,0.967741935,1,1 +8,0,0,6,0.90625,1,1 +1,1,0,6,0.85915493,1,1 +2,1,0,6,0.818181818,0.96969697,1 +3,1,0,6,1,1,1 +4,1,0,6,0.952380952,1,1 +5,1,0,6,0.879237288,0.997881356,1 +6,1,0,6,0.862944162,0.984771574,1 +7,1,0,6,0.9,1,1 +8,1,0,6,1,1,1 +1,0,1,6,0.927835052,0.996563574,1 +2,0,1,6,0.859375,0.9921875,1 +3,0,1,6,1,1,1 +4,0,1,6,1,1,1 +5,0,1,6,0.92,1,1 +6,0,1,6,1,1,1 +7,0,1,6,0.904761905,1,1 +8,0,1,6,1,1,1 +1,1,1,6,0.982758621,1,1 +2,1,1,6,0.927710843,0.987951807,1 +3,1,1,6,0.982954545,1,1 +4,1,1,6,0.938679245,1,1 +5,1,1,6,1,1,1 +6,1,1,6,0.9375,1,1 +7,1,1,6,1,1,1 +8,1,1,6,1,1,1 diff --git a/activitysim/abm/models/util/test/test_flexible_tour_trip_ids.py b/activitysim/abm/models/util/test/test_flexible_tour_trip_ids.py new file mode 100644 index 000000000..178025160 --- /dev/null +++ b/activitysim/abm/models/util/test/test_flexible_tour_trip_ids.py @@ -0,0 +1,106 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import pandas as pd + +from ..canonical_ids import ( + determine_flavors_from_alts_file, + determine_mandatory_tour_flavors, +) + + +def test_mandatory_tour_flavors(): + mtf_settings = {} + default_mandatory_tour_flavors = {"work": 2, "school": 2} + + # first test using default + mandatory_tour_flavors = determine_mandatory_tour_flavors( + mtf_settings, + pd.DataFrame(columns=["random_name"]), + default_mandatory_tour_flavors, + ) + + assert mandatory_tour_flavors == default_mandatory_tour_flavors + + # creating dummy spec with different values + model_spec = pd.DataFrame( + data={ + "Label": ["dummy"], + "Description": ["dummy"], + "Expression": [""], + "work1": [1], + "work2": [1], + "work3": [1], + "school1": [1], + "school2": [1], + "school3": [1], + "work_and_school": [1], + } + ) + + # second test reading from spec + mandatory_tour_flavors = determine_mandatory_tour_flavors( + mtf_settings, model_spec, default_mandatory_tour_flavors + ) + assert mandatory_tour_flavors == {"work": 3, "school": 3} + + # third test is reading flavors from settings + mtf_settings["MANDATORY_TOUR_FLAVORS"] = {"work": 3, "school": 2} + mandatory_tour_flavors = determine_mandatory_tour_flavors( + mtf_settings, model_spec, default_mandatory_tour_flavors + ) + + assert mandatory_tour_flavors == {"work": 3, "school": 2} + + +def test_tour_flavors_from_alt_files(): + # alternative tour frequency files are used in joint, atwork, and non-mandatory tour frequency models + # this unit test checks the output from determining flavors from an alt file + + default_tour_flavors = { + "escort": 2, + "othmaint": 1, + "othdiscr": 1, + } + + # first test using default + tour_flavors = determine_flavors_from_alts_file( + pd.DataFrame(columns=["random_name"]), + provided_flavors=None, + default_flavors=default_tour_flavors, + ) + + assert tour_flavors == default_tour_flavors + + # second test is reading from alts file + alts = pd.DataFrame( + data={ + "Alts": ["alt1", "alt2", "alt3", "alt4"], + "escort": [0, 1, 2, 3], + "othmaint": [0, 0, 0, 0], + "othdiscr": [1, 2, 0, 0], + } + ) + + tour_flavors = determine_flavors_from_alts_file( + alts, provided_flavors=None, default_flavors=default_tour_flavors + ) + assert tour_flavors == {"escort": 3, "othmaint": 0, "othdiscr": 2} + + # now with max extension applied + tour_flavors = determine_flavors_from_alts_file( + alts, + provided_flavors=None, + default_flavors=default_tour_flavors, + max_extension=2, + ) + assert tour_flavors == {"escort": 5, "othmaint": 2, "othdiscr": 4} + + # now with provided tour flavors (which will ignore the max extension supplied too) + tour_flavors = determine_flavors_from_alts_file( + alts, + provided_flavors={"escort": 3, "othmaint": 3, "othdiscr": 3}, + default_flavors=default_tour_flavors, + max_extension=2, + ) + assert tour_flavors == {"escort": 3, "othmaint": 3, "othdiscr": 3} diff --git a/activitysim/abm/models/util/test/test_mandatory_tour_frequency.py b/activitysim/abm/models/util/test/test_mandatory_tour_frequency.py index 2739e1a40..3f0144608 100644 --- a/activitysim/abm/models/util/test/test_mandatory_tour_frequency.py +++ b/activitysim/abm/models/util/test/test_mandatory_tour_frequency.py @@ -8,9 +8,23 @@ import pandas.testing as pdt import pytest +from activitysim.core import inject + from ..tour_frequency import process_mandatory_tours +@pytest.fixture(scope="module") +def configs_dir(): + return os.path.join(os.path.dirname(__file__), "configs") + + +def setup_function(): + configs_dir = os.path.join(os.path.dirname(__file__), "configs") + inject.add_injectable("configs_dir", configs_dir) + output_dir = os.path.join(os.path.dirname(__file__), "output") + inject.add_injectable("output_dir", output_dir) + + def mandatory_tour_frequency_alternatives(): configs_dir = os.path.join(os.path.dirname(__file__), "configs") f = os.path.join(configs_dir, "mandatory_tour_frequency_alternatives.csv") diff --git a/activitysim/abm/models/util/test/test_non_mandatory_tour_frequency.py b/activitysim/abm/models/util/test/test_non_mandatory_tour_frequency.py index 5ae895630..b7fac8044 100644 --- a/activitysim/abm/models/util/test/test_non_mandatory_tour_frequency.py +++ b/activitysim/abm/models/util/test/test_non_mandatory_tour_frequency.py @@ -8,9 +8,18 @@ import pandas.testing as pdt import pytest +from activitysim.core import inject + from ..tour_frequency import process_non_mandatory_tours +def setup_function(): + configs_dir = os.path.join(os.path.dirname(__file__), "configs") + inject.add_injectable("configs_dir", configs_dir) + output_dir = os.path.join(os.path.dirname(__file__), "output") + inject.add_injectable("output_dir", output_dir) + + def test_nmtf(): persons = pd.DataFrame( diff --git a/activitysim/abm/models/util/tour_destination.py b/activitysim/abm/models/util/tour_destination.py index a39037b47..fed9c1557 100644 --- a/activitysim/abm/models/util/tour_destination.py +++ b/activitysim/abm/models/util/tour_destination.py @@ -76,6 +76,7 @@ def _destination_sample( chunk_size, chunk_tag, trace_label, + zone_layer=None, ): model_spec = simulate.spec_for_segment( @@ -98,7 +99,12 @@ def _destination_sample( ) sample_size = 0 - locals_d = {"skims": skims} + locals_d = { + "skims": skims, + "orig_col_name": skims.orig_key, # added for sharrow flows + "dest_col_name": skims.dest_key, # added for sharrow flows + "timeframe": "timeless", + } constants = config.get_model_constants(model_settings) if constants is not None: locals_d.update(constants) @@ -117,11 +123,15 @@ def _destination_sample( chunk_size=chunk_size, chunk_tag=chunk_tag, trace_label=trace_label, + zone_layer=zone_layer, ) + # if special person id is passed + chooser_id_column = model_settings.get("CHOOSER_ID_COLUMN", "person_id") + # remember person_id in chosen alts so we can merge with persons in subsequent steps # (broadcasts person_id onto all alternatives sharing the same tour_id index value) - choices["person_id"] = choosers.person_id + choices[chooser_id_column] = choosers[chooser_id_column] return choices @@ -175,11 +185,6 @@ def destination_sample( ORIG_TAZ = "TAZ" # likewise a temp, but if already in choosers, we assume we can use it opportunistically -def map_maz_to_taz(s, network_los): - maz_to_taz = network_los.maz_taz_df[["MAZ", "TAZ"]].set_index("MAZ").TAZ - return s.map(maz_to_taz) - - def aggregate_size_terms(dest_size_terms, network_los): # # aggregate MAZ_size_terms to TAZ_size_terms @@ -188,7 +193,9 @@ def aggregate_size_terms(dest_size_terms, network_los): MAZ_size_terms = dest_size_terms.copy() # add crosswalk DEST_TAZ column to MAZ_size_terms - MAZ_size_terms[DEST_TAZ] = map_maz_to_taz(MAZ_size_terms.index, network_los) + MAZ_size_terms[DEST_TAZ] = network_los.map_maz_to_taz(MAZ_size_terms.index) + if MAZ_size_terms[DEST_TAZ].isna().any(): + raise ValueError("found NaN MAZ") # aggregate to TAZ TAZ_size_terms = MAZ_size_terms.groupby(DEST_TAZ).agg({"size_term": "sum"}) @@ -216,6 +223,9 @@ def aggregate_size_terms(dest_size_terms, network_los): # print(f"TAZ_size_terms ({TAZ_size_terms.shape})\n{TAZ_size_terms}") # print(f"MAZ_size_terms ({MAZ_size_terms.shape})\n{MAZ_size_terms}") + if np.issubdtype(TAZ_size_terms[DEST_TAZ], np.floating): + raise TypeError("TAZ indexes are not integer") + return MAZ_size_terms, TAZ_size_terms @@ -475,7 +485,7 @@ def destination_presample( orig_maz = model_settings["CHOOSER_ORIG_COL_NAME"] assert orig_maz in choosers if ORIG_TAZ not in choosers: - choosers[ORIG_TAZ] = map_maz_to_taz(choosers[orig_maz], network_los) + choosers[ORIG_TAZ] = network_los.map_maz_to_taz(choosers[orig_maz]) # create wrapper with keys for this lookup - in this case there is a HOME_TAZ in the choosers # and a DEST_TAZ in the alternatives which get merged during interaction @@ -494,6 +504,7 @@ def destination_presample( chunk_size, chunk_tag=chunk_tag, trace_label=trace_label, + zone_layer="taz", ) # choose a MAZ for each DEST_TAZ choice, choice probability based on MAZ size_term fraction of TAZ total @@ -519,14 +530,18 @@ def run_destination_sample( # FIXME - MEMORY HACK - only include columns actually used in spec (omit them pre-merge) chooser_columns = model_settings["SIMULATE_CHOOSER_COLUMNS"] + + # if special person id is passed + chooser_id_column = model_settings.get("CHOOSER_ID_COLUMN", "person_id") + persons_merged = persons_merged[ [c for c in persons_merged.columns if c in chooser_columns] ] tours = tours[ - [c for c in tours.columns if c in chooser_columns or c == "person_id"] + [c for c in tours.columns if c in chooser_columns or c == chooser_id_column] ] choosers = pd.merge( - tours, persons_merged, left_on="person_id", right_index=True, how="left" + tours, persons_merged, left_on=chooser_id_column, right_index=True, how="left" ) # interaction_sample requires that choosers.index.is_monotonic_increasing @@ -576,7 +591,7 @@ def run_destination_sample( # remember person_id in chosen alts so we can merge with persons in subsequent steps # (broadcasts person_id onto all alternatives sharing the same tour_id index value) - choices["person_id"] = tours.person_id + choices[chooser_id_column] = tours[chooser_id_column] return choices @@ -612,6 +627,8 @@ def run_destination_logsums( """ logsum_settings = config.read_model_settings(model_settings["LOGSUM_SETTINGS"]) + # if special person id is passed + chooser_id_column = model_settings.get("CHOOSER_ID_COLUMN", "person_id") chunk_tag = "tour_destination.logsums" @@ -624,7 +641,7 @@ def run_destination_logsums( choosers = pd.merge( destination_sample, persons_merged, - left_on="person_id", + left_on=chooser_id_column, right_index=True, how="left", ) @@ -662,6 +679,7 @@ def run_destination_simulate( estimator, chunk_size, trace_label, + skip_choice=False, ): """ run destination_simulate on tour_destination_sample @@ -678,14 +696,18 @@ def run_destination_simulate( # FIXME - MEMORY HACK - only include columns actually used in spec (omit them pre-merge) chooser_columns = model_settings["SIMULATE_CHOOSER_COLUMNS"] + + # if special person id is passed + chooser_id_column = model_settings.get("CHOOSER_ID_COLUMN", "person_id") + persons_merged = persons_merged[ [c for c in persons_merged.columns if c in chooser_columns] ] tours = tours[ - [c for c in tours.columns if c in chooser_columns or c == "person_id"] + [c for c in tours.columns if c in chooser_columns or c == chooser_id_column] ] choosers = pd.merge( - tours, persons_merged, left_on="person_id", right_index=True, how="left" + tours, persons_merged, left_on=chooser_id_column, right_index=True, how="left" ) # interaction_sample requires that choosers.index.is_monotonic_increasing @@ -721,6 +743,9 @@ def run_destination_simulate( locals_d = { "skims": skims, + "orig_col_name": skims.orig_key, # added for sharrow flows + "dest_col_name": skims.dest_key, # added for sharrow flows + "timeframe": "timeless", } if constants is not None: locals_d.update(constants) @@ -743,6 +768,7 @@ def run_destination_simulate( trace_label=trace_label, trace_choice_name="destination", estimator=estimator, + skip_choice=skip_choice, ) if not want_logsums: @@ -764,6 +790,7 @@ def run_tour_destination( chunk_size, trace_hh_id, trace_label, + skip_choice=False, ): size_term_calculator = SizeTermCalculator(model_settings["SIZE_TERM_SELECTOR"]) @@ -839,6 +866,7 @@ def run_tour_destination( estimator=estimator, chunk_size=chunk_size, trace_label=tracing.extend_trace_label(segment_trace_label, "simulate"), + skip_choice=skip_choice, ) choices_list.append(choices) diff --git a/activitysim/abm/models/util/tour_od.py b/activitysim/abm/models/util/tour_od.py index 55cc9a4a0..91377bd6d 100644 --- a/activitysim/abm/models/util/tour_od.py +++ b/activitysim/abm/models/util/tour_od.py @@ -156,7 +156,12 @@ def _od_sample( ) sample_size = 0 - locals_d = {"skims": skims} + locals_d = { + "skims": skims, + "timeframe": "timeless", + "orig_col_name": ORIG_TAZ, + "dest_col_name": DEST_TAZ, + } constants = config.get_model_constants(model_settings) if constants is not None: locals_d.update(constants) @@ -175,7 +180,7 @@ def _od_sample( ) if skims.orig_key == ORIG_TAZ: - od_alts_df[ORIG_TAZ] = map_maz_to_taz(od_alts_df[origin_id_col], network_los) + od_alts_df[ORIG_TAZ] = network_los.map_maz_to_taz(od_alts_df[origin_id_col]) elif skims.orig_key not in od_alts_df: logger.error("Alts df is missing origin skim key column.") @@ -191,6 +196,7 @@ def _od_sample( chunk_size=chunk_size, chunk_tag=chunk_tag, trace_label=trace_label, + zone_layer="taz", ) return choices @@ -274,7 +280,7 @@ def aggregate_size_terms(dest_size_terms, network_los): MAZ_size_terms = dest_size_terms.copy() # add crosswalk DEST_TAZ column to MAZ_size_terms - MAZ_size_terms[DEST_TAZ] = map_maz_to_taz(MAZ_size_terms.index, network_los) + MAZ_size_terms[DEST_TAZ] = network_los.map_maz_to_taz(MAZ_size_terms.index) # aggregate to TAZ TAZ_size_terms = MAZ_size_terms.groupby(DEST_TAZ).agg({"size_term": "sum"}) @@ -993,6 +999,9 @@ def run_od_simulate( locals_d = { "skims": skims, + "timeframe": "timeless", + "orig_col_name": origin_col_name, + "dest_col_name": dest_col_name, } if constants is not None: locals_d.update(constants) diff --git a/activitysim/abm/models/util/tour_scheduling.py b/activitysim/abm/models/util/tour_scheduling.py index 5936ae81e..1d6de7316 100644 --- a/activitysim/abm/models/util/tour_scheduling.py +++ b/activitysim/abm/models/util/tour_scheduling.py @@ -4,7 +4,7 @@ import pandas as pd -from activitysim.core import config, expressions, inject, simulate, tracing +from activitysim.core import config, expressions, inject, simulate from . import estimation from . import vectorize_tour_scheduling as vts @@ -61,6 +61,7 @@ def run_tour_scheduling( # load segmented specs spec_segment_settings = model_settings.get("SPEC_SEGMENTS", {}) specs = {} + sharrow_skips = {} for spec_segment_name, spec_settings in spec_segment_settings.items(): bundle_name = f"{model_name}_{spec_segment_name}" @@ -76,6 +77,7 @@ def run_tour_scheduling( specs[spec_segment_name] = simulate.eval_coefficients( model_spec, coefficients_df, estimator ) + sharrow_skips[spec_segment_name] = spec_settings.get("sharrow_skip", False) if estimator: estimators[spec_segment_name] = estimator # add to local list @@ -90,6 +92,9 @@ def run_tour_scheduling( tour_segments[tour_segment_name] = {} tour_segments[tour_segment_name]["spec_segment_name"] = spec_segment_name tour_segments[tour_segment_name]["spec"] = specs[spec_segment_name] + tour_segments[tour_segment_name]["sharrow_skip"] = sharrow_skips[ + spec_segment_name + ] tour_segments[tour_segment_name]["estimator"] = estimators.get( spec_segment_name ) @@ -108,6 +113,7 @@ def run_tour_scheduling( spec_file_name = model_settings["SPEC"] model_spec = simulate.read_model_spec(file_name=spec_file_name) + sharrow_skip = model_settings.get("sharrow_skip", False) coefficients_df = simulate.read_model_coefficients(model_settings) model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) @@ -118,7 +124,11 @@ def run_tour_scheduling( estimator.write_coefficients(coefficients_df, model_settings) # - non_mandatory tour scheduling is not segmented by tour type - tour_segments = {"spec": model_spec, "estimator": estimator} + tour_segments = { + "spec": model_spec, + "estimator": estimator, + "sharrow_skip": sharrow_skip, + } if estimators: timetable.begin_transaction(list(estimators.values())) diff --git a/activitysim/abm/models/util/trip.py b/activitysim/abm/models/util/trip.py index a137934f5..870801e27 100644 --- a/activitysim/abm/models/util/trip.py +++ b/activitysim/abm/models/util/trip.py @@ -84,6 +84,14 @@ def cleanup_failed_trips(trips): assign_in_place(trips, patch_trips[["trip_num", "trip_count"]]) + # origin needs to match the previous destination + # (leaving first origin alone as it's already set correctly) + trips["origin"] = np.where( + (trips["trip_num"] == 1) & (trips["outbound"] == True), + trips["origin"], + trips.groupby("tour_id")["destination"].shift(), + ).astype(int) + del trips["patch"] del trips["failed"] diff --git a/activitysim/abm/models/util/vectorize_tour_scheduling.py b/activitysim/abm/models/util/vectorize_tour_scheduling.py index b7698ae11..0882a6f74 100644 --- a/activitysim/abm/models/util/vectorize_tour_scheduling.py +++ b/activitysim/abm/models/util/vectorize_tour_scheduling.py @@ -5,20 +5,10 @@ import numpy as np import pandas as pd -from activitysim.core import ( - chunk, - config, - expressions, - inject, - logit, - los, - mem, - simulate, -) +from activitysim.core import chunk, config, expressions, inject, los, simulate from activitysim.core import timetable as tt from activitysim.core import tracing from activitysim.core.interaction_sample_simulate import interaction_sample_simulate -from activitysim.core.pathbuilder import TransitVirtualPathBuilder from activitysim.core.util import reindex logger = logging.getLogger(__name__) @@ -179,7 +169,7 @@ def dedupe_alt_tdd(alt_tdd, tour_purpose, trace_label): tdd_segments = inject.get_injectable("tdd_alt_segments", None) alt_tdd_periods = None - logger.info(f"tdd_alt_segments specified for representative logsums") + logger.info("tdd_alt_segments specified for representative logsums") with chunk.chunk_log(tracing.extend_trace_label(trace_label, "dedupe_alt_tdd")): @@ -272,8 +262,8 @@ def dedupe_alt_tdd(alt_tdd, tour_purpose, trace_label): dedupe_columns = ["out_period", "in_period", "duration"] logger.warning( - f"No tdd_alt_segments for representative logsums so fallback to " - f"deduping tdd_alts by time_period and duration" + "No tdd_alt_segments for representative logsums so fallback to " + "deduping tdd_alts by time_period and duration" ) # - get list of unique (tour_id, out_period, in_period, duration) in alt_tdd_periods @@ -317,8 +307,13 @@ def compute_logsums( # - in_period and out_period assert "out_period" not in alt_tdd assert "in_period" not in alt_tdd + + # FIXME:MEMORY + # These two lines each generate a massive array of strings, + # using a bunch of RAM and slowing things down. alt_tdd["out_period"] = network_los.skim_time_period_label(alt_tdd["start"]) alt_tdd["in_period"] = network_los.skim_time_period_label(alt_tdd["end"]) + alt_tdd["duration"] = alt_tdd["end"] - alt_tdd["start"] # outside chunk_log context because we extend log_df call for alt_tdd made by our only caller _schedule_tours @@ -352,7 +347,7 @@ def compute_logsums( f"from {len(alt_tdd)} to {len(deduped_alt_tdds)} compared to USE_BRUTE_FORCE_TO_COMPUTE_LOGSUMS" ) - t0 = tracing.print_elapsed_time() + tracing.print_elapsed_time() # - compute logsums for the alt_tdd_periods deduped_alt_tdds["logsums"] = _compute_logsums( @@ -458,9 +453,9 @@ def tdd_interaction_dataset( Parameters ---------- - tours : pandas DataFrame + tours : pandas.DataFrame must have person_id column and index on tour_id - alts : pandas DataFrame + alts : pandas.DataFrame alts index must be timetable tdd id timetable : TimeTable object choice_column : str @@ -484,35 +479,46 @@ def tdd_interaction_dataset( tour_ids = np.repeat(tours.index, len(alts.index)) window_row_ids = np.repeat(tours[window_id_col], len(alts.index)) + chunk.log_df(trace_label, "window_row_ids", window_row_ids) alt_tdd = alts.take(alts_ids) alt_tdd.index = tour_ids - alt_tdd[window_id_col] = window_row_ids + + import xarray as xr + + alt_tdd_ = xr.Dataset.from_dataframe(alt_tdd) + dimname = alt_tdd.index.name or "index" + # alt_tdd_[window_id_col] = xr.DataArray(window_row_ids, dims=(dimname,)) + alt_tdd_[choice_column] = xr.DataArray( + alts_ids, dims=(dimname,), coords=alt_tdd_.coords + ) # add tdd alternative id # by convention, the choice column is the first column in the interaction dataset - alt_tdd.insert(loc=0, column=choice_column, value=alts_ids) + # alt_tdd.insert(loc=0, column=choice_column, value=alts_ids) # slice out all non-available tours - available = timetable.tour_available( - alt_tdd[window_id_col], alt_tdd[choice_column] - ) + available = timetable.tour_available(window_row_ids, alts_ids) + + del window_row_ids + chunk.log_df(trace_label, "window_row_ids", None) + logger.debug( f"tdd_interaction_dataset keeping {available.sum()} of ({len(available)}) available alt_tdds" ) assert available.any() chunk.log_df( - trace_label, "alt_tdd", alt_tdd + trace_label, "alt_tdd_", alt_tdd_ ) # catch this before we slice on available - alt_tdd = alt_tdd[available] + alt_tdd = alt_tdd_.isel({dimname: available}).to_dataframe() chunk.log_df(trace_label, "alt_tdd", alt_tdd) # FIXME - don't need this any more after slicing - del alt_tdd[window_id_col] + # del alt_tdd[window_id_col] return alt_tdd @@ -591,6 +597,7 @@ def _schedule_tours( tour_owner_id_col, estimator, tour_trace_label, + sharrow_skip=False, ): """ previous_tour stores values used to add columns that can be used in the spec @@ -697,6 +704,11 @@ def _schedule_tours( if constants is not None: locals_d.update(constants) + if sharrow_skip: + locals_d["_sharrow_skip"] = True + else: + locals_d["_sharrow_skip"] = False + if not RUN_ALTS_PREPROCESSOR_BEFORE_MERGE: # Note: Clint was running alts_preprocessor here on tdd_interaction_dataset instead of on raw (unmerged) alts # and he was using logsum_tour_purpose as selector, although logically it should be the spec_segment @@ -760,6 +772,7 @@ def schedule_tours( chunk_size, tour_trace_label, tour_chunk_tag, + sharrow_skip=False, ): """ chunking wrapper for _schedule_tours @@ -810,11 +823,12 @@ def schedule_tours( tour_owner_id_col, estimator, tour_trace_label=chunk_trace_label, + sharrow_skip=sharrow_skip, ) result_list.append(choices) - chunk.log_df(tour_trace_label, f"result_list", result_list) + chunk.log_df(tour_trace_label, "result_list", result_list) # FIXME: this will require 2X RAM # if necessary, could append to hdf5 store on disk: @@ -959,6 +973,7 @@ def vectorize_tour_scheduling( chunk_size=chunk_size, tour_trace_label=segment_trace_label, tour_chunk_tag=segment_chunk_tag, + sharrow_skip=tour_segment_info.get("sharrow_skip"), ) choice_list.append(choices) @@ -988,6 +1003,7 @@ def vectorize_tour_scheduling( chunk_size=chunk_size, tour_trace_label=tour_trace_label, tour_chunk_tag=tour_chunk_tag, + sharrow_skip=tour_segments.get("sharrow_skip"), ) choice_list.append(choices) @@ -1006,6 +1022,7 @@ def vectorize_subtour_scheduling( estimator, chunk_size=0, trace_label=None, + sharrow_skip=False, ): """ Like vectorize_tour_scheduling but specifically for atwork subtours @@ -1104,6 +1121,7 @@ def vectorize_subtour_scheduling( chunk_size, tour_trace_label, tour_chunk_tag, + sharrow_skip=sharrow_skip, ) choice_list.append(choices) @@ -1159,6 +1177,7 @@ def vectorize_joint_tour_scheduling( estimator, chunk_size=0, trace_label=None, + sharrow_skip=False, ): """ Like vectorize_tour_scheduling but specifically for joint tours @@ -1251,6 +1270,7 @@ def vectorize_joint_tour_scheduling( chunk_size, tour_trace_label, tour_chunk_tag, + sharrow_skip=sharrow_skip, ) # - update timetables of all joint tour participants diff --git a/activitysim/examples/prototype_semcog/extensions/work_from_home.py b/activitysim/abm/models/work_from_home.py similarity index 88% rename from activitysim/examples/prototype_semcog/extensions/work_from_home.py rename to activitysim/abm/models/work_from_home.py index d5f3c5cd2..97a80d301 100755 --- a/activitysim/examples/prototype_semcog/extensions/work_from_home.py +++ b/activitysim/abm/models/work_from_home.py @@ -15,6 +15,8 @@ def work_from_home(persons_merged, persons, chunk_size, trace_hh_id): """ This model predicts whether a person (worker) works from home. The output from this model is TRUE (if works from home) or FALSE (works away from home). + The workplace location choice is overridden for workers who work from home + and set to -1. """ trace_label = "work_from_home" @@ -22,7 +24,9 @@ def work_from_home(persons_merged, persons, chunk_size, trace_hh_id): choosers = persons_merged.to_frame() model_settings = config.read_model_settings(model_settings_file_name) - chooser_filter_column_name = model_settings.get("CHOOSER_FILTER_COLUMN_NAME") + chooser_filter_column_name = model_settings.get( + "CHOOSER_FILTER_COLUMN_NAME", "is_worker" + ) choosers = choosers[choosers[chooser_filter_column_name]] logger.info("Running %s with %d persons", trace_label, len(choosers)) @@ -154,6 +158,17 @@ def work_from_home(persons_merged, persons, chunk_size, trace_hh_id): persons[chooser_filter_column_name] & ~persons["work_from_home"] ) + # setting workplace_zone_id to -1 if person works from home + # this will exclude them from the telecommute frequency model choosers + # See https://github.com/ActivitySim/activitysim/issues/627 + dest_choice_column_name = model_settings.get( + "DEST_CHOICE_COLUMN_NAME", "workplace_zone_id" + ) + if dest_choice_column_name in persons.columns: + persons[dest_choice_column_name] = np.where( + persons.work_from_home == True, -1, persons[dest_choice_column_name] + ) + pipeline.replace_table("persons", persons) tracing.print_summary("work_from_home", persons.work_from_home, value_counts=True) diff --git a/activitysim/abm/tables/__init__.py b/activitysim/abm/tables/__init__.py index 145c9186c..a78e7ec57 100644 --- a/activitysim/abm/tables/__init__.py +++ b/activitysim/abm/tables/__init__.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. from . import ( accessibility, + disaggregate_accessibility, households, landuse, persons, diff --git a/activitysim/abm/tables/accessibility.py b/activitysim/abm/tables/accessibility.py index 3fd054421..6869da736 100644 --- a/activitysim/abm/tables/accessibility.py +++ b/activitysim/abm/tables/accessibility.py @@ -31,9 +31,23 @@ def accessibility(land_use): "created placeholder accessibility table %s" % (accessibility_df.shape,) ) else: - assert accessibility_df.sort_index().index.equals( - land_use.to_frame().sort_index().index - ), f"loaded accessibility table index does not match index of land_use table" + try: + assert accessibility_df.sort_index().index.equals( + land_use.to_frame().sort_index().index + ), f"loaded accessibility table index does not match index of land_use table" + except AssertionError: + land_use_index = land_use.to_frame().index + if f"_original_{land_use_index.name}" in land_use.to_frame(): + land_use_zone_ids = land_use.to_frame()[ + f"_original_{land_use_index.name}" + ] + remapper = dict(zip(land_use_zone_ids, land_use_zone_ids.index)) + accessibility_df.index = accessibility_df.index.map(remapper.get) + assert accessibility_df.sort_index().index.equals( + land_use.to_frame().sort_index().index + ), f"loaded accessibility table index does not match index of land_use table" + else: + raise logger.info("loaded land_use %s" % (accessibility_df.shape,)) # replace table function with dataframe diff --git a/activitysim/abm/tables/disaggregate_accessibility.py b/activitysim/abm/tables/disaggregate_accessibility.py new file mode 100644 index 000000000..4c4eb9ad4 --- /dev/null +++ b/activitysim/abm/tables/disaggregate_accessibility.py @@ -0,0 +1,270 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging +import os + +import numpy as np +import pandas as pd +import pandas.api.types as ptypes + +from sklearn.naive_bayes import CategoricalNB +from activitysim.core import inject, config, pipeline, util, input + +logger = logging.getLogger(__name__) + + +def find_nearest_accessibility_zone(choosers, accessibility_df, method="skims"): + """ + Matches choosers zone to the nearest accessibility zones. + Can be achieved by querying the skims or by nearest neighbor of centroids + """ + origin_col = "home_zone_id" + + def weighted_average(df, values, weights): + return df[values].T.dot(df[weights]) / df[weights].sum() + + def nearest_skim(oz, zones): + # need to pass equal # of origins and destinations to skim_dict + orig_zones = np.full(shape=len(zones), fill_value=oz, dtype=int) + return ( + oz, + zones[np.argmin(skim_dict.lookup(orig_zones, zones, "DIST"))], + ) + + def nearest_node(oz, zones_df): + _idx = util.nearest_node_index(_centroids.loc[oz].XY, zones_df.to_list()) + return oz, zones_df.index[_idx] + + unique_origin_zones = choosers[origin_col].unique() + accessibility_zones = list(set(accessibility_df[origin_col])) + + # First find any choosers zones that are missing from accessibility zones + matched_zones = list(set(unique_origin_zones).intersection(accessibility_zones)) + unmatched_zones = list(set(unique_origin_zones).difference(accessibility_zones)) + + # Store choosers index to ensure consistency + _idx = choosers.index + + if method == "centroids": + # Extract and vectorize TAZ centroids + centroids = inject.get_table("maz_centroids").to_frame() + + # TODO.NF This is a bit hacky, needs some work for variable zone names + if "TAZ" in centroids.columns: + # Find the TAZ centroid as weighted average of MAZ centroids + _centroids = centroids[centroids.TAZ.isin(accessibility_zones)] + _centroids = _centroids[["TAZ", "X", "Y", "Area"]].set_index("TAZ") + _centroids = _centroids.groupby("TAZ").apply( + weighted_average, ["X", "Y"], "Area" + ) + else: + _centroids = centroids + + # create XY tuple columns to find the nearest node with + _centroids["XY"] = list(zip(_centroids.X, _centroids.Y)) + nearest = [nearest_node(Oz, _centroids.XY) for Oz in unmatched_zones] + + else: + skim_dict = inject.get_injectable("skim_dict") + nearest = [nearest_skim(Oz, accessibility_zones) for Oz in unmatched_zones] + + # Add the nearest zones to the matched zones + matched = [(x, x) for x in matched_zones] + matched += nearest + + # Create a DF and merge to choosers + matched_df = pd.DataFrame( + matched, columns=[origin_col, "nearest_accessibility_zone_id"] + ) + matched_df = choosers.reset_index().merge(matched_df, on=origin_col) + matched_df = matched_df.set_index(choosers.index.name) + + return matched_df.loc[_idx] + + +@inject.injectable() +def disaggregate_suffixes(): + return {"SUFFIX": None, "ROOTS": []} + + +@inject.table() +def maz_centroids(): + df = input.read_input_table("maz_centroids") + + if not df.index.is_monotonic_increasing: + df = df.sort_index() + + logger.info("loaded maz_centroids %s" % (df.shape,)) + + # replace table function with dataframe + inject.add_table("maz_centroids", df) + + return df + + +@inject.table() +def proto_disaggregate_accessibility(): + + # Read existing accessibilities, but is not required to enable model compatibility + df = input.read_input_table("proto_disaggregate_accessibility", required=False) + + # If no df, return empty dataframe to skip this model + if not df: + return pd.DataFrame() + + # Ensure canonical index order + if not df.index.is_monotonic_increasing: + df = df.sort_index() + + logger.info("loaded proto_disaggregate_accessibility %s" % (df.shape,)) + + # replace table function with dataframe + inject.add_table("proto_disaggregate_accessibility", df) + + return df + + +@inject.table() +def disaggregate_accessibility(persons, households, land_use, accessibility): + """ + This step initializes pre-computed disaggregate accessibility and merges it onto the full synthetic population. + Function adds merged all disaggregate accessibility tables to the pipeline but returns nothing. + + """ + + # If disaggregate_accessibilities do not exist in the pipeline, it will try loading csv of that name + proto_accessibility_df = pipeline.get_table("proto_disaggregate_accessibility") + + # If there is no table, skip. We do this first to skip as fast as possible + if proto_accessibility_df.empty: + return pd.DataFrame() + + # Get persons merged manually + persons_merged_df = inject.merge_tables( + persons.name, tables=[persons, households, land_use, accessibility] + ) + + # Extract model settings + model_settings = config.read_model_settings("disaggregate_accessibility.yaml") + merging_params = model_settings.get("MERGE_ON") + nearest_method = model_settings.get("NEAREST_METHOD", "skims") + accessibility_cols = [ + x for x in proto_accessibility_df.columns if "accessibility" in x + ] + + # Parse the merging parameters + assert merging_params is not None + + # Check if already assigned! + if set(accessibility_cols).intersection(persons_merged_df.columns) == set( + accessibility_cols + ): + return + + # Find the nearest zone (spatially) with accessibilities calculated + # Note that from here on the 'home_zone_id' is the matched name + if "nearest_accessibility_zone_id" not in persons_merged_df.columns: + persons_merged_df = find_nearest_accessibility_zone( + persons_merged_df, proto_accessibility_df, nearest_method + ) + + # Copy home_zone_id in proto-table to match the temporary 'nearest_zone_id' + proto_accessibility_df[ + "nearest_accessibility_zone_id" + ] = proto_accessibility_df.home_zone_id + + # Set up the useful columns + exact_cols = merging_params.get("by", []) + if "home_zone_id" in exact_cols: + exact_cols.remove("home_zone_id") + exact_cols.insert(0, "nearest_accessibility_zone_id") + + nearest_cols = merging_params.get("asof", []) + merge_cols = exact_cols + nearest_cols + + assert len(nearest_cols) <= 1 + + # Setup and left and right tables. If asof join is used, it must be sorted. + # Drop duplicate accessibilities once filtered (may expect duplicates on households). + # Simply dropping duplicate households won't work if sampling is not 100% + # because it will get slightly different logsums for households in the same zone. + # This is because different destination zones were selected. To resolve, get mean by cols. + right_df = ( + proto_accessibility_df.groupby(merge_cols)[accessibility_cols] + .mean() + .sort_values(nearest_cols) + .reset_index() + ) + + left_df = persons_merged_df[merge_cols].sort_values(nearest_cols) + + if merging_params.get("method") == "soft": + # a 'soft' merge is possible by finding the nearest neighbor + x_pop, x_proto = left_df[exact_cols], right_df[exact_cols] + y = x_proto.index + + # Note: Naive Bayes is fast but discretely constrained. Some refinement may be necessary + # Index error here means data ranges don't match (e.g., age or hh veh is 0,1,2,3 but proto only has 0,1,2) + # The proto pop must at least be the same size or bigger. + clf = CategoricalNB() + clf.fit(x_proto, y) + + assert not any( + x_proto.duplicated() + ) # Ensure no duplicates, would mean we're missing a variable + # assert all(clf.predict(x_proto) == y) # Ensure it can at least predict on itself. If not there is a problem + # Also can just relax this constraint and report the accuracy to the user + accuracy = round(100 * sum(clf.predict(x_proto) == y) / len(y), 2) + print( + "Disaggregate accessibility merge training accuracy:" + " {}% (<100% typically means insufficient merge-on features.)".format( + accuracy + ) + ) + + # Predict the nearest person ID and pull the logsums + matched_logsums_df = right_df.loc[clf.predict(x_pop)][ + accessibility_cols + ].reset_index(drop=True) + merge_df = pd.concat( + [left_df.reset_index(drop=False), matched_logsums_df], axis=1 + ).set_index("person_id") + + else: + # merge_asof is sensitive to dataframe data types. Ensure consistency by 'upgrading' any int32 to int64 + for col in merge_cols: + if left_df[col].dtype is not right_df[col].dtype: + assert ptypes.is_integer_dtype( + left_df[col] + ) and ptypes.is_integer_dtype(right_df[col]) + datatype = np.max([left_df[col].dtype, right_df[col].dtype]) + left_df[col] = left_df[col].astype(datatype) + right_df[col] = right_df[col].astype(datatype) + + if nearest_cols: + merge_df = pd.merge_asof( + left=left_df, + right=right_df, + by=exact_cols, + on=nearest_cols, + direction="nearest", + ) + else: + merge_df = pd.merge( + left=left_df.reset_index(), right=right_df, on=exact_cols, how="left" + ) + merge_df = merge_df.set_index("person_id") + + # Check that it was correctly left-joined + assert all(persons_merged_df[merge_cols] == merge_df[merge_cols]) + assert any(merge_df[accessibility_cols].isnull()) + + # Inject merged accessibilities so that it can be included in persons_merged function + inject.add_table("disaggregate_accessibility", merge_df[accessibility_cols]) + + return merge_df[accessibility_cols] + + +inject.broadcast( + "disaggregate_accessibility", "persons", cast_index=True, onto_on="person_id" +) diff --git a/activitysim/abm/tables/households.py b/activitysim/abm/tables/households.py index 9c115f17c..e0a42f63b 100644 --- a/activitysim/abm/tables/households.py +++ b/activitysim/abm/tables/households.py @@ -1,5 +1,6 @@ # ActivitySim # See full license in LICENSE.txt. +import io import logging from builtins import range @@ -96,6 +97,9 @@ def households(households_sample_size, override_hh_ids, trace_hh_id): df["sample_rate"] = sample_rate logger.info("loaded households %s" % (df.shape,)) + buffer = io.StringIO() + df.info(buf=buffer) + logger.debug("households.info:\n" + buffer.getvalue()) # replace table function with dataframe inject.add_table("households", df) diff --git a/activitysim/abm/tables/landuse.py b/activitysim/abm/tables/landuse.py index cf4e72420..1161bfe5d 100644 --- a/activitysim/abm/tables/landuse.py +++ b/activitysim/abm/tables/landuse.py @@ -1,10 +1,9 @@ # ActivitySim # See full license in LICENSE.txt. +import io import logging -import pandas as pd - -from activitysim.core import inject +from activitysim.core import config, inject from activitysim.core.input import read_input_table logger = logging.getLogger(__name__) @@ -15,6 +14,15 @@ def land_use(): df = read_input_table("land_use") + sharrow_enabled = config.setting("sharrow", False) + if sharrow_enabled: + # when using sharrow, the land use file must be organized (either in raw + # form or via recoding) so that the index is zero-based and contiguous + assert df.index.is_monotonic_increasing + assert df.index[0] == 0 + assert df.index[-1] == len(df.index) - 1 + assert df.index.dtype.kind == "i" + # try to make life easy for everybody by keeping everything in canonical order # but as long as coalesce_pipeline doesn't sort tables it coalesces, it might not stay in order # so even though we do this, anyone downstream who depends on it, should look out for themselves... @@ -22,6 +30,9 @@ def land_use(): df = df.sort_index() logger.info("loaded land_use %s" % (df.shape,)) + buffer = io.StringIO() + df.info(buf=buffer) + logger.debug("land_use.info:\n" + buffer.getvalue()) # replace table function with dataframe inject.add_table("land_use", df) @@ -30,3 +41,22 @@ def land_use(): inject.broadcast("land_use", "households", cast_index=True, onto_on="home_zone_id") + + +@inject.table() +def land_use_taz(): + + df = read_input_table("land_use_taz") + + if not df.index.is_monotonic_increasing: + df = df.sort_index() + + logger.info("loaded land_use_taz %s" % (df.shape,)) + buffer = io.StringIO() + df.info(buf=buffer) + logger.debug("land_use_taz.info:\n" + buffer.getvalue()) + + # replace table function with dataframe + inject.add_table("land_use_taz", df) + + return df diff --git a/activitysim/abm/tables/persons.py b/activitysim/abm/tables/persons.py index a193b8f6d..a3d3804cf 100644 --- a/activitysim/abm/tables/persons.py +++ b/activitysim/abm/tables/persons.py @@ -1,10 +1,11 @@ # ActivitySim # See full license in LICENSE.txt. +import io import logging import pandas as pd -from activitysim.core import inject, mem, pipeline, tracing +from activitysim.core import inject, pipeline, tracing from activitysim.core.input import read_input_table logger = logging.getLogger(__name__) @@ -27,6 +28,9 @@ def persons(households, trace_hh_id): df = read_raw_persons(households) logger.info("loaded persons %s" % (df.shape,)) + buffer = io.StringIO() + df.info(buf=buffer) + logger.debug("persons.info:\n" + buffer.getvalue()) # replace table function with dataframe inject.add_table("persons", df) @@ -69,8 +73,19 @@ def persons(households, trace_hh_id): # another common merge for persons @inject.table() -def persons_merged(persons, households, land_use, accessibility): - - return inject.merge_tables( - persons.name, tables=[persons, households, land_use, accessibility] - ) +def persons_merged( + persons, households, land_use, accessibility, disaggregate_accessibility +): + + if not disaggregate_accessibility.to_frame().empty: + tables = [ + persons, + households, + land_use, + accessibility, + disaggregate_accessibility, + ] + else: + tables = [persons, households, land_use, accessibility] + + return inject.merge_tables(persons.name, tables=tables) diff --git a/activitysim/abm/tables/shadow_pricing.py b/activitysim/abm/tables/shadow_pricing.py index 053b1dcbc..3a3ba7f56 100644 --- a/activitysim/abm/tables/shadow_pricing.py +++ b/activitysim/abm/tables/shadow_pricing.py @@ -10,7 +10,8 @@ import pandas as pd from activitysim.abm.tables.size_terms import tour_destination_size_terms -from activitysim.core import config, inject, tracing, util +from activitysim.core import config, inject, logit, tracing, util +from activitysim.core.input import read_input_table logger = logging.getLogger(__name__) @@ -19,9 +20,9 @@ See docstrings for documentation on: -update_shadow_prices how shadow_price coefficients are calculated -synchronize_choices interprocess communication to compute aggregate modeled_size -check_fit convergence criteria for shadow_pric iteration +update_shadow_prices how shadow_price coefficients are calculated +synchronize_modeled_size interprocess communication to compute aggregate modeled_size +check_fit convergence criteria for shadow_pric iteration Import concepts and variables: @@ -46,12 +47,19 @@ we use the first two rows of the final column in numpy-wrapped shared data as 'reverse semaphores' (they synchronize concurrent access to shared data resource rather than throttling access) -ShadowPriceCalculator.synchronize_choices coordinates access to the global aggregate zone counts +ShadowPriceCalculator.synchronize_modeled_size coordinates access to the global aggregate zone counts (local_modeled_size summed across all sub-processes) using these two semaphores (which are really only tuples of indexes of locations in the shared data array. """ TALLY_CHECKIN = (0, -1) TALLY_CHECKOUT = (1, -1) +TALLY_PENDING_PERSONS = (2, -1) + +default_segment_to_name_dict = { + # model_selector : persons_segment_name + "school": "school_segment", + "workplace": "income_segment", +} def size_table_name(model_selector): @@ -72,10 +80,16 @@ def size_table_name(model_selector): class ShadowPriceCalculator(object): def __init__( - self, model_settings, num_processes, shared_data=None, shared_data_lock=None + self, + model_settings, + num_processes, + shared_data=None, + shared_data_lock=None, + shared_data_choice=None, + shared_data_choice_lock=None, + shared_sp_choice_df=None, ): """ - Presence of shared_data is used as a flag for multiprocessing If we are multiprocessing, shared_data should be a multiprocessing.RawArray buffer to aggregate modeled_size across all sub-processes, and shared_data_lock should be @@ -99,12 +113,6 @@ def __init__( self.model_selector = model_settings["MODEL_SELECTOR"] - full_model_run = config.setting("households_sample_size") == 0 - if self.use_shadow_pricing and not full_model_run: - logger.warning( - "deprecated combination of use_shadow_pricing and not full_model_run" - ) - if (self.num_processes > 1) and not config.setting("fail_fast"): # if we are multiprocessing, then fail_fast should be true or we will wait forever for failed processes logger.warning( @@ -116,7 +124,7 @@ def __init__( self.segment_ids = model_settings["SEGMENT_IDS"] - # - modeled_size (set by call to set_choices/synchronize_choices) + # - modeled_size (set by call to set_choices/synchronize_modeled_size) self.modeled_size = None if self.use_shadow_pricing: @@ -127,6 +135,31 @@ def __init__( "shadow_settings %s: %s" % (k, self.shadow_settings.get(k)) ) + full_model_run = config.setting("households_sample_size") == 0 + if ( + self.use_shadow_pricing + and not full_model_run + and self.shadow_settings["SHADOW_PRICE_METHOD"] != "simulation" + ): + # ctramp and daysim methods directly compare desired and modeled size to compute shadow prices. + # desination size terms are scaled in add_size_tables only for full model runs + logger.warning( + "only 'simulation' shadow price method can use_shadow_pricing and not full_model_run" + ) + logger.warning(f"Not using shadow pricing for {self.model_selector}") + self.use_shadow_pricing = False + + if ( + self.use_shadow_pricing + and self.model_selector not in ["workplace", "school"] + and self.shadow_settings["SHADOW_PRICE_METHOD"] == "simulation" + ): + logger.warning( + "Shadow price simulation method is only implemented for workplace and school." + ) + logger.warning(f"Not using shadow pricing for {self.model_selector}") + self.use_shadow_pricing = False + # - destination_size_table (desired_size) self.desired_size = inject.get_table( size_table_name(self.model_selector) @@ -147,11 +180,22 @@ def __init__( self.shared_data = shared_data self.shared_data_lock = shared_data_lock + self.shared_data_choice = shared_data_choice + self.shared_data_choice_lock = shared_data_choice_lock + + self.shared_sp_choice_df = shared_sp_choice_df + if shared_sp_choice_df is not None: + self.shared_sp_choice_df = self.shared_sp_choice_df.astype("int") + self.shared_sp_choice_df = self.shared_sp_choice_df.set_index("person_id") + self.shared_sp_choice_df["choice"] = int(0) + # - load saved shadow_prices (if available) and set max_iterations accordingly if self.use_shadow_pricing: self.shadow_prices = None self.shadow_price_method = self.shadow_settings["SHADOW_PRICE_METHOD"] - assert self.shadow_price_method in ["daysim", "ctramp"] + assert self.shadow_price_method in ["daysim", "ctramp", "simulation"] + # ignore convergence criteria for zones smaller than target_threshold + self.target_threshold = self.shadow_settings["TARGET_THRESHOLD"] if self.shadow_settings["LOAD_SAVED_SHADOW_PRICES"]: # read_saved_shadow_prices logs error and returns None if file not found @@ -181,6 +225,50 @@ def __init__( self.num_fail = pd.DataFrame(index=self.desired_size.columns) self.max_abs_diff = pd.DataFrame(index=self.desired_size.columns) self.max_rel_diff = pd.DataFrame(index=self.desired_size.columns) + self.choices_by_iteration = pd.DataFrame() + self.global_pending_persons = 1 + self.sampled_persons = pd.DataFrame() + + if ( + self.use_shadow_pricing + and self.shadow_settings["SHADOW_PRICE_METHOD"] == "simulation" + ): + + assert self.model_selector in ["workplace", "school"] + self.target = {} + land_use = inject.get_table("land_use").to_frame() + + if self.model_selector == "workplace": + employment_targets = self.shadow_settings[ + "workplace_segmentation_targets" + ] + assert ( + employment_targets is not None + ), "Need to supply workplace_segmentation_targets in shadow_pricing.yaml" + + for segment, target in employment_targets.items(): + assert ( + segment in self.shadow_prices.columns + ), f"{segment} is not in {self.shadow_prices.columns}" + assert ( + target in land_use.columns + ), f"{target} is not in {land_use.columns}" + self.target[segment] = land_use[target] + + elif self.model_selector == "school": + school_targets = self.shadow_settings["school_segmentation_targets"] + assert ( + school_targets is not None + ), "Need to supply school_segmentation_targets in shadow_pricing.yaml" + + for segment, target in school_targets.items(): + assert ( + segment in self.shadow_prices.columns + ), f"{segment} is not in {self.shadow_prices.columns}" + assert ( + target in land_use.columns + ), f"{target} is not in landuse columns: {land_use.columns}" + self.target[segment] = land_use[target] def read_saved_shadow_prices(self, model_settings): """ @@ -216,35 +304,25 @@ def read_saved_shadow_prices(self, model_settings): return shadow_prices - def synchronize_choices(self, local_modeled_size): + def synchronize_modeled_size(self, local_modeled_size): """ We have to wait until all processes have computed choices and aggregated them by segment and zone before we can compute global aggregate zone counts (by segment). Since the global zone counts are in shared data, we have to coordinate access to the data structure across sub-processes. - Note that all access to self.shared_data has to be protected by acquiring shared_data_lock - - ShadowPriceCalculator.synchronize_choices coordinates access to the global aggregate + ShadowPriceCalculator.synchronize_modeled_size coordinates access to the global aggregate zone counts (local_modeled_size summed across all sub-processes). - * All processes wait (in case we are iterating) until any stragglers from the previous iteration have exited the building. (TALLY_CHECKOUT goes to zero) - * Processes then add their local counts into the shared_data and increment TALLY_CHECKIN - * All processes wait until everybody has checked in (TALLY_CHECKIN == num_processes) - * Processes make local copy of shared_data and check out (increment TALLY_CHECKOUT) - * first_in process waits until all processes have checked out, then zeros shared_data and clears semaphores - Parameters ---------- local_modeled_size : pandas DataFrame - - Returns ------- global_modeled_size_df : pandas DataFrame @@ -275,15 +353,18 @@ def wait(tally, target): # Ellipsis expands : to fill available dims so [..., 0:-1] is the whole array except for the tallys self.shared_data[..., 0:-1] += local_modeled_size.values self.shared_data[TALLY_CHECKIN] += 1 + if len(self.sampled_persons) > 0: + self.shared_data[TALLY_PENDING_PERSONS] += 1 # - wait until everybody else has checked in wait(TALLY_CHECKIN, self.num_processes) - # - copy shared data, increment TALLY_CHECKIN + # - copy shared data, increment TALLY_CHECKOUT with self.shared_data_lock: logger.info("copy shared_data") # numpy array with sum of local_modeled_size.values from all processes global_modeled_size_array = self.shared_data[..., 0:-1].copy() + self.global_pending_persons = self.shared_data[TALLY_PENDING_PERSONS] self.shared_data[TALLY_CHECKOUT] += 1 # - first in waits until all other processes have checked out, and cleans tub @@ -303,6 +384,77 @@ def wait(tally, target): return global_modeled_size_df + def synchronize_choices(self, local_modeled_size): + """ + Same thing as the above synchronize_modeled_size method with the small + difference of keeping track of the individual choices instead of the + aggregate modeled choices between processes. + + Parameters + ---------- + local_modeled_size : pandas DataFrame + + + Returns + ------- + global_modeled_size_df : pandas DataFrame + local copy of shared global_modeled_size data as dataframe + with same shape and columns as local_modeled_size + """ + + # shouldn't be called if we are not multiprocessing + assert self.shared_data_choice is not None + assert self.num_processes > 1 + + def get_tally(t): + with self.shared_data_choice_lock: + return self.shared_data_choice[t] + + def wait(tally, target): + while get_tally(tally) != target: + time.sleep(1) + + # - nobody checks in until checkout clears + wait(TALLY_CHECKOUT, 0) + + # - add local_modeled_size data, increment TALLY_CHECKIN + with self.shared_data_choice_lock: + first_in = self.shared_data_choice[TALLY_CHECKIN] == 0 + # add local data from df to shared data buffer + # final column is used for tallys, hence the negative index + # Ellipsis expands : to fill available dims so [..., 0:-1] is the whole array except for the tallys + self.shared_data_choice[..., 0:-1] += local_modeled_size.values.astype( + np.int64 + ) + self.shared_data_choice[TALLY_CHECKIN] += 1 + + # - wait until everybody else has checked in + wait(TALLY_CHECKIN, self.num_processes) + + # - copy shared data, increment TALLY_CHECKIN + with self.shared_data_choice_lock: + logger.info("copy shared_data") + # numpy array with sum of local_modeled_size.values from all processes + global_modeled_size_array = self.shared_data_choice[..., 0:-1].copy() + self.shared_data_choice[TALLY_CHECKOUT] += 1 + + # - first in waits until all other processes have checked out, and cleans tub + if first_in: + wait(TALLY_CHECKOUT, self.num_processes) + with self.shared_data_choice_lock: + # zero shared_data, clear TALLY_CHECKIN, and TALLY_CHECKOUT semaphores + self.shared_data_choice[:] = 0 + logger.info("first_in clearing shared_data") + + # convert summed numpy array data to conform to original dataframe + global_modeled_size_df = pd.DataFrame( + data=global_modeled_size_array, + index=local_modeled_size.index, + columns=local_modeled_size.columns, + ) + + return global_modeled_size_df + def set_choices(self, choices, segment_ids): """ aggregate individual location choices to modeled_size by zone and segment @@ -330,10 +482,29 @@ def set_choices(self, choices, segment_ids): if self.num_processes == 1: # - not multiprocessing + self.choices_synced = choices self.modeled_size = modeled_size else: # - if we are multiprocessing, we have to aggregate across sub-processes - self.modeled_size = self.synchronize_choices(modeled_size) + self.modeled_size = self.synchronize_modeled_size(modeled_size) + + # need to also store individual choices if simulation approach + choice_merged = pd.merge( + self.shared_sp_choice_df, + choices, + left_index=True, + right_index=True, + how="left", + suffixes=("_x", "_y"), + ) + + choice_merged["choice_y"] = choice_merged["choice_y"].fillna(0) + choice_merged["choice"] = ( + choice_merged["choice_x"] + choice_merged["choice_y"] + ) + choice_merged = choice_merged.drop(columns=["choice_x", "choice_y"]) + + self.choices_synced = self.synchronize_choices(choice_merged) def check_fit(self, iteration): """ @@ -367,37 +538,87 @@ def check_fit(self, iteration): percent_tolerance = self.shadow_settings["PERCENT_TOLERANCE"] # max percentage of zones allowed to fail fail_threshold = self.shadow_settings["FAIL_THRESHOLD"] + # option to write out choices by iteration for each person to trace folder + write_choices = self.shadow_settings.get("WRITE_ITERATION_CHOICES", False) + if write_choices: + self.choices_by_iteration[iteration] = self.choices_synced + + if self.shadow_settings["SHADOW_PRICE_METHOD"] != "simulation": + + modeled_size = self.modeled_size + desired_size = self.desired_size + + abs_diff = (desired_size - modeled_size).abs() - modeled_size = self.modeled_size - desired_size = self.desired_size + self.rel_diff = abs_diff / modeled_size - abs_diff = (desired_size - modeled_size).abs() + # ignore zones where desired_size < threshold + self.rel_diff.where(desired_size >= size_threshold, 0, inplace=True) - rel_diff = abs_diff / modeled_size + # ignore zones where rel_diff < percent_tolerance + self.rel_diff.where( + self.rel_diff > (percent_tolerance / 100.0), 0, inplace=True + ) + + self.num_fail["iter%s" % iteration] = (self.rel_diff > 0).sum() + self.max_abs_diff["iter%s" % iteration] = abs_diff.max() + self.max_rel_diff["iter%s" % iteration] = self.rel_diff.max() + + total_fails = (self.rel_diff > 0).values.sum() + + # FIXME - should not count zones where desired_size < threshold? (could calc in init) + max_fail = (fail_threshold / 100.0) * util.iprod(desired_size.shape) - # ignore zones where desired_size < threshold - rel_diff.where(desired_size >= size_threshold, 0, inplace=True) + converged = total_fails <= max_fail - # ignore zones where rel_diff < percent_tolerance - rel_diff.where(rel_diff > (percent_tolerance / 100.0), 0, inplace=True) + else: + rel_diff_df = pd.DataFrame(index=self.shadow_prices.index) + abs_diff_df = pd.DataFrame(index=self.shadow_prices.index) + # checking each segment + for segment in self.segment_ids: + desired_size = self.target[segment] + modeled_size = self.modeled_size[segment] + + # loop over other segments and add to modeled share if they have the same target + for other_segment in self.segment_ids: + if (segment != other_segment) & ( + self.target[segment].equals(self.target[other_segment]) + ): + modeled_size = modeled_size + self.modeled_size[other_segment] + + # want to match distribution, not absolute numbers so share is computed + desired_share = desired_size / desired_size.sum() + modeled_share = modeled_size / modeled_size.sum() + + abs_diff_df[segment] = (desired_size - modeled_size).abs() + + rel_diff = desired_share / modeled_share + rel_diff = np.where( + # is the desired size below the threshold? + (desired_size <= self.target_threshold) + # is the difference within the tolerance? + | (np.abs(1 - rel_diff) < (percent_tolerance / 100.0)), + 0, + rel_diff, + ) + rel_diff_df[segment] = rel_diff - self.num_fail["iter%s" % iteration] = (rel_diff > 0).sum() - self.max_abs_diff["iter%s" % iteration] = abs_diff.max() - self.max_rel_diff["iter%s" % iteration] = rel_diff.max() + # relative difference is set to max across segments + self.rel_diff = rel_diff_df.max(axis=1) + abs_diff = abs_diff_df.max(axis=1) - total_fails = (rel_diff > 0).values.sum() + self.num_fail["iter%s" % iteration] = (self.rel_diff > 0).sum() + self.max_abs_diff["iter%s" % iteration] = abs_diff.max() + self.max_rel_diff["iter%s" % iteration] = rel_diff.max() - # FIXME - should not count zones where desired_size < threshold? (could calc in init) - max_fail = (fail_threshold / 100.0) * util.iprod(desired_size.shape) + total_fails = (self.rel_diff > 0).values.sum() - converged = total_fails <= max_fail + # FIXME - should not count zones where desired_size < threshold? (could calc in init) + max_fail = (fail_threshold / 100.0) * util.iprod(desired_size.shape) - # for c in desired_size: - # print("check_fit %s segment %s" % (self.model_selector, c)) - # print(" modeled %s" % (modeled_size[c].sum())) - # print(" desired %s" % (desired_size[c].sum())) - # print(" max abs diff %s" % (abs_diff[c].max())) - # print(" max rel diff %s" % (rel_diff[c].max())) + converged = (total_fails <= np.ceil(max_fail)) | ( + (iteration > 1) & (self.global_pending_persons == 0) + ) logger.info( "check_fit %s iteration: %s converged: %s max_fail: %s total_fails: %s" @@ -410,6 +631,13 @@ def check_fit(self, iteration): logger.info("\nshadow_pricing max_rel_diff\n%s" % self.max_rel_diff) logger.info("\nshadow_pricing num_fail\n%s" % self.num_fail) + if write_choices: + tracing.write_csv( + self.choices_by_iteration, + "%s_choices_by_shadow_price_iteration" % self.model_selector, + transpose=False, + ) + return converged def update_shadow_prices(self): @@ -471,6 +699,7 @@ def update_shadow_prices(self): new_shadow_prices.where( self.modeled_size > 0, self.shadow_prices, inplace=True ) + self.shadow_prices = new_shadow_prices elif shadow_price_method == "daysim": # - Daysim @@ -518,16 +747,108 @@ def update_shadow_prices(self): ) new_shadow_prices = self.shadow_prices + adjustment + self.shadow_prices = new_shadow_prices - else: - raise RuntimeError("unknown SHADOW_PRICE_METHOD %s" % shadow_price_method) + elif shadow_price_method == "simulation": + # - NewMethod + """ + C_j = (emp_j/sum(emp_j))/(workers_j/sum(workers_j)) + + if C_j > 1: #under-estimate workers in zone + + shadow_price_j = 0 + + elif C_j < 1: #over-estimate workers in zone + + shadow_price_j = -999 + resimulate n workers from zone j, with n = int(workers_j-emp_j/sum(emp_j*workers_j)) + """ + percent_tolerance = self.shadow_settings["PERCENT_TOLERANCE"] + sampled_persons = pd.DataFrame() + persons_merged = inject.get_table("persons_merged").to_frame() + + # need to join the segment to the choices to sample correct persons + segment_to_name_dict = self.shadow_settings.get( + "", default_segment_to_name_dict + ) + segment_name = segment_to_name_dict[self.model_selector] + + if type(self.choices_synced) != pd.DataFrame: + self.choices_synced = self.choices_synced.to_frame() + + choices_synced = self.choices_synced.merge( + persons_merged[segment_name], + how="left", + left_index=True, + right_index=True, + ).rename(columns={segment_name: "segment"}) + + for segment in self.segment_ids: + desired_size = self.target[segment] + modeled_size = self.modeled_size[segment] + + # loop over other segments and add to modeled share if they have the same target + for other_segment in self.segment_ids: + if (segment != other_segment) & ( + self.target[segment].equals(self.target[other_segment]) + ): + modeled_size = modeled_size + self.modeled_size[other_segment] + + # want to match distribution, not absolute numbers so share is computed + desired_share = desired_size / desired_size.sum() + modeled_share = modeled_size / modeled_size.sum() + + sprice = desired_share / modeled_share + sprice.fillna(0, inplace=True) + sprice.replace([np.inf, -np.inf], 0, inplace=True) + + # shadow prices are set to -999 if overassigned or 0 if the zone still has room for this segment + self.shadow_prices[segment] = np.where( + (sprice <= 1 + percent_tolerance / 100), -999, 0 + ) - # print("\nself.desired_size\n%s" % self.desired_size.head()) - # print("\nself.modeled_size\n%s" % self.modeled_size.head()) - # print("\nprevious shadow_prices\n%s" % self.shadow_prices.head()) - # print("\nnew_shadow_prices\n%s" % new_shadow_prices.head()) + zonal_sample_rate = 1 - sprice + overpredicted_zones = self.shadow_prices[ + self.shadow_prices[segment] == -999 + ].index + zones_outside_tol = zonal_sample_rate[ + zonal_sample_rate > percent_tolerance / 100 + ].index + small_zones = desired_size[desired_size <= self.target_threshold].index + + choices = choices_synced[ + (choices_synced["choice"].isin(overpredicted_zones)) + & (choices_synced["choice"].isin(zones_outside_tol)) + & ~(choices_synced["choice"].isin(small_zones)) + # sampling only from people in this segment + & (choices_synced["segment"] == self.segment_ids[segment]) + ]["choice"] + + # segment is converged if all zones are overpredicted / within tolerance + # do not want people to be re-simulated if no open zone exists + converged = len(overpredicted_zones) == len(self.shadow_prices) + + # draw persons assigned to overassigned zones to re-simulate if not converged + if (len(choices) > 0) & (~converged): + # person's probability of being selected for re-simulation is from the zonal sample rate + sample_rates = choices.map(zonal_sample_rate.to_dict()) + probs = pd.DataFrame( + data={"0": 1 - sample_rates, "1": sample_rates}, + index=choices.index, + ) + # using ActivitySim's RNG to make choices for repeatability + current_sample, rands = logit.make_choices(probs) + current_sample = current_sample[current_sample == 1] + + if len(sampled_persons) == 0: + sampled_persons = current_sample + else: + sampled_persons = pd.concat([sampled_persons, current_sample]) - self.shadow_prices = new_shadow_prices + self.sampled_persons = sampled_persons + + else: + raise RuntimeError("unknown SHADOW_PRICE_METHOD %s" % shadow_price_method) def dest_size_terms(self, segment): @@ -544,6 +865,8 @@ def dest_size_terms(self, segment): size_term_adjustment = self.shadow_prices[segment] elif shadow_price_method == "daysim": utility_adjustment = self.shadow_prices[segment] + elif shadow_price_method == "simulation": + utility_adjustment = self.shadow_prices[segment] else: raise RuntimeError( "unknown SHADOW_PRICE_METHOD %s" % shadow_price_method @@ -673,6 +996,121 @@ def buffers_for_shadow_pricing(shadow_pricing_info): return data_buffers +def buffers_for_shadow_pricing_choice(shadow_pricing_choice_info): + """ + Same as above buffers_for_shadow_price function except now we need to store + the actual choices for the simulation based shadow pricing method + + This allocates a multiprocessing.Array that can store the choice for each person + and then wraps a dataframe around it. That means the dataframe can be shared + and accessed across all threads. + Parameters + ---------- + shadow_pricing_info : dict + Returns + ------- + data_buffers : dict { : } + dict of multiprocessing.Array keyed by model_selector + and wrapped in a pandas dataframe + """ + + dtype = shadow_pricing_choice_info["dtype"] + block_shapes = shadow_pricing_choice_info["block_shapes"] + + data_buffers = {} + + for block_key, block_shape in block_shapes.items(): + + # buffer_size must be int, not np.int64 + buffer_size = util.iprod(block_shape) + + csz = buffer_size * np.dtype(dtype).itemsize + logger.info( + "allocating shared shadow pricing buffer for choices %s %s buffer_size %s bytes %s (%s)" + % (block_key, buffer_size, block_shape, csz, util.GB(csz)) + ) + + if np.issubdtype(dtype, np.int64): + typecode = ctypes.c_int64 + else: + raise RuntimeError( + "buffer_for_shadow_pricing unrecognized dtype %s" % dtype + ) + + shared_data_buffer = multiprocessing.Array(typecode, buffer_size) + + logger.info("buffer_for_shadow_pricing_choice added block %s" % block_key) + + data_buffers[block_key + "_choice"] = shared_data_buffer + + persons = read_input_table("persons") + sp_choice_df = persons.reset_index()["person_id"].to_frame() + + # declare a shared Array with data from sp_choice_df + mparr = multiprocessing.Array(ctypes.c_double, sp_choice_df.values.reshape(-1)) + + # create a new df based on the shared array + shared_sp_choice_df = pd.DataFrame( + np.frombuffer(mparr.get_obj()).reshape(sp_choice_df.shape), + columns=sp_choice_df.columns, + ) + data_buffers["shadow_price_choice_df"] = shared_sp_choice_df + + return data_buffers + + +def shadow_price_data_from_buffers_choice( + data_buffers, shadow_pricing_info, model_selector +): + """ + + Parameters + ---------- + data_buffers : dict of { : } + multiprocessing.Array is simply a convenient way to bundle Array and Lock + we extract the lock and wrap the RawArray in a numpy array for convenience in indexing + The shared data buffer has shape ( + 1) + extra column is for reverse semaphores with TALLY_CHECKIN and TALLY_CHECKOUT + shadow_pricing_info : dict + dict of useful info + dtype: sp_dtype, + block_shapes : OrderedDict({: }) + dict mapping model_selector to block shape (including extra column for semaphores) + e.g. {'school': (num_zones, num_segments + 1) + model_selector : str + location type model_selector (e.g. school or workplace) + + Returns + ------- + shared_data, shared_data_lock + shared_data : multiprocessing.Array or None (if single process) + shared_data_lock : numpy array wrapping multiprocessing.RawArray or None (if single process) + """ + + assert type(data_buffers) == dict + + dtype = shadow_pricing_info["dtype"] + block_shapes = shadow_pricing_info["block_shapes"] + + if model_selector not in block_shapes: + raise RuntimeError( + "Model selector %s not in shadow_pricing_info" % model_selector + ) + + if block_name(model_selector + "_choice") not in data_buffers: + raise RuntimeError( + "Block %s not in data_buffers" % block_name(model_selector + "_choice") + ) + + data = data_buffers[block_name(model_selector + "_choice")] + shape = ( + int(len(data) / block_shapes[model_selector][1]), + int(block_shapes[model_selector][1]), + ) + + return np.frombuffer(data.get_obj(), dtype=dtype).reshape(shape), data.get_lock() + + def shadow_price_data_from_buffers(data_buffers, shadow_pricing_info, model_selector): """ @@ -747,23 +1185,56 @@ def load_shadow_price_calculator(model_settings): shadow_pricing_info = inject.get_injectable("shadow_pricing_info", None) assert shadow_pricing_info is not None + shadow_pricing_choice_info = inject.get_injectable( + "shadow_pricing_choice_info", None + ) + assert shadow_pricing_choice_info is not None + # - extract data buffer and reshape as numpy array data, lock = shadow_price_data_from_buffers( data_buffers, shadow_pricing_info, model_selector ) + data_choice, lock_choice = shadow_price_data_from_buffers_choice( + data_buffers, shadow_pricing_choice_info, model_selector + ) + if "shadow_price_choice_df" in data_buffers: + shared_sp_choice_df = data_buffers["shadow_price_choice_df"] + else: + shared_sp_choice_df = None + else: assert num_processes == 1 data = None # ShadowPriceCalculator will allocate its own data lock = None + data_choice = None + lock_choice = None + shared_sp_choice_df = None # - ShadowPriceCalculator - spc = ShadowPriceCalculator(model_settings, num_processes, data, lock) + spc = ShadowPriceCalculator( + model_settings, + num_processes, + data, + lock, + data_choice, + lock_choice, + shared_sp_choice_df, + ) return spc +# first define add_size_tables as an orca step with no scale argument at all. @inject.step() -def add_size_tables(): +def add_size_tables(disaggregate_suffixes): + return _add_size_tables(disaggregate_suffixes) + + +# then define _add_size_tables as a second method which also offers an optional +# default argument to not scale sizes. This is used only in disaggregate +# accessibility (for now) and is not called via orca. We need to do this to +# avoid having to create a new orca variable for the scale argument. +def _add_size_tables(disaggregate_suffixes, scale=True): """ inject tour_destination_size_terms tables for each model_selector (e.g. school, workplace) @@ -798,6 +1269,22 @@ def add_size_tables(): # but this allows compatability with existing CTRAMP behavior... scale_size_table = shadow_settings.get("SCALE_SIZE_TABLE", False) + # Suffixes for disaggregate accessibilities + # Set default here incase None is explicitly passed + disaggregate_suffixes = ( + {"SUFFIX": None, "ROOTS": []} + if not disaggregate_suffixes + else disaggregate_suffixes + ) + suffix, roots = disaggregate_suffixes.get("SUFFIX"), disaggregate_suffixes.get( + "ROOTS", [] + ) + + assert isinstance(roots, list) + assert (suffix is not None and roots) or ( + suffix is None and not roots + ), "Expected to find both 'ROOTS' and 'SUFFIX', missing one" + # shadow_pricing_models is dict of {: } # since these are scaled to model size, they have to be created while single-process @@ -805,6 +1292,11 @@ def add_size_tables(): model_settings = config.read_model_settings(model_name) + if suffix is not None and roots: + model_settings = util.suffix_tables_in_settings( + model_settings, suffix, roots + ) + assert model_selector == model_settings["MODEL_SELECTOR"] assert ( @@ -826,7 +1318,15 @@ def add_size_tables(): raw_size = tour_destination_size_terms(land_use, size_terms, model_selector) assert set(raw_size.columns) == set(segment_ids.keys()) - if use_shadow_pricing or scale_size_table: + full_model_run = config.setting("households_sample_size") == 0 + + scale_size_table = scale and scale_size_table + + if (use_shadow_pricing and full_model_run) and scale_size_table: + + # need to scale destination size terms because ctramp and daysim approaches directly + # compare modeled size and target size when computing shadow prices + # Does not apply to simulation approach which compares proportions. # - scale size_table counts to sample population # scaled_size = zone_size * (total_segment_modeled / total_segment_desired) @@ -857,9 +1357,19 @@ def add_size_tables(): segment_scale_factors[c], ) ) + # FIXME - can get zero size if model_settings["CHOOSER_FILTER_COLUMN_NAME"] not yet determined / initialized to 0 + # using raw size if scaled size is 0. Is this an acceptable fix? + # this is happening for external models where extenal identification is not run yet at this stage + if segment_scale_factors[c] <= 0: + logger.warning( + f"scale_factor is <= 0 for {model_selector}:{c}, using raw size instead" + ) + segment_scale_factors[c] = 1 # FIXME - should we be rounding? - scaled_size = (raw_size * segment_scale_factors).round() + # scaled_size = (raw_size * segment_scale_factors).round() + # rounding can cause zero probability errors for small sample sizes + scaled_size = raw_size * segment_scale_factors else: scaled_size = raw_size @@ -871,7 +1381,7 @@ def add_size_tables(): scaled_size.index.is_monotonic_increasing ), f"size table {size_table_name(model_selector)} not is_monotonic_increasing" - inject.add_table(size_table_name(model_selector), scaled_size) + inject.add_table(size_table_name(model_selector), scaled_size, replace=True) def get_shadow_pricing_info(): @@ -918,6 +1428,52 @@ def get_shadow_pricing_info(): return shadow_pricing_info +def get_shadow_pricing_choice_info(): + """ + return dict with info about dtype and shapes of desired and modeled size tables + + block shape is (num_zones, num_segments + 1) + + + Returns + ------- + shadow_pricing_info: dict + dtype: , + block_shapes: dict {: } + """ + + persons = read_input_table("persons") + + shadow_settings = config.read_model_settings("shadow_pricing.yaml") + + # shadow_pricing_models is dict of {: } + shadow_pricing_models = shadow_settings.get("shadow_pricing_models", {}) + + blocks = OrderedDict() + for model_selector in shadow_pricing_models: + + # each person will have a work or school location choice + sp_rows = len(persons) + + # extra tally column for TALLY_CHECKIN and TALLY_CHECKOUT semaphores + blocks[block_name(model_selector)] = (sp_rows, 2) + + sp_dtype = np.int64 + # sp_dtype = np.str + + shadow_pricing_choice_info = { + "dtype": sp_dtype, + "block_shapes": blocks, + } + + for k in shadow_pricing_choice_info: + logger.debug( + "shadow_pricing_choice_info %s: %s" % (k, shadow_pricing_choice_info.get(k)) + ) + + return shadow_pricing_choice_info + + @inject.injectable(cache=True) def shadow_pricing_info(): @@ -926,3 +1482,13 @@ def shadow_pricing_info(): logger.debug("loading shadow_pricing_info injectable") return get_shadow_pricing_info() + + +@inject.injectable(cache=True) +def shadow_pricing_choice_info(): + + # when multiprocessing with shared data mp_tasks has to call network_los methods + # get_shadow_pricing_info() and buffers_for_shadow_pricing() + logger.debug("loading shadow_pricing_choice_info injectable") + + return get_shadow_pricing_choice_info() diff --git a/activitysim/abm/tables/skims.py b/activitysim/abm/tables/skims.py index f7a841d9a..39440b29f 100644 --- a/activitysim/abm/tables/skims.py +++ b/activitysim/abm/tables/skims.py @@ -50,4 +50,6 @@ def log_settings(): "num_processes", "resume_after", "trace_hh_id", + "memory_profile", + "instrument", ] diff --git a/activitysim/abm/tables/vehicles.py b/activitysim/abm/tables/vehicles.py index ab6a9d011..fdc886a25 100644 --- a/activitysim/abm/tables/vehicles.py +++ b/activitysim/abm/tables/vehicles.py @@ -38,7 +38,7 @@ def vehicles(households): inject.add_table("vehicles", vehicles) pipeline.get_rn_generator().add_channel("vehicles", vehicles) - tracing.register_traceable_table("households", vehicles) + tracing.register_traceable_table("vehicles", vehicles) return vehicles diff --git a/activitysim/abm/test/test_misc/test_load_cached_accessibility.py b/activitysim/abm/test/test_misc/test_load_cached_accessibility.py index 505fef890..721b441d4 100644 --- a/activitysim/abm/test/test_misc/test_load_cached_accessibility.py +++ b/activitysim/abm/test/test_misc/test_load_cached_accessibility.py @@ -81,15 +81,17 @@ def test_load_cached_accessibility(): "initialize_households", ] - pipeline.run(models=_MODELS, resume_after=None) + try: + pipeline.run(models=_MODELS, resume_after=None) - accessibility_df = pipeline.get_table("accessibility") + accessibility_df = pipeline.get_table("accessibility") - assert "auPkRetail" in accessibility_df + assert "auPkRetail" in accessibility_df - pipeline.close_pipeline() - inject.clear_cache() - close_handlers() + finally: + pipeline.close_pipeline() + inject.clear_cache() + close_handlers() if __name__ == "__main__": diff --git a/activitysim/benchmarking/asv.conf.json b/activitysim/benchmarking/asv.conf.json index 1b1148955..bb8ab65c3 100644 --- a/activitysim/benchmarking/asv.conf.json +++ b/activitysim/benchmarking/asv.conf.json @@ -85,7 +85,9 @@ "numba": [], "coverage": [], "pytest": [], - "cytoolz": [] + "cytoolz": [], + "zarr": [], + "sharrow": [] }, // Combinations of libraries/python versions can be excluded/included diff --git a/activitysim/benchmarking/benchmarks/mtc1full.py b/activitysim/benchmarking/benchmarks/mtc1full.py index 39115a992..e35e330fc 100644 --- a/activitysim/benchmarking/benchmarks/mtc1full.py +++ b/activitysim/benchmarking/benchmarks/mtc1full.py @@ -52,6 +52,14 @@ VERSION = "1" +try: + from activitysim import __data_compatability__ +except ImportError: + __data_compatability__ = None +else: + OUTPUT_DIR = f"{OUTPUT_DIR}-{__data_compatability__}" + + def setup_cache(): template_setup_cache( EXAMPLE_NAME, diff --git a/activitysim/benchmarking/benchmarks/sandag_example.py b/activitysim/benchmarking/benchmarks/sandag_example.py index b9c74c86e..0ba16a9ba 100644 --- a/activitysim/benchmarking/benchmarks/sandag_example.py +++ b/activitysim/benchmarking/benchmarks/sandag_example.py @@ -33,6 +33,7 @@ ] BENCHMARK_SETTINGS = { "households_sample_size": 48_769, + "sharrow": True, } SKIM_CACHE = True PRELOAD_INJECTABLES = ("skim_dict",) diff --git a/activitysim/benchmarking/componentwise.py b/activitysim/benchmarking/componentwise.py index dda43c454..bb3c909b0 100644 --- a/activitysim/benchmarking/componentwise.py +++ b/activitysim/benchmarking/componentwise.py @@ -63,6 +63,7 @@ def setup_component( data_dir="data", output_dir="output", settings_filename="settings.yaml", + **other_settings, ): """ Prepare to benchmark a model component. @@ -84,6 +85,7 @@ def setup_component( settings_filename, benchmarking=component_name, checkpoints=False, + **other_settings, ) component_logging(component_name) @@ -144,6 +146,7 @@ def setup_component( "use_shadow_pricing", "want_dest_choice_sample_tables", "log_alt_losers", + "sharrow", ] for k in log_settings: logger.info(f"SETTING {k}: {config.setting(k)}") @@ -623,6 +626,7 @@ def setup(self): config_dirs, data_dir, output_dir, + sharrow=True, ) def teardown(self): diff --git a/activitysim/benchmarking/instrument.py b/activitysim/benchmarking/instrument.py new file mode 100644 index 000000000..ede07b2ef --- /dev/null +++ b/activitysim/benchmarking/instrument.py @@ -0,0 +1,28 @@ +import importlib +import os +import webbrowser + +from pyinstrument import Profiler + + +def run_instrument(bench_name, component_name, out_file=None): + bench_module = importlib.import_module( + f"activitysim.benchmarking.benchmarks.{bench_name}" + ) + + component = getattr(bench_module, component_name)() + component.setup() + with Profiler() as profiler: + component.time_component() + component.teardown() + + if out_file is None: + out_file = f"instrument/{bench_name}/{component_name}.html" + dirname = os.path.dirname(out_file) + if dirname: + os.makedirs(dirname, exist_ok=True) + + if out_file: + with open(out_file, "wt") as f: + f.write(profiler.output_html()) + webbrowser.open(f"file://{os.path.realpath(out_file)}") diff --git a/activitysim/cli/benchmark.py b/activitysim/cli/benchmark.py index cbb1512d9..af46766aa 100644 --- a/activitysim/cli/benchmark.py +++ b/activitysim/cli/benchmark.py @@ -37,18 +37,23 @@ "matrix": { "pyarrow": [], "numpy": [], + "scipy": ["1.7"], "openmatrix": [], - "pandas": [], + "pandas": ["1.3"], "pyyaml": [], "pytables": [], "toolz": [], "orca": [], "psutil": [], "requests": [], - "numba": [], - "coverage": [], - "pytest": [], + "numba": ["0.54"], "cytoolz": [], + "zarr": [], + "xarray": [], + "filelock": [], + "dask": [], + "networkx": [], + "sharrow": [], }, # The directory (relative to the current directory) to cache the Python # environments in. If not provided, defaults to "env" diff --git a/activitysim/cli/cli.py b/activitysim/cli/cli.py index 23d07812a..de95e78ff 100644 --- a/activitysim/cli/cli.py +++ b/activitysim/cli/cli.py @@ -25,4 +25,4 @@ def add_subcommand(self, name, args_func, exec_func, description): def execute(self): args = self.parser.parse_args() - args.afunc(args) + return args.afunc(args) diff --git a/activitysim/cli/create.py b/activitysim/cli/create.py index 85187c63e..ec545d378 100644 --- a/activitysim/cli/create.py +++ b/activitysim/cli/create.py @@ -1,8 +1,10 @@ import glob import hashlib +import logging import os import shutil import sys +from pathlib import Path import pkg_resources import requests @@ -53,6 +55,12 @@ def add_create_args(parser): help="path to new project directory (default: %(default)s)", ) + parser.add_argument( + "--link", + action="store_true", + help="cache and reuse downloaded files via symlinking", + ) + def create(args): """ @@ -66,12 +74,15 @@ def create(args): if args.list: list_examples() - sys.exit(0) + return 0 if args.example: - - get_example(args.example, args.destination) - sys.exit(0) + try: + get_example(args.example, args.destination, link=args.link) + except Exception: + logging.getLogger().exception("failure in activitysim create") + return 101 + return 0 def list_examples(): @@ -86,7 +97,9 @@ def list_examples(): return ret -def get_example(example_name, destination, benchmarking=False): +def get_example( + example_name, destination, benchmarking=False, optimize=True, link=True +): """ Copy project data to user-specified directory. @@ -95,8 +108,6 @@ def get_example(example_name, destination, benchmarking=False): `include` field which is a list of files/folders to include in the copied example. - - Parameters ---------- @@ -107,6 +118,13 @@ def get_example(example_name, destination, benchmarking=False): will be copied into a subdirectory with the same name as the example benchmarking: bool + optimize: bool + link: bool or path-like + Files downloaded via http pointers will be cached in + this location. If a path is not given but just a truthy + value, then a cache directory is created using in a location + selected by the appdirs library (or, if not installed, + linking is skipped.) """ if example_name not in EXAMPLES: sys.exit(f"error: could not find example '{example_name}'") @@ -137,7 +155,7 @@ def get_example(example_name, destination, benchmarking=False): sha256 = None if assets.startswith("http"): - download_asset(assets, target_path, sha256) + download_asset(assets, target_path, sha256, link=link) else: for asset_path in glob.glob(_example_path(assets)): @@ -145,6 +163,19 @@ def get_example(example_name, destination, benchmarking=False): print(f"copied! new project files are in {os.path.abspath(dest_path)}") + if optimize: + optimize_func_names = example.get("optimize", None) + if isinstance(optimize_func_names, str): + optimize_func_names = [optimize_func_names] + if optimize_func_names: + from ..examples import optimize_example_data + + for optimize_func_name in optimize_func_names: + getattr( + optimize_example_data, + optimize_func_name, + )(os.path.abspath(dest_path)) + instructions = example.get("instructions") if instructions: print(instructions) @@ -153,6 +184,7 @@ def get_example(example_name, destination, benchmarking=False): def copy_asset(asset_path, target_path, dirs_exist_ok=False): print(f"copying {os.path.basename(asset_path)} ...") + sys.stdout.flush() if os.path.isdir(asset_path): target_path = os.path.join(target_path, os.path.basename(asset_path)) shutil.copytree(asset_path, target_path, dirs_exist_ok=dirs_exist_ok) @@ -164,44 +196,89 @@ def copy_asset(asset_path, target_path, dirs_exist_ok=False): shutil.copy(asset_path, target_path) -def download_asset(url, target_path, sha256=None): +def download_asset(url, target_path, sha256=None, link=True): + if link: + if not isinstance(link, (str, Path)): + try: + import appdirs + except ImportError: + link = False + else: + link = appdirs.user_data_dir("ActivitySim") + original_target_path = target_path + target_path = os.path.join(link, target_path) os.makedirs(os.path.dirname(target_path), exist_ok=True) if url.endswith(".gz") and not target_path.endswith(".gz"): target_path_dl = target_path + ".gz" else: target_path_dl = target_path + download = True if sha256 and os.path.isfile(target_path): computed_sha256 = sha256_checksum(target_path) if sha256 == computed_sha256: print(f"not re-downloading existing {os.path.basename(target_path)} ...") - return + download = False else: print(f"re-downloading existing {os.path.basename(target_path)} ...") print(f" expected checksum {sha256}") print(f" computed checksum {computed_sha256}") else: print(f"downloading {os.path.basename(target_path)} ...") - with requests.get(url, stream=True) as r: - r.raise_for_status() - with open(target_path_dl, "wb") as f: - for chunk in r.iter_content(chunk_size=None): - f.write(chunk) - if target_path_dl != target_path: - import gzip - - with gzip.open(target_path_dl, "rb") as f_in: - with open(target_path, "wb") as f_out: - shutil.copyfileobj(f_in, f_out) - os.remove(target_path_dl) - computed_sha256 = sha256_checksum(target_path) - if sha256 and sha256 != computed_sha256: - raise ValueError( - f"downloaded {os.path.basename(target_path)} has incorrect checksum\n" - f" expected checksum {sha256}\n" - f" computed checksum {computed_sha256}" + sys.stdout.flush() + if download: + with requests.get(url, stream=True) as r: + r.raise_for_status() + print(f"| as {target_path_dl}") + with open(target_path_dl, "wb") as f: + for chunk in r.iter_content(chunk_size=None): + f.write(chunk) + if target_path_dl != target_path: + import gzip + + with gzip.open(target_path_dl, "rb") as f_in: + with open(target_path, "wb") as f_out: + print(f"| unzip to {target_path}") + shutil.copyfileobj(f_in, f_out) + os.remove(target_path_dl) + computed_sha256 = sha256_checksum(target_path) + if sha256 and sha256 != computed_sha256: + raise ValueError( + f"downloaded {os.path.basename(target_path)} has incorrect checksum\n" + f" expected checksum {sha256}\n" + f" computed checksum {computed_sha256}" + ) + elif not sha256: + print(f"| computed checksum {computed_sha256}") + if link: + os.makedirs( + os.path.dirname(os.path.normpath(original_target_path)), + exist_ok=True, ) - elif not sha256: - print(f" computed checksum {computed_sha256}") + + # check if the original_target_path exists and if so check if it is the correct file + if os.path.isfile(os.path.normpath(original_target_path)): + if sha256 is None: + sha256 = sha256_checksum(os.path.normpath(target_path)) + existing_sha256 = sha256_checksum(os.path.normpath(original_target_path)) + if existing_sha256 != sha256: + os.unlink(os.path.normpath(original_target_path)) + + # if the original_target_path exists now it is the correct file, keep it + if not os.path.isfile(os.path.normpath(original_target_path)): + try: + os.symlink( + os.path.normpath(target_path), + os.path.normpath(original_target_path), + ) + except OSError: + # permission errors likely foil symlinking on windows + shutil.copy( + os.path.normpath(target_path), + os.path.normpath(original_target_path), + ) + print(f"| copied to {os.path.normpath(original_target_path)}") + else: + print(f"| symlinked to {os.path.normpath(original_target_path)}") def sha256_checksum(filename, block_size=65536): @@ -210,3 +287,18 @@ def sha256_checksum(filename, block_size=65536): for block in iter(lambda: f.read(block_size), b""): sha256.update(block) return sha256.hexdigest() + + +def display_sha256_checksums(directory=None): + print("SHA 256 CHECKSUMS") + if directory is None: + if len(sys.argv) > 1: + directory = sys.argv[1] + else: + directory = os.getcwd() + print(f" in {directory}") + for dirpath, dirnames, filenames in os.walk(directory): + print(f"- in {dirpath}") + for filename in filenames: + f = os.path.join(dirpath, filename) + print(f"= {sha256_checksum(f)} = {f}") diff --git a/activitysim/cli/main.py b/activitysim/cli/main.py index 92988e863..e4b37ba12 100644 --- a/activitysim/cli/main.py +++ b/activitysim/cli/main.py @@ -2,18 +2,9 @@ import sys -def main(): - - # set all these before we import numpy or any other math library - if len(sys.argv) > 1 and sys.argv[1] == "benchmark": - os.environ["MKL_NUM_THREADS"] = "1" - os.environ["OMP_NUM_THREADS"] = "1" - os.environ["OPENBLAS_NUM_THREADS"] = "1" - os.environ["NUMBA_NUM_THREADS"] = "1" - os.environ["VECLIB_MAXIMUM_THREADS"] = "1" - os.environ["NUMEXPR_NUM_THREADS"] = "1" +def prog(): - from activitysim import __doc__, __version__ + from activitysim import __doc__, __version__, workflows from activitysim.cli import CLI, benchmark, create, run asim = CLI(version=__version__, description=__doc__) @@ -35,4 +26,48 @@ def main(): exec_func=benchmark.benchmark, description=benchmark.benchmark.__doc__, ) - sys.exit(asim.execute()) + asim.add_subcommand( + name="workflow", + args_func=lambda x: None, + exec_func=workflows.main, + description=workflows.main.__doc__, + ) + return asim + + +def main(): + + # set all these before we import numpy or any other math library + if len(sys.argv) > 1 and sys.argv[1] == "benchmark": + os.environ["MKL_NUM_THREADS"] = "1" + os.environ["OMP_NUM_THREADS"] = "1" + os.environ["OPENBLAS_NUM_THREADS"] = "1" + os.environ["NUMBA_NUM_THREADS"] = "1" + os.environ["VECLIB_MAXIMUM_THREADS"] = "1" + os.environ["NUMEXPR_NUM_THREADS"] = "1" + + asim = prog() + try: + if len(sys.argv) >= 2 and sys.argv[1] in ("workflow", "workflow_"): + from activitysim import workflows + + if sys.argv[1] == "workflow_": + result = workflows.main(sys.argv[2:]) + # exit silently on PipelineNotFoundError + if result == 254: + result = 0 + sys.exit(result) + else: + sys.exit(workflows.main(sys.argv[2:])) + else: + sys.exit(asim.execute()) + except Exception: + # if we are in the debugger, re-raise the error instead of exiting + if sys.gettrace() is not None: + raise + sys.exit(99) + + +def parser(): + asim = prog() + return asim.parser diff --git a/activitysim/cli/run.py b/activitysim/cli/run.py index d5c464950..71677e8ad 100644 --- a/activitysim/cli/run.py +++ b/activitysim/cli/run.py @@ -1,6 +1,7 @@ # ActivitySim # See full license in LICENSE.txt. import argparse +import importlib import logging import os import sys @@ -13,7 +14,13 @@ logger = logging.getLogger(__name__) -INJECTABLES = ["data_dir", "configs_dir", "output_dir", "settings_file_name"] +INJECTABLES = [ + "data_dir", + "configs_dir", + "output_dir", + "settings_file_name", + "imported_extensions", +] def add_run_args(parser, multiprocess=True): @@ -64,6 +71,22 @@ def add_run_args(parser, multiprocess=True): parser.add_argument( "--households_sample_size", type=int, metavar="N", help="households sample size" ) + parser.add_argument( + "--fast", + action="store_true", + help="Do not limit process to one thread. " + "Can make single process runs faster, " + "but will cause thrashing on MP runs.", + ) + parser.add_argument( + "-e", + "--ext", + type=str, + action="append", + metavar="PATH", + help="Package of extension modules to load. Use of this option is not " + "generally secure.", + ) if multiprocess: parser.add_argument( @@ -87,7 +110,7 @@ def validate_injectable(name): # injectable is missing, meaning is hasn't been explicitly set # and defaults cannot be found. sys.exit( - "Error: please specify either a --working_dir " + f"Error({name}): please specify either a --working_dir " "containing 'configs', 'data', and 'output' folders " "or all three of --config, --data, and --output" ) @@ -111,6 +134,26 @@ def inject_arg(name, value, cache=False): # 'configs', 'data', and 'output' folders by default os.chdir(args.working_dir) + if args.ext: + for e in args.ext: + basepath, extpath = os.path.split(e) + if not basepath: + basepath = "." + sys.path.insert(0, os.path.abspath(basepath)) + try: + importlib.import_module(extpath) + except ImportError as err: + logger.exception("ImportError") + raise + except Exception as err: + logger.exception(f"Error {err}") + raise + finally: + del sys.path[0] + inject_arg("imported_extensions", args.ext) + else: + inject_arg("imported_extensions", ()) + # settings_file_name should be cached or else it gets squashed by config.py if args.settings_file: inject_arg("settings_file_name", args.settings_file, cache=True) @@ -159,8 +202,16 @@ def cleanup_output_files(): tracing.delete_trace_files() + csv_ignore = [] + if config.setting("memory_profile", False): + # memory profiling is opened potentially before `cleanup_output_files` + # is called, but we want to leave any (newly created) memory profiling + # log files that may have just been created. + mem_prof_log = config.log_file_path("memory_profile.csv") + csv_ignore.append(mem_prof_log) + tracing.delete_output_files("h5") - tracing.delete_output_files("csv") + tracing.delete_output_files("csv", ignore=csv_ignore) tracing.delete_output_files("txt") tracing.delete_output_files("yaml") tracing.delete_output_files("prof") @@ -183,13 +234,27 @@ def run(args): # other callers (e.g. populationsim) will have to arrange to register their own steps and injectables # (presumably) in a custom run_simulation.py instead of using the 'activitysim run' command if not inject.is_injectable("preload_injectables"): - from activitysim import ( # register abm steps and other abm-specific injectables - abm, - ) + # register abm steps and other abm-specific injectables + from activitysim import abm # noqa: F401 tracing.config_logger(basic=True) handle_standard_args(args) # possibly update injectables + if config.setting("rotate_logs", False): + config.rotate_log_directory() + + if config.setting("memory_profile", False) and not config.setting( + "multiprocess", False + ): + # Memory sidecar is only useful for single process runs + # multiprocess runs log memory usage without blocking in the controlling process. + mem_prof_log = config.log_file_path("memory_profile.csv") + from ..core.memory_sidecar import MemorySidecar + + memory_sidecar_process = MemorySidecar(mem_prof_log) + else: + memory_sidecar_process = None + # legacy support for run_list setting nested 'models' and 'resume_after' settings if config.setting("run_list"): warnings.warn( @@ -238,7 +303,12 @@ def run(args): # OMP_NUM_THREADS: openmp # OPENBLAS_NUM_THREADS: openblas # MKL_NUM_THREADS: mkl - for env in ["MKL_NUM_THREADS", "OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS"]: + for env in [ + "MKL_NUM_THREADS", + "OMP_NUM_THREADS", + "OPENBLAS_NUM_THREADS", + "NUMBA_NUM_THREADS", + ]: logger.info(f"ENV {env}: {os.getenv(env)}") np_info_keys = [ @@ -281,7 +351,11 @@ def run(args): else: logger.info("run single process simulation") - pipeline.run(models=config.setting("models"), resume_after=resume_after) + pipeline.run( + models=config.setting("models"), + resume_after=resume_after, + memory_sidecar_process=memory_sidecar_process, + ) if config.setting("cleanup_pipeline_after_run", False): pipeline.cleanup_pipeline() # has side effect of closing open pipeline @@ -298,18 +372,23 @@ def run(args): chunk.consolidate_logs() mem.consolidate_logs() + from ..core.flow import TimeLogger + + TimeLogger.aggregate_summary(logger) + tracing.print_elapsed_time("all models", t0) + if memory_sidecar_process: + memory_sidecar_process.stop() + return 0 if __name__ == "__main__": - from activitysim import abm # register injectables + from activitysim import abm # register injectables # noqa: F401 parser = argparse.ArgumentParser() add_run_args(parser) args = parser.parse_args() - - parser.parse_args(["--sum", "7", "-1", "42"]) sys.exit(run(args)) diff --git a/activitysim/cli/test/test_cli.py b/activitysim/cli/test/test_cli.py index 96c3575c0..b8f0fc384 100644 --- a/activitysim/cli/test/test_cli.py +++ b/activitysim/cli/test/test_cli.py @@ -71,7 +71,7 @@ def test_run(): cp = subprocess.run(["activitysim", "run"], capture_output=True) msg = ( - "Error: please specify either a --working_dir " + "please specify either a --working_dir " "containing 'configs', 'data', and 'output' " "folders or all three of --config, --data, and --output" ) diff --git a/activitysim/core/assign.py b/activitysim/core/assign.py index 4eb55ddc3..f8e73adc6 100644 --- a/activitysim/core/assign.py +++ b/activitysim/core/assign.py @@ -7,7 +7,7 @@ import numpy as np import pandas as pd -from activitysim.core import chunk, config, inject, pipeline, util +from activitysim.core import chunk, config, pipeline, util logger = logging.getLogger(__name__) @@ -264,6 +264,41 @@ def to_series(x): variables = OrderedDict() temps = OrderedDict() + # draw required randomness in one slug + # TODO: generalize to all randomness, not just lognormals + n_randoms = 0 + for expression_idx in assignment_expressions.index: + expression = assignment_expressions.loc[expression_idx, "expression"] + if "rng.lognormal_for_df(df," in expression: + expression = expression.replace( + "rng.lognormal_for_df(df,", f"rng_lognormal(random_draws[{n_randoms}]," + ) + n_randoms += 1 + assignment_expressions.loc[expression_idx, "expression"] = expression + if n_randoms: + from activitysim.core import pipeline + + try: + random_draws = pipeline.get_rn_generator().normal_for_df( + df, broadcast=True, size=n_randoms + ) + except RuntimeError: + pass + else: + _locals_dict["random_draws"] = random_draws + + def rng_lognormal(random_draws, mu, sigma, broadcast=True, scale=False): + if scale: + x = 1 + ((sigma * sigma) / (mu * mu)) + mu = np.log(mu / (np.sqrt(x))) + sigma = np.sqrt(np.log(x)) + assert broadcast + return np.exp(random_draws * sigma + mu) + + _locals_dict["rng_lognormal"] = rng_lognormal + + sharrow_enabled = config.setting("sharrow", False) + # need to be able to identify which variables causes an error, which keeps # this from being expressed more parsimoniously @@ -319,6 +354,18 @@ def to_series(x): globals_dict = {} expr_values = to_series(eval(expression, globals_dict, _locals_dict)) + if ( + sharrow_enabled + and np.issubdtype(expr_values.dtype, np.floating) + and expr_values.dtype.itemsize < 4 + ): + # promote to float32, numba is not presently compatible with + # any float less than 32 (i.e., float16) + # see https://github.com/numba/numba/issues/4402 + # note this only applies to floats, signed and unsigned + # integers are readily supported down to 1 byte + expr_values = expr_values.astype(np.float32) + np.seterr(**save_err) np.seterrcall(saved_handler) diff --git a/activitysim/core/choosing.py b/activitysim/core/choosing.py new file mode 100644 index 000000000..d32f25401 --- /dev/null +++ b/activitysim/core/choosing.py @@ -0,0 +1,202 @@ +import numpy as np +import pandas as pd +from numba import njit + + +@njit +def choice_maker(pr, rn, out=None): + if out is None: + out = np.empty(pr.shape[0], dtype=np.int32) + n_alts = pr.shape[1] + for row in range(pr.shape[0]): + z = rn[row] + for col in range(n_alts): + z = z - pr[row, col] + if z <= 0: + out[row] = col + break + else: + # rare condition, only if a random point is greater than 1 (a bug) + # or if the sum of probabilities is less than 1 and a random point + # is greater than that sum, which due to the limits of numerical + # precision can technically happen + max_pr = 0.0 + for col in range(n_alts): + if pr[row, col] > max_pr: + out[row] = col + max_pr = pr[row, col] + return out + + +@njit +def sample_choices_maker( + prob_array, + random_array, + alts_array, + out_choices=None, + out_choice_probs=None, +): + """ + Random sample of alternatives. + + Parameters + ---------- + prob_array : array of float, shape (n_choosers, n_alts) + random_array : array of float, shape (n_choosers, n_samples) + alts_array : array of int, shape (n_alts) + out_choices : array of int, shape (n_samples, n_choosers), optional + out_choice_probs : array of float, shape (n_samples, n_choosers), optional + + Returns + ------- + out_choices, out_choice_probs + """ + n_choosers = random_array.shape[0] + sample_size = random_array.shape[1] + n_alts = prob_array.shape[1] + if out_choices is None: + out_choices = np.empty((sample_size, n_choosers), dtype=np.int32) + if out_choice_probs is None: + out_choice_probs = np.empty((sample_size, n_choosers), dtype=np.float32) + + for c in range(n_choosers): + random_points = np.sort(random_array[c, :]) + a = 0 + s = 0 + z = 0.0 + for a in range(n_alts): + z += prob_array[c, a] + while s < sample_size and z > random_points[s]: + out_choices[s, c] = alts_array[a] + out_choice_probs[s, c] = prob_array[c, a] + s += 1 + if s >= sample_size: + break + if s < sample_size: + # rare condition, only if a random point is greater than 1 (a bug) + # or if the sum of probabilities is less than 1 and a random point + # is greater than that sum, which due to the limits of numerical + # precision can technically happen + a = n_alts - 1 + while prob_array[c, a] < 1e-30 and a > 0: + # slip back to the last choice with non-trivial prob + a -= 1 + while s < sample_size: + out_choices[s, c] = alts_array[a] + out_choice_probs[s, c] = prob_array[c, a] + s += 1 + + return out_choices, out_choice_probs + + +@njit +def _sample_choices_maker_preserve_ordering( + prob_array, + random_array, + alts_array, + out_choices=None, + out_choice_probs=None, +): + """ + Random sample of alternatives. + + Preserves the ordering of the random array, consistent with the legacy + ActivitySim implementation. + + Parameters + ---------- + prob_array : array of float, shape (n_choosers, n_alts) + random_array : array of float, shape (n_choosers, n_samples) + alts_array : array of int, shape (n_alts) + out_choices : array of int, shape (n_samples, n_choosers), optional + out_choice_probs : array of float, shape (n_samples, n_choosers), optional + + Returns + ------- + out_choices, out_choice_probs + """ + n_choosers = random_array.shape[0] + sample_size = random_array.shape[1] + n_alts = prob_array.shape[1] + if out_choices is None: + out_choices = np.empty((sample_size, n_choosers), dtype=np.int32) + if out_choice_probs is None: + out_choice_probs = np.empty((sample_size, n_choosers), dtype=np.float32) + + for c in range(n_choosers): + sorted_random = np.argsort(random_array[c, :]) + a = 0 + s = 0 + z = 0.0 + for a in range(n_alts): + z += prob_array[c, a] + while s < sample_size and z > random_array[c, sorted_random[s]]: + out_choices[sorted_random[s], c] = alts_array[a] + out_choice_probs[sorted_random[s], c] = prob_array[c, a] + s += 1 + if s >= sample_size: + break + if s < sample_size: + # rare condition, only if a random point is greater than 1 (a bug) + # or if the sum of probabilities is less than 1 and a random point + # is greater than that sum, which due to the limits of numerical + # precision can technically happen + a = n_alts - 1 + while prob_array[c, a] < 1e-30 and a > 0: + # slip back to the last choice with non-trivial prob + a -= 1 + while s < sample_size: + out_choices[sorted_random[s], c] = alts_array[a] + out_choice_probs[sorted_random[s], c] = prob_array[c, a] + s += 1 + + return out_choices, out_choice_probs + + +def sample_choices_maker_preserve_ordering( + prob_array, + random_array, + alts_array, + out_choices=None, + out_choice_probs=None, +): + """ + Make sample choices. + + Parameters + ---------- + prob_array : array[float] + random_array : array[float] + alts_array : array + out_choices : array[alts_array.dtype], optional + out_choice_probs : array[float], optional + + Returns + ------- + out_choices : array[alts_array.dtype] + out_choice_probs : array[float] + """ + if alts_array.dtype.kind != "i": + # when the alternatives array is not integers (which is unusual, but + # does happen in the OD choice model) we need to choose integers in + # numba and then convert those back to whatever dtype the alternatives are + out_choices_, out_choice_probs = _sample_choices_maker_preserve_ordering( + prob_array, + random_array, + np.arange(alts_array.size), + out_choices=None, + out_choice_probs=out_choice_probs, + ) + if out_choices is not None: + out_choices[:] = alts_array[out_choices_] + else: + out_choices = alts_array[out_choices_] + return out_choices, out_choice_probs + + return _sample_choices_maker_preserve_ordering( + prob_array, + random_array, + alts_array, + out_choices=out_choices, + out_choice_probs=out_choice_probs, + ) diff --git a/activitysim/core/chunk.py b/activitysim/core/chunk.py index 547c8f064..6634b479d 100644 --- a/activitysim/core/chunk.py +++ b/activitysim/core/chunk.py @@ -8,10 +8,12 @@ import multiprocessing import os import threading +import warnings from contextlib import contextmanager import numpy as np import pandas as pd +import xarray as xr from . import config, mem, tracing, util from .util import GB @@ -539,10 +541,10 @@ def size_it(df): elif isinstance(df, pd.DataFrame): elements = util.iprod(df.shape) bytes = 0 if not elements else df.memory_usage(index=True).sum() - elif isinstance(df, np.ndarray): + elif isinstance(df, (np.ndarray, xr.DataArray)): elements = util.iprod(df.shape) bytes = df.nbytes - elif isinstance(df, list): + elif isinstance(df, (list, tuple)): # dict of series, dataframe, or ndarray (e.g. assign assign_variables target and temp dicts) elements = 0 bytes = 0 @@ -550,7 +552,7 @@ def size_it(df): e, b = size_it(v) elements += e bytes += b - elif isinstance(df, dict): + elif isinstance(df, (dict, xr.Dataset)): # dict of series, dataframe, or ndarray (e.g. assign assign_variables target and temp dicts) elements = 0 bytes = 0 @@ -581,6 +583,8 @@ def size_it(df): shape = f"list({[x.shape for x in df]})" elif isinstance(df, dict): shape = f"dict({[v.shape for v in df.values()]})" + elif isinstance(df, xr.Dataset): + shape = df.dims else: shape = df.shape @@ -846,7 +850,11 @@ def initial_rows_per_chunk(self): ) estimated_number_of_chunks = None - assert chunk_training_mode() != MODE_PRODUCTION + if chunk_training_mode() == MODE_PRODUCTION: + warnings.warn( + "ActivitySim is running with a chunk_training_mode of " + f"'production' but initial_row_size is zero in {self.trace_label}" + ) # cum_rows is out of phase with cum_overhead # since we won't know observed_chunk_size until AFTER yielding the chunk @@ -1039,6 +1047,10 @@ def chunk_log(trace_label, chunk_tag=None, base=False): # a ChunkSizer class object without actually chunking. This # avoids breaking the assertion below. + if chunk_training_mode() == MODE_CHUNKLESS: + yield + return + assert base == (len(CHUNK_SIZERS) == 0) trace_label = f"{trace_label}.chunk_log" @@ -1073,6 +1085,14 @@ def adaptive_chunked_choosers(choosers, chunk_size, trace_label, chunk_tag=None) # generator to iterate over choosers + if chunk_training_mode() == MODE_CHUNKLESS: + # The adaptive chunking logic is expensive and sometimes results + # in needless data copying. So we short circuit it entirely + # when chunking is disabled. + logger.info(f"Running chunkless with {len(choosers)} choosers") + yield 0, choosers, trace_label + return + chunk_tag = chunk_tag or trace_label num_choosers = len(choosers.index) @@ -1154,6 +1174,18 @@ def adaptive_chunked_choosers_and_alts( chunk of alternatives for chooser chunk """ + if chunk_training_mode() == MODE_CHUNKLESS: + # The adaptive chunking logic is expensive and sometimes results + # in needless data copying. So we short circuit it entirely + # when chunking is disabled. + logger.info(f"Running chunkless with {len(choosers)} choosers") + yield 0, choosers, alternatives, trace_label + return + + check_assertions = False + # set to True if debugging is needed; there are many expensive assertions + # to check data quality in here + chunk_tag = chunk_tag or trace_label num_choosers = len(choosers.index) @@ -1161,18 +1193,17 @@ def adaptive_chunked_choosers_and_alts( assert num_choosers > 0 # alternatives index should match choosers (except with duplicate repeating alt rows) - assert choosers.index.equals( - alternatives.index[~alternatives.index.duplicated(keep="first")] - ) + if check_assertions: + assert choosers.index.equals( + alternatives.index[~alternatives.index.duplicated(keep="first")] + ) last_repeat = alternatives.index != np.roll(alternatives.index, -1) - assert (num_choosers == 1) or choosers.index.equals(alternatives.index[last_repeat]) - assert ( - "pick_count" in alternatives.columns - or choosers.index.name == alternatives.index.name - ) - assert choosers.index.name == alternatives.index.name + if check_assertions: + assert (num_choosers == 1) or choosers.index.equals( + alternatives.index[last_repeat] + ) logger.info( f"{trace_label} Running adaptive_chunked_choosers_and_alts " @@ -1210,12 +1241,13 @@ def adaptive_chunked_choosers_and_alts( alt_end = alt_chunk_ends[offset + rows_per_chunk] alternative_chunk = alternatives[alt_offset:alt_end] - assert len(chooser_chunk.index) == len( - np.unique(alternative_chunk.index.values) - ) - assert ( - chooser_chunk.index == np.unique(alternative_chunk.index.values) - ).all() + if check_assertions: + assert len(chooser_chunk.index) == len( + np.unique(alternative_chunk.index.values) + ) + assert ( + chooser_chunk.index == np.unique(alternative_chunk.index.values) + ).all() logger.info( f"Running chunk {i} of {estimated_number_of_chunks or '?'} " @@ -1245,6 +1277,14 @@ def adaptive_chunked_choosers_by_chunk_id( # all have to be included in the same chunk) # FIXME - we pathologically know name of chunk_id col in households table + if chunk_training_mode() == MODE_CHUNKLESS: + # The adaptive chunking logic is expensive and sometimes results + # in needless data copying. So we short circuit it entirely + # when chunking is disabled. + logger.info(f"Running chunkless with {len(choosers)} choosers") + yield 0, choosers, trace_label + return + chunk_tag = chunk_tag or trace_label num_choosers = choosers["chunk_id"].max() + 1 diff --git a/activitysim/core/cleaning.py b/activitysim/core/cleaning.py new file mode 100644 index 000000000..65b64db15 --- /dev/null +++ b/activitysim/core/cleaning.py @@ -0,0 +1,74 @@ +import logging + +import numpy as np +import pandas as pd + +from . import inject + +logger = logging.getLogger(__name__) + + +def recode_to_zero_based(values, mapping): + values = np.asarray(values) + zone_ids = pd.Index(mapping, dtype=np.int32) + if ( + zone_ids.is_monotonic_increasing + and zone_ids[-1] == len(zone_ids) + zone_ids[0] - 1 + ): + offset = zone_ids[0] + result = values - offset + else: + n = len(zone_ids) + remapper = dict(zip(zone_ids, pd.RangeIndex(n))) + if n < 128: + out_dtype = np.int8 + elif n < (1 << 15): + out_dtype = np.int16 + elif n < (1 << 31): + out_dtype = np.int32 + else: + out_dtype = np.int64 + result = np.fromiter((remapper.get(xi) for xi in values), out_dtype) + return result + + +def should_recode_based_on_table(tablename): + try: + base_df = inject.get_table(tablename).to_frame() + except (KeyError, RuntimeError): + # the basis table is missing, do not + return False + if base_df.index.name and f"_original_{base_df.index.name}" in base_df: + return True + return False + + +def recode_based_on_table(values, tablename): + try: + base_df = inject.get_table(tablename).to_frame() + except (KeyError, RuntimeError): + # the basis table is missing, do nothing + logger.warning(f"unable to recode based on missing {tablename} table") + return values + if base_df.index.name and f"_original_{base_df.index.name}" in base_df: + source_ids = base_df[f"_original_{base_df.index.name}"] + if ( + isinstance(base_df.index, pd.RangeIndex) + and base_df.index.start == 0 + and base_df.index.step == 1 + ): + logger.info(f"recoding to zero-based values based on {tablename} table") + return recode_to_zero_based(values, source_ids) + elif ( + base_df.index.is_monotonic_increasing + and base_df.index[0] == 0 + and base_df.index[-1] == len(base_df) - 1 + ): + logger.info(f"recoding to zero-based values based on {tablename} table") + return recode_to_zero_based(values, source_ids) + else: + logger.info(f"recoding to mapped values based on {tablename} table") + remapper = dict(zip(source_ids, base_df.index)) + return np.fromiter((remapper.get(xi) for xi in values), base_df.index.dtype) + else: + return values diff --git a/activitysim/core/config.py b/activitysim/core/config.py index 02d6fadea..7024d0512 100644 --- a/activitysim/core/config.py +++ b/activitysim/core/config.py @@ -10,7 +10,7 @@ import yaml -from activitysim.core import inject +from activitysim.core import inject, util logger = logging.getLogger(__name__) @@ -65,7 +65,7 @@ def pipeline_file_name(settings): @inject.injectable() def rng_base_seed(): - return 0 + return setting("rng_base_seed", 0) @inject.injectable(cache=True) @@ -77,6 +77,14 @@ def settings_file_name(): def settings(settings_file_name): settings_dict = read_settings_file(settings_file_name, mandatory=True) + # basic settings validation for sharrow + sharrow_enabled = settings_dict.get("sharrow", False) + recode_pipeline_columns = settings_dict.get("recode_pipeline_columns", True) + if sharrow_enabled and not recode_pipeline_columns: + warnings.warn( + "use of `sharrow` setting generally requires `recode_pipeline_columns`" + ) + return settings_dict @@ -108,6 +116,13 @@ def get_cache_dir(): os.mkdir(cache_dir) assert os.path.isdir(cache_dir) + # create a git-ignore in the cache dir if it does not exist. + # this helps prevent accidentally committing cache contents to git + gitignore = os.path.join(cache_dir, ".gitignore") + if not os.path.exists(gitignore): + with open(gitignore, "wt") as f: + f.write("/*") + return cache_dir @@ -248,18 +263,19 @@ def cascading_input_file_path( dir_paths = [dir_paths] if isinstance(dir_paths, str) else dir_paths file_path = None - for dir in dir_paths: - p = os.path.join(dir, file_name) - if os.path.isfile(p): - file_path = p - break + if file_name is not None: + for dir in dir_paths: + p = os.path.join(dir, file_name) + if os.path.isfile(p): + file_path = p + break - if allow_glob and len(glob.glob(p)) > 0: - file_path = p - break + if allow_glob and len(glob.glob(p)) > 0: + file_path = p + break if mandatory and not file_path: - raise RuntimeError( + raise FileNotFoundError( "file_path %s: file '%s' not in %s" % (dir_list_injectable_name, file_name, dir_paths) ) @@ -337,6 +353,20 @@ def output_file_path(file_name): return build_output_file_path(file_name, use_prefix=prefix) +def profiling_file_path(file_name): + + profile_dir = inject.get_injectable("profile_dir", None) + if profile_dir is None: + output_dir = inject.get_injectable("output_dir") + profile_dir = os.path.join( + output_dir, time.strftime("profiling--%Y-%m-%d--%H-%M-%S") + ) + os.makedirs(profile_dir, exist_ok=True) + inject.add_injectable("profile_dir", profile_dir) + + return os.path.join(profile_dir, file_name) + + def trace_file_path(file_name): output_dir = inject.get_injectable("output_dir") @@ -399,6 +429,31 @@ def open_log_file(file_name, mode, header=None, prefix=False): return f +def rotate_log_directory(): + + output_dir = inject.get_injectable("output_dir") + log_dir = os.path.join(output_dir, "log") + if not os.path.exists(log_dir): + return + + from datetime import datetime + from stat import ST_CTIME + + old_log_time = os.stat(log_dir)[ST_CTIME] + rotate_name = os.path.join( + output_dir, + datetime.fromtimestamp(old_log_time).strftime("log--%Y-%m-%d--%H-%M-%S"), + ) + try: + os.rename(log_dir, rotate_name) + except Exception as err: + # if Windows fights us due to permissions or whatever, + print(f"unable to rotate log file, {err!r}") + else: + # on successful rotate, create new empty log directory + os.makedirs(log_dir) + + def pipeline_file_path(file_name): prefix = inject.get_injectable("pipeline_file_prefix", None) @@ -461,6 +516,14 @@ def backfill_settings(settings, backfill): set(configs_dir_list) ), f"repeating file names not allowed in config_dir list: {configs_dir_list}" + args = util.parse_suffix_args(file_name) + file_name = args.filename + + assert isinstance(args.ROOTS, list) + assert (args.SUFFIX is not None and args.ROOTS) or ( + args.SUFFIX is None and not args.ROOTS + ), ("Expected to find both 'ROOTS' and 'SUFFIX' in %s, missing one" % args.filename) + if not file_name.lower().endswith(".yaml"): file_name = "%s.yaml" % (file_name,) @@ -504,7 +567,7 @@ def backfill_settings(settings, backfill): # essentially the current settings firle is an alias for the included file if len(s) > 1: logger.error( - f"'include_settings' must appear alone in settings file." + "'include_settings' must appear alone in settings file." ) additional_settings = list( set(s.keys()).difference({"include_settings"}) @@ -513,7 +576,7 @@ def backfill_settings(settings, backfill): f"Unexpected additional settings: {additional_settings}" ) raise RuntimeError( - f"'include_settings' must appear alone in settings file." + "'include_settings' must appear alone in settings file." ) logger.debug( @@ -565,6 +628,10 @@ def backfill_settings(settings, backfill): if mandatory and not settings: raise SettingsFileNotFound(file_name, configs_dir_list) + # Adds proto_ suffix for disaggregate accessibilities + if args.SUFFIX is not None and args.ROOTS: + settings = util.suffix_tables_in_settings(settings, args.SUFFIX, args.ROOTS) + if include_stack: # if we were called recursively, return an updated list of source_file_paths return settings, source_file_paths @@ -628,6 +695,14 @@ def filter_warnings(): message="`np.object` is a deprecated alias", ) + # Numba triggers a DeprecationWarning from numpy about np.MachAr + warnings.filterwarnings( + "ignore", + category=DeprecationWarning, + module="numba", + message=".np.MachAr. is deprecated", + ) + # beginning pandas version 1.3, various places emit a PerformanceWarning that is # caught in the "strict" filter above, but which are currently unavoidable for complex models. # These warning are left as warnings as an invitation for future enhancement. @@ -635,6 +710,46 @@ def filter_warnings(): warnings.filterwarnings("default", category=PerformanceWarning) + # pandas 1.5 + # beginning in pandas version 1.5, a new warning is emitted when a column is set via iloc + # from an array of different dtype, the update will eventually be done in-place in future + # versions. This is actually the preferred outcome for ActivitySim and no code changes are + # needed. + warnings.filterwarnings( + "ignore", + category=FutureWarning, + message=( + ".*will attempt to set the values inplace instead of always setting a new array. " + "To retain the old behavior, use either.*" + ), + ) + # beginning in pandas version 1.5, a warning is emitted when using pandas.concat on dataframes + # that contain object-dtype columns with all-bool values. ActivitySim plans to address dtypes + # and move away from object-dtype columns anyhow, so this is not a critical problem. + warnings.filterwarnings( + "ignore", + category=FutureWarning, + message=( + ".*object-dtype columns with all-bool values will not be included in reductions.*" + ), + ) + warnings.filterwarnings( + "ignore", + category=DeprecationWarning, + message=".*will attempt to set the values inplace instead of always setting a new array.*", + ) + + # beginning in sharrow version 2.5, a CacheMissWarning is emitted when a sharrow + # flow cannot be loaded from cache and needs to be compiled. These are performance + # warnings for production runs and totally expected when running test or on new + # machines + try: + from sharrow import CacheMissWarning + except ImportError: + pass + else: + warnings.filterwarnings("default", category=CacheMissWarning) + def handle_standard_args(parser=None): diff --git a/activitysim/core/configuration.py b/activitysim/core/configuration.py deleted file mode 100644 index 3174653b2..000000000 --- a/activitysim/core/configuration.py +++ /dev/null @@ -1,291 +0,0 @@ -from typing import Union - -try: - from pydantic import BaseModel as PydanticBase -except ModuleNotFoundError: - - class PydanticBase: - pass - - -class InputTable(PydanticBase): - """ - The features that define an input table to be read by ActivitySim. - """ - - tablename: str - """Name of the injected table""" - - filename: str = None - """ - Name of the CSV or HDF5 file to read. - - If not provided, defaults to `input_store` - """ - - index_col: str = None - """table column to use for the index""" - - rename_columns: dict[str, str] = None - """dictionary of column name mappings""" - - keep_columns: list[str] = None - """ - columns to keep once read in to memory. - - Save only the columns needed for modeling or analysis to save on memory - and file I/O - """ - - h5_tablename: str = None - """table name if reading from HDF5 and different from `tablename`""" - - -class Settings(PydanticBase): - """ - The overall settings for the ActivitySim model system. - - The input for these settings is typically stored in one main YAML file, - usually called ``settings.yaml``. - - Note that this implementation is presently used only for generating - documentation, but future work may migrate the settings implementation to - actually use this pydantic code to validate the settings before running - the model. - """ - - models: list[str] - """ - list of model steps to run - auto ownership, tour frequency, etc. - - See :ref:`model_steps` for more details about each step. - """ - - resume_after: str = None - """to resume running the data pipeline after the last successful checkpoint""" - - input_table_list: list[InputTable] - """list of table names, indices, and column re-maps for each table in `input_store`""" - - input_store: str = None - """HDF5 inputs file""" - - create_input_store: bool = False - """ - Write the inputs as read in back to an HDF5 store. - - If enabled, this writes the store to the outputs folder to use for subsequent - model runs, as reading HDF5 can be faster than reading CSV files.""" - - households_sample_size: int = None - """ - Number of households to sample and simulate - - If omitted or set to 0, ActivitySim will simulate all households. - """ - trace_hh_id: Union[int, list] = None - """ - Trace household id(s) - - If omitted, no tracing is written out - """ - - trace_od: list[int] = None - """ - Trace origin, destination pair in accessibility calculation - - If omitted, no tracing is written out. - """ - - chunk_training_mode: str = None - """ - The method to use for chunk training. - - Valid values include {disabled, training, production, adaptive}. - See :ref:`chunk_size` for more details. - """ - - chunk_size: int = None - """ - Approximate amount of RAM to allocate to ActivitySim for batch processing. - - See :ref:`chunk_size` for more details. - """ - - chunk_method: str = None - """ - Memory use measure to use for chunking. - - See :ref:`chunk_size`. - """ - - checkpoints: Union[bool, list] = True - """ - When to write checkpoint (intermediate table states) to disk. - - If True, checkpoints are written at each step. If False, no intermediate - checkpoints will be written before the end of run. Or, provide an explicit - list of models to checkpoint. - """ - - check_for_variability: bool = False - """ - Debugging feature to find broken model specifications. - - Enabling this check does not alter valid results but slows down model runs. - """ - - log_alt_losers: bool = False - """ - Write out expressions when all alternatives are unavailable. - - This can be useful for model development to catch errors in specifications. - Enabling this check does not alter valid results but slows down model runs. - """ - - use_shadow_pricing: bool = False - """turn shadow_pricing on and off for work and school location""" - - output_tables: list[str] = None - """list of output tables to write to CSV or HDF5""" - - want_dest_choice_sample_tables: bool = False - """turn writing of sample_tables on and off for all models""" - - cleanup_pipeline_after_run: bool = False - """ - Cleans up pipeline after successful run. - - This will clean up pipeline only after successful runs, by creating a - single-checkpoint pipeline file, and deleting any subprocess pipelines. - """ - - sharrow: Union[bool, str] = False - """ - Set the sharrow operating mode. - - .. versionadded:: 1.2 - - * `false` - Do not use sharrow. This is the default if no value is given. - * `true` - Use sharrow optimizations when possible, but fall back to - legacy `pandas.eval` systems when any error is encountered. This is the - preferred mode for running with sharrow if reliability is more important - than performance. - * `require` - Use sharrow optimizations, and raise an error if they fail - unexpectedly. This is the preferred mode for running with sharrow - if performance is a concern. - * `test` - Run every relevant calculation using both sharrow and legacy - systems, and compare them to ensure the results match. This is the slowest - mode of operation, but useful for development and debugging. - """ - - -class ZarrDigitalEncoding(PydanticBase): - """Digital encoding instructions for skim tables. - - .. versionadded:: 1.2 - """ - - regex: str - """A regular expression for matching skim matrix names. - - All skims with names that match under typical regular expression rules - for Python will be processed together. - """ - - joint_dict: str - """The name of the joint dictionary for this group. - - This must be a unique name for this set of skims, and a new array - will be added to the Dataset with this name. It will be an integer- - type array indicating the position of each element in the jointly - encoded dictionary.""" - - -class TAZ_Settings(PydanticBase): - """ - Complex settings for TAZ skims that are not just OMX file(s). - - .. versionadded:: 1.2 - """ - - omx: str = None - """The filename of the data stored in OMX format. - - This is treated as a fallback for the raw input data, if ZARR format data - is not available. - """ - - zarr: str = None - """The filename of the data stored in ZARR format. - - Reading ZARR data can be much faster than reading OMX format data, so if - this filename is given, the ZARR file format is preferred if it exists. If - it does not exist, then OMX data is read in and then ZARR data is written - out for future usage. - - .. versionadded:: 1.2 - """ - - zarr_digital_encoding: list[ZarrDigitalEncoding] = None - """ - A list of encodings to apply before saving skims in ZARR format. - - .. versionadded:: 1.2 - """ - - -class NetworkSettings(PydanticBase): - """ - Network level of service and skims settings - - The input for these settings is typically stored in one YAML file, - usually called ``network_los.yaml``. - """ - - zone_system: int - """Which zone system type is used. - - * 1 - TAZ only. - * 2 - MAZ and TAZ. - * 3 - MAZ, TAZ, and TAP - """ - - taz_skims: Union[str, TAZ_Settings] = None - """Instructions for how to load and pre-process skim matrices. - - If given as a string, it is interpreted as the location for OMX file(s), - either as a single file or as a glob-matching pattern for multiple files. - The time period for the matrix must be represented at the end of the matrix - name and be seperated by a double_underscore (e.g. `BUS_IVT__AM` indicates base - skim BUS_IVT with a time period of AM. - - Alternatively, this can be given as a nested dictionary defined via the - TAZ_Settings class, which allows for ZARR transformation and pre-processing. - """ - - skim_time_periods: dict - """time period upper bound values and labels - - * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours)) - * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole factor of ``time_window``. (Default: 60 minutes) - * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment - * ``labels`` - Labels to define names for aggregate periods for skims and assignment - """ - - read_skim_cache: bool = False - """Read cached skims (using numpy memmap) from output directory. - - Reading from memmap is much faster than omx, but the memmap is a huge - uncompressed file. - """ - - write_skim_cache: bool = False - """Write memmapped cached skims to output directory. - - This is needed if you want to use the cached skims to speed up subsequent - runs. - """ - - cache_dir: str = None - """alternate dir to read/write cache files (defaults to output_dir)""" diff --git a/activitysim/core/configuration/__init__.py b/activitysim/core/configuration/__init__.py new file mode 100644 index 000000000..5cdd2f69c --- /dev/null +++ b/activitysim/core/configuration/__init__.py @@ -0,0 +1,4 @@ +# flake8: noqa + +from .network import * +from .top import * diff --git a/activitysim/core/configuration/base.py b/activitysim/core/configuration/base.py new file mode 100644 index 000000000..d6c5a3ed9 --- /dev/null +++ b/activitysim/core/configuration/base.py @@ -0,0 +1,8 @@ +from typing import Any, Union # noqa: F401 + +try: + from pydantic import BaseModel as PydanticBase +except ModuleNotFoundError: + + class PydanticBase: + pass diff --git a/activitysim/core/configuration/network.py b/activitysim/core/configuration/network.py new file mode 100644 index 000000000..c047f117d --- /dev/null +++ b/activitysim/core/configuration/network.py @@ -0,0 +1,193 @@ +from .base import Any, PydanticBase, Union + + +class DigitalEncoding(PydanticBase): + """Digital encoding instructions for skim tables. + + These encoding instructions are used to digitally encode data prior + to writing that data back to disk in the `zarr` file format. + + See :ref:`digital-encoding` documentation for details. + + .. versionadded:: 1.2 + """ + + name: str = None + """The name of an individual matrix skim to encode. + + Use this setting to encode specific individual skims. To encode + a group of related skims with the same encoding, or together with + a joint encoding, use `regex` instead. You cannot specify both + `name` and `regex` at the same time. + """ + + regex: str = None + """A regular expression for matching skim matrix names. + + All skims with names that match under typical regular expression rules + for Python will be processed using the rules defined in this + DigitalEncoding instruction. To encode one specific skim, + use `name` instead. You cannot specify both `name` and `regex` at + the same time. + """ + + joint_dict: str = None + """The name of the joint dictionary for this group. + + This must be a unique name for this set of skims, and a new array + will be added to the Dataset with this name. It will be an integer- + type array indicating the position of each element in the jointly + encoded dictionary. + + If the `joint_dict` name is given, then all other instructions in this + DigitalEncoding are ignored, except `regex`. + """ + + missing_value: Any = None + """ + Use this value to indicate "missing" values. + + For float variables, it is possible to use NaN to represent missing values, + but other data types do not have a native missing value, so it will need + to be given explicitly. + """ + + bitwidth: int = 16 + """Number of bits to use in encoded integers, either 8, 16 or 32. + + For basic fixed point encoding, it is usually sufficient to simply define + the target bitwidth, and the `missing value` if applicable. The other + necessary parameters can then be inferred from the data. + """ + + min_value: Union[int, float] = None + """ + Explicitly give the minimum value represented in the array. + If not given, it is inferred from the data. It is useful to give + these values if the array does not necessarily include all the values that + might need to be inserted later. + """ + + max_value: Union[int, float] = None + """ + Explicitly give the maximum value represented in the array. + If not given, it is inferred from the data. It is useful to give + these values if the array does not necessarily include all the values that + might need to be inserted later. + """ + + scale: Union[int, float] = None + """ + An explicitly defined scaling factor. + + The scaling factor can be inferred from the min and max values if not provided. + """ + + offset: Union[int, float] = None + """ + An explicitly defined offset factor. + + The offset factor can be inferred from the min and max values if not provided. + """ + + by_dict: Union[int, bool] = None + """ + Encode by dictionary, using a bitwidth from {8, 16, 32}, or `True`. + + If given, all arguments other settings for this data are ignored. If given + as `True`, the bitwidth setting is not ignored, but everything else still + is. + """ + + +class TAZ_Settings(PydanticBase): + """ + Complex settings for TAZ skims that are not just OMX file(s). + + .. versionadded:: 1.2 + """ + + omx: str = None + """The filename of the data stored in OMX format. + + This is treated as a fallback for the raw input data, if ZARR format data + is not available. + """ + + zarr: str = None + """The filename of the data stored in ZARR format. + + Reading ZARR data can be much faster than reading OMX format data, so if + this filename is given, the ZARR file format is preferred if it exists. If + it does not exist, then OMX data is read in and then ZARR data is written + out for future usage. + + .. versionadded:: 1.2 + """ + + zarr_digital_encoding: list[DigitalEncoding] = None + """ + A list of encodings to apply before saving skims in ZARR format. + + .. versionadded:: 1.2 + + Digital encodings transform how data is stored in memory and on disk, + potentially reducing storage requirements without fundamentally changing + the underlying data. + """ + + +class NetworkSettings(PydanticBase): + """ + Network level of service and skims settings + + The input for these settings is typically stored in one YAML file, + usually called ``network_los.yaml``. + """ + + zone_system: int + """Which zone system type is used. + + * 1 - TAZ only. + * 2 - MAZ and TAZ. + * 3 - MAZ, TAZ, and TAP + """ + + taz_skims: Union[str, TAZ_Settings] = None + """Instructions for how to load and pre-process skim matrices. + + If given as a string, it is interpreted as the location for OMX file(s), + either as a single file or as a glob-matching pattern for multiple files. + The time period for the matrix must be represented at the end of the matrix + name and be seperated by a double_underscore (e.g. `BUS_IVT__AM` indicates base + skim BUS_IVT with a time period of AM. + + Alternatively, this can be given as a nested dictionary defined via the + TAZ_Settings class, which allows for ZARR transformation and pre-processing. + """ + + skim_time_periods: dict + """time period upper bound values and labels + + * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours)) + * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole factor of ``time_window``. (Default: 60 minutes) + * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment + * ``labels`` - Labels to define names for aggregate periods for skims and assignment + """ + + read_skim_cache: bool = False + """Read cached skims (using numpy memmap) from output directory. + + Reading from memmap is much faster than omx, but the memmap is a huge + uncompressed file. + """ + + write_skim_cache: bool = False + """Write memmapped cached skims to output directory. + + This is needed if you want to use the cached skims to speed up subsequent + runs. + """ + + cache_dir: str = None + """alternate dir to read/write cache files (defaults to output_dir)""" diff --git a/activitysim/core/configuration/top.py b/activitysim/core/configuration/top.py new file mode 100644 index 000000000..f956bb6ea --- /dev/null +++ b/activitysim/core/configuration/top.py @@ -0,0 +1,459 @@ +from .base import PydanticBase, Union + + +class InputTable(PydanticBase): + """ + The features that define an input table to be read by ActivitySim. + """ + + tablename: str + """Name of the injected table""" + + filename: str = None + """ + Name of the CSV or HDF5 file to read. + + If not provided, defaults to `input_store` + """ + + index_col: str = None + """table column to use for the index""" + + rename_columns: dict[str, str] = None + """ + Dictionary of column name mappings. + + This allows for renaming data columns from the original names found in the + header of the input file itself, into the names used internally by + ActivitySim, in configuration and specification files. + """ + + recode_columns: dict[str, str] = None + """ + Dictionary of column recoding instructions. + + Certain columns of data, notably TAZ and MAZ id's, are more efficiently + stored as the index (offset) position of each value within a fixed array of + values, instead of as the value itself. To recode a column into this offset + format, give the value "zero-based" for that column name. This will replace + the named column with a RangeIndex, starting from zero, and will create a + lookup column of the original values, which is not used by ActivitySim other + than to recode other related variables, or to reconstitute the original + labels for a final output table. This zero-based recoding is typically + done for the `zone_id` field in the land_use table, but might also be done + elsewhere. + + Alternatively, for columns that contain *references* to recoded data, give + the recode instruction as "tablename.fieldname" (often, "land_use.zone_id"). + This will trigger a remapping of each value according to the stored lookup + table for the original values, transforming the values in other columns to + be consistent with the recoded zero-based values. For example, if the + `zone_id` field in the land_use table has been recoded to be zero-based, + then the home_zone_id in the households table needs to be recoded to match. + + Note that recoding is done after renaming, so the key values in this mapping + should correspond to the internally used names and not the original column + names that appear in the input file (if they have been renamed). + """ + + keep_columns: list[str] = None + """ + Columns to keep once read in to memory. + + Save only the columns needed for modeling or analysis to save on memory + and file I/O. If not given, all columns in the input file will be read + and retained. + """ + + h5_tablename: str = None + """table name if reading from HDF5 and different from `tablename`""" + + +class OutputTable(PydanticBase): + tablename: str + """The name of the pipeline table to write out.""" + + decode_columns: dict[str, str] = None + """ + A mapping indicating columns to decode when writing out results. + + Column decoding is the inverse of column recoding, such that the original + mapped values are restored. For example, if TAZ ID's in the zone_id column + of the land_use table have been recoded to zero-based, then any output + column that gives a TAZ (i.e., household home zones, work or school + locations, trip or tour origins and destinations) can and probably should be + decoded from zero-based back into nominal TAZ ID's. If every value in the + column is to be decoded, simply give the decode instruction in the same + manner as the recode instruction, "tablename.fieldname" (often, + "land_use.zone_id"). + + For some columns, like work or school locations, only non-negative values + should be decoded, as negative values indicate an absence of choice. In + these cases, the "tablename.fieldname" can be prefixed with a "nonnegative" + filter, seperated by a pipe character (e.g. "nonnegative | land_use.zone_id"). + """ + + +class OutputTables(PydanticBase): + """Instructions on how to write out final pipeline tables.""" + + h5_store: bool = False + """Write tables into a single HDF5 store instead of individual CSVs.""" + + action: str + """Whether to 'include' or 'skip' the enumerated tables in `tables`.""" + + prefix: str = "final_" + """This prefix is added to the filename of each output table.""" + + sort: bool = False + """Sort output in each table consistent with well defined column names.""" + + tables: list[Union[str, OutputTable]] = None + """ + A list of pipeline tables to include or to skip when writing outputs. + + If `action` is 'skip', the values in this list must all be simple + strings giving the names of tables to skip. Also, no decoding will be + applied to any output tables in this case. + + If `action` is 'include', the values in this list can be either simple + strings giving the names of tables to include, or :class:`OutputTable` + definitions giving a name and decoding instructions. + + If omitted, the all tables are written out and no decoding will be + applied to any output tables. + """ + + +class MultiprocessStepSlice(PydanticBase): + """Instructions on how to slice tables for each subprocess.""" + + tables: list[str] + """ + The names of tables that are to be sliced for multiprocessing. + + The index of the first table in the 'tables' list is the primary_slicer. + Any other tables listed are dependent tables with either ref_cols to the + primary_slicer or with the same index (i.e. having an index with the same + name). This cascades, so any tables dependent on the primary_table can in + turn have dependent tables that will be sliced by index or ref_col. + + For instance, if the primary_slicer is households, then persons can be + sliced because it has a ref_col to (column with the same same name as) the + household table index. And the tours table can be sliced since it has a + ref_col to persons. Tables can also be sliced by index. For instance the + person_windows table can be sliced because it has an index with the same + names as the persons table. + """ + + exclude: Union[bool, str, list[str]] + """ + Optional list of tables not to slice even if they have a sliceable index name. + + Or set to `True` or "*" to exclude all tables not explicitly listed in + `tables`. + """ + + +class MultiprocessStep(PydanticBase): + """ + A contiguous group of model components that are multiprocessed together. + """ + + name: str + """A descriptive name for this multiprocessing step.""" + + begin: str + """The first component that is part of this multiprocessing step.""" + + num_processes: int = None + """ + The number of processes to use in this multiprocessing step. + + If not provided, the default overall number of processes set in the main + settings file is used. + """ + + slice: MultiprocessStepSlice = None + """Instructions on how to slice tables for each subprocess.""" + + +class Settings(PydanticBase): + """ + The overall settings for the ActivitySim model system. + + The input for these settings is typically stored in one main YAML file, + usually called ``settings.yaml``. + + Note that this implementation is presently used only for generating + documentation, but future work may migrate the settings implementation to + actually use this pydantic code to validate the settings before running + the model. + """ + + models: list[str] + """ + list of model steps to run - auto ownership, tour frequency, etc. + + See :ref:`model_steps` for more details about each step. + """ + + multiprocess: bool = False + """Enable multiprocessing for this model.""" + + num_processes: int = None + """ + If running in multiprocessing mode, use this number of processes by default. + + If not given or set to 0, the number of processes to use is set to + half the number of available CPU cores, plus 1. + """ + + multiprocess_steps: list[MultiprocessStep] + """A list of multiprocess steps.""" + + resume_after: str = None + """to resume running the data pipeline after the last successful checkpoint""" + + input_table_list: list[InputTable] + """list of table names, indices, and column re-maps for each table in `input_store`""" + + input_store: str = None + """HDF5 inputs file""" + + create_input_store: bool = False + """ + Write the inputs as read in back to an HDF5 store. + + If enabled, this writes the store to the outputs folder to use for subsequent + model runs, as reading HDF5 can be faster than reading CSV files.""" + + households_sample_size: int = None + """ + Number of households to sample and simulate + + If omitted or set to 0, ActivitySim will simulate all households. + """ + trace_hh_id: Union[int, list] = None + """ + Trace household id(s) + + If omitted, no tracing is written out + """ + + trace_od: list[int] = None + """ + Trace origin, destination pair in accessibility calculation + + If omitted, no tracing is written out. + """ + + chunk_training_mode: str = None + """ + The method to use for chunk training. + + Valid values include {disabled, training, production, adaptive}. + See :ref:`chunk_size` for more details. + """ + + chunk_size: int = None + """ + Approximate amount of RAM to allocate to ActivitySim for batch processing. + + See :ref:`chunk_size` for more details. + """ + + chunk_method: str = None + """ + Memory use measure to use for chunking. + + See :ref:`chunk_size`. + """ + + checkpoints: Union[bool, list] = True + """ + When to write checkpoint (intermediate table states) to disk. + + If True, checkpoints are written at each step. If False, no intermediate + checkpoints will be written before the end of run. Or, provide an explicit + list of models to checkpoint. + """ + + check_for_variability: bool = False + """ + Debugging feature to find broken model specifications. + + Enabling this check does not alter valid results but slows down model runs. + """ + + log_alt_losers: bool = False + """ + Write out expressions when all alternatives are unavailable. + + This can be useful for model development to catch errors in specifications. + Enabling this check does not alter valid results but slows down model runs. + """ + + use_shadow_pricing: bool = False + """turn shadow_pricing on and off for work and school location""" + + output_tables: OutputTables = None + """list of output tables to write to CSV or HDF5""" + + want_dest_choice_sample_tables: bool = False + """turn writing of sample_tables on and off for all models""" + + cleanup_pipeline_after_run: bool = False + """ + Cleans up pipeline after successful run. + + This will clean up pipeline only after successful runs, by creating a + single-checkpoint pipeline file, and deleting any subprocess pipelines. + """ + + cleanup_trace_files_on_resume: bool = False + """Clean all trace files when restarting a model from a checkpoint.""" + + sharrow: Union[bool, str] = False + """ + Set the sharrow operating mode. + + .. versionadded:: 1.2 + + * `false` - Do not use sharrow. This is the default if no value is given. + * `true` - Use sharrow optimizations when possible, but fall back to + legacy `pandas.eval` systems when any error is encountered. This is the + preferred mode for running with sharrow if reliability is more important + than performance. + * `require` - Use sharrow optimizations, and raise an error if they fail + unexpectedly. This is the preferred mode for running with sharrow + if performance is a concern. + * `test` - Run every relevant calculation using both sharrow and legacy + systems, and compare them to ensure the results match. This is the slowest + mode of operation, but useful for development and debugging. + """ + + disable_zarr: bool = False + """ + Disable the use of zarr format skims. + + .. versionadded:: 1.2 + + By default, if sharrow is enabled (any setting other than false), ActivitySim + currently loads data from zarr format skims if a zarr location is provided, + and data is found there. If no data is found there, then original OMX skim + data is loaded, any transformations or encodings are applied, and then this + data is written out to a zarr file at that location. Setting this option to + True will disable the use of zarr. + """ + + instrument: bool = False + """ + Use `pyinstrument` to profile component performance. + + .. versionadded:: 1.2 + + This is generally a developer-only feature and not needed for regular usage + of ActivitySim. + + Use of this setting to enable statistical profiling of ActivitySim code, + using the `pyinstrument` library (an optional dependency which must also be + installed). A separate profiling session is triggered for each model + component. See the pyinstrument + `documentation `__ + for a description of how this tool works. + + When activated, a "profiling--\\*" directory is created in the output directory + of the model, tagged with the date and time of the profiling run. Profile + output is always tagged like this and never overwrites previous profiling + outputs, facilitating serial comparisons of runtimes in response to code or + configuration changes. + """ + + memory_profile: bool = False + """ + Generate a memory profile by sampling memory usage from a secondary process. + + .. versionadded:: 1.2 + + This is generally a developer-only feature and not needed for regular usage + of ActivitySim. + + Using this feature will open a secondary process, whose only job is to poll + memory usage for the main ActivitySim process. The usage is logged to a file + with time stamps, so it can be cross-referenced against ActivitySim logs to + identify what parts of the code are using RAM. The profiling is done from + a separate process to avoid the profiler itself from significantly slowing + the main model core, or (more importantly) generating memory usage on its + own that pollutes the collected data. + """ + + benchmarking: bool = False + """ + Flag this model run as a benchmarking run. + + .. versionadded:: 1.1 + + This is generally a developer-only feature and not needed for regular usage + of ActivitySim. + + By flagging a model run as a benchmark, certain operations of the model are + altered, to ensure valid benchmark readings. For example, in regular + operation, data such as skims are loaded on-demand within the first model + component that needs them. With benchmarking enabled, all data are always + pre-loaded before any component is run, to ensure that recorded times are + the runtime of the component itself, and not data I/O operations that are + neither integral to that component nor necessarily stable over replication. + """ + + write_raw_tables: bool = False + """ + Dump input tables back to disk immediately after loading them. + + This is generally a developer-only feature and not needed for regular usage + of ActivitySim. + + The data tables are written out before any annotation steps, but after + initial processing (renaming, filtering columns, recoding). + """ + + disable_destination_sampling: bool = False + + want_dest_choice_presampling: bool = False + + testing_fail_trip_destination: bool = False + + fail_fast: bool = False + + rotate_logs: bool = False + + offset_preprocessing: bool = False + """ + Flag to indicate whether offset preprocessing has already been done. + + .. versionadded:: 1.2 + + This flag is generally set automatically within ActivitySim during a run, + and not be a user ahead of time. The ability to do so is provided as a + developer-only feature for testing and development. + """ + + recode_pipeline_columns: bool = True + """ + Apply recoding instructions on input and final output for pipeline tables. + + .. versionadded:: 1.2 + + Recoding instructions can be provided in individual + :py:attr:`InputTable.recode_columns` and :py:attr:`OutputTable.decode_columns` + settings. This global setting permits disabling all recoding processes + simultaneously. + + .. warning:: + + Disabling recoding is fine in legacy mode but it is generally not + compatible with using :py:attr:`Settings.sharrow`. + """ + + keep_mem_logs: bool = False diff --git a/activitysim/core/expressions.py b/activitysim/core/expressions.py index a64f66523..728b6d440 100644 --- a/activitysim/core/expressions.py +++ b/activitysim/core/expressions.py @@ -2,11 +2,12 @@ # See full license in LICENSE.txt. import logging -import numpy as np -import pandas as pd - from activitysim.core import assign, config, inject, simulate, tracing -from activitysim.core.util import assign_in_place +from activitysim.core.util import ( + assign_in_place, + parse_suffix_args, + suffix_expressions_df_str, +) logger = logging.getLogger(__name__) @@ -51,6 +52,21 @@ def compute_columns(df, model_settings, locals_dict={}, trace_label=None): helper_table_names = model_settings.get("TABLES", []) expressions_spec_name = model_settings.get("SPEC", None) + # Extract suffix for disaggregate accessibilities. + # The suffix args can either be passed in the model settings or as part of the config file string. + # Awkward, but avoids having to put positional arguments in every single asim function. + args = parse_suffix_args(expressions_spec_name) + + expressions_spec_name = args.filename + suffix = model_settings.get("SUFFIX", args.SUFFIX) + roots = model_settings.get("ROOTS", args.ROOTS) + + assert isinstance(roots, list) + assert (suffix is not None and roots) or (suffix is None and not roots), ( + "Expected to find both 'ROOTS' and 'SUFFIX' in %s, missing one" + % model_settings_name + ) + assert expressions_spec_name is not None, ( "Expected to find 'SPEC' in %s" % model_settings_name ) @@ -62,10 +78,14 @@ def compute_columns(df, model_settings, locals_dict={}, trace_label=None): logger.debug( f"{trace_label} compute_columns using expression spec file {expressions_spec_name}" ) + expressions_spec = assign.read_assignment_spec( config.config_file_path(expressions_spec_name) ) + if suffix is not None and roots: + expressions_spec = suffix_expressions_df_str(expressions_spec, suffix, roots) + assert expressions_spec.shape[0] > 0, ( "Expected to find some assignment expressions in %s" % expressions_spec_name ) @@ -84,12 +104,10 @@ def compute_columns(df, model_settings, locals_dict={}, trace_label=None): _locals_dict.update(tables) # FIXME a number of asim model preprocessors want skim_dict - should they request it in model_settings.TABLES? - _locals_dict.update( - { - # 'los': inject.get_injectable('network_los', None), - "skim_dict": inject.get_injectable("skim_dict", None), - } - ) + if config.setting("sharrow", False): + _locals_dict["skim_dict"] = inject.get_injectable("skim_dataset_dict", None) + else: + _locals_dict["skim_dict"] = inject.get_injectable("skim_dict", None) results, trace_results, trace_assigned_locals = assign.assign_variables( expressions_spec, df, _locals_dict, trace_rows=tracing.trace_targets(df) diff --git a/activitysim/core/fast_mapping.py b/activitysim/core/fast_mapping.py new file mode 100644 index 000000000..5c3a4fd10 --- /dev/null +++ b/activitysim/core/fast_mapping.py @@ -0,0 +1,62 @@ +import numba as nb +import numpy as np +import pandas as pd + + +@nb.njit +def _fast_map(fm, target, dtype=np.int32): + out = np.zeros(len(target), dtype=dtype) + for n in range(target.size): + out[n] = fm[target[n]] + return out + + +class FastMapping: + def __init__(self, source, to_range=np.int64): + if isinstance(source, pd.Series): + m = nb.typed.Dict.empty( + key_type=nb.from_dtype(source.index.dtype), + value_type=nb.from_dtype(source.dtype), + ) + for k, v in source.items(): + m[k] = v + self._in_dtype = source.index.dtype + self._out_dtype = source.dtype + self._mapper = m + elif to_range: + m = nb.typed.Dict.empty( + key_type=nb.from_dtype(source.dtype), + value_type=nb.from_dtype(to_range), + ) + for v, k in enumerate(source): + m[k] = v + self._in_dtype = source.dtype + self._out_dtype = to_range + self._mapper = m + else: + raise ValueError("invalid input") + + def __len__(self): + return len(self._mapper) + + def __contains__(self, item): + return item in self._mapper + + def __getitem__(self, item): + return self._mapper[item] + + def apply_to(self, target): + if isinstance(target, pd.Series): + return pd.Series( + _fast_map( + self._mapper, + target.astype(self._in_dtype).to_numpy(), + dtype=self._out_dtype, + ), + index=target.index, + ) + return _fast_map( + self._mapper, + np.asarray(target, dtype=self._in_dtype), + dtype=self._out_dtype, + ) diff --git a/activitysim/core/flow.py b/activitysim/core/flow.py new file mode 100644 index 000000000..88189ae76 --- /dev/null +++ b/activitysim/core/flow.py @@ -0,0 +1,826 @@ +import contextlib +import glob +import logging +import os +import time +from datetime import timedelta +from functools import partial +from numbers import Number +from stat import ST_MTIME + +import numpy as np +import pandas as pd + +from .. import __version__ +from ..core import tracing +from . import config, inject +from .simulate_consts import SPEC_EXPRESSION_NAME, SPEC_LABEL_NAME +from .timetable import ( + sharrow_tt_adjacent_window_after, + sharrow_tt_adjacent_window_before, + sharrow_tt_max_time_block_available, + sharrow_tt_previous_tour_begins, + sharrow_tt_previous_tour_ends, + sharrow_tt_remaining_periods_available, +) + +try: + import sharrow as sh +except ModuleNotFoundError: + sh = None + + +logger = logging.getLogger(__name__) + +_FLOWS = {} + +if os.environ.get("TRAVIS") == "true": + # The multithreaded dask scheduler causes problems on travis. + # Here, we detect if this code is running on Travis, and if so, we + # change the default scheduler to single-threaded. This should not + # be particularly problematic, as only tiny test cases are run on Travis. + import dask + + dask.config.set( + scheduler="single-threaded" + ) # overwrite default with threaded scheduler + + +@contextlib.contextmanager +def logtime(tag, tag2=""): + logger.info(f"begin {tag} {tag2}") + t0 = time.time() + try: + yield + except Exception: + logger.error(f"error in {tag} after {timedelta(seconds=time.time()-t0)} {tag2}") + raise + else: + logger.info(f"completed {tag} in {timedelta(seconds=time.time()-t0)} {tag2}") + + +class TimeLogger: + + aggregate_timing = {} + + def __init__(self, tag1): + self._time_point = self._time_start = time.time() + self._time_log = [] + self._tag1 = tag1 + + def mark(self, tag, ping=True, logger=None, suffix=""): + if ping: + now = time.time() + elapsed = now - self._time_point + self._time_log.append((tag, timedelta(seconds=elapsed))) + self._time_point = now + if logger is not None: + logger.info( + "elapsed time {0} {1} {2}".format( + tag, + timedelta(seconds=elapsed), + suffix, + ) + ) + else: + self._time_log.append((tag, "skipped")) + elapsed = 0 + if self._tag1: + tag = f"{self._tag1}.{tag}" + if tag not in self.aggregate_timing: + self.aggregate_timing[tag] = elapsed + else: + self.aggregate_timing[tag] += elapsed + + def summary(self, logger, tag, level=20, suffix=None): + gross_elaspsed = time.time() - self._time_start + if suffix: + msg = f"{tag} in {timedelta(seconds=gross_elaspsed)}: ({suffix})\n" + else: + msg = f"{tag} in {timedelta(seconds=gross_elaspsed)}: \n" + msgs = [] + for i in self._time_log: + j = timedelta(seconds=self.aggregate_timing[f"{self._tag1}.{i[0]}"]) + msgs.append(" - {0:24s} {1} [{2}]".format(*i, j)) + msg += "\n".join(msgs) + logger.log(level=level, msg=msg) + + @classmethod + def aggregate_summary( + cls, logger, heading="Aggregate Flow Timing Summary", level=20 + ): + msg = f"{heading}\n" + msgs = [] + for tag, elapsed in cls.aggregate_timing.items(): + msgs.append(" - {0:48s} {1}".format(tag, timedelta(seconds=elapsed))) + msg += "\n".join(msgs) + logger.log(level=level, msg=msg) + + +def only_simple(x, exclude_keys=()): + """ + All the values in a dict that are plain numbers, strings, or lists or tuples thereof. + """ + y = {} + for k, v in x.items(): + if k not in exclude_keys: + if isinstance(v, (Number, str)): + y[k] = v + elif isinstance(v, (list, tuple)): + if all(isinstance(j, (Number, str)) for j in v): + y[k] = v + return y + + +def get_flow( + spec, local_d, trace_label=None, choosers=None, interacts=None, zone_layer=None +): + extra_vars = only_simple(local_d) + orig_col_name = local_d.get("orig_col_name", None) + dest_col_name = local_d.get("dest_col_name", None) + stop_col_name = None + parking_col_name = None + primary_origin_col_name = None + timeframe = local_d.get("timeframe", "tour") + if timeframe == "trip": + orig_col_name = local_d.get("ORIGIN", orig_col_name) + dest_col_name = local_d.get("DESTINATION", dest_col_name) + parking_col_name = local_d.get("PARKING", parking_col_name) + if orig_col_name is None and "od_skims" in local_d: + orig_col_name = local_d["od_skims"].orig_key + if dest_col_name is None and "od_skims" in local_d: + dest_col_name = local_d["od_skims"].dest_key + if stop_col_name is None and "dp_skims" in local_d: + stop_col_name = local_d["dp_skims"].dest_key + if primary_origin_col_name is None and "dnt_skims" in local_d: + primary_origin_col_name = local_d["dnt_skims"].dest_key + local_d = size_terms_on_flow(local_d) + size_term_mapping = local_d.get("size_array", {}) + if "tt" in local_d: + aux_vars = local_d["tt"].export_for_numba() + else: + aux_vars = {} + flow = new_flow( + spec, + extra_vars, + orig_col_name, + dest_col_name, + trace_label, + timeframe=timeframe, + choosers=choosers, + stop_col_name=stop_col_name, + parking_col_name=parking_col_name, + size_term_mapping=size_term_mapping, + interacts=interacts, + zone_layer=zone_layer, + aux_vars=aux_vars, + primary_origin_col_name=primary_origin_col_name, + ) + flow.tree.aux_vars = aux_vars + return flow + + +def should_invalidate_cache_file(cache_filename, *source_filenames): + """ + Check if a cache file should be invalidated. + + It should be invalidated if any source file has a modification time + more recent than the cache file modification time. + + Parameters + ---------- + cache_filename : Path-like + source_filenames : Collection[Path-like] + + Returns + ------- + bool + """ + try: + stat0 = os.stat(cache_filename) + except FileNotFoundError: + # cache file does not even exist + return True + for i in source_filenames: + stat1 = os.stat(i) + if stat0[ST_MTIME] < stat1[ST_MTIME]: + return True + return False + + +def scan_for_unused_names(tokens): + """ + Scan all spec files to find unused skim variable names. + + Parameters + ---------- + tokens : Collection[str] + + Returns + ------- + Set[str] + """ + configs_dir_list = inject.get_injectable("configs_dir") + configs_dir_list = ( + [configs_dir_list] if isinstance(configs_dir_list, str) else configs_dir_list + ) + assert isinstance(configs_dir_list, list) + + for directory in configs_dir_list: + logger.debug(f"scanning for unused skims in {directory}") + filenames = glob.glob(os.path.join(directory, "*.csv")) + for filename in filenames: + with open(filename, "rt") as f: + content = f.read() + missing_tokens = set() + for t in tokens: + if t not in content: + missing_tokens.add(t) + tokens = missing_tokens + if not tokens: + return tokens + return tokens + + +@inject.injectable(cache=True) +def skim_dataset_dict(skim_dataset): + from .skim_dataset import SkimDataset + + return SkimDataset(skim_dataset) + + +def skims_mapping( + orig_col_name, + dest_col_name, + timeframe="tour", + stop_col_name=None, + parking_col_name=None, + zone_layer=None, + primary_origin_col_name=None, +): + logger.info("loading skims_mapping") + logger.info(f"- orig_col_name: {orig_col_name}") + logger.info(f"- dest_col_name: {dest_col_name}") + logger.info(f"- stop_col_name: {stop_col_name}") + logger.info(f"- primary_origin_col_name: {primary_origin_col_name}") + skim_dataset = inject.get_injectable("skim_dataset") + if zone_layer == "maz" or zone_layer is None: + odim = "omaz" if "omaz" in skim_dataset.dims else "otaz" + ddim = "dmaz" if "dmaz" in skim_dataset.dims else "dtaz" + elif zone_layer == "taz": + odim = "otaz" + ddim = "dtaz" + if "omaz" in skim_dataset.dims: + # strip out all MAZ-specific features of the skim_dataset + dropdims = ["omaz", "dmaz"] + skim_dataset = skim_dataset.drop_dims(dropdims, errors="ignore") + for dd in dropdims: + if f"dim_redirection_{dd}" in skim_dataset.attrs: + del skim_dataset.attrs[f"dim_redirection_{dd}"] + for attr_name in list(skim_dataset.attrs): + if attr_name.startswith("blend"): + del skim_dataset.attrs[attr_name] + + else: + raise ValueError(f"unknown zone layer {zone_layer!r}") + if zone_layer: + logger.info(f"- zone_layer: {zone_layer}") + if ( + orig_col_name is not None + and dest_col_name is not None + and stop_col_name is None + and parking_col_name is None + ): + if timeframe == "timeless": + return dict( + skims=skim_dataset, + relationships=( + f"df._orig_col_name -> skims.{odim}", + f"df._dest_col_name -> skims.{ddim}", + ), + ) + if timeframe == "timeless_directional": + return dict( + od_skims=skim_dataset, + do_skims=skim_dataset, + relationships=( + f"df._orig_col_name -> od_skims.{odim}", + f"df._dest_col_name -> od_skims.{ddim}", + f"df._dest_col_name -> do_skims.{odim}", + f"df._orig_col_name -> do_skims.{ddim}", + ), + ) + elif timeframe == "trip": + return dict( + odt_skims=skim_dataset, + dot_skims=skim_dataset, + od_skims=skim_dataset, + relationships=( + f"df._orig_col_name -> odt_skims.{odim}", + f"df._dest_col_name -> odt_skims.{ddim}", + "df.trip_period -> odt_skims.time_period", + f"df._dest_col_name -> dot_skims.{odim}", + f"df._orig_col_name -> dot_skims.{ddim}", + "df.trip_period -> dot_skims.time_period", + f"df._orig_col_name -> od_skims.{odim}", + f"df._dest_col_name -> od_skims.{ddim}", + ), + ) + else: + return dict( + # TODO:SHARROW: organize dimensions. + odt_skims=skim_dataset, + dot_skims=skim_dataset, + odr_skims=skim_dataset, + dor_skims=skim_dataset, + od_skims=skim_dataset, + relationships=( + f"df._orig_col_name -> odt_skims.{odim}", + f"df._dest_col_name -> odt_skims.{ddim}", + "df.out_period @ odt_skims.time_period", + f"df._dest_col_name -> dot_skims.{odim}", + f"df._orig_col_name -> dot_skims.{ddim}", + "df.in_period @ dot_skims.time_period", + f"df._orig_col_name -> odr_skims.{odim}", + f"df._dest_col_name -> odr_skims.{ddim}", + "df.in_period @ odr_skims.time_period", + f"df._dest_col_name -> dor_skims.{odim}", + f"df._orig_col_name -> dor_skims.{ddim}", + "df.out_period @ dor_skims.time_period", + f"df._orig_col_name -> od_skims.{odim}", + f"df._dest_col_name -> od_skims.{ddim}", + ), + ) + elif stop_col_name is not None: # trip_destination + return dict( + od_skims=skim_dataset, + dp_skims=skim_dataset, + op_skims=skim_dataset, + odt_skims=skim_dataset, + dot_skims=skim_dataset, + dpt_skims=skim_dataset, + pdt_skims=skim_dataset, + opt_skims=skim_dataset, + pot_skims=skim_dataset, + ndt_skims=skim_dataset, + dnt_skims=skim_dataset, + relationships=( + f"df._orig_col_name -> od_skims.{odim}", + f"df._dest_col_name -> od_skims.{ddim}", + f"df._dest_col_name -> dp_skims.{odim}", + f"df._stop_col_name -> dp_skims.{ddim}", + f"df._orig_col_name -> op_skims.{odim}", + f"df._stop_col_name -> op_skims.{ddim}", + f"df._orig_col_name -> odt_skims.{odim}", + f"df._dest_col_name -> odt_skims.{ddim}", + "df.trip_period -> odt_skims.time_period", + f"df._dest_col_name -> dot_skims.{odim}", + f"df._orig_col_name -> dot_skims.{ddim}", + "df.trip_period -> dot_skims.time_period", + f"df._dest_col_name -> dpt_skims.{odim}", + f"df._stop_col_name -> dpt_skims.{ddim}", + "df.trip_period -> dpt_skims.time_period", + f"df._stop_col_name -> pdt_skims.{odim}", + f"df._dest_col_name -> pdt_skims.{ddim}", + "df.trip_period -> pdt_skims.time_period", + f"df._orig_col_name -> opt_skims.{odim}", + f"df._stop_col_name -> opt_skims.{ddim}", + "df.trip_period -> opt_skims.time_period", + f"df._stop_col_name -> pot_skims.{odim}", + f"df._orig_col_name -> pot_skims.{ddim}", + "df.trip_period -> pot_skims.time_period", + f"df._primary_origin_col_name -> ndt_skims.{odim}", + f"df._dest_col_name -> ndt_skims.{ddim}", + "df.trip_period -> ndt_skims.time_period", + f"df._dest_col_name -> dnt_skims.{odim}", + f"df._primary_origin_col_name -> dnt_skims.{ddim}", + "df.trip_period -> dnt_skims.time_period", + ), + ) + elif parking_col_name is not None: # parking location + return dict( + od_skims=skim_dataset, + do_skims=skim_dataset, + op_skims=skim_dataset, + pd_skims=skim_dataset, + odt_skims=skim_dataset, + dot_skims=skim_dataset, + opt_skims=skim_dataset, + pdt_skims=skim_dataset, + relationships=( + f"df._orig_col_name -> od_skims.{odim}", + f"df._dest_col_name -> od_skims.{ddim}", + f"df._dest_col_name -> do_skims.{odim}", + f"df._orig_col_name -> do_skims.{ddim}", + f"df._orig_col_name -> op_skims.{odim}", + f"df._park_col_name -> op_skims.{ddim}", + f"df._park_col_name -> pd_skims.{odim}", + f"df._dest_col_name -> pd_skims.{ddim}", + f"df._orig_col_name -> odt_skims.{odim}", + f"df._dest_col_name -> odt_skims.{ddim}", + "df.trip_period -> odt_skims.time_period", + f"df._dest_col_name -> dot_skims.{odim}", + f"df._orig_col_name -> dot_skims.{ddim}", + "df.trip_period -> dot_skims.time_period", + f"df._orig_col_name -> opt_skims.{odim}", + f"df._park_col_name -> opt_skims.{ddim}", + "df.trip_period -> opt_skims.time_period", + f"df._park_col_name -> pdt_skims.{odim}", + f"df._dest_col_name -> pdt_skims.{ddim}", + "df.trip_period -> pdt_skims.time_period", + ), + ) + else: + return {} # flows without LOS characteristics are still valid + + +def new_flow( + spec, + extra_vars, + orig_col_name, + dest_col_name, + trace_label=None, + timeframe="tour", + choosers=None, + stop_col_name=None, + parking_col_name=None, + size_term_mapping=None, + interacts=None, + zone_layer=None, + aux_vars=None, + primary_origin_col_name=None, +): + """ + Setup a new sharrow flow. + + Parameters + ---------- + spec : pandas.DataFrame + The spec, as usual for ActivitySim. The index should either be a basic + single-level index containing the expressions to be evaluated, or a + MultiIndex with at least "Expression" and "Label" levels. + extra_vars : Mapping + Extra values that are available to expressions and which are written + explicitly into compiled code (and cannot be changed later). + orig_col_name : str + The column from the choosers table that gives the origin zone index, + used to attach values from skims. + dest_col_name : str + The column from the choosers table that gives the destination zone index, + used to attach values from skims. + trace_label : str + A descriptive label + timeframe : {"tour", "timeless", "timeless_directional", "trip"}, default "tour" + A framework for how to treat the time and directionality of skims that + will be attached. + choosers : pandas.DataFrame + Attributes of the choosers, possibly interacted with attributes of the + alternatives. Generally this flow can and will be re-used by swapping + out the `choosers` for a new dataframe with the same columns and + different rows. + stop_col_name : str + The column from the choosers table that gives the stop zone index in + trip destination choice, used to attach values from skims. + parking_col_name : str + The column from the choosers table that gives the parking zone index, + used to attach values from skims. + size_term_mapping : Mapping + Size term arrays. + interacts : pd.DataFrame, optional + An unmerged interaction dataset, giving attributes of the alternatives + that are not conditional on the chooser. Use this when the choice model + has some variables that are conditional on the chooser (and included in + the `choosers` dataframe, and some variables that are conditional on the + alternative but not the chooser, and when every chooser has the same set + of possible alternatives. + zone_layer : {'taz', 'maz'}, default 'taz' + Specify which zone layer of the skims is to be used. You cannot use the + 'maz' zone layer in a one-zone model, but you can use the 'taz' layer in + a two- or three-zone model (e.g. for destination pre-sampling). + aux_vars : Mapping + Extra values that are available to expressions and which are written + only by reference into compiled code (and thus can be changed later). + + Returns + ------- + sharrow.Flow + """ + + with logtime(f"setting up flow {trace_label}"): + if choosers is None: + chooser_cols = [] + else: + chooser_cols = list(choosers.columns) + + cache_dir = os.path.join( + config.get_cache_dir(), + "__sharrowcache__", + ) + os.makedirs(cache_dir, exist_ok=True) + logger.debug(f"flow.cache_dir: {cache_dir}") + skims_mapping_ = skims_mapping( + orig_col_name, + dest_col_name, + timeframe, + stop_col_name, + parking_col_name=parking_col_name, + zone_layer=zone_layer, + primary_origin_col_name=primary_origin_col_name, + ) + if size_term_mapping is None: + size_term_mapping = {} + + def _apply_filter(_dataset, renames: list): + renames_keys = set(i for (i, j) in rename_dataset_cols) + ds = _dataset.ensure_integer(renames_keys) + for _k, _v in renames: + if _k in ds: + ds[_v] = ds[_k] + return ds + + if interacts is None: + if choosers is None: + logger.info(f"empty flow on {trace_label}") + else: + logger.info(f"{len(choosers)} chooser rows on {trace_label}") + flow_tree = sh.DataTree(df=[] if choosers is None else choosers) + idx_name = choosers.index.name or "index" + rename_dataset_cols = [ + (idx_name, "chooserindex"), + ] + if orig_col_name is not None: + rename_dataset_cols.append((orig_col_name, "_orig_col_name")) + if dest_col_name is not None: + rename_dataset_cols.append((dest_col_name, "_dest_col_name")) + if stop_col_name is not None: + rename_dataset_cols.append((stop_col_name, "_stop_col_name")) + if parking_col_name is not None: + rename_dataset_cols.append((parking_col_name, "_park_col_name")) + if primary_origin_col_name is not None: + rename_dataset_cols.append( + (primary_origin_col_name, "_primary_origin_col_name") + ) + + flow_tree.replacement_filters[flow_tree.root_node_name] = partial( + _apply_filter, renames=rename_dataset_cols + ) + flow_tree.root_dataset = flow_tree.root_dataset # apply the filter + else: + logger.info( + f"{len(choosers)} chooser rows and {len(interacts)} interact rows on {trace_label}" + ) + top = sh.dataset.from_named_objects( + pd.RangeIndex(len(choosers), name="chooserindex"), + pd.RangeIndex(len(interacts), name="interactindex"), + ) + flow_tree = sh.DataTree(start=top) + rename_dataset_cols = [] + if orig_col_name is not None: + rename_dataset_cols.append((orig_col_name, "_orig_col_name")) + if dest_col_name is not None: + rename_dataset_cols.append((dest_col_name, "_dest_col_name")) + if stop_col_name is not None: + rename_dataset_cols.append((stop_col_name, "_stop_col_name")) + if parking_col_name is not None: + rename_dataset_cols.append((parking_col_name, "_park_col_name")) + if primary_origin_col_name is not None: + rename_dataset_cols.append( + (primary_origin_col_name, "_primary_origin_col_name") + ) + + choosers_ = sh.dataset.construct(choosers) + choosers_ = _apply_filter(choosers_, rename_dataset_cols) + flow_tree.add_dataset( + "df", + choosers_, + f"start.chooserindex -> df.{next(iter(choosers_.dims))}", + ) + interacts_ = sh.dataset.construct(interacts) + interacts_ = _apply_filter(interacts_, rename_dataset_cols) + flow_tree.add_dataset( + "interact_table", + interacts_, + f"start.interactindex -> interact_table.{next(iter(interacts_.dims))}", + ) + flow_tree.subspace_fallbacks["df"] = ["interact_table"] + + flow_tree.add_items(skims_mapping_) + flow_tree.add_items(size_term_mapping) + flow_tree.extra_vars = extra_vars + flow_tree.extra_funcs = ( + sharrow_tt_remaining_periods_available, + sharrow_tt_previous_tour_begins, + sharrow_tt_previous_tour_ends, + sharrow_tt_adjacent_window_after, + sharrow_tt_adjacent_window_before, + sharrow_tt_max_time_block_available, + ) + flow_tree.aux_vars = aux_vars + + # - eval spec expressions + if isinstance(spec.index, pd.MultiIndex): + # spec MultiIndex with expression and label + exprs = spec.index.get_level_values(SPEC_EXPRESSION_NAME) + labels = spec.index.get_level_values(SPEC_LABEL_NAME) + else: + exprs = spec.index + labels = exprs + + defs = {} + # duplicate labels cause problems for sharrow, so we need to dedupe + existing_labels = set() + for (expr, label) in zip(exprs, labels): + while label in existing_labels: + label = label + "_" + existing_labels.add(label) + if expr[0] == "@": + if label == expr: + if expr[1:].isidentifier(): + defs[expr[1:] + "_"] = expr[1:] + else: + defs[expr[1:]] = expr[1:] + else: + defs[label] = expr[1:] + elif expr[0] == "_" and "@" in expr: + # - allow temps of form _od_DIST@od_skim['DIST'] + target = expr[: expr.index("@")] + rhs = expr[expr.index("@") + 1 :] + defs[target] = rhs + else: + if label == expr and expr.isidentifier(): + defs[expr + "_"] = expr + else: + defs[label] = expr + + readme = f""" + activitysim version: {__version__} + trace label: {trace_label} + orig_col_name: {orig_col_name} + dest_col_name: {dest_col_name} + expressions:""" + for (expr, label) in zip(exprs, labels): + readme += f"\n - {label}: {expr}" + if extra_vars: + readme += "\n extra_vars:" + for i, v in extra_vars.items(): + readme += f"\n - {i}: {v}" + + logger.info(f"setting up sharrow flow {trace_label}") + extra_hash_data = () + if zone_layer: + extra_hash_data += (zone_layer,) + return flow_tree.setup_flow( + defs, + cache_dir=cache_dir, + readme=readme[1:], # remove leading newline + flow_library=_FLOWS, + extra_hash_data=extra_hash_data, + hashing_level=0, + boundscheck=False, + ) + + +def size_terms_on_flow(locals_d): + """ + Create size terms to attach to a DataTree based on destination and purpose. + + Parameters + ---------- + locals_d : Mapping[str,Any] + The context for the flow. If it does not contain "size_terms_array" + this function does nothing. Otherwise, the instructions for adding + the size terms to the DataTree are created in a "size_array" variable + in the same context space. + + Returns + ------- + locals_d + """ + if "size_terms_array" in locals_d: + a = sh.Dataset( + { + "sizearray": sh.DataArray( + locals_d["size_terms_array"], + dims=["zoneid", "purpose_index"], + coords={ + "zoneid": np.arange(locals_d["size_terms_array"].shape[0]), + }, + ) + } + ) + locals_d["size_array"] = dict( + size_terms=a, + relationships=( + "df._dest_col_name -> size_terms.zoneid", + "df.purpose_index_num -> size_terms.purpose_index", + ), + ) + return locals_d + + +def apply_flow( + spec, + choosers, + locals_d=None, + trace_label=None, + required=False, + interacts=None, + zone_layer=None, +): + """ + Apply a sharrow flow. + + Parameters + ---------- + spec : pd.DataFrame + choosers : pd.DataFrame + locals_d : Mapping[str,Any], optional + A namespace of local variables to be made available with the + expressions in `spec`. + trace_label : str, optional + A descriptive label used in logging and naming trace files. + required : bool, default False + Require the spec to be compile-able. If set to true, a problem will + the flow will be raised as an error, instead of allowing this function + to return with no result (and activitysim will then fall back to the + legacy eval system). + interacts : pd.DataFrame, optional + An unmerged interaction dataset, giving attributes of the alternatives + that are not conditional on the chooser. Use this when the choice model + has some variables that are conditional on the chooser (and included in + the `choosers` dataframe, and some variables that are conditional on the + alternative but not the chooser, and when every chooser has the same set + of possible alternatives. + zone_layer : {'taz', 'maz'}, default 'taz' + Specify which zone layer of the skims is to be used. You cannot use the + 'maz' zone layer in a one-zone model, but you can use the 'taz' layer in + a two- or three-zone model (e.g. for destination pre-sampling). + + Returns + ------- + flow_result : ndarray + The computed dot-product of the utility function and the coefficients. + flow : sharrow.Flow + The flow object itself. In typical application you probably don't need + it ever again, but having a reference to it available later can be useful + in debugging and tracing. Flows are cached and reused anyway, so it is + generally not important to delete this at any point to free resources. + """ + if sh is None: + return None, None + if locals_d is None: + locals_d = {} + with logtime("apply_flow"): + try: + flow = get_flow( + spec, + locals_d, + trace_label, + choosers=choosers, + interacts=interacts, + zone_layer=zone_layer, + ) + except ValueError as err: + if "unable to rewrite" in str(err): + # There has been an error in preparing this flow. + # If in `require` mode, we report the error and keep it as an error + # Otherwise, we report the error but then swallow it and return + # a None result, allowing ActivitySim to fall back to legacy + # operating mode for this utility function. + logger.error(f"error in apply_flow: {err!s}") + if required: + raise + return None, None + else: + raise + with logtime(f"{flow.name}.load", trace_label or ""): + try: + flow_result = flow.dot( + coefficients=spec.values.astype(np.float32), + dtype=np.float32, + compile_watch=True, + ) + # TODO: are there remaining internal arrays in dot that need to be + # passed out to be seen by the dynamic chunker before they are freed? + except ValueError as err: + if "could not convert" in str(err): + # There has been an error in compiling this flow. + # If in `require` mode, we report the error and keep it as an error + # Otherwise, we report the error but then swallow it and return + # a None result, allowing ActivitySim to fall back to legacy + # operating mode for this utility function. + logger.error(f"error in apply_flow: {err!s}") + if required: + raise + return None, flow + raise + except Exception as err: + logger.error(f"error in apply_flow: {err!s}") + raise + if flow.compiled_recently: + # When compile activity is detected, we make a note in the timing log, + # which can help explain when a component is unexpectedly slow. + # Detecting compilation activity when in production mode is a bug + # that should be investigated. + tracing.timing_notes.add(f"compiled:{flow.name}") + return flow_result, flow diff --git a/activitysim/core/inject.py b/activitysim/core/inject.py index 6e2e9d5fa..208a5658f 100644 --- a/activitysim/core/inject.py +++ b/activitysim/core/inject.py @@ -1,6 +1,7 @@ # ActivitySim # See full license in LICENSE.txt. import logging +import warnings from orca import orca @@ -8,6 +9,7 @@ _DECORATED_TABLES = {} _DECORATED_COLUMNS = {} _DECORATED_INJECTABLES = {} +_BROADCASTS = [] # we want to allow None (any anyting else) as a default value, so just choose an improbable string @@ -25,6 +27,31 @@ def decorator(func): assert not _DECORATED_STEPS.get(name, False), ( "step '%s' already decorated." % name ) + if _DECORATED_STEPS.get(name, False): + warnings.warn( + f"step {name!r} already exists, ignoring default implementation." + ) + else: + _DECORATED_STEPS[name] = func + orca.add_step(name, func) + + return func + + return decorator + + +def custom_step(): + """ + This decorator allows custom steps to potentially overload existing steps. + """ + + def decorator(func): + name = func.__name__ + + logger.debug("inject step %s" % name) + + if _DECORATED_STEPS.get(name, False): + warnings.warn(f"step {name!r} already exists, overwriting it.") _DECORATED_STEPS[name] = func orca.add_step(name, func) @@ -109,6 +136,17 @@ def add_injectable(name, injectable, cache=False): def broadcast( cast, onto, cast_on=None, onto_on=None, cast_index=False, onto_index=False ): + _BROADCASTS.append( + [ + (cast, onto), + dict( + cast_on=cast_on, + onto_on=onto_on, + cast_index=cast_index, + onto_index=onto_index, + ), + ] + ) return orca.broadcast( cast, onto, @@ -145,9 +183,12 @@ def remove_injectable(name): orca._INJECTABLES.pop(name, None) -def reinject_decorated_tables(): +def reinject_decorated_tables(steps=False): """ reinject the decorated tables (and columns) + + This function can be used to completely reset the global state for + ActivitySim. """ logger.info("reinject_decorated_tables") @@ -157,6 +198,9 @@ def reinject_decorated_tables(): orca._COLUMNS.clear() orca._TABLE_CACHE.clear() orca._COLUMN_CACHE.clear() + if steps: + orca._STEPS.clear() + orca._BROADCASTS.clear() for name, func in _DECORATED_TABLES.items(): logger.debug("reinject decorated table %s" % name) @@ -171,6 +215,13 @@ def reinject_decorated_tables(): logger.debug("reinject decorated injectable %s" % name) orca.add_injectable(name, args["func"], cache=args["cache"]) + if steps: + for name, func in _DECORATED_STEPS.items(): + logger.debug("reinject decorated step %s" % name) + orca.add_step(name, func) + for arg, kwarg in _BROADCASTS: + orca.broadcast(*arg, **kwarg) + def clear_cache(): return orca.clear_cache() diff --git a/activitysim/core/input.py b/activitysim/core/input.py index 73183f3e9..41bfdc1c0 100644 --- a/activitysim/core/input.py +++ b/activitysim/core/input.py @@ -7,7 +7,7 @@ import pandas as pd -from activitysim.core import config, inject, mem, util +from activitysim.core import config, inject, util logger = logging.getLogger(__name__) @@ -85,6 +85,7 @@ def read_from_table_info(table_info): column_map = table_info.get("column_map", None) keep_columns = table_info.get("keep_columns", None) rename_columns = table_info.get("rename_columns", None) + recode_columns = table_info.get("recode_columns", None) csv_dtypes = table_info.get("dtypes", {}) # don't require a redundant index_col directive for canonical tables @@ -156,6 +157,37 @@ def read_from_table_info(table_info): logger.debug("renaming columns: %s" % rename_columns) df.rename(columns=rename_columns, inplace=True) + # recode columns, can simplify data structure + if recode_columns and config.setting("recode_pipeline_columns", True): + for colname, recode_instruction in recode_columns.items(): + logger.info(f"recoding column {colname}: {recode_instruction}") + if recode_instruction == "zero-based": + if f"_original_{colname}" in df: + # a recoding of this column has already been completed + # just need to confirm it is zero-based + if (df[colname] != pd.RangeIndex(len(df))).any(): + raise ValueError("column already recoded as non-zero-based") + else: + remapper = {j: i for i, j in enumerate(sorted(set(df[colname])))} + df[f"_original_{colname}"] = df[colname] + df[colname] = df[colname].apply(remapper.get) + if keep_columns and f"_original_{colname}" not in keep_columns: + keep_columns.append(f"_original_{colname}") + if tablename == "land_use" and colname == canonical_index_col: + # We need to keep track if we have recoded the land_use + # table's index to zero-based, as we need to disable offset + # processing for legacy skim access. + config.override_setting("offset_preprocessing", True) + else: + source_table, lookup_col = recode_instruction.split(".") + parent_table = inject.get_table(source_table) + try: + map_col = parent_table[f"_original_{lookup_col}"] + except KeyError: + map_col = parent_table[lookup_col] + remapper = dict(zip(map_col, parent_table.index)) + df[colname] = df[colname].apply(remapper.get) + # set index if index_col is not None: if index_col in df.columns: diff --git a/activitysim/core/interaction_sample.py b/activitysim/core/interaction_sample.py index 6d5d87edf..62cabd861 100644 --- a/activitysim/core/interaction_sample.py +++ b/activitysim/core/interaction_sample.py @@ -1,13 +1,11 @@ # ActivitySim # See full license in LICENSE.txt. import logging -from builtins import range -from math import ceil import numpy as np import pandas as pd -from . import chunk, interaction_simulate, logit, pipeline, tracing +from . import chunk, config, interaction_simulate, logit, pipeline, tracing from .simulate import set_skim_wrapper_targets logger = logging.getLogger(__name__) @@ -62,83 +60,32 @@ def make_sample_choices( probs = probs[~zero_probs] choosers = choosers[~zero_probs] - cum_probs_array = probs.values.cumsum(axis=1) - chunk.log_df(trace_label, "cum_probs_array", cum_probs_array) - - # alt probs in convenient layout to return prob of chose alternative - # (same layout as cum_probs_arr) - alt_probs_array = probs.values.flatten() - chunk.log_df(trace_label, "alt_probs_array", alt_probs_array) - # get sample_size rands for each chooser rands = pipeline.get_rn_generator().random_for_df(probs, n=sample_size) # transform as we iterate over alternatives # reshape so rands[i] is in broadcastable (2-D) shape for cum_probs_arr # i.e rands[i] is a 2-D array of one alt choice rand for each chooser - rands = rands.T.reshape(sample_size, -1, 1) + # rands = rands.T #.reshape(sample_size, -1, 1) chunk.log_df(trace_label, "rands", rands) - # the alternative value chosen - # WHY SHOULD CHOICES COL HAVE TO BE TYPE INT??? - # choices_array = np.empty([sample_size, len(choosers)]).astype(int) - choices_array = np.empty([sample_size, len(choosers)]).astype( - alternatives.index.dtype - ) - # chunk log these later after we populate them... - - # the probability of the chosen alternative - choice_probs_array = np.empty([sample_size, len(choosers)]) - # chunk log these later after we populate them... - - alts = np.tile(alternatives.index.values, len(choosers)) - chunk.log_df(trace_label, "alts", alts) - - # FIXME - do this all at once rather than iterate? - for i in range(sample_size): - - # FIXME - do this in numpy, not pandas? - - # rands for this alt in broadcastable shape - r = rands[i] - - # position of first occurrence of positive value - positions = np.argmax(cum_probs_array > r, axis=1) - - # FIXME - leave positions as numpy array, not pandas series? - # positions is series with the chosen alternative represented as a column index in probs - # which is an integer between zero and num alternatives in the alternative sample - positions = pd.Series(positions, index=probs.index) - - # need to get from an integer offset into the alternative sample to the alternative index - # that is, we want the index value of the row that is offset by rows into the - # tranche of this choosers alternatives created by cross join of alternatives and choosers - - # offsets is the offset into model_design df of first row of chooser alternatives - offsets = np.arange(len(positions)) * alternative_count + # TODO: is `sample_choices_maker` more efficient? The order of samples changes, might change repro-randoms + from .choosing import sample_choices_maker_preserve_ordering - # choices and choice_probs have one element per chooser and is in same order as choosers - choices_array[i] = np.take(alts, positions + offsets) - choice_probs_array[i] = np.take(alt_probs_array, positions + offsets) - - del positions - del offsets + choices_array, choice_probs_array = sample_choices_maker_preserve_ordering( + probs.values, + rands, + alternatives.index.values, + ) chunk.log_df(trace_label, "choices_array", choices_array) chunk.log_df(trace_label, "choice_probs_array", choice_probs_array) - del alts - chunk.log_df(trace_label, "alts", None) - del cum_probs_array - chunk.log_df(trace_label, "cum_probs_array", None) - del alt_probs_array - chunk.log_df(trace_label, "alt_probs_array", None) - # explode to one row per chooser.index, alt_zone_id choices_df = pd.DataFrame( { alt_col_name: choices_array.flatten(order="F"), - "rand": rands.flatten(order="F"), + "rand": rands.T.flatten(order="F"), "prob": choice_probs_array.flatten(order="F"), choosers.index.name: np.repeat(np.asanyarray(choosers.index), sample_size), } @@ -170,6 +117,7 @@ def _interaction_sample( skims=None, locals_d=None, trace_label=None, + zone_layer=None, ): """ Run a MNL simulation in the situation in which alternatives must @@ -227,6 +175,8 @@ def _interaction_sample( """ have_trace_targets = tracing.has_trace_targets(choosers) + trace_ids = None + trace_rows = None num_choosers = len(choosers.index) assert num_choosers > 0 @@ -251,76 +201,159 @@ def _interaction_sample( chooser_index_id = interaction_simulate.ALT_CHOOSER_ID if log_alt_losers else None + sharrow_enabled = config.setting("sharrow", False) + # - cross join choosers and alternatives (cartesian product) # for every chooser, there will be a row for each alternative # index values (non-unique) are from alternatives df alternative_count = alternatives.shape[0] - interaction_df = logit.interaction_dataset( - choosers, - alternatives, - sample_size=alternative_count, - chooser_index_id=chooser_index_id, - ) - chunk.log_df(trace_label, "interaction_df", interaction_df) + interaction_utilities = None + interaction_utilities_sh = None + if sharrow_enabled: - assert alternative_count == len(interaction_df.index) / len(choosers.index) + ( + interaction_utilities, + trace_eval_results, + ) = interaction_simulate.eval_interaction_utilities( + spec, + choosers, + locals_d, + trace_label, + trace_rows, + estimator=None, + log_alt_losers=log_alt_losers, + extra_data=alternatives, + zone_layer=zone_layer, + ) + chunk.log_df(trace_label, "interaction_utilities", interaction_utilities) + if sharrow_enabled == "test" or True: + interaction_utilities_sh, trace_eval_results_sh = ( + interaction_utilities, + trace_eval_results, + ) + if not sharrow_enabled or (sharrow_enabled == "test"): + interaction_df = logit.interaction_dataset( + choosers, + alternatives, + sample_size=alternative_count, + chooser_index_id=chooser_index_id, + ) - if skims is not None: - set_skim_wrapper_targets(interaction_df, skims) + chunk.log_df(trace_label, "interaction_df", interaction_df) - # evaluate expressions from the spec multiply by coefficients and sum - # spec is df with one row per spec expression and one col with utility coefficient - # column names of interaction_df match spec index values - # utilities has utility value for element in the cross product of choosers and alternatives - # interaction_utilities is a df with one utility column and one row per row in interaction_df - if have_trace_targets: - trace_rows, trace_ids = tracing.interaction_trace_rows( - interaction_df, choosers, alternative_count - ) + assert alternative_count == len(interaction_df.index) / len(choosers.index) - tracing.trace_df( - interaction_df[trace_rows], - tracing.extend_trace_label(trace_label, "interaction_df"), - slicer="NONE", - transpose=False, - ) - else: - trace_rows = trace_ids = None - - # interaction_utilities is a df with one utility column and one row per interaction_df row - ( - interaction_utilities, - trace_eval_results, - ) = interaction_simulate.eval_interaction_utilities( - spec, - interaction_df, - locals_d, - trace_label, - trace_rows, - estimator=None, - log_alt_losers=log_alt_losers, - ) - chunk.log_df(trace_label, "interaction_utilities", interaction_utilities) + if skims is not None: + set_skim_wrapper_targets(interaction_df, skims) - # ########### HWM - high water mark (point of max observed memory usage) + # evaluate expressions from the spec multiply by coefficients and sum + # spec is df with one row per spec expression and one col with utility coefficient + # column names of interaction_df match spec index values + # utilities has utility value for element in the cross product of choosers and alternatives + # interaction_utilities is a df with one utility column and one row per row in interaction_df + if have_trace_targets: + trace_rows, trace_ids = tracing.interaction_trace_rows( + interaction_df, choosers, alternative_count + ) - del interaction_df - chunk.log_df(trace_label, "interaction_df", None) + tracing.trace_df( + interaction_df[trace_rows], + tracing.extend_trace_label(trace_label, "interaction_df"), + slicer="NONE", + transpose=False, + ) + else: + trace_rows = trace_ids = None - if have_trace_targets: + # interaction_utilities is a df with one utility column and one row per interaction_df row + ( + interaction_utilities, + trace_eval_results, + ) = interaction_simulate.eval_interaction_utilities( + spec, + interaction_df, + locals_d, + trace_label, + trace_rows, + estimator=None, + log_alt_losers=log_alt_losers, + zone_layer=zone_layer, + ) + chunk.log_df(trace_label, "interaction_utilities", interaction_utilities) + + # ########### HWM - high water mark (point of max observed memory usage) + + del interaction_df + chunk.log_df(trace_label, "interaction_df", None) + + if sharrow_enabled == "test": + try: + if interaction_utilities_sh is not None: + np.testing.assert_allclose( + interaction_utilities_sh.values.reshape( + interaction_utilities.values.shape + ), + interaction_utilities.values, + rtol=1e-2, + atol=0, + err_msg="utility not aligned", + verbose=True, + ) + except AssertionError as err: + print(err) + misses = np.where( + ~np.isclose( + interaction_utilities_sh.values, + interaction_utilities.values, + rtol=1e-2, + atol=0, + ) + ) + _sh_util_miss1 = interaction_utilities_sh.values[ + tuple(m[0] for m in misses) + ] + _u_miss1 = interaction_utilities.values[tuple(m[0] for m in misses)] + diff = _sh_util_miss1 - _u_miss1 + if len(misses[0]) > interaction_utilities_sh.values.size * 0.01: + print("big problem") + print(misses) + if "nan location mismatch" in str(err): + print("nan location mismatch interaction_utilities_sh") + print(np.where(np.isnan(interaction_utilities_sh.values))) + print("nan location mismatch interaction_utilities legacy") + print(np.where(np.isnan(interaction_utilities.values))) + print("misses =>", misses) + j = 0 + while j < len(misses[0]): + print( + f"miss {j} {tuple(m[j] for m in misses)}:", + interaction_utilities_sh.values[tuple(m[j] for m in misses)], + "!=", + interaction_utilities.values[tuple(m[j] for m in misses)], + ) + j += 1 + if j > 10: + break + raise + + if have_trace_targets and trace_ids is not None: tracing.trace_interaction_eval_results( trace_eval_results, trace_ids, tracing.extend_trace_label(trace_label, "eval"), ) - tracing.trace_df( - interaction_utilities[trace_rows], - tracing.extend_trace_label(trace_label, "interaction_utilities"), - slicer="NONE", - transpose=False, - ) + if have_trace_targets and trace_rows is not None: + try: + tracing.trace_df( + interaction_utilities[trace_rows], + tracing.extend_trace_label(trace_label, "interaction_utilities"), + slicer="NONE", + transpose=False, + ) + except ValueError: + pass tracing.dump_df(DUMP, interaction_utilities, trace_label, "interaction_utilities") @@ -457,6 +490,7 @@ def interaction_sample( chunk_size=0, chunk_tag=None, trace_label=None, + zone_layer=None, ): """ @@ -542,6 +576,7 @@ def interaction_sample( skims=skims, locals_d=locals_d, trace_label=chunk_trace_label, + zone_layer=zone_layer, ) if choices.shape[0] > 0: diff --git a/activitysim/core/interaction_sample_simulate.py b/activitysim/core/interaction_sample_simulate.py index 776df91d4..13e65f384 100644 --- a/activitysim/core/interaction_sample_simulate.py +++ b/activitysim/core/interaction_sample_simulate.py @@ -25,6 +25,7 @@ def _interaction_sample_simulate( trace_label, trace_choice_name, estimator, + skip_choice=False, ): """ @@ -121,7 +122,9 @@ def _interaction_sample_simulate( # here, alternatives is sparsely repeated once for each (non-dup) sample # we expect alternatives to have same index of choosers (but with duplicate index values) # so we just need to left join alternatives with choosers - assert alternatives.index.name == choosers.index.name + + # assert alternatives.index.name == choosers.index.name + # asserting the index names are the same tells us nothing about the underlying data so why? interaction_df = alternatives.join(choosers, how="left", rsuffix="_chooser") @@ -266,66 +269,70 @@ def _interaction_sample_simulate( # FIXME this is kind of gnarly, but we force choice of first alt probs.loc[zero_probs, 0] = 1.0 - # make choices - # positions is series with the chosen alternative represented as a column index in probs - # which is an integer between zero and num alternatives in the alternative sample - positions, rands = logit.make_choices( - probs, trace_label=trace_label, trace_choosers=choosers - ) + if skip_choice: + return choosers.join(logsums.to_frame("logsums")) - chunk.log_df(trace_label, "positions", positions) - chunk.log_df(trace_label, "rands", rands) + else: + # make choices + # positions is series with the chosen alternative represented as a column index in probs + # which is an integer between zero and num alternatives in the alternative sample + positions, rands = logit.make_choices( + probs, trace_label=trace_label, trace_choosers=choosers + ) - del probs - chunk.log_df(trace_label, "probs", None) + chunk.log_df(trace_label, "positions", positions) + chunk.log_df(trace_label, "rands", rands) - # shouldn't have chosen any of the dummy pad utilities - assert positions.max() < max_sample_count + del probs + chunk.log_df(trace_label, "probs", None) - # need to get from an integer offset into the alternative sample to the alternative index - # that is, we want the index value of the row that is offset by rows into the - # tranche of this choosers alternatives created by cross join of alternatives and choosers + # shouldn't have chosen any of the dummy pad utilities + assert positions.max() < max_sample_count - # resulting pandas Int64Index has one element per chooser row and is in same order as choosers - choices = alternatives[choice_column].take(positions + first_row_offsets) + # need to get from an integer offset into the alternative sample to the alternative index + # that is, we want the index value of the row that is offset by rows into the + # tranche of this choosers alternatives created by cross join of alternatives and choosers - # create a series with index from choosers and the index of the chosen alternative - choices = pd.Series(choices, index=choosers.index) + # resulting pandas Int64Index has one element per chooser row and is in same order as choosers + choices = alternatives[choice_column].take(positions + first_row_offsets) - chunk.log_df(trace_label, "choices", choices) + # create a series with index from choosers and the index of the chosen alternative + choices = pd.Series(choices, index=choosers.index) - if allow_zero_probs and zero_probs.any(): - # FIXME this is kind of gnarly, patch choice for zero_probs - choices.loc[zero_probs] = zero_prob_choice_val + chunk.log_df(trace_label, "choices", choices) - if have_trace_targets: - tracing.trace_df( - choices, - tracing.extend_trace_label(trace_label, "choices"), - columns=[None, trace_choice_name], - ) - tracing.trace_df( - rands, - tracing.extend_trace_label(trace_label, "rands"), - columns=[None, "rand"], - ) - if want_logsums: + if allow_zero_probs and zero_probs.any() and zero_prob_choice_val is not None: + # FIXME this is kind of gnarly, patch choice for zero_probs + choices.loc[zero_probs] = zero_prob_choice_val + + if have_trace_targets: + tracing.trace_df( + choices, + tracing.extend_trace_label(trace_label, "choices"), + columns=[None, trace_choice_name], + ) tracing.trace_df( - logsums, - tracing.extend_trace_label(trace_label, "logsum"), - columns=[None, "logsum"], + rands, + tracing.extend_trace_label(trace_label, "rands"), + columns=[None, "rand"], ) + if want_logsums: + tracing.trace_df( + logsums, + tracing.extend_trace_label(trace_label, "logsum"), + columns=[None, "logsum"], + ) - if want_logsums: - choices = choices.to_frame("choice") - choices["logsum"] = logsums + if want_logsums: + choices = choices.to_frame("choice") + choices["logsum"] = logsums - chunk.log_df(trace_label, "choices", choices) + chunk.log_df(trace_label, "choices", choices) - # handing this off to our caller - chunk.log_df(trace_label, "choices", None) + # handing this off to our caller + chunk.log_df(trace_label, "choices", None) - return choices + return choices def interaction_sample_simulate( @@ -344,6 +351,7 @@ def interaction_sample_simulate( trace_label=None, trace_choice_name=None, estimator=None, + skip_choice=False, ): """ @@ -383,6 +391,9 @@ def interaction_sample_simulate( when household tracing enabled. No tracing occurs if label is empty or None. trace_choice_name: str This is the column label to be used in trace file csv dump of choices + skip_choice: bool + This skips the logit choice step and simply returns the alternatives table with logsums + (used in disaggregate accessibility) Returns ------- @@ -428,6 +439,7 @@ def interaction_sample_simulate( chunk_trace_label, trace_choice_name, estimator, + skip_choice, ) result_list.append(choices) diff --git a/activitysim/core/interaction_simulate.py b/activitysim/core/interaction_simulate.py index 836fc4a34..38075e5ad 100644 --- a/activitysim/core/interaction_simulate.py +++ b/activitysim/core/interaction_simulate.py @@ -1,8 +1,10 @@ # ActivitySim # See full license in LICENSE.txt. import logging +import time from builtins import zip from collections import OrderedDict +from datetime import timedelta import numpy as np import pandas as pd @@ -17,7 +19,15 @@ def eval_interaction_utilities( - spec, df, locals_d, trace_label, trace_rows, estimator=None, log_alt_losers=False + spec, + df, + locals_d, + trace_label, + trace_rows, + estimator=None, + log_alt_losers=False, + extra_data=None, + zone_layer=None, ): """ Compute the utilities for a single-alternative spec evaluated in the context of df @@ -57,9 +67,23 @@ def eval_interaction_utilities( Will have the index of `df` and a single column of utilities """ + start_time = time.time() + trace_label = tracing.extend_trace_label(trace_label, "eval_interaction_utils") logger.info("Running eval_interaction_utilities on %s rows" % df.shape[0]) + sharrow_enabled = config.setting("sharrow", False) + + if locals_d is not None and locals_d.get("_sharrow_skip", False): + sharrow_enabled = False + + # if trace_label.startswith("trip_destination"): + # sharrow_enabled = False + + logger.info(f"{trace_label} sharrow_enabled is {sharrow_enabled}") + + trace_eval_results = None + with chunk.chunk_log(trace_label): assert len(spec.columns) == 1 @@ -67,190 +91,482 @@ def eval_interaction_utilities( # avoid altering caller's passed-in locals_d parameter (they may be looping) locals_d = locals_d.copy() if locals_d is not None else {} - # add df for startswith('@') eval expressions - locals_d["df"] = df + utilities = None - def to_series(x): - if np.isscalar(x): - return pd.Series([x] * len(df), index=df.index) - if isinstance(x, np.ndarray): - return pd.Series(x, index=df.index) - return x + from .flow import TimeLogger - if trace_rows is not None and trace_rows.any(): - # # convert to numpy array so we can slice ndarrays as well as series - # trace_rows = np.asanyarray(trace_rows) - assert type(trace_rows) == np.ndarray - trace_eval_results = OrderedDict() - else: - trace_eval_results = None + timelogger = TimeLogger("interaction_simulate") - check_for_variability = config.setting("check_for_variability") - - # need to be able to identify which variables causes an error, which keeps - # this from being expressed more parsimoniously + # add df for startswith('@') eval expressions + locals_d["df"] = df - utilities = pd.DataFrame({"utility": 0.0}, index=df.index) + if sharrow_enabled: - chunk.log_df(trace_label, "eval.utilities", utilities) + from .flow import apply_flow - no_variability = has_missing_vals = 0 + spec_sh = spec.copy() - if estimator: - # ensure alt_id from interaction_dataset is available in expression_values_df for - # estimator.write_interaction_expression_values and eventual omnibus table assembly - alt_id = estimator.get_alt_id() - assert alt_id in df.columns - expression_values_df = df[[alt_id]] + def replace_in_index_level(mi, level, *repls): + if isinstance(mi, pd.MultiIndex): + level = mi._get_level_number(level) + content = list(mi.levels[level]) + new_content = [] + for i in content: + for repl in repls: + i = i.replace(*repl) + new_content.append(i) + return mi.set_levels(new_content, level=level) + else: + new_content = [] + for i in mi: + for repl in repls: + i = i.replace(*repl) + new_content.append(i) + return new_content + + spec_sh.index = replace_in_index_level( + spec_sh.index, + simulate.SPEC_EXPRESSION_NAME, + ( + "tt.adjacent_window_before(", + "sharrow_tt_adjacent_window_before(tt_windows, tt_row_mapper, tt_col_mapper, ", + ), + ( + "tt.adjacent_window_after(", + "sharrow_tt_adjacent_window_after(tt_windows, tt_row_mapper, tt_col_mapper, ", + ), + ( + "tt.previous_tour_ends(", + "sharrow_tt_previous_tour_ends(tt_windows, tt_row_mapper, tt_col_mapper, ", + ), + ( + "tt.previous_tour_begins(", + "sharrow_tt_previous_tour_begins(tt_windows, tt_row_mapper, tt_col_mapper, ", + ), + ( + "tt.remaining_periods_available(", + "sharrow_tt_remaining_periods_available(tt_windows, tt_row_mapper, ", + ), + ( + "tt.max_time_block_available(", + "sharrow_tt_max_time_block_available(tt_windows, tt_row_mapper, ", + ), + ) - # FIXME estimation_requires_chooser_id_in_df_column - # estimation requires that chooser_id is either in index or a column of interaction_dataset - # so it can be reformatted (melted) and indexed by chooser_id and alt_id - # we assume caller has this under control if index is named - # bug - location choice has df index_name zone_id but should be person_id???? - if df.index.name is None: - chooser_id = estimator.get_chooser_id() - assert ( - chooser_id in df.columns - ), "Expected to find choose_id column '%s' in interaction dataset" % ( - chooser_id, + # need to zero out any coefficients on temp vars + if isinstance(spec_sh.index, pd.MultiIndex): + exprs = spec_sh.index.get_level_values(simulate.SPEC_EXPRESSION_NAME) + labels = spec_sh.index.get_level_values(simulate.SPEC_LABEL_NAME) + else: + exprs = spec_sh.index + labels = spec_sh.index + for n, (expr, label) in enumerate(zip(exprs, labels)): + if expr.startswith("_") and "@" in expr: + spec_sh.iloc[n, 0] = 0.0 + + for i1, i2 in zip(exprs, labels): + logger.debug(f" - expr: {i1}: {i2}") + + timelogger.mark("sharrow preamble", True, logger, trace_label) + + sh_util, sh_flow = apply_flow( + spec_sh, + df, + locals_d, + trace_label, + interacts=extra_data, + zone_layer=zone_layer, + ) + if sh_util is not None: + chunk.log_df(trace_label, "sh_util", sh_util) + utilities = pd.DataFrame( + {"utility": sh_util.reshape(-1)}, + index=df.index if extra_data is None else None, ) - assert df.index.name is None - expression_values_df[chooser_id] = df[chooser_id] + chunk.log_df(trace_label, "sh_util", None) # hand off to caller - if isinstance(spec.index, pd.MultiIndex): - exprs = spec.index.get_level_values(simulate.SPEC_EXPRESSION_NAME) - labels = spec.index.get_level_values(simulate.SPEC_LABEL_NAME) + timelogger.mark("sharrow flow", True, logger, trace_label) else: - exprs = spec.index - labels = spec.index + sh_util, sh_flow = None, None + timelogger.mark("sharrow flow", False) + + if ( + utilities is None + or estimator + or (sharrow_enabled == "test" and extra_data is None) + ): + + def to_series(x): + if np.isscalar(x): + return pd.Series([x] * len(df), index=df.index) + if isinstance(x, np.ndarray): + return pd.Series(x, index=df.index) + return x + + if trace_rows is not None and trace_rows.any(): + # # convert to numpy array so we can slice ndarrays as well as series + # trace_rows = np.asanyarray(trace_rows) + assert type(trace_rows) == np.ndarray + trace_eval_results = OrderedDict() + else: + trace_eval_results = None + + check_for_variability = config.setting("check_for_variability") + + # need to be able to identify which variables causes an error, which keeps + # this from being expressed more parsimoniously + + utilities = pd.DataFrame({"utility": 0.0}, index=df.index) + + chunk.log_df(trace_label, "eval.utilities", utilities) + + no_variability = has_missing_vals = 0 + + if estimator: + # ensure alt_id from interaction_dataset is available in expression_values_df for + # estimator.write_interaction_expression_values and eventual omnibus table assembly + alt_id = estimator.get_alt_id() + assert alt_id in df.columns + expression_values_df = df[[alt_id]] + + # FIXME estimation_requires_chooser_id_in_df_column + # estimation requires that chooser_id is either in index or a column of interaction_dataset + # so it can be reformatted (melted) and indexed by chooser_id and alt_id + # we assume caller has this under control if index is named + # bug - location choice has df index_name zone_id but should be person_id???? + if df.index.name is None: + chooser_id = estimator.get_chooser_id() + assert chooser_id in df.columns, ( + "Expected to find choose_id column '%s' in interaction dataset" + % (chooser_id,) + ) + assert df.index.name is None + expression_values_df[chooser_id] = df[chooser_id] + + if isinstance(spec.index, pd.MultiIndex): + exprs = spec.index.get_level_values(simulate.SPEC_EXPRESSION_NAME) + labels = spec.index.get_level_values(simulate.SPEC_LABEL_NAME) + else: + exprs = spec.index + labels = spec.index + + for expr, label, coefficient in zip(exprs, labels, spec.iloc[:, 0]): + try: + + # - allow temps of form _od_DIST@od_skim['DIST'] + if expr.startswith("_"): + + target = expr[: expr.index("@")] + rhs = expr[expr.index("@") + 1 :] + v = to_series(eval(rhs, globals(), locals_d)) + + # update locals to allows us to ref previously assigned targets + locals_d[target] = v + chunk.log_df( + trace_label, target, v + ) # track temps stored in locals + + if trace_eval_results is not None: + trace_eval_results[expr] = v[trace_rows] + + # don't add temps to utility sums + # they have a non-zero dummy coefficient to avoid being removed from spec as NOPs + continue + + if expr.startswith("@"): + v = to_series(eval(expr[1:], globals(), locals_d)) + else: + v = df.eval(expr, resolvers=[locals_d]) + + if check_for_variability and v.std() == 0: + logger.info( + "%s: no variability (%s) in: %s" + % (trace_label, v.iloc[0], expr) + ) + no_variability += 1 + + # FIXME - how likely is this to happen? Not sure it is really a problem? + if ( + check_for_variability + and np.count_nonzero(v.isnull().values) > 0 + ): + logger.info("%s: missing values in: %s" % (trace_label, expr)) + has_missing_vals += 1 + + if estimator: + # in case we modified expression_values_df index + expression_values_df.insert( + loc=len(expression_values_df.columns), + column=label, + value=v.values if isinstance(v, pd.Series) else v, + ) - for expr, label, coefficient in zip(exprs, labels, spec.iloc[:, 0]): - try: + utility = (v * coefficient).astype("float") - # - allow temps of form _od_DIST@od_skim['DIST'] - if expr.startswith("_"): + if log_alt_losers: - target = expr[: expr.index("@")] - rhs = expr[expr.index("@") + 1 :] - v = to_series(eval(rhs, globals(), locals_d)) + assert ALT_CHOOSER_ID in df + max_utils_by_chooser = utility.groupby(df[ALT_CHOOSER_ID]).max() - # update locals to allows us to ref previously assigned targets - locals_d[target] = v - chunk.log_df(trace_label, target, v) # track temps stored in locals + if (max_utils_by_chooser < simulate.ALT_LOSER_UTIL).any(): - if trace_eval_results is not None: - trace_eval_results[expr] = v[trace_rows] + losers = max_utils_by_chooser[ + max_utils_by_chooser < simulate.ALT_LOSER_UTIL + ] + logger.warning( + f"{trace_label} - {len(losers)} choosers of {len(max_utils_by_chooser)} " + f"with prohibitive utilities for all alternatives for expression: {expr}" + ) - # don't add temps to utility sums - # they have a non-zero dummy coefficient to avoid being removed from spec as NOPs - continue + # loser_df = df[df[ALT_CHOOSER_ID].isin(losers.index)] + # print(f"\nloser_df\n{loser_df}\n") + # print(f"\nloser_max_utils_by_chooser\n{losers}\n") + # bug - if expr.startswith("@"): - v = to_series(eval(expr[1:], globals(), locals_d)) - else: - v = df.eval(expr) - - if check_for_variability and v.std() == 0: - logger.info( - "%s: no variability (%s) in: %s" - % (trace_label, v.iloc[0], expr) - ) - no_variability += 1 - - # FIXME - how likely is this to happen? Not sure it is really a problem? - if check_for_variability and np.count_nonzero(v.isnull().values) > 0: - logger.info("%s: missing values in: %s" % (trace_label, expr)) - has_missing_vals += 1 - - if estimator: - # in case we modified expression_values_df index - expression_values_df.insert( - loc=len(expression_values_df.columns), - column=label, - value=v.values if isinstance(v, pd.Series) else v, - ) - - utility = (v * coefficient).astype("float") + del max_utils_by_chooser - if log_alt_losers: + utilities.utility.values[:] += utility - assert ALT_CHOOSER_ID in df - max_utils_by_chooser = utility.groupby(df[ALT_CHOOSER_ID]).max() + if trace_eval_results is not None: - if (max_utils_by_chooser < simulate.ALT_LOSER_UTIL).any(): + # expressions should have been uniquified when spec was read + # (though we could do it here if need be...) + # expr = assign.uniquify_key(trace_eval_results, expr, template="{} # ({})") + assert expr not in trace_eval_results - losers = max_utils_by_chooser[ - max_utils_by_chooser < simulate.ALT_LOSER_UTIL - ] - logger.warning( - f"{trace_label} - {len(losers)} choosers of {len(max_utils_by_chooser)} " - f"with prohibitive utilities for all alternatives for expression: {expr}" + trace_eval_results[expr] = v[trace_rows] + k = "partial utility (coefficient = %s) for %s" % ( + coefficient, + expr, ) + trace_eval_results[k] = v[trace_rows] * coefficient - # loser_df = df[df[ALT_CHOOSER_ID].isin(losers.index)] - # print(f"\nloser_df\n{loser_df}\n") - # print(f"\nloser_max_utils_by_chooser\n{losers}\n") - # bug + del v + # chunk.log_df(trace_label, 'v', None) - del max_utils_by_chooser + except Exception as err: + logger.exception( + f"{trace_label} - {type(err).__name__} ({str(err)}) evaluating: {str(expr)}" + ) + raise err - utilities.utility += utility + if estimator: + estimator.log( + "eval_interaction_utilities write_interaction_expression_values %s" + % trace_label + ) + estimator.write_interaction_expression_values(expression_values_df) + del expression_values_df - if trace_eval_results is not None: + if no_variability > 0: + logger.warning( + "%s: %s columns have no variability" % (trace_label, no_variability) + ) - # expressions should have been uniquified when spec was read - # (though we could do it here if need be...) - # expr = assign.uniquify_key(trace_eval_results, expr, template="{} # ({})") - assert expr not in trace_eval_results + if has_missing_vals > 0: + logger.warning( + "%s: %s columns have missing values" + % (trace_label, has_missing_vals) + ) - trace_eval_results[expr] = v[trace_rows] - k = "partial utility (coefficient = %s) for %s" % ( - coefficient, - expr, - ) - trace_eval_results[k] = v[trace_rows] * coefficient + if trace_eval_results is not None: + trace_eval_results["total utility"] = utilities.utility[trace_rows] - del v - # chunk.log_df(trace_label, 'v', None) + trace_eval_results = pd.DataFrame.from_dict(trace_eval_results) + trace_eval_results.index = df[trace_rows].index - except Exception as err: - logger.exception( - f"{trace_label} - {type(err).__name__} ({str(err)}) evaluating: {str(expr)}" + # add df columns to trace_results + trace_eval_results = pd.concat( + [df[trace_rows], trace_eval_results], axis=1 ) - raise err + chunk.log_df(trace_label, "eval.trace_eval_results", trace_eval_results) - if estimator: - estimator.log( - "eval_interaction_utilities write_interaction_expression_values %s" - % trace_label - ) - estimator.write_interaction_expression_values(expression_values_df) - del expression_values_df + chunk.log_df(trace_label, "v", None) + chunk.log_df(trace_label, "eval.utilities", None) # out of out hands... + chunk.log_df(trace_label, "eval.trace_eval_results", None) - if no_variability > 0: - logger.warning( - "%s: %s columns have no variability" % (trace_label, no_variability) - ) + timelogger.mark("regular interact flow", True, logger, trace_label) + else: + timelogger.mark("regular interact flow", False) - if has_missing_vals > 0: - logger.warning( - "%s: %s columns have missing values" % (trace_label, has_missing_vals) + # + # Sharrow tracing + # + if sh_flow is not None and trace_rows is not None and trace_rows.any(): + assert type(trace_rows) == np.ndarray + sh_utility_fat = sh_flow.load_dataarray( + # sh_flow.tree.replace_datasets( + # df=df.iloc[trace_rows], + # ), + dtype=np.float32, ) + sh_utility_fat = sh_utility_fat[trace_rows, :] + sh_utility_fat = sh_utility_fat.to_dataframe("vals") + try: + sh_utility_fat = sh_utility_fat.unstack("expressions") + except ValueError: + exprs = sh_utility_fat.index.levels[-1] + sh_utility_fat = pd.DataFrame( + sh_utility_fat.values.reshape(-1, len(exprs)), + index=sh_utility_fat.index[:: len(exprs)].droplevel(-1), + columns=exprs, + ) + else: + sh_utility_fat = sh_utility_fat.droplevel(0, axis=1) + sh_utility_fat.add_prefix("SH:") + sh_utility_fat_coef = sh_utility_fat * spec.iloc[:, 0].values.reshape(1, -1) + sh_utility_fat_coef.columns = [ + f"{i} * ({j})" + for i, j in zip(sh_utility_fat_coef.columns, spec.iloc[:, 0].values) + ] + if utilities.shape[0] > trace_rows.shape[0]: + trace_rows_ = np.repeat( + trace_rows, utilities.shape[0] // trace_rows.shape[0] + ) + else: + trace_rows_ = trace_rows + if trace_eval_results is None: + trace_eval_results = pd.concat( + [ + sh_utility_fat, + sh_utility_fat_coef, + utilities.utility[trace_rows_] + .rename("total utility") + .to_frame() + .set_index(sh_utility_fat.index), + ], + axis=1, + ) + try: + trace_eval_results.index = df[trace_rows].index + except ValueError: + pass + chunk.log_df(trace_label, "eval.trace_eval_results", trace_eval_results) + else: + # in test mode, trace from non-sharrow exists + trace_eval_results = pd.concat( + [ + trace_eval_results.reset_index(drop=True), + sh_utility_fat.reset_index(drop=True), + sh_utility_fat_coef.reset_index(drop=True), + utilities.utility[trace_rows_] + .rename("total utility") + .reset_index(drop=True), + ], + axis=1, + ) + trace_eval_results.index = df[trace_rows].index + chunk.log_df(trace_label, "eval.trace_eval_results", trace_eval_results) + + # sh_utility_fat1 = np.dot(sh_utility_fat, spec.values) + # sh_utility_fat2 = sh_flow.dot( + # source=sh_flow.tree.replace_datasets( + # df=df.iloc[trace_rows], + # ), + # coefficients=spec.values.astype(np.float32), + # dtype=np.float32, + # ) + timelogger.mark("sharrow interact trace", True, logger, trace_label) + + if sharrow_enabled == "test": + + try: + if sh_util is not None: + np.testing.assert_allclose( + sh_util.reshape(utilities.values.shape), + utilities.values, + rtol=1e-2, + atol=0, + err_msg="utility not aligned", + verbose=True, + ) + except AssertionError as err: + print(err) + misses = np.where( + ~np.isclose(sh_util, utilities.values, rtol=1e-2, atol=0) + ) + _sh_util_miss1 = sh_util[tuple(m[0] for m in misses)] + _u_miss1 = utilities.values[tuple(m[0] for m in misses)] + diff = _sh_util_miss1 - _u_miss1 + if len(misses[0]) > sh_util.size * 0.01: + print("big problem") + if "nan location mismatch" in str(err): + print("nan location mismatch sh_util") + print(np.where(np.isnan(sh_util))) + print("nan location mismatch legacy util") + print(np.where(np.isnan(utilities.values))) + print("misses =>", misses) + j = 0 + while j < len(misses[0]): + print( + f"miss {j} {tuple(m[j] for m in misses)}:", + sh_util[tuple(m[j] for m in misses)], + "!=", + utilities.values[tuple(m[j] for m in misses)], + ) + j += 1 + if j > 10: + break + + re_trace = misses[0] + retrace_eval_data = {} + retrace_eval_parts = {} + re_trace_df = df.iloc[re_trace] + + for expr, label, coefficient in zip(exprs, labels, spec.iloc[:, 0]): + if expr.startswith("_"): + target = expr[: expr.index("@")] + rhs = expr[expr.index("@") + 1 :] + v = to_series(eval(rhs, globals(), locals_d)) + locals_d[target] = v + if trace_eval_results is not None: + trace_eval_results[expr] = v.iloc[re_trace] + continue + if expr.startswith("@"): + v = to_series(eval(expr[1:], globals(), locals_d)) + else: + v = df.eval(expr) + if check_for_variability and v.std() == 0: + logger.info( + "%s: no variability (%s) in: %s" + % (trace_label, v.iloc[0], expr) + ) + no_variability += 1 + retrace_eval_data[expr] = v.iloc[re_trace] + k = "partial utility (coefficient = %s) for %s" % ( + coefficient, + expr, + ) + retrace_eval_parts[k] = (v.iloc[re_trace] * coefficient).astype( + "float" + ) + retrace_eval_data_ = pd.concat(retrace_eval_data, axis=1) + retrace_eval_parts_ = pd.concat(retrace_eval_parts, axis=1) - if trace_eval_results is not None: - trace_eval_results["total utility"] = utilities.utility[trace_rows] + re_sh_flow_load = sh_flow.load( + dtype=np.float32, + ) + re_sh_flow_load_ = re_sh_flow_load[re_trace] + + look_for_problems_here = np.where( + ~np.isclose( + re_sh_flow_load_[ + :, ~spec.index.get_level_values(0).str.startswith("_") + ], + retrace_eval_data_.values.astype(np.float32), + ) + ) - trace_eval_results = pd.DataFrame.from_dict(trace_eval_results) - trace_eval_results.index = df[trace_rows].index + raise # enter debugger now to see what's up + timelogger.mark("sharrow interact test", True, logger, trace_label) - # add df columns to trace_results - trace_eval_results = pd.concat([df[trace_rows], trace_eval_results], axis=1) - chunk.log_df(trace_label, "eval.trace_eval_results", trace_eval_results) + logger.info(f"utilities.dtypes {trace_label}\n{utilities.dtypes}") + end_time = time.time() - chunk.log_df(trace_label, "v", None) - chunk.log_df(trace_label, "eval.utilities", None) # out of out hands... - chunk.log_df(trace_label, "eval.trace_eval_results", None) + timelogger.summary(logger, "TIMING interact_simulate.eval_utils") + logger.info( + f"interact_simulate.eval_utils runtime: {timedelta(seconds=end_time - start_time)} {trace_label}" + ) return utilities, trace_eval_results @@ -351,66 +667,119 @@ def _interaction_simulate( alt_index_id = estimator.get_alt_id() if estimator else None chooser_index_id = ALT_CHOOSER_ID if log_alt_losers else None - interaction_df = logit.interaction_dataset( - choosers, - alternatives, - sample_size, - alt_index_id=alt_index_id, - chooser_index_id=chooser_index_id, - ) - chunk.log_df(trace_label, "interaction_df", interaction_df) + sharrow_enabled = config.setting("sharrow", False) + interaction_utilities = None - if skims is not None: - simulate.set_skim_wrapper_targets(interaction_df, skims) + if locals_d is not None and locals_d.get("_sharrow_skip", False): + sharrow_enabled = False - # evaluate expressions from the spec multiply by coefficients and sum - # spec is df with one row per spec expression and one col with utility coefficient - # column names of model_design match spec index values - # utilities has utility value for element in the cross product of choosers and alternatives - # interaction_utilities is a df with one utility column and one row per row in model_design - if have_trace_targets: - trace_rows, trace_ids = tracing.interaction_trace_rows( - interaction_df, choosers, sample_size - ) + if ( + sharrow_enabled + and skims is None + and not have_trace_targets + and sample_size == len(alternatives) + ): + # no need to create the merged interaction dataset + # TODO: can we still do this if skims is not None? - tracing.trace_df( - interaction_df[trace_rows], - tracing.extend_trace_label(trace_label, "interaction_df"), - slicer="NONE", - transpose=False, - ) - else: + # TODO: re-enable tracing for sharrow so have_trace_targets can be True trace_rows = trace_ids = None - interaction_utilities, trace_eval_results = eval_interaction_utilities( - spec, - interaction_df, - locals_d, - trace_label, - trace_rows, - estimator=estimator, - log_alt_losers=log_alt_losers, - ) - chunk.log_df(trace_label, "interaction_utilities", interaction_utilities) - # print(f"interaction_df {interaction_df.shape}") - # print(f"interaction_utilities {interaction_utilities.shape}") + interaction_utilities, trace_eval_results = eval_interaction_utilities( + spec, + choosers, + locals_d, + trace_label, + trace_rows, + estimator=estimator, + log_alt_losers=log_alt_losers, + extra_data=alternatives, + ) - del interaction_df - chunk.log_df(trace_label, "interaction_df", None) + # set this index here as this is how later code extracts the chosen alt id's + interaction_utilities.index = np.tile(alternatives.index, len(choosers)) - if have_trace_targets: - tracing.trace_interaction_eval_results( - trace_eval_results, - trace_ids, - tracing.extend_trace_label(trace_label, "eval"), + chunk.log_df(trace_label, "interaction_utilities", interaction_utilities) + # mem.trace_memory_info(f"{trace_label}.init interaction_utilities sh", force_garbage_collect=True) + if sharrow_enabled == "test" or True: + interaction_utilities_sh, trace_eval_results_sh = ( + interaction_utilities, + trace_eval_results, + ) + else: + interaction_utilities_sh = trace_eval_results_sh = None + + else: + interaction_utilities_sh = trace_eval_results_sh = None + + if ( + not sharrow_enabled + or (sharrow_enabled == "test") + or interaction_utilities is None + ): + + interaction_df = logit.interaction_dataset( + choosers, + alternatives, + sample_size, + alt_index_id=alt_index_id, + chooser_index_id=chooser_index_id, ) + chunk.log_df(trace_label, "interaction_df", interaction_df) + + if skims is not None: + simulate.set_skim_wrapper_targets(interaction_df, skims) + + # evaluate expressions from the spec multiply by coefficients and sum + # spec is df with one row per spec expression and one col with utility coefficient + # column names of model_design match spec index values + # utilities has utility value for element in the cross product of choosers and alternatives + # interaction_utilities is a df with one utility column and one row per row in model_design + if have_trace_targets: + trace_rows, trace_ids = tracing.interaction_trace_rows( + interaction_df, choosers, sample_size + ) - tracing.trace_df( - interaction_utilities[trace_rows], - tracing.extend_trace_label(trace_label, "interaction_utils"), - slicer="NONE", - transpose=False, + tracing.trace_df( + interaction_df[trace_rows], + tracing.extend_trace_label(trace_label, "interaction_df"), + slicer="NONE", + transpose=False, + ) + else: + trace_rows = trace_ids = None + + interaction_utilities, trace_eval_results = eval_interaction_utilities( + spec, + interaction_df, + locals_d, + trace_label, + trace_rows, + estimator=estimator, + log_alt_losers=log_alt_losers, ) + chunk.log_df(trace_label, "interaction_utilities", interaction_utilities) + # mem.trace_memory_info(f"{trace_label}.init interaction_utilities", force_garbage_collect=True) + + # print(f"interaction_df {interaction_df.shape}") + # print(f"interaction_utilities {interaction_utilities.shape}") + + del interaction_df + chunk.log_df(trace_label, "interaction_df", None) + + if have_trace_targets: + tracing.trace_interaction_eval_results( + trace_eval_results, + trace_ids, + tracing.extend_trace_label(trace_label, "eval"), + ) + + tracing.trace_df( + interaction_utilities[trace_rows], + tracing.extend_trace_label(trace_label, "interaction_utils"), + slicer="NONE", + transpose=False, + ) # reshape utilities (one utility column and one row per row in model_design) # to a dataframe with one row per chooser and one column per alternative @@ -569,7 +938,7 @@ def interaction_simulate( result_list.append(choices) - chunk.log_df(trace_label, f"result_list", result_list) + chunk.log_df(trace_label, "result_list", result_list) # FIXME: this will require 2X RAM # if necessary, could append to hdf5 store on disk: diff --git a/activitysim/core/logit.py b/activitysim/core/logit.py index 784e073f8..ff3128450 100644 --- a/activitysim/core/logit.py +++ b/activitysim/core/logit.py @@ -7,6 +7,7 @@ import pandas as pd from . import config, pipeline, tracing +from .choosing import choice_maker logger = logging.getLogger(__name__) @@ -156,26 +157,35 @@ def utils_to_probs( # fixme - conversion to float not needed in either case? # utils_arr = utils.values.astype('float') utils_arr = utils.values + + if utils_arr.dtype == np.float32 and utils_arr.max() > 85: + # exponentiated utils will overflow, downshift them + utils_arr -= utils_arr.max(1, keepdims=True) + if not exponentiated: + # TODO: reduce memory usage by exponentiating in-place. + # but first we need to make sure the raw utilities + # are not needed elsewhere and overwriting won't hurt. + # try: + # np.exp(utils_arr, out=utils_arr) + # except TypeError: + # utils_arr = np.exp(utils_arr) utils_arr = np.exp(utils_arr) - np.clip(utils_arr, EXP_UTIL_MIN, EXP_UTIL_MAX, out=utils_arr) - - # FIXME - utils_arr = np.where(utils_arr == EXP_UTIL_MIN, 0.0, utils_arr) + np.putmask(utils_arr, utils_arr <= EXP_UTIL_MIN, 0) arr_sum = utils_arr.sum(axis=1) - zero_probs = arr_sum == 0.0 - if zero_probs.any() and not allow_zero_probs: - - report_bad_choices( - zero_probs, - utils, - trace_label=tracing.extend_trace_label(trace_label, "zero_prob_utils"), - msg="all probabilities are zero", - trace_choosers=trace_choosers, - ) + if not allow_zero_probs: + zero_probs = arr_sum == 0.0 + if zero_probs.any(): + report_bad_choices( + zero_probs, + utils, + trace_label=tracing.extend_trace_label(trace_label, "zero_prob_utils"), + msg="all probabilities are zero", + trace_choosers=trace_choosers, + ) inf_utils = np.isinf(arr_sum) if inf_utils.any(): @@ -195,7 +205,7 @@ def utils_to_probs( np.divide(utils_arr, arr_sum.reshape(len(utils_arr), 1), out=utils_arr) # if allow_zero_probs, this will cause EXP_UTIL_MIN util rows to have all zero probabilities - utils_arr[np.isnan(utils_arr)] = PROB_MIN + np.putmask(utils_arr, np.isnan(utils_arr), PROB_MIN) np.clip(utils_arr, PROB_MIN, PROB_MAX, out=utils_arr) @@ -251,13 +261,7 @@ def make_choices(probs, trace_label=None, trace_choosers=None, allow_bad_probs=F rands = pipeline.get_rn_generator().random_for_df(probs) - probs_arr = probs.values.cumsum(axis=1) - rands - - # rows, cols = np.where(probs_arr > 0) - # choices = [s.iat[0] for _, s in pd.Series(cols).groupby(rows)] - choices = np.argmax(probs_arr > 0.0, axis=1) - - choices = pd.Series(choices, index=probs.index) + choices = pd.Series(choice_maker(probs.values, rands), index=probs.index) rands = pd.Series(np.asanyarray(rands).flatten(), index=probs.index) diff --git a/activitysim/core/los.py b/activitysim/core/los.py index b274941f1..e69ed587f 100644 --- a/activitysim/core/los.py +++ b/activitysim/core/los.py @@ -2,21 +2,14 @@ # See full license in LICENSE.txt. import logging -import os import warnings import numpy as np import pandas as pd -from activitysim.core import ( - config, - inject, - mem, - pathbuilder, - skim_dictionary, - tracing, - util, -) +from activitysim.core import skim_dataset # noqa: F401 +from activitysim.core import config, inject, pathbuilder, skim_dictionary, tracing, util +from activitysim.core.cleaning import recode_based_on_table from activitysim.core.skim_dict_factory import MemMapSkimFactory, NumpyArraySkimFactory from activitysim.core.skim_dictionary import NOT_IN_SKIM_ZONE_ID @@ -137,15 +130,19 @@ def setting(self, keys, default=""): s = self.los_settings for key in key_list[:-1]: s = s.get(key) - assert isinstance( - s, dict - ), f"expected key '{key}' not found in '{keys}' in {self.los_settings_file_name}" + if default == "": + assert isinstance( + s, dict + ), f"expected key '{key}' not found in '{keys}' in {self.los_settings_file_name}" key = key_list[-1] # last key if default == "": assert ( key in s ), f"Expected setting {keys} not found in in {LOS_SETTINGS_FILE_NAME}" - return s.get(key, default) + if isinstance(s, dict): + return s.get(key, default) + else: + return default def load_settings(self): """ @@ -276,6 +273,14 @@ def load_data(self): by="MAZ" ) # only fields we need + # recode MAZs if needed + self.maz_taz_df["MAZ"] = recode_based_on_table( + self.maz_taz_df["MAZ"], "land_use" + ) + self.maz_taz_df["TAZ"] = recode_based_on_table( + self.maz_taz_df["TAZ"], "land_use_taz" + ) + self.maz_ceiling = self.maz_taz_df.MAZ.max() + 1 # maz_to_maz_df @@ -325,6 +330,9 @@ def load_data(self): file_name = maz_to_tap_settings["table"] df = pd.read_csv(config.data_file_path(file_name, mandatory=True)) + # recode MAZs if needed + df["MAZ"] = recode_based_on_table(df["MAZ"], "land_use") + # trim tap set # if provided, use tap_line_distance_col together with tap_lines table to trim the near tap set # to only include the nearest tap to origin when more than one tap serves the same line @@ -414,44 +422,67 @@ def load_data(self): self.maz_to_tap_dfs[mode] = df # create taz skim dict - assert "taz" not in self.skim_dicts - self.skim_dicts["taz"] = self.create_skim_dict("taz") - - # make sure skim has all taz_ids - # FIXME - weird that there is no list of tazs? + if not config.setting("sharrow", False): + assert "taz" not in self.skim_dicts + # If offset_preprocessing was completed, then TAZ values + # will be pre-offset and there's no need to re-offset them. + if config.setting("offset_preprocessing", False): + _override_offset_int = 0 + else: + _override_offset_int = None + self.skim_dicts["taz"] = self.create_skim_dict( + "taz", _override_offset_int=_override_offset_int + ) + # make sure skim has all taz_ids + # FIXME - weird that there is no list of tazs? + else: + self.skim_dicts["taz"] = self.get_skim_dict("taz") # create MazSkimDict facade if self.zone_system in [TWO_ZONE, THREE_ZONE]: - # create MazSkimDict facade skim_dict - # (must have already loaded dependencies: taz skim_dict, maz_to_maz_df, and maz_taz_df) - assert "maz" not in self.skim_dicts - maz_skim_dict = self.create_skim_dict("maz") - self.skim_dicts["maz"] = maz_skim_dict - - # make sure skim has all maz_ids - assert not ( - maz_skim_dict.offset_mapper.map(self.maz_taz_df["MAZ"].values) - == NOT_IN_SKIM_ZONE_ID - ).any() + if not config.setting("sharrow", False): + # create MazSkimDict facade skim_dict + # (must have already loaded dependencies: taz skim_dict, maz_to_maz_df, and maz_taz_df) + assert "maz" not in self.skim_dicts + maz_skim_dict = self.create_skim_dict("maz") + self.skim_dicts["maz"] = maz_skim_dict + + # make sure skim has all maz_ids + assert not ( + maz_skim_dict.offset_mapper.map(self.maz_taz_df["MAZ"].values) + == NOT_IN_SKIM_ZONE_ID + ).any(), ( + "every MAZ in the MAZ-to-TAZ mapping must map to a TAZ that exists" + ) + else: + self.skim_dicts["maz"] = self.get_skim_dict("maz") + # TODO:SHARROW: make sure skim has all maz_ids # create tap skim dict if self.zone_system == THREE_ZONE: - assert "tap" not in self.skim_dicts - tap_skim_dict = self.create_skim_dict("tap") - self.skim_dicts["tap"] = tap_skim_dict - # make sure skim has all tap_ids - assert not ( - tap_skim_dict.offset_mapper.map(self.tap_df["TAP"].values) - == NOT_IN_SKIM_ZONE_ID - ).any() + if not config.setting("sharrow", False): + assert "tap" not in self.skim_dicts + tap_skim_dict = self.create_skim_dict("tap") + self.skim_dicts["tap"] = tap_skim_dict + # make sure skim has all tap_ids + assert not ( + tap_skim_dict.offset_mapper.map(self.tap_df["TAP"].values) + == NOT_IN_SKIM_ZONE_ID + ).any() + else: + self.skim_dicts["tap"] = self.get_skim_dict("tap") - def create_skim_dict(self, skim_tag): + def create_skim_dict(self, skim_tag, _override_offset_int=None): """ Create a new SkimDict of type specified by skim_tag (e.g. 'taz', 'maz' or 'tap') Parameters ---------- - skim_tag: str + skim_tag : str + _override_offset_int : int, optional + Override the offset int for this dictionary. Use this to set that + offset to zero when zone id's have been pre-processed to be zero-based + contiguous integers. Returns ------- @@ -477,6 +508,11 @@ def create_skim_dict(self, skim_tag): logger.debug(f"create_skim_dict {skim_tag} omx_shape {skim_dict.omx_shape}") + if _override_offset_int is not None: + skim_dict.offset_mapper.set_offset_int( + _override_offset_int + ) # default is -1 + return skim_dict def omx_file_names(self, skim_tag): @@ -492,9 +528,66 @@ def omx_file_names(self, skim_tag): list of str """ file_names = self.setting(f"{skim_tag}_skims") + if isinstance(file_names, dict): + for i in ("file", "files", "omx"): + if i in file_names: + file_names = file_names[i] + break + if isinstance(file_names, dict): + raise ValueError( + f"must specify `{skim_tag}_skims.file` in network_los settings file" + ) file_names = [file_names] if isinstance(file_names, str) else file_names return file_names + def zarr_file_name(self, skim_tag): + """ + Return zarr directory name from network_los settings file for the specified skim_tag (e.g. 'taz') + + Parameters + ---------- + skim_tag: str (e.g. 'taz') + + Returns + ------- + list of str + """ + skim_setting = self.setting(f"{skim_tag}_skims") + if isinstance(skim_setting, dict): + return skim_setting.get("zarr", None) + else: + return None + + def zarr_pre_encoding(self, skim_tag): + """ + Return digital encoding pre-processing before writing to zarr for the specified skim_tag (e.g. 'taz') + + Parameters + ---------- + skim_tag: str (e.g. 'taz') + + Returns + ------- + list or None + """ + skim_setting = self.setting(f"{skim_tag}_skims") + if isinstance(skim_setting, dict): + return skim_setting.get("zarr-digital-encoding", None) + else: + return None + + def skim_backing_store(self, skim_tag): + name = self.setting("name", "unnamed") + return self.setting( + f"{skim_tag}_skims.backend", f"shared_memory_{skim_tag}_{name}" + ) + + def skim_max_float_precision(self, skim_tag): + return self.setting(f"{skim_tag}_skims.max_float_precision", 32) + + def skim_digital_encoding(self, skim_tag): + return self.setting(f"{skim_tag}_skims.digital-encoding", []) + def multiprocess(self): """ return True if this is a multiprocessing run (even if it is a main or single-process subprocess) @@ -584,11 +677,31 @@ def get_skim_dict(self, skim_tag): ------- SkimDict or subclass (e.g. MazSkimDict) """ - - assert ( - skim_tag in self.skim_dicts - ), f"network_los.get_skim_dict: skim tag '{skim_tag}' not in skim_dicts" - return self.skim_dicts[skim_tag] + sharrow_enabled = config.setting("sharrow", False) + if sharrow_enabled and skim_tag in ("taz", "maz"): + skim_dataset = inject.get_injectable("skim_dataset") + # non-global import avoids circular references + from .skim_dataset import SkimDataset + + if skim_tag == "maz": + return SkimDataset(skim_dataset) + else: + dropdims = ["omaz", "dmaz"] + skim_dataset = skim_dataset.drop_dims(dropdims, errors="ignore") + for dd in dropdims: + if f"dim_redirection_{dd}" in skim_dataset.attrs: + del skim_dataset.attrs[f"dim_redirection_{dd}"] + return SkimDataset(skim_dataset) + elif sharrow_enabled and skim_tag in ("tap"): + tap_dataset = inject.get_injectable("tap_dataset") + from .skim_dataset import SkimDataset + + return SkimDataset(tap_dataset) + else: + assert ( + skim_tag in self.skim_dicts + ), f"network_los.get_skim_dict: skim tag '{skim_tag}' not in skim_dicts" + return self.skim_dicts[skim_tag] def get_default_skim_dict(self): """ @@ -601,6 +714,7 @@ def get_default_skim_dict(self): if self.zone_system == ONE_ZONE: return self.get_skim_dict("taz") else: + # TODO:SHARROW: taz and maz are the same return self.get_skim_dict("maz") def get_mazpairs(self, omaz, dmaz, attribute): @@ -651,9 +765,36 @@ def get_tappairs3d(self, otap, dtap, dim3, key): ------- Numpy.ndarray: list of tap skim values for odt tuples """ + tap_skim = self.get_skim_dict("tap") + + if isinstance(tap_skim, skim_dictionary.SkimDict): + return tap_skim.lookup_3d(otap, dtap, dim3, key) + elif isinstance(dim3, str): + s = ( + tap_skim.dataset[[key]] + .sel(time_period=dim3) + .at( + otap=otap.values, + dtap=dtap.values, + _name=key, + ) + ) + elif dim3.dtype.kind == "i": + s = tap_skim.dataset.at( + otap=otap.values, + dtap=dtap.values, + time_period=tap_skim.dataset.time_period.values[dim3], + _name=key, + ) + else: + s = tap_skim.dataset.at( + otap=otap.values, + dtap=dtap.values, + time_period=dim3, + _name=key, + ) - s = self.get_skim_dict("tap").lookup_3d(otap, dtap, dim3, key) - return s + return s.values def skim_time_period_label(self, time_period): """ @@ -665,7 +806,7 @@ def skim_time_period_label(self, time_period): Returns ------- - numpy.array + pandas Series string time period labels """ @@ -683,22 +824,42 @@ def skim_time_period_label(self, time_period): assert 0 == model_time_window_min % period_minutes total_periods = model_time_window_min / period_minutes - bins = ( - np.digitize( - [np.array(time_period) % total_periods], + # FIXME - eventually test and use np version always? + if np.isscalar(time_period): + bin = ( + np.digitize( + [time_period % total_periods], + self.skim_time_periods["periods"], + right=True, + )[0] + - 1 + ) + result = self.skim_time_periods["labels"][bin] + else: + result = pd.cut( + time_period, self.skim_time_periods["periods"], - right=True, - )[0] - - 1 - ) - return np.array(self.skim_time_periods["labels"])[bins] + labels=self.skim_time_periods["labels"], + ordered=False, + ).astype(str) + + return result def get_tazs(self): # FIXME - should compute on init? if self.zone_system == ONE_ZONE: tazs = inject.get_table("land_use").index.values else: - tazs = self.maz_taz_df.TAZ.unique() + try: + land_use_taz = inject.get_table("land_use_taz").to_frame() + except (RuntimeError, KeyError): + # land_use_taz is missing, use fallback + tazs = self.maz_taz_df.TAZ.unique() + else: + if "_original_TAZ" in land_use_taz: + tazs = land_use_taz["_original_TAZ"].values + else: + tazs = self.maz_taz_df.TAZ.unique() assert isinstance(tazs, np.ndarray) return tazs @@ -715,3 +876,49 @@ def get_taps(self): taps = self.tap_df.TAP.values assert isinstance(taps, np.ndarray) return taps + + @property + def get_maz_to_taz_series(self): + """ + pd.Series: Index is the MAZ, value is the corresponding TAZ + """ + sharrow_enabled = config.setting("sharrow", False) + if sharrow_enabled: + # FIXME:SHARROW - this assumes that both MAZ and TAZ have been recoded to + # zero-based indexes, but what if that was not done? + # Should we check it and error out here or bravely march forward? + skim_dataset = inject.get_injectable("skim_dataset") + maz_to_taz = skim_dataset["_digitized_otaz_of_omaz"].to_series() + else: + maz_to_taz = self.maz_taz_df[["MAZ", "TAZ"]].set_index("MAZ").TAZ + return maz_to_taz + + def map_maz_to_taz(self, s): + """ + Convert MAZ's to TAZ's + + Parameters + ---------- + s : Array-like + Integer MAZ values + + Returns + ------- + pd.Series + Integer TAZ values + """ + if not isinstance(s, (pd.Series, pd.Index)): + s = pd.Series(s) + input_was_series = False + else: + input_was_series = True + out = s.map(self.get_maz_to_taz_series) + if np.issubdtype(out, np.floating): + if out.isna().any(): + raise KeyError("failed in mapping MAZ to TAZ") + else: + out = out.astype(np.int32) + if input_was_series: + return out + else: + return out.to_numpy() diff --git a/activitysim/core/mem.py b/activitysim/core/mem.py index 845ea3554..ae832f250 100644 --- a/activitysim/core/mem.py +++ b/activitysim/core/mem.py @@ -7,7 +7,6 @@ import logging import multiprocessing import os -import platform import threading import time @@ -174,7 +173,7 @@ def log_global_hwm(): ) -def trace_memory_info(event, trace_ticks=0): +def trace_memory_info(event, trace_ticks=0, force_garbage_collect=False): global MEM_TICK @@ -183,14 +182,26 @@ def trace_memory_info(event, trace_ticks=0): return MEM_TICK = tick + if force_garbage_collect: + was_disabled = not gc.isenabled() + if was_disabled: + gc.enable() + gc.collect() + if was_disabled: + gc.disable() + process_name = multiprocessing.current_process().name pid = os.getpid() current_process = psutil.Process() if USS: - info = current_process.memory_full_info() - uss = info.uss + try: + info = current_process.memory_full_info() + uss = info.uss + except (PermissionError, psutil.AccessDenied, RuntimeError): + info = current_process.memory_info() + uss = 0 else: info = current_process.memory_info() uss = 0 @@ -254,8 +265,12 @@ def get_rss(force_garbage_collect=False, uss=False): gc.disable() if uss: - info = psutil.Process().memory_full_info() - return info.rss, info.uss + try: + info = psutil.Process().memory_full_info() + return info.rss, info.uss + except (PermissionError, psutil.AccessDenied, RuntimeError): + info = psutil.Process().memory_info() + return info.rss, 0 else: info = psutil.Process().memory_info() return info.rss, 0 @@ -276,6 +291,11 @@ def shared_memory_size(data_buffers=None): data_buffers = inject.get_injectable("data_buffers", {}) for k, data_buffer in data_buffers.items(): + if isinstance(data_buffer, str) and data_buffer.startswith("sh.Dataset:"): + from sharrow import Dataset + + shared_size += Dataset.shm.preload_shared_memory_size(data_buffer[11:]) + continue try: obj = data_buffer.get_obj() except Exception: diff --git a/activitysim/core/memory_sidecar.py b/activitysim/core/memory_sidecar.py new file mode 100644 index 000000000..b1d8816e3 --- /dev/null +++ b/activitysim/core/memory_sidecar.py @@ -0,0 +1,130 @@ +import datetime +import os +import time +from multiprocessing import Pipe, Process + +import psutil + + +def record_memory_usage( + logstream, event="", event_idx=-1, measure_uss=False, measure_cpu=False, pid=None +): + + if pid is None: + pid = os.getpid() + current_process = psutil.Process(pid) + with current_process.oneshot(): + process_name = current_process.name() + + if measure_uss: + try: + info = current_process.memory_full_info() + uss = info.uss + except (PermissionError, psutil.AccessDenied, RuntimeError): + info = current_process.memory_info() + uss = 0 + else: + info = current_process.memory_info() + uss = 0 + + if measure_cpu: + cpu_pct = current_process.cpu_percent() + else: + cpu_pct = -1 + + full_rss = rss = info.rss + + num_children = 0 + for child in current_process.children(recursive=True): + try: + child_info = child.memory_info() + full_rss += child_info.rss + num_children += 1 + except (psutil.NoSuchProcess, psutil.AccessDenied) as e: + pass + + timestamp = datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S.%f") # sortable + + print( + f"{process_name}," + f"{pid}," + f"{int(rss)}," + f"{int(full_rss)}," + f"{int(uss)}," + f"{cpu_pct}," + f"{event_idx}," + f"{event}," + f"{num_children}," + f"{timestamp}", + file=logstream, + ) + + +def monitor_memory_usage( + pid, + conn, + interval=0.5, + flush_interval=5, + filename="/tmp/sidecar.csv", + measure_uss=True, + measure_cpu=True, +): + event = "" + event_idx = 0 + last_flush = time.time() + if measure_cpu: + psutil.cpu_percent() + with open(filename, "w") as stream: + MEM_LOG_HEADER = ( + "process,pid,rss,full_rss,uss,cpu,event_idx,event,children,time" + ) + print(MEM_LOG_HEADER, file=stream) + while True: + # timestamp = datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S.%f") # sortable + # print(f"{timestamp} [{event}]", file=stream) + record_memory_usage( + stream, + event=event, + event_idx=event_idx, + measure_uss=measure_uss, + measure_cpu=measure_cpu, + pid=pid, + ) + if conn.poll(interval): + event_ = conn.recv() + if event_ != event: + event_idx += 1 + event = event_ + else: + pass + now = time.time() + if now > last_flush + flush_interval: + stream.flush() + last_flush = now + if event == "STOP": + stream.flush() + break + + +class MemorySidecar: + def __init__(self, filename="/tmp/sidecar.csv"): + self.local_conn, child_conn = Pipe() + self.sidecar_process = Process( + target=monitor_memory_usage, + args=(os.getpid(), child_conn), + kwargs=dict(filename=filename), + ) + self.sidecar_process.start() + + def stop(self): + self.set_event("STOP") + self.sidecar_process.join(timeout=5) + if self.sidecar_process.exitcode is None: + self.sidecar_process.kill() + print("memory sidecar stopped") + + def set_event(self, event): + try: + self.local_conn.send(str(event)) + except BrokenPipeError: + pass diff --git a/activitysim/core/mp_tasks.py b/activitysim/core/mp_tasks.py index 1d41c7c96..ebbf1cb37 100644 --- a/activitysim/core/mp_tasks.py +++ b/activitysim/core/mp_tasks.py @@ -1,5 +1,6 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib import logging import multiprocessing import os @@ -381,7 +382,7 @@ def build_slice_rules(slice_info, pipeline_tables): """ slicer_table_names = slice_info["tables"] - slicer_table_exceptions = slice_info.get("except", []) + slicer_table_exceptions = slice_info.get("exclude", slice_info.get("except", [])) primary_slicer = slicer_table_names[0] # - ensure that tables listed in slice_info appear in correct order and before any others @@ -733,15 +734,29 @@ def setup_injectables_and_logging(injectables, locutor=True): # other callers (e.g. piopulationsim) will have to arrange to register their own steps and injectables # (presumably) in a custom run_simulation.py instead of using the 'activitysim run' command if not inject.is_injectable("preload_injectables"): - from activitysim import ( # register abm steps and other abm-specific injectables - abm, - ) + # register abm steps and other abm-specific injectables + from activitysim import abm # noqa: F401 try: for k, v in injectables.items(): inject.add_injectable(k, v) + # re-import extension modules to register injectables + ext = inject.get_injectable("imported_extensions", default=()) + for e in ext: + basepath, extpath = os.path.split(e) + if not basepath: + basepath = "." + sys.path.insert(0, basepath) + try: + importlib.import_module(e) + except ImportError as err: + logger.exception("ImportError") + raise + finally: + del sys.path[0] + inject.add_injectable("is_sub_task", True) inject.add_injectable("locutor", locutor) @@ -1056,6 +1071,33 @@ def allocate_shared_shadow_pricing_buffers(): return shadow_pricing_buffers +def allocate_shared_shadow_pricing_buffers_choice(): + """ + This is called by the main process to allocate memory buffer to share with subprocs + + Returns + ------- + multiprocessing.RawArray + """ + + info("allocate_shared_shadow_pricing_buffers_choice") + + shadow_pricing_choice_info = inject.get_injectable( + "shadow_pricing_choice_info", None + ) + + if shadow_pricing_choice_info is not None: + from activitysim.abm.tables import shadow_pricing + + shadow_pricing_buffers_choice = ( + shadow_pricing.buffers_for_shadow_pricing_choice(shadow_pricing_choice_info) + ) + else: + shadow_pricing_buffers_choice = {} + + return shadow_pricing_buffers_choice + + def run_sub_simulations( injectables, shared_data_buffers, @@ -1385,15 +1427,18 @@ def skip_phase(phase): def find_breadcrumb(crumb, default=None): return old_breadcrumbs.get(step_name, {}).get(crumb, default) + sharrow_enabled = config.setting("sharrow", False) + # - allocate shared data shared_data_buffers = {} mem.trace_memory_info("allocate_shared_skim_buffer.before") t0 = tracing.print_elapsed_time() - shared_data_buffers.update(allocate_shared_skim_buffers()) - t0 = tracing.print_elapsed_time("allocate shared skim buffer", t0) - mem.trace_memory_info("allocate_shared_skim_buffer.completed") + if not sharrow_enabled: + shared_data_buffers.update(allocate_shared_skim_buffers()) + t0 = tracing.print_elapsed_time("allocate shared skim buffer", t0) + mem.trace_memory_info("allocate_shared_skim_buffer.completed") # combine shared_skim_buffer and shared_shadow_pricing_buffer in shared_data_buffer t0 = tracing.print_elapsed_time() @@ -1401,18 +1446,42 @@ def find_breadcrumb(crumb, default=None): t0 = tracing.print_elapsed_time("allocate shared shadow_pricing buffer", t0) mem.trace_memory_info("allocate_shared_shadow_pricing_buffers.completed") + # combine shared_shadow_pricing_buffers to pool choices across all processes + t0 = tracing.print_elapsed_time() + shared_data_buffers.update(allocate_shared_shadow_pricing_buffers_choice()) + t0 = tracing.print_elapsed_time("allocate shared shadow_pricing choice buffer", t0) + mem.trace_memory_info("allocate_shared_shadow_pricing_buffers_choice.completed") + + if sharrow_enabled: + start_time = time.time() + shared_data_buffers["skim_dataset"] = "sh.Dataset:skim_dataset" + + # Loading skim_dataset must be done in the main process, not a subprocess, + # so that this min process can hold on to the shared memory and then cleanly + # release it on exit. + from . import flow # make injectable known # noqa: F401 + + inject.get_injectable("skim_dataset") + + tracing.print_elapsed_time("setup skim_dataset", t0) + mem.trace_memory_info("skim_dataset.completed") + # - mp_setup_skims - if len(shared_data_buffers) > 0: - run_sub_task( - multiprocessing.Process( - target=mp_setup_skims, - name="mp_setup_skims", - args=(injectables,), - kwargs=shared_data_buffers, + else: # not sharrow_enabled + if len(shared_data_buffers) > 0: + start_time = time.time() + run_sub_task( + multiprocessing.Process( + target=mp_setup_skims, + name="mp_setup_skims", + args=(injectables,), + kwargs=shared_data_buffers, + ) ) - ) - t0 = tracing.print_elapsed_time("setup shared_data_buffers", t0) - mem.trace_memory_info("mp_setup_skims.completed") + + tracing.print_elapsed_time("setup shared_data_buffers", t0) + mem.trace_memory_info("mp_setup_skims.completed") + tracing.log_runtime("mp_setup_skims", start_time=start_time, force=True) # - for each step in run list for step_info in run_list["multiprocess_steps"]: @@ -1429,6 +1498,7 @@ def find_breadcrumb(crumb, default=None): # - mp_apportion_pipeline if not skip_phase("apportion") and num_processes > 1: + start_time = time.time() run_sub_task( multiprocessing.Process( target=mp_apportion_pipeline, @@ -1436,6 +1506,9 @@ def find_breadcrumb(crumb, default=None): args=(injectables, sub_proc_names, step_info), ) ) + tracing.log_runtime( + "%s_apportion" % step_name, start_time=start_time, force=True + ) drop_breadcrumb(step_name, "apportion") # - run_sub_simulations @@ -1463,6 +1536,7 @@ def find_breadcrumb(crumb, default=None): # - mp_coalesce_pipelines if not skip_phase("coalesce") and num_processes > 1: + start_time = time.time() run_sub_task( multiprocessing.Process( target=mp_coalesce_pipelines, @@ -1470,6 +1544,9 @@ def find_breadcrumb(crumb, default=None): args=(injectables, sub_proc_names, slice_info), ) ) + tracing.log_runtime( + "%s_coalesce" % step_name, start_time=start_time, force=True + ) drop_breadcrumb(step_name, "coalesce") # add checkpoint with final tables even if not intermediate checkpointing @@ -1786,7 +1863,11 @@ def get_run_list(): # - add resume breadcrumbs if resume_after: - breadcrumbs = get_breadcrumbs(run_list) + try: + breadcrumbs = get_breadcrumbs(run_list) + except IOError: # file does not exist, no resume_after is possible + breadcrumbs = None + resume_after = None if breadcrumbs: run_list["breadcrumbs"] = breadcrumbs diff --git a/activitysim/core/pathbuilder.py b/activitysim/core/pathbuilder.py index 91107e08e..1199f19c5 100644 --- a/activitysim/core/pathbuilder.py +++ b/activitysim/core/pathbuilder.py @@ -55,7 +55,7 @@ def compute_utilities( f"{trace_label} Running compute_utilities with {choosers.shape[0]} choosers" ) - locals_dict = {"np": np, "los": network_los} + locals_dict = {"np": np, "los": network_los, "disable_sharrow": True} locals_dict.update(model_constants) # we don't grok coefficients, but allow them to use constants in spec alt columns @@ -1267,6 +1267,16 @@ def __getitem__(self, path_type): skim: Skim The skim object """ + if self.cache_choices and path_type in self.cache: + # restore out of cache if all logsums are available in cache + # this can happen if the tvpb is called twice for the same thing in a spec + # do we want to allow this? alternatively the onus can be on the + # spec writer not to use them twice + cached = self.cache.get(path_type) + if "logsum" in cached and self.df.index[0] in cached.index: + recalled_logsums = cached.reindex(self.df.index).logsum + if not recalled_logsums.isna().any(): + return recalled_logsums assert self.df is not None, "Call set_df first" assert ( @@ -1308,11 +1318,21 @@ def __getitem__(self, path_type): choices_df = logsum_df[["atap", "btap", "path_set"]] if path_type in self.cache: - assert ( + if ( len(self.cache.get(path_type).index.intersection(logsum_df.index)) == 0 - ) - choices_df = pd.concat([self.cache.get(path_type), choices_df]) + ): + choices_df = pd.concat([self.cache.get(path_type), choices_df]) + else: + intersect = self.cache.get(path_type).index.intersection( + logsum_df.index + ) + choices_df = pd.concat( + [ + self.cache.get(path_type), + choices_df.loc[~choices_df.index.isin(intersect)], + ] + ) self.cache[path_type] = choices_df diff --git a/activitysim/core/pathbuilder_cache.py b/activitysim/core/pathbuilder_cache.py index b5f108913..1bd219693 100644 --- a/activitysim/core/pathbuilder_cache.py +++ b/activitysim/core/pathbuilder_cache.py @@ -1,19 +1,16 @@ # ActivitySim # See full license in LICENSE.txt. -import gc as _gc import itertools import logging import multiprocessing import os -import time from builtins import range from contextlib import contextmanager import numpy as np import pandas as pd -import psutil -from activitysim.core import config, inject, simulate, tracing, util +from activitysim.core import config, inject, simulate, util logger = logging.getLogger(__name__) @@ -30,37 +27,38 @@ @contextmanager def memo(tag, console=False, disable_gc=True): - t0 = time.time() - - MEMO_STACK.append(tag) - - gc_was_enabled = _gc.isenabled() - if gc_was_enabled: - _gc.collect() - if disable_gc: - _gc.disable() - - previous_mem = psutil.Process().memory_info().rss - try: - yield - finally: - elapsed_time = time.time() - t0 - - current_mem = psutil.Process().memory_info().rss - marginal_mem = current_mem - previous_mem - mem_str = f"net {util.GB(marginal_mem)} ({util.INT(marginal_mem)}) total {util.GB(current_mem)}" - - if gc_was_enabled and disable_gc: - _gc.enable() - if _gc.isenabled(): - _gc.collect() - - if console: - print(f"MEMO {tag} Time: {util.SEC(elapsed_time)} Memory: {mem_str} ") - else: - logger.debug(f"MEM {tag} {mem_str} in {util.SEC(elapsed_time)}") - - MEMO_STACK.pop() + yield # make this a noop for performance + # t0 = time.time() + # + # MEMO_STACK.append(tag) + # + # gc_was_enabled = _gc.isenabled() + # if gc_was_enabled: + # _gc.collect() + # if disable_gc: + # _gc.disable() + # + # previous_mem = psutil.Process().memory_info().rss + # try: + # yield + # finally: + # elapsed_time = time.time() - t0 + # + # current_mem = psutil.Process().memory_info().rss + # marginal_mem = current_mem - previous_mem + # mem_str = f"net {util.GB(marginal_mem)} ({util.INT(marginal_mem)}) total {util.GB(current_mem)}" + # + # if gc_was_enabled and disable_gc: + # _gc.enable() + # if _gc.isenabled(): + # _gc.collect() + # + # if console: + # print(f"MEMO {tag} Time: {util.SEC(elapsed_time)} Memory: {mem_str} ") + # else: + # logger.debug(f"MEM {tag} {mem_str} in {util.SEC(elapsed_time)}") + # + # MEMO_STACK.pop() class TVPBCache(object): @@ -97,7 +95,20 @@ def cleanup(self): """ if os.path.isfile(self.cache_path): logger.debug(f"deleting cache {self.cache_path}") - os.unlink(self.cache_path) + try: + os.unlink(self.cache_path) + except PermissionError: + # windows may complain if the cache was not completely closed + # in an earlier run, so let's just cache in a new file + n = 0 + while True: + n += 1 + candidate = os.path.join( + config.get_cache_dir(), f"{self.cache_tag}.{n}.mmap" + ) + if not os.path.isfile(candidate): + self.cache_tag = f"{self.cache_tag}.{n}" + break def write_static_cache(self, data): @@ -400,13 +411,19 @@ def get_unique_ids(self, df, scalar_attributes): if name in df: # if there is a column, use it - uid = uid * cardinality + np.asanyarray(df[name].map(ordinalizer)) + if name == "tod" and df[name].dtype.kind == "i": + # when time of day is an integer, assume it is already ordinalized + ticker = np.asanyarray(df[name]) + else: + ticker = np.asanyarray(df[name].map(ordinalizer)) + uid = uid * cardinality + ticker else: # otherwise it should be in scalar_attributes assert ( name in scalar_attributes ), f"attribute '{name}' not found in df.columns or scalar_attributes." - uid = uid * cardinality + ordinalizer.at[scalar_attributes[name]] + ticker = ordinalizer.at[scalar_attributes[name]] + uid = uid * cardinality + ticker return uid diff --git a/activitysim/core/pipeline.py b/activitysim/core/pipeline.py index ad39cdcfe..fe3ceca45 100644 --- a/activitysim/core/pipeline.py +++ b/activitysim/core/pipeline.py @@ -413,6 +413,14 @@ def load_checkpoint(checkpoint_name): # register it as an orca table rewrap(table_name, df) loaded_tables[table_name] = df + if table_name == "land_use" and "_original_zone_id" in df.columns: + # The presence of _original_zone_id indicates this table index was + # decoded to zero-based, so we need to disable offset + # processing for legacy skim access. + # TODO: this "magic" column name should be replaced with a mechanism + # to write and recover particular settings from the pipeline + # store, but we don't have that mechanism yet + config.override_setting("offset_preprocessing", True) # register for tracing in order that tracing.register_traceable_table wants us to register them traceable_tables = inject.get_injectable("traceable_tables", []) @@ -499,7 +507,26 @@ def run_model(model_name): t0 = print_elapsed_time() logger.info(f"#run_model running step {step_name}") - orca.run([step_name]) + instrument = config.setting("instrument", None) + if instrument is not None: + try: + from pyinstrument import Profiler + except ImportError: + instrument = False + if isinstance(instrument, (list, set, tuple)): + if step_name not in instrument: + instrument = False + else: + instrument = True + + if instrument: + with Profiler() as profiler: + orca.run([step_name]) + out_file = config.profiling_file_path(f"{step_name}.html") + with open(out_file, "wt") as f: + f.write(profiler.output_html()) + else: + orca.run([step_name]) t0 = print_elapsed_time( "#run_model completed step '%s'" % model_name, t0, debug=True @@ -543,7 +570,15 @@ def open_pipeline(resume_after=None, mode="a"): # open existing pipeline logger.debug("open_pipeline - open existing pipeline") open_pipeline_store(overwrite=False, mode=mode) - load_checkpoint(resume_after) + try: + load_checkpoint(resume_after) + except KeyError as err: + if "checkpoints" in err.args[0]: + # no checkpoints initialized, fall back to restart + _PIPELINE.last_checkpoint[CHECKPOINT_NAME] = INITIAL_CHECKPOINT_NAME + add_checkpoint(INITIAL_CHECKPOINT_NAME) + else: + raise else: # open new, empty pipeline logger.debug("open_pipeline - new, empty pipeline") @@ -601,7 +636,7 @@ def intermediate_checkpoint(checkpoint_name=None): return checkpoint_name in checkpoints -def run(models, resume_after=None): +def run(models, resume_after=None, memory_sidecar_process=None): """ run the specified list of models, optionally loading checkpoint and resuming after specified checkpoint. @@ -618,6 +653,8 @@ def run(models, resume_after=None): list of model_names resume_after : str or None model_name of checkpoint to load checkpoint and AFTER WHICH to resume model run + memory_sidecar_process : MemorySidecar, optional + Subprocess that monitors memory usage returns: nothing, but with pipeline open @@ -640,18 +677,25 @@ def run(models, resume_after=None): # preload any bulky injectables (e.g. skims) not in pipeline if inject.get_injectable("preload_injectables", None): + if memory_sidecar_process: + memory_sidecar_process.set_event("preload_injectables") t0 = print_elapsed_time("preload_injectables", t0) mem.trace_memory_info("pipeline.run after preload_injectables") t0 = print_elapsed_time() for model in models: + if memory_sidecar_process: + memory_sidecar_process.set_event(model) t1 = print_elapsed_time() run_model(model) mem.trace_memory_info(f"pipeline.run after {model}") tracing.log_runtime(model_name=model, start_time=t1) + if memory_sidecar_process: + memory_sidecar_process.set_event("finalizing") + # add checkpoint with final tables even if not intermediate checkpointing if not intermediate_checkpoint(): add_checkpoint(FINAL_CHECKPOINT_NAME) diff --git a/activitysim/core/random.py b/activitysim/core/random.py index 12527840f..dc34e2729 100644 --- a/activitysim/core/random.py +++ b/activitysim/core/random.py @@ -247,7 +247,7 @@ def random_for_df(self, df, step_name, n=1): self.row_states.loc[df.index, "offset"] += n return rands - def normal_for_df(self, df, step_name, mu, sigma, lognormal=False): + def normal_for_df(self, df, step_name, mu, sigma, lognormal=False, size=None): """ Return a floating point random number in normal (or lognormal) distribution for each row in df using the appropriate random channel for each row. @@ -296,20 +296,24 @@ def to_series(x): if lognormal: rands = np.asanyarray( [ - prng.lognormal(mean=mu[i], sigma=sigma[i]) + prng.lognormal(mean=mu[i], sigma=sigma[i], size=size) for i, prng in enumerate(generators) ] ) else: rands = np.asanyarray( [ - prng.normal(loc=mu[i], scale=sigma[i]) + prng.normal(loc=mu[i], scale=sigma[i], size=size) for i, prng in enumerate(generators) ] ) # update offset for rows we handled - self.row_states.loc[df.index, "offset"] += 1 + if size is not None: + consume_offsets = int(size) + else: + consume_offsets = 1 + self.row_states.loc[df.index, "offset"] += consume_offsets return rands @@ -531,7 +535,7 @@ def set_base_seed(self, seed=None): assert len(list(self.channels.keys())) == 0 if seed is None: - self.base_seed = np.random.RandomState().randint(_MAX_SEED) + self.base_seed = np.random.RandomState().randint(_MAX_SEED, dtype=np.uint32) logger.debug("Set random seed randomly to %s" % self.base_seed) else: logger.debug("Set random seed base to %s" % seed) @@ -612,7 +616,7 @@ def random_for_df(self, df, n=1): rands = channel.random_for_df(df, self.step_name, n) return rands - def normal_for_df(self, df, mu=0, sigma=1, broadcast=False): + def normal_for_df(self, df, mu=0, sigma=1, broadcast=False, size=None): """ Return a single floating point normal random number in range (-inf, inf) for each row in df using the appropriate random channel for each row. @@ -651,13 +655,16 @@ def normal_for_df(self, df, mu=0, sigma=1, broadcast=False): alts_df = df df = df.index.unique().to_series() rands = channel.normal_for_df( - df, self.step_name, mu=0, sigma=1, lognormal=False + df, self.step_name, mu=0, sigma=1, lognormal=False, size=size ) - rands = reindex(pd.Series(rands, index=df.index), alts_df.index) + if size is not None: + rands = reindex(pd.DataFrame(rands, index=df.index), alts_df.index) + else: + rands = reindex(pd.Series(rands, index=df.index), alts_df.index) rands = rands * sigma + mu else: rands = channel.normal_for_df( - df, self.step_name, mu, sigma, lognormal=False + df, self.step_name, mu, sigma, lognormal=False, size=size ) return rands diff --git a/activitysim/core/simulate.py b/activitysim/core/simulate.py index 01ff42709..6c0fb902d 100644 --- a/activitysim/core/simulate.py +++ b/activitysim/core/simulate.py @@ -2,23 +2,25 @@ # See full license in LICENSE.txt. import logging +import time import warnings from builtins import range from collections import OrderedDict +from datetime import timedelta import numpy as np import pandas as pd from . import assign, chunk, config, logit, pathbuilder, pipeline, tracing, util +from .simulate_consts import ( + ALT_LOSER_UTIL, + SPEC_DESCRIPTION_NAME, + SPEC_EXPRESSION_NAME, + SPEC_LABEL_NAME, +) logger = logging.getLogger(__name__) -SPEC_DESCRIPTION_NAME = "Description" -SPEC_EXPRESSION_NAME = "Expression" -SPEC_LABEL_NAME = "Label" - -ALT_LOSER_UTIL = -900 - def random_rows(df, n): @@ -319,7 +321,7 @@ def get_segment_coefficients(model_settings, segment_name): FutureWarning, ) else: - raise RuntimeError(f"No COEFFICIENTS setting in model_settings") + raise RuntimeError("No COEFFICIENTS setting in model_settings") if legacy: constants = config.get_model_constants(model_settings) @@ -397,6 +399,11 @@ def eval_coefficients(spec, coefficients, estimator): spec[c].apply(lambda x: eval(str(x), {}, coefficients)).astype(np.float32) ) + sharrow_enabled = config.setting("sharrow", False) + if sharrow_enabled: + # keep all zero rows, reduces the number of unique flows to compile and store. + return spec + # drop any rows with all zeros since they won't have any effect (0 marginal utility) # (do not drop rows in estimation mode as it may confuse the estimation package (e.g. larch) zero_rows = (spec == 0).all(axis=1) @@ -420,8 +427,11 @@ def eval_utilities( estimator=None, trace_column_names=None, log_alt_losers=False, + zone_layer=None, + spec_sh=None, ): """ + Evaluate a utility function as defined in a spec file. Parameters ---------- @@ -432,99 +442,163 @@ def eval_utilities( choosers : pandas.DataFrame locals_d : Dict or None This is a dictionary of local variables that will be the environment - for an evaluation of an expression that begins with @ - trace_label: str - have_trace_targets: boolean - choosers has targets to trace - trace_all_rows: boolean - trace all chooser rows, bypassing tracing.trace_targets + for an evaluation of an expression that begins with "@". + trace_label : str + have_trace_targets : bool + Indicates if `choosers` has targets to trace + trace_all_rows : bool + Trace all chooser rows, bypassing tracing.trace_targets estimator : called to report intermediate table results (used for estimation) - trace_column_names: str or list of str + trace_column_names: str or list[str] chooser columns to include when tracing expression_values + log_alt_losers : bool, default False + Write out expressions when all alternatives are unavailable. + This can be useful for model development to catch errors in + specifications. Enabling this check does not alter valid results + but slows down model runs. + zone_layer : {'taz', 'maz'}, optional + Specify which zone layer of the skims is to be used by sharrow. You + cannot use the 'maz' zone layer in a one-zone model, but you can use + the 'taz' layer in a two- or three-zone model (e.g. for destination + pre-sampling). If not given, the default (lowest available) layer is + used. + spec_sh : pandas.DataFrame, optional + An alternative `spec` modified specifically for use with sharrow. + This is meant to give the same result, but allows for some optimizations + or preprocessing outside the sharrow framework (e.g. to run the Python + based transit virtual path builder and cache relevant values). Returns ------- - + utilities : pandas.DataFrame """ + start_time = time.time() - # fixme - restore tracing and _check_for_variability + sharrow_enabled = config.setting("sharrow", False) - trace_label = tracing.extend_trace_label(trace_label, "eval_utils") + expression_values = None - # avoid altering caller's passed-in locals_d parameter (they may be looping) - locals_dict = assign.local_utilities() + from .flow import TimeLogger - if locals_d is not None: - locals_dict.update(locals_d) - globals_dict = {} + timelogger = TimeLogger("simulate") + sh_util = None + sh_flow = None + utilities = None - locals_dict["df"] = choosers + if spec_sh is None: + spec_sh = spec - # - eval spec expressions - if isinstance(spec.index, pd.MultiIndex): - # spec MultiIndex with expression and label - exprs = spec.index.get_level_values(SPEC_EXPRESSION_NAME) + if locals_d is not None and "disable_sharrow" in locals_d: + sharrow_enabled = False + + if sharrow_enabled: + from .flow import apply_flow # import inside func to prevent circular imports + + locals_dict = {} + locals_dict.update(config.get_global_constants()) + if locals_d is not None: + locals_dict.update(locals_d) + sh_util, sh_flow = apply_flow( + spec_sh, + choosers, + locals_dict, + trace_label, + sharrow_enabled == "require", + zone_layer=zone_layer, + ) + utilities = sh_util + timelogger.mark("sharrow flow", True, logger, trace_label) else: - exprs = spec.index + timelogger.mark("sharrow flow", False) - expression_values = np.empty((spec.shape[0], choosers.shape[0])) - chunk.log_df(trace_label, "expression_values", expression_values) + # fixme - restore tracing and _check_for_variability - i = 0 - for expr, coefficients in zip(exprs, spec.values): + if utilities is None or estimator or sharrow_enabled == "test": - try: - with warnings.catch_warnings(record=True) as w: - # Cause all warnings to always be triggered. - warnings.simplefilter("always") - if expr.startswith("@"): - expression_value = eval(expr[1:], globals_dict, locals_dict) - - else: - expression_value = choosers.eval(expr) - - if len(w) > 0: - for wrn in w: - logger.warning( - f"{trace_label} - {type(wrn).__name__} ({wrn.message}) evaluating: {str(expr)}" - ) + trace_label = tracing.extend_trace_label(trace_label, "eval_utils") - except Exception as err: - logger.exception( - f"{trace_label} - {type(err).__name__} ({str(err)}) evaluating: {str(expr)}" - ) - raise err + # avoid altering caller's passed-in locals_d parameter (they may be looping) + locals_dict = assign.local_utilities() - if log_alt_losers: - # utils for each alt for this expression - # FIXME if we always did tis, we cold uem these and skip np.dot below - utils = np.outer(expression_value, coefficients) - losers = np.amax(utils, axis=1) < ALT_LOSER_UTIL + if locals_d is not None: + locals_dict.update(locals_d) + globals_dict = {} - if losers.any(): - logger.warning( - f"{trace_label} - {sum(losers)} choosers of {len(losers)} " - f"with prohibitive utilities for all alternatives for expression: {expr}" + locals_dict["df"] = choosers + + # - eval spec expressions + if isinstance(spec.index, pd.MultiIndex): + # spec MultiIndex with expression and label + exprs = spec.index.get_level_values(SPEC_EXPRESSION_NAME) + else: + exprs = spec.index + + expression_values = np.empty((spec.shape[0], choosers.shape[0])) + chunk.log_df(trace_label, "expression_values", expression_values) + + i = 0 + for expr, coefficients in zip(exprs, spec.values): + + try: + with warnings.catch_warnings(record=True) as w: + # Cause all warnings to always be triggered. + warnings.simplefilter("always") + if expr.startswith("@"): + expression_value = eval(expr[1:], globals_dict, locals_dict) + else: + expression_value = choosers.eval(expr) + + if len(w) > 0: + for wrn in w: + logger.warning( + f"{trace_label} - {type(wrn).__name__} ({wrn.message}) evaluating: {str(expr)}" + ) + + except Exception as err: + logger.exception( + f"{trace_label} - {type(err).__name__} ({str(err)}) evaluating: {str(expr)}" ) + raise err - expression_values[i] = expression_value - i += 1 + if log_alt_losers: + # utils for each alt for this expression + # FIXME if we always did tis, we cold uem these and skip np.dot below + utils = np.outer(expression_value, coefficients) + losers = np.amax(utils, axis=1) < ALT_LOSER_UTIL - chunk.log_df(trace_label, "expression_values", expression_values) + if losers.any(): + logger.warning( + f"{trace_label} - {sum(losers)} choosers of {len(losers)} " + f"with prohibitive utilities for all alternatives for expression: {expr}" + ) - if estimator: - df = pd.DataFrame( - data=expression_values.transpose(), - index=choosers.index, - columns=spec.index.get_level_values(SPEC_LABEL_NAME), + expression_values[i] = expression_value + i += 1 + + chunk.log_df(trace_label, "expression_values", expression_values) + + if estimator: + df = pd.DataFrame( + data=expression_values.transpose(), + index=choosers.index, + columns=spec.index.get_level_values(SPEC_LABEL_NAME), + ) + df.index.name = choosers.index.name + estimator.write_expression_values(df) + + # - compute_utilities + utilities = np.dot( + expression_values.transpose(), spec.astype(np.float64).values ) - df.index.name = choosers.index.name - estimator.write_expression_values(df) - # - compute_utilities - utilities = np.dot(expression_values.transpose(), spec.astype(np.float64).values) - utilities = pd.DataFrame(data=utilities, index=choosers.index, columns=spec.columns) + timelogger.mark("simple flow", True, logger=logger, suffix=trace_label) + else: + timelogger.mark("simple flow", False) + utilities = pd.DataFrame(data=utilities, index=choosers.index, columns=spec.columns) chunk.log_df(trace_label, "utilities", utilities) + timelogger.mark("assemble utilities") # sometimes tvpb will drop rows on the fly and we wind up with an empty # table of choosers. this will just bypass tracing in that case. @@ -539,39 +613,108 @@ def eval_utilities( # get int offsets of the trace_targets (offsets of bool=True values) offsets = np.nonzero(list(trace_targets))[0] + # trace sharrow + if sh_flow is not None: + try: + data_sh = sh_flow.load( + sh_flow.tree.replace_datasets( + df=choosers.iloc[offsets], + ), + dtype=np.float32, + ) + expression_values_sh = pd.DataFrame(data=data_sh.T, index=spec.index) + except ValueError: + expression_values_sh = None + else: + expression_values_sh = None + # get array of expression_values # expression_values.shape = (len(spec), len(choosers)) # data.shape = (len(spec), len(offsets)) - data = expression_values[:, offsets] + if expression_values is not None: + data = expression_values[:, offsets] + + # index is utility expressions (and optional label if MultiIndex) + expression_values_df = pd.DataFrame(data=data, index=spec.index) - # index is utility expressions (and optional label if MultiIndex) - expression_values_df = pd.DataFrame(data=data, index=spec.index) + if trace_column_names is not None: + if isinstance(trace_column_names, str): + trace_column_names = [trace_column_names] + expression_values_df.columns = pd.MultiIndex.from_frame( + choosers.loc[trace_targets, trace_column_names] + ) + else: + expression_values_df = None - if trace_column_names is not None: - if isinstance(trace_column_names, str): - trace_column_names = [trace_column_names] - expression_values_df.columns = pd.MultiIndex.from_frame( - choosers.loc[trace_targets, trace_column_names] + if expression_values_sh is not None: + tracing.trace_df( + expression_values_sh, + tracing.extend_trace_label(trace_label, "expression_values_sh"), + slicer=None, + transpose=False, + ) + if expression_values_df is not None: + tracing.trace_df( + expression_values_df, + tracing.extend_trace_label(trace_label, "expression_values"), + slicer=None, + transpose=False, ) - tracing.trace_df( - expression_values_df, - tracing.extend_trace_label(trace_label, "expression_values"), - slicer=None, - transpose=False, - ) + if len(spec.columns) > 1: - if len(spec.columns) > 1: + for c in spec.columns: + name = f"expression_value_{c}" - for c in spec.columns: - name = f"expression_value_{c}" + tracing.trace_df( + expression_values_df.multiply(spec[c].values, axis=0), + tracing.extend_trace_label(trace_label, name), + slicer=None, + transpose=False, + ) + timelogger.mark("trace", True, logger, trace_label) - tracing.trace_df( - expression_values_df.multiply(spec[c].values, axis=0), - tracing.extend_trace_label(trace_label, name), - slicer=None, - transpose=False, + if sharrow_enabled == "test": + try: + np.testing.assert_allclose( + sh_util, + utilities.values, + rtol=1e-2, + atol=0, + err_msg="utility not aligned", + verbose=True, + ) + except AssertionError as err: + print(err) + misses = np.where(~np.isclose(sh_util, utilities.values, rtol=1e-2, atol=0)) + _sh_util_miss1 = sh_util[tuple(m[0] for m in misses)] + _u_miss1 = utilities.values[tuple(m[0] for m in misses)] + diff = _sh_util_miss1 - _u_miss1 + if len(misses[0]) > sh_util.size * 0.01: + print( + f"big problem: {len(misses[0])} missed close values " + f"out of {sh_util.size} ({100*len(misses[0]) / sh_util.size:.2f}%)" ) + print(f"{sh_util.shape=}") + print(misses) + _sh_flow_load = sh_flow.load() + print("possible problematic expressions:") + for expr_n, expr in enumerate(exprs): + closeness = np.isclose( + _sh_flow_load[:, expr_n], expression_values[expr_n, :] + ) + if not closeness.all(): + print( + f" {closeness.sum()/closeness.size:05.1%} [{expr_n:03d}] {expr}" + ) + raise + except TypeError as err: + print(err) + print("sh_util") + print(sh_util) + print("utilities") + print(utilities) + timelogger.mark("sharrow test", True, logger, trace_label) del expression_values chunk.log_df(trace_label, "expression_values", None) @@ -579,6 +722,11 @@ def eval_utilities( # no longer our problem - but our caller should re-log this... chunk.log_df(trace_label, "utilities", None) + end_time = time.time() + logger.info( + f"simulate.eval_utils runtime: {timedelta(seconds=end_time - start_time)} {trace_label}" + ) + timelogger.summary(logger, "simulate.eval_utils timing") return utilities @@ -1070,8 +1218,10 @@ def eval_nl( if have_trace_targets: tracing.trace_df(choosers, "%s.choosers" % trace_label) + choosers, spec_sh = _preprocess_tvpb_logsums_on_choosers(choosers, spec, locals_d) + raw_utilities = eval_utilities( - spec, + spec_sh, choosers, locals_d, log_alt_losers=log_alt_losers, @@ -1079,6 +1229,7 @@ def eval_nl( have_trace_targets=have_trace_targets, estimator=estimator, trace_column_names=trace_column_names, + spec_sh=spec_sh, ) chunk.log_df(trace_label, "raw_utilities", raw_utilities) @@ -1345,7 +1496,7 @@ def simple_simulate( result_list.append(choices) - chunk.log_df(trace_label, f"result_list", result_list) + chunk.log_df(trace_label, "result_list", result_list) if len(result_list) > 1: choices = pd.concat(result_list) @@ -1396,7 +1547,7 @@ def simple_simulate_by_chunk_id( result_list.append(choices) - chunk.log_df(trace_label, f"result_list", result_list) + chunk.log_df(trace_label, "result_list", result_list) if len(result_list) > 1: choices = pd.concat(result_list) @@ -1452,6 +1603,85 @@ def eval_mnl_logsums(choosers, spec, locals_d, trace_label=None): return logsums +def _preprocess_tvpb_logsums_on_choosers(choosers, spec, locals_d): + """ + Compute TVPB logsums and attach those values to the choosers. + + Also generate a modified spec that uses the replacement value instead of + regenerating the logsums dynamically inline. + + Parameters + ---------- + choosers + spec + locals_d + + Returns + ------- + choosers + spec + + """ + spec_sh = spec.copy() + + def _replace_in_level(multiindex, level_name, *args, **kwargs): + y = multiindex.levels[multiindex.names.index(level_name)].str.replace( + *args, **kwargs + ) + return multiindex.set_levels(y, level=level_name) + + # Preprocess TVPB logsums outside sharrow + if "tvpb_logsum_odt" in locals_d: + tvpb = locals_d["tvpb_logsum_odt"] + path_types = tvpb.tvpb.network_los.setting( + f"TVPB_SETTINGS.{tvpb.recipe}.path_types" + ).keys() + assignments = {} + for path_type in ["WTW", "DTW"]: + if path_type not in path_types: + continue + re_spec = spec_sh.index + re_spec = _replace_in_level( + re_spec, + "Expression", + rf"tvpb_logsum_odt\['{path_type}'\]", + f"df.PRELOAD_tvpb_logsum_odt_{path_type}", + regex=True, + ) + if not all(spec_sh.index == re_spec): + spec_sh.index = re_spec + preloaded = locals_d["tvpb_logsum_odt"][path_type] + assignments[f"PRELOAD_tvpb_logsum_odt_{path_type}"] = preloaded + if assignments: + choosers = choosers.assign(**assignments) + + if "tvpb_logsum_dot" in locals_d: + tvpb = locals_d["tvpb_logsum_dot"] + path_types = tvpb.tvpb.network_los.setting( + f"TVPB_SETTINGS.{tvpb.recipe}.path_types" + ).keys() + assignments = {} + for path_type in ["WTW", "WTD"]: + if path_type not in path_types: + continue + re_spec = spec_sh.index + re_spec = _replace_in_level( + re_spec, + "Expression", + rf"tvpb_logsum_dot\['{path_type}'\]", + f"df.PRELOAD_tvpb_logsum_dot_{path_type}", + regex=True, + ) + if not all(spec_sh.index == re_spec): + spec_sh.index = re_spec + preloaded = locals_d["tvpb_logsum_dot"][path_type] + assignments[f"PRELOAD_tvpb_logsum_dot_{path_type}"] = preloaded + if assignments: + choosers = choosers.assign(**assignments) + + return choosers, spec_sh + + def eval_nl_logsums(choosers, spec, nest_spec, locals_d, trace_label=None): """ like eval_nl except return logsums instead of making choices @@ -1467,16 +1697,19 @@ def eval_nl_logsums(choosers, spec, nest_spec, locals_d, trace_label=None): logit.validate_nest_spec(nest_spec, trace_label) + choosers, spec_sh = _preprocess_tvpb_logsums_on_choosers(choosers, spec, locals_d) + # trace choosers if have_trace_targets: tracing.trace_df(choosers, "%s.choosers" % trace_label) raw_utilities = eval_utilities( - spec, + spec_sh, choosers, locals_d, trace_label=trace_label, have_trace_targets=have_trace_targets, + spec_sh=spec_sh, ) chunk.log_df(trace_label, "raw_utilities", raw_utilities) @@ -1576,7 +1809,7 @@ def simple_simulate_logsums( result_list.append(logsums) - chunk.log_df(trace_label, f"result_list", result_list) + chunk.log_df(trace_label, "result_list", result_list) if len(result_list) > 1: logsums = pd.concat(result_list) diff --git a/activitysim/core/simulate_consts.py b/activitysim/core/simulate_consts.py new file mode 100644 index 000000000..dc36b640d --- /dev/null +++ b/activitysim/core/simulate_consts.py @@ -0,0 +1,5 @@ +SPEC_DESCRIPTION_NAME = "Description" +SPEC_EXPRESSION_NAME = "Expression" +SPEC_LABEL_NAME = "Label" + +ALT_LOSER_UTIL = -900 diff --git a/activitysim/core/skim_dataset.py b/activitysim/core/skim_dataset.py new file mode 100644 index 000000000..9c28082fc --- /dev/null +++ b/activitysim/core/skim_dataset.py @@ -0,0 +1,827 @@ +import glob +import logging +import os + +import numpy as np +import openmatrix +import pandas as pd +import sharrow as sh + +from . import config +from . import flow as __flow # noqa, keep this here for side effects? +from . import inject + +logger = logging.getLogger(__name__) + +POSITIONS_AS_DICT = True + + +class SkimDataset: + """ + A wrapper around xarray.Dataset containing skim data, with time period management. + """ + + def __init__(self, dataset): + self.dataset = dataset + self.time_map = { + j: i for i, j in enumerate(self.dataset.indexes["time_period"]) + } + self.usage = set() # track keys of skims looked up + + @property + def odim(self): + if "omaz" in self.dataset.dims: + return "omaz" + else: + return "otaz" + + @property + def ddim(self): + if "dmaz" in self.dataset.dims: + return "dmaz" + else: + return "dtaz" + + def get_skim_usage(self): + """ + return set of keys of skims looked up. e.g. {'DIST', 'SOV'} + + Returns + ------- + set: + """ + return self.usage + + def wrap(self, orig_key, dest_key): + """ + Get a wrapper for the given keys. + + Parameters + ---------- + orig_key, dest_key : str + + Returns + ------- + DatasetWrapper + """ + return DatasetWrapper(self.dataset, orig_key, dest_key, time_map=self.time_map) + + def wrap_3d(self, orig_key, dest_key, dim3_key): + """ + Get a 3d wrapper for the given keys. + + Parameters + ---------- + orig_key, dest_key : str + + Returns + ------- + DatasetWrapper + """ + return DatasetWrapper( + self.dataset, orig_key, dest_key, dim3_key, time_map=self.time_map + ) + + def lookup(self, orig, dest, key): + """ + Return list of skim values of skims(s) at orig/dest in skim with the specified key (e.g. 'DIST') + + Parameters + ---------- + orig: list of orig zone_ids + dest: list of dest zone_ids + key: str + + Returns + ------- + Numpy.ndarray: list of skim values for od pairs + """ + + self.usage.add(key) + use_index = None + + # orig or dest might be a list instead of a series, in which case `index` + # is a builtin method instead of an array of coordinates, we don't want that. + if use_index is None and hasattr(orig, "index") and not isinstance(orig, list): + use_index = orig.index + if use_index is None and hasattr(dest, "index") and not isinstance(dest, list): + use_index = dest.index + + orig = np.asanyarray(orig).astype(int) + dest = np.asanyarray(dest).astype(int) + + # TODO offset mapper if required + positions = {self.odim: orig, self.ddim: dest} + + # When asking for a particular time period + if isinstance(key, tuple) and len(key) == 2: + main_key, time_key = key + if time_key in self.time_map: + positions["time_period"] = np.full_like(orig, self.time_map[time_key]) + key = main_key + else: + raise KeyError(key) + + result = self.dataset.iat( + **positions, _name=key + ) # Dataset.iat as implemented by sharrow strips data encoding + + result = result.to_series() + + if use_index is not None: + result.index = use_index + return result + + def map_time_periods_from_series(self, time_period_labels): + logger.info(f"vectorize lookup for time_period={time_period_labels.name}") + time_period_idxs = pd.Series( + np.vectorize(self.time_map.get)(time_period_labels), + index=time_period_labels.index, + ) + return time_period_idxs + + +class DatasetWrapper: + """ + Mimics the SkimWrapper interface to allow legacy code to access data. + + Parameters + ---------- + dataset: Dataset + orig_key, dest_key: str + name of columns in target dataframe to use as origin and destination + lookups, respectively + time_key : str, optional + time_map : Mapping, optional + A mapping from time period index numbers to (more aggregate) time + period names. + """ + + def __init__(self, dataset, orig_key, dest_key, time_key=None, *, time_map=None): + """ + Mimics the SkimWrapper interface to allow legacy code to access data. + + """ + self.dataset = dataset + self.orig_key = orig_key + self.dest_key = dest_key + self.time_key = time_key + self.df = None + if time_map is None: + self.time_map = { + j: i for i, j in enumerate(self.dataset.indexes["time_period"]) + } + else: + self.time_map = time_map + + @property + def odim(self): + if "omaz" in self.dataset.dims: + return "omaz" + else: + return "otaz" + + @property + def ddim(self): + if "dmaz" in self.dataset.dims: + return "dmaz" + else: + return "dtaz" + + def map_time_periods(self, df): + if self.time_key: + logger.info(f"vectorize lookup for time_period={self.time_key}") + time_period_idxs = pd.Series( + np.vectorize(self.time_map.get)(df[self.time_key]), + index=df.index, + ) + return time_period_idxs + + def set_df(self, df): + """ + Set the dataframe + + Parameters + ---------- + df : DataFrame + The dataframe which contains the origin and destination ids + + Returns + ------- + self (to facilitate chaining) + """ + assert ( + self.orig_key in df + ), f"orig_key '{self.orig_key}' not in df columns: {list(df.columns)}" + assert ( + self.dest_key in df + ), f"dest_key '{self.dest_key}' not in df columns: {list(df.columns)}" + if self.time_key: + assert ( + self.time_key in df + ), f"time_key '{self.time_key}' not in df columns: {list(df.columns)}" + self.df = df + + # TODO allow offsets if needed + positions = { + self.odim: df[self.orig_key], + self.ddim: df[self.dest_key], + } + if self.time_key: + if ( + np.issubdtype(df[self.time_key].dtype, np.integer) + and df[self.time_key].max() < self.dataset.dims["time_period"] + ): + logger.info(f"natural use for time_period={self.time_key}") + positions["time_period"] = df[self.time_key] + else: + logger.info(f"vectorize lookup for time_period={self.time_key}") + positions["time_period"] = pd.Series( + np.vectorize(self.time_map.get)(df[self.time_key]), + index=df.index, + ) + + if POSITIONS_AS_DICT: + self.positions = {} + for k, v in positions.items(): + self.positions[k] = v.astype(int) + else: + self.positions = pd.DataFrame(positions).astype(int) + + return self + + def lookup(self, key, reverse=False): + """ + Generally not called by the user - use __getitem__ instead + + Parameters + ---------- + key : hashable + The key (identifier) for this skim object + + od : bool (optional) + od=True means lookup standard origin-destination skim value + od=False means lookup destination-origin skim value + + Returns + ------- + impedances: pd.Series + A Series of impedances which are elements of the Skim object and + with the same index as df + """ + + assert self.df is not None, "Call set_df first" + if reverse: + if isinstance(self.positions, dict): + x = self.positions.copy() + x.update( + { + self.odim: self.positions[self.ddim], + self.ddim: self.positions[self.odim], + } + ) + else: + x = self.positions.rename( + columns={self.odim: self.ddim, self.ddim: self.odim} + ) + else: + if isinstance(self.positions, dict): + x = self.positions.copy() + else: + x = self.positions + + # When asking for a particular time period + if isinstance(key, tuple) and len(key) == 2: + main_key, time_key = key + if time_key in self.time_map: + if isinstance(x, dict): + x["time_period"] = np.full_like( + x[self.odim], fill_value=self.time_map[time_key] + ) + else: + x = x.assign(time_period=self.time_map[time_key]) + key = main_key + else: + raise KeyError(key) + + result = self.dataset.iat(**x, _name=key) # iat strips data encoding + # if 'digital_encoding' in self.dataset[key].attrs: + # result = array_decode(result, self.dataset[key].attrs['digital_encoding']) + + # Return a series, consistent with ActivitySim SkimWrapper + out = result.to_series() + out.index = self.df.index + return out + + def reverse(self, key): + """ + return skim value in reverse (d-o) direction + """ + return self.lookup(key, reverse=True) + + def max(self, key): + """ + return max skim value in either o-d or d-o direction + """ + assert self.df is not None, "Call set_df first" + + s = np.maximum( + self.lookup(key), + self.lookup(key, True), + ) + + return pd.Series(s, index=self.df.index) + + def __getitem__(self, key): + """ + Get the lookup for an available skim object (df and orig/dest and column names implicit) + + Parameters + ---------- + key : hashable + The key (identifier) for the skim object + + Returns + ------- + impedances: pd.Series with the same index as df + A Series of impedances values from the single Skim with specified key, indexed byt orig/dest pair + """ + return self.lookup(key) + + +def _should_invalidate_cache_file(cache_filename, *source_filenames): + """ + Check if a cache file should be invalidated. + + It should be invalidated if any source file has a modification time + more recent than the cache file modification time. + + Parameters + ---------- + cache_filename : Path-like + source_filenames : Collection[Path-like] + + Returns + ------- + bool + """ + from stat import ST_MTIME + + try: + stat0 = os.stat(cache_filename) + except FileNotFoundError: + # cache file does not even exist + return True + for i in source_filenames: + stat1 = os.stat(i) + if stat0[ST_MTIME] < stat1[ST_MTIME]: + return True + return False + + +def _use_existing_backing_if_valid(backing, omx_file_paths, skim_tag): + """ + Open an xarray dataset from a backing store if possible. + + Parameters + ---------- + backing : str + What kind of memory backing to use. Memmaps always start + with "memmap:" and then have a file system location, so + if this pattern does not apply the backing is not a memmap, + and instead the backing string is used as the key to find + the data in ephemeral shared memory. + omx_file_paths : Collection[Path-like] + These are the original source files. If the file modification + time for any of these files is more recent than the memmap, + the memmap files are invalid and will be deleted so they + can be rebuilt. + skim_tag : str + For error message reporting only + + Returns + ------- + xarray.Dataset or None + """ + out = None + if backing.startswith("memmap:"): + # when working with a memmap, check if the memmap file on disk + # needs to be invalidated, because the source skims have been + # modified more recently. + if not _should_invalidate_cache_file(backing[7:], *omx_file_paths): + try: + out = sh.Dataset.shm.from_shared_memory(backing, mode="r") + except FileNotFoundError as err: + logger.info(f"skim dataset {skim_tag!r} not found {err!s}") + logger.info(f"loading skim dataset {skim_tag!r} from original sources") + out = None + else: + logger.info("using skim_dataset from shared memory") + else: + sh.Dataset.shm.delete_shared_memory_files(backing) + else: + # when working in ephemeral shared memory, assume that if that data + # is loaded then it is good to use without further checks. + try: + out = sh.Dataset.shm.from_shared_memory(backing, mode="r") + except FileNotFoundError as err: + logger.info(f"skim dataset {skim_tag!r} not found {err!s}") + logger.info(f"loading skim dataset {skim_tag!r} from original sources") + out = None + return out + + +def _dedupe_time_periods(network_los_preload): + raw_time_periods = network_los_preload.los_settings["skim_time_periods"]["labels"] + # deduplicate time period names + time_periods = [] + for t in raw_time_periods: + if t not in time_periods: + time_periods.append(t) + return time_periods + + +def _apply_digital_encoding(dataset, digital_encodings): + """ + Apply digital encoding to compress skims with minimal information loss. + + Parameters + ---------- + dataset : xarray.Dataset + digital_encodings : Collection[Dict] + A collection of digital encoding instructions. To apply the same + encoding for multiple variables, the Dict should have a 'regex' key + that gives a regular expressing to match. Otherwise, see + sharrow's digital_encoding for other details. + + Returns + ------- + dataset : xarray.Dataset + As modified + """ + if digital_encodings: + import re + + # apply once, before saving to zarr, will stick around in cache + for encoding in digital_encodings: + logger.info(f"applying zarr digital-encoding: {encoding}") + regex = encoding.pop("regex", None) + joint_dict = encoding.pop("joint_dict", None) + if joint_dict: + joins = [] + for k in dataset.variables: + assert isinstance(k, str) # variable names should be strings + if re.match(regex, k): + joins.append(k) + dataset = dataset.digital_encoding.set( + joins, joint_dict=joint_dict, **encoding + ) + elif regex: + if "name" in encoding: + raise ValueError( + "cannot give both name and regex for digital_encoding" + ) + for k in dataset.variables: + assert isinstance(k, str) # variable names should be strings + if re.match(regex, k): + dataset = dataset.digital_encoding.set(k, **encoding) + else: + dataset = dataset.digital_encoding.set(**encoding) + return dataset + + +def _scan_for_unused_names(tokens): + """ + Scan all spec files to find unused skim variable names. + + Parameters + ---------- + tokens : Collection[str] + + Returns + ------- + Set[str] + """ + configs_dir_list = inject.get_injectable("configs_dir") + configs_dir_list = ( + [configs_dir_list] if isinstance(configs_dir_list, str) else configs_dir_list + ) + assert isinstance(configs_dir_list, list) + + for directory in configs_dir_list: + logger.debug(f"scanning for unused skims in {directory}") + filenames = glob.glob(os.path.join(directory, "*.csv")) + for filename in filenames: + with open(filename, "rt") as f: + content = f.read() + missing_tokens = set() + for t in tokens: + if t not in content: + missing_tokens.add(t) + tokens = missing_tokens + if not tokens: + return tokens + return tokens + + +def _drop_unused_names(dataset): + logger.info("scanning for unused skims") + tokens = set(dataset.variables.keys()) - set(dataset.coords.keys()) + unused_tokens = _scan_for_unused_names(tokens) + if unused_tokens: + baggage = dataset.digital_encoding.baggage(None) + unused_tokens -= baggage + # retain sparse matrix tables + unused_tokens = set(i for i in unused_tokens if not i.startswith("_s_")) + # retain lookup tables + unused_tokens = set(i for i in unused_tokens if not i.startswith("_digitized_")) + logger.info(f"dropping unused skims: {unused_tokens}") + dataset = dataset.drop_vars(unused_tokens) + else: + logger.info("no unused skims found") + return dataset + + +def load_sparse_maz_skims( + dataset, + land_use_index, + remapper, + zone_system, + maz2taz_file_name, + maz_to_maz_tables=(), + max_blend_distance=None, + data_file_resolver=None, +): + """ + Load sparse MAZ data on top of TAZ skim data. + + Parameters + ---------- + dataset : xarray.Dataset + The existing dataset at TAZ resolution only. + land_use_index : pandas.Index + The index of the land use table. For two and three zone systems, + these index values should be MAZ identifiers. + remapper : dict, optional + A dictionary mapping where the keys are the original (nominal) zone + id's, and the values are the recoded (typically zero-based contiguous) + zone id's. Recoding improves runtime efficiency. + zone_system : int + Currently 1, 2 and 3 are supported. + maz2taz_file_name : str + maz_to_maz_tables : Collection[] + max_blend_distance : optional + data_file_resolver : function + + Returns + ------- + xarray.Dataset + """ + from ..core.los import THREE_ZONE, TWO_ZONE + + if data_file_resolver is None: + data_file_resolver = config.data_file_path + + if zone_system in [TWO_ZONE, THREE_ZONE]: + + # maz + maz_taz = pd.read_csv(data_file_resolver(maz2taz_file_name, mandatory=True)) + maz_taz = maz_taz[["MAZ", "TAZ"]].set_index("MAZ").sort_index() + + # MAZ alignment is ensured here, so no re-alignment check is + # needed below for TWO_ZONE or THREE_ZONE systems + try: + pd.testing.assert_index_equal( + maz_taz.index, land_use_index, check_names=False + ) + except AssertionError: + if remapper is not None: + maz_taz.index = maz_taz.index.map(remapper.get) + maz_taz = maz_taz.sort_index() + assert maz_taz.index.equals( + land_use_index.sort_values() + ), "maz-taz lookup index does not match index of land_use table" + else: + raise + + dataset.redirection.set( + maz_taz, + map_to="otaz", + name="omaz", + map_also={"dtaz": "dmaz"}, + ) + + maz_to_maz_tables = ( + [maz_to_maz_tables] + if isinstance(maz_to_maz_tables, str) + else maz_to_maz_tables + ) + + if max_blend_distance is None: + max_blend_distance = {} + if isinstance(max_blend_distance, int): + max_blend_distance = {"DEFAULT": max_blend_distance} + + for file_name in maz_to_maz_tables: + + df = pd.read_csv(data_file_resolver(file_name, mandatory=True)) + if remapper is not None: + df.OMAZ = df.OMAZ.map(remapper.get) + df.DMAZ = df.DMAZ.map(remapper.get) + for colname in df.columns: + if colname in ["OMAZ", "DMAZ"]: + continue + max_blend_distance_i = max_blend_distance.get("DEFAULT", None) + max_blend_distance_i = max_blend_distance.get( + colname, max_blend_distance_i + ) + dataset.redirection.sparse_blender( + colname, + df.OMAZ, + df.DMAZ, + df[colname], + max_blend_distance=max_blend_distance_i, + index=land_use_index, + ) + + return dataset + + +def load_skim_dataset_to_shared_memory(skim_tag="taz"): + """ + Load skims from disk into shared memory. + + Parameters + ---------- + skim_tag : str, default "taz" + + Returns + ------- + xarray.Dataset + """ + from ..core.los import ONE_ZONE + + # TODO:SHARROW: taz and maz are the same + network_los_preload = inject.get_injectable("network_los_preload", None) + if network_los_preload is None: + raise ValueError("missing network_los_preload") + + # find which OMX files are to be used. + omx_file_paths = config.expand_input_file_list( + network_los_preload.omx_file_names(skim_tag), + ) + zarr_file = network_los_preload.zarr_file_name(skim_tag) + + if config.setting("disable_zarr", False): + # we can disable the zarr optimizations by setting the `disable_zarr` + # flag in the master config file to True + zarr_file = None + + if zarr_file is not None: + zarr_file = os.path.join(config.get_cache_dir(), zarr_file) + + max_float_precision = network_los_preload.skim_max_float_precision(skim_tag) + + skim_digital_encoding = network_los_preload.skim_digital_encoding(skim_tag) + zarr_digital_encoding = network_los_preload.zarr_pre_encoding(skim_tag) + + # The backing can be plain shared_memory, or a memmap + backing = network_los_preload.skim_backing_store(skim_tag) + if backing == "memmap": + # if memmap is given without a path, create a cache file + mmap_file = os.path.join( + config.get_cache_dir(), f"sharrow_dataset_{skim_tag}.mmap" + ) + backing = f"memmap:{mmap_file}" + + land_use = inject.get_table("land_use") + + if f"_original_{land_use.index.name}" in land_use.to_frame(): + land_use_zone_ids = land_use.to_frame()[f"_original_{land_use.index.name}"] + remapper = dict(zip(land_use_zone_ids, land_use_zone_ids.index)) + else: + remapper = None + + d = _use_existing_backing_if_valid(backing, omx_file_paths, skim_tag) + + if d is None: + time_periods = _dedupe_time_periods(network_los_preload) + if zarr_file: + logger.info(f"looking for zarr skims at {zarr_file}") + if zarr_file and os.path.exists(zarr_file): + # TODO: check if the OMX skims or sparse MAZ are modified more + # recently than the cached ZARR versions; if so do not use + # the ZARR + logger.info("found zarr skims, loading them") + d = sh.dataset.from_zarr_with_attr(zarr_file).max_float_precision( + max_float_precision + ) + else: + if zarr_file: + logger.info("did not find zarr skims, loading omx") + d = sh.dataset.from_omx_3d( + [openmatrix.open_file(f, mode="r") for f in omx_file_paths], + index_names=( + ("otap", "dtap", "time_period") + if skim_tag == "tap" + else ("otaz", "dtaz", "time_period") + ), + time_periods=time_periods, + max_float_precision=max_float_precision, + ) + + if zarr_file: + try: + import zarr # noqa + + # ensure zarr is available before we do all this work + except ModuleNotFoundError: + logger.warning( + "the 'zarr' package is not installed, " + "cannot cache skims to zarr" + ) + else: + if zarr_digital_encoding: + d = _apply_digital_encoding(d, zarr_digital_encoding) + logger.info(f"writing zarr skims to {zarr_file}") + d.to_zarr_with_attr(zarr_file) + + if skim_tag in ("taz", "maz"): + # load sparse MAZ skims, if any + # these are processed after the ZARR stuff as the GCXS sparse array + # is not yet compatible with ZARR directly. + # see https://github.com/pydata/sparse/issues/222 + # or https://github.com/zarr-developers/zarr-python/issues/424 + maz2taz_file_name = network_los_preload.setting("maz", None) + if maz2taz_file_name: + d = load_sparse_maz_skims( + d, + land_use.index, + remapper, + zone_system=network_los_preload.zone_system, + maz2taz_file_name=network_los_preload.setting("maz"), + maz_to_maz_tables=network_los_preload.setting("maz_to_maz.tables"), + max_blend_distance=network_los_preload.setting( + "maz_to_maz.max_blend_distance", default={} + ), + ) + + d = _drop_unused_names(d) + # apply non-zarr dependent digital encoding + d = _apply_digital_encoding(d, skim_digital_encoding) + + if skim_tag in ("taz", "maz"): + # check alignment of TAZs that it matches land_use table + logger.info("checking skims alignment with land_use") + try: + land_use_zone_id = land_use[f"_original_{land_use.index.name}"] + except KeyError: + land_use_zone_id = land_use.index + else: + land_use_zone_id = None + + if network_los_preload.zone_system == ONE_ZONE: + # check TAZ alignment for ONE_ZONE system. + # other systems use MAZ for most lookups, which dynamically + # resolves to TAZ inside the Dataset code. + if d["otaz"].attrs.get("preprocessed") != "zero-based-contiguous": + try: + np.testing.assert_array_equal(land_use_zone_id, d.otaz) + except AssertionError as err: + logger.info(f"otaz realignment required\n{err}") + d = d.reindex(otaz=land_use_zone_id) + else: + logger.info("otaz alignment ok") + d["otaz"] = land_use.index.to_numpy() + d["otaz"].attrs["preprocessed"] = "zero-based-contiguous" + else: + np.testing.assert_array_equal(land_use.index, d.otaz) + + if d["dtaz"].attrs.get("preprocessed") != "zero-based-contiguous": + try: + np.testing.assert_array_equal(land_use_zone_id, d.dtaz) + except AssertionError as err: + logger.info(f"dtaz realignment required\n{err}") + d = d.reindex(dtaz=land_use_zone_id) + else: + logger.info("dtaz alignment ok") + d["dtaz"] = land_use.index.to_numpy() + d["dtaz"].attrs["preprocessed"] = "zero-based-contiguous" + else: + np.testing.assert_array_equal(land_use.index, d.dtaz) + + if d.shm.is_shared_memory: + return d + else: + logger.info("writing skims to shared memory") + return d.shm.to_shared_memory(backing, mode="r") + + +@inject.injectable(cache=True) +def skim_dataset(): + return load_skim_dataset_to_shared_memory() + + +@inject.injectable(cache=True) +def tap_dataset(): + return load_skim_dataset_to_shared_memory("tap") diff --git a/activitysim/core/skim_dict_factory.py b/activitysim/core/skim_dict_factory.py index 6c1c8f222..450b98d25 100644 --- a/activitysim/core/skim_dict_factory.py +++ b/activitysim/core/skim_dict_factory.py @@ -5,12 +5,13 @@ import logging import multiprocessing import os -from abc import ABC, abstractmethod +import warnings +from abc import ABC import numpy as np import openmatrix as omx -from activitysim.core import config, inject, skim_dictionary, tracing, util +from activitysim.core import config, inject, skim_dictionary, util logger = logging.getLogger(__name__) @@ -87,7 +88,8 @@ def __init__(self, skim_tag, network_los): self.base_keys = None self.block_offsets = None - self.load_skim_info(skim_tag) + if skim_tag: + self.load_skim_info(skim_tag) def load_skim_info(self, skim_tag): """ @@ -114,7 +116,7 @@ def load_skim_info(self, skim_tag): logger.debug(f"load_skim_info {skim_tag} reading {omx_file_path}") - with omx.open_file(omx_file_path) as omx_file: + with omx.open_file(omx_file_path, mode="r") as omx_file: # fixme call to omx_file.shape() failing in windows p3.5 if self.omx_shape is None: @@ -127,9 +129,10 @@ def load_skim_info(self, skim_tag): ), f"Mismatch shape {self.omx_shape} != {omx_file.shape()}" for skim_name in omx_file.listMatrices(): - assert ( - skim_name not in self.omx_manifest - ), f"duplicate skim '{skim_name}' found in {self.omx_manifest[skim_name]} and {omx_file}" + if skim_name in self.omx_manifest: + warnings.warn( + f"duplicate skim '{skim_name}' found in {self.omx_manifest[skim_name]} and {omx_file}" + ) self.omx_manifest[skim_name] = omx_file_path for m in omx_file.listMappings(): @@ -281,7 +284,7 @@ def _read_skims_from_omx(self, skim_info, skim_data): logger.info(f"_read_skims_from_omx {omx_file_path}") # read skims into skim_data - with omx.open_file(omx_file_path) as omx_file: + with omx.open_file(omx_file_path, mode="r") as omx_file: for skim_key, omx_key in omx_keys.items(): if omx_manifest[omx_key] == omx_file_path: diff --git a/activitysim/core/skim_dictionary.py b/activitysim/core/skim_dictionary.py index d707e560e..a1897b536 100644 --- a/activitysim/core/skim_dictionary.py +++ b/activitysim/core/skim_dictionary.py @@ -7,8 +7,6 @@ import numpy as np import pandas as pd -from activitysim.core.util import quick_loc_series - logger = logging.getLogger(__name__) NOT_IN_SKIM_ZONE_ID = -1 @@ -126,9 +124,6 @@ def map(self, zone_ids): assert self.offset_int is None assert isinstance(self.offset_series, pd.Series) - # FIXME - turns out it is faster to use series.map if zone_ids is a series - # offsets = quick_loc_series(zone_ids, self.offset_series).fillna(NOT_IN_SKIM_ZONE_ID).astype(int) - if isinstance(zone_ids, np.ndarray): zone_ids = pd.Series(zone_ids) offsets = ( @@ -266,6 +261,7 @@ def _lookup(self, orig, dest, block_offsets): result = self.skim_data[mapped_orig, mapped_dest, block_offsets] # FIXME - should return nan if not in skim (negative indices wrap around) + # FIXME - this check only works if # of origin zones match # of dest zones! in_skim = ( (mapped_orig >= 0) & (mapped_orig < self.omx_shape[0]) @@ -279,9 +275,12 @@ def _lookup(self, orig, dest, block_offsets): # print(f"in_skim\n{in_skim}") # check for bad indexes (other than NOT_IN_SKIM_ZONE_ID) - assert ( + if not ( in_skim | (orig == NOT_IN_SKIM_ZONE_ID) | (dest == NOT_IN_SKIM_ZONE_ID) - ).all(), f"{(~in_skim).sum()} od pairs not in skim" + ).all(): + raise AssertionError( + f"{(~in_skim).sum()} od pairs not in skim including [{orig[~in_skim][:5]}]->[{dest[~in_skim][:5]}]" + ) if not in_skim.all(): result = np.where(in_skim, result, NOT_IN_SKIM_NAN).astype(self.dtype) @@ -634,7 +633,33 @@ def __init__(self, skim_tag, network_los, taz_skim_dict): self.network_los = network_los - super().__init__(skim_tag, taz_skim_dict.skim_info, taz_skim_dict.skim_data) + from activitysim.core.cleaning import ( + recode_based_on_table, + should_recode_based_on_table, + ) + + if should_recode_based_on_table("land_use_taz"): + from .skim_dict_factory import SkimInfo + + skim_info = SkimInfo(None, network_los) + skim_info.skim_tag = taz_skim_dict.skim_info.skim_tag + skim_info.dtype_name = network_los.skim_dtype_name + skim_info.omx_manifest = taz_skim_dict.skim_info.omx_manifest + skim_info.omx_shape = taz_skim_dict.skim_info.omx_shape + skim_info.num_skims = taz_skim_dict.skim_info.num_skims + skim_info.skim_data_shape = taz_skim_dict.skim_info.skim_data_shape + skim_info.offset_map_name = taz_skim_dict.skim_info.offset_map_name + skim_info.omx_keys = taz_skim_dict.skim_info.omx_keys + skim_info.base_keys = taz_skim_dict.skim_info.base_keys + skim_info.block_offsets = taz_skim_dict.skim_info.block_offsets + + skim_info.offset_map = recode_based_on_table( + taz_skim_dict.skim_info.offset_map, "land_use_taz" + ) + else: + skim_info = taz_skim_dict.skim_info + + super().__init__(skim_tag, skim_info, taz_skim_dict.skim_data) assert ( self.offset_mapper is not None ) # should have been set with _init_offset_mapper @@ -658,20 +683,15 @@ def _offset_mapper(self): OffsetMapper """ - # start with a series with MAZ zone_id index and TAZ zone id values - maz_to_taz = ( - self.network_los.maz_taz_df[["MAZ", "TAZ"]] - .set_index("MAZ") - .sort_values(by="TAZ") - .TAZ - ) - # use taz offset_mapper to create series mapping directly from MAZ to TAZ skim index taz_offset_mapper = super()._offset_mapper() - maz_to_skim_offset = taz_offset_mapper.map(maz_to_taz) + maz_taz = self.network_los.get_maz_to_taz_series + maz_to_skim_offset = taz_offset_mapper.map(maz_taz) if isinstance(maz_to_skim_offset, np.ndarray): - maz_to_skim_offset = pd.Series(maz_to_skim_offset, maz_to_taz.index) # bug + maz_to_skim_offset = pd.Series( + maz_to_skim_offset, self.network_los.get_maz_to_taz_series.index + ) # bug # MAZ # 19062 330 <- The TAZ would be, say, 331, and the offset is 330 @@ -685,6 +705,8 @@ def _offset_mapper(self): offset_mapper = OffsetMapper(offset_series=maz_to_skim_offset) elif isinstance(maz_to_skim_offset, np.ndarray): offset_mapper = OffsetMapper(offset_list=maz_to_skim_offset) + else: + raise NotImplementedError return offset_mapper @@ -877,3 +899,9 @@ def get(self, row_ids, col_ids): result = pd.Series(result, index=row_ids.index) return result + + def get_rows(self, row_ids): + return self.offset_mapper.map(np.asanyarray(row_ids)) + + def get_cols(self, col_ids): + return np.vectorize(self.cols_to_indexes.get)(col_ids) diff --git a/activitysim/core/steps/output.py b/activitysim/core/steps/output.py index 93397b914..8b8a8e3be 100644 --- a/activitysim/core/steps/output.py +++ b/activitysim/core/steps/output.py @@ -37,9 +37,16 @@ def track_skim_usage(output_dir): for key in skim_dict.get_skim_usage(): print(key, file=output_file) - unused = set(k for k in skim_dict.skim_info.base_keys) - set( - k for k in skim_dict.get_skim_usage() - ) + try: + unused = set(k for k in skim_dict.skim_info.base_keys) - set( + k for k in skim_dict.get_skim_usage() + ) + except AttributeError: + base_keys = set(skim_dict.dataset.variables.keys()) - set( + skim_dict.dataset.coords.keys() + ) + # using dataset + unused = base_keys - set(k for k in skim_dict.get_skim_usage()) for key in unused: print(key, file=output_file) @@ -121,7 +128,16 @@ def write_data_dictionary(output_dir): schema = dict() final_shapes = dict() for table_name in table_names: - df = pipeline.get_table(table_name) + try: + df = pipeline.get_table(table_name) + except RuntimeError as run_err: + if run_err.args and "dropped" in run_err.args[0]: + # if a checkpointed table was dropped, that's not ideal, so we should + # log a warning about it, but not allow the error to stop execution here + logger.warning(run_err.args[0]) + # note actually emitting a warnings.warn instead of a logger message will + # unfortunately cause some of our excessively strict tests to fail + continue final_shapes[table_name] = df.shape @@ -157,15 +173,15 @@ def write_data_dictionary(output_dir): info = schema.get(table_name, None) - # tag any new columns with checkpoint name - prev_columns = info[info.checkpoint != ""].column_name.values - new_cols = [c for c in df.columns.values if c not in prev_columns] - is_new_column_this_checkpoont = info.column_name.isin(new_cols) - info.checkpoint = np.where( - is_new_column_this_checkpoont, checkpoint_name, info.checkpoint - ) - - schema[table_name] = info + if info is not None: + # tag any new columns with checkpoint name + prev_columns = info[info.checkpoint != ""].column_name.values + new_cols = [c for c in df.columns.values if c not in prev_columns] + is_new_column_this_checkpoont = info.column_name.isin(new_cols) + info.checkpoint = np.where( + is_new_column_this_checkpoont, checkpoint_name, info.checkpoint + ) + schema[table_name] = info schema_df = pd.concat(schema.values()) @@ -180,7 +196,8 @@ def write_data_dictionary(output_dir): for table_name in table_names: info = schema.get(table_name, None) - + if info is None: + continue columns_to_print = ["column_name", "dtype", "checkpoint"] info = info[columns_to_print].copy() @@ -269,6 +286,12 @@ def write_tables(output_dir): for table_name in output_tables_list: + if not isinstance(table_name, str): + table_decode_cols = table_name.get("decode_columns", {}) + table_name = table_name["tablename"] + else: + table_decode_cols = {} + if table_name == "checkpoints": df = pipeline.get_checkpoints() else: @@ -304,6 +327,43 @@ def write_tables(output_dir): ) df = df.sort_index() + if config.setting("recode_pipeline_columns", True): + for colname, decode_instruction in table_decode_cols.items(): + if "|" in decode_instruction: + decode_filter, decode_instruction = decode_instruction.split("|") + decode_filter = decode_filter.strip() + decode_instruction = decode_instruction.strip() + else: + decode_filter = None + if "." not in decode_instruction: + lookup_col = decode_instruction + source_table = table_name + parent_table = df + else: + source_table, lookup_col = decode_instruction.split(".") + parent_table = inject.get_table(source_table) + try: + map_col = parent_table[f"_original_{lookup_col}"] + except KeyError: + map_col = parent_table[lookup_col] + map_col = np.asarray(map_col) + map_func = map_col.__getitem__ + if decode_filter: + if decode_filter == "nonnegative": + + def map_func(x): + return x if x < 0 else map_col[x] + + else: + raise ValueError(f"unknown decode_filter {decode_filter}") + if colname in df.columns: + df[colname] = df[colname].astype(int).map(map_func) + elif colname == df.index.name: + df.index = df.index.astype(int).map(map_func) + # drop _original_x from table if it is duplicative + if source_table == table_name and f"_original_{lookup_col}" in df: + df = df.drop(columns=[f"_original_{lookup_col}"]) + if h5_store: file_path = config.output_file_path("%soutput_tables.h5" % prefix) df.to_hdf(file_path, key=table_name, mode="a", format="fixed") diff --git a/activitysim/core/test/test_logit.py b/activitysim/core/test/test_logit.py index 225fdbb3c..8253149e1 100644 --- a/activitysim/core/test/test_logit.py +++ b/activitysim/core/test/test_logit.py @@ -116,14 +116,20 @@ def test_make_choices_only_one(): ) choices, rands = logit.make_choices(probs) - pdt.assert_series_equal(choices, pd.Series([0, 1], index=["x", "y"])) + pdt.assert_series_equal( + choices, pd.Series([0, 1], index=["x", "y"]), check_dtype=False + ) def test_make_choices_real_probs(utilities): probs = logit.utils_to_probs(utilities, trace_label=None) choices, rands = logit.make_choices(probs) - pdt.assert_series_equal(choices, pd.Series([1, 2], index=[0, 1])) + pdt.assert_series_equal( + choices, + pd.Series([1, 2], index=[0, 1]), + check_dtype=False, + ) @pytest.fixture(scope="module") diff --git a/activitysim/core/test/test_simulate.py b/activitysim/core/test/test_simulate.py index 9eaa66534..ab100f6a9 100644 --- a/activitysim/core/test/test_simulate.py +++ b/activitysim/core/test/test_simulate.py @@ -75,7 +75,7 @@ def test_simple_simulate(data, spec): choices = simulate.simple_simulate(choosers=data, spec=spec, nest_spec=None) expected = pd.Series([1, 1, 1], index=data.index) - pdt.assert_series_equal(choices, expected) + pdt.assert_series_equal(choices, expected, check_dtype=False) def test_simple_simulate_chunked(data, spec): @@ -86,4 +86,4 @@ def test_simple_simulate_chunked(data, spec): choosers=data, spec=spec, nest_spec=None, chunk_size=2 ) expected = pd.Series([1, 1, 1], index=data.index) - pdt.assert_series_equal(choices, expected) + pdt.assert_series_equal(choices, expected, check_dtype=False) diff --git a/activitysim/core/test/test_timetable.py b/activitysim/core/test/test_timetable.py index 17b871dcf..e8ac8e555 100644 --- a/activitysim/core/test/test_timetable.py +++ b/activitysim/core/test/test_timetable.py @@ -7,6 +7,7 @@ import pandas as pd import pandas.testing as pdt import pytest +from numpy.testing import assert_array_equal from .. import chunk from .. import timetable as tt @@ -150,9 +151,8 @@ def test_basic(persons, tdd_alts): 17, # START + MIDDLE + END collides with same ] ) - pdt.assert_series_equal( - timetable.tour_available(person_ids, tdds), - pd.Series([True, True, False, False], index=person_ids.index), + assert_array_equal( + timetable.tour_available(person_ids, tdds), [True, True, False, False] ) # assigning overlapping trip END,START should convert END to START_END @@ -196,13 +196,17 @@ def test_basic(persons, tdd_alts): person_ids = pd.Series([0, 1, 2, 3, 4, 5]) periods = pd.Series([5, 5, 5, 5, 5, 5]) adjacent_run_length = timetable.adjacent_window_after(person_ids, periods) - pdt.assert_series_equal(adjacent_run_length, pd.Series([5, 5, 0, 5, 5, 3])) + pdt.assert_series_equal( + adjacent_run_length, pd.Series([5, 5, 0, 5, 5, 3]), check_dtype=False + ) # - adjacent_window_before person_ids = pd.Series([0, 1, 2, 3, 4, 5]) periods = pd.Series([10, 10, 10, 10, 10, 10]) adjacent_run_length = timetable.adjacent_window_before(person_ids, periods) - pdt.assert_series_equal(adjacent_run_length, pd.Series([5, 5, 1, 5, 5, 0])) + pdt.assert_series_equal( + adjacent_run_length, pd.Series([5, 5, 1, 5, 5, 0]), check_dtype=False + ) # - remaining_periods_available person_ids = pd.Series([0, 1, 2, 3]) diff --git a/activitysim/core/timetable.py b/activitysim/core/timetable.py index fe2aeee3c..a2106bbe2 100644 --- a/activitysim/core/timetable.py +++ b/activitysim/core/timetable.py @@ -4,6 +4,7 @@ import logging from builtins import object, range +import numba as nb import numpy as np import pandas as pd @@ -37,7 +38,7 @@ ] COLLISION_LIST = [a + (b << I_BIT_SHIFT) for a, b in COLLISIONS] - +COLLISION_ARRAY = np.asarray(COLLISION_LIST) # str versions of time windows period states C_EMPTY = str(I_EMPTY) @@ -47,6 +48,161 @@ C_START_END = str(I_START_END) +@nb.njit +def _fast_tour_available( + tdds, + tdd_footprints, + window_row_ids, + window_row_ix__mapper, + self_windows, +): + """ + + Parameters + ---------- + tdds : array-like, shape (k) + tdd_footprints : array-like, shape (c, t) + window_row_ids : array-like, shape (k) + window_row_ix__mapper : FastMapping._mapper + self_windows : array-like + + Returns + ------- + array of bool, shape (k) + """ + out = np.ones_like(tdds, dtype=np.bool_) + for k in range(tdds.shape[0]): + tour_footprints = tdd_footprints[tdds[k]] # -> shape (t) + row_ix = window_row_ix__mapper[window_row_ids[k]] + windows = self_windows[row_ix] + x = tour_footprints + (windows << I_BIT_SHIFT) + stop = False + for j in range(COLLISION_ARRAY.size): + for i in range(x.size): + if x[i] == COLLISION_ARRAY[j]: + out[k] = False + stop = True + break + if stop: + break + return out + + +@nb.njit +def _available_run_length( + available, + before, + periods, + time_ix_mapper, +): + num_rows = available.shape[0] + num_cols = available.shape[1] + _time_col_ix_map = np.arange(num_cols) + available_run_length = np.zeros(num_rows, dtype=np.int32) + for row in range(num_rows): + _time_col_ix = time_ix_mapper[periods[row]] # scalar + if before: + mask = (_time_col_ix_map < _time_col_ix) * 1 + # index of first unavailable window after time + first_unavailable = np.where( + (1 - available[row]) * mask, _time_col_ix_map, 0 + ).max() + available_run_length[row] = _time_col_ix - first_unavailable - 1 + else: + # ones after specified time, zeroes before + mask = (_time_col_ix_map > _time_col_ix) * 1 + # index of first unavailable window after time + first_unavailable = np.where( + (1 - available[row]) * mask, _time_col_ix_map, num_cols + ).min() + available_run_length[row] = first_unavailable - _time_col_ix - 1 + return available_run_length + + +@nb.njit +def _available_run_length_1( + windows, + window_row_mapper, + time_ix_mapper, + before, + window_row_id, + period, +): + num_cols = windows.shape[1] + _time_col_ix_map = np.arange(num_cols) + available = np.ones(num_cols, dtype=np.int8) + available[0] = 0 + available[-1] = 0 + + window_row = windows[window_row_mapper[window_row_id], :] + for j in range(1, num_cols - 1): + if window_row[j] != I_MIDDLE: + available[j] = 1 + else: + available[j] = 0 + + _time_col_ix = time_ix_mapper[period] # scalar + if before: + mask = (_time_col_ix_map < _time_col_ix) * 1 + # index of first unavailable window after time + first_unavailable = np.where((1 - available) * mask, _time_col_ix_map, 0).max() + available_run_length = _time_col_ix - first_unavailable - 1 + else: + # ones after specified time, zeroes before + mask = (_time_col_ix_map > _time_col_ix) * 1 + # index of first unavailable window after time + first_unavailable = np.where( + (1 - available) * mask, _time_col_ix_map, num_cols + ).min() + available_run_length = first_unavailable - _time_col_ix - 1 + return available_run_length + + +@nb.njit +def _available_run_length_2( + windows, + window_row_mapper, + time_ix_mapper, + before, + window_row_id_values, + periods, +): + num_rows = window_row_id_values.shape[0] + num_cols = windows.shape[1] + _time_col_ix_map = np.arange(num_cols) + available_run_length = np.zeros(num_rows, dtype=np.int32) + available = np.ones(num_cols, dtype=np.int8) + available[0] = 0 + available[-1] = 0 + for row in range(num_rows): + + row_ix = window_row_mapper[window_row_id_values[row]] + window_row = windows[row_ix] + for j in range(1, num_cols - 1): + if window_row[j] != I_MIDDLE: + available[j] = 1 + else: + available[j] = 0 + + _time_col_ix = time_ix_mapper[periods[row]] # scalar + if before: + mask = (_time_col_ix_map < _time_col_ix) * 1 + # index of first unavailable window after time + first_unavailable = np.where( + (1 - available) * mask, _time_col_ix_map, 0 + ).max() + available_run_length[row] = _time_col_ix - first_unavailable - 1 + else: + # ones after specified time, zeroes before + mask = (_time_col_ix_map > _time_col_ix) * 1 + # index of first unavailable window after time + first_unavailable = np.where( + (1 - available) * mask, _time_col_ix_map, num_cols + ).min() + available_run_length[row] = first_unavailable - _time_col_ix - 1 + return available_run_length + + def tour_map(persons, tours, tdd_alts, persons_id_col="person_id"): sigil = { @@ -196,13 +352,15 @@ def __init__(self, windows_df, tdd_alts_df, table_name=None): self.checkpoint_df = None # series to map window row index value to window row's ordinal index - self.window_row_ix = pd.Series( - list(range(len(windows_df.index))), index=windows_df.index + from ..core.fast_mapping import FastMapping + + self.window_row_ix = FastMapping( + pd.Series(list(range(len(windows_df.index))), index=windows_df.index) ) int_time_periods = [int(c) for c in windows_df.columns.values] - self.time_ix = pd.Series( - list(range(len(windows_df.columns))), index=int_time_periods + self.time_ix = FastMapping( + pd.Series(list(range(len(windows_df.columns))), index=int_time_periods) ) # - pre-compute window state footprints for every tdd_alt @@ -246,12 +404,19 @@ def rollback(self): self.checkpoint_df = None self.transaction_loggers = None + def export_for_numba(self): + return dict( + tt_row_mapper=self.window_row_ix._mapper, + tt_col_mapper=self.time_ix._mapper, + tt_windows=self.windows, + ) + def slice_windows_by_row_id(self, window_row_ids): """ return windows array slice containing rows for specified window_row_ids (in window_row_ids order) """ - row_ixs = window_row_ids.map(self.window_row_ix).values + row_ixs = self.window_row_ix.apply_to(window_row_ids.values) windows = self.windows[row_ixs] return windows @@ -259,10 +424,10 @@ def slice_windows_by_row_id(self, window_row_ids): def slice_windows_by_row_id_and_period(self, window_row_ids, periods): # row ixs of tour_df group rows in windows - row_ixs = window_row_ids.map(self.window_row_ix).values + row_ixs = self.window_row_ix.apply_to(window_row_ids) # col ixs of periods in windows - time_col_ixs = periods.map(self.time_ix).values + time_col_ixs = self.time_ix.apply_to(periods) windows = self.windows[row_ixs, time_col_ixs] @@ -317,21 +482,30 @@ def tour_available(self, window_row_ids, tdds): available : pandas Series of bool with same index as window_row_ids.index (presumably tour_id, but we don't care) """ - - assert len(window_row_ids) == len(tdds) - - # numpy array with one tdd_footprints_df row for tdds - tour_footprints = self.tdd_footprints[tdds.values.astype(int)] - - # numpy array with one windows row for each person - windows = self.slice_windows_by_row_id(window_row_ids) - - # t0 = tracing.print_elapsed_time("slice_windows_by_row_id", t0, debug=True) - - x = tour_footprints + (windows << I_BIT_SHIFT) - - available = ~np.isin(x, COLLISION_LIST).any(axis=1) - available = pd.Series(available, index=window_row_ids.index) + if isinstance(tdds, pd.Series): + tdds = tdds.astype(np.int32).to_numpy() + else: + tdds = tdds.astype(np.int32) + if isinstance(window_row_ids, pd.Series): + window_row_ids = window_row_ids.astype(np.int64).to_numpy() + else: + window_row_ids = window_row_ids.astype(np.int64) + + try: + available = _fast_tour_available( + tdds, + self.tdd_footprints, + window_row_ids, + self.window_row_ix._mapper, + self.windows, + ) + except KeyError: + # key error messages here may not have enough detail to be useful, + # so we'll also log the row ids and mapper to help diagnose errors. + logger.error("KeyError in _fast_tour_available") + logger.error(f"{window_row_ids=}") + logger.error(f"{self.window_row_ix._mapper=}") + raise return available @@ -359,7 +533,7 @@ def assign(self, window_row_ids, tdds): tour_footprints = self.tdd_footprints[tdds.values.astype(int)] # row idxs of windows to assign to - row_ixs = window_row_ids.map(self.window_row_ix).values + row_ixs = self.window_row_ix.apply_to(window_row_ids) self.windows[row_ixs] = np.bitwise_or(self.windows[row_ixs], tour_footprints) @@ -396,7 +570,7 @@ def assign_subtour_mask(self, window_row_ids, tdds): tour_footprints = self.tdd_footprints[tdds.values.astype(int)] # row idxs of windows to assign to - row_ixs = window_row_ids.map(self.window_row_ix).values + row_ixs = self.window_row_ix.apply_to(window_row_ids) self.windows[row_ixs] = (tour_footprints == 0) * I_MIDDLE @@ -424,7 +598,7 @@ def assign_footprints(self, window_row_ids, footprints): assert len(window_row_ids.values) == len(np.unique(window_row_ids.values)) # row idxs of windows to assign to - row_ixs = window_row_ids.map(self.window_row_ix).values + row_ixs = self.window_row_ix.apply_to(window_row_ids) self.windows[row_ixs] = np.bitwise_or(self.windows[row_ixs], footprints) @@ -459,47 +633,15 @@ def adjacent_window_run_length(self, window_row_ids, periods, before): trace_label = "tt.adjacent_window_run_length" with chunk.chunk_log(trace_label): - - time_col_ixs = periods.map(self.time_ix).values - chunk.log_df(trace_label, "time_col_ixs", time_col_ixs) - - # sliced windows with 1s where windows state is I_MIDDLE and 0s elsewhere - available = (self.slice_windows_by_row_id(window_row_ids) != I_MIDDLE) * 1 - chunk.log_df(trace_label, "available", available) - - # padding periods not available - available[:, 0] = 0 - available[:, -1] = 0 - - # column idxs of windows - num_rows, num_cols = available.shape - time_col_ix_map = np.tile(np.arange(0, num_cols), num_rows).reshape( - num_rows, num_cols + available_run_length = _available_run_length_2( + self.windows, + self.window_row_ix._mapper, + self.time_ix._mapper, + before, + window_row_ids.values, + periods.to_numpy(), ) - # 0 1 2 3 4 5... - # 0 1 2 3 4 5... - # 0 1 2 3 4 5... - chunk.log_df(trace_label, "time_col_ix_map", time_col_ix_map) - - if before: - # ones after specified time, zeroes before - mask = (time_col_ix_map < time_col_ixs.reshape(num_rows, 1)) * 1 - # index of first unavailable window after time - first_unavailable = np.where( - (1 - available) * mask, time_col_ix_map, 0 - ).max(axis=1) - available_run_length = time_col_ixs - first_unavailable - 1 - else: - # ones after specified time, zeroes before - mask = (time_col_ix_map > time_col_ixs.reshape(num_rows, 1)) * 1 - # index of first unavailable window after time - first_unavailable = np.where( - (1 - available) * mask, time_col_ix_map, num_cols - ).min(axis=1) - available_run_length = first_unavailable - time_col_ixs - 1 - - chunk.log_df(trace_label, "mask", mask) - chunk.log_df(trace_label, "first_unavailable", first_unavailable) + chunk.log_df(trace_label, "available_run_length", available_run_length) return pd.Series(available_run_length, index=window_row_ids.index) @@ -645,21 +787,8 @@ def remaining_periods_available(self, window_row_ids, starts, ends): available : pandas Series int number periods available indexed by window_row_ids.index """ - - assert len(window_row_ids) == len(starts) - assert len(window_row_ids) == len(ends) - - available = (self.slice_windows_by_row_id(window_row_ids) != I_MIDDLE).sum( - axis=1 - ) - - # don't count time window padding at both ends of day - available -= 2 - - available -= np.clip((ends - starts - 1), a_min=0, a_max=None) - available = pd.Series(available, index=window_row_ids.index) - - return available + result = tt_remaining_periods_available(self, window_row_ids, starts, ends) + return result def max_time_block_available(self, window_row_ids): """ @@ -673,37 +802,393 @@ def max_time_block_available(self, window_row_ids): ------- pandas.Series with same index as window_row_ids, and integer max_run_length of """ + result = pd.Series( + _max_time_blocks_available_1( + self.window_row_ix._mapper, self.windows, np.asarray(window_row_ids) + ), + index=window_row_ids.index, + ) + return result + + +@nb.njit +def _max_time_block_available_1(windows_row): + """ + + Parameters + ---------- + tt_windows : array[int8], 1 dimension + Array of currently scheduled stuff + tt_row_mapper : numba.typed.Dict[int,int] + Maps value in `window_row_id` to row position in `tt_windows`. + window_row_id : int - # FIXME consider dedupe/redupe window_row_ids for performance - # as this may be called for alts with lots of duplicates (e.g. trip scheduling time pressure calculations) + Returns + ------- + + """ + max_block_avail = 0 + current_block = 0 + for i in range(1, windows_row.size - 1): + if windows_row[i] != I_MIDDLE: + current_block += 1 + else: + current_block = 0 + if current_block > max_block_avail: + max_block_avail = current_block + return max_block_avail + + +@nb.njit +def _max_time_blocks_available_1(tt_window_row_ix, tt_windows, window_row_ids): + max_blocks = np.zeros(window_row_ids.size, dtype=np.uint8) + # FIXME consider dedupe/redupe window_row_ids for performance + # as this may be called for alts with lots of duplicates (e.g. trip scheduling time pressure calculations) + for j in range(window_row_ids.size): + max_blocks[j] = _max_time_block_available_1( + tt_windows[tt_window_row_ix[window_row_ids[j]]] + ) + return max_blocks - # sliced windows with 1s where windows state is I_MIDDLE and 0s elsewhere - available = (self.slice_windows_by_row_id(window_row_ids) != I_MIDDLE) * 1 - # np.set_printoptions(edgeitems=25, linewidth = 180) - # print(f"self.slice_windows_by_row_id(window_row_ids)\n{self.slice_windows_by_row_id(window_row_ids)}") +@nb.njit +def sharrow_tt_max_time_block_available(tt_windows, tt_row_mapper, window_row_id): + return _max_time_block_available_1(tt_windows[tt_row_mapper[window_row_id]]) - # padding periods not available - available[:, 0] = 0 - available[:, -1] = 0 - diffs = np.diff( - available - ) # 1 at start of run of availables, -1 at end, 0 everywhere else - start_row_index, starts = np.asarray( - diffs > 0 - ).nonzero() # indices of run starts - end_row_index, ends = np.asarray(diffs < 0).nonzero() # indices of run ends - assert ( - start_row_index == end_row_index - ).all() # because bounded, expect same number of starts and ends +def tt_slice_windows_by_row_id(tt_window_row_ix, tt_windows, window_row_ids): + """ + return windows array slice containing rows for specified window_row_ids + (in window_row_ids order) + """ + row_ixs = tt_window_row_ix.apply_to(window_row_ids.values) + windows = tt_windows[row_ixs] + + return windows + + +@nb.njit +def _count_windows_that_are_not_middles( + window_row_id, # int + windows_mapper, # nb.Dict[int,int] + windows, # ndarray +): + row_ix = windows_mapper[window_row_id] + x = 0 + for i in range(windows.shape[1]): + if windows[row_ix, i] != I_MIDDLE: + x += 1 + return x + + +@nb.njit +def sharrow_tt_remaining_periods_available( + tt_windows, # ndarray + tt_row_mapper, # nb.Dict[int,int] + window_row_id, # int + starter, # int + ender, # int +): + """ + Number of periods remaining available after hypothetical scheduling - # run_lengths like availability but with run length at start of every run and zeros elsewhere - # (row_indices of starts and ends are aligned, so end - start is run_length) - run_lengths = np.zeros_like(available) - run_lengths[start_row_index, starts] = ends - starts + This is what's left after a new tour or trip from `starts` to + `ends` is hypothetically scheduled. - # we just want to know the the longest one for each window_row_id - max_run_lengths = run_lengths.max(axis=1) + Implements MTC TM1 @@remainingPeriodsAvailableAlt - return pd.Series(max_run_lengths, index=window_row_ids.index) + The start and end periods will always be available after + scheduling, so ignore them. The periods between start and end + must be currently unscheduled, so assume they will become + unavailable after scheduling this window. + + Parameters + ---------- + tt_windows : array[int8], 2 dimensions + Array of currently scheduled stuff + tt_row_mapper : numba.typed.Dict[int,int] + Maps value in the `window_row_ids` to row positions in `windows`. + window_row_id : int + An identifier for which window row to use. + starter : int + The starting period of the new tour that will block windows. + ender : int + The ending period of the new tour that will block windows. + + Returns + ------- + int + """ + available = _count_windows_that_are_not_middles( + window_row_id, + tt_row_mapper, + tt_windows, + ) + # don't count time window padding at both ends of day + available -= 2 + this_block = ender - starter - 1 + if this_block > 0: + available -= this_block + return available + + +@nb.njit +def _remaining_periods_available( + windows, # ndarray + windows_row_mapper, + window_row_ids, # ndarray[int] + starts, # ndarray[int] + ends, # ndarray[int] +): + """ + Number of periods remaining available after hypothetical scheduling + + This is what's left after a new tour or trip from `starts` to + `ends` is hypothetically scheduled. + + Implements MTC TM1 @@remainingPeriodsAvailableAlt + + The start and end periods will always be available after + scheduling, so ignore them. The periods between start and end + must be currently unscheduled, so assume they will become + unavailable after scheduling this window. + + Parameters + ---------- + windows_row_mapper : numba.typed.Dict[int,int] + Maps value in the `window_row_ids` to row positions in `windows`. + windows : array[int8], 2 dimensions + window_row_ids : array[int], 1-dimension + starts : array[int] + A 1-dimension array the same shape as `window_row_ids` which + gives the starting period of the new tour that will block windows. + ends : array[int], 1-dimension + A 1-dimension array the same shape as `window_row_ids` which + gives the ending period of the new tour that will block windows. + + Returns + ------- + array[int] + A 1-dimension array the same shape as `window_row_ids` which + gives the number of available periods remaining. + """ + result = np.empty(window_row_ids.shape, dtype=np.int64) + for i in range(window_row_ids.shape[0]): + result[i] = sharrow_tt_remaining_periods_available( + windows, + windows_row_mapper, + window_row_ids[i], + starts[i], + ends[i], + ) + return result + + +def tt_remaining_periods_available(tt, window_row_ids, starts, ends): + """ + Number of periods remaining available after hypothetical scheduling + + That is, what's left after something from starts to ends is + hypothetically scheduled + + Implements MTC TM1 @@remainingPeriodsAvailableAlt + + The start and end periods will always be available after + scheduling, so ignore them. The periods between start and end + must be currently unscheduled, so assume they will become + unavailable after scheduling this window. + + Parameters + ---------- + tt : TimeTable + window_row_ids : pandas.Series[int] + series of window_row_ids indexed by tour_id + starts : pandas.Series[int] + series of tdd_alt ids, index irrelevant (one per window_row_id) + ends : pandas.Series[int] + series of tdd_alt ids, index irrelevant (one per window_row_id) + + Returns + ------- + available : pandas Series int + number periods available indexed by window_row_ids.index + """ + + result = _remaining_periods_available( + tt.windows, + tt.window_row_ix._mapper, + window_row_ids.values, + starts.values, + ends.values, + ) + if isinstance(window_row_ids, pd.Series): + result = pd.Series(result, index=window_row_ids.index) + return result + + +@nb.njit +def _window_period_in_states( + windows, + windows_row_mapper, + windows_col_mapper, + window_row_id, + period, + state1, + state2, +): + """ + Return boolean indicating whether specified window periods are in list of states. + + Internal DRY method to implement previous_tour_ends and previous_tour_begins + + Parameters + ---------- + windows : array of int8, 2 dimensions + Array of currently scheduled stuff + windows_row_mapper : numba.typed.Dict[int,int] + Maps value in the `window_row_ids` to row positions in `windows`. + windows_col_mapper : numba.typed.Dict[int,int] + Array of currently scheduled stuff + window_row_id : int + An identifier for which window row to use. + period : int + An identifier for which window col to use. + state1, state2 : int + presumably (e.g. I_EMPTY, I_START...) + + Returns + ------- + bool + """ + w = windows[windows_row_mapper[window_row_id], windows_col_mapper[period]] + if w == state1 or w == state2: + return True + return False + + +@nb.njit +def _windows_periods_in_states( + windows, + windows_row_mapper, + windows_col_mapper, + window_row_ids, + periods, + state1, + state2, +): + result = np.empty(window_row_ids.shape, dtype=np.int8) + for i in range(window_row_ids.shape[0]): + result[i] = _window_period_in_states( + windows, + windows_row_mapper, + windows_col_mapper, + window_row_ids[i], + periods[i], + state1, + state2, + ) + return result + + +def tt_previous_tour_ends(tt, window_row_ids, periods): + return _windows_periods_in_states( + tt.windows, + tt.window_row_ix._mapper, + tt.time_ix._mapper, + window_row_ids.values, + periods.values, + I_END, + I_START_END, + ) + + +@nb.njit +def sharrow_tt_previous_tour_ends( + tt_windows, tt_row_mapper, tt_col_mapper, window_row_id, period +): + return _window_period_in_states( + tt_windows, + tt_row_mapper, + tt_col_mapper, + window_row_id, + period, + I_END, + I_START_END, + ) + + +def tt_previous_tour_begins(tt, window_row_ids, periods): + return _windows_periods_in_states( + tt.windows, + tt.window_row_ix._mapper, + tt.time_ix._mapper, + window_row_ids.values, + periods.values, + I_START, + I_START_END, + ) + + +@nb.njit +def sharrow_tt_previous_tour_begins( + tt_windows, tt_row_mapper, tt_col_mapper, window_row_id, period +): + return _window_period_in_states( + tt_windows, + tt_row_mapper, + tt_col_mapper, + window_row_id, + period, + I_START, + I_START_END, + ) + + +def tt_adjacent_window_before(tt, window_row_ids, periods): + return _available_run_length_2( + tt.windows, + tt.window_row_ix._mapper, + tt.time_ix._mapper, + True, + window_row_ids.values, + periods.to_numpy(), + ) + + +@nb.njit +def sharrow_tt_adjacent_window_before( + tt_windows, tt_row_mapper, tt_col_mapper, window_row_id, period +): + return _available_run_length_1( + tt_windows, + tt_row_mapper, + tt_col_mapper, + True, + window_row_id, + period, + ) + + +def tt_adjacent_window_after(tt, window_row_ids, periods): + return _available_run_length_2( + tt.windows, + tt.window_row_ix._mapper, + tt.time_ix._mapper, + False, + window_row_ids.values, + periods.to_numpy(), + ) + + +@nb.njit +def sharrow_tt_adjacent_window_after( + tt_windows, tt_row_mapper, tt_col_mapper, window_row_id, period +): + return _available_run_length_1( + tt_windows, + tt_row_mapper, + tt_col_mapper, + False, + window_row_id, + period, + ) diff --git a/activitysim/core/tracing.py b/activitysim/core/tracing.py index 22098065e..44707c0ae 100644 --- a/activitysim/core/tracing.py +++ b/activitysim/core/tracing.py @@ -26,6 +26,8 @@ logger = logging.getLogger(__name__) +timing_notes = set() + class ElapsedTimeFormatter(logging.Formatter): def format(self, record): @@ -64,7 +66,8 @@ def print_elapsed_time(msg=None, t0=None, debug=False): return t1 -def log_runtime(model_name, start_time=None, timing=None): +def log_runtime(model_name, start_time=None, timing=None, force=False): + global timing_notes assert (start_time or timing) and not (start_time and timing) @@ -74,7 +77,7 @@ def log_runtime(model_name, start_time=None, timing=None): process_name = multiprocessing.current_process().name - if config.setting("multiprocess", False): + if config.setting("multiprocess", False) and not force: # when benchmarking, log timing for each processes in its own log if config.setting("benchmarking", False): header = "component_name,duration" @@ -86,9 +89,12 @@ def log_runtime(model_name, start_time=None, timing=None): if not inject.get_injectable("locutor", False): return - header = "process_name,model_name,seconds,minutes" + header = "process_name,model_name,seconds,minutes,notes" + note = " ".join(timing_notes) with config.open_log_file("timing_log.csv", "a", header) as log_file: - print(f"{process_name},{model_name},{seconds},{minutes}", file=log_file) + print(f"{process_name},{model_name},{seconds},{minutes},{note}", file=log_file) + + timing_notes.clear() def delete_output_files(file_type, ignore=None, subdir=None): @@ -303,7 +309,8 @@ def register_traceable_table(table_name, df): return new_traced_ids = [] - if table_name == "households": + # if table_name == "households": + if table_name in ["households", "proto_households"]: if trace_hh_id not in df.index: logger.warning("trace_hh_id %s not in dataframe" % trace_hh_id) new_traced_ids = [] @@ -755,15 +762,32 @@ def interaction_trace_rows(interaction_df, choosers, sample_size=None): traceable_table_ids = inject.get_injectable("traceable_table_ids", {}) - if choosers.index.name == "person_id" and "persons" in traceable_table_ids: + # Determine whether actual tables or proto_ tables for disaggregate accessibilities + persons_table_name = set(traceable_table_ids).intersection( + ["persons", "proto_persons"] + ) + households_table_name = set(traceable_table_ids).intersection( + ["households", "proto_households"] + ) + + assert len(persons_table_name) == 1 and len(persons_table_name) == 1 + persons_table_name, households_table_name = ( + persons_table_name.pop(), + households_table_name.pop(), + ) + + if choosers.index.name == "person_id" and persons_table_name in traceable_table_ids: slicer_column_name = choosers.index.name targets = traceable_table_ids["persons"] + elif choosers.index.name == "household_id" and "households" in traceable_table_ids: + slicer_column_name = choosers.index.name + targets = traceable_table_ids["households"] elif "household_id" in choosers.columns and "households" in traceable_table_ids: slicer_column_name = "household_id" - targets = traceable_table_ids["households"] - elif "person_id" in choosers.columns and "persons" in traceable_table_ids: + targets = traceable_table_ids[households_table_name] + elif "person_id" in choosers.columns and persons_table_name in traceable_table_ids: slicer_column_name = "person_id" - targets = traceable_table_ids["persons"] + targets = traceable_table_ids[persons_table_name] else: print(choosers.columns) raise RuntimeError( @@ -833,7 +857,10 @@ def trace_interaction_eval_results(trace_results, trace_ids, label): slicer_column_name = trace_ids[0] - trace_results[slicer_column_name] = trace_ids[1] + try: + trace_results[slicer_column_name] = trace_ids[1] + except ValueError: + trace_results[slicer_column_name] = int(trace_ids[1]) targets = np.unique(trace_ids[1]) diff --git a/activitysim/core/util.py b/activitysim/core/util.py index 9a2f5c18d..217cdb837 100644 --- a/activitysim/core/util.py +++ b/activitysim/core/util.py @@ -1,6 +1,9 @@ # ActivitySim # See full license in LICENSE.txt. +import argparse +import collections +import itertools import logging import os from builtins import zip @@ -10,6 +13,7 @@ import cytoolz.curried import numpy as np import pandas as pd +import yaml logger = logging.getLogger(__name__) @@ -168,15 +172,12 @@ def reindex(series1, series2): """ - # turns out the merge is much faster than the .loc below - df = pd.merge( - series2.to_frame(name="left"), - series1.to_frame(name="right"), - left_on="left", - right_index=True, - how="left", - ) - return df.right + result = series1.reindex(series2) + try: + result.index = series2.index + except AttributeError: + pass + return result # return pd.Series(series1.loc[series2.values].values, index=series2.index) @@ -378,3 +379,92 @@ def df_from_dict(values, index=None): # del values[c] return df + + +# for disaggregate accessibilities + + +def ordered_load( + stream, Loader=yaml.SafeLoader, object_pairs_hook=collections.OrderedDict +): + class OrderedLoader(Loader): + pass + + def construct_mapping(loader, node): + loader.flatten_mapping(node) + return object_pairs_hook(loader.construct_pairs(node)) + + OrderedLoader.add_constructor( + yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, construct_mapping + ) + return yaml.load(stream, OrderedLoader) + + +def named_product(**d): + names = d.keys() + vals = d.values() + for res in itertools.product(*vals): + yield dict(zip(names, res)) + + +def recursive_replace(obj, search, replace): + if isinstance(obj, dict): + for k, v in obj.items(): + obj[k] = recursive_replace(v, search, replace) + if isinstance(obj, list): + obj = [replace if x == search else x for x in obj] + if search == obj: + obj = replace + return obj + + +def suffix_tables_in_settings( + model_settings, + suffix="proto_", + tables=["persons", "households", "tours", "persons_merged"], +): + for k in tables: + model_settings = recursive_replace(model_settings, k, suffix + k) + return model_settings + + +def suffix_expressions_df_str( + df, suffix="proto_", tables=["persons", "households", "tours", "persons_merged"] +): + for k in tables: + df["expression"] = df.expression.str.replace(k, suffix + k) + return df + + +def parse_suffix_args(args): + parser = argparse.ArgumentParser() + parser.add_argument("filename", help="file name") + parser.add_argument("-s", "--SUFFIX", "-s", help="suffix to replace root targets") + parser.add_argument( + "-r", "--ROOTS", nargs="*", help="roots be suffixed", default=[] + ) + return parser.parse_args(args.split()) + + +def concat_suffix_dict(args): + if isinstance(args, dict): + args = sum([["--" + k, v] for k, v in args.items()], []) + if isinstance(args, list): + args = list(flatten(args)) + return args + + +def flatten(lst): + for sublist in lst: + if isinstance(sublist, list): + for item in sublist: + yield item + else: + yield sublist + + +def nearest_node_index(node, nodes): + nodes = np.asarray(nodes) + deltas = nodes - node + dist_2 = np.einsum("ij,ij->i", deltas, deltas) + return np.argmin(dist_2) diff --git a/activitysim/estimation/larch/general.py b/activitysim/estimation/larch/general.py index 1844d5964..4e4fc3adf 100644 --- a/activitysim/estimation/larch/general.py +++ b/activitysim/estimation/larch/general.py @@ -1,18 +1,15 @@ -import itertools import logging import os -import re from pathlib import Path from typing import Mapping import numpy as np import pandas as pd -import yaml -from larch import DataFrames, Model, P, X +from larch import DataFrames, Model, P, X # noqa: F401 from larch.log import logger_name from larch.model.abstract_model import AbstractChoiceModel from larch.model.tree import NestingTree -from larch.util import Dict +from larch.util import Dict # noqa: F401 _logger = logging.getLogger(logger_name) @@ -490,13 +487,24 @@ def clean_values( return values -def update_coefficients(model, data, result_dir=Path("."), output_file=None): +def update_coefficients( + model, data, result_dir=Path("."), output_file=None, relabel_coef=None +): if isinstance(data, pd.DataFrame): coefficients = data.copy() else: coefficients = data.coefficients.copy() - est_names = [j for j in coefficients.index if j in model.pf.index] - coefficients.loc[est_names, "value"] = model.pf.loc[est_names, "value"] + if relabel_coef is not None and len(relabel_coef): + for j in coefficients.index: + if j in model.pf.index: + coefficients.loc[j, "value"] = model.pf.loc[j, "value"] + else: + j_ = relabel_coef.get(j, None) + if j_ is not None and j_ in model.pf.index: + coefficients.loc[j, "value"] = model.pf.loc[j_, "value"] + else: + est_names = [j for j in coefficients.index if j in model.pf.index] + coefficients.loc[est_names, "value"] = model.pf.loc[est_names, "value"] if output_file is not None: os.makedirs(result_dir, exist_ok=True) coefficients.reset_index().to_csv( diff --git a/activitysim/estimation/larch/nonmand_tour_freq.py b/activitysim/estimation/larch/nonmand_tour_freq.py index 22f026d05..9dfdac73a 100644 --- a/activitysim/estimation/larch/nonmand_tour_freq.py +++ b/activitysim/estimation/larch/nonmand_tour_freq.py @@ -1,14 +1,10 @@ -import itertools import logging import os -import re from pathlib import Path -from typing import Mapping -import numpy as np import pandas as pd import yaml -from larch import DataFrames, Model, P, X +from larch import DataFrames, Model from larch.log import logger_name from larch.util import Dict @@ -125,7 +121,23 @@ def unavail(model, x_ca): def nonmand_tour_freq_model( edb_directory="output/estimation_data_bundle/{name}/", return_data=False, + condense_parameters=False, ): + """ + Prepare nonmandatory tour frequency models for estimation. + + Parameters + ---------- + edb_directory : str + Location of estimation data bundle for these models. + return_data : bool, default False + Whether to return the data used in preparing this function. + If returned, data is a dict in the second return value. + condense_parameters : bool, default False + Apply a transformation whereby all parameters in each model that + have the same initial value are converted to have the same name + (and thus to be the same parameter, used in various places). + """ data = interaction_simulate_data( name="non_mandatory_tour_frequency", edb_directory=edb_directory, @@ -133,9 +145,10 @@ def nonmand_tour_freq_model( settings = data.settings segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]] - data.relabel_coef = link_same_value_coefficients( - segment_names, data.coefficients, data.spec - ) + if condense_parameters: + data.relabel_coef = link_same_value_coefficients( + segment_names, data.coefficients, data.spec + ) spec = data.spec coefficients = data.coefficients chooser_data = data.chooser_data diff --git a/activitysim/estimation/test/test_larch_estimation.py b/activitysim/estimation/test/test_larch_estimation.py index b90a0ee70..b148009f3 100644 --- a/activitysim/estimation/test/test_larch_estimation.py +++ b/activitysim/estimation/test/test_larch_estimation.py @@ -279,12 +279,24 @@ def test_tour_and_subtour_mode_choice(est_data, num_regression, dataframe_regres def test_nonmand_tour_freq(est_data, num_regression, dataframe_regression): from activitysim.estimation.larch.nonmand_tour_freq import nonmand_tour_freq_model - m = nonmand_tour_freq_model() + m = nonmand_tour_freq_model(condense_parameters=True) loglike_prior = {} + expected_n_params = { + "PTYPE_FULL": 72, + "PTYPE_PART": 51, + "PTYPE_UNIVERSITY": 70, + "PTYPE_NONWORK": 77, + "PTYPE_RETIRED": 53, + "PTYPE_DRIVING": 43, + "PTYPE_SCHOOL": 34, + "PTYPE_PRESCHOOL": 25, + } for segment_name in m: m[segment_name].load_data() m[segment_name].doctor(repair_ch_av="-") loglike_prior[segment_name] = m[segment_name].loglike() + assert len(m[segment_name].pf) == expected_n_params[segment_name] + assert len(m[segment_name].utility_ca) == 210 r = {} for segment_name in m: r[segment_name] = m[segment_name].maximize_loglike( @@ -297,3 +309,14 @@ def test_nonmand_tour_freq(est_data, num_regression, dataframe_regression): basename="test_nonmand_tour_freq_loglike", ) _regression_check(dataframe_regression, pd.concat([x.pf for x in m.values()])) + + +def test_nonmand_tour_freq_not_condensed( + est_data, num_regression, dataframe_regression +): + from activitysim.estimation.larch.nonmand_tour_freq import nonmand_tour_freq_model + + m = nonmand_tour_freq_model(condense_parameters=False) + for segment_name in m: + assert len(m[segment_name].pf) == 210 + assert len(m[segment_name].utility_ca) == 210 diff --git a/activitysim/estimation/test/test_larch_estimation/test_joint_tour_scheduling_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_joint_tour_scheduling_loglike.csv index cf0a9aeef..a8e60cc38 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_joint_tour_scheduling_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_joint_tour_scheduling_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-1295.265265023259,-173.82837615906058 +0,-1295.3015770898169,-175.64109886722656 diff --git a/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_school_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_school_loglike.csv index e113b20b4..e76a45b3f 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_school_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_school_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-2431.4832345854475,-2331.7641653716646 +0,-2475.6453326598094,-2344.7409183750583 diff --git a/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_work_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_work_loglike.csv index 7cf505c93..6970d3e66 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_work_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_work_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-9139.1009551683674,-8399.8219678385103 +0,-9138.134506637969,-8404.2487574734187 diff --git a/activitysim/estimation/test/test_larch_estimation/test_non_mandatory_tour_scheduling_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_non_mandatory_tour_scheduling_loglike.csv index 3a5c071b3..3282b45e7 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_non_mandatory_tour_scheduling_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_non_mandatory_tour_scheduling_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-12768.066280736257,-9499.3997160849394 +0,-12772.911183835131,-9506.0541725800285 diff --git a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_joint_tour_scheduling_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_joint_tour_scheduling_SLSQP_.csv index b11e454f6..091e76401 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_joint_tour_scheduling_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_joint_tour_scheduling_SLSQP_.csv @@ -1,60 +1,60 @@ ,value,initvalue,nullvalue,minimum,maximum,best -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-13.52657987575712,-0.025700000000000001,0,-25,25,-13.52657987575712 -coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,-14.169340715131355,-0.02734,0,-25,25,-14.169340715131355 -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,-13.495922438017288,0.0084419999999999999,0,-25,25,-13.495922438017288 -coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,-7.7162465780101046,-0.059299999999999999,0,-25,25,-7.7162465780101046 -coef_adult_with_children_in_hh_arrive_19_21,-0.0025406451956376816,0.33600000000000002,0,-25,25,-0.0025406451956376816 -coef_arrival_constants_am_peak,4.6476727087794183,-8.7288800000000002,0,-25,25,4.6476727087794183 -coef_arrival_constants_early,24.793142364658262,-8.7288800000000002,0,-25,25,24.793142364658262 -coef_arrival_constants_evening,-4.2917831342034436,-2.7489400000000002,0,-25,25,-4.2917831342034436 -coef_arrival_constants_late,-9.6170378774836092,-4.2425300000000004,0,-25,25,-9.6170378774836092 +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.028774249414067657,-0.025700000000000001,0,-25,25,-0.028774249414067657 +coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,0.91901966813332936,-0.02734,0,-25,25,0.91901966813332936 +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.03464166423412067,0.0084419999999999999,0,-25,25,0.03464166423412067 +coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,-18.311237242705495,-0.059299999999999999,0,-25,25,-18.311237242705495 +coef_adult_with_children_in_hh_arrive_19_21,0.04436348324103874,0.33600000000000002,0,-25,25,0.04436348324103874 +coef_arrival_constants_am_peak,4.7614100686980576,-8.7288800000000002,0,-25,25,4.7614100686980576 +coef_arrival_constants_early,24.924968513710187,-8.7288800000000002,0,-25,25,24.924968513710187 +coef_arrival_constants_evening,-4.3200714359409025,-2.7489400000000002,0,-25,25,-4.3200714359409025 +coef_arrival_constants_late,-9.6327188982749732,-4.2425300000000004,0,-25,25,-9.6327188982749732 coef_arrival_constants_midday_1,0,0,0,0,0,0 -coef_arrival_constants_midday_2,2.6925192121364452,1.40804,0,-25,25,2.6925192121364452 -coef_arrival_constants_pm_peak_1,1.8492366917320286,1.0203599999999999,0,-25,25,1.8492366917320286 -coef_arrival_constants_pm_peak_2,1.5184104637159339,1.06863,0,-25,25,1.5184104637159339 +coef_arrival_constants_midday_2,2.6541662776744461,1.40804,0,-25,25,2.6541662776744461 +coef_arrival_constants_pm_peak_1,1.8388653479429438,1.0203599999999999,0,-25,25,1.8388653479429438 +coef_arrival_constants_pm_peak_2,1.4625561851708289,1.06863,0,-25,25,1.4625561851708289 coef_arrival_constants_pm_peak_3,0,0,0,0,0,0 -coef_arrival_constants_pm_peak_4,-1.1827680035278383,-0.59626000000000001,0,-25,25,-1.1827680035278383 +coef_arrival_constants_pm_peak_4,-1.2333276504506303,-0.59626000000000001,0,-25,25,-1.2333276504506303 coef_departure_constants_am_peak_1,-25,-11.595050000000001,0,-25,25,-25 -coef_departure_constants_am_peak_2,-4.2368491295195421,-9.0051900000000007,0,-25,25,-4.2368491295195421 -coef_departure_constants_am_peak_3,0.80622324441230619,-2.7331500000000002,0,-25,25,0.80622324441230619 -coef_departure_constants_am_peak_4,1.4081419130496329,0.26654,0,-25,25,1.4081419130496329 -coef_departure_constants_early,-24.418086041106228,-14.477080000000001,0,-25,25,-24.418086041106228 -coef_departure_constants_evening,-14.557517186260492,-18.987369999999999,0,-25,25,-14.557517186260492 -coef_departure_constants_late,-20.421814010799007,-20.27807,0,-25,25,-20.421814010799007 +coef_departure_constants_am_peak_2,-4.2487039295223656,-9.0051900000000007,0,-25,25,-4.2487039295223656 +coef_departure_constants_am_peak_3,0.75112055157913948,-2.7331500000000002,0,-25,25,0.75112055157913948 +coef_departure_constants_am_peak_4,1.3921313310397618,0.26654,0,-25,25,1.3921313310397618 +coef_departure_constants_early,-24.394585604972157,-14.477080000000001,0,-25,25,-24.394585604972157 +coef_departure_constants_evening,-14.12448484303447,-18.987369999999999,0,-25,25,-14.12448484303447 +coef_departure_constants_late,-19.873640270611908,-20.27807,0,-25,25,-19.873640270611908 coef_departure_constants_midday_1,0,0,0,0,0,0 -coef_departure_constants_midday_2,-4.5012376516121435,-1.6026,0,-25,25,-4.5012376516121435 -coef_departure_constants_pm_peak,-12.638402978350928,-17.695979999999999,0,-25,25,-12.638402978350928 -coef_destination_in_cbd_duration_shift_effects,0.57232969025524438,0.1067,0,-25,25,0.57232969025524438 -coef_discretionary_tour_duration_lt_2_hours,-1.0224876940373548,-0.69740000000000002,0,-25,25,-1.0224876940373548 -coef_duration_constants_0_to_1_hours,-2.7504813190145132,-2.2282600000000001,0,-25,25,-2.7504813190145132 -coef_duration_constants_11_to_13_hours,-6.1627365477909208,-0.81518999999999997,0,-25,25,-6.1627365477909208 -coef_duration_constants_14_to_18_hours,-2.8007056063538349,-2.7384400000000002,0,-25,25,-2.8007056063538349 +coef_departure_constants_midday_2,-4.3735430875047783,-1.6026,0,-25,25,-4.3735430875047783 +coef_departure_constants_pm_peak,-12.231456579430871,-17.695979999999999,0,-25,25,-12.231456579430871 +coef_destination_in_cbd_duration_shift_effects,0.56926682397665584,0.1067,0,-25,25,0.56926682397665584 +coef_discretionary_tour_duration_lt_2_hours,-0.96979691863694628,-0.69740000000000002,0,-25,25,-0.96979691863694628 +coef_duration_constants_0_to_1_hours,-2.7866437326016174,-2.2282600000000001,0,-25,25,-2.7866437326016174 +coef_duration_constants_11_to_13_hours,-5.6834374392880065,-0.81518999999999997,0,-25,25,-5.6834374392880065 +coef_duration_constants_14_to_18_hours,-2.7347130621577005,-2.7384400000000002,0,-25,25,-2.7347130621577005 coef_duration_constants_2_to_3_hours,0,0,0,0,0,0 -coef_duration_constants_4_to_5_hours,0.30114528727921769,-0.56174000000000002,0,-25,25,0.30114528727921769 -coef_duration_constants_6_to_7_hours,0.14407175378681686,-0.65547,0,-25,25,0.14407175378681686 -coef_duration_constants_8_to_10_hours,-10.570858039965891,-0.74061999999999995,0,-25,25,-10.570858039965891 -coef_eat_out_tour_departure_shift_effects,-0.024648889447639735,0.075490000000000002,0,-25,25,-0.024648889447639735 -coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-1.3293508348128713,-0.2364,0,-25,25,-1.3293508348128713 -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.017752136350677013,0.0031949999999999999,0,-25,25,0.017752136350677013 -coef_maintenance_tour_depart_before_7,1.1545842619546469,-0.88260000000000005,0,-25,25,1.1545842619546469 -coef_maintenance_tour_departure_shift_effects,-0.15921016616264547,-0.1489,0,-25,25,-0.15921016616264547 -coef_maintenance_tour_duration_shift_effects,-0.14645897223903356,-0.083720000000000003,0,-25,25,-0.14645897223903356 -coef_number_of_joint_tours_departure_shift_effects,1.3561727548582434,0.052080000000000001,0,-25,25,1.3561727548582434 -coef_number_of_mandatory_tours_departure_shift_effects,0.076325869327416057,0.046730000000000001,0,-25,25,0.076325869327416057 +coef_duration_constants_4_to_5_hours,0.35927958946513583,-0.56174000000000002,0,-25,25,0.35927958946513583 +coef_duration_constants_6_to_7_hours,0.25829995673545386,-0.65547,0,-25,25,0.25829995673545386 +coef_duration_constants_8_to_10_hours,-10.443272918037978,-0.74061999999999995,0,-25,25,-10.443272918037978 +coef_eat_out_tour_departure_shift_effects,-0.026030105628492072,0.075490000000000002,0,-25,25,-0.026030105628492072 +coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-1.6735672025262123,-0.2364,0,-25,25,-1.6735672025262123 +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.016957888892283637,0.0031949999999999999,0,-25,25,0.016957888892283637 +coef_maintenance_tour_depart_before_7,1.1216498838609328,-0.88260000000000005,0,-25,25,1.1216498838609328 +coef_maintenance_tour_departure_shift_effects,-0.16045036076756827,-0.1489,0,-25,25,-0.16045036076756827 +coef_maintenance_tour_duration_shift_effects,-0.15774061753063057,-0.083720000000000003,0,-25,25,-0.15774061753063057 +coef_number_of_joint_tours_departure_shift_effects,1.3426208902212988,0.052080000000000001,0,-25,25,1.3426208902212988 +coef_number_of_mandatory_tours_departure_shift_effects,0.073116531424716663,0.046730000000000001,0,-25,25,0.073116531424716663 coef_school_child_age_16_plus_departure_shift_effects,0.072660000000000002,0.072660000000000002,0,-25,25,0.072660000000000002 coef_school_child_age_16_plus_duration_shift_effects,0.20949999999999999,0.20949999999999999,0,-25,25,0.20949999999999999 -coef_school_child_age_under_16_departure_shift_effects,0.23900726126553401,0.04657,0,-25,25,0.23900726126553401 -coef_school_child_age_under_16_duration_shift_effects,-0.56786310052985978,0.32719999999999999,0,-25,25,-0.56786310052985978 -coef_school_child_under_16_arrive_after_22,-6.1472373285382957,-1.1799999999999999,0,-25,25,-6.1472373285382957 -coef_shopping_tour_arrive_after_22,-7.2210408448157928,-0.60270000000000001,0,-25,25,-7.2210408448157928 -coef_shopping_tour_depart_before_8,1.4584880566051988,-1.0369999999999999,0,-25,25,1.4584880566051988 -coef_shopping_tour_departure_shift_effects,-0.045633495185504684,-0.060150000000000002,0,-25,25,-0.045633495185504684 -coef_shopping_tour_duration_lt_2_hours,0.18942741647184094,0.51680000000000004,0,-25,25,0.18942741647184094 -coef_shopping_tour_duration_shift_effects,-0.35970475378100253,-0.1208,0,-25,25,-0.35970475378100253 -coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,0.41197887481555961,-0.3992,0,-25,25,0.41197887481555961 -coef_some_previously_scheduled_tour_ends_in_this_departure_hour,0.28202909641105622,-0.45619999999999999,0,-25,25,0.28202909641105622 -coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,0.19126381823461158,-0.1731,0,-25,25,0.19126381823461158 +coef_school_child_age_under_16_departure_shift_effects,0.22437768613209164,0.04657,0,-25,25,0.22437768613209164 +coef_school_child_age_under_16_duration_shift_effects,-0.55690620785460743,0.32719999999999999,0,-25,25,-0.55690620785460743 +coef_school_child_under_16_arrive_after_22,-6.2179802555750401,-1.1799999999999999,0,-25,25,-6.2179802555750401 +coef_shopping_tour_arrive_after_22,-6.8841149173847738,-0.60270000000000001,0,-25,25,-6.8841149173847738 +coef_shopping_tour_depart_before_8,1.6329282274649815,-1.0369999999999999,0,-25,25,1.6329282274649815 +coef_shopping_tour_departure_shift_effects,-0.032901677188934476,-0.060150000000000002,0,-25,25,-0.032901677188934476 +coef_shopping_tour_duration_lt_2_hours,0.069480688387001235,0.51680000000000004,0,-25,25,0.069480688387001235 +coef_shopping_tour_duration_shift_effects,-0.41367749415560207,-0.1208,0,-25,25,-0.41367749415560207 +coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,0.46823426868427825,-0.3992,0,-25,25,0.46823426868427825 +coef_some_previously_scheduled_tour_ends_in_this_departure_hour,0.26003373330967577,-0.45619999999999999,0,-25,25,0.26003373330967577 +coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.015340339657372392,-0.1731,0,-25,25,-0.015340339657372392 coef_unavailable,-999,-999,0,-999,-999,-999 -coef_university_student_arrive_after_22,-9.1810949757532487,0.54659999999999997,0,-25,25,-9.1810949757532487 -coef_visit_tour_departure_shift_effects,-0.1095149196358212,0.096879999999999994,0,-25,25,-0.1095149196358212 -coef_visit_tour_duration_shift_effects,-0.12381401024065011,0.1638,0,-25,25,-0.12381401024065011 +coef_university_student_arrive_after_22,-9.1734571378829219,0.54659999999999997,0,-25,25,-9.1734571378829219 +coef_visit_tour_departure_shift_effects,-0.11236540231411092,0.096879999999999994,0,-25,25,-0.11236540231411092 +coef_visit_tour_duration_shift_effects,-0.13492097024550309,0.1638,0,-25,25,-0.13492097024550309 diff --git a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_school_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_school_SLSQP_.csv index b82e6aef3..93c03c449 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_school_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_school_SLSQP_.csv @@ -1,56 +1,56 @@ ,value,initvalue,nullvalue,minimum,maximum,best -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.41422850345277012,-0.0030490000000000001,0,-25,25,-0.41422850345277012 -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.6401855452227263,-0.52710000000000001,0,-25,25,-0.6401855452227263 -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,1.6569196041966843,0.089749999999999996,0,-25,25,1.6569196041966843 -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,0.22572243506435116,-0.44,0,-25,25,0.22572243506435116 -coef_all_adults_ft_worker_duration,0.052335612401385102,0.10929999999999999,0,-25,25,0.052335612401385102 -coef_arrival_constants_am_peak,-2.2391622684315053,-2.4287183990000001,0,-25,25,-2.2391622684315053 -coef_arrival_constants_early,-1.0659800055630519,-2.4287183990000001,0,-25,25,-1.0659800055630519 -coef_arrival_constants_evening,-1.0333285070293219,-0.87014690400000005,0,-25,25,-1.0333285070293219 -coef_arrival_constants_late,-1.315909676881561,-1.7520004899999999,0,-25,25,-1.315909676881561 -coef_arrival_constants_midday_1,-1.3023302498849483,-1.237908768,0,-25,25,-1.3023302498849483 -coef_arrival_constants_midday_2,-0.55467258320436619,-0.53976893100000001,0,-25,25,-0.55467258320436619 +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,11.716776044898809,-0.0030490000000000001,0,-25,25,11.716776044898809 +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.58735581754454202,-0.52710000000000001,0,-25,25,-0.58735581754454202 +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,9.2169111822551333,0.089749999999999996,0,-25,25,9.2169111822551333 +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-15.186947397585952,-0.44,0,-25,25,-15.186947397585952 +coef_all_adults_ft_worker_duration,0.0083360428258746171,0.10929999999999999,0,-25,25,0.0083360428258746171 +coef_arrival_constants_am_peak,-2.2525597933199202,-2.4287183990000001,0,-25,25,-2.2525597933199202 +coef_arrival_constants_early,-1.0600927435131249,-2.4287183990000001,0,-25,25,-1.0600927435131249 +coef_arrival_constants_evening,-1.024968926440146,-0.87014690400000005,0,-25,25,-1.024968926440146 +coef_arrival_constants_late,-1.3622777646923283,-1.7520004899999999,0,-25,25,-1.3622777646923283 +coef_arrival_constants_midday_1,-1.3329442334964623,-1.237908768,0,-25,25,-1.3329442334964623 +coef_arrival_constants_midday_2,-0.56302790885535348,-0.53976893100000001,0,-25,25,-0.56302790885535348 coef_arrival_constants_pm_peak_1,0,0,0,0,0,0 -coef_arrival_constants_pm_peak_2,-0.44138742316895407,-0.38916924800000002,0,-25,25,-0.44138742316895407 -coef_arrival_constants_pm_peak_3,-0.28124697553233768,-0.198120349,0,-25,25,-0.28124697553233768 -coef_arrival_constants_pm_peak_4,-0.37036768408938353,-0.25362468399999999,0,-25,25,-0.37036768408938353 -coef_departure_constants_am_peak_1,-1.8826940382143509,-1.6176440560000001,0,-25,25,-1.8826940382143509 +coef_arrival_constants_pm_peak_2,-0.47120087260372584,-0.38916924800000002,0,-25,25,-0.47120087260372584 +coef_arrival_constants_pm_peak_3,-0.2889635829589125,-0.198120349,0,-25,25,-0.2889635829589125 +coef_arrival_constants_pm_peak_4,-0.42904089312882804,-0.25362468399999999,0,-25,25,-0.42904089312882804 +coef_departure_constants_am_peak_1,-1.8402276885639066,-1.6176440560000001,0,-25,25,-1.8402276885639066 coef_departure_constants_am_peak_2,0,0,0,0,0,0 -coef_departure_constants_am_peak_3,-0.18398491652900856,-0.073826841000000004,0,-25,25,-0.18398491652900856 -coef_departure_constants_am_peak_4,-2.0164025628346165,-2.0805707689999999,0,-25,25,-2.0164025628346165 -coef_departure_constants_early,-3.6367697723264993,-3.8206624040000001,0,-25,25,-3.6367697723264993 -coef_departure_constants_evening,-5.2587949264840343,-5.2302878359999996,0,-25,25,-5.2587949264840343 -coef_departure_constants_late,-21.447622138036181,-11.88604728,0,-25,25,-21.447622138036181 -coef_departure_constants_midday_1,-3.2798639404148568,-2.9857394570000002,0,-25,25,-3.2798639404148568 -coef_departure_constants_midday_2,-3.7505480975849346,-3.6284346460000001,0,-25,25,-3.7505480975849346 -coef_departure_constants_pm_peak,-3.0931673015219667,-3.1025051499999998,0,-25,25,-3.0931673015219667 -coef_duration_constants_0_to_2_hours,-1.3721102503108922,-1.409955689,0,-25,25,-1.3721102503108922 -coef_duration_constants_10_hours,-0.87644803457973108,-0.90478898299999999,0,-25,25,-0.87644803457973108 -coef_duration_constants_11_hours,-1.1272644347425629,-1.5211626039999999,0,-25,25,-1.1272644347425629 -coef_duration_constants_12_to_13_hours,-2.6683455157140479,-2.4184889169999999,0,-25,25,-2.6683455157140479 -coef_duration_constants_14_to_18_hours,-2.6759742908121606,-2.5031372950000002,0,-25,25,-2.6759742908121606 -coef_duration_constants_3_to_4_hours,-0.55220033048204353,-0.74589325200000001,0,-25,25,-0.55220033048204353 -coef_duration_constants_5_to_6_hours,-0.53262716577049274,-0.56763662199999998,0,-25,25,-0.53262716577049274 +coef_departure_constants_am_peak_3,-0.14762834941122654,-0.073826841000000004,0,-25,25,-0.14762834941122654 +coef_departure_constants_am_peak_4,-2.0052907227909373,-2.0805707689999999,0,-25,25,-2.0052907227909373 +coef_departure_constants_early,-3.6385102149182318,-3.8206624040000001,0,-25,25,-3.6385102149182318 +coef_departure_constants_evening,-5.1709311530899091,-5.2302878359999996,0,-25,25,-5.1709311530899091 +coef_departure_constants_late,-25,-11.88604728,0,-25,25,-25 +coef_departure_constants_midday_1,-3.235655391222839,-2.9857394570000002,0,-25,25,-3.235655391222839 +coef_departure_constants_midday_2,-3.7122959166670451,-3.6284346460000001,0,-25,25,-3.7122959166670451 +coef_departure_constants_pm_peak,-2.9909526474284074,-3.1025051499999998,0,-25,25,-2.9909526474284074 +coef_duration_constants_0_to_2_hours,-1.3817519840859342,-1.409955689,0,-25,25,-1.3817519840859342 +coef_duration_constants_10_hours,-0.85439568067710703,-0.90478898299999999,0,-25,25,-0.85439568067710703 +coef_duration_constants_11_hours,-1.0970644700531507,-1.5211626039999999,0,-25,25,-1.0970644700531507 +coef_duration_constants_12_to_13_hours,-2.6774099148644868,-2.4184889169999999,0,-25,25,-2.6774099148644868 +coef_duration_constants_14_to_18_hours,-2.6488356018470931,-2.5031372950000002,0,-25,25,-2.6488356018470931 +coef_duration_constants_3_to_4_hours,-0.56119226026187308,-0.74589325200000001,0,-25,25,-0.56119226026187308 +coef_duration_constants_5_to_6_hours,-0.52869720683509602,-0.56763662199999998,0,-25,25,-0.52869720683509602 coef_duration_constants_7_to_8_hours,0,0,0,0,0,0 -coef_duration_constants_9_hours,-0.48760897429357175,-0.65080668399999997,0,-25,25,-0.48760897429357175 -coef_first_of_2plus_school_lt_6_hours,0.16905521319272807,1.4870000000000001,0,-25,25,0.16905521319272807 -coef_first_of_2plus_school_tours_departure,-0.3338303791406505,-0.30020000000000002,0,-25,25,-0.3338303791406505 -coef_first_of_2plus_school_tours_duration,-0.042558365362996578,-0.1593,0,-25,25,-0.042558365362996578 +coef_duration_constants_9_hours,-0.47578654406256893,-0.65080668399999997,0,-25,25,-0.47578654406256893 +coef_first_of_2plus_school_lt_6_hours,0.36465272850981928,1.4870000000000001,0,-25,25,0.36465272850981928 +coef_first_of_2plus_school_tours_departure,-0.28345842741237504,-0.30020000000000002,0,-25,25,-0.28345842741237504 +coef_first_of_2plus_school_tours_duration,-0.0072205064801072029,-0.1593,0,-25,25,-0.0072205064801072029 coef_ft_worker_departure,0.39710000000000001,0.39710000000000001,0,-25,25,0.39710000000000001 coef_ft_worker_duration,-0.1908,-0.1908,0,-25,25,-0.1908 -coef_hh_income_early_departure,-13.757852371659974,-0.88370000000000004,0,-25,25,-13.757852371659974 -coef_hh_income_late_arrival,0.10172645562322212,-0.3533,0,-25,25,0.10172645562322212 -coef_mode_choice_logsum,0.056409818603392878,2.1269999999999998,0,-25,25,0.056409818603392878 +coef_hh_income_early_departure,-12.781415662231149,-0.88370000000000004,0,-25,25,-12.781415662231149 +coef_hh_income_late_arrival,0.13481375603358139,-0.3533,0,-25,25,0.13481375603358139 +coef_mode_choice_logsum,-0.15485890248282916,2.1269999999999998,0,-25,25,-0.15485890248282916 coef_non_worker_departure,0.55389999999999995,0.55389999999999995,0,-25,25,0.55389999999999995 -coef_previous_tour_begins_this_arrival_hour,-10.370632174066369,-1.1020000000000001,0,-25,25,-10.370632174066369 -coef_previous_tour_ends_this_departure_hour,0.29855098378969142,-0.59950000000000003,0,-25,25,0.29855098378969142 -coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,-25,-16.670000000000002,0,-25,25,-25 -coef_roundtrip_auto_time_to_work,0.005137149637746443,0.0031949999999999999,0,-25,25,0.005137149637746443 -coef_school_plus_work_tours_by_student_lt_6_hours,-2.4833134339994589,1.73,0,-25,25,-2.4833134339994589 -coef_school_plus_work_tours_by_worker_lt_6_hours,-2.0713134339992108,2.1419999999999999,0,-25,25,-2.0713134339992108 -coef_student_driver_duration,-0.0039575686674964382,0.034639999999999997,0,-25,25,-0.0039575686674964382 -coef_subsequent_2plus_school_tours_duration,-0.18829905225489668,-0.23380000000000001,0,-25,25,-0.18829905225489668 -coef_subsequent_of_2plus_school_lt_6_hours,25,2.1419999999999999,0,-25,25,25 +coef_previous_tour_begins_this_arrival_hour,-5.5053579900953125,-1.1020000000000001,0,-25,25,-5.5053579900953125 +coef_previous_tour_ends_this_departure_hour,-15.038466619861389,-0.59950000000000003,0,-25,25,-15.038466619861389 +coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,-24.999999999999979,-16.670000000000002,0,-25,25,-24.999999999999979 +coef_roundtrip_auto_time_to_work,0.0053019952333040361,0.0031949999999999999,0,-25,25,0.0053019952333040361 +coef_school_plus_work_tours_by_student_lt_6_hours,-1.1660913354672906,1.73,0,-25,25,-1.1660913354672906 +coef_school_plus_work_tours_by_worker_lt_6_hours,-0.75409133546729057,2.1419999999999999,0,-25,25,-0.75409133546729057 +coef_student_driver_duration,-0.0068046815074113429,0.034639999999999997,0,-25,25,-0.0068046815074113429 +coef_subsequent_2plus_school_tours_duration,-0.21896654709477803,-0.23380000000000001,0,-25,25,-0.21896654709477803 +coef_subsequent_of_2plus_school_lt_6_hours,24.999999999999982,2.1419999999999999,0,-25,25,24.999999999999982 coef_subsequent_tour_must_start_after_previous_tour_ends,-100,-100,0,-100,-100,-100 -coef_univ_departure,0.23436737071396405,0.28000000000000003,0,-25,25,0.23436737071396405 -coef_univ_duration,-0.4091323980277653,-0.29070000000000001,0,-25,25,-0.4091323980277653 +coef_univ_departure,0.22263900806749343,0.28000000000000003,0,-25,25,0.22263900806749343 +coef_univ_duration,-0.41672805296299825,-0.29070000000000001,0,-25,25,-0.41672805296299825 diff --git a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_work_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_work_SLSQP_.csv index e101827da..9465192ef 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_work_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_work_SLSQP_.csv @@ -1,64 +1,64 @@ ,value,initvalue,nullvalue,minimum,maximum,best -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,0.32667520282267543,0.36270000000000002,0,-25,25,0.32667520282267543 -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,0.2989032139409768,-0.1012,0,-25,25,0.2989032139409768 -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,-0.038388513238313653,0.17710000000000001,0,-25,25,-0.038388513238313653 -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.012603680222033091,-0.21229999999999999,0,-25,25,-0.012603680222033091 -coef_arrival_constants_am_peak,-2.5271385106886068,-1.854520626,0,-25,25,-2.5271385106886068 +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,25,0.36270000000000002,0,-25,25,25 +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,0.67172411345437255,-0.1012,0,-25,25,0.67172411345437255 +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,-0.2705642011106818,0.17710000000000001,0,-25,25,-0.2705642011106818 +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,2.3126663890343506,-0.21229999999999999,0,-25,25,2.3126663890343506 +coef_arrival_constants_am_peak,-2.5608583720316691,-1.854520626,0,-25,25,-2.5608583720316691 coef_arrival_constants_early,0,0,0,0,0,0 -coef_arrival_constants_evening,-0.085796024190049405,0.103566251,0,-25,25,-0.085796024190049405 -coef_arrival_constants_late,-1.2214089672341761,-0.96595733900000003,0,-25,25,-1.2214089672341761 -coef_arrival_constants_midday_1,-0.48973614520419595,-0.49597203699999998,0,-25,25,-0.48973614520419595 -coef_arrival_constants_midday_2,-0.38325967338204053,-0.37855408099999999,0,-25,25,-0.38325967338204053 +coef_arrival_constants_evening,-0.11313543070951976,0.103566251,0,-25,25,-0.11313543070951976 +coef_arrival_constants_late,-1.2047708015331426,-0.96595733900000003,0,-25,25,-1.2047708015331426 +coef_arrival_constants_midday_1,-0.4666152062959858,-0.49597203699999998,0,-25,25,-0.4666152062959858 +coef_arrival_constants_midday_2,-0.37297675905912303,-0.37855408099999999,0,-25,25,-0.37297675905912303 coef_arrival_constants_pm_peak_1,0,0,0,0,0,0 -coef_arrival_constants_pm_peak_2,0.20200494978830774,0.27608389999999999,0,-25,25,0.20200494978830774 -coef_arrival_constants_pm_peak_3,0.57252378326592945,0.699587132,0,-25,25,0.57252378326592945 -coef_arrival_constants_pm_peak_4,0.70559147854432169,0.79928937700000002,0,-25,25,0.70559147854432169 -coef_departure_constants_am_peak_1,-0.6418973650064711,-0.61618090599999997,0,-25,25,-0.6418973650064711 +coef_arrival_constants_pm_peak_2,0.20356469833981647,0.27608389999999999,0,-25,25,0.20356469833981647 +coef_arrival_constants_pm_peak_3,0.55940049285496218,0.699587132,0,-25,25,0.55940049285496218 +coef_arrival_constants_pm_peak_4,0.69955932869966053,0.79928937700000002,0,-25,25,0.69955932869966053 +coef_departure_constants_am_peak_1,-0.65012576984935755,-0.61618090599999997,0,-25,25,-0.65012576984935755 coef_departure_constants_am_peak_2,0,0,0,0,0,0 -coef_departure_constants_am_peak_3,-0.32300636563617879,-0.254714726,0,-25,25,-0.32300636563617879 -coef_departure_constants_am_peak_4,-1.220041820613047,-1.2513460240000001,0,-25,25,-1.220041820613047 -coef_departure_constants_early,-0.88937510260312602,-0.95272526999999996,0,-25,25,-0.88937510260312602 -coef_departure_constants_evening,-1.9893998781318789,-1.610513243,0,-25,25,-1.9893998781318789 -coef_departure_constants_late,-2.9366745684271129,-2.8834152230000001,0,-25,25,-2.9366745684271129 -coef_departure_constants_midday_1,-1.8267001521037016,-1.7058689920000001,0,-25,25,-1.8267001521037016 -coef_departure_constants_midday_2,-1.8787827704964197,-1.6935705830000001,0,-25,25,-1.8787827704964197 -coef_departure_constants_pm_peak,-1.6918462289474312,-1.4399919619999999,0,-25,25,-1.6918462289474312 -coef_destination_in_cbd_departure_shift_effects,0.028678306805682247,0.047169999999999997,0,-25,25,0.028678306805682247 -coef_destination_in_cbd_duration_shift_effects,0.096673084899908696,0.086790000000000006,0,-25,25,0.096673084899908696 -coef_destination_in_cbd_early_departure_interaction,-0.41995880475080311,-0.45660000000000001,0,-25,25,-0.41995880475080311 -coef_destination_in_cbd_late_arrival_interaction,-0.26693459750183979,-0.2334,0,-25,25,-0.26693459750183979 -coef_duration_constants_0_to_2_hours,-2.0624023323687144,-2.5282663900000002,0,-25,25,-2.0624023323687144 +coef_departure_constants_am_peak_3,-0.32740035887947389,-0.254714726,0,-25,25,-0.32740035887947389 +coef_departure_constants_am_peak_4,-1.2190845361495699,-1.2513460240000001,0,-25,25,-1.2190845361495699 +coef_departure_constants_early,-0.92470832388900248,-0.95272526999999996,0,-25,25,-0.92470832388900248 +coef_departure_constants_evening,-1.8906229365299492,-1.610513243,0,-25,25,-1.8906229365299492 +coef_departure_constants_late,-2.9986176789858154,-2.8834152230000001,0,-25,25,-2.9986176789858154 +coef_departure_constants_midday_1,-1.8241536006077277,-1.7058689920000001,0,-25,25,-1.8241536006077277 +coef_departure_constants_midday_2,-1.8783523324805591,-1.6935705830000001,0,-25,25,-1.8783523324805591 +coef_departure_constants_pm_peak,-1.6396663560770837,-1.4399919619999999,0,-25,25,-1.6396663560770837 +coef_destination_in_cbd_departure_shift_effects,0.04109611455586798,0.047169999999999997,0,-25,25,0.04109611455586798 +coef_destination_in_cbd_duration_shift_effects,0.10130692865710192,0.086790000000000006,0,-25,25,0.10130692865710192 +coef_destination_in_cbd_early_departure_interaction,-0.38984353468069122,-0.45660000000000001,0,-25,25,-0.38984353468069122 +coef_destination_in_cbd_late_arrival_interaction,-0.27055298689359897,-0.2334,0,-25,25,-0.27055298689359897 +coef_duration_constants_0_to_2_hours,-2.1189652588431978,-2.5282663900000002,0,-25,25,-2.1189652588431978 coef_duration_constants_10_hours,0,0,0,0,0,0 -coef_duration_constants_11_hours,-0.35847896432255638,-0.34779539100000001,0,-25,25,-0.35847896432255638 -coef_duration_constants_12_to_13_hours,-1.1320004478276953,-1.0082223459999999,0,-25,25,-1.1320004478276953 -coef_duration_constants_14_to_18_hours,-1.8548759254550073,-1.701858847,0,-25,25,-1.8548759254550073 -coef_duration_constants_3_to_4_hours,-0.60027688746955321,-0.91897445700000002,0,-25,25,-0.60027688746955321 -coef_duration_constants_5_to_6_hours,-0.3435610046131029,-0.71855028799999998,0,-25,25,-0.3435610046131029 -coef_duration_constants_7_to_8_hours,-0.033979868351848914,-0.139623566,0,-25,25,-0.033979868351848914 -coef_duration_constants_9_hours,0.13725128186580737,0.055706243000000003,0,-25,25,0.13725128186580737 -coef_first_of_2plus_work_tours_departure_shift_effects,-0.2896837904560684,-0.30330000000000001,0,-25,25,-0.2896837904560684 -coef_first_of_2plus_work_tours_duration_lt_8_hrs,1.9167861172628315,1.98,0,-25,25,1.9167861172628315 -coef_first_of_2plus_work_tours_duration_shift_effects,-0.28618628658489031,-0.18609999999999999,0,-25,25,-0.28618628658489031 -coef_free_flow_round_trip_auto_time_shift_effects_departure,0.00059861431451224852,-0.00114,0,-25,25,0.00059861431451224852 -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.0042157097953458792,0.0022100000000000002,0,-25,25,0.0042157097953458792 -coef_full_time_worker_10_to_12_departure_interaction,-0.67825091233923496,-0.51819999999999999,0,-25,25,-0.67825091233923496 -coef_full_time_worker_duration_lt_9_hours_interaction,-1.3572941093466813,-1.2569999999999999,0,-25,25,-1.3572941093466813 -coef_household_income_departure_shift_effects,0.00011131783637255824,0.00020799999999999999,0,-25,25,0.00011131783637255824 -coef_household_income_early_departure_interaction,-0.58277482301281736,-0.4854,0,-25,25,-0.58277482301281736 -coef_household_income_late_arrival_interaction,0.02995300537436358,-0.38390000000000002,0,-25,25,0.02995300537436358 -coef_mode_choice_logsum,0.24711457366198325,1.0269999999999999,0,-25,25,0.24711457366198325 +coef_duration_constants_11_hours,-0.35443610468830039,-0.34779539100000001,0,-25,25,-0.35443610468830039 +coef_duration_constants_12_to_13_hours,-1.1153352032051054,-1.0082223459999999,0,-25,25,-1.1153352032051054 +coef_duration_constants_14_to_18_hours,-1.8418267064323726,-1.701858847,0,-25,25,-1.8418267064323726 +coef_duration_constants_3_to_4_hours,-0.63573505547017051,-0.91897445700000002,0,-25,25,-0.63573505547017051 +coef_duration_constants_5_to_6_hours,-0.35577473775309831,-0.71855028799999998,0,-25,25,-0.35577473775309831 +coef_duration_constants_7_to_8_hours,-0.033757586303302736,-0.139623566,0,-25,25,-0.033757586303302736 +coef_duration_constants_9_hours,0.12934760728519379,0.055706243000000003,0,-25,25,0.12934760728519379 +coef_first_of_2plus_work_tours_departure_shift_effects,-0.27106753260112321,-0.30330000000000001,0,-25,25,-0.27106753260112321 +coef_first_of_2plus_work_tours_duration_lt_8_hrs,1.8948098471889567,1.98,0,-25,25,1.8948098471889567 +coef_first_of_2plus_work_tours_duration_shift_effects,-0.29362220747793244,-0.18609999999999999,0,-25,25,-0.29362220747793244 +coef_free_flow_round_trip_auto_time_shift_effects_departure,0.00054374628944530747,-0.00114,0,-25,25,0.00054374628944530747 +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.0040398779948456849,0.0022100000000000002,0,-25,25,0.0040398779948456849 +coef_full_time_worker_10_to_12_departure_interaction,-0.66614758973349719,-0.51819999999999999,0,-25,25,-0.66614758973349719 +coef_full_time_worker_duration_lt_9_hours_interaction,-1.3694302807642016,-1.2569999999999999,0,-25,25,-1.3694302807642016 +coef_household_income_departure_shift_effects,0.00011200417166098945,0.00020799999999999999,0,-25,25,0.00011200417166098945 +coef_household_income_early_departure_interaction,-0.57489507784478577,-0.4854,0,-25,25,-0.57489507784478577 +coef_household_income_late_arrival_interaction,0.036426020508396037,-0.38390000000000002,0,-25,25,0.036426020508396037 +coef_mode_choice_logsum,0.20613495321472861,1.0269999999999999,0,-25,25,0.20613495321472861 coef_non_working_adult_duration_shift_effects,-0.1207,-0.1207,0,-25,25,-0.1207 -coef_part_time_worker_13_to_15_arrival_interaction,0.37532108740462561,0.54330000000000001,0,-25,25,0.37532108740462561 -coef_part_time_worker_departure_shift_effects,0.09509355721343847,0.067360000000000003,0,-25,25,0.09509355721343847 -coef_previously_scheduled_tour_begins_in_this_arrival_hour,-6.7245570005227204,-1.3340000000000001,0,-25,25,-6.7245570005227204 -coef_previously_scheduled_tour_ends_in_this_departure_hour,0.2588531712397476,-0.89349999999999996,0,-25,25,0.2588531712397476 -coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,-6.8650722180171249,-18.68,0,-25,25,-6.8650722180171249 +coef_part_time_worker_13_to_15_arrival_interaction,0.33677938786979689,0.54330000000000001,0,-25,25,0.33677938786979689 +coef_part_time_worker_departure_shift_effects,0.089714285580281661,0.067360000000000003,0,-25,25,0.089714285580281661 +coef_previously_scheduled_tour_begins_in_this_arrival_hour,-6.6033938783133914,-1.3340000000000001,0,-25,25,-6.6033938783133914 +coef_previously_scheduled_tour_ends_in_this_departure_hour,2.4216789099176217,-0.89349999999999996,0,-25,25,2.4216789099176217 +coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,-6.3408961734536069,-18.68,0,-25,25,-6.3408961734536069 coef_rural_household_early_departure_interaction,0.40389999999999998,0.40389999999999998,0,-25,25,0.40389999999999998 coef_rural_household_late_arrival_interaction,-0.34510000000000002,-0.34510000000000002,0,-25,25,-0.34510000000000002 -coef_subsequent_2plus_work_departure_tours_shift_effects,0.060824095014172706,-0.53810000000000002,0,-25,25,0.060824095014172706 -coef_subsequent_2plus_work_duration_tours_shift_effects,-0.08095682334415219,-0.31740000000000002,0,-25,25,-0.08095682334415219 -coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs,5.4433872561126897,2.5819999999999999,0,-25,25,5.4433872561126897 +coef_subsequent_2plus_work_departure_tours_shift_effects,0.015346535529312815,-0.53810000000000002,0,-25,25,0.015346535529312815 +coef_subsequent_2plus_work_duration_tours_shift_effects,-0.12773109887960765,-0.31740000000000002,0,-25,25,-0.12773109887960765 +coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs,3.5917959509926001,2.5819999999999999,0,-25,25,3.5917959509926001 coef_subsequent_tour_must_start_after_previous_tour_ends,-100,-100,0,-100,-100,-100 -coef_tours_by_student_duration_lt_8_hrs,20.916124133444324,2.5819999999999999,0,-25,25,20.916124133444324 -coef_tours_by_worker_duration_lt_8_hrs,19.246724133444353,0.91259999999999997,0,-25,25,19.246724133444353 -coef_university_student_departure_shift_effects,0.076480195754151201,0.05747,0,-25,25,0.076480195754151201 +coef_tours_by_student_duration_lt_8_hrs,14.705420185896203,2.5819999999999999,0,-25,25,14.705420185896203 +coef_tours_by_worker_duration_lt_8_hrs,13.036020185896209,0.91259999999999997,0,-25,25,13.036020185896209 +coef_university_student_departure_shift_effects,0.052139288632078368,0.05747,0,-25,25,0.052139288632078368 diff --git a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_non_mandatory_tour_scheduling_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_non_mandatory_tour_scheduling_SLSQP_.csv index e733c6608..68cc071e8 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_non_mandatory_tour_scheduling_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_non_mandatory_tour_scheduling_SLSQP_.csv @@ -1,90 +1,90 @@ ,value,initvalue,nullvalue,minimum,maximum,best -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.12795390836606826,-0.025700000000000001,0,-25,25,-0.12795390836606826 -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,0.093953618800295624,-0.02734,0,-25,25,0.093953618800295624 -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.25374862846174484,0.0084419999999999999,0,-25,25,0.25374862846174484 -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.19834303389910271,-0.059299999999999999,0,-25,25,-0.19834303389910271 -coef_adult_with_children_in_hh_arrive_19_21,0.30942398582506869,0.33600000000000002,0,-25,25,0.30942398582506869 -coef_arrival_constants_am_peak,0.42338231194541198,-1.814822602,0,-25,25,0.42338231194541198 -coef_arrival_constants_early,2.9125415992060972,-0.051990748000000003,0,-25,25,2.9125415992060972 +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,0.4470328861441506,-0.025700000000000001,0,-25,25,0.4470328861441506 +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,0.56540266502429914,-0.02734,0,-25,25,0.56540266502429914 +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,1.0259323894447316,0.0084419999999999999,0,-25,25,1.0259323894447316 +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,0.39614317482379957,-0.059299999999999999,0,-25,25,0.39614317482379957 +coef_adult_with_children_in_hh_arrive_19_21,0.31331407963369601,0.33600000000000002,0,-25,25,0.31331407963369601 +coef_arrival_constants_am_peak,0.29950075228772494,-1.814822602,0,-25,25,0.29950075228772494 +coef_arrival_constants_early,2.725267289110473,-0.051990748000000003,0,-25,25,2.725267289110473 coef_arrival_constants_evening,0,0,0,0,0,0 -coef_arrival_constants_late,-1.1910368663347299,-0.86667131500000005,0,-25,25,-1.1910368663347299 -coef_arrival_constants_midday_1,1.4838084605550812,0.00037150099999999999,0,-25,25,1.4838084605550812 -coef_arrival_constants_midday_2,1.5740135366172114,0.53211603100000004,0,-25,25,1.5740135366172114 -coef_arrival_constants_pm_peak_1,1.4096316362715051,0.62848156700000002,0,-25,25,1.4096316362715051 -coef_arrival_constants_pm_peak_2,1.6015853090922501,0.65052141600000002,0,-25,25,1.6015853090922501 -coef_arrival_constants_pm_peak_3,1.1104118303036004,0.40289440599999998,0,-25,25,1.1104118303036004 -coef_arrival_constants_pm_peak_4,0.71897054480267941,0.154213293,0,-25,25,0.71897054480267941 -coef_departure_constants_am_peak_1,-0.46298108436506397,-0.65416357300000005,0,-25,25,-0.46298108436506397 -coef_departure_constants_am_peak_2,1.2593758274992846,0.554282571,0,-25,25,1.2593758274992846 -coef_departure_constants_am_peak_3,1.4967305972755718,1.0505610869999999,0,-25,25,1.4967305972755718 -coef_departure_constants_am_peak_4,1.5743607357021454,0.97156822799999998,0,-25,25,1.5743607357021454 -coef_departure_constants_early,-1.0295111494115279,-1.7401356610000001,0,-25,25,-1.0295111494115279 -coef_departure_constants_evening,-1.9057735658380073,-1.856475096,0,-25,25,-1.9057735658380073 -coef_departure_constants_late,-5.8630966842838257,-8.2288801409999994,0,-25,25,-5.8630966842838257 -coef_departure_constants_midday_1,1.4060318633333218,0.88199198599999995,0,-25,25,1.4060318633333218 -coef_departure_constants_midday_2,0.67297573507452191,0.411103634,0,-25,25,0.67297573507452191 +coef_arrival_constants_late,-1.1446323020070934,-0.86667131500000005,0,-25,25,-1.1446323020070934 +coef_arrival_constants_midday_1,1.3878018540712422,0.00037150099999999999,0,-25,25,1.3878018540712422 +coef_arrival_constants_midday_2,1.5044255895039795,0.53211603100000004,0,-25,25,1.5044255895039795 +coef_arrival_constants_pm_peak_1,1.3575212925702953,0.62848156700000002,0,-25,25,1.3575212925702953 +coef_arrival_constants_pm_peak_2,1.5579462028570845,0.65052141600000002,0,-25,25,1.5579462028570845 +coef_arrival_constants_pm_peak_3,1.0806181769202525,0.40289440599999998,0,-25,25,1.0806181769202525 +coef_arrival_constants_pm_peak_4,0.69735759210419612,0.154213293,0,-25,25,0.69735759210419612 +coef_departure_constants_am_peak_1,-0.52735012006812276,-0.65416357300000005,0,-25,25,-0.52735012006812276 +coef_departure_constants_am_peak_2,1.1919565496484956,0.554282571,0,-25,25,1.1919565496484956 +coef_departure_constants_am_peak_3,1.4522880996476126,1.0505610869999999,0,-25,25,1.4522880996476126 +coef_departure_constants_am_peak_4,1.5114204226663379,0.97156822799999998,0,-25,25,1.5114204226663379 +coef_departure_constants_early,-0.92104869293940328,-1.7401356610000001,0,-25,25,-0.92104869293940328 +coef_departure_constants_evening,-1.892558890126933,-1.856475096,0,-25,25,-1.892558890126933 +coef_departure_constants_late,-5.6265216713840269,-8.2288801409999994,0,-25,25,-5.6265216713840269 +coef_departure_constants_midday_1,1.3642337556324493,0.88199198599999995,0,-25,25,1.3642337556324493 +coef_departure_constants_midday_2,0.64556751877721019,0.411103634,0,-25,25,0.64556751877721019 coef_departure_constants_pm_peak,0,0,0,0,0,0 -coef_destination_in_cbd_duration_shift_effects,0.1161201042988334,0.1067,0,-25,25,0.1161201042988334 -coef_discretionary_tour_duration_lt_2_hours,-0.2639348382984561,-0.69740000000000002,0,-25,25,-0.2639348382984561 +coef_destination_in_cbd_duration_shift_effects,0.11545735776937631,0.1067,0,-25,25,0.11545735776937631 +coef_discretionary_tour_duration_lt_2_hours,-0.28570443543294716,-0.69740000000000002,0,-25,25,-0.28570443543294716 coef_duration_constants_0_to_1_hours,0,0,0,0,0,0 -coef_duration_constants_11_to_13_hours,-0.87732408480084789,-0.955635554,0,-25,25,-0.87732408480084789 -coef_duration_constants_14_to_18_hours,-2.8212978244929676,-1.042580879,0,-25,25,-2.8212978244929676 -coef_duration_constants_2_to_3_hours,0.43539369018467328,0.051385565000000001,0,-25,25,0.43539369018467328 -coef_duration_constants_4_to_5_hours,-0.22360076510836863,-0.59395132100000003,0,-25,25,-0.22360076510836863 -coef_duration_constants_6_to_7_hours,-0.58405912079172817,-0.95115532800000002,0,-25,25,-0.58405912079172817 -coef_duration_constants_8_to_10_hours,-0.52790593216985049,-0.828108399,0,-25,25,-0.52790593216985049 -coef_eat_out_tour_departure_shift_effects,0.10565687284340203,0.075490000000000002,0,-25,25,0.10565687284340203 +coef_duration_constants_11_to_13_hours,-1.0074136237311473,-0.955635554,0,-25,25,-1.0074136237311473 +coef_duration_constants_14_to_18_hours,-2.9449650044604194,-1.042580879,0,-25,25,-2.9449650044604194 +coef_duration_constants_2_to_3_hours,0.3987726313758776,0.051385565000000001,0,-25,25,0.3987726313758776 +coef_duration_constants_4_to_5_hours,-0.27952507767146284,-0.59395132100000003,0,-25,25,-0.27952507767146284 +coef_duration_constants_6_to_7_hours,-0.65619904538586438,-0.95115532800000002,0,-25,25,-0.65619904538586438 +coef_duration_constants_8_to_10_hours,-0.62326492676484069,-0.828108399,0,-25,25,-0.62326492676484069 +coef_eat_out_tour_departure_shift_effects,0.10408922995281709,0.075490000000000002,0,-25,25,0.10408922995281709 coef_escort_tour_arrival_constants_am_peak,0,0,0,0,0,0 coef_escort_tour_arrival_constants_early,0,0,0,0,0,0 -coef_escort_tour_arrival_constants_evening,-0.87885259097970236,-0.53691872799999996,0,-25,25,-0.87885259097970236 -coef_escort_tour_arrival_constants_late,-1.2544150933448788,-1.008290213,0,-25,25,-1.2544150933448788 +coef_escort_tour_arrival_constants_evening,-0.8931637699400693,-0.53691872799999996,0,-25,25,-0.8931637699400693 +coef_escort_tour_arrival_constants_late,-1.1770797692787565,-1.008290213,0,-25,25,-1.1770797692787565 coef_escort_tour_arrival_constants_midday_1,0,0,0,0,0,0 coef_escort_tour_arrival_constants_midday_2,0,0,0,0,0,0 coef_escort_tour_arrival_constants_pm_peak_1,0,0,0,0,0,0 coef_escort_tour_arrival_constants_pm_peak_2,0,0,0,0,0,0 coef_escort_tour_arrival_constants_pm_peak_3,0,0,0,0,0,0 coef_escort_tour_arrival_constants_pm_peak_4,0,0,0,0,0,0 -coef_escort_tour_departure_constants_am_peak_1,0.70430916557367373,-1.1123577529999999,0,-25,25,0.70430916557367373 -coef_escort_tour_departure_constants_am_peak_2,2.276345350741189,0.69878818499999995,0,-25,25,2.276345350741189 -coef_escort_tour_departure_constants_am_peak_3,2.3008963449265081,1.1962688130000001,0,-25,25,2.3008963449265081 -coef_escort_tour_departure_constants_am_peak_4,0.49313656586014942,-0.22525822100000001,0,-25,25,0.49313656586014942 -coef_escort_tour_departure_constants_early,-0.49391001353581915,-1.7401356610000001,0,-25,25,-0.49391001353581915 -coef_escort_tour_departure_constants_evening,-4.8022685133416925,-3.9487328110000002,0,-25,25,-4.8022685133416925 -coef_escort_tour_departure_constants_late,-6.8574861910304179,-8.2288801409999994,0,-25,25,-6.8574861910304179 -coef_escort_tour_departure_constants_midday_1,0.7464465452279021,0.028662017000000001,0,-25,25,0.7464465452279021 +coef_escort_tour_departure_constants_am_peak_1,0.48291466233476604,-1.1123577529999999,0,-25,25,0.48291466233476604 +coef_escort_tour_departure_constants_am_peak_2,2.0132225505169186,0.69878818499999995,0,-25,25,2.0132225505169186 +coef_escort_tour_departure_constants_am_peak_3,2.1327074544894873,1.1962688130000001,0,-25,25,2.1327074544894873 +coef_escort_tour_departure_constants_am_peak_4,0.29911257358855436,-0.22525822100000001,0,-25,25,0.29911257358855436 +coef_escort_tour_departure_constants_early,-0.5151665525123571,-1.7401356610000001,0,-25,25,-0.5151665525123571 +coef_escort_tour_departure_constants_evening,-4.7638859598327503,-3.9487328110000002,0,-25,25,-4.7638859598327503 +coef_escort_tour_departure_constants_late,-6.5766274559402813,-8.2288801409999994,0,-25,25,-6.5766274559402813 +coef_escort_tour_departure_constants_midday_1,0.64080123578544568,0.028662017000000001,0,-25,25,0.64080123578544568 coef_escort_tour_departure_constants_midday_2,0,0,0,0,0,0 -coef_escort_tour_departure_constants_pm_peak,-1.8233595967851086,-1.180140161,0,-25,25,-1.8233595967851086 +coef_escort_tour_departure_constants_pm_peak,-1.78947739154685,-1.180140161,0,-25,25,-1.78947739154685 coef_escort_tour_duration_constants_0_to_1_hours,0,0,0,0,0,0 -coef_escort_tour_duration_constants_11_to_13_hours,-6.3931476306146751,-2.9743649759999999,0,-25,25,-6.3931476306146751 -coef_escort_tour_duration_constants_14_to_18_hours,-9.4197927932485896,-2.5074471460000001,0,-25,25,-9.4197927932485896 -coef_escort_tour_duration_constants_2_to_3_hours,-2.5955922813145555,-2.0420138969999999,0,-25,25,-2.5955922813145555 -coef_escort_tour_duration_constants_4_to_5_hours,-3.5228888691144959,-2.880293896,0,-25,25,-3.5228888691144959 -coef_escort_tour_duration_constants_6_to_7_hours,-3.979277237854689,-2.9735337309999998,0,-25,25,-3.979277237854689 -coef_escort_tour_duration_constants_8_to_10_hours,-5.1583159232705968,-3.0202137580000001,0,-25,25,-5.1583159232705968 -coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.28520075435325948,-0.2364,0,-25,25,-0.28520075435325948 -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.0049919355433122436,0.0047410000000000004,0,-25,25,0.0049919355433122436 -coef_maintenance_tour_depart_before_7,0.041996701210282046,-0.88260000000000005,0,-25,25,0.041996701210282046 -coef_maintenance_tour_departure_shift_effects,-0.036635545903427789,-0.1489,0,-25,25,-0.036635545903427789 -coef_maintenance_tour_duration_shift_effects,-0.1159437577045862,-0.083720000000000003,0,-25,25,-0.1159437577045862 -coef_number_of_escort_tours_departure_shift_effects,0.1152135976407198,0.020129999999999999,0,-25,25,0.1152135976407198 -coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,0.10437475708930999,0.038960000000000002,0,-25,25,0.10437475708930999 -coef_number_of_joint_tours_departure_shift_effects,0.12192044424883711,0.052080000000000001,0,-25,25,0.12192044424883711 -coef_number_of_mandatory_tours_departure_shift_effects,0.17081676715336708,0.046730000000000001,0,-25,25,0.17081676715336708 -coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,5.6436190430109816,-13.630000000000001,0,-25,25,5.6436190430109816 -coef_school_child_age_16_plus_departure_shift_effects,0.00028295279835436216,0.072660000000000002,0,-25,25,0.00028295279835436216 -coef_school_child_age_16_plus_duration_shift_effects,0.30612186264250524,0.20949999999999999,0,-25,25,0.30612186264250524 -coef_school_child_age_under_16_departure_shift_effects,0.11079613464663726,0.04657,0,-25,25,0.11079613464663726 -coef_school_child_age_under_16_duration_shift_effects,0.33821236944890215,0.32719999999999999,0,-25,25,0.33821236944890215 -coef_school_child_under_16_arrive_after_22,-0.40240965942253298,-1.1799999999999999,0,-25,25,-0.40240965942253298 -coef_shopping_tour_arrive_after_22,-2.5452262719424956,-0.60270000000000001,0,-25,25,-2.5452262719424956 -coef_shopping_tour_depart_before_8,-0.78710037865743798,-1.0369999999999999,0,-25,25,-0.78710037865743798 -coef_shopping_tour_departure_shift_effects,0.015067331790855299,-0.060150000000000002,0,-25,25,0.015067331790855299 -coef_shopping_tour_duration_lt_2_hours,0.94341972947847441,0.51680000000000004,0,-25,25,0.94341972947847441 -coef_shopping_tour_duration_shift_effects,-0.075331047793831374,-0.1208,0,-25,25,-0.075331047793831374 -coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.05438519391868582,-0.3992,0,-25,25,-0.05438519391868582 -coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.34572651304702828,-0.45619999999999999,0,-25,25,-0.34572651304702828 -coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.11510735547695737,-0.1731,0,-25,25,-0.11510735547695737 +coef_escort_tour_duration_constants_11_to_13_hours,-6.4203324936004007,-2.9743649759999999,0,-25,25,-6.4203324936004007 +coef_escort_tour_duration_constants_14_to_18_hours,-9.2410499900666672,-2.5074471460000001,0,-25,25,-9.2410499900666672 +coef_escort_tour_duration_constants_2_to_3_hours,-2.600430326093385,-2.0420138969999999,0,-25,25,-2.600430326093385 +coef_escort_tour_duration_constants_4_to_5_hours,-3.5279045037998431,-2.880293896,0,-25,25,-3.5279045037998431 +coef_escort_tour_duration_constants_6_to_7_hours,-3.9930263301758195,-2.9735337309999998,0,-25,25,-3.9930263301758195 +coef_escort_tour_duration_constants_8_to_10_hours,-5.1822459061747068,-3.0202137580000001,0,-25,25,-5.1822459061747068 +coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.30486234400858059,-0.2364,0,-25,25,-0.30486234400858059 +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.0050429319781780269,0.0047410000000000004,0,-25,25,0.0050429319781780269 +coef_maintenance_tour_depart_before_7,0.052350894248540496,-0.88260000000000005,0,-25,25,0.052350894248540496 +coef_maintenance_tour_departure_shift_effects,-0.041432645424667555,-0.1489,0,-25,25,-0.041432645424667555 +coef_maintenance_tour_duration_shift_effects,-0.11717847942290449,-0.083720000000000003,0,-25,25,-0.11717847942290449 +coef_number_of_escort_tours_departure_shift_effects,0.1003621581289256,0.020129999999999999,0,-25,25,0.1003621581289256 +coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,0.098215723941853808,0.038960000000000002,0,-25,25,0.098215723941853808 +coef_number_of_joint_tours_departure_shift_effects,0.12273335177942903,0.052080000000000001,0,-25,25,0.12273335177942903 +coef_number_of_mandatory_tours_departure_shift_effects,0.1726876041064854,0.046730000000000001,0,-25,25,0.1726876041064854 +coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,5.7502384461866223,-13.630000000000001,0,-25,25,5.7502384461866223 +coef_school_child_age_16_plus_departure_shift_effects,0.002334001767561128,0.072660000000000002,0,-25,25,0.002334001767561128 +coef_school_child_age_16_plus_duration_shift_effects,0.30512330091593426,0.20949999999999999,0,-25,25,0.30512330091593426 +coef_school_child_age_under_16_departure_shift_effects,0.1028104094868405,0.04657,0,-25,25,0.1028104094868405 +coef_school_child_age_under_16_duration_shift_effects,0.33667040040989843,0.32719999999999999,0,-25,25,0.33667040040989843 +coef_school_child_under_16_arrive_after_22,-0.29870062575080597,-1.1799999999999999,0,-25,25,-0.29870062575080597 +coef_shopping_tour_arrive_after_22,-2.4414941640980703,-0.60270000000000001,0,-25,25,-2.4414941640980703 +coef_shopping_tour_depart_before_8,-0.84790437236499061,-1.0369999999999999,0,-25,25,-0.84790437236499061 +coef_shopping_tour_departure_shift_effects,0.0090862416686240035,-0.060150000000000002,0,-25,25,0.0090862416686240035 +coef_shopping_tour_duration_lt_2_hours,0.912982413595951,0.51680000000000004,0,-25,25,0.912982413595951 +coef_shopping_tour_duration_shift_effects,-0.08012681553606732,-0.1208,0,-25,25,-0.08012681553606732 +coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.004765610611209883,-0.3992,0,-25,25,-0.004765610611209883 +coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.28646699211356075,-0.45619999999999999,0,-25,25,-0.28646699211356075 +coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.10477927540016083,-0.1731,0,-25,25,-0.10477927540016083 coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,-999,-999,0,-999,-999,-999 -coef_university_student_arrive_after_22,-0.53729852771430953,0.54659999999999997,0,-25,25,-0.53729852771430953 -coef_visit_tour_departure_shift_effects,0.16959832172498893,0.096879999999999994,0,-25,25,0.16959832172498893 -coef_visit_tour_duration_shift_effects,0.15578338984481574,0.1638,0,-25,25,0.15578338984481574 +coef_university_student_arrive_after_22,-0.4840281170137713,0.54659999999999997,0,-25,25,-0.4840281170137713 +coef_visit_tour_departure_shift_effects,0.1674492151765343,0.096879999999999994,0,-25,25,0.1674492151765343 +coef_visit_tour_duration_shift_effects,0.15890840399796446,0.1638,0,-25,25,0.15890840399796446 diff --git a/activitysim/examples/__init__.py b/activitysim/examples/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb b/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb index f6ce37d14..81092083f 100644 --- a/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb +++ b/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb @@ -34,11 +34,24 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'1.1.0'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import pandas as pd\n", + "import activitysim\n", + "activitysim.__version__" ] }, { @@ -73,13 +86,22 @@ "modelname = \"nonmand_tour_freq\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "model, data = component_model(modelname, return_data=True, condense_parameters=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "The prototype model spec we are re-estimating has 210 rows for each person type, but the\n", + "accompanying dataset is not large enough to successfully estimate anywhere near than many\n", + "parameters. The `condense_parameters` option is activated here as a short cut to making\n", + "a model that can be estimated with stable parameter results. When activated, it merges\n", + "parameters not only by name (i.e. when the same name appears twice it is the same parameter)\n", + "but also by value, so that if the initial value of any two parameters is identical\n", + "then they are treated as the same parameter. Using \"condense_parameters\" in actual model\n", + "estimation efforts is ill advised and may generate confusing or unexpected results.\n", + "\n", "This component actually has a distinct choice model for each person type, so\n", "instead of a single model there's a `dict` of models." ] @@ -712,13 +734,49 @@ "source": [ "# Estimate\n", "\n", - "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution. Larch has a built-in estimation methods including BHHH, and also offers access to more advanced general purpose non-linear optimizers in the `scipy` package, including SLSQP, which allows for bounds and constraints on parameters. BHHH is the default and typically runs faster, but does not follow constraints on parameters." + "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution. The prototype model spec we are re-estimating has 210 rows for each person type, but the accompanying dataset is not large enough to successfully estimate anywhere near than many parameters, so a short cut is applied by having one parameter only per unique existing parameter value." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Person type PTYPE_FULL has 210 utility terms and 72 unique parameters.\n", + "Person type PTYPE_PART has 210 utility terms and 51 unique parameters.\n", + "Person type PTYPE_UNIVERSITY has 210 utility terms and 70 unique parameters.\n", + "Person type PTYPE_NONWORK has 210 utility terms and 77 unique parameters.\n", + "Person type PTYPE_RETIRED has 210 utility terms and 53 unique parameters.\n", + "Person type PTYPE_DRIVING has 210 utility terms and 43 unique parameters.\n", + "Person type PTYPE_SCHOOL has 210 utility terms and 34 unique parameters.\n", + "Person type PTYPE_PRESCHOOL has 210 utility terms and 25 unique parameters.\n" + ] + } + ], + "source": [ + "for k, m in model.items():\n", + " print(f\"Person type {k} has {len(m.utility_ca)} utility terms and {len(m.pf)} unique parameters.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For future estimation work, parameters can be intelligently named and applied to match the model developer's desired structure (by using the same named parameter for multiple rows of the spec file). If this is done, the \"short cut\" should be disabled by setting `condense_parameters=False` in the loading step above.\n", + "\n", + "Larch has a built-in estimation methods including BHHH, and also offers access to more advanced general purpose non-linear optimizers in the `scipy` package, including SLSQP, which allows for bounds and constraints on parameters. BHHH is the default and typically runs faster, but does not follow constraints on parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stderr", @@ -742,7 +800,7 @@ { "data": { "text/html": [ - "

Best LL = -1831.0744991065408

" + "

Best LL = -1831.0744991680774

" ], "text/plain": [ "" @@ -788,8 +846,8 @@ " -2.000000\n", " -2.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -2.0\n", + " -2.0\n", " 1\n", " \n", " -2.000000\n", @@ -829,14 +887,14 @@ " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " 10.543980\n", + " 10.543979\n", " 0.7412\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 10.543980\n", + " 10.543979\n", " \n", " \n", " ...\n", @@ -915,7 +973,7 @@ "coef_1_escort_tour_constant 0.319037 0.0298 \n", "coef_1_plus_eating_out_tours_constant -1.012856 0.0097 \n", "coef_1_plus_maintenance_tours_constant -2.842643 0.1202 \n", - "coef_1_plus_other_discretionary_tours_constant 10.543980 0.7412 \n", + "coef_1_plus_other_discretionary_tours_constant 10.543979 0.7412 \n", "... ... ... \n", "coef_walk_access_to_retail_and_discretionary 0.160520 0.0567 \n", "coef_walk_access_to_retail_and_eating_out 0.211289 0.1450 \n", @@ -924,7 +982,7 @@ "coef_zero_car_ownership_and_tour_frequency_is_5... -0.227492 -0.3486 \n", "\n", " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 NaN \n", + "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", "coef_1_escort_tour_constant 0.0 NaN \n", "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", @@ -937,7 +995,7 @@ "coef_zero_car_ownership_and_tour_frequency_is_5... 0.0 NaN \n", "\n", " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour NaN 1 \n", + "coef_0_auto_household_and_escorting_tour -2.0 1 \n", "coef_1_escort_tour_constant NaN 0 \n", "coef_1_plus_eating_out_tours_constant NaN 0 \n", "coef_1_plus_maintenance_tours_constant NaN 0 \n", @@ -954,7 +1012,7 @@ "coef_1_escort_tour_constant 0.319037 \n", "coef_1_plus_eating_out_tours_constant -1.012856 \n", "coef_1_plus_maintenance_tours_constant -2.842643 \n", - "coef_1_plus_other_discretionary_tours_constant 10.543980 \n", + "coef_1_plus_other_discretionary_tours_constant 10.543979 \n", "... ... \n", "coef_walk_access_to_retail_and_discretionary 0.160520 \n", "coef_walk_access_to_retail_and_eating_out 0.211289 \n", @@ -972,11 +1030,16 @@ "name": "stderr", "output_type": "stream", "text": [ - ":2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.981360340588202e-07 in general_inverse\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 5.456681257677148e-07 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":2: RuntimeWarning: invalid value encountered in sqrt\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 3 parameter estimators with negative variance\n", + "- coef_1_escort_tour_constant\n", + "- coef_2_plus_escort_tours_constant\n", + "- coef_urban_and_escorting_tour\n", + " m.estimate(method='SLSQP')\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", " m.estimate(method='SLSQP')\n", "req_data does not request avail_ca or avail_co but it is set and being provided\n" ] @@ -996,7 +1059,7 @@ { "data": { "text/html": [ - "

Best LL = -856.8595081575636

" + "

Best LL = -856.8595081667797

" ], "text/plain": [ "" @@ -1042,8 +1105,8 @@ " -2.000000\n", " -2.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -2.0\n", + " -2.0\n", " 1\n", " \n", " -2.000000\n", @@ -1317,8 +1380,8 @@ " -999.000000\n", " -999.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -999.0\n", + " -999.0\n", " 1\n", " \n", " -999.000000\n", @@ -1446,14 +1509,14 @@ " \n", " \n", " coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1\n", - " 0.308612\n", + " 0.308611\n", " -0.1559\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 0.308612\n", + " 0.308611\n", " \n", " \n", " coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5\n", @@ -1548,8 +1611,8 @@ " 0.000000\n", " 0.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", " \n", " 0.000000\n", @@ -1641,7 +1704,7 @@ "coef_presence_of_part_time_worker_and_maintenan... -1.034909 -0.5621 \n", "coef_presence_of_pre_driving_school_kid_and_esc... 1.808890 1.5795 \n", "coef_presence_of_pre_school_kid_and_escorting_tour 0.291702 0.5414 \n", - "coef_presence_of_preschool_kid_in_household_and... 0.308612 -0.1559 \n", + "coef_presence_of_preschool_kid_in_household_and... 0.308611 -0.1559 \n", "coef_presence_of_preschool_kid_in_household_and... -0.530685 -0.5681 \n", "coef_presence_of_retiree_and_eating_out_tour 0.109209 -1.3890 \n", "coef_presence_of_retiree_and_escorting_tour -0.461150 -0.7516 \n", @@ -1657,7 +1720,7 @@ "coef_walk_access_to_retail_and_tour_frequency_i... 0.050631 0.3479 \n", "\n", " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 NaN \n", + "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", "coef_1_escort_tour_constant 0.0 NaN \n", "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", @@ -1682,7 +1745,7 @@ "coef_mediumlow_income_group_and_tour_frequency_... 0.0 NaN \n", "coef_mediumlow_income_group_and_tour_frequency_... 0.0 NaN \n", "coef_number_of_joint_tours_and_tour_frequency_is_4 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 NaN \n", + "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 -999.0 \n", "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", @@ -1703,14 +1766,14 @@ "coef_total_number_of_tours_is_2 0.0 NaN \n", "coef_total_number_of_tours_is_3 0.0 NaN \n", "coef_total_number_of_tours_is_4 0.0 NaN \n", - "coef_urban_and_discretionary_tour 0.0 NaN \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", "coef_urban_and_escorting_tour 0.0 NaN \n", "coef_walk_access_to_retail_and_tour_frequency_is_1 0.0 NaN \n", "coef_walk_access_to_retail_and_tour_frequency_is_2 0.0 NaN \n", "coef_walk_access_to_retail_and_tour_frequency_i... 0.0 NaN \n", "\n", " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour NaN 1 \n", + "coef_0_auto_household_and_escorting_tour -2.0 1 \n", "coef_1_escort_tour_constant NaN 0 \n", "coef_1_plus_eating_out_tours_constant NaN 0 \n", "coef_1_plus_maintenance_tours_constant NaN 0 \n", @@ -1735,7 +1798,7 @@ "coef_mediumlow_income_group_and_tour_frequency_... NaN 0 \n", "coef_mediumlow_income_group_and_tour_frequency_... NaN 0 \n", "coef_number_of_joint_tours_and_tour_frequency_is_4 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... NaN 1 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 1 \n", "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", @@ -1756,7 +1819,7 @@ "coef_total_number_of_tours_is_2 NaN 0 \n", "coef_total_number_of_tours_is_3 NaN 0 \n", "coef_total_number_of_tours_is_4 NaN 0 \n", - "coef_urban_and_discretionary_tour NaN 1 \n", + "coef_urban_and_discretionary_tour 0.0 1 \n", "coef_urban_and_escorting_tour NaN 0 \n", "coef_walk_access_to_retail_and_tour_frequency_is_1 NaN 0 \n", "coef_walk_access_to_retail_and_tour_frequency_is_2 NaN 0 \n", @@ -1800,7 +1863,7 @@ "coef_presence_of_part_time_worker_and_maintenan... -1.034909 \n", "coef_presence_of_pre_driving_school_kid_and_esc... 1.808890 \n", "coef_presence_of_pre_school_kid_and_escorting_tour 0.291702 \n", - "coef_presence_of_preschool_kid_in_household_and... 0.308612 \n", + "coef_presence_of_preschool_kid_in_household_and... 0.308611 \n", "coef_presence_of_preschool_kid_in_household_and... -0.530685 \n", "coef_presence_of_retiree_and_eating_out_tour 0.109209 \n", "coef_presence_of_retiree_and_escorting_tour -0.461150 \n", @@ -1823,11 +1886,15 @@ "name": "stderr", "output_type": "stream", "text": [ - ":2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 5.859529741365759e-08 in general_inverse\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.818666298527715e-07 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":2: RuntimeWarning: invalid value encountered in sqrt\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 2 parameter estimators with negative variance\n", + "- coef_1_escort_tour_constant\n", + "- coef_2_plus_escort_tours_constant\n", + " m.estimate(method='SLSQP')\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", " m.estimate(method='SLSQP')\n", "req_data does not request avail_ca or avail_co but it is set and being provided\n" ] @@ -1847,7 +1914,7 @@ { "data": { "text/html": [ - "

Best LL = -324.85841724064665

" + "

Best LL = -324.8584172756779

" ], "text/plain": [ "" @@ -1893,8 +1960,8 @@ " -2.000000\n", " -2.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -2.0\n", + " -2.0\n", " 1\n", " \n", " -2.000000\n", @@ -2029,7 +2096,7 @@ "coef_walk_access_to_retail_and_shopping 0.102379 0.0972 \n", "\n", " nullvalue minimum maximum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 NaN NaN \n", + "coef_0_auto_household_and_escorting_tour 0.0 -2.0 -2.0 \n", "coef_1_escort_tour_constant 0.0 NaN NaN \n", "coef_1_plus_eating_out_tours_constant 0.0 NaN NaN \n", "coef_1_plus_maintenance_tours_constant 0.0 NaN NaN \n", @@ -2064,11 +2131,18 @@ "name": "stderr", "output_type": "stream", "text": [ - ":2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.866421268239863e-13 in general_inverse\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.8717456834857003e-13 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":2: RuntimeWarning: invalid value encountered in sqrt\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 13 parameter estimators with negative variance\n", + "- coef_1_plus_eating_out_tours_constant\n", + "- coef_1_plus_shopping_tours_constant\n", + "- coef_1_plus_visting_tours_constant\n", + "- coef_logged_maximum_residual_window_tour_frequency_is_0\n", + "- and 9 more\n", + " m.estimate(method='SLSQP')\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", " m.estimate(method='SLSQP')\n", "req_data does not request avail_ca or avail_co but it is set and being provided\n" ] @@ -2088,7 +2162,7 @@ { "data": { "text/html": [ - "

Best LL = -1038.960565924978

" + "

Best LL = -1038.9605658891858

" ], "text/plain": [ "" @@ -2134,55 +2208,55 @@ " -2.000000\n", " -2.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -2.0\n", + " -2.0\n", " 1\n", " \n", " -2.000000\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " -4.317861\n", + " -4.317862\n", " -0.0629\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -4.317861\n", + " -4.317862\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " -4.459266\n", + " -4.459267\n", " -0.1429\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -4.459266\n", + " -4.459267\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " -9.609589\n", + " -9.609591\n", " -0.0653\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -9.609589\n", + " -9.609591\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " -4.488177\n", + " -4.488178\n", " 0.3334\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -4.488177\n", + " -4.488178\n", " \n", " \n", " ...\n", @@ -2258,10 +2332,10 @@ "text/plain": [ " value initvalue \\\n", "coef_0_auto_household_and_escorting_tour -2.000000 -2.0000 \n", - "coef_1_escort_tour_constant -4.317861 -0.0629 \n", - "coef_1_plus_eating_out_tours_constant -4.459266 -0.1429 \n", - "coef_1_plus_maintenance_tours_constant -9.609589 -0.0653 \n", - "coef_1_plus_other_discretionary_tours_constant -4.488177 0.3334 \n", + "coef_1_escort_tour_constant -4.317862 -0.0629 \n", + "coef_1_plus_eating_out_tours_constant -4.459267 -0.1429 \n", + "coef_1_plus_maintenance_tours_constant -9.609591 -0.0653 \n", + "coef_1_plus_other_discretionary_tours_constant -4.488178 0.3334 \n", "... ... ... \n", "coef_walk_access_to_retail_and_discretionary 0.214072 0.0772 \n", "coef_walk_access_to_retail_and_shopping 0.039849 0.0598 \n", @@ -2270,7 +2344,7 @@ "coef_walk_access_to_retail_and_tour_frequency_i... -0.260693 0.1508 \n", "\n", " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 NaN \n", + "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", "coef_1_escort_tour_constant 0.0 NaN \n", "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", @@ -2283,7 +2357,7 @@ "coef_walk_access_to_retail_and_tour_frequency_i... 0.0 NaN \n", "\n", " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour NaN 1 \n", + "coef_0_auto_household_and_escorting_tour -2.0 1 \n", "coef_1_escort_tour_constant NaN 0 \n", "coef_1_plus_eating_out_tours_constant NaN 0 \n", "coef_1_plus_maintenance_tours_constant NaN 0 \n", @@ -2297,10 +2371,10 @@ "\n", " best \n", "coef_0_auto_household_and_escorting_tour -2.000000 \n", - "coef_1_escort_tour_constant -4.317861 \n", - "coef_1_plus_eating_out_tours_constant -4.459266 \n", - "coef_1_plus_maintenance_tours_constant -9.609589 \n", - "coef_1_plus_other_discretionary_tours_constant -4.488177 \n", + "coef_1_escort_tour_constant -4.317862 \n", + "coef_1_plus_eating_out_tours_constant -4.459267 \n", + "coef_1_plus_maintenance_tours_constant -9.609591 \n", + "coef_1_plus_other_discretionary_tours_constant -4.488178 \n", "... ... \n", "coef_walk_access_to_retail_and_discretionary 0.214072 \n", "coef_walk_access_to_retail_and_shopping 0.039849 \n", @@ -2318,11 +2392,18 @@ "name": "stderr", "output_type": "stream", "text": [ - ":2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.6153105979883785e-16 in general_inverse\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.673077519570165e-16 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":2: RuntimeWarning: invalid value encountered in sqrt\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 21 parameter estimators with negative variance\n", + "- coef_1_escort_tour_constant\n", + "- coef_1_plus_eating_out_tours_constant\n", + "- coef_1_plus_maintenance_tours_constant\n", + "- coef_1_plus_other_discretionary_tours_constant\n", + "- and 17 more\n", + " m.estimate(method='SLSQP')\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", " m.estimate(method='SLSQP')\n", "req_data does not request avail_ca or avail_co but it is set and being provided\n" ] @@ -2342,7 +2423,7 @@ { "data": { "text/html": [ - "

Best LL = -787.4000431643133

" + "

Best LL = -787.4000431560225

" ], "text/plain": [ "" @@ -2388,8 +2469,8 @@ " -2.000000\n", " -2.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -2.0\n", + " -2.0\n", " 1\n", " \n", " -2.000000\n", @@ -2462,14 +2543,14 @@ " \n", " \n", " coef_2_plus_escort_tours_constant\n", - " -4.935936\n", + " -4.935937\n", " 0.5175\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -4.935936\n", + " -4.935937\n", " \n", " \n", " coef_car_surplus_vs_workers_and_tour_frequency_is_1\n", @@ -2729,8 +2810,8 @@ " -999.000000\n", " -999.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -999.0\n", + " -999.0\n", " 1\n", " \n", " -999.000000\n", @@ -2949,8 +3030,8 @@ " 0.000000\n", " 0.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", " \n", " 0.000000\n", @@ -2979,7 +3060,7 @@ "coef_1_plus_other_discretionary_tours_constant -2.243196 0.4282 \n", "coef_1_plus_shopping_tours_constant -1.916643 0.5947 \n", "coef_1_plus_visting_tours_constant -2.344018 0.2789 \n", - "coef_2_plus_escort_tours_constant -4.935936 0.5175 \n", + "coef_2_plus_escort_tours_constant -4.935937 0.5175 \n", "coef_car_surplus_vs_workers_and_tour_frequency_... 3.094754 0.7965 \n", "coef_car_surplus_vs_workers_and_tour_frequency_... 3.475854 2.1302 \n", "coef_female_and_discretionary_tour 0.541528 0.4954 \n", @@ -3027,7 +3108,7 @@ "coef_walk_access_to_retail_and_tour_frequency_i... 0.691082 0.0616 \n", "\n", " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 NaN \n", + "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", "coef_1_escort_tour_constant 0.0 NaN \n", "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", @@ -3058,7 +3139,7 @@ "coef_number_of_joint_shopping_tours 0.0 NaN \n", "coef_number_of_joint_tours_and_tour_frequency_is_2 0.0 NaN \n", "coef_number_of_joint_tours_and_tour_frequency_is_3 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 NaN \n", + "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 -999.0 \n", "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", "coef_presence_of_full_time_worker_and_discretio... 0.0 NaN \n", "coef_presence_of_full_time_worker_and_shopping_... 0.0 NaN \n", @@ -3078,11 +3159,11 @@ "coef_total_number_of_tours_is_3 0.0 NaN \n", "coef_total_number_of_tours_is_4 0.0 NaN \n", "coef_total_number_of_tours_is_5 0.0 NaN \n", - "coef_urban_and_discretionary_tour 0.0 NaN \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", "coef_walk_access_to_retail_and_tour_frequency_i... 0.0 NaN \n", "\n", " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour NaN 1 \n", + "coef_0_auto_household_and_escorting_tour -2.0 1 \n", "coef_1_escort_tour_constant NaN 0 \n", "coef_1_plus_eating_out_tours_constant NaN 0 \n", "coef_1_plus_maintenance_tours_constant NaN 0 \n", @@ -3113,7 +3194,7 @@ "coef_number_of_joint_shopping_tours NaN 0 \n", "coef_number_of_joint_tours_and_tour_frequency_is_2 NaN 0 \n", "coef_number_of_joint_tours_and_tour_frequency_is_3 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... NaN 1 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 1 \n", "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", "coef_presence_of_full_time_worker_and_discretio... NaN 0 \n", "coef_presence_of_full_time_worker_and_shopping_... NaN 0 \n", @@ -3133,7 +3214,7 @@ "coef_total_number_of_tours_is_3 NaN 0 \n", "coef_total_number_of_tours_is_4 NaN 0 \n", "coef_total_number_of_tours_is_5 NaN 0 \n", - "coef_urban_and_discretionary_tour NaN 1 \n", + "coef_urban_and_discretionary_tour 0.0 1 \n", "coef_walk_access_to_retail_and_tour_frequency_i... NaN 0 \n", "\n", " best \n", @@ -3144,7 +3225,7 @@ "coef_1_plus_other_discretionary_tours_constant -2.243196 \n", "coef_1_plus_shopping_tours_constant -1.916643 \n", "coef_1_plus_visting_tours_constant -2.344018 \n", - "coef_2_plus_escort_tours_constant -4.935936 \n", + "coef_2_plus_escort_tours_constant -4.935937 \n", "coef_car_surplus_vs_workers_and_tour_frequency_... 3.094754 \n", "coef_car_surplus_vs_workers_and_tour_frequency_... 3.475854 \n", "coef_female_and_discretionary_tour 0.541528 \n", @@ -3199,10 +3280,19 @@ "name": "stderr", "output_type": "stream", "text": [ - ":2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 9.97417709684196e-16 in general_inverse\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.7375419460797773e-15 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 11 parameter estimators with negative variance\n", + "- coef_1_escort_tour_constant\n", + "- coef_1_plus_eating_out_tours_constant\n", + "- coef_1_plus_maintenance_tours_constant\n", + "- coef_1_plus_other_discretionary_tours_constant\n", + "- and 7 more\n", + " m.estimate(method='SLSQP')\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", + " m.estimate(method='SLSQP')\n", "req_data does not request avail_ca or avail_co but it is set and being provided\n" ] }, @@ -3221,7 +3311,7 @@ { "data": { "text/html": [ - "

Best LL = -13.427352349597697

" + "

Best LL = -13.427352167831913

" ], "text/plain": [ "" @@ -3264,476 +3354,476 @@ " \n", " \n", " coef_0_auto_household_and_escorting_tour\n", - " -289.822903\n", + " -289.819438\n", " -2.0000\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -289.822903\n", + " -289.819438\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " -6435.039018\n", + " -6434.961317\n", " -0.4934\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -6435.039018\n", + " -6434.961317\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " -3885.458878\n", + " -3885.411913\n", " -0.0242\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -3885.458878\n", + " -3885.411913\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " -1195.820538\n", + " -1195.807014\n", " -0.4344\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1195.820538\n", + " -1195.807014\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " -2406.835103\n", + " -2406.806121\n", " -0.2602\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -2406.835103\n", + " -2406.806121\n", " \n", " \n", " coef_1_plus_shopping_tours_constant\n", - " -2389.332994\n", + " -2389.305945\n", " 0.5320\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -2389.332994\n", + " -2389.305945\n", " \n", " \n", " coef_1_plus_visting_tours_constant\n", - " -2389.333045\n", + " -2389.305996\n", " 0.2367\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -2389.333045\n", + " -2389.305996\n", " \n", " \n", " coef_2_plus_escort_tours_constant\n", - " -5667.753696\n", + " -5667.685401\n", " 1.4155\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -5667.753696\n", + " -5667.685401\n", " \n", " \n", " coef_auto_access_to_retail_and_tour_frequency_is_5_plus\n", - " -44.188317\n", + " -44.188373\n", " 0.1004\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -44.188317\n", + " -44.188373\n", " \n", " \n", " coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus\n", - " -17233.038190\n", + " -17232.832071\n", " -0.6369\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -17233.038190\n", + " -17232.832071\n", " \n", " \n", " coef_car_surplus_vs_workers_and_tour_frequency_is_1\n", - " -4035.668234\n", + " -4035.619542\n", " 0.2902\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -4035.668234\n", + " -4035.619542\n", " \n", " \n", " coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus\n", - " 15226.177337\n", + " 15225.992542\n", " 2.0352\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 15226.177337\n", + " 15225.992542\n", " \n", " \n", " coef_high_income_group_and_discretionary_tour\n", - " 4481.138425\n", + " 4481.084393\n", " 2.3270\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 4481.138425\n", + " 4481.084393\n", " \n", " \n", " coef_high_income_group_and_eating_out_tour\n", - " -3157.311928\n", + " -3157.273834\n", " 0.4916\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -3157.311928\n", + " -3157.273834\n", " \n", " \n", " coef_high_income_group_and_maintenance_tour\n", - " 4461.536961\n", + " 4461.484862\n", " 0.3982\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 4461.536961\n", + " 4461.484862\n", " \n", " \n", " coef_high_income_group_and_shopping_tour\n", - " 4461.442902\n", + " 4461.390803\n", " 0.2443\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 4461.442902\n", + " 4461.390803\n", " \n", " \n", " coef_high_income_group_and_visiting_tour\n", - " 4461.442950\n", + " 4461.390852\n", " 0.2858\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 4461.442950\n", + " 4461.390852\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_1\n", - " 16.594317\n", + " 16.594329\n", " 1.3298\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 16.594317\n", + " 16.594329\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_2\n", - " -19743.176298\n", + " -19742.937757\n", " 1.3759\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -19743.176298\n", + " -19742.937757\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_5_plus\n", - " -11491.799030\n", + " -11491.665678\n", " 3.2808\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -11491.799030\n", + " -11491.665678\n", " \n", " \n", " coef_mediumhigh_income_group_and_discretionary_tour\n", - " 572.853274\n", + " 572.847509\n", " 1.4050\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 572.853274\n", + " 572.847509\n", " \n", " \n", " coef_mediumlow_income_group_and_discretionary_tour\n", - " -4429.995709\n", + " -4429.942211\n", " 0.9169\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -4429.995709\n", + " -4429.942211\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_1\n", - " -4180.405770\n", + " -4180.353968\n", " -0.2162\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -4180.405770\n", + " -4180.353968\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_2\n", - " -1606.522520\n", + " -1606.502880\n", " -0.3587\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1606.522520\n", + " -1606.502880\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_3\n", - " -109.820520\n", + " -109.819329\n", " -4.2701\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -109.820520\n", + " -109.819329\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_5_plus\n", " -999.000000\n", " -999.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -999.0\n", + " -999.0\n", " 1\n", " \n", " -999.000000\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_1\n", - " -16877.228974\n", + " -16877.023970\n", " -0.2340\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -16877.228974\n", + " -16877.023970\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_2\n", - " 12620.915951\n", + " 12620.764595\n", " -0.9231\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 12620.915951\n", + " 12620.764595\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_3\n", - " -4862.054249\n", + " -4861.997912\n", " -6.5835\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -4862.054249\n", + " -4861.997912\n", " \n", " \n", " coef_presence_of_driving_school_kid_and_discretionary_tour\n", - " -578.879026\n", + " -578.872073\n", " -0.9202\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -578.879026\n", + " -578.872073\n", " \n", " \n", " coef_presence_of_driving_school_kid_and_eating_out_tour\n", - " 6.381808\n", + " 6.381728\n", " -0.6377\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 6.381808\n", + " 6.381728\n", " \n", " \n", " coef_presence_of_non_worker_and_tour_frequency_is_2\n", - " 878.020904\n", + " 878.008827\n", " -0.6571\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 878.020904\n", + " 878.008827\n", " \n", " \n", " coef_presence_of_non_worker_and_tour_frequency_is_5\n", - " -1944.945175\n", + " -1944.922644\n", " -1.4044\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1944.945175\n", + " -1944.922644\n", " \n", " \n", " coef_presence_of_pre_driving_school_kid_and_eating_out_tour\n", - " -164.908265\n", + " -164.906255\n", " -1.5698\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -164.908265\n", + " -164.906255\n", " \n", " \n", " coef_presence_of_pre_school_kid_and_eating_out_tour\n", - " 313.480011\n", + " 313.476309\n", " -0.2987\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 313.480011\n", + " 313.476309\n", " \n", " \n", " coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1\n", - " 4033.615564\n", + " 4033.566871\n", " -0.3219\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 4033.615564\n", + " 4033.566871\n", " \n", " \n", " coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5\n", - " 10149.453410\n", + " 10149.331976\n", " -1.0874\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 10149.453410\n", + " 10149.331976\n", " \n", " \n", " coef_presence_of_university_student_and_discretionary_tour\n", - " 3906.426671\n", + " 3906.378403\n", " -1.2834\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 3906.426671\n", + " 3906.378403\n", " \n", " \n", " coef_total_number_of_tours_is_1\n", - " 15200.841768\n", + " 15200.662337\n", " -7.1506\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 15200.841768\n", + " 15200.662337\n", " \n", " \n", " coef_total_number_of_tours_is_2\n", - " -1638.576969\n", + " -1638.556186\n", " -11.1214\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1638.576969\n", + " -1638.556186\n", " \n", " \n", " coef_total_number_of_tours_is_3\n", - " -14010.773566\n", + " -14010.608324\n", " -13.1750\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -14010.773566\n", + " -14010.608324\n", " \n", " \n", " coef_urban_and_discretionary_tour\n", " 0.000000\n", " 0.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", " \n", " 0.000000\n", " \n", " \n", " coef_urban_and_maintenance_tour\n", - " -1194.346738\n", + " -1194.333214\n", " 1.0394\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -1194.346738\n", + " -1194.333214\n", " \n", " \n", "\n", @@ -3741,49 +3831,49 @@ ], "text/plain": [ " value initvalue \\\n", - "coef_0_auto_household_and_escorting_tour -289.822903 -2.0000 \n", - "coef_1_escort_tour_constant -6435.039018 -0.4934 \n", - "coef_1_plus_eating_out_tours_constant -3885.458878 -0.0242 \n", - "coef_1_plus_maintenance_tours_constant -1195.820538 -0.4344 \n", - "coef_1_plus_other_discretionary_tours_constant -2406.835103 -0.2602 \n", - "coef_1_plus_shopping_tours_constant -2389.332994 0.5320 \n", - "coef_1_plus_visting_tours_constant -2389.333045 0.2367 \n", - "coef_2_plus_escort_tours_constant -5667.753696 1.4155 \n", - "coef_auto_access_to_retail_and_tour_frequency_i... -44.188317 0.1004 \n", - "coef_car_shortage_vs_workers_and_tour_frequency... -17233.038190 -0.6369 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... -4035.668234 0.2902 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 15226.177337 2.0352 \n", - "coef_high_income_group_and_discretionary_tour 4481.138425 2.3270 \n", - "coef_high_income_group_and_eating_out_tour -3157.311928 0.4916 \n", - "coef_high_income_group_and_maintenance_tour 4461.536961 0.3982 \n", - "coef_high_income_group_and_shopping_tour 4461.442902 0.2443 \n", - "coef_high_income_group_and_visiting_tour 4461.442950 0.2858 \n", - "coef_logged_maximum_residual_window_tour_freque... 16.594317 1.3298 \n", - "coef_logged_maximum_residual_window_tour_freque... -19743.176298 1.3759 \n", - "coef_logged_maximum_residual_window_tour_freque... -11491.799030 3.2808 \n", - "coef_mediumhigh_income_group_and_discretionary_... 572.853274 1.4050 \n", - "coef_mediumlow_income_group_and_discretionary_tour -4429.995709 0.9169 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_1 -4180.405770 -0.2162 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 -1606.522520 -0.3587 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 -109.820520 -4.2701 \n", + "coef_0_auto_household_and_escorting_tour -289.819438 -2.0000 \n", + "coef_1_escort_tour_constant -6434.961317 -0.4934 \n", + "coef_1_plus_eating_out_tours_constant -3885.411913 -0.0242 \n", + "coef_1_plus_maintenance_tours_constant -1195.807014 -0.4344 \n", + "coef_1_plus_other_discretionary_tours_constant -2406.806121 -0.2602 \n", + "coef_1_plus_shopping_tours_constant -2389.305945 0.5320 \n", + "coef_1_plus_visting_tours_constant -2389.305996 0.2367 \n", + "coef_2_plus_escort_tours_constant -5667.685401 1.4155 \n", + "coef_auto_access_to_retail_and_tour_frequency_i... -44.188373 0.1004 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... -17232.832071 -0.6369 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... -4035.619542 0.2902 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 15225.992542 2.0352 \n", + "coef_high_income_group_and_discretionary_tour 4481.084393 2.3270 \n", + "coef_high_income_group_and_eating_out_tour -3157.273834 0.4916 \n", + "coef_high_income_group_and_maintenance_tour 4461.484862 0.3982 \n", + "coef_high_income_group_and_shopping_tour 4461.390803 0.2443 \n", + "coef_high_income_group_and_visiting_tour 4461.390852 0.2858 \n", + "coef_logged_maximum_residual_window_tour_freque... 16.594329 1.3298 \n", + "coef_logged_maximum_residual_window_tour_freque... -19742.937757 1.3759 \n", + "coef_logged_maximum_residual_window_tour_freque... -11491.665678 3.2808 \n", + "coef_mediumhigh_income_group_and_discretionary_... 572.847509 1.4050 \n", + "coef_mediumlow_income_group_and_discretionary_tour -4429.942211 0.9169 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_1 -4180.353968 -0.2162 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 -1606.502880 -0.3587 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 -109.819329 -4.2701 \n", "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.0000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -16877.228974 -0.2340 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 12620.915951 -0.9231 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -4862.054249 -6.5835 \n", - "coef_presence_of_driving_school_kid_and_discret... -578.879026 -0.9202 \n", - "coef_presence_of_driving_school_kid_and_eating_... 6.381808 -0.6377 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 878.020904 -0.6571 \n", - "coef_presence_of_non_worker_and_tour_frequency_... -1944.945175 -1.4044 \n", - "coef_presence_of_pre_driving_school_kid_and_eat... -164.908265 -1.5698 \n", - "coef_presence_of_pre_school_kid_and_eating_out_... 313.480011 -0.2987 \n", - "coef_presence_of_predriving_school_kid_in_house... 4033.615564 -0.3219 \n", - "coef_presence_of_predriving_school_kid_in_house... 10149.453410 -1.0874 \n", - "coef_presence_of_university_student_and_discret... 3906.426671 -1.2834 \n", - "coef_total_number_of_tours_is_1 15200.841768 -7.1506 \n", - "coef_total_number_of_tours_is_2 -1638.576969 -11.1214 \n", - "coef_total_number_of_tours_is_3 -14010.773566 -13.1750 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -16877.023970 -0.2340 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 12620.764595 -0.9231 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -4861.997912 -6.5835 \n", + "coef_presence_of_driving_school_kid_and_discret... -578.872073 -0.9202 \n", + "coef_presence_of_driving_school_kid_and_eating_... 6.381728 -0.6377 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 878.008827 -0.6571 \n", + "coef_presence_of_non_worker_and_tour_frequency_... -1944.922644 -1.4044 \n", + "coef_presence_of_pre_driving_school_kid_and_eat... -164.906255 -1.5698 \n", + "coef_presence_of_pre_school_kid_and_eating_out_... 313.476309 -0.2987 \n", + "coef_presence_of_predriving_school_kid_in_house... 4033.566871 -0.3219 \n", + "coef_presence_of_predriving_school_kid_in_house... 10149.331976 -1.0874 \n", + "coef_presence_of_university_student_and_discret... 3906.378403 -1.2834 \n", + "coef_total_number_of_tours_is_1 15200.662337 -7.1506 \n", + "coef_total_number_of_tours_is_2 -1638.556186 -11.1214 \n", + "coef_total_number_of_tours_is_3 -14010.608324 -13.1750 \n", "coef_urban_and_discretionary_tour 0.000000 0.0000 \n", - "coef_urban_and_maintenance_tour -1194.346738 1.0394 \n", + "coef_urban_and_maintenance_tour -1194.333214 1.0394 \n", "\n", " nullvalue minimum \\\n", "coef_0_auto_household_and_escorting_tour 0.0 NaN \n", @@ -3811,7 +3901,7 @@ "coef_number_of_joint_tours_and_tour_frequency_is_1 0.0 NaN \n", "coef_number_of_joint_tours_and_tour_frequency_is_2 0.0 NaN \n", "coef_number_of_joint_tours_and_tour_frequency_is_3 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 NaN \n", + "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 -999.0 \n", "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", @@ -3827,7 +3917,7 @@ "coef_total_number_of_tours_is_1 0.0 NaN \n", "coef_total_number_of_tours_is_2 0.0 NaN \n", "coef_total_number_of_tours_is_3 0.0 NaN \n", - "coef_urban_and_discretionary_tour 0.0 NaN \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", "coef_urban_and_maintenance_tour 0.0 NaN \n", "\n", " maximum holdfast note \\\n", @@ -3856,7 +3946,7 @@ "coef_number_of_joint_tours_and_tour_frequency_is_1 NaN 0 \n", "coef_number_of_joint_tours_and_tour_frequency_is_2 NaN 0 \n", "coef_number_of_joint_tours_and_tour_frequency_is_3 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... NaN 1 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 1 \n", "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", @@ -3872,53 +3962,53 @@ "coef_total_number_of_tours_is_1 NaN 0 \n", "coef_total_number_of_tours_is_2 NaN 0 \n", "coef_total_number_of_tours_is_3 NaN 0 \n", - "coef_urban_and_discretionary_tour NaN 1 \n", + "coef_urban_and_discretionary_tour 0.0 1 \n", "coef_urban_and_maintenance_tour NaN 0 \n", "\n", " best \n", - "coef_0_auto_household_and_escorting_tour -289.822903 \n", - "coef_1_escort_tour_constant -6435.039018 \n", - "coef_1_plus_eating_out_tours_constant -3885.458878 \n", - "coef_1_plus_maintenance_tours_constant -1195.820538 \n", - "coef_1_plus_other_discretionary_tours_constant -2406.835103 \n", - "coef_1_plus_shopping_tours_constant -2389.332994 \n", - "coef_1_plus_visting_tours_constant -2389.333045 \n", - "coef_2_plus_escort_tours_constant -5667.753696 \n", - "coef_auto_access_to_retail_and_tour_frequency_i... -44.188317 \n", - "coef_car_shortage_vs_workers_and_tour_frequency... -17233.038190 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... -4035.668234 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 15226.177337 \n", - "coef_high_income_group_and_discretionary_tour 4481.138425 \n", - "coef_high_income_group_and_eating_out_tour -3157.311928 \n", - "coef_high_income_group_and_maintenance_tour 4461.536961 \n", - "coef_high_income_group_and_shopping_tour 4461.442902 \n", - "coef_high_income_group_and_visiting_tour 4461.442950 \n", - "coef_logged_maximum_residual_window_tour_freque... 16.594317 \n", - "coef_logged_maximum_residual_window_tour_freque... -19743.176298 \n", - "coef_logged_maximum_residual_window_tour_freque... -11491.799030 \n", - "coef_mediumhigh_income_group_and_discretionary_... 572.853274 \n", - "coef_mediumlow_income_group_and_discretionary_tour -4429.995709 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_1 -4180.405770 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 -1606.522520 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 -109.820520 \n", + "coef_0_auto_household_and_escorting_tour -289.819438 \n", + "coef_1_escort_tour_constant -6434.961317 \n", + "coef_1_plus_eating_out_tours_constant -3885.411913 \n", + "coef_1_plus_maintenance_tours_constant -1195.807014 \n", + "coef_1_plus_other_discretionary_tours_constant -2406.806121 \n", + "coef_1_plus_shopping_tours_constant -2389.305945 \n", + "coef_1_plus_visting_tours_constant -2389.305996 \n", + "coef_2_plus_escort_tours_constant -5667.685401 \n", + "coef_auto_access_to_retail_and_tour_frequency_i... -44.188373 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... -17232.832071 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... -4035.619542 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 15225.992542 \n", + "coef_high_income_group_and_discretionary_tour 4481.084393 \n", + "coef_high_income_group_and_eating_out_tour -3157.273834 \n", + "coef_high_income_group_and_maintenance_tour 4461.484862 \n", + "coef_high_income_group_and_shopping_tour 4461.390803 \n", + "coef_high_income_group_and_visiting_tour 4461.390852 \n", + "coef_logged_maximum_residual_window_tour_freque... 16.594329 \n", + "coef_logged_maximum_residual_window_tour_freque... -19742.937757 \n", + "coef_logged_maximum_residual_window_tour_freque... -11491.665678 \n", + "coef_mediumhigh_income_group_and_discretionary_... 572.847509 \n", + "coef_mediumlow_income_group_and_discretionary_tour -4429.942211 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_1 -4180.353968 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 -1606.502880 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 -109.819329 \n", "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -16877.228974 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 12620.915951 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -4862.054249 \n", - "coef_presence_of_driving_school_kid_and_discret... -578.879026 \n", - "coef_presence_of_driving_school_kid_and_eating_... 6.381808 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 878.020904 \n", - "coef_presence_of_non_worker_and_tour_frequency_... -1944.945175 \n", - "coef_presence_of_pre_driving_school_kid_and_eat... -164.908265 \n", - "coef_presence_of_pre_school_kid_and_eating_out_... 313.480011 \n", - "coef_presence_of_predriving_school_kid_in_house... 4033.615564 \n", - "coef_presence_of_predriving_school_kid_in_house... 10149.453410 \n", - "coef_presence_of_university_student_and_discret... 3906.426671 \n", - "coef_total_number_of_tours_is_1 15200.841768 \n", - "coef_total_number_of_tours_is_2 -1638.576969 \n", - "coef_total_number_of_tours_is_3 -14010.773566 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -16877.023970 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 12620.764595 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -4861.997912 \n", + "coef_presence_of_driving_school_kid_and_discret... -578.872073 \n", + "coef_presence_of_driving_school_kid_and_eating_... 6.381728 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 878.008827 \n", + "coef_presence_of_non_worker_and_tour_frequency_... -1944.922644 \n", + "coef_presence_of_pre_driving_school_kid_and_eat... -164.906255 \n", + "coef_presence_of_pre_school_kid_and_eating_out_... 313.476309 \n", + "coef_presence_of_predriving_school_kid_in_house... 4033.566871 \n", + "coef_presence_of_predriving_school_kid_in_house... 10149.331976 \n", + "coef_presence_of_university_student_and_discret... 3906.378403 \n", + "coef_total_number_of_tours_is_1 15200.662337 \n", + "coef_total_number_of_tours_is_2 -1638.556186 \n", + "coef_total_number_of_tours_is_3 -14010.608324 \n", "coef_urban_and_discretionary_tour 0.000000 \n", - "coef_urban_and_maintenance_tour -1194.346738 " + "coef_urban_and_maintenance_tour -1194.333214 " ] }, "metadata": {}, @@ -3928,11 +4018,18 @@ "name": "stderr", "output_type": "stream", "text": [ - ":2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 0.0 in general_inverse\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 0.0 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":2: RuntimeWarning: invalid value encountered in sqrt\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 14 parameter estimators with negative variance\n", + "- coef_1_plus_maintenance_tours_constant\n", + "- coef_1_plus_other_discretionary_tours_constant\n", + "- coef_1_plus_shopping_tours_constant\n", + "- coef_1_plus_visting_tours_constant\n", + "- and 10 more\n", + " m.estimate(method='SLSQP')\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", " m.estimate(method='SLSQP')\n", "req_data does not request avail_ca or avail_co but it is set and being provided\n" ] @@ -3952,7 +4049,7 @@ { "data": { "text/html": [ - 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Best LL = -256.78783280769073

" + "

Best LL = -256.78783276592117

" ], "text/plain": [ "" @@ -3998,8 +4095,8 @@ " -2.000000\n", " -2.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -2.0\n", + " -2.0\n", " 1\n", " \n", " -2.000000\n", @@ -4116,14 +4213,14 @@ " \n", " \n", " coef_high_income_group_and_tour_frequency_is_5_plus\n", - " 16.017263\n", + " 16.017262\n", " 2.0175\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " 16.017263\n", + " 16.017262\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_5_plus\n", @@ -4185,22 +4282,22 @@ " -999.000000\n", " -999.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -999.0\n", + " -999.0\n", " 1\n", " \n", " -999.000000\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_1\n", - " -9.889680\n", + " -9.889679\n", " -1.0331\n", " 0.0\n", " NaN\n", " NaN\n", " 0\n", " \n", - " -9.889680\n", + " -9.889679\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_3\n", @@ -4339,8 +4436,8 @@ " 0.000000\n", " 0.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", " \n", " 0.000000\n", @@ -4384,14 +4481,14 @@ "coef_auto_access_to_retail_and_escorting 0.555108 0.0629 \n", "coef_high_income_group_and_eating_out_tour -1.361980 -0.7010 \n", "coef_high_income_group_and_shopping_tour -3.736670 -0.6506 \n", - "coef_high_income_group_and_tour_frequency_is_5_... 16.017263 2.0175 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 16.017262 2.0175 \n", "coef_logged_maximum_residual_window_tour_freque... 1.526025 1.5603 \n", "coef_mediumhigh_income_group_and_tour_frequency... 15.096814 1.5197 \n", "coef_mediumlow_income_group_and_tour_frequency_... 14.227759 1.0873 \n", "coef_number_of_joint_maintenance_tours -1.347600 -1.3476 \n", "coef_number_of_joint_tours_and_tour_frequency_is_2 -1.197115 -0.6149 \n", "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.0000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -9.889680 -1.0331 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -9.889679 -1.0331 \n", "coef_number_of_mandatory_tours_and_tour_frequen... -10.675107 -2.7445 \n", "coef_presence_of_full_time_worker_and_discretio... 0.167217 0.7526 \n", "coef_presence_of_non_worker_and_eating_out_tour -1.295662 -1.3074 \n", @@ -4409,7 +4506,7 @@ "coef_walk_access_to_retail_and_eating_out 0.400103 0.0738 \n", "\n", " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 NaN \n", + "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", "coef_1_escort_tour_constant 0.0 NaN \n", "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", @@ -4426,7 +4523,7 @@ "coef_mediumlow_income_group_and_tour_frequency_... 0.0 NaN \n", "coef_number_of_joint_maintenance_tours 0.0 NaN \n", "coef_number_of_joint_tours_and_tour_frequency_is_2 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 NaN \n", + "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 -999.0 \n", "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", "coef_presence_of_full_time_worker_and_discretio... 0.0 NaN \n", @@ -4440,12 +4537,12 @@ "coef_total_number_of_tours_is_1 0.0 NaN \n", "coef_total_number_of_tours_is_2 0.0 NaN \n", "coef_total_number_of_tours_is_3 0.0 NaN \n", - "coef_urban_and_discretionary_tour 0.0 NaN \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", "coef_urban_and_escorting_tour 0.0 NaN \n", "coef_walk_access_to_retail_and_eating_out 0.0 NaN \n", "\n", " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour NaN 1 \n", + "coef_0_auto_household_and_escorting_tour -2.0 1 \n", "coef_1_escort_tour_constant NaN 0 \n", "coef_1_plus_eating_out_tours_constant NaN 0 \n", "coef_1_plus_maintenance_tours_constant NaN 0 \n", @@ -4462,7 +4559,7 @@ "coef_mediumlow_income_group_and_tour_frequency_... NaN 0 \n", "coef_number_of_joint_maintenance_tours NaN 0 \n", "coef_number_of_joint_tours_and_tour_frequency_is_2 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... NaN 1 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 1 \n", "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", "coef_presence_of_full_time_worker_and_discretio... NaN 0 \n", @@ -4476,7 +4573,7 @@ "coef_total_number_of_tours_is_1 NaN 0 \n", "coef_total_number_of_tours_is_2 NaN 0 \n", "coef_total_number_of_tours_is_3 NaN 0 \n", - "coef_urban_and_discretionary_tour NaN 1 \n", + "coef_urban_and_discretionary_tour 0.0 1 \n", "coef_urban_and_escorting_tour NaN 0 \n", "coef_walk_access_to_retail_and_eating_out NaN 0 \n", "\n", @@ -4492,14 +4589,14 @@ "coef_auto_access_to_retail_and_escorting 0.555108 \n", "coef_high_income_group_and_eating_out_tour -1.361980 \n", "coef_high_income_group_and_shopping_tour -3.736670 \n", - "coef_high_income_group_and_tour_frequency_is_5_... 16.017263 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 16.017262 \n", "coef_logged_maximum_residual_window_tour_freque... 1.526025 \n", "coef_mediumhigh_income_group_and_tour_frequency... 15.096814 \n", "coef_mediumlow_income_group_and_tour_frequency_... 14.227759 \n", "coef_number_of_joint_maintenance_tours -1.347600 \n", "coef_number_of_joint_tours_and_tour_frequency_is_2 -1.197115 \n", "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -9.889680 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -9.889679 \n", "coef_number_of_mandatory_tours_and_tour_frequen... -10.675107 \n", "coef_presence_of_full_time_worker_and_discretio... 0.167217 \n", "coef_presence_of_non_worker_and_eating_out_tour -1.295662 \n", @@ -4524,11 +4621,16 @@ "name": "stderr", "output_type": "stream", "text": [ - ":2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.618193006591901e-16 in general_inverse\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.064162929131762e-17 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":2: RuntimeWarning: invalid value encountered in sqrt\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 3 parameter estimators with negative variance\n", + "- coef_1_escort_tour_constant\n", + "- coef_2_plus_escort_tours_constant\n", + "- coef_urban_and_escorting_tour\n", + " m.estimate(method='SLSQP')\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", " m.estimate(method='SLSQP')\n", "req_data does not request avail_ca or avail_co but it is set and being provided\n" ] @@ -4548,7 +4650,7 @@ { "data": { "text/html": [ - "

Best LL = -225.00971520122775

" + "

Best LL = -225.00971520122772

" ], "text/plain": [ "" @@ -4594,8 +4696,8 @@ " -2.000000\n", " -2.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -2.0\n", + " -2.0\n", " 1\n", " \n", " -2.000000\n", @@ -4836,8 +4938,8 @@ " -999.000000\n", " -999.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " -999.0\n", + " -999.0\n", " 1\n", " \n", " -999.000000\n", @@ -4847,8 +4949,8 @@ " 0.000000\n", " 0.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", " \n", " 0.000000\n", @@ -4897,7 +4999,7 @@ "coef_visiting_or_social_tour 0.315314 0.7690 \n", "\n", " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 NaN \n", + "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", "coef_1_escort_tour_constant 0.0 NaN \n", "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", @@ -4919,12 +5021,12 @@ "coef_total_number_of_tours_is_2 0.0 NaN \n", "coef_total_number_of_tours_is_3 0.0 NaN \n", "coef_total_number_of_tours_is_4 0.0 NaN \n", - "coef_total_number_of_tours_is_6_plus 0.0 NaN \n", - "coef_urban_and_discretionary_tour 0.0 NaN \n", + "coef_total_number_of_tours_is_6_plus 0.0 -999.0 \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", "coef_visiting_or_social_tour 0.0 NaN \n", "\n", " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour NaN 1 \n", + "coef_0_auto_household_and_escorting_tour -2.0 1 \n", "coef_1_escort_tour_constant NaN 0 \n", "coef_1_plus_eating_out_tours_constant NaN 0 \n", "coef_1_plus_maintenance_tours_constant NaN 0 \n", @@ -4946,8 +5048,8 @@ "coef_total_number_of_tours_is_2 NaN 0 \n", "coef_total_number_of_tours_is_3 NaN 0 \n", "coef_total_number_of_tours_is_4 NaN 0 \n", - "coef_total_number_of_tours_is_6_plus NaN 1 \n", - "coef_urban_and_discretionary_tour NaN 1 \n", + "coef_total_number_of_tours_is_6_plus -999.0 1 \n", + "coef_urban_and_discretionary_tour 0.0 1 \n", "coef_visiting_or_social_tour NaN 0 \n", "\n", " best \n", @@ -4985,11 +5087,18 @@ "name": "stderr", "output_type": "stream", "text": [ - ":2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.954982550090504e-08 in general_inverse\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.8528068249273466e-08 in general_inverse\n", " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":2: RuntimeWarning: invalid value encountered in sqrt\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 14 parameter estimators with negative variance\n", + "- coef_1_escort_tour_constant\n", + "- coef_1_plus_eating_out_tours_constant\n", + "- coef_1_plus_maintenance_tours_constant\n", + "- coef_1_plus_shopping_tours_constant\n", + "- and 10 more\n", + " m.estimate(method='SLSQP')\n", + "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", " m.estimate(method='SLSQP')\n" ] } @@ -5008,749 +5117,767 @@ }, { "cell_type": "code", - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coef_0_auto_household_and_escorting_tour-2.00 NA NA NA 0.00fixed value
coef_1_escort_tour_constant 0.319 362. 0.00 NA 0.00
coef_1_plus_eating_out_tours_constant-1.01 143.-0.01 NA 0.00
coef_1_plus_maintenance_tours_constant-2.84 143.-0.02 NA 0.00
coef_1_plus_other_discretionary_tours_constant 10.5 144. 0.07 NA 0.00
coef_1_plus_shopping_tours_constant 7.18 143. 0.05 NA 0.00
coef_1_plus_visting_tours_constant-0.321 143.-0.00 NA 0.00
coef_2_plus_escort_tours_constant 0.607 725. 0.00 NA 0.00
coef_at_home_pre_driving_school_kid_and_escorting_tour-0.926 1.05-0.89 NA 0.00
coef_at_home_pre_school_kid_and_discretionary_tour-0.656 0.743-0.88 NA 0.00
coef_at_home_pre_school_kid_and_escorting_tour-0.793 0.821-0.97 NA 0.00
coef_auto_access_to_retail_and_discretionary-1.05 0.955-1.10 NA 0.00
coef_auto_access_to_retail_and_maintenance 0.279 0.886 0.31 NA 0.00
coef_auto_access_to_retail_and_shopping-0.631 0.855-0.74 NA 0.00
coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus 0.168 0.233 0.72 NA 0.00
coef_female_and_escorting_tour 0.601 0.266 2.26* NA 0.00
coef_female_and_tour_frequency_is_1-0.316 0.137-2.31* NA 0.00
coef_female_and_tour_frequency_is_2-0.583 0.277-2.11* NA 0.00
coef_female_and_tour_frequency_is_5 0.0227 0.671 0.03 NA 0.00
coef_high_income_group_and_discretionary_tour-0.0604 0.241-0.25 NA 0.00
coef_high_income_group_and_eating_out_tour 0.726 0.282 2.58** NA 0.00
coef_high_income_group_and_tour_frequency_is_1 1.14 0.408 2.79** NA 0.00
coef_high_income_group_and_tour_frequency_is_2 2.22 0.869 2.55* NA 0.00
coef_high_income_group_and_tour_frequency_is_5_plus 0.105 1.42 0.07 NA 0.00
coef_high_income_group_and_visiting_tour-0.863 0.350-2.46* NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_1 1.34 0.232 5.76*** NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_2 1.43 0.289 4.94*** NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 0.640 1.09 0.59 NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_1 1.09 0.394 2.77** NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_2 1.81 0.850 2.13* NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_5_plus 1.04 1.25 0.83 NA 0.00
coef_number_of_joint_eating_out_tours-9.98 147.-0.07 NA 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_2-1.43 0.489-2.93** NA 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus-1.64 2.28-0.72 NA 0.00
coef_presence_of_driving_school_kid_and_discretionary_tour 0.384 0.452 0.85 NA 0.00
coef_presence_of_driving_school_kid_and_escorting_tour-0.211 0.529-0.40 NA 0.00
coef_presence_of_full_time_worker_and_discretionary_tour-0.783 0.222-3.53*** NA 0.00
coef_presence_of_full_time_worker_and_eating_out_tour-0.748 0.221-3.38*** NA 0.00
coef_presence_of_full_time_worker_and_maintenance_tour-0.342 0.271-1.27 NA 0.00
coef_presence_of_full_time_worker_and_shopping_tour-0.631 0.190-3.32*** NA 0.00
coef_presence_of_non_worker_and_discretionary_tour-1.27 0.384-3.30*** NA 0.00
coef_presence_of_non_worker_and_eating_out_tour-0.445 0.327-1.36 NA 0.00
coef_presence_of_non_worker_and_escorting_tour-0.854 0.404-2.11* NA 0.00
coef_presence_of_non_worker_and_maintenance_tour-0.209 0.364-0.58 NA 0.00
coef_presence_of_non_worker_and_shopping_tour-0.844 0.301-2.80** NA 0.00
coef_presence_of_part_time_worker_and_discretionary_tour-0.0412 0.265-0.16 NA 0.00
coef_presence_of_part_time_worker_and_maintenance_tour-0.183 0.328-0.56 NA 0.00
coef_presence_of_part_time_worker_and_shopping_tour-0.149 0.224-0.66 NA 0.00
coef_presence_of_pre_driving_school_kid_and_discretionary_tour-0.126 0.314-0.40 NA 0.00
coef_presence_of_pre_driving_school_kid_and_escorting_tour 1.40 0.268 5.23*** NA 0.00
coef_presence_of_pre_school_kid_and_discretionary_tour-0.0171 0.349-0.05 NA 0.00
coef_presence_of_pre_school_kid_and_eating_out_tour-0.845 0.421-2.01* NA 0.00
coef_presence_of_pre_school_kid_and_escorting_tour 0.748 0.309 2.42* NA 0.00
coef_presence_of_pre_school_kid_and_shopping_tour-0.00614 0.294-0.02 NA 0.00
coef_presence_of_retiree_and_discretionary_tour-0.597 0.461-1.29 NA 0.00
coef_presence_of_retiree_and_eating_out_tour-1.29 0.618-2.08* NA 0.00
coef_presence_of_retiree_and_escorting_tour-12.9 250.-0.05 NA 0.00
coef_presence_of_university_student_and_discretionary_tour-0.407 0.376-1.08 NA 0.00
coef_total_number_of_tours_is_1-7.52 143.-0.05 NA 0.00
coef_total_number_of_tours_is_2-10.1 286.-0.04 NA 0.00
coef_total_number_of_tours_is_3-11.1 429.-0.03 NA 0.00
coef_total_number_of_tours_is_4-12.8 573.-0.02 NA 0.00
coef_total_number_of_tours_is_5-21.8 717.-0.03 NA 0.00
coef_total_number_of_tours_is_6_plus-999. NA NA NA 0.00fixed value
coef_transit_access_to_retail_and_tour_frequency_is_5_plus 0.0745 0.114 0.65 NA 0.00
coef_urban_and_discretionary_tour 0.00 NA NA NA 0.00fixed value
coef_urban_and_escorting_tour-0.409 NA NA[***] 6.54 0.00
coef_walk_access_to_retail_and_discretionary 0.161 0.169 0.95 NA 0.00
coef_walk_access_to_retail_and_eating_out 0.211 0.128 1.65 NA 0.00
coef_walk_access_to_retail_and_escorting-0.105 0.147-0.72 NA 0.00
coef_walk_access_to_retail_and_shopping 0.0302 0.146 0.21 NA 0.00
coef_zero_car_ownership_and_tour_frequency_is_5_plus-0.227 0.175-1.30 NA 0.00
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 ValueStd Errt StatSignifLike RatioNull ValueConstrained
coef_0_auto_household_and_escorting_tour-2.00 NA NA NA 0.00fixed value
coef_1_escort_tour_constant 0.319 NA NA[*] 2.36 0.00
coef_1_plus_eating_out_tours_constant-1.01 124.-0.01 NA 0.00
coef_1_plus_maintenance_tours_constant-2.84 124.-0.02 NA 0.00
coef_1_plus_other_discretionary_tours_constant 10.5 123. 0.09 NA 0.00
coef_1_plus_shopping_tours_constant 7.18 124. 0.06 NA 0.00
coef_1_plus_visting_tours_constant-0.321 124.-0.00 NA 0.00
coef_2_plus_escort_tours_constant 0.607 NA NA[] 0.97 0.00
coef_at_home_pre_driving_school_kid_and_escorting_tour-0.926 1.05-0.89 NA 0.00
coef_at_home_pre_school_kid_and_discretionary_tour-0.656 0.743-0.88 NA 0.00
coef_at_home_pre_school_kid_and_escorting_tour-0.793 0.821-0.97 NA 0.00
coef_auto_access_to_retail_and_discretionary-1.05 0.957-1.10 NA 0.00
coef_auto_access_to_retail_and_maintenance 0.279 0.886 0.31 NA 0.00
coef_auto_access_to_retail_and_shopping-0.631 0.855-0.74 NA 0.00
coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus 0.168 0.233 0.72 NA 0.00
coef_female_and_escorting_tour 0.601 0.266 2.26* NA 0.00
coef_female_and_tour_frequency_is_1-0.316 0.137-2.31* NA 0.00
coef_female_and_tour_frequency_is_2-0.583 0.277-2.11* NA 0.00
coef_female_and_tour_frequency_is_5 0.0227 0.671 0.03 NA 0.00
coef_high_income_group_and_discretionary_tour-0.0604 0.241-0.25 NA 0.00
coef_high_income_group_and_eating_out_tour 0.726 0.282 2.58** NA 0.00
coef_high_income_group_and_tour_frequency_is_1 1.14 0.408 2.79** NA 0.00
coef_high_income_group_and_tour_frequency_is_2 2.22 0.869 2.55* NA 0.00
coef_high_income_group_and_tour_frequency_is_5_plus 0.105 1.42 0.07 NA 0.00
coef_high_income_group_and_visiting_tour-0.863 0.350-2.46* NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_1 1.34 0.232 5.76*** NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_2 1.43 0.289 4.94*** NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 0.640 1.09 0.59 NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_1 1.09 0.394 2.77** NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_2 1.81 0.850 2.13* NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_5_plus 1.04 1.25 0.83 NA 0.00
coef_number_of_joint_eating_out_tours-9.98 147.-0.07 NA 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_2-1.43 0.489-2.93** NA 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus-1.64 2.28-0.72 NA 0.00
coef_presence_of_driving_school_kid_and_discretionary_tour 0.384 0.452 0.85 NA 0.00
coef_presence_of_driving_school_kid_and_escorting_tour-0.211 0.529-0.40 NA 0.00
coef_presence_of_full_time_worker_and_discretionary_tour-0.783 0.222-3.53*** NA 0.00
coef_presence_of_full_time_worker_and_eating_out_tour-0.748 0.221-3.38*** NA 0.00
coef_presence_of_full_time_worker_and_maintenance_tour-0.342 0.271-1.27 NA 0.00
coef_presence_of_full_time_worker_and_shopping_tour-0.631 0.190-3.32*** NA 0.00
coef_presence_of_non_worker_and_discretionary_tour-1.27 0.384-3.30*** NA 0.00
coef_presence_of_non_worker_and_eating_out_tour-0.445 0.327-1.36 NA 0.00
coef_presence_of_non_worker_and_escorting_tour-0.854 0.404-2.11* NA 0.00
coef_presence_of_non_worker_and_maintenance_tour-0.209 0.364-0.58 NA 0.00
coef_presence_of_non_worker_and_shopping_tour-0.844 0.301-2.80** NA 0.00
coef_presence_of_part_time_worker_and_discretionary_tour-0.0412 0.265-0.16 NA 0.00
coef_presence_of_part_time_worker_and_maintenance_tour-0.183 0.328-0.56 NA 0.00
coef_presence_of_part_time_worker_and_shopping_tour-0.149 0.224-0.66 NA 0.00
coef_presence_of_pre_driving_school_kid_and_discretionary_tour-0.126 0.314-0.40 NA 0.00
coef_presence_of_pre_driving_school_kid_and_escorting_tour 1.40 0.268 5.23*** NA 0.00
coef_presence_of_pre_school_kid_and_discretionary_tour-0.0171 0.349-0.05 NA 0.00
coef_presence_of_pre_school_kid_and_eating_out_tour-0.845 0.421-2.01* NA 0.00
coef_presence_of_pre_school_kid_and_escorting_tour 0.748 0.309 2.42* NA 0.00
coef_presence_of_pre_school_kid_and_shopping_tour-0.00614 0.294-0.02 NA 0.00
coef_presence_of_retiree_and_discretionary_tour-0.597 0.461-1.29 NA 0.00
coef_presence_of_retiree_and_eating_out_tour-1.29 0.618-2.08* NA 0.00
coef_presence_of_retiree_and_escorting_tour-12.9 250.-0.05 NA 0.00
coef_presence_of_university_student_and_discretionary_tour-0.407 0.376-1.08 NA 0.00
coef_total_number_of_tours_is_1-7.52 124.-0.06 NA 0.00
coef_total_number_of_tours_is_2-10.1 247.-0.04 NA 0.00
coef_total_number_of_tours_is_3-11.1 371.-0.03 NA 0.00
coef_total_number_of_tours_is_4-12.8 494.-0.03 NA 0.00
coef_total_number_of_tours_is_5-21.8 623.-0.03 NA 0.00
coef_total_number_of_tours_is_6_plus-999. NA NA NA 0.00fixed value
coef_transit_access_to_retail_and_tour_frequency_is_5_plus 0.0745 0.114 0.65 NA 0.00
coef_urban_and_discretionary_tour 0.00 NA NA NA 0.00fixed value
coef_urban_and_escorting_tour-0.409 NA NA[***] 6.54 0.00
coef_walk_access_to_retail_and_discretionary 0.161 0.169 0.95 NA 0.00
coef_walk_access_to_retail_and_eating_out 0.211 0.128 1.65 NA 0.00
coef_walk_access_to_retail_and_escorting-0.105 0.147-0.72 NA 0.00
coef_walk_access_to_retail_and_shopping 0.0302 0.146 0.21 NA 0.00
coef_zero_car_ownership_and_tour_frequency_is_5_plus-0.227 0.175-1.30 NA 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -5771,7 +5898,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -5781,6 +5908,7 @@ " update_coefficients(\n", " m, data.coefficients[k], result_dir,\n", " output_file=f\"{modelname}_{k}_coefficients_revised.csv\",\n", + " relabel_coef=data.relabel_coef.get(k),\n", " );" ] }, @@ -5793,9 +5921,32 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", + " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", + " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", + " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", + " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", + " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", + " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", + " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", + "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", + " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n" + ] + } + ], "source": [ "for k, m in model.items():\n", " result_dir = data.edb_directory/k/\"estimated\"\n", @@ -5816,7 +5967,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -5903,7 +6054,7 @@ " \n", " 208\n", " coef_1_plus_other_discretionary_tours_constant\n", - " 10.543980\n", + " 10.543979\n", " F\n", " \n", " \n", @@ -5928,13 +6079,13 @@ "205 coef_1_plus_maintenance_tours_constant -2.842643 F\n", "206 coef_1_plus_eating_out_tours_constant -1.012856 F\n", "207 coef_1_plus_visting_tours_constant -0.320820 F\n", - "208 coef_1_plus_other_discretionary_tours_constant 10.543980 F\n", + "208 coef_1_plus_other_discretionary_tours_constant 10.543979 F\n", "209 coef_0_auto_household_and_escorting_tour -2.000000 T\n", "\n", "[210 rows x 3 columns]" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -5952,7 +6103,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -5966,7 +6117,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.9.13" }, "toc": { "base_numbering": 1, @@ -5984,4 +6135,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/activitysim/examples/example_manifest.yaml b/activitysim/examples/example_manifest.yaml index 5c29a9924..6af4302c1 100644 --- a/activitysim/examples/example_manifest.yaml +++ b/activitysim/examples/example_manifest.yaml @@ -56,7 +56,7 @@ # activitysim run -c configs_mp -c configs_extended/configs -c configs -o output -d data # cd .. include: - - prototype_mtc/data + # prototype_mtc/data # data loaded from activitysim_resources - prototype_mtc/configs - prototype_mtc_extended/configs configs_extended @@ -81,7 +81,7 @@ include: - prototype_mtc/configs - prototype_mtc/configs_mp - - prototype_mtc/data + # prototype_mtc/data # load data from activitysim_resources - prototype_mtc/output - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/mtc_data_sf/skims.omx data/skims.omx @@ -136,7 +136,7 @@ include: - prototype_mtc/configs - placeholder_multiple_zone/configs_2_zone - - placeholder_multiple_zone/data_2 + # placeholder_multiple_zone/data_2 # data loaded from activitysim_resources - placeholder_multiple_zone/output_2 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/data_2/households.csv data_2/households.csv @@ -177,7 +177,7 @@ - prototype_mtc/configs - placeholder_multiple_zone/configs_3_zone - placeholder_multiple_zone/configs_local - - placeholder_multiple_zone/data_3 + # placeholder_multiple_zone/data_3 # data loaded from activitysim_resources - placeholder_multiple_zone/output_3 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/data_3/households.csv data_3/households.csv @@ -247,7 +247,7 @@ # cd .. include: - prototype_marin/configs - - prototype_marin/data + # prototype_marin/data # data loaded from activitysim_resources - prototype_marin/output - prototype_marin/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/marin_data_marin_sf/highway_skims_AM.omx @@ -319,7 +319,7 @@ # cd .. include: - prototype_marin/configs - - prototype_marin/data + # prototype_marin/data # data loaded from activitysim_resources - prototype_marin/output - prototype_marin/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/marin_data_full/highway_skims_AM.omx @@ -406,7 +406,7 @@ - placeholder_psrc/configs - placeholder_psrc/configs_skip_accessibility - placeholder_psrc/configs_accessibility - - placeholder_psrc/data + # placeholder_psrc/data # data loaded from activitysim_resources - placeholder_psrc/output - placeholder_psrc/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/psrc_data_seattle/skims.omx @@ -448,7 +448,7 @@ - placeholder_psrc/configs - placeholder_psrc/configs_skip_accessibility - placeholder_psrc/configs_accessibility - - placeholder_psrc/data + # placeholder_psrc/data # data loaded from activitysim_resources - placeholder_psrc/output - placeholder_psrc/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/psrc_data_full/skims.omx @@ -579,7 +579,7 @@ # activitysim run -c configs -o output -d data # cd .. include: - - prototype_arc/data + # prototype_arc/data # data loaded from activitysim_resources - prototype_arc/configs - prototype_arc/output - prototype_arc/README.MD @@ -652,7 +652,7 @@ - placeholder_sandag/../prototype_mtc/configs prototype_mtc - placeholder_sandag/configs_1_zone - - placeholder_sandag/data_1 + # placeholder_sandag/data_1 # load data from activitysim_resources instead - placeholder_sandag/output_1 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/households.csv data_1/households.csv @@ -708,7 +708,7 @@ - placeholder_sandag/../placeholder_psrc/configs placeholder_psrc - placeholder_sandag/configs_2_zone - - placeholder_sandag/data_2 + # placeholder_sandag/data_2 # load data from activitysim_resources instead - placeholder_sandag/output_2 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/households.csv data_2/households.csv @@ -771,12 +771,14 @@ # activitysim run -c configs_3_zone -c prototype_mtc/configs -d data_3 -o output_3 -s settings_mp.yaml # cd .. include: - - placeholder_sandag/data_3 + # placeholder_sandag/data_3 # load data from activitysim_resources instead - placeholder_sandag/../prototype_mtc/configs prototype_mtc - placeholder_sandag/configs_3_zone - placeholder_sandag/configs_skip_accessibility - placeholder_sandag/output_3 + - placeholder_sandag/data_3/cached_accessibility.csv.gz + data_3/cached_accessibility.csv.gz - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/taz_skims1.omx data_3/taz_skims1.omx 5b56d0e79ec671e37f8c71f7fedd741d7bf32d2bced866ab1f03f3973fccce8c @@ -912,4 +914,52 @@ - prototype_mwcog/output - prototype_mwcog/README.MD - prototype_mwcog/simulation.py - \ No newline at end of file + +- name: prototype_mwcog_full + description: full scale test example for the MWCOG region + # activitysim create -e prototype_mwcog_full -d test_prototype_mwcog_full + # cd test_prototype_mwcog_full + # python simulation.py -c configs -o output -d data + # cd .. + include: + - prototype_mwcog/extensions + # prototype_mwcog/data # data is provided below + - prototype_mwcog/configs + - prototype_mwcog/configs_mp + - prototype_mwcog/configs_sharrow + - prototype_mwcog/output + - prototype_mwcog/README.MD + - prototype_mwcog/simulation.py + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/LU_taz3722_rnd91a_2018_adj_enrollment.csv + data/LU_taz3722_rnd91a_2018_adj_enrollment.csv + 9cec393becd718175168b6638e4b0e7f70510ea4c53079819e21eee8bfbbd455 + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/combined_synthetic_hh_2018.csv + data/combined_synthetic_hh_2018.csv + a904ac91505c7e21999e5bb790ea599dcc4657d30463600038a8ec25be1575c8 + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/combined_synthetic_per_2018.csv + data/combined_synthetic_per_2018.csv + b5d29a29ab04c36e142456a25397c0eca28dd9fade46f5a3ca986a79f42bdd07 + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/land_use.csv + data/land_use.csv + cf7d7aec7854204a3a938d03b9f9acc8d9e33865a453cf8283b93ea762c07a9b + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/skims0.omx + data/skims0.omx + 22640ec3c87775b42209abedb28e1feca5f4653e2645fabb9c71127473292cfa + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/skims1.omx + data/skims1.omx + 49ab4b9c673d7528320657caa0b71ffe150d8ed418c49c54a1de75b72027f980 + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/skims2.omx + data/skims2.omx + a756f4f871297cdbe693f4eaf39ee01405f32dd9f60927f87927fa2611ad8ae3 + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/skims3.omx + data/skims3.omx + e9334eaa17e2dbc55583128387b834284f70d5c289d8ecfb0f5a98ffab62a14e + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/skims4.omx + data/skims4.omx + 63b8fa22a7890615c8771bc1ff23f0d00f7853c16bc8fc4a0e826222d7c37e46 + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/skims5.omx + data/skims5.omx + a6408d6a4d501dfb759c83fb9a757fd4045378b4a7a7b7a69b8bce6381215331 + - https://media.githubusercontent.com/media/ActivitySim/activitysim_resources/master/mwcog_data_full/skims6.omx + data/skims6.omx + 783113cd748f78d1d2b933dd45502d342625f7aa6cfe6d21c98e1e15d02db838 diff --git a/activitysim/examples/optimize_example_data.py b/activitysim/examples/optimize_example_data.py new file mode 100644 index 000000000..490bea110 --- /dev/null +++ b/activitysim/examples/optimize_example_data.py @@ -0,0 +1,41 @@ +import os + +import openmatrix +import pandas as pd + + +def patch_example_sandag_1_zone(example_dir): + + cwd = os.getcwd() + try: + os.chdir(example_dir) + skims = openmatrix.open_file("data_1/skims1.omx", mode="a") + skims_lookup = skims.root["lookup"] + + zone_name = None + if len(skims_lookup._v_children) == 1: + zone_name = list(skims_lookup._v_children)[0] + zone_data = skims_lookup[zone_name] + rezone = pd.Series( + pd.RangeIndex(1, zone_data.shape[0] + 1), + index=zone_data[:], + ) + else: + rezone = None + + if rezone is not None: + households = pd.read_csv("data_1/households.csv") + households["TAZ"] = households["TAZ"].map(rezone) + households.to_csv("data_1/households.csv", index=False) + + land_use = pd.read_csv("data_1/land_use.csv") + land_use["TAZ"] = land_use["TAZ"].map(rezone) + land_use.to_csv("data_1/land_use.csv", index=False) + + if zone_name: + skims_lookup[zone_name]._f_remove() + + skims.close() + + finally: + os.chdir(cwd) diff --git a/activitysim/examples/placeholder_multiple_zone/configs_2_zone/settings.yaml b/activitysim/examples/placeholder_multiple_zone/configs_2_zone/settings.yaml index a775eaf68..68675f302 100644 --- a/activitysim/examples/placeholder_multiple_zone/configs_2_zone/settings.yaml +++ b/activitysim/examples/placeholder_multiple_zone/configs_2_zone/settings.yaml @@ -64,6 +64,11 @@ input_table_list: - TOPOLOGY - TERMINAL - access_dist_transit + - tablename: land_use_taz + filename: taz.csv + index_col: TAZ + recode_columns: + TAZ: zero-based #resume_after: initialize_landuse @@ -82,3 +87,5 @@ output_tables: # trace origin, destination in accessibility calculation; comment out or leave empty for no trace trace_od: [5000, 11000] + +recode_pipeline_columns: False diff --git a/activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings.yaml index 0cde54e8e..245a1b66c 100644 --- a/activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings.yaml +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings.yaml @@ -83,3 +83,5 @@ output_tables: # trace origin, destination in accessibility calculation; comment out or leave empty for no trace #trace_od: [5000, 11000] + +recode_pipeline_columns: False diff --git a/activitysim/examples/placeholder_multiple_zone/test/regress/final_tours_2_zone.csv b/activitysim/examples/placeholder_multiple_zone/test/regress/final_tours_2_zone.csv index 38d480be5..64da1ba53 100644 --- a/activitysim/examples/placeholder_multiple_zone/test/regress/final_tours_2_zone.csv +++ b/activitysim/examples/placeholder_multiple_zone/test/regress/final_tours_2_zone.csv @@ -26,7 +26,7 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,to 58514946,1427193,shopping,1,1,1,1,non_mandatory,1,16000.0,25000.0,703381,130.0,13.0,19.0,6.0,,12.523150856688828,WALK,-1.1307531749443571,,,0out_0in,shopping 58514982,1427194,othmaint,1,1,1,2,non_mandatory,1,25000.0,25000.0,703381,43.0,7.0,13.0,6.0,,13.741081970110171,WALK,0.7102432373197154,,,0out_0in,othmaint 58514990,1427194,social,1,1,2,2,non_mandatory,1,11000.0,25000.0,703381,160.0,16.0,22.0,6.0,,13.587548572119903,WALK,-0.3434452146802574,,,1out_2in,social -64479052,1572659,shopping,1,1,1,1,non_mandatory,1,19000.0,6000.0,763879,50.0,7.0,20.0,13.0,,12.695032127873429,TNC_SINGLE,-1.5395149128365382,,,3out_0in,shopping +64479052,1572659,shopping,1,1,1,1,non_mandatory,1,19000.0,6000.0,763879,50.0,7.0,20.0,13.0,,12.695032127873429,TNC_SINGLE,-1.5395149128365384,,,3out_0in,shopping 64490158,1572930,othmaint,1,1,2,2,non_mandatory,1,14000.0,9000.0,764150,57.0,8.0,11.0,3.0,,13.876979292356584,WALK_LRF,-0.8284966847360843,,,1out_1in,othmaint 64490163,1572930,shopping,1,1,1,2,non_mandatory,1,21000.0,9000.0,764150,104.0,11.0,16.0,5.0,,12.652319439420722,WALK,-0.7117743765170423,,,0out_0in,shopping 66923525,1632281,eat,1,1,1,1,atwork,1,5000.0,14000.0,823501,125.0,13.0,14.0,1.0,,15.582337201253203,WALK,5.627962154350834,,66923560.0,1out_0in,atwork @@ -36,7 +36,7 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,to 76904608,1875722,eatout,1,1,1,1,non_mandatory,1,17000.0,16000.0,982875,91.0,10.0,16.0,6.0,,14.206952836182584,WALK,2.455435658701909,,,1out_0in,eatout 88521376,2159057,work,1,1,1,1,mandatory,1,21000.0,20000.0,1099626,48.0,7.0,18.0,11.0,,,WALK,0.012760765917479,no_subtours,,0out_0in,work 88521409,2159058,school,1,1,1,1,mandatory,1,10000.0,20000.0,1099626,145.0,15.0,15.0,0.0,,,WALK_LOC,0.7098750900924855,,,0out_0in,univ -88521450,2159059,school,1,1,1,1,mandatory,1,11000.0,20000.0,1099626,58.0,8.0,12.0,4.0,,,WALK,-0.6251066468993006,,,0out_0in,school +88521450,2159059,school,1,1,1,1,mandatory,1,11000.0,20000.0,1099626,58.0,8.0,12.0,4.0,,,WALK,-0.6251066468993004,,,0out_0in,school 100798519,2458502,shopping,1,1,1,1,joint,2,5000.0,8000.0,1173905,54.0,8.0,8.0,0.0,children,12.849118307199914,SHARED2FREE,-0.5111781667673684,,,0out_0in,shopping 100798528,2458500,othmaint,1,1,1,1,non_mandatory,1,9000.0,8000.0,1173905,120.0,12.0,20.0,8.0,,13.933008315199084,WALK,-0.0534146023738154,,,0out_0in,othmaint 100798550,2458501,escort,1,1,1,1,non_mandatory,1,17000.0,8000.0,1173905,146.0,15.0,16.0,1.0,,12.452436194889506,WALK_LRF,-2.030352537291469,,,0out_0in,escort @@ -51,14 +51,14 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,to 120410774,2936848,eatout,1,1,2,2,non_mandatory,1,6000.0,11000.0,1286557,136.0,14.0,15.0,1.0,,13.385908233280974,DRIVEALONEFREE,0.5446813508509882,,,0out_0in,eatout 120410793,2936848,othdiscr,1,1,1,2,non_mandatory,1,25000.0,11000.0,1286557,146.0,15.0,16.0,1.0,,13.998407599705123,DRIVEALONEFREE,0.0531249030428947,,,1out_0in,othdiscr 125537687,3061894,shopping,1,1,1,1,non_mandatory,1,5000.0,24000.0,1363467,113.0,12.0,13.0,1.0,,12.67844496957057,WALK,0.1794050478758298,,,0out_0in,shopping -125537720,3061895,othdiscr,1,1,2,2,non_mandatory,1,9000.0,24000.0,1363467,164.0,17.0,19.0,2.0,,13.855717382920384,WALK_HVY,0.1180104930269069,,,0out_0in,othdiscr +125537720,3061895,othdiscr,1,1,2,2,non_mandatory,1,9000.0,24000.0,1363467,164.0,17.0,19.0,2.0,,13.855717382920384,WALK_HVY,0.1180104930269067,,,0out_0in,othdiscr 125537723,3061895,othmaint,1,1,1,2,non_mandatory,1,4000.0,24000.0,1363467,146.0,15.0,16.0,1.0,,13.843895836691548,WALK,0.4346074611532325,,,0out_0in,othmaint 125537734,3061895,work,1,1,1,1,mandatory,1,2000.0,24000.0,1363467,26.0,6.0,13.0,7.0,,,WALK,0.4289367938120317,no_subtours,,0out_0in,work 130727777,3188483,othdiscr,1,1,1,1,joint,3,3000.0,25000.0,1402945,70.0,9.0,9.0,0.0,adults,14.490562913707262,WALK,-2.2237959104289464,,,0out_0in,othdiscr 130727801,3188482,work,1,1,1,1,mandatory,1,14000.0,25000.0,1402945,63.0,8.0,17.0,9.0,,,WALK,1.470809103053866,no_subtours,,1out_0in,work 130727842,3188483,work,1,1,1,1,mandatory,1,15000.0,25000.0,1402945,77.0,9.0,16.0,7.0,,,WALK_LOC,1.476215281092868,no_subtours,,0out_0in,work -130727875,3188484,school,1,1,2,2,mandatory,1,12000.0,25000.0,1402945,154.0,16.0,16.0,0.0,,,WALK,3.3072495217751663,,,0out_1in,univ -130727883,3188484,work,1,1,1,2,mandatory,1,5000.0,25000.0,1402945,71.0,9.0,10.0,1.0,,,WALK_LOC,2.1088774790757463,no_subtours,,0out_0in,work +130727875,3188484,school,1,1,2,2,mandatory,1,12000.0,25000.0,1402945,155.0,16.0,17.0,1.0,,,WALK,3.303113744185396,,,0out_2in,univ +130727883,3188484,work,1,1,1,2,mandatory,1,5000.0,25000.0,1402945,73.0,9.0,12.0,3.0,,,WALK_LOC,2.0839353031691825,no_subtours,,0out_0in,work 130727924,3188485,work,1,1,1,1,mandatory,1,1000.0,25000.0,1402945,96.0,10.0,21.0,11.0,,,WALK_LOC,1.7666619623619302,no_subtours,,1out_1in,work 132571948,3233462,eatout,1,1,3,3,non_mandatory,1,13000.0,17000.0,1445222,146.0,15.0,16.0,1.0,,15.090110400259128,WALK,3.6847853902920167,,,0out_0in,eatout 132571975,3233462,shopping,2,1,1,3,non_mandatory,1,16000.0,17000.0,1445222,162.0,17.0,17.0,0.0,,14.059936928241624,WALK_LRF,2.5837938479671587,,,0out_0in,shopping @@ -111,5 +111,5 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,to 308013110,7512514,social,1,1,3,3,non_mandatory,1,16000.0,8000.0,2821179,154.0,16.0,16.0,0.0,,13.794538448328227,WALK_LOC,0.0836844432714275,,,0out_0in,social 308050751,7513432,work,1,1,1,1,mandatory,1,9000.0,8000.0,2822097,10.0,5.0,15.0,10.0,,,BIKE,0.1541226783048151,no_subtours,,0out_0in,work 308055753,7513554,work,1,1,1,1,mandatory,1,7000.0,8000.0,2822219,11.0,5.0,16.0,11.0,,,WALK,5.8674311173207885,no_subtours,,0out_0in,work -308056204,7513565,work,1,1,1,1,mandatory,1,12000.0,8000.0,2822230,82.0,9.0,21.0,12.0,,,WALK,5.818260825925703,no_subtours,,1out_1in,work +308056204,7513565,work,1,1,1,1,mandatory,1,12000.0,8000.0,2822230,82.0,9.0,21.0,12.0,,,WALK,5.818260825925704,no_subtours,,1out_1in,work 308464222,7523517,othdiscr,1,1,1,1,non_mandatory,1,16000.0,7000.0,2832182,59.0,8.0,13.0,5.0,,14.941709019837395,WALK_LOC,1.725989964213542,,,0out_0in,othdiscr diff --git a/activitysim/examples/placeholder_multiple_zone/test/regress/final_trips_2_zone.csv b/activitysim/examples/placeholder_multiple_zone/test/regress/final_trips_2_zone.csv index a26e48e7b..8a555ba05 100644 --- a/activitysim/examples/placeholder_multiple_zone/test/regress/final_trips_2_zone.csv +++ b/activitysim/examples/placeholder_multiple_zone/test/regress/final_trips_2_zone.csv @@ -17,7 +17,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 36757261,112064,112064,shopping,1,False,1,16000,11000,4594657,home,,17,WALK,3.9642911737176 86627409,264107,226869,work,1,True,1,15000,9000,10828426,work,,7,WALK_LRF,1.451342899763582 86627413,264107,226869,work,1,False,1,9000,15000,10828426,home,,19,WALK_LRF,1.4433154732713112 -86627625,264108,226869,othdiscr,1,True,1,2000,9000,10828453,othdiscr,,10,WALK_LRF,0.8013218089436901 +86627625,264108,226869,othdiscr,1,True,1,2000,9000,10828453,othdiscr,,10,WALK_LRF,0.8013218089436903 86627629,264108,226869,othdiscr,1,False,2,25000,2000,10828453,escort,32.83572582954258,11,WALK,0.1335656823355492 86627630,264108,226869,othdiscr,2,False,2,9000,25000,10828453,home,,11,WALK_LOC,12.099030970556203 106170305,323689,256660,work,1,True,1,12000,10000,13271288,work,,11,WALK_LOC,3.790246557613184 @@ -51,8 +51,8 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 211388445,644476,386761,work,1,False,1,16000,12000,26423555,home,,17,WALK,3.516861371044758 211388721,644477,386761,shopping,1,True,1,12000,16000,26423590,shopping,,14,WALK,3.1625662738073435 211388725,644477,386761,shopping,1,False,1,16000,12000,26423590,home,,14,WALK,3.162369002002967 -211389033,644478,386761,school,1,True,1,22000,16000,26423629,school,,8,WALK_LOC,1.1439151605607298 -211389037,644478,386761,school,1,False,1,16000,22000,26423629,home,,16,WALK,1.2205853035954894 +211389033,644478,386761,school,1,True,1,22000,16000,26423629,school,,8,WALK_LOC,1.1439151605607296 +211389037,644478,386761,school,1,False,1,16000,22000,26423629,home,,16,WALK,1.2205853035954892 415214745,1265898,568785,othdiscr,1,True,1,9000,17000,51901843,othdiscr,,9,WALK_LRF,10.8917991771908 415214749,1265898,568785,othdiscr,1,False,1,17000,9000,51901843,home,,20,WALK_LRF,11.199024497487024 468119569,1427193,703381,shopping,1,True,1,16000,25000,58514946,shopping,,13,WALK,5.874499232376766 @@ -107,7 +107,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 841877257,2566698,1196298,work,1,True,1,4000,25000,105234657,work,,6,WALK,1.0198269854172115 841877261,2566698,1196298,work,1,False,1,25000,4000,105234657,home,,18,WALK,0.9937585802563552 841877849,2566700,1196298,school,1,True,1,5000,25000,105234731,school,,7,WALK_LOC,4.047398168186926 -841877853,2566700,1196298,school,1,False,1,25000,5000,105234731,home,,15,WALK_LOC,3.9643518377422713 +841877853,2566700,1196298,school,1,False,1,25000,5000,105234731,home,,15,WALK_LOC,3.964351837742272 841878177,2566701,1196298,school,1,True,1,8000,25000,105234772,school,,8,WALK_LOC,11.36280438733663 841878181,2566701,1196298,school,1,False,1,25000,8000,105234772,home,,13,WALK_LOC,10.81430067240017 841878505,2566702,1196298,school,1,True,1,25000,25000,105234813,school,,12,WALK,12.82461586997922 @@ -137,10 +137,11 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 1045822737,3188483,1402945,work,1,True,1,15000,25000,130727842,work,,9,WALK,1.1621136398928025 1045822741,3188483,1402945,work,1,False,1,25000,15000,130727842,home,,16,WALK_LOC,1.0332661932426703 1045823001,3188484,1402945,univ,1,True,1,12000,25000,130727875,univ,,16,WALK,3.8010257036427793 -1045823005,3188484,1402945,univ,1,False,2,25000,12000,130727875,shopping,40.59787148529891,16,WALK,3.725146070305983 -1045823006,3188484,1402945,univ,2,False,2,25000,25000,130727875,home,,16,WALK,12.824615870159588 +1045823005,3188484,1402945,univ,1,False,3,25000,12000,130727875,shopping,40.597871512333725,17,WALK,3.725146077579001 +1045823006,3188484,1402945,univ,2,False,3,7000,25000,130727875,othmaint,56.60102011468401,17,WALK,12.03329580904219 +1045823007,3188484,1402945,univ,3,False,3,25000,7000,130727875,home,,17,WALK,12.646537396714637 1045823065,3188484,1402945,work,1,True,1,5000,25000,130727883,work,,9,WALK,4.08199251781282 -1045823069,3188484,1402945,work,1,False,1,25000,5000,130727883,home,,10,WALK_LOC,3.9154781479629737 +1045823069,3188484,1402945,work,1,False,1,25000,5000,130727883,home,,12,WALK_LOC,3.90954672164702 1045823393,3188485,1402945,work,1,True,2,7000,25000,130727924,escort,30.71610667465545,10,WALK_LOC,11.388230172922947 1045823394,3188485,1402945,work,2,True,2,1000,7000,130727924,work,,12,WALK,-0.1910319526120294 1045823397,3188485,1402945,work,1,False,2,7000,1000,130727924,eatout,30.600694971829377,20,WALK_LOC,-0.1403015748111612 @@ -229,8 +230,8 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 1767186578,5387763,2223027,work,2,True,2,5000,7000,220898322,work,,8,WALK,4.031252242930563 1767186581,5387763,2223027,work,1,False,2,8000,5000,220898322,othmaint,36.964562786529065,17,WALK,3.9594482660927697 1767186582,5387763,2223027,work,2,False,2,9000,8000,220898322,home,,17,WALK,10.001230529962584 -1767666161,5389226,2223759,atwork,1,True,1,13000,21000,220958270,atwork,,13,WALK,-0.8469767119587287 -1767666165,5389226,2223759,atwork,1,False,1,21000,13000,220958270,work,,13,WALK,-0.8469846953017646 +1767666161,5389226,2223759,atwork,1,True,1,13000,21000,220958270,atwork,,13,WALK,-0.8469767119587291 +1767666165,5389226,2223759,atwork,1,False,1,21000,13000,220958270,work,,13,WALK,-0.8469846953017649 1767666233,5389226,2223759,eatout,1,True,1,16000,16000,220958279,eatout,,20,WALK,6.618624772185435 1767666237,5389226,2223759,eatout,1,False,1,16000,16000,220958279,home,,20,WALK,6.618624772185435 1767666441,5389226,2223759,work,1,True,1,21000,16000,220958305,work,,9,WALK,2.520654632402534 @@ -254,7 +255,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 2464104645,7512514,2821179,eatout,1,False,1,8000,19000,308013080,home,,16,WALK,-1.474310526482455 2464104857,7512514,2821179,shopping,1,True,1,6000,8000,308013107,shopping,,17,WALK,12.612248978887928 2464104861,7512514,2821179,shopping,1,False,1,8000,6000,308013107,home,,19,WALK,12.322088998148194 -2464104881,7512514,2821179,social,1,True,1,16000,8000,308013110,social,,16,WALK_LOC,3.494452156419123 +2464104881,7512514,2821179,social,1,True,1,16000,8000,308013110,social,,16,WALK_LOC,3.4944521564191238 2464104885,7512514,2821179,social,1,False,1,8000,16000,308013110,home,,16,WALK_LOC,3.4381818382545646 2464406009,7513432,2822097,work,1,True,1,9000,8000,308050751,work,,5,BIKE,8.0614192494138 2464406013,7513432,2822097,work,1,False,1,8000,9000,308050751,home,,15,BIKE,8.061419249304613 @@ -264,5 +265,5 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 2464449634,7513565,2822230,work,2,True,2,12000,9000,308056204,work,,9,WALK,2.6765203030859817 2464449637,7513565,2822230,work,1,False,2,6000,12000,308056204,escort,33.34376957078302,12,WALK,3.1913129432158387 2464449638,7513565,2822230,work,2,False,2,8000,6000,308056204,home,,21,WALK,9.3863428519612 -2467713777,7523517,2832182,othdiscr,1,True,1,16000,7000,308464222,othdiscr,,8,WALK,6.612046694416055 +2467713777,7523517,2832182,othdiscr,1,True,1,16000,7000,308464222,othdiscr,,8,WALK,6.612046694416056 2467713781,7523517,2832182,othdiscr,1,False,1,7000,16000,308464222,home,,13,WALK_LOC,6.681639068176178 diff --git a/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py index c0f19d3b6..40d4a41a0 100644 --- a/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py +++ b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import os import subprocess +import sys import pandas as pd import pandas.testing as pdt @@ -28,12 +29,12 @@ def mtc_example_path(dirname): def build_data(): if os.environ.get("TRAVIS") != "true": - subprocess.check_call( - ["coverage", "run", example_path("scripts/two_zone_example_data.py")] - ) - subprocess.check_call( - ["coverage", "run", example_path("scripts/three_zone_example_data.py")] - ) + if os.environ.get("GITHUB_ACTIONS") == "true": + go = ["coverage", "run"] + else: + go = [sys.executable] + subprocess.check_call(go + [example_path("scripts/two_zone_example_data.py")]) + subprocess.check_call(go + [example_path("scripts/three_zone_example_data.py")]) @pytest.fixture(scope="module") @@ -89,7 +90,10 @@ def regress(zone): elif zone == "3": run_args = run_args + ["-s", "settings_static"] - subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + if os.environ.get("GITHUB_ACTIONS") == "true": + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + else: + subprocess.run([sys.executable, file_path] + run_args, check=True) regress(zone) diff --git a/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency.csv b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency.csv index 06e9f8878..eb5c0658a 100755 --- a/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency.csv +++ b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency.csv @@ -4,7 +4,7 @@ util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_ti util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business -util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business +util_zero_cars_owned_by_hh_dummy,auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business diff --git a/activitysim/examples/placeholder_psrc/configs/logging.yaml b/activitysim/examples/placeholder_psrc/configs/logging.yaml index 71ac15cc1..46838a7a2 100755 --- a/activitysim/examples/placeholder_psrc/configs/logging.yaml +++ b/activitysim/examples/placeholder_psrc/configs/logging.yaml @@ -1,54 +1,70 @@ -# Config for logging -# ------------------ -# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema - -logging: - version: 1 - disable_existing_loggers: true - - - # Configuring the default (root) logger is highly recommended - root: - level: NOTSET - handlers: [console, logfile] - - loggers: - - activitysim: - level: INFO - handlers: [console, logfile] - propagate: false - - orca: - level: WARN - handlers: [console, logfile] - propagate: false - - handlers: - - logfile: - class: logging.FileHandler - filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] - mode: w - formatter: fileFormatter - level: NOTSET - - console: - class: logging.StreamHandler - stream: ext://sys.stdout - formatter: simpleFormatter - level: NOTSET - - formatters: - - simpleFormatter: - class: logging.Formatter - # format: '%(levelname)s - %(name)s - %(message)s' - format: '%(levelname)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - - fileFormatter: - class: logging.Formatter - format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + filelock: + level: WARN + + sharrow: + level: INFO + + blib2to3: + level: WARN + + black: + level: WARN + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: elapsedFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' diff --git a/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency.csv b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency.csv index 848bbf77a..51094ee82 100755 --- a/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency.csv +++ b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency.csv @@ -25,10 +25,10 @@ util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5 util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 diff --git a/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_scheduling.yaml index d02c60c0e..5c1d82113 100755 --- a/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_scheduling.yaml +++ b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_scheduling.yaml @@ -1,6 +1,14 @@ SPEC: tour_scheduling_nonmandatory.csv COEFFICIENTS: tour_scheduling_nonmandatory_coeffs.csv +sharrow_skip: true +# The `sharrow_skip` option is activated to disable the use of sharrow for this +# model, because the utility specification as currently written includes a +# large number of string comparisons. Sharrow can process these expressions +# correctly, but it is slow. If the specification is rewritten to remove most +# of these string comparisons (see for example in the equivalent file for the +# prototype_mtc models) then sharrow can be re-enabled for a significant speed +# benefit. LOGIT_TYPE: MNL diff --git a/activitysim/examples/placeholder_psrc/configs/settings.yaml b/activitysim/examples/placeholder_psrc/configs/settings.yaml index 779d6a182..739077cbd 100755 --- a/activitysim/examples/placeholder_psrc/configs/settings.yaml +++ b/activitysim/examples/placeholder_psrc/configs/settings.yaml @@ -1,176 +1,200 @@ -#inherit_settings: True - -# activitysim run -c configs -d data -o output - - -# number of households to simulate -households_sample_size: 100 -# simulate all households -# households_sample_size: 0 - -chunk_size: 0 - -# assume enough RAM to not chunk -chunk_training_mode: disabled - -# set false to disable variability check in simple_simulate and interaction_simulate -check_for_variability: False - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - -# turn writing of sample_tables on and off for all models -# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) -want_dest_choice_sample_tables: False - -# - tracing - -# trace household id; comment out or leave empty for no trace -# households with all tour types -trace_hh_id: - -# trace origin, destination in accessibility calculation; comment out or leave empty for no trace -trace_od: - - -# input tables -input_table_list: - - tablename: households - filename: households.csv - index_col: household_id - rename_columns: - HHID: household_id - PERSONS: hhsize - workers: num_workers - VEHICL: auto_ownership - MAZ: home_zone_id - keep_columns: - - home_zone_id - - income - - hhsize - - HHT - - auto_ownership - - num_workers - - tablename: persons - filename: persons.csv - index_col: person_id - rename_columns: - PERID: person_id - keep_columns: - - household_id - - age - - PNUM - - sex - - pemploy - - pstudent - - ptype - - tablename: land_use - filename: land_use.csv - index_col: zone_id - rename_columns: - MAZ: zone_id - COUNTY: county_id - keep_columns: - - TAZ - - DISTRICT - - SD - - county_id - - TOTHH - - TOTPOP - - TOTACRE - - RESACRE - - CIACRE - - TOTEMP - - AGE0519 - - RETEMPN - - FPSEMPN - - HEREMPN - - OTHEMPN - - AGREMPN - - MWTEMPN - - PRKCST - - OPRKCST - - area_type - - HSENROLL - - COLLFTE - - COLLPTE - - TOPOLOGY - - TERMINAL -# - access_dist_transit - -# to resume after last successful checkpoint, specify resume_after: _ -#resume_after: trip_scheduling - -models: - - initialize_landuse - - initialize_households - - compute_accessibility - - school_location - - workplace_location - - auto_ownership_simulate - - free_parking - - cdap_simulate - - mandatory_tour_frequency - - mandatory_tour_scheduling - - joint_tour_frequency - - joint_tour_composition - - joint_tour_participation - - joint_tour_destination - - joint_tour_scheduling - - non_mandatory_tour_frequency - - non_mandatory_tour_destination - - non_mandatory_tour_scheduling - - tour_mode_choice_simulate - - atwork_subtour_frequency - - atwork_subtour_destination - - atwork_subtour_scheduling - - atwork_subtour_mode_choice - - stop_frequency - - trip_purpose - - trip_destination - - trip_purpose_and_destination - - trip_scheduling - - trip_mode_choice - - write_data_dictionary - - track_skim_usage - - write_trip_matrices - - write_tables - - -output_tables: - h5_store: False - action: include - prefix: final_ - sort: True - tables: - - checkpoints - - accessibility - - land_use - - households - - persons - - tours - - trips - - joint_tour_participants - -# area_types less than this are considered urban -urban_threshold: 4 -cbd_threshold: 2 -rural_threshold: 6 - - -# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) - -min_value_of_time: 1 -max_value_of_time: 50 -distributed_vot_mu: 0.684 -distributed_vot_sigma: 0.85 - -household_median_value_of_time: - 1: 6.01 - 2: 8.81 - 3: 10.44 - 4: 12.86 +#inherit_settings: True + +# activitysim run -c configs -d data -o output + + +# number of households to simulate +households_sample_size: 100 +# simulate all households +# households_sample_size: 0 + +chunk_size: 0 + +# assume enough RAM to not chunk +chunk_training_mode: disabled + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +trace_hh_id: + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +trace_od: + + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + HHID: household_id + PERSONS: hhsize + workers: num_workers + VEHICL: auto_ownership + MAZ: home_zone_id + recode_columns: + home_zone_id: land_use.zone_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + PERID: person_id + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + COUNTY: county_id + recode_columns: + zone_id: zero-based + TAZ: land_use_taz.TAZ + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL +# - access_dist_transit + - tablename: land_use_taz + filename: taz.csv + index_col: TAZ + recode_columns: + TAZ: zero-based + +# to resume after last successful checkpoint, specify resume_after: _ +#resume_after: trip_scheduling + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables + + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + + +# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) + +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 diff --git a/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv index 845e55a0b..8cc5fb59e 100755 --- a/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv @@ -27,10 +27,10 @@ util_previous_tour_begins_this_arrival_hour,Previously-scheduled tour begins in #,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & tt.adjacent_window_after(df.person_id, df.end)",-0.5271 util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) * (df.tour_num == 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) * (df.tour_num == 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction util_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours util_departure_constants_early_up_to_5,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early util_departure_constants_am_peak_1 _6,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 diff --git a/activitysim/examples/placeholder_psrc/configs/tour_scheduling_work.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_work.csv index 0d8b8aeff..3d9104547 100755 --- a/activitysim/examples/placeholder_psrc/configs/tour_scheduling_work.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_work.csv @@ -31,10 +31,10 @@ util_previously_scheduled_tour_begins_in_this_arrival_hour,Previously-scheduled #,FIXME - use temps as timetable ops can be very time-consuming,, util_dummy_adjacent_before,local temp variable,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy util_dummy_adjacent_after,local temp variable,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy -util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) * (df.tour_num == 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) * (df.tour_num == 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction util_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours util_departure_constants_early,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 @@ -65,72 +65,3 @@ util_duration_constants_10_hours,Duration Constants -- 10 hours,duration == 10,c util_duration_constants_11_hours,Duration Constants -- 11 hours,duration == 11,coef_duration_constants_11_hours util_duration_constants_12_to_13_hours,Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),coef_duration_constants_12_to_13_hours util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/activitysim/examples/placeholder_psrc/configs/trip_destination.csv b/activitysim/examples/placeholder_psrc/configs/trip_destination.csv index 99434ae2c..0b5a0ba15 100644 --- a/activitysim/examples/placeholder_psrc/configs/trip_destination.csv +++ b/activitysim/examples/placeholder_psrc/configs/trip_destination.csv @@ -1,8 +1,8 @@ Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork # size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 # no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -size term,"@np.log1p(size_terms.get(df.alt_dest, df.purpose))",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.alt_dest, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +size term,"@np.log1p(size_terms.get(df.alt_dest, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.alt_dest, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),-0.04972591574229,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),0.147813278663948,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 diff --git a/activitysim/examples/placeholder_psrc/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/trip_destination_annotate_trips_preprocessor.csv index 1a1afb074..9f2d502d3 100755 --- a/activitysim/examples/placeholder_psrc/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/placeholder_psrc/configs/trip_destination_annotate_trips_preprocessor.csv @@ -7,4 +7,7 @@ Description,Target,Expression #,,not needed as school is not chosen as an intermediate trip destination #,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" #,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" -,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" \ No newline at end of file +,purpose_index_num,"size_terms.get_cols(df.purpose)" +,tour_mode_is_walk,"reindex(tours.tour_mode, df.tour_id)=='WALK'" +,tour_mode_is_bike,"reindex(tours.tour_mode, df.tour_id)=='BIKE'" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id)).astype(int)" diff --git a/activitysim/examples/placeholder_psrc/configs/trip_destination_sample.csv b/activitysim/examples/placeholder_psrc/configs/trip_destination_sample.csv index ecf943d7d..65c8f619b 100644 --- a/activitysim/examples/placeholder_psrc/configs/trip_destination_sample.csv +++ b/activitysim/examples/placeholder_psrc/configs/trip_destination_sample.csv @@ -8,8 +8,8 @@ Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & #If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,, # size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 # no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -size term,"@np.log1p(size_terms.get(df.alt_dest, df.purpose))",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.alt_dest, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +size term,"@np.log1p(size_terms.get(df.alt_dest, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.alt_dest, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.04972591574229,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813278663948,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 diff --git a/activitysim/examples/placeholder_psrc/test/configs/settings.yaml b/activitysim/examples/placeholder_psrc/test/configs/settings.yaml index 12820a341..e01bf1e83 100644 --- a/activitysim/examples/placeholder_psrc/test/configs/settings.yaml +++ b/activitysim/examples/placeholder_psrc/test/configs/settings.yaml @@ -37,3 +37,5 @@ output_tables: sort: True tables: - trips + +recode_pipeline_columns: False diff --git a/activitysim/examples/placeholder_psrc/test/configs_sharrow/network_los.yaml b/activitysim/examples/placeholder_psrc/test/configs_sharrow/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/placeholder_psrc/test/configs_sharrow/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/placeholder_psrc/test/configs_sharrow/settings.yaml b/activitysim/examples/placeholder_psrc/test/configs_sharrow/settings.yaml new file mode 100644 index 000000000..480f52f9f --- /dev/null +++ b/activitysim/examples/placeholder_psrc/test/configs_sharrow/settings.yaml @@ -0,0 +1,19 @@ +inherit_settings: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +sharrow: require +recode_pipeline_columns: True diff --git a/activitysim/examples/placeholder_psrc/test/regress/final_trips.csv b/activitysim/examples/placeholder_psrc/test/regress/final_trips.csv index 7dd3b5fa1..8f8328add 100644 --- a/activitysim/examples/placeholder_psrc/test/regress/final_trips.csv +++ b/activitysim/examples/placeholder_psrc/test/regress/final_trips.csv @@ -3,7 +3,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 91698949,279569,270725,work,1,False,2,29610,15669,11462368,shopping,11.008441668275683,18,BIKE,0.5886135998450344 91698950,279569,270725,work,2,False,2,973,29610,11462368,home,,19,BIKE,-0.6310803160253456 91701985,279579,270725,escort,1,True,1,19339,973,11462748,escort,,11,TNC_SHARED,-1.9095545921503028 -91701989,279579,270725,escort,1,False,3,11062,19339,11462748,othmaint,2.334885323775696,11,TNC_SINGLE,-2.1198277065919595 +91701989,279579,270725,escort,1,False,3,11062,19339,11462748,othmaint,2.3348853237756946,11,TNC_SINGLE,-2.1198277065919595 91701990,279579,270725,escort,2,False,3,24984,11062,11462748,othmaint,3.707466699501644,11,TNC_SINGLE,-2.3805512729030873 91701991,279579,270725,escort,3,False,3,973,24984,11462748,home,,11,TNC_SINGLE,-1.9771890941421908 92588481,282281,272321,work,1,True,1,25879,21360,11573560,work,,7,DRIVEALONEFREE,-0.4591205183942871 @@ -13,7 +13,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 96160401,293171,287712,work,1,True,1,11062,15634,12020050,work,,10,DRIVEALONEFREE,-0.23879695478284943 96160405,293171,287712,work,1,False,4,57,11062,12020050,othdiscr,11.446003474931256,17,DRIVEALONEFREE,-0.30417034896125145 96160406,293171,287712,work,2,False,4,19682,57,12020050,shopping,13.108805318596527,19,WALK,1.0687113416849714 -96160407,293171,287712,work,3,False,4,19446,19682,12020050,work,11.610203781380497,19,DRIVEALONEFREE,-0.3843616938183148 +96160407,293171,287712,work,3,False,4,19446,19682,12020050,work,11.610203781380498,19,DRIVEALONEFREE,-0.3843616938183148 96160408,293171,287712,work,4,False,4,15634,19446,12020050,home,,19,WALK,0.3685287952829796 96187033,293253,287712,eatout,1,True,1,11062,15634,12023379,eatout,,9,WALK,-2.3139746826971415 96187037,293253,287712,eatout,1,False,1,15634,11062,12023379,home,,20,WALK,-2.3138588610052215 @@ -23,10 +23,10 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 98164725,299282,277087,othmaint,1,False,1,54,29610,12270590,home,,10,DRIVEALONEFREE,0.5950248538601501 98164761,299282,277087,shopping,1,True,1,29610,54,12270595,shopping,,12,DRIVEALONEFREE,-0.05863290327000465 98164765,299282,277087,shopping,1,False,1,54,29610,12270595,home,,17,WALK,-0.05863290327000465 -99858601,304446,250897,work,1,True,1,4691,29642,12482325,work,,6,WALK,0.005868850942480171 -99858605,304446,250897,work,1,False,1,29642,4691,12482325,home,,11,WALK,0.005868850942480171 -99858609,304446,250897,work,1,True,1,4691,29642,12482326,work,,13,WALK,0.005868850942480171 -99858613,304446,250897,work,1,False,1,29642,4691,12482326,home,,17,WALK,0.005868850942480171 +99858601,304446,250897,work,1,True,1,4691,29642,12482325,work,,5,WALK,0.005868850942480171 +99858605,304446,250897,work,1,False,1,29642,4691,12482325,home,,10,WALK,0.005868850942480171 +99858609,304446,250897,work,1,True,1,4691,29642,12482326,work,,12,WALK,0.005868850942480171 +99858613,304446,250897,work,1,False,1,29642,4691,12482326,home,,12,WALK,0.005868850942480171 99914409,304617,250897,atwork,1,True,1,11062,11062,12489301,atwork,,11,WALK,-0.4085873729867728 99914413,304617,250897,atwork,1,False,3,30391,11062,12489301,work,15.610155649473146,13,WALK,-0.6211759061266833 99914414,304617,250897,atwork,2,False,3,34578,30391,12489301,escort,17.34865514925389,13,WALK,4.727873905420292 @@ -36,10 +36,11 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 99914694,304617,250897,work,2,False,2,29642,20981,12489336,home,,22,WALK,-0.8174519257532181 100166265,305384,251378,work,1,True,1,29643,15673,12520783,work,,6,WALK,0.8737060466605951 100166269,305384,251378,work,1,False,1,15673,29643,12520783,home,,18,SHARED2FREE,0.8725336323381478 -101549769,309602,320221,work,1,True,1,15668,5353,12693721,work,,6,DRIVEALONEFREE,-0.4416858596858487 -101549773,309602,320221,work,1,False,1,5353,15668,12693721,home,,13,DRIVEALONEFREE,-0.4437773390273746 -101549777,309602,320221,work,1,True,1,15668,5353,12693722,work,,15,DRIVEALONEFREE,-0.4416858596858487 -101549781,309602,320221,work,1,False,1,5353,15668,12693722,home,,18,DRIVEALONEFREE,-0.4437773390273746 +101549769,309602,320221,work,1,True,2,15446,5353,12693721,othdiscr,9.89687476466247,5,DRIVEALONEFREE,0.0317353991298851 +101549770,309602,320221,work,2,True,2,15668,15446,12693721,work,,5,WALK,-0.22454055979971102 +101549773,309602,320221,work,1,False,1,5353,15668,12693721,home,,12,DRIVEALONEFREE,-0.4437773390273746 +101549777,309602,320221,work,1,True,1,15668,5353,12693722,work,,14,DRIVEALONEFREE,-0.4416858596858487 +101549781,309602,320221,work,1,False,1,5353,15668,12693722,home,,17,DRIVEALONEFREE,-0.4437773390273746 102198705,311581,318938,othmaint,1,True,1,20981,21809,12774838,othmaint,,18,SHARED2FREE,0.7109418031062801 102198709,311581,318938,othmaint,1,False,1,21809,20981,12774838,home,,18,TNC_SHARED,0.7112990246442743 102198881,311581,318938,work,1,True,1,15669,21809,12774860,work,,7,BIKE,0.5933951347687365 diff --git a/activitysim/examples/placeholder_psrc/test/test_psrc.py b/activitysim/examples/placeholder_psrc/test/test_psrc.py index 10863cc1d..b265d7d5f 100644 --- a/activitysim/examples/placeholder_psrc/test/test_psrc.py +++ b/activitysim/examples/placeholder_psrc/test/test_psrc.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import os import subprocess +import sys import pandas as pd import pandas.testing as pdt @@ -15,7 +16,7 @@ def teardown_function(func): inject.reinject_decorated_tables() -def test_psrc(): +def _test_psrc(sharrow=False): def example_path(dirname): resource = os.path.join("examples", "placeholder_psrc", dirname) return pkg_resources.resource_filename("activitysim", resource) @@ -34,27 +35,38 @@ def regress(): file_path = os.path.join(os.path.dirname(__file__), "simulation.py") - subprocess.run( - [ - "coverage", - "run", - "-a", - file_path, - "-c", - test_path("configs"), - "-c", - example_path("configs"), - "-d", - example_path("data"), - "-o", - test_path("output"), - ], - check=True, - ) + if sharrow: + run_args = ["-c", test_path("configs_sharrow")] + else: + run_args = [] + + run_args += [ + "-c", + test_path("configs"), + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] + + if os.environ.get("GITHUB_ACTIONS") == "true": + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + else: + subprocess.run([sys.executable, file_path] + run_args, check=True) regress() +def test_psrc(): + _test_psrc(sharrow=False) + + +def test_psrc_sharrow(): + _test_psrc(sharrow=True) + + if __name__ == "__main__": test_psrc() diff --git a/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/logging.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/logging.yaml new file mode 100644 index 000000000..3d62e8064 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/logging.yaml @@ -0,0 +1,54 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARNING + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + + formatters: + + simpleFormatter: + class: logging.Formatter + format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ + '%(processName)-10s %(levelname)s - %(name)s - %(message)s', + '%(levelname)s - %(name)s - %(message)s'] + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/network_los.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/network_los.yaml new file mode 100644 index 000000000..8560d7d2f --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/network_los.yaml @@ -0,0 +1,17 @@ +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +#alternate dir to read/write skim cache (defaults to output_dir) +#cache_dir: data/cache + +zone_system: 1 + +taz_skims: skims*.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59 + labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/settings.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/settings.yaml new file mode 100644 index 000000000..4c3317401 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/settings.yaml @@ -0,0 +1,188 @@ +inherit_settings: True + +# input tables +# +# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) +# as well as for created tables (tours, joint_tour_participants, trips) +# e.g. the households table must have an index column 'household_id' and the foreign key to households in the +# persons table is also household_id. This naming convention allows activitysim to intuit the relationship +# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips +# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by +# household_id. +# +# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id +# you cannot change the well-known name of the index by modifying this directive. However, if your input file +# has a different id column name, you can rename it to the required index name with the rename_columns directive. +# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. +# +input_table_list: + # + # households (table index 'household_id') + # + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + HHID: household_id # household_id is the required index column + PERSONS: hhsize + workers: num_workers + VEHICL: auto_ownership + TAZ: home_zone_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + # + # persons (table index 'person_id') + # + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + PERID: person_id # person_id is the required index column + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + + # + # land_use (table index 'zone_id') + # + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + TAZ: zone_id # person_id is the required index column + COUNTY: county_id + keep_columns: + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL + +# convert input CSVs to HDF5 format and save to outputs directory +# create_input_store: True + +#input_store: ../output/input_data.h5 + +# number of households to simulate +# simulate all households +households_sample_size: 0 + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] +trace_hh_id: + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +trace_od: + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables + +output_tables: + h5_store: False + action: include + prefix: final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + +# - value of time +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 diff --git a/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv new file mode 100644 index 000000000..9db6a951d --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_1_zone/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv @@ -0,0 +1,12 @@ +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" +#,,not needed as school is not chosen as an intermediate trip destination +#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" +#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" +,tour_mode_is_walk,"reindex(tours.tour_mode, df.tour_id)=='WALK'" +,tour_mode_is_bike,"reindex(tours.tour_mode, df.tour_id)=='BIKE'" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id)).astype(int)" diff --git a/activitysim/examples/placeholder_sandag/configs_1_zone/logging.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/logging.yaml new file mode 100644 index 000000000..13e533abb --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_1_zone/logging.yaml @@ -0,0 +1,69 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + filelock: + level: WARN + + sharrow: + level: INFO + + blib2to3: + level: WARN + + black: + level: WARN + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + formatter: elapsedFormatter + + formatters: + + simpleFormatter: + class: logging.Formatter + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' diff --git a/activitysim/examples/placeholder_sandag/configs_1_zone/network_los.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/network_los.yaml index 8560d7d2f..d1a9b9d4e 100644 --- a/activitysim/examples/placeholder_sandag/configs_1_zone/network_los.yaml +++ b/activitysim/examples/placeholder_sandag/configs_1_zone/network_los.yaml @@ -8,7 +8,25 @@ write_skim_cache: False zone_system: 1 -taz_skims: skims*.omx +taz_skims: + omx: skims*.omx + zarr: skims.zarr + zarr-digital-encoding: + - {regex: .*_BOARDS , joint_dict: true} + - {regex: .*_DDIST , joint_dict: true} + - {regex: .*_DTIM , joint_dict: true} + - {regex: .*_FAR , joint_dict: true} + - {regex: .*_IWAIT , joint_dict: true} + - {regex: .*_KEYIVT , joint_dict: true} + - {regex: .*_TOTIVT , joint_dict: true} + - {regex: .*_FERRYIVT , joint_dict: true} + - {regex: .*_WAIT , joint_dict: true} + - {regex: .*_WAUX , joint_dict: true} + - {regex: .*_XWAIT , joint_dict: true} + - {regex: .*_BTOLL , joint_dict: true} + - {regex: .*_DIST , joint_dict: true} + - {regex: .*_TIME , joint_dict: true} + - {regex: .*_VTOLL , joint_dict: true} skim_time_periods: time_window: 1440 diff --git a/activitysim/examples/placeholder_sandag/configs_1_zone/settings.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/settings.yaml index 4c3317401..e478e64fa 100644 --- a/activitysim/examples/placeholder_sandag/configs_1_zone/settings.yaml +++ b/activitysim/examples/placeholder_sandag/configs_1_zone/settings.yaml @@ -28,6 +28,8 @@ input_table_list: workers: num_workers VEHICL: auto_ownership TAZ: home_zone_id + recode_columns: + home_zone_id: land_use.zone_id keep_columns: - home_zone_id - income @@ -61,6 +63,8 @@ input_table_list: rename_columns: TAZ: zone_id # person_id is the required index column COUNTY: county_id + recode_columns: + zone_id: zero-based keep_columns: - DISTRICT - SD @@ -186,3 +190,4 @@ household_median_value_of_time: 2: 8.81 3: 10.44 4: 12.86 + diff --git a/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/logging.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/logging.yaml new file mode 100644 index 000000000..3d62e8064 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/logging.yaml @@ -0,0 +1,54 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARNING + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + + formatters: + + simpleFormatter: + class: logging.Formatter + format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ + '%(processName)-10s %(levelname)s - %(name)s - %(message)s', + '%(levelname)s - %(name)s - %(message)s'] + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/network_los.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/network_los.yaml new file mode 100644 index 000000000..8aee14644 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/network_los.yaml @@ -0,0 +1,34 @@ +#inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +zone_system: 2 + +# glob 'skims*.omx' will match one or more files: skims.omx, skims1.omx, skims2.omx... +taz_skims: skims*.omx + + +maz: maz.csv + +maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + # blend distance of 0 means no blending + DISTBIKE: 0 + DISTWALK: 0 + + # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) + #blend_distance_skim_name: DIST + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/settings.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/settings.yaml new file mode 100644 index 000000000..9a047df3d --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/settings.yaml @@ -0,0 +1,167 @@ +inherit_settings: True + +# number of households to simulate +# simulate all households +households_sample_size: 0 + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# presampling +want_dest_choice_presampling: True + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +trace_hh_id: + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +trace_od: + + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + HHID: household_id + PERSONS: hhsize + workers: num_workers + VEHICL: auto_ownership + MAZ: home_zone_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + PERID: person_id + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + COUNTY: county_id + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL +# - access_dist_transit + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables + + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + + +# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) + +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 diff --git a/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/settings_mp.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/settings_mp.yaml new file mode 100644 index 000000000..5002c6284 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/settings_mp.yaml @@ -0,0 +1,47 @@ +inherit_settings: settings.yaml + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +households_sample_size: 0 +multiprocess: True +num_processes: 28 + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + +# presampling +want_dest_choice_presampling: True + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + slice: + tables: + - accessibility + - name: mp_households + begin: school_location + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_data_dictionary + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips diff --git a/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv new file mode 100644 index 000000000..9db6a951d --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_2_zone/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv @@ -0,0 +1,12 @@ +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" +#,,not needed as school is not chosen as an intermediate trip destination +#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" +#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" +,tour_mode_is_walk,"reindex(tours.tour_mode, df.tour_id)=='WALK'" +,tour_mode_is_bike,"reindex(tours.tour_mode, df.tour_id)=='BIKE'" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id)).astype(int)" diff --git a/activitysim/examples/placeholder_sandag/configs_2_zone/network_los.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/network_los.yaml index 8aee14644..600c542fd 100644 --- a/activitysim/examples/placeholder_sandag/configs_2_zone/network_los.yaml +++ b/activitysim/examples/placeholder_sandag/configs_2_zone/network_los.yaml @@ -8,7 +8,25 @@ write_skim_cache: False zone_system: 2 # glob 'skims*.omx' will match one or more files: skims.omx, skims1.omx, skims2.omx... -taz_skims: skims*.omx +taz_skims: + omx: skims*.omx + zarr: skims.zarr + zarr-digital-encoding: + - {regex: .*_BOARDS , joint_dict: true} + - {regex: .*_DDIST , joint_dict: true} + - {regex: .*_DTIM , joint_dict: true} + - {regex: .*_FAR , joint_dict: true} + - {regex: .*_IWAIT , joint_dict: true} + - {regex: .*_KEYIVT , joint_dict: true} + - {regex: .*_TOTIVT , joint_dict: true} + - {regex: .*_FERRYIVT , joint_dict: true} + - {regex: .*_WAIT , joint_dict: true} + - {regex: .*_WAUX , joint_dict: true} + - {regex: .*_XWAIT , joint_dict: true} + - {regex: .*_BTOLL , joint_dict: true} + - {regex: .*_TIME , joint_dict: true} + - {regex: .*_VTOLL , joint_dict: true} + - {regex: "^(?!_s_).*_DIST" , joint_dict: true} maz: maz.csv diff --git a/activitysim/examples/placeholder_sandag/configs_2_zone/settings.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/settings.yaml index 9a047df3d..2d341f552 100644 --- a/activitysim/examples/placeholder_sandag/configs_2_zone/settings.yaml +++ b/activitysim/examples/placeholder_sandag/configs_2_zone/settings.yaml @@ -42,6 +42,8 @@ input_table_list: workers: num_workers VEHICL: auto_ownership MAZ: home_zone_id + recode_columns: + home_zone_id: land_use.zone_id keep_columns: - home_zone_id - income @@ -68,6 +70,9 @@ input_table_list: rename_columns: MAZ: zone_id COUNTY: county_id + recode_columns: + zone_id: zero-based + TAZ: land_use_taz.TAZ keep_columns: - TAZ - DISTRICT @@ -95,6 +100,11 @@ input_table_list: - TOPOLOGY - TERMINAL # - access_dist_transit + - tablename: land_use_taz + filename: taz.csv + index_col: TAZ + recode_columns: + TAZ: zero-based models: - initialize_landuse @@ -140,11 +150,25 @@ output_tables: tables: - checkpoints - accessibility - - land_use - - households - - persons - - tours - - trips + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id - joint_tour_participants # area_types less than this are considered urban diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/logging.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/logging.yaml new file mode 100644 index 000000000..3d62e8064 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/logging.yaml @@ -0,0 +1,54 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARNING + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + + formatters: + + simpleFormatter: + class: logging.Formatter + format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ + '%(processName)-10s %(levelname)s - %(name)s - %(message)s', + '%(levelname)s - %(name)s - %(message)s'] + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/network_los.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/network_los.yaml new file mode 100644 index 000000000..64bd95fd0 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/network_los.yaml @@ -0,0 +1,164 @@ +inherit_settings: True + +zone_system: 3 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +# rebuild and overwrite existing tap_tap_utilities cache +rebuild_tvpb_cache: True + +# write a csv version of tvpb cache for tracing when checkpointing cache. +# (writes csv file when writing/checkpointing cache i.e. when cached changed) +# (n.b. csv file could be quite large if cache is STATIC!) +trace_tvpb_cache_as_csv: False + +taz_skims: taz_skims*.omx + +# we require that skims for all tap_tap sets have unique names +# and can therefor share a single skim_dict without name collision +# e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM +tap_skims: tap_skims*.omx + +maz: maz.csv + +tap: tap.csv + +tap_lines: tap_lines.csv + +maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + # blend distance of 0 means no blending + DISTBIKE: 0 + DISTWALK: 1 + + # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) + blend_distance_skim_name: DIST + +maz_to_tap: + walk: + table: maz_to_tap_walk.csv + # if provided, this column will be used (together with tap_lines table) to trim the near tap set + # to only include the nearest tap to origin when more than oFne tap serves the same line + tap_line_distance_col: DISTWALK + max_dist: 1.2 + drive: + table: maz_to_tap_drive.csv + # although maz_tap untility calculations use both DIST and drive_time, + # it looks like drive_time is a linera function of DIST so we can use either column to trim to nearest + tap_line_distance_col: DIST + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + +# transit virtual path builder settings +TVPB_SETTINGS: + + tour_mode_choice: + units: utility + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + DTW: + access: drive + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + WTD: + access: walk + egress: drive + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + demographic_segment: *demographic_segments + tod: *skim_time_period_labels + access_mode: ['drive', 'walk'] + attributes_as_columns: + - demographic_segment + - tod + maz_tap_settings: + walk: + SPEC: tvpb_utility_walk_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - walk_time + drive: + SPEC: tvpb_utility_drive_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - drive_time + - DIST + CONSTANTS: + C_LOW_INCOME_SEGMENT_ID: *low_income_segment_id + C_HIGH_INCOME_SEGMENT_ID: *high_income_segment_id + TVPB_demographic_segments_by_income_segment: + 1: *low_income_segment_id + 2: *low_income_segment_id + 3: *high_income_segment_id + 4: *high_income_segment_id + c_ivt_high_income: -0.028 + c_ivt_low_income: -0.0175 + c_cost_high_income: -0.00112 + c_cost_low_income: -0.00112 + c_wait: 1.5 + c_walk: 1.7 + c_drive: 1.5 + c_auto_operating_cost_per_mile: 18.29 + C_UNAVAILABLE: -999 + C_FASTEST_IVT_MULTIPLIER: 2 + C_FASTEST_COST_MULTIPLIER: 1 + C_CHEAPEST_IVT_MULTIPLIER: 1 + C_CHEAPEST_COST_MULTIPLIER: 500 + C_SHORTEST_IVT_MULTIPLIER: 1 + C_SHORTEST_COST_MULTIPLIER: 1 + C_SHORTEST_DIST_MULTIPLIER: 1 + # illustrate using access mode in tat-tap expressions files + C_DRIVE_TRANSFER_PENALTY: -1 + + accessibility: + units: time + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 1 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_accessibility_tap_tap_.csv + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + tod: *skim_time_period_labels + maz_tap_settings: + walk: + SPEC: tvpb_accessibility_walk_maz_tap.csv + CHOOSER_COLUMNS: + - walk_time + CONSTANTS: + out_of_vehicle_walk_time_weight: 1.5 + out_of_vehicle_wait_time_weight: 2.0 diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/settings.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/settings.yaml new file mode 100644 index 000000000..d569385c0 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/settings.yaml @@ -0,0 +1,131 @@ +inherit_settings: True + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +trace_hh_id: + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +trace_od: + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + HHID: household_id + PERSONS: hhsize + workers: num_workers + VEHICL: auto_ownership + MAZ: home_zone_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + PERID: person_id + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + COUNTY: county_id + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL + - access_dist_transit + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + # --- STATIC cache prebuild steps + # single-process step to create attribute_combination list + - initialize_los + # multi-processable step to build STATIC cache + # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) + - initialize_tvpb + # --- + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/settings_mp.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/settings_mp.yaml new file mode 100644 index 000000000..3642cb1e1 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/settings_mp.yaml @@ -0,0 +1,34 @@ +inherit_settings: settings.yaml + +multiprocess: True +households_sample_size: 0 +num_processes: 28 + +# raise error if any sub-process fails without waiting for others to complete +# (Shadow pricing requires fail_fast setting in multiprocessing mode) +fail_fast: True + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + slice: + tables: + - accessibility + - name: mp_los + begin: initialize_los + - name: mp_tvpb + begin: initialize_tvpb + num_processes: 20 + slice: + tables: + - attribute_combinations + - name: mp_models + begin: school_location + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_data_dictionary diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv new file mode 100644 index 000000000..9db6a951d --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv @@ -0,0 +1,12 @@ +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" +#,,not needed as school is not chosen as an intermediate trip destination +#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" +#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" +,tour_mode_is_walk,"reindex(tours.tour_mode, df.tour_id)=='WALK'" +,tour_mode_is_bike,"reindex(tours.tour_mode, df.tour_id)=='BIKE'" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id)).astype(int)" diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/settings.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/settings.yaml index ad373d8ff..97221aca7 100644 --- a/activitysim/examples/placeholder_sandag/configs_3_zone/settings.yaml +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/settings.yaml @@ -9,6 +9,9 @@ trace_hh_id: # trace origin, destination in accessibility calculation; comment out or leave empty for no trace trace_od: +# presampling +want_dest_choice_presampling: True + # input tables input_table_list: - tablename: households @@ -20,6 +23,8 @@ input_table_list: workers: num_workers VEHICL: auto_ownership MAZ: home_zone_id + recode_columns: + home_zone_id: land_use.zone_id keep_columns: - home_zone_id - income @@ -46,6 +51,9 @@ input_table_list: rename_columns: MAZ: zone_id COUNTY: county_id + recode_columns: + zone_id: zero-based + TAZ: land_use_taz.TAZ keep_columns: - TAZ - DISTRICT @@ -73,6 +81,11 @@ input_table_list: - TOPOLOGY - TERMINAL - access_dist_transit + - tablename: land_use_taz + filename: taz.csv + index_col: TAZ + recode_columns: + TAZ: zero-based output_tables: h5_store: False @@ -82,11 +95,26 @@ output_tables: tables: - checkpoints - accessibility - - land_use - - households - - persons - - tours - - trips + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - joint_tour_participants models: - initialize_landuse @@ -129,7 +157,3 @@ models: - track_skim_usage - write_trip_matrices - write_tables - - - - diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice.csv index 609454d2e..920c21625 100644 --- a/activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice.csv +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice.csv @@ -187,3 +187,6 @@ util_Walk_not_available_for_long_distances,Walk not available for long distances util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.distance_bike_od > 8,,,,,,,,-999,,,,, util_Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,-999,,,,,,,,,,, #, max(c_densityIndexOrigin*originDensityIndex,originDensityIndexMax),,,,,,,,,1,1,,, +#, School Escorting eligibility,,,,,,,,,,,,,,,,,,,,,, +util_one_or_more_school_escort,No SOV if on school escort tour,"@(df.get('num_escortees', 0) >= 1)",-999,-999,,,,,,,,,,, +util_two_or_more_school_escort,Can't take HOV2 if taking two children and yourself,"@(df.get('num_escortees', 0) >= 2)",,,-999,-999,,,,,,,,, \ No newline at end of file diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.csv index 30952ec5d..e6bb38fdd 100644 --- a/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.csv +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.csv @@ -193,3 +193,6 @@ util_Bike_not_available_for_long_distances,Bike not available for long distances util_origin_density_index,Origin density index,@origin_density_applied*(origin_density_index_multiplier*df.origin_density_index).clip(origin_density_index_max),,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,coef_ivt,coef_ivt util_walk_express_penalty,Walk-express penalty for intermediate stops,@walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,,,,,, util_adjust_tnc_shared,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,coef_ivt +#, School Escorting eligibility,,,,,,,,,,,,,,,,,,,,,, +util_one_or_more_school_escort,No SOV if on school escort tour,(num_escortees >= 1),-999,-999,,,,,,,,,,, +util_two_or_more_school_escort,Can't take HOV2 if taking two children and yourself,(num_escortees >= 2),,,-999,-999,,,,,,,,, \ No newline at end of file diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.yaml index 081d5d842..cdb5c7fef 100644 --- a/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.yaml +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.yaml @@ -76,16 +76,8 @@ CONSTANTS: SHARED3PAY: 6 WALK: 7 BIKE: 8 - WALK_LOC: 9 - WALK_LRF: 10 - WALK_EXP: 11 - WALK_HVY: 12 - WALK_COM: 13 - DRIVE_LOC: 14 - DRIVE_LRF: 15 - DRIVE_EXP: 16 - DRIVE_HVY: 17 - DRIVE_COM: 18 + WALK_TRANSIT: 9 + DRIVE_TRANSIT: 14 TAXI: 19 TNC_SINGLE: 20 TNC_SHARED: 21 @@ -95,8 +87,8 @@ CONSTANTS: I_AUTO_MODES: [1, 2, 3, 4, 5, 6] I_WALK_MODE: 7 I_BIKE_MODE: 8 - I_WALK_TRANSIT_MODES: [9, 10, 11, 12, 13] - I_DRIVE_TRANSIT_MODES: [14, 15, 16, 17, 18] + I_WALK_TRANSIT_MODES: [9] + I_DRIVE_TRANSIT_MODES: [14] I_RIDE_HAIL_MODES: [19, 20, 21] # RIDEHAIL Settings Taxi_baseFare: 2.20 @@ -108,7 +100,7 @@ CONSTANTS: 3: 13.3 4: 17.3 5: 26.5 - Taxi_waitTime_sd: + Taxi_waitTime_sd: 1: 0 2: 0 3: 0 @@ -118,13 +110,13 @@ CONSTANTS: TNC_single_costPerMile: 1.33 TNC_single_costPerMinute: 0.24 TNC_single_costMinimum: 7.20 - TNC_single_waitTime_mean: + TNC_single_waitTime_mean: 1: 3.0 2: 6.3 3: 8.4 4: 8.5 5: 10.3 - TNC_single_waitTime_sd: + TNC_single_waitTime_sd: 1: 0 2: 0 3: 0 @@ -135,13 +127,13 @@ CONSTANTS: TNC_shared_costPerMinute: 0.10 TNC_shared_costMinimum: 3.00 TNC_shared_IVTFactor: 1.5 - TNC_shared_waitTime_mean: + TNC_shared_waitTime_mean: 1: 5.0 2: 8.0 3: 11.0 4: 15.0 5: 15.0 - TNC_shared_waitTime_sd: + TNC_shared_waitTime_sd: 1: 0 2: 0 3: 0 diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv index e07c28f0b..ea84ab8c8 100644 --- a/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv @@ -63,7 +63,7 @@ dest terminal time not counted at home,_dest_terminal_time,"np.where(inbound & l ,destination_walk_time,shortWalk*60/walkSpeed # RIDEHAIL,, ,origin_density_measure,"(reindex(land_use.TOTPOP, df[orig_col_name]) + reindex(land_use.TOTEMP, df[orig_col_name])) / (reindex(land_use.TOTACRE, df[orig_col_name]) / 640)" -,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,origin_density,"pd.cut(origin_density_measure.fillna(0), bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" ,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" ,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" # ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from @@ -108,3 +108,5 @@ dest terminal time not counted at home,_dest_terminal_time,"np.where(inbound & l ,distance,od_skims['DIST'] ,distance_walk_od,od_skims['DIST'] ,distance_bike_od,od_skims['DISTBIKE'] +# added for school escorting model,, +Number of school children in vehicle on trip,num_escortees,"0 if 'escort_participants' not in df.columns else df.escort_participants.fillna('').apply(lambda x: len(x.split('_')))" \ No newline at end of file diff --git a/activitysim/examples/placeholder_sandag/data_2/.gitignore b/activitysim/examples/placeholder_sandag/data_2/.gitignore new file mode 100644 index 000000000..f1c7b387e --- /dev/null +++ b/activitysim/examples/placeholder_sandag/data_2/.gitignore @@ -0,0 +1 @@ +maz_to_maz_bike.csv diff --git a/activitysim/examples/placeholder_sandag/test/configs_1_sharrow/network_los.yaml b/activitysim/examples/placeholder_sandag/test/configs_1_sharrow/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/configs_1_sharrow/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/placeholder_sandag/test/configs_1_sharrow/settings.yaml b/activitysim/examples/placeholder_sandag/test/configs_1_sharrow/settings.yaml new file mode 100644 index 000000000..5ad20bda7 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/configs_1_sharrow/settings.yaml @@ -0,0 +1,19 @@ +inherit_settings: True + +output_tables: + h5_store: False + action: include + prefix: final_1_zone_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +sharrow: require +recode_pipeline_columns: True diff --git a/activitysim/examples/placeholder_sandag/test/configs_1_zone/settings.yaml b/activitysim/examples/placeholder_sandag/test/configs_1_zone/settings.yaml index bf0e418e9..92b4d430a 100644 --- a/activitysim/examples/placeholder_sandag/test/configs_1_zone/settings.yaml +++ b/activitysim/examples/placeholder_sandag/test/configs_1_zone/settings.yaml @@ -24,3 +24,5 @@ output_tables: tables: - trips - tours + +recode_pipeline_columns: False diff --git a/activitysim/examples/placeholder_sandag/test/configs_2_sharrow/network_los.yaml b/activitysim/examples/placeholder_sandag/test/configs_2_sharrow/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/configs_2_sharrow/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/placeholder_sandag/test/configs_2_sharrow/settings.yaml b/activitysim/examples/placeholder_sandag/test/configs_2_sharrow/settings.yaml new file mode 100644 index 000000000..5372d9aac --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/configs_2_sharrow/settings.yaml @@ -0,0 +1,23 @@ +inherit_settings: True + +output_tables: + h5_store: False + action: include + prefix: final_2_zone_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: proto_disaggregate_accessibility + decode_columns: + home_zone_id: land_use.zone_id + TAZ: land_use_taz.TAZ + +sharrow: require +recode_pipeline_columns: True diff --git a/activitysim/examples/placeholder_sandag/test/configs_2_sharrow/settings_mp.yaml b/activitysim/examples/placeholder_sandag/test/configs_2_sharrow/settings_mp.yaml new file mode 100644 index 000000000..5372d9aac --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/configs_2_sharrow/settings_mp.yaml @@ -0,0 +1,23 @@ +inherit_settings: True + +output_tables: + h5_store: False + action: include + prefix: final_2_zone_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: proto_disaggregate_accessibility + decode_columns: + home_zone_id: land_use.zone_id + TAZ: land_use_taz.TAZ + +sharrow: require +recode_pipeline_columns: True diff --git a/activitysim/examples/placeholder_sandag/test/configs_2_zone/annotate_proto_households.csv b/activitysim/examples/placeholder_sandag/test/configs_2_zone/annotate_proto_households.csv new file mode 100644 index 000000000..95720faef --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/configs_2_zone/annotate_proto_households.csv @@ -0,0 +1,4 @@ +# This is an example use of annotation for dissaggregate accessibilities +# But this particular case could probably be handled using variable mapping +Description,Target,Expression +auto ownership,auto_ownership,"np.clip(households.veh, 0, 2)" diff --git a/activitysim/examples/placeholder_sandag/test/configs_2_zone/disaggregate_accessibility.yaml b/activitysim/examples/placeholder_sandag/test/configs_2_zone/disaggregate_accessibility.yaml new file mode 100644 index 000000000..e8f7ecba1 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/configs_2_zone/disaggregate_accessibility.yaml @@ -0,0 +1,170 @@ +# Sampling size. 0 = no limit +# can be whole integer value or a sample rate (percent of zones) +# zero or missing defaults to full sample! +DESTINATION_SAMPLE_SIZE: 0.1 +ORIGIN_SAMPLE_SIZE: 200 + +# select origin zones weighted by population (or another landuse variable) +ORIGIN_WEIGHTING_COLUMN: TOTPOP + +# How the nearest sampled zone is found: skims or centroid distance +NEAREST_METHOD: skims + +# Specify the tables to be created and their variables. +# Single values are constants +# Lists are varying and will generate the cartesian product (i.e., all possible non-repeating combinations) + +# Example result for households below: +#hhid veh hinccat1 hinc hworkers persons hht bldgsz +# 1 0 1 14000 1 2 1 2 +# 2 1 1 14000 1 2 1 2 +# 3 2 1 14000 1 2 1 2 +# 4 0 2 67000 1 2 1 2 +# 5 1 2 67000 1 2 1 2 +# 6 2 2 67000 1 2 1 2 +# .... + +# Use FROM_TEMPLATES or CREATE_TABLES to generate the proto-population +# Alternative can be manually specified in csv templates +# FROM_TEMPLATES: True +# CREATE_TABLES: +# PROTO_HOUSEHOLDS: proto_households_template.csv +# PROTO_PERSONS: proto_persons_template.csv +# PROTO_TOURS: proto_tours_template.csv + +##Note: parameters in ALL CAPS below are required by the program for synthesis +CREATE_TABLES: + # Note: Only one household-member composition is possible through direct definition. + # e.g., you cannot have one household with 2 people and another with 3 people + PROTO_HOUSEHOLDS: + index_col: proto_household_id + zone_col: home_zone_id + rename_columns: + zone_id: home_zone_id + VARIABLES: + hinccat1: [1, 2, 3, 4] # Income categories + hworkers: 1 # Household workers + veh: [0, 1, 2] # Household vehicles + persons: 2 # Two persons household + HHT: 1 # Married-couple family household + bldgsz: 2 # Building size - Single family detached + # Additional columns that are mapped to another (e.g., hhinccat1: 1 = hinc: 14000) + mapped_fields: + hinccat1: # List new fields mapped to this field + income: # Median income within each quartile + 1: 15000 # 14000 (for 3 level) + 2: 45000 # 67000 (for 3 level) + 3: 80000 # 120000 (for 3 level) + 4: 150000 + persons: + hhsize: + 1: 1 + 2: 2 + 3: 3 + hworkers: + num_workers: + 1: 1 + 2: 2 + 3: 3 + + PROTO_PERSONS: + index_col: proto_person_id + VARIABLES: + pnum: [1, 2] # Person number + military: 4 # Not military + pstudent: 3 # Not attending school + educ: 13 # Bachelor's + grade: 0 # Not attending + mapped_fields: + pnum: + age: + 1: 35 + 2: 55 + sex: # Female male + 1: 2 + 2: 1 + ptype: # Person type, full-time and non-working adult + 1: 1 + 2: 4 + pemploy: # Full-time and unemployed + 1: 1 + 2: 3 + weeks: # 50-52 weeks, none + 1: 1 + 2: 0 + hours: # Hours per week + 1: 35 + 2: 0 + DAP: # Mandatory, Non-mandatory + 1: "M" + 2: "N" + + PROTO_TOURS: + index_col: proto_tour_id + VARIABLES: + tour_num: 1 # Tour number, 1 tour per person + purpose: [1, 2, 3] + mapped_fields: + purpose: + person_num: # In this case it was easier to map the person number directly to the purposez + 1: 1 + 2: 2 + 3: 2 + tour_type: + 1: "work" + 2: "shopping" + 3: "othdiscr" + tour_category: # tour purpose category, mandatory/non-mandatory + 1: "mandatory" + 2: "non_mandatory" + 3: "non_mandatory" + filter_rows: # list any matching conditions as pandas expression + - ~((df.tour_type == "work") & (df.person_num == 1)) + JOIN_ON: + person_num: pnum # Specifies which person variable to join the tours on + +# Merge on variables +MERGE_ON: + by: # These should be categorical variables at the household level + - home_zone_id + - auto_ownership + - income_segment + +# Include any annotations for persons, households, land_use, or tours. +# The purpose of a separate annotation setup is that annotation expressions for the main model +# may require data that aren't in the proto population. +# This step enables users to annotate the proto-population by referencing custom annotation scripts. +# Of course, users can also just reference existing configs if they work, but they must be referenced here too. + +annotate_proto_tables: + - tablename: proto_persons + annotate: + SPEC: annotate_persons + DF: proto_persons + TABLES: + - proto_households + + - tablename: proto_households + annotate: + SPEC: annotate_proto_households + DF: proto_households + TABLES: + - proto_persons + - land_use + +# Annotate the proto_households table using the main model annotations files + - tablename: proto_households + annotate: + SPEC: annotate_households + DF: proto_households + TABLES: + - proto_persons + - land_use + +# Annotate the proto_persons table using the main model annotations files + - tablename: proto_persons + annotate: + SPEC: annotate_persons_after_hh + DF: proto_persons + TABLES: + - proto_households diff --git a/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings.yaml b/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings.yaml index 5625718fa..27a426684 100644 --- a/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings.yaml +++ b/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings.yaml @@ -17,7 +17,47 @@ want_dest_choice_sample_tables: False # global switch to turn on or off presampling of destination alternatives at TAZ level (multizone models only) want_dest_choice_presampling: True -cleanup_pipeline_after_run: True +models: + ## Disaggregate Accessibility steps + # only including for 2 zone sandag test + - initialize_proto_population + - compute_disaggregate_accessibility + ### mp_initialize step + - initialize_landuse + - initialize_households + ### mp_accessibility step + - compute_accessibility + ### mp_households step + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + ### mp_summarize step + - write_data_dictionary + - write_trip_matrices + - write_tables output_tables: h5_store: False @@ -25,5 +65,9 @@ output_tables: prefix: final_2_zone_ sort: True tables: - - trips - tours + - trips + - vehicles + - proto_disaggregate_accessibility + +recode_pipeline_columns: False diff --git a/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings_mp.yaml b/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings_mp.yaml index 7877a85c3..d965a26ec 100644 --- a/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings_mp.yaml +++ b/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings_mp.yaml @@ -14,6 +14,10 @@ write_skim_cache: False fail_fast: True models: + ## Disaggregate Accessibility steps + # only including for 2 zone sandag test + - initialize_proto_population + - compute_disaggregate_accessibility ### mp_initialize step - initialize_landuse - initialize_households @@ -52,6 +56,15 @@ models: - write_tables multiprocess_steps: + - name: mp_proto_initialize + begin: initialize_proto_population + - name: mp_disaggregate_accessibility + begin: compute_disaggregate_accessibility + slice: + tables: + - proto_households + - proto_persons + - proto_tours - name: mp_initialize begin: initialize_landuse - name: mp_accessibility @@ -64,3 +77,14 @@ multiprocess_steps: - persons - name: mp_summarize begin: write_data_dictionary + +output_tables: + h5_store: False + action: include + prefix: final_2_zone_ + sort: True + tables: + - tours + - trips + - vehicles + - proto_disaggregate_accessibility \ No newline at end of file diff --git a/activitysim/examples/placeholder_sandag/test/configs_3_sharrow/network_los.yaml b/activitysim/examples/placeholder_sandag/test/configs_3_sharrow/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/configs_3_sharrow/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/placeholder_sandag/test/configs_3_sharrow/settings.yaml b/activitysim/examples/placeholder_sandag/test/configs_3_sharrow/settings.yaml new file mode 100644 index 000000000..57445acbc --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/configs_3_sharrow/settings.yaml @@ -0,0 +1,19 @@ +inherit_settings: True + +output_tables: + h5_store: False + action: include + prefix: final_3_zone_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +sharrow: test +recode_pipeline_columns: True diff --git a/activitysim/examples/placeholder_sandag/test/output_1/.gitignore b/activitysim/examples/placeholder_sandag/test/output_1/.gitignore new file mode 100644 index 000000000..bf5bf15e3 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/output_1/.gitignore @@ -0,0 +1,7 @@ +*.csv +*.log +*.prof +*.h5 +*.txt +*.yaml +*.omx diff --git a/activitysim/examples/placeholder_sandag/test/output_1/cache/.gitignore b/activitysim/examples/placeholder_sandag/test/output_1/cache/.gitignore new file mode 100644 index 000000000..3dd2e62f9 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/output_1/cache/.gitignore @@ -0,0 +1,2 @@ +*.mmap +*.feather diff --git a/activitysim/examples/placeholder_sandag/test/output_1/trace/.gitignore b/activitysim/examples/placeholder_sandag/test/output_1/trace/.gitignore new file mode 100644 index 000000000..8edb80678 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/output_1/trace/.gitignore @@ -0,0 +1,3 @@ +*.csv +*.log +*.txt diff --git a/activitysim/examples/placeholder_sandag/test/output_2/.gitignore b/activitysim/examples/placeholder_sandag/test/output_2/.gitignore new file mode 100644 index 000000000..77cae3dbf --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/output_2/.gitignore @@ -0,0 +1,8 @@ +*.csv +*.log +*.prof +*.h5 +*.txt +*.yaml +*.omx +** diff --git a/activitysim/examples/placeholder_sandag/test/output_2/cache/.gitignore b/activitysim/examples/placeholder_sandag/test/output_2/cache/.gitignore new file mode 100644 index 000000000..3dd2e62f9 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/output_2/cache/.gitignore @@ -0,0 +1,2 @@ +*.mmap +*.feather diff --git a/activitysim/examples/placeholder_sandag/test/output_2/trace/.gitignore b/activitysim/examples/placeholder_sandag/test/output_2/trace/.gitignore new file mode 100644 index 000000000..8edb80678 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/output_2/trace/.gitignore @@ -0,0 +1,3 @@ +*.csv +*.log +*.txt diff --git a/activitysim/examples/placeholder_sandag/test/output_3/.gitignore b/activitysim/examples/placeholder_sandag/test/output_3/.gitignore new file mode 100644 index 000000000..bf5bf15e3 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/output_3/.gitignore @@ -0,0 +1,7 @@ +*.csv +*.log +*.prof +*.h5 +*.txt +*.yaml +*.omx diff --git a/activitysim/examples/placeholder_sandag/test/output_3/cache/.gitignore b/activitysim/examples/placeholder_sandag/test/output_3/cache/.gitignore new file mode 100644 index 000000000..3dd2e62f9 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/output_3/cache/.gitignore @@ -0,0 +1,2 @@ +*.mmap +*.feather diff --git a/activitysim/examples/placeholder_sandag/test/output_3/trace/.gitignore b/activitysim/examples/placeholder_sandag/test/output_3/trace/.gitignore new file mode 100644 index 000000000..8edb80678 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/output_3/trace/.gitignore @@ -0,0 +1,3 @@ +*.csv +*.log +*.txt diff --git a/activitysim/examples/placeholder_sandag/test/regress/.gitignore b/activitysim/examples/placeholder_sandag/test/regress/.gitignore new file mode 100644 index 000000000..c5200d0c4 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/regress/.gitignore @@ -0,0 +1 @@ +*_last_run.csv diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_tours.csv b/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_tours.csv index 84449693e..c5f84186b 100644 --- a/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_tours.csv +++ b/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_tours.csv @@ -1,63 +1,63 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,tour_category,number_of_participants,destination,origin,household_id,tdd,start,end,duration,composition,destination_logsum,tour_mode,mode_choice_logsum,atwork_subtour_frequency,parent_tour_id,stop_frequency,primary_purpose -1359025,33146,work,1,1,1,1,mandatory,1,3131.0,3259.0,12593,11.0,5.0,16.0,11.0,,,SHARED2FREE,0.0449709966797433,no_subtours,,2out_0in,work +1359025,33146,work,1,1,1,1,mandatory,1,3131.0,3259.0,12593,11.0,5.0,16.0,11.0,,,SHARED2FREE,0.04497099667974337,no_subtours,,2out_0in,work 1359066,33147,work,1,1,1,1,mandatory,1,3342.0,3259.0,12593,59.0,8.0,13.0,5.0,,,SHARED2FREE,0.923483008481964,no_subtours,,0out_3in,work -1494647,36454,shopping,2,1,1,2,non_mandatory,1,3342.0,3134.0,13797,135.0,14.0,14.0,0.0,,11.401050563880888,DRIVEALONEFREE,0.2595703435486286,,,0out_1in,shopping -1494648,36454,shopping,2,2,2,2,non_mandatory,1,3497.0,3134.0,13797,154.0,16.0,16.0,0.0,,11.439449857137804,SHARED2FREE,-0.0006093410329746,,,1out_0in,shopping +1494647,36454,shopping,2,1,1,2,non_mandatory,1,3342.0,3134.0,13797,135.0,14.0,14.0,0.0,,11.401050563880887,DRIVEALONEFREE,0.2595703435486286,,,0out_1in,shopping +1494648,36454,shopping,2,2,2,2,non_mandatory,1,3497.0,3134.0,13797,154.0,16.0,16.0,0.0,,11.439449857137804,SHARED2FREE,-0.0006093410329746202,,,1out_0in,shopping 1494694,36455,work,1,1,1,1,mandatory,1,3317.0,3134.0,13797,53.0,7.0,23.0,16.0,,,SHARED3FREE,0.2738771147247297,no_subtours,,0out_0in,work -1709911,41705,eatout,1,1,1,1,non_mandatory,1,3437.0,3342.0,15777,85.0,10.0,10.0,0.0,,11.909980318826896,SHARED2FREE,0.7916769643872005,,,0out_0in,eatout +1709911,41705,eatout,1,1,1,1,non_mandatory,1,3437.0,3342.0,15777,85.0,10.0,10.0,0.0,,11.909980318826895,SHARED2FREE,0.7916769643872005,,,0out_0in,eatout 1709946,41706,business,1,1,1,1,atwork,1,3131.0,3398.0,15777,117.0,12.0,17.0,5.0,,18.502192217204307,SHARED2FREE,-1.0113258602519364,,1709985.0,0out_0in,atwork 1709985,41706,work,1,1,1,1,mandatory,1,3398.0,3342.0,15777,65.0,8.0,19.0,11.0,,,DRIVEALONEFREE,0.5212273251520757,business1,,0out_1in,work -2051456,50035,social,1,1,1,1,joint,2,3497.0,3493.0,18261,146.0,15.0,16.0,1.0,mixed,11.601381369295757,SHARED2FREE,-1.4037659073121551,,,0out_2in,social +2051456,50035,social,1,1,1,1,joint,2,3497.0,3493.0,18261,146.0,15.0,16.0,1.0,mixed,11.601381369295755,SHARED2FREE,-1.4037659073121553,,,0out_2in,social 2051466,50035,school,1,1,1,1,mandatory,1,3340.0,3493.0,18261,42.0,7.0,12.0,5.0,,,SHARED3FREE,-1.3497203179845243,,,0out_0in,school -2051468,50035,shopping,1,1,1,1,non_mandatory,1,3396.0,3493.0,18261,172.0,18.0,21.0,3.0,,11.366590261069064,DRIVEALONEFREE,0.6130171387405682,,,0out_0in,shopping -2051509,50036,shopping,1,1,1,1,non_mandatory,1,3497.0,3493.0,18261,99.0,11.0,11.0,0.0,,11.55566952538272,DRIVEALONEFREE,0.6647948166112536,,,0out_1in,shopping +2051468,50035,shopping,1,1,1,1,non_mandatory,1,3396.0,3493.0,18261,172.0,18.0,21.0,3.0,,11.366590261069065,DRIVEALONEFREE,0.6130171387405682,,,0out_0in,shopping +2051509,50036,shopping,1,1,1,1,non_mandatory,1,3497.0,3493.0,18261,99.0,11.0,11.0,0.0,,11.555669525382719,DRIVEALONEFREE,0.6647948166112536,,,0out_1in,shopping 2051556,50037,work,1,1,1,1,mandatory,1,3396.0,3493.0,18261,44.0,7.0,14.0,7.0,,,DRIVEALONEFREE,0.5299854110237027,no_subtours,,0out_0in,work -2268889,55338,school,1,1,1,1,mandatory,1,3592.0,3592.0,19758,40.0,7.0,10.0,3.0,,,WALK,0.3318320919975738,,,0out_0in,school +2268889,55338,school,1,1,1,1,mandatory,1,3592.0,3592.0,19758,40.0,7.0,10.0,3.0,,,WALK,0.33183209199757385,,,0out_0in,school 2268938,55339,work,1,1,1,1,mandatory,1,3497.0,3592.0,19758,11.0,5.0,16.0,11.0,,,WALK,0.537772515990635,no_subtours,,0out_0in,work 2373816,57897,work,1,1,1,1,mandatory,1,3176.0,3746.0,20552,50.0,7.0,20.0,13.0,,,DRIVEALONEFREE,-0.28388579338221,no_subtours,,0out_0in,work 2373822,57898,eat,1,1,1,1,atwork,1,3410.0,3347.0,20552,125.0,13.0,14.0,1.0,,11.100624000564997,DRIVEALONEFREE,0.7681389958489114,,2373857.0,0out_0in,atwork 2373857,57898,work,1,1,1,1,mandatory,1,3347.0,3746.0,20552,103.0,11.0,15.0,4.0,,,DRIVEALONEFREE,0.520379765074684,eat,,0out_1in,work 2373898,57899,work,1,1,1,1,mandatory,1,3402.0,3746.0,20552,47.0,7.0,17.0,10.0,,,WALK,1.0388895039783694,no_subtours,,0out_0in,work -2373980,57901,work,2,1,1,2,mandatory,1,3115.0,3746.0,20552,24.0,6.0,11.0,5.0,,,SHARED3FREE,0.6022315390131013,no_subtours,,0out_0in,work -2373981,57901,work,2,2,2,2,mandatory,1,3115.0,3746.0,20552,148.0,15.0,18.0,3.0,,,SHARED2FREE,0.6101482774173842,no_subtours,,1out_0in,work 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-2563863,62533,escort,3,2,2,4,non_mandatory,1,3519.0,3316.0,21869,99.0,11.0,11.0,0.0,,12.466623656700463,SHARED2FREE,-0.9326373013150776,,,0out_0in,escort +2563863,62533,escort,3,2,2,4,non_mandatory,1,3519.0,3316.0,21869,99.0,11.0,11.0,0.0,,12.466623656700463,SHARED2FREE,-0.9326373013150777,,,0out_0in,escort 2563864,62533,escort,3,3,3,4,non_mandatory,1,3398.0,3316.0,21869,135.0,14.0,14.0,0.0,,12.485628371830364,SHARED3FREE,-1.4467093822325976,,,0out_0in,escort -2563878,62533,othdiscr,1,1,4,4,non_mandatory,1,3383.0,3316.0,21869,99.0,11.0,11.0,0.0,,12.893824062601642,DRIVEALONEFREE,0.0900147292900378,,,0out_0in,othdiscr +2563878,62533,othdiscr,1,1,4,4,non_mandatory,1,3383.0,3316.0,21869,99.0,11.0,11.0,0.0,,12.893824062601642,DRIVEALONEFREE,0.09001472929003788,,,0out_0in,othdiscr 2563925,62534,school,1,1,1,1,mandatory,1,3316.0,3316.0,21869,55.0,8.0,9.0,1.0,,,SHARED3FREE,0.4282867689528016,,,0out_1in,school 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-52915705,1290626,work,1,1,1,1,mandatory,1,3502.0,3278.0,442036,64.0,8.0,18.0,10.0,,,BIKE,-0.0288128226314909,eat,,0out_2in,work +52915670,1290626,eat,1,1,1,1,atwork,1,3340.0,3502.0,442036,86.0,10.0,11.0,1.0,,18.513981739804258,BIKE,14.60281456532542,,52915705.0,0out_1in,atwork +52915705,1290626,work,1,1,1,1,mandatory,1,3502.0,3278.0,442036,64.0,8.0,18.0,10.0,,,BIKE,-0.028812822631490976,eat,,0out_2in,work 76379130,1862905,othdiscr,1,1,1,1,non_mandatory,1,3460.0,3598.0,721960,151.0,15.0,21.0,6.0,,12.841187222979508,WALK,0.5769914886883216,,,0out_0in,othdiscr 76379171,1862906,othdiscr,1,1,1,1,non_mandatory,1,3705.0,3598.0,721960,104.0,11.0,16.0,5.0,,12.884342308539214,WALK,0.7912388057447584,,,0out_0in,othdiscr -80946571,1974306,othdiscr,1,1,1,1,non_mandatory,1,3176.0,3396.0,760593,59.0,8.0,13.0,5.0,,13.385057255164831,SHARED2FREE,-0.2639203430422295,,,0out_0in,othdiscr -80946591,1974307,eat,1,1,1,1,atwork,1,3574.0,3316.0,760593,113.0,12.0,13.0,1.0,,18.535862390079988,TNC_SINGLE,-1.395417499795803,,80946626.0,0out_0in,atwork -80946626,1974307,work,1,1,1,1,mandatory,1,3316.0,3396.0,760593,78.0,9.0,17.0,8.0,,,SHARED2FREE,-0.1094760736283397,eat,,1out_1in,work -80946637,1974308,escort,1,1,1,1,non_mandatory,1,3315.0,3396.0,760593,0.0,5.0,5.0,0.0,,12.64221480734466,WALK,-0.4993149033005503,,,0out_0in,escort +80946571,1974306,othdiscr,1,1,1,1,non_mandatory,1,3176.0,3396.0,760593,59.0,8.0,13.0,5.0,,13.385057255164831,SHARED2FREE,-0.26392034304222955,,,0out_0in,othdiscr +80946591,1974307,eat,1,1,1,1,atwork,1,3574.0,3316.0,760593,113.0,12.0,13.0,1.0,,18.535862390079988,TNC_SINGLE,-1.3954174997958033,,80946626.0,0out_0in,atwork +80946626,1974307,work,1,1,1,1,mandatory,1,3316.0,3396.0,760593,78.0,9.0,17.0,8.0,,,SHARED2FREE,-0.10947607362833972,eat,,1out_1in,work +80946637,1974308,escort,1,1,1,1,non_mandatory,1,3315.0,3396.0,760593,0.0,5.0,5.0,0.0,,12.64221480734466,WALK,-0.49931490330055034,,,0out_0in,escort 81048440,1976791,escort,1,1,1,1,non_mandatory,1,3314.0,3281.0,761445,124.0,13.0,13.0,0.0,,12.689453354918568,SHARED2FREE,-0.8229389480344289,,,0out_0in,escort 81048476,1976792,eat,1,1,1,1,atwork,1,3176.0,3176.0,761445,85.0,10.0,10.0,0.0,,19.43069515696065,DRIVEALONEFREE,0.6823981998609486,,81048511.0,0out_0in,atwork 81048497,1976792,othdiscr,1,1,1,1,non_mandatory,1,3398.0,3281.0,761445,137.0,14.0,16.0,2.0,,12.916929977944218,DRIVEALONEFREE,0.4546141474460643,,,0out_0in,othdiscr 81048511,1976792,work,1,1,1,1,mandatory,1,3176.0,3281.0,761445,9.0,5.0,14.0,9.0,,,DRIVEALONEFREE,-0.6065542396018551,eat,,0out_0in,work 81130344,1978788,social,1,1,1,1,non_mandatory,1,3498.0,3598.0,762159,66.0,8.0,20.0,12.0,,11.919599153230113,SHARED3FREE,0.5693977967774873,,,0out_0in,social -81130399,1978790,escort,1,1,1,1,non_mandatory,1,3410.0,3598.0,762159,54.0,8.0,8.0,0.0,,12.580660633126431,SHARED2FREE,-0.7876904915091273,,,0out_0in,escort -81130429,1978790,work,1,1,1,1,mandatory,1,3574.0,3598.0,762159,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,-0.1419369306489097,no_subtours,,0out_0in,work -81130470,1978791,work,1,1,1,1,mandatory,1,3176.0,3598.0,762159,47.0,7.0,17.0,10.0,,,SHARED2FREE,-0.9914397742001736,no_subtours,,0out_0in,work +81130399,1978790,escort,1,1,1,1,non_mandatory,1,3410.0,3598.0,762159,54.0,8.0,8.0,0.0,,12.580660633126433,SHARED2FREE,-0.7876904915091273,,,0out_0in,escort +81130429,1978790,work,1,1,1,1,mandatory,1,3574.0,3598.0,762159,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,-0.14193693064890978,no_subtours,,0out_0in,work +81130470,1978791,work,1,1,1,1,mandatory,1,3176.0,3598.0,762159,47.0,7.0,17.0,10.0,,,SHARED2FREE,-0.9914397742001735,no_subtours,,0out_0in,work 102419958,2498047,school,1,1,1,1,mandatory,1,3340.0,3383.0,922602,76.0,9.0,15.0,6.0,,,WALK_LRF,2.873022403355501,,,0out_0in,school 102420007,2498048,work,1,1,1,1,mandatory,1,3383.0,3383.0,922602,46.0,7.0,16.0,9.0,,,WALK,2.281344345550102,no_subtours,,0out_0in,work 102420048,2498049,work,1,1,1,1,mandatory,1,3115.0,3383.0,922602,29.0,6.0,16.0,10.0,,,BIKE,0.5927455937959631,no_subtours,,0out_0in,work @@ -65,10 +65,10 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,to 107509922,2622193,escort,1,1,1,2,non_mandatory,1,3376.0,3505.0,952720,19.0,6.0,6.0,0.0,,12.84512392061393,SHARED2FREE,-0.7469201256899531,,,0out_0in,escort 107509941,2622193,othmaint,1,1,2,2,non_mandatory,1,3176.0,3505.0,952720,59.0,8.0,13.0,5.0,,12.428456977574594,DRIVEALONEFREE,-0.748210620713289,,,0out_0in,othmaint 107509987,2622194,shopping,1,1,1,1,non_mandatory,1,3131.0,3505.0,952720,75.0,9.0,14.0,5.0,,11.82505917210944,TNC_SINGLE,-0.6891536748380889,,,0out_0in,shopping -107510034,2622195,work,1,1,1,1,mandatory,1,3363.0,3505.0,952720,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.3860087172282153,no_subtours,,0out_0in,work -116640406,2844887,work,1,1,1,1,mandatory,1,3497.0,3597.0,1028031,107.0,11.0,19.0,8.0,,,DRIVEALONEFREE,0.1107975465014056,no_subtours,,0out_1in,work +107510034,2622195,work,1,1,1,1,mandatory,1,3363.0,3505.0,952720,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.38600871722821534,no_subtours,,0out_0in,work +116640406,2844887,work,1,1,1,1,mandatory,1,3497.0,3597.0,1028031,107.0,11.0,19.0,8.0,,,DRIVEALONEFREE,0.11079754650140568,no_subtours,,0out_1in,work 120287676,2933845,school,1,1,1,1,mandatory,1,3536.0,3134.0,1048898,122.0,12.0,22.0,10.0,,,SHARED3FREE,-1.0938793779365912,,,0out_0in,school 120287717,2933846,school,1,1,1,1,mandatory,1,3536.0,3134.0,1048898,62.0,8.0,16.0,8.0,,,SHARED2FREE,-1.1274402981153004,,,0out_0in,school 120287752,2933847,othdiscr,1,1,1,1,non_mandatory,1,3272.0,3134.0,1048898,42.0,7.0,12.0,5.0,,12.770313936483376,SHARED2FREE,0.5634771825641499,,,0out_1in,othdiscr -120287807,2933848,work,1,1,1,1,mandatory,1,3536.0,3134.0,1048898,31.0,6.0,18.0,12.0,,,DRIVEALONEFREE,0.0371166413339574,no_subtours,,0out_1in,work +120287807,2933848,work,1,1,1,1,mandatory,1,3536.0,3134.0,1048898,31.0,6.0,18.0,12.0,,,DRIVEALONEFREE,0.037116641333957465,no_subtours,,0out_1in,work 131881533,3216622,school,1,1,1,1,mandatory,1,3112.0,3098.0,1148260,138.0,14.0,17.0,3.0,,,WALK,-0.8109781504489971,,,0out_2in,univ diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_tours_sh.csv b/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_tours_sh.csv new file mode 100644 index 000000000..c5f84186b --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_tours_sh.csv @@ -0,0 +1,74 @@ +tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,tour_category,number_of_participants,destination,origin,household_id,tdd,start,end,duration,composition,destination_logsum,tour_mode,mode_choice_logsum,atwork_subtour_frequency,parent_tour_id,stop_frequency,primary_purpose +1359025,33146,work,1,1,1,1,mandatory,1,3131.0,3259.0,12593,11.0,5.0,16.0,11.0,,,SHARED2FREE,0.04497099667974337,no_subtours,,2out_0in,work +1359066,33147,work,1,1,1,1,mandatory,1,3342.0,3259.0,12593,59.0,8.0,13.0,5.0,,,SHARED2FREE,0.923483008481964,no_subtours,,0out_3in,work +1494647,36454,shopping,2,1,1,2,non_mandatory,1,3342.0,3134.0,13797,135.0,14.0,14.0,0.0,,11.401050563880887,DRIVEALONEFREE,0.2595703435486286,,,0out_1in,shopping +1494648,36454,shopping,2,2,2,2,non_mandatory,1,3497.0,3134.0,13797,154.0,16.0,16.0,0.0,,11.439449857137804,SHARED2FREE,-0.0006093410329746202,,,1out_0in,shopping +1494694,36455,work,1,1,1,1,mandatory,1,3317.0,3134.0,13797,53.0,7.0,23.0,16.0,,,SHARED3FREE,0.2738771147247297,no_subtours,,0out_0in,work +1709911,41705,eatout,1,1,1,1,non_mandatory,1,3437.0,3342.0,15777,85.0,10.0,10.0,0.0,,11.909980318826895,SHARED2FREE,0.7916769643872005,,,0out_0in,eatout +1709946,41706,business,1,1,1,1,atwork,1,3131.0,3398.0,15777,117.0,12.0,17.0,5.0,,18.502192217204307,SHARED2FREE,-1.0113258602519364,,1709985.0,0out_0in,atwork +1709985,41706,work,1,1,1,1,mandatory,1,3398.0,3342.0,15777,65.0,8.0,19.0,11.0,,,DRIVEALONEFREE,0.5212273251520757,business1,,0out_1in,work +2051456,50035,social,1,1,1,1,joint,2,3497.0,3493.0,18261,146.0,15.0,16.0,1.0,mixed,11.601381369295755,SHARED2FREE,-1.4037659073121553,,,0out_2in,social +2051466,50035,school,1,1,1,1,mandatory,1,3340.0,3493.0,18261,42.0,7.0,12.0,5.0,,,SHARED3FREE,-1.3497203179845243,,,0out_0in,school +2051468,50035,shopping,1,1,1,1,non_mandatory,1,3396.0,3493.0,18261,172.0,18.0,21.0,3.0,,11.366590261069065,DRIVEALONEFREE,0.6130171387405682,,,0out_0in,shopping +2051509,50036,shopping,1,1,1,1,non_mandatory,1,3497.0,3493.0,18261,99.0,11.0,11.0,0.0,,11.555669525382719,DRIVEALONEFREE,0.6647948166112536,,,0out_1in,shopping +2051556,50037,work,1,1,1,1,mandatory,1,3396.0,3493.0,18261,44.0,7.0,14.0,7.0,,,DRIVEALONEFREE,0.5299854110237027,no_subtours,,0out_0in,work +2268889,55338,school,1,1,1,1,mandatory,1,3592.0,3592.0,19758,40.0,7.0,10.0,3.0,,,WALK,0.33183209199757385,,,0out_0in,school +2268938,55339,work,1,1,1,1,mandatory,1,3497.0,3592.0,19758,11.0,5.0,16.0,11.0,,,WALK,0.537772515990635,no_subtours,,0out_0in,work +2373816,57897,work,1,1,1,1,mandatory,1,3176.0,3746.0,20552,50.0,7.0,20.0,13.0,,,DRIVEALONEFREE,-0.28388579338221,no_subtours,,0out_0in,work +2373822,57898,eat,1,1,1,1,atwork,1,3410.0,3347.0,20552,125.0,13.0,14.0,1.0,,11.100624000564997,DRIVEALONEFREE,0.7681389958489114,,2373857.0,0out_0in,atwork +2373857,57898,work,1,1,1,1,mandatory,1,3347.0,3746.0,20552,103.0,11.0,15.0,4.0,,,DRIVEALONEFREE,0.520379765074684,eat,,0out_1in,work +2373898,57899,work,1,1,1,1,mandatory,1,3402.0,3746.0,20552,47.0,7.0,17.0,10.0,,,WALK,1.0388895039783694,no_subtours,,0out_0in,work +2373980,57901,work,2,1,1,2,mandatory,1,3115.0,3746.0,20552,25.0,6.0,12.0,6.0,,,SHARED3FREE,0.6022315390131013,no_subtours,,0out_0in,work +2373981,57901,work,2,2,2,2,mandatory,1,3115.0,3746.0,20552,150.0,15.0,20.0,5.0,,,SHARED2FREE,0.6232767878249469,no_subtours,,1out_0in,work +2563802,62531,school,1,1,1,1,mandatory,1,3460.0,3316.0,21869,180.0,20.0,20.0,0.0,,,SHARED3FREE,-0.7094603590463964,,,0out_0in,school +2563821,62532,escort,1,1,1,1,non_mandatory,1,3398.0,3316.0,21869,20.0,6.0,7.0,1.0,,12.499268454965652,SHARED2FREE,-1.4604154628072699,,,0out_0in,escort +2563862,62533,escort,3,1,1,4,non_mandatory,1,3402.0,3316.0,21869,1.0,5.0,6.0,1.0,,12.534424209198946,SHARED3FREE,-1.2940574569954848,,,0out_3in,escort +2563863,62533,escort,3,2,2,4,non_mandatory,1,3519.0,3316.0,21869,99.0,11.0,11.0,0.0,,12.466623656700463,SHARED2FREE,-0.9326373013150777,,,0out_0in,escort +2563864,62533,escort,3,3,3,4,non_mandatory,1,3398.0,3316.0,21869,135.0,14.0,14.0,0.0,,12.485628371830364,SHARED3FREE,-1.4467093822325976,,,0out_0in,escort +2563878,62533,othdiscr,1,1,4,4,non_mandatory,1,3383.0,3316.0,21869,99.0,11.0,11.0,0.0,,12.893824062601642,DRIVEALONEFREE,0.09001472929003788,,,0out_0in,othdiscr +2563925,62534,school,1,1,1,1,mandatory,1,3316.0,3316.0,21869,55.0,8.0,9.0,1.0,,,SHARED3FREE,0.4282867689528016,,,0out_1in,school +2787968,67999,escort,1,1,1,2,non_mandatory,1,3410.0,3378.0,23619,124.0,13.0,13.0,0.0,,12.955709049606325,TNC_SINGLE,-0.5513480052414359,,,0out_2in,escort +2787995,67999,social,1,1,2,2,non_mandatory,1,3400.0,3378.0,23619,165.0,17.0,20.0,3.0,,11.998752822352907,WALK,0.8829112299755879,,,0out_0in,social +2788039,68000,work,1,1,1,1,mandatory,1,3375.0,3378.0,23619,50.0,7.0,20.0,13.0,,,WALK,0.6308240193619252,no_subtours,,0out_0in,work +3238088,78977,school,1,1,1,1,mandatory,1,3339.0,3339.0,26897,44.0,7.0,14.0,7.0,,,WALK,2.7965374977721846,,,0out_0in,school +52627721,1283602,work,1,1,1,1,mandatory,1,3410.0,3315.0,435012,64.0,8.0,18.0,10.0,,,DRIVEALONEFREE,-0.6005520767047482,no_subtours,,0out_1in,work +52638594,1283868,eatout,1,1,1,1,non_mandatory,1,3314.0,3314.0,435278,172.0,18.0,21.0,3.0,,11.417188649277557,WALK,1.465324908192731,,,0out_0in,eatout +52638611,1283868,maint,1,1,1,1,atwork,1,3176.0,3176.0,435278,154.0,16.0,16.0,0.0,,19.272682099619168,WALK,0.6803915404396673,,52638627.0,0out_0in,atwork +52638627,1283868,work,1,1,1,1,mandatory,1,3176.0,3314.0,435278,79.0,9.0,18.0,9.0,,,DRIVEALONEFREE,-1.2414138285222476,maint,,0out_1in,work +52641825,1283946,work,1,1,1,1,mandatory,1,3619.0,3315.0,435356,48.0,7.0,18.0,11.0,,,TNC_SINGLE,-0.20853332274708639,no_subtours,,0out_0in,work +52668557,1284598,work,1,1,1,1,mandatory,1,3464.0,3261.0,436008,79.0,9.0,18.0,9.0,,,DRIVEALONEFREE,-0.10356442936745472,no_subtours,,0out_0in,work +52734819,1286215,eat,1,1,1,1,atwork,1,3176.0,3176.0,437625,88.0,10.0,13.0,3.0,,19.30442083196423,WALK,0.6365738436949087,,52734854.0,0out_0in,atwork +52734854,1286215,work,1,1,1,1,mandatory,1,3176.0,3317.0,437625,65.0,8.0,19.0,11.0,,,DRIVEALONEFREE,-1.216142661769996,eat,,0out_2in,work +52897577,1290184,shopping,2,1,1,2,non_mandatory,1,3131.0,3195.0,441594,71.0,9.0,10.0,1.0,,11.507230007796327,SHARED2FREE,-0.24241712512773075,,,0out_0in,shopping +52897578,1290184,shopping,2,2,2,2,non_mandatory,1,3131.0,3195.0,441594,154.0,16.0,16.0,0.0,,11.519310381558867,WALK,-0.24594444222498252,,,0out_0in,shopping +52897583,1290184,work,1,1,1,1,mandatory,1,3176.0,3195.0,441594,91.0,10.0,16.0,6.0,,,DRIVEALONEFREE,-0.4742431906622577,no_subtours,,0out_0in,work +52915670,1290626,eat,1,1,1,1,atwork,1,3340.0,3502.0,442036,86.0,10.0,11.0,1.0,,18.513981739804258,BIKE,14.60281456532542,,52915705.0,0out_1in,atwork +52915705,1290626,work,1,1,1,1,mandatory,1,3502.0,3278.0,442036,64.0,8.0,18.0,10.0,,,BIKE,-0.028812822631490976,eat,,0out_2in,work +76379130,1862905,othdiscr,1,1,1,1,non_mandatory,1,3460.0,3598.0,721960,151.0,15.0,21.0,6.0,,12.841187222979508,WALK,0.5769914886883216,,,0out_0in,othdiscr +76379171,1862906,othdiscr,1,1,1,1,non_mandatory,1,3705.0,3598.0,721960,104.0,11.0,16.0,5.0,,12.884342308539214,WALK,0.7912388057447584,,,0out_0in,othdiscr +80946571,1974306,othdiscr,1,1,1,1,non_mandatory,1,3176.0,3396.0,760593,59.0,8.0,13.0,5.0,,13.385057255164831,SHARED2FREE,-0.26392034304222955,,,0out_0in,othdiscr 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+131881533,3216622,school,1,1,1,1,mandatory,1,3112.0,3098.0,1148260,138.0,14.0,17.0,3.0,,,WALK,-0.8109781504489971,,,0out_2in,univ diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_trips.csv b/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_trips.csv index f66d5e1aa..3e4eecce6 100644 --- a/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_trips.csv +++ b/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_trips.csv @@ -1,17 +1,17 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,tour_id,purpose,destination_logsum,depart,trip_mode,mode_choice_logsum -10872201,33146,12593,work,1,True,3,3290,3259,1359025,escort,12.50569587831954,5,WALK,0.8796640848644649 +10872201,33146,12593,work,1,True,3,3290,3259,1359025,escort,12.505695878319539,5,WALK,0.8796640848644649 10872202,33146,12593,work,2,True,3,3317,3290,1359025,escort,12.74437072347788,7,WALK,0.7528069371143918 -10872203,33146,12593,work,3,True,3,3131,3317,1359025,work,,7,SHARED2FREE,-0.2771584910567812 +10872203,33146,12593,work,3,True,3,3131,3317,1359025,work,,7,SHARED2FREE,-0.27715849105678125 10872205,33146,12593,work,1,False,1,3259,3131,1359025,home,,16,SHARED2FREE,-0.3682899649069351 10872529,33147,12593,work,1,True,1,3342,3259,1359066,work,,8,SHARED2FREE,0.4187992443742946 10872533,33147,12593,work,1,False,4,3383,3342,1359066,escort,14.576775128994838,12,SHARED2FREE,0.8686143194682551 10872534,33147,12593,work,2,False,4,3274,3383,1359066,social,13.66341661662671,13,SHARED2FREE,0.5107943560451084 10872535,33147,12593,work,3,False,4,3340,3274,1359066,escort,14.112546915053883,13,SHARED2FREE,0.4230067105850933 -10872536,33147,12593,work,4,False,4,3259,3340,1359066,home,,13,DRIVEALONEFREE,0.2491163354013192 -11957177,36454,13797,shopping,1,True,1,3342,3134,1494647,shopping,,14,DRIVEALONEFREE,0.2941685907193185 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+4793,2397,12.113353382243144,13.852249483468231,12.628850625174163,2,4,3,13,0,55,1,4,3,0,0,N,False,True,True,True,False,False,False,False,False,False,True,False,False,False,False,False,False,False,False,0,False,1087,9.72542862493836,1,2,2,True,9.72542862493836,2,4,False,False,2397,1,150000,150.0,4,12.86,False,2,0,0,0,0,2,1,1,0,0,2,2,3273,1,1,1,75,173,10.0629,7.50267414,0.0,8,26,0,0,5,3,0,0,0.0,0,5,0,0.0,0.0,1,0.0,9.996435750840167,1.0662864800896177,0.963511879599052 +4794,2397,12.113353382243144,13.852249483468231,12.628850625174163,1,4,3,13,0,35,2,1,1,1,35,M,False,True,True,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,0,True,1087,9.72542862493836,1,2,2,True,9.72542862493836,2,4,False,False,2397,1,150000,150.0,4,12.86,False,2,0,0,0,0,2,1,1,0,0,2,2,3273,1,1,1,75,173,10.0629,7.50267414,0.0,8,26,0,0,5,3,0,0,0.0,0,5,0,0.0,0.0,1,0.0,9.996435750840167,1.0662864800896177,0.963511879599052 +4795,2398,12.025004408499438,13.551837183912088,12.34243891247209,2,4,3,13,0,55,1,4,3,0,0,N,False,True,True,True,False,False,False,False,False,False,True,False,False,False,False,False,False,False,False,0,False,1092,3.5242037446361527,1,2,2,True,3.5242037446361527,2,4,False,False,2398,1,150000,150.0,4,12.86,False,2,0,0,0,0,2,1,1,0,0,2,2,3278,1,1,1,85,202,17.8938,15.42102208,0.0,114,30,0,0,113,1,0,0,0.0,0,5,0,0.0,0.0,1,0.0,5.511956312561093,7.392506113317231,3.1576031137284652 +4796,2398,12.025004408499438,13.551837183912088,12.34243891247209,1,4,3,13,0,35,2,1,1,1,35,M,False,True,True,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,0,True,1092,3.5242037446361527,1,2,2,True,3.5242037446361527,2,4,False,False,2398,1,150000,150.0,4,12.86,False,2,0,0,0,0,2,1,1,0,0,2,2,3278,1,1,1,85,202,17.8938,15.42102208,0.0,114,30,0,0,113,1,0,0,0.0,0,5,0,0.0,0.0,1,0.0,5.511956312561093,7.392506113317231,3.1576031137284652 +4797,2399,12.01624188709364,13.52246487749587,12.253058151748906,2,4,3,13,0,55,1,4,3,0,0,N,False,True,True,True,False,False,False,False,False,False,True,False,False,False,False,False,False,False,False,0,False,1098,5.879146530670945,1,2,2,True,5.879146530670945,2,4,False,False,2399,1,150000,150.0,4,12.86,False,2,0,0,0,0,2,1,1,0,0,2,2,3195,1,1,1,362,818,15.3018,9.5027121225,3.1675707075,14,126,0,0,9,5,0,0,0.0,0,5,0,0.0,0.0,1,0.0,28.570790791100283,1.1049477101530496,1.0638060400941596 +4798,2399,12.01624188709364,13.52246487749587,12.253058151748906,1,4,3,13,0,35,2,1,1,1,35,M,False,True,True,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,0,True,1098,5.879146530670945,1,2,2,True,5.879146530670945,2,4,False,False,2399,1,150000,150.0,4,12.86,False,2,0,0,0,0,2,1,1,0,0,2,2,3195,1,1,1,362,818,15.3018,9.5027121225,3.1675707075,14,126,0,0,9,5,0,0,0.0,0,5,0,0.0,0.0,1,0.0,28.570790791100283,1.1049477101530496,1.0638060400941596 +4799,2400,12.00113892117717,13.591330096022038,12.271419406640174,1,4,3,13,0,35,2,1,1,1,35,M,False,True,True,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,0,True,1099,6.800770958975652,1,2,2,True,6.800770958975652,2,4,False,False,2400,1,150000,150.0,4,12.86,False,2,0,0,0,0,2,1,1,0,0,2,2,3195,1,1,1,136,322,34.9367,27.80936417,0.0,5,56,0,3,0,2,0,0,0.0,0,5,0,0.0,0.0,1,0.0,4.890439032285146,0.17979555265754213,0.1734198238398988 +4800,2400,12.00113892117717,13.591330096022038,12.271419406640174,2,4,3,13,0,55,1,4,3,0,0,N,False,True,True,True,False,False,False,False,False,False,True,False,False,False,False,False,False,False,False,0,False,1099,6.800770958975652,1,2,2,True,6.800770958975652,2,4,False,False,2400,1,150000,150.0,4,12.86,False,2,0,0,0,0,2,1,1,0,0,2,2,3195,1,1,1,136,322,34.9367,27.80936417,0.0,5,56,0,3,0,2,0,0,0.0,0,5,0,0.0,0.0,1,0.0,4.890439032285146,0.17979555265754213,0.1734198238398988 diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv index 4b0273f67..8bef88172 100644 --- a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv +++ b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv @@ -19,8 +19,9 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,to 2373822,57898,eat,1,1,1,1,atwork,1,1070.0,904.0,20552,125.0,13.0,14.0,1.0,,11.039771243389376,DRIVEALONEFREE,-0.5383115099180752,,2373857.0,0out_0in,atwork 2373857,57898,work,1,1,1,1,mandatory,1,904.0,829.0,20552,103.0,11.0,15.0,4.0,,,DRIVEALONEFREE,0.41082360290734365,eat,,0out_1in,work 2373898,57899,work,1,1,1,1,mandatory,1,687.0,829.0,20552,47.0,7.0,17.0,10.0,,,WALK,1.295796465229812,no_subtours,,0out_0in,work -2373980,57901,work,2,1,1,2,mandatory,1,706.0,829.0,20552,24.0,6.0,11.0,5.0,,,SHARED2FREE,0.5267612467815088,no_subtours,,0out_0in,work -2373981,57901,work,2,2,2,2,mandatory,1,706.0,829.0,20552,148.0,15.0,18.0,3.0,,,DRIVEALONEFREE,0.5171713294052263,no_subtours,,1out_0in,work +2373942,57901,business,1,1,1,1,atwork,1,990.0,706.0,20552,124.0,13.0,13.0,0.0,,11.247393445255824,DRIVEALONEFREE,0.6164510097064706,,2373981.0,0out_0in,atwork +2373980,57901,work,2,1,1,2,mandatory,1,706.0,829.0,20552,4.0,5.0,9.0,4.0,,,SHARED2FREE,0.5267612467815088,no_subtours,,0out_0in,work +2373981,57901,work,2,2,2,2,mandatory,1,706.0,829.0,20552,119.0,12.0,19.0,7.0,,,DRIVEALONEFREE,0.5171713294052263,business1,,1out_0in,work 2563802,62531,school,1,1,1,1,mandatory,1,943.0,900.0,21869,181.0,20.0,21.0,1.0,,,SHARED3FREE,-1.2267647178249026,,,0out_0in,school 2563821,62532,escort,1,1,1,1,non_mandatory,1,647.0,900.0,21869,20.0,6.0,7.0,1.0,,12.495263261084341,SHARED2FREE,-1.2864424308629347,,,0out_0in,escort 2563862,62533,escort,3,1,1,4,non_mandatory,1,695.0,900.0,21869,1.0,5.0,6.0,1.0,,12.556993224775415,SHARED3FREE,-1.2677737648720784,,,0out_3in,escort @@ -49,7 +50,7 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,to 52897571,1290184,othdiscr,3,3,3,4,non_mandatory,1,725.0,1096.0,441594,189.0,23.0,23.0,0.0,,12.816550040191554,DRIVEALONEFREE,0.10969889039862483,,,1out_0in,othdiscr 52897583,1290184,work,1,1,1,1,mandatory,1,952.0,1096.0,441594,81.0,9.0,20.0,11.0,,,DRIVEALONEFREE,-0.27927777887955096,business1,,0out_1in,work 52915670,1290626,eat,1,1,1,1,atwork,1,1070.0,1070.0,442036,86.0,10.0,11.0,1.0,,19.46157380226947,WALK,0.547115313877139,,52915705.0,0out_1in,atwork -52915705,1290626,work,1,1,1,1,mandatory,1,1070.0,1100.0,442036,64.0,8.0,18.0,10.0,,,WALK_LRF,-0.47850756825328533,eat,,1out_0in,work +52915705,1290626,work,1,1,1,1,mandatory,1,1070.0,1100.0,442036,64.0,8.0,18.0,10.0,,,WALK_LRF,-0.47850756825328516,eat,,1out_0in,work 76379130,1862905,othdiscr,1,1,1,1,non_mandatory,1,975.0,960.0,721960,151.0,15.0,21.0,6.0,,12.772851804931955,SHARED3FREE,0.21983705793331118,,,0out_0in,othdiscr 76379171,1862906,othdiscr,1,1,1,1,non_mandatory,1,980.0,960.0,721960,104.0,11.0,16.0,5.0,,12.8503916377897,SHARED2FREE,0.22336856671451474,,,0out_1in,othdiscr 80946571,1974306,othdiscr,1,1,1,1,non_mandatory,1,564.0,509.0,760593,62.0,8.0,16.0,8.0,,13.379667308006209,SHARED3FREE,1.164517752717716,,,0out_0in,othdiscr @@ -60,7 +61,7 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,to 81048476,1976792,eat,1,1,1,1,atwork,1,517.0,517.0,761445,70.0,9.0,9.0,0.0,,17.917187712862642,DRIVEALONEFREE,1.0395075013303896,,81048511.0,0out_0in,atwork 81048508,1976792,social,1,1,1,1,non_mandatory,1,831.0,613.0,761445,140.0,14.0,19.0,5.0,,12.06743909439602,WALK,0.46364302229357707,,,0out_0in,social 81048511,1976792,work,1,1,1,1,mandatory,1,517.0,613.0,761445,7.0,5.0,12.0,7.0,,,DRIVEALONEFREE,0.03163142089216641,eat,,0out_0in,work -81130344,1978788,social,1,1,1,1,non_mandatory,1,739.0,961.0,762159,66.0,8.0,20.0,12.0,,11.906202943037743,SHARED3FREE,0.6662525964318254,,,0out_0in,social +81130344,1978788,social,1,1,1,1,non_mandatory,1,739.0,961.0,762159,66.0,8.0,20.0,12.0,,11.906202943037743,SHARED3FREE,0.6662525964318252,,,0out_0in,social 81130399,1978790,escort,1,1,1,1,non_mandatory,1,992.0,961.0,762159,54.0,8.0,8.0,0.0,,12.577144975000508,SHARED2FREE,-0.8831308547168382,,,0out_0in,escort 81130429,1978790,work,1,1,1,1,mandatory,1,681.0,961.0,762159,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,-0.01697772512269618,no_subtours,,0out_0in,work 81130470,1978791,work,1,1,1,1,mandatory,1,1070.0,961.0,762159,47.0,7.0,17.0,10.0,,,SHARED2FREE,-0.928003496988582,no_subtours,,0out_0in,work diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours_sh.csv b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours_sh.csv new file mode 100644 index 000000000..b2f1a3c29 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours_sh.csv @@ -0,0 +1,81 @@ +tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,tour_category,number_of_participants,destination,origin,household_id,tdd,start,end,duration,composition,destination_logsum,tour_mode,mode_choice_logsum,atwork_subtour_frequency,parent_tour_id,stop_frequency,primary_purpose +1359025,33146,work,1,1,1,1,mandatory,1,1100,560,12593,13.0,5.0,18.0,13.0,,,SHARED2FREE,0.36538018644207043,no_subtours,,2out_0in,work +1359066,33147,work,1,1,1,1,mandatory,1,1070,560,12593,59.0,8.0,13.0,5.0,,,SHARED3FREE,0.09941178487064588,no_subtours,,0out_3in,work +1494647,36454,shopping,2,1,1,2,non_mandatory,1,725,580,13797,128.0,13.0,17.0,4.0,,11.588883,SHARED2FREE,-0.0992827123431451,,,0out_1in,shopping +1494648,36454,shopping,2,2,2,2,non_mandatory,1,729,580,13797,169.0,18.0,18.0,0.0,,11.589278,WALK,0.22215181107342957,,,0out_2in,shopping +1494680,36455,othdiscr,1,1,1,1,non_mandatory,1,621,580,13797,159.0,16.0,21.0,5.0,,12.871272,DRIVEALONEFREE,0.3181692351526671,,,0out_2in,othdiscr +1494694,36455,work,1,1,1,1,mandatory,1,555,580,13797,58.0,8.0,12.0,4.0,,,WALK,0.4370819786779232,no_subtours,,0out_0in,work +1709911,41705,eatout,1,1,1,1,non_mandatory,1,1070,645,15777,76.0,9.0,15.0,6.0,,11.872931,SHARED2FREE,-0.7287327063424008,,,0out_0in,eatout +1709946,41706,business,1,1,1,1,atwork,1,988,763,15777,116.0,12.0,16.0,4.0,,18.66777,WALK,0.137119353213824,,1709985.0,0out_0in,atwork +1709985,41706,work,1,1,1,1,mandatory,1,763,645,15777,65.0,8.0,19.0,11.0,,,DRIVEALONEFREE,0.441411411669672,business1,,0out_1in,work +2051448,50035,eatout,1,1,1,1,joint,3,986,757,18261,156.0,16.0,18.0,2.0,mixed,11.548074,BIKE,-4.315590206938891,,,0out_1in,eatout +2051466,50035,school,1,1,1,1,mandatory,1,919,757,18261,43.0,7.0,13.0,6.0,,,SHARED3FREE,-1.4990946171231887,,,0out_0in,school +2051468,50035,shopping,1,1,1,1,non_mandatory,1,991,757,18261,175.0,19.0,19.0,0.0,,11.432129,DRIVEALONEFREE,-0.49743352602371993,,,0out_0in,shopping +2051504,50036,othmaint,1,1,1,1,non_mandatory,1,874,757,18261,44.0,7.0,14.0,7.0,,12.126692,DRIVEALONEFREE,-0.20958674876271485,,,0out_0in,othmaint +2051556,50037,work,1,1,1,1,mandatory,1,1070,757,18261,46.0,7.0,16.0,9.0,,,DRIVEALONEFREE,-1.259416062462369,no_subtours,,0out_0in,work +2268889,55338,school,1,1,1,1,mandatory,1,684,794,19758,40.0,7.0,10.0,3.0,,,WALK,-1.256172025738767,,,0out_0in,school +2268938,55339,work,1,1,1,1,mandatory,1,699,794,19758,11.0,5.0,16.0,11.0,,,WALK,0.16257796649302067,no_subtours,,0out_0in,work +2373816,57897,work,1,1,1,1,mandatory,1,1070,829,20552,50.0,7.0,20.0,13.0,,,DRIVEALONEFREE,-0.30338142975106963,no_subtours,,0out_0in,work +2373822,57898,eat,1,1,1,1,atwork,1,1070,904,20552,125.0,13.0,14.0,1.0,,11.039771,DRIVEALONEFREE,-0.5383114957971221,,2373857.0,0out_0in,atwork +2373857,57898,work,1,1,1,1,mandatory,1,904,829,20552,103.0,11.0,15.0,4.0,,,DRIVEALONEFREE,0.41082359419482434,eat,,0out_1in,work +2373898,57899,work,1,1,1,1,mandatory,1,687,829,20552,47.0,7.0,17.0,10.0,,,WALK,1.295796532158761,no_subtours,,0out_0in,work +2373942,57901,business,1,1,1,1,atwork,1,990,706,20552,124.0,13.0,13.0,0.0,,11.247394,DRIVEALONEFREE,0.616450956316715,,2373981.0,0out_0in,atwork +2373980,57901,work,2,1,1,2,mandatory,1,706,829,20552,4.0,5.0,9.0,4.0,,,SHARED2FREE,0.5267612673613843,no_subtours,,0out_0in,work +2373981,57901,work,2,2,2,2,mandatory,1,706,829,20552,119.0,12.0,19.0,7.0,,,DRIVEALONEFREE,0.5171713630296526,business1,,1out_0in,work +2563802,62531,school,1,1,1,1,mandatory,1,943,900,21869,181.0,20.0,21.0,1.0,,,SHARED3FREE,-1.226764637052788,,,0out_0in,school +2563821,62532,escort,1,1,1,1,non_mandatory,1,647,900,21869,20.0,6.0,7.0,1.0,,12.495263,SHARED2FREE,-1.2864424041376865,,,0out_0in,escort +2563862,62533,escort,3,1,1,4,non_mandatory,1,695,900,21869,1.0,5.0,6.0,1.0,,12.5569935,SHARED3FREE,-1.2677737730011323,,,0out_3in,escort +2563863,62533,escort,3,2,2,4,non_mandatory,1,518,900,21869,99.0,11.0,11.0,0.0,,12.4978695,SHARED2FREE,-1.6806379182094977,,,0out_0in,escort +2563864,62533,escort,3,3,3,4,non_mandatory,1,844,900,21869,135.0,14.0,14.0,0.0,,12.529105,SHARED3FREE,-1.4300972332149253,,,0out_0in,escort +2563878,62533,othdiscr,1,1,4,4,non_mandatory,1,1070,900,21869,100.0,11.0,12.0,1.0,,12.891527,DRIVEALONEFREE,-0.1277668073387934,,,0out_0in,othdiscr +2563925,62534,school,1,1,1,1,mandatory,1,916,900,21869,55.0,8.0,9.0,1.0,,,SHARED2FREE,-0.036284049053444734,,,0out_1in,school +2787968,67999,escort,1,1,1,2,non_mandatory,1,767,973,23619,124.0,13.0,13.0,0.0,,12.967598,TNC_SINGLE,-0.7500927100255308,,,0out_2in,escort +2787995,67999,social,1,1,2,2,non_mandatory,1,913,973,23619,165.0,17.0,20.0,3.0,,12.107248,WALK,1.0240294566725847,,,0out_0in,social +2788039,68000,work,1,1,1,1,mandatory,1,1044,973,23619,49.0,7.0,19.0,12.0,,,SHARED2FREE,-0.007107189151378274,no_subtours,,1out_1in,work +3238088,78977,school,1,1,1,1,mandatory,1,984,1081,26897,44.0,7.0,14.0,7.0,,,WALK,0.12444784050821488,,,0out_0in,school +3238143,78979,eat,1,1,1,1,atwork,1,877,871,26897,72.0,9.0,11.0,2.0,,18.13034,WALK,0.5663422365804629,,3238178.0,0out_0in,atwork +3238178,78979,work,1,1,1,1,mandatory,1,871,1081,26897,48.0,7.0,18.0,11.0,,,DRIVEALONEFREE,-0.014910558388757844,eat,,0out_0in,work +52627721,1283602,work,1,1,1,1,mandatory,1,1078,521,435012,64.0,8.0,18.0,10.0,,,DRIVEALONEFREE,-1.1965658036522242,no_subtours,,0out_1in,work +52638594,1283868,eatout,1,1,1,1,non_mandatory,1,1070,537,435278,172.0,18.0,21.0,3.0,,11.528167,SHARED2FREE,-1.1519134997360956,,,1out_0in,eatout +52638611,1283868,maint,1,1,1,1,atwork,1,713,923,435278,154.0,16.0,16.0,0.0,,18.36072,DRIVEALONEFREE,-0.27103028035417925,,52638627.0,0out_0in,atwork +52638627,1283868,work,1,1,1,1,mandatory,1,923,537,435278,79.0,9.0,18.0,9.0,,,DRIVEALONEFREE,-0.43545363448393387,maint,,0out_1in,work +52641825,1283946,work,1,1,1,1,mandatory,1,1005,523,435356,48.0,7.0,18.0,11.0,,,TNC_SINGLE,-0.15324518765147138,no_subtours,,0out_0in,work +52668557,1284598,work,1,1,1,1,mandatory,1,551,562,436008,80.0,9.0,19.0,10.0,,,DRIVEALONEFREE,0.5908314031713293,no_subtours,,0out_0in,work +52734819,1286215,eat,1,1,1,1,atwork,1,1077,606,437625,88.0,10.0,13.0,3.0,,18.12966,DRIVEALONEFREE,-1.0786065781751473,,52734854.0,0out_0in,atwork +52734854,1286215,work,1,1,1,1,mandatory,1,606,656,437625,65.0,8.0,19.0,11.0,,,DRIVEALONEFREE,0.34770109667460397,eat,,0out_2in,work +52897544,1290184,business,1,1,1,1,atwork,1,763,952,441594,99.0,11.0,11.0,0.0,,17.608831,DRIVEALONEFREE,-0.2638632508540483,,52897583.0,0out_0in,atwork +52897550,1290184,eatout,1,1,4,4,non_mandatory,1,946,1096,441594,181.0,20.0,21.0,1.0,,11.792316,DRIVEALONEFREE,-0.27383046206140366,,,0out_1in,eatout +52897569,1290184,othdiscr,3,1,1,4,non_mandatory,1,684,1096,441594,19.0,6.0,6.0,0.0,,12.786691,DRIVEALONEFREE,0.009380535229135779,,,0out_0in,othdiscr +52897570,1290184,othdiscr,3,2,2,4,non_mandatory,1,762,1096,441594,185.0,21.0,22.0,1.0,,12.837735,DRIVEALONEFREE,-0.10046306805513741,,,0out_0in,othdiscr +52897571,1290184,othdiscr,3,3,3,4,non_mandatory,1,725,1096,441594,189.0,23.0,23.0,0.0,,12.81655,DRIVEALONEFREE,0.10969887272263075,,,1out_0in,othdiscr +52897583,1290184,work,1,1,1,1,mandatory,1,952,1096,441594,81.0,9.0,20.0,11.0,,,DRIVEALONEFREE,-0.27927776481025,business1,,0out_1in,work +52915670,1290626,eat,1,1,1,1,atwork,1,1070,1070,442036,86.0,10.0,11.0,1.0,,19.461575,WALK,0.5471153271447997,,52915705.0,0out_1in,atwork +52915705,1290626,work,1,1,1,1,mandatory,1,1070,1100,442036,64.0,8.0,18.0,10.0,,,WALK_LRF,-0.4785075925903294,eat,,1out_0in,work +76379130,1862905,othdiscr,1,1,1,1,non_mandatory,1,975,960,721960,151.0,15.0,21.0,6.0,,12.772852,SHARED3FREE,0.21983705470524506,,,0out_0in,othdiscr +76379171,1862906,othdiscr,1,1,1,1,non_mandatory,1,980,960,721960,104.0,11.0,16.0,5.0,,12.850391,SHARED2FREE,0.22336853092936326,,,0out_1in,othdiscr +80946571,1974306,othdiscr,1,1,1,1,non_mandatory,1,564,509,760593,62.0,8.0,16.0,8.0,,13.379667,SHARED3FREE,1.1645177740991923,,,0out_0in,othdiscr +80946591,1974307,eat,1,1,1,1,atwork,1,853,696,760593,113.0,12.0,13.0,1.0,,18.752243,TNC_SINGLE,0.28357719040363577,,80946626.0,0out_0in,atwork +80946626,1974307,work,1,1,1,1,mandatory,1,696,509,760593,79.0,9.0,18.0,9.0,,,WALK,0.32952249428941843,eat,,1out_0in,work +80946637,1974308,escort,1,1,1,1,non_mandatory,1,716,509,760593,0.0,5.0,5.0,0.0,,12.669005,SHARED3FREE,-0.8421136510342611,,,0out_0in,escort +81048440,1976791,escort,1,1,1,1,non_mandatory,1,908,613,761445,124.0,13.0,13.0,0.0,,12.820903,SHARED2FREE,-0.9700895291217932,,,0out_0in,escort +81048476,1976792,eat,1,1,1,1,atwork,1,517,517,761445,70.0,9.0,9.0,0.0,,17.917187,DRIVEALONEFREE,1.039507486293145,,81048511.0,0out_0in,atwork +81048508,1976792,social,1,1,1,1,non_mandatory,1,831,613,761445,140.0,14.0,19.0,5.0,,12.067439,WALK,0.4636430464295935,,,0out_0in,social +81048511,1976792,work,1,1,1,1,mandatory,1,517,613,761445,7.0,5.0,12.0,7.0,,,DRIVEALONEFREE,0.03163141281794101,eat,,0out_0in,work +81130344,1978788,social,1,1,1,1,non_mandatory,1,739,961,762159,66.0,8.0,20.0,12.0,,11.906203,SHARED3FREE,0.6662526170664789,,,0out_0in,social +81130399,1978790,escort,1,1,1,1,non_mandatory,1,992,961,762159,54.0,8.0,8.0,0.0,,12.577145,SHARED2FREE,-0.8831308815924643,,,0out_0in,escort +81130429,1978790,work,1,1,1,1,mandatory,1,681,961,762159,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,-0.016977745800912,no_subtours,,0out_0in,work +81130470,1978791,work,1,1,1,1,mandatory,1,1070,961,762159,47.0,7.0,17.0,10.0,,,SHARED2FREE,-0.9280034994526589,no_subtours,,0out_0in,work +102419958,2498047,school,1,1,1,1,mandatory,1,737,730,922602,77.0,9.0,16.0,7.0,,,WALK,2.6747421575246584,,,0out_0in,school +102420007,2498048,work,1,1,1,1,mandatory,1,722,730,922602,46.0,7.0,16.0,9.0,,,WALK,2.041191360849634,no_subtours,,0out_0in,work +102420048,2498049,work,1,1,1,1,mandatory,1,755,730,922602,29.0,6.0,16.0,10.0,,,WALK,1.1684000891097426,no_subtours,,0out_0in,work +107509903,2622192,school,1,1,1,1,mandatory,1,1004,1025,952720,44.0,7.0,14.0,7.0,,,WALK,0.28186425446408825,,,0out_0in,school +107509922,2622193,escort,1,1,1,2,non_mandatory,1,773,1025,952720,19.0,6.0,6.0,0.0,,12.838724,SHARED2FREE,-0.7024294415801873,,,0out_0in,escort +107509941,2622193,othmaint,1,1,2,2,non_mandatory,1,550,1025,952720,61.0,8.0,15.0,7.0,,12.457189,DRIVEALONEFREE,-0.0718658187188987,,,0out_0in,othmaint +107509987,2622194,shopping,1,1,1,1,non_mandatory,1,989,1025,952720,72.0,9.0,11.0,2.0,,11.894073,WALK,0.541554508705619,,,0out_0in,shopping +107510034,2622195,work,1,1,1,1,mandatory,1,1021,1025,952720,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.576165950576846,no_subtours,,0out_0in,work +116640406,2844887,work,1,1,1,1,mandatory,1,796,846,1028031,109.0,11.0,21.0,10.0,,,DRIVEALONEFREE,0.411732781392635,no_subtours,,0out_1in,work +120287676,2933845,school,1,1,1,1,mandatory,1,563,574,1048898,121.0,12.0,21.0,9.0,,,WALK,0.07010832880583706,,,0out_0in,school +120287717,2933846,school,1,1,1,1,mandatory,1,515,574,1048898,62.0,8.0,16.0,8.0,,,SHARED2FREE,-1.0988306831746049,,,0out_0in,school +120287752,2933847,othdiscr,1,1,1,1,non_mandatory,1,623,574,1048898,42.0,7.0,12.0,5.0,,12.852489,DRIVEALONEFREE,0.12407657987930228,,,0out_1in,othdiscr +120287807,2933848,work,1,1,1,1,mandatory,1,502,574,1048898,31.0,6.0,18.0,12.0,,,DRIVEALONEFREE,0.25163331437904624,no_subtours,,0out_1in,work +131881533,3216622,school,1,1,1,1,mandatory,1,1074,1076,1148260,136.0,14.0,15.0,1.0,,,WALK,-0.49868912988606207,,,0out_2in,univ diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv index 466427db8..409e2c7e3 100644 --- a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv +++ b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv @@ -21,7 +21,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 11957447,36455,13797,othdiscr,3,False,3,580,715,1494680,home,,21,DRIVEALONEFREE,0.30871306064437815 11957553,36455,13797,work,1,True,1,555,580,1494694,work,,8,WALK,0.3634416376281795 11957557,36455,13797,work,1,False,1,580,555,1494694,home,,12,WALK,0.3634848237755095 -13679289,41705,15777,eatout,1,True,1,1070,645,1709911,eatout,,9,DRIVEALONEFREE,0.33225829451467775 +13679289,41705,15777,eatout,1,True,1,1070,645,1709911,eatout,,9,DRIVEALONEFREE,0.33225829451467803 13679293,41705,15777,eatout,1,False,1,645,1070,1709911,home,,15,SHARED2FREE,0.2798579035187319 13679569,41706,15777,atwork,1,True,1,988,763,1709946,atwork,,12,WALK,-1.7814320077964532 13679573,41706,15777,atwork,1,False,1,763,988,1709946,work,,16,WALK,-1.781485908054844 @@ -29,7 +29,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 13679885,41706,15777,work,1,False,2,1058,763,1709985,escort,12.95511617078327,18,DRIVEALONEFREE,-0.2304178431510166 13679886,41706,15777,work,2,False,2,645,1058,1709985,home,,19,DRIVEALONEFREE,-0.2819678461804502 16411585,50035,18261,eatout,1,True,1,986,757,2051448,eatout,,16,BIKE,-0.7708542602773313 -16411589,50035,18261,eatout,1,False,2,676,986,2051448,escort,11.368855031767414,17,BIKE,-0.5723144319848409 +16411589,50035,18261,eatout,1,False,2,676,986,2051448,escort,11.368855031767414,17,BIKE,-0.5723144319848411 16411590,50035,18261,eatout,2,False,2,757,676,2051448,home,,18,BIKE,0.6522763557669851 16411729,50035,18261,school,1,True,1,919,757,2051466,school,,7,SHARED3FREE,-0.6140707131031844 16411733,50035,18261,school,1,False,1,757,919,2051466,home,,13,DRIVEALONEFREE,-0.609643926614725 @@ -52,11 +52,13 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 18990862,57898,20552,work,2,False,2,829,733,2373857,home,,15,DRIVEALONEFREE,-0.13240927771932942 18991185,57899,20552,work,1,True,1,687,829,2373898,work,,7,WALK,0.7784185661273756 18991189,57899,20552,work,1,False,1,829,687,2373898,home,,17,WALK,0.7784242205760281 -18991841,57901,20552,work,1,True,1,706,829,2373980,work,,6,DRIVEALONEFREE,0.3008027159078227 -18991845,57901,20552,work,1,False,1,829,706,2373980,home,,11,DRIVEALONEFREE,0.26506806828181284 -18991849,57901,20552,work,1,True,2,712,829,2373981,othmaint,11.4872953609327,15,DRIVEALONEFREE,-0.19068246123655475 -18991850,57901,20552,work,2,True,2,706,712,2373981,work,,16,DRIVEALONEFREE,0.29088948362646183 -18991853,57901,20552,work,1,False,1,829,706,2373981,home,,18,DRIVEALONEFREE,-0.23582413156669893 +18991537,57901,20552,atwork,1,True,1,990,706,2373942,atwork,,13,DRIVEALONEFREE,-0.008138339522985559 +18991541,57901,20552,atwork,1,False,1,706,990,2373942,work,,13,DRIVEALONEFREE,-0.01627417365187152 +18991841,57901,20552,work,1,True,1,706,829,2373980,work,,5,DRIVEALONEFREE,0.3008027159078227 +18991845,57901,20552,work,1,False,1,829,706,2373980,home,,9,DRIVEALONEFREE,0.26506806828181284 +18991849,57901,20552,work,1,True,2,712,829,2373981,othmaint,11.4872953609327,12,DRIVEALONEFREE,-0.19068246123655475 +18991850,57901,20552,work,2,True,2,706,712,2373981,work,,13,DRIVEALONEFREE,0.29088948362646183 +18991853,57901,20552,work,1,False,1,829,706,2373981,home,,19,DRIVEALONEFREE,-0.23582413156669893 20510417,62531,21869,school,1,True,1,943,900,2563802,school,,20,SHARED3FREE,-1.9772115284881657 20510421,62531,21869,school,1,False,1,900,943,2563802,home,,21,SHARED3FREE,-1.9596696603848989 20510569,62532,21869,escort,1,True,1,647,900,2563821,escort,,6,SHARED2FREE,0.33462552915364907 @@ -70,14 +72,14 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 20510909,62533,21869,escort,1,False,1,900,518,2563863,home,,11,SHARED2FREE,0.028638087667856384 20510913,62533,21869,escort,1,True,1,844,900,2563864,escort,,14,SHARED2FREE,0.5953832095412978 20510917,62533,21869,escort,1,False,1,900,844,2563864,home,,14,DRIVEALONEFREE,0.5982622294593943 -20511025,62533,21869,othdiscr,1,True,1,1070,900,2563878,othdiscr,,11,DRIVEALONEFREE,-0.1485066877504566 +20511025,62533,21869,othdiscr,1,True,1,1070,900,2563878,othdiscr,,11,DRIVEALONEFREE,-0.14850668775045647 20511029,62533,21869,othdiscr,1,False,1,900,1070,2563878,home,,12,DRIVEALONEFREE,-0.1534836148666948 20511401,62534,21869,school,1,True,1,916,900,2563925,school,,8,WALK,-1.2930940704581566 20511405,62534,21869,school,1,False,2,1082,916,2563925,shopping,7.845318248721952,9,SHARED2FREE,-1.5272294838127598 20511406,62534,21869,school,2,False,2,900,1082,2563925,home,,9,WALK,1.1927964486618703 -22303745,67999,23619,escort,1,True,1,767,973,2787968,escort,,13,TNC_SHARED,0.1443338083297466 +22303745,67999,23619,escort,1,True,1,767,973,2787968,escort,,13,TNC_SHARED,0.1443338083297464 22303749,67999,23619,escort,1,False,3,1070,767,2787968,eatout,12.697641790480269,13,TNC_SINGLE,0.07988923204934381 -22303750,67999,23619,escort,2,False,3,1078,1070,2787968,escort,13.449805856854407,13,TNC_SINGLE,0.7932468294267513 +22303750,67999,23619,escort,2,False,3,1078,1070,2787968,escort,13.449805856854407,13,TNC_SINGLE,0.7932468294267512 22303751,67999,23619,escort,3,False,3,973,1078,2787968,home,,13,TNC_SINGLE,0.22765715549891172 22303961,67999,23619,social,1,True,1,913,973,2787995,social,,17,WALK,-0.5148523644112458 22303965,67999,23619,social,1,False,1,973,913,2787995,home,,20,WALK,-0.5150737325237639 @@ -94,9 +96,9 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 421021769,1283602,435012,work,1,True,1,1078,521,52627721,work,,8,DRIVEALONEFREE,-0.6750290087916829 421021773,1283602,435012,work,1,False,2,560,1078,52627721,social,10.565217359540531,14,DRIVEALONEFREE,-1.1907987969099594 421021774,1283602,435012,work,2,False,2,521,560,52627721,home,,18,DRIVEALONEFREE,0.3055101717954503 -421108753,1283868,435278,eatout,1,True,2,1077,537,52638594,escort,14.320986232374429,18,SHARED2FREE,-0.4350484117498945 +421108753,1283868,435278,eatout,1,True,2,1077,537,52638594,escort,14.320986232374427,18,SHARED2FREE,-0.4350484117498945 421108754,1283868,435278,eatout,2,True,2,1070,1077,52638594,eatout,,19,WALK,1.8006997854536941 -421108757,1283868,435278,eatout,1,False,1,537,1070,52638594,home,,21,SHARED2FREE,-0.4928453511919577 +421108757,1283868,435278,eatout,1,False,1,537,1070,52638594,home,,21,SHARED2FREE,-0.49284535119195805 421108889,1283868,435278,atwork,1,True,1,713,923,52638611,atwork,,16,DRIVEALONEFREE,-0.3206205769695649 421108893,1283868,435278,atwork,1,False,1,923,713,52638611,work,,16,DRIVEALONEFREE,-0.30825542970214137 421109017,1283868,435278,work,1,True,1,923,537,52638627,work,,9,DRIVEALONEFREE,-0.44703657084141896 @@ -106,7 +108,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 421134605,1283946,435356,work,1,False,1,523,1005,52641825,home,,18,TNC_SINGLE,0.13396244489330475 421348457,1284598,436008,work,1,True,1,551,562,52668557,work,,9,DRIVEALONEFREE,0.41482590970511396 421348461,1284598,436008,work,1,False,1,562,551,52668557,home,,19,WALK,0.41482590970511396 -421878553,1286215,437625,atwork,1,True,1,1077,606,52734819,atwork,,10,DRIVEALONEFREE,-0.7443646546844188 +421878553,1286215,437625,atwork,1,True,1,1077,606,52734819,atwork,,10,DRIVEALONEFREE,-0.7443646546844186 421878557,1286215,437625,atwork,1,False,1,606,1077,52734819,work,,13,DRIVEALONEFREE,-0.7600790313114658 421878833,1286215,437625,work,1,True,1,606,656,52734854,work,,8,DRIVEALONEFREE,0.15855289638925027 421878837,1286215,437625,work,1,False,3,934,606,52734854,othmaint,11.777501072314317,17,DRIVEALONEFREE,-0.32712136667920466 @@ -115,7 +117,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 423180353,1290184,441594,atwork,1,True,1,763,952,52897544,atwork,,11,DRIVEALONEFREE,-0.32097715508720903 423180357,1290184,441594,atwork,1,False,1,952,763,52897544,work,,11,DRIVEALONEFREE,-0.3082313192247495 423180401,1290184,441594,eatout,1,True,1,946,1096,52897550,eatout,,20,DRIVEALONEFREE,-0.3549315977740098 -423180405,1290184,441594,eatout,1,False,2,1070,946,52897550,othmaint,11.98171181287322,21,DRIVEALONEFREE,-0.641641315087032 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-647572733,1974307,760593,atwork,1,False,1,696,853,80946591,work,,13,TNC_SINGLE,0.4374827076991478 +647572729,1974307,760593,atwork,1,True,1,853,696,80946591,atwork,,12,TNC_SINGLE,0.5588801307317726 +647572733,1974307,760593,atwork,1,False,1,696,853,80946591,work,,13,TNC_SINGLE,0.43748270769914765 647573009,1974307,760593,work,1,True,2,670,509,80946626,shopping,7.846782621068262,9,WALK,-0.2893554768721311 647573010,1974307,760593,work,2,True,2,696,670,80946626,work,,10,WALK,-0.5062069503521934 647573013,1974307,760593,work,1,False,1,509,696,80946626,home,,18,WALK,-1.3939945233876567 @@ -187,7 +189,7 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 962301737,2933846,1048898,school,1,True,1,515,574,120287717,school,,8,WALK,-2.077872684673323 962301741,2933846,1048898,school,1,False,1,574,515,120287717,home,,16,SHARED2FREE,-2.083095264404366 962302017,2933847,1048898,othdiscr,1,True,1,623,574,120287752,othdiscr,,7,TNC_SINGLE,0.16536386122450294 -962302021,2933847,1048898,othdiscr,1,False,2,877,623,120287752,eatout,12.331068629649982,12,TAXI,0.10034850012383481 +962302021,2933847,1048898,othdiscr,1,False,2,877,623,120287752,eatout,12.331068629649982,12,TAXI,0.10034850012383502 962302022,2933847,1048898,othdiscr,2,False,2,574,877,120287752,home,,12,DRIVEALONEFREE,0.0157047931957291 962302457,2933848,1048898,work,1,True,1,502,574,120287807,work,,6,DRIVEALONEFREE,-0.05432573151273244 962302461,2933848,1048898,work,1,False,2,728,502,120287807,escort,12.16357348271453,17,DRIVEALONEFREE,-0.0901887950029015 diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips_sh.csv b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips_sh.csv new file mode 100644 index 000000000..b06592953 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips_sh.csv @@ -0,0 +1,200 @@ 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a/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_tours.csv b/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_tours.csv index 23434cccc..a27174eec 100644 --- a/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_tours.csv +++ b/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_tours.csv @@ -1,79 +1,78 @@ tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,tour_category,number_of_participants,destination,origin,household_id,tdd,start,end,duration,composition,destination_logsum,tour_mode,mode_choice_logsum,od_atap,od_btap,od_path_set,do_atap,do_btap,do_path_set,atwork_subtour_frequency,parent_tour_id,stop_frequency,primary_purpose -1359025,33146,work,1,1,1,1,mandatory,1,1100.0,560.0,12593,12.0,5.0,17.0,12.0,,,DRIVEALONEFREE,1.0261372219238083,,,,,,,no_subtours,,2out_0in,work -1359066,33147,work,1,1,1,1,mandatory,1,1070.0,560.0,12593,61.0,8.0,15.0,7.0,,,WALK_TRANSIT,2.96683804953191,1500.0,1558.0,fastest,1558.0,1500.0,fastest,no_subtours,,0out_1in,work -1494647,36454,shopping,2,1,1,2,non_mandatory,1,725.0,580.0,13797,128.0,13.0,17.0,4.0,,11.688295162318989,DRIVEALONEFREE,0.185754815801926,,,,,,,,,0out_1in,shopping -1494648,36454,shopping,2,2,2,2,non_mandatory,1,729.0,580.0,13797,169.0,18.0,18.0,0.0,,11.689029568940938,DRIVEALONEFREE,0.443644139698639,,,,,,,,,1out_0in,shopping -1494680,36455,othdiscr,1,1,1,1,non_mandatory,1,623.0,580.0,13797,159.0,16.0,21.0,5.0,,13.239720693390336,SHARED2FREE,0.738377038878971,,,,,,,,,0out_2in,othdiscr -1494694,36455,work,1,1,1,1,mandatory,1,562.0,580.0,13797,58.0,8.0,12.0,4.0,,,SHARED3FREE,0.8851264867711228,,,,,,,no_subtours,,0out_0in,work -1709911,41705,eatout,1,1,1,1,non_mandatory,1,1070.0,645.0,15777,76.0,9.0,15.0,6.0,,12.070246786563017,WALK_TRANSIT,0.4629895150090414,1500.0,1611.0,fastest,1611.0,1500.0,shortest,,,0out_0in,eatout -1709950,41706,eat,1,1,1,1,atwork,1,989.0,854.0,15777,112.0,12.0,12.0,0.0,,18.50954665100095,WALK_TRANSIT,-0.6180823333381719,1666.0,1662.0,shortest,1748.0,1618.0,fastest,,1709985.0,0out_0in,atwork -1709985,41706,work,1,1,1,1,mandatory,1,854.0,645.0,15777,65.0,8.0,19.0,11.0,,,SHARED2FREE,0.613674174636675,,,,,,,eat,,0out_1in,work -2051448,50035,eatout,1,1,1,1,joint,3,1070.0,757.0,18261,156.0,16.0,18.0,2.0,mixed,11.85828127629614,SHARED3FREE,-5.832306485220664,,,,,,,,,1out_0in,eatout -2051466,50035,school,1,1,1,1,mandatory,1,919.0,757.0,18261,43.0,7.0,13.0,6.0,,,WALK_TRANSIT,1.2158966517539809,1618.0,1748.0,shortest,1748.0,1618.0,fastest,,,0out_0in,school -2051468,50035,shopping,1,1,1,1,non_mandatory,1,996.0,757.0,18261,175.0,19.0,19.0,0.0,,11.577466521097683,DRIVEALONEFREE,-0.1846581633337935,,,,,,,,,0out_0in,shopping -2051504,50036,othmaint,1,1,1,1,non_mandatory,1,877.0,757.0,18261,44.0,7.0,14.0,7.0,,12.412335687839308,SHARED2FREE,0.0818069109613803,,,,,,,,,0out_0in,othmaint -2051556,50037,work,1,1,1,1,mandatory,1,1070.0,757.0,18261,46.0,7.0,16.0,9.0,,,WALK_TRANSIT,1.2858135302346076,1500.0,1651.0,shortest,1651.0,1500.0,shortest,no_subtours,,0out_0in,work -2268889,55338,school,1,1,1,1,mandatory,1,699.0,794.0,19758,40.0,7.0,10.0,3.0,,,WALK_TRANSIT,1.426179634142238,1608.0,1652.0,cheapest,1652.0,1608.0,fastest,,,0out_0in,school -2268938,55339,work,1,1,1,1,mandatory,1,762.0,794.0,19758,11.0,5.0,16.0,11.0,,,DRIVEALONEFREE,0.6319219718888117,,,,,,,no_subtours,,0out_0in,work -2373816,57897,work,1,1,1,1,mandatory,1,1070.0,829.0,20552,50.0,7.0,20.0,13.0,,,WALK_TRANSIT,1.815253620726848,1500.0,1754.0,fastest,1754.0,1500.0,shortest,no_subtours,,0out_0in,work -2373818,57898,business,1,1,1,1,atwork,1,948.0,948.0,20552,101.0,11.0,13.0,2.0,,10.856328670591866,WALK,1.0849001811607144,,,,,,,,2373857.0,1out_0in,atwork -2373857,57898,work,1,1,1,1,mandatory,1,948.0,829.0,20552,105.0,11.0,17.0,6.0,,,DRIVEALONEFREE,0.2266199059946182,,,,,,,business1,,0out_1in,work -2373898,57899,work,1,1,1,1,mandatory,1,687.0,829.0,20552,47.0,7.0,17.0,10.0,,,WALK,1.4925958516365831,,,,,,,no_subtours,,0out_0in,work -2373980,57901,work,2,1,1,2,mandatory,1,708.0,829.0,20552,29.0,6.0,16.0,10.0,,,DRIVEALONEFREE,0.5654424599486602,,,,,,,no_subtours,,0out_0in,work -2373981,57901,work,2,2,2,2,mandatory,1,708.0,829.0,20552,172.0,18.0,21.0,3.0,,,SHARED2FREE,0.7217981727639898,,,,,,,no_subtours,,1out_0in,work -2563802,62531,school,1,1,1,1,mandatory,1,938.0,900.0,21869,180.0,20.0,20.0,0.0,,,WALK_TRANSIT,1.0406873179045706,1673.0,1633.0,shortest,1633.0,1673.0,fastest,,,0out_0in,school -2563821,62532,escort,1,1,1,1,non_mandatory,1,647.0,900.0,21869,20.0,6.0,7.0,1.0,,12.49976194812137,SHARED2FREE,-1.2299200748899617,,,,,,,,,0out_0in,escort -2563862,62533,escort,3,1,1,4,non_mandatory,1,695.0,900.0,21869,1.0,5.0,6.0,1.0,,12.55798878217292,SHARED3FREE,-1.221458591960288,,,,,,,,,0out_3in,escort -2563863,62533,escort,3,2,2,4,non_mandatory,1,518.0,900.0,21869,99.0,11.0,11.0,0.0,,12.502003827155296,SHARED3FREE,-1.66067049305692,,,,,,,,,0out_0in,escort -2563864,62533,escort,3,3,3,4,non_mandatory,1,844.0,900.0,21869,135.0,14.0,14.0,0.0,,12.530459798779978,SHARED2FREE,-1.4032965315757244,,,,,,,,,0out_0in,escort -2563878,62533,othdiscr,1,1,4,4,non_mandatory,1,1070.0,900.0,21869,100.0,11.0,12.0,1.0,,13.216136797297771,WALK_TRANSIT,1.0118533227067692,1500.0,73.0,fastest,73.0,1500.0,shortest,,,0out_0in,othdiscr -2563925,62534,school,1,1,1,1,mandatory,1,793.0,900.0,21869,55.0,8.0,9.0,1.0,,,WALK_TRANSIT,-0.605105363231441,1748.0,73.0,shortest,73.0,1748.0,shortest,,,0out_0in,school -2787968,67999,escort,1,1,1,2,non_mandatory,1,767.0,973.0,23619,124.0,13.0,13.0,0.0,,12.970512717862755,SHARED3FREE,-0.7145790466379958,,,,,,,,,0out_2in,escort -2787995,67999,social,1,1,2,2,non_mandatory,1,988.0,973.0,23619,165.0,17.0,20.0,3.0,,12.777797143559908,WALK,1.394261833722182,,,,,,,,,0out_0in,social -2788039,68000,work,1,1,1,1,mandatory,1,1070.0,973.0,23619,51.0,7.0,21.0,14.0,,,WALK_TRANSIT,1.4899770411640134,1500.0,1588.0,shortest,1588.0,1500.0,fastest,no_subtours,,1out_0in,work -3238088,78977,school,1,1,1,1,mandatory,1,984.0,1081.0,26897,44.0,7.0,14.0,7.0,,,WALK,1.0391837359866254,,,,,,,,,0out_0in,school -3238143,78979,eat,1,1,1,1,atwork,1,1070.0,897.0,26897,72.0,9.0,11.0,2.0,,18.50007970900582,DRIVEALONEFREE,-0.2864770605138423,,,,,,,,3238178.0,0out_0in,atwork -3238178,78979,work,1,1,1,1,mandatory,1,897.0,1081.0,26897,48.0,7.0,18.0,11.0,,,DRIVEALONEFREE,0.5747485518145101,,,,,,,eat,,0out_0in,work -52627721,1283602,work,1,1,1,1,mandatory,1,1078.0,521.0,435012,64.0,8.0,18.0,10.0,,,WALK_TRANSIT,5.710266024459359,1584.0,1238.0,shortest,1238.0,1584.0,cheapest,no_subtours,,0out_0in,work -52638588,1283868,business,1,1,1,1,atwork,1,1070.0,1070.0,435278,112.0,12.0,12.0,0.0,,19.38117027561944,WALK,0.6487888616012731,,,,,,,,52638627.0,0out_0in,atwork -52638594,1283868,eatout,1,1,1,1,non_mandatory,1,1070.0,537.0,435278,172.0,18.0,21.0,3.0,,11.755720899317405,WALK_TRANSIT,0.6406085296048486,1500.0,1558.0,fastest,1558.0,1500.0,shortest,,,0out_1in,eatout -52638627,1283868,work,1,1,1,1,mandatory,1,1070.0,537.0,435278,79.0,9.0,18.0,9.0,,,WALK_TRANSIT,1.206681423660966,1500.0,1558.0,shortest,1558.0,1500.0,shortest,business1,,0out_0in,work -52641825,1283946,work,1,1,1,1,mandatory,1,1070.0,523.0,435356,49.0,7.0,19.0,12.0,,,WALK_TRANSIT,1.1978831616543206,1500.0,1604.0,cheapest,1604.0,1500.0,shortest,no_subtours,,0out_0in,work -52668557,1284598,work,1,1,1,1,mandatory,1,553.0,562.0,436008,80.0,9.0,19.0,10.0,,,DRIVEALONEFREE,0.7312528815647517,,,,,,,no_subtours,,0out_0in,work -52734819,1286215,eat,1,1,1,1,atwork,1,1077.0,606.0,437625,88.0,10.0,13.0,3.0,,18.149830935609163,WALK_TRANSIT,-0.8798916104770301,1500.0,1562.0,shortest,1562.0,1500.0,shortest,,52734854.0,0out_0in,atwork -52734854,1286215,work,1,1,1,1,mandatory,1,606.0,656.0,437625,65.0,8.0,19.0,11.0,,,DRIVEALONEFREE,0.8985332322364314,,,,,,,eat,,0out_2in,work -52897544,1290184,business,1,1,1,1,atwork,1,732.0,977.0,441594,99.0,11.0,11.0,0.0,,18.610370946991612,SHARED2FREE,0.4316303329603235,,,,,,,,52897583.0,0out_0in,atwork -52897550,1290184,eatout,1,1,4,4,non_mandatory,1,949.0,1096.0,441594,184.0,21.0,21.0,0.0,,11.904706692046382,SHARED2FREE,-0.1665710953231831,,,,,,,,,0out_1in,eatout -52897569,1290184,othdiscr,3,1,1,4,non_mandatory,1,684.0,1096.0,441594,2.0,5.0,7.0,2.0,,12.918529902113356,DRIVEALONEFREE,0.2317787046274241,,,,,,,,,0out_0in,othdiscr -52897570,1290184,othdiscr,3,2,2,4,non_mandatory,1,762.0,1096.0,441594,185.0,21.0,22.0,1.0,,12.959861282775266,WALK_TRANSIT,0.2522845990878585,1748.0,1516.0,fastest,1516.0,1748.0,fastest,,,0out_0in,othdiscr -52897571,1290184,othdiscr,3,3,3,4,non_mandatory,1,725.0,1096.0,441594,189.0,23.0,23.0,0.0,,13.001363665570665,WALK,0.3854413463230495,,,,,,,,,0out_0in,othdiscr -52897583,1290184,work,1,1,1,1,mandatory,1,977.0,1096.0,441594,82.0,9.0,21.0,12.0,,,DRIVEALONEFREE,-0.0423820492992088,,,,,,,business1,,0out_1in,work -52915705,1290626,work,1,1,1,1,mandatory,1,1070.0,1100.0,442036,64.0,8.0,18.0,10.0,,,WALK_TRANSIT,1.4095982213888998,1500.0,1584.0,cheapest,1584.0,1500.0,fastest,no_subtours,,1out_0in,work -76379130,1862905,othdiscr,1,1,1,1,non_mandatory,1,986.0,960.0,721960,151.0,15.0,21.0,6.0,,12.957346522442643,WALK_TRANSIT,0.3212522660901984,1618.0,1762.0,fastest,1762.0,1618.0,fastest,,,0out_0in,othdiscr -76379171,1862906,othdiscr,1,1,1,1,non_mandatory,1,992.0,960.0,721960,104.0,11.0,16.0,5.0,,12.9976638956563,SHARED3FREE,0.3134675853717786,,,,,,,,,0out_1in,othdiscr -80946571,1974306,othdiscr,1,1,1,1,non_mandatory,1,564.0,509.0,760593,62.0,8.0,16.0,8.0,,13.57415728090829,WALK,1.2942555315594169,,,,,,,,,0out_0in,othdiscr -80946591,1974307,eat,1,1,1,1,atwork,1,913.0,739.0,760593,113.0,12.0,13.0,1.0,,18.622653002301583,SHARED3FREE,-0.7672328773497228,,,,,,,,80946626.0,0out_0in,atwork -80946626,1974307,work,1,1,1,1,mandatory,1,739.0,509.0,760593,79.0,9.0,18.0,9.0,,,WALK_TRANSIT,0.6201259698287482,1748.0,1603.0,fastest,1665.0,1662.0,cheapest,eat,,1out_1in,work -80946637,1974308,escort,1,1,1,1,non_mandatory,1,716.0,509.0,760593,0.0,5.0,5.0,0.0,,12.671799977139084,SHARED3FREE,-0.8180421450990675,,,,,,,,,0out_0in,escort -81048440,1976791,escort,1,1,1,1,non_mandatory,1,908.0,613.0,761445,124.0,13.0,13.0,0.0,,12.82099191373982,SHARED2FREE,-0.9528167859302504,,,,,,,,,0out_0in,escort -81048476,1976792,eat,1,1,1,1,atwork,1,517.0,517.0,761445,70.0,9.0,9.0,0.0,,17.910037480926228,SHARED2FREE,0.7181216397530412,,,,,,,,81048511.0,0out_0in,atwork -81048478,1976792,eatout,1,1,1,1,non_mandatory,1,648.0,613.0,761445,130.0,13.0,19.0,6.0,,11.955235691700665,DRIVEALONEFREE,1.3804088714164349,,,,,,,,,0out_0in,eatout -81048511,1976792,work,1,1,1,1,mandatory,1,517.0,613.0,761445,7.0,5.0,12.0,7.0,,,WALK_TRANSIT,0.5051019359212883,1681.0,1238.0,fastest,1238.0,1681.0,fastest,eat,,0out_0in,work -81130344,1978788,social,1,1,1,1,non_mandatory,1,763.0,961.0,762159,66.0,8.0,20.0,12.0,,12.393316604403664,SHARED2FREE,1.1233925360416968,,,,,,,,,0out_0in,social -81130399,1978790,escort,1,1,1,1,non_mandatory,1,992.0,961.0,762159,54.0,8.0,8.0,0.0,,12.58022321361327,SHARED2FREE,-0.9361958649826466,,,,,,,,,0out_0in,escort -81130429,1978790,work,1,1,1,1,mandatory,1,672.0,961.0,762159,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.7319548477569705,,,,,,,no_subtours,,0out_0in,work -81130470,1978791,work,1,1,1,1,mandatory,1,1070.0,961.0,762159,47.0,7.0,17.0,10.0,,,WALK_TRANSIT,1.223231536517431,1500.0,1762.0,shortest,1762.0,1500.0,shortest,no_subtours,,0out_0in,work -102419958,2498047,school,1,1,1,1,mandatory,1,865.0,730.0,922602,77.0,9.0,16.0,7.0,,,WALK_TRANSIT,4.515115660099912,1698.0,1608.0,shortest,1608.0,1698.0,shortest,,,0out_0in,school -102420007,2498048,work,1,1,1,1,mandatory,1,735.0,730.0,922602,46.0,7.0,16.0,9.0,,,WALK_TRANSIT,2.37613290143943,1592.0,1612.0,shortest,1612.0,1592.0,shortest,no_subtours,,0out_0in,work -102420048,2498049,work,1,1,1,1,mandatory,1,763.0,730.0,922602,29.0,6.0,16.0,10.0,,,WALK,2.522839906360249,,,,,,,no_subtours,,0out_0in,work -107509903,2622192,school,1,1,1,1,mandatory,1,995.0,1025.0,952720,44.0,7.0,14.0,7.0,,,WALK_TRANSIT,0.7240925397746193,1664.0,1666.0,fastest,1666.0,1664.0,fastest,,,0out_0in,school -107509922,2622193,escort,1,1,1,2,non_mandatory,1,773.0,1025.0,952720,19.0,6.0,6.0,0.0,,12.840047018957565,SHARED3FREE,-0.7318852867656347,,,,,,,,,0out_0in,escort -107509941,2622193,othmaint,1,1,2,2,non_mandatory,1,550.0,1025.0,952720,61.0,8.0,15.0,7.0,,12.86750275018236,WALK_TRANSIT,0.3002279969733954,1238.0,1666.0,fastest,1666.0,1238.0,cheapest,,,0out_0in,othmaint -107509987,2622194,shopping,1,1,1,1,non_mandatory,1,989.0,1025.0,952720,72.0,9.0,11.0,2.0,,11.974087902319576,SHARED3FREE,0.5678792550825605,,,,,,,,,0out_0in,shopping -107510034,2622195,work,1,1,1,1,mandatory,1,1021.0,1025.0,952720,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.8640931880479102,,,,,,,no_subtours,,0out_0in,work -116640406,2844887,work,1,1,1,1,mandatory,1,845.0,846.0,1028031,111.0,11.0,23.0,12.0,,,WALK_TRANSIT,0.9802398468480144,1730.0,1695.0,shortest,1695.0,1730.0,cheapest,no_subtours,,0out_0in,work -120287676,2933845,school,1,1,1,1,mandatory,1,666.0,574.0,1048898,121.0,12.0,21.0,9.0,,,WALK,-0.0074452524495141,,,,,,,,,0out_0in,school -120287717,2933846,school,1,1,1,1,mandatory,1,515.0,574.0,1048898,62.0,8.0,16.0,8.0,,,SHARED2FREE,-1.016021399976598,,,,,,,,,0out_0in,school -120287752,2933847,othdiscr,1,1,1,1,non_mandatory,1,657.0,574.0,1048898,42.0,7.0,12.0,5.0,,13.002449945790438,WALK_TRANSIT,0.8636594074657715,1238.0,1559.0,shortest,1559.0,1238.0,shortest,,,0out_1in,othdiscr -120287807,2933848,work,1,1,1,1,mandatory,1,502.0,574.0,1048898,31.0,6.0,18.0,12.0,,,WALK_TRANSIT,0.3885773372794717,1333.0,1559.0,fastest,1559.0,1333.0,fastest,no_subtours,,0out_0in,work -131881533,3216622,school,1,1,1,1,mandatory,1,1074.0,1076.0,1148260,136.0,14.0,15.0,1.0,,,WALK_TRANSIT,10.725339744357893,1500.0,1516.0,fastest,1516.0,1500.0,fastest,,,0out_0in,univ +1359025,33146,work,1,1,1,1,mandatory,1,608.0,68.0,12593,12.0,5.0,17.0,12.0,,,DRIVEALONEFREE,1.0261372219238083,,,,,,,no_subtours,,2out_0in,work +1359066,33147,work,1,1,1,1,mandatory,1,578.0,68.0,12593,61.0,8.0,15.0,7.0,,,WALK_TRANSIT,2.96683804953191,1500.0,1558.0,fastest,1558.0,1500.0,fastest,no_subtours,,0out_1in,work +1494647,36454,shopping,2,1,1,2,non_mandatory,1,233.0,88.0,13797,128.0,13.0,17.0,4.0,,11.594151670549255,DRIVEALONEFREE,0.054207142123309696,,,,,,,,,0out_1in,shopping +1494648,36454,shopping,2,2,2,2,non_mandatory,1,237.0,88.0,13797,169.0,18.0,18.0,0.0,,11.643159811140553,DRIVEALONEFREE,0.28212638921439376,,,,,,,,,1out_0in,shopping +1494680,36455,othdiscr,1,1,1,1,non_mandatory,1,131.0,88.0,13797,159.0,16.0,21.0,5.0,,13.153011009083952,DRIVEALONEFREE,0.527847608692733,,,,,,,,,0out_2in,othdiscr +1494694,36455,work,1,1,1,1,mandatory,1,70.0,88.0,13797,58.0,8.0,12.0,4.0,,,WALK,0.9189166731374394,,,,,,,no_subtours,,0out_0in,work +1709911,41705,eatout,1,1,1,1,non_mandatory,1,578.0,153.0,15777,76.0,9.0,15.0,6.0,,12.194280696346672,WALK_TRANSIT,0.4629895150090414,1500.0,1611.0,fastest,1611.0,1500.0,fastest,,,0out_0in,eatout +1709950,41706,eat,1,1,1,1,atwork,1,497.0,362.0,15777,112.0,12.0,12.0,0.0,,18.60610008578578,WALK_TRANSIT,-0.8459966967797716,1666.0,1662.0,shortest,1662.0,1666.0,shortest,,1709985.0,0out_0in,atwork +1709985,41706,work,1,1,1,1,mandatory,1,362.0,153.0,15777,65.0,8.0,19.0,11.0,,,SHARED2FREE,0.6600577445845757,,,,,,,eat,,0out_1in,work +2051448,50035,eatout,1,1,1,1,joint,3,578.0,265.0,18261,156.0,16.0,18.0,2.0,mixed,11.830383451121103,SHARED3FREE,-5.832306485220664,,,,,,,,,1out_0in,eatout +2051466,50035,school,1,1,1,1,mandatory,1,427.0,265.0,18261,43.0,7.0,13.0,6.0,,,WALK_TRANSIT,1.2158966517539807,1618.0,1748.0,shortest,1748.0,1618.0,shortest,,,0out_0in,school +2051468,50035,shopping,1,1,1,1,non_mandatory,1,504.0,265.0,18261,175.0,19.0,19.0,0.0,,11.561744392977129,DRIVEALONEFREE,-0.18465816333379353,,,,,,,,,0out_0in,shopping +2051504,50036,othmaint,1,1,1,1,non_mandatory,1,385.0,265.0,18261,44.0,7.0,14.0,7.0,,12.388911839471218,SHARED2FREE,0.07492591448018238,,,,,,,,,0out_0in,othmaint +2051556,50037,work,1,1,1,1,mandatory,1,578.0,265.0,18261,46.0,7.0,16.0,9.0,,,WALK_TRANSIT,1.2858135302346076,1500.0,1651.0,shortest,1651.0,1500.0,fastest,no_subtours,,0out_0in,work +2268889,55338,school,1,1,1,1,mandatory,1,207.0,302.0,19758,40.0,7.0,10.0,3.0,,,WALK_TRANSIT,1.4625238798878268,1608.0,1652.0,cheapest,1652.0,1608.0,shortest,,,0out_0in,school +2268938,55339,work,1,1,1,1,mandatory,1,254.0,302.0,19758,11.0,5.0,16.0,11.0,,,DRIVEALONEFREE,0.899253309377072,,,,,,,no_subtours,,0out_0in,work +2373816,57897,work,1,1,1,1,mandatory,1,578.0,337.0,20552,50.0,7.0,20.0,13.0,,,WALK_TRANSIT,1.8152536207268481,1500.0,1754.0,fastest,1754.0,1500.0,fastest,no_subtours,,0out_0in,work +2373818,57898,business,1,1,1,1,atwork,1,456.0,456.0,20552,101.0,11.0,13.0,2.0,,10.851045587482702,WALK,0.8933720529791014,,,,,,,,2373857.0,1out_0in,atwork +2373857,57898,work,1,1,1,1,mandatory,1,456.0,337.0,20552,105.0,11.0,17.0,6.0,,,DRIVEALONEFREE,0.22661990599461826,,,,,,,business1,,0out_1in,work +2373898,57899,work,1,1,1,1,mandatory,1,195.0,337.0,20552,47.0,7.0,17.0,10.0,,,WALK,1.4775079963774336,,,,,,,no_subtours,,0out_0in,work +2373980,57901,work,2,1,1,2,mandatory,1,216.0,337.0,20552,30.0,6.0,17.0,11.0,,,DRIVEALONEFREE,0.5418945770189671,,,,,,,no_subtours,,0out_0in,work +2373981,57901,work,2,2,2,2,mandatory,1,216.0,337.0,20552,175.0,19.0,19.0,0.0,,,SHARED2FREE,0.7907156266134642,,,,,,,no_subtours,,1out_0in,work +2563802,62531,school,1,1,1,1,mandatory,1,442.0,408.0,21869,180.0,20.0,20.0,0.0,,,WALK_TRANSIT,0.9438904813360477,1682.0,1633.0,shortest,1633.0,1682.0,fastest,,,0out_0in,school +2563821,62532,escort,1,1,1,1,non_mandatory,1,155.0,408.0,21869,20.0,6.0,7.0,1.0,,12.523318372217064,SHARED2FREE,-1.2299200748899615,,,,,,,,,0out_0in,escort +2563862,62533,escort,3,1,1,4,non_mandatory,1,203.0,408.0,21869,1.0,5.0,6.0,1.0,,12.561374920193618,SHARED3FREE,-1.221458591960288,,,,,,,,,0out_3in,escort +2563863,62533,escort,3,2,2,4,non_mandatory,1,26.0,408.0,21869,99.0,11.0,11.0,0.0,,12.495641230564226,SHARED3FREE,-1.66067049305692,,,,,,,,,0out_0in,escort +2563864,62533,escort,3,3,3,4,non_mandatory,1,352.0,408.0,21869,135.0,14.0,14.0,0.0,,12.525318452376542,SHARED2FREE,-1.4032965315757244,,,,,,,,,0out_0in,escort +2563878,62533,othdiscr,1,1,4,4,non_mandatory,1,578.0,408.0,21869,100.0,11.0,12.0,1.0,,13.255455507967595,WALK_TRANSIT,1.0602578337312834,1500.0,73.0,fastest,73.0,1500.0,shortest,,,0out_0in,othdiscr +2563925,62534,school,1,1,1,1,mandatory,1,301.0,408.0,21869,55.0,8.0,9.0,1.0,,,WALK_TRANSIT,-0.605105363231441,1748.0,73.0,shortest,73.0,1748.0,fastest,,,0out_0in,school +2787968,67999,escort,1,1,1,2,non_mandatory,1,269.0,481.0,23619,124.0,13.0,13.0,0.0,,12.995994495540678,SHARED3FREE,-0.6687154899908945,,,,,,,,,0out_2in,escort +2787995,67999,social,1,1,2,2,non_mandatory,1,496.0,481.0,23619,165.0,17.0,20.0,3.0,,12.759866800729268,WALK,1.4266623253551904,,,,,,,,,0out_0in,social +2788039,68000,work,1,1,1,1,mandatory,1,578.0,481.0,23619,51.0,7.0,21.0,14.0,,,WALK_TRANSIT,1.4899770411640134,1500.0,1588.0,shortest,1588.0,1500.0,shortest,no_subtours,,1out_0in,work +3238088,78977,school,1,1,1,1,mandatory,1,492.0,589.0,26897,44.0,7.0,14.0,7.0,,,WALK_TRANSIT,0.8204920756912042,1618.0,1584.0,shortest,1584.0,1618.0,cheapest,,,0out_0in,school +3238143,78979,eat,1,1,1,1,atwork,1,578.0,405.0,26897,72.0,9.0,11.0,2.0,,18.599435534464497,DRIVEALONEFREE,-0.2744746714261923,,,,,,,,3238178.0,0out_0in,atwork +3238178,78979,work,1,1,1,1,mandatory,1,405.0,589.0,26897,48.0,7.0,18.0,11.0,,,DRIVEALONEFREE,0.5976010496480395,,,,,,,eat,,0out_0in,work +52627721,1283602,work,1,1,1,1,mandatory,1,586.0,29.0,435012,64.0,8.0,18.0,10.0,,,WALK_TRANSIT,5.710266024459359,1584.0,1238.0,shortest,1608.0,1584.0,fastest,no_subtours,,0out_0in,work +52638588,1283868,business,1,1,1,1,atwork,1,578.0,578.0,435278,112.0,12.0,12.0,0.0,,19.462265786986954,WALK,0.6243659427953904,,,,,,,,52638627.0,0out_0in,atwork +52638594,1283868,eatout,1,1,1,1,non_mandatory,1,578.0,45.0,435278,172.0,18.0,21.0,3.0,,11.758619181788253,WALK_TRANSIT,0.6406085296048486,1500.0,1558.0,fastest,1558.0,1500.0,shortest,,,0out_1in,eatout +52638627,1283868,work,1,1,1,1,mandatory,1,578.0,45.0,435278,79.0,9.0,18.0,9.0,,,WALK_TRANSIT,1.206681423660966,1500.0,1558.0,shortest,1558.0,1500.0,shortest,business1,,0out_0in,work +52641825,1283946,work,1,1,1,1,mandatory,1,578.0,31.0,435356,49.0,7.0,19.0,12.0,,,WALK_TRANSIT,1.1978831616543206,1500.0,1604.0,cheapest,1604.0,1500.0,shortest,no_subtours,,0out_0in,work +52668557,1284598,work,1,1,1,1,mandatory,1,61.0,70.0,436008,80.0,9.0,19.0,10.0,,,DRIVEALONEFREE,0.7713829364314543,,,,,,,no_subtours,,0out_0in,work +52734819,1286215,eat,1,1,1,1,atwork,1,585.0,114.0,437625,88.0,10.0,13.0,3.0,,17.98372758878566,WALK_TRANSIT,-0.8798916104770301,1500.0,1562.0,shortest,1562.0,1500.0,cheapest,,52734854.0,0out_0in,atwork +52734854,1286215,work,1,1,1,1,mandatory,1,114.0,164.0,437625,65.0,8.0,19.0,11.0,,,DRIVEALONEFREE,0.8500450570238016,,,,,,,eat,,0out_2in,work +52897544,1290184,business,1,1,1,1,atwork,1,240.0,485.0,441594,99.0,11.0,11.0,0.0,,18.649706295366613,WALK,0.7361914154560637,,,,,,,,52897583.0,0out_0in,atwork +52897550,1290184,eatout,1,1,4,4,non_mandatory,1,347.0,604.0,441594,184.0,21.0,21.0,0.0,,11.9769068256242,SHARED2FREE,-0.10785441309565102,,,,,,,,,0out_1in,eatout +52897569,1290184,othdiscr,3,1,1,4,non_mandatory,1,22.0,604.0,441594,2.0,5.0,7.0,2.0,,13.340592587029276,DRIVEALONEFREE,0.9729989937926249,,,,,,,,,0out_0in,othdiscr +52897570,1290184,othdiscr,3,2,2,4,non_mandatory,1,241.0,604.0,441594,185.0,21.0,22.0,1.0,,13.194495693775464,BIKE,0.24171550040319387,,,,,,,,,0out_0in,othdiscr +52897571,1290184,othdiscr,3,3,3,4,non_mandatory,1,161.0,604.0,441594,189.0,23.0,23.0,0.0,,13.296474125502314,WALK_TRANSIT,0.4347637705876943,1238.0,1516.0,fastest,1516.0,1238.0,fastest,,,0out_1in,othdiscr +52897583,1290184,work,1,1,1,1,mandatory,1,485.0,604.0,441594,82.0,9.0,21.0,12.0,,,DRIVEALONEFREE,-0.05454824120553145,,,,,,,business1,,0out_1in,work +52915705,1290626,work,1,1,1,1,mandatory,1,578.0,608.0,442036,64.0,8.0,18.0,10.0,,,WALK_TRANSIT,1.4241213520203073,1500.0,1584.0,cheapest,1584.0,1500.0,fastest,no_subtours,,1out_0in,work +76379130,1862905,othdiscr,1,1,1,1,non_mandatory,1,494.0,468.0,721960,151.0,15.0,21.0,6.0,,13.046748957111205,BIKE,0.6336063394695053,,,,,,,,,0out_0in,othdiscr +76379171,1862906,othdiscr,1,1,1,1,non_mandatory,1,500.0,468.0,721960,104.0,11.0,16.0,5.0,,13.068781711323547,WALK,0.6246154614956402,,,,,,,,,0out_0in,othdiscr +80946571,1974306,othdiscr,1,1,1,1,non_mandatory,1,72.0,17.0,760593,62.0,8.0,16.0,8.0,,13.609717599377104,WALK,1.2877790624306358,,,,,,,,,0out_0in,othdiscr +80946591,1974307,eat,1,1,1,1,atwork,1,324.0,204.0,760593,113.0,12.0,13.0,1.0,,18.711722700372903,SHARED3FREE,-1.1971419362787026,,,,,,,,80946626.0,0out_0in,atwork +80946626,1974307,work,1,1,1,1,mandatory,1,204.0,17.0,760593,79.0,9.0,18.0,9.0,,,WALK_TRANSIT,0.9146373054676561,1748.0,1603.0,shortest,1603.0,1748.0,shortest,eat,,1out_1in,work +80946637,1974308,escort,1,1,1,1,non_mandatory,1,224.0,17.0,760593,0.0,5.0,5.0,0.0,,12.668506375964688,SHARED3FREE,-0.8073126044826399,,,,,,,,,0out_0in,escort +81048440,1976791,escort,1,1,1,1,non_mandatory,1,483.0,121.0,761445,124.0,13.0,13.0,0.0,,12.714018122401603,SHARED2FREE,-0.7186631667509052,,,,,,,,,0out_0in,escort +81048508,1976792,social,1,1,1,1,non_mandatory,1,494.0,121.0,761445,140.0,14.0,19.0,5.0,,12.159236859223718,DRIVEALONEFREE,0.5721200907291696,,,,,,,,,0out_0in,social +81048511,1976792,work,1,1,1,1,mandatory,1,25.0,121.0,761445,7.0,5.0,12.0,7.0,,,WALK_TRANSIT,0.4973097110196049,1681.0,1238.0,fastest,1238.0,1681.0,fastest,no_subtours,,0out_0in,work +81130344,1978788,social,1,1,1,1,non_mandatory,1,271.0,469.0,762159,66.0,8.0,20.0,12.0,,12.430877422027173,SHARED2FREE,1.1233925360416968,,,,,,,,,0out_0in,social +81130399,1978790,escort,1,1,1,1,non_mandatory,1,500.0,469.0,762159,54.0,8.0,8.0,0.0,,12.604750255865364,SHARED2FREE,-0.7891363476365268,,,,,,,,,0out_0in,escort +81130429,1978790,work,1,1,1,1,mandatory,1,180.0,469.0,762159,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.7319548477569705,,,,,,,no_subtours,,0out_0in,work +81130470,1978791,work,1,1,1,1,mandatory,1,578.0,469.0,762159,47.0,7.0,17.0,10.0,,,WALK_TRANSIT,1.2232315365174313,1500.0,1762.0,shortest,1762.0,1500.0,shortest,no_subtours,,0out_0in,work +102419958,2498047,school,1,1,1,1,mandatory,1,432.0,238.0,922602,77.0,9.0,16.0,7.0,,,WALK_TRANSIT,4.547714059900562,1673.0,1608.0,shortest,1608.0,1673.0,fastest,,,0out_0in,school +102420007,2498048,work,1,1,1,1,mandatory,1,243.0,238.0,922602,46.0,7.0,16.0,9.0,,,WALK_TRANSIT,2.571846550195381,1592.0,1612.0,shortest,1612.0,1592.0,shortest,no_subtours,,0out_0in,work +102420048,2498049,work,1,1,1,1,mandatory,1,271.0,238.0,922602,29.0,6.0,16.0,10.0,,,WALK,2.3529244532782645,,,,,,,no_subtours,,0out_0in,work +107509903,2622192,school,1,1,1,1,mandatory,1,501.0,533.0,952720,44.0,7.0,14.0,7.0,,,WALK_TRANSIT,0.7689513133515259,1664.0,1666.0,fastest,1666.0,1664.0,shortest,,,0out_0in,school +107509922,2622193,escort,1,1,1,2,non_mandatory,1,281.0,533.0,952720,19.0,6.0,6.0,0.0,,13.120197714243572,WALK,-0.6633544461803778,,,,,,,,,0out_0in,escort +107509941,2622193,othmaint,1,1,2,2,non_mandatory,1,58.0,533.0,952720,61.0,8.0,15.0,7.0,,13.296284550786224,WALK_TRANSIT,0.6229120227429399,1238.0,1666.0,fastest,1666.0,1238.0,shortest,,,0out_0in,othmaint +107509987,2622194,shopping,1,1,1,1,non_mandatory,1,504.0,533.0,952720,74.0,9.0,13.0,4.0,,12.281026585137033,SHARED3FREE,0.8228058752894183,,,,,,,,,0out_0in,shopping +107510034,2622195,work,1,1,1,1,mandatory,1,578.0,533.0,952720,63.0,8.0,17.0,9.0,,,WALK_TRANSIT,1.4377271736671302,1500.0,1666.0,fastest,1666.0,1500.0,shortest,no_subtours,,0out_0in,work +116640406,2844887,work,1,1,1,1,mandatory,1,385.0,354.0,1028031,108.0,11.0,20.0,9.0,,,WALK_TRANSIT,0.6883028572332556,1754.0,1695.0,shortest,1748.0,1754.0,fastest,no_subtours,,0out_0in,work +120287676,2933845,school,1,1,1,1,mandatory,1,197.0,82.0,1048898,121.0,12.0,21.0,9.0,,,WALK_TRANSIT,-0.05133861963844917,1608.0,1559.0,shortest,1559.0,1608.0,fastest,,,0out_0in,school +120287717,2933846,school,1,1,1,1,mandatory,1,23.0,82.0,1048898,62.0,8.0,16.0,8.0,,,SHARED2FREE,-1.0238153653421556,,,,,,,,,0out_0in,school +120287752,2933847,othdiscr,1,1,1,1,non_mandatory,1,165.0,82.0,1048898,42.0,7.0,12.0,5.0,,12.971588539704879,WALK_TRANSIT,0.8435740874298338,1238.0,1559.0,shortest,1559.0,1238.0,fastest,,,0out_1in,othdiscr +120287807,2933848,work,1,1,1,1,mandatory,1,10.0,82.0,1048898,31.0,6.0,18.0,12.0,,,WALK_TRANSIT,0.3435861763727887,1333.0,1559.0,fastest,1559.0,1333.0,shortest,no_subtours,,0out_0in,work +131881533,3216622,school,1,1,1,1,mandatory,1,582.0,584.0,1148260,136.0,14.0,15.0,1.0,,,WALK_TRANSIT,10.730879630807463,1500.0,1516.0,fastest,1516.0,1500.0,shortest,,,0out_0in,univ diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_tours_sh.csv b/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_tours_sh.csv new file mode 100644 index 000000000..2016fde26 --- /dev/null +++ b/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_tours_sh.csv @@ -0,0 +1,79 @@ +tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,tour_category,number_of_participants,destination,origin,household_id,tdd,start,end,duration,composition,destination_logsum,tour_mode,mode_choice_logsum,od_atap,od_btap,od_path_set,do_atap,do_btap,do_path_set,atwork_subtour_frequency,parent_tour_id,stop_frequency,primary_purpose +1359025,33146,work,1,1,1,1,mandatory,1,1100,560,12593,12.0,5.0,17.0,12.0,,,DRIVEALONEFREE,1.0261372219238083,,,,,,,no_subtours,,2out_0in,work +1359066,33147,work,1,1,1,1,mandatory,1,1070,560,12593,61.0,8.0,15.0,7.0,,,WALK_TRANSIT,2.96683804953191,1500.0,1558.0,fastest,1558.0,1500.0,fastest,no_subtours,,0out_1in,work +1494647,36454,shopping,2,1,1,2,non_mandatory,1,725,580,13797,128.0,13.0,17.0,4.0,,11.688295162318989,DRIVEALONEFREE,0.18575481580192602,,,,,,,,,0out_1in,shopping +1494648,36454,shopping,2,2,2,2,non_mandatory,1,729,580,13797,169.0,18.0,18.0,0.0,,11.689029568940938,DRIVEALONEFREE,0.44364413969863903,,,,,,,,,1out_0in,shopping +1494680,36455,othdiscr,1,1,1,1,non_mandatory,1,623,580,13797,159.0,16.0,21.0,5.0,,13.239720693390337,SHARED2FREE,0.738377038878971,,,,,,,,,0out_2in,othdiscr +1494694,36455,work,1,1,1,1,mandatory,1,562,580,13797,58.0,8.0,12.0,4.0,,,SHARED3FREE,0.8851264867711228,,,,,,,no_subtours,,0out_0in,work +1709911,41705,eatout,1,1,1,1,non_mandatory,1,1070,645,15777,76.0,9.0,15.0,6.0,,12.070246786563015,WALK_TRANSIT,0.4629895150090414,1500.0,1611.0,fastest,1611.0,1500.0,fastest,,,0out_0in,eatout +1709950,41706,eat,1,1,1,1,atwork,1,989,854,15777,112.0,12.0,12.0,0.0,,18.50954665100095,WALK_TRANSIT,-0.6180823333381719,1666.0,1662.0,shortest,1662.0,1666.0,shortest,,1709985.0,0out_0in,atwork +1709985,41706,work,1,1,1,1,mandatory,1,854,645,15777,65.0,8.0,19.0,11.0,,,SHARED2FREE,0.613674174636675,,,,,,,eat,,0out_1in,work 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+2268938,55339,work,1,1,1,1,mandatory,1,762,794,19758,11.0,5.0,16.0,11.0,,,DRIVEALONEFREE,0.6319219718888117,,,,,,,no_subtours,,0out_0in,work +2373816,57897,work,1,1,1,1,mandatory,1,1070,829,20552,50.0,7.0,20.0,13.0,,,WALK_TRANSIT,1.8152536207268481,1500.0,1754.0,fastest,1754.0,1500.0,fastest,no_subtours,,0out_0in,work +2373818,57898,business,1,1,1,1,atwork,1,948,948,20552,101.0,11.0,13.0,2.0,,10.856328670591866,WALK,1.0849001811607144,,,,,,,,2373857.0,1out_0in,atwork +2373857,57898,work,1,1,1,1,mandatory,1,948,829,20552,105.0,11.0,17.0,6.0,,,DRIVEALONEFREE,0.22661990599461826,,,,,,,business1,,0out_1in,work +2373898,57899,work,1,1,1,1,mandatory,1,687,829,20552,47.0,7.0,17.0,10.0,,,WALK,1.4925958516365831,,,,,,,no_subtours,,0out_0in,work +2373980,57901,work,2,1,1,2,mandatory,1,708,829,20552,30.0,6.0,17.0,11.0,,,DRIVEALONEFREE,0.5418945770189671,,,,,,,no_subtours,,0out_0in,work +2373981,57901,work,2,2,2,2,mandatory,1,708,829,20552,175.0,19.0,19.0,0.0,,,SHARED2FREE,0.7907156266134642,,,,,,,no_subtours,,1out_0in,work +2563802,62531,school,1,1,1,1,mandatory,1,938,900,21869,180.0,20.0,20.0,0.0,,,WALK_TRANSIT,1.0406873179045706,1673.0,1633.0,shortest,1633.0,1673.0,fastest,,,0out_0in,school +2563821,62532,escort,1,1,1,1,non_mandatory,1,647,900,21869,20.0,6.0,7.0,1.0,,12.49976194812137,SHARED2FREE,-1.2299200748899615,,,,,,,,,0out_0in,escort +2563862,62533,escort,3,1,1,4,non_mandatory,1,695,900,21869,1.0,5.0,6.0,1.0,,12.55798878217292,SHARED3FREE,-1.221458591960288,,,,,,,,,0out_3in,escort +2563863,62533,escort,3,2,2,4,non_mandatory,1,518,900,21869,99.0,11.0,11.0,0.0,,12.502003827155296,SHARED3FREE,-1.66067049305692,,,,,,,,,0out_0in,escort +2563864,62533,escort,3,3,3,4,non_mandatory,1,844,900,21869,135.0,14.0,14.0,0.0,,12.530459798779978,SHARED2FREE,-1.4032965315757244,,,,,,,,,0out_0in,escort +2563878,62533,othdiscr,1,1,4,4,non_mandatory,1,1070,900,21869,100.0,11.0,12.0,1.0,,13.216136797297771,WALK_TRANSIT,1.0118533227067692,1500.0,73.0,fastest,73.0,1500.0,shortest,,,0out_0in,othdiscr +2563925,62534,school,1,1,1,1,mandatory,1,793,900,21869,55.0,8.0,9.0,1.0,,,WALK_TRANSIT,-0.605105363231441,1748.0,73.0,shortest,73.0,1748.0,fastest,,,0out_0in,school +2787968,67999,escort,1,1,1,2,non_mandatory,1,767,973,23619,124.0,13.0,13.0,0.0,,12.970512717862757,SHARED3FREE,-0.7145790466379958,,,,,,,,,0out_2in,escort +2787995,67999,social,1,1,2,2,non_mandatory,1,988,973,23619,165.0,17.0,20.0,3.0,,12.777797143559908,WALK,1.394261833722182,,,,,,,,,0out_0in,social +2788039,68000,work,1,1,1,1,mandatory,1,1070,973,23619,51.0,7.0,21.0,14.0,,,WALK_TRANSIT,1.4899770411640134,1500.0,1588.0,shortest,1588.0,1500.0,shortest,no_subtours,,1out_0in,work +3238088,78977,school,1,1,1,1,mandatory,1,984,1081,26897,44.0,7.0,14.0,7.0,,,WALK,1.0391837359866254,,,,,,,,,0out_0in,school +3238143,78979,eat,1,1,1,1,atwork,1,1070,897,26897,72.0,9.0,11.0,2.0,,18.50007970900582,DRIVEALONEFREE,-0.28647706051384236,,,,,,,,3238178.0,0out_0in,atwork +3238178,78979,work,1,1,1,1,mandatory,1,897,1081,26897,48.0,7.0,18.0,11.0,,,DRIVEALONEFREE,0.5747485518145101,,,,,,,eat,,0out_0in,work +52627721,1283602,work,1,1,1,1,mandatory,1,1078,521,435012,64.0,8.0,18.0,10.0,,,WALK_TRANSIT,5.710266024459359,1584.0,1238.0,shortest,1608.0,1584.0,fastest,no_subtours,,0out_0in,work +52638588,1283868,business,1,1,1,1,atwork,1,1070,1070,435278,112.0,12.0,12.0,0.0,,19.38117027561944,WALK,0.6487888616012731,,,,,,,,52638627.0,0out_0in,atwork +52638594,1283868,eatout,1,1,1,1,non_mandatory,1,1070,537,435278,172.0,18.0,21.0,3.0,,11.755720899317405,WALK_TRANSIT,0.6406085296048486,1500.0,1558.0,fastest,1558.0,1500.0,shortest,,,0out_1in,eatout +52638627,1283868,work,1,1,1,1,mandatory,1,1070,537,435278,79.0,9.0,18.0,9.0,,,WALK_TRANSIT,1.206681423660966,1500.0,1558.0,shortest,1558.0,1500.0,shortest,business1,,0out_0in,work +52641825,1283946,work,1,1,1,1,mandatory,1,1070,523,435356,49.0,7.0,19.0,12.0,,,WALK_TRANSIT,1.1978831616543206,1500.0,1604.0,cheapest,1604.0,1500.0,shortest,no_subtours,,0out_0in,work +52668557,1284598,work,1,1,1,1,mandatory,1,553,562,436008,80.0,9.0,19.0,10.0,,,DRIVEALONEFREE,0.7312528815647517,,,,,,,no_subtours,,0out_0in,work +52734819,1286215,eat,1,1,1,1,atwork,1,1077,606,437625,88.0,10.0,13.0,3.0,,18.149830935609163,WALK_TRANSIT,-0.8798916104770301,1500.0,1562.0,shortest,1562.0,1500.0,cheapest,,52734854.0,0out_0in,atwork +52734854,1286215,work,1,1,1,1,mandatory,1,606,656,437625,65.0,8.0,19.0,11.0,,,DRIVEALONEFREE,0.8985332322364314,,,,,,,eat,,0out_2in,work +52897544,1290184,business,1,1,1,1,atwork,1,732,977,441594,99.0,11.0,11.0,0.0,,18.610370946991615,SHARED2FREE,0.43163033296032355,,,,,,,,52897583.0,0out_0in,atwork +52897550,1290184,eatout,1,1,4,4,non_mandatory,1,949,1096,441594,184.0,21.0,21.0,0.0,,11.904706692046382,SHARED2FREE,-0.16657109532318312,,,,,,,,,0out_1in,eatout +52897569,1290184,othdiscr,3,1,1,4,non_mandatory,1,684,1096,441594,2.0,5.0,7.0,2.0,,12.918529902113356,DRIVEALONEFREE,0.23177870462742411,,,,,,,,,0out_0in,othdiscr +52897570,1290184,othdiscr,3,2,2,4,non_mandatory,1,762,1096,441594,185.0,21.0,22.0,1.0,,12.959861282775266,WALK_TRANSIT,0.25228459908785855,1748.0,1516.0,fastest,1516.0,1748.0,shortest,,,0out_0in,othdiscr +52897571,1290184,othdiscr,3,3,3,4,non_mandatory,1,725,1096,441594,189.0,23.0,23.0,0.0,,13.001363665570663,WALK,0.38544134632304955,,,,,,,,,0out_0in,othdiscr +52897583,1290184,work,1,1,1,1,mandatory,1,977,1096,441594,82.0,9.0,21.0,12.0,,,DRIVEALONEFREE,-0.04238204929920889,,,,,,,business1,,0out_1in,work +52915705,1290626,work,1,1,1,1,mandatory,1,1070,1100,442036,64.0,8.0,18.0,10.0,,,WALK_TRANSIT,1.4095982213888998,1500.0,1584.0,cheapest,1584.0,1500.0,fastest,no_subtours,,1out_0in,work +76379130,1862905,othdiscr,1,1,1,1,non_mandatory,1,986,960,721960,151.0,15.0,21.0,6.0,,12.957346522442645,WALK_TRANSIT,0.32125226609019847,1618.0,1762.0,fastest,1762.0,1618.0,cheapest,,,0out_0in,othdiscr +76379171,1862906,othdiscr,1,1,1,1,non_mandatory,1,992,960,721960,104.0,11.0,16.0,5.0,,12.9976638956563,SHARED3FREE,0.3134675853717786,,,,,,,,,0out_1in,othdiscr +80946571,1974306,othdiscr,1,1,1,1,non_mandatory,1,564,509,760593,62.0,8.0,16.0,8.0,,13.57415728090829,WALK,1.2942555315594169,,,,,,,,,0out_0in,othdiscr +80946591,1974307,eat,1,1,1,1,atwork,1,913,739,760593,113.0,12.0,13.0,1.0,,18.622653002301586,SHARED3FREE,-0.7672328773497228,,,,,,,,80946626.0,0out_0in,atwork +80946626,1974307,work,1,1,1,1,mandatory,1,739,509,760593,79.0,9.0,18.0,9.0,,,WALK_TRANSIT,0.6201259698287482,1748.0,1603.0,fastest,1603.0,1748.0,fastest,eat,,1out_1in,work +80946637,1974308,escort,1,1,1,1,non_mandatory,1,716,509,760593,0.0,5.0,5.0,0.0,,12.671799977139083,SHARED3FREE,-0.8180421450990675,,,,,,,,,0out_0in,escort +81048440,1976791,escort,1,1,1,1,non_mandatory,1,908,613,761445,124.0,13.0,13.0,0.0,,12.820991913739821,SHARED2FREE,-0.9528167859302505,,,,,,,,,0out_0in,escort +81048476,1976792,eat,1,1,1,1,atwork,1,517,517,761445,70.0,9.0,9.0,0.0,,17.910037480926228,SHARED2FREE,0.7181216397530412,,,,,,,,81048511.0,0out_0in,atwork +81048478,1976792,eatout,1,1,1,1,non_mandatory,1,648,613,761445,130.0,13.0,19.0,6.0,,11.955235691700665,DRIVEALONEFREE,1.3804088714164349,,,,,,,,,0out_0in,eatout +81048511,1976792,work,1,1,1,1,mandatory,1,517,613,761445,7.0,5.0,12.0,7.0,,,WALK_TRANSIT,0.5051019359212883,1681.0,1238.0,fastest,1238.0,1681.0,fastest,eat,,0out_0in,work +81130344,1978788,social,1,1,1,1,non_mandatory,1,763,961,762159,66.0,8.0,20.0,12.0,,12.393316604403664,SHARED2FREE,1.1233925360416968,,,,,,,,,0out_0in,social +81130399,1978790,escort,1,1,1,1,non_mandatory,1,992,961,762159,54.0,8.0,8.0,0.0,,12.58022321361327,SHARED2FREE,-0.9361958649826466,,,,,,,,,0out_0in,escort +81130429,1978790,work,1,1,1,1,mandatory,1,672,961,762159,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.7319548477569705,,,,,,,no_subtours,,0out_0in,work +81130470,1978791,work,1,1,1,1,mandatory,1,1070,961,762159,47.0,7.0,17.0,10.0,,,WALK_TRANSIT,1.2232315365174313,1500.0,1762.0,shortest,1762.0,1500.0,shortest,no_subtours,,0out_0in,work +102419958,2498047,school,1,1,1,1,mandatory,1,865,730,922602,77.0,9.0,16.0,7.0,,,WALK_TRANSIT,4.515115660099912,1698.0,1608.0,shortest,1608.0,1698.0,fastest,,,0out_0in,school +102420007,2498048,work,1,1,1,1,mandatory,1,735,730,922602,46.0,7.0,16.0,9.0,,,WALK_TRANSIT,2.37613290143943,1592.0,1612.0,shortest,1612.0,1592.0,shortest,no_subtours,,0out_0in,work +102420048,2498049,work,1,1,1,1,mandatory,1,763,730,922602,29.0,6.0,16.0,10.0,,,WALK,2.522839906360249,,,,,,,no_subtours,,0out_0in,work +107509903,2622192,school,1,1,1,1,mandatory,1,995,1025,952720,44.0,7.0,14.0,7.0,,,WALK_TRANSIT,0.7240925397746193,1664.0,1666.0,fastest,1666.0,1664.0,shortest,,,0out_0in,school +107509922,2622193,escort,1,1,1,2,non_mandatory,1,773,1025,952720,19.0,6.0,6.0,0.0,,12.840047018957563,SHARED3FREE,-0.7318852867656347,,,,,,,,,0out_0in,escort +107509941,2622193,othmaint,1,1,2,2,non_mandatory,1,550,1025,952720,61.0,8.0,15.0,7.0,,12.867502750182359,WALK_TRANSIT,0.3002279969733954,1238.0,1666.0,fastest,1666.0,1238.0,shortest,,,0out_0in,othmaint +107509987,2622194,shopping,1,1,1,1,non_mandatory,1,989,1025,952720,72.0,9.0,11.0,2.0,,11.974087902319576,SHARED3FREE,0.5678792550825605,,,,,,,,,0out_0in,shopping +107510034,2622195,work,1,1,1,1,mandatory,1,1021,1025,952720,63.0,8.0,17.0,9.0,,,DRIVEALONEFREE,0.8640931880479102,,,,,,,no_subtours,,0out_0in,work +116640406,2844887,work,1,1,1,1,mandatory,1,845,846,1028031,111.0,11.0,23.0,12.0,,,WALK_TRANSIT,0.9802398468480145,1730.0,1695.0,shortest,1695.0,1748.0,fastest,no_subtours,,0out_0in,work +120287676,2933845,school,1,1,1,1,mandatory,1,666,574,1048898,121.0,12.0,21.0,9.0,,,WALK,-0.007445252449514138,,,,,,,,,0out_0in,school +120287717,2933846,school,1,1,1,1,mandatory,1,515,574,1048898,62.0,8.0,16.0,8.0,,,SHARED2FREE,-1.016021399976598,,,,,,,,,0out_0in,school +120287752,2933847,othdiscr,1,1,1,1,non_mandatory,1,657,574,1048898,42.0,7.0,12.0,5.0,,13.002449945790438,WALK_TRANSIT,0.8636594074657715,1238.0,1559.0,shortest,1559.0,1238.0,fastest,,,0out_1in,othdiscr +120287807,2933848,work,1,1,1,1,mandatory,1,502,574,1048898,31.0,6.0,18.0,12.0,,,WALK_TRANSIT,0.38857733727947175,1333.0,1559.0,fastest,1559.0,1333.0,shortest,no_subtours,,0out_0in,work +131881533,3216622,school,1,1,1,1,mandatory,1,1074,1076,1148260,136.0,14.0,15.0,1.0,,,WALK_TRANSIT,10.725339744357893,1500.0,1516.0,fastest,1516.0,1500.0,shortest,,,0out_0in,univ diff --git a/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_trips.csv b/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_trips.csv index 03a015e70..8c93f8ec8 100644 --- a/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_trips.csv +++ b/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_trips.csv @@ -1,185 +1,183 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,tour_id,purpose,destination_logsum,depart,trip_mode,mode_choice_logsum,atap,btap,path_set -10872201,33146,12593,work,1,True,3,879,560,1359025,escort,11.70885284520791,5,DRIVEALONEFREE,-0.3548746577733072,,, -10872202,33146,12593,work,2,True,3,989,879,1359025,escort,12.022433281644073,7,DRIVEALONEFREE,-0.361684345054043,,, -10872203,33146,12593,work,3,True,3,1100,989,1359025,work,,7,DRIVEALONEFREE,0.1801428827020189,,, -10872205,33146,12593,work,1,False,1,560,1100,1359025,home,,17,DRIVEALONEFREE,-0.368339088809198,,, -10872529,33147,12593,work,1,True,1,1070,560,1359066,work,,8,WALK_TRANSIT,2.7567263588191,1500.0,1558.0,shortest -10872533,33147,12593,work,1,False,2,942,1070,1359066,escort,19.449635104296835,15,DRIVEALONEFREE,1.4771881293112104,,, -10872534,33147,12593,work,2,False,2,560,942,1359066,home,,15,SHARED2FREE,1.0374930881113709,,, -11957177,36454,13797,shopping,1,True,1,725,580,1494647,shopping,,13,DRIVEALONEFREE,0.1717448995480592,,, -11957181,36454,13797,shopping,1,False,2,803,725,1494647,shopping,12.016029658018422,17,TAXI,0.0352267728440656,,, -11957182,36454,13797,shopping,2,False,2,580,803,1494647,home,,17,DRIVEALONEFREE,0.07359791064631,,, -11957185,36454,13797,shopping,1,True,2,687,580,1494648,escort,13.593599471819292,18,WALK,0.7788942047635835,,, -11957186,36454,13797,shopping,2,True,2,729,687,1494648,shopping,,18,DRIVEALONEFREE,0.489210171281661,,, -11957189,36454,13797,shopping,1,False,1,580,729,1494648,home,,18,TAXI,0.2313076964301194,,, -11957441,36455,13797,othdiscr,1,True,1,623,580,1494680,othdiscr,,16,DRIVEALONEFREE,0.7744647479734814,,, -11957445,36455,13797,othdiscr,1,False,3,784,623,1494680,othmaint,15.224351954248831,21,SHARED2FREE,0.6723499661375587,,, -11957446,36455,13797,othdiscr,2,False,3,687,784,1494680,social,14.665238994089853,21,SHARED2FREE,0.8916379345159234,,, -11957447,36455,13797,othdiscr,3,False,3,580,687,1494680,home,,21,WALK,1.6583917720273251,,, -11957553,36455,13797,work,1,True,1,562,580,1494694,work,,8,WALK,1.0769131864309256,,, -11957557,36455,13797,work,1,False,1,580,562,1494694,home,,12,SHARED3FREE,1.1126409173456748,,, -13679289,41705,15777,eatout,1,True,1,1070,645,1709911,eatout,,9,WALK_TRANSIT,3.1199845193778537,1500.0,1611.0,fastest -13679293,41705,15777,eatout,1,False,1,645,1070,1709911,home,,15,WALK_TRANSIT,1.5023958487827092,1611.0,1500.0,shortest -13679601,41706,15777,atwork,1,True,1,989,854,1709950,atwork,,12,WALK_TRANSIT,1.744638602675838,1618.0,1748.0,fastest -13679605,41706,15777,atwork,1,False,1,854,989,1709950,work,,12,SHARED2FREE,1.1051431249206285,,, -13679881,41706,15777,work,1,True,1,854,645,1709985,work,,8,SHARED2FREE,0.484767983512682,,, -13679885,41706,15777,work,1,False,2,1080,854,1709985,escort,14.795756318103102,18,SHARED2FREE,0.4510582957043532,,, -13679886,41706,15777,work,2,False,2,645,1080,1709985,home,,19,SHARED2FREE,0.4925057974553318,,, -16411585,50035,18261,eatout,1,True,2,897,757,2051448,shopping,9.84722541528066,16,SHARED3FREE,-0.8214696655451806,,, -16411586,50035,18261,eatout,2,True,2,1070,897,2051448,eatout,,17,WALK,0.1729919081975451,,, -16411589,50035,18261,eatout,1,False,1,757,1070,2051448,home,,18,SHARED3FREE,-1.5064572485047776,,, -16411729,50035,18261,school,1,True,1,919,757,2051466,school,,7,SHARED2FREE,0.9176970231226664,,, -16411733,50035,18261,school,1,False,1,757,919,2051466,home,,13,WALK_TRANSIT,1.0330239915909305,1748.0,1618.0,fastest -16411745,50035,18261,shopping,1,True,1,996,757,2051468,shopping,,19,DRIVEALONEFREE,-0.4672462178894699,,, -16411749,50035,18261,shopping,1,False,1,757,996,2051468,home,,19,DRIVEALONEFREE,-0.4597066008562396,,, -16412033,50036,18261,othmaint,1,True,1,877,757,2051504,othmaint,,7,SHARED2FREE,0.8103017184288349,,, -16412037,50036,18261,othmaint,1,False,1,757,877,2051504,home,,14,SHARED2FREE,0.7939513107930013,,, -16412449,50037,18261,work,1,True,1,1070,757,2051556,work,,7,DRIVEALONEFREE,2.631618309801618,,, -16412453,50037,18261,work,1,False,1,757,1070,2051556,home,,16,WALK_TRANSIT,1.2835436251782917,1651.0,1500.0,fastest -18151113,55338,19758,school,1,True,1,699,794,2268889,school,,7,WALK_TRANSIT,1.353066071452224,1608.0,1652.0,cheapest -18151117,55338,19758,school,1,False,1,794,699,2268889,home,,10,SHARED2FREE,1.118204426327703,,, -18151505,55339,19758,work,1,True,1,762,794,2268938,work,,5,DRIVEALONEFREE,-0.0975185793139585,,, -18151509,55339,19758,work,1,False,1,794,762,2268938,home,,16,DRIVEALONEFREE,-0.0075877722519422,,, -18990529,57897,20552,work,1,True,1,1070,829,2373816,work,,7,WALK_TRANSIT,2.259124587532973,1500.0,1754.0,fastest -18990533,57897,20552,work,1,False,1,829,1070,2373816,home,,20,DRIVEALONEFREE,1.0944715206029072,,, -18990545,57898,20552,atwork,1,True,2,948,948,2373818,othmaint,7.505984009752515,11,WALK,-0.4339456610259058,,, -18990546,57898,20552,atwork,2,True,2,948,948,2373818,atwork,,11,WALK,-0.4339456610259058,,, -18990549,57898,20552,atwork,1,False,1,948,948,2373818,work,,13,WALK,-0.4339456610259058,,, -18990857,57898,20552,work,1,True,1,948,829,2373857,work,,11,DRIVEALONEFREE,-0.4249793219573366,,, -18990861,57898,20552,work,1,False,2,739,948,2373857,escort,11.536722530937924,17,DRIVEALONEFREE,-0.4351552970051177,,, -18990862,57898,20552,work,2,False,2,829,739,2373857,home,,17,DRIVEALONEFREE,-0.0991454272062134,,, -18991185,57899,20552,work,1,True,1,687,829,2373898,work,,7,WALK,0.5220452718494769,,, -18991189,57899,20552,work,1,False,1,829,687,2373898,home,,17,WALK,0.6195549538462863,,, -18991841,57901,20552,work,1,True,1,708,829,2373980,work,,6,DRIVEALONEFREE,-0.2156500818117738,,, -18991845,57901,20552,work,1,False,1,829,708,2373980,home,,16,DRIVEALONEFREE,-0.82405708338336,,, -18991849,57901,20552,work,1,True,2,687,829,2373981,othmaint,13.312507041737812,18,WALK,1.203803918419165,,, -18991850,57901,20552,work,2,True,2,708,687,2373981,work,,21,SHARED2FREE,0.5645998198729414,,, -18991853,57901,20552,work,1,False,1,829,708,2373981,home,,21,DRIVEALONEFREE,0.1538836282621585,,, -20510417,62531,21869,school,1,True,1,938,900,2563802,school,,20,TAXI,1.0846519720828849,,, -20510421,62531,21869,school,1,False,1,900,938,2563802,home,,20,WALK,1.0317475525946904,,, -20510569,62532,21869,escort,1,True,1,647,900,2563821,escort,,6,SHARED2FREE,0.335383128230131,,, -20510573,62532,21869,escort,1,False,1,900,647,2563821,home,,7,DRIVEALONEFREE,0.3214092822815929,,, -20510897,62533,21869,escort,1,True,1,695,900,2563862,escort,,5,SHARED3FREE,0.7146320921116115,,, -20510901,62533,21869,escort,1,False,4,996,695,2563862,shopping,13.9010789936427,6,SHARED3FREE,0.8524136187385734,,, -20510902,62533,21869,escort,2,False,4,565,996,2563862,eatout,14.39637292108163,6,SHARED2FREE,0.5414028775934869,,, -20510903,62533,21869,escort,3,False,4,1099,565,2563862,escort,14.873416692380914,6,SHARED3FREE,0.5359149885298558,,, -20510904,62533,21869,escort,4,False,4,900,1099,2563862,home,,6,SHARED2FREE,0.8757485870243559,,, -20510905,62533,21869,escort,1,True,1,518,900,2563863,escort,,11,SHARED2FREE,0.403734784317824,,, -20510909,62533,21869,escort,1,False,1,900,518,2563863,home,,11,SHARED3FREE,0.4094721213082503,,, -20510913,62533,21869,escort,1,True,1,844,900,2563864,escort,,14,SHARED2FREE,0.2145990810721203,,, -20510917,62533,21869,escort,1,False,1,900,844,2563864,home,,14,SHARED2FREE,0.2175421651558981,,, -20511025,62533,21869,othdiscr,1,True,1,1070,900,2563878,othdiscr,,11,BIKE,3.2327250107654435,,, -20511029,62533,21869,othdiscr,1,False,1,900,1070,2563878,home,,12,BIKE,1.820941282963293,,, -20511401,62534,21869,school,1,True,1,793,900,2563925,school,,8,SHARED3FREE,0.6855068986830097,,, -20511405,62534,21869,school,1,False,1,900,793,2563925,home,,9,SHARED3FREE,0.6540064639192135,,, -22303745,67999,23619,escort,1,True,1,767,973,2787968,escort,,13,DRIVEALONEFREE,0.890462913926255,,, -22303749,67999,23619,escort,1,False,3,1023,767,2787968,eatout,15.21145768299369,13,DRIVEALONEFREE,0.9380038576909148,,, -22303750,67999,23619,escort,2,False,3,993,1023,2787968,escort,16.38782485475755,13,DRIVEALONEFREE,1.1641324346709314,,, -22303751,67999,23619,escort,3,False,3,973,993,2787968,home,,13,SHARED3FREE,1.1940117377263124,,, -22303961,67999,23619,social,1,True,1,988,973,2787995,social,,17,WALK,-0.5648561131494263,,, -22303965,67999,23619,social,1,False,1,973,988,2787995,home,,20,WALK,-0.532826088016878,,, -22304313,68000,23619,work,1,True,2,949,973,2788039,social,22.753813938189783,7,SHARED3FREE,1.149142351882263,,, -22304314,68000,23619,work,2,True,2,1070,949,2788039,work,,8,WALK_TRANSIT,3.309993337941638,1500.0,1747.0,fastest -22304317,68000,23619,work,1,False,1,973,1070,2788039,home,,21,WALK_TRANSIT,1.875153102901004,1588.0,1500.0,fastest -25904705,78977,26897,school,1,True,1,984,1081,3238088,school,,7,WALK,-0.4961040893656598,,, -25904709,78977,26897,school,1,False,1,1081,984,3238088,home,,14,WALK,-0.4863570793073567,,, -25905145,78979,26897,atwork,1,True,1,1070,897,3238143,atwork,,9,DRIVEALONEFREE,-0.0790579016887315,,, -25905149,78979,26897,atwork,1,False,1,897,1070,3238143,work,,11,DRIVEALONEFREE,0.0896991164812165,,, -25905425,78979,26897,work,1,True,1,897,1081,3238178,work,,7,DRIVEALONEFREE,0.0543705077473041,,, -25905429,78979,26897,work,1,False,1,1081,897,3238178,home,,18,DRIVEALONEFREE,0.2237278343269858,,, -421021769,1283602,435012,work,1,True,1,1078,521,52627721,work,,8,WALK_TRANSIT,1.8939124714977291,1584.0,1608.0,fastest -421021773,1283602,435012,work,1,False,1,521,1078,52627721,home,,18,WALK_TRANSIT,0.9194329115009408,1238.0,1584.0,shortest -421108705,1283868,435278,atwork,1,True,1,1070,1070,52638588,atwork,,12,WALK,2.4750620100011926,,, -421108709,1283868,435278,atwork,1,False,1,1070,1070,52638588,work,,12,WALK,2.4750620100011926,,, -421108753,1283868,435278,eatout,1,True,1,1070,537,52638594,eatout,,18,WALK_TRANSIT,3.4974794156920828,1500.0,1558.0,shortest -421108757,1283868,435278,eatout,1,False,2,895,1070,52638594,shopping,17.316225456083114,21,WALK,2.514244617152652,,, -421108758,1283868,435278,eatout,2,False,2,537,895,52638594,home,,21,DRIVEALONEFREE,1.0614628362390803,,, -421109017,1283868,435278,work,1,True,1,1070,537,52638627,work,,9,WALK_TRANSIT,2.6311889287575188,1500.0,1558.0,cheapest -421109021,1283868,435278,work,1,False,1,537,1070,52638627,home,,18,SHARED2FREE,1.283382912854287,,, -421134601,1283946,435356,work,1,True,1,1070,523,52641825,work,,7,WALK_TRANSIT,2.648329293837569,1500.0,1604.0,shortest -421134605,1283946,435356,work,1,False,1,523,1070,52641825,home,,19,TNC_SINGLE,1.3977246630790026,,, -421348457,1284598,436008,work,1,True,1,553,562,52668557,work,,9,WALK,0.3477587678510122,,, -421348461,1284598,436008,work,1,False,1,562,553,52668557,home,,19,WALK,0.3521744962789443,,, -421878553,1286215,437625,atwork,1,True,1,1077,606,52734819,atwork,,10,WALK_TRANSIT,1.186699589189828,1500.0,1562.0,cheapest -421878557,1286215,437625,atwork,1,False,1,606,1077,52734819,work,,13,WALK_TRANSIT,1.681572479856284,1562.0,1500.0,fastest -421878833,1286215,437625,work,1,True,1,606,656,52734854,work,,8,DRIVEALONEFREE,0.1102478757651215,,, -421878837,1286215,437625,work,1,False,3,934,606,52734854,othmaint,11.66765616556947,17,DRIVEALONEFREE,-0.3317318054812608,,, -421878838,1286215,437625,work,2,False,3,730,934,52734854,othmaint,11.283765093022993,19,DRIVEALONEFREE,-0.1643308499157778,,, -421878839,1286215,437625,work,3,False,3,656,730,52734854,home,,19,DRIVEALONEFREE,0.0934059914274113,,, -423180353,1290184,441594,atwork,1,True,1,732,977,52897544,atwork,,11,WALK,2.454959930326644,,, -423180357,1290184,441594,atwork,1,False,1,977,732,52897544,work,,11,SHARED2FREE,2.474151530078552,,, -423180401,1290184,441594,eatout,1,True,1,949,1096,52897550,eatout,,21,SHARED2FREE,0.0292772650529429,,, -423180405,1290184,441594,eatout,1,False,2,1070,949,52897550,othmaint,13.725541816697769,21,DRIVEALONEFREE,-0.5109700761493449,,, -423180406,1290184,441594,eatout,2,False,2,1096,1070,52897550,home,,21,WALK,1.478188695251212,,, -423180553,1290184,441594,othdiscr,1,True,1,684,1096,52897569,othdiscr,,5,DRIVEALONEFREE,-0.0552559508415055,,, -423180557,1290184,441594,othdiscr,1,False,1,1096,684,52897569,home,,7,TAXI,-0.0112939168809909,,, -423180561,1290184,441594,othdiscr,1,True,1,762,1096,52897570,othdiscr,,21,WALK_TRANSIT,1.8308609456730651,1748.0,1516.0,fastest -423180565,1290184,441594,othdiscr,1,False,1,1096,762,52897570,home,,22,DRIVEALONEFREE,1.1096229314740322,,, -423180569,1290184,441594,othdiscr,1,True,1,725,1096,52897571,othdiscr,,23,WALK,-3.2668547335399403,,, -423180573,1290184,441594,othdiscr,1,False,1,1096,725,52897571,home,,23,WALK,-3.2660886555615467,,, -423180665,1290184,441594,work,1,True,1,977,1096,52897583,work,,9,DRIVEALONEFREE,-0.1968641598702008,,, -423180669,1290184,441594,work,1,False,2,996,977,52897583,shopping,10.57412967355008,20,WALK,0.2087211377734316,,, -423180670,1290184,441594,work,2,False,2,1096,996,52897583,home,,21,DRIVEALONEFREE,-0.1682790295025423,,, -423325641,1290626,442036,work,1,True,2,1070,1100,52915705,work,23.18866767080017,8,WALK_TRANSIT,2.8246315365649477,1500.0,1584.0,cheapest -423325642,1290626,442036,work,2,True,2,1070,1070,52915705,work,,9,TAXI,3.132049389519477,,, -423325645,1290626,442036,work,1,False,1,1100,1070,52915705,home,,18,WALK_TRANSIT,1.8829102540483185,1584.0,1500.0,shortest -611033041,1862905,721960,othdiscr,1,True,1,986,960,76379130,othdiscr,,15,WALK_TRANSIT,1.4885049380905109,1618.0,1762.0,shortest -611033045,1862905,721960,othdiscr,1,False,1,960,986,76379130,home,,21,TAXI,1.3532506038209091,,, -611033369,1862906,721960,othdiscr,1,True,1,992,960,76379171,othdiscr,,11,SHARED3FREE,0.8858713264739554,,, -611033373,1862906,721960,othdiscr,1,False,2,551,992,76379171,eatout,14.365988124910029,16,DRIVEALONEFREE,0.6551993708892542,,, -611033374,1862906,721960,othdiscr,2,False,2,960,551,76379171,home,,16,SHARED2FREE,0.5117735347645973,,, -647572569,1974306,760593,othdiscr,1,True,1,564,509,80946571,othdiscr,,8,WALK,0.1247752862685705,,, -647572573,1974306,760593,othdiscr,1,False,1,509,564,80946571,home,,16,WALK,0.116845013634565,,, -647572729,1974307,760593,atwork,1,True,1,913,739,80946591,atwork,,12,SHARED3FREE,2.326963701825421,,, -647572733,1974307,760593,atwork,1,False,1,739,913,80946591,work,,13,SHARED3FREE,2.32846863119764,,, -647573009,1974307,760593,work,1,True,2,741,509,80946626,shopping,18.29690262001317,9,WALK_TRANSIT,1.757205419198924,1663.0,1333.0,fastest -647573010,1974307,760593,work,2,True,2,739,741,80946626,work,,10,BIKE,2.23760901377759,,, -647573013,1974307,760593,work,1,False,2,518,739,80946626,work,19.081960514674996,17,WALK_TRANSIT,2.9100248711462138,1681.0,1662.0,cheapest -647573014,1974307,760593,work,2,False,2,509,518,80946626,home,,18,WALK,2.5433977364719564,,, -647573097,1974308,760593,escort,1,True,1,716,509,80946637,escort,,5,SHARED3FREE,0.8908384736539703,,, -647573101,1974308,760593,escort,1,False,1,509,716,80946637,home,,5,SHARED3FREE,0.8921116412562625,,, -648387521,1976791,761445,escort,1,True,1,908,613,81048440,escort,,13,SHARED2FREE,0.431874605975145,,, -648387525,1976791,761445,escort,1,False,1,613,908,81048440,home,,13,SHARED2FREE,0.4276938639754877,,, -648387809,1976792,761445,atwork,1,True,1,517,517,81048476,atwork,,9,WALK,3.011532825794776,,, -648387813,1976792,761445,atwork,1,False,1,517,517,81048476,work,,9,WALK,3.011532825794776,,, -648387825,1976792,761445,eatout,1,True,1,648,613,81048478,eatout,,13,DRIVEALONEFREE,0.4724646811902892,,, -648387829,1976792,761445,eatout,1,False,1,613,648,81048478,home,,19,TAXI,0.5571365851477499,,, -648388089,1976792,761445,work,1,True,1,517,613,81048511,work,,5,WALK_TRANSIT,1.796194897875614,1681.0,1238.0,cheapest -648388093,1976792,761445,work,1,False,1,613,517,81048511,home,,12,DRIVEALONEFREE,1.474443497861856,,, -649042753,1978788,762159,social,1,True,1,763,961,81130344,social,,8,SHARED2FREE,0.3356357191681573,,, -649042757,1978788,762159,social,1,False,1,961,763,81130344,home,,20,SHARED2FREE,0.3505516593935055,,, -649043193,1978790,762159,escort,1,True,1,992,961,81130399,escort,,8,SHARED2FREE,0.5305214939922914,,, -649043197,1978790,762159,escort,1,False,1,961,992,81130399,home,,8,SHARED2FREE,0.5188517472186277,,, -649043433,1978790,762159,work,1,True,1,672,961,81130429,work,,8,DRIVEALONEFREE,-0.3341036694423639,,, -649043437,1978790,762159,work,1,False,1,961,672,81130429,home,,17,DRIVEALONEFREE,-0.3318966430958522,,, -649043761,1978791,762159,work,1,True,1,1070,961,81130470,work,,7,WALK_TRANSIT,2.6922733054492127,1500.0,1762.0,fastest -649043765,1978791,762159,work,1,False,1,961,1070,81130470,home,,17,TAXI,1.4645945681666606,,, -819359665,2498047,922602,school,1,True,1,865,730,102419958,school,,9,TAXI,1.202896883865351,,, -819359669,2498047,922602,school,1,False,1,730,865,102419958,home,,16,SHARED2FREE,1.846403103708924,,, -819360057,2498048,922602,work,1,True,1,735,730,102420007,work,,7,SHARED3FREE,1.7808819076757447,,, -819360061,2498048,922602,work,1,False,1,730,735,102420007,home,,16,WALK_TRANSIT,2.022371451590549,1612.0,1592.0,shortest -819360385,2498049,922602,work,1,True,1,763,730,102420048,work,,6,WALK,-0.7841986127882443,,, -819360389,2498049,922602,work,1,False,1,730,763,102420048,home,,16,WALK,-0.8403247829423018,,, -860079225,2622192,952720,school,1,True,1,995,1025,107509903,school,,7,TNC_SINGLE,1.737007403541493,,, -860079229,2622192,952720,school,1,False,1,1025,995,107509903,home,,14,SHARED3FREE,1.6399355775465674,,, -860079377,2622193,952720,escort,1,True,1,773,1025,107509922,escort,,6,SHARED3FREE,0.9453815876415104,,, -860079381,2622193,952720,escort,1,False,1,1025,773,107509922,home,,6,SHARED2FREE,0.9690872179986344,,, -860079529,2622193,952720,othmaint,1,True,1,550,1025,107509941,othmaint,,8,TAXI,1.6791134568237254,,, -860079533,2622193,952720,othmaint,1,False,1,1025,550,107509941,home,,15,WALK_TRANSIT,1.6848126498987464,1666.0,1238.0,shortest -860079897,2622194,952720,shopping,1,True,1,989,1025,107509987,shopping,,9,WALK,1.6280881165461685,,, -860079901,2622194,952720,shopping,1,False,1,1025,989,107509987,home,,11,SHARED3FREE,1.5894514263370938,,, -860080273,2622195,952720,work,1,True,1,1021,1025,107510034,work,,8,DRIVEALONEFREE,0.1307608725915786,,, -860080277,2622195,952720,work,1,False,1,1025,1021,107510034,home,,17,DRIVEALONEFREE,0.1511979688414838,,, -933123249,2844887,1028031,work,1,True,1,845,846,116640406,work,,11,SHARED3FREE,1.662725895784853,,, -933123253,2844887,1028031,work,1,False,1,846,845,116640406,home,,23,DRIVEALONEFREE,1.6190252500331577,,, -962301409,2933845,1048898,school,1,True,1,666,574,120287676,school,,12,WALK,-1.1062441750768537,,, -962301413,2933845,1048898,school,1,False,1,574,666,120287676,home,,21,WALK,-1.227339677708164,,, -962301737,2933846,1048898,school,1,True,1,515,574,120287717,school,,8,SHARED2FREE,-2.095040038174043,,, -962301741,2933846,1048898,school,1,False,1,574,515,120287717,home,,16,SHARED2FREE,-2.102822435414125,,, -962302017,2933847,1048898,othdiscr,1,True,1,657,574,120287752,othdiscr,,7,WALK_TRANSIT,1.5362116862704658,1238.0,1559.0,cheapest -962302021,2933847,1048898,othdiscr,1,False,2,1070,657,120287752,eatout,17.04052424640584,12,WALK_TRANSIT,3.494101406816589,3.0,1238.0,shortest -962302022,2933847,1048898,othdiscr,2,False,2,574,1070,120287752,home,,12,WALK_TRANSIT,1.3230638799617858,1559.0,1500.0,fastest -962302457,2933848,1048898,work,1,True,1,502,574,120287807,work,,6,BIKE,1.3439545881637307,,, -962302461,2933848,1048898,work,1,False,1,574,502,120287807,home,,18,WALK_TRANSIT,1.3169033457581467,1559.0,1333.0,shortest -1055052265,3216622,1148260,univ,1,True,1,1074,1076,131881533,univ,,14,TAXI,0.933892780827025,,, -1055052269,3216622,1148260,univ,1,False,1,1076,1074,131881533,home,,15,BIKE,0.8615934658413547,,, +10872201,33146,12593,work,1,True,3,350,68,1359025,escort,11.62710514749821,5,DRIVEALONEFREE,-0.3153159028910412,,, +10872202,33146,12593,work,2,True,3,491,350,1359025,escort,12.424454211009824,7,DRIVEALONEFREE,-0.1330752275781039,,, +10872203,33146,12593,work,3,True,3,608,491,1359025,work,,7,DRIVEALONEFREE,0.1534821760951137,,, +10872205,33146,12593,work,1,False,1,68,608,1359025,home,,17,WALK,-0.3102996602636911,,, +10872529,33147,12593,work,1,True,1,578,68,1359066,work,,8,WALK_TRANSIT,2.7167630046939766,1500.0,1558.0,shortest +10872533,33147,12593,work,1,False,2,578,578,1359066,escort,19.372183137979995,15,WALK,2.9941670645943352,,, +10872534,33147,12593,work,2,False,2,68,578,1359066,home,,15,WALK,1.93207085094047,,, +11957177,36454,13797,shopping,1,True,1,233,88,1494647,shopping,,13,DRIVEALONEFREE,0.1611535351527045,,, +11957181,36454,13797,shopping,1,False,2,423,233,1494647,shopping,12.025184457266546,17,TAXI,-0.0282876166283608,,, +11957182,36454,13797,shopping,2,False,2,88,423,1494647,home,,17,DRIVEALONEFREE,0.0612368483543265,,, +11957185,36454,13797,shopping,1,True,2,195,88,1494648,escort,13.40305488007496,18,WALK,0.3896474852723683,,, +11957186,36454,13797,shopping,2,True,2,237,195,1494648,shopping,,18,DRIVEALONEFREE,0.4918839198395139,,, +11957189,36454,13797,shopping,1,False,1,88,237,1494648,home,,18,TAXI,0.2042092214903569,,, 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58db5d2d3..8619b8a54 100644 --- a/activitysim/examples/placeholder_sandag/test/test_sandag.py +++ b/activitysim/examples/placeholder_sandag/test/test_sandag.py @@ -3,6 +3,7 @@ import os import shutil import subprocess +import sys import pandas as pd import pandas.testing as pdt @@ -44,41 +45,80 @@ def data(): build_data() -def run_test(zone, multiprocess=False): +def run_test(zone, multiprocess=False, sharrow=False): def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) def regress(zone): # ## regress tours - regress_tours_df = pd.read_csv( - test_path(f"regress/final_{zone}_zone_tours.csv") - ) - tours_df = pd.read_csv(test_path(f"output/final_{zone}_zone_tours.csv")) + if sharrow and os.path.isfile( + test_path(f"regress/final_{zone}_zone_tours_sh.csv") + ): + regress_tours_df = pd.read_csv( + test_path(f"regress/final_{zone}_zone_tours_sh.csv") + ) + else: + regress_tours_df = pd.read_csv( + test_path(f"regress/final_{zone}_zone_tours.csv") + ) + tours_df = pd.read_csv(test_path(f"output_{zone}/final_{zone}_zone_tours.csv")) tours_df.to_csv( test_path(f"regress/final_{zone}_zone_tours_last_run.csv"), index=False ) - print(f"regress tours") - pdt.assert_frame_equal(tours_df, regress_tours_df, rtol=1e-03) + print("regress tours") + pdt.assert_frame_equal( + tours_df, regress_tours_df, rtol=1e-03, check_dtype=False + ) # ## regress trips - regress_trips_df = pd.read_csv( - test_path(f"regress/final_{zone}_zone_trips.csv") - ) - trips_df = pd.read_csv(test_path(f"output/final_{zone}_zone_trips.csv")) + if sharrow and os.path.isfile( + test_path(f"regress/final_{zone}_zone_trips_sh.csv") + ): + regress_trips_df = pd.read_csv( + test_path(f"regress/final_{zone}_zone_trips_sh.csv") + ) + else: + regress_trips_df = pd.read_csv( + test_path(f"regress/final_{zone}_zone_trips.csv") + ) + trips_df = pd.read_csv(test_path(f"output_{zone}/final_{zone}_zone_trips.csv")) trips_df.to_csv( test_path(f"regress/final_{zone}_zone_trips_last_run.csv"), index=False ) - print(f"regress trips") - pdt.assert_frame_equal(trips_df, regress_trips_df, rtol=1e-03) + print("regress trips") + pdt.assert_frame_equal( + trips_df, regress_trips_df, rtol=1e-03, check_dtype=False + ) + + if zone == "2": + # also test accessibility for the 2-zone system + regress_accessibility_df = pd.read_csv( + test_path( + f"regress/final_{zone}_zone_proto_disaggregate_accessibility.csv" + ) + ) + final_accessibility_df = pd.read_csv( + test_path( + f"output_{zone}/final_{zone}_zone_proto_disaggregate_accessibility.csv" + ) + ) + final_accessibility_df = final_accessibility_df[ + [ + c + for c in final_accessibility_df.columns + if not c.startswith("_original_") + ] + ] + pdt.assert_frame_equal(final_accessibility_df, regress_accessibility_df) # run test file_path = os.path.join(os.path.dirname(__file__), "simulation.py") if zone == "2": - base_configs = psrc_example_path(f"configs") + base_configs = psrc_example_path("configs") else: - base_configs = mtc_example_path(f"configs") + base_configs = mtc_example_path("configs") run_args = [ "-c", @@ -90,13 +130,46 @@ def regress(zone): "-d", example_path(f"data_{zone}"), "-o", - test_path("output"), + test_path(f"output_{zone}"), ] if multiprocess: run_args = run_args + ["-s", "settings_mp.yaml"] - subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + if sharrow: + run_args = ["-c", test_path(f"configs_{zone}_sharrow")] + run_args + + try: + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + except FileNotFoundError: + subprocess.run([sys.executable, file_path] + run_args, check=True) + from tempfile import TemporaryFile + from time import sleep + + with TemporaryFile() as outputstream: + env = os.environ.copy() + pythonpath = env.pop("PYTHONPATH", None) + process = subprocess.Popen( + args=[sys.executable, file_path] + run_args, + shell=True, + stdout=outputstream, + stderr=subprocess.STDOUT, + cwd=os.getcwd(), + env=env, + ) + while process.poll() is None: + where = outputstream.tell() + lines = outputstream.read() + if not lines: + # Adjust the sleep interval to your needs + sleep(0.25) + # make sure pointing to the last place we read + outputstream.seek(where) + else: + # Windows adds an extra carriage return and then chokes on + # it when displaying (or, as it were, not displaying) the + # output. So we give Windows a little helping hand. + print(lines.decode().replace("\r\n", "\n"), end="") regress(zone) @@ -109,6 +182,13 @@ def test_1_zone_mp(data): run_test(zone="1", multiprocess=True) +def test_1_zone_sharrow(data): + # Run both single and MP in one test function + # guarantees that compile happens in single + run_test(zone="1", multiprocess=False, sharrow=True) + run_test(zone="1", multiprocess=True, sharrow=True) + + def test_2_zone(data): run_test(zone="2", multiprocess=False) @@ -117,6 +197,13 @@ def test_2_zone_mp(data): run_test(zone="2", multiprocess=True) +def test_2_zone_sharrow(data): + # Run both single and MP in one test function + # guarantees that compile happens in single + run_test(zone="2", multiprocess=False, sharrow=True) + run_test(zone="2", multiprocess=True, sharrow=True) + + def test_3_zone(data): run_test(zone="3", multiprocess=False) @@ -125,15 +212,28 @@ def test_3_zone_mp(data): run_test(zone="3", multiprocess=True) +def test_3_zone_sharrow(data): + # Run both single and MP in one test function + # guarantees that compile happens in single + run_test(zone="3", multiprocess=False, sharrow=True) + run_test(zone="3", multiprocess=True, sharrow=True) + + if __name__ == "__main__": # call each test explicitly so we get a pass/fail for each build_data() run_test(zone="1", multiprocess=False) run_test(zone="1", multiprocess=True) + run_test(zone="1", multiprocess=False, sharrow=True) + run_test(zone="1", multiprocess=True, sharrow=True) run_test(zone="2", multiprocess=False) run_test(zone="2", multiprocess=True) + run_test(zone="2", multiprocess=False, sharrow=True) + run_test(zone="2", multiprocess=True, sharrow=True) run_test(zone="3", multiprocess=False) run_test(zone="3", multiprocess=True) + run_test(zone="3", multiprocess=False, sharrow=True) + run_test(zone="3", multiprocess=True, sharrow=True) diff --git a/activitysim/examples/prototype_arc/configs/annotate_persons_workplace.csv b/activitysim/examples/prototype_arc/configs/annotate_persons_workplace.csv index 9156b5293..7fd0a892f 100644 --- a/activitysim/examples/prototype_arc/configs/annotate_persons_workplace.csv +++ b/activitysim/examples/prototype_arc/configs/annotate_persons_workplace.csv @@ -36,4 +36,4 @@ Total IVT,students_totalTrn_ivt,"np.where((_PremOnlyPremIVT_Sch > 0) * (_PremOnl Usual work location is home,usualWorkLocationIsHome,"np.where(persons.workplace_zone_id == persons.home_zone_id, 1, 0)" #Followed MTC convention - not sure why workplace taz equal to 0 was not used,, No usual work location,noUsualWorkLocation,"np.where(persons.workplace_zone_id < 0, 1, 0)" -Distance to work,distance_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('SOV_FREE_DISTANCE', 'MD'))" +Distance to work,distance_to_work,"np.where(persons.workplace_zone_id>=0, skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('SOV_FREE_DISTANCE', 'MD')),np.nan)" diff --git a/activitysim/examples/prototype_arc/configs/joint_tour_scheduling.yaml b/activitysim/examples/prototype_arc/configs/joint_tour_scheduling.yaml index e81afe238..2281b0467 100644 --- a/activitysim/examples/prototype_arc/configs/joint_tour_scheduling.yaml +++ b/activitysim/examples/prototype_arc/configs/joint_tour_scheduling.yaml @@ -2,6 +2,14 @@ LOGIT_TYPE: MNL SPEC: tour_scheduling_joint.csv COEFFICIENTS: tour_scheduling_joint_coeffs.csv +sharrow_skip: True +# The `sharrow_skip` option is activated to disable the use of sharrow for this +# model, because the utility specification as currently written includes a +# large number of string comparisons (nearly every one of 400+ expressions has +# such an operation). Sharrow can process these expressions correctly, but it is +# slow. If the specification is rewritten to remove most of these string +# comparisons (see for example in the equivalent file for the prototype_mtc +# models) then sharrow can be re-enabled for a significant speed benefit. preprocessor: SPEC: joint_tour_scheduling_annotate_tours_preprocessor diff --git a/activitysim/examples/prototype_arc/configs/legacy-1.1.3/logging.yaml b/activitysim/examples/prototype_arc/configs/legacy-1.1.3/logging.yaml new file mode 100644 index 000000000..b2a89a10e --- /dev/null +++ b/activitysim/examples/prototype_arc/configs/legacy-1.1.3/logging.yaml @@ -0,0 +1,53 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: activitysim.log + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/examples/prototype_arc/configs/legacy-1.1.3/network_los.yaml b/activitysim/examples/prototype_arc/configs/legacy-1.1.3/network_los.yaml new file mode 100644 index 000000000..6c851bbbe --- /dev/null +++ b/activitysim/examples/prototype_arc/configs/legacy-1.1.3/network_los.yaml @@ -0,0 +1,15 @@ +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +zone_system: 1 + +# glob 'skims*.omx' will match one or more files: skims.omx, skims1.omx, skims2.omx... +taz_skims: skims*.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + periods: [0, 6, 14, 24, 32, 48] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/examples/prototype_arc/configs/legacy-1.1.3/settings.yaml b/activitysim/examples/prototype_arc/configs/legacy-1.1.3/settings.yaml new file mode 100644 index 000000000..bd3c24a24 --- /dev/null +++ b/activitysim/examples/prototype_arc/configs/legacy-1.1.3/settings.yaml @@ -0,0 +1,208 @@ +# input tables +input_table_list: + - tablename: households + filename: households.csv + # The index column is set before keep_columns, + # so don't put index in keep columns + index_col: household_id + rename_columns: + maz: home_zone_id + np: hhsize + nwrkrs_esr: num_workers + keep_columns: + #- household_id + - home_zone_id + - hhsize + - num_workers + - hincp + #- hhincAdj + #- adjinc + #- veh + - hht + #- bld + #- type + - tablename: persons + filename: persons.csv + # The index column is set before keep_columns, + # so don't put index in keep columns + index_col: person_id + rename_columns: + maz: home_zone_id + sporder: PNUM + keep_columns: + #- person_id + - household_id + - home_zone_id + - PNUM + - agep + #- employed + - pecasOcc + - sex + - esr + - wkw + - wkhp + #-mil + - schg + #-schl + #-indp02 + #-indp07 + #-occp02 + #-occp10 + #-n + - tablename: land_use + filename: land_use.csv + # The index column is set before keep_columns, + # so don't put index in keep columns + index_col: zone_id +# rename_columns: +# + keep_columns: + #- TAZ +# - construc +# - manufac +# - TCU +# - wholesl + - retail +# - FIRE + - service +# - private +# - govt + - emp + - pop + - hshld + - univ + - acres +# - otherEmp +# - district + - PARKTOT + - PARKLNG + - PROPFREE + - PARKRATE + - areatype +# - county + - CBDFlag + - N11 + - N21 + - N22 + - N23 + - N313233 + - N42 + - N4445 + - N4849 + - N51 + - N52 + - N53 + - N54 + - N55 + - N56 + - N61 + - N62 + - N71 + - N72 + - N81 + - N92 + - EnrollDS + - EnrollPD + - I_PCTLT10K + - I_PCT10TO20 + - I_PCT20TO40 + - I_PCTGT40 + - RetailEmp30 + - PARKING_ZONE + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# use_shadow_pricing: True + +# chunk_size: + +# assume enough RAM to not chunk +chunk_training_mode: disabled + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling_choice + - trip_departure_choice + - trip_mode_choice + - parking_location + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables + +# resume_after: + +multiprocess: False +fail_fast: True + +multiprocess_steps: + - name: mp_initialize_landuse + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + num_processes: 5 + slice: + tables: + - accessibility + # don't slice any tables not explicitly listed above in slice.tables + except: True + - name: mp_households + begin: school_location + num_processes: 5 + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_tables + + +output_tables: + h5_store: False + action: include + prefix: final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +trace_od: +trace_hh_id: + +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 diff --git a/activitysim/examples/prototype_arc/configs/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_arc/configs/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv new file mode 100644 index 000000000..fd39cda93 --- /dev/null +++ b/activitysim/examples/prototype_arc/configs/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv @@ -0,0 +1,13 @@ +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,tour_mode_is_transit,(tour_mode == 'WALK_ALLTRN')|(tour_mode == 'WALK_PRMTRN')|(tour_mode == 'PNR_ALLTRN')|(tour_mode == 'PNR_PRMTRN')|(tour_mode == 'KNR_ALLTRN')|(tour_mode == 'KNR_PRMTRN') +,tour_mode_is_shared_ride,(tour_mode == 'SHARED2FREE')|(tour_mode == 'SHARED2PAY')|(tour_mode == 'SHARED3FREE')|(tour_mode == 'SHARED3PAY') +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" +#,is_CBD,"reindex(land_use.areatype, df.dest_zone_id) == 1" +#,,not needed as school is not chosen as an intermediate trip destination +#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" +#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" diff --git a/activitysim/examples/prototype_arc/configs/logging.yaml b/activitysim/examples/prototype_arc/configs/logging.yaml index a30a25c82..1db43e998 100644 --- a/activitysim/examples/prototype_arc/configs/logging.yaml +++ b/activitysim/examples/prototype_arc/configs/logging.yaml @@ -24,6 +24,18 @@ logging: handlers: [console, logfile] propagate: false + filelock: + level: WARN + + sharrow: + level: INFO + + blib2to3: + level: WARN + + black: + level: WARN + handlers: logfile: @@ -36,7 +48,7 @@ logging: console: class: logging.StreamHandler stream: ext://sys.stdout - formatter: simpleFormatter + formatter: elapsedFormatter level: NOTSET formatters: @@ -44,7 +56,7 @@ logging: simpleFormatter: class: logging.Formatter # format: '%(levelname)s - %(name)s - %(message)s' - format: '%(levelname)s - %(message)s' + format: '%(name)s - %(levelname)s - %(message)s' datefmt: '%d/%m/%Y %H:%M:%S' fileFormatter: @@ -52,3 +64,7 @@ logging: format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' datefmt: '%d/%m/%Y %H:%M:%S' + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' diff --git a/activitysim/examples/prototype_arc/configs/mandatory_tour_frequency.csv b/activitysim/examples/prototype_arc/configs/mandatory_tour_frequency.csv index 4e7a9fef4..b3ce0e569 100644 --- a/activitysim/examples/prototype_arc/configs/mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_arc/configs/mandatory_tour_frequency.csv @@ -25,10 +25,10 @@ Can walk to work - Retired interaction,(ptype == 5) & (distance_to_work < 3),0,0 Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),0,0,0,0.7114,0 Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),0,0,0,0.7114,0 Can walk to school - Pre-driving age child who is in school interaction,((ptype == 7)|(ptype == 8)) & (distance_to_school < 3),0,0,0,0.7114,0 -Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),0,0,0,0,0.1391 -Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),0,0,0,0,0.1391 -Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),0,0,0,0,0.1391 -Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),0,0,0,0,0.1391 +Can walk to work or school - Full-time worker interaction,(ptype == 1) & ((distance_to_work < 3) | (distance_to_school < 3)),0,0,0,0,0.1391 +Can walk to work or school - Part-time worker interaction,(ptype == 2) & ((distance_to_work < 3) | (distance_to_school < 3)),0,0,0,0,0.1391 +Can walk to work or school - University student interaction,(ptype == 3) & ((distance_to_work < 3) | (distance_to_school < 3)),0,0,0,0,0.1391 +Can walk to work or school - Driving-age child interaction,(ptype == 6) & ((distance_to_work < 3) | (distance_to_school < 3)),0,0,0,0,0.1391 Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,0,-0.003518,0,0,-0.003124 Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,0,-0.003518,0,0,-0.003124 Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,0,-0.003518,0,0,-0.003124 diff --git a/activitysim/examples/prototype_arc/configs/network_los.yaml b/activitysim/examples/prototype_arc/configs/network_los.yaml index 6c851bbbe..fca9b4621 100644 --- a/activitysim/examples/prototype_arc/configs/network_los.yaml +++ b/activitysim/examples/prototype_arc/configs/network_los.yaml @@ -4,9 +4,31 @@ read_skim_cache: False write_skim_cache: False zone_system: 1 +name: prototype_arc # glob 'skims*.omx' will match one or more files: skims.omx, skims1.omx, skims2.omx... -taz_skims: skims*.omx +taz_skims: + omx: skims*.omx + zarr: skims.zarr + zarr-digital-encoding: + - {regex: .*_IVT , joint_dict: true} + - {regex: .*_DISTANCE , joint_dict: true} + - {regex: .*_TIME , joint_dict: true} + - {regex: .*_WALK , joint_dict: true} + - {regex: .*_FAR , joint_dict: true} + - {regex: .*_LRT , joint_dict: true} + - {regex: .*_TOLL , joint_dict: true} + - {regex: .*_XPEN , joint_dict: true} + - {regex: .*_HRT , joint_dict: true} + - {regex: .*_IWAIT , joint_dict: true} + - {regex: .*_LOCAL , joint_dict: true} + - {regex: .*_BRT , joint_dict: true} + - {regex: .*_XBUS , joint_dict: true} + - {regex: .*_COMRAIL , joint_dict: true} + - {regex: .*_BRDS , joint_dict: true} + - {regex: .*_XWAIT , joint_dict: true} + - {regex: .*_DIST , joint_dict: true} + - {regex: .*_AUTO , joint_dict: true} skim_time_periods: time_window: 1440 diff --git a/activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination.csv index 0f3c647c1..fd1e6bf9e 100644 --- a/activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination.csv +++ b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination.csv @@ -1,6 +1,6 @@ Description,Expression,escort,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr Mode choice logsum,mode_choice_logsum,1,1,1,coef_shopping_mode_logsum,coef_eatout_mode_logsum,coef_othmaint_mode_logsum,1,1 -Intrazonal,@(df['zone_id']==df['home_zone_id']),0,0,0,0,0,0,0,0 +#Intrazonal,@(df['zone_id']==df['home_zone_id']),0,0,0,0,0,0,0,0 CBD area type,@df['is_CBD'],0,0,0,coef_shopping_cbd,coef_eatout_cbd,coef_othmaint_cbd,coef_social_cbd,0 Urban high area type,@df['is_urban'],0,0,0,coef_shopping_urban,coef_eatout_urban,coef_othmaint_urban,coef_social_urban,coef_othdiscr_urban "# CTRAMP expression has two 'Distance' coefficients. For example, 0.2553 and 0.0100 for Escort, so used their sum here",,,,,,,,, @@ -8,7 +8,7 @@ Distance,"@skims[('SOV_FREE_DISTANCE', 'MD')]",coef_escort_distance,coef_escortk Distance squared,"@(skims[('SOV_FREE_DISTANCE', 'MD')] ** 2)",coef_escort_distance_squared,coef_escortkids_distance_squared,coef_escortnokids_distance_squared,coef_shopping_distance_squared,coef_eatout_distance_squared,coef_othmaint_distance_squared,coef_social_distance_squared,coef_othdiscr_distance_squared Distance cubed,"@(skims[('SOV_FREE_DISTANCE', 'MD')] ** 3)",coef_escort_distance_cubed,coef_escortkids_distance_cubed,coef_escortnokids_distance_cubed,coef_shopping_distance_cubed,coef_eatout_distance_cubed,coef_othmaint_distance_cubed,coef_social_distance_cubed,coef_othdiscr_distance_cubed Log of distance,"@np.log(skims[('SOV_FREE_DISTANCE', 'MD')])",coef_escort_log_distance,coef_escortkids_log_distance,coef_escortnokids_log_distance,coef_shopping_log_distance,coef_eatout_log_distance,coef_othmaint_log_distance,coef_social_log_distance,coef_othdiscr_log_distance -Distance - high income,"@((df['income_in_thousands']>50) & skims[('SOV_FREE_DISTANCE', 'MD')])",0,0,0,0,coef_eatout_distance_high_income,0,0,0 +Distance - high income,"@((df['income_in_thousands']>50) * skims[('SOV_FREE_DISTANCE', 'MD')])",0,0,0,0,coef_eatout_distance_high_income,0,0,0 Distance - non-driving age student in hh,"@np.where(df['num_nondriving_age_children']>0, skims[('SOV_FREE_DISTANCE', 'MD')], 0)",coef_escort_distance_nondrive,coef_escortkids_distance_nondrive,coef_escortnokids_distance_nondrive,coef_shopping_distance_nondrive,0,0,coef_social_distance_nondrive,coef_othdiscr_distance_nondrive Distance bin 0 to 1,"@((skims[('SOV_FREE_DISTANCE', 'MD')]>=0) & (skims[('SOV_FREE_DISTANCE', 'MD')]<1))",0,0,0,coef_shopping_dist_0_1,coef_eatout_dist_0_1,coef_othmaint_dist_0_1,0,coef_othdiscr_dist_0_1 Distance bin 1 to 2,"@((skims[('SOV_FREE_DISTANCE', 'MD')]>=1) & (skims[('SOV_FREE_DISTANCE', 'MD')]<2))",0,0,0,coef_shopping_dist_1_2,coef_eatout_dist_1_2,coef_othmaint_dist_1_2,0,coef_othdiscr_dist_1_2 diff --git a/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency.csv index 36da94ff9..2056cec3d 100644 --- a/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency.csv @@ -139,8 +139,8 @@ util_presence_of_university_student_and_maintenance_tour,Dummy for Presence of U util_presence_of_driving_school_kid_and_maintenance_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour util_presence_of_pre_driving_school_kid_and_maintenance_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour util_presence_of_pre_school_kid_and_maintenance_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour -util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour -util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour +#util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour +#util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour util_presence_of_full_time_worker_and_eating_out_tour,Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour util_presence_of_part_time_worker_and_eating_out_tour,Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour util_presence_of_non_worker_and_eating_out_tour,Dummy for Presence of Non-Worker (other than modeled person) & Eating Out tour ,has_non_worker * eatout,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour diff --git a/activitysim/examples/prototype_arc/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_scheduling.yaml index d89ad8b33..974b11991 100644 --- a/activitysim/examples/prototype_arc/configs/non_mandatory_tour_scheduling.yaml +++ b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_scheduling.yaml @@ -1,3 +1,12 @@ +sharrow_skip: true +# The `sharrow_skip` option is activated to disable the use of sharrow for this +# model, because the utility specification as currently written includes a +# large number of string comparisons (nearly every one of 300+ expressions has +# such an operation). Sharrow can process these expressions correctly, but it is +# slow. If the specification is rewritten to remove most of these string +# comparisons (see for example in the equivalent file for the prototype_mtc +# models) then sharrow can be re-enabled for a significant speed benefit. + SPEC: tour_scheduling_nonmandatory.csv COEFFICIENTS: tour_scheduling_nonmandatory_coeffs.csv @@ -9,9 +18,9 @@ preprocessor: TABLES: - land_use - joint_tour_participants - + SIMULATE_CHOOSER_COLUMNS: - + - age - is_female - adult @@ -31,4 +40,3 @@ SIMULATE_CHOOSER_COLUMNS: - num_non_escort_tours - num_add_shop_maint_tours - num_add_soc_discr_tours - diff --git a/activitysim/examples/prototype_arc/configs/settings.yaml b/activitysim/examples/prototype_arc/configs/settings.yaml index 5dea2f633..aa1c297ac 100644 --- a/activitysim/examples/prototype_arc/configs/settings.yaml +++ b/activitysim/examples/prototype_arc/configs/settings.yaml @@ -5,6 +5,8 @@ input_table_list: # The index column is set before keep_columns, # so don't put index in keep columns index_col: household_id + recode_columns: + home_zone_id: land_use.zone_id rename_columns: maz: home_zone_id np: hhsize @@ -39,6 +41,8 @@ input_table_list: # The index column is set before keep_columns, # so don't put index in keep columns index_col: zone_id + recode_columns: + zone_id: zero-based keep_columns: - retail - service @@ -130,8 +134,8 @@ models: - track_skim_usage - write_trip_matrices - write_tables - -# resume_after: + +# resume_after: multiprocess: False fail_fast: True @@ -165,15 +169,29 @@ output_tables: tables: - checkpoints - accessibility - - land_use - - households - - persons - - tours - - trips + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id - joint_tour_participants - + #trace_od: [100,10] -trace_hh_id: 88 +trace_hh_id: # trace_hh_id: 2 #trace_hh_id: 1643904 @@ -181,3 +199,5 @@ min_value_of_time: 1 max_value_of_time: 50 distributed_vot_mu: 0.684 distributed_vot_sigma: 0.85 + +checkpoints: true diff --git a/activitysim/examples/prototype_arc/configs/settings_mp.yaml b/activitysim/examples/prototype_arc/configs/settings_mp.yaml index 103c07bbb..5701b4029 100644 --- a/activitysim/examples/prototype_arc/configs/settings_mp.yaml +++ b/activitysim/examples/prototype_arc/configs/settings_mp.yaml @@ -5,6 +5,8 @@ input_table_list: # The index column is set before keep_columns, # so don't put index in keep columns index_col: household_id + recode_columns: + home_zone_id: land_use.zone_id rename_columns: maz: home_zone_id np: hhsize @@ -39,6 +41,8 @@ input_table_list: # The index column is set before keep_columns, # so don't put index in keep columns index_col: zone_id + recode_columns: + zone_id: zero-based keep_columns: - retail - service @@ -170,11 +174,25 @@ output_tables: tables: - checkpoints - accessibility - - land_use - - households - - persons - - tours - - trips + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id - joint_tour_participants diff --git a/activitysim/examples/prototype_arc/configs/tour_mode_choice.csv b/activitysim/examples/prototype_arc/configs/tour_mode_choice.csv index 9aee67ee9..2091f5c09 100644 --- a/activitysim/examples/prototype_arc/configs/tour_mode_choice.csv +++ b/activitysim/examples/prototype_arc/configs/tour_mode_choice.csv @@ -254,8 +254,8 @@ SCHOOLBUS - Grade school pre-driver,"@np.where(df.is_school, (coef_schBus_PreDri SCHOOLBUS - Grade school driver,"@np.where(df.is_school, coef_schBus_Driver * df.is_driving_age, 0)",,,,,,,,,,,,,,,1 SCHOOLBUS - Rural/exurban destination,"@np.where(df.is_school, coef_schBus_Rural * df.is_rural_or_exurban, 0)",,,,,,,,,,,,,,,1 SCHOOLBUS - o/d distance greater than 10 miles,"@np.where(df.is_school, coef_schBus_odDistgre10Mile * (odt_skims['SOV_FREE_DISTANCE']>10), 0)",,,,,,,,,,,,,,,1 -SCHOOLBUS - Depart from home before 8 am (downstream model),"@np.where(df.is_school, todOut<2 , 0)",,,,,,,,,,,,,,, -SCHOOLBUS - Return to home before 5 pm (downstream model),"@np.where(df.is_school, todIn<6, 0)",,,,,,,,,,,,,,, +#SCHOOLBUS - Depart from home before 8 am (downstream model),"@np.where(df.is_school, todOut<2 , 0)",,,,,,,,,,,,,,, +#SCHOOLBUS - Return to home before 5 pm (downstream model),"@np.where(df.is_school, todIn<6, 0)",,,,,,,,,,,,,,, SCHOOLBUS - In-vehicle time (ERROR-all probabilities are zero for 1204 rows),"@np.where(df.is_school, coef_ivt*(odt_skims['SOV_FREE_DISTANCE']+dot_skims['SOV_FREE_DISTANCE'])*60/schoolBusSpeed, 0)",,,,,,,,,,,,,,,1 SCHOOLBUS - Out-of-vehicle time,"@np.where(df.is_school, coef_ovt*schoolBusOvt, 0)",,,,,,,,,,,,,,,1 #,,,,,,,,,,,,,,,, diff --git a/activitysim/examples/prototype_arc/configs/tour_scheduling_joint.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_joint.csv index ed7609cc6..16b284cc3 100644 --- a/activitysim/examples/prototype_arc/configs/tour_scheduling_joint.csv +++ b/activitysim/examples/prototype_arc/configs/tour_scheduling_joint.csv @@ -68,7 +68,7 @@ ESCORT - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_ty ESCORT - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==4)),-1.071871358 ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>4)),-1.691098421 ESCORT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),-0.047200214 -ESCORT - Calibration Constant - Duration = 2,@(((df.tour_category == 'joint') & (df.tour_type == 'escort') & df.duration==1)),0.035611332 +ESCORT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==1)),0.035611332 ESCORT - Calibration Constant - Departure = 9,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),0.106814756 ESCORT - Calibration Constant - Departure = 10,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),0.215386864 ESCORT - Calibration Constant - Departure = 23,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),-0.255087318 @@ -111,7 +111,7 @@ SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),-0.309421311 SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),-0.541073357 SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",-0.072013428 -SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",-0.000653398 +SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)).astype(np.float32) ** 2), 0)",-0.000653398 SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",-0.18376635 SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),-0.716195343 SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),-0.502714001 @@ -176,7 +176,7 @@ MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'jo MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),-0.427568963 MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),-0.520863411 MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",0.042879095 -MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",-0.003157293 +MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)).astype(np.float32) ** 2), 0)",-0.003157293 MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",-0.226803619 MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),-0.223212258 MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),0 @@ -366,7 +366,9 @@ DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - DISCRETIONARY - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",-0.559947083 DISCRETIONARY - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",-0.115347031 DISCRETIONARY - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.number_of_participants > 2)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",0.104494637 -DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.origin_to_destination_distance)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",-0.162965435 +# FIXME? The following line might be incorrect, it has been modified to avoid a (int & float) operation +# originally: DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.origin_to_destination_distance)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",-0.162965435 +DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)) * (df.origin_to_destination_distance), 0)",-0.162965435 DISCRETIONARY - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",0.006797399 #The expressions below are for individual tours,, #DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",-0.229264474 diff --git a/activitysim/examples/prototype_arc/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_nonmandatory.csv index 9749a6303..e7c209951 100644 --- a/activitysim/examples/prototype_arc/configs/tour_scheduling_nonmandatory.csv +++ b/activitysim/examples/prototype_arc/configs/tour_scheduling_nonmandatory.csv @@ -69,7 +69,7 @@ util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5h util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_calibration_constant_duration_2 util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 @@ -103,7 +103,7 @@ util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)).astype(np.float32) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm @@ -159,7 +159,7 @@ util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Co util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)).astype(np.float32) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am @@ -302,7 +302,7 @@ util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_li util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear "# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3) & (df.origin_to_destination_distance)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear +util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') * (df.duration<3) * (df.origin_to_destination_distance)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr diff --git a/activitysim/examples/prototype_arc/configs/tour_scheduling_school.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_school.csv index b50c83338..63b3f3fb3 100644 --- a/activitysim/examples/prototype_arc/configs/tour_scheduling_school.csv +++ b/activitysim/examples/prototype_arc/configs/tour_scheduling_school.csv @@ -57,4 +57,4 @@ SCHOOL - Duration Constant: 8.5 hours,@(df.duration==17),-0.139412248 SCHOOL - Duration Constant: 9 hours,@(df.duration==18),-0.509620713 SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),-0.561449384 SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * ((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",0.379484906 -SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * (((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19))) ** 2)",-0.028814477 +SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * (((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19))).astype(np.float32) ** 2)",-0.028814477 diff --git a/activitysim/examples/prototype_arc/configs/trip_destination.csv b/activitysim/examples/prototype_arc/configs/trip_destination.csv index dc92ce84e..6ca461e9c 100644 --- a/activitysim/examples/prototype_arc/configs/trip_destination.csv +++ b/activitysim/examples/prototype_arc/configs/trip_destination.csv @@ -8,14 +8,12 @@ All - Tour origin zone,@(df.origin == df.dest_zone_id),0.7112,0.7112,0.7112,0.71 All - Tour destinatoin zone,@(df.destination == df.dest_zone_id),1.037,1.037,1.037,1.037,1.037,1.037,1.037,1.037,1.037,1.037 "# dest_zone_id is not available in the preprocessor file and reindex is not available here, so the expression was commented out. Similar expression was commented out in the ActivitySim example file as well. ",,,,,,,,,,, # All - CBD area type,"@reindex(land_use.areatype, df.dest_zone_id) == 1",-0.285,-0.285,-0.285,-0.285,-0.285,-0.285,-0.285,-0.285,-0.285,-0.285 -All - Size variable ,"@np.log1p(size_terms.get(df.dest_zone_id, df.purpose))",1,1,1,1,1,1,1,1,1,1 -All - No attractions,"@size_terms.get(df.dest_zone_id, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +All - Size variable ,"@np.log1p(size_terms.get(df.dest_zone_id, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 +All - No attractions,"@size_terms.get(df.dest_zone_id, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #Applied the conventions used in the destination sample file,,,,,,,,,,, ,"_org_to_stop_dist@od_skims[('SOV_FREE_DISTANCE', 'MD')]",1,1,1,1,1,1,1,1,1,1 ,"_stop_to_dest_dist@dp_skims[('SOV_FREE_DISTANCE', 'MD')]",1,1,1,1,1,1,1,1,1,1 -# THIS IS WRONG ***** RIGHT HERE **** I CHANGED THE BELOW TO MAKE WORK MECHANICALLY ,,,,,,,,,,, -# ,"_od_distance@op_skims[('SOV_FREE_DISTANCE', 'MD')]",1,1,1,1,1,1,1,1,1,1 -,"_od_distance@od_skims[('SOV_FREE_DISTANCE', 'MD')]",1,1,1,1,1,1,1,1,1,1 +,"_od_distance@op_skims[('SOV_FREE_DISTANCE', 'MD')]",1,1,1,1,1,1,1,1,1,1 #WORK,,,,,,,,,,, WORK - Distance squared,"@((od_skims[('SOV_FREE_DISTANCE', 'MD')] + dp_skims[('SOV_FREE_DISTANCE', 'MD')]) ** 2)",0.0026,,,,,,,,, WORK - Distance cubed,"@((od_skims[('SOV_FREE_DISTANCE', 'MD')] + dp_skims[('SOV_FREE_DISTANCE', 'MD')]) ** 3)",-0.0000109,,,,,,,,, diff --git a/activitysim/examples/prototype_arc/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_arc/configs/trip_destination_annotate_trips_preprocessor.csv index e94b9ea22..c740b72fd 100644 --- a/activitysim/examples/prototype_arc/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_arc/configs/trip_destination_annotate_trips_preprocessor.csv @@ -10,4 +10,5 @@ Description,Target,Expression #,,not needed as school is not chosen as an intermediate trip destination #,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" #,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" -,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" \ No newline at end of file +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" +,purpose_index_num,"size_terms.get_cols(df.purpose)" \ No newline at end of file diff --git a/activitysim/examples/prototype_arc/configs/trip_destination_sample.csv b/activitysim/examples/prototype_arc/configs/trip_destination_sample.csv index 63b0031be..c41acb946 100644 --- a/activitysim/examples/prototype_arc/configs/trip_destination_sample.csv +++ b/activitysim/examples/prototype_arc/configs/trip_destination_sample.csv @@ -13,5 +13,5 @@ not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (odt_skims['SOV_FREE_DISTANCE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dpt_skims['SOV_FREE_DISTANCE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 "stop not available if tourMode is transit, and stop isn't walkable or accessible by transit","@np.where((((df.tour_mode=='WALK_ALLTRN')|(df.tour_mode=='WALK_PRMTRN')) & (((_od_AllIVT > 0) & (_od_DIST > 3) & (_dp_AllIVT > 0) & (_dp_DIST > 3)) | ((_od_AllIVT > 0) & (_od_DIST > 3) & (_dp_AllIVT > 0) & (_dp_DIST > 3)))), 1, 0)",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Size variable ,"@np.log1p(size_terms.get(df.dest_zone_id, df.purpose))",1,1,1,1,1,1,1,1,1,1 -No attractions,"@np.where(size_terms.get(df.dest_zone_id, df.purpose) == 0, 1, 0)",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Size variable ,"@np.log1p(size_terms.get(df.dest_zone_id, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 +No attractions,"@np.where(size_terms.get(df.dest_zone_id, df.purpose) == 0, 1, 0) # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 diff --git a/activitysim/examples/prototype_arc/configs/trip_mode_choice.csv b/activitysim/examples/prototype_arc/configs/trip_mode_choice.csv index b098d95bc..13a5db2f7 100644 --- a/activitysim/examples/prototype_arc/configs/trip_mode_choice.csv +++ b/activitysim/examples/prototype_arc/configs/trip_mode_choice.csv @@ -178,8 +178,8 @@ KNRPRM Inbound - Drive time,@df.inbound * c_knrTime * (dot_skims['WLK_PRMTRN_KNR KNRPRM Inbound - Cost,@df.inbound * df.c_cost*(dot_skims['WLK_PRMTRN_KNR_FARE']*100+(dot_skims['WLK_PRMTRN_KNR_AUTO']*20/60*costPerMile))*(df.knr_prmTransit_available_in) * (c_filter_atwrk == 0),,,,,,,,,,,,,,1, #,,,,,,,,,,,,,,,, #SCHOOL,,,,,,,,,,,,,,,, -SCHOOL - Grade school pre-driver,"@np.where(df.school_mode_available & df.age<16, 1, 0) * 2.9620 * (c_filter_atwrk == 0) * (c_filter_sch == 1)",,,,,,,,,,,,,,,1 -SCHOOL - Grade school driver,"@np.where(df.school_mode_available & df.age>15, 1, 0) * 0.1473 * (c_filter_atwrk == 0) * (c_filter_sch == 1)",,,,,,,,,,,,,,,1 +SCHOOL - Grade school pre-driver,"@np.where(df.school_mode_available & (df.age<16), 1, 0) * 2.9620 * (c_filter_atwrk == 0) * (c_filter_sch == 1)",,,,,,,,,,,,,,,1 +SCHOOL - Grade school driver,"@np.where(df.school_mode_available & (df.age>15), 1, 0) * 0.1473 * (c_filter_atwrk == 0) * (c_filter_sch == 1)",,,,,,,,,,,,,,,1 SCHOOL - Rural/exurban destination,"@np.where(df.school_mode_available & df.ruralDestination, 1, 0) * 1.3400 * (c_filter_atwrk == 0) * (c_filter_sch == 1)",,,,,,,,,,,,,,,1 SCHOOL - O/d distance greater than 10 miles,"@np.where((df.school_mode_available) & (odt_skims['SOV_FREE_DISTANCE']>10) , 1, 0) * (-2.372) * (c_filter_atwrk == 0) * (c_filter_sch == 1)",,,,,,,,,,,,,,,1 SCHOOL - Depart from home before 8 am,"@np.where((df.school_mode_available) & (df.time_out_school) , 1, 0) * 0.5960 * (c_filter_atwrk == 0) * (c_filter_sch == 1)",,,,,,,,,,,,,,,1 diff --git a/activitysim/examples/prototype_arc/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_arc/configs/trip_mode_choice_annotate_trips_preprocessor.csv index 3e55f253f..8bab206a5 100644 --- a/activitysim/examples/prototype_arc/configs/trip_mode_choice_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_arc/configs/trip_mode_choice_annotate_trips_preprocessor.csv @@ -13,8 +13,8 @@ free parking availability,free_parking_available,(df.tour_type == 'work') & df.f #cost coefficients,, ,c_cost,(c_cost_low * df.is_low_income)+(c_cost_med * df.is_medium_income)+(c_cost_high * df.is_high_income)+(c_cost_high * df.is_very_high_income) #,, -is_morning_period,_out_period,"(df.trip_period.isin(['EA','AM','MD']))" -is_afternoon_period,_in_period,"(df.trip_period.isin(['MD','PM']))" +is_morning_period,_out_period,"(df.trip_period.isin(['EA','AM','MD',0,1,2]))" +is_afternoon_period,_in_period,"(df.trip_period.isin(['MD','PM',2,3]))" #,, WLKALL transit in-vehicle time - outbound direction,walk_allTransit_ivt,odt_skims['WLK_ALLTRN_WLK_LOCAL']+expr*odt_skims['WLK_ALLTRN_WLK_XBUS']+hrt*odt_skims['WLK_ALLTRN_WLK_HRT']+brt*odt_skims['WLK_ALLTRN_WLK_BRT']+lrt*odt_skims['WLK_ALLTRN_WLK_LRT']+crl*odt_skims['WLK_ALLTRN_WLK_COMRAIL'] WLKPRM transit in-vehicle time - outbound direction,walk_prmTransit_ivt,odt_skims['WLK_PRMTRN_WLK_LOCAL']+expr*odt_skims['WLK_PRMTRN_WLK_XBUS']+hrt*odt_skims['WLK_PRMTRN_WLK_HRT']+brt*odt_skims['WLK_PRMTRN_WLK_BRT']+lrt*odt_skims['WLK_PRMTRN_WLK_LRT']+crl*odt_skims['WLK_PRMTRN_WLK_COMRAIL'] diff --git a/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.csv b/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.csv index 2933ca495..7516a038e 100644 --- a/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.csv +++ b/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.csv @@ -6,7 +6,7 @@ Discretionary tour-ASC for Legtime = 0,@(df['main_leg_duration'] == 0) & (df['to "Discretionary tour,ASC for Legtime = 3",@(df['main_leg_duration'] == 3) & (df['tour_type']=='othdiscr'),-1.0313 "Discretionary tour,ASC for Legtime = 4",@(df['main_leg_duration'] == 4) & (df['tour_type']=='othdiscr'),-0.46489 Discretionary tour - Main Leg time,"@np.where(df['tour_type']=='othdiscr', df['main_leg_duration'], 0)",0.060382 -Eatout tour - Shift,"@np.where(df['tour_type']=='eatout', np.where(df['main_leg_duration']> 4.5, (df['main_leg_duration'] - 4.5), (4.5 - df['main_leg_duration'])), 0)",-0.7508 +Eatout tour - Shift,"@np.where(df['tour_type']=='eatout', np.absolute(df['main_leg_duration'] - 4.5), 0)",-0.7508 Eatout tour - Main leg time,"@np.where(df['tour_type']=='eatout', df['main_leg_duration'], 0)",0.53247 "Maintenance tour,ASC for Legtime = 0",@(df['main_leg_duration'] == 0) & (df['tour_type']=='othmaint'),-3.6079 "Maintenance tour,ASC for Legtime = 1",@(df['main_leg_duration'] == 1) & (df['tour_type']=='othmaint'),-1.9376 @@ -78,8 +78,8 @@ Calibration,@(df['main_leg_duration'] == 0) & (df['tour_type']=='shopping'),-2.0 Calibration,@(df['main_leg_duration'] == 1) & (df['tour_type']=='shopping'),-1.0205 Calibration,@(df['main_leg_duration'] == 2) & (df['tour_type']=='shopping'),-0.0582 Calibration,@(df['main_leg_duration'] == 3) & (df['tour_type']=='shopping'),0.5533 -Calibration,@(df['main_leg_duration'] == 0) & (df['tour_type']=='eatout'),-100 -Calibration,@(df['main_leg_duration'] == 1) & (df['tour_type']=='eatout'),-100 +Calibration,@(df['main_leg_duration'] == 0) & (df['tour_type']=='eatout'),-50 +Calibration,@(df['main_leg_duration'] == 1) & (df['tour_type']=='eatout'),-50 Calibration,@(df['main_leg_duration'] == 2) & (df['tour_type']=='eatout'),-6.8372 Calibration,@(df['main_leg_duration'] == 3) & (df['tour_type']=='eatout'),-0.3319 Calibration,@(df['main_leg_duration'] == 4) & (df['tour_type']=='eatout'),0.8709 diff --git a/activitysim/examples/prototype_arc/output/cache/.gitignore b/activitysim/examples/prototype_arc/output/cache/.gitignore index 3dd2e62f9..1d085cacc 100644 --- a/activitysim/examples/prototype_arc/output/cache/.gitignore +++ b/activitysim/examples/prototype_arc/output/cache/.gitignore @@ -1,2 +1 @@ -*.mmap -*.feather +** diff --git a/activitysim/examples/prototype_arc/test/configs/settings.yaml b/activitysim/examples/prototype_arc/test/configs/settings.yaml index d2b21a346..52e164345 100644 --- a/activitysim/examples/prototype_arc/test/configs/settings.yaml +++ b/activitysim/examples/prototype_arc/test/configs/settings.yaml @@ -38,3 +38,5 @@ output_tables: sort: True tables: - trips + +recode_pipeline_columns: False diff --git a/activitysim/examples/prototype_arc/test/configs_recode/network_los.yaml b/activitysim/examples/prototype_arc/test/configs_recode/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/prototype_arc/test/configs_recode/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/prototype_arc/test/configs_recode/settings.yaml b/activitysim/examples/prototype_arc/test/configs_recode/settings.yaml new file mode 100644 index 000000000..7e9d6f92a --- /dev/null +++ b/activitysim/examples/prototype_arc/test/configs_recode/settings.yaml @@ -0,0 +1,30 @@ +inherit_settings: True + +# EXAMPLE_HAS_WARNINGS +strict: False + +# number of households to simulate +households_sample_size: 10 +chunk_size: 0 + +# - shadow pricing global switches +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +recode_pipeline_columns: True diff --git a/activitysim/examples/prototype_arc/test/configs_sharrow/network_los.yaml b/activitysim/examples/prototype_arc/test/configs_sharrow/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/prototype_arc/test/configs_sharrow/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/prototype_arc/test/configs_sharrow/settings.yaml b/activitysim/examples/prototype_arc/test/configs_sharrow/settings.yaml new file mode 100644 index 000000000..ad0541e5f --- /dev/null +++ b/activitysim/examples/prototype_arc/test/configs_sharrow/settings.yaml @@ -0,0 +1,31 @@ +inherit_settings: True + +# EXAMPLE_HAS_WARNINGS +strict: False + +# number of households to simulate +households_sample_size: 10 +chunk_size: 0 + +# - shadow pricing global switches +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +sharrow: require +recode_pipeline_columns: True diff --git a/activitysim/examples/prototype_arc/test/output/cache/.gitignore b/activitysim/examples/prototype_arc/test/output/cache/.gitignore index 3dd2e62f9..1d085cacc 100644 --- a/activitysim/examples/prototype_arc/test/output/cache/.gitignore +++ b/activitysim/examples/prototype_arc/test/output/cache/.gitignore @@ -1,2 +1 @@ -*.mmap -*.feather +** diff --git a/activitysim/examples/prototype_arc/test/regress/final_trips.csv b/activitysim/examples/prototype_arc/test/regress/final_trips.csv index 59d47d4b4..3cfe9e642 100644 --- a/activitysim/examples/prototype_arc/test/regress/final_trips.csv +++ b/activitysim/examples/prototype_arc/test/regress/final_trips.csv @@ -1,91 +1,91 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,tour_id,purpose,destination_logsum,depart,trip_mode,mode_choice_logsum,parking_zone_id -37314161,113762,42730,othmaint,1,True,1,106,103,4664270,othmaint,,10,DRIVEALONEFREE,-0.3567815833091734,-1 -37314165,113762,42730,othmaint,1,False,1,103,106,4664270,home,,14,DRIVEALONEFREE,-0.3564603142738344,-1 -38194977,116448,43843,atwork,1,True,1,106,101,4774372,atwork,,20,DRIVEALONEFREE,-0.3217517094135284,-1 -38194981,116448,43843,atwork,1,False,1,101,106,4774372,work,,21,DRIVEALONEFREE,-0.3217517094135284,-1 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+56358805,171825,63802,othdiscr,1,False,2,132,131,7044850,social,10.225653,35,SHARED2FREE,-0.20277185632585107,-1 +56358806,171825,63802,othdiscr,2,False,2,135,132,7044850,home,,39,SHARED3FREE,-0.36521793162300004,-1 +56358809,171825,63802,othdiscr,1,True,4,135,135,7044851,othmaint,5.3795877,26,WALK,-0.7460585832595825,-1 +56358810,171825,63802,othdiscr,2,True,4,131,135,7044851,othmaint,5.4266872,27,WALK,-2.0398435592651363,-1 +56358811,171825,63802,othdiscr,3,True,4,130,131,7044851,othmaint,5.7105064,28,WALK,-1.2828608751297,-1 +56358812,171825,63802,othdiscr,4,True,4,130,130,7044851,othdiscr,,28,WALK,-0.78075897693634,-1 +56358813,171825,63802,othdiscr,1,False,1,135,130,7044851,home,,28,WALK,-1.4660019874572756,-1 +56358849,171825,63802,school,1,True,1,135,135,7044856,school,,9,SHARED3FREE,0.10569338088788001,-1 +56358853,171825,63802,school,1,False,1,135,135,7044856,home,,24,SHARED3FREE,0.10569338088788001,-1 +56359177,171826,63802,school,1,True,1,135,135,7044897,school,,10,SHARED3FREE,0.10569338088788001,-1 +56359181,171826,63802,school,1,False,1,135,135,7044897,home,,22,SHARED3FREE,0.10569338088788001,-1 diff --git a/activitysim/examples/prototype_arc/test/test_arc.py b/activitysim/examples/prototype_arc/test/test_arc.py index 5f0c9baaf..5b3513249 100644 --- a/activitysim/examples/prototype_arc/test/test_arc.py +++ b/activitysim/examples/prototype_arc/test/test_arc.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import os import subprocess +import sys import pandas as pd import pandas.testing as pdt @@ -15,7 +16,7 @@ def teardown_function(func): inject.reinject_decorated_tables() -def test_arc(): +def _test_arc(recode=False, sharrow=False): def example_path(dirname): resource = os.path.join("examples", "prototype_arc", dirname) return pkg_resources.resource_filename("activitysim", resource) @@ -24,7 +25,11 @@ def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) def regress(): - regress_trips_df = pd.read_csv(test_path("regress/final_trips.csv")) + if sharrow: + # sharrow results in tiny changes (one trip moving one time period earlier) + regress_trips_df = pd.read_csv(test_path("regress/final_trips_sh.csv")) + else: + regress_trips_df = pd.read_csv(test_path("regress/final_trips.csv")) final_trips_df = pd.read_csv(test_path("output/final_trips.csv")) # person_id,household_id,tour_id,primary_purpose,trip_num,outbound,trip_count,purpose, @@ -34,12 +39,30 @@ def regress(): file_path = os.path.join(os.path.dirname(__file__), "simulation.py") - subprocess.run( - [ - "coverage", - "run", - "-a", - file_path, + if recode: + run_args = [ + "-c", + test_path("configs_recode"), + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] + elif sharrow: + run_args = [ + "-c", + test_path("configs_sharrow"), + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] + else: + run_args = [ "-c", test_path("configs"), "-c", @@ -48,13 +71,30 @@ def regress(): example_path("data"), "-o", test_path("output"), - ], - check=True, - ) + ] + + if os.environ.get("GITHUB_ACTIONS") == "true": + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + else: + subprocess.run([sys.executable, file_path] + run_args, check=True) regress() +def test_arc(): + _test_arc() + + +def test_arc_recode(): + _test_arc(recode=True) + + +def test_arc_sharrow(): + _test_arc(sharrow=True) + + if __name__ == "__main__": - test_arc() + _test_arc() + _test_arc(recode=True) + _test_arc(sharrow=True) diff --git a/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency.csv b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency.csv index 36b1bf20a..2a319621e 100644 --- a/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency.csv +++ b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency.csv @@ -4,7 +4,7 @@ util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_ti util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business -util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business +util_zero_cars_owned_by_hh_dummy,auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business diff --git a/activitysim/examples/prototype_mtc/configs/data_dictionary.yaml b/activitysim/examples/prototype_mtc/configs/data_dictionary.yaml new file mode 100644 index 000000000..1fd9e670f --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs/data_dictionary.yaml @@ -0,0 +1,14 @@ + + +land_use: + county_id: + 1: San Francisco + 2: San Mateo + 3: Santa Clara + 4: Alameda + 5: Contra Costa + 6: Solano + 7: Napa + 8: Sonoma + 9: Marin + diff --git a/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/logging.yaml b/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/logging.yaml new file mode 100644 index 000000000..3d62e8064 --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/logging.yaml @@ -0,0 +1,54 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARNING + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + + formatters: + + simpleFormatter: + class: logging.Formatter + format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ + '%(processName)-10s %(levelname)s - %(name)s - %(message)s', + '%(levelname)s - %(name)s - %(message)s'] + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/network_los.yaml b/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/network_los.yaml new file mode 100644 index 000000000..ab3a7d5c6 --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/network_los.yaml @@ -0,0 +1,14 @@ +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +zone_system: 1 + +taz_skims: skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59 + labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/settings.yaml b/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/settings.yaml new file mode 100644 index 000000000..543992d83 --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/settings.yaml @@ -0,0 +1,245 @@ +# input tables +# +# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) +# as well as for created tables (tours, joint_tour_participants, trips) +# e.g. the households table must have an index column 'household_id' and the foreign key to households in the +# persons table is also household_id. This naming convention allows activitysim to intuit the relationship +# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips +# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by +# household_id. +# +# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id +# you cannot change the well-known name of the index by modifying this directive. However, if your input file +# has a different id column name, you can rename it to the required index name with the rename_columns directive. +# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. +# +input_table_list: + # + # households (table index 'household_id') + # + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + HHID: household_id # household_id is the required index column + PERSONS: hhsize + workers: num_workers + VEHICL: auto_ownership + TAZ: home_zone_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + # + # persons (table index 'person_id') + # + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + PERID: person_id # person_id is the required index column + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + # + # land_use (table index 'zone_id') + # + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + TAZ: zone_id # person_id is the required index column + COUNTY: county_id + keep_columns: + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL + +# convert input CSVs to HDF5 format and save to outputs directory +# create_input_store: True + +#input_store: ../output/input_data.h5 + +# number of households to simulate +households_sample_size: 100 +# simulate all households +# households_sample_size: 0 + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# log interaction simulate/sample expressions that return prohibitive utility values that exclude all alternatives +log_alt_losers: False + +# alternate dir to read/write cache (defaults to output_dir) +# used for skim cache, tvpb, and chunk_log caches +#cache_dir: data/cache + +############## +# +# chunking +# + +# chooser chunk size in gigabytes +# target top memory usage during activitysim run (including shared memory, loaded tables, and transient memory usage) +#chunk_size: 12_000_000_000 +chunk_size: 0 + +# minimum fraction of total chunk_size to reserve for adaptive chunking +min_available_chunk_ratio: 0.05 + +# initial number of chooser rows for first chunk in training mode +# when there is no pre-existing chunk_cache to set initial value +# ordinarily bigger is better as long as it is not so big it causes memory issues (e.g. accessibility with lots of zones) +default_initial_rows_per_chunk: 500 + +# method to calculate memory overhead when chunking is enabled +chunk_method: hybrid_uss + +# chunk training mode +# training to determine the chunking settings written to a cache file that is re-used for production runs +# training +# production +# disabled +# assume enough RAM to not chunk +chunk_training_mode: disabled + +# whether to preserve or delete subprocess chunk and mem logs when they are consolidated at end of multiprocess run +keep_chunk_logs: True +keep_mem_logs: True + +############## + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] +trace_hh_id: 982875 + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +# trace_od: [5, 11] +trace_od: + + + +# to resume after last successful checkpoint, specify resume_after: _ +#resume_after: trip_destination +resume_after: + +checkpoints: True +# if checkpoints is False, no intermediate checkpoints will be written before the end of run +# (or if multiprocessing, at the end of each multiprocess_step) +#checkpoints: False +# explicit list of models to checkpoint +#checkpoints: +# - mandatory_tour_scheduling +# - non_mandatory_tour_scheduling +# - trip_mode_choice + + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables + +output_tables: + h5_store: False + action: include + prefix: final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + +# - value of time +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 diff --git a/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv new file mode 100644 index 000000000..9db6a951d --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv @@ -0,0 +1,12 @@ +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" +#,,not needed as school is not chosen as an intermediate trip destination +#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" +#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" +,tour_mode_is_walk,"reindex(tours.tour_mode, df.tour_id)=='WALK'" +,tour_mode_is_bike,"reindex(tours.tour_mode, df.tour_id)=='BIKE'" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id)).astype(int)" diff --git a/activitysim/examples/prototype_mtc/configs/logging.yaml b/activitysim/examples/prototype_mtc/configs/logging.yaml index 43a70cb58..3b2851ddd 100644 --- a/activitysim/examples/prototype_mtc/configs/logging.yaml +++ b/activitysim/examples/prototype_mtc/configs/logging.yaml @@ -24,6 +24,18 @@ logging: handlers: [console, logfile] propagate: false + filelock: + level: WARN + + sharrow: + level: INFO + + blib2to3: + level: WARN + + black: + level: WARN + handlers: logfile: diff --git a/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency.csv b/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency.csv index 848bbf77a..51094ee82 100644 --- a/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency.csv @@ -25,10 +25,10 @@ util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5 util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 diff --git a/activitysim/examples/prototype_mtc/configs/network_los.yaml b/activitysim/examples/prototype_mtc/configs/network_los.yaml index eb5ce609f..2e0309aa8 100644 --- a/activitysim/examples/prototype_mtc/configs/network_los.yaml +++ b/activitysim/examples/prototype_mtc/configs/network_los.yaml @@ -4,8 +4,28 @@ read_skim_cache: False write_skim_cache: True zone_system: 1 +name: prototype_mtc -taz_skims: skims.omx +taz_skims: + omx: skims.omx + zarr: skims.zarr + zarr-digital-encoding: + - regex: ".*_BOARDS" + joint_dict: joined_board + - regex: ".*_FAR" + joint_dict: joined_fare + - regex: ".*_XWAIT" + joint_dict: joined_xwait + - regex: ".*_WAUX" + joint_dict: joined_waux + - regex: ".*_IWAIT" + joint_dict: joined_iwait + - regex: ".*_KEYIVT" + joint_dict: joined_keyivt + - regex: ".*_WAIT" + joint_dict: joined_wait + - regex: "DISTWALK|DISTBIKE" + joint_dict: joined_walkbike skim_time_periods: time_window: 1440 diff --git a/activitysim/examples/prototype_mtc/configs/settings.yaml b/activitysim/examples/prototype_mtc/configs/settings.yaml index 6b1258bcc..6577b94b9 100644 --- a/activitysim/examples/prototype_mtc/configs/settings.yaml +++ b/activitysim/examples/prototype_mtc/configs/settings.yaml @@ -26,6 +26,8 @@ input_table_list: workers: num_workers VEHICL: auto_ownership TAZ: home_zone_id + recode_columns: + home_zone_id: land_use.zone_id keep_columns: - home_zone_id - income @@ -58,6 +60,8 @@ input_table_list: rename_columns: TAZ: zone_id # person_id is the required index column COUNTY: county_id + recode_columns: + zone_id: zero-based keep_columns: - DISTRICT - SD @@ -90,7 +94,7 @@ input_table_list: #input_store: ../output/input_data.h5 # number of households to simulate -households_sample_size: 100 +households_sample_size: 100000 # simulate all households # households_sample_size: 0 @@ -155,7 +159,7 @@ keep_mem_logs: True # trace household id; comment out or leave empty for no trace # households with all tour types # [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] -trace_hh_id: 982875 +trace_hh_id: # trace origin, destination in accessibility calculation; comment out or leave empty for no trace # trace_od: [5, 11] @@ -221,11 +225,25 @@ output_tables: tables: - checkpoints - accessibility - - land_use - - households - - persons - - tours - - trips + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id - joint_tour_participants # area_types less than this are considered urban @@ -244,3 +262,4 @@ household_median_value_of_time: 2: 8.81 3: 10.44 4: 12.86 + diff --git a/activitysim/examples/prototype_mtc/configs/shadow_pricing.yaml b/activitysim/examples/prototype_mtc/configs/shadow_pricing.yaml index b61ec4192..89816475a 100644 --- a/activitysim/examples/prototype_mtc/configs/shadow_pricing.yaml +++ b/activitysim/examples/prototype_mtc/configs/shadow_pricing.yaml @@ -1,7 +1,7 @@ shadow_pricing_models: school: school_location workplace: workplace_location - + # global switch to enable/disable loading of saved shadow prices # (ignored if global use_shadow_pricing switch is False) LOAD_SAVED_SHADOW_PRICES: True @@ -32,3 +32,4 @@ DAMPING_FACTOR: 1 # FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? DAYSIM_ABSOLUTE_TOLERANCE: 50 DAYSIM_PERCENT_TOLERANCE: 10 + \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc/configs/tour_scheduling_joint.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_joint.csv index 3c116bc9a..e5d35053c 100644 --- a/activitysim/examples/prototype_mtc/configs/tour_scheduling_joint.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_joint.csv @@ -29,14 +29,14 @@ util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 1 #,Mode Choice Logsum,mode_choice_logsum, #,, #,,, FIXME - use temps as timetable ops can be very time-consuming -util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.tour_id, df.start)",coef_dummy -util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.tour_id, df.end)",coef_dummy +util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.tour_id, df.start)>0",coef_dummy +util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.tour_id, df.end)>0",coef_dummy util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.tour_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.tour_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour -util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) * (df.tour_type_num == 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) * (df.tour_type_num == 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction #,, util_departure_constants_early,Departure Constants -- Early (up to 5),"(tour_type != 'escort') & (start < 6)",coef_departure_constants_early util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),"(tour_type != 'escort') & (start == 6)",coef_departure_constants_am_peak_1 diff --git a/activitysim/examples/prototype_mtc/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_nonmandatory.csv index e81bddda6..a000c1dde 100644 --- a/activitysim/examples/prototype_mtc/configs/tour_scheduling_nonmandatory.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_nonmandatory.csv @@ -1,12 +1,17 @@ Label,Description,Expression,Coefficient +is_escort,is_escort,_is_escort@df.tour_type == 'escort',0.000000000000001 +is_not_escort,is_not_escort,_is_not_escort@df.tour_type != 'escort',0.000000000000001 +is_shopping,is_shopping,_is_shopping@df.tour_type == 'shopping',0.000000000000001 +is_othmaint,is_othmaint,_is_othmaint@df.tour_type == 'othmaint',0.000000000000001 +is_social,is_social,_is_social@df.tour_type == 'social',0.000000000000001 util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,Subsequent tour must start after previous tour for this purpose ends,(start < end_previous) & (tour_type_num > 1),coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration -util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,(tour_type == 'shopping') * start,coef_shopping_tour_departure_shift_effects -util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,(tour_type == 'shopping') * duration,coef_shopping_tour_duration_shift_effects -util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * start,coef_maintenance_tour_departure_shift_effects -util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * duration,coef_maintenance_tour_duration_shift_effects -util_visit_tour_departure_shift_effects_start,Visit tour - departure shift effects,(tour_type == 'social') * start,coef_visit_tour_departure_shift_effects -util_visit_tour_duration_shift_effects_duration,Visit tour - departure shift effects,(tour_type == 'social') * duration,coef_visit_tour_duration_shift_effects +util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,@(_is_shopping) * df.start,coef_shopping_tour_departure_shift_effects +util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,@(_is_shopping) * df.duration,coef_shopping_tour_duration_shift_effects +util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,@(_is_othmaint) * df.start,coef_maintenance_tour_departure_shift_effects +util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,@(_is_othmaint) * df.duration,coef_maintenance_tour_duration_shift_effects +util_visit_tour_departure_shift_effects_start,Visit tour - departure shift effects,@(_is_social) * df.start,coef_visit_tour_departure_shift_effects +util_visit_tour_duration_shift_effects_duration,Visit tour - departure shift effects,@(_is_social) * df.duration,coef_visit_tour_duration_shift_effects util_eat_out_tour_departure_shift_effects,Eat Out tour - departure shift effects,(tour_type == 'eatout') * start,coef_eat_out_tour_departure_shift_effects util_school_child_age_16_plus_departure_shift_effects,School child age 16+ - departure shift effects,(ptype == 6) * start,coef_school_child_age_16_plus_departure_shift_effects util_school_child_age_16_plus_duration_shift_effects,School child age 16+ - duration shift effects,(ptype == 6) * duration,coef_school_child_age_16_plus_duration_shift_effects @@ -19,78 +24,78 @@ util_number_of_escort_tours_departure_shift_effects,Number of escort tours - dep util_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,Number of idividual non-mandatory tours (excluding escort) - departure shift effects,num_non_escort_tours * start,coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects util_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,First of 2+ tours for same purpose - departure shift effect,((tour_type_count>1) & (tour_type_num == 1)) * start,coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect util_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,subsequent of 2+ tours for same purpose - duration shift effect,(tour_type_num > 1) * duration,coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect -util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,(tour_type == 'othmaint') & (start < 7),coef_maintenance_tour_depart_before_7 -util_shopping_tour_depart_before_8,Shopping tour - depart before 8,(tour_type == 'shopping') & (start < 8),coef_shopping_tour_depart_before_8 -util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,(tour_type == 'shopping') & (end > 22),coef_shopping_tour_arrive_after_22 +util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,@(_is_othmaint) * (df.start < 7),coef_maintenance_tour_depart_before_7 +util_shopping_tour_depart_before_8,Shopping tour - depart before 8,@(_is_shopping) * (df.start < 8),coef_shopping_tour_depart_before_8 +util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,@(_is_shopping) * (df.end > 22),coef_shopping_tour_arrive_after_22 util_school_child_under_16_arrive_after_22,School child under 16 - arrive after 22,(ptype == 7) & (end > 22),coef_school_child_under_16_arrive_after_22 util_university_student_arrive_after_22,University student - arrive after 22,(ptype == 3) & (end > 22),coef_university_student_arrive_after_22 -util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,(tour_type == 'shopping') & (duration < 2),coef_shopping_tour_duration_lt_2_hours +util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,@(_is_shopping) * (df.duration < 2),coef_shopping_tour_duration_lt_2_hours util_discretionary_tour_duration_lt_2_hours,Discretionary tour - duration < 2 hours,(tour_type == 'othdiscr') & (duration < 2),coef_discretionary_tour_duration_lt_2_hours util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 19 - 21,adult & (num_children > 0) & ( end > 18 ) & ( end < 22 ),coef_adult_with_children_in_hh_arrive_19_21 #,,, #,Mode Choice Logsum,mode_choice_logsum,#mode_choice_logsum #,,,# -util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy -util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy +util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)>0",coef_dummy +util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)>0",coef_dummy util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour -util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) * (df.tour_type_num == 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) * (df.tour_type_num == 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction util_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,Remaining individual non-mandatory tours to be scheduled / number of unscheduled hours,"@((1.0 + df.tour_count - df.tour_num)) / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours #,#,,# -util_departure_constants_early,Departure Constants -- Early (up to 5),(tour_type != 'escort') & (start < 6),coef_departure_constants_early -util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),(tour_type != 'escort') & (start == 6),coef_departure_constants_am_peak_1 -util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),(tour_type != 'escort') & (start == 7),coef_departure_constants_am_peak_2 -util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),(tour_type != 'escort') & (start == 8),coef_departure_constants_am_peak_3 -util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),(tour_type != 'escort') & (start == 9),coef_departure_constants_am_peak_4 -util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (start > 9) & (start < 13),coef_departure_constants_midday_1 -util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(tour_type != 'escort') & (start > 12) & (start < 16),coef_departure_constants_midday_2 -util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(tour_type != 'escort') & (start > 15) & (start < 19),coef_departure_constants_pm_peak -util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(tour_type != 'escort') & (start > 18) & (start < 22),coef_departure_constants_evening -util_departure_constants_late,Departure Constants -- Late (22 and later),(tour_type != 'escort') & (start > 21),coef_departure_constants_late -util_arrival_constants_early,Arrival Constants -- Early (up to 6),(tour_type != 'escort') & (end < 7),coef_arrival_constants_early -util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(tour_type != 'escort') & (end > 6) & (end < 10),coef_arrival_constants_am_peak -util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (end > 9) & (end < 13),coef_arrival_constants_midday_1 -util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(tour_type != 'escort') & (end > 12) & (end < 15),coef_arrival_constants_midday_2 -util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),(tour_type != 'escort') & (end == 15),coef_arrival_constants_pm_peak_1 -util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),(tour_type != 'escort') & (end == 16),coef_arrival_constants_pm_peak_2 -util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),(tour_type != 'escort') & (end == 17),coef_arrival_constants_pm_peak_3 -util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),(tour_type != 'escort') & (end == 18),coef_arrival_constants_pm_peak_4 -util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(tour_type != 'escort') & (end > 18) & (end < 22),coef_arrival_constants_evening -util_arrival_constants_late,Arrival Constants -- Late (22 and later),(tour_type != 'escort') & (end > 21),coef_arrival_constants_late -util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,(tour_type != 'escort') & (duration < 2),coef_duration_constants_0_to_1_hours -util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,(tour_type != 'escort') & (duration > 1) & (duration < 4),coef_duration_constants_2_to_3_hours -util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,(tour_type != 'escort') & (duration > 3) & (duration < 6),coef_duration_constants_4_to_5_hours -util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,(tour_type != 'escort') & (duration > 5) & (duration < 8),coef_duration_constants_6_to_7_hours -util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,(tour_type != 'escort') & (duration > 7) & (duration < 11),coef_duration_constants_8_to_10_hours -util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,(tour_type != 'escort') & (duration > 10) & (duration < 14),coef_duration_constants_11_to_13_hours -util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(tour_type != 'escort') & (duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours -util_escort_tour_departure_constants_early,Escort Tour Departure Constants -- Early (up to 5),(tour_type == 'escort') & (start < 6),coef_escort_tour_departure_constants_early -util_escort_tour_departure_constants_am_peak_1,Escort Tour Departure Constants -- AM peak 1 (6),(tour_type == 'escort') & (start == 6),coef_escort_tour_departure_constants_am_peak_1 -util_escort_tour_departure_constants_am_peak_2,Escort Tour Departure Constants -- AM peak 2 (7),(tour_type == 'escort') & (start == 7),coef_escort_tour_departure_constants_am_peak_2 -util_escort_tour_departure_constants_am_peak_3,Escort Tour Departure Constants -- AM peak 3 (8),(tour_type == 'escort') & (start == 8),coef_escort_tour_departure_constants_am_peak_3 -util_escort_tour_departure_constants_am_peak_4,Escort Tour Departure Constants -- AM peak 4 (9),(tour_type == 'escort') & (start == 9),coef_escort_tour_departure_constants_am_peak_4 -util_escort_tour_departure_constants_midday_1,Escort Tour Departure Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (start > 9) & (start < 13),coef_escort_tour_departure_constants_midday_1 -util_escort_tour_departure_constants_midday_2,Escort Tour Departure Constants -- Midday 2 (13 to 15),(tour_type == 'escort') & (start > 12) & (start < 16),coef_escort_tour_departure_constants_midday_2 -util_escort_tour_departure_constants_pm_peak,Escort Tour Departure Constants -- PM peak (16 to 18),(tour_type == 'escort') & (start > 15) & (start < 19),coef_escort_tour_departure_constants_pm_peak -util_escort_tour_departure_constants_evening,Escort Tour Departure Constants -- Evening (19 to 21),(tour_type == 'escort') & (start > 18) & (start < 22),coef_escort_tour_departure_constants_evening -util_escort_tour_departure_constants_late,Escort Tour Departure Constants -- Late (22 and later),(tour_type == 'escort') & (start > 21),coef_escort_tour_departure_constants_late -util_escort_tour_arrival_constants_early,Escort Tour Arrival Constants -- Early (up to 6),(tour_type == 'escort') & (end < 7),coef_escort_tour_arrival_constants_early -util_escort_tour_arrival_constants_am_peak,Escort Tour Arrival Constants -- AM peak (7 to 9),(tour_type == 'escort') & (end > 6) & (end < 10),coef_escort_tour_arrival_constants_am_peak -util_escort_tour_arrival_constants_midday_1,Escort Tour Arrival Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (end > 9) & (end < 13),coef_escort_tour_arrival_constants_midday_1 -util_escort_tour_arrival_constants_midday_2,Escort Tour Arrival Constants -- Midday 2 (13 to 14),(tour_type == 'escort') & (end > 12) & (end < 15),coef_escort_tour_arrival_constants_midday_2 -util_escort_tour_arrival_constants_pm_peak_1,Escort Tour Arrival Constants -- PM peak 1 (15),(tour_type == 'escort') & (end == 15),coef_escort_tour_arrival_constants_pm_peak_1 -util_escort_tour_arrival_constants_pm_peak_2,Escort Tour Arrival Constants -- PM peak 2 (16),(tour_type == 'escort') & (end == 16),coef_escort_tour_arrival_constants_pm_peak_2 -util_escort_tour_arrival_constants_pm_peak_3,Escort Tour Arrival Constants -- PM peak 3 (17),(tour_type == 'escort') & (end == 17),coef_escort_tour_arrival_constants_pm_peak_3 -util_escort_tour_arrival_constants_pm_peak_4,Escort Tour Arrival Constants -- PM peak 4 (18),(tour_type == 'escort') & (end == 18),coef_escort_tour_arrival_constants_pm_peak_4 -util_escort_tour_arrival_constants_evening,Escort Tour Arrival Constants -- Evening (19 to 21),(tour_type == 'escort') & (end > 18) & (end < 22),coef_escort_tour_arrival_constants_evening -util_escort_tour_arrival_constants_late,Escort Tour Arrival Constants -- Late (22 and later),(tour_type == 'escort') & (end > 21),coef_escort_tour_arrival_constants_late -util_escort_tour_duration_constants_0_to_1_hours,Escort Tour Duration Constants -- 0 to 1 hours,(tour_type == 'escort') & (duration < 2),coef_escort_tour_duration_constants_0_to_1_hours -util_escort_tour_duration_constants_2_to_3_hours,Escort Tour Duration Constants -- 2 to 3 hours,(tour_type == 'escort') & (duration > 1) & (duration < 4),coef_escort_tour_duration_constants_2_to_3_hours -util_escort_tour_duration_constants_4_to_5_hours,Escort Tour Duration Constants -- 4 to 5 hours,(tour_type == 'escort') & (duration > 3) & (duration < 6),coef_escort_tour_duration_constants_4_to_5_hours -util_escort_tour_duration_constants_6_to_7_hours,Escort Tour Duration Constants -- 6 to 7 hours,(tour_type == 'escort') & (duration > 5) & (duration < 8),coef_escort_tour_duration_constants_6_to_7_hours -util_escort_tour_duration_constants_8_to_10_hours,Escort Tour Duration Constants -- 8 to 10 hours,(tour_type == 'escort') & (duration > 7) & (duration < 11),coef_escort_tour_duration_constants_8_to_10_hours -util_escort_tour_duration_constants_11_to_13_hours,Escort Tour Duration Constants -- 11 to 13 hours,(tour_type == 'escort') & (duration > 10) & (duration < 14),coef_escort_tour_duration_constants_11_to_13_hours -util_escort_tour_duration_constants_14_to_18_hours,Escort Tour Duration Constants -- 14 to 18 hours,(tour_type == 'escort') & (duration > 13) & (duration < 19),coef_escort_tour_duration_constants_14_to_18_hours +util_departure_constants_early,Departure Constants -- Early (up to 5),@(_is_not_escort) * (df.start < 6),coef_departure_constants_early +util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),@(_is_not_escort) * (df.start == 6),coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),@(_is_not_escort) * (df.start == 7),coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),@(_is_not_escort) * (df.start == 8),coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),@(_is_not_escort) * (df.start == 9),coef_departure_constants_am_peak_4 +util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),@(_is_not_escort) * (df.start > 9) * (df.start < 13),coef_departure_constants_midday_1 +util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),@(_is_not_escort) * (df.start > 12) * (df.start < 16),coef_departure_constants_midday_2 +util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),@(_is_not_escort) * (df.start > 15) * (df.start < 19),coef_departure_constants_pm_peak +util_departure_constants_evening,Departure Constants -- Evening (19 to 21),@(_is_not_escort) * (df.start > 18) * (df.start < 22),coef_departure_constants_evening +util_departure_constants_late,Departure Constants -- Late (22 and later),@(_is_not_escort) * (df.start > 21),coef_departure_constants_late +util_arrival_constants_early,Arrival Constants -- Early (up to 6),@(_is_not_escort) * (df.end < 7),coef_arrival_constants_early +util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),@(_is_not_escort) * (df.end > 6) * (df.end < 10),coef_arrival_constants_am_peak +util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),@(_is_not_escort) * (df.end > 9) * (df.end < 13),coef_arrival_constants_midday_1 +util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),@(_is_not_escort) * (df.end > 12) * (df.end < 15),coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),@(_is_not_escort) * (df.end == 15),coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),@(_is_not_escort) * (df.end == 16),coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),@(_is_not_escort) * (df.end == 17),coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),@(_is_not_escort) * (df.end == 18),coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),@(_is_not_escort) * (df.end > 18) * (df.end < 22),coef_arrival_constants_evening +util_arrival_constants_late,Arrival Constants -- Late (22 and later),@(_is_not_escort) * (df.end > 21),coef_arrival_constants_late +util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,@(_is_not_escort) * (df.duration < 2),coef_duration_constants_0_to_1_hours +util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,@(_is_not_escort) * (df.duration > 1) * (df.duration < 4),coef_duration_constants_2_to_3_hours +util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,@(_is_not_escort) * (df.duration > 3) * (df.duration < 6),coef_duration_constants_4_to_5_hours +util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,@(_is_not_escort) * (df.duration > 5) * (df.duration < 8),coef_duration_constants_6_to_7_hours +util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,@(_is_not_escort) * (df.duration > 7) * (df.duration < 11),coef_duration_constants_8_to_10_hours +util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,@(_is_not_escort) * (df.duration > 10) * (df.duration < 14),coef_duration_constants_11_to_13_hours +util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,@(_is_not_escort) * (df.duration > 13) * (df.duration < 19),coef_duration_constants_14_to_18_hours +util_escort_tour_departure_constants_early,Escort Tour Departure Constants -- Early (up to 5),@(_is_escort) * (df.start < 6),coef_escort_tour_departure_constants_early +util_escort_tour_departure_constants_am_peak_1,Escort Tour Departure Constants -- AM peak 1 (6),@(_is_escort) * (df.start == 6),coef_escort_tour_departure_constants_am_peak_1 +util_escort_tour_departure_constants_am_peak_2,Escort Tour Departure Constants -- AM peak 2 (7),@(_is_escort) * (df.start == 7),coef_escort_tour_departure_constants_am_peak_2 +util_escort_tour_departure_constants_am_peak_3,Escort Tour Departure Constants -- AM peak 3 (8),@(_is_escort) * (df.start == 8),coef_escort_tour_departure_constants_am_peak_3 +util_escort_tour_departure_constants_am_peak_4,Escort Tour Departure Constants -- AM peak 4 (9),@(_is_escort) * (df.start == 9),coef_escort_tour_departure_constants_am_peak_4 +util_escort_tour_departure_constants_midday_1,Escort Tour Departure Constants -- Midday 1 (10 to 12),@(_is_escort) * (df.start > 9) * (df.start < 13),coef_escort_tour_departure_constants_midday_1 +util_escort_tour_departure_constants_midday_2,Escort Tour Departure Constants -- Midday 2 (13 to 15),@(_is_escort) * (df.start > 12) * (df.start < 16),coef_escort_tour_departure_constants_midday_2 +util_escort_tour_departure_constants_pm_peak,Escort Tour Departure Constants -- PM peak (16 to 18),@(_is_escort) * (df.start > 15) * (df.start < 19),coef_escort_tour_departure_constants_pm_peak +util_escort_tour_departure_constants_evening,Escort Tour Departure Constants -- Evening (19 to 21),@(_is_escort) * (df.start > 18) * (df.start < 22),coef_escort_tour_departure_constants_evening +util_escort_tour_departure_constants_late,Escort Tour Departure Constants -- Late (22 and later),@(_is_escort) * (df.start > 21),coef_escort_tour_departure_constants_late +util_escort_tour_arrival_constants_early,Escort Tour Arrival Constants -- Early (up to 6),@(_is_escort) * (df.end < 7),coef_escort_tour_arrival_constants_early +util_escort_tour_arrival_constants_am_peak,Escort Tour Arrival Constants -- AM peak (7 to 9),@(_is_escort) * (df.end > 6) * (df.end < 10),coef_escort_tour_arrival_constants_am_peak +util_escort_tour_arrival_constants_midday_1,Escort Tour Arrival Constants -- Midday 1 (10 to 12),@(_is_escort) * (df.end > 9) * (df.end < 13),coef_escort_tour_arrival_constants_midday_1 +util_escort_tour_arrival_constants_midday_2,Escort Tour Arrival Constants -- Midday 2 (13 to 14),@(_is_escort) * (df.end > 12) * (df.end < 15),coef_escort_tour_arrival_constants_midday_2 +util_escort_tour_arrival_constants_pm_peak_1,Escort Tour Arrival Constants -- PM peak 1 (15),@(_is_escort) * (df.end == 15),coef_escort_tour_arrival_constants_pm_peak_1 +util_escort_tour_arrival_constants_pm_peak_2,Escort Tour Arrival Constants -- PM peak 2 (16),@(_is_escort) * (df.end == 16),coef_escort_tour_arrival_constants_pm_peak_2 +util_escort_tour_arrival_constants_pm_peak_3,Escort Tour Arrival Constants -- PM peak 3 (17),@(_is_escort) * (df.end == 17),coef_escort_tour_arrival_constants_pm_peak_3 +util_escort_tour_arrival_constants_pm_peak_4,Escort Tour Arrival Constants -- PM peak 4 (18),@(_is_escort) * (df.end == 18),coef_escort_tour_arrival_constants_pm_peak_4 +util_escort_tour_arrival_constants_evening,Escort Tour Arrival Constants -- Evening (19 to 21),@(_is_escort) * (df.end > 18) * (df.end < 22),coef_escort_tour_arrival_constants_evening +util_escort_tour_arrival_constants_late,Escort Tour Arrival Constants -- Late (22 and later),@(_is_escort) * (df.end > 21),coef_escort_tour_arrival_constants_late +util_escort_tour_duration_constants_0_to_1_hours,Escort Tour Duration Constants -- 0 to 1 hours,@(_is_escort) * (df.duration < 2),coef_escort_tour_duration_constants_0_to_1_hours +util_escort_tour_duration_constants_2_to_3_hours,Escort Tour Duration Constants -- 2 to 3 hours,@(_is_escort) * (df.duration > 1) * (df.duration < 4),coef_escort_tour_duration_constants_2_to_3_hours +util_escort_tour_duration_constants_4_to_5_hours,Escort Tour Duration Constants -- 4 to 5 hours,@(_is_escort) * (df.duration > 3) * (df.duration < 6),coef_escort_tour_duration_constants_4_to_5_hours +util_escort_tour_duration_constants_6_to_7_hours,Escort Tour Duration Constants -- 6 to 7 hours,@(_is_escort) * (df.duration > 5) * (df.duration < 8),coef_escort_tour_duration_constants_6_to_7_hours +util_escort_tour_duration_constants_8_to_10_hours,Escort Tour Duration Constants -- 8 to 10 hours,@(_is_escort) * (df.duration > 7) * (df.duration < 11),coef_escort_tour_duration_constants_8_to_10_hours +util_escort_tour_duration_constants_11_to_13_hours,Escort Tour Duration Constants -- 11 to 13 hours,@(_is_escort) * (df.duration > 10) * (df.duration < 14),coef_escort_tour_duration_constants_11_to_13_hours +util_escort_tour_duration_constants_14_to_18_hours,Escort Tour Duration Constants -- 14 to 18 hours,@(_is_escort) * (df.duration > 13) * (df.duration < 19),coef_escort_tour_duration_constants_14_to_18_hours diff --git a/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv index 4c34c1edc..6c735c423 100644 --- a/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv @@ -21,14 +21,14 @@ util_school_plus_work_tours_by_worker_lt_6_hours,School+work tours by worker- du util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum #,,, #,,, FIXME - use temps as timetable ops can be very time-consuming -util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy -util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy +util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)>0",coef_dummy +util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)>0",coef_dummy util_previous_tour_ends_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previous_tour_ends_this_departure_hour util_previous_tour_begins_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previous_tour_begins_this_arrival_hour -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) * (df.tour_num == 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) * (df.tour_num == 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction util_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours #,,, util_departure_constants_early_up_to_5,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early diff --git a/activitysim/examples/prototype_mtc/configs/tour_scheduling_work.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_work.csv index 7aa6a363a..e15fd96d4 100644 --- a/activitysim/examples/prototype_mtc/configs/tour_scheduling_work.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_work.csv @@ -29,14 +29,14 @@ util_tours_by_student_duration_lt_8_hrs,School+work tours by student- duration<8 util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum #,,, #,FIXME - use temps _adjacent_window_before and _adjacent_window_after because timetable ops can be very time-consuming,, -util_dummy_adjacent_before,local temp variable,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy -util_dummy_adjacent_after,local temp variable,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy +util_dummy_adjacent_before,local temp variable,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)>0",coef_dummy +util_dummy_adjacent_after,local temp variable,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)>0",coef_dummy util_previous_tour_ends_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previously_scheduled_tour_ends_in_this_departure_hour util_previously_scheduled_tour_begins_in_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previously_scheduled_tour_begins_in_this_arrival_hour -util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) * (df.tour_num == 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) * (df.tour_num == 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) * _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction util_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours #,,, util_departure_constants_early,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early diff --git a/activitysim/examples/prototype_mtc/configs/trip_destination.csv b/activitysim/examples/prototype_mtc/configs/trip_destination.csv index b12246c13..a43c88c4b 100644 --- a/activitysim/examples/prototype_mtc/configs/trip_destination.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_destination.csv @@ -1,8 +1,8 @@ Label,Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork local_dist_od,,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 local_dist_dp,,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -util_size_term,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one -util_no_attractions,no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE +util_size_term,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_no_attractions,no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE util_stop_zone_CDB_are_type,#stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, util_distance_inbound,distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),coef_util_distance_work_outbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork util_distance_outbound,distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),coef_util_distance_work_inbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork diff --git a/activitysim/examples/prototype_mtc/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/trip_destination_annotate_trips_preprocessor.csv index 1a1afb074..aeedee02c 100644 --- a/activitysim/examples/prototype_mtc/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_destination_annotate_trips_preprocessor.csv @@ -1,5 +1,4 @@ Description,Target,Expression -#,, ,tour_mode,"reindex(tours.tour_mode, df.tour_id)" ,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" ,trip_period,network_los.skim_time_period_label(_tod) @@ -7,4 +6,8 @@ Description,Target,Expression #,,not needed as school is not chosen as an intermediate trip destination #,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" #,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" -,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" \ No newline at end of file +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" +,purpose_index_num,"size_terms.get_cols(df.purpose)" +# the following two lines have been migrated from the utility spec files because `reindex` is not compatible with sharrow +,tour_mode_is_walk,"reindex(tours.tour_mode, df.tour_id)=='WALK'" +,tour_mode_is_bike,"reindex(tours.tour_mode, df.tour_id)=='BIKE'" diff --git a/activitysim/examples/prototype_mtc/configs/trip_destination_sample.csv b/activitysim/examples/prototype_mtc/configs/trip_destination_sample.csv index 857fb44c0..38e85d52a 100644 --- a/activitysim/examples/prototype_mtc/configs/trip_destination_sample.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_destination_sample.csv @@ -1,13 +1,13 @@ Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork ,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 ,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if walk tour not within walking distance,@(df.tour_mode_is_walk) & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if walk tour not within walking distance,@(df.tour_mode_is_walk) & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode_is_bike) & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode_is_bike) & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,, -size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.04972591574229,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813278663948,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 diff --git a/activitysim/examples/prototype_mtc/configs/trip_mode_choice.csv b/activitysim/examples/prototype_mtc/configs/trip_mode_choice.csv index 3ac15254b..590f47073 100644 --- a/activitysim/examples/prototype_mtc/configs/trip_mode_choice.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_mode_choice.csv @@ -330,18 +330,18 @@ util_tour_mode_is_walk_transit,Walk to Transit tour mode availability,tour_mode_ util_tour_mode_is_drive_transit,Drive to Transit tour modes availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,,,,,,, util_tour_mode_is_ride_hail,Ride hail tour modes availability,tour_mode_is_ride_hail,-999,-999,,,,,,-999,,,,,,-999,-999,-999,-999,-999,,, ,#indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_shared_ride_2_df_is_indiv,Drive Alone tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,sov_ASC_sr2,sov_ASC_sr2,,,,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_shared_ride_3_plus,Drive Alone tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,sov_ASC_sr3p,sov_ASC_sr3p,,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,sov_ASC_walk,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,,,,sov_ASC_rh,sov_ASC_rh,sov_ASC_rh -util_Shared_Ride_2_tour_mode_ASC_shared_ride_2,Shared Ride 2 tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,sr2_ASC_sr2,sr2_ASC_sr2,,,,,,,,,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_shared_ride_3_plus,Shared Ride 2 tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,sr2_ASC_sr3p,sr2_ASC_sr3p,,,,,,,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,sr2_ASC_walk,,,,,,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,,,,sr2_ASC_rh,sr2_ASC_rh,sr2_ASC_rh -util_Shared_Ride_3_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,sr3p_ASC_sr2,sr3p_ASC_sr2,,,,,,,,,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_shared_ride_3_plus,Shared Ride 3+ tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,sr3p_ASC_sr3p,sr3p_ASC_sr3p,,,,,,,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,sr3p_ASC_walk,,,,,,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,,,,sr3p_ASC_rh,sr3p_ASC_rh,sr3p_ASC_rh +util_Drive_Alone_tour_mode_ASC_shared_ride_2_df_is_indiv,Drive Alone tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_SOV),,,sov_ASC_sr2,sov_ASC_sr2,,,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_shared_ride_3_plus,Drive Alone tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_SOV),,,,,sov_ASC_sr3p,sov_ASC_sr3p,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_SOV),,,,,,,sov_ASC_walk,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_SOV),,,,,,,,,,,,,,,,,,,sov_ASC_rh,sov_ASC_rh,sov_ASC_rh +util_Shared_Ride_2_tour_mode_ASC_shared_ride_2,Shared Ride 2 tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_SR2),,,sr2_ASC_sr2,sr2_ASC_sr2,,,,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_shared_ride_3_plus,Shared Ride 2 tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_SR2),,,,,sr2_ASC_sr3p,sr2_ASC_sr3p,,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_SR2),,,,,,,sr2_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_SR2),,,,,,,,,,,,,,,,,,,sr2_ASC_rh,sr2_ASC_rh,sr2_ASC_rh +util_Shared_Ride_3_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_SR3P),,,sr3p_ASC_sr2,sr3p_ASC_sr2,,,,,,,,,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_shared_ride_3_plus,Shared Ride 3+ tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_SR3P),,,,,sr3p_ASC_sr3p,sr3p_ASC_sr3p,,,,,,,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_SR3P),,,,,,,sr3p_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_SR3P),,,,,,,,,,,,,,,,,,,sr3p_ASC_rh,sr3p_ASC_rh,sr3p_ASC_rh util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,,,,walk_ASC_rh,walk_ASC_rh,walk_ASC_rh util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,bike_ASC_walk,,,,,,,,,,,,,, util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,bike_ASC_rh,bike_ASC_rh,bike_ASC_rh @@ -364,15 +364,15 @@ util_Ride_Hail_tour_mode_ASC_shared_ride_2,Ride Hail tour mode ASC -- shared rid util_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,Ride Hail tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,ride_hail_ASC_sr3p,ride_hail_ASC_sr3p,,,,,,,,,,,,,,, util_Ride_Hail_tour_mode_ASC_walk,Ride Hail tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,ride_hail_ASC_walk,,,,,,,,,,,,,, util_Ride_Hail_tour_mode_ASC_walk_to_transit,Ride Hail tour mode ASC -- walk to transit,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,,,,,,,, -util_Ride_Hail_tour_mode_ASC_ride_hail_taxi,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,ride_hail_ASC_taxi,, -util_Ride_Hail_tour_mode_ASC_ride_hail_single,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_single, -util_Ride_Hail_tour_mode_ASC_ride_hail_shared,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_shared +util_Ride_Hail_tour_mode_ASC_ride_hail_taxi,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,,,,,,,,,,,ride_hail_ASC_taxi,, +util_Ride_Hail_tour_mode_ASC_ride_hail_single,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_single, +util_Ride_Hail_tour_mode_ASC_ride_hail_shared,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_shared #,joint tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_shared_ride_2,joint - auto tour mode ASC -- shared ride 2,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,joint_auto_ASC_sr2,joint_auto_ASC_sr2,,,,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_shared_ride_3_,joint - auto tour mode ASC -- shared ride 3+,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,joint_auto_ASC_sr3p,joint_auto_ASC_sr3p,,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_walk,joint - auto tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,,,joint_auto_ASC_walk,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_ride_hail,joint - auto tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_auto_ASC_rh,joint_auto_ASC_rh,joint_auto_ASC_rh -util_joint_Walk_tour_mode_ASC_ride_hail,joint - Walk tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,joint_walk_ASC_rh,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_shared_ride_2,joint - auto tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_auto),,,joint_auto_ASC_sr2,joint_auto_ASC_sr2,,,,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_shared_ride_3_,joint - auto tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_auto),,,,,joint_auto_ASC_sr3p,joint_auto_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_walk,joint - auto tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_auto),,,,,,,joint_auto_ASC_walk,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_ride_hail,joint - auto tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,,,,,,,,,,,joint_auto_ASC_rh,joint_auto_ASC_rh,joint_auto_ASC_rh +util_joint_Walk_tour_mode_ASC_ride_hail,joint - Walk tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,joint_walk_ASC_rh,,,,,,,,,,,,,, util_joint_Bike_tour_mode_ASC_walk,joint - Bike tour mode ASC -- walk,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,joint_bike_ASC_walk,,,,,,,,,,,,,, util_joint_Bike_tour_mode_ASC_ride_hail,joint - Bike tour mode ASC -- ride hail,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,joint_bike_ASC_rh,joint_bike_ASC_rh,joint_bike_ASC_rh util_joint_Walk_to_Transit_tour_mode_ASC_light_rail,joint - Walk to Transit tour mode ASC -- light rail,@(df.is_joint & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_lightrail,,,,,,,,,,, @@ -394,9 +394,9 @@ util_joint_Ride_Hail_tour_mode_ASC_shared_ride_2,joint - Ride Hail tour mode ASC util_joint_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,joint - Ride Hail tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,joint_ride_hail_ASC_sr3p,joint_ride_hail_ASC_sr3p,,,,,,,,,,,,,,, util_joint_Ride_Hail_tour_mode_ASC_walk,joint - Ride Hail tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,joint_ride_hail_ASC_walk,,,,,,,,,,,,,, util_joint_Ride_Hail_tour_mode_ASC_walk_to_transit,joint - Ride Hail tour mode ASC -- walk to transit,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_taxi,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_taxi,, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_single,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_single, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_shared,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_shared +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_taxi,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_taxi,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_single,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_single, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_shared,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_shared #,#,,,,,,,,,,,,,,,,,,,,,, util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,,,,-999,,,,,,,,,,,,,, util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,,,,-999,,,,,,,,,,,,, diff --git a/activitysim/examples/prototype_mtc/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/trip_mode_choice_annotate_trips_preprocessor.csv index b0b82a22b..edab18258 100644 --- a/activitysim/examples/prototype_mtc/configs/trip_mode_choice_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_mode_choice_annotate_trips_preprocessor.csv @@ -12,6 +12,8 @@ Description,Target,Expression #,, ,i_tour_mode,df.tour_mode.map(I_MODE_MAP) ,tour_mode_is_SOV,i_tour_mode.isin(I_SOV_MODES) +,tour_mode_is_SR2,i_tour_mode.isin(I_SR2_MODES) +,tour_mode_is_SR3P,i_tour_mode.isin(I_SR3P_MODES) ,tour_mode_is_auto,i_tour_mode.isin(I_AUTO_MODES) ,tour_mode_is_walk,i_tour_mode == I_WALK_MODE ,tour_mode_is_bike,i_tour_mode == I_BIKE_MODE diff --git a/activitysim/examples/prototype_mtc/configs_chunktrain/logging.yaml b/activitysim/examples/prototype_mtc/configs_chunktrain/logging.yaml new file mode 100644 index 000000000..74fc31def --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs_chunktrain/logging.yaml @@ -0,0 +1,76 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: DEBUG + handlers: [console, logfile] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARNING + handlers: [console, logfile] + propagate: false + + filelock: + level: WARN + handlers: [console, logfile] + propagate: false + + sharrow: + level: INFO + handlers: [console, logfile] + propagate: false + + black: + level: WARN + + blib2to3: + level: WARN + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: elapsedFormatter + level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + + formatters: + + simpleFormatter: + class: logging.Formatter + #format: '%(processName)-10s %(levelname)s - %(name)s - %(message)s' + format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ + '%(processName)-10s %(levelname)s - %(name)s - %(message)s', + '%(levelname)s - %(name)s - %(message)s'] + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' diff --git a/activitysim/examples/prototype_mtc/configs_chunktrain/settings.yaml b/activitysim/examples/prototype_mtc/configs_chunktrain/settings.yaml new file mode 100644 index 000000000..ecd18ec9e --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs_chunktrain/settings.yaml @@ -0,0 +1,85 @@ + +inherit_settings: True + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + +chunk_training_mode: training +multiprocess: True +strict: False +use_shadow_pricing: False + +households_sample_size: 4000 +chunk_size: 40_000_000_000 +num_processes: 8 + +# - ------------------------- + +# not recommended or supported for multiprocessing +want_dest_choice_sample_tables: False + + +# - tracing +# trace_hh_id: +# trace_od: + +# to resume after last successful checkpoint, specify resume_after: _ +# resume_after: trip_purpose_and_destination + +models: + ### mp_initialize step + - initialize_landuse + - initialize_households + ### mp_accessibility step + - compute_accessibility + ### mp_households step + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + ### mp_summarize step + - write_data_dictionary + - write_trip_matrices + - write_tables + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + slice: + tables: + - accessibility + # don't slice any tables not explicitly listed above in slice.tables + except: True + - name: mp_households + begin: school_location + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_data_dictionary + diff --git a/activitysim/examples/prototype_mtc/configs_mp/logging.yaml b/activitysim/examples/prototype_mtc/configs_mp/logging.yaml index e932009c5..13e533abb 100644 --- a/activitysim/examples/prototype_mtc/configs_mp/logging.yaml +++ b/activitysim/examples/prototype_mtc/configs_mp/logging.yaml @@ -9,21 +9,33 @@ logging: # Configuring the default (root) logger is highly recommended root: - level: DEBUG + level: NOTSET handlers: [console, logfile] loggers: activitysim: - level: DEBUG + level: INFO handlers: [console, logfile] propagate: false orca: - level: WARNING + level: WARN handlers: [console, logfile] propagate: false + filelock: + level: WARN + + sharrow: + level: INFO + + blib2to3: + level: WARN + + black: + level: WARN + handlers: logfile: @@ -36,18 +48,14 @@ logging: console: class: logging.StreamHandler stream: ext://sys.stdout - formatter: simpleFormatter - #level: NOTSET level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + formatter: elapsedFormatter formatters: simpleFormatter: class: logging.Formatter - #format: '%(processName)-10s %(levelname)s - %(name)s - %(message)s' - format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ - '%(processName)-10s %(levelname)s - %(name)s - %(message)s', - '%(levelname)s - %(name)s - %(message)s'] + format: '%(levelname)s - %(message)s' datefmt: '%d/%m/%Y %H:%M:%S' fileFormatter: @@ -55,3 +63,7 @@ logging: format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' datefmt: '%d/%m/%Y %H:%M:%S' + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' diff --git a/activitysim/examples/prototype_mtc/configs_production/logging.yaml b/activitysim/examples/prototype_mtc/configs_production/logging.yaml new file mode 100644 index 000000000..8939607c8 --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs_production/logging.yaml @@ -0,0 +1,71 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: DEBUG + handlers: [console, logfile] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARNING + handlers: [console, logfile] + propagate: false + + filelock: + level: WARN + handlers: [console, logfile] + propagate: false + + sharrow: + level: INFO + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: elapsedFormatter + level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + + formatters: + + simpleFormatter: + class: logging.Formatter + #format: '%(processName)-10s %(levelname)s - %(name)s - %(message)s' + format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ + '%(processName)-10s %(levelname)s - %(name)s - %(message)s', + '%(levelname)s - %(name)s - %(message)s'] + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' + diff --git a/activitysim/examples/prototype_mtc/configs_production/settings.yaml b/activitysim/examples/prototype_mtc/configs_production/settings.yaml new file mode 100644 index 000000000..8b3f59cb5 --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs_production/settings.yaml @@ -0,0 +1,85 @@ + +inherit_settings: True + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + +chunk_training_mode: production +multiprocess: True +strict: False +use_shadow_pricing: False + +households_sample_size: 0 +chunk_size: 40_000_000_000 +num_processes: 8 + +# - ------------------------- + +# not recommended or supported for multiprocessing +want_dest_choice_sample_tables: False + + +# - tracing +# trace_hh_id: +# trace_od: + +# to resume after last successful checkpoint, specify resume_after: _ +# resume_after: trip_purpose_and_destination + +models: + ### mp_initialize step + - initialize_landuse + - initialize_households + ### mp_accessibility step + - compute_accessibility + ### mp_households step + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + ### mp_summarize step + - write_data_dictionary + - write_trip_matrices + - write_tables + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + slice: + tables: + - accessibility + # don't slice any tables not explicitly listed above in slice.tables + except: True + - name: mp_households + begin: school_location + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_data_dictionary + diff --git a/activitysim/examples/prototype_mtc/configs_sh/settings.yaml b/activitysim/examples/prototype_mtc/configs_sh/settings.yaml new file mode 100644 index 000000000..9a5a2162c --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs_sh/settings.yaml @@ -0,0 +1,3 @@ + +inherit_settings: True +sharrow: require diff --git a/activitysim/examples/prototype_mtc/configs_sh_compile/settings.yaml b/activitysim/examples/prototype_mtc/configs_sh_compile/settings.yaml new file mode 100644 index 000000000..598f84799 --- /dev/null +++ b/activitysim/examples/prototype_mtc/configs_sh_compile/settings.yaml @@ -0,0 +1,5 @@ + +inherit_settings: True +sharrow: test +chunk_training_mode: disabled +households_sample_size: 100 diff --git a/activitysim/examples/prototype_mtc/output/cache/.gitignore b/activitysim/examples/prototype_mtc/output/cache/.gitignore index 3dd2e62f9..1d085cacc 100644 --- a/activitysim/examples/prototype_mtc/output/cache/.gitignore +++ b/activitysim/examples/prototype_mtc/output/cache/.gitignore @@ -1,2 +1 @@ -*.mmap -*.feather +** diff --git a/activitysim/examples/prototype_mtc/test/configs/settings.yaml b/activitysim/examples/prototype_mtc/test/configs/settings.yaml index abaebfa7b..7626237ca 100644 --- a/activitysim/examples/prototype_mtc/test/configs/settings.yaml +++ b/activitysim/examples/prototype_mtc/test/configs/settings.yaml @@ -23,3 +23,5 @@ output_tables: sort: True tables: - trips + +recode_pipeline_columns: False diff --git a/activitysim/examples/prototype_mtc/test/configs_chunkless/settings.yaml b/activitysim/examples/prototype_mtc/test/configs_chunkless/settings.yaml index 3e670aca9..7639c323e 100644 --- a/activitysim/examples/prototype_mtc/test/configs_chunkless/settings.yaml +++ b/activitysim/examples/prototype_mtc/test/configs_chunkless/settings.yaml @@ -24,3 +24,5 @@ output_tables: sort: True tables: - trips + +recode_pipeline_columns: False diff --git a/activitysim/examples/prototype_mtc/test/configs_mp/settings.yaml b/activitysim/examples/prototype_mtc/test/configs_mp/settings.yaml index 88c031193..038c09a5f 100644 --- a/activitysim/examples/prototype_mtc/test/configs_mp/settings.yaml +++ b/activitysim/examples/prototype_mtc/test/configs_mp/settings.yaml @@ -29,3 +29,5 @@ output_tables: sort: True tables: - trips + +recode_pipeline_columns: False diff --git a/activitysim/examples/prototype_mtc/test/configs_recode/network_los.yaml b/activitysim/examples/prototype_mtc/test/configs_recode/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/prototype_mtc/test/configs_recode/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/prototype_mtc/test/configs_recode/settings.yaml b/activitysim/examples/prototype_mtc/test/configs_recode/settings.yaml new file mode 100644 index 000000000..c563f1312 --- /dev/null +++ b/activitysim/examples/prototype_mtc/test/configs_recode/settings.yaml @@ -0,0 +1,30 @@ +inherit_settings: True + +# treat warnings as errors +strict: True + +# number of households to simulate +households_sample_size: 10 +chunk_size: 0 + +# - shadow pricing global switches +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +recode_pipeline_columns: True diff --git a/activitysim/examples/prototype_mtc/test/configs_sharrow/network_los.yaml b/activitysim/examples/prototype_mtc/test/configs_sharrow/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/prototype_mtc/test/configs_sharrow/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/prototype_mtc/test/configs_sharrow/settings.yaml b/activitysim/examples/prototype_mtc/test/configs_sharrow/settings.yaml new file mode 100644 index 000000000..3bfe65d0f --- /dev/null +++ b/activitysim/examples/prototype_mtc/test/configs_sharrow/settings.yaml @@ -0,0 +1,31 @@ +inherit_settings: True + +# treat warnings as errors +strict: True + +# number of households to simulate +households_sample_size: 10 +chunk_size: 0 + +# - shadow pricing global switches +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +sharrow: require +recode_pipeline_columns: True diff --git a/activitysim/examples/prototype_mtc/test/output/cache/.gitignore b/activitysim/examples/prototype_mtc/test/output/cache/.gitignore index 3dd2e62f9..1d085cacc 100644 --- a/activitysim/examples/prototype_mtc/test/output/cache/.gitignore +++ b/activitysim/examples/prototype_mtc/test/output/cache/.gitignore @@ -1,2 +1 @@ -*.mmap -*.feather +** diff --git a/activitysim/examples/prototype_mtc/test/test_mtc.py b/activitysim/examples/prototype_mtc/test/test_mtc.py index fb5751a29..b79266f8f 100644 --- a/activitysim/examples/prototype_mtc/test/test_mtc.py +++ b/activitysim/examples/prototype_mtc/test/test_mtc.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import os import subprocess +import sys import pandas as pd import pandas.testing as pdt @@ -15,7 +16,7 @@ def teardown_function(func): inject.reinject_decorated_tables() -def run_test_mtc(multiprocess=False, chunkless=False): +def run_test_mtc(multiprocess=False, chunkless=False, recode=False, sharrow=False): def example_path(dirname): resource = os.path.join("examples", "prototype_mtc", dirname) return pkg_resources.resource_filename("activitysim", resource) @@ -58,6 +59,28 @@ def regress(): "-o", test_path("output"), ] + elif recode: + run_args = [ + "-c", + test_path("configs_recode"), + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] + elif sharrow: + run_args = [ + "-c", + test_path("configs_sharrow"), + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] else: run_args = [ "-c", @@ -70,7 +93,10 @@ def regress(): test_path("output"), ] - subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + if os.environ.get("GITHUB_ACTIONS") == "true": + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + else: + subprocess.run([sys.executable, file_path] + run_args, check=True) regress() @@ -87,8 +113,18 @@ def test_mtc_mp(): run_test_mtc(multiprocess=True) +def test_mtc_recode(): + run_test_mtc(recode=True) + + +def test_mtc_sharrow(): + run_test_mtc(sharrow=True) + + if __name__ == "__main__": run_test_mtc(multiprocess=False) run_test_mtc(multiprocess=True) run_test_mtc(multiprocess=False, chunkless=True) + run_test_mtc(recode=True) + run_test_mtc(sharrow=True) diff --git a/activitysim/examples/prototype_mtc_extended/configs/annotate_landuse.csv b/activitysim/examples/prototype_mtc_extended/configs/annotate_landuse.csv new file mode 100644 index 000000000..a434db9db --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/annotate_landuse.csv @@ -0,0 +1,17 @@ +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.TOTHH / (land_use.RESACRE + land_use.CIACRE) +employment_density,employment_density,land_use.TOTEMP / (land_use.RESACRE + land_use.CIACRE) +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) +,is_cbd,land_use.area_type == 1 +# additions put in place for simulation shadow pricing approach,, +total university enrollment,TOTENR_univ,land_use.COLLFTE + land_use.COLLPTE +# example external worker implementation,, +Example with 10 percent external workers across all zones,ext_work_share,0.1 +scaling employment fields,RETEMPN_scaled,land_use.RETEMPN * (1 - ext_work_share) +,FPSEMPN_scaled,land_use.FPSEMPN * (1 - ext_work_share) +,HEREMPN_scaled,land_use.HEREMPN * (1 - ext_work_share) +,OTHEMPN_scaled,land_use.OTHEMPN * (1 - ext_work_share) +,AGREMPN_scaled,land_use.AGREMPN * (1 - ext_work_share) +,MWTEMPN_scaled,land_use.MWTEMPN * (1 - ext_work_share) +,TOTEMP_scaled,land_use.TOTEMP * (1 - ext_work_share) diff --git a/activitysim/examples/prototype_mtc_extended/configs/annotate_non_mandatory_destination.csv b/activitysim/examples/prototype_mtc_extended/configs/annotate_non_mandatory_destination.csv new file mode 100644 index 000000000..cdd627bb6 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/annotate_non_mandatory_destination.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +overwrite non-mandatory tour destination for school escort tours,_school_escort_tour_destination,"reindex(school_escort_tours.destination, df.index)" +,destination,"np.where(_school_escort_tour_destination.isna(), df.destination, _school_escort_tour_destination)" \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/annotate_non_mandatory_tour_frequency.csv b/activitysim/examples/prototype_mtc_extended/configs/annotate_non_mandatory_tour_frequency.csv new file mode 100644 index 000000000..a4861c99d --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/annotate_non_mandatory_tour_frequency.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +# re-calculating tour counts with school escorting tours included,, +,tour_type_num,"df.groupby(['person_id', 'tour_type']).cumcount() + 1" +,tour_type_count,"tour_type_num + df.groupby(['person_id', 'tour_type']).cumcount(ascending=False)" +,tour_num,"df.groupby('person_id').cumcount() + 1" +,tour_count,"tour_num + df.groupby('person_id').cumcount(ascending=False)" \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/annotate_proto_households.csv b/activitysim/examples/prototype_mtc_extended/configs/annotate_proto_households.csv new file mode 100644 index 000000000..95720faef --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/annotate_proto_households.csv @@ -0,0 +1,4 @@ +# This is an example use of annotation for dissaggregate accessibilities +# But this particular case could probably be handled using variable mapping +Description,Target,Expression +auto ownership,auto_ownership,"np.clip(households.veh, 0, 2)" diff --git a/activitysim/examples/prototype_mtc_extended/configs/destination_choice_size_terms.csv b/activitysim/examples/prototype_mtc_extended/configs/destination_choice_size_terms.csv new file mode 100644 index 000000000..f07de5d48 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/destination_choice_size_terms.csv @@ -0,0 +1,28 @@ +model_selector,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE,RETEMPN_scaled,FPSEMPN_scaled,HEREMPN_scaled,OTHEMPN_scaled,AGREMPN_scaled,MWTEMPN_scaled +workplace,work_low,0,0,0,0,0,0,0,0,0,0,0,0.129,0.193,0.383,0.12,0.01,0.164 +workplace,work_med,0,0,0,0,0,0,0,0,0,0,0,0.12,0.197,0.325,0.139,0.008,0.21 +workplace,work_high,0,0,0,0,0,0,0,0,0,0,0,0.11,0.207,0.284,0.154,0.006,0.239 +workplace,work_veryhigh,0,0,0,0,0,0,0,0,0,0,0,0.093,0.27,0.241,0.146,0.004,0.246 +school,university,0,0,0,0,0,0,0,0,0,0.592,0.408,0,0,0,0,0,0 +school,gradeschool,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +school,highschool,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +non_mandatory,escort,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0,0,0,0,0,0,0 +#non_mandatory,escort_kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0,0,0,0,0,0,0 +#non_mandatory,escort_nokids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0,0,0,0,0,0,0 +non_mandatory,shopping,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,othmaint,0,0.482,0,0.518,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,social,0,0.522,0,0.478,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0,0,0,0,0,0,0 +atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0,0,0,0,0,0,0 +trip,work,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0 +trip,escort,0.001,0.225,0,0.144,0,0,0,0.464,0.166,0,0,0,0,0,0,0,0 +trip,shopping,0.001,0.999,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +trip,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0,0,0,0,0,0,0 +trip,othmaint,0.001,0.481,0,0.518,0,0,0,0,0,0,0,0,0,0,0,0,0 +trip,social,0.001,0.521,0,0.478,0,0,0,0,0,0,0,0,0,0,0,0,0 +trip,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0,0,0,0,0,0,0 +trip,univ,0.001,0,0,0,0,0,0,0,0,0.592,0.408,0,0,0,0,0,0 +# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,, +#trip,gradeschool,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 +#trip,highschool,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 diff --git a/activitysim/examples/prototype_mtc_extended/configs/disaggregate_accessibility.yaml b/activitysim/examples/prototype_mtc_extended/configs/disaggregate_accessibility.yaml new file mode 100644 index 000000000..41ec677ab --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/disaggregate_accessibility.yaml @@ -0,0 +1,170 @@ +# Sampling size. 0 = no limit +# can be whole integer value or a sample rate (percent of zones) +# zero or missing defaults to full sample! +DESTINATION_SAMPLE_SIZE: 0.5 +ORIGIN_SAMPLE_SIZE: 5000 + +# select origin zones weighted by population (or another landuse variable) +ORIGIN_WEIGHTING_COLUMN: TOTPOP + +# How the nearest sampled zone is found: skims or centroid distance +NEAREST_METHOD: skims + +# Specify the tables to be created and their variables. +# Single values are constants +# Lists are varying and will generate the cartesian product (i.e., all possible non-repeating combinations) + +# Example result for households below: +#hhid veh hinccat1 hinc hworkers persons hht bldgsz +# 1 0 1 14000 1 2 1 2 +# 2 1 1 14000 1 2 1 2 +# 3 2 1 14000 1 2 1 2 +# 4 0 2 67000 1 2 1 2 +# 5 1 2 67000 1 2 1 2 +# 6 2 2 67000 1 2 1 2 +# .... + +# Use FROM_TEMPLATES or CREATE_TABLES to generate the proto-population +# Alternative can be manually specified in csv templates +FROM_TEMPLATES: True +CREATE_TABLES: + PROTO_HOUSEHOLDS: proto_households_template.csv + PROTO_PERSONS: proto_persons_template.csv + PROTO_TOURS: proto_tours_template.csv + +##Note: parameters in ALL CAPS below are required by the program for synthesis +# CREATE_TABLES: +# # Note: Only one household-member composition is possible through direct definition. +# # e.g., you cannot have one household with 2 people and another with 3 people +# PROTO_HOUSEHOLDS: +# index_col: proto_household_id +# zone_col: home_zone_id +# rename_columns: +# zone_id: home_zone_id +# VARIABLES: +# hinccat1: [1, 2, 3, 4] # Income categories +# hworkers: 1 # Household workers +# veh: [0, 1, 2] # Household vehicles +# persons: 2 # Two persons household +# HHT: 1 # Married-couple family household +# bldgsz: 2 # Building size - Single family detached +# # Additional columns that are mapped to another (e.g., hhinccat1: 1 = hinc: 14000) +# mapped_fields: +# hinccat1: # List new fields mapped to this field +# income: # Median income within each quartile +# 1: 15000 # 14000 (for 3 level) +# 2: 45000 # 67000 (for 3 level) +# 3: 80000 # 120000 (for 3 level) +# 4: 150000 +# persons: +# hhsize: +# 1: 1 +# 2: 2 +# 3: 3 +# hworkers: +# num_workers: +# 1: 1 +# 2: 2 +# 3: 3 + +# PROTO_PERSONS: +# index_col: proto_person_id +# VARIABLES: +# pnum: [1, 2] # Person number +# military: 4 # Not military +# pstudent: 3 # Not attending school +# educ: 13 # Bachelor's +# grade: 0 # Not attending +# mapped_fields: +# pnum: +# age: +# 1: 35 +# 2: 55 +# sex: # Female male +# 1: 2 +# 2: 1 +# ptype: # Person type, full-time and non-working adult +# 1: 1 +# 2: 4 +# pemploy: # Full-time and unemployed +# 1: 1 +# 2: 3 +# weeks: # 50-52 weeks, none +# 1: 1 +# 2: 0 +# hours: # Hours per week +# 1: 35 +# 2: 0 +# DAP: # Mandatory, Non-mandatory +# 1: "M" +# 2: "N" + +# PROTO_TOURS: +# index_col: proto_tour_id +# VARIABLES: +# tour_num: 1 # Tour number, 1 tour per person +# purpose: [1, 2, 3] +# mapped_fields: +# purpose: +# person_num: # In this case it was easier to map the person number directly to the purposez +# 1: 1 +# 2: 2 +# 3: 2 +# tour_type: +# 1: "work" +# 2: "shopping" +# 3: "othdiscr" +# tour_category: # tour purpose category, mandatory/non-mandatory +# 1: "mandatory" +# 2: "non_mandatory" +# 3: "non_mandatory" +# filter_rows: # list any matching conditions as pandas expression +# - ~((df.tour_type == "work") & (df.person_num == 1)) +# JOIN_ON: +# person_num: pnum # Specifies which person variable to join the tours on + +# Merge on variables +MERGE_ON: + by: # These should be categorical variables at the household level + - home_zone_id + - auto_ownership + - income_segment + +# Include any annotations for persons, households, land_use, or tours. +# The purpose of a separate annotation setup is that annotation expressions for the main model +# may require data that aren't in the proto population. +# This step enables users to annotate the proto-population by referencing custom annotation scripts. +# Of course, users can also just reference existing configs if they work, but they must be referenced here too. + +annotate_proto_tables: + - tablename: proto_persons + annotate: + SPEC: annotate_persons + DF: proto_persons + TABLES: + - proto_households + + - tablename: proto_households + annotate: + SPEC: annotate_proto_households + DF: proto_households + TABLES: + - proto_persons + - land_use + +# Annotate the proto_households table using the main model annotations files + - tablename: proto_households + annotate: + SPEC: annotate_households + DF: proto_households + TABLES: + - proto_persons + - land_use + +# Annotate the proto_persons table using the main model annotations files + - tablename: proto_persons + annotate: + SPEC: annotate_persons_after_hh + DF: proto_persons + TABLES: + - proto_households diff --git a/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_destination.yaml b/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_destination.yaml new file mode 100644 index 000000000..48d8ca8d9 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_destination.yaml @@ -0,0 +1,57 @@ +SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv +SPEC: non_mandatory_tour_destination.csv +COEFFICIENTS: non_mandatory_tour_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: non_mandatory + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample + + +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - home_zone_id + - person_id + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +annotate_tours: + SPEC: annotate_non_mandatory_destination + DF: df + TABLES: + - tours + - school_escort_tours + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: + shopping: 14 + othmaint: 14 + othdiscr: 14 + eatout: 14 + social: 14 + escort: 14 +OUT_PERIOD: + shopping: 14 + othmaint: 14 + othdiscr: 14 + eatout: 14 + social: 14 + escort: 14 \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_frequency_alternatives.csv b/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_frequency_alternatives.csv new file mode 100644 index 000000000..0bea47c6f --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_frequency_alternatives.csv @@ -0,0 +1,100 @@ +escort,shopping,othmaint,othdiscr,eatout,social +0,0,0,0,0,0 +0,0,0,1,0,0 +0,0,0,0,0,1 +0,0,0,1,0,1 +0,0,0,0,1,0 +0,0,0,1,1,0 +0,0,0,0,1,1 +0,0,0,1,1,1 +0,0,1,0,0,0 +0,0,1,1,0,0 +0,0,1,0,0,1 +0,0,1,1,0,1 +0,0,1,0,1,0 +0,0,1,1,1,0 +0,0,1,0,1,1 +0,0,1,1,1,1 +0,1,0,0,0,0 +0,1,0,1,0,0 +0,1,0,0,0,1 +0,1,0,1,0,1 +0,1,0,0,1,0 +0,1,0,1,1,0 +0,1,0,0,1,1 +0,1,0,1,1,1 +0,1,1,0,0,0 +0,1,1,1,0,0 +0,1,1,0,0,1 +0,1,1,1,0,1 +0,1,1,0,1,0 +0,1,1,1,1,0 +0,1,1,0,1,1 +0,1,1,1,1,1 +1,0,0,0,0,0 +1,0,0,1,0,0 +1,0,0,0,0,1 +1,0,0,1,0,1 +1,0,0,0,1,0 +1,0,0,1,1,0 +1,0,0,0,1,1 +1,0,0,1,1,1 +1,0,1,0,0,0 +1,0,1,1,0,0 +1,0,1,0,0,1 +1,0,1,1,0,1 +1,0,1,0,1,0 +1,0,1,1,1,0 +1,0,1,0,1,1 +1,0,1,1,1,1 +1,1,0,0,0,0 +1,1,0,1,0,0 +1,1,0,0,0,1 +1,1,0,1,0,1 +1,1,0,0,1,0 +1,1,0,1,1,0 +1,1,0,0,1,1 +1,1,0,1,1,1 +1,1,1,0,0,0 +1,1,1,1,0,0 +1,1,1,0,0,1 +1,1,1,1,0,1 +1,1,1,0,1,0 +1,1,1,1,1,0 +1,1,1,0,1,1 +1,1,1,1,1,1 +2,0,0,0,0,0 +2,0,0,1,0,0 +2,0,0,0,0,1 +2,0,0,1,0,1 +2,0,0,0,1,0 +2,0,0,1,1,0 +2,0,0,0,1,1 +2,0,0,1,1,1 +2,0,1,0,0,0 +2,0,1,1,0,0 +2,0,1,0,0,1 +2,0,1,1,0,1 +2,0,1,0,1,0 +2,0,1,1,1,0 +2,0,1,0,1,1 +2,0,1,1,1,1 +2,1,0,0,0,0 +2,1,0,1,0,0 +2,1,0,0,0,1 +2,1,0,1,0,1 +2,1,0,0,1,0 +2,1,0,1,1,0 +2,1,0,0,1,1 +2,1,0,1,1,1 +2,1,1,0,0,0 +2,1,1,1,0,0 +2,1,1,0,0,1 +2,1,1,1,0,1 +2,1,1,0,1,0 +2,1,1,1,1,0 +2,1,1,0,1,1 +2,1,1,1,1,1 +# extension for flexible ids demonstration,,,,, +# should be removed for actual model run,,,,, +0,0,0,2,0,0 diff --git a/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_scheduling.yaml new file mode 100644 index 000000000..488ba6b82 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_scheduling.yaml @@ -0,0 +1,22 @@ + +SPEC: tour_scheduling_nonmandatory.csv +COEFFICIENTS: tour_scheduling_nonmandatory_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: non_mandatory_tour_scheduling_annotate_tours_preprocessor + DF: non_mandatory_tours + TABLES: + - land_use + - joint_tour_participants + - school_escort_tours + +SIMULATE_CHOOSER_COLUMNS: + - ptype + - num_children + - roundtrip_auto_time_to_work + - num_mand + - num_escort_tours + - num_non_escort_tours + - adult diff --git a/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv new file mode 100644 index 000000000..c0281468f --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv @@ -0,0 +1,11 @@ +Description,Target,Expression +destination in central business district,destination_in_cbd,"(reindex(land_use.area_type, non_mandatory_tours.destination) < setting('cbd_threshold')) * 1" +#,, +,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), non_mandatory_tours.person_id)" +# included for school escorting model,, +flag to denote outbound school escort tours,is_outbound_school_escort_tour,"non_mandatory_tours.index.isin(school_escort_tours[school_escort_tours['school_escort_direction'] == 'outbound'].index)" +flag to denote inbound school escort tours,is_inbound_school_escort_tour,"non_mandatory_tours.index.isin(school_escort_tours[school_escort_tours['school_escort_direction'] == 'inbound'].index)" +school escort tour start time,school_escort_tour_start,"reindex(school_escort_tours.start, non_mandatory_tours.index)" +school escort tour next start time,school_escort_tour_next_start,"reindex(school_escort_tours.next_pure_escort_start, non_mandatory_tours.index)" +school escort tour end time,school_escort_tour_end,"reindex(school_escort_tours.end, non_mandatory_tours.index)" + diff --git a/activitysim/examples/prototype_mtc_extended/configs/proto_households_template.csv b/activitysim/examples/prototype_mtc_extended/configs/proto_households_template.csv new file mode 100644 index 000000000..01da5b375 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/proto_households_template.csv @@ -0,0 +1,25 @@ +proto_household_id,HHT,bldgsz,hinccat1,hworkers,persons,veh,income,num_workers,hhsize +1,1,2,1,1,1,0,15000,1,1 +2,1,2,1,1,1,1,15000,1,1 +3,1,2,1,1,1,2,15000,1,1 +4,1,2,2,1,1,0,45000,1,1 +5,1,2,2,1,1,1,45000,1,1 +6,1,2,2,1,1,2,45000,1,1 +7,1,2,3,1,1,0,80000,1,1 +8,1,2,3,1,1,1,80000,1,1 +9,1,2,3,1,1,2,80000,1,1 +10,1,2,4,1,1,0,150000,1,1 +11,1,2,4,1,1,1,150000,1,1 +12,1,2,4,1,1,2,150000,1,1 +13,1,2,1,1,2,0,15000,1,2 +14,1,2,1,1,2,1,15000,1,2 +15,1,2,1,1,2,2,15000,1,2 +16,1,2,2,1,2,0,45000,1,2 +17,1,2,2,1,2,1,45000,1,2 +18,1,2,2,1,2,2,45000,1,2 +19,1,2,3,1,2,0,80000,1,2 +20,1,2,3,1,2,1,80000,1,2 +21,1,2,3,1,2,2,80000,1,2 +22,1,2,4,1,2,0,150000,1,2 +23,1,2,4,1,2,1,150000,1,2 +24,1,2,4,1,2,2,150000,1,2 diff --git a/activitysim/examples/prototype_mtc_extended/configs/proto_persons_template.csv b/activitysim/examples/prototype_mtc_extended/configs/proto_persons_template.csv new file mode 100644 index 000000000..4e6106d3e --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/proto_persons_template.csv @@ -0,0 +1,37 @@ +proto_person_id,proto_household_id,educ,grade,military,pnum,pstudent,DAP,age,hours,pemploy,ptype,sex,weeks +1,1,13,0,4,1,3,N,70,0,3,4,1,0 +2,2,13,0,4,1,3,N,70,0,3,4,1,0 +3,3,13,0,4,1,3,N,70,0,3,4,1,0 +4,4,13,0,4,1,3,N,70,0,3,4,1,0 +5,5,13,0,4,1,3,N,70,0,3,4,1,0 +6,6,13,0,4,1,3,N,70,0,3,4,1,0 +7,7,13,0,4,1,3,N,70,0,3,4,1,0 +8,8,13,0,4,1,3,N,70,0,3,4,1,0 +9,9,13,0,4,1,3,N,70,0,3,4,1,0 +10,10,13,0,4,1,3,N,70,0,3,4,1,0 +11,11,13,0,4,1,3,N,70,0,3,4,1,0 +12,12,13,0,4,1,3,N,70,0,3,4,1,0 +13,13,13,0,4,1,3,M,35,35,1,1,2,1 +14,13,13,0,4,2,3,N,55,0,3,4,1,0 +15,14,13,0,4,1,3,M,35,35,1,1,2,1 +16,14,13,0,4,2,3,N,55,0,3,4,1,0 +17,15,13,0,4,1,3,M,35,35,1,1,2,1 +18,15,13,0,4,2,3,N,55,0,3,4,1,0 +19,16,13,0,4,1,3,M,35,35,1,1,2,1 +20,16,13,0,4,2,3,N,55,0,3,4,1,0 +21,17,13,0,4,1,3,M,35,35,1,1,2,1 +22,17,13,0,4,2,3,N,55,0,3,4,1,0 +23,18,13,0,4,1,3,M,35,35,1,1,2,1 +24,18,13,0,4,2,3,N,55,0,3,4,1,0 +25,19,13,0,4,1,3,M,35,35,1,1,2,1 +26,19,13,0,4,2,3,N,55,0,3,4,1,0 +27,20,13,0,4,1,3,M,35,35,1,1,2,1 +28,20,13,0,4,2,3,N,55,0,3,4,1,0 +29,21,13,0,4,1,3,M,35,35,1,1,2,1 +30,21,13,0,4,2,3,N,55,0,3,4,1,0 +31,22,13,0,4,1,3,M,35,35,1,1,2,1 +32,22,13,0,4,2,3,N,55,0,3,4,1,0 +33,23,13,0,4,1,3,M,35,35,1,1,2,1 +34,23,13,0,4,2,3,N,55,0,3,4,1,0 +35,24,13,0,4,1,3,M,35,35,1,1,2,1 +36,24,13,0,4,2,3,N,55,0,3,4,1,0 diff --git a/activitysim/examples/prototype_mtc_extended/configs/proto_tours_template.csv b/activitysim/examples/prototype_mtc_extended/configs/proto_tours_template.csv new file mode 100644 index 000000000..e30e7a278 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/proto_tours_template.csv @@ -0,0 +1,61 @@ +proto_household_id,proto_person_id,purpose,tour_num,person_num,tour_category,tour_type +1,1,2,1,1,non_mandatory,shopping +1,1,3,1,1,non_mandatory,othdiscr +2,2,2,1,1,non_mandatory,shopping +2,2,3,1,1,non_mandatory,othdiscr +3,3,2,1,1,non_mandatory,shopping +3,3,3,1,1,non_mandatory,othdiscr +4,4,2,1,1,non_mandatory,shopping +4,4,3,1,1,non_mandatory,othdiscr +5,5,2,1,1,non_mandatory,shopping +5,5,3,1,1,non_mandatory,othdiscr +6,6,2,1,1,non_mandatory,shopping +6,6,3,1,1,non_mandatory,othdiscr +7,7,2,1,1,non_mandatory,shopping +7,7,3,1,1,non_mandatory,othdiscr +8,8,2,1,1,non_mandatory,shopping +8,8,3,1,1,non_mandatory,othdiscr +9,9,2,1,1,non_mandatory,shopping +9,9,3,1,1,non_mandatory,othdiscr +10,10,2,1,1,non_mandatory,shopping +10,10,3,1,1,non_mandatory,othdiscr +11,11,2,1,1,non_mandatory,shopping +11,11,3,1,1,non_mandatory,othdiscr +12,12,2,1,1,non_mandatory,shopping +12,12,3,1,1,non_mandatory,othdiscr +13,13,1,1,1,mandatory,work +13,14,2,1,2,non_mandatory,shopping +13,14,3,1,2,non_mandatory,othdiscr +14,15,1,1,1,mandatory,work +14,16,2,1,2,non_mandatory,shopping +14,16,3,1,2,non_mandatory,othdiscr +15,17,1,1,1,mandatory,work +15,18,2,1,2,non_mandatory,shopping +15,18,3,1,2,non_mandatory,othdiscr +16,19,1,1,1,mandatory,work +16,20,2,1,2,non_mandatory,shopping +16,20,3,1,2,non_mandatory,othdiscr +17,21,1,1,1,mandatory,work +17,22,2,1,2,non_mandatory,shopping +17,22,3,1,2,non_mandatory,othdiscr +18,23,1,1,1,mandatory,work +18,24,2,1,2,non_mandatory,shopping +18,24,3,1,2,non_mandatory,othdiscr +19,25,1,1,1,mandatory,work +19,26,2,1,2,non_mandatory,shopping +19,26,3,1,2,non_mandatory,othdiscr +20,27,1,1,1,mandatory,work +20,28,2,1,2,non_mandatory,shopping +20,28,3,1,2,non_mandatory,othdiscr +21,29,1,1,1,mandatory,work +21,30,2,1,2,non_mandatory,shopping +21,30,3,1,2,non_mandatory,othdiscr +22,31,1,1,1,mandatory,work +22,32,2,1,2,non_mandatory,shopping +22,32,3,1,2,non_mandatory,othdiscr +23,33,1,1,1,mandatory,work +23,34,2,1,2,non_mandatory,shopping +23,34,3,1,2,non_mandatory,othdiscr +24,35,1,1,1,mandatory,work +24,36,2,1,2,non_mandatory,shopping +24,36,3,1,2,non_mandatory,othdiscr diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting.yaml b/activitysim/examples/prototype_mtc_extended/configs/school_escorting.yaml new file mode 100644 index 000000000..31527cb1d --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting.yaml @@ -0,0 +1,54 @@ +# The school escort model as written in this prototype is not +# compatible with sharrow, so "sharrow_skip" must be activated here. +# Currently the spec file has a few lines that evaluate differently in +# the sharrow implementation, resulting in failure that are flagged by +# the `test` mode. Once these are fixed (and string comparisons are +# minimized for performance) this `sharrow_skip` setting can be removed. +sharrow_skip: true + +OUTBOUND_SPEC: school_escorting_outbound.csv +OUTBOUND_COEFFICIENTS: school_escorting_coefficients_outbound.csv + +INBOUND_SPEC: school_escorting_inbound.csv +INBOUND_COEFFICIENTS: school_escorting_coefficients_inbound.csv + +OUTBOUND_COND_SPEC: school_escorting_outbound_cond.csv +OUTBOUND_COND_COEFFICIENTS: school_escorting_coefficients_outbound_cond.csv + +ALTS: school_escorting_alts.csv + +LOGIT_TYPE: MNL + +NUM_ESCORTEES: 3 +NUM_CHAPERONES: 2 + +preprocessor_outbound: + SPEC: school_escorting_preprocessor_outbound + DF: df + TABLES: + - persons + - tours + +preprocessor_inbound: + SPEC: school_escorting_preprocessor_inbound + DF: df + TABLES: + - persons + - tours + +preprocessor_outbound_cond: + SPEC: school_escorting_preprocessor_outbound + DF: df + TABLES: + - persons + - tours + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - income + - auto_ownership + - num_workers + +CONSTANTS: + max_bin_difference_between_departure_times: 1 + mins_per_time_bin: 60 diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_alts.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_alts.csv new file mode 100644 index 000000000..eca661755 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_alts.csv @@ -0,0 +1,158 @@ +Alt,bundle1,bundle2,bundle3,chauf1,chauf2,chauf3,nbund1,nbund2,nbundles,nrs1,npe1,nrs2,npe2,Description +1,0,0,0,0,0,0,0,0,0,0,0,0,0,no one is escorted +2,1,0,0,1,0,0,1,0,1,1,0,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +3,1,0,0,2,0,0,1,0,1,0,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +4,1,0,0,3,0,0,0,1,1,0,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +5,1,0,0,4,0,0,0,1,1,0,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +6,0,1,0,0,1,0,1,0,1,1,0,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing +7,0,1,0,0,2,0,1,0,1,0,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort +8,0,1,0,0,3,0,0,1,1,0,0,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing +9,0,1,0,0,4,0,0,1,1,0,0,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort +10,0,0,1,0,0,1,1,0,1,1,0,0,0,child 3 is escorted in bundle 1 by chauffeur 1 as ride sharing +11,0,0,1,0,0,2,1,0,1,0,1,0,0,child 3 is escorted in bundle 1 by chauffeur 1 as pure escort +12,0,0,1,0,0,3,0,1,1,0,0,1,0,child 3 is escorted in bundle 1 by chauffeur 2 as ride sharing +13,0,0,1,0,0,4,0,1,1,0,0,0,1,child 3 is escorted in bundle 1 by chauffeur 2 as pure escort +14,1,1,0,1,1,0,1,0,1,1,0,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing +15,1,1,0,2,2,0,1,0,1,0,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort +16,1,1,0,3,3,0,0,1,1,0,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing +17,1,1,0,4,4,0,0,1,1,0,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort +18,1,0,1,1,0,1,1,0,1,1,0,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing +19,1,0,1,2,0,2,1,0,1,0,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort +20,1,0,1,3,0,3,0,1,1,0,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing +21,1,0,1,4,0,4,0,1,1,0,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort +22,0,1,1,0,1,1,1,0,1,1,0,0,0,child 2 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing +23,0,1,1,0,2,2,1,0,1,0,1,0,0,child 2 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort +24,0,1,1,0,3,3,0,1,1,0,0,1,0,child 2 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing +25,0,1,1,0,4,4,0,1,1,0,0,0,1,child 2 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort +26,1,2,0,1,2,0,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +27,1,2,0,1,3,0,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +28,1,2,0,1,4,0,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +29,1,2,0,2,1,0,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +30,1,2,0,2,2,0,2,0,2,0,2,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +31,1,2,0,2,3,0,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +32,1,2,0,2,4,0,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +33,1,2,0,3,1,0,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +34,1,2,0,3,2,0,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +35,1,2,0,3,4,0,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +36,1,2,0,4,1,0,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +37,1,2,0,4,2,0,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +38,1,2,0,4,3,0,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +39,1,2,0,4,4,0,0,2,2,0,0,0,2,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +40,1,0,2,1,0,2,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +41,1,0,2,1,0,3,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +42,1,0,2,1,0,4,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +43,1,0,2,2,0,1,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +44,1,0,2,2,0,2,2,0,2,0,2,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +45,1,0,2,2,0,3,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +46,1,0,2,2,0,4,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +47,1,0,2,3,0,1,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +48,1,0,2,3,0,2,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +49,1,0,2,3,0,4,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +50,1,0,2,4,0,1,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +51,1,0,2,4,0,2,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +52,1,0,2,4,0,3,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +53,1,0,2,4,0,4,0,2,2,0,0,0,2,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +54,0,1,2,0,1,2,2,0,2,1,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +55,0,1,2,0,1,3,1,1,2,1,0,1,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +56,0,1,2,0,1,4,1,1,2,1,0,0,1,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +57,0,1,2,0,2,1,2,0,2,1,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +58,0,1,2,0,2,2,2,0,2,0,2,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +59,0,1,2,0,2,3,1,1,2,0,1,1,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +60,0,1,2,0,2,4,1,1,2,0,1,0,1,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +61,0,1,2,0,3,1,1,1,2,1,0,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +62,0,1,2,0,3,2,1,1,2,0,1,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +63,0,1,2,0,3,4,0,2,2,0,0,1,1,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +64,0,1,2,0,4,1,1,1,2,1,0,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +65,0,1,2,0,4,2,1,1,2,0,1,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +66,0,1,2,0,4,3,0,2,2,0,0,1,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +67,0,1,2,0,4,4,0,2,2,0,0,0,2,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +68,1,1,1,1,1,1,1,0,1,1,0,0,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing" +69,1,1,1,2,2,2,1,0,1,0,1,0,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort" +70,1,1,1,3,3,3,0,1,1,0,0,1,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing" +71,1,1,1,4,4,4,0,1,1,0,0,0,1,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort" +72,1,2,3,1,2,2,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +73,1,2,3,1,2,3,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +74,1,2,3,1,2,4,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +75,1,2,3,1,3,2,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +76,1,2,3,1,3,4,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +77,1,2,3,1,4,2,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +78,1,2,3,1,4,3,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +79,1,2,3,1,4,4,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +80,1,2,3,2,1,2,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +81,1,2,3,2,1,3,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +82,1,2,3,2,1,4,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +83,1,2,3,2,2,1,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +84,1,2,3,2,2,2,3,0,3,0,3,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +85,1,2,3,2,2,3,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +86,1,2,3,2,2,4,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +87,1,2,3,2,3,1,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +88,1,2,3,2,3,2,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +89,1,2,3,2,3,4,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +90,1,2,3,2,4,1,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +91,1,2,3,2,4,2,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +92,1,2,3,2,4,3,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +93,1,2,3,2,4,4,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +94,1,2,3,3,1,2,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +95,1,2,3,3,1,4,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +96,1,2,3,3,2,1,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +97,1,2,3,3,2,2,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +98,1,2,3,3,2,4,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +99,1,2,3,3,4,1,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +100,1,2,3,3,4,2,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +101,1,2,3,3,4,4,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +102,1,2,3,4,1,2,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +103,1,2,3,4,1,3,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +104,1,2,3,4,1,4,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +105,1,2,3,4,2,1,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +106,1,2,3,4,2,2,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +107,1,2,3,4,2,3,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +108,1,2,3,4,2,4,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +109,1,2,3,4,3,1,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +110,1,2,3,4,3,2,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +111,1,2,3,4,3,4,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +112,1,2,3,4,4,1,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +113,1,2,3,4,4,2,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +114,1,2,3,4,4,3,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +115,1,2,3,4,4,4,0,3,3,0,0,0,3,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +116,1,1,2,1,1,2,2,0,2,1,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +117,1,1,2,1,1,3,1,1,2,1,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +118,1,1,2,1,1,4,1,1,2,1,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +119,1,1,2,2,2,1,2,0,2,1,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +120,1,1,2,2,2,2,2,0,2,0,2,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +121,1,1,2,2,2,3,1,1,2,0,1,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +122,1,1,2,2,2,4,1,1,2,0,1,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +123,1,1,2,3,3,1,1,1,2,1,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +124,1,1,2,3,3,2,1,1,2,0,1,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +125,1,1,2,3,3,4,0,2,2,0,0,1,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +126,1,1,2,4,4,1,1,1,2,1,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +127,1,1,2,4,4,2,1,1,2,0,1,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +128,1,1,2,4,4,3,0,2,2,0,0,1,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +129,1,1,2,4,4,4,0,2,2,0,0,0,2,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +130,1,2,1,1,2,1,2,0,2,1,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +131,1,2,1,1,3,1,1,1,2,1,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +132,1,2,1,1,4,1,1,1,2,1,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +133,1,2,1,2,1,2,2,0,2,1,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +134,1,2,1,2,2,2,2,0,2,0,2,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +135,1,2,1,2,3,2,1,1,2,0,1,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +136,1,2,1,2,4,2,1,1,2,0,1,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +137,1,2,1,3,1,3,1,1,2,1,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +138,1,2,1,3,2,3,1,1,2,0,1,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +139,1,2,1,3,4,3,0,2,2,0,0,1,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +140,1,2,1,4,1,4,1,1,2,1,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +141,1,2,1,4,2,4,1,1,2,0,1,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +142,1,2,1,4,3,4,0,2,2,0,0,1,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +143,1,2,1,4,4,4,0,2,2,0,0,0,2,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +144,1,2,2,1,2,2,2,0,2,1,1,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +145,1,2,2,1,3,3,1,1,2,1,0,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +146,1,2,2,1,4,4,1,1,2,1,0,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +147,1,2,2,2,1,1,2,0,2,1,1,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +148,1,2,2,2,2,2,2,0,2,0,2,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +149,1,2,2,2,3,3,1,1,2,0,1,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +150,1,2,2,2,4,4,1,1,2,0,1,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +151,1,2,2,3,1,1,1,1,2,1,0,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +152,1,2,2,3,2,2,1,1,2,0,1,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +153,1,2,2,3,4,4,0,2,2,0,0,1,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +154,1,2,2,4,1,1,1,1,2,1,0,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +155,1,2,2,4,2,2,1,1,2,0,1,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +156,1,2,2,4,3,3,0,2,2,0,0,1,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +157,1,2,2,4,4,4,0,2,2,0,0,0,2,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_coefficients_inbound.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_coefficients_inbound.csv new file mode 100644 index 000000000..d6bc1947f --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_coefficients_inbound.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,0.17488,F +coef_child_age_6_to_15_noes,-0.41751,F +coef_child_age_u5_noes,-1.36718,F +coef_ln_dist_from_school_noes,-0.01787,F +coef_ln_dist_from_school_u6_noes,-0.23304,F +coef_ln_dist_from_school_6to9_noes,-0.07286,F +coef_child_age_16p_rs,1.97184 +coef_child_age_10to15_rs,1.73544 +coef_child_age_6to9_rs,1.73544 +coef_child_age_u6_rs,1.34996 +coef_hh_inc_u25k_noes,0.0,F +coef_hh_inc_25to50k_noes,0.0,F +coef_zero_auto_hh_noes,0.13165,F +coef_cars_lt_workers_rs,-3.35586,F +coef_cars_lt_workers_pe,-1.59062,F +coef_chauf_female_rs,-0.44827,F +coef_chauf_male_rs,-0.90832,F +coef_chauf_female_pe,-0.68399,F +coef_chauf_male_pe,-1.01783,F +coef_chauf_pt_worker_rs,0.51244,F +coef_chauf_pt_worker_pe,0.23496,F +coef_chauf_non_worker_pe,0.69245,F +coef_chauf_univ_stud_re,0.47395,F +coef_chauf_age_u35_pe,0.00000,F +coef_chauf_time_to_work_or_univ_rs,-0.01974,F +coef_chauf_walk_dist_to_work_or_univ_rs,-0.73155,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.00000,F +coef_abs_dev_distance,-0.08011,F +coef_rel_dev_distance,0.0,F +coef_same_taz_escort,1.44053,F +coef_same_taz_no_escort,1.96760,F +coef_no_escort_outbound,2.04553,F +coef_outbound_rs,0.0,F +coef_same_chauf,1.14533,F +coef_calib_child_age_u6_rs,8.75011,F +coef_calib_child_age_16p_rs,-0.20,F +coef_calib_child_age_6to15_noes,-3.46362,F +coef_calib_child_age_u6_noes,-0.36565,F +coef_calib_child_age_6to15_rs,-1.45344,F diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_coefficients_outbound.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_coefficients_outbound.csv new file mode 100644 index 000000000..14f1e7d5f --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_coefficients_outbound.csv @@ -0,0 +1,40 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,1.07391,F +coef_child_age_10_to_15_noes,0.46127,F +coef_child_age_u9_noes,0.13590,F +coef_ln_dist_to_school_noes,-0.33583,F +coef_ln_dist_to_school_u6_noes,-1.00920,F +coef_ln_dist_to_school_6to9_noes,-0.11156,F +coef_child_age_16p_rs,0.34244,F +coef_child_age_10to15_rs,.27494,F +coef_child_age_6to9_rs,0.20757,F +coef_child_age_u6_rs,0.33051,F +coef_child_dist_pe,-0.04593,F +coef_hh_inc_u25k_noes,0.18901,F +coef_hh_inc_25to50k_noes,0.12172,F +coef_zero_auto_hh_noes,9.0,F +coef_cars_lt_workers_rs,-0.88274,F +coef_cars_lt_workers_pe,-0.55291,F +coef_chauf_female_rs,-0.27828,F +coef_chauf_male_rs,-0.67320,F +coef_chauf_female_pe,-0.84859,F +coef_chauf_male_pe,-0.80965,F +coef_chauf_pt_worker_rs,-0.11422,F +coef_chauf_pt_worker_pe,0.51792,F +coef_chauf_non_worker_pe,0.51577,F +coef_chauf_univ_stud_re,0.0,F +coef_chauf_age_u35_pe,-0.33715,F +coef_chauf_time_to_work_or_univ_rs,0.0,F +coef_chauf_walk_dist_to_work_or_univ_rs,0.77326,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.0,F +coef_abs_dev_distance,-0.07136,F +coef_rel_dev_distance,-0.09951,F +coef_same_taz_escort,2.77591,F +coef_same_taz_no_escort,2.62969,F +coef_same_taz_no_escort_23,2.21201,F +coef_calib_child_age_u6_rs,8.55400,F +coef_calib_child_age_16p_rs,-0.2000,F +coef_calib_child_age_6to15_noes,-4.53067,F +coef_calib_child_age_6to15_rs,-2.41923,F +coef_calib_child_age_u6_noes,-0.67902,F diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_coefficients_outbound_cond.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_coefficients_outbound_cond.csv new file mode 100644 index 000000000..079d1e845 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_coefficients_outbound_cond.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,-0.33924,F +coef_child_age_10_to_15_noes,-0.78691,F +coef_child_age_u9_noes,-0.97142,F +coef_ln_dist_to_school_noes,-0.02112,F +coef_ln_dist_to_school_u6_noes,-0.81356,F +coef_ln_dist_to_school_6to9_noes,-0.12910,F +coef_child_age_16p_rs,0.93518,F +coef_child_age_10to15_rs,0.58009,F +coef_child_age_6to9_rs,0.36698,F +coef_child_age_u6_rs,0.29043,F +coef_child_dist_pe,-0.03624,F +coef_hh_inc_u25k_noes,0.33839,F +coef_hh_inc_25to50k_noes,0.05888,F +coef_zero_auto_hh_noes,9.00000,F +coef_cars_lt_workers_rs,-0.38588,F +coef_cars_lt_workers_pe,-0.01213,F +coef_chauf_female_rs,-0.49717,F +coef_chauf_male_rs,-0.94654,F +coef_chauf_female_pe,-0.98546,F +coef_chauf_male_pe,-1.05266,F +coef_chauf_pt_worker_rs,-0.74807,F +coef_chauf_pt_worker_pe,0.31729,F +coef_chauf_non_worker_pe,0.19211,F +coef_chauf_univ_stud_re,0.0,F +coef_chauf_age_u35_pe,-0.41194,F +coef_chauf_time_to_work_or_univ_rs,0.0,F +coef_chauf_walk_dist_to_work_or_univ_rs,0.38819,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.0,F +coef_abs_dev_distance,-0.04497,F +coef_rel_dev_distance,-0.09067,F +coef_same_taz_escort,2.56855,F +coef_same_taz_no_escort,2.21201,F +coef_no_escort_inbound,1.72902,F +coef_same_chauf,0.99276,F +coef_calib_child_age_u6_rs,8.55400,F +coef_calib_child_age_16p_rs,-0.2000,F +coef_calib_child_age_6to15_noes,-4.53067,F +coef_calib_child_age_6to15_rs,-2.41923,F +coef_calib_child_age_u6_noes,-0.67902,F diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_inbound.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_inbound.csv new file mode 100644 index 000000000..5dcc1ec37 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_inbound.csv @@ -0,0 +1,175 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school1 / mins_per_time_bin) - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school1 / mins_per_time_bin) - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school2 / mins_per_time_bin) - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school2 / mins_per_time_bin) - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school3 / mins_per_time_bin) - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school3 / mins_per_time_bin) - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school1 - (df.time_home_to_school1 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school1 - (df.time_home_to_school1 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school2 - (df.time_home_to_school2 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school2 - (df.time_home_to_school2 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school3 - (df.time_home_to_school3 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school3 - (df.time_home_to_school3 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_time_span11,Chauffeur 1 and Child 1 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf1 == 1) | (chauf1 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school1),coef_unavail +util_time_span12,Chauffeur 1 and Child 2 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf2 == 1) | (chauf2 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school2),coef_unavail +util_time_span13,Chauffeur 1 and Child 3 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf3 == 1) | (chauf3 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school3),coef_unavail +util_time_span21,Chauffeur 2 and Child 1 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf1 == 3) | (chauf1 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school1),coef_unavail +util_time_span22,Chauffeur 2 and Child 2 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf2 == 3) | (chauf2 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school2),coef_unavail +util_time_span23,Chauffeur 2 and Child 3 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf3 == 3) | (chauf3 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_6_to_15_noes,Child 1 age 6 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_child_age_6_to_15_noes +util_child2_age_6_to_15_noes,Child 2 age 6 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_child_age_6_to_15_noes +util_child3_age_6_to_15_noes,Child 3 age 6 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_child_age_6_to_15_noes +util_child1_age_u5_noes,Child 1 age 5 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_child_age_u5_noes +util_child2_age_u5_noes,Child 2 age 5 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_child_age_u5_noes +util_child3_age_u5_noes,Child 3 age 5 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_child_age_u5_noes +util_ln_dist_from_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_6to9_noes +util_ln_dist_from_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_6to9_noes +util_ln_dist_from_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_school_to_home1 > 30),coef_unavail +util_child2_dist_pe,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_school_to_home2 > 30),coef_unavail +util_child3_dist_pe,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_school_to_home3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_mand_to_home1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_mand_to_home2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_mand1_to_home < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_mand2_to_home < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_in_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_in_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_in_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_in_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort +# ,Outbound Terms,, +util_no_escort_outbound_child1,No escorting in outbound direction - Child 1,(bundle1 == 0) & (child_id1 > 0) & (bundle1_outbound == 0),coef_no_escort_outbound +util_no_escort_outbound_child2,No escorting in outbound direction - Child 2,(bundle2 == 0) & (child_id2 > 0) & (bundle2_outbound == 0),coef_no_escort_outbound +util_no_escort_outbound_child3,No escorting in outbound direction - Child 3,(bundle3 == 0) & (child_id3 > 0) & (bundle3_outbound == 0),coef_no_escort_outbound +util_outbound_rs_child1,Ride sharing in the outbound direction - Child 1,((chauf1 == 1) | (chauf1 == 3)) & ((chauf1_outbound == 1) | (chauf1_outbound == 3)),coef_outbound_rs +util_outbound_rs_child1,Ride sharing in the outbound direction - Child 2,((chauf2 == 1) | (chauf2 == 3)) & ((chauf2_outbound == 1) | (chauf2_outbound == 3)),coef_outbound_rs +util_outbound_rs_child1,Ride sharing in the outbound direction - Child 3,((chauf3 == 1) | (chauf3 == 3)) & ((chauf3_outbound == 1) | (chauf3_outbound == 3)),coef_outbound_rs +util_same_chauf,Same chauffeur in both directions (not child specific) - chauf 1 inbound & outbound,((chauf1 == 1) | (chauf1 == 2) | (chauf2 == 1) | (chauf2 == 2) | (chauf3 == 1) | (chauf3 == 2)) & ((nbund1_outbound > 0)),coef_same_chauf +util_same_chauf,Same chauffeur in both directions (not child specific) - chauf 2 inbound & outbound,((chauf1 == 3) | (chauf1 == 4) | (chauf2 == 3) | (chauf2 == 4) | (chauf3 == 3) | (chauf3 == 4)) & ((nbund2_outbound > 0)),coef_same_chauf +# ,Calibration Constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_u6_noes,Child 1 age 5 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 5 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 5 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_outbound.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_outbound.csv new file mode 100644 index 000000000..53f5f4ccb --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_outbound.csv @@ -0,0 +1,163 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_10_to_15_noes,Child 1 age 10 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10_to_15_noes +util_child2_age_10_to_15_noes,Child 2 age 10 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10_to_15_noes +util_child3_age_10_to_15_noes,Child 3 age 10 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10_to_15_noes +util_child1_age_u9_noes,Child 1 age 9 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 10),coef_child_age_u9_noes +util_child2_age_u9_noes,Child 2 age 9 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 10),coef_child_age_u9_noes +util_child3_age_u9_noes,Child 3 age 9 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 10),coef_child_age_u9_noes +util_ln_dist_to_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe,Child 1 distance to school - Pure escorting,((chauf1 == 2) | (chauf1 == 4)) * dist_home_to_school1,coef_child_dist_pe +util_child2_dist_pe,Child 2 distance to school - Pure escorting,((chauf2 == 2) | (chauf2 == 4)) * dist_home_to_school2,coef_child_dist_pe +util_child3_dist_pe,Child 3 distance to school - Pure escorting,((chauf3 == 2) | (chauf3 == 4)) * dist_home_to_school3,coef_child_dist_pe +util_child1_dist_pe,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_home_to_school1 > 30),coef_unavail +util_child2_dist_pe,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_home_to_school2 > 30),coef_unavail +util_child3_dist_pe,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_home_to_school3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_home_to_mand1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_home_to_mand2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_home_to_mand1 < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_home_to_mand2 < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort_23 +# ,Calibration constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs +util_calib_child1_age_u6_noes,Child 1 age 6 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 6 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 6 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_outbound_cond.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_outbound_cond.csv new file mode 100644 index 000000000..00527edc3 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_outbound_cond.csv @@ -0,0 +1,169 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_10_to_15_noes,Child 1 age 10 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10_to_15_noes +util_child2_age_10_to_15_noes,Child 2 age 10 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10_to_15_noes +util_child3_age_10_to_15_noes,Child 3 age 10 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10_to_15_noes +util_child1_age_u9_noes,Child 1 age 9 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 10),coef_child_age_u9_noes +util_child2_age_u9_noes,Child 2 age 9 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 10),coef_child_age_u9_noes +util_child3_age_u9_noes,Child 3 age 9 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 10),coef_child_age_u9_noes +util_ln_dist_to_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe,Child 1 distance to school - Pure escorting,((chauf1 == 2) | (chauf1 == 4)) * dist_home_to_school1,coef_child_dist_pe +util_child2_dist_pe,Child 2 distance to school - Pure escorting,((chauf2 == 2) | (chauf2 == 4)) * dist_home_to_school2,coef_child_dist_pe +util_child3_dist_pe,Child 3 distance to school - Pure escorting,((chauf3 == 2) | (chauf3 == 4)) * dist_home_to_school3,coef_child_dist_pe +util_child1_dist_pe,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_home_to_school1 > 30),coef_unavail +util_child2_dist_pe,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_home_to_school2 > 30),coef_unavail +util_child3_dist_pe,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_home_to_school3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_home_to_mand1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_home_to_mand2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_home_to_mand1 < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_home_to_mand2 < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort +# ,Constants related to inbound choice,, +util_no_escort_inbound_child1,No escorting in inbound direction - Child 1,(bundle1 == 0) & (child_id1 > 0) & (bundle1_inbound == 0),coef_no_escort_inbound +util_no_escort_inbound_child2,No escorting in inbound direction - Child 2,(bundle2 == 0) & (child_id2 > 0) & (bundle2_inbound == 0),coef_no_escort_inbound +util_no_escort_inbound_child3,No escorting in inbound direction - Child 3,(bundle3 == 0) & (child_id3 > 0) & (bundle3_inbound == 0),coef_no_escort_inbound +util_same_chauf,Same chauffeur in both directions (not child specific) - chauf 1 inbound & inbound,((chauf1 == 1) | (chauf1 == 2) | (chauf2 == 1) | (chauf2 == 2) | (chauf3 == 1) | (chauf3 == 2)) & ((nbund1_inbound > 0)),coef_same_chauf +util_same_chauf,Same chauffeur in both directions (not child specific) - chauf 2 inbound & inbound,((chauf1 == 3) | (chauf1 == 4) | (chauf2 == 3) | (chauf2 == 4) | (chauf3 == 3) | (chauf3 == 4)) & ((nbund2_inbound > 0)),coef_same_chauf +# ,Calibration constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs +util_calib_child1_age_u6_noes,Child 1 age 6 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 6 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 6 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_preprocessor_inbound.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_preprocessor_inbound.csv new file mode 100644 index 000000000..5efc87882 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_preprocessor_inbound.csv @@ -0,0 +1,148 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.sex, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.sex, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times from school and work ,, +Preferred departure time from school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred departure time from school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred departure time from school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +# setting preffered departure time for chauffer to the last mandatory tour of the day for inbound escorting ,, +Preferred departure time from work / univ - chauffer 1 - tour 1,pref_depart_time_chauf1_tour1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred departure time from work / univ - chauffer 2 - tour 1,pref_depart_time_chauf2_tour1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" +Preferred departure time from work / univ - chauffer 1 - tour 2,pref_depart_time_chauf1_tour2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 2)].set_index('person_id').end, df.chauf_id1)" +Preferred departure time from work / univ - chauffer 2 - tour 2,pref_depart_time_chauf2_tour2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 2)].set_index('person_id').end, df.chauf_id2)" +Preferred departure time from work / univ - chauffer 1,pref_depart_time_chauf1,"np.where(pref_depart_time_chauf1_tour2 > pref_depart_time_chauf1_tour1, pref_depart_time_chauf1_tour2, pref_depart_time_chauf1_tour1)" +Preferred departure time from work / univ - chauffer 1,pref_depart_time_chauf2,"np.where(pref_depart_time_chauf2_tour2 > pref_depart_time_chauf2_tour1, pref_depart_time_chauf2_tour2, pref_depart_time_chauf2_tour1)" +# Distances and times to school and work ,, +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SOV_TIME', 'PM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SOV_TIME', 'PM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SOV_TIME', 'PM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SOV_TIME', 'PM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SOV_TIME', 'PM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SOV_TIME', 'PM')), 0)" +Auto dist home to school - child 1,dist_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SOV_DIST', 'PM')), 0)" +Auto dist home to school - child 2,dist_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SOV_DIST', 'PM')), 0)" +Auto dist home to school - child 3,dist_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SOV_DIST', 'PM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SOV_DIST', 'PM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SOV_DIST', 'PM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SOV_DIST', 'PM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SOV_TIME', 'PM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SOV_TIME', 'PM')), 0)" +Auto time work or university to home - chauffer 1,time_mand_to_home1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(_valid_mandatory_location_chauf1, df.home_zone_id, ('SOV_TIME', 'PM')), 0)" +Auto time work or university to home - chauffer 2,time_mand_to_home2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(_valid_mandatory_location_chauf2, df.home_zone_id, ('SOV_TIME', 'PM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SOV_DIST', 'PM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SOV_DIST', 'PM')), 0)" +Auto dist work or university to home - chauffer 1,dist_mand1_to_home,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(_valid_mandatory_location_chauf1, df.home_zone_id, ('SOV_DIST', 'PM')), 0)" +Auto dist work or university to home - chauffer 2,dist_mand2_to_home,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(_valid_mandatory_location_chauf2, df.home_zone_id, ('SOV_DIST', 'PM')), 0)" +# inbound distance combinations between chauffeurs and children,, +Distance from chauffeur 1 mandatory location to child 1 school,time_mand1_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 2 school,time_mand1_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 3 school,time_mand1_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 1 school,time_mand2_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 2 school,time_mand2_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 3 school,time_mand2_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 1 school,_dist_mand1_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child1, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 1 school,_dist_mand2_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child1, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 2 school,_dist_mand1_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child2, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 2 school,_dist_mand2_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child2, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 3 school,_dist_mand1_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child3, ('SOV_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 3 school,_dist_mand2_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child3, ('SOV_TIME', 'PM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('SOV_TIME', 'PM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('SOV_TIME', 'PM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('SOV_TIME', 'PM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('SOV_TIME', 'PM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('SOV_TIME', 'PM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('SOV_TIME', 'PM')), 0)" +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# absolute deviation distance inbound,, +Absolute deviation inbound distance Child 1 Chauffer 1,abs_dev_dist_in_child1_chauf1,"np.maximum(_dist_mand1_to_school1 + dist_school_to_home1 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 1 Chauffer 2,abs_dev_dist_in_child1_chauf2,"np.maximum(_dist_mand2_to_school1 + dist_school_to_home1 - dist_mand2_to_home,0)" +Absolute deviation inbound distance Child 2 Chauffer 1,abs_dev_dist_in_child2_chauf1,"np.maximum(_dist_mand1_to_school2 + dist_school_to_home2 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 2 Chauffer 2,abs_dev_dist_in_child2_chauf2,"np.maximum(_dist_mand2_to_school2 + dist_school_to_home2 - dist_mand2_to_home,0)" +Absolute deviation inbound distance Child 3 Chauffer 1,abs_dev_dist_in_child3_chauf1,"np.maximum(_dist_mand1_to_school3 + dist_school_to_home3 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 3 Chauffer 2,abs_dev_dist_in_child3_chauf2,"np.maximum(_dist_mand2_to_school3 + dist_school_to_home3 - dist_mand2_to_home,0)" +Absolute deviation inbound distance child12 Chauffer 1,abs_dev_dist_in_child12_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school1_to_school2 + dist_school_to_home2, _dist_mand1_to_school2 + _dist_school2_to_school1 + dist_school_to_home1) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child12 Chauffer 2,abs_dev_dist_in_child12_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school1_to_school2 + dist_school_to_home2, _dist_mand2_to_school2 + _dist_school2_to_school1 + dist_school_to_home1) - dist_mand2_to_home, 0)" +Absolute deviation inbound distance child13 Chauffer 1,abs_dev_dist_in_child13_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school1_to_school3 + dist_school_to_home2, _dist_mand1_to_school3 + _dist_school3_to_school1 + dist_school_to_home3) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child13 Chauffer 2,abs_dev_dist_in_child13_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2, _dist_mand2_to_school3 + _dist_school3_to_school1 + dist_school_to_home3) - dist_mand2_to_home, 0)" +Absolute deviation inbound distance child23 Chauffer 1,abs_dev_dist_in_child23_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school2_to_school3 + dist_school_to_home3, _dist_mand1_to_school3 + _dist_school3_to_school2 + dist_school_to_home2) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child23 Chauffer 2,abs_dev_dist_in_child23_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school2_to_school3 + dist_school_to_home3, _dist_mand2_to_school3 + _dist_school3_to_school2 + dist_school_to_home2) - dist_mand2_to_home, 0)" +,_dist_mand1_school1_school2_school3,"_dist_mand1_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + dist_school_to_home1" +,_dist_mand1_school1_school3_school2,"_dist_mand1_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + dist_school_to_home1" +,_dist_mand1_school2_school1_school3,"_dist_mand1_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2" +,_dist_mand1_school2_school3_school1,"_dist_mand1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + dist_school_to_home2" +,_dist_mand1_school3_school1_school2,"_dist_mand1_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + dist_school_to_home3" +,_dist_mand1_school3_school2_school1,"_dist_mand1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + dist_school_to_home3" +,_min_dist_dropoff_order_in_child123_chauf1,_dist_mand1_school1_school2_school3 +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school1_school3_school2 > 0) & (_dist_mand1_school1_school3_school2 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school1_school3_school2, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school2_school1_school3 > 0) & (_dist_mand1_school2_school1_school3 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school2_school1_school3, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school2_school3_school1 > 0) & (_dist_mand1_school2_school3_school1 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school2_school3_school1, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school3_school1_school2 > 0) & (_dist_mand1_school3_school1_school2 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school3_school1_school2, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school3_school2_school1 > 0) & (_dist_mand1_school3_school2_school1 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school3_school2_school1, _min_dist_dropoff_order_in_child123_chauf1)" +Absolute deviation inbound distance child123 Chauffer 1,abs_dev_dist_in_child123_chauf1,"np.maximum(_min_dist_dropoff_order_in_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_mand2_school1_school2_school3,"_dist_mand2_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + dist_school_to_home1" +,_dist_mand2_school1_school3_school2,"_dist_mand2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + dist_school_to_home1" +,_dist_mand2_school2_school1_school3,"_dist_mand2_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2" +,_dist_mand2_school2_school3_school1,"_dist_mand2_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + dist_school_to_home2" +,_dist_mand2_school3_school1_school2,"_dist_mand2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + dist_school_to_home3" +,_dist_mand2_school3_school2_school1,"_dist_mand2_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + dist_school_to_home3" +,_min_dist_dropoff_order_in_child123_chauf2,_dist_mand2_school1_school2_school3 +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school1_school3_school2 > 0) & (_dist_mand2_school1_school3_school2 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school1_school3_school2, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school2_school1_school3 > 0) & (_dist_mand2_school2_school1_school3 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school2_school1_school3, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school2_school3_school1 > 0) & (_dist_mand2_school2_school3_school1 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school2_school3_school1, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school3_school1_school2 > 0) & (_dist_mand2_school3_school1_school2 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school3_school1_school2, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school3_school2_school1 > 0) & (_dist_mand2_school3_school2_school1 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school3_school2_school1, _min_dist_dropoff_order_in_child123_chauf2)" +Absolute deviation inbound distance child123 Chauffer 2,abs_dev_dist_in_child123_chauf2,"np.maximum(_min_dist_dropoff_order_in_child123_chauf2 - dist_home_to_mand1, 0)" +# overlapping time windows from outbound escorting,, +Preferred departure time to school - child 1,_pref_depart_time_to_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,_pref_depart_time_to_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,_pref_depart_time_to_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +,return_min_taking_outbound_child1_pe,"(_pref_depart_time_to_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,return_min_taking_outbound_child2_pe,"(_pref_depart_time_to_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,return_min_taking_outbound_child3_pe,"(_pref_depart_time_to_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +finding latest time chauffer 1 returns from outbound pure escort tour,outbound_pe_return_time_home_chauf1,-1 +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf1_outbound == 2) & (return_min_taking_outbound_child1_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child1_pe, outbound_pe_return_time_home_chauf1)" +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf2_outbound == 2) & (return_min_taking_outbound_child2_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child2_pe, outbound_pe_return_time_home_chauf1)" +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf3_outbound == 2) & (return_min_taking_outbound_child3_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child3_pe, outbound_pe_return_time_home_chauf1)" +finding latest time chauffer 2 returns from outbound pure escort tour,outbound_pe_return_time_home_chauf2,-1 +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf1_outbound == 4) & (return_min_taking_outbound_child1_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child1_pe, outbound_pe_return_time_home_chauf2)" +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf2_outbound == 4) & (return_min_taking_outbound_child2_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child2_pe, outbound_pe_return_time_home_chauf2)" +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf3_outbound == 4) & (return_min_taking_outbound_child3_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child3_pe, outbound_pe_return_time_home_chauf2)" +finding latest time chauffer 1 returns from outbound ride share tour,outbound_rs_return_time_home_chauf1,pref_depart_time_chauf1 +finding latest time chauffer 2 returns from outbound ride share tour,outbound_rs_return_time_home_chauf2,pref_depart_time_chauf2 +return time of outbound school escoring tour - chauffeur 1,return_bin_outbound_school_escorting1,"np.where(df.nrs1_outbound > 0, pref_depart_time_chauf1, outbound_pe_return_time_home_chauf1 / mins_per_time_bin)" +return time of outbound school escoring tour - chauffeur 2,return_bin_outbound_school_escorting2,"np.where(df.nrs2_outbound > 0, pref_depart_time_chauf2, outbound_pe_return_time_home_chauf2 / mins_per_time_bin)" diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_preprocessor_outbound.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_preprocessor_outbound.csv new file mode 100644 index 000000000..78b7f2578 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_preprocessor_outbound.csv @@ -0,0 +1,122 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.sex, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.sex, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times to school and work +Preferred departure time to school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +Preferred departure time to work / univ - chauffer 1,pref_depart_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id1)" +Preferred departure time to work / univ - chauffer 2,pref_depart_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id2)" +# Distances and times to school and work +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SOV_TIME', 'AM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SOV_TIME', 'AM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SOV_TIME', 'AM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SOV_TIME', 'AM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SOV_TIME', 'AM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SOV_TIME', 'AM')), 0)" +Auto dist home to school - child 1,dist_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SOV_DIST', 'AM')), 0)" +Auto dist home to school - child 2,dist_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SOV_DIST', 'AM')), 0)" +Auto dist home to school - child 3,dist_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SOV_DIST', 'AM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SOV_DIST', 'AM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SOV_DIST', 'AM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SOV_DIST', 'AM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SOV_TIME', 'AM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SOV_TIME', 'AM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SOV_DIST', 'AM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SOV_DIST', 'AM')), 0)" +# outbound distance combinations between chauffeurs and children,, +Distance from child 1 school to chauffeur 1 mandatory location,_dist_school1_to_mand1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('SOV_TIME', 'AM')), 0)" +Distance from child 1 school to chauffeur 2 mandatory location,_dist_school1_to_mand2,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('SOV_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 1 mandatory location,_dist_school2_to_mand1,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('SOV_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 2 mandatory location,_dist_school2_to_mand2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('SOV_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 1 mandatory location,_dist_school3_to_mand1,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('SOV_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 2 mandatory location,_dist_school3_to_mand2,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('SOV_TIME', 'AM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('SOV_TIME', 'AM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('SOV_TIME', 'AM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('SOV_TIME', 'AM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('SOV_TIME', 'AM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('SOV_TIME', 'AM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('SOV_TIME', 'AM')), 0)" +# absolute deviation distance outbound,, +Absolute deviation outbound distance Child 1 Chauffer 1,abs_dev_dist_out_child1_chauf1,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 1 Chauffer 2,abs_dev_dist_out_child1_chauf2,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 2 Chauffer 1,abs_dev_dist_out_child2_chauf1,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 2 Chauffer 2,abs_dev_dist_out_child2_chauf2,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 3 Chauffer 1,abs_dev_dist_out_child3_chauf1,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 3 Chauffer 2,abs_dev_dist_out_child3_chauf2,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance child12 Chauffer 1,abs_dev_dist_out_child12_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child12 Chauffer 2,abs_dev_dist_out_child12_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child13 Chauffer 1,abs_dev_dist_out_child13_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child13 Chauffer 2,abs_dev_dist_out_child13_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child23 Chauffer 1,abs_dev_dist_out_child23_chauf1,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child23 Chauffer 2,abs_dev_dist_out_child23_chauf2,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2) - dist_home_to_mand2, 0)" +,_dist_school1_school2_school3_mand1,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1" +,_dist_school1_school3_school2_mand1,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1" +,_dist_school2_school1_school3_mand1,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1" +,_dist_school2_school3_school1_mand1,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1" +,_dist_school3_school1_school2_mand1,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1" +,_dist_school3_school2_school1_mand1,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1" +,_min_dist_dropoff_order_out_child123_chauf1,_dist_school1_school2_school3_mand1 +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school1_school3_school2_mand1 > 0) & (_dist_school1_school3_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school1_school3_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school1_school3_mand1 > 0) & (_dist_school2_school1_school3_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school1_school3_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school3_school1_mand1 > 0) & (_dist_school2_school3_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school3_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school1_school2_mand1 > 0) & (_dist_school3_school1_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school1_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school2_school1_mand1 > 0) & (_dist_school3_school2_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school2_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf1,"np.maximum(_min_dist_dropoff_order_out_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_school1_school2_school3_mand2,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2" +,_dist_school1_school3_school2_mand2,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2" +,_dist_school2_school1_school3_mand2,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2" +,_dist_school2_school3_school1_mand2,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2" +,_dist_school3_school1_school2_mand2,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2" +,_dist_school3_school2_school1_mand2,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2" +,_min_dist_dropoff_order_out_child123_chauf2,_dist_school1_school2_school3_mand2 +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school1_school3_school2_mand2 > 0) & (_dist_school1_school3_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school1_school3_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school1_school3_mand2 > 0) & (_dist_school2_school1_school3_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school1_school3_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school3_school1_mand2 > 0) & (_dist_school2_school3_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school3_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school1_school2_mand2 > 0) & (_dist_school3_school1_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school1_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school2_school1_mand2 > 0) & (_dist_school3_school2_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school2_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf2,"np.maximum(_min_dist_dropoff_order_out_child123_chauf2 - dist_home_to_mand2, 0)" +# Availability for multiple bundles,, +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# ,, +# Inbound specific terms ,, +Preferred return time from school - child 1,pref_return_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred return time from school - child 2,pref_return_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred return time from school - child 3,pref_return_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +Preferred return time from work / univ - chauffer 1,pref_return_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred return time from work / univ - chauffer 2,pref_return_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" diff --git a/activitysim/examples/prototype_mtc_extended/configs/school_escorting_preprocessor_outbound_cond.csv b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_preprocessor_outbound_cond.csv new file mode 100644 index 000000000..ede805b09 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/school_escorting_preprocessor_outbound_cond.csv @@ -0,0 +1,138 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.sex, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.sex, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times to school and work +Preferred departure time to school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +Preferred departure time to work / univ - chauffer 1,pref_depart_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id1)" +Preferred departure time to work / univ - chauffer 2,pref_depart_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id2)" +# Distances and times to school and work +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SOV_TIME', 'AM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SOV_TIME', 'AM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SOV_TIME', 'AM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('SOV_TIME', 'AM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('SOV_TIME', 'AM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('SOV_TIME', 'AM')), 0)" +Auto dist home to school - child 1,dist_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SOV_DIST', 'AM')), 0)" +Auto dist home to school - child 2,dist_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SOV_DIST', 'AM')), 0)" +Auto dist home to school - child 3,dist_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SOV_DIST', 'AM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('SOV_DIST', 'AM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('SOV_DIST', 'AM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('SOV_DIST', 'AM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SOV_TIME', 'AM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SOV_TIME', 'AM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('SOV_DIST', 'AM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('SOV_DIST', 'AM')), 0)" +# outbound distance combinations between chauffeurs and children,, +Distance from child 1 school to chauffeur 1 mandatory location,_dist_school1_to_mand1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('SOV_TIME', 'AM')), 0)" +Distance from child 1 school to chauffeur 2 mandatory location,_dist_school1_to_mand2,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('SOV_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 1 mandatory location,_dist_school2_to_mand1,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('SOV_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 2 mandatory location,_dist_school2_to_mand2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('SOV_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 1 mandatory location,_dist_school3_to_mand1,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('SOV_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 2 mandatory location,_dist_school3_to_mand2,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('SOV_TIME', 'AM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('SOV_TIME', 'AM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('SOV_TIME', 'AM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('SOV_TIME', 'AM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('SOV_TIME', 'AM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('SOV_TIME', 'AM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('SOV_TIME', 'AM')), 0)" +# absolute deviation distance outbound,, +Absolute deviation outbound distance Child 1 Chauffer 1,abs_dev_dist_out_child1_chauf1,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 1 Chauffer 2,abs_dev_dist_out_child1_chauf2,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 2 Chauffer 1,abs_dev_dist_out_child2_chauf1,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 2 Chauffer 2,abs_dev_dist_out_child2_chauf2,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 3 Chauffer 1,abs_dev_dist_out_child3_chauf1,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 3 Chauffer 2,abs_dev_dist_out_child3_chauf2,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance child12 Chauffer 1,abs_dev_dist_out_child12_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child12 Chauffer 2,abs_dev_dist_out_child12_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child13 Chauffer 1,abs_dev_dist_out_child13_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child13 Chauffer 2,abs_dev_dist_out_child13_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child23 Chauffer 1,abs_dev_dist_out_child23_chauf1,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child23 Chauffer 2,abs_dev_dist_out_child23_chauf2,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2) - dist_home_to_mand2, 0)" +,_dist_school1_school2_school3_mand1,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1" +,_dist_school1_school3_school2_mand1,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1" +,_dist_school2_school1_school3_mand1,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1" +,_dist_school2_school3_school1_mand1,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1" +,_dist_school3_school1_school2_mand1,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1" +,_dist_school3_school2_school1_mand1,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1" +,_min_dist_dropoff_order_out_child123_chauf1,_dist_school1_school2_school3_mand1 +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school1_school3_school2_mand1 > 0) & (_dist_school1_school3_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school1_school3_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school1_school3_mand1 > 0) & (_dist_school2_school1_school3_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school1_school3_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school3_school1_mand1 > 0) & (_dist_school2_school3_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school3_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school1_school2_mand1 > 0) & (_dist_school3_school1_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school1_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school2_school1_mand1 > 0) & (_dist_school3_school2_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school2_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf1,"np.maximum(_min_dist_dropoff_order_out_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_school1_school2_school3_mand2,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2" +,_dist_school1_school3_school2_mand2,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2" +,_dist_school2_school1_school3_mand2,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2" +,_dist_school2_school3_school1_mand2,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2" +,_dist_school3_school1_school2_mand2,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2" +,_dist_school3_school2_school1_mand2,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2" +,_min_dist_dropoff_order_out_child123_chauf2,_dist_school1_school2_school3_mand2 +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school1_school3_school2_mand2 > 0) & (_dist_school1_school3_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school1_school3_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school1_school3_mand2 > 0) & (_dist_school2_school1_school3_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school1_school3_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school3_school1_mand2 > 0) & (_dist_school2_school3_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school3_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school1_school2_mand2 > 0) & (_dist_school3_school1_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school1_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school2_school1_mand2 > 0) & (_dist_school3_school2_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school2_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf2,"np.maximum(_min_dist_dropoff_order_out_child123_chauf2 - dist_home_to_mand2, 0)" +# Availability for multiple bundles,, +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# ,, +# Inbound specific terms ,, +Preferred return time from school - child 1,pref_return_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred return time from school - child 2,pref_return_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred return time from school - child 3,pref_return_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +Preferred return time from work / univ - chauffer 1,pref_return_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred return time from work / univ - chauffer 2,pref_return_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" +# overlapping time windows from inbound escorting,, +# ,_return_min_taking_inbound_child1_pe,"(_pref_depart_time_to_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +# ,_return_min_taking_inbound_child2_pe,"(_pref_depart_time_to_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +# ,_return_min_taking_inbound_child3_pe,"(_pref_depart_time_to_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +# finding earliest time chauffer 1 returns from inbound pure escort tour,inbound_pe_return_time_home_chauf1,-1 +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf1_inbound == 2) & (_return_min_taking_inbound_child1_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child1_pe, inbound_pe_return_time_home_chauf1)" +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf2_inbound == 2) & (_return_min_taking_inbound_child2_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child2_pe, inbound_pe_return_time_home_chauf1)" +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf3_inbound == 2) & (_return_min_taking_inbound_child3_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child3_pe, inbound_pe_return_time_home_chauf1)" +# finding earliest time chauffer 2 returns from inbound pure escort tour,inbound_pe_return_time_home_chauf2,-1 +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf1_inbound == 2) & (_return_min_taking_inbound_child1_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child1_pe, inbound_pe_return_time_home_chauf2)" +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf2_inbound == 2) & (_return_min_taking_inbound_child2_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child2_pe, inbound_pe_return_time_home_chauf2)" +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf3_inbound == 2) & (_return_min_taking_inbound_child3_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child3_pe, inbound_pe_return_time_home_chauf2)" +# finding latest time chauffer 1 returns from inbound ride share tour,inbound_rs_return_time_home_chauf1,pref_depart_time_chauf1 +# finding latest time chauffer 2 returns from inbound ride share tour,inbound_rs_return_time_home_chauf2,pref_depart_time_chauf2 +# return time of inbound school escoring tour - chauffeur 1,return_bin_inbound_school_escorting1,"np.where(df.nrs1_inbound > 0, pref_depart_time_chauf1, inbound_pe_return_time_home_chauf1)" +# return time of inbound school escoring tour - chauffeur 2,return_bin_inbound_school_escorting2,"np.where(df.nrs2_inbound > 0, pref_depart_time_chauf2, inbound_pe_return_time_home_chauf2)" diff --git a/activitysim/examples/prototype_mtc_extended/configs/settings.yaml b/activitysim/examples/prototype_mtc_extended/configs/settings.yaml index b1faa5c25..1e96b19f4 100644 --- a/activitysim/examples/prototype_mtc_extended/configs/settings.yaml +++ b/activitysim/examples/prototype_mtc_extended/configs/settings.yaml @@ -26,6 +26,8 @@ input_table_list: workers: num_workers VEHICL: auto_ownership TAZ: home_zone_id + recode_columns: + home_zone_id: land_use.zone_id keep_columns: - home_zone_id - income @@ -58,6 +60,8 @@ input_table_list: rename_columns: TAZ: zone_id # person_id is the required index column COUNTY: county_id + recode_columns: + zone_id: zero-based keep_columns: - DISTRICT - SD @@ -102,7 +106,7 @@ check_for_variability: False # turn shadow_pricing on and off for all models (e.g. school and work) # shadow pricing is deprecated for less than full samples # see shadow_pricing.yaml for additional settings -use_shadow_pricing: False +use_shadow_pricing: True # turn writing of sample_tables on and off for all models # (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) @@ -155,7 +159,7 @@ keep_mem_logs: True # trace household id; comment out or leave empty for no trace # households with all tour types # [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] -trace_hh_id: 982875 +trace_hh_id: # trace origin, destination in accessibility calculation; comment out or leave empty for no trace # trace_od: [5, 11] @@ -165,7 +169,6 @@ trace_od: # to resume after last successful checkpoint, specify resume_after: _ #resume_after: trip_destination -resume_after: checkpoints: True # if checkpoints is False, no intermediate checkpoints will be written before the end of run @@ -179,6 +182,10 @@ checkpoints: True models: + ## Disaggregate Accessibility steps + - initialize_proto_population # Separate step so proto tables can be split for multiprocess. + - compute_disaggregate_accessibility + ## Main model steps - initialize_landuse - initialize_households - compute_accessibility @@ -190,6 +197,7 @@ models: - cdap_simulate - mandatory_tour_frequency - mandatory_tour_scheduling + - school_escorting - joint_tour_frequency - joint_tour_composition - joint_tour_participation @@ -222,11 +230,26 @@ output_tables: tables: - checkpoints - accessibility - - land_use - - households - - persons - - tours - - trips + - proto_disaggregate_accessibility + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id - joint_tour_participants - vehicles diff --git a/activitysim/examples/prototype_mtc_extended/configs/shadow_pricing.yaml b/activitysim/examples/prototype_mtc_extended/configs/shadow_pricing.yaml new file mode 100644 index 000000000..a8c1b62c6 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/shadow_pricing.yaml @@ -0,0 +1,56 @@ +shadow_pricing_models: + school: school_location + workplace: workplace_location + +# global switch to enable/disable loading of saved shadow prices +# (ignored if global use_shadow_pricing switch is False) +# (warm start not available for shadow price simulation method) +LOAD_SAVED_SHADOW_PRICES: False + +# write out choices by iteration to trace folder +WRITE_ITERATION_CHOICES: False + +# number of shadow price iterations for cold start +MAX_ITERATIONS: 10 + +# number of shadow price iterations for warm start (after loading saved shadow_prices) +MAX_ITERATIONS_SAVED: 1 + +## Shadow pricing method +# SHADOW_PRICE_METHOD: ctramp +# SHADOW_PRICE_METHOD: daysim +SHADOW_PRICE_METHOD: simulation + +# ignore criteria for zones smaller than size_threshold +SIZE_THRESHOLD: 10 +# ignore criteria for zones smaller than target_threshold (total employmnet or enrollment) +TARGET_THRESHOLD: 20 +# zone passes if modeled is within percent_tolerance of predicted_size +PERCENT_TOLERANCE: 5 +# max percentage of zones allowed to fail +FAIL_THRESHOLD: 1 + +# --- simulation method settings +# apply different targets for each segment specified in destination_size_terms.csv +school_segmentation_targets: + # format is segment: land_use_column + university: TOTENR_univ + highschool: HSENROLL + gradeschool: AGE0519 + +# if target names are the same, they will be combined together +workplace_segmentation_targets: + # using total employment scaled to remove external workers. see annotate_landuse.csv + work_low: TOTEMP_scaled + work_med: TOTEMP_scaled + work_high: TOTEMP_scaled + work_veryhigh: TOTEMP_scaled + +# --- ctramp method settings +DAMPING_FACTOR: 1 + +# --- daysim method settings +# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? +DAYSIM_ABSOLUTE_TOLERANCE: 50 +DAYSIM_PERCENT_TOLERANCE: 10 + diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_alternatives.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_alternatives.csv new file mode 100644 index 000000000..04a03d64f --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_alternatives.csv @@ -0,0 +1,21 @@ +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,out,in +0out_0in,0,0 +0out_1in,0,1 +0out_2in,0,2 +0out_3in,0,3 +1out_0in,1,0 +1out_1in,1,1 +1out_2in,1,2 +1out_3in,1,3 +2out_0in,2,0 +2out_1in,2,1 +2out_2in,2,2 +2out_3in,2,3 +3out_0in,3,0 +3out_1in,3,1 +3out_2in,3,2 +3out_3in,3,3 +# extension for flexible ids demonstration,, +# should be removed for actual model run,, +4out_3in,4,3 \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_escort.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_escort.csv new file mode 100644 index 000000000..40e0f4145 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_escort.csv @@ -0,0 +1,29 @@ +Description,value,coefficient_name +Number of HH Persons,-0.24,coef_number_of_hh_persons +Number of Students in HH,0.19,coef_number_of_students_in_hh +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.91,coef_dummy_for_walking_to_all_stops +Number of work tours undertaken by the person,-0.29,coef_number_of_work_tours_undertaken_by_the_person +Number of escort tours tours undertaken by the person,-0.15,coef_number_of_escort_tours_tours_undertaken_by_the_person +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.59,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +dummy for distance less than 5 Miles,0.32,coef_dummy_for_distance_less_than_5_miles +dummy for distance in miles,0.01,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-0.968,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in +Alternative specific constant for return stops,-2.41,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops on joint tours,-2.796,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in +Alternative specific constant for the total number of stops on joint tours,0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in +Alternative specific constant for return stops,-3.024,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for return stops on joint tours,-3.379,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +Alternative specific constant for outbound stops,-2.173,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for outbound stops on joint tours,-1.783,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in +Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in +Alternative specific constant for outbound stops,-4.294,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in +Alternative specific constant for the total number of stops,-1.807,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for the total number of stops on joint tours,1.497,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +Alternative specific constant for outbound stops,-4.758,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in +Unavailable,-999,coef_unavail diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_school.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_school.csv new file mode 100644 index 000000000..8efa5941f --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_school.csv @@ -0,0 +1,22 @@ +Description,value,coefficient_name +Number of HH Persons,-0.506,coef_number_of_hh_persons +Presence of kids between 5 and 15 (including) years old,0.3299,coef_presence_of_kids_between_5_and_15_including_years_old +Number of Cars > Number of Workers,0.5331,coef_number_of_cars_number_of_workers +Dummy for female,0.4099,coef_dummy_for_female +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.8163,coef_dummy_for_walking_to_all_stops +Number of escort tours tours undertaken by the person,1.2365,coef_number_of_escort_tours_tours_undertaken_by_the_person +Arrival later than 17:00.,1.8377,coef_arrival_later_than_17_00_ +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.9549,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +dummy for distance in miles,0.0438,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-1.206,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops,-2.6719999999999997,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops,-3.364,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for outbound stops,-2.123,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for the total number of stops,0.701,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for outbound stops,-3.798,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for the total number of stops,1.135,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for outbound stops,-5.85,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Coefficient for unavailable alternatives,-999,coef_unavail \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_univ.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_univ.csv new file mode 100644 index 000000000..b074f4087 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_univ.csv @@ -0,0 +1,21 @@ +Description,value,coefficient_name +Number of HH Persons,-0.2827,coef_number_of_hh_persons +Presence of kids between 5 and 15 (including) years old,0.6823,coef_presence_of_kids_between_5_and_15_including_years_old +Number of Vehicles,0.1703,coef_number_of_vehicles +Dummy for female,0.7349,coef_dummy_for_female +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Number of escort tours tours undertaken by the person,0.9018,coef_number_of_escort_tours_tours_undertaken_by_the_person +Arrival later than 17:00.,0.389,coef_arrival_later_than_17_00_ +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.8434,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +HH accesibility for inbound tours. Interaction,0.2481,coef_hh_accesibility_for_inbound_tours_interaction +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-2.003,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops,-3.51,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops,-3.677,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for outbound stops,-2.628,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for the total number of stops,1.272,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for outbound stops,-3.741,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for the total number of stops,1.871,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for outbound stops,-4.981,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Coefficient for unavailable alternatives,-999,coef_unavail \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_work.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_work.csv new file mode 100644 index 000000000..61008890f --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_coefficients_work.csv @@ -0,0 +1,35 @@ +Description,value,coefficient_name +Middle to Low Income HH,0.17,coef_middle_to_low_income_hh +Mid to High Income HH,0.23,coef_mid_to_high_income_hh +High Income HH,0.24,coef_high_income_hh +Number of HH Persons,-0.31,coef_number_of_hh_persons +Number of Students in HH,0.21,coef_number_of_students_in_hh +Presence of Kids between 0 and 4 (including) years old,0.74,coef_presence_of_kids_between_0_and_4_including_years_old +Num kids between 5 and 15 (including) years old,0.08,coef_num_kids_between_5_and_15_including_years_old +Presence of kids between 5 and 15 (including) years old,0.26,coef_presence_of_kids_between_5_and_15_including_years_old +Number of Adults (>= 16 years old),0.03,coef_number_of_adults_16_years_old_ +Number of Cars > Number of Workers,0.16,coef_number_of_cars_number_of_workers +Dummy for female,0.22,coef_dummy_for_female +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.54,coef_dummy_for_walking_to_all_stops +Number of work tours undertaken by the person,-0.15,coef_number_of_work_tours_undertaken_by_the_person +Number of university tours tours undertaken by the person,-0.48,coef_number_of_university_tours_tours_undertaken_by_the_person +Number of school tours tours undertaken by the person,-1.55,coef_number_of_school_tours_tours_undertaken_by_the_person +Number of escort tours tours undertaken by the person,0.2,coef_number_of_escort_tours_tours_undertaken_by_the_person +Number of shop tours undertaken by the houshold,-0.05,coef_number_of_shop_tours_undertaken_by_the_houshold +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,-1.93,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours +Evening Arrival (>=19:00) Interacted with return tours,0.31,coef_evening_arrival_19_00_interacted_with_return_tours +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,0.6,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours +dummy for distance less than 20 Miles,-0.22,coef_dummy_for_distance_less_than_20_miles +dummy for distance in miles,0.01,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-0.445,coef_alternative_specific_constant_for_return_stops_0out_1in +Number of subtours in the tour,0.19,coef_number_of_subtours_in_the_tour +Alternative specific constant for return stops,-1.775,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops,-2.1390000000000002,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for outbound stops,-0.833,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for outbound stops,-2.613,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for the total number of stops,0.695,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for outbound stops,-3.9339999999999997,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Coefficient for unavailable alternatives,-999,coef_unavail \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_escort.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_escort.csv new file mode 100644 index 000000000..e522097e5 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_escort.csv @@ -0,0 +1,49 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,female,,,,,,,,,,,,,,,, +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, +util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,,,,,,,,,,,,,,, +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_5_miles,dummy for distance less than 5 Miles,(distance_in_miles < 5),,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in +util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +# added for school escorting,,,,,,,,,,,,,,,,,, +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_othdiscr.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_othdiscr.csv new file mode 100644 index 000000000..ef0a49437 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_othdiscr.csv @@ -0,0 +1,50 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in,4out_3in +util_middle_to_low_income_hh_,Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,,,,,,,,,,,,,,,, +util_number_of_full_time_workes_in_hh,Number of full time workes in HH,num_full,,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,,, +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,,, +util_num_kids_between_4_and_15_including_years_old,Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,,, +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,~is_joint & female,,,,,,,,,,,,,,,,, +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,,, +util_number_of_shool_tours_tours_undertaken_by_the_person,Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,,, +util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,,, +util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,,, +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_ +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_10_miles_,dummy for distance less than 10 Miles ,(distance_in_miles < 10),,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_ +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in +util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +# last column and below constant was added to demonstrate flexible id extension,,,,,,,,,,,,,,,,,,, +util_flexible_id_ext_constant,Constant to control addex example extension,True,,,,,,,,,,,,,,,,,8 diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_school.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_school.csv new file mode 100644 index 000000000..9aa97f549 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_school.csv @@ -0,0 +1,46 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,, +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, +util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_ +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_15_miles,dummy for distance less than 15 Miles,(distance_in_miles < 15),,,,,,,,,,,,,,,, +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +# No stops for half tour that includes school escorting,,,,,,,,,,,,,,,,,, +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_univ.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_univ.csv new file mode 100644 index 000000000..8b4b53031 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_univ.csv @@ -0,0 +1,46 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,, +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles +util_dummy_for_female,Dummy for female,female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,,,,,,,,,,,,,,, +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, +util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_ +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_20_miles,dummy for distance less than 20 Miles,(distance_in_miles < 20),,,,,,,,,,,,,,,, +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,,,,,,,,,,,,,,, +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +# No stops for half tour that includes school escorting,,,,,,,,,,,,,,,,,, +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail diff --git a/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_work.csv b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_work.csv new file mode 100644 index 000000000..764064c83 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/stop_frequency_work.csv @@ -0,0 +1,47 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old +util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_ +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person +util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,,,,,,,,,,,,,,, +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_20_miles,dummy for distance less than 20 Miles,(distance_in_miles < 20),,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_number_of_subtours_in_the_tour,Number of subtours in the tour,num_atwork_subtours,,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour +# No stops for half tour that includes school escorting,,,,,,,,,,,,,,,,,, +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail,coef_unavail +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail,,coef_unavail,coef_unavail,coef_unavail diff --git a/activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice.csv b/activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice.csv index 2ba4c6d0d..7679f5731 100644 --- a/activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice.csv +++ b/activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice.csv @@ -342,3 +342,6 @@ util_Walk_not_available_for_long_distances,Walk not available for long distances util_Bike_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,,,,-999,,,,,,,,,,,,, util_Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,-999,,,,,,,,,,,,,,,,,,, #, max(c_densityIndexOrigin*originDensityIndex,originDensityIndexMax),,,,,,,,,1,1,1,1,1,1,1,,,,,, +#, School Escorting eligibility-odd looking where/isnan is to allow this to work with numba fastmath,,,,,,,,,,,,,,,,,,,,,, +util_one_or_more_school_escort,No SOV if on school escort tour,"@(np.where(np.isnan(df.get('num_escortees', 0)), 0 , df.get('num_escortees', 0)) >= 1)",-999,-999,,,,,,,,,,,,,,,,,,, +util_two_or_more_school_escort,Can't take HOV2 if taking two children and yourself,"@(np.where(np.isnan(df.get('num_escortees', 0)), 0 , df.get('num_escortees', 0)) >= 2)",,,-999,-999,,,,,,,,,,,,,,,,, diff --git a/activitysim/examples/prototype_mtc_extended/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/prototype_mtc_extended/configs/tour_scheduling_nonmandatory.csv new file mode 100644 index 000000000..19ef4dfc3 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/tour_scheduling_nonmandatory.csv @@ -0,0 +1,100 @@ +Label,Description,Expression,Coefficient +util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,Subsequent tour must start after previous tour for this purpose ends,(start < end_previous) & (tour_type_num > 1),coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends +util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration +util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,(tour_type == 'shopping') * start,coef_shopping_tour_departure_shift_effects +util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,(tour_type == 'shopping') * duration,coef_shopping_tour_duration_shift_effects +util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * start,coef_maintenance_tour_departure_shift_effects +util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * duration,coef_maintenance_tour_duration_shift_effects +util_visit_tour_departure_shift_effects_start,Visit tour - departure shift effects,(tour_type == 'social') * start,coef_visit_tour_departure_shift_effects +util_visit_tour_duration_shift_effects_duration,Visit tour - departure shift effects,(tour_type == 'social') * duration,coef_visit_tour_duration_shift_effects +util_eat_out_tour_departure_shift_effects,Eat Out tour - departure shift effects,(tour_type == 'eatout') * start,coef_eat_out_tour_departure_shift_effects +util_school_child_age_16_plus_departure_shift_effects,School child age 16+ - departure shift effects,(ptype == 6) * start,coef_school_child_age_16_plus_departure_shift_effects +util_school_child_age_16_plus_duration_shift_effects,School child age 16+ - duration shift effects,(ptype == 6) * duration,coef_school_child_age_16_plus_duration_shift_effects +util_school_child_age_under_16_departure_shift_effects,School child age under 16 - departure shift effects,(ptype == 7) * start,coef_school_child_age_under_16_departure_shift_effects +util_school_child_age_under_16_duration_shift_effects,School child age under 16 - duration shift effects,(ptype == 7) * duration,coef_school_child_age_under_16_duration_shift_effects +util_destination_in_cbd_duration_shift_effects,Destination in CBD - duration shift effects,destination_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects +util_number_of_mandatory_tours_departure_shift_effects,Number of mandatory tours - departure shift effects,num_mand * start,coef_number_of_mandatory_tours_departure_shift_effects +util_number_of_joint_tours_departure_shift_effects,Number of joint tours - departure shift effects,num_person_joint_tours * start,coef_number_of_joint_tours_departure_shift_effects +util_number_of_escort_tours_departure_shift_effects,Number of escort tours - departure shift effects,num_escort_tours * start,coef_number_of_escort_tours_departure_shift_effects +util_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,Number of idividual non-mandatory tours (excluding escort) - departure shift effects,num_non_escort_tours * start,coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects +util_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,First of 2+ tours for same purpose - departure shift effect,((tour_type_count>1) & (tour_type_num == 1)) * start,coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect +util_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,subsequent of 2+ tours for same purpose - duration shift effect,(tour_type_num > 1) * duration,coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect +util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,(tour_type == 'othmaint') & (start < 7),coef_maintenance_tour_depart_before_7 +util_shopping_tour_depart_before_8,Shopping tour - depart before 8,(tour_type == 'shopping') & (start < 8),coef_shopping_tour_depart_before_8 +util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,(tour_type == 'shopping') & (end > 22),coef_shopping_tour_arrive_after_22 +util_school_child_under_16_arrive_after_22,School child under 16 - arrive after 22,(ptype == 7) & (end > 22),coef_school_child_under_16_arrive_after_22 +util_university_student_arrive_after_22,University student - arrive after 22,(ptype == 3) & (end > 22),coef_university_student_arrive_after_22 +util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,(tour_type == 'shopping') & (duration < 2),coef_shopping_tour_duration_lt_2_hours +util_discretionary_tour_duration_lt_2_hours,Discretionary tour - duration < 2 hours,(tour_type == 'othdiscr') & (duration < 2),coef_discretionary_tour_duration_lt_2_hours +util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 19 - 21,adult & (num_children > 0) & ( end > 18 ) & ( end < 22 ),coef_adult_with_children_in_hh_arrive_19_21 +#,,, +#,Mode Choice Logsum,mode_choice_logsum,#mode_choice_logsum +#,,,# +util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy +util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy +util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour +util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & (_adjacent_window_before!=0)",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & (_adjacent_window_after!=0)",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & (_adjacent_window_before!=0)",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & (_adjacent_window_after!=0)",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,Remaining individual non-mandatory tours to be scheduled / number of unscheduled hours,"@((1.0 + df.tour_count - df.tour_num)) / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours +#,#,,# +util_departure_constants_early,Departure Constants -- Early (up to 5),(tour_type != 'escort') & (start < 6),coef_departure_constants_early +util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),(tour_type != 'escort') & (start == 6),coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),(tour_type != 'escort') & (start == 7),coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),(tour_type != 'escort') & (start == 8),coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),(tour_type != 'escort') & (start == 9),coef_departure_constants_am_peak_4 +util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (start > 9) & (start < 13),coef_departure_constants_midday_1 +util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(tour_type != 'escort') & (start > 12) & (start < 16),coef_departure_constants_midday_2 +util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(tour_type != 'escort') & (start > 15) & (start < 19),coef_departure_constants_pm_peak +util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(tour_type != 'escort') & (start > 18) & (start < 22),coef_departure_constants_evening +util_departure_constants_late,Departure Constants -- Late (22 and later),(tour_type != 'escort') & (start > 21),coef_departure_constants_late +util_arrival_constants_early,Arrival Constants -- Early (up to 6),(tour_type != 'escort') & (end < 7),coef_arrival_constants_early +util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(tour_type != 'escort') & (end > 6) & (end < 10),coef_arrival_constants_am_peak +util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (end > 9) & (end < 13),coef_arrival_constants_midday_1 +util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(tour_type != 'escort') & (end > 12) & (end < 15),coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),(tour_type != 'escort') & (end == 15),coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),(tour_type != 'escort') & (end == 16),coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),(tour_type != 'escort') & (end == 17),coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),(tour_type != 'escort') & (end == 18),coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(tour_type != 'escort') & (end > 18) & (end < 22),coef_arrival_constants_evening +util_arrival_constants_late,Arrival Constants -- Late (22 and later),(tour_type != 'escort') & (end > 21),coef_arrival_constants_late +util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,(tour_type != 'escort') & (duration < 2),coef_duration_constants_0_to_1_hours +util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,(tour_type != 'escort') & (duration > 1) & (duration < 4),coef_duration_constants_2_to_3_hours +util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,(tour_type != 'escort') & (duration > 3) & (duration < 6),coef_duration_constants_4_to_5_hours +util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,(tour_type != 'escort') & (duration > 5) & (duration < 8),coef_duration_constants_6_to_7_hours +util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,(tour_type != 'escort') & (duration > 7) & (duration < 11),coef_duration_constants_8_to_10_hours +util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,(tour_type != 'escort') & (duration > 10) & (duration < 14),coef_duration_constants_11_to_13_hours +util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(tour_type != 'escort') & (duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours +util_escort_tour_departure_constants_early,Escort Tour Departure Constants -- Early (up to 5),(tour_type == 'escort') & (start < 6),coef_escort_tour_departure_constants_early +util_escort_tour_departure_constants_am_peak_1,Escort Tour Departure Constants -- AM peak 1 (6),(tour_type == 'escort') & (start == 6),coef_escort_tour_departure_constants_am_peak_1 +util_escort_tour_departure_constants_am_peak_2,Escort Tour Departure Constants -- AM peak 2 (7),(tour_type == 'escort') & (start == 7),coef_escort_tour_departure_constants_am_peak_2 +util_escort_tour_departure_constants_am_peak_3,Escort Tour Departure Constants -- AM peak 3 (8),(tour_type == 'escort') & (start == 8),coef_escort_tour_departure_constants_am_peak_3 +util_escort_tour_departure_constants_am_peak_4,Escort Tour Departure Constants -- AM peak 4 (9),(tour_type == 'escort') & (start == 9),coef_escort_tour_departure_constants_am_peak_4 +util_escort_tour_departure_constants_midday_1,Escort Tour Departure Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (start > 9) & (start < 13),coef_escort_tour_departure_constants_midday_1 +util_escort_tour_departure_constants_midday_2,Escort Tour Departure Constants -- Midday 2 (13 to 15),(tour_type == 'escort') & (start > 12) & (start < 16),coef_escort_tour_departure_constants_midday_2 +util_escort_tour_departure_constants_pm_peak,Escort Tour Departure Constants -- PM peak (16 to 18),(tour_type == 'escort') & (start > 15) & (start < 19),coef_escort_tour_departure_constants_pm_peak +util_escort_tour_departure_constants_evening,Escort Tour Departure Constants -- Evening (19 to 21),(tour_type == 'escort') & (start > 18) & (start < 22),coef_escort_tour_departure_constants_evening +util_escort_tour_departure_constants_late,Escort Tour Departure Constants -- Late (22 and later),(tour_type == 'escort') & (start > 21),coef_escort_tour_departure_constants_late +util_escort_tour_arrival_constants_early,Escort Tour Arrival Constants -- Early (up to 6),(tour_type == 'escort') & (end < 7),coef_escort_tour_arrival_constants_early +util_escort_tour_arrival_constants_am_peak,Escort Tour Arrival Constants -- AM peak (7 to 9),(tour_type == 'escort') & (end > 6) & (end < 10),coef_escort_tour_arrival_constants_am_peak +util_escort_tour_arrival_constants_midday_1,Escort Tour Arrival Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (end > 9) & (end < 13),coef_escort_tour_arrival_constants_midday_1 +util_escort_tour_arrival_constants_midday_2,Escort Tour Arrival Constants -- Midday 2 (13 to 14),(tour_type == 'escort') & (end > 12) & (end < 15),coef_escort_tour_arrival_constants_midday_2 +util_escort_tour_arrival_constants_pm_peak_1,Escort Tour Arrival Constants -- PM peak 1 (15),(tour_type == 'escort') & (end == 15),coef_escort_tour_arrival_constants_pm_peak_1 +util_escort_tour_arrival_constants_pm_peak_2,Escort Tour Arrival Constants -- PM peak 2 (16),(tour_type == 'escort') & (end == 16),coef_escort_tour_arrival_constants_pm_peak_2 +util_escort_tour_arrival_constants_pm_peak_3,Escort Tour Arrival Constants -- PM peak 3 (17),(tour_type == 'escort') & (end == 17),coef_escort_tour_arrival_constants_pm_peak_3 +util_escort_tour_arrival_constants_pm_peak_4,Escort Tour Arrival Constants -- PM peak 4 (18),(tour_type == 'escort') & (end == 18),coef_escort_tour_arrival_constants_pm_peak_4 +util_escort_tour_arrival_constants_evening,Escort Tour Arrival Constants -- Evening (19 to 21),(tour_type == 'escort') & (end > 18) & (end < 22),coef_escort_tour_arrival_constants_evening +util_escort_tour_arrival_constants_late,Escort Tour Arrival Constants -- Late (22 and later),(tour_type == 'escort') & (end > 21),coef_escort_tour_arrival_constants_late +util_escort_tour_duration_constants_0_to_1_hours,Escort Tour Duration Constants -- 0 to 1 hours,(tour_type == 'escort') & (duration < 2),coef_escort_tour_duration_constants_0_to_1_hours +util_escort_tour_duration_constants_2_to_3_hours,Escort Tour Duration Constants -- 2 to 3 hours,(tour_type == 'escort') & (duration > 1) & (duration < 4),coef_escort_tour_duration_constants_2_to_3_hours +util_escort_tour_duration_constants_4_to_5_hours,Escort Tour Duration Constants -- 4 to 5 hours,(tour_type == 'escort') & (duration > 3) & (duration < 6),coef_escort_tour_duration_constants_4_to_5_hours +util_escort_tour_duration_constants_6_to_7_hours,Escort Tour Duration Constants -- 6 to 7 hours,(tour_type == 'escort') & (duration > 5) & (duration < 8),coef_escort_tour_duration_constants_6_to_7_hours +util_escort_tour_duration_constants_8_to_10_hours,Escort Tour Duration Constants -- 8 to 10 hours,(tour_type == 'escort') & (duration > 7) & (duration < 11),coef_escort_tour_duration_constants_8_to_10_hours +util_escort_tour_duration_constants_11_to_13_hours,Escort Tour Duration Constants -- 11 to 13 hours,(tour_type == 'escort') & (duration > 10) & (duration < 14),coef_escort_tour_duration_constants_11_to_13_hours +util_escort_tour_duration_constants_14_to_18_hours,Escort Tour Duration Constants -- 14 to 18 hours,(tour_type == 'escort') & (duration > 13) & (duration < 19),coef_escort_tour_duration_constants_14_to_18_hours +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable diff --git a/activitysim/examples/prototype_mtc_extended/configs/tour_scheduling_nonmandatory_coefficients.csv b/activitysim/examples/prototype_mtc_extended/configs/tour_scheduling_nonmandatory_coefficients.csv new file mode 100644 index 000000000..bbb01bb05 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/tour_scheduling_nonmandatory_coefficients.csv @@ -0,0 +1,97 @@ +coefficient_name,value,constrain +coef_dummy,1,T +coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,-999,T +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.004741,F +coef_shopping_tour_departure_shift_effects,-0.06015,F +coef_shopping_tour_duration_shift_effects,-0.1208,F +coef_maintenance_tour_departure_shift_effects,-0.1489,F +coef_maintenance_tour_duration_shift_effects,-0.08372,F +coef_visit_tour_departure_shift_effects,0.09688,F +coef_visit_tour_duration_shift_effects,0.1638,F +coef_eat_out_tour_departure_shift_effects,0.07549,F +coef_school_child_age_16_plus_departure_shift_effects,0.07266,F +coef_school_child_age_16_plus_duration_shift_effects,0.2095,F +coef_school_child_age_under_16_departure_shift_effects,0.04657,F +coef_school_child_age_under_16_duration_shift_effects,0.3272,F +coef_destination_in_cbd_duration_shift_effects,0.1067,F +coef_number_of_mandatory_tours_departure_shift_effects,0.04673,F +coef_number_of_joint_tours_departure_shift_effects,0.05208,F +coef_number_of_escort_tours_departure_shift_effects,0.02013,F +coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,0.03896,F +coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.2364,F +coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.1731,F +coef_maintenance_tour_depart_before_7,-0.8826,F +coef_shopping_tour_depart_before_8,-1.037,F +coef_shopping_tour_arrive_after_22,-0.6027,F +coef_school_child_under_16_arrive_after_22,-1.18,F +coef_university_student_arrive_after_22,0.5466,F +coef_shopping_tour_duration_lt_2_hours,0.5168,F +coef_discretionary_tour_duration_lt_2_hours,-0.6974,F +coef_adult_with_children_in_hh_arrive_19_21,0.336,F +#,, +#mode_choice_logsum,, +#,, +coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.4562,F +coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.3992,F +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.008442,F +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.0257,F +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.0593,F +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.02734,F +coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,-13.63,F +#,,F +coef_departure_constants_early,-1.740135661,F +coef_departure_constants_am_peak_1,-0.654163573,F +coef_departure_constants_am_peak_2,0.554282571,F +coef_departure_constants_am_peak_3,1.050561087,F +coef_departure_constants_am_peak_4,0.971568228,F +coef_departure_constants_midday_1,0.881991986,F +coef_departure_constants_midday_2,0.411103634,F +coef_departure_constants_pm_peak,0,T +coef_departure_constants_evening,-1.856475096,F +coef_departure_constants_late,-8.228880141,F +coef_arrival_constants_early,-0.051990748,F +coef_arrival_constants_am_peak,-1.814822602,F +coef_arrival_constants_midday_1,0.000371501,F +coef_arrival_constants_midday_2,0.532116031,F +coef_arrival_constants_pm_peak_1,0.628481567,F +coef_arrival_constants_pm_peak_2,0.650521416,F +coef_arrival_constants_pm_peak_3,0.402894406,F +coef_arrival_constants_pm_peak_4,0.154213293,F +coef_arrival_constants_evening,0,T +coef_arrival_constants_late,-0.866671315,F +coef_duration_constants_0_to_1_hours,0,T +coef_duration_constants_2_to_3_hours,0.051385565,F +coef_duration_constants_4_to_5_hours,-0.593951321,F +coef_duration_constants_6_to_7_hours,-0.951155328,F +coef_duration_constants_8_to_10_hours,-0.828108399,F +coef_duration_constants_11_to_13_hours,-0.955635554,F +coef_duration_constants_14_to_18_hours,-1.042580879,F +coef_escort_tour_departure_constants_early,-1.740135661,F +coef_escort_tour_departure_constants_am_peak_1,-1.112357753,F +coef_escort_tour_departure_constants_am_peak_2,0.698788185,F +coef_escort_tour_departure_constants_am_peak_3,1.196268813,F +coef_escort_tour_departure_constants_am_peak_4,-0.225258221,F +coef_escort_tour_departure_constants_midday_1,0.028662017,F +coef_escort_tour_departure_constants_midday_2,0,T +coef_escort_tour_departure_constants_pm_peak,-1.180140161,F +coef_escort_tour_departure_constants_evening,-3.948732811,F +coef_escort_tour_departure_constants_late,-8.228880141,F +coef_escort_tour_arrival_constants_early,0,T +coef_escort_tour_arrival_constants_am_peak,0,T +coef_escort_tour_arrival_constants_midday_1,0,T +coef_escort_tour_arrival_constants_midday_2,0,T +coef_escort_tour_arrival_constants_pm_peak_1,0,T +coef_escort_tour_arrival_constants_pm_peak_2,0,T +coef_escort_tour_arrival_constants_pm_peak_3,0,T +coef_escort_tour_arrival_constants_pm_peak_4,0,T +coef_escort_tour_arrival_constants_evening,-0.536918728,F +coef_escort_tour_arrival_constants_late,-1.008290213,F +coef_escort_tour_duration_constants_0_to_1_hours,0,T +coef_escort_tour_duration_constants_2_to_3_hours,-2.042013897,F +coef_escort_tour_duration_constants_4_to_5_hours,-2.880293896,F +coef_escort_tour_duration_constants_6_to_7_hours,-2.973533731,F +coef_escort_tour_duration_constants_8_to_10_hours,-3.020213758,F +coef_escort_tour_duration_constants_11_to_13_hours,-2.974364976,F +coef_escort_tour_duration_constants_14_to_18_hours,-2.507447146,F +# Added for school escorting,, +coef_unavailable,-999,F \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.csv b/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.csv index 0253954db..e9318c206 100644 --- a/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.csv +++ b/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.csv @@ -403,3 +403,6 @@ util_Bike_not_available_for_long_distances,Bike not available for long distances util_origin_density_index,Origin density index,@origin_density_applied*(origin_density_index_multiplier*df.origin_density_index).clip(origin_density_index_max),,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,,,,,coef_ivt,coef_ivt util_walk_express_penalty,Walk-express penalty for intermediate stops,@walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,,,,coef_ivt,,,,,,,,,, util_adjust_tnc_shared,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,,,,coef_ivt +#, School Escorting eligibility,,,,,,,,,,,,,,,,,,,,,, +util_one_or_more_school_escort,No SOV if on school escort tour,(num_escortees >= 1),-999,-999,,,,,,,,,,,,,,,,,,, +util_two_or_more_school_escort,Can't take HOV2 if taking two children and yourself,(num_escortees >= 2),,,-999,-999,,,,,,,,,,,,,,,,, \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.yaml b/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.yaml index 712761863..93658ea04 100644 --- a/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.yaml +++ b/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.yaml @@ -186,10 +186,11 @@ preprocessor: - tours - vehicles -# - SPEC: trip_mode_choice_annotate_trips_preprocessor2 -# DF: df -# TABLES: -# - land_use +# annotate_trips: +# SPEC: trip_mode_choice_annotate_trips_postprocessor +# DF: df +# TABLES: +# - tours # to reduce memory needs filter chooser table to these fields TOURS_MERGED_CHOOSER_COLUMNS: diff --git a/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice_annotate_trips_preprocessor.csv index cc1f223d5..fefe54a13 100644 --- a/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice_annotate_trips_preprocessor.csv @@ -91,3 +91,5 @@ dest terminal time not counted at home,_dest_terminal_time,"np.where(inbound & l #,max_dist_walk,od_skims.max('DISTWALK') #,dist_bike,od_skims['DISTBIKE'] #,dist_only,od_skims['DIST'] +# added for school escorting model,, +Number of school children in vehicle on trip,num_escortees,df.escort_participants.fillna('').apply(lambda x: len(x.split('_'))) \ No newline at end of file diff --git a/activitysim/examples/prototype_mtc_extended/configs/trip_scheduling.yaml b/activitysim/examples/prototype_mtc_extended/configs/trip_scheduling.yaml new file mode 100644 index 000000000..53b07924c --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/trip_scheduling.yaml @@ -0,0 +1,11 @@ +PROBS_SPEC: trip_scheduling_probs.csv +COEFFICIENTS: trip_scheduling_coefficients.csv + +# int to add to probs column index to get time period it represents. +# e.g. depart_alt_base = 5 means first column (column 0) represents 5 am +DEPART_ALT_BASE: 5 + +MAX_ITERATIONS: 100 + +# FAILFIX: drop_and_cleanup +FAILFIX: choose_most_initial diff --git a/activitysim/examples/prototype_mtc_extended/configs/trip_scheduling_probs.csv b/activitysim/examples/prototype_mtc_extended/configs/trip_scheduling_probs.csv new file mode 100644 index 000000000..00beef381 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs/trip_scheduling_probs.csv @@ -0,0 +1,1388 @@ +primary_purpose,outbound,tour_hour,trip_num,HR5,HR6,HR7,HR8,HR9,HR10,HR11,HR12,HR13,HR14,HR15,HR16,HR17,HR18,HR19,HR20,HR21,HR22,HR23 +work,TRUE,5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +work,TRUE,5,2,0.249730906,0.477180111,0.215788882,0.02257625,0.009653299,0.001272067,0.002559828,0.005345297,0.012868196,0.000858457,0,0.00130551,0,0.000861198,0,0,0,0,0 +work,TRUE,5,3,0.269166724,0.331378773,0.290398422,0.047428828,0.032211326,0.003681738,0,0.00648104,0.007547054,0.006178507,0,0.005527589,0,0,0,0,0,0,0 +work,TRUE,5,4,0.087782501,0.257488508,0.384088251,0.077346978,0.060562922,0,0,0.049138541,0,0.014538525,0,0,0,0.041701151,0.018235082,0,0.009117541,0,0 +work,TRUE,6,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +work,TRUE,6,2,0,0.218769369,0.568056029,0.16549898,0.028654735,0.007305391,0.002067083,0.003148838,0.000503641,0.003688829,0.002307106,0,0,0,0,0,0,0,0 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households_sample_size: 0 chunk_size: 0 @@ -35,19 +35,23 @@ want_dest_choice_sample_tables: False #write_skim_cache: True # - tracing -#trace_hh_id: +trace_hh_id: trace_od: # to resume after last successful checkpoint, specify resume_after: _ #resume_after: trip_purpose_and_destination models: - ### mp_initialize step + ### mp_initialize_proto step (Create the proto tables in single process to be shared across cores) + - initialize_proto_population + ### mp_disaggregate_accessibility (Slice and run proto tables in multiprocess) + - compute_disaggregate_accessibility + ### mp_initialize_hhs step - initialize_landuse - initialize_households ### mp_accessibility step - compute_accessibility - ### mp_households step + ### mp_simulate step - school_location - workplace_location - auto_ownership_simulate @@ -56,6 +60,7 @@ models: - cdap_simulate - mandatory_tour_frequency - mandatory_tour_scheduling + - school_escorting - joint_tour_frequency - joint_tour_composition - joint_tour_participation @@ -83,6 +88,15 @@ models: multiprocess_steps: - name: mp_initialize + begin: initialize_proto_population + - name: mp_disaggregate_accessibility + begin: compute_disaggregate_accessibility + slice: + tables: + - proto_households + - proto_persons + - proto_tours + - name: mp_initialize_hhs begin: initialize_landuse - name: mp_accessibility begin: compute_accessibility @@ -90,8 +104,8 @@ multiprocess_steps: tables: - accessibility # don't slice any tables not explicitly listed above in slice.tables - except: True - - name: mp_households + except: True # This is needed after disaggregate accessibilities, otherwise it will return empty logsums tables + - name: mp_simulate begin: school_location slice: tables: diff --git a/activitysim/examples/prototype_mtc_extended/configs_sharrow/settings.yaml b/activitysim/examples/prototype_mtc_extended/configs_sharrow/settings.yaml new file mode 100644 index 000000000..288ec21d9 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/configs_sharrow/settings.yaml @@ -0,0 +1,3 @@ +inherit_settings: True +sharrow: test +recode_pipeline_columns: True diff --git a/activitysim/examples/prototype_mtc_extended/output/cache/.gitignore b/activitysim/examples/prototype_mtc_extended/output/cache/.gitignore index 3dd2e62f9..1d085cacc 100644 --- a/activitysim/examples/prototype_mtc_extended/output/cache/.gitignore +++ b/activitysim/examples/prototype_mtc_extended/output/cache/.gitignore @@ -1,2 +1 @@ -*.mmap -*.feather +** diff --git a/activitysim/examples/prototype_mtc_extended/sampling_scenarios.py b/activitysim/examples/prototype_mtc_extended/sampling_scenarios.py new file mode 100644 index 000000000..84e72bedd --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/sampling_scenarios.py @@ -0,0 +1,179 @@ +import argparse +import os +import pkg_resources +import yaml +import pandas as pd +import shutil +from activitysim.cli.run import add_run_args, run +from activitysim.core.util import named_product + +START_ITER = 0 +SAMPLING_PARAMS = { + "DESTINATION_SAMPLE_SIZE": [0.1, 1 / 3, 2 / 3, 0], + "ORIGIN_SAMPLE_SIZE": [0.1, 1 / 3, 2 / 3, 0], + "ORIGIN_SAMPLE_METHOD": [None], # , 'kmeans'] +} + + +def integer_params(params): + n_zones = count_lines_enumerate(base_path("data_2/land_use.csv")) + + d_zones = 1 if params.DESTINATION_SAMPLE_SIZE > 1 else n_zones + o_zones = 1 if params.ORIGIN_SAMPLE_SIZE > 1 else n_zones + + params.DESTINATION_SAMPLE_SIZE = round(params.DESTINATION_SAMPLE_SIZE * d_zones) + params.ORIGIN_SAMPLE_SIZE = round(params.ORIGIN_SAMPLE_SIZE * o_zones) + + return params + + +def base_path(dirname): + resource = os.path.join("examples", "placeholder_sandag_2_zone", dirname) + return pkg_resources.resource_filename("activitysim", resource) + + +def extended_path(dirname): + resource = os.path.join("examples", "placeholder_sandag_2_zone_extended", dirname) + return pkg_resources.resource_filename("activitysim", resource) + + +def run_model(): + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + # add in the arguments + args.config = [ + extended_path("configs_mp"), + extended_path("configs"), + base_path("configs_2_zone"), + base_path("placeholder_psrc/configs"), + ] + args.output = extended_path("output") + args.data = [extended_path("data"), base_path("data_2")] + run(args) + + +def count_lines_enumerate(file_name): + fp = open(file_name, "r") + line_count = list(enumerate(fp))[-1][0] + return line_count + + +def update_configs(scene, model_settings, config_path): + # Update the model settings + scene = integer_params(scene) + model_settings["DESTINATION_SAMPLE_SIZE"] = scene.DESTINATION_SAMPLE_SIZE + model_settings["ORIGIN_SAMPLE_SIZE"] = scene.ORIGIN_SAMPLE_SIZE + model_settings["ORIGIN_SAMPLE_METHOD"] = scene.ORIGIN_SAMPLE_METHOD + + with open(config_path, "w") as file: + yaml.dump(model_settings, file) + + return model_settings + + +def make_scene_name(it, params): + d_samp = params["DESTINATION_SAMPLE_SIZE"] + o_samp = params["ORIGIN_SAMPLE_SIZE"] + method = params["ORIGIN_SAMPLE_METHOD"] + + scene_name = "scene-{}_dsamp-{}_osamp-{}_method-{}".format( + it + START_ITER, + d_samp, + o_samp, + method, + ) + + return scene_name + + +def copy_output(scene_name, model_settings): + + scene_dir_name = os.path.join("scenarios_output", scene_name) + + if os.path.exists(extended_path(scene_dir_name)): + shutil.rmtree(extended_path(scene_dir_name)) + os.makedirs(extended_path(scene_dir_name)) + + log = pd.read_csv("scenarios_output/log.csv") + filt = ( + (log.DESTINATION_SAMPLE_SIZE == model_settings["DESTINATION_SAMPLE_SIZE"]) + & (log.ORIGIN_SAMPLE_SIZE == model_settings["ORIGIN_SAMPLE_SIZE"]) + & (log.ORIGIN_SAMPLE_METHOD == model_settings["ORIGIN_SAMPLE_METHOD"]) + ) + log.loc[filt, "COMPLETED_ID"] = scene_name + log.to_csv("scenarios_output/log.csv", index=False) + + files_list = [ + x + for x in os.listdir(extended_path("output")) + if "pipeline" not in x and "cache" not in x + ] + + for file in files_list: + copyargs = { + "src": extended_path(os.path.join("output", file)), + "dst": extended_path(os.path.join(scene_dir_name, file)), + } + if os.path.isfile(copyargs["src"]): + shutil.copy(**copyargs) + else: + if os.path.exists(copyargs["dst"]): + shutil.rmtree(copyargs["dst"]) + shutil.copytree(**copyargs) + return + + +def run_scenarios(): + config_path = extended_path("configs/disaggregate_accessibility.yaml") + with open(config_path) as file: + model_settings = yaml.load(file, Loader=yaml.FullLoader) + + if not os.path.exists(extended_path("scenarios_output")): + os.makedirs(extended_path("scenarios_output")) + + if os.path.exists(extended_path("scenarios_output/log.csv")): + log = pd.read_csv(extended_path("scenarios_output/log.csv")) + # assert scenarios[['DESTINATION_SAMPLE_SIZE','ORIGIN_SAMPLE_SIZE', 'ORIGIN_SAMPLE_METHOD']].equals( + # log[['DESTINATION_SAMPLE_SIZE','ORIGIN_SAMPLE_SIZE', 'ORIGIN_SAMPLE_METHOD']] + # ) + scenarios = log + else: + scenarios = pd.DataFrame(named_product(**SAMPLING_PARAMS)) + scenarios["COMPLETED_ID"] = "" + scenarios["SKIP"] = False + scenarios.to_csv(extended_path("scenarios_output/log.csv"), index=False) + + for it, scene in scenarios.iterrows(): + # Update model settings + model_settings = update_configs(scene, model_settings, config_path) + + # Check if already run + scene_name = make_scene_name(it, scene) + scene_dir_name = extended_path(os.path.join("scenarios_output", scene_name)) + + if ( + any(scenarios.COMPLETED_ID == scene_name) + and os.path.exists(scene_dir_name) + or scene.SKIP + ): + continue + + # Run the model + print( + f"Running model {it} of {len(scenarios.index)}: {chr(10)}" + + f"{chr(10)}".join( + [f"{var}={model_settings[var]}" for var in SAMPLING_PARAMS.keys()] + ) + ) + try: + run_model() + # Copy results to named folder + copy_output(scene_name, model_settings) + except: + print(f"Failed on scene {scene_name}") + + +if __name__ == "__main__": + run_scenarios() diff --git a/activitysim/examples/prototype_mtc_extended/test/configs/settings.yaml b/activitysim/examples/prototype_mtc_extended/test/configs/settings.yaml index 78bdcf922..8528ba239 100644 --- a/activitysim/examples/prototype_mtc_extended/test/configs/settings.yaml +++ b/activitysim/examples/prototype_mtc_extended/test/configs/settings.yaml @@ -11,9 +11,8 @@ chunk_size: 0 # - shadow pricing global switches # turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples # see shadow_pricing.yaml for additional settings -use_shadow_pricing: False +use_shadow_pricing: True # turn writing of sample_tables on and off for all models # (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) @@ -36,5 +35,11 @@ output_tables: prefix: final_ sort: True tables: - - trips + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id - vehicles + - proto_disaggregate_accessibility + +recode_pipeline_columns: False diff --git a/activitysim/examples/prototype_mtc_extended/test/configs_mp/logging.yaml b/activitysim/examples/prototype_mtc_extended/test/configs_mp/logging.yaml new file mode 100644 index 000000000..e932009c5 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/test/configs_mp/logging.yaml @@ -0,0 +1,57 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: DEBUG + handlers: [console, logfile] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARNING + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + #level: NOTSET + level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + + formatters: + + simpleFormatter: + class: logging.Formatter + #format: '%(processName)-10s %(levelname)s - %(name)s - %(message)s' + format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ + '%(processName)-10s %(levelname)s - %(name)s - %(message)s', + '%(levelname)s - %(name)s - %(message)s'] + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + diff --git a/activitysim/examples/prototype_mtc_extended/test/configs_mp/network_los.yaml b/activitysim/examples/prototype_mtc_extended/test/configs_mp/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/test/configs_mp/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/prototype_mtc_extended/test/configs_mp/settings.yaml b/activitysim/examples/prototype_mtc_extended/test/configs_mp/settings.yaml new file mode 100644 index 000000000..060e9f873 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/test/configs_mp/settings.yaml @@ -0,0 +1,45 @@ +inherit_settings: True + +# treat warnings as errors +strict: True + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + +multiprocess: True + +chunk_size: 0 +num_processes: 2 + +# number of households to simulate +households_sample_size: 10 + +# - shadow pricing global switches +# turn shadow_pricing on and off for all models (e.g. school and work) +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: True + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +trace_hh_id: +# global switch to turn on or off presampling of destination alternatives at TAZ level (multizone models only) +want_dest_choice_presampling: True + +recode_pipeline_columns: False + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - vehicles + - proto_disaggregate_accessibility diff --git a/activitysim/examples/prototype_mtc_extended/test/no-shadow-pricing/settings.yaml b/activitysim/examples/prototype_mtc_extended/test/no-shadow-pricing/settings.yaml new file mode 100644 index 000000000..a9accd37f --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/test/no-shadow-pricing/settings.yaml @@ -0,0 +1,2 @@ +inherit_settings: True +use_shadow_pricing: False diff --git a/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore b/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore index 3dd2e62f9..1d085cacc 100644 --- a/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore +++ b/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore @@ -1,2 +1 @@ -*.mmap -*.feather +** diff --git a/activitysim/examples/prototype_mtc_extended/test/regress/final_proto_disaggregate_accessibility-shadowpriced-sharrow.csv b/activitysim/examples/prototype_mtc_extended/test/regress/final_proto_disaggregate_accessibility-shadowpriced-sharrow.csv new file mode 100644 index 000000000..36b1e1a2c --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/test/regress/final_proto_disaggregate_accessibility-shadowpriced-sharrow.csv @@ -0,0 +1,901 @@ +proto_person_id,proto_household_id,workplace_location_accessibility,othdiscr_accessibility,shopping_accessibility,educ,grade,military,pnum,pstudent,DAP,age,hours,pemploy,ptype,sex,weeks,age_16_to_19,age_16_p,adult,male,female,has_non_worker,has_retiree,has_preschool_kid,has_driving_kid,has_school_kid,has_full_time,has_part_time,has_university,student_is_employed,nonstudent_to_school,is_student,is_gradeschool,is_highschool,is_university,school_segment,is_worker,home_zone_id,value_of_time,HHT,auto_ownership,bldgsz,family,hh_value_of_time,hhsize,hinccat1,home_is_rural,home_is_urban,household_serial_no,hworkers,income,income_in_thousands,income_segment,median_value_of_time,non_family,num_adults,num_children,num_children_16_to_17,num_children_5_to_15,num_college_age,num_drivers,num_non_workers,num_workers,num_young_adults,num_young_children,persons,veh,DISTRICT,SD,county_id,TOTHH,TOTPOP,TOTACRE,RESACRE,CIACRE,TOTEMP,AGE0519,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,PRKCST,OPRKCST,area_type,HSENROLL,COLLFTE,COLLPTE,TOPOLOGY,TERMINAL,_original_zone_id,household_density,employment_density,density_index,is_cbd,TOTENR_univ,ext_work_share,RETEMPN_scaled,FPSEMPN_scaled,HEREMPN_scaled,OTHEMPN_scaled,AGREMPN_scaled,MWTEMPN_scaled,TOTEMP_scaled 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a/activitysim/examples/prototype_mtc_extended/test/regress/final_proto_disaggregate_accessibility-shadowpriced.csv b/activitysim/examples/prototype_mtc_extended/test/regress/final_proto_disaggregate_accessibility-shadowpriced.csv new file mode 100644 index 000000000..c3109fed9 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/test/regress/final_proto_disaggregate_accessibility-shadowpriced.csv @@ -0,0 +1,901 @@ 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a/activitysim/examples/prototype_mtc_extended/test/regress/final_proto_disaggregate_accessibility.csv b/activitysim/examples/prototype_mtc_extended/test/regress/final_proto_disaggregate_accessibility.csv new file mode 100644 index 000000000..cc6fe12dd --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/test/regress/final_proto_disaggregate_accessibility.csv @@ -0,0 +1,901 @@ 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+trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,tour_id,escort_participants,school_escort_direction,purpose,destination_logsum,depart,trip_mode,mode_choice_logsum +309348571,644476,386761,escort,1,True,1,13,16,30934857,,,escort,,9,TNC_SHARED,-0.06869446957292388 +309348576,644476,386761,escort,1,False,2,7,13,30934857,,,othdiscr,34.81437482524035,9,WALK_LOC,-0.08996283484500224 +309348577,644476,386761,escort,2,False,2,16,7,30934857,,,home,,9,WALK_LOC,13.429241204717705 +309348581,644476,386761,escort,1,True,1,10,16,30934858,,,escort,,11,WALK,5.409024682061962 +309348586,644476,386761,escort,1,False,1,16,10,30934858,,,home,,14,WALK,5.435046360173753 +309348941,644476,386761,work,1,True,1,4,16,30934894,,,work,,17,WALK,-0.3895987614665479 +309348946,644476,386761,work,1,False,2,7,4,30934894,,,othdiscr,28.173622215668733,20,WALK,-0.3544041726968209 +309348947,644476,386761,work,2,False,2,16,7,30934894,,,home,,23,WALK,9.584561380116599 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+2002378961,4171622,1810015,shopping,1,True,1,16,16,200237896,,,shopping,,17,WALK,7.330879449572421 +2002378966,4171622,1810015,shopping,1,False,1,16,16,200237896,,,home,,20,WALK,7.330879449572421 +2002379081,4171623,1810015,atwork,1,True,1,11,21,200237908,,,atwork,,11,WALK,5.104591162652175 +2002379086,4171623,1810015,atwork,1,False,1,21,11,200237908,,,work,,13,WALK,5.104591162652175 +2002379501,4171623,1810015,work,1,True,1,21,16,200237950,,,work,,8,WALK,2.5206381139237855 +2002379506,4171623,1810015,work,1,False,1,16,21,200237950,,,home,,18,WALK,2.7406374416940373 +3606007011,7512514,2821179,othmaint,1,True,1,1,8,360600701,,,othmaint,,9,WALK_LRF,0.7751207751649315 +3606007016,7512514,2821179,othmaint,1,False,1,8,1,360600701,,,home,,9,WALK_LRF,0.7208609722719941 +3606007121,7512514,2821179,shopping,1,True,1,5,8,360600712,,,shopping,,9,WALK,4.457538179557397 +3606007126,7512514,2821179,shopping,1,False,1,8,5,360600712,,,home,,10,WALK,4.457538178961434 +3606511661,7513565,2822230,work,1,True,1,25,8,360651166,,,work,,17,WALK,10.208443061976432 +3606511666,7513565,2822230,work,1,False,1,8,25,360651166,,,home,,21,WALK_LOC,10.194153968488326 diff --git a/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py b/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py index cc7bb7b54..758a291d5 100644 --- a/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py +++ b/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import os import subprocess +import sys import pandas as pd import pandas.testing as pdt @@ -15,7 +16,9 @@ def teardown_function(func): inject.reinject_decorated_tables() -def test_prototype_mtc_extended(): +def _test_prototype_mtc_extended( + multiprocess=False, sharrow=False, shadow_pricing=True +): def example_path(dirname): resource = os.path.join("examples", "prototype_mtc_extended", dirname) return pkg_resources.resource_filename("activitysim", resource) @@ -28,28 +31,75 @@ def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) def regress(): - regress_trips_df = pd.read_csv(test_path("regress/final_trips.csv")) - final_trips_df = pd.read_csv(test_path("output/final_trips.csv")) + regress_suffix = "" + if shadow_pricing: + regress_suffix += "-shadowpriced" + if sharrow: + regress_suffix += "-sharrow" + + output_dir = "output" + regress_trips_df = pd.read_csv( + test_path(f"regress/final_trips{regress_suffix}.csv") + ) + final_trips_df = pd.read_csv(test_path(f"{output_dir}/final_trips.csv")) regress_vehicles_df = pd.read_csv(test_path("regress/final_vehicles.csv")) - final_vehicles_df = pd.read_csv(test_path("output/final_vehicles.csv")) - - # person_id,household_id,tour_id,primary_purpose,trip_num,outbound,trip_count,purpose, - # destination,origin,destination_logsum,depart,trip_mode,mode_choice_logsum - # compare_cols = [] - pdt.assert_frame_equal(final_trips_df, regress_trips_df) - pdt.assert_frame_equal(final_vehicles_df, regress_vehicles_df) + final_vehicles_df = pd.read_csv(test_path(f"{output_dir}/final_vehicles.csv")) + + regress_accessibility_df = pd.read_csv( + test_path( + f"regress/final_proto_disaggregate_accessibility{regress_suffix}.csv" + ) + ) + final_accessibiliy_df = pd.read_csv( + test_path(f"{output_dir}/final_proto_disaggregate_accessibility.csv") + ) + # new transforms may add columns to final_accessibiliy_df, but that is + # not a test breakage if the existing columns still match. + final_accessibiliy_df = final_accessibiliy_df.drop( + columns=[ + i + for i in final_accessibiliy_df.columns + if i not in regress_accessibility_df.columns + ] + ) + pdt.assert_frame_equal( + final_accessibiliy_df, regress_accessibility_df, rtol=1.0e-4 + ) + + pdt.assert_frame_equal(final_trips_df, regress_trips_df, rtol=1.0e-4) + pdt.assert_frame_equal(final_vehicles_df, regress_vehicles_df, rtol=1.0e-4) file_path = os.path.join(os.path.dirname(__file__), "simulation.py") - - subprocess.run( - [ - "coverage", - "run", - "-a", - file_path, + shadowprice_configs = ( + [] if shadow_pricing else ["-c", test_path("no-shadow-pricing")] + ) + if sharrow: + sh_configs = ["-c", example_path("configs_sharrow")] + else: + sh_configs = [] + if multiprocess: + mp_configs = [ + "-c", + test_path("configs_mp"), + "-c", + example_path("configs_mp"), + ] + elif sharrow: + mp_configs = [ "-c", test_path("configs"), + ] + else: + mp_configs = [ + "-c", + test_path("configs"), + ] + run_args = ( + shadowprice_configs + + sh_configs + + mp_configs + + [ "-c", example_path("configs"), "-c", @@ -58,13 +108,46 @@ def regress(): example_mtc_path("data"), "-o", test_path("output"), - ], - check=True, + ] ) + if os.environ.get("GITHUB_ACTIONS") == "true": + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + else: + subprocess.run( + [sys.executable, "-m", "activitysim", "run"] + run_args, check=True + ) regress() +def test_prototype_mtc_extended(): + _test_prototype_mtc_extended( + multiprocess=False, sharrow=False, shadow_pricing=False + ) + + +def test_prototype_mtc_extended_sharrow(): + _test_prototype_mtc_extended(multiprocess=False, sharrow=True, shadow_pricing=False) + + +def test_prototype_mtc_extended_mp(): + _test_prototype_mtc_extended(multiprocess=True, sharrow=False, shadow_pricing=False) + + +def test_prototype_mtc_extended_shadow_pricing(): + _test_prototype_mtc_extended(multiprocess=False, sharrow=False, shadow_pricing=True) + + +def test_prototype_mtc_extended_sharrow_shadow_pricing(): + _test_prototype_mtc_extended(multiprocess=False, sharrow=True, shadow_pricing=True) + + +def test_prototype_mtc_extended_mp_shadow_pricing(): + _test_prototype_mtc_extended(multiprocess=True, sharrow=False, shadow_pricing=True) + + if __name__ == "__main__": test_prototype_mtc_extended() + test_prototype_mtc_extended_sharrow() + test_prototype_mtc_extended_mp() diff --git a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.csv b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.csv index a4eec1a88..a07639de7 100644 --- a/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.csv +++ b/activitysim/examples/prototype_mwcog/configs/atwork_subtour_frequency.csv @@ -4,7 +4,7 @@ util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_ti util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business -util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business +util_zero_cars_owned_by_hh_dummy,auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business diff --git a/activitysim/examples/prototype_mwcog/configs/legacy-1.1.3/settings.yaml b/activitysim/examples/prototype_mwcog/configs/legacy-1.1.3/settings.yaml new file mode 100644 index 000000000..4f8110237 --- /dev/null +++ b/activitysim/examples/prototype_mwcog/configs/legacy-1.1.3/settings.yaml @@ -0,0 +1,155 @@ +# input tables +# +# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) +# as well as for created tables (tours, joint_tour_participants, trips) +# e.g. the households table must have an index column 'household_id' and the foreign key to households in the +# persons table is also household_id. This naming convention allows activitysim to intuit the relationship +# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips +# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by +# household_id. +# +# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id +# you cannot change the well-known name of the index by modifying this directive. However, if your input file +# has a different id column name, you can rename it to the required index name with the rename_columns directive. +# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. +# +# input tables +input_table_list: + # + # households (table index 'household_id') + # + - tablename: households + filename: combined_synthetic_hh_2018.csv + index_col: household_id + rename_columns: + NP: hhsize + VEH: auto_ownership + TAZ: home_zone_id + # + # persons (table index 'person_id') + # + - tablename: persons + filename: combined_synthetic_per_2018.csv + index_col: person_id + rename_columns: + AGEP: age + hh_id: household_id + # + # land_use (table index 'zone_id') + # + - tablename: land_use + filename: LU_taz3722_rnd91a_2018_adj_enrollment.csv + index_col: zone_id + rename_columns: + TAZ: zone_id + +# convert input CSVs to HDF5 format and save to outputs directory +# create_input_store: True + +# number of households to simulate +households_sample_size: 100 +# simulate all households +#households_sample_size: 0 + +#household_ids: override_household_ids.csv + +chunk_size: 0 + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] +#trace_hh_id: 94944 + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +# trace_od: [5, 11] +trace_od: + + +models: + - initialize_landuse + - compute_accessibility + - initialize_households + - school_location + - workplace_location + - work_from_home + - transit_pass_subsidy + - transit_pass_ownership + - auto_ownership_simulate + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_tables + - write_trip_matrices + +# to resume after last successful checkpoint, specify resume_after: _ +resume_after: + +output_tables: + h5_store: False + action: include + prefix: final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + +# - value of time + +# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) + +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 diff --git a/activitysim/examples/prototype_mwcog/configs/logging.yaml b/activitysim/examples/prototype_mwcog/configs/logging.yaml index 6dfca578a..5fc7798a3 100644 --- a/activitysim/examples/prototype_mwcog/configs/logging.yaml +++ b/activitysim/examples/prototype_mwcog/configs/logging.yaml @@ -15,7 +15,7 @@ logging: loggers: activitysim: - level: DEBUG + level: INFO handlers: [console, logfile] propagate: false @@ -24,6 +24,20 @@ logging: handlers: [console, logfile] propagate: false + filelock: + level: WARN + + sharrow: + level: INFO + handlers: [console, logfile] + propagate: false + + blib2to3: + level: WARN + + black: + level: WARN + handlers: logfile: @@ -36,7 +50,7 @@ logging: console: class: logging.StreamHandler stream: ext://sys.stdout - formatter: simpleFormatter + formatter: elapsedFormatter level: NOTSET formatters: @@ -51,3 +65,8 @@ logging: class: logging.Formatter format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' datefmt: '%d/%m/%Y %H:%M:%S' + + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' diff --git a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.csv b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.csv index 848bbf77a..51094ee82 100644 --- a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_frequency.csv @@ -25,10 +25,10 @@ util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5 util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 diff --git a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling.yaml index 120959d36..ceda1517d 100644 --- a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling.yaml +++ b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling.yaml @@ -20,7 +20,7 @@ SIMULATE_CHOOSER_COLUMNS: - school_zone_id - home_zone_id - TAZ - + LOGSUM_SETTINGS: tour_mode_choice.yaml TOUR_SPEC_SEGMENTS: @@ -28,6 +28,10 @@ TOUR_SPEC_SEGMENTS: school: school univ: univ +preprocessor: + SPEC: mandatory_tour_scheduling_annotate_choosers_preprocessor.csv + DF: df + ALTS_PREPROCESSOR: work: SPEC: mandatory_tour_scheduling_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling_annotate_choosers_preprocessor.csv new file mode 100644 index 000000000..9b6fde9c9 --- /dev/null +++ b/activitysim/examples/prototype_mwcog/configs/mandatory_tour_scheduling_annotate_choosers_preprocessor.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +subsequent_tour_is_work,subsequent_tour_is_work,df.tour_type.shift(-1) == 'work' +subsequent_tour_is_school,subsequent_tour_is_school,df.tour_type.shift(-1) == 'school' diff --git a/activitysim/examples/prototype_mwcog/configs/network_los.yaml b/activitysim/examples/prototype_mwcog/configs/network_los.yaml index 3e4862903..8852c17f6 100644 --- a/activitysim/examples/prototype_mwcog/configs/network_los.yaml +++ b/activitysim/examples/prototype_mwcog/configs/network_los.yaml @@ -8,7 +8,28 @@ write_skim_cache: False zone_system: 1 -taz_skims: skims.omx +taz_skims: + omx: skims*.omx + zarr: skims.zarr + zarr-digital-encoding: + - {regex: .*_DIST.* , joint_dict: true} + - {regex: .*_DDIST , joint_dict: true} + - {regex: .*_DTIME , joint_dict: true} + - {regex: .*_FARE , joint_dict: true} + - {regex: .*_IVTBR , joint_dict: true} + - {regex: .*_IVTCR , joint_dict: true} + - {regex: .*_IVTLB , joint_dict: true} + - {regex: .*_IVTLR , joint_dict: true} + - {regex: .*_IVTMR , joint_dict: true} + - {regex: .*_IVTXB , joint_dict: true} + - {regex: .*_IWAIT , joint_dict: true} + - {regex: .*_TOTIVT , joint_dict: true} + - {regex: .*_TOTTT , joint_dict: true} + - {regex: .*_WACC , joint_dict: true} + - {regex: .*_WAUX , joint_dict: true} + - {regex: .*_XFERS , joint_dict: true} + - {regex: .*_XPEN , joint_dict: true} + - {regex: .*_XWAIT , joint_dict: true} skim_time_periods: time_window: 1440 @@ -16,4 +37,4 @@ skim_time_periods: periods: [0, 6, 12, 24, 32, 48] labels: ['NT', 'AM', 'MD', 'PM', 'NT'] -#NT is repeated twice as it's on both sides of the day. \ No newline at end of file +#NT is repeated twice as it's on both sides of the day. diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.csv b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.csv index 6abd71a70..cb99f1840 100644 --- a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_frequency.csv @@ -215,9 +215,9 @@ util_calibration_mand_gt0_nonmandtours_gt3,Mandatory tours > 0 and Non-mandatory util_calibration_mand_0_nonmandtours_2,Mandatory tours = 0 and Non-mandatory tours = 2 Calibration Constant,(num_mand == 0)&(tot_tours == 2),coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0,coef_calib_nonmandtours_2_mand_0 util_calibration_mand_0_nonmandtours_3,Mandatory tours = 0 and Non-mandatory tours = 3 Calibration Constant,(num_mand == 0)&(tot_tours == 3),coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0,coef_calib_nonmandtours_3_mand_0 util_calibration_mand_0_nonmandtours_gt4,Mandatory tours = 0 and Non-mandatory tours = 4+ Calibration Constant,(num_mand == 0)&(tot_tours >= 4),coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0,coef_calib_nonmandtours_gt4_mand_0 -util_tc_1dayperweek_1,Person that telecommutes 1 day per week,telecommute_frequency=='1_day_week' & tot_tours == 1,coef_tc_1dayperweek_1,coef_tc_1dayperweek_1,coef_tc_1dayperweek_1,0,0,0,0 -util_tc_23dayperweek_1,Person that telecommutes 2 to 3 days per week,telecommute_frequency=='2_3_days_week' & tot_tours == 1,coef_tc_23dayperweek_1,coef_tc_23dayperweek_1,coef_tc_23dayperweek_1,0,0,0,0 -util_tc_4pdayperweek_1,Person that telecommutes 2 to 3 days per week,telecommute_frequency=='4_days_week' & tot_tours == 1,coef_tc_4pdayperweek_1,coef_tc_4pdayperweek_1,coef_tc_4pdayperweek_1,0,0,0,0 -util_tc_1dayperweek_2p,Person that telecommutes 1 day per week,telecommute_frequency=='1_day_week' & tot_tours > 1,coef_tc_1dayperweek_2p,coef_tc_1dayperweek_2p,coef_tc_1dayperweek_2p,0,0,0,0 -util_tc_23dayperweek_2p,Person that telecommutes 2 to 3 days per week,telecommute_frequency=='2_3_days_week' & tot_tours > 1,coef_tc_23dayperweek_2p,coef_tc_23dayperweek_2p,coef_tc_23dayperweek_2p,0,0,0,0 -util_tc_4pdayperweek_2p,Person that telecommutes 2 to 3 days per week,telecommute_frequency=='4_days_week' & tot_tours > 1,coef_tc_4pdayperweek_2p,coef_tc_4pdayperweek_2p,coef_tc_4pdayperweek_2p,0,0,0,0 \ No newline at end of file +util_tc_1dayperweek_1,Person that telecommutes 1 day per week,(telecommute_frequency=='1_day_week') & (tot_tours == 1),coef_tc_1dayperweek_1,coef_tc_1dayperweek_1,coef_tc_1dayperweek_1,0,0,0,0 +util_tc_23dayperweek_1,Person that telecommutes 2 to 3 days per week,(telecommute_frequency=='2_3_days_week') & (tot_tours == 1),coef_tc_23dayperweek_1,coef_tc_23dayperweek_1,coef_tc_23dayperweek_1,0,0,0,0 +util_tc_4pdayperweek_1,Person that telecommutes 2 to 3 days per week,(telecommute_frequency=='4_days_week') & (tot_tours == 1),coef_tc_4pdayperweek_1,coef_tc_4pdayperweek_1,coef_tc_4pdayperweek_1,0,0,0,0 +util_tc_1dayperweek_2p,Person that telecommutes 1 day per week,(telecommute_frequency=='1_day_week') & (tot_tours > 1),coef_tc_1dayperweek_2p,coef_tc_1dayperweek_2p,coef_tc_1dayperweek_2p,0,0,0,0 +util_tc_23dayperweek_2p,Person that telecommutes 2 to 3 days per week,(telecommute_frequency=='2_3_days_week') & (tot_tours > 1),coef_tc_23dayperweek_2p,coef_tc_23dayperweek_2p,coef_tc_23dayperweek_2p,0,0,0,0 +util_tc_4pdayperweek_2p,Person that telecommutes 2 to 3 days per week,(telecommute_frequency=='4_days_week') & (tot_tours > 1),coef_tc_4pdayperweek_2p,coef_tc_4pdayperweek_2p,coef_tc_4pdayperweek_2p,0,0,0,0 diff --git a/activitysim/examples/prototype_mwcog/configs/settings.yaml b/activitysim/examples/prototype_mwcog/configs/settings.yaml index 4f8110237..fc126b771 100644 --- a/activitysim/examples/prototype_mwcog/configs/settings.yaml +++ b/activitysim/examples/prototype_mwcog/configs/settings.yaml @@ -25,6 +25,8 @@ input_table_list: NP: hhsize VEH: auto_ownership TAZ: home_zone_id + recode_columns: + home_zone_id: land_use.zone_id # # persons (table index 'person_id') # @@ -34,6 +36,8 @@ input_table_list: rename_columns: AGEP: age hh_id: household_id + recode_columns: + TAZ: land_use.zone_id # # land_use (table index 'zone_id') # @@ -42,6 +46,8 @@ input_table_list: index_col: zone_id rename_columns: TAZ: zone_id + recode_columns: + zone_id: zero-based # convert input CSVs to HDF5 format and save to outputs directory # create_input_store: True @@ -126,12 +132,30 @@ output_tables: prefix: final_ tables: - checkpoints - - accessibility - - land_use - - households - - persons - - tours - - trips + - tablename: accessibility + decode_columns: + zone_id: land_use.zone_id + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + TAZ: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + TAZ: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id - joint_tour_participants # area_types less than this are considered urban @@ -153,3 +177,5 @@ household_median_value_of_time: 2: 8.81 3: 10.44 4: 12.86 + +recode_pipeline_columns: False diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.csv index 93d8fc472..12b681419 100644 --- a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.csv +++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_atwork.csv @@ -19,22 +19,22 @@ util_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,Subtour purpose: Eat- util_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Subtour purpose: Eat-out - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_type == 'eat') & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period util__Departure_constants,# Departure constants,,coef__Departure_constants util_Shift_for_every_30_minutes_before_1030_am_Linear,Shift for every 30 minutes before 10:30 am - Linear,"@np.where((df.start<16), (np.where((df.start< 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start> 21), np.minimum(df.start - 21, 11), 0)), 0)",coef_Shift_for_every_30_minutes_before_1030_am_Linear -util_Before_1100_AM,Before 11:00 AM,@(df.start<17),coef_start_Before_1100_AM -util_1100_AM_1130_AM,11:00 AM - 11:30 AM,@(df.start==17),coef_start_1100_AM_1130_AM -util_1130_AM_1200_PM,11:30 AM - 12:00 PM,@(df.start==18),coef_start_1130_AM_1200_PM -util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.start==19),coef_start_1200_AM_1230_PM -util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.start==20),coef_start_1230_PM_0100_PM -util_After_0100_PM,After 01:00 PM,@(df.start>20),coef_start_After_0100_PM +util_Before_1100_AM_start,Before 11:00 AM,@(df.start<17),coef_start_Before_1100_AM +util_1100_AM_1130_AM_start,11:00 AM - 11:30 AM,@(df.start==17),coef_start_1100_AM_1130_AM +util_1130_AM_1200_PM_start,11:30 AM - 12:00 PM,@(df.start==18),coef_start_1130_AM_1200_PM +util_1200_AM_1230_PM_start,12:00 AM - 12:30 PM,@(df.start==19),coef_start_1200_AM_1230_PM +util_1230_PM_0100_PM_start,12:30 PM - 01:00 PM,@(df.start==20),coef_start_1230_PM_0100_PM +util_After_0100_PM_start,After 01:00 PM,@(df.start>20),coef_start_After_0100_PM util_Shift_for_every_30_minutes_after_130_pm_Square_Root,Shift for every 30 minutes after 1:30 pm - Square Root,"@np.where((df.start>21), ((np.where((df.start < 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start > 21), np.minimum(df.start - 21, 11), 0))** 0.5), 0)",coef_Shift_for_every_30_minutes_after_130_pm_Square_Root util__Arrival_constants,# Arrival constants,,coef__Arrival_constants util_Shift_for_every_30_minutes_before_1130_am_Linear,Shift for every 30 minutes before 11:30 am - Linear,"@np.where((df.end<18), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_before_1130_am_Linear -util_Before_1200_PM,Before 12:00 PM,@(df.end<19),coef_end_Before_1200_PM -util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.end==19),coef_end_1200_AM_1230_PM -util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.end==20),coef_end_1230_PM_0100_PM -util_0100_PM_0130_PM,01:00 PM - 01:30 PM,@(df.end==21),coef_end_0100_PM_0130_PM -util_0130_PM_0200_PM,01:30 PM - 02:00 PM,@(df.end==22),coef_end_0130_PM_0200_PM -util_0200_PM_0230_PM,02:00 PM - 02:30 PM,@(df.end==23),coef_end_0200_PM_0230_PM -util_After_0230_PM,After 02:30 PM,@(df.end>23),coef_end_After_0230_PM +util_Before_1200_PM_end,Before 12:00 PM,@(df.end<19),coef_end_Before_1200_PM +util_1200_AM_1230_PM_end,12:00 AM - 12:30 PM,@(df.end==19),coef_end_1200_AM_1230_PM +util_1230_PM_0100_PM_end,12:30 PM - 01:00 PM,@(df.end==20),coef_end_1230_PM_0100_PM +util_0100_PM_0130_PM_end,01:00 PM - 01:30 PM,@(df.end==21),coef_end_0100_PM_0130_PM +util_0130_PM_0200_PM_end,01:30 PM - 02:00 PM,@(df.end==22),coef_end_0130_PM_0200_PM +util_0200_PM_0230_PM_end,02:00 PM - 02:30 PM,@(df.end==23),coef_end_0200_PM_0230_PM +util_After_0230_PM_end,After 02:30 PM,@(df.end>23),coef_end_After_0230_PM util_Shift_for_every_30_minutes_after_300_pm_Linear,Shift for every 30 minutes after 3:00 pm - Linear,"@np.where((df.end>24), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_after_300_pm_Linear util__Duration_constants,# Duration constants,,coef__Duration_constants util_0_hrs,0 hrs,@(df.duration==0),coef_0_hrs diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint.csv index a9f0a5186..bf7686c48 100644 --- a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint.csv +++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_joint.csv @@ -68,7 +68,7 @@ util_escort_Duration_Constant_1p5hrs,ESCORT - Duration Constant: 1.5hrs,@((df.to util_escort_Duration_Constant_2_hrs,ESCORT - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==4)),coef_escort_Duration_Constant_2_hrs util_escort_Duration_Constant_Longer_than_2_hrs,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>4)),coef_escort_Duration_Constant_Longer_than_2_hrs util_escort_Calibration_Constant_Duration_eq_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Calibration_Constant_Duration_eq_1 -util_escort_Calibration_Constant_Duration_eq_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_category == 'joint') & (df.tour_type == 'escort') & df.duration==1)),coef_escort_Calibration_Constant_Duration_eq_2 +util_escort_Calibration_Constant_Duration_eq_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==1)),coef_escort_Calibration_Constant_Duration_eq_2 util_escort_Calibration_Constant_Departure_eq_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Calibration_Constant_Departure_eq_9 util_escort_Calibration_Constant_Departure_eq_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Calibration_Constant_Departure_eq_10 util_escort_Calibration_Constant_Departure_eq_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Calibration_Constant_Departure_eq_23 @@ -95,7 +95,7 @@ util_shop_Departure_Constant_10_00_AM_10_30_AM,SHOPPING - Departure Constant: 10 util_shop_Departure_Constant_10_30_AM_11_00_AM,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),coef_shop_Departure_Constant_10_30_AM_11_00_AM util_shop_Departure_Constant_After_11_00_AM,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),coef_shop_Departure_Constant_After_11_00_AM util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear -util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)).astype(np.float32) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear util_shop_Arrival_Constant_Before_12_30_PM,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),coef_shop_Arrival_Constant_Before_12_30_PM util_shop_Arrival_Constant_12_30_PM_03_00_PM,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shop_Arrival_Constant_12_30_PM_03_00_PM @@ -144,7 +144,7 @@ util_maint_Departure_Constant_10_00_AM_10_30_AM,MAINTENANCE - Departure Constant util_maint_Departure_Constant_10_30_AM_11_00_AM,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),coef_maint_Departure_Constant_10_30_AM_11_00_AM util_maint_Departure_Constant_After_11_00_AM,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),coef_maint_Departure_Constant_After_11_00_AM util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear -util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)).astype(np.float32) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear util_maint_Arrival_Constant_Before_10_30_AM,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),coef_maint_Arrival_Constant_Before_10_30_AM util_maint_Arrival_Constant_10_30_AM_11_00_AM,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),coef_maint_Arrival_Constant_10_30_AM_11_00_AM diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint.csv index 04f26223e..370db4e47 100644 --- a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint.csv +++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_othmaint.csv @@ -31,7 +31,7 @@ util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Co util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.start>16)),coef_maintenance_departure_constant_after_11_am util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)).astype(np.float32) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.end<16)),coef_maintenance_arrival_constant_before_10_30_am util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping.csv index 11211303e..437314f8d 100644 --- a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping.csv +++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_nonmandatory_shopping.csv @@ -28,7 +28,7 @@ util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.start>16)),coef_shopping_departure_constant_after_11_am util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)).astype(np.float32) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.end<20)),coef_shopping_arrival_constant_before_12_30_pm util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@(( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school.csv index 434942040..44b1f75ab 100644 --- a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school.csv +++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_school.csv @@ -22,11 +22,11 @@ util_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__L util_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,SCHOOL - All adults in the household are fulltime workers - Departure after 8:00 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear util_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration < 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end<27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs util_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration > 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end>27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs -util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work')) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours -util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work')) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours -util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Subsequent_tour_is_school_tour_Departure_after_800_am -util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours -util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours +util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.subsequent_tour_is_work)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours +util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.subsequent_tour_is_work)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours +util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.subsequent_tour_is_school)) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Subsequent_tour_is_school_tour_Departure_after_800_am +util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.subsequent_tour_is_school)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours +util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.subsequent_tour_is_school)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours util_Departure_Constant_Before_0600_AM,SCHOOL - Departure Constant: Before 06:00 AM,@(df.start<7),coef_Departure_Constant_Before_0600_AM @@ -56,4 +56,4 @@ util_Duration_Constant_8p5_hours,SCHOOL - Duration Constant: 8.5 hours,@(df.dura util_Duration_Constant_9_hours,SCHOOL - Duration Constant: 9 hours,@(df.duration==18),coef_Duration_Constant_9_hours util_Duration_Constant_Longer_than_9_hrs,SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),coef_Duration_Constant_Longer_than_9_hrs util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * ((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear -util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * (((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19))) ** 2)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * (((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19))).astype(np.float32) ** 2)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared diff --git a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university.csv b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university.csv index 160311a16..0f476a08e 100644 --- a/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university.csv +++ b/activitysim/examples/prototype_mwcog/configs/tour_scheduling_university.csv @@ -12,12 +12,12 @@ util_Distance_to_destination_Departure_after_830_am_Linear,UNIVERSITY - Distance util_Distance_to_destination_Durationlt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration< 4 hrs -Linear,"@((df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Linear util_Distance_to_destination_Durationgt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration> 4 hrs- Linear,"@((df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationgt_4_hrs_Linear util_Distance_to_destination_Durationlt_4_hrs_Square_Root,UNIVERSITY - Distance to destination - Duration< 4 hrs - Square Root,"@((df.duration<8))*(abs(((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))) ** 0.5)*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Square_Root -util_Subsequent_tour_is_work_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is work tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work')) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_work_tour_Departure_after_830_am -util_Subsequent_tour_is_work_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work')) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours -util_Subsequent_tour_is_work_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work')) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours -util_Subsequent_tour_is_school_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is school tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_school_tour_Departure_after_830_am -util_Subsequent_tour_is_school_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours -util_Subsequent_tour_is_school_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school')) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours +util_Subsequent_tour_is_work_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is work tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & df.subsequent_tour_is_work) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_work_tour_Departure_after_830_am +util_Subsequent_tour_is_work_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & df.subsequent_tour_is_work) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours +util_Subsequent_tour_is_work_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & df.subsequent_tour_is_work) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours +util_Subsequent_tour_is_school_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is school tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & df.subsequent_tour_is_school) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_school_tour_Departure_after_830_am +util_Subsequent_tour_is_school_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & df.subsequent_tour_is_school) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours +util_Subsequent_tour_is_school_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & df.subsequent_tour_is_school) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours util_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,UNIVERSITY - Departure Constant: Shift for every 30 minutes before 07:00 am - Linear,"@((df.start<9))*((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination.csv b/activitysim/examples/prototype_mwcog/configs/trip_destination.csv index 3f950aa96..3a0e04cec 100644 --- a/activitysim/examples/prototype_mwcog/configs/trip_destination.csv +++ b/activitysim/examples/prototype_mwcog/configs/trip_destination.csv @@ -1,6 +1,6 @@ Label,Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork -util_size_term,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one -util_no_attractions,no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_size_term,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_no_attractions,no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable #util_stop_zone_CDB_are_type,#stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, util_distance_inbound,distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),coef_util_distance_work_outbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork util_distance_outbound,distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),coef_util_distance_work_inbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv index 0217a6855..3f60fe017 100644 --- a/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv @@ -7,3 +7,4 @@ Description,Target,Expression #,,not needed as school is not chosen as an intermediate trip destination #,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" #,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" +,purpose_index_num,"size_terms.get_cols(df.purpose)" diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination_sample.csv b/activitysim/examples/prototype_mwcog/configs/trip_destination_sample.csv index 6a0a53480..5d03dd6c0 100644 --- a/activitysim/examples/prototype_mwcog/configs/trip_destination_sample.csv +++ b/activitysim/examples/prototype_mwcog/configs/trip_destination_sample.csv @@ -6,8 +6,8 @@ Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,, -size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 diff --git a/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.csv b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.csv index fa8def35f..0ce978b1e 100644 --- a/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.csv +++ b/activitysim/examples/prototype_mwcog/configs/trip_mode_choice.csv @@ -8,7 +8,7 @@ util_DRIVEALONE_In_vehicle_time,DRIVEALONE - In-vehicle time,@odt_skims['SOV_TIM util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,, util_DRIVEALONE_Operating_cost_,DRIVEALONE - Operating cost ,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,,,,,,,,,, util_DRIVEALONE_Parking_cost_,DRIVEALONE - Parking cost ,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@c_age1619_da * ((df.age >= 16) & (df.age <= 19)),coef_age1619_da,,,,,,,,,,,,,,,,,,,, +#util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@c_age1619_da * ((df.age >= 16) & (df.age <= 19)),coef_age1619_da,,,,,,,,,,,,,,,,,,,, #Shared_ride_2,#Shared ride 2,,,,,,,,,,,,,,,,,,,,,, util_SHARED2_Unavailable,SHARED2 - Unavailable,hov2_available == False,,coef_unavailable,,,,,,,,,,,,,,,,,,, util_SHARED2_Unavailable_based_on_party_size,SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,coef_unavailable,,,,,,,,,,,,,,,,,,, @@ -18,7 +18,7 @@ util_SHARED2_Operating_cost,SHARED2 - Operating cost,@ivt_cost_multiplier * df.i util_SHARED2_Parking_cost,SHARED2 - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,coef_ivt,,,,,,,,,,,,,,,,,,, util_SHARED2_One_person_household,SHARED2 - One person household,@(df.hhsize == 1),,coef_hhsize1_sr,,,,,,,,,,,,,,,,,,, util_SHARED2_Two_person_household,SHARED2 - Two person household,@(df.hhsize == 2),,coef_hhsize2_sr,,,,,,,,,,,,,,,,,,, -util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age1619_da,,,,,,,,,,,,,,,,,,, +#util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age1619_da,,,,,,,,,,,,,,,,,,, #Shared_ride_3+,#Shared ride 3+,,,,,,,,,,,,,,,,,,,,,, util_SHARED3_Unavailable,SHARED3 - Unavailable,hov3_available == False,,,coef_unavailable,,,,,,,,,,,,,,,,,, util_SHARED3_Unavailable_based_joint_tour_mode,SHARED3 - Unavailable based joint tour mode,@df.is_joint & df.i_tour_mode.isin(I_SR2_MODES),,,coef_unavailable,,,,,,,,,,,,,,,,,, diff --git a/activitysim/examples/prototype_mwcog/configs/work_from_home.csv b/activitysim/examples/prototype_mwcog/configs/work_from_home.csv index b6f9853df..fed5611bc 100644 --- a/activitysim/examples/prototype_mwcog/configs/work_from_home.csv +++ b/activitysim/examples/prototype_mwcog/configs/work_from_home.csv @@ -2,9 +2,9 @@ Label,Description,Expression,work_at_home,work_away_from_home util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant,0 util_full_time_worker,Full time worker (1 if true),@df.pemploy==PEMPLOY_FULL,coef_full_time_worker,0 util_female_worker,Female Worker,@df.SEX==2,coef_female_worker,0 -util_female_worker_preschool_child,Female worker with a Preschool Child in Household,"@df.SEX==2 & other_than(df.household_id, df.ptype == PTYPE_SCHOOL)",coef_female_worker_preschool_child,0 +util_female_worker_preschool_child,Female worker with a Preschool Child in Household,female_worker_with_preschool_child,coef_female_worker_preschool_child,0 util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.workplace_location_logsum,coef_access_to_workplaces,0 -util_non_working_adult_in_hh,Presence of Non Working Adult in the Household,"@other_than(df.household_id, df.ptype == PTYPE_NONWORK)",coef_non_working_adult_in_hh,0 +util_non_working_adult_in_hh,Presence of Non Working Adult in the Household,nonworking_adult_in_hh,coef_non_working_adult_in_hh,0 util_education_ba_plus,Education Level Bachelors or higher degree,0,coef_education_ba_plus,0 util_low_income,Household income Less than 30K,@df.income < 30000,coef_low_income,0 util_age_lt_35,Age Group - Less than 35 years,@df.age < 35,coef_age_lt_35,0 diff --git a/activitysim/examples/prototype_mwcog/configs/work_from_home.yaml b/activitysim/examples/prototype_mwcog/configs/work_from_home.yaml index c4f265ced..893876145 100644 --- a/activitysim/examples/prototype_mwcog/configs/work_from_home.yaml +++ b/activitysim/examples/prototype_mwcog/configs/work_from_home.yaml @@ -10,3 +10,7 @@ LOGIT_TYPE: MNL WORK_FROM_HOME_ALT: 0 DEST_CHOICE_COLUMN_NAME: workplace_zone_id + +preprocessor: + SPEC: work_from_home_annotate_persons_preprocessor + DF: persons diff --git a/activitysim/examples/prototype_mwcog/configs/work_from_home_annotate_persons_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/work_from_home_annotate_persons_preprocessor.csv new file mode 100644 index 000000000..299f422f7 --- /dev/null +++ b/activitysim/examples/prototype_mwcog/configs/work_from_home_annotate_persons_preprocessor.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +,female_worker_with_preschool_child,"df.SEX==2 & other_than(df.household_id, df.ptype == PTYPE_SCHOOL)" +,nonworking_adult_in_hh,"other_than(df.household_id, df.ptype == PTYPE_NONWORK)" diff --git a/activitysim/examples/prototype_mwcog/configs_sharrow/network_los.yaml b/activitysim/examples/prototype_mwcog/configs_sharrow/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/prototype_mwcog/configs_sharrow/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/prototype_mwcog/configs_sharrow/settings.yaml b/activitysim/examples/prototype_mwcog/configs_sharrow/settings.yaml new file mode 100644 index 000000000..7ddeaad9c --- /dev/null +++ b/activitysim/examples/prototype_mwcog/configs_sharrow/settings.yaml @@ -0,0 +1,3 @@ +inherit_settings: True +sharrow: require +recode_pipeline_columns: True diff --git a/activitysim/examples/prototype_mwcog/extensions/__init__.py b/activitysim/examples/prototype_mwcog/extensions/__init__.py deleted file mode 100644 index a5b766f31..000000000 --- a/activitysim/examples/prototype_mwcog/extensions/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from . import work_from_home -from . import telecommute_frequency -from . import transit_pass_ownership -from . import transit_pass_subsidy diff --git a/activitysim/examples/prototype_mwcog/extensions/telecommute_frequency.py b/activitysim/examples/prototype_mwcog/extensions/telecommute_frequency.py deleted file mode 100644 index c37081491..000000000 --- a/activitysim/examples/prototype_mwcog/extensions/telecommute_frequency.py +++ /dev/null @@ -1,105 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. -import logging - -import pandas as pd - -from activitysim.core import tracing -from activitysim.core import config -from activitysim.core import pipeline -from activitysim.core import simulate -from activitysim.core import inject -from activitysim.core import expressions - -from activitysim.abm.models.util import estimation - -logger = logging.getLogger(__name__) - - -@inject.step() -def telecommute_frequency(persons_merged, persons, chunk_size, trace_hh_id): - """ - This model predicts the frequency of telecommute for a person (worker) who - does not works from home. The alternatives of this model are 'No Telecommute', - '1 day per week', '2 to 3 days per week' and '4 days per week'. This model - reflects the choices of people who prefer a combination of working from home and - office during a week. - - The main interface to the telecommute frequency model is the telecommute_frequency() function. - This function is registered as an orca step in the example Pipeline. - """ - - trace_label = "telecommute_frequency" - model_settings_file_name = "telecommute_frequency.yaml" - - choosers = persons_merged.to_frame() - choosers = choosers[choosers.workplace_zone_id > -1] - - logger.info("Running %s with %d persons", trace_label, len(choosers)) - - model_settings = config.read_model_settings(model_settings_file_name) - estimator = estimation.manager.begin_estimation("telecommute_frequency") - - constants = config.get_model_constants(model_settings) - - # - preprocessor - preprocessor_settings = model_settings.get("preprocessor", None) - if preprocessor_settings: - - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) - - model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) - coefficients_df = simulate.read_model_coefficients(model_settings) - model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) - - nest_spec = config.get_logit_model_settings(model_settings) - - if estimator: - estimator.write_model_settings(model_settings, model_settings_file_name) - estimator.write_spec(model_settings) - estimator.write_coefficients(coefficients_df) - estimator.write_choosers(choosers) - - choices = simulate.simple_simulate( - choosers=choosers, - spec=model_spec, - nest_spec=nest_spec, - locals_d=constants, - chunk_size=chunk_size, - trace_label=trace_label, - trace_choice_name="telecommute_frequency", - estimator=estimator, - ) - - choices = pd.Series(model_spec.columns[choices.values], index=choices.index) - - if estimator: - estimator.write_choices(choices) - choices = estimator.get_survey_values( - choices, "persons", "telecommute_frequency" - ) - estimator.write_override_choices(choices) - estimator.end_estimation() - - persons = persons.to_frame() - persons["telecommute_frequency"] = ( - choices.reindex(persons.index).fillna("").astype(str) - ) - - pipeline.replace_table("persons", persons) - - tracing.print_summary( - "telecommute_frequency", persons.telecommute_frequency, value_counts=True - ) - - if trace_hh_id: - tracing.trace_df(persons, label=trace_label, warn_if_empty=True) diff --git a/activitysim/examples/prototype_mwcog/extensions/transit_pass_ownership.py b/activitysim/examples/prototype_mwcog/extensions/transit_pass_ownership.py deleted file mode 100644 index ebe43953f..000000000 --- a/activitysim/examples/prototype_mwcog/extensions/transit_pass_ownership.py +++ /dev/null @@ -1,92 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. -import logging - -import numpy as np - -from activitysim.core import tracing -from activitysim.core import config -from activitysim.core import pipeline -from activitysim.core import simulate -from activitysim.core import inject -from activitysim.core import expressions - -from activitysim.abm.models.util import estimation - -logger = logging.getLogger("activitysim") - - -@inject.step() -def transit_pass_ownership(persons_merged, persons, chunk_size, trace_hh_id): - """ - Transit pass ownership model. - """ - - trace_label = "transit_pass_ownership" - model_settings_file_name = "transit_pass_ownership.yaml" - - choosers = persons_merged.to_frame() - logger.info("Running %s with %d persons", trace_label, len(choosers)) - - model_settings = config.read_model_settings(model_settings_file_name) - estimator = estimation.manager.begin_estimation("transit_pass_ownership") - - constants = config.get_model_constants(model_settings) - - # - preprocessor - preprocessor_settings = model_settings.get("preprocessor", None) - if preprocessor_settings: - - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) - - model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) - coefficients_df = simulate.read_model_coefficients(model_settings) - model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) - - nest_spec = config.get_logit_model_settings(model_settings) - - if estimator: - estimator.write_model_settings(model_settings, model_settings_file_name) - estimator.write_spec(model_settings) - estimator.write_coefficients(coefficients_df) - estimator.write_choosers(choosers) - - choices = simulate.simple_simulate( - choosers=choosers, - spec=model_spec, - nest_spec=nest_spec, - locals_d=constants, - chunk_size=chunk_size, - trace_label=trace_label, - trace_choice_name="transit_pass_ownership", - estimator=estimator, - ) - - if estimator: - estimator.write_choices(choices) - choices = estimator.get_survey_values( - choices, "persons", "transit_pass_ownership" - ) - estimator.write_override_choices(choices) - estimator.end_estimation() - - persons = persons.to_frame() - persons["transit_pass_ownership"] = choices.reindex(persons.index) - - pipeline.replace_table("persons", persons) - - tracing.print_summary( - "transit_pass_ownership", persons.transit_pass_ownership, value_counts=True - ) - - if trace_hh_id: - tracing.trace_df(persons, label=trace_label, warn_if_empty=True) diff --git a/activitysim/examples/prototype_mwcog/extensions/transit_pass_subsidy.py b/activitysim/examples/prototype_mwcog/extensions/transit_pass_subsidy.py deleted file mode 100644 index e902753cb..000000000 --- a/activitysim/examples/prototype_mwcog/extensions/transit_pass_subsidy.py +++ /dev/null @@ -1,92 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. -import logging - -import numpy as np - -from activitysim.core import tracing -from activitysim.core import config -from activitysim.core import pipeline -from activitysim.core import simulate -from activitysim.core import inject -from activitysim.core import expressions - -from activitysim.abm.models.util import estimation - -logger = logging.getLogger("activitysim") - - -@inject.step() -def transit_pass_subsidy(persons_merged, persons, chunk_size, trace_hh_id): - """ - Transit pass subsidy model. - """ - - trace_label = "transit_pass_subsidy" - model_settings_file_name = "transit_pass_subsidy.yaml" - - choosers = persons_merged.to_frame() - logger.info("Running %s with %d persons", trace_label, len(choosers)) - - model_settings = config.read_model_settings(model_settings_file_name) - estimator = estimation.manager.begin_estimation("transit_pass_subsidy") - - constants = config.get_model_constants(model_settings) - - # - preprocessor - preprocessor_settings = model_settings.get("preprocessor", None) - if preprocessor_settings: - - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) - - model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) - coefficients_df = simulate.read_model_coefficients(model_settings) - model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) - - nest_spec = config.get_logit_model_settings(model_settings) - - if estimator: - estimator.write_model_settings(model_settings, model_settings_file_name) - estimator.write_spec(model_settings) - estimator.write_coefficients(coefficients_df) - estimator.write_choosers(choosers) - - choices = simulate.simple_simulate( - choosers=choosers, - spec=model_spec, - nest_spec=nest_spec, - locals_d=constants, - chunk_size=chunk_size, - trace_label=trace_label, - trace_choice_name="transit_pass_subsidy", - estimator=estimator, - ) - - if estimator: - estimator.write_choices(choices) - choices = estimator.get_survey_values( - choices, "persons", "transit_pass_subsidy" - ) - estimator.write_override_choices(choices) - estimator.end_estimation() - - persons = persons.to_frame() - persons["transit_pass_subsidy"] = choices.reindex(persons.index) - - pipeline.replace_table("persons", persons) - - tracing.print_summary( - "transit_pass_subsidy", persons.transit_pass_subsidy, value_counts=True - ) - - if trace_hh_id: - tracing.trace_df(persons, label=trace_label, warn_if_empty=True) diff --git a/activitysim/examples/prototype_mwcog/extensions/work_from_home.py b/activitysim/examples/prototype_mwcog/extensions/work_from_home.py deleted file mode 100644 index 82d6618d6..000000000 --- a/activitysim/examples/prototype_mwcog/extensions/work_from_home.py +++ /dev/null @@ -1,104 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. -import logging - -import numpy as np - -from activitysim.core import tracing -from activitysim.core import config -from activitysim.core import pipeline -from activitysim.core import simulate -from activitysim.core import inject -from activitysim.core import expressions - -from activitysim.abm.models.util import estimation - -logger = logging.getLogger(__name__) - - -@inject.step() -def work_from_home(persons_merged, persons, chunk_size, trace_hh_id): - """ - This model predicts the whether a person (worker) works from home. The output - from this model is TRUE (if works from home) or FALSE (works away from home). - The workplace location choice is overridden for workers who work from home - and set to -1. - - The main interface to the work from home model is the work_from_home() function. - This function is registered as an orca step in the example Pipeline. - """ - - trace_label = "work_from_home" - model_settings_file_name = "work_from_home.yaml" - - choosers = persons_merged.to_frame() - choosers = choosers[choosers.workplace_zone_id > -1] - logger.info("Running %s with %d persons", trace_label, len(choosers)) - - model_settings = config.read_model_settings(model_settings_file_name) - estimator = estimation.manager.begin_estimation("work_from_home") - - constants = config.get_model_constants(model_settings) - - # - preprocessor - preprocessor_settings = model_settings.get("preprocessor", None) - if preprocessor_settings: - - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) - - model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) - coefficients_df = simulate.read_model_coefficients(model_settings) - model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) - - nest_spec = config.get_logit_model_settings(model_settings) - - if estimator: - estimator.write_model_settings(model_settings, model_settings_file_name) - estimator.write_spec(model_settings) - estimator.write_coefficients(coefficients_df) - estimator.write_choosers(choosers) - - choices = simulate.simple_simulate( - choosers=choosers, - spec=model_spec, - nest_spec=nest_spec, - locals_d=constants, - chunk_size=chunk_size, - trace_label=trace_label, - trace_choice_name="work_from_home", - estimator=estimator, - ) - - work_from_home_alt = model_settings["WORK_FROM_HOME_ALT"] - choices = choices == work_from_home_alt - - dest_choice_column_name = model_settings["DEST_CHOICE_COLUMN_NAME"] - print(dest_choice_column_name) - - if estimator: - estimator.write_choices(choices) - choices = estimator.get_survey_values(choices, "persons", "work_from_home") - estimator.write_override_choices(choices) - estimator.end_estimation() - - persons = persons.to_frame() - persons["work_from_home"] = choices.reindex(persons.index).fillna(0).astype(bool) - persons[dest_choice_column_name] = np.where( - persons.work_from_home is True, -1, persons[dest_choice_column_name] - ) - - pipeline.replace_table("persons", persons) - - tracing.print_summary("work_from_home", persons.work_from_home, value_counts=True) - - if trace_hh_id: - tracing.trace_df(persons, label=trace_label, warn_if_empty=True) diff --git a/activitysim/examples/prototype_mwcog/simulation.py b/activitysim/examples/prototype_mwcog/simulation.py index 3126777b8..8313dd45e 100644 --- a/activitysim/examples/prototype_mwcog/simulation.py +++ b/activitysim/examples/prototype_mwcog/simulation.py @@ -1,13 +1,11 @@ # ActivitySim # See full license in LICENSE.txt. -import sys import argparse +import sys from activitysim.cli.run import add_run_args, run -import extensions - if __name__ == "__main__": parser = argparse.ArgumentParser() diff --git a/activitysim/examples/prototype_mwcog/test/configs/settings.yaml b/activitysim/examples/prototype_mwcog/test/configs/settings.yaml index 12820a341..b9597e83a 100644 --- a/activitysim/examples/prototype_mwcog/test/configs/settings.yaml +++ b/activitysim/examples/prototype_mwcog/test/configs/settings.yaml @@ -24,16 +24,8 @@ want_dest_choice_presampling: True # - tracing # trace household id; comment out or leave empty for no trace -trace_hh_id: +#trace_hh_id: # trace origin, destination in accessibility calculation; comment out or leave empty for no trace # trace_od: [5, 11] trace_od: - -output_tables: - h5_store: False - action: include - prefix: final_ - sort: True - tables: - - trips diff --git a/activitysim/examples/prototype_mwcog/test/output/cache/.gitignore b/activitysim/examples/prototype_mwcog/test/output/cache/.gitignore index 3dd2e62f9..1d085cacc 100644 --- a/activitysim/examples/prototype_mwcog/test/output/cache/.gitignore +++ b/activitysim/examples/prototype_mwcog/test/output/cache/.gitignore @@ -1,2 +1 @@ -*.mmap -*.feather +** diff --git a/activitysim/examples/prototype_mwcog/test/output/trace/.gitignore b/activitysim/examples/prototype_mwcog/test/output/trace/.gitignore index 8edb80678..1d085cacc 100644 --- a/activitysim/examples/prototype_mwcog/test/output/trace/.gitignore +++ b/activitysim/examples/prototype_mwcog/test/output/trace/.gitignore @@ -1,3 +1 @@ -*.csv -*.log -*.txt +** diff --git a/activitysim/examples/prototype_mwcog/test/regress/.gitignore b/activitysim/examples/prototype_mwcog/test/regress/.gitignore new file mode 100644 index 000000000..b4a783f63 --- /dev/null +++ b/activitysim/examples/prototype_mwcog/test/regress/.gitignore @@ -0,0 +1 @@ +final_checkpoints.csv diff --git a/activitysim/examples/prototype_mwcog/test/regress/final_accessibility.csv b/activitysim/examples/prototype_mwcog/test/regress/final_accessibility.csv new file mode 100644 index 000000000..b2580500a --- /dev/null +++ b/activitysim/examples/prototype_mwcog/test/regress/final_accessibility.csv @@ -0,0 +1,54 @@ +zone_id,auPkRetail,auPkTotal,auOpRetail,auOpTotal,trPkRetail,trPkTotal,trOpRetail,trOpTotal,nmRetail,nmTotal +1,9.81029707051306,12.151784326376733,10.255286731654158,12.56149659149444,6.876715840695639,9.397924315741278,6.053229178214222,8.480448597281763,7.862428648487154,10.55193711294014 +2,9.788277306830071,12.132040532912322,10.214609253692055,12.52197012399085,6.037116576669806,8.690508031671603,4.980478571350719,7.992674074446341,7.084710260664416,9.826611142334702 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+270473601,1100105,36,2704736,1,47,1,40,3,1,2,1,-9,4,722Z,8680.0,False,False,False,True,True,False,False,True,False,1,40,3,-9,4,1,3.0,1,False,False,False,False,False,False,False,False,False,False,False,False,False,False,0,True,36,1,20021,20.021,1,True,False,5.707035026149024,True,False,False,False,False,False,True,False,False,False,0,-1,,0.0,37,16.11584096402891,0.2,True,1.0,1.96,14.04,0.117,False,1,1,False,No_Telecommute,H,False,False,False,False,,False,False,0,0,False,False,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/activitysim/examples/prototype_mwcog/test/regress/final_tours.csv b/activitysim/examples/prototype_mwcog/test/regress/final_tours.csv new file mode 100644 index 000000000..057e169fe --- /dev/null +++ b/activitysim/examples/prototype_mwcog/test/regress/final_tours.csv @@ -0,0 +1,22 @@ +tour_id,person_id,tour_type,tour_type_count,tour_type_num,tour_num,tour_count,tour_category,number_of_participants,destination,origin,household_id,tdd,start,end,duration,composition,destination_logsum,tour_mode,mode_choice_logsum,atwork_subtour_frequency,parent_tour_id,stop_frequency,primary_purpose +6519154,159003,school,1,1,1,1,mandatory,1,27.0,25.0,1590,451.0,11.0,27.0,16.0,,,WALK,2.758076567942611,,,0out_0in,school +6519236,159005,school,1,1,1,1,mandatory,1,27.0,25.0,1590,293.0,7.0,27.0,20.0,,,WALK,2.7353689305227307,,,0out_0in,school +6519318,159007,school,1,1,1,1,mandatory,1,19.0,25.0,1590,451.0,11.0,27.0,16.0,,,WALK,2.713144660446043,,,0out_0in,school +6642121,162002,work,1,1,1,1,mandatory,1,38.0,25.0,1620,604.0,15.0,38.0,23.0,,,KNR_MR,2.7209542315290243,eat,,0out_0in,work +6642162,162003,work,1,1,1,1,mandatory,1,26.0,25.0,1620,527.0,13.0,30.0,17.0,,,WALK,3.5666322634386933,eat,,0out_0in,work 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+6642074,162001,shopping,1,1,1,2,non_mandatory,1,24.0,25.0,1620,742.0,20.0,21.0,1.0,,15.04014514701597,WALK,1.7064715572448472,,,0out_0in,shopping +6642066,162001,othdiscr,1,1,2,2,non_mandatory,1,25.0,25.0,1620,516.0,13.0,19.0,6.0,,15.116038427975054,WALK,2.012854024573718,,,0out_0in,othdiscr +6691291,163202,escort,2,1,1,2,non_mandatory,1,39.0,25.0,1632,946.0,28.0,29.0,1.0,,13.51585783844801,SHARED2,0.021552688601505547,,,0out_0in,escort +6691292,163202,escort,2,2,2,2,non_mandatory,1,23.0,25.0,1632,852.0,24.0,25.0,1.0,,13.533137735669488,WALK,0.8533704840550164,,,0out_0in,escort +6691356,163203,shopping,1,1,1,1,non_mandatory,1,22.0,25.0,1632,690.0,18.0,28.0,10.0,,14.593476482584549,WALK,0.041425542945739295,,,0out_0in,shopping +51996247,1268201,eatout,1,1,1,1,non_mandatory,1,39.0,47.0,12682,713.0,19.0,21.0,2.0,,13.057026256478705,WALK,0.3187482301360538,,,0out_0in,eatout +58552166,1428101,othdiscr,1,1,1,1,non_mandatory,1,38.0,49.0,14281,879.0,25.0,28.0,3.0,,14.354797920912523,WALK,0.6250888340229916,,,1out_3in,othdiscr +6642086,162002,eat,1,1,1,1,atwork,1,38.0,38.0,1620,799.0,22.0,23.0,1.0,,15.4963111559122,WALK,2.9189754365251126,,6642121.0,0out_0in,atwork +6642127,162003,eat,1,1,1,1,atwork,1,40.0,26.0,1620,711.0,19.0,19.0,0.0,,15.418258993054184,WALK,1.8680736528431738,,6642162.0,0out_0in,atwork diff --git a/activitysim/examples/prototype_mwcog/test/regress/final_trips.csv b/activitysim/examples/prototype_mwcog/test/regress/final_trips.csv index 81d05d0dd..b69e6a62d 100644 --- a/activitysim/examples/prototype_mwcog/test/regress/final_trips.csv +++ b/activitysim/examples/prototype_mwcog/test/regress/final_trips.csv @@ -6,9 +6,9 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 52154545,159007,1590,school,1,True,1,19,25,6519318,school,,11,WALK,3.1673109534958903 52154549,159007,1590,school,1,False,1,25,19,6519318,home,,27,WALK,3.1673109534958903 53136529,162001,1620,othdiscr,1,True,1,25,25,6642066,othdiscr,,13,WALK,2.0354872001527298 -53136533,162001,1620,othdiscr,1,False,1,25,25,6642066,home,,17,WALK,2.0354872001527298 +53136533,162001,1620,othdiscr,1,False,1,25,25,6642066,home,,19,WALK,2.0354872001527298 53136593,162001,1620,shopping,1,True,1,24,25,6642074,shopping,,20,WALK,0.5873669693786134 -53136597,162001,1620,shopping,1,False,1,25,24,6642074,home,,23,WALK,0.5873669693786134 +53136597,162001,1620,shopping,1,False,1,25,24,6642074,home,,21,WALK,0.5873669693786134 53136689,162002,1620,atwork,1,True,1,38,38,6642086,atwork,,22,WALK,-0.611999989181757 53136693,162002,1620,atwork,1,False,1,38,38,6642086,work,,23,WALK,-0.611999989181757 53136969,162002,1620,work,1,True,1,38,25,6642121,work,,15,KNR_MR,-0.8976646759623489 @@ -17,19 +17,19 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 53137021,162003,1620,atwork,1,False,1,26,40,6642127,work,,19,WALK,0.8548156977447776 53137297,162003,1620,work,1,True,1,26,25,6642162,work,,13,WALK,-0.16346785221758944 53137301,162003,1620,work,1,False,1,25,26,6642162,home,,30,WALK,-0.16346785221758944 -53530241,163201,1632,work,1,True,1,24,25,6691280,work,,9,PNR_MR,-0.4392097253621265 +53530241,163201,1632,work,1,True,1,24,25,6691280,work,,9,PNR_MR,-0.4392097253621263 53530245,163201,1632,work,1,False,1,25,24,6691280,home,,33,PNR_MR,-0.2591827350249029 53530329,163202,1632,escort,1,True,1,39,25,6691291,escort,,28,DRIVEALONE,-0.06197964360005489 53530333,163202,1632,escort,1,False,1,25,39,6691291,home,,29,SHARED2,-0.1701462043767353 53530337,163202,1632,escort,1,True,1,23,25,6691292,escort,,24,WALK,-0.92999997921288 53530341,163202,1632,escort,1,False,1,25,23,6691292,home,,25,WALK,-0.92999997921288 -53530849,163203,1632,shopping,1,True,1,22,25,6691356,shopping,,19,WALK,-1.409999968484044 -53530853,163203,1632,shopping,1,False,1,25,22,6691356,home,,20,WALK,-1.289999971166253 +53530849,163203,1632,shopping,1,True,1,22,25,6691356,shopping,,18,WALK,-1.409999968484044 +53530853,163203,1632,shopping,1,False,1,25,22,6691356,home,,28,WALK,-1.289999971166253 278275841,848401,8484,work,1,True,1,1,42,34784480,work,,16,DRIVEALONE,-0.3047444691565592 278275845,848401,8484,work,1,False,1,42,1,34784480,home,,30,DRIVEALONE,-0.3737130471632631 -381661281,1163601,11636,shopping,1,True,1,25,47,47707660,shopping,,39,SHARED2,0.13405080458662966 -381661285,1163601,11636,shopping,1,False,2,37,25,47707660,shopping,12.938418648448124,40,SHARED2,-0.19334951027125485 -381661286,1163601,11636,shopping,2,False,2,47,37,47707660,home,,40,SHARED2,0.11558833086308477 +381661281,1163601,11636,shopping,1,True,1,25,47,47707660,shopping,,38,SHARED2,0.13405080458662966 +381661285,1163601,11636,shopping,1,False,2,37,25,47707660,shopping,12.616106774095424,41,SHARED2,-0.2823603086688386 +381661286,1163601,11636,shopping,2,False,2,47,37,47707660,home,,42,SHARED2,0.03716309792422327 381661441,1163601,11636,work,1,True,1,55,47,47707680,work,,14,DRIVEALONE,-0.2776450324248023 381661445,1163601,11636,work,1,False,3,49,55,47707680,shopping,8.755660261900594,37,DRIVEALONE,-1.318377909968898 381661446,1163601,11636,work,2,False,3,26,49,47707680,shopping,11.445671730580356,37,DRIVEALONE,-0.628015657476555 @@ -47,4 +47,4 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 468417335,1428101,14281,othdiscr,3,False,4,45,54,58552166,othmaint,26.28617562432176,26,WALK,4.039591798974065 468417336,1428101,14281,othdiscr,4,False,4,49,45,58552166,home,,28,WALK,2.895014407185858 650752641,1984001,19840,work,1,True,1,28,57,81344080,work,,9,WALK,0.21761578208744412 -650752645,1984001,19840,work,1,False,1,57,28,81344080,home,,13,WALK_MR,0.22380439703983218 +650752645,1984001,19840,work,1,False,1,57,28,81344080,home,,13,WALK_MR,0.22380439703983235 diff --git a/activitysim/examples/prototype_mwcog/test/test_mwcog.py b/activitysim/examples/prototype_mwcog/test/test_mwcog.py index bd2307768..98652504f 100644 --- a/activitysim/examples/prototype_mwcog/test/test_mwcog.py +++ b/activitysim/examples/prototype_mwcog/test/test_mwcog.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import os import subprocess +import sys import pandas as pd import pandas.testing as pdt @@ -15,7 +16,7 @@ def teardown_function(func): inject.reinject_decorated_tables() -def test_mwcog(): +def _test_mwcog(sharrow=False): def example_path(dirname): resource = os.path.join("examples", "prototype_mwcog", dirname) return pkg_resources.resource_filename("activitysim", resource) @@ -34,12 +35,23 @@ def regress(): file_path = os.path.join(os.path.dirname(__file__), ".." + os.sep + "simulation.py") + if os.environ.get("GITHUB_ACTIONS") == "true": + executable = ["coverage", "run", "-a"] + else: + executable = [sys.executable] + + if sharrow: + sh_configs = ["-c", example_path("configs_sharrow")] + else: + sh_configs = [] + subprocess.run( - [ - "coverage", - "run", - "-a", + executable + + [ file_path, + ] + + sh_configs + + [ "-c", test_path("configs"), "-c", @@ -55,6 +67,14 @@ def regress(): regress() +def test_mwcog(): + _test_mwcog() + + +def test_mwcog_sharrow(): + _test_mwcog(sharrow=True) + + if __name__ == "__main__": test_mwcog() diff --git a/activitysim/examples/prototype_sandag_xborder/configs/legacy-1.1.3/logging.yaml b/activitysim/examples/prototype_sandag_xborder/configs/legacy-1.1.3/logging.yaml new file mode 100755 index 000000000..1e92b76a3 --- /dev/null +++ b/activitysim/examples/prototype_sandag_xborder/configs/legacy-1.1.3/logging.yaml @@ -0,0 +1,53 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/examples/prototype_sandag_xborder/configs/legacy-1.1.3/settings.yaml b/activitysim/examples/prototype_sandag_xborder/configs/legacy-1.1.3/settings.yaml new file mode 100755 index 000000000..66ce52012 --- /dev/null +++ b/activitysim/examples/prototype_sandag_xborder/configs/legacy-1.1.3/settings.yaml @@ -0,0 +1,157 @@ +inherit_settings: False + +# number of households to simulate + +households_sample_size: 0 +multiprocess: True +strict: False +mem_tick: 30 +num_processes: 30 +fail_fast: True + +############## +# +# chunking +# + +# chooser chunk size in gigabytes +# target top memory usage during activitysim run (including shared memory, loaded tables, and transient memory usage) +chunk_size: 400_000_000_000 +#chunk_size: 0 + +# minimum fraction of total chunk_size to reserve for adaptive chunking +min_available_chunk_ratio: 0.05 + +# initial number of chooser rows for first chunk in training mode +# when there is no pre-existing chunk_cache to set initial value +# ordinarily bigger is better as long as it is not so big it causes memory issues (e.g. accessibility with lots of zones) +default_initial_rows_per_chunk: 500 + +# method to calculate memory overhead when chunking is enabled +chunk_method: hybrid_uss + +# chunk training mode +# training to determine the chunking settings written to a cache file that is re-used for production runs +# training +# production +# disabled +chunk_training_mode: disabled + +# whether to preserve or delete subprocess chunk and mem logs when they are consolidated at end of multiprocess run +keep_chunk_logs: True +keep_mem_logs: True + +############## + +trace_hh_id: + +# input tables +input_table_list: + - tablename: households + filename: households_xborder.csv + index_col: household_id + + - tablename: persons + filename: persons_xborder.csv + index_col: person_id + + - tablename: land_use + filename: mazs_xborder.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + + - tablename: tours + filename: tours_xborder.csv + index_col: tour_id + keep_columns: + - pass_type + - tour_type + - purpose_id + - tour_category + - number_of_participants + - tour_num + - tour_count + - household_id + - person_id + + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False +want_dest_choice_presampling: True + +#resume_after: trip_scheduling + +models: + - initialize_landuse + - initialize_households + - initialize_tours + # --- STATIC cache prebuild steps + # single-process step to create attribute_combination list + - initialize_los + # multi-processable step to build STATIC cache + # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) + - initialize_tvpb + # # --- + - tour_scheduling_probabilistic + - tour_od_choice + - reassign_tour_purpose_by_poe + - tour_mode_choice_simulate + - stop_frequency + - trip_purpose + - trip_scheduling + - trip_destination + - trip_mode_choice + - write_trip_matrices + - write_tables + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_tvpb + begin: initialize_tvpb + num_processes: 5 + slice: + tables: + - attribute_combinations + - name: mp_households + begin: tour_scheduling_probabilistic + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_trip_matrices + +output_tables: + h5_store: False + action: include + prefix: final_ + # FIXME sort is an undocumented feature - sorts table by best index or ref_col according to traceable_table_indexes + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - attribute_combinations + +output_summaries: + tours: + - tour_mode + - od_path_set + - do_path_set diff --git a/activitysim/examples/prototype_sandag_xborder/configs/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv new file mode 100644 index 000000000..d7563cc82 --- /dev/null +++ b/activitysim/examples/prototype_sandag_xborder/configs/legacy-1.1.3/trip_destination_annotate_trips_preprocessor.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,tour_duration,"reindex(((tours.end - tours.start + 1) * 0.5), df.tour_id)" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id), reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) diff --git a/activitysim/examples/prototype_sandag_xborder/configs/logging.yaml b/activitysim/examples/prototype_sandag_xborder/configs/logging.yaml index df20cf0c7..33072a571 100755 --- a/activitysim/examples/prototype_sandag_xborder/configs/logging.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/logging.yaml @@ -24,6 +24,18 @@ logging: handlers: [console, logfile] propagate: false + filelock: + level: WARN + + sharrow: + level: INFO + + blib2to3: + level: WARN + + black: + level: WARN + handlers: logfile: @@ -36,7 +48,7 @@ logging: console: class: logging.StreamHandler stream: ext://sys.stdout - formatter: simpleFormatter + formatter: elapsedFormatter level: NOTSET formatters: @@ -52,3 +64,7 @@ logging: format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' datefmt: '%d/%m/%Y %H:%M:%S' + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' diff --git a/activitysim/examples/prototype_sandag_xborder/configs/settings.yaml b/activitysim/examples/prototype_sandag_xborder/configs/settings.yaml index 66ce52012..33bb5b840 100755 --- a/activitysim/examples/prototype_sandag_xborder/configs/settings.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/settings.yaml @@ -60,6 +60,15 @@ input_table_list: index_col: zone_id rename_columns: MAZ: zone_id + recode_columns: + zone_id: zero-based + TAZ: land_use_taz.TAZ + + - tablename: land_use_taz + filename: taz_xborder.csv + index_col: TAZ + recode_columns: + TAZ: zero-based - tablename: tours filename: tours_xborder.csv @@ -147,7 +156,10 @@ output_tables: - households - persons - tours - - trips + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id - attribute_combinations output_summaries: diff --git a/activitysim/examples/prototype_sandag_xborder/configs/trip_destination.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination.csv index 12fe38ccd..a5a52d062 100644 --- a/activitysim/examples/prototype_sandag_xborder/configs/trip_destination.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination.csv @@ -1,6 +1,6 @@ Description,Expression,work,school,visit,shop,other -size term for purpose,"@np.log1p(size_terms.get(df.trip_dest, df.purpose))",1,1,1,1,1 -no attractions for purpose,"@size_terms.get(df.trip_dest, df.purpose) == 0",-999,-999,-999,-999,-999 +Size variable,"@np.log1p(size_terms.get(df.trip_dest, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1 +No attractions,"@size_terms.get(df.trip_dest, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999 ,"_od_DIST@odt_skims['SOV_NT_M_DIST']",1,1,1,1,1 ,"_dp_DIST@dpt_skims['SOV_NT_M_DIST']",1,1,1,1,1 intermediate stops on half-tour,_stops_on_leg@df.trip_count - 1,1,1,1,1,1 @@ -9,8 +9,8 @@ mode choice logsum,od_logsum + dp_logsum,coef_mode_choice_logsum,coef_mode_choic ,@(df.tour_duration <= 2) * (df.od_logsum + df.dp_logsum),coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2 ,@(df.tour_duration >= 8) * (df.od_logsum + df.dp_logsum),coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8 first outbound trip,"@(df.trip_num == 1) & df.outbound & (_od_DIST > 1)",coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi -last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound & (_dp_DIST > 1)",coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi -last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound * np.clip(_dp_DIST - 1, 0, 3)",coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3 -first inbound trip,"@(df.trip_num == 1) & (1 - df.outbound) & (_od_DIST > 1)",coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi -last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) & (_dp_DIST > 1)",coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi -last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) * np.clip(_dp_DIST - 1, 0, 3)",coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3 \ No newline at end of file +last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) * df.outbound * (_dp_DIST > 1)",coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi +last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) * df.outbound * np.clip(_dp_DIST - 1, 0, 3)",coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3 +first inbound trip,"@(df.trip_num == 1) * (1 - df.outbound) * (_od_DIST > 1)",coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi +last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) * (1 - df.outbound) * (_dp_DIST > 1)",coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi +last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) * (1 - df.outbound) * np.clip(_dp_DIST - 1, 0, 3)",coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3 diff --git a/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_annotate_trips_preprocessor.csv index d7563cc82..430f04cfe 100644 --- a/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_annotate_trips_preprocessor.csv @@ -5,3 +5,4 @@ Description,Target,Expression ,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" ,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id), reindex_i(tours.end, df.tour_id))" ,trip_period,network_los.skim_time_period_label(_tod) +,purpose_index_num,"size_terms.get_cols(df.purpose)" diff --git a/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_sample.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_sample.csv index 029c9f7ba..6fa26e218 100644 --- a/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_sample.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_sample.csv @@ -1,12 +1,12 @@ Description,Expression,work,school,visit,shop,other -Size variable,"@np.log1p(size_terms.get(df.trip_dest, df.purpose))",1,1,1,1,1 -No attractions,"@size_terms.get(df.trip_dest, df.purpose) == 0",-999,-999,-999,-999,-999 +Size variable,"@np.log1p(size_terms.get(df.trip_dest, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1 +No attractions,"@size_terms.get(df.trip_dest, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999 ,"_od_DIST@odt_skims['SOV_NT_M_DIST']",1,1,1,1,1 ,"_dp_DIST@dpt_skims['SOV_NT_M_DIST']",1,1,1,1,1 intermediate stops on half-tour,_stops_on_leg@df.trip_count - 1,1,1,1,1,1 first outbound trip,"@(df.trip_num == 1) & df.outbound & (_od_DIST > 1)",coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound & (_dp_DIST > 1)",coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi -last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound * np.clip(_dp_DIST - 1, 0, 3)",coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3 +last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) * df.outbound * np.clip(_dp_DIST - 1, 0, 3)",coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3 first inbound trip,"@(df.trip_num == 1) & (1 - df.outbound) & (_od_DIST > 1)",coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) & (_dp_DIST > 1)",coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi -last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) * np.clip(_dp_DIST - 1, 0, 3)",coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3 +last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) * (1 - df.outbound) * np.clip(_dp_DIST - 1, 0, 3)",coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3 diff --git a/activitysim/examples/prototype_sandag_xborder/configs_sharrow/settings.yaml b/activitysim/examples/prototype_sandag_xborder/configs_sharrow/settings.yaml new file mode 100644 index 000000000..7ddeaad9c --- /dev/null +++ b/activitysim/examples/prototype_sandag_xborder/configs_sharrow/settings.yaml @@ -0,0 +1,3 @@ +inherit_settings: True +sharrow: require +recode_pipeline_columns: True diff --git a/activitysim/examples/prototype_sandag_xborder/data/taz_xborder.csv b/activitysim/examples/prototype_sandag_xborder/data/taz_xborder.csv new file mode 100644 index 000000000..4813009ea --- /dev/null +++ b/activitysim/examples/prototype_sandag_xborder/data/taz_xborder.csv @@ -0,0 +1,95 @@ +TAZ +1 +2 +4 +3093 +3098 +3112 +3115 +3129 +3131 +3134 +3135 +3148 +3176 +3184 +3208 +3252 +3259 +3261 +3272 +3274 +3277 +3278 +3281 +3286 +3289 +3290 +3314 +3315 +3316 +3317 +3339 +3340 +3342 +3343 +3347 +3363 +3375 +3376 +3378 +3383 +3395 +3396 +3397 +3398 +3399 +3400 +3402 +3410 +3412 +3433 +3437 +3442 +3460 +3464 +3493 +3497 +3498 +3501 +3502 +3505 +3519 +3525 +3536 +3540 +3548 +3553 +3574 +3590 +3592 +3595 +3597 +3598 +3619 +3626 +3640 +3654 +3655 +3661 +3670 +3687 +3698 +3704 +3705 +3713 +3715 +3729 +3746 +3761 +3796 +3822 +3849 +4831 +4989 +4993 diff --git a/activitysim/examples/prototype_sandag_xborder/test/configs/settings.yaml b/activitysim/examples/prototype_sandag_xborder/test/configs/settings.yaml index 32e44ac90..12039e3ab 100644 --- a/activitysim/examples/prototype_sandag_xborder/test/configs/settings.yaml +++ b/activitysim/examples/prototype_sandag_xborder/test/configs/settings.yaml @@ -23,4 +23,9 @@ output_tables: prefix: final_ sort: True tables: - - trips + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +recode_pipeline_columns: False diff --git a/activitysim/examples/prototype_sandag_xborder/test/configs_sharrow/settings.yaml b/activitysim/examples/prototype_sandag_xborder/test/configs_sharrow/settings.yaml new file mode 100644 index 000000000..7ddeaad9c --- /dev/null +++ b/activitysim/examples/prototype_sandag_xborder/test/configs_sharrow/settings.yaml @@ -0,0 +1,3 @@ +inherit_settings: True +sharrow: require +recode_pipeline_columns: True diff --git a/activitysim/examples/prototype_sandag_xborder/test/configs_single_process/settings.yaml b/activitysim/examples/prototype_sandag_xborder/test/configs_single_process/settings.yaml new file mode 100644 index 000000000..7e203b9e2 --- /dev/null +++ b/activitysim/examples/prototype_sandag_xborder/test/configs_single_process/settings.yaml @@ -0,0 +1,3 @@ +inherit_settings: True +num_processes: 1 +multiprocess: False diff --git a/activitysim/examples/prototype_sandag_xborder/test/regress/final_trips.csv b/activitysim/examples/prototype_sandag_xborder/test/regress/final_trips.csv index 4f5cca972..bddf1263e 100644 --- a/activitysim/examples/prototype_sandag_xborder/test/regress/final_trips.csv +++ b/activitysim/examples/prototype_sandag_xborder/test/regress/final_trips.csv @@ -5,8 +5,8 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 88253,19755,85270,other,1,False,1,123003,107090,False,11031,home,32,SHARED2,0.5131543999496662,,, 95817,73905,28840,shop,1,True,4,107090,123003,False,11977,other,29,DRIVEALONE,1.0469859921509128,,, 95818,73905,28840,shop,2,True,4,107090,107090,False,11977,other,30,WALK,1.0920564936223314,,, -95819,73905,28840,shop,3,True,4,100770,107090,False,11977,shop,33,SHARED2,-0.744935577346182,,, -95820,73905,28840,shop,4,True,4,100773,100770,False,11977,shop,36,SHARED2,1.2959664265830488,,, +95819,73905,28840,shop,3,True,4,100752,107090,False,11977,shop,33,SHARED2,-0.7374660553434764,,, +95820,73905,28840,shop,4,True,4,100773,100752,False,11977,shop,36,SHARED2,1.229684959352721,,, 95821,73905,28840,shop,1,False,2,100628,100773,False,11977,shop,36,SHARED2,1.1468706118720493,,, 95822,73905,28840,shop,2,False,2,123003,100628,False,11977,home,36,SHARED2,-0.8665412088436409,,, 359641,53300,105709,shop,1,True,1,100949,123003,False,44955,shop,24,SHARED2,-1.5409695208772292,,, @@ -32,8 +32,8 @@ trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,dest 660085,95667,12980,shop,1,False,3,100853,107090,False,82510,shop,46,DRIVEALONE,-1.2731966496833342,,, 660086,95667,12980,shop,2,False,3,101016,100853,False,82510,shop,47,WALK_TRANSIT,1.4417223835339454,1666.0,1660.0,set1 660087,95667,12980,shop,3,False,3,123003,101016,False,82510,home,48,WALK_TRANSIT,-1.6156533142902623,2485.0,1682.0,set3 -670945,15965,35585,work,1,True,2,107090,123003,False,83868,other,19,SHARED2,1.007548486967705,,, -670946,15965,35585,work,2,True,2,100511,107090,False,83868,work,20,SHARED3,-0.962582956214035,,, +670945,15965,35585,work,1,True,2,100761,123003,False,83868,other,19,SHARED2,-0.9886686904689685,,, +670946,15965,35585,work,2,True,2,100511,100761,False,83868,work,20,SHARED3,1.0345212732658378,,, 670949,15965,35585,work,1,False,1,123003,100511,False,83868,home,36,SHARED3,-1.048070004386589,,, 739817,12419,106831,shop,1,True,1,107090,123003,False,92477,shop,19,WALK,2.0003935353035707,,, 739821,12419,106831,shop,1,False,3,107090,107090,False,92477,visit,21,WALK,2.0261525497602557,,, diff --git a/activitysim/examples/prototype_sandag_xborder/test/regress/final_trips_1_process.csv b/activitysim/examples/prototype_sandag_xborder/test/regress/final_trips_1_process.csv new file mode 100644 index 000000000..f8a8a2646 --- /dev/null +++ b/activitysim/examples/prototype_sandag_xborder/test/regress/final_trips_1_process.csv @@ -0,0 +1,41 @@ +trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,failed,tour_id,purpose,depart,trip_mode,trip_mode_choice_logsum,atap,btap,path_set +52689,29512,20525,work,1,True,1,100629,123004,False,6586,work,28,DRIVEALONE,-2.2927062285592363,,, +52693,29512,20525,work,1,False,1,123004,100629,False,6586,home,43,SHARED3,-2.385303760737826,,, +88249,19755,85270,other,1,True,1,107090,123003,False,11031,other,24,WALK,0.6298754465072887,,, +88253,19755,85270,other,1,False,1,123003,107090,False,11031,home,32,SHARED2,0.5131543999496662,,, +95817,73905,28840,shop,1,True,4,107090,123003,False,11977,other,29,DRIVEALONE,1.0469859921509128,,, +95818,73905,28840,shop,2,True,4,107090,107090,False,11977,other,30,WALK,1.0920564936223314,,, +95819,73905,28840,shop,3,True,4,100752,107090,False,11977,shop,33,SHARED2,-0.7374660553434764,,, +95820,73905,28840,shop,4,True,4,100773,100752,False,11977,shop,36,SHARED2,1.229684959352721,,, +95821,73905,28840,shop,1,False,2,100628,100773,False,11977,shop,36,SHARED2,1.1468706118720493,,, +95822,73905,28840,shop,2,False,2,123003,100628,False,11977,home,36,SHARED2,-0.8665412088436409,,, +359641,53300,105709,shop,1,True,1,100949,123003,False,44955,shop,24,SHARED2,-1.5409695208772292,,, +359645,53300,105709,shop,1,False,4,107090,100949,False,44955,visit,36,DRIVEALONE,-1.5234073810744422,,, +359646,53300,105709,shop,2,False,4,100702,107090,False,44955,shop,37,WALK_TRANSIT,-0.792098750685573,1748.0,2485.0,set2 +359647,53300,105709,shop,3,False,4,107090,100702,False,44955,other,37,DRIVEALONE,-0.8957791792725553,,, +359648,53300,105709,shop,4,False,4,123003,107090,False,44955,home,37,WALK,1.9934609006686892,,, +466729,101126,85142,shop,1,True,1,101060,123003,False,58341,shop,18,SHARED2,-0.8545911170875646,,, +466733,101126,85142,shop,1,False,4,107090,101060,False,58341,visit,24,WALK_TRANSIT,-0.8640844181864032,2485.0,1857.0,set3 +466734,101126,85142,shop,2,False,4,100681,107090,False,58341,shop,25,WALK_TRANSIT,-0.9762391471647144,1612.0,2485.0,set2 +466735,101126,85142,shop,3,False,4,100721,100681,False,58341,shop,25,WALK,2.107292435978293,,, +466736,101126,85142,shop,4,False,4,123003,100721,False,58341,home,26,SHARED3,-1.033275093362878,,, +524585,60566,5596,shop,1,True,2,107090,123003,False,65573,other,15,DRIVEALONE,-0.1835895560485719,,, +524586,60566,5596,shop,2,True,2,100672,107090,False,65573,shop,26,DRIVEALONE,-2.0960686016264316,,, +524589,60566,5596,shop,1,False,4,100803,100672,False,65573,shop,32,SHARED2,-0.07701004684751338,,, +524590,60566,5596,shop,2,False,4,101070,100803,False,65573,shop,32,SHARED2,-0.7578421416787123,,, +524591,60566,5596,shop,3,False,4,107090,101070,False,65573,other,33,DRIVEALONE,-2.804919142326608,,, +524592,60566,5596,shop,4,False,4,123003,107090,False,65573,home,33,DRIVEALONE,-0.3137968667991837,,, +586217,35438,45199,shop,1,True,2,107090,123003,False,73277,other,20,SHARED3,1.4766852636113235,,, +586218,35438,45199,shop,2,True,2,100771,107090,False,73277,shop,21,SHARED3,-0.4943992108199083,,, +586221,35438,45199,shop,1,False,1,123003,100771,False,73277,home,32,SHARED3,-0.5803723599047195,,, +660081,95667,12980,shop,1,True,1,107090,123003,False,82510,shop,20,DRIVEALONE,1.5561582438604589,,, +660085,95667,12980,shop,1,False,3,100853,107090,False,82510,shop,46,DRIVEALONE,-1.2731966496833342,,, +660086,95667,12980,shop,2,False,3,101016,100853,False,82510,shop,47,WALK_TRANSIT,1.4417223835339454,1666.0,1660.0,set1 +660087,95667,12980,shop,3,False,3,123003,101016,False,82510,home,48,WALK_TRANSIT,-1.6156533142902623,2485.0,1682.0,set3 +670945,15965,35585,work,1,True,2,100761,123003,False,83868,other,19,DRIVEALONE,-1.3985538024019755,,, +670946,15965,35585,work,2,True,2,100511,100761,False,83868,work,20,SHARED2,0.60013961889779,,, +670949,15965,35585,work,1,False,1,123003,100511,False,83868,home,36,SHARED2,-1.4626936851073165,,, +739817,12419,106831,shop,1,True,1,107090,123003,False,92477,shop,19,WALK,2.0003935353035707,,, +739821,12419,106831,shop,1,False,3,107090,107090,False,92477,visit,21,WALK,2.0261525497602557,,, +739822,12419,106831,shop,2,False,3,101060,107090,False,92477,shop,22,SHARED3,-0.9555192827260166,,, +739823,12419,106831,shop,3,False,3,123003,101060,False,92477,home,24,WALK_TRANSIT,-1.0341377256289952,2485.0,1977.0,set2 diff --git a/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py b/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py index afa29c29d..261190248 100644 --- a/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py +++ b/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import os import subprocess +import sys import pandas as pd import pandas.testing as pdt @@ -15,7 +16,7 @@ def teardown_function(func): inject.reinject_decorated_tables() -def test_sandag_xborder(): +def _test_sandag_xborder(sharrow=False, mp=True): def example_path(dirname): resource = os.path.join("examples", "prototype_sandag_xborder", dirname) return pkg_resources.resource_filename("activitysim", resource) @@ -24,33 +25,53 @@ def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) def regress(): - regress_trips_df = pd.read_csv(test_path("regress/final_trips.csv")) + if mp: + regress_trips_df = pd.read_csv(test_path("regress/final_trips.csv")) + else: + regress_trips_df = pd.read_csv( + test_path("regress/final_trips_1_process.csv") + ) final_trips_df = pd.read_csv(test_path("output/final_trips.csv")) pdt.assert_frame_equal(final_trips_df, regress_trips_df) file_path = os.path.join(os.path.dirname(__file__), "../simulation.py") - subprocess.run( - [ - "coverage", - "run", - "-a", - file_path, - "-c", - test_path("configs"), - "-c", - example_path("configs"), - "-d", - example_path("data"), - "-o", - test_path("output"), - ], - check=True, - ) + run_args = [file_path] + if sharrow: + run_args += ["-c", test_path("configs_sharrow")] + if not mp: + run_args += ["-c", test_path("configs_single_process")] + run_args += [ + "-c", + test_path("configs"), + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] + + if os.environ.get("GITHUB_ACTIONS") == "true": + subprocess.run(["coverage", "run", "-a"] + run_args, check=True) + else: + subprocess.run([sys.executable] + run_args, check=True) regress() +def test_sandag_xborder(): + _test_sandag_xborder(mp=False) + + +def test_sandag_xborder_mp(): + _test_sandag_xborder(mp=True) + + +def test_sandag_xborder_sharrow(): + _test_sandag_xborder(sharrow=True, mp=False) + + if __name__ == "__main__": test_sandag_xborder() diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.csv b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.csv index a4eec1a88..a07639de7 100755 --- a/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.csv +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.csv @@ -4,7 +4,7 @@ util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_ti util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business -util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business +util_zero_cars_owned_by_hh_dummy,auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business diff --git a/activitysim/examples/prototype_semcog/configs/logging.yaml b/activitysim/examples/prototype_semcog/configs/logging.yaml index 6dfca578a..657507ee6 100755 --- a/activitysim/examples/prototype_semcog/configs/logging.yaml +++ b/activitysim/examples/prototype_semcog/configs/logging.yaml @@ -1,53 +1,70 @@ -# Config for logging -# ------------------ -# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema - -logging: - version: 1 - disable_existing_loggers: true - - - # Configuring the default (root) logger is highly recommended - root: - level: NOTSET - handlers: [console] - - loggers: - - activitysim: - level: DEBUG - handlers: [console, logfile] - propagate: false - - orca: - level: WARN - handlers: [console, logfile] - propagate: false - - handlers: - - logfile: - class: logging.FileHandler - filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] - mode: w - formatter: fileFormatter - level: NOTSET - - console: - class: logging.StreamHandler - stream: ext://sys.stdout - formatter: simpleFormatter - level: NOTSET - - formatters: - - simpleFormatter: - class: logging.Formatter - # format: '%(levelname)s - %(name)s - %(message)s' - format: '%(levelname)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - - fileFormatter: - class: logging.Formatter - format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + filelock: + level: WARN + + sharrow: + level: INFO + + blib2to3: + level: WARN + + black: + level: WARN + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: elapsedFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + elapsedFormatter: + (): activitysim.core.tracing.ElapsedTimeFormatter + format: '[{elapsedTime}] {levelname:s}: {message:s}' + style: '{' diff --git a/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv index 848bbf77a..51094ee82 100755 --- a/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv @@ -25,10 +25,10 @@ util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5 util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & ((distance_to_work < 3) | (distance_to_school < 3)),,,,,coef_can_walk_to_work_and_school util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 diff --git a/activitysim/examples/prototype_semcog/configs/settings.yaml b/activitysim/examples/prototype_semcog/configs/settings.yaml index 3d1bb870f..2aeff430a 100755 --- a/activitysim/examples/prototype_semcog/configs/settings.yaml +++ b/activitysim/examples/prototype_semcog/configs/settings.yaml @@ -1,156 +1,175 @@ -# input tables -# -# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) -# as well as for created tables (tours, joint_tour_participants, trips) -# e.g. the households table must have an index column 'household_id' and the foreign key to households in the -# persons table is also household_id. This naming convention allows activitysim to intuit the relationship -# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips -# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by -# household_id. -# -# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id -# you cannot change the well-known name of the index by modifying this directive. However, if your input file -# has a different id column name, you can rename it to the required index name with the rename_columns directive. -# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. -# -# input tables -input_table_list: - # - # households (table index 'household_id') - # - - tablename: households - filename: households.csv - index_col: household_id - rename_columns: - persons: hhsize - cars: auto_ownership - zone_id: home_zone_id - # - # persons (table index 'person_id') - # - - tablename: persons - filename: persons.csv - index_col: person_id - # - # land_use (table index 'zone_id') - # - - tablename: land_use - filename: land_use.csv - index_col: zone_id - rename_columns: - ZONE: zone_id - -# convert input CSVs to HDF5 format and save to outputs directory -# create_input_store: True - -# number of households to simulate -households_sample_size: 100 -# simulate all households -#households_sample_size: 0 - -#hh_ids: override_hh_ids.csv - -chunk_size: 0 - -# assume enough RAM to not chunk -chunk_training_mode: disabled - -# set false to disable variability check in simple_simulate and interaction_simulate -check_for_variability: False - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - - -# - tracing - -# trace household id; comment out or leave empty for no trace -# households with all tour types -# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] -trace_hh_id: 1311364 - -# trace origin, destination in accessibility calculation; comment out or leave empty for no trace -# trace_od: [5, 11] -trace_od: - - -models: - - initialize_landuse - - initialize_households - - compute_accessibility - - work_from_home - - add_size_tables - - school_location - - workplace_location - - transit_pass_subsidy - - transit_pass_ownership - - auto_ownership_simulate - - free_parking - - telecommute_frequency - - cdap_simulate - - mandatory_tour_frequency - - mandatory_tour_scheduling - - joint_tour_frequency - - joint_tour_composition - - joint_tour_participation - - joint_tour_destination - - joint_tour_scheduling - - non_mandatory_tour_frequency - - non_mandatory_tour_destination - - non_mandatory_tour_scheduling - - tour_mode_choice_simulate - - atwork_subtour_frequency - - atwork_subtour_destination - - atwork_subtour_scheduling - - atwork_subtour_mode_choice - - stop_frequency - - trip_purpose - - trip_destination - - trip_purpose_and_destination - - trip_scheduling - - trip_mode_choice - - write_data_dictionary - - track_skim_usage - - write_tables - - write_trip_matrices - -# to resume after last successful checkpoint, specify resume_after: _ -resume_after: - -output_tables: - h5_store: False - action: include - prefix: final_ - tables: - - checkpoints - - accessibility - - land_use - - households - - persons - - tours - - trips - - joint_tour_participants - -# area_types less than this are considered urban -urban_threshold: 4 -cbd_threshold: 2 -rural_threshold: 6 - -# - value of time - -# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) - -min_value_of_time: 1 -max_value_of_time: 50 -distributed_vot_mu: 0.684 -distributed_vot_sigma: 0.85 - -household_median_value_of_time: - 1: 6.01 - 2: 8.81 - 3: 10.44 - 4: 12.86 +# input tables +# +# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) +# as well as for created tables (tours, joint_tour_participants, trips) +# e.g. the households table must have an index column 'household_id' and the foreign key to households in the +# persons table is also household_id. This naming convention allows activitysim to intuit the relationship +# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips +# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by +# household_id. +# +# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id +# you cannot change the well-known name of the index by modifying this directive. However, if your input file +# has a different id column name, you can rename it to the required index name with the rename_columns directive. +# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. +# +# input tables +input_table_list: + # + # households (table index 'household_id') + # + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + persons: hhsize + cars: auto_ownership + zone_id: home_zone_id + recode_columns: + home_zone_id: land_use.zone_id + # + # persons (table index 'person_id') + # + - tablename: persons + filename: persons.csv + index_col: person_id + # + # land_use (table index 'zone_id') + # + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + ZONE: zone_id + recode_columns: + zone_id: zero-based + +# convert input CSVs to HDF5 format and save to outputs directory +# create_input_store: True + +# number of households to simulate +households_sample_size: 100 +# simulate all households +#households_sample_size: 0 + +#hh_ids: override_hh_ids.csv + +chunk_size: 0 + +# assume enough RAM to not chunk +chunk_training_mode: disabled + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] +trace_hh_id: 1311364 + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +# trace_od: [5, 11] +trace_od: + + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - work_from_home + - add_size_tables + - school_location + - workplace_location + - transit_pass_subsidy + - transit_pass_ownership + - auto_ownership_simulate + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_tables + - write_trip_matrices + +# to resume after last successful checkpoint, specify resume_after: _ +resume_after: + +output_tables: + h5_store: False + action: include + prefix: final_ + tables: + - checkpoints + - accessibility + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + +# - value of time + +# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) + +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 + diff --git a/activitysim/examples/prototype_semcog/configs/tour_mode_choice.csv b/activitysim/examples/prototype_semcog/configs/tour_mode_choice.csv index 14e13a802..dfeb0a526 100755 --- a/activitysim/examples/prototype_semcog/configs/tour_mode_choice.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_mode_choice.csv @@ -8,7 +8,7 @@ util_DRIVEALONE_In_vehicle_time,DRIVEALONE - In-vehicle time,@odt_skims['SOV_TIM util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,0,,,,,,,,,,,,,,,,, util_DRIVEALONE_Operating_cost_,DRIVEALONE - Operating cost ,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,,,,,,, util_DRIVEALONE_Parking_cost_,DRIVEALONE - Parking cost ,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Bridge_toll_,DRIVEALONE - Bridge toll ,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),0,,,,,,,,,,,,,,,,, +#util_DRIVEALONE_Bridge_toll_,DRIVEALONE - Bridge toll ,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),0,,,,,,,,,,,,,,,,, util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,,,,,,, #Shared_ride_2,#Shared ride 2,,,,,,,,,,,,,,,,,,, util_SHARED2_Unavailable,SHARED2 - Unavailable,hov2_available == False,,-999,,,,,,,,,,,,,,,, @@ -17,7 +17,7 @@ util_SHARED2_In_vehicle_time,SHARED2 - In-vehicle time,@(odt_skims['HOV2_TIME'] util_SHARED2_Terminal_time,SHARED2 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,0,,,,,,,,,,,,,,,, util_SHARED2_Operating_cost,SHARED2 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,coef_ivt,,,,,,,,,,,,,,,, util_SHARED2_Parking_cost,SHARED2 - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED2_Bridge_toll,SHARED2 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,0,,,,,,,,,,,,,,,, +#util_SHARED2_Bridge_toll,SHARED2 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,0,,,,,,,,,,,,,,,, util_SHARED2_One_person_household,SHARED2 - One person household,@(df.hhsize == 1),,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,, util_SHARED2_Two_person_household,SHARED2 - Two person household,@(df.hhsize == 2),,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,, util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,, @@ -27,7 +27,7 @@ util_SHARED3_In_vehicle_time,SHARED3 - In-vehicle time,@(odt_skims['HOV3_TIME'] util_SHARED3_Terminal_time,SHARED3 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,0,,,,,,,,,,,,,,, util_SHARED3_Operating_cost,SHARED3 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,coef_ivt,,,,,,,,,,,,,,, util_SHARED3_Parking_cost,SHARED3 - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3_Bridge_toll,SHARED3 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,0,,,,,,,,,,,,,,, +#util_SHARED3_Bridge_toll,SHARED3 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,0,,,,,,,,,,,,,,, util_SHARED3_One_person_household,SHARED3 - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,, util_SHARED3_Two_person_household,SHARED3 - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,, util_SHARED3_Person_is_16_years_old_or_older,SHARED3 - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,, diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv index a9f0a5186..351eaf77e 100755 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv @@ -68,7 +68,7 @@ util_escort_Duration_Constant_1p5hrs,ESCORT - Duration Constant: 1.5hrs,@((df.to util_escort_Duration_Constant_2_hrs,ESCORT - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==4)),coef_escort_Duration_Constant_2_hrs util_escort_Duration_Constant_Longer_than_2_hrs,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>4)),coef_escort_Duration_Constant_Longer_than_2_hrs util_escort_Calibration_Constant_Duration_eq_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Calibration_Constant_Duration_eq_1 -util_escort_Calibration_Constant_Duration_eq_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_category == 'joint') & (df.tour_type == 'escort') & df.duration==1)),coef_escort_Calibration_Constant_Duration_eq_2 +util_escort_Calibration_Constant_Duration_eq_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==1)),coef_escort_Calibration_Constant_Duration_eq_2 util_escort_Calibration_Constant_Departure_eq_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Calibration_Constant_Departure_eq_9 util_escort_Calibration_Constant_Departure_eq_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Calibration_Constant_Departure_eq_10 util_escort_Calibration_Constant_Departure_eq_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Calibration_Constant_Departure_eq_23 diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv index 3e2c109d9..f345af80a 100755 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv @@ -69,7 +69,7 @@ util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5h util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_calibration_constant_duration_2 util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv index c8eaf1bbd..318f1b695 100755 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv @@ -69,7 +69,7 @@ util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5h util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_calibration_constant_duration_2 util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv index 93b77cbe4..05b6b8ba9 100755 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv @@ -69,7 +69,7 @@ util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5h util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_calibration_constant_duration_2 util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 diff --git a/activitysim/examples/prototype_semcog/configs/trip_destination.csv b/activitysim/examples/prototype_semcog/configs/trip_destination.csv index 2d9307ab5..79b73f1ea 100755 --- a/activitysim/examples/prototype_semcog/configs/trip_destination.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_destination.csv @@ -1,6 +1,6 @@ Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork -size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 diff --git a/activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv index 1a1afb074..9f2d502d3 100755 --- a/activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv @@ -7,4 +7,7 @@ Description,Target,Expression #,,not needed as school is not chosen as an intermediate trip destination #,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" #,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" -,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" \ No newline at end of file +,purpose_index_num,"size_terms.get_cols(df.purpose)" +,tour_mode_is_walk,"reindex(tours.tour_mode, df.tour_id)=='WALK'" +,tour_mode_is_bike,"reindex(tours.tour_mode, df.tour_id)=='BIKE'" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id)).astype(int)" diff --git a/activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv b/activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv index 6a0a53480..4a2f5de66 100755 --- a/activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv @@ -1,13 +1,13 @@ Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork ,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 ,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if walk tour not within walking distance,@(df.tour_mode_is_walk) & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if walk tour not within walking distance,@(df.tour_mode_is_walk) & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode_is_bike) & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode_is_bike) & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,, -size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 diff --git a/activitysim/examples/prototype_semcog/configs/work_from_home.csv b/activitysim/examples/prototype_semcog/configs/work_from_home.csv index 11cfe76b4..b5afec73f 100755 --- a/activitysim/examples/prototype_semcog/configs/work_from_home.csv +++ b/activitysim/examples/prototype_semcog/configs/work_from_home.csv @@ -2,9 +2,9 @@ Label,Description,Expression,work_at_home,work_away_from_home util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant,0 util_full_time_worker,Full time worker (1 if true),@df.pemploy==PEMPLOY_FULL,coef_full_time_worker,0 util_female_worker,Female Worker,@df.sex==2,coef_female_worker,0 -util_female_worker_preschool_child,Female worker with a Preschool Child in Household,"@df.sex==2 & other_than(df.household_id, df.ptype == PTYPE_SCHOOL)",coef_female_worker_preschool_child,0 +util_female_worker_preschool_child,Female worker with a Preschool Child in Household,female_worker_with_preschool_child,coef_female_worker_preschool_child,0 util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.auPkTotal,coef_access_to_workplaces,0 -util_non_working_adult_in_hh,Presence of Non Working Adult in the Household,"@other_than(df.household_id, df.ptype == PTYPE_NONWORK)",coef_non_working_adult_in_hh,0 +util_non_working_adult_in_hh,Presence of Non Working Adult in the Household,nonworking_adult_in_hh,coef_non_working_adult_in_hh,0 util_education_ba_plus,Education Level Bachelors or higher degree,0,coef_education_ba_plus,0 util_low_income,Household income Less than 30K,@df.income < 30000,coef_low_income,0 util_age_lt_35,Age Group - Less than 35 years,@df.age < 35,coef_age_lt_35,0 diff --git a/activitysim/examples/prototype_semcog/configs/work_from_home.yaml b/activitysim/examples/prototype_semcog/configs/work_from_home.yaml index b94c13883..623c57b35 100755 --- a/activitysim/examples/prototype_semcog/configs/work_from_home.yaml +++ b/activitysim/examples/prototype_semcog/configs/work_from_home.yaml @@ -1,19 +1,23 @@ - -SPEC: work_from_home.csv -COEFFICIENTS: work_from_home_coeffs.csv - -#LOGIT_TYPE: NL -LOGIT_TYPE: MNL - -WORK_FROM_HOME_ALT: 0 - -# boolean column to filter choosers (True means keep) -CHOOSER_FILTER_COLUMN_NAME: is_worker - -# iterative what-if analysis example -# omit these settings to not iterate -# WORK_FROM_HOME_ITERATIONS: 3 -# WORK_FROM_HOME_CHOOSER_FILTER: is_worker -# WORK_FROM_HOME_TARGET_PERCENT: 0.1 -# WORK_FROM_HOME_TARGET_PERCENT_TOLERANCE: 0.01 -# WORK_FROM_HOME_COEFFICIENT_CONSTANT: coef_work_from_home_constant \ No newline at end of file + +SPEC: work_from_home.csv +COEFFICIENTS: work_from_home_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +WORK_FROM_HOME_ALT: 0 + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_worker + +# iterative what-if analysis example +# omit these settings to not iterate +# WORK_FROM_HOME_ITERATIONS: 3 +# WORK_FROM_HOME_CHOOSER_FILTER: is_worker +# WORK_FROM_HOME_TARGET_PERCENT: 0.1 +# WORK_FROM_HOME_TARGET_PERCENT_TOLERANCE: 0.01 +# WORK_FROM_HOME_COEFFICIENT_CONSTANT: coef_work_from_home_constant + +preprocessor: + SPEC: work_from_home_annotate_persons_preprocessor + DF: persons diff --git a/activitysim/examples/prototype_semcog/configs/work_from_home_annotate_persons_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/work_from_home_annotate_persons_preprocessor.csv new file mode 100644 index 000000000..1eb7f1b2e --- /dev/null +++ b/activitysim/examples/prototype_semcog/configs/work_from_home_annotate_persons_preprocessor.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +,female_worker_with_preschool_child,"df.sex==2 & other_than(df.household_id, df.ptype == PTYPE_SCHOOL)" +,nonworking_adult_in_hh,"other_than(df.household_id, df.ptype == PTYPE_NONWORK)" diff --git a/activitysim/examples/prototype_semcog/extensions/__init__.py b/activitysim/examples/prototype_semcog/extensions/__init__.py deleted file mode 100644 index 0b9ada033..000000000 --- a/activitysim/examples/prototype_semcog/extensions/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from . import ( - telecommute_frequency, - transit_pass_ownership, - transit_pass_subsidy, - work_from_home, -) diff --git a/activitysim/examples/prototype_semcog/simulation.py b/activitysim/examples/prototype_semcog/simulation.py index d53f5d25c..8313dd45e 100755 --- a/activitysim/examples/prototype_semcog/simulation.py +++ b/activitysim/examples/prototype_semcog/simulation.py @@ -4,8 +4,6 @@ import argparse import sys -import extensions - from activitysim.cli.run import add_run_args, run if __name__ == "__main__": diff --git a/activitysim/examples/prototype_semcog/test/test_semcog.py b/activitysim/examples/prototype_semcog/test/test_semcog.py index 8dc2dc2fc..e941238c3 100644 --- a/activitysim/examples/prototype_semcog/test/test_semcog.py +++ b/activitysim/examples/prototype_semcog/test/test_semcog.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import os import subprocess +import sys import pandas as pd import pandas.testing as pdt @@ -34,23 +35,21 @@ def regress(): file_path = os.path.join(os.path.dirname(__file__), "../simulation.py") - subprocess.run( - [ - "coverage", - "run", - "-a", - file_path, - "-c", - test_path("configs"), - "-c", - example_path("configs"), - "-d", - example_path("data"), - "-o", - test_path("output"), - ], - check=True, - ) + run_args = [ + "-c", + test_path("configs"), + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] + + if os.environ.get("GITHUB_ACTIONS") == "true": + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + else: + subprocess.run([sys.executable, file_path] + run_args, check=True) regress() diff --git a/activitysim/preconfigure.py b/activitysim/preconfigure.py new file mode 100644 index 000000000..73eb6902b --- /dev/null +++ b/activitysim/preconfigure.py @@ -0,0 +1,14 @@ +import os + + +def main(): + from .cli.main import main + + os.environ["MKL_NUM_THREADS"] = "1" + os.environ["OMP_NUM_THREADS"] = "1" + os.environ["OPENBLAS_NUM_THREADS"] = "1" + os.environ["NUMBA_NUM_THREADS"] = "1" + os.environ["VECLIB_MAXIMUM_THREADS"] = "1" + os.environ["NUMEXPR_NUM_THREADS"] = "1" + + return main() diff --git a/activitysim/standalone/__init__.py b/activitysim/standalone/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/activitysim/standalone/compare.py b/activitysim/standalone/compare.py new file mode 100644 index 000000000..024c720a3 --- /dev/null +++ b/activitysim/standalone/compare.py @@ -0,0 +1,319 @@ +import os +import warnings + +import altair as alt +import pandas as pd + +from .data_dictionary import check_data_dictionary +from .pipeline import load_checkpointed_tables + + +def load_pipelines(pipelines, tables=None, checkpoint_name=None): + """ + Parameters + ---------- + pipelines : Dict[Str, Path-like] + Mapping run name to path of pipeline file. + checkpoint : str + Name of checkpoint to load for all pipelines + """ + return { + key: load_checkpointed_tables( + pth, + tables=tables, + checkpoint_name=checkpoint_name, + )[1] + for key, pth in pipelines.items() + } + + +def load_final_tables(output_dirs, tables=None, index_cols=None): + result = {} + for key, pth in output_dirs.items(): + if not os.path.exists(pth): + warnings.warn(f"{key} directory does not exist: {pth}") + continue + result[key] = {} + for tname, tfile in tables.items(): + tpath = os.path.join(pth, tfile) + kwargs = {} + if index_cols is not None and tname in index_cols: + kwargs["index_col"] = index_cols[tname] + if os.path.exists(tpath): + result[key][tname] = pd.read_csv(tpath, **kwargs) + if len(result[key]) == 0: + # no tables were loaded, delete the entire group + del result[key] + return result + + +def compare_trip_mode_choice( + tablesets, title="Trip Mode Choice", grouping="primary_purpose" +): + + d = {} + groupings = [ + grouping, + ] + + for key, tableset in tablesets.items(): + df = ( + tableset["trips"] + .groupby(groupings + ["trip_mode"]) + .size() + .rename("n_trips") + .reset_index() + ) + df["share_trips"] = df["n_trips"] / df.groupby(groupings)["n_trips"].transform( + "sum" + ) + d[key] = df + + all_d = pd.concat(d, names=["source"]).reset_index() + + selection = alt.selection_multi( + fields=["trip_mode"], + bind="legend", + ) + + fig = ( + alt.Chart(all_d) + .mark_bar() + .encode( + color="trip_mode", + y=alt.Y("source", axis=alt.Axis(grid=False, title=""), sort=None), + x=alt.X( + "share_trips", + axis=alt.Axis(grid=False, labels=False, title="Mode Share"), + ), + row="primary_purpose", + opacity=alt.condition(selection, alt.value(1), alt.value(0.2)), + tooltip=[ + "trip_mode", + "source", + "n_trips", + alt.Tooltip("share_trips:Q", format=".2%"), + ], + ) + .add_selection( + selection, + ) + ) + + if title: + fig = fig.properties(title=title).configure_title( + fontSize=20, + anchor="start", + color="black", + ) + + return fig + + +def compare_trip_distance( + tablesets, + skims, + dist_skim_name, + otaz_col="origin", + dtaz_col="destination", + time_col="depart", + dist_bins=20, + grouping="primary_purpose", + title="Trip Length Distribution", + max_dist=None, +): + groupings = [grouping] + if not isinstance(skims, dict): + skims = {i: skims for i in tablesets.keys()} + + distances = {} + for key, tableset in tablesets.items(): + skim_dist = skims[key][[dist_skim_name]] + + zone_ids = tableset["land_use"].index + if ( + zone_ids.is_monotonic_increasing + and zone_ids[-1] == len(zone_ids) + zone_ids[0] - 1 + ): + offset = zone_ids[0] + looks = [ + tableset["trips"][otaz_col].rename("otaz") - offset, + tableset["trips"][dtaz_col].rename("dtaz") - offset, + ] + else: + remapper = dict(zip(zone_ids, pd.RangeIndex(len(zone_ids)))) + looks = [ + tableset["trips"][otaz_col].rename("otaz").apply(remapper.get), + tableset["trips"][dtaz_col].rename("dtaz").apply(remapper.get), + ] + if "time_period" in skim_dist.dims: + looks.append( + tableset["trips"][time_col] + .apply(skims[key].attrs["time_period_imap"].get) + .rename("time_period"), + ) + look = pd.concat(looks, axis=1) + distances[key] = skims[key][[dist_skim_name]].iat.df(look) + + if dist_bins is not None: + result = pd.concat(distances, names=["source"]) + if max_dist is not None: + result = result[result <= max_dist] + result = pd.cut(result.iloc[:, 0], dist_bins).to_frame() + distances = {k: result.loc[k] for k in tablesets.keys()} + + data = {} + for key, tableset in tablesets.items(): + data[key] = tableset["trips"].assign(**{"distance": distances[key]}) + + d = {} + for key, dat in data.items(): + df = ( + dat.groupby(groupings + ["distance"]) + .size() + .rename("n_trips") + .unstack("distance") + .fillna(0) + .stack() + .rename("n_trips") + .reset_index() + ) + df["share_trips"] = df["n_trips"] / df.groupby(groupings)["n_trips"].transform( + "sum" + ) + d[key] = df + + all_d = pd.concat(d, names=["source"]).reset_index() + all_d["distance"] = all_d["distance"].apply(lambda x: x.mid) + + fig = ( + alt.Chart(all_d) + .mark_line( + interpolate="monotone", + ) + .encode( + color="source", + y=alt.Y("share_trips", axis=alt.Axis(grid=False, title="")), + x=alt.X("distance", axis=alt.Axis(grid=False, title="Distance")), + # opacity=alt.condition(selection, alt.value(1), alt.value(0.2)), + # tooltip = ['trip_mode', 'source', 'n_trips', alt.Tooltip('share_trips:Q', format='.2%')], + facet=alt.Facet(grouping, columns=3), + strokeWidth="source", + ) + .properties( + width=200, + height=120, + ) + ) + + if title: + fig = fig.properties(title=title).configure_title( + fontSize=20, + anchor="start", + color="black", + ) + + return fig + + +def compare_work_district( + tablesets, + district_id, + label="district", + hometaz_col="home_zone_id", + worktaz_col="workplace_zone_id", + data_dictionary=None, +): + data_dictionary = check_data_dictionary(data_dictionary) + + d = {} + h = f"home_{label}" + w = f"work_{label}" + + for key, tableset in tablesets.items(): + persons = tableset["persons"] + workers = persons[persons[worktaz_col] >= 0].copy() + district_map = tableset["land_use"][district_id] + # workers[f"home_{label}_"] = workers[hometaz_col].map(district_map) + # workers[f"work_{label}_"] = workers[worktaz_col].map(district_map) + home_district = workers[hometaz_col].map(district_map).rename(h) + work_district = workers[worktaz_col].map(district_map).rename(w) + df = ( + workers.groupby( + [home_district, work_district] + # [f"home_{label}_", f"work_{label}_"] + ) + .size() + .rename("n_workers") + ) + d[key] = df + + all_d = pd.concat(d, names=["source"]).reset_index() + + district_names = data_dictionary.get("land_use", {}).get(district_id, None) + if district_names is not None: + all_d[h] = all_d[h].map(district_names) + all_d[w] = all_d[w].map(district_names) + + selection = alt.selection_multi( + fields=[w], + bind="legend", + ) + + fig = ( + alt.Chart(all_d) + .mark_bar() + .encode( + color=f"{w}:N", + y=alt.Y("source", axis=alt.Axis(grid=False, title=""), sort=None), + x=alt.X("n_workers", axis=alt.Axis(grid=False)), + row=f"{h}:N", + opacity=alt.condition(selection, alt.value(1), alt.value(0.2)), + tooltip=[f"{h}:N", f"{w}:N", "source", "n_workers"], + ) + .add_selection( + selection, + ) + ) + + return fig + + +def compare_runtime(combo_timing_log): + df = pd.read_csv(combo_timing_log, index_col="model_name") + df1 = ( + df[["sharrow", "legacy"]] + .rename_axis(columns="source") + .unstack() + .rename("seconds") + .reset_index() + ) + c = alt.Chart( + df1, + height={"step": 20}, + ) + + result = c.mark_bar(yOffset=-3, size=6,).transform_filter( + (alt.datum.source == "legacy") + ).encode( + x=alt.X("seconds:Q", stack=None), + y=alt.Y("model_name", type="nominal", sort=None), + color="source", + tooltip=["source", "model_name", "seconds"], + ) + c.mark_bar( + yOffset=4, + size=6, + ).transform_filter( + (alt.datum.source == "sharrow") + ).encode( + x=alt.X("seconds:Q", stack=None), + y=alt.Y("model_name", type="nominal", sort=None), + color="source", + tooltip=["source", "model_name", "seconds"], + ) | alt.Chart( + df1 + ).mark_bar().encode( + color="source", x="source", y="sum(seconds)", tooltip=["source", "sum(seconds)"] + ) + + return result diff --git a/activitysim/standalone/data_dictionary.py b/activitysim/standalone/data_dictionary.py new file mode 100644 index 000000000..a4fc9e4b7 --- /dev/null +++ b/activitysim/standalone/data_dictionary.py @@ -0,0 +1,34 @@ +import os.path +import warnings +from typing import Mapping + +import yaml + + +def check_data_dictionary(input): + """ + Read and validate a data dictionary. + + The dictionary should be a nested mapping, with top level keys + giving table names, second level keys giving column names, and + then finally a mapping of codes to values. + """ + if input is None: + return {} + elif isinstance(input, str): + if not os.path.exists(input): + warnings.warn(f"data dictionary file {input} is missing") + return {} + with open(input, "rt") as f: + content = yaml.safe_load(f) + else: + content = input + + for i, v in content.items(): + assert isinstance(i, str) + assert isinstance(v, Mapping) + for c, j in v.items(): + assert isinstance(c, str) + assert isinstance(j, Mapping) + + return content diff --git a/activitysim/standalone/pipeline.py b/activitysim/standalone/pipeline.py new file mode 100644 index 000000000..948914d10 --- /dev/null +++ b/activitysim/standalone/pipeline.py @@ -0,0 +1,45 @@ +import pandas as pd + +from ..core import pipeline + + +def load_checkpointed_tables( + pipeline_file_path, + tables=None, + checkpoint_name=None, +): + pipeline_store = pd.HDFStore(pipeline_file_path, mode="r") + + checkpoints = pipeline_store[pipeline.CHECKPOINT_TABLE_NAME] + + # checkpoint row as series + if checkpoint_name is None: + checkpoint = checkpoints.iloc[-1] + checkpoint_name = checkpoint.loc[pipeline.CHECKPOINT_NAME] + else: + i = checkpoints.set_index(pipeline.CHECKPOINT_NAME).index.get_loc( + checkpoint_name + ) + checkpoint = checkpoints.iloc[i] + + # series with table name as index and checkpoint_name as value + checkpoint_tables = checkpoint[~checkpoint.index.isin(pipeline.NON_TABLE_COLUMNS)] + + # omit dropped tables with empty checkpoint name + checkpoint_tables = checkpoint_tables[checkpoint_tables != ""] + + # hdf5 key is / + checkpoint_tables = { + table_name: pipeline.pipeline_table_key(table_name, checkpoint_name) + for table_name, checkpoint_name in checkpoint_tables.items() + } + + data = {} + for table_name, table_key in checkpoint_tables.items(): + if tables is None or table_name in tables: + data[table_name] = pipeline_store[table_key] + + pipeline_store.close() + + # checkpoint name and series mapping table name to hdf5 key for tables in that checkpoint + return checkpoint_name, data diff --git a/activitysim/standalone/render.py b/activitysim/standalone/render.py new file mode 100644 index 000000000..ad672a7d1 --- /dev/null +++ b/activitysim/standalone/render.py @@ -0,0 +1,64 @@ +import logging +import os +import textwrap +from contextlib import contextmanager +from pathlib import Path + +import nbclient +import nbformat as nbf +from nbconvert import HTMLExporter +from xmle import NumberedCaption, Reporter + +from .. import __version__ + +# from jupyter_contrib_nbextensions.nbconvert_support import TocExporter # problematic + + +@contextmanager +def chdir(path: Path): + """ + Sets the cwd within the context + + Args: + path (Path): The path to the cwd + + Yields: + None + """ + + cwd = Path().absolute() + try: + os.chdir(path) + yield + finally: + os.chdir(cwd) + + +def render_notebook(nb_filename, cellcontent): + nb_filename = os.path.splitext(nb_filename)[0] + nb = nbf.v4.new_notebook() + + cells = [] + for c in cellcontent: + c = textwrap.dedent(c).strip() + if c[:4] == "[md]": + cells.append(nbf.v4.new_markdown_cell(c[4:])) + else: + cells.append(nbf.v4.new_code_cell(c)) + nb["cells"] = cells + nbf.write(nb, nb_filename + ".ipynb") + + nb = nbclient.execute(nb, cwd=os.path.dirname(nb_filename)) + nbf.write(nb, nb_filename + "-e.ipynb") + + html_exporter = HTMLExporter( + embed_images=True, + exclude_input_prompt=True, + exclude_output_prompt=True, + exclude_input=True, + # template_name = 'classic' + ) + (body, resources) = html_exporter.from_notebook_node(nb) + + with open(nb_filename + ".html", "w") as f: + f.write(body) diff --git a/activitysim/standalone/skims.py b/activitysim/standalone/skims.py new file mode 100644 index 000000000..1065383c5 --- /dev/null +++ b/activitysim/standalone/skims.py @@ -0,0 +1,64 @@ +import glob +import logging +import os + +import numpy as np +import openmatrix +import sharrow as sh +import yaml + +logger = logging.getLogger(__name__) + + +def load_skims( + network_los_settings_filename, + data_dir, +): + with open(network_los_settings_filename, "rt") as f: + settings = yaml.safe_load(f) + + skim_settings = settings["taz_skims"] + if isinstance(skim_settings, str): + skims_omx_fileglob = skim_settings + else: + skims_omx_fileglob = skim_settings.get("omx", None) + skims_omx_fileglob = skim_settings.get("files", skims_omx_fileglob) + skims_filenames = glob.glob(os.path.join(data_dir, skims_omx_fileglob)) + index_names = ("otaz", "dtaz", "time_period") + indexes = None + time_period_breaks = settings.get("skim_time_periods", {}).get("periods") + time_periods = settings.get("skim_time_periods", {}).get("labels") + time_period_sep = "__" + + time_window = settings.get("skim_time_periods", {}).get("time_window") + period_minutes = settings.get("skim_time_periods", {}).get("period_minutes") + n_periods = int(time_window / period_minutes) + + tp_map = {} + tp_imap = {} + label = time_periods[0] + i = 0 + for t in range(n_periods): + if t in time_period_breaks: + i = time_period_breaks.index(t) + label = time_periods[i] + tp_map[t + 1] = label + tp_imap[t + 1] = i + + omxs = [ + openmatrix.open_file(skims_filename, mode="r") + for skims_filename in skims_filenames + ] + if isinstance(time_periods, (list, tuple)): + time_periods = np.asarray(time_periods) + result = sh.dataset.from_omx_3d( + omxs, + index_names=index_names, + indexes=indexes, + time_periods=time_periods, + time_period_sep=time_period_sep, + ) + result.attrs["time_period_map"] = tp_map + result.attrs["time_period_imap"] = tp_imap + + return result diff --git a/activitysim/standalone/utils.py b/activitysim/standalone/utils.py new file mode 100644 index 000000000..7ba7c4ca4 --- /dev/null +++ b/activitysim/standalone/utils.py @@ -0,0 +1,24 @@ +import os +from contextlib import contextmanager +from pathlib import Path + + +@contextmanager +def chdir(path: Path): + """ + Sets the cwd within the context + + Args: + path (Path): The path to the cwd + + Yields: + None + """ + + cwd = Path().absolute() + try: + if path is not None: + os.chdir(path) + yield + finally: + os.chdir(cwd) diff --git a/activitysim/workflows/__init__.py b/activitysim/workflows/__init__.py new file mode 100644 index 000000000..35ea4784b --- /dev/null +++ b/activitysim/workflows/__init__.py @@ -0,0 +1,7 @@ +try: + import pypyr +except ImportError: + pass +else: + from .steps import cmd, py + from .steps.main import get_pipeline_definition, main diff --git a/activitysim/workflows/demo.yaml b/activitysim/workflows/demo.yaml new file mode 100644 index 000000000..750ddd997 --- /dev/null +++ b/activitysim/workflows/demo.yaml @@ -0,0 +1,39 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- name: pypyr.steps.default + in: + defaults: + example_name: prototype_mtc + workspace: workspace + legacy: True + tag: + stress: 22 + +- name: activitysim.workflows.steps.title + in: + label: "[bold blue]activitysim workflow demo {example_name}" + +- description: Make {workspace} directory if it does not exist + name: activitysim.workflows.steps.py + in: + py: | + import os + os.makedirs(f"{workspace}", exist_ok=True) + +- description: Generate a tag based on datetime if tag is not given + name: activitysim.workflows.steps.py + in: + label: Generate tag + py: | + import time + if tag is None: + tag = time.strftime("%Y-%m-%d-%H%M%S") + time.sleep(2) + save(tag=tag) + print(f"tag is {tag}") + +- description: Memory Stress Test + name: activitysim.workflows.steps.memory_stress_test + in: + n: '{stress}' diff --git a/activitysim/workflows/exercise-mini.yaml b/activitysim/workflows/exercise-mini.yaml new file mode 100644 index 000000000..fc41849ae --- /dev/null +++ b/activitysim/workflows/exercise-mini.yaml @@ -0,0 +1,49 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + workspace: workspace + reference_asim_version: "1.1.3" + tag: + +- name: activitysim.workflows.steps.py + in: + label: Initialize common tag if not given + py: | + import time + if tag is None: + if os.environ.get("GITHUB_ACTIONS") == "true": + tag = "CI" + else: + tag = time.strftime("%Y-%m-%d-%H%M%S") + save("tag") + +- name: pypyr.steps.pype + in: + pype: + name: sharrow-contrast/mtc_mini + args: + workspace: '{workspace}' + reference_asim_version: '{reference_asim_version}' + tag: '{tag}' + +- name: pypyr.steps.pype + in: + pype: + name: sharrow-contrast/arc_mini + args: + workspace: '{workspace}' + reference_asim_version: '{reference_asim_version}' + tag: '{tag}' + +- name: pypyr.steps.pype + in: + pype: + name: sharrow-contrast/mwcog_mini + args: + workspace: '{workspace}' + reference_asim_version: '{reference_asim_version}' + tag: '{tag}' diff --git a/activitysim/workflows/sharrow-contrast/_contrast_runner.yaml b/activitysim/workflows/sharrow-contrast/_contrast_runner.yaml new file mode 100644 index 000000000..ae362ebf1 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/_contrast_runner.yaml @@ -0,0 +1,708 @@ +# activitysim workflow example_runner example_name=prototype_mtc + +context_parser: pypyr.parser.keyvaluepairs + +on_failure: +- name: activitysim.workflows.steps.py + in: + py: | + import time + print("FAILURE", time.strftime("%Y-%m-%d %I:%M:%S%p")) + +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_mtc + workflow_name: sharrow-contrast + workspace: workspace + multiprocess: 0 + chunk_size: 0 + create: True + compile: True + sharrow: True + legacy: True + reference: False + reference_asim_version: "1.1.3" + tag: + resume_after: + fast: False + compile_n_households: 1000 + main_n_households: 100000 + config_dirs: configs + data_dir: data + ext_dirs: + instrument: False + memory_profile: False + trace_hh_id: + trace_od: + chunk_method: hybrid_uss + chunk_training_mode: disabled + machine_name: + disable_zarr: False + settings_file: settings.yaml + report_skip: [] + copy_report_to: + +- description: Ensure integer types for numbers + name: activitysim.workflows.steps.py + in: + label: Ensure integer types for numbers + py: | + def int_or_none(i): + return int(i) if i is not None else i + save( + compile_n_households=int_or_none(compile_n_households), + main_n_households=int_or_none(main_n_households), + multiprocess=int_or_none(multiprocess), + chunk_size=int_or_none(chunk_size), + ) + +- name: activitysim.workflows.steps.title + in: + label: "activitysim workflow {workflow_name}" + formatting: bold cyan + +- name: activitysim.workflows.steps.py + in: + label: Make {workspace} directory if it does not exist + py: | + import os + os.makedirs(f"{workspace}", exist_ok=True) + +- name: activitysim.workflows.steps.py + in: + label: Detect if debugging + py: | + def is_debug(): + import sys + gettrace = getattr(sys, 'gettrace', None) + if gettrace is None: + return False + else: + v = gettrace() + if v is None: + return False + else: + return True + should_swallow_errors = not is_debug() + save('should_swallow_errors') + +- activitysim.workflows.steps.contrast.contrast_setup + +- name: activitysim.workflows.steps.create + run: '{create}' + in: + destination: "{workspace}" + +- activitysim.workflows.steps.contrast.directory_prep + +- name: pypyr.steps.call + run: '{compile}' + in: + call: run-compile + swallow: False + +- name: pypyr.steps.call + run: '{sharrow}' + in: + call: run-sharrow + swallow: '{should_swallow_errors}' + +- name: pypyr.steps.call + run: '{legacy}' + in: + call: run-legacy + swallow: '{should_swallow_errors}' + +- name: pypyr.steps.call + run: '{reference}' + in: + call: run-reference + swallow: '{should_swallow_errors}' + +- activitysim.workflows.steps.contrast.composite_log + +- name: pypyr.steps.call + in: + call: + groups: reporting + success: report-save + failure: report-save + swallow: False + +################################################################################ +run-compile: + # This step group runs activitysim with a (usually) smaller sample of + # household, to generate the compiled numba code for the local machine + +- description: write configs_sh_compile + name: pypyr.steps.filewriteyaml + in: + fileWriteYaml: + path: "{workspace}/{example_name}/configs_sh_compile/{settings_file}" + payload: + inherit_settings: True + sharrow: test + chunk_training_mode: disabled + households_sample_size: '{compile_n_households}' + cache_dir: cache_sharrow + trace_hh_id: '{trace_hh_id}' + trace_od: '{trace_od}' + instrument: '{instrument}' + disable_zarr: '{disable_zarr}' + multiprocess: False + recode_pipeline_columns: True + +- description: Run activitysim to compile and test sharrow-enabled model + name: activitysim.workflows.steps.run_subprocess + in: + pre_config_dirs: configs_sh_compile + output_dir: 'output-{tag}/output-compile' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- sharrow compile" + + +################################################################################ +run-sharrow: + +- description: write configs_sh + name: pypyr.steps.filewriteyaml + in: + fileWriteYaml: + path: "{workspace}/{example_name}/configs_sh/{settings_file}" + payload: + inherit_settings: True + sharrow: require + cache_dir: cache_sharrow + households_sample_size: '{main_n_households}' + trace_hh_id: '{trace_hh_id}' + trace_od: '{trace_od}' + instrument: '{instrument}' + memory_profile: '{memory_profile}' + chunk_size: '{chunk_size}' + num_processes: '{num_processes}' + multiprocess: '{is_multiprocess}' + chunk_method: '{chunk_method}' + chunk_training_mode: '{chunk_training_mode}' + disable_zarr: '{disable_zarr}' + recode_pipeline_columns: True + +- description: Run activitysim to evaluate sharrow-enabled model + name: activitysim.workflows.steps.run_subprocess + in: + pre_config_dirs: configs_sh + output_dir: 'output-{tag}/output-sharrow' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- sharrow run" + +################################################################################ +run-legacy: + +- description: write.configs_legacy + name: pypyr.steps.filewriteyaml + in: + fileWriteYaml: + path: "{workspace}/{example_name}/configs_legacy/{settings_file}" + payload: + inherit_settings: True + recode_pipeline_columns: False + cache_dir: cache_legacy + households_sample_size: '{main_n_households}' + trace_hh_id: '{trace_hh_id}' + trace_od: '{trace_od}' + instrument: '{instrument}' + memory_profile: '{memory_profile}' + chunk_size: '{chunk_size}' + num_processes: '{num_processes}' + multiprocess: '{is_multiprocess}' + chunk_method: '{chunk_method}' + chunk_training_mode: '{chunk_training_mode}' + +- description: Run activitysim to evaluate legacy model + name: activitysim.workflows.steps.run_subprocess + in: + pre_config_dirs: configs_legacy + output_dir: 'output-{tag}/output-legacy' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- legacy run" + + +################################################################################ +run-reference: + +- description: Install a reference environment + name: activitysim.workflows.steps.install_env + in: + label: "{example_name} -- install ref env {reference_asim_version}" + env_prefix: "{workspace}/env/asim-ref-{reference_asim_version}" + asim_version: '{reference_asim_version}' + +- name: activitysim.workflows.steps.py + in: + label: Identify first config dir + py: | + if isinstance(config_dirs, str): + first_config_dir = config_dirs + else: + first_config_dir = config_dirs[0] + save('first_config_dir') + +- description: Copy required reference settings + name: activitysim.workflows.steps.copy_files + in: + source_glob: + - "{workspace}/{example_name}/{first_config_dir}/legacy-{reference_asim_version}/*.yaml" + - "{workspace}/{example_name}/{first_config_dir}/legacy-{reference_asim_version}/*.csv" + dest_dir: "{workspace}/{example_name}/configs_reference" + +- name: activitysim.workflows.steps.py + in: + label: Allow resume_after in ref only if pipeline exists + # Older versions of ActivitySim choke on resume_after if pipeline is missing. + py: | + import os + ref_pipeline = f"{workspace}/{example_name}/output-{tag}/output-reference/pipeline.h5" + if os.path.exists(ref_pipeline): + resume_after_ref = resume_after + else: + resume_after_ref = None + save('resume_after_ref') + +- description: write.configs_reference + name: activitysim.workflows.steps.update_yaml + in: + updateYaml: + path: "{workspace}/{example_name}/configs_reference/{settings_file}" + payload: + inherit_settings: True + households_sample_size: '{main_n_households}' + trace_hh_id: '{trace_hh_id}' + trace_od: '{trace_od}' + resume_after: '{resume_after_ref}' + chunk_size: '{chunk_size}' + num_processes: '{num_processes}' + multiprocess: '{is_multiprocess}' + chunk_method: '{chunk_method}' + chunk_training_mode: '{chunk_training_mode}' + +- description: Run activitysim to evaluate reference model + name: activitysim.workflows.steps.run_subprocess + in: + resume_after: + pre_config_dirs: configs_reference + output_dir: 'output-{tag}/output-reference' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- reference run" + conda_prefix: "../env/asim-ref-{reference_asim_version}" + + +################################################################################ +reporting: + +- name: activitysim.workflows.steps.contrast.load_tables + in: + common_output_directory: "{workspace}/{example_name}/output-{tag}" + databases: + sharrow: "output-sharrow" + legacy: "output-legacy" + reference: "output-reference" + tables: + households: + filename: final_households.csv + index_col: household_id + persons: + filename: final_persons.csv + index_col: person_id + tours: + filename: final_tours.csv + index_col: tour_id + trips: + filename: final_trips.csv + index_col: trip_id + land_use: + filename: final_land_use.csv + index_col: zone_id + +- name: activitysim.workflows.steps.contrast.load_skims + in: + common_directory: "{workspace}/{example_name}" + +- name: activitysim.workflows.steps.reporting.init_report + in: + title: "{example_name} report" + common_directory: "{workspace}/{example_name}" + +- name: activitysim.workflows.steps.reporting.machine_info + in: + caption: Machine Info + +- name: activitysim.workflows.steps.reporting.settings + in: + caption: Settings + names: + - disable_zarr + - resume_after + - instrument + - memory_profile + - fast + - chunk_method + - chunk_training_mode + - chunk_size + - multiprocess + + +#### Runtime and Data Inventory #### + +- name: activitysim.workflows.steps.contrast.runtime + in: + caption: Model Runtime + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Memory Usage + +- name: activitysim.workflows.steps.contrast.memory_use + in: + caption: USS + caption_level: 3 + memory_measure: uss + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.contrast.memory_use + in: + caption: RSS + caption_level: 3 + memory_measure: rss + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.contrast.memory_use + in: + caption: Full RSS + caption_level: 3 + memory_measure: full_rss + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.contrast.memory_use_peak + in: + caption: Peak RSS by Component + caption_level: 3 + memory_measure: rss + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.contrast.memory_use_peak + in: + caption: Peak USS by Component + caption_level: 3 + memory_measure: uss + include_runs: + - reference + - legacy + - sharrow + + +- name: activitysim.workflows.steps.contrast.data_inventory + + +#### Workplace Location #### + +- name: activitysim.workflows.steps.reporting.load_data_dictionary + in: + cwd: "{workspace}/{example_name}" + +- name: pypyr.steps.default + in: + defaults: + workplace_zone_agg: + +- name: activitysim.workflows.steps.reporting.section_title + skip: !py "'work location' in report_skip" + run: '{workplace_zone_agg}' + in: + title: Workplace Location + +- name: activitysim.workflows.steps.contrast.district_to_district + skip: !py "'work location' in report_skip" + run: '{workplace_zone_agg}' + in: + tablename: persons + caption: '{workplace_zone_agg[caption]}' + caption_level: 3 + district_id: '{workplace_zone_agg[district_id]}' + orig_label: home district + dest_label: work district + orig_col: home_zone_id + dest_col: workplace_zone_id + filter: workplace_zone_id >= 0 + size_label: n_workers + +- name: activitysim.workflows.steps.contrast.transform_data + skip: !py "household_income is None" + in: + tablename: households + column: '{household_income}' + out: income_tertile + qcut: + q: 3 + labels: + - low + - mid + - high + +- name: activitysim.workflows.steps.contrast.join_table_data + skip: !py "household_income is None" + in: + caption: Join Income to Persons + tablename: persons + from_tablename: households + columns: income_tertile + on: household_id + +- name: activitysim.workflows.steps.contrast.trip_distance + skip: !py "'work location' in report_skip" + in: + caption: Workplace Distance by Income + caption_level: 3 + dist_bins: 20 + dist_skim_name: 'distance_to_work' + tablename: persons + grouping: income_tertile + +- name: activitysim.workflows.steps.contrast.trip_distance + skip: !py "'school location' in report_skip" + in: + caption: School Distance by Income + caption_level: 3 + dist_bins: 20 + dist_skim_name: 'distance_to_school' + tablename: persons + grouping: income_tertile + +#### Auto Ownership #### + +- name: activitysim.workflows.steps.reporting.section_title + skip: !py "'auto ownership' in report_skip" + in: + title: Auto Ownership + +- name: activitysim.workflows.steps.contrast.transform_data + skip: !py "'auto ownership' in report_skip" + in: + tablename: households + column: hhsize + out: hhsize_to5 + clip: + upper: 5 + +- name: activitysim.workflows.steps.contrast.nominal_choice + skip: !py "'auto ownership' in report_skip" + in: + caption: Household Auto Ownership Counts by Household Size + caption_level: 3 + tablename: households + nominal_col: auto_ownership + row_grouping: + field: hhsize_to5 + title: Household Size (up to 5) + col_grouping: + field: income_tertile + sort: [low, mid, high] + title: Income Tertile + ordinal: true + plot_type: count + axis_label: "# of Households" + +- name: activitysim.workflows.steps.contrast.nominal_choice + skip: !py "'auto ownership' in report_skip" + in: + caption: Household Auto Ownership Shares by Household Size + caption_level: 3 + tablename: households + nominal_col: auto_ownership + row_grouping: + field: hhsize_to5 + title: Household Size (up to 5) + col_grouping: + field: income_tertile + sort: [low, mid, high] + title: Income Tertile + ordinal: true + + +#### CDAP #### + +- name: activitysim.workflows.steps.reporting.section_title + skip: !py "'cdap' in report_skip" + in: + title: Coordinated Daily Activity Pattern + +- name: activitysim.workflows.steps.contrast.nominal_choice + skip: !py "'cdap' in report_skip" + in: + caption: Coordinated Daily Activity Pattern by Person Type + caption_level: 3 + tablename: persons + nominal_col: cdap_activity + row_grouping: + field: ptype + title: Person Type + axis_label: Daily Activity Pattern Count + plot_type: count + + +#### Tour Mode Choice #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Tour Mode Choice + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Tour Mode Choice by Primary Purpose + caption_level: 3 + tablename: tours + nominal_col: tour_mode + row_grouping: primary_purpose + axis_label: Tour Mode Share + +- name: activitysim.workflows.steps.contrast.nominal_choice + skip: !py "'tour mode by composition' in report_skip" + in: + caption: Tour Mode Choice by Composition + caption_level: 3 + tablename: tours + nominal_col: tour_mode + row_grouping: composition + axis_label: Tour Mode Share + + +#### Tour Schedule #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Tour Scheduling + +- name: activitysim.workflows.steps.contrast.ordinal_distribution + in: + caption: Tour Start Time by Primary Purpose + caption_level: 3 + tablename: tours + ordinal_col: start + facet_grouping: primary_purpose + plot_type: count + +- name: activitysim.workflows.steps.contrast.ordinal_distribution + in: + caption: Tour Duration by Primary Purpose + caption_level: 3 + tablename: tours + ordinal_col: duration + facet_grouping: primary_purpose + plot_type: count + + +#### Trip Mode Choice #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Trip Mode Choice + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Trip Mode Choice by Primary Purpose + caption_level: 3 + tablename: trips + nominal_col: trip_mode + row_grouping: primary_purpose + axis_label: Trip Mode Share + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Trip Mode Choice by Departure Time + caption_level: 3 + tablename: trips + nominal_col: trip_mode + row_grouping: depart + axis_label: Trip Mode Share + + +#### Trip Distance #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Trip Distance + +- name: activitysim.workflows.steps.contrast.attach_skim_data + in: + tablename: trips + otaz_col: origin + dtaz_col: destination + time_col: depart + skim_vars: '{distance_skim}' + +- name: activitysim.workflows.steps.contrast.transform_data + in: + tablename: trips + column: '{distance_skim}' + out: distance_to10_binned + censor: + left: 0 + right: 10 + cut: + bins: 20 + labels: midpoint + +- name: activitysim.workflows.steps.contrast.ordinal_distribution + in: + caption: Trip Distance by the Primary Purpose, <10 miles + caption_level: 3 + tablename: trips + ordinal_col: distance_to10_binned + facet_grouping: primary_purpose + plot_type: count + interpolate: step + value_format: "0.2f" + axis_label: Distance (to 10 miles) + +- name: activitysim.workflows.steps.contrast.trip_distance + in: + caption: Trip Distance by Primary Purpose, <10 miles + caption_level: 3 + grouping: primary_purpose + dist_bins: 20 + dist_skim_name: '{distance_skim}' + max_dist: 10 + +- name: activitysim.workflows.steps.contrast.trip_distance + in: + caption: Trip Distance by Primary Purpose + caption_level: 3 + grouping: primary_purpose + dist_bins: 20 + dist_skim_name: '{distance_skim}' + + +################################################################################ +report-save: +- name: activitysim.workflows.steps.reporting.save_report + in: + html_filename: "{workspace}/{example_name}/output-{tag}/report-{tag}.html" + copy_filename: '{copy_report_to}' diff --git a/activitysim/workflows/sharrow-contrast/_contrast_runner_chunked.yaml b/activitysim/workflows/sharrow-contrast/_contrast_runner_chunked.yaml new file mode 100644 index 000000000..7d947a8b3 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/_contrast_runner_chunked.yaml @@ -0,0 +1,670 @@ +# activitysim workflow example_runner example_name=prototype_mtc + +context_parser: pypyr.parser.keyvaluepairs + +on_failure: +- name: activitysim.workflows.steps.py + in: + py: | + import time + print("FAILURE", time.strftime("%Y-%m-%d %I:%M:%S%p")) + +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_mtc + workflow_name: sharrow-contrast + workspace: workspace + multiprocess: 0 + chunk_size: + chunk_size_pct_of_available: 0.75 + create: True + compile: True + sharrow: True + legacy: False + reference: False + reference_asim_version: "1.1.3" + tag: + resume_after: + fast: False + compile_n_households: 1000 + train_n_households: 20000 + main_n_households: 100000 + config_dirs: configs + data_dir: data + instrument: False + memory_profile: False + trace_hh_id: + trace_od: + chunk_method: hybrid_uss + chunk_training: True + chunk_training_mode: training + machine_name: + disable_zarr: False + settings_file: settings.yaml + +- name: activitysim.workflows.steps.title + in: + label: "activitysim workflow {workflow_name}" + formatting: bold cyan + +- name: activitysim.workflows.steps.py + in: + label: Make {workspace} directory if it does not exist + py: | + import os + os.makedirs(f"{workspace}", exist_ok=True) + +- activitysim.workflows.steps.contrast.contrast_setup + +- activitysim.workflows.steps.chunk_sizing + +- name: activitysim.workflows.steps.create + run: '{create}' + in: + destination: "{workspace}" + +- activitysim.workflows.steps.contrast.directory_prep + +- name: pypyr.steps.call + run: '{compile}' + in: + call: run-compile + swallow: True + +- name: pypyr.steps.call + run: '{sharrow}' + in: + call: run-sharrow + swallow: True + +- name: pypyr.steps.call + run: '{legacy}' + in: + call: run-legacy + swallow: True + +- name: pypyr.steps.call + run: '{reference}' + in: + call: run-reference + swallow: True + +- activitysim.workflows.steps.contrast.composite_log + +- name: pypyr.steps.call + in: + call: + groups: reporting + success: report-save + failure: report-save + +################################################################################ +run-compile: + # This step group runs activitysim with a (usually) smaller sample of + # household, to generate the compiled numba code for the local machine + +- description: write configs_sh_compile + name: pypyr.steps.filewriteyaml + in: + fileWriteYaml: + path: "{workspace}/{example_name}/configs_sh_compile/{settings_file}" + payload: + inherit_settings: True + sharrow: test + chunk_training_mode: disabled + households_sample_size: '{compile_n_households}' + cache_dir: cache_sharrow + trace_hh_id: + trace_od: + instrument: '{instrument}' + disable_zarr: '{disable_zarr}' + multiprocess: False + recode_pipeline_columns: True + +- description: Run activitysim to compile and test sharrow-enabled model + name: activitysim.workflows.steps.run + in: + pre_config_dirs: configs_sh_compile + output_dir: 'output-{tag}/output-compile' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- sharrow compile" + + +################################################################################ +run-sharrow: + +- description: write configs_sh_chunktrain + name: pypyr.steps.filewriteyaml + run: '{chunk_training}' + in: + fileWriteYaml: + path: "{workspace}/{example_name}/configs_sh_chunktrain/{settings_file}" + payload: + inherit_settings: True + chunk_training_mode: '{chunk_training_mode}' + households_sample_size: '{train_n_households}' + num_processes: 2 + +- description: write configs_sh + name: pypyr.steps.filewriteyaml + in: + fileWriteYaml: + path: "{workspace}/{example_name}/configs_sh/{settings_file}" + payload: + inherit_settings: True + sharrow: require + cache_dir: cache_sharrow + households_sample_size: '{main_n_households}' + trace_hh_id: + trace_od: + instrument: '{instrument}' + memory_profile: '{memory_profile}' + chunk_size: '{chunk_size}' + num_processes: '{num_processes}' + multiprocess: '{is_multiprocess}' + chunk_method: '{chunk_method}' + chunk_training_mode: '{chunk_application_mode}' + disable_zarr: '{disable_zarr}' + recode_pipeline_columns: True + +- description: Run activitysim to chunk-train sharrow-enabled model + name: activitysim.workflows.steps.run_subprocess + run: '{chunk_training}' + in: + pre_config_dirs: [configs_sh_chunktrain, configs_sh] + output_dir: 'output-{tag}/output-sharrow-training' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- sharrow chunk training run" + +- description: Run activitysim to evaluate sharrow-enabled model + name: activitysim.workflows.steps.run_subprocess + in: + pre_config_dirs: configs_sh + output_dir: 'output-{tag}/output-sharrow' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- sharrow run" + +################################################################################ +run-legacy: + +- description: write configs_legacy_chunktrain + name: pypyr.steps.filewriteyaml + run: '{chunk_training}' + in: + fileWriteYaml: + path: "{workspace}/{example_name}/configs_legacy_chunktrain/{settings_file}" + payload: + inherit_settings: True + chunk_training_mode: '{chunk_training_mode}' + households_sample_size: '{train_n_households}' + num_processes: 2 + +- description: write.configs_legacy + name: pypyr.steps.filewriteyaml + in: + fileWriteYaml: + path: "{workspace}/{example_name}/configs_legacy/{settings_file}" + payload: + inherit_settings: True + recode_pipeline_columns: False + cache_dir: cache_legacy + households_sample_size: '{main_n_households}' + trace_hh_id: '{trace_hh_id}' + trace_od: '{trace_od}' + instrument: '{instrument}' + memory_profile: '{memory_profile}' + chunk_size: '{chunk_size}' + num_processes: '{num_processes}' + multiprocess: '{is_multiprocess}' + chunk_method: '{chunk_method}' + chunk_training_mode: '{chunk_application_mode}' + +- description: Run activitysim to chunk-train legacy model + name: activitysim.workflows.steps.run_subprocess + run: '{chunk_training}' + in: + pre_config_dirs: [configs_legacy_chunktrain, configs_legacy] + output_dir: 'output-{tag}/output-legacy-training' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- legacy chunk training run" + +- description: Run activitysim to evaluate legacy model + name: activitysim.workflows.steps.run_subprocess + in: + pre_config_dirs: configs_legacy + output_dir: 'output-{tag}/output-legacy' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- legacy run" + + +################################################################################ +run-reference: + +- description: Install a reference environment + name: activitysim.workflows.steps.install_env + in: + label: "{example_name} -- install ref env {reference_asim_version}" + env_prefix: "{workspace}/env/asim-ref-{reference_asim_version}" + asim_version: '{reference_asim_version}' + +- description: Copy required reference settings + name: activitysim.workflows.steps.copy_files + in: + source_glob: + - "{workspace}/{example_name}/configs/legacy-{reference_asim_version}/*.yaml" + - "{workspace}/{example_name}/configs/legacy-{reference_asim_version}/*.csv" + dest_dir: "{workspace}/{example_name}/configs_reference" + +- name: activitysim.workflows.steps.py + in: + label: Allow resume_after in ref only if pipeline exists + # Older versions of ActivitySim choke on resume_after if pipeline is missing. + py: | + import os + ref_pipeline = f"{workspace}/{example_name}/output-{tag}/output-reference/pipeline.h5" + if os.path.exists(ref_pipeline): + resume_after_ref = resume_after + else: + resume_after_ref = None + save('resume_after_ref') + +- description: write.configs_reference + name: activitysim.workflows.steps.update_yaml + in: + updateYaml: + path: "{workspace}/{example_name}/configs_reference/settings.yaml" + payload: + inherit_settings: True + households_sample_size: '{main_n_households}' + trace_hh_id: '{trace_hh_id}' + trace_od: '{trace_od}' + resume_after: '{resume_after_ref}' + chunk_size: '{chunk_size}' + num_processes: '{num_processes}' + multiprocess: '{is_multiprocess}' + chunk_method: '{chunk_method}' + chunk_training_mode: '{chunk_application_mode}' + +- description: Run activitysim to evaluate reference model + name: activitysim.workflows.steps.run_subprocess + in: + resume_after: + pre_config_dirs: configs_reference + output_dir: 'output-{tag}/output-reference' + cwd: "{workspace}/{example_name}" + label: "{example_name} -- reference run" + conda_prefix: "../env/asim-ref-{reference_asim_version}" + + +################################################################################ +reporting: + +- name: activitysim.workflows.steps.contrast.load_tables + in: + common_output_directory: "{workspace}/{example_name}/output-{tag}" + databases: + sharrow: "output-sharrow" + legacy: "output-legacy" + reference: "output-reference" + tables: + households: + filename: final_households.csv + index_col: household_id + persons: + filename: final_persons.csv + index_col: person_id + tours: + filename: final_tours.csv + index_col: tour_id + trips: + filename: final_trips.csv + index_col: trip_id + land_use: + filename: final_land_use.csv + index_col: zone_id + +- name: activitysim.workflows.steps.contrast.load_skims + in: + common_directory: "{workspace}/{example_name}" + +- name: activitysim.workflows.steps.reporting.init_report + in: + title: "{example_name} report" + common_directory: "{workspace}/{example_name}" + +- name: activitysim.workflows.steps.reporting.machine_info + in: + caption: Machine Info + +- name: activitysim.workflows.steps.reporting.settings + in: + caption: Settings + names: + - disable_zarr + - resume_after + - instrument + - memory_profile + - fast + - chunk_method + - chunk_training_mode + - chunk_size + - multiprocess + + +#### Runtime and Data Inventory #### + +- name: activitysim.workflows.steps.contrast.runtime + in: + caption: Model Runtime + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Memory Usage + +- name: activitysim.workflows.steps.contrast.memory_use + in: + caption: USS + caption_level: 3 + memory_measure: uss + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.contrast.memory_use + in: + caption: RSS + caption_level: 3 + memory_measure: rss + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.contrast.memory_use + in: + caption: Full RSS + caption_level: 3 + memory_measure: full_rss + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.contrast.memory_use_peak + in: + caption: Peak RSS by Component + caption_level: 3 + memory_measure: rss + include_runs: + - reference + - legacy + - sharrow + +- name: activitysim.workflows.steps.contrast.memory_use_peak + in: + caption: Peak USS by Component + caption_level: 3 + memory_measure: uss + include_runs: + - reference + - legacy + - sharrow + + +- name: activitysim.workflows.steps.contrast.data_inventory + + +#### Workplace Location #### + +- name: activitysim.workflows.steps.reporting.load_data_dictionary + in: + cwd: "{workspace}/{example_name}" + +- name: pypyr.steps.default + in: + defaults: + workplace_zone_agg: + +- name: activitysim.workflows.steps.reporting.section_title + run: '{workplace_zone_agg}' + in: + title: Workplace Location + +- name: activitysim.workflows.steps.contrast.district_to_district + run: '{workplace_zone_agg}' + in: + tablename: persons + caption: '{workplace_zone_agg[caption]}' + caption_level: 3 + district_id: '{workplace_zone_agg[district_id]}' + orig_label: home district + dest_label: work district + orig_col: home_zone_id + dest_col: workplace_zone_id + filter: workplace_zone_id >= 0 + size_label: n_workers + + +#### Auto Ownership #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Auto Ownership + +- name: activitysim.workflows.steps.contrast.transform_data + in: + tablename: households + column: hhsize + out: hhsize_to5 + clip: + upper: 5 + +- name: activitysim.workflows.steps.contrast.transform_data + in: + tablename: households + column: '{household_income}' + out: income_tertile + qcut: + q: 3 + labels: + - low + - mid + - high + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Household Auto Ownership Counts by Household Size + caption_level: 3 + tablename: households + nominal_col: auto_ownership + row_grouping: + field: hhsize_to5 + title: Household Size (up to 5) + col_grouping: + field: income_tertile + sort: [low, mid, high] + title: Income Tertile + ordinal: true + plot_type: count + axis_label: "# of Households" + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Household Auto Ownership Shares by Household Size + caption_level: 3 + tablename: households + nominal_col: auto_ownership + row_grouping: + field: hhsize_to5 + title: Household Size (up to 5) + col_grouping: + field: income_tertile + sort: [low, mid, high] + title: Income Tertile + ordinal: true + + +#### CDAP #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Coordinated Daily Activity Pattern + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Coordinated Daily Activity Pattern by Person Type + caption_level: 3 + tablename: persons + nominal_col: cdap_activity + row_grouping: + field: ptype + title: Person Type + axis_label: Daily Activity Pattern Count + plot_type: count + + +#### Tour Mode Choice #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Tour Mode Choice + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Tour Mode Choice by Primary Purpose + caption_level: 3 + tablename: tours + nominal_col: tour_mode + row_grouping: primary_purpose + axis_label: Tour Mode Share + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Tour Mode Choice by Composition + caption_level: 3 + tablename: tours + nominal_col: tour_mode + row_grouping: composition + axis_label: Tour Mode Share + + +#### Tour Schedule #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Tour Scheduling + +- name: activitysim.workflows.steps.contrast.ordinal_distribution + in: + caption: Tour Start Time by Primary Purpose + caption_level: 3 + tablename: tours + ordinal_col: start + facet_grouping: primary_purpose + plot_type: count + +- name: activitysim.workflows.steps.contrast.ordinal_distribution + in: + caption: Tour Duration by Primary Purpose + caption_level: 3 + tablename: tours + ordinal_col: duration + facet_grouping: primary_purpose + plot_type: count + + +#### Trip Mode Choice #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Trip Mode Choice + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Trip Mode Choice by Primary Purpose + caption_level: 3 + tablename: trips + nominal_col: trip_mode + row_grouping: primary_purpose + axis_label: Trip Mode Share + +- name: activitysim.workflows.steps.contrast.nominal_choice + in: + caption: Trip Mode Choice by Departure Time + caption_level: 3 + tablename: trips + nominal_col: trip_mode + row_grouping: depart + axis_label: Trip Mode Share + + +#### Trip Distance #### + +- name: activitysim.workflows.steps.reporting.section_title + in: + title: Trip Distance + +- name: activitysim.workflows.steps.contrast.attach_skim_data + in: + tablename: trips + otaz_col: origin + dtaz_col: destination + time_col: depart + skim_vars: '{distance_skim}' + +- name: activitysim.workflows.steps.contrast.transform_data + in: + tablename: trips + column: '{distance_skim}' + out: distance_to10_binned + censor: + left: 0 + right: 10 + cut: + bins: 20 + labels: midpoint + +- name: activitysim.workflows.steps.contrast.ordinal_distribution + in: + caption: Trip Distance by the Primary Purpose, <10 miles + caption_level: 3 + tablename: trips + ordinal_col: distance_to10_binned + facet_grouping: primary_purpose + plot_type: count + interpolate: step + value_format: "0.2f" + axis_label: Distance (to 10 miles) + +- name: activitysim.workflows.steps.contrast.trip_distance + in: + caption: Trip Distance by Primary Purpose, <10 miles + caption_level: 3 + grouping: primary_purpose + dist_bins: 20 + dist_skim_name: '{distance_skim}' + max_dist: 10 + +- name: activitysim.workflows.steps.contrast.trip_distance + in: + caption: Trip Distance by Primary Purpose + caption_level: 3 + grouping: primary_purpose + dist_bins: 20 + dist_skim_name: '{distance_skim}' + + +################################################################################ +report-save: +- name: activitysim.workflows.steps.reporting.save_report + in: + html_filename: "{workspace}/{example_name}/output-{tag}/report-{tag}.html" diff --git a/activitysim/workflows/sharrow-contrast/arc_full.yaml b/activitysim/workflows/sharrow-contrast/arc_full.yaml new file mode 100644 index 000000000..ac7464c41 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/arc_full.yaml @@ -0,0 +1,33 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_arc_full + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: True + reference_asim_version: "1.1.3" + tag: + resume_after: _ + fast: False + compile_n_households: 500 + main_n_households: 10000 + config_dirs: configs + data_dir: data + instrument: False +# workplace_zone_agg: +# caption: Workplaces by County +# district_id: county_id + distance_skim: SOV_FREE_DISTANCE + household_income: income_in_thousands + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/arc_mini.yaml b/activitysim/workflows/sharrow-contrast/arc_mini.yaml new file mode 100644 index 000000000..ed6031fbd --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/arc_mini.yaml @@ -0,0 +1,33 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_arc_fulton + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: True + reference_asim_version: "1.1.3" + tag: + resume_after: + fast: False + compile_n_households: 500 + main_n_households: 10000 + config_dirs: configs + data_dir: data + instrument: False +# workplace_zone_agg: +# caption: Workplaces by County +# district_id: county_id + distance_skim: SOV_FREE_DISTANCE + household_income: income_in_thousands + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/arc_mp.yaml b/activitysim/workflows/sharrow-contrast/arc_mp.yaml new file mode 100644 index 000000000..f494f6296 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/arc_mp.yaml @@ -0,0 +1,42 @@ +# +# arc_mp +# - activitysim workflow sharrow-contrast/arc_mp +# +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_arc_full + workflow_name: sharrow-contrast/arc_mp + workspace: workspace + multiprocess: -4 + settings_file: settings_mp.yaml + chunk_size_pct_of_available: 0.75 + chunk_size_minimum_gb: 40 + create: True + compile: True + sharrow: True + legacy: True + reference: False + tag: + resume_after: + fast: False + compile_n_households: 5000 + main_n_households: 0 + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner_chunked diff --git a/activitysim/workflows/sharrow-contrast/comprehensive-mini.yaml b/activitysim/workflows/sharrow-contrast/comprehensive-mini.yaml new file mode 100644 index 000000000..9d995deeb --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/comprehensive-mini.yaml @@ -0,0 +1,200 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: True + reference_asim_version: "1.1.3" + tag: + multiprocess: 8 + resume_after: + +- name: pypyr.steps.pyimport + comment: any subsequent !py strings can use these objects + in: + pyImport: | + import os + +- name: activitysim.workflows.steps.py + description: Generate a tag based on date if tag is not given + in: + label: Initialize common tag if not given + py: | + import time, os + if tag is None: + if os.environ.get("GITHUB_ACTIONS") == "true": + tag = "CI" + else: + tag = time.strftime("%Y-%m-%d-Comprehensive") + save("tag") + +- name: activitysim.workflows.steps.pype + description: Prototype MTC Model, Single Process, Zarr Disabled + skip: !py "os.path.exists(f'{workspace}/comprehensive-mini-reports/prototype_mtc-{tag}.html')" + swallow: true + in: + pype: + name: sharrow-contrast/mtc_mini + args: + workspace: '{workspace}' + copy_report_to: '{workspace}/comprehensive-mini-reports/prototype_mtc-{tag}.html' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + disable_zarr: True + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + +- name: activitysim.workflows.steps.pype + description: Prototype MTC Model, Single Process + skip: !py "os.path.exists(f'{workspace}/comprehensive-mini-reports/prototype_mtc-{tag}-zarr.html')" + swallow: true + in: + pype: + name: sharrow-contrast/mtc_mini + args: + workspace: '{workspace}' + copy_report_to: '{workspace}/comprehensive-mini-reports/prototype_mtc-{tag}-zarr.html' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: False # Zarr mode incompatible with older versions + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess-Zarr' + resume_after: '{resume_after}' + +- name: activitysim.workflows.steps.pype + description: Prototype ARC Model, Single Process + skip: !py "os.path.exists(f'{workspace}/comprehensive-mini-reports/prototype_arc-{tag}.html')" + swallow: true + in: + pype: + name: sharrow-contrast/arc_mini + args: + copy_report_to: '{workspace}/comprehensive-mini-reports/prototype_arc-{tag}.html' + workspace: '{workspace}' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + +- name: activitysim.workflows.steps.pype + description: Placeholder SANDAG 1-Zone Model, Single Process + skip: !py "os.path.exists(f'{workspace}/comprehensive-mini-reports/placeholder_sandag_1-{tag}.html')" + swallow: true + in: + pype: + name: sharrow-contrast/sandag_1zone_mini + args: + workspace: '{workspace}' + copy_report_to: '{workspace}/comprehensive-mini-reports/placeholder_sandag_1-{tag}.html' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + +- name: activitysim.workflows.steps.pype + description: Placeholder SANDAG 2-Zone Model, Single Process + skip: !py "os.path.exists(f'{workspace}/comprehensive-mini-reports/placeholder_sandag_2-{tag}.html')" + swallow: true + in: + pype: + name: sharrow-contrast/sandag_2zone_mini + args: + workspace: '{workspace}' + copy_report_to: '{workspace}/comprehensive-mini-reports/placeholder_sandag_2-{tag}.html' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + +- name: activitysim.workflows.steps.pype + description: Placeholder SANDAG 3-Zone Model, Single Process + skip: !py "os.path.exists(f'{workspace}/comprehensive-mini-reports/placeholder_sandag_3-{tag}.html')" + swallow: true + in: + pype: + name: sharrow-contrast/sandag_3zone_mini + args: + workspace: '{workspace}' + copy_report_to: '{workspace}/comprehensive-mini-reports/placeholder_sandag_3-{tag}.html' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + +- name: activitysim.workflows.steps.pype + description: Prototype SANDAG Cross-Border Model, Single Process + skip: !py "os.path.exists(f'{workspace}/comprehensive-mini-reports/prototype_sandag_xborder-{tag}.html')" + swallow: true + in: + pype: + name: sharrow-contrast/sandag_xborder_mini + args: + workspace: '{workspace}' + copy_report_to: '{workspace}/comprehensive-mini-reports/prototype_sandag_xborder-{tag}.html' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + + +- name: activitysim.workflows.steps.pype + description: Placeholder PSRC Seattle Model, Single Process + skip: !py "os.path.exists(f'{workspace}/comprehensive-mini-reports/placeholder_psrc-{tag}.html')" + swallow: true + in: + pype: + name: sharrow-contrast/psrc_mini + args: + workspace: '{workspace}' + copy_report_to: '{workspace}/comprehensive-mini-reports/placeholder_psrc-{tag}.html' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + compile_n_households: 500 + main_n_households: 50000 diff --git a/activitysim/workflows/sharrow-contrast/comprehensive.yaml b/activitysim/workflows/sharrow-contrast/comprehensive.yaml new file mode 100644 index 000000000..bf5a2f713 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/comprehensive.yaml @@ -0,0 +1,123 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: True + reference_asim_version: "1.1.3" + tag: + compile_n_households: 1000 + main_n_households_singleprocess: 100000 + main_n_households_multiprocess: 100000 + multiprocess: 8 + resume_after: + +- description: Ensure integer types for numbers + name: activitysim.workflows.steps.py + in: + label: Ensure integer types for numbers + py: | + save( + compile_n_households=int(compile_n_households), + main_n_households_singleprocess=int(main_n_households_singleprocess), + main_n_households_multiprocess=int(main_n_households_multiprocess), + multiprocess=int(multiprocess), + ) + +- description: Generate a tag based on date if tag is not given + name: activitysim.workflows.steps.py + in: + label: Generate tag + py: | + import time + if tag is None: + tag = time.strftime("%Y-%m-%d-Comprehensive") + save(tag=tag) + +- name: activitysim.workflows.steps.pype + description: Prototype MTC Model, Single Process, Zarr Disabled + skip: false + in: + pype: + name: sharrow-contrast/mtc_full + args: + workspace: '{workspace}' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + disable_zarr: True + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + compile_n_households: '{compile_n_households}' + main_n_households: '{main_n_households_singleprocess}' + +- name: activitysim.workflows.steps.pype + description: Prototype MTC Model, Single Process + skip: false + in: + pype: + name: sharrow-contrast/mtc_full + args: + workspace: '{workspace}' + create: False + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: False + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess-Zarr' + resume_after: '{resume_after}' + compile_n_households: '{compile_n_households}' + main_n_households: '{main_n_households_singleprocess}' + +- name: activitysim.workflows.steps.pype + description: Prototype MTC Model, Multiple Processes + skip: false + in: + pype: + name: sharrow-contrast/mtc_mp + args: + workspace: '{workspace}' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-MultiProcess' + resume_after: '{resume_after}' + multiprocess: '{multiprocess}' + compile_n_households: '{compile_n_households}' + main_n_households: '{main_n_households_multiprocess}' + +- name: activitysim.workflows.steps.pype + description: Prototype ARC Model, Single Process + skip: false + in: + pype: + name: sharrow-contrast/arc_full + args: + workspace: '{workspace}' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + compile_n_households: '{compile_n_households}' + main_n_households: '{main_n_households_singleprocess}' diff --git a/activitysim/workflows/sharrow-contrast/mtc_chunked.yaml b/activitysim/workflows/sharrow-contrast/mtc_chunked.yaml new file mode 100644 index 000000000..943e486a3 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/mtc_chunked.yaml @@ -0,0 +1,50 @@ +# +# mtc_chunked +# +# This workflow runs the Prototype MTC model using full skims (1454 zones), +# in a chunked multi-process runner. Chunking is complicated and recommended +# only when running without chunking is infeasible due to excessive memory +# usage. +# + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_mtc_full + workflow_name: sharrow-contrast/mtc_chunked + multiprocess: 4 + chunk_size: 30_000_000_000 # 30 GB + chunk_training: True + create: False + compile: False + sharrow: True + legacy: True + reference: True + workspace: workspace + tag: '2022-06-complete' + resume_after: + fast: False + compile_n_households: 10000 + main_n_households: 0 # complete data + config_dirs: + - configs_mp + - configs + data_dir: data + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + distance_skim: DIST + household_income: income + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner_chunked diff --git a/activitysim/workflows/sharrow-contrast/mtc_comprehensive.yaml b/activitysim/workflows/sharrow-contrast/mtc_comprehensive.yaml new file mode 100644 index 000000000..14c274a59 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/mtc_comprehensive.yaml @@ -0,0 +1,113 @@ +# +# mtc_comprehensive +# +# This workflow runs the Prototype MTC model using full skims (1454 zones), +# running it multiple times in different configurations (single process, +# single process with zarr compression, multi-process). +# + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: True + reference_asim_version: "1.1.3" + tag: + compile_n_households: 2000 + main_n_households_singleprocess: 100000 + main_n_households_multiprocess: 0 + multiprocess: -4 + resume_after: + +- description: Ensure integer types for numbers + name: activitysim.workflows.steps.py + in: + label: Ensure integer types for numbers + py: | + save( + compile_n_households=int(compile_n_households), + main_n_households_singleprocess=int(main_n_households_singleprocess), + main_n_households_multiprocess=int(main_n_households_multiprocess), + multiprocess=int(multiprocess), + ) + +- description: Generate a tag based on date if tag is not given + name: activitysim.workflows.steps.py + in: + label: Generate tag + py: | + import time + if tag is None: + tag = time.strftime("%Y-%m-%d-Comprehensive") + save(tag=tag) + +- name: activitysim.workflows.steps.pype + description: Prototype MTC Model, Single Process, Zarr Disabled + skip: false + in: + pype: + name: sharrow-contrast/mtc_full + args: + workflow_name: sharrow-contrast/mtc_full (zarr disabled) + workspace: '{workspace}' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + disable_zarr: True + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess' + resume_after: '{resume_after}' + compile_n_households: '{compile_n_households}' + main_n_households: '{main_n_households_singleprocess}' + +- name: activitysim.workflows.steps.pype + description: Prototype MTC Model, Single Process + skip: false + in: + pype: + name: sharrow-contrast/mtc_full + args: + workflow_name: sharrow-contrast/mtc_full (zarr enabled) + workspace: '{workspace}' + create: False + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: False + memory_profile: True + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-SingleProcess-Zarr' + resume_after: '{resume_after}' + compile_n_households: '{compile_n_households}' + main_n_households: '{main_n_households_singleprocess}' + +- name: activitysim.workflows.steps.pype + description: Prototype MTC Model, Multiple Processes + skip: false + in: + pype: + name: sharrow-contrast/mtc_mp + args: + workspace: '{workspace}' + create: '{create}' + compile: '{compile}' + sharrow: '{sharrow}' + legacy: '{legacy}' + reference: '{reference}' + reference_asim_version: "{reference_asim_version}" + tag: '{tag}-MultiProcess' + resume_after: '{resume_after}' + multiprocess: '{multiprocess}' + compile_n_households: '{compile_n_households}' + main_n_households: '{main_n_households_multiprocess}' diff --git a/activitysim/workflows/sharrow-contrast/mtc_full.yaml b/activitysim/workflows/sharrow-contrast/mtc_full.yaml new file mode 100644 index 000000000..fabe888bf --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/mtc_full.yaml @@ -0,0 +1,44 @@ +# +# mtc_full +# +# This workflow runs the Prototype MTC model using full skims (1454 zones), +# in a single process runner. It is well suited for conducting timing and +# memory usage studies, as well as smaller model quality and validation +# studies. For running the full population, consider running in multiprocess +# mode with the `mtc_mp` workflow. +# + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_mtc_full + workflow_name: sharrow-contrast/mtc_full + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: True + reference_asim_version: "1.1.3" + tag: + resume_after: _ + fast: False + compile_n_households: 1000 + main_n_households: 100000 + config_dirs: configs + data_dir: data + instrument: False + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/mtc_mini.yaml b/activitysim/workflows/sharrow-contrast/mtc_mini.yaml new file mode 100644 index 000000000..fa71929cd --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/mtc_mini.yaml @@ -0,0 +1,44 @@ +# +# mtc_mini +# +# This workflow runs the Prototype MTC model using mini skims (190 zones), +# in a single process runner. It is for mainly for rapid testing of the code +# and specification files for errors, not for policy analysis. +# + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_mtc_sf + workflow_name: sharrow-contrast/mtc_mini + workspace: workspace # this is the directory where model runs are stored + create: True + compile: True + sharrow: True + legacy: True + reference: True + tag: + resume_after: + fast: False + compile_n_households: 500 + main_n_households: 50000 + config_dirs: configs + data_dir: data + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/mtc_mp.yaml b/activitysim/workflows/sharrow-contrast/mtc_mp.yaml new file mode 100644 index 000000000..bf87c2b2d --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/mtc_mp.yaml @@ -0,0 +1,59 @@ +# +# mtc_mp +# +# This workflow runs the Prototype MTC model using full skims (1454 zones), +# in a multi-process runner. It is well suited for large runs with the full +# population. To do detailed analysis of performance or debugging, consider +# running in single-process mode with the `mtc_full` workflow. +# + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_mtc_full + workflow_name: sharrow-contrast/mtc_mp + workspace: workspace + multiprocess: 8 + chunk_size: 0 + create: True + compile: True + sharrow: True + legacy: True + reference: True + reference_asim_version: "1.1.3" + tag: + resume_after: + fast: False + compile_n_households: 2000 + main_n_households: 100000 + config_dirs: + - configs_mp + - configs + data_dir: data + instrument: False + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- description: Ensure integer types for numbers + name: activitysim.workflows.steps.py + in: + label: Ensure integer types for numbers + py: | + save( + compile_n_households=int(compile_n_households), + main_n_households=int(main_n_households), + multiprocess=int(multiprocess), + chunk_size=int(chunk_size), + ) + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/mwcog_mini.yaml b/activitysim/workflows/sharrow-contrast/mwcog_mini.yaml new file mode 100644 index 000000000..f51daa528 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/mwcog_mini.yaml @@ -0,0 +1,45 @@ +# +# mwcog_mini +# +# This workflow runs the Prototype MWCOG model using mini skims, +# in a single process runner. It is for mainly for rapid testing of the code +# and specification files for errors, not for policy analysis. +# + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_mwcog + workflow_name: sharrow-contrast/mwcog_mini + workspace: workspace # this is the directory where model runs are stored + create: True + compile: True + sharrow: True + legacy: True + reference: True + tag: + resume_after: + fast: False + compile_n_households: 5000 + main_n_households: 100000 + config_dirs: configs + data_dir: data + ext_dirs: extensions + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/psrc_mini.yaml b/activitysim/workflows/sharrow-contrast/psrc_mini.yaml new file mode 100644 index 000000000..33bb4512f --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/psrc_mini.yaml @@ -0,0 +1,31 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: placeholder_psrc_seattle + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: True + tag: + resume_after: + fast: False + compile_n_households: 500 + main_n_households: 50000 + config_dirs: + #- configs_skip_accessibility + - configs + data_dir: data + instrument: False + distance_skim: DIST + household_income: income_in_thousands + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/sandag_1zone_complete.yaml b/activitysim/workflows/sharrow-contrast/sandag_1zone_complete.yaml new file mode 100644 index 000000000..925253e5b --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/sandag_1zone_complete.yaml @@ -0,0 +1,38 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: placeholder_sandag_1_zone_full + multiprocess: 4 + chunk_size_minimum_gb: 30 + chunk_training: True + create: False + compile: True + sharrow: True + legacy: False + reference: False + workspace: workspace + tag: '2022-07' + resume_after: + fast: False + compile_n_households: 2000 + main_n_households: 0 # complete data + config_dirs: + - configs_1_zone + - prototype_mtc/configs + data_dir: data_1 + settings_file: settings_mp.yaml + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner_chunked diff --git a/activitysim/workflows/sharrow-contrast/sandag_1zone_full.yaml b/activitysim/workflows/sharrow-contrast/sandag_1zone_full.yaml new file mode 100644 index 000000000..3aa5d2f08 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/sandag_1zone_full.yaml @@ -0,0 +1,39 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: placeholder_sandag_1_zone_full + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: False + tag: '2022-07-1zone-1process' + resume_after: + fast: False + compile_n_households: 10000 + main_n_households: 100000 + config_dirs: + - configs_1_zone + - prototype_mtc/configs + data_dir: data_1 + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + chunk_training: True + chunk_training_mode: training + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/sandag_1zone_mini.yaml b/activitysim/workflows/sharrow-contrast/sandag_1zone_mini.yaml new file mode 100644 index 000000000..4c3692856 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/sandag_1zone_mini.yaml @@ -0,0 +1,37 @@ +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: placeholder_sandag_1_zone + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: True + tag: + resume_after: + fast: False + compile_n_households: 500 + main_n_households: 50000 + config_dirs: + - configs_1_zone + - prototype_mtc/configs + data_dir: data_1 + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/sandag_1zone_mp.yaml b/activitysim/workflows/sharrow-contrast/sandag_1zone_mp.yaml new file mode 100644 index 000000000..f08056ed0 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/sandag_1zone_mp.yaml @@ -0,0 +1,46 @@ +# +# sandag_1zone_mp +# - activitysim workflow sharrow-contrast/sandag_1zone_mp +# +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: placeholder_sandag_1_zone_full + workflow_name: sharrow-contrast/sandag_1zone_mp + workspace: workspace + multiprocess: -4 + settings_file: settings_mp.yaml + chunk_size_pct_of_available: 0.75 + chunk_size_minimum_gb: 40 + create: True + compile: True + sharrow: True + legacy: True + reference: False + tag: + resume_after: + fast: False + compile_n_households: 1000 + main_n_households: 0 + config_dirs: + - configs_1_zone + - prototype_mtc/configs + data_dir: data_1 + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner_chunked diff --git a/activitysim/workflows/sharrow-contrast/sandag_2zone_full.yaml b/activitysim/workflows/sharrow-contrast/sandag_2zone_full.yaml new file mode 100644 index 000000000..524ce0816 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/sandag_2zone_full.yaml @@ -0,0 +1,45 @@ +# +# sandag_2zone_full +# +# This workflow runs the Placeholder SANDAG model using the 2-Zone system +# in a single process runner. +# +# > activitysim workflow sharrow-contrast/sandag_2zone_full + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: placeholder_sandag_2_zone_full + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: False + tag: + resume_after: + fast: False + compile_n_households: 100 + main_n_households: 10000 + config_dirs: + - configs_2_zone + - placeholder_psrc/configs + data_dir: data_2 + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/sandag_2zone_mini.yaml b/activitysim/workflows/sharrow-contrast/sandag_2zone_mini.yaml new file mode 100644 index 000000000..559a19c9d --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/sandag_2zone_mini.yaml @@ -0,0 +1,46 @@ +# +# sandag_2zone_mini +# +# This workflow runs the Placeholder SANDAG model using the 2-Zone system +# in a single process runner. It is for mainly for rapid testing of the code +# and specification files for errors, not for policy analysis. +# +# > activitysim workflow sharrow-contrast/sandag_2zone_mini + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: placeholder_sandag_2_zone + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: False + tag: + resume_after: + fast: False + compile_n_households: 500 + main_n_households: 50000 + config_dirs: + - configs_2_zone + - placeholder_psrc/configs + data_dir: data_2 + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/sandag_2zone_test.yaml b/activitysim/workflows/sharrow-contrast/sandag_2zone_test.yaml new file mode 100644 index 000000000..db6390550 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/sandag_2zone_test.yaml @@ -0,0 +1,43 @@ +# +# sandag_2zone_mini +# +# This workflow runs the Placeholder SANDAG model using the 2-Zone system +# in a single process runner. It is for mainly for rapid testing of the code +# and specification files for errors, not for policy analysis. +# +# > activitysim workflow sharrow-contrast/sandag_2zone_test + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: placeholder_sandag_2_zone + workspace: worktest + create: True + compile: False + sharrow: False + legacy: True + reference: True + tag: '2022-08-2zone' + resume_after: + fast: False + compile_n_households: 30 + main_n_households: 30 + config_dirs: + - configs_2_zone + - placeholder_psrc/configs + data_dir: data_2 + instrument: False + memory_profile: True + trace_hh_id: + trace_od: + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/sandag_3zone_mini.yaml b/activitysim/workflows/sharrow-contrast/sandag_3zone_mini.yaml new file mode 100644 index 000000000..056d19a34 --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/sandag_3zone_mini.yaml @@ -0,0 +1,46 @@ +# +# sandag_3zone_mini +# +# This workflow runs the Placeholder SANDAG model using the 3-Zone system +# in a single process runner. It is for mainly for rapid testing of the code +# and specification files for errors, not for policy analysis. +# +# > activitysim workflow sharrow-contrast/sandag_3zone_mini + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: placeholder_sandag_3_zone + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: False + tag: + resume_after: + fast: False + compile_n_households: 500 + main_n_households: 50000 + config_dirs: + - configs_3_zone + - prototype_mtc/configs + data_dir: data_3 + instrument: False + memory_profile: False + trace_hh_id: + trace_od: + workplace_zone_agg: + caption: Workplaces by County + district_id: county_id + distance_skim: DIST + household_income: income + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/sharrow-contrast/sandag_xborder_mini.yaml b/activitysim/workflows/sharrow-contrast/sandag_xborder_mini.yaml new file mode 100644 index 000000000..62317ebef --- /dev/null +++ b/activitysim/workflows/sharrow-contrast/sandag_xborder_mini.yaml @@ -0,0 +1,49 @@ +# +# sandag_xborder_mini +# +# This workflow runs the Prototype SANDAG cross-border model in a single- +# process runner. +# +# > activitysim workflow sharrow-contrast/sandag_xborder_mini + +context_parser: pypyr.parser.keyvaluepairs +steps: + +- description: Setting default workflow parameters + name: pypyr.steps.default + in: + defaults: + example_name: prototype_sandag_xborder + workspace: workspace + create: True + compile: True + sharrow: True + legacy: True + reference: False + tag: + resume_after: + fast: False + compile_n_households: 500 + main_n_households: 50000 + config_dirs: + - configs + data_dir: data + ext_dirs: + - extensions + instrument: False + memory_profile: False + trace_hh_id: + trace_od: + distance_skim: SOV_NT_M_DIST + household_income: + report_skip: + - auto ownership + - cdap + - tour mode by composition + - work location + - school location + +- name: activitysim.workflows.steps.pype + in: + pype: + name: sharrow-contrast/_contrast_runner diff --git a/activitysim/workflows/steps/__init__.py b/activitysim/workflows/steps/__init__.py new file mode 100644 index 000000000..c4ac3b54b --- /dev/null +++ b/activitysim/workflows/steps/__init__.py @@ -0,0 +1,31 @@ +# from . import archive_outputs +from pypyr.context import Context +from pypyr.errors import KeyNotInContextError + +from . import cmd, py + + +def get_formatted_or_default(self: Context, key: str, default): + try: + return self.get_formatted(key) + except (KeyNotInContextError, KeyError): + return default + except TypeError: + return self.get(key) + except Exception as err: + raise ValueError(f"extracting {key} from context") from err + + +Context.get_formatted_or_default = get_formatted_or_default + + +def get_formatted_or_raw(self: Context, key: str): + try: + return self.get_formatted(key) + except TypeError: + return self.get(key) + except Exception as err: + raise ValueError(f"extracting {key} from context") from err + + +Context.get_formatted_or_raw = get_formatted_or_raw diff --git a/activitysim/workflows/steps/chunk_sizing.py b/activitysim/workflows/steps/chunk_sizing.py new file mode 100644 index 000000000..ee0ceca69 --- /dev/null +++ b/activitysim/workflows/steps/chunk_sizing.py @@ -0,0 +1,50 @@ +import warnings + +import psutil + +from .progression import reset_progress_step +from .wrapping import workstep + + +@workstep(updates_context=True) +def chunk_sizing( + chunk_size=None, + chunk_size_pct_of_available=None, + chunk_size_pct_of_total=None, + chunk_size_minimum_gb=0, +): + reset_progress_step(description="Figuring chunk size") + + # by default, if neither pct_of_available or pct_of_total is given, + # and chunk_size is not set explicitly, then use 85% of available RAM + if chunk_size_pct_of_available is None and chunk_size_pct_of_total is None: + chunk_size_pct_of_available = 0.85 + + vm = psutil.virtual_memory() + available_ram = vm.available + total_ram = vm.total + + # if chunk size is set explicitly, use it without regard to other settings + if chunk_size is None: + + if chunk_size_pct_of_available is not None: + if chunk_size_pct_of_available > 1: + chunk_size_pct_of_available /= 100 + chunk_size = int(available_ram * chunk_size_pct_of_available) + elif chunk_size_pct_of_total is not None: + if chunk_size_pct_of_total > 1: + chunk_size_pct_of_total /= 100 + chunk_size = int(total_ram * chunk_size_pct_of_total) + + min_chunk_size = int(chunk_size_minimum_gb * 1e9) + if chunk_size < min_chunk_size: + chunk_size = min_chunk_size + + if chunk_size > total_ram: + warnings.warn( + f"chunk size of {chunk_size/ 2**30:.2f}GB exceeds " + f"total RAM of {total_ram/ 2**30:.2f}" + ) + + out = dict(chunk_size=chunk_size) + return out diff --git a/activitysim/workflows/steps/cmd/__init__.py b/activitysim/workflows/steps/cmd/__init__.py new file mode 100644 index 000000000..e62b19e85 --- /dev/null +++ b/activitysim/workflows/steps/cmd/__init__.py @@ -0,0 +1,54 @@ +"""pypyr step that executes a cmd as a sub-process. + +You cannot use things like exit, return, shell pipes, filename wildcards, +environment,variable expansion, and expansion of ~ to a user’s home +directory. +""" +import logging + +from .dsl import CmdStep + +# logger means the log level will be set correctly +logger = logging.getLogger(__name__) + + +def run_step(context): + """Run command, program or executable. + + Context is a dictionary or dictionary-like. + + Context must contain the following keys: + cmd: <> (command + args to execute.) + + OR, as a dict + cmd: + run: str. mandatory. <> command + args to execute. + save: bool. defaults False. save output to cmdOut. + + Will execute the command string in the shell as a sub-process. + Escape curly braces: if you want a literal curly brace, double it like + {{ or }}. + + If save is True, will save the output to context as follows: + cmdOut: + returncode: 0 + stdout: 'stdout str here. None if empty.' + stderr: 'stderr str here. None if empty.' + + cmdOut.returncode is the exit status of the called process. Typically 0 + means OK. A negative value -N indicates that the child was terminated by + signal N (POSIX only). + + context['cmd'] will interpolate anything in curly braces for values + found in context. So if your context looks like this: + key1: value1 + key2: value2 + cmd: mything --arg1 {key1} + + The cmd passed to the shell will be "mything --arg value1" + """ + logger.debug("started") + + CmdStep(name=__name__, context=context).run_step(is_shell=False) + + logger.debug("done") diff --git a/activitysim/workflows/steps/cmd/dsl.py b/activitysim/workflows/steps/cmd/dsl.py new file mode 100644 index 000000000..1d05a7d3b --- /dev/null +++ b/activitysim/workflows/steps/cmd/dsl.py @@ -0,0 +1,167 @@ +"""pypyr step yaml definition for commands - domain specific language.""" +import logging +import os +import shlex +import subprocess +import sys +import time + +import pypyr.errors +from pypyr.config import config +from pypyr.errors import ContextError +from pypyr.utils import types + +from ..progression import reset_progress_step + +# logger means the log level will be set correctly +logger = logging.getLogger(__name__) + +RED = "\033[91m" +END = "\033[0m" + + +def stream_process(process, label): + go = True + while go: + go = process.poll() is None + for line in process.stdout: + print(line.decode().rstrip()) + for line in process.stderr: + print(RED + line.decode().rstrip() + END, file=sys.stderr) + + +class CmdStep: + """A pypyr step that represents a command runner step. + + This models a step that takes config like this: + cmd: <> + + OR, as a dict + cmd: + run: str. mandatory. command + args to execute. + save: bool. defaults False. save output to cmdOut. + + If save is True, will save the output to context as follows: + cmdOut: + returncode: 0 + stdout: 'stdout str here. None if empty.' + stderr: 'stderr str here. None if empty.' + + cmdOut.returncode is the exit status of the called process. Typically 0 + means OK. A negative value -N indicates that the child was terminated by + signal N (POSIX only). + + The run_step method does the actual work. init loads the yaml. + """ + + def __init__(self, name, context): + """Initialize the CmdStep. + + The step config in the context dict looks like this: + cmd: <> + + OR, as a dict + cmd: + run: str. mandatory. command + args to execute. + save: bool. optional. defaults False. save output to cmdOut. + cwd: str/path. optional. if specified, change the working + directory just for the duration of the command. + + Args: + name: Unique name for step. Likely __name__ of calling step. + context: pypyr.context.Context. Look for config in this context + instance. + + """ + assert name, "name parameter must exist for CmdStep." + assert context, "context param must exist for CmdStep." + # this way, logs output as the calling step, which makes more sense + # to end-user than a mystery steps.dsl.blah logging output. + self.logger = logging.getLogger(name) + + context.assert_key_has_value(key="cmd", caller=name) + + self.context = context + + cmd_config = context.get_formatted("cmd") + + if isinstance(cmd_config, str): + self.cmd_text = cmd_config + self.cwd = None + self.logger.debug("Processing command string: %s", cmd_config) + self.label = cmd_config + elif isinstance(cmd_config, dict): + context.assert_child_key_has_value(parent="cmd", child="run", caller=name) + + self.cmd_text = cmd_config["run"] + self.label = cmd_config.get("label", self.cmd_text) + self.conda_path = cmd_config.get("conda_path", None) + + cwd_string = cmd_config.get("cwd", None) + if cwd_string: + self.cwd = cwd_string + self.logger.debug( + "Processing command string in dir " "%s: %s", + self.cwd, + self.cmd_text, + ) + else: + self.cwd = None + self.logger.debug("Processing command string: %s", self.cmd_text) + + else: + raise ContextError( + f"{name} cmd config should be either a simple " + "string cmd='mycommandhere' or a dictionary " + "cmd={'run': 'mycommandhere', 'save': False}." + ) + + def run_step(self, is_shell): + """Run a command. + + Runs a program or executable. If is_shell is True, executes the command + through the shell. + + Args: + is_shell: bool. defaults False. Set to true to execute cmd through + the default shell. + """ + assert is_shell is not None, "is_shell param must exist for CmdStep." + conda_path = self.conda_path or sys.exec_prefix + + # why? If shell is True, it is recommended to pass args as a string + # rather than as a sequence. + # But not on windows, because windows wants strings, not sequences. + if is_shell or config.is_windows: + args = f'conda run -p "{conda_path}" ' + self.cmd_text + else: + args = shlex.split(self.cmd_text) + args = ["conda", "run", "-p", conda_path] + list(args) # TODO windows? + + reset_progress_step(description=f"{self.label}", prefix="[bold green]") + + env = os.environ.copy() + pythonpath = env.pop("PYTHONPATH", None) + + process = subprocess.Popen( + args, + shell=is_shell, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + cwd=self.cwd, + env=env, + ) + stream_process(process, self.label) + self.context["cmdOut"] = { + "returncode": process.returncode, + } + + # don't swallow the error, because it's the Step swallow decorator + # responsibility to decide to ignore or not. + if process.returncode: + raise subprocess.CalledProcessError( + process.returncode, + process.args, + process.stdout, + process.stderr, + ) diff --git a/activitysim/workflows/steps/contrast/__init__.py b/activitysim/workflows/steps/contrast/__init__.py new file mode 100644 index 000000000..5ef49ba90 --- /dev/null +++ b/activitysim/workflows/steps/contrast/__init__.py @@ -0,0 +1,2 @@ +from .load_skims import load_skims +from .load_tables import load_tables diff --git a/activitysim/workflows/steps/contrast/attach_skim_data.py b/activitysim/workflows/steps/contrast/attach_skim_data.py new file mode 100644 index 000000000..b872afd24 --- /dev/null +++ b/activitysim/workflows/steps/contrast/attach_skim_data.py @@ -0,0 +1,89 @@ +import logging + +import numpy as np +import pandas as pd + +from ..progression import reset_progress_step +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +def _as_int(x): + if x.dtype.kind == "i": + return x + else: + return x.astype(np.int32) + + +@workstep(updates_context=True) +def attach_skim_data( + tablesets, + skims, + skim_vars, + tablename, + otaz_col, + dtaz_col, + time_col=None, + rename_skim_vars=None, +) -> dict: + if isinstance(skim_vars, str): + skim_vars = [skim_vars] + if len(skim_vars) == 1: + skim_vars_note = skim_vars[0] + else: + skim_vars_note = f"{len(skim_vars)} skim vars" + + reset_progress_step( + description=f"attach skim data / {tablename} <- {skim_vars_note}" + ) + + if not isinstance(skims, dict): + skims = {i: skims for i in tablesets.keys()} + + for key, tableset in tablesets.items(): + # skim_subset = skims[key][list(skims[key].coords) + skim_vars] + skim_subset = skims[key][skim_vars] + + otag = "omaz" if "omaz" in skims[key].coords else "otaz" + dtag = "dmaz" if "dmaz" in skims[key].coords else "dtaz" + + zone_ids = tableset["land_use"].index + if ( + zone_ids.is_monotonic_increasing + and zone_ids[-1] == len(zone_ids) + zone_ids[0] - 1 + ): + offset = zone_ids[0] + looks = [ + _as_int(tableset[tablename][otaz_col].rename(otag) - offset), + _as_int(tableset[tablename][dtaz_col].rename(dtag) - offset), + ] + else: + remapper = dict(zip(zone_ids, pd.RangeIndex(len(zone_ids)))) + looks = [ + _as_int(tableset[tablename][otaz_col].rename(otag).apply(remapper.get)), + _as_int(tableset[tablename][dtaz_col].rename(dtag).apply(remapper.get)), + ] + if "time_period" in skim_subset.dims: + if time_col is None: + raise KeyError("time_period in skims to slice but time_col is missing") + looks.append( + tableset[tablename][time_col] + .apply(skims[key].attrs["time_period_imap"].get) + .rename("time_period"), + ) + look = pd.concat(looks, axis=1) + try: + out = skim_subset.iat.df(look) + except KeyError as err: + # KeyError is triggered when reading TAZ data from MAZ-enabled skims + lookr = {i: look[i].values for i in look.columns} + out = skims[key].iat(**lookr, _names=skim_vars).to_dataframe() + out = out.set_index(tablesets[key][tablename].index) + if rename_skim_vars is not None: + if isinstance(rename_skim_vars, str): + rename_skim_vars = [rename_skim_vars] + out = out.rename(columns=dict(zip(skim_vars, rename_skim_vars))) + tablesets[key][tablename] = tablesets[key][tablename].assign(**out) + + return dict(tablesets=tablesets) diff --git a/activitysim/workflows/steps/contrast/composite_log.py b/activitysim/workflows/steps/contrast/composite_log.py new file mode 100644 index 000000000..81c90c599 --- /dev/null +++ b/activitysim/workflows/steps/contrast/composite_log.py @@ -0,0 +1,72 @@ +import os + +import pandas as pd + +from ..progression import reset_progress_step +from ..wrapping import workstep + + +def to_csv_safe(obj, filename, *args, **kwargs): + """ + Write a csv as normal, changing the filename if a permission error occurs. + """ + try: + obj.to_csv(filename, *args, **kwargs) + except PermissionError: + n = 1 + f1, f2 = os.path.splitext(filename) + while os.path.exists(f"{f1} ({n}){f2}"): + n += 1 + obj.to_csv(f"{f1} ({n}){f2}", *args, **kwargs) + + +@workstep(updates_context=True) +def composite_log( + tag, + archive_dir, + compares=("compile", "sharrow", "legacy", "reference"), +) -> dict: + + reset_progress_step(description="composite timing and memory logs") + + timings = {} + compares = list(compares) + for t in compares: + filename = f"{archive_dir}/output-{t}/log/timing_log.csv" + if os.path.exists(filename): + df = pd.read_csv(filename) + df = df.set_index("model_name")["seconds"] + timings[t] = df.loc[~df.index.duplicated()] + if timings: + composite_timing = pd.concat(timings, axis=1) + to_csv_safe(composite_timing, f"{archive_dir}/combined_timing_log-{tag}.csv") + mems = {} + for t in compares: + filename = f"{archive_dir}/output-{t}/log/mem.csv" + if os.path.exists(filename): + df = pd.read_csv(filename) + df = df.set_index("event")[["rss", "full_rss", "uss"]] + mems[t] = df.loc[~df.index.duplicated()] + if mems: + composite_mem = pd.concat(mems, axis=1) + to_csv_safe(composite_mem, f"{archive_dir}/combined_mem_log-{tag}.csv") + peaks = {} + for t in compares: + filename = f"{archive_dir}/output-{t}/log/memory_profile.csv" + if os.path.exists(filename): + df = pd.read_csv(filename) + df["time"] = pd.to_datetime(df["time"]) + peak_by_event = ( + df.groupby("event")[["rss", "full_rss", "uss", "time"]] + .max() + .sort_values("time") + ) + peaks[t] = peak_by_event[["rss", "full_rss", "uss"]] + if peaks: + composite_peaks = pd.concat(peaks, axis=1) + to_csv_safe(composite_peaks, f"{archive_dir}/combined_mem_peak-{tag}.csv") + return dict( + combined_timing_log=f"{archive_dir}/combined_timing_log-{tag}.csv", + combined_mem_log=f"{archive_dir}/combined_mem_log-{tag}.csv", + combined_peakmem_log=f"{archive_dir}/combined_mem_peak-{tag}.csv", + ) diff --git a/activitysim/workflows/steps/contrast/contrast_setup.py b/activitysim/workflows/steps/contrast/contrast_setup.py new file mode 100644 index 000000000..fd91a814c --- /dev/null +++ b/activitysim/workflows/steps/contrast/contrast_setup.py @@ -0,0 +1,57 @@ +import multiprocessing +import time + +from ..progression import reset_progress_step +from ..wrapping import workstep + + +@workstep(updates_context=True) +def contrast_setup( + example_name, + tag=None, + compile=True, + sharrow=True, + legacy=True, + resume_after=True, + fast=True, + reference=None, + reference_asim_version="0.0.0", + multiprocess=0, + chunk_training_mode=None, + main_n_households=None, +): + reset_progress_step(description="Constrast Setup") + if tag is None: + tag = time.strftime("%Y-%m-%d-%H%M%S") + contrast = sharrow and legacy + + flags = [] + if resume_after: + flags.append(f" -r {resume_after}") + if fast: + flags.append("--fast") + + out = dict(tag=tag, contrast=contrast, flags=" ".join(flags)) + if isinstance(reference, str) and "." in reference: + out["reference_asim_version"] = reference + out["reference"] = True + out["relabel_tablesets"] = {"reference": f"v{reference_asim_version}"} + multiprocess = int(multiprocess) + out["is_multiprocess"] = (multiprocess > 1) or (multiprocess < 0) + if multiprocess >= 0: + out["num_processes"] = multiprocess + else: + # when negative, count the number of cpu cores, and run on half the + # cores except the absolute value of `multiprocess`, so e.g. + # if set to -3 and you have 48 cores, then run on (48/2)-3 = 21 + # processes (but at least 2). + out["num_processes"] = int(multiprocessing.cpu_count() / 2) + multiprocess + if out["num_processes"] < 2: + out["num_processes"] = 2 + + if chunk_training_mode and chunk_training_mode != "disabled": + out["chunk_application_mode"] = "production" + else: + out["chunk_application_mode"] = chunk_training_mode + + return out diff --git a/activitysim/workflows/steps/contrast/data_inventory.py b/activitysim/workflows/steps/contrast/data_inventory.py new file mode 100644 index 000000000..a3ff172a3 --- /dev/null +++ b/activitysim/workflows/steps/contrast/data_inventory.py @@ -0,0 +1,76 @@ +import logging + +import altair as alt +import pandas as pd +import xmle +from pypyr.context import Context +from pypyr.errors import KeyNotInContextError + +from ..error_handler import error_logging +from ..progression import reset_progress_step + +logger = logging.getLogger(__name__) + + +@error_logging +def run_step(context: Context) -> None: + reset_progress_step(description="report model inventory") + + context.assert_key_has_value(key="report", caller=__name__) + report = context.get("report") + fig = context.get("fig") + tab = context.get("tab") + + tablesets = context.get("tablesets") + skims = context.get("skims") + try: + title = context.get_formatted("title") + except (KeyError, KeyNotInContextError): + title = "Data Inventory" + + lens = {} + dtypes = {} + for source, tableset in tablesets.items(): + lens[source] = {} + dtypes[source] = {} + for tablename, tabledata in tableset.items(): + lens[source][tablename] = len(tabledata) + for k, kt in tabledata.dtypes.items(): + dtypes[source][tablename, k] = kt + + with report: + report << f"## {title}" + + with report: + with pd.option_context( + "display.max_rows", 1_000_000, "display.max_columns", 10_000 + ): + report << tab("Table Row Count", level=3) + report << pd.DataFrame(lens).applymap( + lambda x: f"{x:,}" if isinstance(x, int) else x + ) + + with report: + with pd.option_context( + "display.max_rows", 1_000_000, "display.max_columns", 10_000 + ): + report << tab("Table Contents", level=3) + dtypes_table = ( + pd.DataFrame(dtypes).rename_axis(index=["table", "column"]).fillna("") + ) + dtypes_table[""] = pd.Series( + (dtypes_table.iloc[:, 0].to_frame().values == dtypes_table.values).all( + 1 + ), + index=dtypes_table.index, + ).apply(lambda x: "" if x else "\u2B05") + report << dtypes_table + + with report: + report << tab("Skims Contents", level=3) + ul = xmle.Elem("ul") + for k in skims: + i = xmle.Elem("li") + i << xmle.Elem("b", text=k, tail=f" {skims[k].dtype} {skims[k].dims}") + ul << i + report << ul diff --git a/activitysim/workflows/steps/contrast/directory_prep.py b/activitysim/workflows/steps/contrast/directory_prep.py new file mode 100644 index 000000000..a7f124539 --- /dev/null +++ b/activitysim/workflows/steps/contrast/directory_prep.py @@ -0,0 +1,46 @@ +import os +from pathlib import Path + +from ..wrapping import workstep + + +def _prep_dir(directory): + directory = Path(directory) + os.makedirs(directory, exist_ok=True) + os.makedirs(directory / "log", exist_ok=True) + os.makedirs(directory / "trace", exist_ok=True) + gitignore = directory / ".gitignore" + if not os.path.exists(gitignore): + with open(gitignore, "wt") as f: + f.write("/*") + + +@workstep(updates_context=True) +def directory_prep( + tag, + example_name, + workspace, + compile=True, + sharrow=True, + legacy=True, + reference=True, + chunk_training=None, +): + archive_dir = f"{workspace}/{example_name}/output-{tag}" + os.makedirs(archive_dir, exist_ok=True) + if compile: + _prep_dir(f"{archive_dir}/output-compile") + if sharrow: + _prep_dir(f"{archive_dir}/output-sharrow") + if chunk_training: + _prep_dir(f"{archive_dir}/output-sharrow-training") + if legacy: + _prep_dir(f"{archive_dir}/output-legacy") + if chunk_training: + _prep_dir(f"{archive_dir}/output-legacy-training") + if reference: + _prep_dir(f"{archive_dir}/output-reference") + return dict( + archive_dir=archive_dir, + archive_base=os.path.basename(archive_dir), + ) diff --git a/activitysim/workflows/steps/contrast/district_to_district.py b/activitysim/workflows/steps/contrast/district_to_district.py new file mode 100644 index 000000000..93358ea78 --- /dev/null +++ b/activitysim/workflows/steps/contrast/district_to_district.py @@ -0,0 +1,74 @@ +import logging + +import altair as alt +import pandas as pd +from pypyr.context import Context + +from ....standalone.data_dictionary import check_data_dictionary +from ..progression import reset_progress_step +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep +def compare_district_to_district( + tablesets, + tablename, + district_id, + orig_col="home_zone_id", + dest_col="workplace_zone_id", + orig_label="home_district", + dest_label="work_district", + filter=None, + data_dictionary=None, + size_label="n_workers", + viz_engine="altair", +): + data_dictionary = check_data_dictionary(data_dictionary) + + d = {} + + for key, tableset in tablesets.items(): + if filter is not None: + subtable = tableset[tablename].query(filter) + else: + subtable = tableset[tablename] + district_map = tableset["land_use"][district_id] + orig_district = subtable[orig_col].map(district_map).rename(orig_label) + dest_district = subtable[dest_col].map(district_map).rename(dest_label) + df = subtable.groupby([orig_district, dest_district]).size().rename(size_label) + d[key] = df + + all_d = pd.concat(d, names=["source"]).reset_index() + + district_names = data_dictionary.get("land_use", {}).get(district_id, None) + if district_names is not None: + all_d[orig_label] = all_d[orig_label].map(district_names) + all_d[dest_label] = all_d[dest_label].map(district_names) + + if viz_engine is None: + return all_d + + selection = alt.selection_multi( + fields=[dest_label], + bind="legend", + ) + + fig = ( + alt.Chart(all_d) + .mark_bar() + .encode( + color=f"{dest_label}:N", + y=alt.Y("source", axis=alt.Axis(grid=False, title=""), sort=None), + x=alt.X(size_label, axis=alt.Axis(grid=False)), + row=f"{orig_label}:N", + opacity=alt.condition(selection, alt.value(1), alt.value(0.2)), + tooltip=[f"{orig_label}:N", f"{dest_label}:N", "source", size_label], + ) + .add_selection( + selection, + ) + ) + + return fig diff --git a/activitysim/workflows/steps/contrast/join_table_data.py b/activitysim/workflows/steps/contrast/join_table_data.py new file mode 100644 index 000000000..528f169d4 --- /dev/null +++ b/activitysim/workflows/steps/contrast/join_table_data.py @@ -0,0 +1,30 @@ +import logging + +from ..progression import reset_progress_step +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep(updates_context=True) +def join_table_data( + tablesets, + tablename, + from_tablename, + columns, + on, +) -> dict: + if isinstance(columns, str): + columns = [columns] + if len(columns) == 1: + columns_note = columns[0] + else: + columns_note = f"{len(columns)} columns" + + reset_progress_step(description=f"join table data / {tablename} <- {columns_note}") + + for key, tableset in tablesets.items(): + other = tableset[from_tablename][columns] + tablesets[key][tablename] = tablesets[key][tablename].join(other, on=on) + + return dict(tablesets=tablesets) diff --git a/activitysim/workflows/steps/contrast/load_skims.py b/activitysim/workflows/steps/contrast/load_skims.py new file mode 100644 index 000000000..85071b8f7 --- /dev/null +++ b/activitysim/workflows/steps/contrast/load_skims.py @@ -0,0 +1,164 @@ +import glob +import logging +import os +from pathlib import Path + +import numpy as np +import openmatrix +import pandas as pd +import sharrow as sh +import yaml + +from activitysim.standalone.utils import chdir + +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +def load_skims_per_settings( + network_los_settings_filename, + data_dir, +): + with open(network_los_settings_filename, "rt") as f: + settings = yaml.safe_load(f) + + skim_settings = settings["taz_skims"] + if isinstance(skim_settings, str): + skims_omx_fileglob = skim_settings + skims_filenames = glob.glob(os.path.join(data_dir, skims_omx_fileglob)) + elif isinstance(skim_settings, list): + skims_filenames = [os.path.join(data_dir, i) for i in skim_settings] + else: + skims_omx_fileglob = skim_settings.get("omx", None) + skims_omx_fileglob = skim_settings.get("files", skims_omx_fileglob) + skims_filenames = glob.glob(os.path.join(data_dir, skims_omx_fileglob)) + index_names = ("otaz", "dtaz", "time_period") + indexes = None + time_period_breaks = settings.get("skim_time_periods", {}).get("periods") + time_periods = settings.get("skim_time_periods", {}).get("labels") + time_period_sep = "__" + + time_window = settings.get("skim_time_periods", {}).get("time_window") + period_minutes = settings.get("skim_time_periods", {}).get("period_minutes") + n_periods = int(time_window / period_minutes) + + tp_map = {} + tp_imap = {} + label = time_periods[0] + i = 0 + for t in range(n_periods): + if t in time_period_breaks: + i = time_period_breaks.index(t) + label = time_periods[i] + tp_map[t + 1] = label + tp_imap[t + 1] = i + + omxs = [ + openmatrix.open_file(skims_filename, mode="r") + for skims_filename in skims_filenames + ] + if isinstance(time_periods, (list, tuple)): + time_periods = np.asarray(time_periods) + result = sh.dataset.from_omx_3d( + omxs, + index_names=index_names, + indexes=indexes, + time_periods=time_periods, + time_period_sep=time_period_sep, + ) + result.attrs["time_period_map"] = tp_map + result.attrs["time_period_imap"] = tp_imap + + # load sparse MAZ skims, if any + from activitysim.core.skim_dataset import load_sparse_maz_skims + + zone_system = int(settings.get("zone_system")) + + if zone_system > 1: + maz2taz_file_name = settings.get("maz") + maz_to_maz = settings.get("maz_to_maz", {}) + maz_to_maz_tables = maz_to_maz.get("tables", ()) + max_blend_distance = maz_to_maz.get("max_blend_distance", {}) + + maz2taz = pd.read_csv(os.path.join(data_dir, maz2taz_file_name), index_col=0) + maz2taz = maz2taz.rename_axis("MAZ") + maz2taz = maz2taz.reset_index() + remapper = dict(zip(maz2taz.MAZ, maz2taz.index)) + + result = load_sparse_maz_skims( + result, + maz2taz.index, + remapper, + zone_system, + maz2taz_file_name, + maz_to_maz_tables=maz_to_maz_tables, + max_blend_distance=max_blend_distance, + data_file_resolver=lambda f, mandatory=True: os.path.join(data_dir, f), + ) + + return result + + +@workstep("skims") +def load_skims( + config_dirs=("configs",), + data_dir="data", + working_directory=None, + common_directory=None, +): + """ + Open and prepare one or more sets of skims for use. + + The xarray library can use dask to delay loading skim matrices until they + are actually needed, so this may not actually load the skims into RAM. + + Parameters + ---------- + config_dirs : Tuple[Path-like] or Mapping[str, Tuple[Path-like]], default ('configs',) + Location of the config directory(s). The skims are loaded by finding + the `network_los.yaml` definition files in one of these directories. + If a mapping is provided, the keys label multiple different sets of skims + to load, which can be useful in contrasting model results from different + input values. + data_dir : Path-like or Mapping[str, Path-like], default 'data' + Loc + working_directory : Path-like, optional + common_directory : Path-like, optional + Deprecated, use working_directory + + Returns + ------- + xarray.Dataset or Dict[str, xarray.Dataset] + A single dataset is returned when a single set of config and data + directories are provided, otherwise a dict is returned with keys + that match the inputs. + """ + if common_directory is not None: + working_directory = common_directory + + if isinstance(config_dirs, str): + config_dirs = [config_dirs] + + if working_directory is None: + working_directory = os.getcwd() + + with chdir(working_directory): + network_los_file = None + for config_dir in config_dirs: + network_los_file = os.path.join(config_dir, "network_los.yaml") + if os.path.exists(network_los_file): + break + if network_los_file is None: + raise FileNotFoundError("<>/network_los.yaml") + if isinstance(data_dir, (str, Path)) and isinstance( + network_los_file, (str, Path) + ): + skims = load_skims_per_settings(network_los_file, data_dir) + else: + skims = {} + for k in data_dir.keys(): + skims[k] = load_skims_per_settings(network_los_file, data_dir[k]) + # TODO: allow for different network_los_file + + return skims diff --git a/activitysim/workflows/steps/contrast/load_tables.py b/activitysim/workflows/steps/contrast/load_tables.py new file mode 100644 index 000000000..16e33920e --- /dev/null +++ b/activitysim/workflows/steps/contrast/load_tables.py @@ -0,0 +1,78 @@ +import os +from pathlib import Path + +from pypyr.context import Context + +from activitysim.standalone.compare import load_final_tables +from activitysim.standalone.utils import chdir + +from ..error_handler import error_logging +from ..progression import reset_progress_step +from ..wrapping import workstep + +# databases = context.get_formatted('databases') +# # the various different output directories to process, for example: +# # { +# # "sharrow": "output-sharrow", +# # "legacy": "output-legacy", +# # } +# +# tables = context.get_formatted('tables') +# # the various tables in the output directories to read, for example: +# # trips: +# # filename: final_trips.csv +# # index_col: trip_id +# # persons: +# # filename: final_persons.csv +# # index_col: person_id +# # land_use: +# # filename: final_land_use.csv +# # index_col: zone_id + + +@workstep("tablesets") +def load_tables(databases, tables, common_output_directory=None) -> dict: + """ + Load tables from one or more tablesets. + + Parameters + ---------- + databases : Dict[str,Path-like] + Defines one or more tablesets to load, out of input or output + directories. Each included database should include all the tables + referenced by the `tables` argument. + tables : Dict[str,Dict[str,str]] + The keys give names of tables to load, and the values are dictionaries + with keys at least including `filename`, and possibly also `index_col`. + common_output_directory : Path-like, optional + The directory in which each of the database directories can be found. + If they are not in the same place, the user should set this to a common + root directory, and include the full relative path for each database. + If not given, defaults to the current working directory. + + Returns + ------- + dict + The loaded tables are under the key 'tablesets'. + """ + + tablefiles = {} + index_cols = {} + for t, v in tables.items(): + if isinstance(v, str): + tablefiles[t] = v + else: + tablefiles[t] = v.get("filename") + index_cols[t] = v.get("index_col", None) + + if common_output_directory is None: + common_output_directory = os.getcwd() + + with chdir(common_output_directory): + tablesets = load_final_tables( + databases, + tablefiles, + index_cols, + ) + + return tablesets diff --git a/activitysim/workflows/steps/contrast/memory_use.py b/activitysim/workflows/steps/contrast/memory_use.py new file mode 100644 index 000000000..c703188d9 --- /dev/null +++ b/activitysim/workflows/steps/contrast/memory_use.py @@ -0,0 +1,65 @@ +import logging + +import altair as alt +import numpy as np +import pandas as pd + +from ..progression import reset_progress_step +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep +def memory_use( + combined_mem_log, + include_runs=("legacy", "sharrow", "reference"), + relabel_tablesets=None, + memory_measure="uss", +): + reset_progress_step(description="report model memory usage") + + if relabel_tablesets is None: + relabel_tablesets = {} + + include_runs = list(include_runs) + + logger.info(f"building memory use report from {combined_mem_log}") + + df = ( + pd.read_csv( + combined_mem_log, + header=[0, 1], + skipinitialspace=True, + index_col=0, + ) + .fillna(0) + .astype(np.float64) + ) + df.columns = df.columns.set_names(["source", "mem"]) + mem = df.stack().query(f"mem == '{memory_measure}'") + include_runs = [i for i in include_runs if i in mem.columns] + mem = ( + mem[include_runs] + .reset_index() + .reset_index() + .set_index(["event", "index"]) + .drop(columns=["mem"]) + .stack() + .rename("mem_use") + .reset_index() + ) + mem["mem_gigs"] = mem["mem_use"] / 1e9 + mem["source"] = mem["source"].map(lambda x: relabel_tablesets.get(x, x)) + + chart = ( + alt.Chart(mem) + .mark_line() + .encode( + y="mem_gigs", + color="source", + x=alt.X("index", sort=None), + tooltip=["event", "source", "mem_gigs"], + ) + ) + return chart diff --git a/activitysim/workflows/steps/contrast/memory_use_peak.py b/activitysim/workflows/steps/contrast/memory_use_peak.py new file mode 100644 index 000000000..b6a8b1ed7 --- /dev/null +++ b/activitysim/workflows/steps/contrast/memory_use_peak.py @@ -0,0 +1,65 @@ +import logging + +import altair as alt +import numpy as np +import pandas as pd + +from ..progression import reset_progress_step +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep +def memory_use_peak( + combined_peakmem_log, + include_runs=("legacy", "sharrow", "reference"), + relabel_tablesets=None, + memory_measure="uss", +): + reset_progress_step(description="report component peak memory use") + + if relabel_tablesets is None: + relabel_tablesets = {} + + include_runs = list(include_runs) + + logger.info(f"building peak memory use report from {combined_peakmem_log}") + + try: + df = pd.read_csv( + combined_peakmem_log, + header=[0, 1], + skipinitialspace=True, + index_col=0, + ) + except FileNotFoundError: + return f"File Not Found: {combined_peakmem_log}" + df = df.fillna(0).astype(np.float64) + df.columns = df.columns.set_names(["source", "mem"]) + mem = df.stack().query(f"mem == '{memory_measure}'") + include_runs = [i for i in include_runs if i in mem.columns] + mem = ( + mem[include_runs] + .reset_index() + .reset_index() + .set_index(["event", "index"]) + .drop(columns=["mem"]) + .stack() + .rename("mem_use") + .reset_index() + ) + mem["mem_gigs"] = mem["mem_use"] / 1e9 + mem["source"] = mem["source"].map(lambda x: relabel_tablesets.get(x, x)) + + chart = ( + alt.Chart(mem) + .mark_line(interpolate="step") + .encode( + y="mem_gigs", + color="source", + x=alt.X("event", sort=None), + tooltip=["event", "source", "mem_gigs"], + ) + ) + return chart diff --git a/activitysim/workflows/steps/contrast/nominal_choice.py b/activitysim/workflows/steps/contrast/nominal_choice.py new file mode 100644 index 000000000..c9756b72b --- /dev/null +++ b/activitysim/workflows/steps/contrast/nominal_choice.py @@ -0,0 +1,148 @@ +import logging + +import altair as alt +import pandas as pd +from pypyr.context import Context + +from ..progression import reset_progress_step +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +def parse_grouping(g): + if isinstance(g, str): + return g, {"shorthand": g} + elif isinstance(g, dict): + return g.get("field"), g + elif g is None: + return None, None + else: + raise ValueError(g) + + +@workstep +def compare_nominal_choice( + tablesets, + tablename, + nominal_col, + row_grouping=None, + col_grouping=None, + count_label=None, + share_label=None, + axis_label="Share", + title=None, + ordinal=False, + plot_type="share", + relabel_tablesets=None, +): + """ + Parameters + ---------- + tablesets : Mapping + title : str, optional + grouping : str + relabel_tablesets : Mapping[str,str] + Remap the keys in `tablesets` with these values. Any + missing values are retained. This allows you to modify + the figure to e.g. change "reference" to "v1.0.4" without + editing the original input data. + """ + if count_label is None: + count_label = f"# of {tablename}" + if share_label is None: + share_label = f"share of {tablename}" + if relabel_tablesets is None: + relabel_tablesets = {} + + row_g, row_g_kwd = parse_grouping(row_grouping) + col_g, col_g_kwd = parse_grouping(col_grouping) + + d = {} + groupings = [] + if row_g is not None: + groupings.append(row_g) + if col_g is not None: + groupings.append(col_g) + + for key, tableset in tablesets.items(): + df = ( + tableset[tablename] + .groupby(groupings + [nominal_col]) + .size() + .rename(count_label) + .reset_index() + ) + if not groupings: + df[share_label] = df[count_label] / df[count_label].sum() + else: + df[share_label] = df[count_label] / df.groupby(groupings)[ + count_label + ].transform("sum") + d[relabel_tablesets.get(key, key)] = df + + all_d = pd.concat(d, names=["source"]).reset_index() + + selection = alt.selection_multi( + fields=[nominal_col], + bind="legend", + ) + + if plot_type == "count": + x = alt.X( + count_label, + axis=alt.Axis(grid=False, labels=False, title=axis_label), + ) + elif plot_type == "share": + x = alt.X( + share_label, + axis=alt.Axis(grid=False, labels=False, title=axis_label), + scale=alt.Scale(domain=[0.0, 1.0]), + ) + else: + raise ValueError(f"unknown plot_type {plot_type}") + + encode = dict( + color=alt.Color( + nominal_col, + type="ordinal" if ordinal else "nominal", + ), + y=alt.Y("source", axis=alt.Axis(grid=False, title=""), sort=None), + x=x, + opacity=alt.condition(selection, alt.value(1), alt.value(0.2)), + tooltip=[ + nominal_col, + "source", + count_label, + alt.Tooltip(f"{share_label}:Q", format=".2%"), + ], + ) + if row_g is not None: + encode["row"] = alt.Row(**row_g_kwd) + if col_g is not None: + encode["column"] = alt.Column(**col_g_kwd) + + fig = ( + alt.Chart(all_d) + .mark_bar() + .encode( + **encode, + ) + .add_selection( + selection, + ) + ) + + if title: + fig = fig.properties(title=title).configure_title( + fontSize=20, + anchor="start", + color="black", + ) + + if col_grouping is not None: + fig = fig.properties( + width=100, + ) + + return fig diff --git a/activitysim/workflows/steps/contrast/ordinal_distribution.py b/activitysim/workflows/steps/contrast/ordinal_distribution.py new file mode 100644 index 000000000..a50d25b40 --- /dev/null +++ b/activitysim/workflows/steps/contrast/ordinal_distribution.py @@ -0,0 +1,112 @@ +import logging + +import altair as alt +import pandas as pd +from pypyr.context import Context + +from ..progression import reset_progress_step +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep +def ordinal_distribution( + tablesets, + tablename, + ordinal_col, + facet_grouping="primary_purpose", + title=None, + axis_label=None, + count_label=None, + share_label=None, + interpolate="monotone", + plot_type="share", + value_format=None, +): + if count_label is None: + count_label = f"# of {tablename}" + + if share_label is None: + share_label = f"share of {tablename}" + + if axis_label is None: + axis_label = ordinal_col + groupings = [] + if isinstance(facet_grouping, str): + groupings = [facet_grouping] + + d = {} + for key, tableset in tablesets.items(): + df = ( + tableset[tablename] + .groupby(groupings + [ordinal_col]) + .size() + .rename(count_label) + .unstack(ordinal_col) + .fillna(0) + .stack() + .rename(count_label) + .reset_index() + ) + df[share_label] = df[count_label] / df.groupby(groupings)[ + count_label + ].transform("sum") + d[key] = df + + # This is sorted in reverse alphabetical order by source, so that + # the stroke width for the first line plotted is fattest, and progressively + # thinner lines are plotted over that, so all data is visible on the figure. + all_d = ( + pd.concat(d, names=["source"]) + .reset_index() + .sort_values("source", ascending=False) + ) + + if plot_type == "count": + y = alt.Y(count_label, axis=alt.Axis(grid=False, title=""), stack=None) + elif plot_type == "share": + y = alt.Y(share_label, axis=alt.Axis(grid=False, title=""), stack=None) + else: + raise ValueError(f"unknown plot_type {plot_type}") + + val_format = {} + if value_format is not None: + val_format["format"] = value_format + + fig = ( + alt.Chart(all_d) + .mark_area( + interpolate=interpolate, + fillOpacity=0.3, + line=True, + ) + .encode( + color="source", + y=y, + x=alt.X( + ordinal_col, axis=alt.Axis(grid=False, title=axis_label, **val_format) + ), + tooltip=[ + alt.Tooltip(ordinal_col, **val_format), + "source", + count_label, + alt.Tooltip(f"{share_label}:Q", format=".2%"), + ], + facet=alt.Facet(facet_grouping, columns=3), + strokeWidth="source", + ) + .properties( + width=200, + height=120, + ) + ) + + if title: + fig = fig.properties(title=title).configure_title( + fontSize=20, + anchor="start", + color="black", + ) + + return fig diff --git a/activitysim/workflows/steps/contrast/runtime.py b/activitysim/workflows/steps/contrast/runtime.py new file mode 100644 index 000000000..7437f331b --- /dev/null +++ b/activitysim/workflows/steps/contrast/runtime.py @@ -0,0 +1,149 @@ +import logging + +import altair as alt +import pandas as pd + +from ..progression import reset_progress_step +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep +def contrast_runtime( + combined_timing_log, + include_runs=( + "legacy", + "sharrow", + ), + relabel_tablesets=None, +): + """ + Generate a figure contrasting runtime for each model component. + + Parameters + ---------- + combined_timing_log + include_runs + relabel_tablesets : Mapping[str,str] + Remap the keys in `include_runs` with these values. Any + missing values are retained. This allows you to modify + the figure to e.g. change "reference" to "v1.0.4" without + editing the original input data. + + Returns + ------- + + """ + reset_progress_step(description="report model runtime") + + if relabel_tablesets is None: + relabel_tablesets = {} + + include_runs = list(include_runs) + + logger.info(f"building runtime report from {combined_timing_log}") + + df = pd.read_csv(combined_timing_log, index_col="model_name") + include_runs = [i for i in include_runs if i in df.columns] + df1 = ( + df[include_runs] + .rename_axis(columns="source") + .unstack() + .rename("seconds") + .reset_index() + .fillna(0) + ) + + def relabel_source(x): + return relabel_tablesets.get(x, x) + + df1["source"] = df1["source"].map(relabel_source) + c = alt.Chart( + df1, + height={"step": 20}, + ) + + if len(include_runs) == 1: + + result = c.mark_bar(size=6,).encode( + x=alt.X("seconds:Q", stack=None), + y=alt.Y("model_name", type="nominal", sort=None), + color="source", + tooltip=["source", "model_name", "seconds"], + ) | alt.Chart(df1).mark_bar().encode( + color="source", + x="source", + y=alt.Y("sum(seconds)", title="Total Seconds"), + tooltip=["source", "sum(seconds)"], + ) + + elif len(include_runs) == 2: + + result = c.mark_bar(yOffset=-3, size=6,).transform_filter( + (alt.datum.source == relabel_source(include_runs[0])) + ).encode( + x=alt.X("seconds:Q", stack=None), + y=alt.Y("model_name", type="nominal", sort=None), + color="source", + tooltip=["source", "model_name", "seconds"], + ) + c.mark_bar( + yOffset=4, + size=6, + ).transform_filter( + (alt.datum.source == relabel_source(include_runs[1])) + ).encode( + x=alt.X("seconds:Q", stack=None), + y=alt.Y("model_name", type="nominal", sort=None), + color="source", + tooltip=["source", "model_name", "seconds"], + ) | alt.Chart( + df1 + ).mark_bar().encode( + color="source", + x="source", + y=alt.Y("sum(seconds)", title="Total Seconds"), + tooltip=["source", "sum(seconds)"], + ) + + elif len(include_runs) == 3: + result = c.mark_bar(yOffset=-5, size=4,).transform_filter( + (alt.datum.source == relabel_source(include_runs[0])) + ).encode( + x=alt.X("seconds:Q", stack=None), + y=alt.Y("model_name", type="nominal", sort=None), + color="source", + tooltip=["source", "model_name", "seconds"], + ) + c.mark_bar( + yOffset=0, + size=4, + ).transform_filter( + (alt.datum.source == relabel_source(include_runs[1])) + ).encode( + x=alt.X("seconds:Q", stack=None), + y=alt.Y("model_name", type="nominal", sort=None), + color="source", + tooltip=["source", "model_name", "seconds"], + ) + c.mark_bar( + yOffset=5, + size=4, + ).transform_filter( + (alt.datum.source == relabel_source(include_runs[2])) + ).encode( + x=alt.X("seconds:Q", stack=None), + y=alt.Y("model_name", type="nominal", sort=None), + color="source", + tooltip=["source", "model_name", "seconds"], + ) | alt.Chart( + df1 + ).mark_bar().encode( + color="source", + x=alt.X("source", type="nominal", sort=None), + y=alt.Y("sum(seconds)", title="Total Seconds"), + tooltip=["source", "sum(seconds)"], + ) + + else: + raise ValueError(f"len(include_runs) == {len(include_runs)}") + + return result diff --git a/activitysim/workflows/steps/contrast/transform_data.py b/activitysim/workflows/steps/contrast/transform_data.py new file mode 100644 index 000000000..1b621631e --- /dev/null +++ b/activitysim/workflows/steps/contrast/transform_data.py @@ -0,0 +1,72 @@ +import logging + +import numpy as np +import pandas as pd +from pypyr.context import Context + +from ..progression import reset_progress_step +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep(updates_context=True) +def transform_data( + tablesets, + tablename, + column, + out, + qcut=None, + cut=None, + clip=None, + censor=None, + eval=None, +) -> dict: + + if qcut is None and cut is None and clip is None and censor is None: + raise ValueError("must give at least one of {cut, qcut, clip, censor}") + + reset_progress_step(description=f"attach transformed data / {tablename} <- {out}") + + if eval is not None: + for key, tableset in tablesets.items(): + for target, expr in eval.items(): + tableset[tablename][target] = tableset[tablename].eval(expr) + return dict(tablesets=tablesets) + + # collect all series into a common vector, so bins are common + pieces = {} + for key, tableset in tablesets.items(): + pieces[key] = tableset[tablename][column] + + common = pd.concat(pieces, names=["source"]) + + if clip is not None: + common = common.clip(**clip) + + if censor is not None: + common = common.where(common.between(**censor)) + + use_midpoint = False + if cut is not None: + if cut.get("labels", None) == "midpoint": + use_midpoint = True + cut.pop("labels") + common = pd.cut(common, **cut) + elif qcut is not None: + if qcut.get("labels", None) == "midpoint": + use_midpoint = True + qcut.pop("labels") + common = pd.qcut(common, **qcut) + + if use_midpoint: + common = common.apply(lambda x: x.mid) + + pieces = {k: common.loc[k] for k in tablesets.keys()} + + for key in tablesets: + tablesets[key][tablename] = tablesets[key][tablename].assign( + **{out: pieces[key]} + ) + + return dict(tablesets=tablesets) diff --git a/activitysim/workflows/steps/contrast/trip_distance.py b/activitysim/workflows/steps/contrast/trip_distance.py new file mode 100644 index 000000000..d9bb106ba --- /dev/null +++ b/activitysim/workflows/steps/contrast/trip_distance.py @@ -0,0 +1,120 @@ +import logging + +import altair as alt +import pandas as pd + +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep +def trip_distance( + tablesets, + skims, + dist_skim_name, + dist_bins=20, + grouping="primary_purpose", + title=None, + max_dist=None, + relabel_tablesets=None, + tablename="trips", +): + """ + + Parameters + ---------- + tablesets + skims + dist_skim_name + dist_bins + grouping + title + max_dist + relabel_tablesets : Mapping[str,str] + Remap the keys in `tablesets` with these values. Any + missing values are retained. This allows you to modify + the figure to e.g. change "reference" to "v1.0.4" without + editing the original input data. + + Returns + ------- + altair.Chart + """ + if relabel_tablesets is None: + relabel_tablesets = {} + + groupings = [grouping] + if not isinstance(skims, dict): + skims = {i: skims for i in tablesets.keys()} + + distances = {} + for key, tableset in tablesets.items(): + distances[key] = tableset[tablename][dist_skim_name] + + if dist_bins is not None: + result = pd.concat(distances, names=["source"]) + if max_dist is not None: + result = result[result <= max_dist] + result = pd.cut(result, dist_bins).to_frame() + distances = {k: result.loc[k] for k in tablesets.keys()} + + data = {} + for key, tableset in tablesets.items(): + data[key] = tableset[tablename].assign(**{"distance": distances[key]}) + + d = {} + for key, dat in data.items(): + df = ( + dat.groupby(groupings + ["distance"]) + .size() + .rename("n_trips") + .unstack("distance") + .fillna(0) + .stack() + .rename("n_trips") + .reset_index() + ) + df["share_trips"] = df["n_trips"] / df.groupby(groupings)["n_trips"].transform( + "sum" + ) + d[relabel_tablesets.get(key, key)] = df + + # This is sorted in reverse alphabetical order by source, so that + # the stroke width for the first line plotted is fattest, and progressively + # thinner lines are plotted over that, so all data is visible on the figure. + all_d = ( + pd.concat(d, names=["source"]) + .reset_index() + .sort_values("source", ascending=False) + ) + all_d["distance"] = all_d["distance"].apply(lambda x: x.mid) + + fig = ( + alt.Chart(all_d) + .mark_line( + interpolate="monotone", + ) + .encode( + color="source", + y=alt.Y("share_trips", axis=alt.Axis(grid=False, title="")), + x=alt.X("distance", axis=alt.Axis(grid=False, title="Distance")), + # opacity=alt.condition(selection, alt.value(1), alt.value(0.2)), + # tooltip = ['trip_mode', 'source', 'n_trips', alt.Tooltip('share_trips:Q', format='.2%')], + facet=alt.Facet(grouping, columns=3), + strokeWidth="source", + ) + .properties( + width=200, + height=120, + ) + ) + + if title: + fig = fig.properties(title=title).configure_title( + fontSize=20, + anchor="start", + color="black", + ) + + return fig diff --git a/activitysim/workflows/steps/copy_files.py b/activitysim/workflows/steps/copy_files.py new file mode 100644 index 000000000..e9ee247af --- /dev/null +++ b/activitysim/workflows/steps/copy_files.py @@ -0,0 +1,34 @@ +import glob +import os +import shutil + +from pypyr.errors import KeyNotInContextError +from pypyr.steps.fetchyaml import run_step as _fetch +from pypyr.steps.filewriteyaml import run_step as _write +from pypyr.steps.py import run_step as _run_step + +from .progression import progress, progress_overall, progress_step +from .wrapping import workstep + + +@workstep +def copy_files( + source_glob, + dest_dir, +) -> None: + """ + Copy files. + + Parameters + ---------- + source_glob : str or Sequence[str] + One or more file glob patterns to copy + dest_dir : path-like + Files that match the source_glob(s) will be copied here. + """ + os.makedirs(dest_dir, exist_ok=True) + if isinstance(source_glob, str): + source_glob = [source_glob] + for pattern in source_glob: + for filename in glob.glob(pattern): + shutil.copy2(filename, dest_dir) diff --git a/activitysim/workflows/steps/create.py b/activitysim/workflows/steps/create.py new file mode 100644 index 000000000..70857240c --- /dev/null +++ b/activitysim/workflows/steps/create.py @@ -0,0 +1,53 @@ +import os +import shlex + +from ...standalone.utils import chdir +from .progression import reset_progress_step +from .wrapping import workstep + + +@workstep +def create(example_name, destination=None) -> None: + """ + Install a functioning example from the ActivitySim resources collection. + + The behavior of this workstep differs slightly from the CLI of + `create`: on the command line, if the destination directory does not + already exist, it is created and becomes the target location to install + the example model. This can result in the example model being installed + into a (newly created) directory with any name, not necessarily the name + of the example being installed. + + This workstep function creates the destination directory if it does not + exist, but then always creates a subdirectory within the destination, named + according to the `example_name`. This ensures stability and guarantees the + resulting installed example is in the same location whether that directory + existed before or not. + + Parameters + ---------- + example_name : str + The name of the example to be installed, which should be listed in + the example manifest. + destination : path-like, optional + The directory where the example should be installed, defaulting to the + current working directory. The example is always then installed into + "destination/example_name". + + """ + reset_progress_step( + description=f"activitysim create {example_name}", prefix="[bold green]" + ) + + args = f"create -e {example_name} --link -d ." + if destination: + os.makedirs(destination, exist_ok=True) + else: + destination = "." + + # Call the run program inside this process + from activitysim.cli.main import prog + + with chdir(destination): + namespace = prog().parser.parse_args(shlex.split(args)) + namespace.afunc(namespace) diff --git a/activitysim/workflows/steps/error_handler.py b/activitysim/workflows/steps/error_handler.py new file mode 100644 index 000000000..97e85bb61 --- /dev/null +++ b/activitysim/workflows/steps/error_handler.py @@ -0,0 +1,14 @@ +import logging + + +def error_logging(func): + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except Exception as err: + logging.error(f"===== ERROR IN {func.__name__} =====") + logging.exception(f"{err}") + logging.error(f"===== / =====") + raise + + return wrapper diff --git a/activitysim/workflows/steps/install_env.py b/activitysim/workflows/steps/install_env.py new file mode 100644 index 000000000..0a30d53c8 --- /dev/null +++ b/activitysim/workflows/steps/install_env.py @@ -0,0 +1,60 @@ +import logging +import os.path +import subprocess + +from .cmd.dsl import stream_process +from .progression import reset_progress_step +from .wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep(returns_names="install_env_returncode") +def install_env( + env_prefix, + asim_version="1.0.4", + cwd=None, + label=None, +): + if os.path.exists(env_prefix): + return 0 + reset_progress_step(description=f"Creating activitysim v{asim_version} environment") + + os.makedirs(os.path.dirname(env_prefix), exist_ok=True) + command = [ + "mamba", + "create", + "--prefix", + env_prefix, + f"python=3.9", + f"activitysim={asim_version}", + "-c", + "conda-forge", + "--override-channels", + "--yes", + ] + + if label is None: + label = f"Creating {asim_version} Environment" + + logger.info(f"running command:\n{' '.join(command)}") + process = subprocess.Popen( + " ".join(command), + shell=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + cwd=cwd, + ) + stream_process(process, label) + + # don't swallow the error, because it's the Step swallow decorator + # responsibility to decide to ignore or not. + if process.returncode: + raise subprocess.CalledProcessError( + process.returncode, + process.args, + process.stdout, + process.stderr, + ) + + return process.returncode diff --git a/activitysim/workflows/steps/main.py b/activitysim/workflows/steps/main.py new file mode 100644 index 000000000..9fd7c7f5c --- /dev/null +++ b/activitysim/workflows/steps/main.py @@ -0,0 +1,103 @@ +"""Naive custom loader without any error handling.""" +import os +import signal +import sys +import traceback +from pathlib import Path + +from .progression import get_progress + + +def get_pipeline_definition(pipeline_name, parent): + """Simplified loader that gets pipeline_name.yaml in working dir.""" + import pypyr.yaml + from pypyr.loaders.file import get_pipeline_definition + + search_dir = Path(os.path.dirname(__file__)).parent + workflow_file = search_dir.joinpath(f"{pipeline_name}.yaml") + if os.path.exists(workflow_file): + with open(workflow_file) as yaml_file: + return pypyr.yaml.get_pipeline_yaml(yaml_file) + else: + + return get_pipeline_definition(pipeline_name, parent) + + +def enable_vt_support(): + # allow printing in color on windows terminal + if os.name == "nt": + import ctypes + + hOut = ctypes.windll.kernel32.GetStdHandle(-11) + out_modes = ctypes.c_uint32() + ENABLE_VT_PROCESSING = ctypes.c_uint32(0x0004) + ctypes.windll.kernel32.GetConsoleMode(hOut, ctypes.byref(out_modes)) + out_modes = ctypes.c_uint32(out_modes.value | 0x0004) + ctypes.windll.kernel32.SetConsoleMode(hOut, out_modes) + + +def main(args): + """ + Run a named workflow. + + Each workflow defines its own arguments, refer to the workflow itself to + learn what the arguments and options are. + """ + if args is None: + args = sys.argv[2:] + + if "--no-rich" in args: + args.remove("--no-rich") + os.environ["NO_RICH"] = "1" + + with get_progress(): + + try: + import pypyr.log.logger + import pypyr.pipelinerunner + import pypyr.yaml + from pypyr.cli import get_args + from pypyr.config import config + except ImportError: + raise ImportError("activitysim.workflows requires pypyr") + + parsed_args = get_args(args) + + try: + config.init() + pypyr.log.logger.set_root_logger( + log_level=parsed_args.log_level, log_path=parsed_args.log_path + ) + + pypyr.pipelinerunner.run( + pipeline_name=parsed_args.pipeline_name, + args_in=parsed_args.context_args, + parse_args=True, + groups=parsed_args.groups, + success_group=parsed_args.success_group, + failure_group=parsed_args.failure_group, + py_dir=parsed_args.py_dir, + loader="activitysim.workflows", + ) + + except KeyboardInterrupt: + # Shell standard is 128 + signum = 130 (SIGINT = 2) + sys.stdout.write("\n") + return 128 + signal.SIGINT + except Exception as e: + # stderr and exit code 255 + sys.stderr.write("\n") + sys.stderr.write(f"\033[91m{type(e).__name__}: {str(e)}\033[0;0m") + sys.stderr.write("\n") + # at this point, you're guaranteed to have args and thus log_level + if parsed_args.log_level: + if parsed_args.log_level < 10: + # traceback prints to stderr by default + traceback.print_exc() + + # if we are in the debugger, re-raise the error instead of returning + if sys.gettrace() is not None: + raise + from pypyr.errors import PipelineNotFoundError + + return 254 if isinstance(e, PipelineNotFoundError) else 255 diff --git a/activitysim/workflows/steps/make_tag.py b/activitysim/workflows/steps/make_tag.py new file mode 100644 index 000000000..59a8a5e3b --- /dev/null +++ b/activitysim/workflows/steps/make_tag.py @@ -0,0 +1,47 @@ +import time + +from .progression import reset_progress_step +from .wrapping import workstep + + +@workstep(updates_context=True) +def make_tag( + example_name, + tag=None, + compile=True, + sharrow=True, + legacy=True, + resume_after=True, + fast=True, + mp=False, + reference=None, + reference_asim_version="0.0.0", + multiprocess=0, + chunk_training_mode=None, + main_n_households=None, +): + reset_progress_step(description="Initialize Tag") + if tag is None: + tag = time.strftime("%Y-%m-%d-%H%M%S") + contrast = sharrow and legacy + + flags = [] + if resume_after: + flags.append(f" -r {resume_after}") + if fast: + flags.append("--fast") + + out = dict(tag=tag, contrast=contrast, flags=" ".join(flags)) + if isinstance(reference, str) and "." in reference: + out["reference_asim_version"] = reference + out["reference"] = True + out["relabel_tablesets"] = {"reference": f"v{reference_asim_version}"} + out["is_multiprocess"] = multiprocess > 1 + out["num_processes"] = int(multiprocess) + + if chunk_training_mode and chunk_training_mode != "disabled": + out["chunk_application_mode"] = "production" + else: + out["chunk_application_mode"] = chunk_training_mode + + return out diff --git a/activitysim/workflows/steps/memory_stress_test.py b/activitysim/workflows/steps/memory_stress_test.py new file mode 100644 index 000000000..0fc42d7c2 --- /dev/null +++ b/activitysim/workflows/steps/memory_stress_test.py @@ -0,0 +1,35 @@ +import logging +import os +import time + +import numpy as np +import psutil + +from ...core.util import si_units +from .wrapping import workstep + + +def ping_mem(pid=None): + if pid is None: + pid = os.getpid() + current_process = psutil.Process(pid) + with current_process.oneshot(): + info = current_process.memory_full_info() + uss = info.uss + rss = info.rss + + return f"USS={si_units(uss)} RSS={si_units(rss)}" + + +@workstep(updates_context=True) +def memory_stress_test(n=37): + + logging.critical(f"ping_mem = {ping_mem()}") + big = np.arange(int(2 ** float(n) / 8), dtype=np.float64) + big *= 2.0 + time.sleep(1.0) + logging.critical(f"ping_mem = {ping_mem()}") + time.sleep(5.0) + logging.critical(f"ping_mem = {ping_mem()}") + logging.critical(f"bye") + return {} diff --git a/activitysim/workflows/steps/progression.py b/activitysim/workflows/steps/progression.py new file mode 100644 index 000000000..50c128ac2 --- /dev/null +++ b/activitysim/workflows/steps/progression.py @@ -0,0 +1,104 @@ +import os +import time +from datetime import timedelta + + +class DummyProgress: + def __init__(self, *args, **kwargs): + pass + + def __enter__(self): + pass + + def __exit__(self, exc_type, exc_val, exc_tb): + pass + + def add_task(self, *args, **kwargs): + pass + + def reset(self, *args, **kwargs): + pass + + def update(self, *args, **kwargs): + pass + + +dummy_progress = DummyProgress() + +try: + + from rich.panel import Panel + from rich.progress import Progress, ProgressColumn, Text + +except ImportError: + + # allow printing in color on windows terminal + if os.name == "nt": + import ctypes + + hOut = ctypes.windll.kernel32.GetStdHandle(-11) + out_modes = ctypes.c_uint32() + ENABLE_VT_PROCESSING = ctypes.c_uint32(0x0004) + ctypes.windll.kernel32.GetConsoleMode(hOut, ctypes.byref(out_modes)) + out_modes = ctypes.c_uint32(out_modes.value | 0x0004) + ctypes.windll.kernel32.SetConsoleMode(hOut, out_modes) + + Progress = DummyProgress + progress_overall = None + progress = dummy_progress + +else: + + class MyProgress(Progress): + def get_renderables(self): + yield Panel(self.make_tasks_table(self.tasks)) + + class TimeElapsedColumn(ProgressColumn): + """Renders time elapsed.""" + + def render(self, task: "Task") -> Text: + """Show time remaining.""" + elapsed = task.finished_time if task.finished else task.elapsed + if elapsed is None: + return Text("-:--:--", style="progress.elapsed") + delta = timedelta(seconds=(elapsed)) + return Text(str(delta)[:-5], style="progress.elapsed") + + progress = MyProgress( + TimeElapsedColumn(), + "[progress.description]{task.description}", + ) + + progress_step = progress.add_task("step") + progress_overall = progress.add_task("overall") + + +def get_progress(): + if os.environ.get("NO_RICH", False): + return dummy_progress + else: + return progress + + +def update_progress_overall(description, formatting=""): + if progress_overall is not None: + if formatting: + if not formatting.startswith("[") and not formatting.endswith("]"): + formatting = f"[{formatting}]" + progress.update(progress_overall, description=f"{formatting}{description}") + else: + progress.update(progress_overall, description=f"{description}") + else: + print("╔" + "═" * (len(description) + 2) + "╗") + print(f"║ {description} ║ {time.strftime('%I:%M:%S %p')}") + print("╚" + "═" * (len(description) + 2) + "╝") + + +def reset_progress_step(*args, description="", prefix="", **kwargs): + if not os.environ.get("NO_RICH", False): + print(f"\u23F1 {time.strftime('%I:%M:%S %p')} - {description}") + progress.reset(progress_step, *args, description=prefix + description, **kwargs) + else: + print("╭" + "─" * (len(description) + 2) + "╮") + print(f"│ {description} │ {time.strftime('%I:%M:%S %p')}") + print("╰" + "─" * (len(description) + 2) + "╯") diff --git a/activitysim/workflows/steps/py.py b/activitysim/workflows/steps/py.py new file mode 100644 index 000000000..83759d61f --- /dev/null +++ b/activitysim/workflows/steps/py.py @@ -0,0 +1,27 @@ +from pypyr.errors import KeyNotInContextError +from pypyr.steps.py import run_step as _run_step + +from .progression import reset_progress_step + + +def run_step(context): + """Execute dynamic python code. + + Takes two forms of input: + py: exec contents as dynamically interpreted python statements, with + contents of context available as vars. + pycode: exec contents as dynamically interpreted python statements, + with the context object itself available as a var. + + Args: + context (pypyr.context.Context): Mandatory. + Context is a dictionary or dictionary-like. + Context must contain key 'py' or 'pycode' + """ + try: + label = context.get_formatted("label") + except KeyNotInContextError: + label = None + if label is not None: + reset_progress_step(description=label) + _run_step(context) diff --git a/activitysim/workflows/steps/pype.py b/activitysim/workflows/steps/pype.py new file mode 100644 index 000000000..ffd263919 --- /dev/null +++ b/activitysim/workflows/steps/pype.py @@ -0,0 +1,10 @@ +from pypyr.steps.pype import run_step as _run_step + + +def run_step(context): + pype = context.get("pype") + if isinstance(pype, str): + pype = {"name": pype} + pype["loader"] = "activitysim.workflows" + context["pype"] = pype + return _run_step(context) diff --git a/activitysim/workflows/steps/reporting/__init__.py b/activitysim/workflows/steps/reporting/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/activitysim/workflows/steps/reporting/init_report.py b/activitysim/workflows/steps/reporting/init_report.py new file mode 100644 index 000000000..3191b9987 --- /dev/null +++ b/activitysim/workflows/steps/reporting/init_report.py @@ -0,0 +1,16 @@ +from xmle import NumberedCaption, Reporter + +from ..progression import reset_progress_step +from ..wrapping import workstep + + +@workstep(updates_context=True) +def init_report( + title, +): + reset_progress_step(description="initialize report") + return dict( + report=Reporter(title=title), + fig=NumberedCaption("Figure", level=2, anchor=True), + tab=NumberedCaption("Table", level=2, anchor=True), + ) diff --git a/activitysim/workflows/steps/reporting/load_data_dictionary.py b/activitysim/workflows/steps/reporting/load_data_dictionary.py new file mode 100644 index 000000000..80d4aebf5 --- /dev/null +++ b/activitysim/workflows/steps/reporting/load_data_dictionary.py @@ -0,0 +1,23 @@ +import os + +import yaml + +from ..wrapping import workstep + + +@workstep("data_dictionary") +def load_data_dictionary( + config_dirs, + data_dict_filename="data_dictionary.yaml", + cwd=".", +): + if isinstance(config_dirs, str): + config_dirs = [config_dirs] + dd = {} + for config_dir in config_dirs: + if os.path.isdir(os.path.join(cwd, config_dir)): + f = os.path.join(cwd, config_dir, data_dict_filename) + if os.path.exists(f): + with open(f, "rt") as stream: + dd.update(yaml.safe_load(stream)) + return dd diff --git a/activitysim/workflows/steps/reporting/machine_info.py b/activitysim/workflows/steps/reporting/machine_info.py new file mode 100644 index 000000000..6edbdacaf --- /dev/null +++ b/activitysim/workflows/steps/reporting/machine_info.py @@ -0,0 +1,55 @@ +import multiprocessing +import platform +import subprocess + +import pandas as pd +import psutil + +from ..wrapping import workstep + + +def get_processor_info(): + out = "" + if platform.system() == "Windows": + out = platform.processor() + elif platform.system() == "Darwin": + out = subprocess.check_output( + ["/usr/sbin/sysctl", "-n", "machdep.cpu.brand_string"] + ).strip() + elif platform.system() == "Linux": + command = "cat /proc/cpuinfo" + out = subprocess.check_output(command, shell=True).strip() + if isinstance(out, bytes): + out = out.decode("utf-8") + return out + + +def get_size(bytes, suffix="B"): + """ + Scale bytes to its proper format + """ + factor = 1024 + for unit in ["", "K", "M", "G", "T"]: + if bytes < factor: + return f"{bytes:.2f}{unit}{suffix}" + bytes /= factor + unit = "P" + return f"{bytes:.2f}{unit}{suffix}" + + +@workstep +def machine_info(name=None): + uname = platform.uname() + ram = get_size(psutil.virtual_memory().total) + out = { + "Processor": get_processor_info(), + "Number of Cores": multiprocessing.cpu_count(), + "OS": uname.system, + "Release": uname.release, + "RAM": ram, + } + if name is True: + name = uname.node + if name is None: + name = "" + return pd.DataFrame(pd.Series(out).rename(name)) diff --git a/activitysim/workflows/steps/reporting/save_report.py b/activitysim/workflows/steps/reporting/save_report.py new file mode 100644 index 000000000..172208457 --- /dev/null +++ b/activitysim/workflows/steps/reporting/save_report.py @@ -0,0 +1,127 @@ +import logging +import os +import shutil + +from .... import __version__ +from ..wrapping import workstep + +# Get decorated version when in development +try: + top_package = __import__(__name__.split(".")[0]) + import setuptools_scm + + version = setuptools_scm.get_version(os.path.dirname(top_package.__path__[0])) +except: # noqa: E722 + version = __version__ + + +@workstep +def save_report( + report, + html_filename, + toc_color="forest", + copy_filename=None, +): + + bootstrap_font_family = ( + 'font-family: -apple-system, "system-ui", "Segoe UI",' + ' "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji",' + ' "Segoe UI Emoji", "Segoe UI Symbol";' + ) + toc_width = 200 + signature_font = "font-size:70%; font-weight:200;" + signature_name_font = "font-weight:400; font-style:normal;" + + css = ( + """ + html { """ + + bootstrap_font_family + + """ } + div.xmle_title { font-weight:700; font-size: 2em; color: rgb(35, 144, 98);} + body { margin-left: """ + + str(toc_width) + + """px; } + div.xmle_html_report { padding-left: 5px; } + .table_of_contents_frame { width: """ + + str(toc_width - 13) + + """px; position: fixed; margin-left: -""" + + str(toc_width) + + """px; top:0; padding-top:10px; z-index:2000;} + .table_of_contents { width: """ + + str(toc_width - 13) + + """px; position: fixed; margin-left: -""" + + str(toc_width) + + """px; font-size:85%;} + .table_of_contents_head { font-weight:700; padding-left:25px; } + .table_of_contents ul { padding-left:25px; } + .table_of_contents ul ul { font-size:75%; padding-left:15px; } + .xmle_signature {""" + + signature_font + + """ width: """ + + str(toc_width - 30) + + """px; position: fixed; left: 0px; bottom: 0px; padding-left:20px;""" + + """padding-bottom:2px; background-color:rgba(255,255,255,0.9);} + .xmle_name_signature {""" + + signature_name_font + + """} + a.parameter_reference {font-style: italic; text-decoration: none} + .strut2 {min-width:2in} + .histogram_cell { padding-top:1; padding-bottom:1; vertical-align:center; } + table { border-spacing: 0; border: none; } + .dataframe tbody tr:nth-child(odd) { + background: #f5f5f5; + } + .dataframe tr, .dataframe th, .dataframe td { + text-align: right; + vertical-align: middle; + padding: 0.2em 0.2em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; + } + .dataframe table { + margin-left: auto; + margin-right: auto; + border: none; + border-collapse: collapse; + border-spacing: 0; + color: black; + font-size: 12px; + table-layout: fixed; + } + table.floatinghead thead {background-color:#FFF;} + table.dataframe thead {background-color:#FFF;} + @media print { + body { color: #000; background: #fff; width: 100%; margin: 0; padding: 0;} + /*.table_of_contents { display: none; }*/ + @page { + margin: 1in; + } + h1, h2, h3 { page-break-after: avoid; } + img { max-width: 100% !important; } + ul, img, table { page-break-inside: avoid; } + .xmle_signature {""" + + signature_font + + """ padding:0; background-color:#fff; position: fixed; bottom: 0;} + .xmle_name_signature {""" + + signature_name_font + + """} + .xmle_signature img {display:none;} + .xmle_signature .noprint {display:none;} + } + """ + ) + + logging.critical(f"SAVING REPORT TO {(html_filename)}") + report.save( + html_filename, + overwrite=True, + css=css, + toc_font=bootstrap_font_family, + toc_color=toc_color, + branding=f"ActivitySim {version.replace('+', ' +')}", + ) + if copy_filename is not None: + os.makedirs(os.path.dirname(copy_filename), exist_ok=True) + shutil.copy2(html_filename, copy_filename) diff --git a/activitysim/workflows/steps/reporting/section_title.py b/activitysim/workflows/steps/reporting/section_title.py new file mode 100644 index 000000000..edf209907 --- /dev/null +++ b/activitysim/workflows/steps/reporting/section_title.py @@ -0,0 +1,17 @@ +import logging + +from ..wrapping import workstep + +logger = logging.getLogger(__name__) + + +@workstep(returns_names="report") +def section_title( + report, + title, + level=2, +): + with report: + t = "#" * level + report << f"{t} {title}" + return report diff --git a/activitysim/workflows/steps/reporting/settings.py b/activitysim/workflows/steps/reporting/settings.py new file mode 100644 index 000000000..f2ff77b19 --- /dev/null +++ b/activitysim/workflows/steps/reporting/settings.py @@ -0,0 +1,18 @@ +import multiprocessing +import platform +import subprocess + +import pandas as pd +import psutil + +from ..wrapping import workstep + + +@workstep +def settings(names, **kwargs): + if isinstance(names, str): + names = [names] + out = {} + for name in names: + out[name] = kwargs.get(name, None) + return pd.DataFrame(pd.Series(out).rename("")) diff --git a/activitysim/workflows/steps/run.py b/activitysim/workflows/steps/run.py new file mode 100644 index 000000000..bce4d1d30 --- /dev/null +++ b/activitysim/workflows/steps/run.py @@ -0,0 +1,79 @@ +import shlex + +from pypyr.errors import KeyNotInContextError + +from ...standalone.utils import chdir +from .progression import reset_progress_step +from .wrapping import workstep + + +def _get_formatted(context, key, default): + try: + out = context.get_formatted(key) + except KeyNotInContextError: + out = None + if out is None: + out = default + return out + + +@workstep +def run_activitysim( + label=None, + cwd=None, + pre_config_dirs=(), + config_dirs=("configs",), + data_dir="data", + output_dir="output", + ext_dirs=None, + resume_after=None, + fast=True, + settings_file=None, +) -> None: + if isinstance(pre_config_dirs, str): + pre_config_dirs = [pre_config_dirs] + else: + pre_config_dirs = list(pre_config_dirs) + if isinstance(config_dirs, str): + config_dirs = [config_dirs] + else: + config_dirs = list(config_dirs) + if isinstance(ext_dirs, str): + ext_dirs = [ext_dirs] + elif ext_dirs is None: + ext_dirs = [] + else: + ext_dirs = list(ext_dirs) + flags = [] + if resume_after: + flags.append(f" -r {resume_after}") + if fast: + flags.append("--fast") + if settings_file: + flags.append(f" -s {settings_file}") + flags = " ".join(flags) + cfgs = " ".join(f"-c {c}" for c in pre_config_dirs + config_dirs) + exts = "".join(f" -e {e}" for e in ext_dirs) + args = f"run {cfgs}{exts} -d {data_dir} -o {output_dir} {flags}" + if label is None: + label = f"activitysim {args}" + + reset_progress_step(description=f"{label}", prefix="[bold green]") + + # Clear all saved state from ORCA + import orca + + orca.clear_cache() + orca.clear_all() + + # Re-inject everything from ActivitySim + from ...core.inject import reinject_decorated_tables + + reinject_decorated_tables(steps=True) + + # Call the run program inside this process + from activitysim.cli.main import prog + + with chdir(cwd): + namespace = prog().parser.parse_args(shlex.split(args)) + namespace.afunc(namespace) diff --git a/activitysim/workflows/steps/run_subprocess.py b/activitysim/workflows/steps/run_subprocess.py new file mode 100644 index 000000000..35db46347 --- /dev/null +++ b/activitysim/workflows/steps/run_subprocess.py @@ -0,0 +1,161 @@ +import logging +import os +import subprocess +from tempfile import TemporaryFile +from time import sleep + +from pypyr.errors import KeyNotInContextError + +from .progression import reset_progress_step +from .wrapping import workstep + + +def _get_formatted(context, key, default): + try: + out = context.get_formatted(key) + except KeyNotInContextError: + out = None + if out is None: + out = default + return out + + +def _stream_subprocess(args, cwd, env): + with TemporaryFile() as outputstream: + process = subprocess.Popen( + args=args, + shell=True, + stdout=outputstream, + stderr=subprocess.STDOUT, + cwd=cwd, + env=env, + ) + while process.poll() is None: + where = outputstream.tell() + lines = outputstream.read() + if not lines: + # Adjust the sleep interval to your needs + sleep(0.25) + # make sure pointing to the last place we read + outputstream.seek(where) + else: + # Windows adds an extra carriage return and then chokes on + # it when displaying (or, as it were, not displaying) the + # output. So we give Windows a little helping hand. + print(lines.decode().replace("\r\n", "\n"), end="") + return process + + +@workstep +def run_activitysim_as_subprocess( + label=None, + cwd=None, + pre_config_dirs=(), + config_dirs=("configs",), + data_dir="data", + output_dir="output", + ext_dirs=None, + settings_file=None, + resume_after=None, + fast=True, + conda_prefix=None, + single_thread=True, + multi_thread=None, +) -> None: + if isinstance(pre_config_dirs, str): + pre_config_dirs = [pre_config_dirs] + else: + pre_config_dirs = list(pre_config_dirs) + if isinstance(config_dirs, str): + config_dirs = [config_dirs] + else: + config_dirs = list(config_dirs) + if isinstance(ext_dirs, str): + ext_dirs = [ext_dirs] + elif ext_dirs is None: + ext_dirs = [] + else: + ext_dirs = list(ext_dirs) + flags = [] + if resume_after: + flags.append(f" -r {resume_after}") + if fast: + flags.append("--fast") + if settings_file: + flags.append(f"-s {settings_file}") + flags = " ".join(flags) + cfgs = " ".join(f"-c {c}" for c in pre_config_dirs + config_dirs) + exts = "".join(f" -e {e}" for e in ext_dirs) + args = f"activitysim run {cfgs}{exts} -d {data_dir} -o {output_dir} {flags}" + if label is None: + label = f"{args}" + else: + logging.getLogger(__name__).critical(f"\n=======\nSUBPROC {args}\n=======") + + reset_progress_step(description=f"{label}", prefix="[bold green]") + + # args = shlex.split(args) + + env = os.environ.copy() + pythonpath = env.pop("PYTHONPATH", None) + + if single_thread: + env["MKL_NUM_THREADS"] = "1" + env["OMP_NUM_THREADS"] = "1" + env["OPENBLAS_NUM_THREADS"] = "1" + env["NUMBA_NUM_THREADS"] = "1" + env["VECLIB_MAXIMUM_THREADS"] = "1" + env["NUMEXPR_NUM_THREADS"] = "1" + + if multi_thread: + env["MKL_NUM_THREADS"] = str(multi_thread.get("MKL", 1)) + env["OMP_NUM_THREADS"] = str(multi_thread.get("OMP", 1)) + env["OPENBLAS_NUM_THREADS"] = str(multi_thread.get("OPENBLAS", 1)) + env["NUMBA_NUM_THREADS"] = str(multi_thread.get("NUMBA", 1)) + env["VECLIB_MAXIMUM_THREADS"] = str(multi_thread.get("VECLIB", 1)) + env["NUMEXPR_NUM_THREADS"] = str(multi_thread.get("NUMEXPR", 1)) + + # if pythonpath: + # print(f"removed PYTHONPATH from ENV: {pythonpath}") + # else: + # print(f"no removed PYTHONPATH from ENV!") + # + # for k, v in env.items(): + # print(f" - {k}: {v}") + + # if conda_prefix is not None: + # args = ["conda", "init", "bash", "&&", 'conda', 'activate', conda_prefix, '&&'] + list(args) + # args = ['conda', 'run', '-p', conda_prefix] + list(args) + + if conda_prefix: + conda_prefix_1 = os.environ.get("CONDA_PREFIX_1", None) + if conda_prefix_1 is None: + conda_prefix_1 = os.environ.get("CONDA_PREFIX", None) + if os.name == "nt": + conda_prefix_1 = conda_prefix_1.replace("\\", "/") + conda_prefix = conda_prefix.replace("\\", "/") + joiner = "; " + else: + joiner = " && " + script = [ + f"source {conda_prefix_1}/etc/profile.d/conda.sh", + f'conda activate "{conda_prefix}"', + args, + ] + args = "bash -c '" + joiner.join(script) + "'" + + process = _stream_subprocess( + args=args, + cwd=cwd, + env=env, + ) + + # don't swallow any error, because it's the Step swallow decorator + # responsibility to decide to ignore or not. + if process.returncode: + raise subprocess.CalledProcessError( + process.returncode, + process.args, + process.stdout, + process.stderr, + ) diff --git a/activitysim/workflows/steps/title.py b/activitysim/workflows/steps/title.py new file mode 100644 index 000000000..8dc7baf60 --- /dev/null +++ b/activitysim/workflows/steps/title.py @@ -0,0 +1,10 @@ +from .progression import update_progress_overall +from .wrapping import workstep + + +@workstep +def title( + label="ActivitySim Workflow", + formatting="bold blue", +) -> None: + update_progress_overall(label, formatting) diff --git a/activitysim/workflows/steps/update_yaml.py b/activitysim/workflows/steps/update_yaml.py new file mode 100644 index 000000000..079eb878f --- /dev/null +++ b/activitysim/workflows/steps/update_yaml.py @@ -0,0 +1,50 @@ +from pypyr.errors import KeyNotInContextError +from pypyr.steps.fetchyaml import run_step as _fetch +from pypyr.steps.filewriteyaml import run_step as _write +from pypyr.steps.py import run_step as _run_step + +from .progression import progress, progress_overall, progress_step + + +def run_step(context): + """Update yaml file with payload. + + If you do not set encoding, will use the system default, which is utf-8 + for everything except windows. + + Args: + context: pypyr.context.Context. Mandatory. + The following context keys expected: + - updateYaml + - path. mandatory. path-like. Read and write file + here. + - payload. Add this to output file. + - encoding. string. Defaults None (platform default, + usually 'utf-8'). + + Returns: + None. + + Raises: + pypyr.errors.KeyNotInContextError: fileWriteYaml or + fileWriteYaml['path'] missing in context. + pypyr.errors.KeyInContextHasNoValueError: fileWriteYaml or + fileWriteYaml['path'] exists but is None. + + """ + context.assert_key_has_value("updateYaml", __name__) + fetch_yaml_input = context.get_formatted("updateYaml") + context["fetchYaml"] = { + "path": fetch_yaml_input["path"], + "key": "file_payload", + "encoding": fetch_yaml_input.get("encoding", None), + } + _fetch(context) + payload = fetch_yaml_input["payload"] + context["file_payload"].update(payload) + context["fileWriteYaml"] = { + "path": fetch_yaml_input["path"], + "payload": context["file_payload"], + "encoding": fetch_yaml_input.get("encoding", None), + } + _write(context) diff --git a/activitysim/workflows/steps/wrapping.py b/activitysim/workflows/steps/wrapping.py new file mode 100644 index 000000000..ca3f48c8b --- /dev/null +++ b/activitysim/workflows/steps/wrapping.py @@ -0,0 +1,204 @@ +import importlib +import logging +from inspect import getfullargspec +from typing import Mapping + +from pypyr.context import Context + +from . import get_formatted_or_default +from .error_handler import error_logging +from .progression import reset_progress_step + +logger = logging.getLogger(__name__) + + +class MockReport: + def __enter__(self): + pass + + def __exit__(self, exc_type, exc_val, exc_tb): + if exc_type or exc_val or exc_tb: + logger.exception(repr(exc_val)) + + def __lshift__(self, other): + logger.info(str(other)) + + +class workstep: + """ + Decorator for functions that write report sections or update context. + + The decorator will generate a `run_step` function in the same module, + wrapped with additional arguments and appropriately annotated for use + with the pypyr workflow model. The original function also remains + available to import and use without changes. + + When called as a step inside a pypyr workflow, the following context + variables are potentially accessed: + + report : xmle.Reporter + The active report into which new figures or tables are added. + caption : str + A caption for the item being processed. This is used both in + writing out the output (if any) in the report and for logging + step progression during a run. + caption_type : str + The caption type (typically, 'fig' for figures or 'tab' + for tables). + progress_tag : str + Use this instead of `caption` to log step progression during a run. + + If the function returns values that should update the context, that + can be done in one of three ways: + + - Set `updates_context` to True and return a `dict`, and use that + dict to update the context directly. + - Return a single object, and set `returns_names` to a string + giving the name that object should take in the context. + - Return a sequence of objects, and set `returns_names` to a + matching sequence of names that those objects should take + in the context. + + Otherwise, the return value is appended to the report. To declare that + there is no return value and no reporting should be done, you must + explicitly annotate the function with a return value of `-> None`. + + Important: there can be only one `workstep` in + each module. If you need more than one, make another separate module. + + Parameters + ---------- + wrapped_func : Callable + returns_names : str or tuple[str], optional + updates_context : bool, default False + + Returns + ------- + wrapped_func : Callable + The original wrapped function + + """ + + def __new__(cls, wrapped_func=None, *, returns_names=None, updates_context=False): + """ + Initialize a work step wrapper. + + Parameters + ---------- + wrapped_func : Callable + The function being decorated. + """ + if isinstance(wrapped_func, (str, tuple, list)): + # the returns_names are provided instead of the wrapped func + returns_names = wrapped_func + wrapped_func = None + self = super().__new__(cls) + self._returns_names = returns_names + self._updates_context = updates_context + if wrapped_func is not None: + return self(wrapped_func) + else: + return self + + def __call__(self, wrapped_func): + returns_names = self._returns_names + updates_context = self._updates_context + ( + _args, + _varargs, + _varkw, + _defaults, + _kwonlyargs, + _kwonlydefaults, + _annotations, + ) = getfullargspec(wrapped_func) + + if isinstance(returns_names, str): + returns_names = (returns_names,) + + def run_step(context: Context = None) -> None: + + caption = get_formatted_or_default(context, "caption", None) + progress_tag = get_formatted_or_default(context, "progress_tag", caption) + if progress_tag is not None: + reset_progress_step(description=progress_tag) + + return_type = _annotations.get("return", "") + _updates_context = updates_context or return_type in {dict, Context} + if return_type not in {None, dict, Context}: + if returns_names is None and not _updates_context: + context.assert_key_has_value( + key="report", caller=wrapped_func.__module__ + ) + report = context.get("report", MockReport()) + with report: + caption_type = get_formatted_or_default(context, "caption_type", "fig") + caption_maker = get_formatted_or_default(context, caption_type, None) + # parse and run function itself + if _defaults is None: + ndefault = 0 + _required_args = _args + else: + ndefault = len(_defaults) + _required_args = _args[:-ndefault] + args = [] + for arg in _required_args: + context.assert_key_has_value( + key=arg, caller=wrapped_func.__module__ + ) + try: + args.append(context.get_formatted_or_raw(arg)) + except Exception as err: + raise ValueError(f"extracting {arg} from context") from err + if ndefault: + for arg, default in zip(_args[-ndefault:], _defaults): + args.append(get_formatted_or_default(context, arg, default)) + kwargs = {} + for karg in _kwonlyargs: + if karg in _kwonlydefaults: + kwargs[karg] = get_formatted_or_default( + context, karg, _kwonlydefaults[karg] + ) + else: + context.assert_key_has_value( + key=karg, caller=wrapped_func.__module__ + ) + try: + kwargs[karg] = context.get_formatted_or_raw(karg) + except Exception as err: + raise ValueError(f"extracting {karg} from context") from err + if _varkw: + kwargs.update(context) + for arg in _required_args: + if arg in kwargs: + kwargs.pop(arg) + outcome = error_logging(wrapped_func)(*args, **kwargs) + if returns_names: + if len(returns_names) == 1: + context[returns_names[0]] = outcome + else: + for returns_name, out in zip(returns_names, outcome): + context[returns_name] = out + elif updates_context: + if not isinstance(outcome, Mapping): + raise ValueError( + f"{wrapped_func.__name__} is marked as updates_context, " + f"it should return a mapping" + ) + context.update(outcome) + elif outcome is not None: + caption_level = get_formatted_or_default( + context, "caption_level", None + ) + if caption is not None: + report << caption_maker(caption, level=caption_level) + report << outcome + + module = importlib.import_module(wrapped_func.__module__) + if hasattr(module, "run_step"): + raise ValueError( + f"{wrapped_func.__module__}.run_step exists, there can be only one per module" + ) + setattr(module, "run_step", run_step) + + return wrapped_func diff --git a/conda-environments/activitysim-dev-base.yml b/conda-environments/activitysim-dev-base.yml new file mode 100644 index 000000000..166503e34 --- /dev/null +++ b/conda-environments/activitysim-dev-base.yml @@ -0,0 +1,72 @@ +# Environment for Advanced Development +# This is a set of recommended dependencies for ActivitySim developers. +# It includes a variety of tools and packages not necessary for actually +# running models, but useful for writing code, running tests and +# experiments, etc. +# +# This file does not install ActivitySim or Sharrow, you must do that yourself +# (so that you can install a preferred branch or fork) +# +# usage: $ mamba env create --file=activitysim-dev-base.yml -n ASIM-DEV + +channels: +- conda-forge +- nodefaults +dependencies: +- python=3.9 +- pip +- asv # for benchmarking +- black +- bump2version # for making a release +- coveralls +- cytoolz >= 0.8.1 +- dask +- descartes +- filelock +- fsspec +- geopandas +- gh +- git +- ipykernel # so this env will appear in jupyter as a selection +- isort +- jupyterlab +- larch >= 5.5.8 +- matplotlib +- myst-parser # allows markdown in sphinx +- nbconvert +- nbformat +- numba >= 0.55.2 +- numexpr +- numpy >= 1.16.1,<=1.21 +- numpydoc +- openmatrix >= 0.3.4.1 +- orca >= 1.6 +- pandas >= 1.1.0 +- pre-commit +- psutil >= 4.1 +- pyarrow >= 2.0 +- pycodestyle +- pydantic +- pydata-sphinx-theme +- pyinstrument +- pypyr +- pytables >=3.5.1,<3.7 +- pytest +- pytest-cov +- pytest-regressions +- pyyaml >= 5.1 +- requests >= 2.7 +- rich +- ruby # required for benchmarking pre-commit hooks +- setuptools_scm +- simwrapper > 1.7 +- snakeviz # for profiling +- sphinx +- sphinx_rtd_theme +- sphinx-argparse +- xarray >= 0.21 +- xmle +- zarr + +- pip: + - autodoc_pydantic diff --git a/conda-environments/activitysim-dev.yml b/conda-environments/activitysim-dev.yml index 373e03c55..a788b9e74 100644 --- a/conda-environments/activitysim-dev.yml +++ b/conda-environments/activitysim-dev.yml @@ -1,11 +1,18 @@ # Environment for development -# usage: $ conda env create --file=activitysim-dev.yml -n ASIM-DEV-TMP +# This is a set of recommended dependencies for ActivitySim developers. +# It includes a variety of tools and packages not necessary for actually +# running models, but useful for writing code, running tests and +# experiments, etc. +# +# usage: $ mamba env create --file=activitysim-dev.yml -n ASIM-SH channels: - conda-forge +- nodefaults dependencies: - python=3.9 - pip - asv # for benchmarking +- black - bump2version # for making a release - coveralls - cytoolz >= 0.8.1 @@ -17,15 +24,16 @@ dependencies: - gh - git - ipykernel # so this env will appear in jupyter as a selection +- isort - jupyterlab - larch >= 5.5.8 - matplotlib - myst-parser # allows markdown in sphinx - nbconvert - nbformat -- numba >= 0.51.2 +- numba >= 0.56.4 - numexpr -- numpy >= 1.16.1,<=1.21 +- numpy >= 1.16.1 - numpydoc - openmatrix >= 0.3.4.1 - orca >= 1.6 @@ -34,6 +42,7 @@ dependencies: - psutil >= 4.1 - pyarrow >= 2.0 - pycodestyle +- pydantic - pydata-sphinx-theme - pyinstrument - pypyr @@ -45,6 +54,8 @@ dependencies: - requests >= 2.7 - rich - ruby # required for benchmarking pre-commit hooks +- setuptools_scm +- sharrow >= 2.5.2 - simwrapper > 1.7 - snakeviz # for profiling - sphinx @@ -55,4 +66,5 @@ dependencies: - zarr - pip: + - autodoc_pydantic - -e .. diff --git a/conda-environments/activitysim-test-larch.yml b/conda-environments/activitysim-test-larch.yml deleted file mode 100644 index 5f4b1f687..000000000 --- a/conda-environments/activitysim-test-larch.yml +++ /dev/null @@ -1,32 +0,0 @@ -# Environment for testing estimation in travis -# usage: $ conda env create --file=activitysim-test.yml -name: asimtest -channels: -- conda-forge -dependencies: -- python=${TRAVIS_PYTHON_VERSION} -- pip -- black -- coveralls -- cytoolz >= 0.8.1 -- isort -- larch >=5.5.3 -- nbmake -- numba >= 0.55.2 -- numpy >= 1.16.1,<=1.21 -- openmatrix >= 0.3.4.1 -- orca >= 1.6 -- pandas >= 1.1.0 -- psutil >= 4.1 -- pyarrow >= 2.0 -- pypyr >= 5.3 -- pytables >= 3.5.1,<3.7 # orca's constraint -- pytest -- pytest-cov -- pytest-regressions -- pyyaml >= 5.1 -- requests >= 2.7 -- sharrow >= 2.2.4 -- simwrapper > 1.7 -- xarray >= 0.21 -- zarr diff --git a/conda-environments/activitysim-test.yml b/conda-environments/activitysim-test.yml deleted file mode 100644 index bcd8e7d40..000000000 --- a/conda-environments/activitysim-test.yml +++ /dev/null @@ -1,30 +0,0 @@ -# Environment for testing in travis -# usage: $ conda env create --file=activitysim-test.yml -name: asimtest -channels: -- conda-forge -dependencies: -- python=${TRAVIS_PYTHON_VERSION} -- black -- coveralls -- cytoolz >= 0.8.1 -- isort -- nbmake -- numba >= 0.55.2 -- numpy >= 1.16.1,<=1.21 -- openmatrix >= 0.3.4.1 -- orca >= 1.6 -- pandas >= 1.1.0 -- psutil >= 4.1 -- pyarrow >= 2.0 -- pypyr >= 5.3 -- pytables >= 3.5.1,<3.7 # orca's constraint -- pytest -- pytest-cov -- pytest-regressions -- pyyaml >= 5.1 -- requests >= 2.7 -- sharrow >= 2.2 -- simwrapper > 1.7 -- xarray >= 0.21 -- zarr diff --git a/conda-environments/docbuild.yml b/conda-environments/docbuild.yml index c598d8606..b6d1ebf5c 100644 --- a/conda-environments/docbuild.yml +++ b/conda-environments/docbuild.yml @@ -25,8 +25,8 @@ dependencies: - larch >=5.5.3 - matplotlib - myst-parser -- numba >= 0.51.2 -- numpy >= 1.16.1,<=1.21 +- numba >= 0.56.4 +- numpy >= 1.16.1 - numpydoc - openmatrix >= 0.3.4.1 - orca >= 1.6 @@ -41,7 +41,7 @@ dependencies: - pytest-regressions - pyyaml >= 5.1 - requests >= 2.7 -- sharrow >= 2.2.4 +- sharrow >= 2.5.2 - simwrapper > 1.7 - sphinx-argparse - sphinx_rtd_theme @@ -50,3 +50,4 @@ dependencies: - pip: - autodoc_pydantic + - -e .. diff --git a/conda-environments/github-actions-tests.yml b/conda-environments/github-actions-tests.yml index 01108bdc4..4e5dce407 100644 --- a/conda-environments/github-actions-tests.yml +++ b/conda-environments/github-actions-tests.yml @@ -1,4 +1,7 @@ # Environment for testing in GitHub Actions +# This environment contains a minimal set of dependencies needed to run most tests. +# It does not install ActivitySim itself (which is done by the test scripts) and +# is not meant for use outside the CI tools. name: asim-test channels: - conda-forge @@ -9,8 +12,8 @@ dependencies: - cytoolz >= 0.8.1 - isort - nbmake -- numba >= 0.55.2 -- numpy >= 1.16.1,<=1.21 +- numba >= 0.56.4 +- numpy >= 1.16.1 - openmatrix >= 0.3.4.1 - orca >= 1.6 - pandas >= 1.1.0 @@ -23,7 +26,8 @@ dependencies: - pytest-regressions - pyyaml >= 5.1 - requests >= 2.7 -- sharrow >= 2.2.4 +- sharrow >= 2.5.2 - simwrapper > 1.7 +- scikit-learn >= 1.1.1 - xarray >= 0.21 - zarr diff --git a/docs/_templates/autopydantic.rst b/docs/_templates/autopydantic.rst new file mode 100644 index 000000000..5536fa108 --- /dev/null +++ b/docs/_templates/autopydantic.rst @@ -0,0 +1,5 @@ +{{ name | escape | underline}} + +.. currentmodule:: {{ module }} + +.. auto{{ objtype }}:: {{ objname }} diff --git a/docs/core.rst b/docs/core.rst index d2af1c54a..c8438e3fe 100644 --- a/docs/core.rst +++ b/docs/core.rst @@ -514,7 +514,7 @@ Table columns can also be aggregated, or "remapped," during the preprocessing st - column: major_trip_mode # Column on which to operate label: major_trip_mode # New column to make map: - DRIVEALONEFREE: SOV # Keys: Existing values to map from + DRIVEALONEFREE: SOV # Keys: Existing values to map from DRIVEALONEPAY: SOV # Values: New values to map to SHARED2FREE: HOV SHARED2PAY: HOV @@ -557,7 +557,7 @@ The latest information about the Simwrapper package is available on its `PyPI pa To run SimWrapper, navigate on the command line to ``output\summarize`` within the model directory, or a directory where you may have copied outputs, and run: :: - > simwrapper open asim + > simwrapper open asim This will start SimWrapper in your default browser. If this directory contains the appropriate configuration files for a dashboard (see above), the dashboard will open automatically. Otherwise, SimWrapper will show a file browser with the contents of the directory. @@ -616,7 +616,7 @@ Chunking management. of chunk_size simplifies model setup since it is the approximate amount of RAM available to ActivitySim as opposed to the obscure number of doubles (64-bit numbers) in a chunk of a choosers table. -The ``chunk_size`` is the approximate amount of RAM in GBs to allocate to ActivitySim for batch +The ``chunk_size`` is the approximate amount of RAM in bytes (1 Gigabyte or 1 GB is equal to 1,000,000,000 bytes) to allocate to ActivitySim for batch processing choosers across all processes. It is specified in bytes, for example ``chunk_size: 500_000_000_000`` is 500 GBs. If set ``chunk_training_mode: disabled`` then no chunking will be performed and ActivitySim will attempt to solve all the choosers at once across all the processes. Chunking is required when all the chooser data required to process all the diff --git a/docs/dev-guide/components/index.rst b/docs/dev-guide/components/index.rst new file mode 100644 index 000000000..e4ffabfb0 --- /dev/null +++ b/docs/dev-guide/components/index.rst @@ -0,0 +1,15 @@ +========== +Components +========== + +.. toctree:: + :maxdepth: 1 + + trip_destination + + + +.. note:: + + The documentation for individual components is being migrated to this + section. See :ref:`models` for other components. diff --git a/docs/dev-guide/components/trip_destination.md b/docs/dev-guide/components/trip_destination.md new file mode 100644 index 000000000..b6be0398d --- /dev/null +++ b/docs/dev-guide/components/trip_destination.md @@ -0,0 +1,133 @@ +(component-trip-destination)= +# Trip Destination + +```{eval-rst} +.. currentmodule:: activitysim.abm.models.trip_destination +``` + +The trip location choice model predicts the location of trips (or stops) made +along the tour other than the primary destination. Final trips already have a +destination (the primary tour destination for outbound trips, and home for +inbound trips) so no choice is needed in those cases. + +## Structure + +- *Configuration File*: `trip_destination.yaml` +- *Core Table*: `trips` +- *Result Field*: `destination` + +This model is structured as +a multinomial logit model using a zone attraction size variable +and route deviation measure as impedance. The alternatives are sampled +from the full set of zones, subject to availability of a zonal attraction size +term. The sampling mechanism is also based on accessibility between tour origin +and primary destination, and is subject to certain rules based on tour mode. + +All destinations are available for auto tour modes, so long as there is a positive +size term for the zone. Intermediate stops on walk tours must be within X miles of both the tour +origin and primary destination zones. Intermediate stops on bike tours must be within X miles of both +the tour origin and primary destination zones. Intermediate stops on walk-transit tours must either be +within X miles walking distance of both the tour origin and primary destination, or have transit access to +both the tour origin and primary destination. Additionally, only short and long walk zones are +available destinations on walk-transit tours. + +The intermediate stop location choice model works by cycling through stops on tours. The level-of-service +variables (including mode choice logsums) are calculated as the additional utility between the +last location and the next known location on the tour. For example, the LOS variable for the first stop +on the outbound direction of the tour is based on additional impedance between the tour origin and the +tour primary destination. The LOS variable for the next outbound stop is based on the additional +impedance between the previous stop and the tour primary destination. Stops on return tour legs work +similarly, except that the location of the first stop is a function of the additional impedance between the +tour primary destination and the tour origin. The next stop location is based on the additional +impedance between the first stop on the return leg and the tour origin, and so on. + +Trip location choice for [two- and three-zone](multiple_zone_systems) models +uses [presampling](presampling) by default. + +The main interface to the trip destination choice model is the +[trip_destination](activitysim.abm.models.trip_destination.trip_destination) function. +This function is registered as an Inject step in the example Pipeline. +See [writing_logsums](writing_logsums) for how to write logsums for estimation. + +```{note} +Trip purpose and trip destination choice can be run iteratively together via +[trip_purpose_and_destination](trip_purpose_and_destination_model). +``` + +## Configuration + +```{eval-rst} +.. autopydantic_model:: TripDestinationSettings +``` + +### Examples + +- [Prototype MTC](https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/prototype_mtc/configs/trip_destination.yaml) +- [Prototype ARC](https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/prototype_arc/configs/trip_destination.yaml) + +## Implementation + +```{eval-rst} +.. autofunction:: trip_destination +``` + +## Context Variables + +The following variables are made available for use in this component's utility +specifications: + +- `network_los` ([Network_LOS](activitysim.core.los.Network_LOS)): A reference + to the main network_los object for this model. +- `size_terms` ([DataFrameMatrix](activitysim.core.skim_dictionary.DataFrameMatrix)): + This DataFrameMatrix offers a specialized two-dimension lookup wrapper + on an array, to access the size terms by alternative zone and trip + purpose by label simultaneously. When sharrow is enabled for this model, + this value also embeds a special linked array accessible as + `size_terms['sizearray']`, which automatically compiles to point to the + alternative destination zone and the 'purpose_index_num' (an integer + offset value instead of a label, which should be set in a preprocessor). +- `size_terms_array` (numpy.ndarray): A numpy array of size term values, + with rows for zones and columns for trip purposes. This is just the + raw data underneath the `size_terms` value above. +- `timeframe` (str): Contains the constant value "trip". +- `odt_skims` ([Skim3dWrapper](activitysim.core.skim_dictionary.Skim3dWrapper) + or [DatasetWrapper](activitysim.core.skim_dataset.DatasetWrapper)): Skims + giving the LOS characteristics from the fixed trip origin to each + alternative destination, by time period. +- `dot_skims` ([Skim3dWrapper](activitysim.core.skim_dictionary.Skim3dWrapper) + or [DatasetWrapper](activitysim.core.skim_dataset.DatasetWrapper)): Skims giving the LOS + characteristics from each alternative destination backwards to the + fixed trip origin, by time period. +- `dpt_skims` ([Skim3dWrapper](activitysim.core.skim_dictionary.Skim3dWrapper) + or [DatasetWrapper](activitysim.core.skim_dataset.DatasetWrapper)): Skims giving the LOS + characteristics from each alternative destination to the tour final + destination, by time period. +- `pdt_skims` ([Skim3dWrapper](activitysim.core.skim_dictionary.Skim3dWrapper) + or [DatasetWrapper](activitysim.core.skim_dataset.DatasetWrapper)): Skims giving the LOS + characteristics backwards to each alternative destination from the tour + final destination, by time period. +- `od_skims` ([SkimWrapper](activitysim.core.skim_dictionary.SkimWrapper) + or [DatasetWrapper](activitysim.core.skim_dataset.DatasetWrapper)): Skims giving the LOS + characteristics from the fixed trip origin to each alternative + destination. +- `dpt_skims` ([SkimWrapper](activitysim.core.skim_dictionary.SkimWrapper) + or [DatasetWrapper](activitysim.core.skim_dataset.DatasetWrapper)): Skims giving the LOS + characteristics from each alternative destination to the tour final + destination. + +The following TransitVirtualPathLogsumWrapper values are also available, +only for 3-zone models: + +- `tvpb_logsum_odt` +- `tvpb_logsum_dot` +- `tvpb_logsum_dpt` +- `tvpb_logsum_pdt` + +## Additional Related Functions + +```{eval-rst} +.. autofunction:: trip_destination_sample +.. autofunction:: compute_logsums +.. autofunction:: compute_ood_logsums +.. autofunction:: trip_destination_simulate +``` diff --git a/docs/dev-guide/index.rst b/docs/dev-guide/index.rst index e18240afa..2ec96f83e 100644 --- a/docs/dev-guide/index.rst +++ b/docs/dev-guide/index.rst @@ -23,7 +23,11 @@ Contents :maxdepth: 3 install + using-sharrow + skim-dataset + workflows ../development ../models + components/index ../core ../benchmarking diff --git a/docs/dev-guide/install.md b/docs/dev-guide/install.md index 755a6dd29..b7b6030a0 100644 --- a/docs/dev-guide/install.md +++ b/docs/dev-guide/install.md @@ -8,7 +8,7 @@ testing. ## Package Manager ActivitySim has a lot of dependencies. It's easiest and fastest to install -them using a package manager like conda. There's a faster version called +them using a package manager like conda, or its faster and free sibling [Mambaforge](https://github.com/conda-forge/miniforge#mambaforge). Depending on your security settings, you might need to install in a container like docker, instructions for that are coming soon. @@ -44,23 +44,35 @@ want to install in a new directory called "workspace" run: ```sh mkdir workspace cd workspace -mamba env create -p ASIM-ENV --file https://raw.githubusercontent.com/camsys/activitysim/sharrow-black/conda-environments/activitysim-dev-base.yml +mamba env create -p ASIM-ENV --file https://raw.githubusercontent.com/ActivitySim/activitysim/main/conda-environments/activitysim-dev-base.yml conda activate ./ASIM-ENV git clone https://github.com/ActivitySim/sharrow.git python -m pip install -e ./sharrow -git clone https://github.com/camsys/activitysim.git +git clone https://github.com/ActivitySim/activitysim.git cd activitysim -git switch sharrow-black +git switch develop cd .. python -m pip install -e ./activitysim ``` +```{note} +If the environment create step above fails due to a 404 missing error, +the main repository may not be up to date with these docs, try this instead: +https://raw.githubusercontent.com/camsys/activitysim/sharrow-black/conda-environments/activitysim-dev-base.yml +``` + Note the above commands will create an environment with all the necessary dependencies, clone both ActivitySim and sharrow from GitHub, and `pip install` each of these libraries in editable mode, which will allow your code changes to be reflected when running ActivitySim in this environment. +Depending on what you are working on, you may want to check out a branch +other than `develop`. To do so, you can point the `git switch` command +above to any other existing branch name. If you want to start an new +branch, first create it with `git branch cool-new-feature` and then switch +to it with `git switch cool-new-feature`. + Now your environment should be ready to use. Happy coding! ```{important} diff --git a/docs/dev-guide/skim-dataset.md b/docs/dev-guide/skim-dataset.md new file mode 100644 index 000000000..39fdce353 --- /dev/null +++ b/docs/dev-guide/skim-dataset.md @@ -0,0 +1,38 @@ +(skim-datasets)= +# Using Skim Dataset + +ActivitySim 1.2 offers two internal frameworks for managing skim data. + +- [`SkimDict`](activitysim.core.skim_dictionary.SkimDict), the legacy + framework, that stores all skim data in one large omnibus array, with + various offset lookups and tools to access values. +- [`SkimDataset`](activitysim.core.skim_dataset.SkimDataset), an + [xarray.Dataset]() based framework, which mimics the + [`SkimDict`](activitysim.core.skim_dictionary.SkimDict) interface, and + adds a number of features optimized specifically for use with `sharrow`. + This framework is automatically used when sharrow is enabled, and there + is no user configuration to enable it separately. + +## Skims in Shared Memory + +These two internal frameworks manage shared memory differently when running +ActivitySim in multiprocessing mode. + +For [`SkimDict`](activitysim.core.skim_dictionary.SkimDict), shared memory is +used only when running with multiprocessing active, and is allocated via the +[`allocate_shared_skim_buffers`](activitysim.core.mp_tasks.allocate_shared_skim_buffers) +function, which in turn invokes +[Network_LOS.allocate_shared_skim_buffers](activitysim.core.los.Network_LOS.allocate_shared_skim_buffers). + +For [`SkimDataset`](activitysim.core.skim_dataset.SkimDataset), shared memory is +used regardless of whether multiprocessing is active or not. The shared memory +allocation is done via the `Dataset.shm.to_shared_memory` method called at the end +of the [`load_skim_dataset_to_shared_memory`](activitysim.core.skim_dataset.load_skim_dataset_to_shared_memory) +function. + +## Skim Dataset API + +```{eval-rst} +.. automodule:: activitysim.core.skim_dataset + :members: +``` diff --git a/docs/dev-guide/using-sharrow.md b/docs/dev-guide/using-sharrow.md new file mode 100644 index 000000000..4e7cb9083 --- /dev/null +++ b/docs/dev-guide/using-sharrow.md @@ -0,0 +1,390 @@ + +# Using Sharrow + +This page will walk through an exercise of running a model with `sharrow`. + +## How it Works + +Sharrow accelerates ActivitySim in part by using numba to create optimized and +pre-compiled versions of utility specification files, and caching those bits +of code to disk. + +```{important} +Running the compiler needs to be done in single-process mode, otherwise the +various process all do the compiling and compete to write to the same cache +location on disk, which is likely to fail. You can safely run in +multiprocessing mode after all the compilation for all model components is +complete. +``` + +## Model Design Requirements + +Activating the `sharrow` optimizations also requires using the new +[`SkimDataset`](skim-datasets) interface for skims instead of the legacy +[`SkimDict`](activitysim.core.skim_dictionary.SkimDict), and internally +recoding zones into a zero-based contiguous indexing scheme. + +### Zero-based Recoding of Zones + +Using sharrow requires recoding zone id's to be zero-based contiguous index +values, at least for internal usage. This recoding needs to be written into +the input table list explicitly. For example, the following snippet of a +`settings.yaml` settings file shows the process of recoding zone ids in +the input files. + +```yaml +input_table_list: + - tablename: land_use + filename: land_use.csv + index_col: zone_id + recode_columns: + zone_id: zero-based + - tablename: households + filename: households.csv + index_col: household_id + recode_columns: + home_zone_id: land_use.zone_id +``` + +For the `land_use` table, the `zone_id` field is marked for recoding explicitly +as `zero-based`, which will turn whatever nominal id's appear in that column into +zero-based index values, as well as store a mapping of the original values that +is used to recode and decode zone id's when used elsewhere. + +The first "elsewhere" recoding is in the households input table, where we will +map the `home_zone_id` to the new zone id's by pointing the recoding instruction +at the `land_use.zone_id` field. If zone id's appear in other input files, they +need to be recoded in those fields as well, using the same process. + +The other places where we need to handle zone id's is in output files. The +following snippet of a `settings.yaml` settings file shows how those id's are +decoded in various output files. Generally, for columns that are fully populated +with zone id's (e.g. tour and trip ends) we can apply the `decode_columns` settings +to reverse the mapping and restore the nominal zone id's globally for the entire +column of data. For columns where there is some missing data flagged by negative +values, the "nonnegative" filter is prepended to the instruction. + +```yaml +output_tables: + action: include + tables: + - tablename: land_use + decode_columns: + zone_id: land_use.zone_id + - tablename: households + decode_columns: + home_zone_id: land_use.zone_id + - tablename: persons + decode_columns: + home_zone_id: land_use.zone_id + school_zone_id: nonnegative | land_use.zone_id + workplace_zone_id: nonnegative | land_use.zone_id + - tablename: tours + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id +``` + +## Measuring Performance + +Testing with sharrow requires two steps: test mode and production mode. + +In test mode, the code is run to compile all the spec files and +ascertain whether the functions are working correctly. Test mode is expected +to be slow, potentially much slower than older versions of ActivitySim, +especially for models with small populations and zone systems, as the compile +time is a function of the complexity of the utility functions and *not* a +function of the number of households or zones. Once the compile and test is +complete, production mode can then just run the pre-compiled functions with +sharrow, which is much faster. + +It is possible to run test mode and production mode independently using the +existing `activitysim run` command line tool, pointing that tool to the test +and production configurations directories as appropriate. + +To generate a meaningful measure of performance enhancement, it is necessary +to compare the runtimes in production mode against equivalent runtimes with +sharrow disabled. This is facilitated by the `activitysim workflow` command +line tool, which permits the use of pre-made batches of activitysim runs, as +well as automatic report generation from the results. For more details on the +use of this tool, see [workflows](workflows). + + +## Sharrow Compatability and Limitations + +In general, utility specification files contain a sequence of :ref:`expressions` that +are evaluated for each row in a main DataFrame. In legacy ActivitySim, +there are two fundamental evaluation modes: + +- `pandas.DataFrame.eval`, which is the default, and +- plain Python `eval`, which is used when the expression is prefixed with an `@` symbol. + +Under the `pandas.DataFrame.eval` mode, expressions are evaluated within the context +of the current main dataframe only. References can be made to other columns +in that dataframe directly, i.e. if the dataframe contains a column named "income", +the expression can reference that value as "income". However, the expression can +*only* reference other values that are stored in the current row of the dataframe. +The available syntax for these expressions is a subset of regular python, see the +[supported syntax](https://pandas.pydata.org/docs/user_guide/enhancingperf.html#supported-syntax) +section of the pandas documentation for details. + +Under the plain `eval` mode, expressions are evaluated within a broader context +that potentially includes other variables (which other variables is component +dependent) and constants defined in the model settings file. References to +columns in the main dataframe must be made indirectly via attribution, i.e. if +the dataframe contains a column named "income", the expression needs to reference +that value as "df.income". Typically, references to skims, time tables, and anything +else that isn't the simple original dataframe use this mode. While it is sometimes +not as fast as `pandas.DataFrame.eval`, this mode is far more flexible, as you +can write basically any valid Python expression, including calling other functions, +accessing or manipulating table metadata, indexes, or adjacent rows, etc. + +Within sharrow, the distinction between these two modes is ignored, as sharrow +uses a completely different evaluation system routed through numba. The `@` prefix +does not need to be stripped or added anywhere, it is simply ignored. The expression +can reference other columns of the main dataframe directly or indirectly, so that +either "income" or "df.income" is a valid reference to that column in the main +dataframe. You can also reference other variables, including skims as usual for each component +and constants defined in in the model settings file. Within scheduling models, +references can also be made to the timetable as "tt", accessing that interfaces +methods to compute presence and length of adjacent and available time windows. +However, you *cannot* write arbitrary Python code. External functions are not allowed unless they have +already been compiled with numba's `njit` wrapper. Typically, unless specifically +constructed to be allowed for a model component, cross-referencing, merging or +reindexing against tables other than the main dataframe is not allowed. Such +customizations will generally require a custom extension. + +Sharrow only runs for utility specification files. ActivitySim also features the +ability to apply pre-processors and post-processors to annotate tables, which use +specification files that look very similar to utility specifications. These +functions do *not* run through sharrow, and are a great place to relocate expressions +that need to run arbitrary code or join data from other tables. + +### Temporary Variables + +Temporary variables can be created from `@` mode expressions by adding a variable +name beginning with an underscore before the `@`, e.g. `_stops_on_leg@df.trip_count-1`. + +In legacy mode, temporary variables are useful but they can consume substantial +memory, as the variable is computed and stored for every row in the entire dataframe +before it can be used in other expressions. In sharrow, temporary variables are +allocated, used, and freed for each row separately, so no extra memory is required. + +### Switchable Expressions + +As a general rule, it is best to write each utility expression in a manner that +is cross-compatible between sharrow and legacy evaluation modes, even if that +means transitioning a few expressions or parts of expressions into preprocessors. + +However, sometimes this is not possible or makes writing the expressions excessively +complex. In this case, it is possible to write a toggling expression, where the +individual expression evaluated is different for sharrow and legacy modes. The +special comment string `# sharrow:` splits the expression, with everything before this +comment evaluated under the legacy process, and everything after evaluated only +when sharrow is enabled. + +An example of a switching expression is found in the trip destination utilities found +in several examples: + + `@np.log1p(size_terms.get(df.alt_dest, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])` + +Here, `size_terms` is a DataFrameMatrix class instance, a special class written into +ActivitySim to facilitate reading from a DataFrame as it it were a 2-d array. As it +is a special purpose class written in Python, the numba compiler cannot handle it directly. +Fortunately, sharrow provides an alternative: passing the size terms as a xarray `DataArray`. +This has a slightly different interface, so the sharrow and legacy evaluation modes require +different expressions. The switching expression is used to handle the DataFrameMatrix +on the left (before "# sharrow:") and the DataArray on the right. + +### Performance Considerations + +Sharrow is usually expected to bring significant performance gains to ActivitySim. +However, the numba engine beneath sharrow has a few known performance limitations, +most notably when using [strings](https://numba.readthedocs.io/en/stable/reference/pysupported.html#str). +To enhance performance, it is best to limit the number of string operations, +including simple equality checks (e.g. `df.tour_purpose == 'work'`). Ideally, +such string operations won't appear in utility specifications at all, or if they +do appear, they are executed only once and stored in a temporary value for re-use +as needed. + +For models with utility expressions that include a lot of string comparisons, +(e.g. because they are built for the legacy `pandas.eval` interpreter and have not +been updated) sharrow can be disabled by setting `sharrow_skip: true` in the +component's configuration yaml file. + +### Limited Tracing and Estimation Mode Capabilities + +When running sharrow-optimized code, large parts of certain calculations are routed +through numba to enhance speed and reduce memory footprint. Many intermediate arrays +are not created, or are allocated and released by numba in a manner not visible to the +general Python interpreter. As a result, in many places the "trace" capabilities of +ActivitySim are limited. These capabilities output intermediate calculations in +extreme detail and are typically only used in debugging and model development, not +in production model runs that need to be optimized for speed. Tracing features can +be re-enabled by switching sharrow off or by using "test" mode, which runs both sharrow +and legacy evaluations together to confirm they are substantially identical. In this +fashion, detailed tracing (albeit slow) remains available for users on all models when +needed. + +Similar constraints apply to estimation mode, as complete estimation mode output +capabilities are not yet integrated with the sharrow engine. Estimation mode remains +fully available when running with sharrow disabled. + +(digital-encoding)= +## Digital Encoding + +Sharrow is compatible with and able to efficiently use +[digital encoding](https://activitysim.github.io/sharrow/walkthrough/encoding.html). +These encodings are applied to data either prospectively (i.e. before ActivitySim +ever sees the skim data), or dynamically within a run using the +`taz_skims.digital-encoding` or `taz_skims.zarr-digital-encoding` settings in +the `network_los.yaml` settings file. The only difference between these two +settings is that the former applies this digital encoding internally every +time you run the model, while the latter applies it prior to caching encoded +skims to disk in Zarr format (and then reuses those values without re-encoding +on subsequent runs with the same data). Dictionary encoding (especially joint +dictionary encodings) can take a long time to prepare, so caching the values can +be useful. As read/write speeds for zarr files are fast, you usually won't +notice a meaningful performance degradation when caching, so the default is +generally to use `zarr-digital-encoding`. + +Very often, data can be expressed adequately with far less memory than is +needed to store a standard 32-bit floating point representation. There are +two simple ways to reduce the memory footprint for data: fixed point +encoding, or dictionary encoding. + +### Fixed Point Encoding + +In fixed point encoding, which is also sometimes called scaled integers, +data is multiplied by some factor, then rounded to the nearest integer. +The integer is stored in memory instead of a floating point value, but the +original value can be (approximately) restored by reversing the process. +An offset factor can also be applied, so that the domain of values does not +need to start at zero. + +For example, instead of storing matrix table values as 32-bit floating point values, +they could be multiplied by a scale factor (e.g., 100) +and then converted to 16-bit integers. This uses half the +RAM and can still express any value (to two decimal point +precision) up to positive or negative 327.68. If the lowest +values in that range are never needed, it can also be shifted, +moving both the bottom and top limits by a fixed amount. Then, +for a particular scale $\mu$ and shift $\xi$ (stored in metadata), +from any array element $i$ the implied (original) value $x$ +can quickly be recovered by evaluating $(i / \mu) - \xi$. + +Fixed point digital encoding can be applied to matrix tables in the skims +using options in the `network_los.yaml` settings file. Making transformations +currently also requires shifting the data from OMX to ZARR file formats; +future versions of ActivitySim may accept digitally encoded data directly +from external sources. + +To apply the default 16-bit encoding to individual named skim variables in the +TAZ skims, just give their names under the `zarr-digital-encoding` setting +like this: + +```yaml +taz_skims: + omx: skims.omx + zarr: skims.zarr + zarr-digital-encoding: + - name: SOV_TIME + - name: HOV2_TIME +``` + +If some variables can use less RAM and still be represented adequately with only +8-bit integers, you can specify the bitwidth as well: + +```yaml +taz_skims: + omx: skims.omx + zarr: skims.zarr + zarr-digital-encoding: + - name: SOV_TIME + - name: HOV2_TIME + - name: SOV_TOLL + bitwidth: 8 + - name: HOV2_TOLL + bitwidth: 8 +``` + +If groups of similarly named variables should have the same encoding applied, +they can be identifed by regular expressions ("regex") instead of explicitly +giving each name. For example: + +```yaml +taz_skims: + omx: skims.omx + zarr: skims.zarr + zarr-digital-encoding: + - regex: .*_TIME + - regex: .*_TOLL + bitwidth: 8 +``` + + +### Dictionary Encoding + +For dictionary encoding, a limited number of unique values are stored in a +lookup array, and then each encoded value is stored as the position of the +value (or its closest approximation) in the lookup array. If there are fewer +than 256 unique values, this can allow the storage of those values to any level +of precision (even float64 if needed) while using only a single byte per array +element, plus a small fixed amount of overhead for the dictionary itself. The +overhead memory doesn't scale with the dimensions of the array, so this works +particularly well for models with thousands of zones. + +Dictionary encoding can be applied to a single variable in a similar fashion as +fixed point encoding, giving the dictionary bit width in the `by_dict` setting, +or as an additional setting value. + +```yaml +taz_skims: + omx: skims.omx + zarr: skims.zarr + zarr-digital-encoding: + - name: TRANSIT_FARE + by_dict: 8 + - name: TRANSIT_XFERS + by_dict: true + bitwidth: 8 +``` + +The most dramatic memory savings can be found when the categorical correlation +(also known as [Cramér's V](https://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V)) +between multiple variables is high. In this case, we can encode more than one +matrix table using the same dictionary lookup indexes. There may be some +duplication in the lookup table, (e.g. if FARE and XFER are joint encoded, +and if a FARE of 2.25 can be matched with either 0 or 1 XFER, the 2.25 would +appear twice in the lookup array for FARE, once for each value of XFER.) + +Since it is the lookup *indexes* that scale with the number of zones and consume most +of the memory for large zone systems, putting multiple variables together into one +set of indexes can save a ton of memory, so long as the overhead of the lookup array +does not combinatorially explode (hence the need for categorical correlation). + +Practical testing for large zone systems suggest this method of encoding can +reduce the footprint of some low variance data tables (especially transit data) +by 95% or more. + +Applying joint dictionary encoding requires more than one variable name, so only +the `regex` form works here. Use wildcards to match on name patterns, or select a +few specific names by joining them with the pipe operator (|). + +```yaml +taz_skims: + omx: skims.omx + zarr: skims.zarr + zarr-digital-encoding: + - regex: .*_FARE|.*_WAIT|.*_XFERS + joint_dict: true + - regex: FERRYTIME|FERRYFARE|FERRYWAIT + joint_dict: true +``` + +For more details on all the settings available for digital encoding, see +[DigitalEncoding](activitysim.core.configuration.network.DigitalEncoding). diff --git a/docs/dev-guide/workflows.md b/docs/dev-guide/workflows.md new file mode 100644 index 000000000..d9bc2e2d3 --- /dev/null +++ b/docs/dev-guide/workflows.md @@ -0,0 +1,70 @@ +(workflows)= +# Workflows + +ActivitySim workflows use the [`pypyr`](https://pypyr.io/) library. This +task runner is more flexible than orca, and relies on isolated 'context' +namespaces rather than storing tables and configurations in Python's +global state. Workflows are activated using the `activitysim workflow` +command line interface. + +You can run a workflow from a local working directory, or one of +a number of pre-packaged workflows that are included with ActivitySim. + + +## Workflows for Performance Monitoring + +A collection of workflows used to compare the new *sharrow* code against +legacy implementations can be found in the +[sharrow-contrast](https://github.com/camsys/activitysim/tree/sharrow-black/activitysim/workflows/sharrow-contrast) +workflow subdirectory. Each of these first runs the relevant example model in +test mode to compile the relevant functions, and then runs in production mode +to measure runtime and memory usage. This is followed by another run in +"legacy" mode that does not use sharrow, as well as a run in a reference +implementation (version 1.1.3). Finally, a report is generated comparing the +results of these various runs, to illustrate performance and validiate output +quality. + +### Mini Examples + +A collection of "mini" example workflows is provided for typical performance analysis. +Each mini example is based on a small or medium size subarea for each model, +and each runs in single-process mode to illustrate the performance and memory +usage succinctly. These mini examples are sized so they should run on a typical +modeler's laptop (e.g. a modern CPU, 32 GB of RAM). + +| Workflow | Description | +|---------------------|:---------------------------------------------------------------------------------------| +| mtc_mini | Prototype MTC model on 50K households in San Francisco (190 zones) | +| arc_mini | Prototype ARC model on 10K households in Fulton County (1,296 zones) | +| sandag_1zone_mini | Placeholder SANDAG 1-Zone model on 50K households in a test region (90 zones) | +| sandag_2zone_mini | Placeholder SANDAG 2-Zone model on 50K households in a test region (90 TAZs, 690 MAZs) | +| sandag_3zone_mini | Placeholder SANDAG 3-Zone model on 50K households in a test region (90 TAZs, 690 MAZs) | +| sandag_xborder_mini | Prototype SANDAG Cross-Border model on 50K tours in a test region (94 TAZs, 586 MAZs) | +| psrc_mini | Placeholder PSRC Model on 50K households in Seattle (8400 MAZs) | +| comprehensive-mini | Runs all "mini" workflows listed above (warning, this takes a long time!) | + +To invoke a pre-packaged workflow, you can provide the workflow's +name and location within the `activitysim/workflows` directory. +For example, to run the workflow of all "mini" examples, run: + +```shell +activitysim workflow sharrow-contrast/comprehensive-mini +``` + +### Full Size Examples + +Running a "full size" example of an ActivitySim model generally requires more +compute resources than the smaller performance tests shown above. These models +generally require [chunking](chunk_size) to run, and servers with substantial +amounts of RAM. + +The "mp" workflows listed below will run each listed example in multiprocess mode. +They will attempt to automatically scale the number of processes and the chunk +size to the available resources of the server where they are run, but some models +require substantial resources and may not run correctly on inadequate hardware. + +| Workflow | Description | +|-----------------|:-----------------------------------------------------------------| +| mtc_mp | Prototype MTC model | +| arc_mp | Prototype ARC model, 5,922 zones | +| sandag_1zone_mp | Placeholder SANDAG 1-Zone model, 1.2M households and 4,984 zones | diff --git a/docs/development.rst b/docs/development.rst index fef5684c3..1c891115b 100644 --- a/docs/development.rst +++ b/docs/development.rst @@ -8,44 +8,44 @@ Software Design --------------- The core software components of ActivitySim are described below. ActivitySim is -implemented in Python, and makes heavy use of the vectorized backend C/C++ libraries in -`pandas `__ and `numpy `__ in order to be quite performant. -The core design principle of the system is vectorization of for loops, and this principle -is woven into the system wherever reasonable. As a result, the Python portions of the software -can be thought of as more of an orchestrator, data processor, etc. that integrates a series of -C/C++ vectorized data table and matrix operations. The model system formulates -each simulation as a series of vectorized table operations and the Python layer +implemented in Python, and makes heavy use of the vectorized backend C/C++ libraries in +`pandas `__ and `numpy `__ in order to be quite performant. +The core design principle of the system is vectorization of for loops, and this principle +is woven into the system wherever reasonable. As a result, the Python portions of the software +can be thought of as more of an orchestrator, data processor, etc. that integrates a series of +C/C++ vectorized data table and matrix operations. The model system formulates +each simulation as a series of vectorized table operations and the Python layer is responsible for setting up and providing expressions to operate on these large data tables. -In developing this software platform, we strive to adhere to a best practices approach to scientific computing, +In developing this software platform, we strive to adhere to a best practices approach to scientific computing, as summarized in `this article. `__ Model Orchestrator ~~~~~~~~~~~~~~~~~~ An ActivitySim model is a sequence of model / data processing steps, commonly known as a data pipeline. -A well defined data pipeline has the ability to resume jobs at a known point, which facilitates +A well defined data pipeline has the ability to resume jobs at a known point, which facilitates debugging of problems with data and/or calculations. It also allows for checkpointing model resources, such as the state of each person at a point in the model simulation. Checkpointing also allows for regression testing of results at specified points in overall model run. -ActivitySim's model orchestrator makes use of depedency injection, which is where one object (or method) +ActivitySim's model orchestrator makes use of depedency injection, which is where one object (or method) supplies the dependencies of another object. Dependency injection is done by the :mod:`activitysim.core.inject` -module, which wraps `ORCA `__, an orchestration/pipeline tool. Inject defines model -steps, dynamic data sources, and connects them to processing functions. It also defines dynamic data tables -based on pandas DataFrames, columns based on pandas Series, and injectables (functions). Model steps are executed +module, which wraps `ORCA `__, an orchestration/pipeline tool. Inject defines model +steps, dynamic data sources, and connects them to processing functions. It also defines dynamic data tables +based on pandas DataFrames, columns based on pandas Series, and injectables (functions). Model steps are executed as steps registered with the model orchestration engine. Over time Inject has extended ORCA's functionality by -adding a :ref:`pipeline_in_detail` that runs a series of model steps, manages the state of the data -tables throughout the model run, allows for restarting at any model step, and integrates with the +adding a :ref:`pipeline_in_detail` that runs a series of model steps, manages the state of the data +tables throughout the model run, allows for restarting at any model step, and integrates with the random number generation procedures (see :ref:`random_in_detail`). Data Handling ~~~~~~~~~~~~~ -ActivitySim works with three open data formats, `HDF5 `__ -, `Open Matrix (OMX) `__, and `CSV `__ . +ActivitySim works with three open data formats, `HDF5 `__ +, `Open Matrix (OMX) `__, and `CSV `__ . The HDF5 binary data container is used for the :ref:`pipeline_in_detail` data store. -OMX, which is based on HDF5, is used for input and output matrices (skims and demand matrices). CSV files +OMX, which is based on HDF5, is used for input and output matrices (skims and demand matrices). CSV files are used for various inputs and outputs as well. Three key data structures in ActivitySim are: @@ -58,34 +58,34 @@ Expressions ~~~~~~~~~~~ ActivitySim exposes all model expressions in CSV files. These model expression CSV files -contain Python expressions, mainly pandas/numpy expression, and reference input data tables -and network skim matrices. With this design, the Python code, which can be thought of as a generic expression -engine, and the specific model calculations, such as the utilities, are separate. This helps to avoid -modifying the actual Python code when making changes to the models, such as during model calibration. An -example of model expressions is found in the example auto ownership model specification file - +contain Python expressions, mainly pandas/numpy expression, and reference input data tables +and network skim matrices. With this design, the Python code, which can be thought of as a generic expression +engine, and the specific model calculations, such as the utilities, are separate. This helps to avoid +modifying the actual Python code when making changes to the models, such as during model calibration. An +example of model expressions is found in the example auto ownership model specification file - `auto_ownership.csv `__. Refer to the :ref:`expressions` section for more detail. -Many of the models have pre- and post-processor table annotators, which read a CSV file of expression, calculate -required additional table fields, and join the fields to the target tables. An example table annotation expressions -file is found in the example configuration files for households for the CDAP model - +Many of the models have pre- and post-processor table annotators, which read a CSV file of expression, calculate +required additional table fields, and join the fields to the target tables. An example table annotation expressions +file is found in the example configuration files for households for the CDAP model - `annotate_households_cdap.csv `__. Refer to :ref:`table_annotation` for more information and the :func:`activitysim.abm.models.util.expressions.assign_columns` function. Choice Models ~~~~~~~~~~~~~ -ActivitySim currently supports multinomial (MNL) and nested logit (NL) choice models. Refer to :ref:`logit_in_detail` -for more information. It also supports custom expressions as noted above, which can often be used to -code additional types of choice models. In addition, developers can write their own choice models -in Python and expose these through the framework. +ActivitySim currently supports multinomial (MNL) and nested logit (NL) choice models. Refer to :ref:`logit_in_detail` +for more information. It also supports custom expressions as noted above, which can often be used to +code additional types of choice models. In addition, developers can write their own choice models +in Python and expose these through the framework. Person Time Windows ~~~~~~~~~~~~~~~~~~~ -The departure time and duration models require person time windows. Time windows are adjacent time -periods that are available for travel. ActivitySim maintains time windows in a pandas table where each row is -a person and each time period is a column. As travel is scheduled throughout the simulation, the relevant +The departure time and duration models require person time windows. Time windows are adjacent time +periods that are available for travel. ActivitySim maintains time windows in a pandas table where each row is +a person and each time period is a column. As travel is scheduled throughout the simulation, the relevant columns for the tour, trip, etc. are updated as needed. Refer to :ref:`time_windows` for more information. Models @@ -103,7 +103,7 @@ typically does the following: * runs a data postprocessor on the output table data that needs additional fields for later models * writes the resulting table data to the pipeline -See :ref:`models` for more information. +See :ref:`models` for more information. Development Install @@ -129,14 +129,14 @@ ActivitySim development adheres to the following standards. Style ~~~~~ -* Python code should follow the `pycodestyle style guide `__ +* Python code should follow the `black code style `__ * Python docstrings should follow the `numpydoc documentation format `__ Imports ~~~~~~~ * Imports should be one per line. -* Imports should be grouped into standard library, third-party, and intra-library imports. +* Imports should be grouped into standard library, third-party, and intra-library imports. * ``from`` import should follow regular ``imports``. * Within each group the imports should be alphabetized. * Imports of scientific Python libraries should follow these conventions: @@ -150,7 +150,7 @@ Imports Working Together in the Repository ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -We use `GitHub Flow `__. The key points to +We use `GitHub Flow `__. The key points to our GitHub workflow are: * The ``main`` branch contains the latest release version of the ActivitySim resources @@ -197,9 +197,11 @@ ActivitySim uses the following `versioning convention `__, a tool to check Python code against the pycodestyle style conventions +* `black `__, a tool to check and enforce black + code style on Python code +* `isort ` to organize imports * `pytest `__, a Python testing tool * `coveralls `__, a tool for measuring code coverage and publishing code coverage stats online @@ -207,18 +209,18 @@ To run the tests locally, first make sure the required packages are installed. :: - pycodestyle + black --check py.test These same tests are run by Travis with each push to the repository. These tests need to pass in order to merge the revisions into main. -In some cases, test targets need to be updated to match the new results produced by the code since these -are now the correct results. In order to update the test targets, first determine which tests are -failing and then review the failing lines in the source files. These are easy to identify since each -test ultimately comes down to one of Python's various types of `assert` statements. Once you identify -which `assert` is failing, you can work your way back through the code that creates the test targets in -order to update it. After updating the test targets, re-run the tests to confirm the new code passes all +In some cases, test targets need to be updated to match the new results produced by the code since these +are now the correct results. In order to update the test targets, first determine which tests are +failing and then review the failing lines in the source files. These are easy to identify since each +test ultimately comes down to one of Python's various types of `assert` statements. Once you identify +which `assert` is failing, you can work your way back through the code that creates the test targets in +order to update it. After updating the test targets, re-run the tests to confirm the new code passes all the tests. See :ref:`adding_agency_examples` for more information on testing, most notably, agency example models. @@ -226,28 +228,28 @@ See :ref:`adding_agency_examples` for more information on testing, most notably, Profiling ~~~~~~~~~ -A handy way to profile ActivitySim model runs is to use `snakeviz `__. -To do so, first install snakeviz and then run ActivitySim with the Python profiler (cProfile) to create +A handy way to profile ActivitySim model runs is to use `snakeviz `__. +To do so, first install snakeviz and then run ActivitySim with the Python profiler (cProfile) to create a profiler file. Then run snakeviz on the profiler file to interactively explore the component runtimes. Documentation ~~~~~~~~~~~~~ -The documentation is written in `reStructuredText `__ markup +The documentation is written in `reStructuredText `__ markup and built with `Sphinx `__. In addition to converting rst files to html and other document formats, these tools also read the inline Python docstrings and convert them into html as well. ActivitySim's docstrings are written in `numpydoc format -`__ since it is easier to use +`__ since it is easier to use than standard rst format. -To build the documentation, first make sure the required packages are installed. Next, build the +To build the documentation, first make sure the required packages are installed. Next, build the documentation in html format with the ``make html`` command run from the ``docs`` folder. -If the activitysim package is installed, then the documentation will be built from that version of -the source code instead of the git repo version. When pushing revisions to the repo, the documentation +If the activitysim package is installed, then the documentation will be built from that version of +the source code instead of the git repo version. When pushing revisions to the repo, the documentation is automatically built by Travis after successfully passing the tests. -GitHub automatically publishes the gh-pages branch at https://activitysim.github.io/activitysim. +GitHub automatically publishes the gh-pages branch at https://activitysim.github.io/activitysim. .. _release_steps : @@ -262,26 +264,26 @@ document. Issues and Support ~~~~~~~~~~~~~~~~~~ -Issue tracking and support is done through GitHub `issues `__. +Issue tracking and support is done through GitHub `issues `__. License ~~~~~~~ -ActivitySim is provided "as is." See the +ActivitySim is provided "as is." See the `License `__ for more information. Contribution Review Criteria ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -When contributing to ActivitySim, the set of questions below will be asked of the contribution. Make sure to also -review the documentation above before making a submittal. The automated test system also provides some helpful +When contributing to ActivitySim, the set of questions below will be asked of the contribution. Make sure to also +review the documentation above before making a submittal. The automated test system also provides some helpful information where identified. -To submit a contribution for review, issue a pull request with a comment introducing your contribution. The comment -should include a brief overview, responses to the questions, and pointers to related information. The entire submittal -should ideally be self contained so any additional documentation should be in the pull request as well. -The `PMC `__ and/or its Contractor will handle the review request, comment on each -question, complete the feedback form, and reply to the pull request. If accepted, the commit(s) will +To submit a contribution for review, issue a pull request with a comment introducing your contribution. The comment +should include a brief overview, responses to the questions, and pointers to related information. The entire submittal +should ideally be self contained so any additional documentation should be in the pull request as well. +The `PMC `__ and/or its Contractor will handle the review request, comment on each +question, complete the feedback form, and reply to the pull request. If accepted, the commit(s) will be `squashed and merged `__. Its a good idea to setup a pre-submittal meeting to discuss questions and better understand expectations. @@ -292,10 +294,10 @@ Its a good idea to setup a pre-submittal meeting to discuss questions and better 3. Are the runtimes reasonable and does it provide documentation justifying this claim? 4. Does it include non-Python code, such as C/C++? If so, does it compile on any OS and are compilation instructions included? 5. Is it licensed with the ActivitySim license that allows the code to be freely distributed and modified and includes attribution so that the provenance of the code can be tracked? Does it include an official release of ownership from the funding agency if applicable? - 6. Does it appropriately interact with the data pipeline (i.e. it doesn't create new ways of managing data)? + 6. Does it appropriately interact with the data pipeline (i.e. it doesn't create new ways of managing data)? 7. Does it include regression tests to enable checking that consistent results will be returned when updates are made to the framework? - 8. Does it include sufficient test coverage and test data for existing and proposed features? - 9. Any other comments or suggestions for improving the developer experience? + 8. Does it include sufficient test coverage and test data for existing and proposed features? + 9. Any other comments or suggestions for improving the developer experience? **Feedback** @@ -303,7 +305,7 @@ The PMC and/or its Contractor will provide feedback for each review criteria abo +-----------------------------------+-------------+-------------------+-------------------+ | Status | Code | Documentation | Tests/Examples | -+===================================+=============+===================+===================+ ++===================================+=============+===================+===================+ | Accept | | | | +-----------------------------------+-------------+-------------------+-------------------+ | Accept but recommend revisions | | | | @@ -403,7 +405,7 @@ to maintain the dictionary of the examples and how to get and run them. Running the Test System ~~~~~~~~~~~~~~~~~~~~~~~ -The automatic TravisCI test system runs the test examples and the cropped agency examples. Examples of the testing +The automatic TravisCI test system runs the test examples and the cropped agency examples. Examples of the testing resources for each agency example that need to be up-to-date are: * `scripts folder (including crop script) `_ @@ -438,7 +440,7 @@ To help understand this case, the inclusion of :ref:`placeholder_psrc` as an age When an agency example includes new submodels and/or contributions to the core that need to be reviewed and then pulled/accepted: * First, the agency example must comply with the steps outlined above under "When an agency wants to update their example". -* Second, the agency example must be up-to-date with the latest develop version of the code so the revisions to the code are only the exact revisions for the new submodels and/or contributions to the core. +* Second, the agency example must be up-to-date with the latest develop version of the code so the revisions to the code are only the exact revisions for the new submodels and/or contributions to the core. * The new submodels and/or contributions to the core will then be reviewed by the repository manager and it's likely some revisions will be required for acceptance. Key items in the review include python code, user documentation, and testable examples for all new components. If the contribution is just new submodels, then the agency example that exercises the new submodel is sufficient for test coverage since TravisCI will automatically test the cropped version of the new submodel. If the contribution includes revisions to the core that impact other test examples, then the developer is responsible for ensuring all the other tests that are up-to-date are updated/passing as well. This includes other agency examples that are up-to-date. This is required to ensure the contribution to the core is adequately complete. To help understand this case, the addition of the parking location choice model for :ref:`prototype_arc` is discussed. First, ARC gets their example in good working order - i.e. updates to develop, makes any required revisions to their model to get it working, creates a cropped and full scaled example, and creates the expected test results. In addition, this use case includes additional submodel and/or core code so ARC also authors the new feature, including documentation and any other relevant requirements such as logging, tracing, support for estimation, etc. With the new example and feature working offline, then ARC issues a pull request to add prototype_arc and the new submodel/core code and makes sure the automatic tests are passing. Once accepted, the automatic test system will run the test example tests and the cropped agency examples. Since the new feature - parking location choice model - is included in prototype_arc, then new feature is now tested. Any testing of downstream impacts from the parking location choice model would also need to be implemented in the example. diff --git a/docs/examples.rst b/docs/examples.rst index cf2431efc..add56f8b3 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -507,13 +507,13 @@ Area types The prototype_mtc_sf inputs were created by the ``other_resources\scripts\create_sf_example.py`` script, which creates the land use, synthetic population, and skim inputs for a subset of user-defined zones. -.. index:: configuration Configuration ^^^^^^^^^^^^^ This section has been moved to :ref:`configuration`. + .. _sub-model-spec-files: Sub-Model Specification Files @@ -1054,6 +1054,17 @@ in this example are: * :ref:`vehicle_type_choice`: Selects a vehicle type for each household vehicle. Runs after auto_ownership. * :ref:`vehicle_allocation`: Allocates a vehicle for each tour and each occupancy level. Tour and trip mode choice auto operating costs are modified to reflect the allocated vehicle option. +* :ref:`school_escorting`: Explicitly models school drop-off / pick-up of students to and from school. + +The prototype_mtc_extended example also contains changes to test the flexible number of tour and trip ids. +(Information in why this is important can be found `here `__.) +The following changes were made to demonstrate this: + +* An additional alternative was added to the non-mandatory tour frequency alternatives file containing 2 other discretionary tours. +* An additional alternative was added to the stop_frequency_alts.csv for 4 outbound stops and 3 inbound stops. This alternative was then + included as part of the stop_frequency_othdiscr.csv specification with an added calibration constant to control that alternative. + Because an additional trip may now happen in the outbound direction, the trip scheduling probabilities table was extended for the + other discretionary tour purpose where the fourth outbound trip rows were copied for the now availabile fifth trip. .. _example_estimation : @@ -1487,8 +1498,8 @@ prototype_semcog The prototype_semcog added a :ref:`work_from_home`, :ref:`telecommute_frequency`, :ref:`transit_pass_subsidy` and :ref:`transit_pass_ownership` submodel. These submodel specification files are below, and are in addition to the :ref:`prototype_mtc` submodel :ref:`sub-model-spec-files`. These submodels were added to prototype_semcog as extensions, which is a way for users to add -submodels within their model setup as opposed to formally adding them to the activitysim package. Extension submodels are run through -the `models` settings. However, the model must be run with the `simulation.py` script instead of the command line interface +submodels within their model setup as opposed to formally adding them to the activitysim package. Extension submodels are run through +the `models` settings. However, the model must be run with the `simulation.py` script instead of the command line interface in order to load the extensions folder. .. _semcog-sub-model-spec-files: diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst index 00db3e792..8f75c21e6 100644 --- a/docs/gettingstarted.rst +++ b/docs/gettingstarted.rst @@ -65,7 +65,7 @@ Additional libraries can also be installed later. You may want to consider thes tools for certain development tasks:: # packages for testing - mamba install pytest pytest-cov coveralls pycodestyle pytest-regressions -c conda-forge --override-channels -n asim + mamba install pytest pytest-cov coveralls black flake8 pytest-regressions -c conda-forge --override-channels -n asim # packages for building documentation mamba install sphinx numpydoc sphinx_rtd_theme==0.5.2 -c conda-forge --override-channels -n asim @@ -172,11 +172,11 @@ To setup and run the primary example (see :ref:`examples`), do the following: * Review the outputs in the output directory .. note:: - Common configuration settings can be overridden at runtime. See ``activitysim -h``, ``activitysim create -h`` and ``activitysim run -h``. + Common configuration settings can be overridden at runtime. See ``activitysim -h``, ``activitysim create -h`` and ``activitysim run -h``. ActivitySim model runs can be configured with settings file inheritance to avoid duplicating settings across model configurations. See :ref:`cli` for more information. Additional examples, including the full scale prototype MTC regional demand model, estimation integration examples, multiple zone system examples, -and examples for agency partners are available for creation by typing ``activitysim create -l``. To create these examples, ActivitySim downloads the (large) input files from +and examples for agency partners are available for creation by typing ``activitysim create -l``. To create these examples, ActivitySim downloads the (large) input files from the `ActivitySim resources `__ repository. See :ref:`examples` for more information. Try the Notebooks @@ -213,7 +213,7 @@ on the amount of RAM and number of processors allocated. See :ref:`multiprocess .. note:: ActivitySim has been run in the cloud, on both Windows and Linux using - `Microsoft Azure `__. Example configurations, + `Microsoft Azure `__. Example configurations, scripts, and runtimes are in the ``other_resources\example_azure`` folder. .. _mkl_settings : diff --git a/docs/models.rst b/docs/models.rst index 00dd28aa4..ef79a38cd 100644 --- a/docs/models.rst +++ b/docs/models.rst @@ -98,32 +98,114 @@ Core Table: ``skims`` | Result Table: ``accessibility`` | Skims Keys: ``O-D, D-O .. automodule:: activitysim.abm.models.accessibility :members: + +.. _disaggregate_accessibility: + +Disaggregate Accessibility +-------------- + +The disaggregate accessibility model is an extension of the base accessibility model. +While the base accessibility model is based on a mode-specific decay function and uses fixed market +segments in the population (i.e., income), the disaggregate accessibility model extracts the actual +destination choice logsums by purpose (i.e., mandatory fixed school/work location and non-mandatory +tour destinations by purpose) from the actual model calculations using a user-defined proto-population. +This enables users to include features that may be more critical to destination +choice than just income (e.g., automobile ownership). + + +Inputs: + * disaggregate_accessibility.yaml - Configuration settings for disaggregate accessibility model. + * annotate.csv [optional] - Users can specify additional annotations specific to disaggregate accessibility. For example, annotating the proto-population tables. + +Outputs: + * final_disaggregate_accessibility.csv [optional] + * final_non_mandatory_tour_destination_accesibility.csv [optional] + * final_workplace_location_accessibility.csv [optional] + * final_school_location_accessibility.csv [optional] + * final_proto_persons.csv [optional] + * final_proto_households.csv [optional] + * final_proto_tours.csv [optional] + +The above tables are created in the model pipeline, but the model will not save +any outputs unless specified in settings.yaml - output_tables. Users can return +the proto population tables for inspection, as well as the raw logsum accessibilities +for mandatory school/work and non-mandatory destinations. The logsums are then merged +at the household level in final_disaggregate_accessibility.csv, which each tour purpose +logsums shown as separate columns. + + +**Usage** +The disaggregate accessibility model is run as a model step in the model list. +There are two necessary steps: + +``- initialize_proto_population`` | ``- compute_disaggregate_accessibility`` + +The reason the steps must be separate is to enable multiprocessing. +The proto-population must be fully generated and initialized before activitysim +slices the tables into separate threads. These steps must also occur before +initialize_households in order to avoid conflict with the shadow_pricing model. + + +The model steps can be run either as part the activitysim model run, or setup +to run as a standalone run to pre-computing the accessibility values. +For standalone implementations, the final_disaggregate_accessibility.csv is read +into the pipeline and initialized with the initialize_household model step. + + +**Configuration of disaggregate_accessibility.yaml:** + * CREATE_TABLES - Users define the variables to be generated for PROTO_HOUSEHOLDS, PROTO_PERSONS, and PROTO_TOURS tables. These tables must include all basic fields necessary for running the actual model. Additional fields can be annotated in pre-processing using the annotation settings of this file. The base variables in each table are defined using the following parameters: + + - VARIABLES - The base variable, must be a value or a list. Results in the cartesian product (all non-repeating combinations) of the fields. + - mapped_fields [optional] - For non-combinatorial fields, users can map a variable to the fields generated in VARIABLES (e.g., income category bins mapped to median dollar values). + - filter_rows [optional] - Users can also filter rows using pandas expressions if specific variable combinations are not desired. + - JOIN_ON [required only for PROTO_TOURS] - specify the persons variable to join the tours to (e.g., person_number). + * MERGE_ON - User specified fields to merge the proto-population logsums onto the full synthetic population. The proto-population should be designed such that the logsums are able to be joined exactly on these variables specified to the full population. Users specify the to join on using: + + - by: An exact merge will be attempted using these discrete variables. + - asof [optional]: The model can peform an "asof" join for continuous variables, which finds the nearest value. This method should not be necessary since synthetic populations are all discrete. + + - method [optional]: Optional join method can be "soft", default is None. For cases where a full inner join is not possible, a Naive Bayes clustering method is fast but discretely constrained method. The proto-population is treated as the "training data" to match the synthetic population value to the best possible proto-population candidate. The Some refinement may be necessary to make this procedure work. + + * annotate_proto_tables [optional] - Annotation configurations if users which to modify the proto-population beyond basic generation in the YAML. + * DESTINATION_SAMPLE_SIZE - The *destination* sample size (0 = all zones), e.g., the number of destination zone alternatives sampled for calculating the destination logsum. Decimal values < 1 will be interpreted as a percentage, e.g., 0.5 = 50% sample. + * ORIGIN_SAMPLE_SIZE - The *origin* sample size (0 = all zones), e.g., the number of origins where logsum is calculated. Origins without a logsum will draw from the nearest zone with a logsum. This parameter is useful for systems with a large number of zones with similar accessibility. Decimal values < 1 will be interpreted as a percentage, e.g., 0.5 = 50% sample. + * ORIGIN_SAMPLE_METHOD - The method in which origins are sampled. Population weighted sampling can be TAZ-based or "TAZ-agnostic" using KMeans clustering. The potential advantage of KMeans is to provide a more geographically even spread of MAZs sampled that do not rely on TAZ hierarchies. Unweighted sampling is also possible using 'uniform' and 'uniform-taz'. + + - None [Default] - Sample zones weighted by population, ensuring at least one TAZ is sampled per MAZ. If n-samples > n-tazs then sample 1 MAZ from each TAZ until n-remaining-samples < n-tazs, then sample n-remaining-samples TAZs and sample an MAZ within each of those TAZs. If n-samples < n-tazs, then it proceeds to the above 'then' condition. + + - "kmeans" - K-Means clustering is performed on the zone centroids (must be provided as maz_centroids.csv), weighted by population. The clustering yields k XY coordinates weighted by zone population for n-samples = k-clusters specified. Once k new cluster centroids are found, these are then approximated into the nearest available zone centroid and used to calculate accessibilities on. By default, the k-means method is run on 10 different initial cluster seeds (n_init) using using "k-means++" seeding algorithm (https://en.wikipedia.org/wiki/K-means%2B%2B). The k-means method runs for max_iter iterations (default=300). + + - "uniform" - Unweighted sample of N zones independent of each other. + + - "uniform-taz" - Unweighted sample of 1 zone per taz up to the N samples specified. + + .. _work_from_home: Work From Home -------------- -Telecommuting is defined as workers who work from home instead of going -to work. It only applies to workers with a regular workplace outside of home. -The telecommute model consists of two submodels - this work from home model and a -person :ref:`telecommute_frequency` model. This model predicts for all workers whether they +Telecommuting is defined as workers who work from home instead of going +to work. It only applies to workers with a regular workplace outside of home. +The telecommute model consists of two submodels - this work from home model and a +person :ref:`telecommute_frequency` model. This model predicts for all workers whether they usually work from home. The work from home model includes the ability to adjust a work from home alternative -constant to attempt to realize a work from home percent for what-if type analysis. -This iterative single process procedure takes as input a number of iterations, a filter on -the choosers to use for the calculation, a target work from home percent, a tolerance percent -for convergence, and the name of the coefficient to adjust. An example setup is provided and -the coefficient adjustment at each iteration is: +constant to attempt to realize a work from home percent for what-if type analysis. +This iterative single process procedure takes as input a number of iterations, a filter on +the choosers to use for the calculation, a target work from home percent, a tolerance percent +for convergence, and the name of the coefficient to adjust. An example setup is provided and +the coefficient adjustment at each iteration is: ``new_coefficient = log( target_percent / current_percent ) + current_coefficient``. -The main interface to the work from home model is the -:py:func:`~activitysim.examples.prototype_semcog.extensions.work_from_home` function. This +The main interface to the work from home model is the +:py:func:`~activitysim.abm.models.work_from_home` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``persons`` | Result Field: ``work_from_home`` | Skims Keys: NA -.. automodule:: activitysim.examples.prototype_semcog.extensions.work_from_home +.. automodule:: activitysim.abm.models.work_from_home :members: .. _school_location: @@ -197,40 +279,81 @@ The shadow pricing calculator used by work and school location choice. **Turning on and saving shadow prices** -Shadow pricing is activated by setting the ``use_shadow_pricing`` to True in the settings.yaml file. Once this setting has -been activated, ActivitySim will search for shadow pricing configuration in the shadow_pricing.yaml file. When shadow pricing is -activated, the shadow pricing outputs will be exported by the tracing engine. As a result, the shadow pricing output files will -be prepended with ``trace`` followed by the iteration number the results represent. For example, the shadow pricing outputs -for iteration 3 of the school location model will be called ``trace.shadow_price_school_shadow_prices_3.csv``. +Shadow pricing is activated by setting the ``use_shadow_pricing`` to True in the settings.yaml file. +Once this setting has been activated, ActivitySim will search for shadow pricing configuration in +the shadow_pricing.yaml file. When shadow pricing is activated, the shadow pricing outputs will be +exported by the tracing engine. As a result, the shadow pricing output files will be prepended with +``trace`` followed by the iteration number the results represent. For example, the shadow pricing +outputs for iteration 3 of the school location model will be called +``trace.shadow_price_school_shadow_prices_3.csv``. In total, ActivitySim generates three types of output files for each model with shadow pricing: -- ``trace.shadow_price__desired_size.csv`` - The size terms by zone that shadow pricing is attempting to target. These usually will match the size terms identified - in the land_use input file. - -- ``trace.shadow_price__modeled_size_.csv`` - These are the modeled size terms after the iteration of shadow pricing identified by the number. In other - words, these are the predicted choices by zone for the model after the iteration completes. - -- ``trace.shadow_price__shadow_prices_.csv`` - The actual shadow price for each zone and segment after the of shadow pricing. This the file that can be - used to warm start the shadow pricing mechanism in ActivitySim. +- ``trace.shadow_price__desired_size.csv`` The size terms by zone that the ctramp and daysim + methods are attempting to target. These equal the size term columns in the land use data + multiplied by size term coefficients. + +- ``trace.shadow_price__modeled_size_.csv`` These are the modeled size terms after + the iteration of shadow pricing identified by the number. In other words, these are + the predicted choices by zone and segment for the model after the iteration completes. (Not + applicable for ``simulation`` option.) + +- ``trace.shadow_price__shadow_prices_.csv`` The actual shadow price for each zone + and segment after the of shadow pricing. This is the file that can be used to warm + start the shadow pricing mechanism in ActivitySim. (Not applicable for ``simulation`` option.) + +There are three shadow pricing methods in activitysim: ``ctramp``, ``daysim``, and ``simulation``. +The first two methods try to match model output with workplace/school location model size terms, +while the last method matches model output with actual employment/enrollmment data. + +The simulation approach operates the following steps. First, every worker / student will be +assigned without shadow prices applied. The modeled share and the target share for each zone are +compared. If the zone is overassigned, a sample of people from the over-assigned zones will be +selected for re-simulation. Shadow prices are set to -999 for the next iteration for overassigned +zones which removes the zone from the set of alternatives in the next iteration. The sampled people +will then be forced to choose from one of the under-assigned zones that still have the initial +shadow price of 0. (In this approach, the shadow price variable is really just a switch turning that +zone on or off for selection in the subsequent iterations. For this reason, warm-start functionality +for this approach is not applicable.) This process repeats until the overall convergence criteria +is met or the maximum number of allowed iterations is reached. + +Because the simulation approach only re-simulates workers / students who were over-assigned in the +previous iteration, run time is significantly less (~90%) than the CTRAMP or DaySim approaches which +re-simulate all workers and students at each iteration. **shadow_pricing.yaml Attributes** -- ``shadow_pricing_models`` List model_selectors and model_names of models that use shadow pricing. This list identifies which size_terms to preload which must be done in single process mode, so predicted_size tables can be scaled to population) -- ``LOAD_SAVED_SHADOW_PRICES`` global switch to enable/disable loading of saved shadow prices. From the above example, this would be trace.shadow_price__shadow_prices_.csv renamed and stored in the ``data_dir``. -- ``MAX_ITERATIONS`` If no loaded shadow prices, maximum number of times shadow pricing can be run on each model before proceeding to the next model. -- ``MAX_ITERATIONS_SAVED`` If loaded shadow prices, maximum number of times shadow pricing can be run. -- ``SIZE_THRESHOLD`` Ignore zones in failure calculation with fewer choices than specified here. +- ``shadow_pricing_models`` List model_selectors and model_names of models that use shadow pricing. + This list identifies which size_terms to preload which must be done in single process mode, so + predicted_size tables can be scaled to population +- ``LOAD_SAVED_SHADOW_PRICES`` global switch to enable/disable loading of saved shadow prices. From + the above example, this would be trace.shadow_price__shadow_prices_.csv renamed + and stored in the ``data_dir``. +- ``MAX_ITERATIONS`` If no loaded shadow prices, maximum number of times shadow pricing can be run + on each model before proceeding to the next model. +- ``MAX_ITERATIONS_SAVED`` If loaded shadow prices, maximum number of times shadow pricing can be + run. +- ``SIZE_THRESHOLD`` Ignore zones in failure calculation (ctramp or daysim method) with smaller size + term value than size_threshold. +- ``TARGET_THRESHOLD`` Ignore zones in failure calculation (simulation method) with smaller + employment/enrollment than target_threshold. - ``PERCENT_TOLERANCE`` Maximum percent difference between modeled and desired size terms -- ``FAIL_THRESHOLD`` Number of zones exceeding the PERCENT_TOLERANCE considered a failure -- ``SHADOW_PRICE_METHOD`` [ctramp | daysim] -- ``DAMPING_FACTOR`` On each iteration, ActivitySim will attempt to adjust the model to match desired size terms. The number is multiplied by adjustment factor to dampen or amplify the ActivitySim calculation. (only for CT-RAMP) -- ``DAYSIM_ABSOLUTE_TOLERANCE`` +- ``FAIL_THRESHOLD`` percentage of zones exceeding the PERCENT_TOLERANCE considered a failure +- ``SHADOW_PRICE_METHOD`` [ctramp | daysim | simulation] +- ``workplace_segmentation_targets`` dict matching school segment to landuse employment column + target. Only used as part of simulation option. If mutiple segments list the same target column, + the segments will be added together for comparison. (Same with the school option below.) +- ``school_segmentation_targets`` dict matching school segment to landuse enrollment column target. + Only used as part of simulation option. +- ``DAMPING_FACTOR`` On each iteration, ActivitySim will attempt to adjust the model to match + desired size terms. The number is multiplied by adjustment factor to dampen or amplify the + ActivitySim calculation. (only for CTRAMP) +- ``DAYSIM_ABSOLUTE_TOLERANCE`` Absolute tolerance for DaySim option +- ``DAYSIM_PERCENT_TOLERANCE`` Relative tolerance for DaySim option +- ``WRITE_ITERATION_CHOICES`` [True | False ] Writes the choices of each person out to the trace + folder. Used for debugging or checking itration convergence. WARNING: every person is written for + each sub-process so the disc space can get large. -- ``DAYSIM_PERCENT_TOLERANCE`` .. automodule:: activitysim.abm.tables.shadow_pricing :members: @@ -248,12 +371,12 @@ person :ref:`transit_pass_ownership` model and the tour and trip mode choice mod via fare discount adjustments. The main interface to the transit pass subsidy model is the -:py:func:`~activitysim.examples.prototype_semcog.extensions.transit_pass_subsidy` function. This +:py:func:`~activitysim.abm.models.transit_pass_subsidy` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``persons`` | Result Field: ``transit_pass_subsidy`` | Skims Keys: NA -.. automodule:: activitysim.examples.prototype_semcog.extensions.transit_pass_subsidy +.. automodule:: activitysim.abm.models.transit_pass_subsidy :members: .. _transit_pass_ownership: @@ -268,12 +391,12 @@ result of this model can be used to condition downstream models such as the tour mode choice models via fare discount adjustments. The main interface to the transit pass ownership model is the -:py:func:`~activitysim.examples.prototype_semcog.extensions.transit_pass_ownership` function. This +:py:func:`~activitysim.abm.models.transit_pass_ownership` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``persons`` | Result Field: ``transit_pass_ownership`` | Skims Keys: NA -.. automodule:: activitysim.examples.prototype_semcog.extensions.transit_pass_ownership +.. automodule:: activitysim.abm.models.transit_pass_ownership :members: .. _auto_ownership: @@ -281,11 +404,11 @@ Core Table: ``persons`` | Result Field: ``transit_pass_ownership`` | Skims Keys: Auto Ownership -------------- -The auto ownership model selects a number of autos for each household in the simulation. +The auto ownership model selects a number of autos for each household in the simulation. The primary model components are household demographics, zonal density, and accessibility. -The main interface to the auto ownership model is the -:py:func:`~activitysim.abm.models.auto_ownership.auto_ownership_simulate` +The main interface to the auto ownership model is the +:py:func:`~activitysim.abm.models.auto_ownership.auto_ownership_simulate` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``households`` | Result Field: ``auto_ownership`` | Skims Keys: NA @@ -293,7 +416,7 @@ Core Table: ``households`` | Result Field: ``auto_ownership`` | Skims Keys: NA .. automodule:: activitysim.abm.models.auto_ownership :members: - + .. _vehicle_type_choice: Vehicle Type Choice @@ -375,12 +498,12 @@ level of telecommuting. The model alternatives are the frequency of telecommutin days per week (0 days, 1 day, 2 to 3 days, 4+ days). The main interface to the work from home model is the -:py:func:`~activitysim.examples.prototype_semcog.extensions.telecommute_frequency` function. This +:py:func:`~activitysim.abm.models.telecommute_frequency` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``persons`` | Result Field: ``telecommute_frequency`` | Skims Keys: NA -.. automodule:: activitysim.examples.prototype_semcog.extensions.telecommute_frequency +.. automodule:: activitysim.abm.models.telecommute_frequency :members: .. _freeparking: @@ -389,12 +512,12 @@ Free Parking Eligibility ------------------------ The Free Parking Eligibility model predicts the availability of free parking at a person's -workplace. It is applied for people who work in zones that have parking charges, which are -generally located in the Central Business Districts. The purpose of the model is to adequately -reflect the cost of driving to work in subsequent models, particularly in mode choice. +workplace. It is applied for people who work in zones that have parking charges, which are +generally located in the Central Business Districts. The purpose of the model is to adequately +reflect the cost of driving to work in subsequent models, particularly in mode choice. -The main interface to the free parking eligibility model is the -:py:func:`~activitysim.abm.models.free_parking.free_parking` function. This function is registered +The main interface to the free parking eligibility model is the +:py:func:`~activitysim.abm.models.free_parking.free_parking` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``persons`` | Result Field: ``free_parking_at_work`` | Skims Keys: NA @@ -493,6 +616,155 @@ Core Table: ``tours`` | Result Field: ``start, end, duration`` | Skims Keys: ``T :members: +.. _school_escorting: + +School Escorting +---------------- + +The school escort model determines whether children are dropped-off at or picked-up from school, +simultaneously with the chaperone responsible for chauffeuring the children, +which children are bundled together on half-tours, and the type of tour (pure escort versus rideshare). +The model is run after work and school locations have been chosen for all household members, +and after work and school tours have been generated and scheduled. +The model labels household members of driving age as potential ‘chauffeurs’ and children with school tours as potential ‘escortees’. +The model then attempts to match potential chauffeurs with potential escortees in a choice model whose alternatives +consist of ‘bundles’ of escortees with a chauffeur for each half tour. + +School escorting is a household level decision – each household will choose an alternative from the ``school_escorting_alts.csv`` file, +with the first alternative being no escorting. This file contains the following columns: + ++------------------------------------------------+--------------------------------------------------------------------+ +| Column Name | Column Description | ++================================================+====================================================================+ +| Alt | Alternative number | ++------------------------------------------------+--------------------------------------------------------------------+ +| bundle[1,2,3] | bundle number for child 1,2, and 3 | ++------------------------------------------------+--------------------------------------------------------------------+ +| chauf[1,2,3] | chauffeur number for child 1,2, and 3 | +| | - 0 = child not escorted | +| | - 1 = chauffeur 1 as ride share | +| | - 2 = chauffeur 1 as pure escort | +| | - 3 = chauffeur 2 as ride share | +| | - 4 = chauffeur 3 as pure escort | ++------------------------------------------------+--------------------------------------------------------------------+ +| nbund[1,2] | - number of escorting bundles for chauffeur 1 and 2 | ++------------------------------------------------+--------------------------------------------------------------------+ +| nbundles | - total number of bundles | +| | - equals nbund1 + nbund2 | ++------------------------------------------------+--------------------------------------------------------------------+ +| nrs1 | - number of ride share bundles for chauffeur 1 | ++------------------------------------------------+--------------------------------------------------------------------+ +| npe1 | - number of pure escort bundles for chauffeur 1 | ++------------------------------------------------+--------------------------------------------------------------------+ +| nrs2 | - number of ride share bundles for chauffeur 2 | ++------------------------------------------------+--------------------------------------------------------------------+ +| npe2 | - number of pure escort bundles for chauffeur 2 | ++------------------------------------------------+--------------------------------------------------------------------+ +| Description | - text description of alternative | ++------------------------------------------------+--------------------------------------------------------------------+ + +The model as currently implemented contains three escortees and two chauffeurs. +Escortees are students under age 16 with a mandatory tour whereas chaperones are all persons in the household over the age of 18. +For households that have more than three possible escortees, the three youngest children are selected for the model. +The two chaperones are selected as the adults of the household with the highest weight according to the following calculation: +:math:`Weight = 100*personType + 10*gender + 1*age(0,1)` +Where *personType* is the person type number from 1 to 5, *gender* is 1 for male and 2 for female, and +*age* is a binary indicator equal to 1 if age is over 25 else 0. + +The model is run sequentially three times, once in the outbound direction, once in the inbound direction, +and again in the outbound direction with additional conditions on what happened in the inbound direction. +There are therefore three sets of utility specifications, coefficients, and pre-processor files. +Each of these files is specified in the school_escorting.yaml file along with the number of escortees and number of chaperones. + +There is also a constants section in the school_escorting.yaml file which contain two constants. +One which sets the maximum time bin difference to match school and work tours for ride sharing +and another to set the number of minutes per time bin. +In the :ref:`prototype_mtc_extended` example, these are set to 1 and 60 respectively. + +After a school escorting alternative is chosen for the inbound and outbound direction, the model will +create the tours and trips associated with the decision. Pure escort tours are created, +and the mandatory tour start and end times are changed to match the school escort bundle start and end times. +(Outbound tours have their start times matched and inbound tours have their end times matched.) +Escortee drop-off / pick-up order is determined by the distance from home to the school locations. +They are ordered from smallest to largest in the outbound direction, and largest to smallest in the inbound direction. +Trips are created for each half-tour that includes school escorting according to the provided order. + +The created pure escort tours are joined to the already created mandatory tour table in the pipeline +and are also saved separately to the pipeline under the table name “school_escort_tours”. +Created school escorting trips are saved to the pipeline under the table name “school_escort_trips”. +By saving these to the pipeline, their data can be queried in downstream models to set correct purposes, +destinations, and schedules to satisfy the school escorting model choice. + +There are a host of downstream model changes that are involved when including the school escorting model. +The following list contains the models that are changed in some way when school escorting is included: + + * **Joint tour scheduling:** Joint tours are not allowed to be scheduled over school escort tours. + This happens automatically by updating the timetable object with the updated mandatory tour times + and created pure escort tour times after the school escorting model is run. + There were no code or config changes in this model, but it is still affected by school escorting. + * **Non-Mandatory tour frequency:** Pure school escort tours are joined to the tours created in the + non-mandatory tour frequency model and tour statistics (such as tour_count and tour_num) are re-calculated. + * **Non-Mandatory tour destination:** Since the primary destination of pure school escort tours is known, + they are removed from the choosers table and have their destination set according to the destination in\ + school_escort_tours table. They are also excluded from the estimation data bundle. + * **Non-Mandatory tour scheduling:** Pure escort tours need to have the non-escorting portion of their tour scheduled. + This is done by inserting availability conditions in the model specification that ensures the alternative + chosen for the start of the tour is equal to the alternative start time for outbound tours and the end time + is equal to the alternative end time for the inbound tours. There are additional terms that ensure the tour + does not overlap with subsequent school escorting tours as well. Beware -- If the availability conditions + in the school escorting model are not set correctly, the tours created may not be consistent with each other + and this model will fail. + * **Tour mode choice:** Availability conditions are set in tour mode choice to prohibit the drive alone mode + if the tour contains an escortee and the shared-ride 2 mode if the tour contains more than one escortee. + * **Stop Frequency:** No stops are allowed on half-tours that include school escorting. + This is enforced by adding availability conditions in the stop frequency model. After the stop frequency + model is run, the school escorting trips are merged from the trips created by the stop frequency model + and a new stop frequency is computed along with updated trip numbers. + * **Trip purpose, destination, and scheduling:** Trip purpose, destination, and departure times are known + for school escorting trips. As such they are removed from their respective chooser tables and the estimation + data bundles, and set according to the values in the school_escort_trips table residing in the pipeline. + * **Trip mode choice:** Like in tour mode choice, availability conditions are set to prohibit trip containing + an escortee to use the drive alone mode or the shared-ride 2 mode for trips with more than one escortee. + +Many of the changes discussed in the above list are handled in the code and the user is not required to make any +changes when implementing the school escorting model. However, it is the users responsibility to include the +changes in the following model configuration files for models downstream of the school escorting model: + ++--------------------------------------------------------------------+------------------------------------------------------------------+ +| File Name(s) | Change(s) Needed | ++====================================================================+==================================================================+ +| - `non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv` | | +| - `tour_scheduling_nonmandatory.csv` | - Set availability conditions based on those times | +| | - Do not schedule over other school escort tours | ++--------------------------------------------------------------------+------------------------------------------------------------------+ +| - `tour_mode_choice_annotate_choosers_preprocessor.csv` | - count number of escortees on tour by parsing the | +| - `tour_mode_choice.csv` | ``escort_participants`` column | +| | - set mode choice availability based on number of escortees | +| | | ++--------------------------------------------------------------------+------------------------------------------------------------------+ +| - `stop_frequency_school.csv` | Do not allow stops for half-tours that include school escorting | +| - `stop_frequency_work.csv` | | +| - `stop_frequency_univ.csv` | | +| - `stop_frequency_escort.csv` | | ++--------------------------------------------------------------------+------------------------------------------------------------------+ +| - `trip_mode_choice_annotate_trips_preprocessor.csv` | - count number of escortees on trip by parsing the | +| - `trip_mode_choice.csv` | ``escort_participants`` column | +| | - set mode choice availability based on number of escortees | +| | | ++--------------------------------------------------------------------+------------------------------------------------------------------+ + +When not including the school escorting model, all of the escort trips to and from school are counted implicitly in +escort tours determined in the non-mandatory tour frequency model. Thus, when including the school escort model and +accounting for these tours explicitly, extra care should be taken not to double count them in the non-mandatory +tour frequency model. The non-mandatory tour frequency model should be re-evaluated and likely changed to decrease +the number of escort tours generated by that model. This was not implemented in the :ref:`prototype_mtc_extended` +implementation due to a lack of data surrounding the number of escort tours in the region. + + +.. automodule:: activitysim.abm.models.school_escorting + :members: + + .. _joint_tour_frequency: Joint Tour Frequency @@ -904,7 +1176,7 @@ function. This function is registered as an Inject step in the example Pipeline Core Table: ``trips`` | Result Field: ``purpose`` | Skims Keys: NA .. note:: - Trip purpose and trip destination choice can be run iteratively together via :ref:`trip_purpose_and_destination`. + Trip purpose and trip destination choice can be run iteratively together via :ref:`trip_purpose_and_destination_model`. .. automodule:: activitysim.abm.models.trip_purpose @@ -916,48 +1188,10 @@ Core Table: ``trips`` | Result Field: ``purpose`` | Skims Keys: NA Trip Destination Choice ----------------------- -The trip (or stop) location choice model predicts the location of trips (or stops) along the tour other than the primary -destination. The stop-location model is structured as a multinomial logit model using a zone -attraction size variable and route deviation measure as impedance. The alternatives are sampled from -the full set of zones, subject to availability of a zonal attraction size term. The sampling mechanism -is also based on accessibility between tour origin and primary destination, and is subject to certain rules -based on tour mode. - -All destinations are available for auto tour modes, so long as there is a positive -size term for the zone. Intermediate stops on walk tours must be within X miles of both the tour -origin and primary destination zones. Intermediate stops on bike tours must be within X miles of both -the tour origin and primary destination zones. Intermediate stops on walk-transit tours must either be -within X miles walking distance of both the tour origin and primary destination, or have transit access to -both the tour origin and primary destination. Additionally, only short and long walk zones are -available destinations on walk-transit tours. - -The intermediate stop location choice model works by cycling through stops on tours. The level-of-service -variables (including mode choice logsums) are calculated as the additional utility between the -last location and the next known location on the tour. For example, the LOS variable for the first stop -on the outbound direction of the tour is based on additional impedance between the tour origin and the -tour primary destination. The LOS variable for the next outbound stop is based on the additional -impedance between the previous stop and the tour primary destination. Stops on return tour legs work -similarly, except that the location of the first stop is a function of the additional impedance between the -tour primary destination and the tour origin. The next stop location is based on the additional -impedance between the first stop on the return leg and the tour origin, and so on. - -Trip location choice for :ref:`multiple_zone_systems` models uses :ref:`presampling` by default. - -The main interface to the trip destination choice model is the -:py:func:`~activitysim.abm.models.trip_destination.trip_destination` function. -This function is registered as an Inject step in the example Pipeline. -See :ref:`writing_logsums` for how to write logsums for estimation. - -Core Table: ``trips`` | Result Field: ``(trip) destination`` | Skims Keys: ``origin, (tour primary) destination, dest_taz, trip_period`` - -.. note:: - Trip purpose and trip destination choice can be run iteratively together via :ref:`trip_purpose_and_destination`. +See :ref:`Trip Destination `. -.. automodule:: activitysim.abm.models.trip_destination - :members: - -.. _trip_purpose_and_destination: +.. _trip_purpose_and_destination_model: Trip Purpose and Destination ---------------------------- diff --git a/docs/users-guide/cli-tools.rst b/docs/users-guide/cli-tools.rst new file mode 100644 index 000000000..84feb4070 --- /dev/null +++ b/docs/users-guide/cli-tools.rst @@ -0,0 +1,31 @@ +================== +Command Line Tools +================== + + +activitysim run +--------------- + +.. argparse:: + :module: activitysim.cli.main + :func: parser + :prog: activitysim + :path: run + + +activitysim create +------------------ + +.. argparse:: + :module: activitysim.cli.main + :func: parser + :prog: activitysim + :path: create + + -d --destination : @replace + Path to new project directory. If this directory already exists, the + newly created example will be copied to a subdirectory within the + existing directory, and named according to the example name. Otherwise, + a new directory is created with this name and the newly created example + will be copied directly into it. + diff --git a/docs/users-guide/configuration.rst b/docs/users-guide/configuration.rst index e726301e6..ebd67d826 100644 --- a/docs/users-guide/configuration.rst +++ b/docs/users-guide/configuration.rst @@ -13,12 +13,30 @@ component. .. currentmodule:: activitysim.core.configuration +Top Level Settings +------------------ + .. autosummary:: :toctree: _generated + :template: autopydantic.rst :recursive: Settings InputTable + OutputTable + OutputTables + MultiprocessStep + MultiprocessStepSlice + + +Network Level of Service +------------------------ + +.. autosummary:: + :toctree: _generated + :template: autopydantic.rst + :recursive: + NetworkSettings TAZ_Settings - ZarrDigitalEncoding + DigitalEncoding diff --git a/docs/users-guide/index.rst b/docs/users-guide/index.rst index 266d01aa4..0bffb2f9f 100644 --- a/docs/users-guide/index.rst +++ b/docs/users-guide/index.rst @@ -24,6 +24,7 @@ Contents :maxdepth: 3 ../gettingstarted + cli-tools ../examples configuration ../cli diff --git a/other_resources/installer/build.sh b/other_resources/installer/build.sh new file mode 100644 index 000000000..e30ce1a99 --- /dev/null +++ b/other_resources/installer/build.sh @@ -0,0 +1,53 @@ +#!/usr/bin/env bash + +set -xe + +env | sort + +echo "***** Start: Building Activitysim installer *****" +CONSTRUCT_ROOT="${CONSTRUCT_ROOT:-${PWD}}" + +cd "${CONSTRUCT_ROOT}" + +# Constructor should be latest for non-native building +# See https://github.com/conda/constructor +echo "***** Install constructor *****" +conda install -y "constructor>=3.3.1" jinja2 curl libarchive -c conda-forge --override-channels + +if [[ "$(uname)" == "Darwin" ]]; then + conda install -y coreutils -c conda-forge --override-channels +fi +# shellcheck disable=SC2154 +if [[ "${TARGET_PLATFORM}" == win-* ]]; then + conda install -y "nsis=3.01" -c conda-forge --override-channels +fi +# pip install git+git://github.com/conda/constructor@3.3.1#egg=constructor --force --no-deps +conda list + +echo "***** Make temp directory *****" +TEMP_DIR=$(mktemp -d --tmpdir=C:/Users/RUNNER~1/AppData/Local/Temp/); + +echo "***** Copy file for installer construction *****" +cp -R other_resources/installer "${TEMP_DIR}/" +cp LICENSE.txt "${TEMP_DIR}/installer/" + +ls -al "${TEMP_DIR}" + +echo "***** Construct the installer *****" +# Transmutation requires the current directory is writable +cd "${TEMP_DIR}" +# shellcheck disable=SC2086 +constructor "${TEMP_DIR}/installer/" --output-dir "${TEMP_DIR}" +cd - + +cd "${TEMP_DIR}" + +# This line will break if there is more than one installer in the folder. +INSTALLER_PATH=$(find . -name "Activitysim*.${EXT}" | head -n 1) + +echo "***** Move installer to build folder *****" +mkdir -p "${CONSTRUCT_ROOT}/build" +mv "${INSTALLER_PATH}" "${CONSTRUCT_ROOT}/build/" + +echo "***** Done: Building ActivitySim installer *****" +cd "${CONSTRUCT_ROOT}" diff --git a/other_resources/installer/build_win.sh b/other_resources/installer/build_win.sh new file mode 100644 index 000000000..433462c02 --- /dev/null +++ b/other_resources/installer/build_win.sh @@ -0,0 +1,6 @@ +#!/usr/bin/env bash + +set -ex + +conda install posix --yes +source other_resources/installer/build.sh diff --git a/other_resources/installer/construct.yaml b/other_resources/installer/construct.yaml new file mode 100644 index 000000000..dd8f72e84 --- /dev/null +++ b/other_resources/installer/construct.yaml @@ -0,0 +1,22 @@ +{% set version = os.environ.get("ACTIVITYSIM_VERSION", "0.42.0-0") %} + +name: Activitysim +version: {{ version }} + +initialize_by_default: false +register_python_default: false + +license_file: LICENSE.txt + +menu_packages: [] + +installer_type: exe + +channels: + - conda-forge + +specs: + - python 3.9.* + - conda + - activitysim {{ version }} + - pip diff --git a/other_resources/scripts/verify_results.py b/other_resources/scripts/verify_results.py index 9c8fb8e32..d5868ae16 100644 --- a/other_resources/scripts/verify_results.py +++ b/other_resources/scripts/verify_results.py @@ -88,7 +88,7 @@ ############################################################# # read distance matrix (DIST) -distmat = omx.open_file(distance_matrix_filename)["DIST"][:] +distmat = omx.open_file(distance_matrix_filename, mode="r")["DIST"][:] ############################################################# # EXPORT TABLES diff --git a/setup.cfg b/setup.cfg index bf02916f3..6eaa64d18 100644 --- a/setup.cfg +++ b/setup.cfg @@ -35,7 +35,7 @@ install_requires = pypyr >= 5.3 pyyaml >= 5.1 requests >= 2.7 - sharrow >= 2.2.4 + sharrow >= 2.5 simwrapper > 1.7 tables >= 3.5.1 xarray >= 0.21 diff --git a/test/random_seed/.gitignore b/test/random_seed/.gitignore new file mode 100644 index 000000000..4d692c93a --- /dev/null +++ b/test/random_seed/.gitignore @@ -0,0 +1,2 @@ +# Configuration files created by test +configs_random_seed_*/ \ No newline at end of file diff --git a/test/random_seed/simulation.py b/test/random_seed/simulation.py new file mode 100644 index 000000000..8313dd45e --- /dev/null +++ b/test/random_seed/simulation.py @@ -0,0 +1,15 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import argparse +import sys + +from activitysim.cli.run import add_run_args, run + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/test/random_seed/test_random_seed.py b/test/random_seed/test_random_seed.py new file mode 100644 index 000000000..1a8641ded --- /dev/null +++ b/test/random_seed/test_random_seed.py @@ -0,0 +1,125 @@ +# ActivitySim +# See full license in LICENSE.txt. +import os +import subprocess +from shutil import copytree + +import pandas as pd +import pandas.testing as pdt +import pkg_resources +import yaml + + +def update_settings(settings_file, key, value): + with open(settings_file, "r") as f: + settings = yaml.safe_load(f) + f.close() + + settings[key] = value + + with open(settings_file, "w") as f: + yaml.safe_dump(settings, f) + f.close() + + +def run_test_random_seed(): + + steps_to_run = [ + "initialize_landuse", + "initialize_households", + "compute_accessibility", + "workplace_location", + "write_tables", + ] + + def example_path(dirname): + resource = os.path.join("examples", "prototype_mtc", dirname) + return pkg_resources.resource_filename("activitysim", resource) + + def test_path(dirname): + return os.path.join(os.path.dirname(__file__), dirname) + + def create_rng_configs(rng_base_seed=None): + new_configs_dir = test_path("configs_random_seed__{}".format(rng_base_seed)) + new_settings_file = os.path.join(new_configs_dir, "settings.yaml") + copytree(example_path("configs"), new_configs_dir) + + update_settings(new_settings_file, "models", steps_to_run) + if rng_base_seed != "": # Undefined + update_settings(new_settings_file, "rng_base_seed", rng_base_seed) + + # (run name, rng_base_seed value) + runs = [ + ("0-a", 0), + ("0-b", 0), + ("1-a", 1), + ("1-b", 1), + ("None-a", None), + ("None-b", None), + ("Undefined", ""), + ] + + seeds = list(set([run[1] for run in runs])) + for seed in seeds: + create_rng_configs(seed) + + outputs = {} + + def check_outputs(df1, df2, should_be_equal=True): + """ + Compares df1 and df2 and raises an AssertionError if they are unequal when `should_be_equal` is True and equal when `should_be_equal` is False + """ + if should_be_equal: + pdt.assert_frame_equal(outputs[df1], outputs[df2]) + else: + try: + pdt.assert_frame_equal(outputs[df1], outputs[df2]) + except AssertionError: + pass + else: + raise AssertionError + + file_path = os.path.join(os.path.dirname(__file__), "simulation.py") + + # running prototype mtc model through workplace_location with 3 random seed settings, 0, 1, None, and undefined. + # final_persons.csv is compared to ensure the same setting returns the same variables and a different setting returns something different. + for name, seed in runs: + + run_args = [ + "-c", + test_path("configs_random_seed__{}".format(seed)), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] + + try: + os.mkdir(test_path("output")) + except FileExistsError: + pass + + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + + # Read in output to memory to compare later + outputs[name] = pd.read_csv( + os.path.join(test_path("output"), "final_persons.csv") + ) + + check_outputs("0-a", "0-b", True) + check_outputs("0-a", "Undefined", True) + check_outputs("1-a", "1-b", True) + check_outputs("None-a", "None-b", False) + check_outputs("0-a", "1-a", False) + check_outputs("None-a", "0-a", False) + check_outputs("None-a", "1-a", False) + check_outputs("None-b", "0-a", False) + check_outputs("None-b", "1-a", False) + + +def test_random_seed(): + run_test_random_seed() + + +if __name__ == "__main__": + run_test_random_seed()