Merge branch 'recipe' into master

examples/recipe.py

@@ -11,14 +11,21 @@ def main():
 
     log.basicConfig(level=log.INFO)
     data = lciafmt.get_method(lciafmt.Method.RECIPE_2016)
-
+
+    #export lcia to csv before mapping
+    data.to_csv(outputpath+'Recipe_source.csv', index=False)
+
+    # 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
-    mapped_data = lciafmt.map_flows(data, system="ReCiPe2016")
+    mapped_data = lciafmt.map_flows(data, system="ReCiPe2016", case_insensitive=True)
 
     # write the result to JSON-LD and CSV
-    mapped_data.to_csv(outputpath+"recipe_2016.csv", index=False)
+    for method in mapped_data['Method'].unique():
+        mapped_data[mapped_data['Method']==method].to_csv(outputpath+method.replace('/','_')+".csv", index=False)
     json_pack = outputpath+"recipe_2016_json.zip"
     if os.path.exists(json_pack):
         os.remove(json_pack)

lciafmt/__init__.py

@@ -33,7 +33,7 @@ def get_method(method_id, add_factors_for_missing_contexts=True, file=None, url=
     if method_id == Method.TRACI.value or method_id == Method.TRACI:
         return traci.get(add_factors_for_missing_contexts, file=file, url=None)
     if method_id == Method.RECIPE_2016.value or method_id == Method.RECIPE_2016:
-        return recipe.get(file=file, url=url)
+        return recipe.get(add_factors_for_missing_contexts, file=file, url=url)
 
 
 def clear_cache():
@@ -47,11 +47,12 @@ def to_jsonld(df: pd.DataFrame, zip_file: str, description: str="", write_flows=
 
 
 def map_flows(df: pd.DataFrame, system=None, mapping=None,
-              preserve_unmapped=False) -> pd.DataFrame:
+              preserve_unmapped=False, case_insensitive=False) -> pd.DataFrame:
     """Maps the flows in the given data frame using the given target system. It
        returns a new data frame with the mapped flows."""
     mapper = fmap.Mapper(df, system=system, mapping=mapping,
-                         preserve_unmapped=preserve_unmapped)
+                         preserve_unmapped=preserve_unmapped,
+                         case_insensitive=case_insensitive)
     return mapper.run()
 
 

lciafmt/fmap.py

@@ -85,11 +85,11 @@ def _is_strv(val) -> bool:
 
 class _FlowInfo(object):
 
-    def __init__(self, uuid="", name="", category="", unit=""):
+    def __init__(self, uuid="", name="", category="", unit="", conversionfactor="1.0"):
         self.name = name
         self.category = category
         self.unit = "kg" if not _is_strv(unit) else unit
-
+        self.conversionfactor = str(conversionfactor)
         if not _is_strv(uuid):
             self.uuid = make_uuid(self.name, self.category, self.unit)
         else:
@@ -99,12 +99,15 @@ def __init__(self, uuid="", name="", category="", unit=""):
 class Mapper(object):
 
     def __init__(self, df: pandas.DataFrame, system=None,
-                 mapping=None, preserve_unmapped=False):
+                 mapping=None, preserve_unmapped=False, case_insensitive=False):
         self.__df = df
         self.__system = system
+        self.__case_insensitive = case_insensitive
         if mapping is None:
             log.info("load flow mapping v=%s from fed.elem.flows")
             mapping = flowlist.get_flowmapping(source=system)
+            if self.__case_insensitive:
+                mapping['SourceFlowName'] = mapping['SourceFlowName'].str.lower()
         self.__mapping = mapping  # type: pandas.DataFrame
         self.__preserve_unmapped = preserve_unmapped
 
@@ -137,6 +140,7 @@ def run(self) -> pandas.DataFrame:
                 r[6] = target.uuid
                 r[7] = target.category
                 r[8] = target.unit
+                r[12] = r[12]/float(target.conversionfactor)
                 records.append(r)
                 mapped += 1
         log.info("created %i factors for mapped flows; " +
@@ -166,6 +170,7 @@ def _build_map_index(self) -> dict:
                 name=row["TargetFlowName"],
                 category=row["TargetFlowContext"],
                 unit=row["TargetUnit"],
+                conversionfactor=row["ConversionFactor"]
             ))
 
         log.info("indexed %i mappings for %i flows",

lciafmt/recipe.py

@@ -8,8 +8,25 @@
 import lciafmt.util as util
 import lciafmt.xls as xls
 
-
-def get(file=None, url=None) -> pandas.DataFrame:
+contexts = {
+        'urban air' : 'air/urban',
+        'urban  air' : 'air/urban',
+        'Urban air' : 'air/urban',
+        'Rural air' : 'air/rural',
+        'rural air' : 'air/rural',
+        'agricultural soil' : 'soil/agricultural',
+        'Agricultural soil' : 'soil/agricultural',
+        'industrial soil' : 'soil/industrial',
+        'Industrial soil' : 'soil/industrial',
+        'freshwater' : 'water/freshwater',
+        'Freshwater' : 'water/freshwater',
+        'fresh water' : 'water/freshwater',
+        'seawater' : 'water/sea water',
+        'sea water' : 'water/sea water',
+        'Sea water' : 'water/sea water',
+        'marine water' : 'water/sea water'}
+
+def get(add_factors_for_missing_contexts=True, file=None, url=None) -> pandas.DataFrame:
     log.info("get method ReCiPe 2016")
     f = file
     if f is None:
@@ -18,7 +35,11 @@ def get(file=None, url=None) -> pandas.DataFrame:
             url = ("http://www.rivm.nl/sites/default/files/2018-11/" +
                    "ReCiPe2016_CFs_v1.1_20180117.xlsx")
         f = cache.get_or_download(fname, url)
-    return _read(f)
+    df = _read(f)
+    if add_factors_for_missing_contexts:
+        log.info("Adding average factors for primary contexts")
+        df = util.aggregate_factors_for_primary_contexts(df)    
+    return df
 
 
 def _read(file: str) -> pandas.DataFrame:
@@ -50,14 +71,15 @@ def _read_mid_points(sheet: xlrd.book.sheet, records: list):
     indicator_unit, flow_unit, unit_col = _determine_units(sheet)
     compartment, compartment_col = _determine_compartments(sheet)
 
+
+
     perspectives = ["I", "H", "E"]
     factor_count = 0
     for row in range(start_row, sheet.nrows):
-        if xls.cell_f64(sheet, row, data_col) == 0.0:
-            continue
-
         if compartment_col > -1:
             compartment = xls.cell_str(sheet, row, compartment_col)
+        if compartment in contexts:
+            compartment = contexts[compartment]
         if unit_col > -1:
             flow_unit = xls.cell_str(sheet, row, unit_col)
             if "/" in flow_unit:
@@ -217,13 +239,16 @@ def _determine_compartments(sheet: xlrd.book.sheet) -> (str, int):
             or _containstr(sheet.name, "ozone") \
             or _containstr(sheet.name, "particulate") \
             or _containstr(sheet.name, "acidification"):
-        log.warning("no compartment column; assuming 'emission/air'")
-        return "emission/air", -1
+        log.warning("no compartment column; assuming 'air'")
+        return "air", -1
 
-    elif _containstr(sheet.name, "mineral", "resource", "scarcity") \
-            or _containstr(sheet.name, "fossil", "resource", "scarcity"):
+    elif _containstr(sheet.name, "mineral", "resource", "scarcity"):
         log.warning("no compartment column; assuming 'resource/ground'")
         return "resource/ground", -1
+
+    elif _containstr(sheet.name, "fossil", "resource", "scarcity"):
+        log.warning("no compartment column; assuming 'resource'")
+        return "resource", -1
 
     if _containstr(sheet.name, "water", "consumption"):
         log.warning("no compartment column; assuming 'resource/fresh water'")

lciafmt/util.py

@@ -58,7 +58,18 @@ def aggregate_factors_for_primary_contexts(df) -> pd.DataFrame:
     :param df: a pandas dataframe for an LCIA method
     :return: a pandas dataframe for an LCIA method
     """
+    #Ignore the following impact categories for generating averages
+    ignored_categories = ['Land transformation', 'Land occupation',
+                          'Water consumption','Mineral resource scarcity',
+                          'Fossil resource scarcity']    
     indices = df['Context'].str.find('/')
+    ignored_list = df['Indicator'].isin(ignored_categories)
+    i = 0
+    for k in ignored_list.iteritems():
+        if k[1] == True:
+            indices.update(pd.Series([-1], index=[i]))
+        i = i + 1
+
     primary_context = []
     i = 0
     for c in df['Context']:

lciafmt/xls.py

@@ -26,6 +26,9 @@ def cell_val(sheet: xlrd.book.sheet, row: int, col: int):
     cell = sheet.cell(row, col)
     if cell is None:
         return None
+    #checks for errortype N/A and returns None
+    if cell.ctype == 5:
+        return None
     return cell.value