Update library to reflect the renaming from rmnd_lca to premise.

lca2rmnd/activity_select.py

@@ -1,12 +1,12 @@
-from rmnd_lca import InventorySet
+from premise import InventorySet
 from bw2data.backends.peewee.proxies import Activity, ActivityDataset as Act
 from functools import reduce
 
 class ActivitySelector():
     """
     Maps ecoinvent activites to REMIND technologies.
 
-    Uses filter definitions from :class:`rmnd_lca.InventorySet`.
+    Uses filter definitions from :class:`premise.InventorySet`.
 
     :param db: A lice cycle inventory database
     :type db: brightway2 database object
@@ -16,7 +16,7 @@ def create_expr(self, fltr={}, mask={}, filter_exact=False, mask_exact=False):
         Create a :class:`peewee.Expression` from a filter dictionary.
 
         For the specification of the filter dictionary please refer to the
-        documentation of :class:`rmnd_lca.InventorySet`.
+        documentation of :class:`premise.InventorySet`.
 
         :param fltr: string, list of strings or dictionary.
             If a string is provided, it is used to match the name field from the start (*startswith*).

lca2rmnd/prepare_inventories.py

@@ -18,7 +18,7 @@
 
 """
 
-import rmnd_lca
+import premise
 from bw2data.utils import merge_databases
 
 from carculator import CarInputParameters, \
@@ -29,6 +29,8 @@
 import brightway2 as bw
 import numpy as np
 
+fpei36 = "/home/alois/ecoinvent/ecoinvent 3.6_cut-off_ecoSpold02/datasets/"
+model = "remind"
 
 def create_project(project_name, ecoinvent_path,
                    years, scenario, remind_data_path, from_scratch=True):
@@ -37,7 +39,7 @@ def create_project(project_name, ecoinvent_path,
     inventories for electricity markets according to
     REMIND data.
 
-    Relies on `rmnd_lca.NewDatabase`. Existing databases are
+    Relies on `premise.NewDatabase`. Existing databases are
     deleted.
 
     :param str project_name: name of the brightway2 project to modify
@@ -64,20 +66,20 @@ def create_project(project_name, ecoinvent_path,
         print("Database has already been imported")
     else:
         ei36 = bw.SingleOutputEcospold2Importer(
-            ecoinvent_path, 'ecoinvent 3.6 cutoff')
+            fpei36, 'ecoinvent 3.6 cutoff')
         ei36.apply_strategies()
         ei36.statistics()
         ei36.write_database()
 
     for year in years:
         print("Create modified database for scenario {} and year {}"
               .format(scenario, year))
-        ndb = rmnd_lca.NewDatabase(
+        ndb = premise.NewDatabase(
             scenario=scenario,
             year=year,
             source_db='ecoinvent 3.6 cutoff',
             source_version=3.6,
-            filepath_to_remind_files=remind_data_path)
+            filepath_to_iam_files=remind_data_path)
         ndb.update_all()
         ndb.write_db_to_brightway()
 
@@ -117,7 +119,7 @@ def relink_electricity_demand(scenario, year):
     :param year: REMIND year.
 
     """
-    eidb = bw.Database(rmnd_lca.utils.eidb_label(scenario, year))
+    eidb = bw.Database(premise.utils.eidb_label(model, scenario, year))
     remind_regions = [
         'LAM', 'OAS', 'SSA', 'EUR',
         'NEU', 'MEA', 'REF', 'CAZ',
@@ -193,7 +195,7 @@ def load_and_merge(scenario, years, relink=True):
         from market groups in REMIND regions
     """
     for year in years:
-        eidb = rmnd_lca.utils.eidb_label(scenario, year)
+        eidb = premise.utils.eidb_label(model, scenario, year)
         inv = load_car_activities(np.array([year]))
         inv.apply_strategies()
 

lca2rmnd/reporting.py

@@ -3,8 +3,8 @@
 from .activity_select import ActivitySelector
 from .utils import project_string
 
-from rmnd_lca import Geomap, InventorySet
-from rmnd_lca.utils import eidb_label
+from premise import Geomap, InventorySet
+from premise.utils import eidb_label
 
 from bw2data.backends.peewee.proxies import Activity, ActivityDataset as Act
 import brightway2 as bw
@@ -34,10 +34,12 @@ class LCAReporting():
     :vartype source_db: str
     """
     def __init__(self, scenario, years, project,
-                 remind_output_folder,
+                 remind_output_folder, regions,
                  indicatorgroup='ReCiPe Midpoint (H) V1.13'):
         self.years = years
         self.scenario = scenario
+        self.model = "remind"
+        self.regions = regions
         bw.projects.set_current(project)
         self.selector = ActivitySelector()
         self.methods = [m for m in bw.methods if m[0] == indicatorgroup
@@ -59,6 +61,8 @@ def __init__(self, scenario, years, project,
         rdc = RemindDataCollection(self.scenario, remind_output_folder)
         self.data = rdc.data[rdc.data.Year.isin(self.years) &
                              (rdc.data.Region != "World")]
+        # all regions there?
+        assert self.regions in self.data.Region.unique()
         self.geo = Geomap()
 
 
@@ -92,26 +96,56 @@ def _act_from_variable(self, variable, db, year, region, scale=1):
         """
         Find the activity for a given REMIND transport reporting variable.
         """
-        def get_act(variable):
-            techmap = {
-                "BEV": "BEV",
-                "FCEV": "FCEV",
-                "Gases": "ICEV-g",
-                "Hybrid Electric": "PHEV-p",
-                "Hybrid Liquids": "HEV-p",
-                "Liquids": "ICEV-p"
+        techmap = {
+            "BEV": "BEV",
+            "FCEV": "FCEV",
+            "Gases": "ICEV-g",
+            "Hybrid Electric": {
+                "diesel": "PHEV-d",
+                "petrol": "PHEV-p"
+            },
+            "Hybrid Liquids": {
+                "diesel": "HEV-d",
+                "petrol": "HEV-p"
+            },
+            "Liquids": {
+                "diesel": "ICEV-d",
+                "petrol": "ICEV-p"
             }
-            return Activity(
-                Act.get((Act.name.startswith(
-                    "Passenger car, fleet average, {}, {}".format(
-                        variable.split("|")[-1], year)
-                ))
-                        & (Act.location == region)
-                        & (Act.database == db.name)))
-
-        return {
-            get_act(variable): scale
         }
+        tech = variable.split("|")[-1]
+        liq_share = {
+            "diesel": 0.4,
+            "petrol": 0.6
+        }
+        if tech in ["Hybrid Electric", "Hybrid Liquids", "Liquids"]:
+            if region in ["CHA", "REF", "IND"]:
+                demand = {
+                    Activity(Act.get(
+                        (Act.name == "transport, passenger car, fleet average, {}, {}".format(
+                            techmap[tech]["petrol"], year))
+                        & (Act.location == region)
+                        & (Act.database == db.name))): scale
+                }
+            else:
+                demand = {
+                    Activity(Act.get(
+                        (Act.name == "transport, passenger car, fleet average, {}, {}".format(
+                            techmap[tech][liq], year))
+                        & (Act.location == region)
+                        & (Act.database == db.name))): scale * liq_share[liq]
+                    for liq in ["diesel", "petrol"]
+                }
+            return demand
+        else:
+            return  {
+                Activity(
+                    Act.get(
+                        (Act.name == "transport, passenger car, fleet average, {}, {}".format(
+                            techmap[tech], year))
+                        & (Act.location == region)
+                        & (Act.database == db.name))): scale
+            }
 
     def report_LDV_LCA(self):
         """
@@ -134,12 +168,13 @@ def report_LDV_LCA(self):
 
         df.set_index(["Year", "Region", "Variable", "Method"], inplace=True)
         start = time.time()
+
         # calc score
         for year in self.years:
             # find activities which at the moment do not depend
             # on regions
-            db = bw.Database(eidb_label(self.scenario, year))
-            for region in df.index.get_level_values(1).unique():
+            db = bw.Database(eidb_label(self.model, self.scenario, year))
+            for region in self.regions:
                 for var in (df.loc[(year, region)]
                             .index.get_level_values(0)
                             .unique()):
@@ -168,15 +203,15 @@ def _get_material_bioflows_for_bev(self):
         method = ('ILCD 2.0 2018 midpoint',
                   'resources', 'minerals and metals')
         year = self.years[0]
-        act_str = "Passenger car, fleet average, BEV, {}".format(year)
+        act_str = "transport, passenger car, fleet average, BEV, {}".format(year)
 
         # upstream material demands are the same for all regions
         # so we can use GLO here
         act = Activity(
             Act.get((Act.name == act_str)
                     & (Act.database == eidb_label(
-                        self.scenario, year))
-                    & (Act.location == "RER")))
+                        self.model, self.scenario, year))
+                    & (Act.location == "EUR")))
         lca = bw.LCA({act: 1}, method=method)
         lca.lci()
         lca.lcia()
@@ -199,12 +234,13 @@ def report_materials(self):
         df = self.data[self.data.Variable.isin(self.variables)]
 
         df.set_index(["Year", "Region", "Variable"], inplace=True)
+
         start = time.time()
         result = {}
         # calc score
         for year in self.years:
-            db = bw.Database(eidb_label(self.scenario, year))
-            for region in df.index.get_level_values(1).unique():
+            db = bw.Database(eidb_label(self.model, self.scenario, year))
+            for region in self.regions:
                 # create large lca demand object
                 demand = [
                     self._act_from_variable(
@@ -242,12 +278,13 @@ def report_direct_emissions(self):
         df = self.data[self.data.Variable.isin(self.variables)]
 
         df.set_index(["Year", "Region", "Variable"], inplace=True)
+
         start = time.time()
         result = {}
         # calc score
         for year in self.years:
-            db = bw.Database(eidb_label(self.scenario, year))
-            for region in df.index.get_level_values(1).unique():
+            db = bw.Database(eidb_label(self.model, self.scenario, year))
+            for region in self.regions:
                 for var in (df.loc[(year, region)]
                             .index.get_level_values(0)
                             .unique()):
@@ -282,8 +319,8 @@ def report_endpoint(self):
         result = {}
         # calc score
         for year in self.years:
-            db = bw.Database(eidb_label(self.scenario, year))
-            for region in df.index.get_level_values(1).unique():
+            db = bw.Database(eidb_label(self.model, self.scenario, year))
+            for region in self.regions:
                 # create large lca demand object
                 demand = [
                     self._act_from_variable(
@@ -326,8 +363,8 @@ def report_midpoint(self):
         result = {}
         # calc score
         for year in self.years:
-            db = bw.Database(eidb_label(self.scenario, year))
-            for region in df.index.get_level_values(1).unique():
+            db = bw.Database(eidb_label(self.model, self.scenario, year))
+            for region in self.regions:
                 # create large lca demand object
                 demand = [
                     self._act_from_variable(
@@ -376,8 +413,8 @@ def report_midpoint_to_endpoint(self):
         result = {}
         # calc score
         for year in self.years:
-            db = bw.Database(eidb_label(self.scenario, year))
-            for region in df.index.get_level_values(1).unique():
+            db = bw.Database(eidb_label(self.model, self.scenario, year))
+            for region in self.regions:
                 # create large lca demand object
                 demand = [
                     self._act_from_variable(
@@ -483,8 +520,8 @@ def _sum_variables_and_add_scores(self, market, variables):
 
         # calc score
         for year in self.years:
-            db = bw.Database(eidb_label(self.scenario, year))
-            for region in df.Region.unique():
+            db = bw.Database(eidb_label(self.model, self.scenario, year))
+            for region in self.regions:
                 # import ipdb;ipdb.set_trace()
                 # find activity
                 act = [a for a in db if a["name"] == market and
@@ -521,14 +558,13 @@ def report_tech_LCA(self, year):
 
         db = bw.Database("_".join(["ecoinvent", self.scenario, str(year)]))
 
-        regions = self._get_rmnd_regions()
         result = self._cartesian_product({
-            "region": regions,
+            "region": self.regions,
             "tech": list(tecdict.keys()),
             "method": self.methods
         }).sort_index()
 
-        for region in regions:
+        for region in self.regions:
             # read the ecoinvent techs for the entries
             shares = self.supplier_shares(db, region)
 
@@ -559,13 +595,6 @@ def _cartesian_product(self, idx):
         index = pd.MultiIndex.from_product(idx.values(), names=idx.keys())
         return pd.DataFrame(index=index)
 
-    def _get_rmnd_regions(self):
-        """Obtain a list of REMIND regions."""
-        regionmap = pd.read_csv(
-            DATA_DIR/"remind/regionmappingH12.csv",
-            sep=";")
-        return regionmap.RegionCode.unique()
-
     def _find_suppliers(self, db, expr, locs):
         """
         Return a list of supplier activites in locations `locs` matching
@@ -609,7 +638,7 @@ def supplier_shares(self, db, region):
         vols = pd.read_csv(DATA_DIR/"electricity_production_volumes_per_tech.csv",
                            sep=";", index_col=["dataset", "location"])
 
-        # the filters come from the rmnd_lca package
+        # the filters come from the premise package
         # this package is also used to modify the techs in the first place
         fltrs = InventorySet(db).powerplant_filters
         act_shares = {}

tests/test_reporting.py

@@ -5,8 +5,8 @@
 import math
 
 from lca2rmnd.reporting import ElectricityLCAReporting, TransportLCAReporting
-from rmnd_lca import InventorySet
-from rmnd_lca.utils import eidb_label
+from premise import InventorySet
+from premise.utils import eidb_label
 
 remind_regions = [
     'LAM', 'OAS', 'SSA', 'EUR',
@@ -18,7 +18,7 @@
 
 years = [2050]
 scenario = "BAU"
-
+model = "remind"
 
 def test_electricity_sectoral_reporting():
     rep = ElectricityLCAReporting(scenario, years)
@@ -32,7 +32,7 @@ def test_electricity_supplier_shares_random():
     yr = random.choice(years)
     region = random.choice(remind_regions)
 
-    db = bw.Database(eidb_label(scenario, yr))
+    db = bw.Database(eidb_label(model, scenario, yr))
 
     shares = rep.supplier_shares(db, region)
 
@@ -48,7 +48,7 @@ def test_electricity_tech_reporting():
     yr = random.choice(years)
     region = random.choice(remind_regions)
 
-    db = bw.Database(eidb_label(scenario, yr))
+    db = bw.Database(eidb_label(model, scenario, yr))
     fltrs = InventorySet(db).powerplant_filters
     tech = random.choice(list(fltrs.keys()))
 

tests/test_selector.py

@@ -1,7 +1,7 @@
 # content of test_activity_maps.py
 from lca2rmnd.activity_select import ActivitySelector
 
-from rmnd_lca import InventorySet, Geomap
+from premise import InventorySet, Geomap
 from bw2data.database import DatabaseChooser
 from bw2data.backends.peewee.proxies import ActivityDataset as Act