bar, vmin=None, vmax=None, reduce=True, style='original', cmap=None, ax=None, data=None)[source]

Plot a bar chart of the classification abundances.

  • class_map (numpy.ndarray[int], ndim=2 or 3) – Array of classifications. If the array is three-dimensional, it is assumed that the first dimension is time, and a time average classification will be plotted. The time average is the most common positive (valid) classification at each pixel.

  • vmin (int, optional, default=None) – Minimum classification integer to plot. Must be greater or equal to zero. Defaults to min positive integer in class_map.

  • vmax (int, optional, default=None) – Maximum classification integer to plot. Must be greater than zero. Defaults to max positive integer in class_map.

  • reduce (bool, optional, default=True) – Whether to perform the time average described in class_map info.

  • style (str, optional, default='original') – The named matplotlib colormap to extract a ListedColormap from. Colours are selected from vmin to vmax at equidistant values in the range [0, 1]. The ListedColormap produced will also show bad classifications and classifications out of range in grey. The default ‘original’ is a special case used since early versions of this code. It is a hardcoded list of 5 colours. When the number of classifications exceeds 5, style='viridis' will be used.

  • cmap (str or matplotlib.colors.Colormap, optional, default=None) – Parameter to pass to matplotlib.axes.Axes.imshow. This parameter overrides any cmap requested via the style parameter.

  • ax (matplotlib.axes.Axes, optional, default=None) – Axes into which the velocity map will be plotted. Defaults to the current axis of the current figure.

  • data (dict, optional, default=None) – Dictionary of common classification plotting settings generated by init_class_data(). If present, all other parameters are ignored except and ax.


b – The object returned by after plotting abundances.

Return type


See also


Classify spectra.


Average a 3D array of classifications.


Visualisation assumes that all integers between vmin and vmax are valid classifications, even if they do not appear in class_map.