def test_add_tapestry_demograhpics():
add_demographics_pipe = Pipeline([
('get_origin_df',
preprocessing.OriginGeographyFeatureClassToDataframe(
block_group_id_field)),
('add_demographics',
preprocessing.AddTapestryDemographicsToOriginDataframe(
origin_geography_layer=block_groups,
geography_id_field=block_group_id_field,
interim_data_directory=scratch_dir,
rebuild_if_output_exists=True))
])
df = add_demographics_pipe.fit_transform(block_groups)
assert 'tapestryhouseholdsNEW_TSEGCODE_6C' in df.columns
def test_add_demographics():
add_demographics_pipe = Pipeline([
('get_origin_df',
preprocessing.OriginGeographyFeatureClassToDataframe(
block_group_id_field)),
('add_demographics',
preprocessing.AddDemographicsToOriginDataframe(
origin_geography_layer=block_groups,
geography_id_field=block_group_id_field,
interim_data_directory=scratch_dir,
rebuild_if_output_exists=True))
])
df = add_demographics_pipe.fit_transform(block_groups)
out_path = scratch_dir / 'origin_demographics.csv'
assert (isinstance(df, pd.DataFrame) and out_path.exists())
def test_add_selected_demographics():
add_demographics_pipe = Pipeline([
('get_origin_df',
preprocessing.OriginGeographyFeatureClassToDataframe(
block_group_id_field)),
('add_demographics',
preprocessing.AddSelectedDemographicsToOriginDataframe(
origin_geography_layer=block_groups,
geography_id_field=block_group_id_field,
enrich_variable_list=data.enrich_vars_dataframe.sample(
10)['enrich_str'],
interim_data_directory=scratch_dir,
rebuild_if_output_exists=True))
])
df = add_demographics_pipe.fit_transform(block_groups)
assert len(df.columns) == 10
def test_add_nearest_competition_locations():
add_nearest_comp_loc_pipe = Pipeline([
('get_origin_df',
preprocessing.OriginGeographyFeatureClassToDataframe(
block_group_id_field)),
('add_demographics',
preprocessing.AddNearestCompetitionLocationsToOriginDataframe(
origin_geography_layer=block_groups,
origin_id_field=block_group_id_field,
competition_location_layer=comp_locs,
competition_location_id_field=comp_id_field,
destination_count=6,
interim_data_directory=scratch_dir,
rebuild_if_output_exists=False))
])
df = add_nearest_comp_loc_pipe.fit_transform(block_groups)
out_path = scratch_dir / 'nearest_locations.csv'
assert (isinstance(df, pd.DataFrame) and out_path.exists())
def test_origins_to_dataframe():
origins_to_dataframe = preprocessing.OriginGeographyFeatureClassToDataframe(
geography_id_field=block_group_id_field)
origin_df = origins_to_dataframe.fit_transform(block_groups)
assert isinstance(origin_df, pd.DataFrame)