def test_search(query, require_all_on, expected):
df = pd.DataFrame({
'A': [
'NCAR', 'IPSL', 'IPSL', 'CSIRO', 'IPSL', 'NCAR', 'NOAA', 'NCAR',
'NASA'
],
'B':
['CESM', 'FOO', 'FOO', 'BAR', 'FOO', 'CESM', 'GCM', 'WACM', 'foo'],
'C': [
'hist', 'control', 'hist', 'control', 'hist', 'control', 'hist',
'hist', 'HiSt'
],
'D': ['O2', 'O2', 'O2', 'O2', 'NO2', 'O2', 'O2', 'TA', 'tAs'],
})
x = search(df, require_all_on=require_all_on,
**query).to_dict(orient='records')
assert x == expected
def test_search_columns_with_iterables(df, query, expected):
results = search(df, **query).to_dict(orient='records')
assert results == expected
'compat': 'override'
}
},
'time_range': {
'type': 'join_existing',
'options': {
'dim': 'time'
}
},
}
subset_df = search(
df,
activity_id='CMIP',
institution_id='CNRM-CERFACS',
source_id='CNRM-CM6-1',
experiment_id='historical',
table_id='Amon',
grid_label='gr',
)
@pytest.fixture(scope='module')
def group_args():
group_args = dict(
key='foo',
df=subset_df,
aggregation_dict=aggregation_dict,
path_column='path',
variable_column='variable_id',
data_format='netcdf',