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models.py
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models.py
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import os
import yaml
import operator
from multiprocessing import Pool
import numpy as np
import orca
import pandana as pdna
import pandas as pd
from urbansim.models import transition, relocation
from urbansim.utils import misc, networks
from urbansim_parcels import utils as parcel_utils
import utils
import lcm_utils
import variables
# Set up location choice model objects.
# Register as injectable to be used throughout simulation
location_choice_models = {}
hlcm_step_names = []
elcm_step_names = []
model_configs = lcm_utils.get_model_category_configs()
for model_category_name, model_category_attributes in model_configs.items():
if model_category_attributes['model_type'] == 'location_choice':
model_config_files = model_category_attributes['config_filenames']
for model_config in model_config_files:
model = lcm_utils.create_lcm_from_config(model_config,
model_category_attributes)
location_choice_models[model.name] = model
if model_category_name == 'hlcm':
hlcm_step_names.append(model.name)
if model_category_name == 'elcm':
elcm_step_names.append(model.name)
orca.add_injectable('location_choice_models', location_choice_models)
orca.add_injectable('hlcm_step_names', sorted(hlcm_step_names, reverse=True))
orca.add_injectable('elcm_step_names', sorted(elcm_step_names, reverse=True))
for name, model in location_choice_models.items():
lcm_utils.register_choice_model_step(model.name,
model.choosers,
choice_function=lcm_utils.unit_choices)
@orca.step()
def elcm_home_based(jobs, households):
wrap_jobs = jobs
_print_number_unplaced(wrap_jobs, 'building_id')
jobs = wrap_jobs.to_frame(['building_id', 'home_based_status', 'large_area_id'])
jobs = jobs[(jobs.home_based_status >= 1) & (jobs.building_id == -1)]
hh = households.to_frame(['building_id', 'large_area_id'])
hh = hh[hh.building_id > 0]
for la, la_job in jobs.groupby('large_area_id'):
la_hh = hh[hh.large_area_id == la]
la_job['building_id'] = la_hh.sample(len(la_job), replace=True).building_id.values
wrap_jobs.update_col_from_series('building_id',
la_job['building_id'],
cast=True)
_print_number_unplaced(wrap_jobs, 'building_id')
@orca.step()
def diagnostic(parcels, buildings, jobs, households, nodes, iter_var):
parcels = parcels.to_frame()
buildings = buildings.to_frame()
jobs = jobs.to_frame()
households = households.to_frame()
nodes = nodes.to_frame()
import pdb
pdb.set_trace()
def make_repm_func(model_name, yaml_file, dep_var):
"""
Generator function for single-model REPMs.
"""
@orca.step(model_name)
def func():
buildings = orca.get_table('buildings')
nodes_walk = orca.get_table('nodes_walk')
print yaml_file
return utils.hedonic_simulate(yaml_file, buildings,
nodes_walk, dep_var)
return func
repm_step_names = []
for repm_config in os.listdir('./configs/repm'):
model_name = repm_config.split('.')[0]
if repm_config.startswith('res'):
dep_var = 'sqft_price_res'
elif repm_config.startswith('nonres'):
dep_var = 'sqft_price_nonres'
make_repm_func(model_name, "repm/" + repm_config, dep_var)
repm_step_names.append(model_name)
orca.add_injectable('repm_step_names', repm_step_names)
@orca.step()
def increase_property_values(buildings, income_growth_rates):
# Hack to make more feasibility
# dfinc = pd.read_csv("income_growth_rates.csv", index_col=['year'])
dfinc = income_growth_rates.to_frame().loc[:orca.get_injectable('year')]
dfrates = dfinc.prod().to_frame(name='cumu_rates').fillna(1.0) # get cumulative increase from base to current year
dfrates.index = dfrates.index.astype(float)
bd = buildings.to_frame(['large_area_id', 'sqft_price_res', 'sqft_price_nonres'])
bd = pd.merge(bd, dfrates, left_on='large_area_id', right_index=True, how='left')
buildings.update_col_from_series('sqft_price_res', bd.sqft_price_res * bd.cumu_rates)
buildings.update_col_from_series('sqft_price_nonres', bd.sqft_price_nonres * bd.cumu_rates)
@orca.step()
def households_relocation(households, annual_relocation_rates_for_households):
relocation_rates = annual_relocation_rates_for_households.to_frame()
relocation_rates = relocation_rates.rename(columns={'age_max': 'age_of_head_max', 'age_min': 'age_of_head_min'})
relocation_rates.probability_of_relocating *= .2
reloc = relocation.RelocationModel(relocation_rates, 'probability_of_relocating')
_print_number_unplaced(households, 'building_id')
print "un-placing"
hh = households.to_frame(households.local_columns)
idx_reloc = reloc.find_movers(hh)
households.update_col_from_series('building_id',
pd.Series(-1, index=idx_reloc),
cast=True)
_print_number_unplaced(households, 'building_id')
@orca.step()
def jobs_relocation(jobs, annual_relocation_rates_for_jobs):
relocation_rates = annual_relocation_rates_for_jobs.to_frame().reset_index()
reloc = relocation.RelocationModel(relocation_rates, 'job_relocation_probability')
_print_number_unplaced(jobs, 'building_id')
print "un-placing"
j = jobs.to_frame(jobs.local_columns)
idx_reloc = reloc.find_movers(j[j.home_based_status <= 0])
jobs.update_col_from_series('building_id',
pd.Series(-1, index=idx_reloc),
cast=True)
_print_number_unplaced(jobs, 'building_id')
def presses_trans((ct, hh, p, target, iter_var)):
ct_finite = ct[ct.persons_max <= 100]
ct_inf = ct[ct.persons_max > 100]
tran = transition.TabularTotalsTransition(ct_finite, 'total_number_of_households')
model = transition.TransitionModel(tran)
new, added_hh_idx, new_linked = \
model.transition(hh, iter_var,
linked_tables={'linked': (p, 'household_id')})
new.loc[added_hh_idx, "building_id"] = -1
pers = new_linked['linked']
pers = pers[pers.household_id.isin(new.index)]
out = [[new, pers]]
target -= len(pers)
best_qal = np.inf
best = []
for _ in range(3):
tran = transition.TabularTotalsTransition(ct_inf, 'total_number_of_households')
model = transition.TransitionModel(tran)
new, added_hh_idx, new_linked = \
model.transition(hh, iter_var,
linked_tables={'linked': (p, 'household_id')})
new.loc[added_hh_idx, "building_id"] = -1
pers = new_linked['linked']
pers = pers[pers.household_id.isin(new.index)]
qal = abs(target - len(pers))
if qal < best_qal:
best = (new, pers)
best_qal = qal
out.append(best)
return out
@orca.step()
def households_transition(households, persons, annual_household_control_totals, remi_pop_total, iter_var):
region_ct = annual_household_control_totals.to_frame()
max_cols = region_ct.columns[region_ct.columns.str.endswith('_max') & (region_ct == -1).any(axis=0)]
region_ct[max_cols] = region_ct[max_cols].replace(-1, np.inf)
region_ct[max_cols] += 1
region_hh = households.to_frame(households.local_columns + ['large_area_id'])
region_p = persons.to_frame(persons.local_columns)
region_target = remi_pop_total.to_frame()
def cut_to_la((large_area_id, hh)):
p = region_p[region_p.household_id.isin(hh.index)]
target = int(region_target.loc[large_area_id, str(iter_var)])
ct = region_ct[region_ct.large_area_id == large_area_id]
del ct["large_area_id"]
return ct, hh, p, target, iter_var
arg_per_la = map(cut_to_la, region_hh.groupby('large_area_id'))
# cunks_per_la = map(presses_trans, arg_per_la)
pool = Pool(8)
cunks_per_la = pool.map(presses_trans, arg_per_la)
pool.close()
pool.join()
out = reduce(operator.concat, cunks_per_la)
# fix indexes
out_hh_fixed = []
out_p_fixed = []
hhidmax = region_hh.index.values.max() + 1
pidmax = region_p.index.values.max() + 1
for hh, p in out:
hh.index.name = 'household_id'
hh = hh.reset_index()
hh['household_id_old'] = hh['household_id']
new_hh = (hh.building_id == -1).sum()
hh.loc[hh.building_id == -1, 'household_id'] = range(hhidmax, hhidmax + new_hh)
hhidmax += new_hh
hhid_map = hh[['household_id_old', 'household_id']].set_index('household_id_old')
p.index.name = 'person_id'
p = pd.merge(p.reset_index(), hhid_map, left_on='household_id', right_index=True)
new_p = (p.household_id_x != p.household_id_y).sum()
p.loc[p.household_id_x != p.household_id_y, 'person_id'] = range(pidmax, pidmax + new_p)
pidmax += new_p
p['household_id'] = p['household_id_y']
new_hh_df = hh.set_index('household_id')
out_hh_fixed.append(new_hh_df[households.local_columns])
out_p_fixed.append(p.set_index('person_id')[persons.local_columns])
orca.add_table("households", pd.concat(out_hh_fixed, verify_integrity=True))
orca.add_table("persons", pd.concat(out_p_fixed, verify_integrity=True))
@orca.step()
def fix_lpr(households, persons, iter_var, workers_employment_rates_by_large_area):
from numpy.random import choice
hh = households.to_frame(households.local_columns + ['large_area_id'])
hh["target_workers"] = 0
p = persons.to_frame(persons.local_columns + ['large_area_id'])
lpr = workers_employment_rates_by_large_area.to_frame(['age_min', 'age_max', str(iter_var)])
employed = p.worker == True
p["weight"] = 1.0 / np.sqrt(p.join(hh["workers"], "household_id").workers + 1.0)
colls = ['persons', 'race_id', 'workers', 'children', 'large_area_id'] # , 'age_of_head'
same = {tuple(idx): df[['income', 'cars']] for idx, df in hh.groupby(colls)}
new_employ = []
new_unemploy = []
for large_area_id, row in lpr.iterrows():
select = (p.large_area_id == large_area_id) & (p.age >= row.age_min) & (p.age <= row.age_max)
lpr = row[str(iter_var)]
lpr_workers = int(select.sum() * lpr)
num_workers = (select & employed).sum()
if lpr_workers > num_workers:
# employ some persons
new_employ.append(choice(p[select & (~employed)].index, int(lpr_workers - num_workers), False))
else:
# unemploy some persons
prob = p[select & employed].weight
prob /= prob.sum()
new_unemploy.append(choice(p[select & employed].index, int(num_workers - lpr_workers), False, p=prob))
# print large_area_id, row.age_min, row.age_max, select.sum(), num_workers, lpr_workers, lpr
if len(new_employ) > 0:
p.loc[np.concatenate(new_employ), "worker"] = 1
if len(new_unemploy):
p.loc[np.concatenate(new_unemploy), "worker"] = 0
hh["old_workers"] = hh.workers
hh.workers = p.groupby("household_id").worker.sum()
hh.workers = hh.workers.fillna(0)
changed = (hh.workers != hh.old_workers)
for match_colls, chh in hh[changed].groupby(colls):
try:
match = same[tuple(match_colls)]
new_workers = choice(match.index, len(chh), True)
hh.loc[chh.index, ['income', 'cars']] = match.loc[new_workers, ['income', 'cars']].values
except KeyError:
pass
# todo: something better!?
orca.add_table("households", hh[households.local_columns])
orca.add_table("persons", p[persons.local_columns])
@orca.step()
def jobs_transition(jobs, annual_employment_control_totals, iter_var):
ct_emp = annual_employment_control_totals.to_frame()
ct_emp = ct_emp.reset_index().set_index('year')
tran = transition.TabularTotalsTransition(ct_emp, 'total_number_of_jobs')
model = transition.TransitionModel(tran)
j = jobs.to_frame(jobs.local_columns + ['large_area_id'])
new, added_jobs_idx, _ = model.transition(j, iter_var)
orca.add_injectable("jobs_large_area_lookup",
new.large_area_id,
autocall=False, cache=True)
new.loc[added_jobs_idx, "building_id"] = -1
orca.add_table("jobs", new[jobs.local_columns])
@orca.step()
def jobs_scaling_model(jobs):
wrap_jobs = jobs
jobs = jobs.to_frame(jobs.local_columns + ['large_area_id'])
regional_sectors = {1, 7, 12, 13, 15, 18}
la_sectors = []
def random_choice(chooser_ids, alternative_ids, probabilities):
return pd.Series(np.random.choice(
alternative_ids, size=len(chooser_ids), replace=True, p=probabilities), index=chooser_ids)
jobs_to_place = jobs[jobs.building_id.isnull() | (jobs.building_id == -1)]
selected = jobs_to_place.sector_id.isin(regional_sectors)
for (sec, la) in la_sectors:
selected |= ((jobs_to_place.sector_id == sec) & (jobs_to_place.large_area_id == la))
jobs_to_place = jobs_to_place[selected]
if len(jobs_to_place) > 0:
for (large_area_id, sector), segment in jobs_to_place.groupby(['large_area_id', 'sector_id']):
counts_by_bid = jobs[(jobs.sector_id == sector) & (jobs.large_area_id == large_area_id)].groupby(
['building_id']).size()
prop_by_bid = counts_by_bid / counts_by_bid.sum()
choices = random_choice(segment.index.values, prop_by_bid.index.values, prop_by_bid.values)
wrap_jobs.update_col_from_series('building_id',
choices,
cast=True)
@orca.step()
def gq_pop_scaling_model(group_quarters, group_quarters_control_totals, year):
gqpop = group_quarters.to_frame(group_quarters.local_columns + ['city_id'])
target_gq = group_quarters_control_totals.to_frame()
target_gq = target_gq[target_gq.year == year].set_index('city_id')
for city_id, local_gqpop in gqpop.groupby('city_id'):
diff = target_gq.loc[city_id]['count'] - len(local_gqpop)
protected = (((local_gqpop.gq_code > 100) & (local_gqpop.gq_code < 200)) |
((local_gqpop.gq_code > 500) & (local_gqpop.gq_code < 600)) |
(local_gqpop.gq_code == 701))
local_gqpop = local_gqpop[~protected]
if diff > 0:
diff = min(len(local_gqpop), abs(diff))
if diff > 0:
newgq = local_gqpop.sample(diff, replace=True)
newgq.index = gqpop.index.values.max() + 1 + np.arange(len(newgq))
gqpop = gqpop.append(newgq)
elif diff < 0:
diff = min(len(local_gqpop), abs(diff))
if diff > 0:
removegq = local_gqpop.sample()
gqpop.drop(removegq.index, inplace=True)
orca.add_table('group_quarters', gqpop[group_quarters.local_columns])
@orca.step()
def refiner(jobs, households, buildings, persons, year, refiner_events, group_quarters):
location_ids = ['b_zone_id', 'zone_id', 'b_city_id', 'city_id', 'large_area_id']
jobs_columns = jobs.local_columns
jobs = jobs.to_frame(jobs_columns + location_ids)
group_quarters_columns = group_quarters.local_columns
group_quarters = group_quarters.to_frame(group_quarters_columns + location_ids)
households_columns = households.local_columns
households = households.to_frame(households_columns + location_ids)
households["household_id_old"] = households.index.values
buildings = buildings.to_frame(buildings.local_columns + location_ids + ["gq_building"])
dic_agent = {'jobs': jobs, 'households': households, 'gq': group_quarters}
refinements = refiner_events.to_frame()
refinements = refinements[refinements.year == year]
assert refinements.action.isin(
{'clone', 'subtract_pop', 'subtract', 'add_pop', 'add', 'target_pop', 'target'}).all(), "Unknown action"
assert refinements.agents.isin({'jobs', 'households', 'gq'}).all(), "Unknown agents"
def add_agents(agents, agents_pool, agent_expression, location_expression, number_of_agents):
"""Move from pool to data"""
bselect = buildings.query(location_expression)
if len(bselect) <= 0:
print("We can't find a building to place these agents")
return agents, agents_pool
new_building_ids = bselect.sample(number_of_agents, replace=True).index.values
# maybe use job reallocation instead of random
if len(agents_pool) > 0:
agents_sub_pool = agents_pool.query(agent_expression)
if len(agents_sub_pool) >= number_of_agents:
agents_sample = agents_sub_pool.sample(number_of_agents, replace=False)
else:
agents_sample = agents_sub_pool.sample(number_of_agents, replace=True)
agents_sample.building_id = new_building_ids
agents_pool.drop(agents_sample.index, inplace=True)
agents_sample.index = agents.index.values.max() + 1 + np.arange(len(agents_sample))
else:
agents_sample = agents.query(agent_expression)
if len(agents_sample) > 0:
agents_sample = agents_sample.sample(number_of_agents, replace=True)
agents_sample.index = agents.index.values.max() + 1 + np.arange(len(agents_sample))
agents_sample.building_id = new_building_ids
agents = agents.append(agents_sample)
return agents, agents_pool
def add_pop_agents(agents, agents_pool, agent_expression, location_expression, number_of_agents):
"""Move from pool to data"""
bselect = buildings.query(location_expression)
if len(bselect) <= 0:
print("We can't fined a building to place these agents")
return agents, agents_pool
if len(agents_pool) > 0:
available_agents = agents_pool.query(agent_expression)
available_agents = available_agents[available_agents.persons <= number_of_agents]
while len(available_agents) > 0 and number_of_agents > 0:
available_agents = available_agents[available_agents.persons <= number_of_agents]
if len(available_agents) >= number_of_agents:
agents_sample = available_agents.sample(number_of_agents, replace=False)
else:
agents_sample = available_agents.sample(number_of_agents, replace=True)
agents_sample = agents_sample[agents_sample.persons.cumsum() <= number_of_agents]
agents_sample.index = agents.index.values.max() + 1 + np.arange(len(agents_sample))
agents_sample.building_id = bselect.sample(len(agents_sample), replace=True).index.values
agents = agents.append(agents_sample)
number_of_agents -= agents_sample.persons.sum()
else:
available_agents = agents.query(agent_expression)
available_agents = available_agents[available_agents.persons <= number_of_agents]
while len(available_agents) > 0 and number_of_agents > 0:
available_agents = available_agents[available_agents.persons <= number_of_agents]
agents_sample = available_agents.sample(number_of_agents, replace=True)
agents_sample = agents_sample[agents_sample.persons.cumsum() <= number_of_agents]
agents_sample.index = agents.index.values.max() + 1 + np.arange(len(agents_sample))
agents_sample.building_id = bselect.sample(len(agents_sample), replace=True).index.values
agents = agents.append(agents_sample)
number_of_agents -= agents_sample.persons.sum()
return agents, agents_pool
def subtract_agents(agents, agents_pool, agent_expression, location_expression, number_of_agents):
"""Move from data to pool"""
# TODO: Share code with clone_agents then call drop
available_agents = agents.query(agent_expression)
bselect = buildings.query(location_expression)
local_agents = available_agents.loc[available_agents.building_id.isin(bselect.index.values)]
if len(local_agents) > 0 and number_of_agents > 0:
selected_agents = local_agents.sample(min(len(local_agents), number_of_agents))
agents_pool = agents_pool.append(selected_agents, ignore_index=True)
agents.drop(selected_agents.index, inplace=True)
return agents, agents_pool
def subtract_pop_agents(agents, agents_pool, agent_expression, location_expression, number_of_agents):
"""Move from data to pool"""
available_agents = agents.query(agent_expression)
bselect = buildings.query(location_expression)
local_agents = available_agents.loc[available_agents.building_id.isin(bselect.index.values)]
local_agents = local_agents[local_agents.persons <= number_of_agents]
while len(local_agents) > 0 and number_of_agents > 0:
local_agents = local_agents[local_agents.persons <= number_of_agents]
selected_agents = local_agents.sample(min(len(local_agents), number_of_agents))
selected_agents = selected_agents[selected_agents.persons.cumsum() <= number_of_agents]
number_of_agents -= selected_agents.persons.sum()
agents_pool = agents_pool.append(selected_agents, ignore_index=True)
local_agents.drop(selected_agents.index, inplace=True)
agents.drop(selected_agents.index, inplace=True)
return agents, agents_pool
def clone_agents(agents, agents_pool, agent_expression, location_expression, number_of_agents):
"""Copy from data to pool. Don't remove from data!"""
available_agents = agents.query(agent_expression)
bselect = buildings.query(location_expression)
local_agents = available_agents.loc[available_agents.building_id.isin(bselect.index.values)]
if len(local_agents) > 0:
selected_agents = local_agents.sample(min(len(local_agents), number_of_agents))
agents_pool = agents_pool.append(selected_agents, ignore_index=True)
return agents, agents_pool
def target_agents(agents, agent_expression, location_expression, number_of_agents, by_pop=False):
"""Determine whether to add or subtract based on data"""
# use for employment event model
exist_agents = agents.query(agent_expression)
bselect = buildings.query(location_expression)
local_agents = exist_agents.loc[exist_agents.building_id.isin(bselect.index.values)]
if by_pop:
return local_agents.persons.sum() - number_of_agents
return len(local_agents) - number_of_agents
for tid, trecords in refinements.groupby("transaction_id"):
print '** processing transcaction ', tid
agent_types = trecords.agents.drop_duplicates()
assert len(agent_types) == 1, "different agents in same transaction_id"
agent_type = agent_types.iloc[0]
agents = dic_agent[agent_type]
pool = pd.DataFrame(data=None, columns=agents.columns)
for _, record in trecords[trecords.action == 'clone'].iterrows():
print record
agents, pool = clone_agents(agents,
pool,
record.agent_expression,
record.location_expression,
record.amount)
for _, record in trecords[trecords.action == 'subtract_pop'].iterrows():
print record
assert agent_type == 'households'
agents, pool = subtract_pop_agents(agents,
pool,
record.agent_expression,
record.location_expression,
record.amount)
for _, record in trecords[trecords.action == 'subtract'].iterrows():
print record
agents, pool = subtract_agents(agents,
pool,
record.agent_expression,
record.location_expression,
record.amount)
for _, record in trecords[trecords.action == 'add_pop'].iterrows():
print record
assert agent_type == 'households'
agents, pool = add_pop_agents(agents,
pool,
record.agent_expression,
record.location_expression,
record.amount)
for _, record in trecords[trecords.action == 'add'].iterrows():
print record
agents, pool = add_agents(agents,
pool,
record.agent_expression,
record.location_expression,
record.amount)
for _, record in trecords[trecords.action == 'target_pop'].iterrows():
print record
assert agent_type == 'households'
diff = target_agents(dic_agent[record.agents],
record.agent_expression,
record.location_expression,
record.amount,
by_pop=True)
if diff < 0:
agents, pool = add_pop_agents(agents,
pool,
record.agents_expression,
record.location_expression,
abs(diff))
elif diff > 0:
agents, pool = subtract_pop_agents(agents,
pool,
record.agent_expression,
record.location_expression,
diff)
for _, record in trecords[trecords.action == 'target'].iterrows():
print record
diff = target_agents(dic_agent[record.agents],
record.agent_expression,
record.location_expression,
record.amount)
if diff < 0:
print 'add: ', abs(diff)
agents, pool = add_agents(agents,
pool,
record.agent_expression,
record.location_expression,
abs(diff))
elif diff > 0:
print 'subtract: ', abs(diff)
agents, pool = subtract_agents(agents,
pool,
record.agent_expression,
record.location_expression,
diff)
dic_agent[agent_type] = agents
if refinements.agents.isin({'jobs'}).sum() > 0:
jobs = dic_agent['jobs']
assert jobs.index.duplicated().sum() == 0, "duplicated index in jobs"
jobs['large_area_id'] = misc.reindex(buildings.large_area_id, jobs.building_id)
orca.add_table('jobs', jobs[jobs_columns])
if refinements.agents.isin({'gq'}).sum() > 0:
group_quarters = dic_agent['gq']
assert group_quarters.index.duplicated().sum() == 0, "duplicated index in group_quarters"
orca.add_table('group_quarters', group_quarters[group_quarters_columns])
if refinements.agents.isin({'households'}).sum() > 0:
households = dic_agent['households']
assert households.index.duplicated().sum() == 0, "duplicated index in households"
households['large_area_id'] = misc.reindex(buildings.large_area_id, households.building_id)
orca.add_table('households', households[households_columns])
persons_columns = persons.local_columns
persons = persons.to_frame(persons_columns)
pidmax = persons.index.values.max() + 1
hh_index_lookup = households[["household_id_old"]].reset_index().set_index("household_id_old")
hh_index_lookup.columns = ['household_id']
p = pd.merge(persons.reset_index(), hh_index_lookup, left_on='household_id', right_index=True)
new_p = (p.household_id_x != p.household_id_y).sum()
p.loc[p.household_id_x != p.household_id_y, 'person_id'] = range(pidmax, pidmax + new_p)
p['household_id'] = p['household_id_y']
persons = p.set_index('person_id')
assert persons.household_id.isin(households.index).all(), "persons.household_id not in households"
assert len(persons.groupby('household_id').size()) == len(households.persons), "households with no persons"
assert persons.index.duplicated().sum() == 0, "duplicated index in persons"
orca.add_table('persons', persons[persons_columns])
@orca.step()
def scheduled_development_events(buildings, iter_var, events_addition):
sched_dev = events_addition.to_frame()
sched_dev = sched_dev[sched_dev.year_built == iter_var].reset_index(drop=True)
if len(sched_dev) > 0:
sched_dev["stories"] = 0
zone = sched_dev.b_zone_id
city = sched_dev.b_city_id
sched_dev = add_extra_columns_res(sched_dev)
sched_dev['b_zone_id'] = zone
sched_dev['b_city_id'] = city
b = buildings.to_frame(buildings.local_columns)
max_id = orca.get_injectable("max_building_id")
all_buildings = parcel_utils.merge_buildings(b, sched_dev[b.columns], False, max_id)
orca.add_injectable("max_building_id", max(all_buildings.index.max(), max_id))
orca.add_table("buildings", all_buildings)
# Todo: maybe we need to impute some columns
# Todo: parcel use need to be updated
# Todo: record dev_id -> building_id
@orca.step()
def scheduled_demolition_events(buildings, households, jobs, iter_var, events_deletion):
sched_dev = events_deletion.to_frame()
sched_dev = sched_dev[sched_dev.year_built == iter_var].reset_index(drop=True)
if len(sched_dev) > 0:
buildings = buildings.to_frame(buildings.local_columns)
drop_buildings = buildings[buildings.index.isin(sched_dev.building_id)].copy()
buildings_idx = drop_buildings.index
drop_buildings['year_demo'] = iter_var
if orca.is_table("dropped_buildings"):
prev_drops = orca.get_table("dropped_buildings").to_frame()
orca.add_table("dropped_buildings",
pd.concat([drop_buildings, prev_drops]))
else:
orca.add_table("dropped_buildings", drop_buildings)
orca.add_table("buildings", buildings.drop(buildings_idx))
# unplace HH
# todo: use orca.update_col_from_series
households = households.to_frame(households.local_columns)
households.loc[households.building_id.isin(sched_dev.building_id), "building_id"] = -1
orca.add_table("households", households)
# unplace jobs
# todo: use orca.update_col_from_series
jobs = jobs.to_frame(jobs.local_columns)
jobs.loc[jobs.building_id.isin(sched_dev.building_id), "building_id"] = -1
orca.add_table("jobs", jobs)
# Todo: parcel use need to be updated
@orca.step()
def random_demolition_events(buildings, households, jobs, year, demolition_rates):
demolition_rates = demolition_rates.to_frame()
demolition_rates *= 0.1 + (1.0 - 0.1) * (2045 - year) / (2045 - 2015)
buildings_columns = buildings.local_columns
buildings = buildings.to_frame(buildings.local_columns + ['b_total_jobs', 'b_total_households'])
b = buildings.copy()
allowed = variables.parcel_is_allowed()
b = b[b.parcel_id.isin(allowed[allowed].index)]
buildings_idx = []
def sample(targets, type_b, accounting, weights):
for b_city_id, target in targets[targets > 0].iteritems():
rel_b = type_b[type_b.b_city_id == b_city_id]
rel_b = rel_b[rel_b[accounting] <= target]
size = min(len(rel_b), int(target))
if size > 0:
rel_b = rel_b.sample(size, weights=rel_b[weights])
rel_b = rel_b[rel_b[accounting].cumsum() <= int(target)]
buildings_idx.append(rel_b.copy())
b['wj'] = 1.0 / (1.0 + np.log1p(b.b_total_jobs))
sample(demolition_rates.typenonsqft, b[b.non_residential_sqft > 0], 'non_residential_sqft', 'wj')
b = b[b.non_residential_sqft == 0]
b['wh'] = 1.0 / (1.0 + np.log1p(b.b_total_households))
sample(demolition_rates.type81units, b[b.building_type_id == 81], 'residential_units', 'wh')
sample(demolition_rates.type82units, b[b.building_type_id == 82], 'residential_units', 'wh')
sample(demolition_rates.type83units, b[b.building_type_id == 83], 'residential_units', 'wh')
# sample(demolition_rates.type84units, b[b.building_type_id == 84], 'residential_units', 'wh')
drop_buildings = pd.concat(buildings_idx).copy()[buildings_columns]
drop_buildings = drop_buildings[~drop_buildings.index.duplicated(keep='first')]
buildings_idx = drop_buildings.index
drop_buildings['year_demo'] = year
if orca.is_table("dropped_buildings"):
prev_drops = orca.get_table("dropped_buildings").to_frame()
orca.add_table("dropped_buildings",
pd.concat([drop_buildings, prev_drops]))
else:
orca.add_table("dropped_buildings", drop_buildings)
orca.add_table("buildings", buildings[buildings_columns].drop(buildings_idx))
# unplace HH
# todo: use orca.update_col_from_series
households = households.to_frame(households.local_columns)
households.loc[households.building_id.isin(buildings_idx), "building_id"] = -1
orca.add_table("households", households)
# unplace jobs
# todo: use orca.update_col_from_series
jobs = jobs.to_frame(jobs.local_columns)
jobs.loc[jobs.building_id.isin(buildings_idx), "building_id"] = -1
orca.add_table("jobs", jobs)
# Todo: parcel use need to be updated
def parcel_average_price(use):
# Copied from variables.py
parcels_wrapper = orca.get_table('parcels')
if len(orca.get_table('nodes_walk')) == 0:
# if nodes isn't generated yet
return pd.Series(index=parcels_wrapper.index)
return misc.reindex(orca.get_table('nodes_walk')[use],
parcels_wrapper.nodeid_walk)
@orca.injectable('cost_shifters')
def shifters():
with open(os.path.join(misc.configs_dir(), "cost_shifters.yaml")) as f:
cfg = yaml.load(f)
return cfg
def cost_shifter_callback(self, form, df, costs):
shifter_cfg = orca.get_injectable('cost_shifters')['calibration']
geography = shifter_cfg['calibration_geography_id']
shift_type = 'residential' if form == 'residential' else 'non_residential'
shifters = shifter_cfg['proforma_cost_shifters'][shift_type]
for geo, geo_df in df.reset_index().groupby(geography):
shifter = shifters[geo]
costs[:, geo_df.index] *= shifter
return costs
@orca.step('feasibility')
def feasibility(parcels):
parcel_utils.run_feasibility(parcels,
parcel_average_price,
variables.parcel_is_allowed,
cfg='proforma.yaml',
modify_costs=cost_shifter_callback
)
feasibility = orca.get_table('feasibility').to_frame()
for lid, df in parcels.large_area_id.to_frame().groupby('large_area_id'):
orca.add_table('feasibility_' + str(lid), feasibility[feasibility.index.isin(df.index)])
def add_extra_columns_nonres(df):
# type: (pd.DataFrame) -> pd.DataFrame
for col in ['improvement_value', 'land_area', 'tax_exempt', 'sqft_price_nonres',
'sqft_price_res', 'sqft_per_unit', 'hu_filter']:
df[col] = 0
df['year_built'] = orca.get_injectable('year')
p = orca.get_table('parcels').to_frame(['zone_id', 'city_id'])
for col in ['zone_id', 'city_id']:
df['b_' + col] = misc.reindex(p[col], df.parcel_id)
return df.fillna(0)
def add_extra_columns_res(df):
# type: (pd.DataFrame) -> pd.DataFrame
df = add_extra_columns_nonres(df)
if 'ave_unit_size' in df.columns:
df['sqft_per_unit'] = df['ave_unit_size']
else:
df['sqft_per_unit'] = misc.reindex(orca.get_table('parcels').ave_unit_size, df.parcel_id)
return df
def probable_type(row):
"""
Function to pass to form_to_btype_callback in parcels_utils.add_buildings.
Reads form for the given row in new buildings DataFrame, gets the
building type probabilities from the form_btype_distributions injectable,
and chooses a building type based on those probabilities.
Parameters
----------
row : Series
Returns
-------
btype : int
"""
form = row['form']
form_to_btype_dists = orca.get_injectable("form_btype_distributions")
btype_dists = form_to_btype_dists[form]
# keys() and values() guaranteed to be in same order
btype = np.random.choice(a=btype_dists.keys(), p=btype_dists.values())
return btype
def register_btype_distributions(buildings):
"""
Prior to adding new buildings selected by the developer model, this
function registers a dictionary where keys are forms, and values are
dictionaries of building types. In each sub-dictionary, the keys are
building type IDs, and values are probabilities. These probabilities are
generated from distributions of building types for each form in each
large area.
Parameters
----------
buildings : DataFrame
Buildings DataFrame (not wrapper) at the time of registration. Must
include building_type_id column.
Returns
-------
None
"""
form_to_btype = orca.get_injectable("form_to_btype")
form_btype_dists = {}
for form in form_to_btype.keys():
bldgs = buildings.loc[buildings.building_type_id
.isin(form_to_btype[form])]
bldgs_by_type = bldgs.groupby('building_type_id').size()
normed = bldgs_by_type / sum(bldgs_by_type)
form_btype_dists[form] = normed.to_dict()
orca.add_injectable("form_btype_distributions", form_btype_dists)
def run_developer(target_units, lid, forms, buildings, supply_fname,
parcel_size, ave_unit_size, current_units, cfg,
add_more_columns_callback=None,
unplace_agents=('households', 'jobs'),
profit_to_prob_func=None,
custom_selection_func=None, pipeline=False):
"""
copied form parcel_utils and modified
"""
from developer import develop
print 'processing large area id:', lid
cfg = misc.config(cfg)
dev = develop.Developer.from_yaml(orca.get_table('feasibility_' + str(lid)).to_frame(), forms,
target_units, parcel_size,
ave_unit_size, current_units,
orca.get_injectable('year'), str_or_buffer=cfg)
print("{:,} feasible buildings before running developer".format(
len(dev.feasibility)))
new_buildings = dev.pick(profit_to_prob_func, custom_selection_func)
orca.add_table('feasibility_' + str(lid), dev.feasibility)
if new_buildings is None or len(new_buildings) == 0:
return
parcel_utils.add_buildings(dev.feasibility,
buildings,
new_buildings,
probable_type,
add_more_columns_callback,
supply_fname, True,
unplace_agents, pipeline)
@orca.step('residential_developer')
def residential_developer(households, parcels, target_vacancies):
target_vacancies = target_vacancies.to_frame()
target_vacancies = target_vacancies[target_vacancies.year == orca.get_injectable('year')]
orig_buildings = orca.get_table('buildings').to_frame(["residential_units", "large_area_id", "building_type_id"])
for lid, _ in parcels.large_area_id.to_frame().groupby('large_area_id'):
la_orig_buildings = orig_buildings[orig_buildings.large_area_id == lid]
target_vacancy = float(target_vacancies[target_vacancies.large_area_id == lid].res_target_vacancy_rate)
target_units = parcel_utils.compute_units_to_build((households.large_area_id == lid).sum(),
la_orig_buildings.residential_units.sum(),
target_vacancy)
register_btype_distributions(la_orig_buildings)
run_developer(
target_units,
lid,
"residential",
orca.get_table('buildings'),
'residential_units',
parcels.parcel_size,
parcels.ave_unit_size,
parcels.total_units,
'res_developer.yaml',
add_more_columns_callback=add_extra_columns_res)
@orca.step()
def non_residential_developer(jobs, parcels, target_vacancies):
target_vacancies = target_vacancies.to_frame()
target_vacancies = target_vacancies[target_vacancies.year == orca.get_injectable('year')]
orig_buildings = orca.get_table('buildings').to_frame(["job_spaces", "large_area_id", "building_type_id"])
for lid, _ in parcels.large_area_id.to_frame().groupby('large_area_id'):
la_orig_buildings = orig_buildings[orig_buildings.large_area_id == lid]
target_vacancy = float(target_vacancies[target_vacancies.large_area_id == lid].non_res_target_vacancy_rate)
num_jobs = ((jobs.large_area_id == lid) & (jobs.home_based_status == 0)).sum()
target_units = parcel_utils.compute_units_to_build(num_jobs,
la_orig_buildings.job_spaces.sum(),
target_vacancy)
register_btype_distributions(la_orig_buildings)
run_developer(
target_units,
lid,
["office", "retail", "industrial", "medical"],
orca.get_table('buildings'),
"job_spaces",
parcels.parcel_size,
parcels.ave_unit_size,
parcels.total_job_spaces,
'nonres_developer.yaml',
add_more_columns_callback=add_extra_columns_nonres)
@orca.step()
def build_networks(parcels):
import yaml
pdna.network.reserve_num_graphs(2)
# networks in semcog_networks.h5
with open(r"configs/available_networks.yaml", 'r') as stream:
dic_net = yaml.load(stream)
st = pd.HDFStore(os.path.join(misc.data_dir(), "semcog_networks.h5"), "r")
lstnet = [
{
'name': 'mgf14_ext_walk',
'cost': 'cost1',
'prev': 10560, # 2 miles
'net': 'net_walk'
},
{
'name': 'tdm_ext',
'cost': 'cost1',
'prev': 60, # 60 minutes
'net': 'net_drv'
},
]
for n in lstnet:
n_dic_net = dic_net[n['name']]
nodes, edges = st[n_dic_net['nodes']], st[n_dic_net['edges']]
net = pdna.Network(nodes["x"], nodes["y"], edges["from"], edges["to"],
edges[[n_dic_net[n['cost']]]])
net.precompute(n['prev'])
net.init_pois(num_categories=10, max_dist=n['prev'], max_pois=5)
orca.add_injectable(n['net'], net)
# spatially join node ids to parcels
p = parcels.local
p['nodeid_walk'] = orca.get_injectable('net_walk').get_node_ids(p['centroid_x'], p['centroid_y'])
p['nodeid_drv'] = orca.get_injectable('net_drv').get_node_ids(p['centroid_x'], p['centroid_y'])
orca.add_table("parcels", p)
@orca.step()
def neighborhood_vars(jobs, households, buildings):
b = buildings.to_frame(['large_area_id'])
j = jobs.to_frame(jobs.local_columns)
h = households.to_frame(households.local_columns)
idx_invalid_building_id = np.in1d(j.building_id, b.index.values) == False
if idx_invalid_building_id.sum() > 0:
print("we have jobs with bad building id's there are #", idx_invalid_building_id.sum())
j.loc[idx_invalid_building_id, 'building_id'] = np.random.choice(
b.index.values,
idx_invalid_building_id.sum())
# TODO: keep LA the same
j['large_area_id'] = misc.reindex(b.large_area_id, j.building_id)
orca.add_table("jobs", j)
idx_invalid_building_id = np.in1d(h.building_id, b.index.values) == False
if idx_invalid_building_id.sum() > 0: