How to use the intake.open_esm_datastore function in intake

def test_contains(key, expected):
    col = intake.open_esm_datastore(cdf_col_sample_cmip6)
    actual = key in col
    assert actual == expected

def test_search(esmcol_path, query):
    col = intake.open_esm_datastore(esmcol_path)
    cat = col.search(**query)
    assert len(cat.df) > 0
    assert len(col.df.columns) == len(cat.df.columns)

def test_opendap_endpoint():
    col = intake.open_esm_datastore('http://haden.ldeo.columbia.edu/catalogs/hyrax_cmip6.json')
    cat = col.search(
        source_id='CAMS-CSM1-0',
        experiment_id='historical',
        member_id='r1i1p1f1',
        table_id='Amon',
        grid_label='gn',
        version='v1',
    )
    dsets = cat.to_dataset_dict(cdf_kwargs={'chunks': {'time': 36}})
    _, ds = dsets.popitem()
    assert isinstance(ds, xr.Dataset)

def test_serialize_to_json():
    with TemporaryDirectory() as local_store:
        col = intake.open_esm_datastore(catalog_dict_records)
        name = 'test_serialize_dict'
        col.serialize(name=name, directory=local_store, catalog_type='dict')
        output_catalog = os.path.join(local_store, name + '.json')
        col2 = intake.open_esm_datastore(output_catalog)
        pd.testing.assert_frame_equal(col.df, col2.df)

def test_to_dataset_dict_aggfalse(esmcol_path, query):
    col = intake.open_esm_datastore(esmcol_path)
    cat = col.search(**query)
    nds = len(cat.df)
    dsets = cat.to_dataset_dict(zarr_kwargs={'consolidated': True}, aggregate=False)
    assert len(dsets.keys()) == nds
    key, ds = dsets.popitem()
    assert 'tasmax' in key

def test_read_catalog_dict():
    col = intake.open_esm_datastore(catalog_dict_records)
    assert isinstance(col.df, pd.DataFrame)
    assert col.catalog_file is None

def zarr_aws_cesmle_col():
    url = 'https://raw.githubusercontent.com/NCAR/cesm-lens-aws/master/intake-catalogs/aws-cesm1-le.json'
    return intake.open_esm_datastore(url)

def test_progressbar(progressbar):
    c = intake.open_esm_datastore(cdf_col_sample_cmip5)
    cat = c.search(variable=['hfls'], frequency='mon', modeling_realm='atmos', model=['CNRM-CM5'])
    _ = cat.to_dataset_dict(cdf_kwargs=dict(chunks={}), progressbar=progressbar)

def test_df_property():
    col = intake.open_esm_datastore(catalog_dict_records)
    assert len(col.df) == 5
    col.df = col.df.iloc[0:2, :]
    assert isinstance(col.df, pd.DataFrame)
    assert len(col) == 1
    assert len(col.df) == 2

def test_to_dataset_dict(esmcol_path, query):
    col = intake.open_esm_datastore(esmcol_path)
    cat = col.search(**query)
    _, ds = cat.to_dataset_dict(
        zarr_kwargs={'consolidated': True},
        cdf_kwargs={'chunks': {'time': 1}},
        storage_options={'token': 'anon'},
    ).popitem()
    assert 'member_id' in ds.dims
    assert len(ds.__dask_keys__()) > 0
    assert ds.time.encoding