Update docs

docs/consequential.rst

@@ -154,6 +154,6 @@ If not, the following default arguments value are used:
         system_args=args
     )
 
-    ndb.update_electricity()
+    ndb.update("electricity")
 
     ndb.write_db_to_brightway()

docs/extract.rst

@@ -840,7 +840,7 @@ They introduce the following datasets:
   carbon dioxide, captured at cement production plant, for subsequent reuse                                        RER
  =============================================================================================================== ===========
 
-Using the transformation function `update_dac()`, *premise* creates various configurations of these processes,
+Using the transformation function `update("dac")`, *premise* creates various configurations of these processes,
 using different sources for heat (industrial steam heat, high-temp heat
 pump heat and excess heat), which are found under the following names, for each IAM region:
 

docs/faq.rst

@@ -153,7 +153,7 @@ In theory, there is consistency between the IAM scenario and pLCA database
 when 100% of the IAM variables and related projections are integrated
 into the pLCA database.
 
-This is not the case today, as ``premise`` only integrates a subset of IAM variables, namely those that relate to:
+This is not the case today, as ``premise`` only integrates a subset of IAM variables, notably those that relate to:
 
 - power production
 - steel production

docs/transform.rst

@@ -85,7 +85,7 @@ Run
         source_version="3.7.1",
         key='xxxxxxxxxxxxxxxxxxxxxxxxx'
     )
-    ndb.update_electricity()
+    ndb.update("electricity")
 
 
 Efficiency adjustment
@@ -257,7 +257,7 @@ emissions outputs, are re-scaled by 1/1.03 (=0.97).
 
 While non-CO2 emissions (e.g., CO) are reduced because of the reduction in fuel consumption,
 the emission factor per energy unit remains the same (i.e., gCO/MJ natural gas)).
-It can be re-scaled using the `update_emissions` function, which updates emission factors according
+It can be re-scaled using the `.update("emissions")` function, which updates emission factors according
 to GAINS projections.
 
 
@@ -684,7 +684,7 @@ Run
         source_version="3.7.1",
         key='xxxxxxxxxxxxxxxxxxxxxxxxx'
     )
-    ndb.update_cement()
+    ndb.update("cement")
 
 
 
@@ -752,7 +752,7 @@ Run
         source_version="3.7.1",
         key='xxxxxxxxxxxxxxxxxxxxxxxxx'
     )
-    ndb.update_steel()
+    ndb.update("steel")"
 
 
 
@@ -925,10 +925,10 @@ Run
         source_version="3.7.1",
         key='xxxxxxxxxxxxxxxxxxxxxxxxx'
     )
-    ndb.update_two_wheelers()
-    ndb.update_cars()
-    ndb.update_trucks()
-    ndb.update_buses()
+    ndb.update("two_wheelers")
+    ndb.update("cars")
+    ndb.update("trucks")
+    ndb.update("buses")
 
 
 *premise* imports inventories for transport activity operated by:
@@ -1147,7 +1147,7 @@ Run
         source_version="3.7.1",
         key='xxxxxxxxxxxxxxxxxxxxxxxxx'
     )
-    ndb.update_dac()
+    ndb.update("dac")
 
 
 
@@ -1209,7 +1209,7 @@ Run
         source_version="3.7.1",
         key='xxxxxxxxxxxxxxxxxxxxxxxxx'
     )
-    ndb.update_fuels()
+    ndb.update("fuels")
 
 
 
@@ -1539,10 +1539,10 @@ Here is a list of the heat and steam production datasets that are regionalized:
 - heat production, hardwood chips from forest, ...
 
 These datasets are relinked to the corresponding regionalized fuel market only
-if `.update_fuels()` has been run.
+if `.update("fuels")` has been run.
 Also, heat production datasets that use biomass as fuel input (e.g., softwood and
 hardwood chips) relink to the dataset `market for biomass, used as fuel` if
-`update_biomass()` has been run previously.
+`update("biomass")` has been run previously.
 
 
 CO2 emissions update
@@ -2025,7 +2025,7 @@ Final Steps
 GAINS emission factors
 """"""""""""""""""""""
 
-When using `update_emissions()`, emission factors from the GAINS-EU_ and GAINS-IAM_ models are used to scale
+When using `update("emissions")`, emission factors from the GAINS-EU_ and GAINS-IAM_ models are used to scale
 non-CO2 emissions in various datasets.
 
 .. _GAINS-EU: https://gains.iiasa.ac.at/gains/EUN/index.login

docs/user_scenarios.rst

@@ -51,27 +51,29 @@ Example
     ]
 
 
-The function **ndb.update_external_scenario()** can be called after that
+The function **ndb.update("external")** can be called after that
 to implement the user-defined scenario in the database.
 
 .. code-block:: python
 
-    ndb.update_external_scenario()
+    ndb.update("external")
 
 Of course, if you wish your database to also integrate the projections
-of the global IAM model, you can run the function **ndb.update_all()**.
+of the global IAM model, you can run the function **ndb.update()**.
 
 .. code-block:: python
 
-    ndb.update_all()
+    ndb.update()
 
 Or if you just want the IAM projections relating to, for example, electricity and steel:
 
 .. code-block:: python
 
-    ndb.update_electricity()
-    ndb.update_steel()
-    ndb.update_external_scenario()
+    ndb.update([
+        "electricity",
+        "steel",
+        "external"
+    ])
 
 Once the integrations are complete, you can export your databases to
 Brightway2, within the activated project:

examples/.ipynb_checkpoints/examples-checkpoint.ipynb

@@ -179,7 +179,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ndb.update_electricity()"
+    "ndb.update(\"electricity\")"
    ]
   },
   {
@@ -240,15 +240,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndb.update_all()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -264,14 +255,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ndb.update_electricity()"
+    "ndb.update(\"electricity\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or you can proceed instead to doing all the transformations available (minus any transformation you have listed in `exclude`), like so:"
+    "Or you can proceed instead to doing all the sectoral transformations available, like so:"
    ]
   },
   {
@@ -280,7 +271,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ndb.update_all()"
+    "ndb.update() # <- updates all sectors"
    ]
   },
   {
@@ -443,45 +434,36 @@
     "\n",
     "These functions modify the extracted database:\n",
     "\n",
-    "* **update_electricity()**: alignment of regional electricity production mixes as well as efficiencies for a number of electricity production technologies, including Carbon Capture and Storage technologies and photovoltaic panels. Also updated the natural gas extraction datasets.\n",
+    "* **update(\"electricity\")**: alignment of regional electricity production mixes as well as efficiencies for a number of electricity production technologies, including Carbon Capture and Storage technologies and photovoltaic panels. Also updated the natural gas extraction datasets.\n",
     "\n",
-    "* **update_cement()**: adjustment of technologies for cement production (dry, semi-dry, wet, with pre-heater or not), fuel efficiency of kilns, fuel mix of kilns (including biomass and waste fuels).\n",
+    "* **update(\"cement\")**: adjustment of technologies for cement production (dry, semi-dry, wet, with pre-heater or not), fuel efficiency of kilns, fuel mix of kilns (including biomass and waste fuels).\n",
     "\n",
-    "* **update_steel()**: adjustment of process efficiency, fuel mix and share of secondary steel in steel markets.\n",
+    "* **update(\"steel\")**: adjustment of process efficiency, fuel mix and share of secondary steel in steel markets.\n",
     "\n",
-    "* **update_dac()**: creates region- and scenario-specific inventories for DAC and DACCS systems. Applies a learning rate on energy and infrastructure needs if the IAM provides the variable.\n",
+    "* **update(\"dac\")**: creates region- and scenario-specific inventories for DAC and DACCS systems. Applies a learning rate on energy and infrastructure needs if the IAM provides the variable.\n",
     "\n",
-    "* **update_fuels()**: creates regional markets for liquid and gaseous fuels and relinks fuel-conusming activities to them.\n",
+    "* **update(\"fuels\")**: creates regional markets for liquid and gaseous fuels and relinks fuel-conusming activities to them.\n",
     "\n",
-    "* **update_heat()**: creates regionalized versions of heat and steam production datasets and relink them to heat-consuming activities.\n",
+    "* **update(\"heat\")**: creates regionalized versions of heat and steam production datasets and relink them to heat-consuming activities.\n",
     "\n",
-    "* **update_emissions()**: adjusts emission of local air pollutants according to GAINS projections.\n",
+    "* **update(\"emissions\")**: adjusts emission of local air pollutants according to GAINS projections.\n",
     "\n",
-    "* **update_cars()**: creates updated inventories for fleet average passenger cars and links back to activities that consume transport.\n",
+    "* **update(\"cars\")**: creates updated inventories for fleet average passenger cars and links back to activities that consume transport.\n",
     "\n",
-    "* **update_trucks()**: creates updated inventories for fleet average lorry trucks and links back to activities that consume transport.\n",
+    "* **update(\"trucks\")**: creates updated inventories for fleet average lorry trucks and links back to activities that consume transport.\n",
     "\n",
-    "* **update_two_wheelers()**: create inventories for two-wheelers.\n",
+    "* **update(\"two_wheelers\")**: create inventories for two-wheelers.\n",
     "\n",
-    "* **update_buses()**: create inventories for buses.\n",
+    "* **update(\"buses\")**: create inventories for buses.\n",
     "\n",
     "A look at the documentation is advised.\n",
     "\n",
     "\n",
-    "These functions can be applied *separately*, *consecutively* or *altogether* (using instead **.update_all()**).\n",
+    "These functions can be applied *separately*, *consecutively* or *altogether* (using instead **.update()** without arguments).\n",
     "\n",
     "They will apply to all the scenario-specific databases listed in `scenarios`."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndb.update_all()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -516,7 +498,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ndb.update_all()"
+    "ndb.update()"
    ]
   },
   {
@@ -544,34 +526,6 @@
     "ndb.write_db_to_brightway(name=[\"my_custom_name_1\", \"my_custom_name_2\"])"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exclude specific functions\n",
-    "Finally, we can exclude some transformation functions when executing `update_all()` like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndb = NewDatabase(\n",
-    "            scenarios=[\n",
-    "                {\"model\":\"remind\", \"pathway\":\"SSP2-Base\", \"year\":2030,\n",
-    "                 \"exclude\": [\"update_steel\"], # <-- do not execute update_seel()\n",
-    "                 \"passenger cars\": {\"regions\":[\"EUR\"]},\"trucks\": {\"regions\":[\"EUR\"]}\n",
-    "                },\n",
-    "                {\"model\":\"remind\", \"pathway\":\"SSP2-Base\", \"year\":2030,},\n",
-    "            ],\n",
-    "            key='xxxxxxxxxxxxxxxxxxxxxxxxx',\n",
-    "            source_db=\"ecoinvent 3.7 cutoff\",\n",
-    "            source_version=\"3.7\", \n",
-    ")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -752,6 +706,22 @@
     "ndb.write_db_to_simapro(filepath=r\"C:/Users/sacchi_r/Downloads/exported_simapro_file\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### As a SimaPro CSV file for OpenLCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndb.write_db_to_olca(filepath=r\"C:/Users/sacchi_r/Downloads/exported_simapro_file\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},