def test_construct_signals_proportions(self):
"""Tests that constructed signals are actual proportions."""
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
assert np.all(cbg_df['completely_home_prop'].values <= 1)
assert np.all(cbg_df['full_time_work_prop'].values <= 1)
assert np.all(cbg_df['part_time_work_prop'].values <= 1)
def test_construct_signals_present(self):
"""Tests that all signals are constructed."""
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
assert 'completely_home_prop' in set(cbg_df.columns)
assert 'full_time_work_prop' in set(cbg_df.columns)
assert 'part_time_work_prop' in set(cbg_df.columns)
assert 'median_home_dwell_time' in set(cbg_df.columns)
def test_aggregate_hhs(self):
"""Tests that aggregation at the state level creates non-zero-valued signals."""
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
df = aggregate(cbg_df, SIGNALS, 'hhs')
assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)
x = df[f'{SIGNALS[0]}_se'].values
assert np.all(x[~np.isnan(x)] >= 0)
assert df.shape == (10, 17)