Beispiel #1
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def test_upload_normalized_dfs_from_csv():
    dfs = upload.read_in_csv_files("base_scenario_less")
    data, scalar, timeseries = upload.map_concrete_to_normalized_df(
        dfs["oed_scalar"], dfs["oed_timeseries"])
    normalized_dfs = {
        "oed_scenario": dfs["oed_scenario"],
        "oed_data": data,
        "oed_scalar": scalar,
        "oed_timeseries": timeseries
    }
    filtered_normalized_dfs = upload.adapt_metadata_attributes_and_types(
        normalized_dfs)
    upload.upload_normalized_dfs(filtered_normalized_dfs, schema="model_draft")
Beispiel #2
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def test_map_dfs():
    dfs = upload.read_in_excel_sheets("scenario_scalar_timeseries_less.xlsx",
                                      sheets=("scenario", "scalar",
                                              "timeseries"))
    assert len(dfs) == 3
    data, scalar, timeseries = upload.map_concrete_to_normalized_df(
        dfs["scalar"], dfs["timeseries"])
    assert len(scalar) == 71
    assert len(scalar.columns) == 3
    assert len(timeseries) == 10
    assert len(timeseries.columns) == 5
    assert len(data) == 81
    assert len(data.columns) == 14
Beispiel #3
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def test_upload_normalized_dfs_from_excel():
    sheets = ("scenario", "scalar", "timeseries")
    dfs = upload.read_in_excel_sheets("scenario_scalar_timeseries_less.xlsx",
                                      sheets=sheets,
                                      sheet_table_map=dict(
                                          zip(sheets, CONCRETE_TABLES)))
    data, scalar, timeseries = upload.map_concrete_to_normalized_df(
        dfs["scalar"], dfs["timeseries"])
    normalized_dfs = {
        "oed_scenario": dfs["scenario"],
        "oed_data": data,
        "oed_scalar": scalar,
        "oed_timeseries": timeseries
    }
    filtered_normalized_dfs = upload.adapt_metadata_attributes_and_types(
        normalized_dfs)
    upload.upload_normalized_dfs(filtered_normalized_dfs, schema="model_draft")
Beispiel #4
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def test_adapt_metadata():
    sheets = ("scenario", "scalar", "timeseries")
    dfs = upload.read_in_excel_sheets("scenario_scalar_timeseries_less.xlsx",
                                      sheets=sheets,
                                      sheet_table_map=dict(
                                          zip(sheets, CONCRETE_TABLES)))
    data, scalar, timeseries = upload.map_concrete_to_normalized_df(
        dfs["scalar"], dfs["timeseries"])
    normalized_dfs = {
        "oed_scenario": dfs["scenario"],
        "oed_data": data,
        "oed_scalar": scalar,
        "oed_timeseries": timeseries
    }
    filtered_dfs = upload.adapt_metadata_attributes_and_types(normalized_dfs)
    region = filtered_dfs["oed_scenario"]["region"][0]
    assert isinstance(region, list)
    assert region[0] == "DE"
    series = filtered_dfs["oed_timeseries"]["series"][0]
    assert isinstance(series, list)
    assert isinstance(series[0], float)