def test_all_optional_args_multiple_sites():
"""Test we create a datafram even when we have all optional
arguements set and multiple spots"""
# Set up expected dataframe.
data = [[600, "air_temperature", 0, 280.],
[600, "air_temperature", 1, 280.],
[600, "air_temperature", 2, 280.]]
columns = ["validity_time", "cf_name", "site", "T+000"]
expected_df = pd.DataFrame(data, columns=columns)
expected_df.set_index(["validity_time", "cf_name", "site"],
inplace=True)
expected_df.columns.name = "forecast_period"
plugin = SpotDatabase(
"csv",
"output",
"improver",
"time",
primary_map=['validity_time'],
primary_func=[lambda x: dt.utcfromtimestamp(x).hour * 100],
pivot_dim='forecast_period',
pivot_map=lambda x: 'T+{:03d}'.format(int(x / 3600)),
column_dims=['name', "index"],
column_maps=['cf_name', "site"],
coord_to_slice_over="index")
# Call the method.
cube = set_up_spot_cube(
280,
number_of_sites=3,
)
plugin.to_dataframe(CubeList([cube]))
assert_frame_equal(plugin.df, expected_df)
def test_all_optional_args_multiple_input_cubes(self):
"""Test we create a dataframe even when we have no optional
arguements set and multiple cubes"""
# Set up expected dataframe.
data = [[600, "air_temperature", 280., np.nan, np.nan],
[700, "air_temperature", np.nan, 281., np.nan],
[800, "air_temperature", np.nan, np.nan, 282.]]
columns = ["validity_time", "cf_name", "T+000", "T+001", "T+002"]
expected_df = pd.DataFrame(data, columns=columns)
expected_df.set_index(["validity_time", "cf_name"], inplace=True)
expected_df.columns.name = "forecast_period"
plugin = SpotDatabase(
"csv",
"output",
"improver",
"time",
primary_map=['validity_time'],
primary_func=[lambda x: dt.utcfromtimestamp(x).hour * 100],
pivot_dim='forecast_period',
pivot_map=lambda x: 'T+{:03d}'.format(int(x / 3600)),
column_dims=['name'],
column_maps=['cf_name'],
coord_to_slice_over="index")
# Call the method.
plugin.to_dataframe(self.cubelist_multiple)
assert_frame_equal(plugin.df, expected_df)
class Test_determine_schema(IrisTest):
"""A set of tests for the determine_schema method"""
def setUp(self):
"""Set up the plugin and dataframe needed for this test"""
cubes = CubeList([set_up_spot_cube(280)])
self.plugin = SpotDatabase("csv", "output", "improver", "time",
"index")
self.dataframe = self.plugin.to_dataframe(cubes)
def test_full_schema(self):
"""Basic test using a basic dataframe as input"""
schema = self.plugin.determine_schema("improver")
expected_schema = 'CREATE TABLE "improver" (\n"index" INTEGER,\n '\
'"values" REAL,\n CONSTRAINT improver_pk '\
'PRIMARY KEY ("index")\n)'
self.assertEqual(schema, expected_schema)
class Test_to_dataframe(IrisTest):
"""Test the to_dataframe method"""
def setUp(self):
"""Set up the plugin and dataframe needed for these tests"""
self.cube = set_up_spot_cube(280, number_of_sites=1)
self.cube2 = set_up_spot_cube(281,
number_of_sites=1,
validity_time=1487311200 + 3600,
forecast_period=1)
self.cube3 = set_up_spot_cube(282,
number_of_sites=1,
validity_time=1487311200 + 7200,
forecast_period=2)
self.cubelist = CubeList([self.cube])
self.cubelist_multiple = CubeList([self.cube, self.cube2, self.cube3])
self.plugin = SpotDatabase("csv", "output", "improver", "time",
"index")
data = [[1487311200, 280.]]
columns = ["time", "values"]
self.input_df = pd.DataFrame(data, columns=columns)
@ManageWarnings(ignored_messages=IGNORED_MESSAGES,
warning_types=WARNING_TYPES)
def test_no_optional_args(self):
"""Test we create a datafram even when we have no optional
arguements set"""
# Set up expected dataframe.
data = [[280.]]
columns = ["values"]
expected_df = pd.DataFrame(data, index=[1487311200], columns=columns)
# Call the method.
self.plugin.to_dataframe(self.cubelist)
assert_frame_equal(self.plugin.df, expected_df)
@ManageWarnings(ignored_messages=IGNORED_MESSAGES,
warning_types=WARNING_TYPES)
def test_all_optional_args(self):
"""Test we create a datafram even when we have all optional
arguements set"""
# Set up expected dataframe.
data = [[600, "air_temperature", 280.]]
columns = ["validity_time", "cf_name", "T+000"]
expected_df = pd.DataFrame(data, columns=columns)
expected_df.set_index(["validity_time", "cf_name"], inplace=True)
expected_df.columns.name = "forecast_period"
plugin = SpotDatabase(
"csv",
"output",
"improver",
"time",
primary_map=['validity_time'],
primary_func=[lambda x: dt.utcfromtimestamp(x).hour * 100],
pivot_dim='forecast_period',
pivot_map=lambda x: 'T+{:03d}'.format(int(x / 3600)),
column_dims=['name'],
column_maps=['cf_name'],
coord_to_slice_over="index")
# Call the method.
plugin.to_dataframe(self.cubelist)
assert_frame_equal(plugin.df, expected_df)
@ManageWarnings(ignored_messages=IGNORED_MESSAGES,
warning_types=WARNING_TYPES)
def test_all_optional_args_multiple_input_cubes(self):
"""Test we create a dataframe even when we have no optional
arguements set and multiple cubes"""
# Set up expected dataframe.
data = [[600, "air_temperature", 280., np.nan, np.nan],
[700, "air_temperature", np.nan, 281., np.nan],
[800, "air_temperature", np.nan, np.nan, 282.]]
columns = ["validity_time", "cf_name", "T+000", "T+001", "T+002"]
expected_df = pd.DataFrame(data, columns=columns)
expected_df.set_index(["validity_time", "cf_name"], inplace=True)
expected_df.columns.name = "forecast_period"
plugin = SpotDatabase(
"csv",
"output",
"improver",
"time",
primary_map=['validity_time'],
primary_func=[lambda x: dt.utcfromtimestamp(x).hour * 100],
pivot_dim='forecast_period',
pivot_map=lambda x: 'T+{:03d}'.format(int(x / 3600)),
column_dims=['name'],
column_maps=['cf_name'],
coord_to_slice_over="index")
# Call the method.
plugin.to_dataframe(self.cubelist_multiple)
assert_frame_equal(plugin.df, expected_df)
@staticmethod
@ManageWarnings(ignored_messages=IGNORED_MESSAGES,
warning_types=WARNING_TYPES)
def test_all_optional_args_multiple_sites():
"""Test we create a datafram even when we have all optional
arguements set and multiple spots"""
# Set up expected dataframe.
data = [[600, "air_temperature", 0, 280.],
[600, "air_temperature", 1, 280.],
[600, "air_temperature", 2, 280.]]
columns = ["validity_time", "cf_name", "site", "T+000"]
expected_df = pd.DataFrame(data, columns=columns)
expected_df.set_index(["validity_time", "cf_name", "site"],
inplace=True)
expected_df.columns.name = "forecast_period"
plugin = SpotDatabase(
"csv",
"output",
"improver",
"time",
primary_map=['validity_time'],
primary_func=[lambda x: dt.utcfromtimestamp(x).hour * 100],
pivot_dim='forecast_period',
pivot_map=lambda x: 'T+{:03d}'.format(int(x / 3600)),
column_dims=['name', "index"],
column_maps=['cf_name', "site"],
coord_to_slice_over="index")
# Call the method.
cube = set_up_spot_cube(
280,
number_of_sites=3,
)
plugin.to_dataframe(CubeList([cube]))
assert_frame_equal(plugin.df, expected_df)