Draft UNEP scripts for Global Materials Database file

flowsa/data_source_scripts/UNEP_IRP_GMFD.py

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+# -*- coding: utf-8 -*-
+# -*- coding: utf-8 -*-
+"""
+Global Material Flows Database
+https://unep-irp.fineprint.global/mfa13/export
+"""
+
+
+import io
+import pandas as pd
+import numpy as np
+from flowsa.flowbyfunctions import assign_fips_location_system
+from flowsa.flowsa_log import log
+from flowsa.flowbyactivity import FlowByActivity
+
+
+def unep_mfa_call(*, resp, url,  **_):
+    """
+    Convert response for calling url to pandas dataframe, begin
+        parsing df into FBA format
+    :param resp: df, response from url call
+    :return: pandas dataframe of original source data
+    """
+    df = pd.read_csv('https://unep-irp.fineprint.global/mfa13/export',skiprows=0,
+    skipfooter=0)
+    return df
+
+def unep_mfa_parse(*, df_list, year, config, **_):
+    """
+    Combine, parse, and format the provided dataframes
+    :param df_list: list of dataframes to concat and format
+    :param year: year
+    :return: df, parsed and partially formatted to
+        flowbyactivity specifications
+    """
+    for df in df_list:
+        df= df.query("Country == 'United States of America'")
+        year_list = [range(2012, 2025)]
+
+        df= df.melt( id_vars=['Country','Category', 'Flow name', 'Flow code', 'Flow unit'], var_name= 'Year', value_name='FlowAmount')
+        df = (df
+              .query("Year ==  ['2012','2013','2014','2015','2016','2017','2018','2019','2020','2021','2022','2023','2024']")  
+              .rename(columns={'Category':'FlowName','Flow name':'ActivityProducedBy', 'Flow unit':'Unit'})  
+              .assign(ActivityProducedBy = lambda x: x['ActivityProducedBy'].str.strip())
+              .assign(ActivityProducedBy = lambda x: x['FlowName'] + ', '+ x['ActivityProducedBy'])  
+              .drop(columns= ['Country', 'Flow code'])
+              .reset_index(drop= True)
+              )
+        if len(df) == 0:
+            log.warning(' Data is missing')
+
+# hard code data
+    df['Year'] = df['Year'].astype(str)
+    df['SourceName'] = 'UNEP_IRP_GMFD'
+    df['FlowType'] = 'ELEMENTARY_FLOW'
+    df['Location'] = '00000'
+    df['Unit'] = 'MT'
+
+# Add tmp DQ scores
+    df['DataReliability'] = 5
+    df['DataCollection'] = 5
+    df['Compartment'] = 'resource'
+    df['Class'] = 'Chemicals'
+    return df
+
+if __name__ == "__main__":
+    import flowsa
+    year = '2012-2024'
+    flowsa.generateflowbyactivity.main(source='UNEP_IRP_GMFD', year=year)
+    fba = pd.DataFrame()
+    for y in range(2012, 2025):
+        fba = pd.concat([fba, flowsa.getFlowByActivity('UNEP_IRP_GMFD', year=y)],
+                        ignore_index=True)
+
+

flowsa/methods/flowbyactivitymethods/UNEP_IRP_GMFD.yaml

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+author: UN Environment Programme
+source_name: 'Inventory of US Greenhouse Gas Emissions and Sinks: 1990-2020'
+source_url: https://www.resourcepanel.org/global-material-flows-database
+#bib_id: UNEP_IRP_GMFD
+format: csv
+url:
+    base_url:  https://unep-irp.fineprint.global/mfa13/export
+
+call_response_fxn: !script_function:UNEP_IRP_GMFD unep_mfa_call
+parse_response_fxn: !script_function:UNEP_IRP_GMFD unep_mfa_parse
+call_all_years: True
+
+years:
+- 2010
+- 2011
+- 2012
+- 2013
+- 2014
+- 2015
+- 2016
+- 2017
+- 2018
+- 2019
+- 2020
+- 2021
+- 2022
+- 2023
+- 2024
+
+