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
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")
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)