check_valid_dataframe

postprocessinglib.utilities._helper_functions.check_valid_dataframe(observed: DataFrame, simulated: DataFrame)

Check if all observations or simulations are invalid and raise an exception/error if this is the case.

Invalid in this case refers to all values in the dataframe being NaN, negative or zero. It goes through both the observed and simulated seperately and makes sure that its values are not all Nan, negative or zero. If any of these conditions are not met, it raises an AllInvalidError specifying which condition was not met.

Parameters:

• observed (pd.DataFrame) – The observed dataframe being checked.

• simulated (pd.DataFrame) – The simulated dataframe being checked.

Raises:

AllInvalidError: – If all observations or all simulations are NaN or negative.

Example

>>> import numpy as np
>>> import pandas as pd
>>> from postprocessinglib.utilities import _helper_functions
>>> # Create your index as an array
>>> index = np.array([1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990])
>>> .
>>> # Create a test dataframe
>>> test_df = pd.DataFrame(data = data, columns = ("obs1", "sim1", "obs2", "sim2"), index = index)
>>> obs = test_df.iloc[:, ::2]
>>> sim = test_df.iloc[:, 1::2]
>>> print(test_df)
        obs1     sim1     obs2     sim2
1981    NaN     -inf       -1     -inf
1982    NaN      inf      NaN      NaN
1983    inf     -inf      NaN     -inf
1984   -inf      NaN      inf      NaN
1985    NaN     -inf     -inf      inf
1986   -inf      NaN      NaN      inf
1987    NaN     -inf      inf      NaN
1988    inf     -inf      NaN      NaN
1989    inf      inf      NaN     -inf
1990   -inf      inf      NaN     -inf
>>> .
>>> _helper_functions.check_valid_dataframe(observed=obs, simulated=sim)
>>> # Error is raised due to all the observed values being invalid
>>> # The error message is as follows:
AllInvalidError: All observed values are invalid(negative)