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@@ -30,6 +30,7 @@ def read_UNFCCC_DI_for_country(
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pm2if_specifications: Optional[dict]=None,
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default_gwp: Optional[str]=None,
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debug: Optional[bool]=False,
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+ use_zenodo: Optional[bool]=True,
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):
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"""
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reads data for a country from the UNFCCC DI interface and saves to native and
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@@ -37,12 +38,20 @@ def read_UNFCCC_DI_for_country(
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"""
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# read the data
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- data_df = read_UNFCCC_DI_for_country_df(
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- country_code=country_code,
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- category_groups=category_groups,
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- read_subsectors=read_subsectors,
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- debug=debug,
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- )
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+ if use_zenodo:
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+ data_df = read_UNFCCC_DI_for_country_df_zenodo(
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+ country_code=country_code,
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+ category_groups=category_groups,
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+ read_subsectors=read_subsectors,
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+ debug=debug,
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+ )
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+ else:
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+ data_df = read_UNFCCC_DI_for_country_df(
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+ country_code=country_code,
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+ category_groups=category_groups,
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+ read_subsectors=read_subsectors,
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+ debug=debug,
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+ )
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# set date_str if not given
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if date_str is None:
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@@ -243,6 +252,78 @@ def read_UNFCCC_DI_for_country_df(
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return di_data
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+def read_UNFCCC_DI_for_country_df_zenodo(
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+ country_code: str,
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+ category_groups: Optional[Dict]=None,
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+ read_subsectors: bool=False,
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+ debug: Optional[bool]=False,
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+)->pd.DataFrame:
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+ """
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+ read UNFCCC DI data for a given country. All data will be read
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+ including all categories, gases, measures, and classifications
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+ Filtering is done later on conversion to PRIMAP2 format
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+
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+ Parameters
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+ ----------
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+ country_code: str
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+ ISO3 code of the country (country names don't work, use the wrapper function)
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+
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+ category_groups: dict (optional)
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+ define which categories to read including filters on classification, measure,
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+ gases
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+
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+ cat_groups = {
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+ "4.A Enteric Fermentation": { #4.A Enteric Fermentation[14577]
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+ "measure": [
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+ 'Net emissions/removals',
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+ 'Total population',
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+ ],
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+ "gases": ["CH4"],
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+ },
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+ }
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+
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+ Returns
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+ -------
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+ pd.DataFrame with read data
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+
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+ """
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+ if read_subsectors:
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+ raise ValueError("Subsector reading is not possible with the Zenodo reader "
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+ "yet")
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+
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+ reader = unfccc_di_api.ZenodoReader()
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+
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+ di_data = reader.query(party_code=country_code)
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+ # remove the "no_gas" data
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+ di_data = di_data[di_data["gas"] != "No gas"]
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+
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+ if category_groups is not None:
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+ di_data = di_data[di_data["category"].isin(category_groups)]
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+
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+ # if data has been collected print some information and save the data
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+ if di_data is None:
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+ raise ValueError(f"No data collected for country {country_code} and category "
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+ f"groups "
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+ f"{category_groups}")
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+ elif debug:
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+ # print some information on collected data
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+ print(f"Collected data for country {country_code}")
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+ print("### Categories ###")
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+ categories = di_data["category"].unique()
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+ categories.sort()
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+ print(categories)
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+ print("### Classifications ###")
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+ classifications = di_data["classification"].unique()
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+ classifications.sort()
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+ print(classifications)
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+ print("### Measures ###")
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+ measures = di_data["measure"].unique()
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+ measures.sort()
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+ print(measures)
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+
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+ return di_data
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+
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+
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def convert_DI_data_to_pm2_if(
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data: pd.DataFrame,
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pm2if_specifications: Optional[dict]=None,
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@@ -266,7 +347,6 @@ def convert_DI_data_to_pm2_if(
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data_temp = data.copy(deep=True)
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# check which country group we have
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- reader = unfccc_di_api.UNFCCCApiReader()
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parties_present_ai = [party for party in data_temp["party"].unique() if party
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in AI_countries]
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parties_present_nai = [party for party in data_temp["party"].unique() if party
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