def test_nonunique_values_in_column(self):
"""Test if there are multiple non-unique values in a column of the
dataframe."""
df = self.forecast_df.copy()
df.at[0, "period"] = pd.Timedelta(7200, units="seconds")
msg = "Multiple values provided for the period"
with self.assertRaisesRegex(ValueError, msg):
forecast_dataframe_to_cube(df, self.date_range,
self.forecast_period)
def test_three_day_training_instantaneous_diag(self):
"""Test an input DataFrame is converted correctly into an Iris Cube
for a three day training length for an instantaneous diagnostic."""
self.forecast_df["period"] = pd.Timedelta("NaT")
result = forecast_dataframe_to_cube(self.forecast_df, self.date_range,
self.forecast_period)
self.assertCubeEqual(result, self.expected_instantaneous_forecast)
def test_two_day_training(self):
"""Test an input DataFrame is converted correctly into an Iris Cube
for a two day training length."""
result = forecast_dataframe_to_cube(self.forecast_df,
self.date_range_two_days,
self.forecast_period)
self.assertCubeEqual(result, self.expected_period_forecast[:, 1:])
def test_empty_dataframe(self):
"""Test if none of the required data is available in the dataframe."""
forecast_period = 7 * 3600
result = forecast_dataframe_to_cube(self.forecast_df, self.date_range,
forecast_period)
self.assertIsNone(result)
def test_three_day_training_period_diag(self):
"""Test an input DataFrame is converted correctly into an Iris Cube
for a three day training length for a period diagnostic."""
result = forecast_dataframe_to_cube(self.forecast_df, self.date_range,
self.forecast_period)
self.assertCubeEqual(result, self.expected_period_forecast)