SanityCheck.py

SanityCheck.py is a script aimed at quickly validating the validity of data by providing simple statistical information.

Often, it is necessary to inspect data, such as model output, to determine if the results are reasonable or not. Instead of elaborate plots, the script focuses on presenting relevant statistics to avoid confusion while conveying as much information about the data as possible.

The script can handle various types of data and automatically adjusts the colorbar according to upper and lower percentiles to account for outliers. This prevents outliers from distorting the colorbar or histogram. Therefore it is important to check the values on on each colorbar, as they may vary from plot to plot.

One use case for this script is to run it automatically after each individual simulation in a long-term climate simulation. This allows for easy verification of the simulation results’ reasonableness, detecting problems like time shifts or instability.

Another use case is to run the script manually on specific data sets by passing them as command line arguments.

For examples of how to use this script, refer to the ‘examples/’ directory.

sloth.SanityCheck.get_PlotMinMaxMid_Percentil(data, lower=5, upper=95)[source]

Calculate the minimum, maximum, and midpoint values based on percentiles of the input data.

Parameters:

  • data (numpy.ma.MaskedArray) – Input data array.

  • lower (int, optional) – Lower percentile value. Default is 5.

  • upper (int, optional) – Upper percentile value. Default is 95.

Returns:

A tuple containing the minimum, maximum, and midpoint values.

Return type:

tuple

Examples

>>> import numpy as np
>>> import numpy.ma as ma
>>> data = ma.masked_array([1.2, 3.4, 5.6, 7.8, 9.0], mask=[False, False, True, False, False])
>>> min_val, max_val, mid_val = get_PlotMinMaxMid_Percentil(data, lower=10, upper=90)
>>> print("Minimum value:", min_val)
Minimum value: 1.86
>>> print("Maximum value:", max_val)
Maximum value: 8.64
>>> print("Midpoint value:", mid_val)
Midpoint value: 5.25
sloth.SanityCheck.plot_SanityCheck(data, kind='mean', figname='./SanityCheck_3D.pdf', fig_title=None, lowerP=2, upperP=98, fixValueRange=None, interactive=False, cmapName='Spectral')[source]

Plot a sanity check for given data.

The Sanity Plot is a plot consisting of 4 sub-plots, trying to visualise some important statistics in a compact way, aimed to determine if the data set inspected is plausible or not (sanity).

Parameters:

  • data (numpy.ma.MaskedArray) – 3D array of data.

  • kind ({'sum', 'mean'}, optional) – Calculation type for the data statistics. Default is ‘mean’.

  • figname (str, optional) – File name to save the plot. Default is ‘./SanityCheck_3D.pdf’.

  • fig_title (str, optional) – Title of the plot.

  • lowerP (int, optional) – Lower percentile value for plot limits. Default is 2.

  • upperP (int, optional) – Upper percentile value for plot limits. Default is 98.

  • fixValueRange (list) – A list of values to define a fix value range.

  • interactive (bool, optional) – If True, display the plot interactively. If False, save the plot to ‘figname’. Default is False.

  • cmapName (str, optional) – Name of the colormap. Default is ‘Spectral’.

Return type:

None

Notes

  • The ‘data’ input must be a 3D numpy masked array (t, y, x).

  • The ‘kind’ parameter specifies whether to calculate the sum or mean of the data.

  • The function generates a plot with subplots for the minimum, maximum, kind (sum or mean), and histogram of the data.

  • The colormap normalization is determined based on the percentiles of the data.

  • The plot can be displayed interactively or saved to a file.

  • If the ‘interactive’ parameter is set to True, the plot is displayed using plt.show(). If False, the plot is saved to the file specified by ‘figname’.