Merge pull request #78 from USEPA/develop

final linting for JOSS release

README.md

@@ -1,4 +1,6 @@
 # LCIA formatter
+[![build](https://github.com/USEPA/LCIAformatter/actions/workflows/python-package.yml/badge.svg)](https://github.com/USEPA/LCIAformatter/actions/workflows/python-package.yml)
+
 The LCIA formatter, or `lciafmt`, is a Python tool for standardizing the format and flows of life cycle impact assessment (LCIA) data. The tool acquires LCIA data transparently from its original 
 source, cleans the data, shapes them into a standard format using the [LCIAmethod format](./format%20specs/LCIAmethod.md), and optionally applies flow mappings as defined in the [Federal LCA Commons Elementary Flow List](https://github.com/USEPA/Federal-LCA-Commons-Elementary-Flow-List). The result can be exported to all formats supported by the
 `pandas` package (e.g. Excel, CSV) or the [openLCA JSON-LD format](https://github.com/GreenDelta/olca-schema).

examples/fedefl_inventory_methods.py

@@ -5,16 +5,18 @@
 
 method = lciafmt.Method.FEDEFL_INV
 
+
 def main():
 
     log.debug('Subsets available: ' + ", ".join(map(str, fedefl.subset_list.get_subsets())))
 
     subsets = None
 
-    inventory_methods = lciafmt.get_method(method_id='FEDEFL Inventory',subset=subsets)
-    
+    inventory_methods = lciafmt.get_method(method_id='FEDEFL Inventory', subset=subsets)
+
     store_method(inventory_methods, method)
     save_json(method, inventory_methods)
-
+
+
 if __name__ == "__main__":
     main()

examples/impact_world.py

@@ -3,6 +3,7 @@
 
 method = lciafmt.Method.ImpactWorld
 
+
 def main():
 
     data = lciafmt.get_method(method)

examples/recipe.py

@@ -3,29 +3,31 @@
 
 method = lciafmt.Method.RECIPE_2016
 
-#To obtain summary LCIA endpoint categories (damage assessment) set to True
+# To obtain summary LCIA endpoint categories (damage assessment) set to True
 apply_summary = False
 
+
 def main():
 
-    data = lciafmt.get_method(method, endpoint = True, 
-                              summary = apply_summary)
-    
+    data = lciafmt.get_method(method, endpoint=True,
+                              summary=apply_summary)
+
     # make flowables case insensitive to handle lack of consistent structure in source file
     data['Flowable'] = data['Flowable'].str.lower()
-    
+
     # map the flows to the Fed.LCA commons flows
     # set preserve_unmapped=True if you want to keep unmapped
     # flows in the resulting data frame
     mapping = method.get_metadata()['mapping']
     mapped_data = lciafmt.map_flows(data, system=mapping, case_insensitive=True)
-    
+
     mapped_data = collapse_indicators(mapped_data)
-    
+
     # write the result to parquet and JSON-LD
     store_method(mapped_data, method)
     for m in mapped_data['Method'].unique():
         save_json(method, mapped_data, m)
 
+
 if __name__ == "__main__":
     main()

examples/traci.py

@@ -6,18 +6,19 @@
 
 method = lciafmt.Method.TRACI
 
+
 def main():
 
     data = lciafmt.get_method(method)
-    
+
     if mod is not None:
         log.info("getting modified CFs")
-        modified_cfs=get_modification(mod,"TRACI2.1")
-        data = data.merge(modified_cfs,how='left',on=['Flowable','Context','Indicator'])
-        data.loc[data['Updated CF'].notnull(),'Characterization Factor']=data['Updated CF']
-        data = data.drop(columns=['Updated CF','Note'])
-        data['Method']="TRACI 2.1 ("+mod+" mod)"
-    
+        modified_cfs = get_modification(mod, "TRACI2.1")
+        data = data.merge(modified_cfs, how='left', on=['Flowable', 'Context', 'Indicator'])
+        data.loc[data['Updated CF'].notnull(), 'Characterization Factor'] = data['Updated CF']
+        data = data.drop(columns=['Updated CF', 'Note'])
+        data['Method'] = "TRACI 2.1 (" + mod + " mod)"
+
     # map the flows to the Fed.LCA commons flows
     # set preserve_unmapped=True if you want to keep unmapped
     # flows in the resulting data frame
@@ -28,5 +29,6 @@ def main():
     store_method(mapped_data, method)
     save_json(method, mapped_data)
 
+
 if __name__ == "__main__":
     main()

examples/valuation.py

@@ -1,14 +1,16 @@
 import lciafmt
 from lciafmt.util import store_method
 
+
 def main():
 
-    methods = lciafmt.generate_endpoints('Weidema_valuation', name = 'Weidema Valuation',
-                                        matching_fields=['Indicator unit'])
-    # matching_fields = ['Indicator unit'] is used to avoid listing all 
+    methods = lciafmt.generate_endpoints('Weidema_valuation', name='Weidema Valuation',
+                                         matching_fields=['Indicator unit'])
+    # matching_fields = ['Indicator unit'] is used to avoid listing all
     # indicators separately in source data file
-
-    store_method(methods, method_id = None)
+
+    store_method(methods, method_id=None)
+
 
 if __name__ == "__main__":
     main()

lciafmt/df.py

@@ -28,8 +28,9 @@ def data_frame(records: list) -> pandas.DataFrame:
 
 def as_list(df: pandas.DataFrame, row=-1) -> list:
     """Converts the given data frame into a list of lists. When the `row`
-       paremeter is given with a value >= 0, only that row is extracted as
-       list from the data frame."""
+    paremeter is given with a value >= 0, only that row is extracted as
+    list from the data frame.
+    """
     if df is None:
         return []
     if row >= 0:
@@ -72,4 +73,3 @@ def record(records: list,
         location_uuid,
         factor])
     return records
-

lciafmt/iw.py

@@ -97,7 +97,7 @@ def _read(access_file: str) -> pd.DataFrame:
                        ("CF - regionalized - LandTrans - aggregated", "Elementary Flow"),
                        ("CF - regionalized - LandOcc - aggregated", "Elementary Flow"),
                        ("CF - regionalized - EutroMar - aggregated", "Compartment"),
-                       ("CF - regionalized - PartMatterForm - aggregated","Compartment"),
+                       ("CF - regionalized - PartMatterForm - aggregated", "Compartment"),
                        ("CF - regionalized - AcidFW - aggregated", "Compartment"),
                        ("CF - regionalized - AcidTerr - aggregated", "Compartment"),
                        ("CF - regionalized - EutroFW - aggregated", "Compartment"),

lciafmt/jsonld.py

@@ -94,7 +94,7 @@ def __method(self, row) -> olca.ImpactMethod:
         m.name = row[0]
         m.version = pkg_version_number
         m.impact_categories = []
-        m.description=description
+        m.description = description
         self.__methods[uid] = m
         return m
 

lciafmt/recipe.py

@@ -34,7 +34,7 @@
         'sea water': 'water/sea water',
         'Sea water': 'water/sea water',
         'marine water': 'water/sea water'}
-flowables_split = pd.read_csv(datapath+'ReCiPe2016_split.csv')
+flowables_split = pd.read_csv(datapath + 'ReCiPe2016_split.csv')
 
 
 def get(add_factors_for_missing_contexts=True, endpoint=True,
@@ -77,7 +77,7 @@ def get(add_factors_for_missing_contexts=True, endpoint=True,
         df2 = df.merge(endpoint_df, how="inner", on=["Method", "Indicator"])
         df2 = df2.append(flowdf, ignore_index=True, sort=False)
         # reformat dataframe and apply conversion
-        df2['Characterization Factor'] = df2['Characterization Factor']*df2['EndpointConversion']
+        df2['Characterization Factor'] = df2['Characterization Factor'] * df2['EndpointConversion']
         df2['Method'] = df2['EndpointMethod']
         df2['Indicator'] = df2['EndpointIndicator']
         df2['Indicator unit'] = df2['EndpointUnit']
@@ -97,7 +97,7 @@ def get(add_factors_for_missing_contexts=True, endpoint=True,
 
     length = len(df)
     df.drop_duplicates(keep='first', inplace=True)
-    length = length-len(df)
+    length = length - len(df)
     log.info(f"{length} duplicate entries removed")
 
     if summary:
@@ -179,11 +179,11 @@ def _read_endpoints(file: str) -> pd.DataFrame:
     log.debug("extracted %i endpoint factors", endpoint_factor_count)
 
     log.info("processing endpoint factors")
-    endpoint.loc[endpoint['EndpointUnit'].str.contains('daly', case=False),'EndpointUnit'] = 'DALY'
+    endpoint.loc[endpoint['EndpointUnit'].str.contains('daly', case=False), 'EndpointUnit'] = 'DALY'
     endpoint.loc[endpoint['EndpointUnit'].str.contains('species', case=False), 'EndpointUnit'] = 'species-year'
     endpoint.loc[endpoint['EndpointUnit'].str.contains('USD', case=False), 'EndpointUnit'] = 'USD2013'
 
-    endpoint_map = pd.read_csv(datapath+'ReCiPe2016_endpoint_to_midpoint.csv')
+    endpoint_map = pd.read_csv(datapath + 'ReCiPe2016_endpoint_to_midpoint.csv')
     endpoint = endpoint.merge(endpoint_map, how="left", on='EndpointIndicator')
 
     # split into two dataframes

lciafmt/traci.py

@@ -66,7 +66,7 @@ def get(add_factors_for_missing_contexts=True, file=None,
 
     length = len(df)
     df.drop_duplicates(keep='first', inplace=True)
-    length = length-len(df)
+    length = length - len(df)
     log.info(f"{length} duplicate entries removed")
 
     return df

tests/test_mapped_method.py

@@ -3,6 +3,7 @@
 import lciafmt
 from lciafmt.util import log, read_method
 
+
 class TestInputFiles(unittest.TestCase):
 
     def test_duplicate_flows(self):
@@ -13,12 +14,12 @@ def test_duplicate_flows(self):
         for m in method_list:
             method = read_method(lciafmt.Method.get_class(m['id']))
             if method is not None:
-                dup_flowables = method[method[['Method','Indicator','Flow UUID']].duplicated(keep=False)]
+                dup_flowables = method[method[['Method', 'Indicator', 'Flow UUID']].duplicated(keep=False)]
                 duplicates = len(dup_flowables)
                 if duplicates > 0:
-                    log.warning('duplicate factors in method '+ m['name'])
+                    log.warning('duplicate factors in method ' + m['name'])
                 total_duplicates += duplicates
-        self.assertTrue(total_duplicates==0,'Duplicate factors in one or more methods')
+        self.assertTrue(total_duplicates == 0, 'Duplicate factors in one or more methods')
 
 
 if __name__ == "__main__":