serpentTools.plot.cartMeshPlot

serpentTools.plot.cartMeshPlot(data, xticks=None, yticks=None, ax=None, cmap=None, logColor=False, normalizer=None, cbarLabel=None, thresh=None, **kwargs)

Create a cartesian mesh plot of the data

Parameters

  • data (numpy.array) – 2D array of data to be plotted

  • xticks (iterable or None) –

  • yticks (iterable or None) – Values corresponding to lower x/y boundary of meshes. If not given, treat this as a matrix plot like matplotlib.pyplot.imshow(). If given, they should contain one more item than number of elements in their dimension to give dimensions for all meshes.

  • ax (matplotlib.axes.Axes, optional) – Ax on which to plot the data. If not provided, create a new plot

  • cmap (str, optional) – Valid Matplotlib colormap to apply to the plot.

  • logColor (bool) – If true, apply a logarithmic coloring

  • normalizer (callable or matplotlib.colors.Normalize) – Custom normalizer for this plot. If an instance of matplotlib.colors.Normalize,

  • normalizer – Custom normalizer for this plot. If an instance of matplotlib.colors.Normalize, use directly. Otherwise, assume a callable object and call as norm = normalizer(data, xticks, yticks)

  • cbarLabel (None or str) – Label to apply to colorbar

  • {thresh} –

  • kwargs (dict, optional) – Addition keyword arguments to pass to matplotlib.pyplot.pcolormesh() or matplotlib.pyplot.imshow() if xticks and yticks are None

Returns

Ax on which the data was plotted.

Return type

matplotlib.axes.Axes

Raises

  • ValueError – If logColor and data contains negative quantities

  • TypeError – If only one of xticks or yticks is None.

Examples

>>> from serpentTools.plot import cartMeshPlot
>>> from numpy import arange
>>> data = arange(100).reshape(10, 10)
>>> x = y = arange(11)
>>> cartMeshPlot(data, x, y, cbarLabel='Demo')

(Source code, png, hires.png, pdf)

../../_images/serpentTools-plot-cartMeshPlot-1.png 
>>> from serpentTools.plot import cartMeshPlot
>>> from numpy import  eye
>>> data = eye(10)
>>> for indx in range(10):
...     data[indx] *= indx
>>> cartMeshPlot(data, logColor=True)

(Source code, png, hires.png, pdf)

../../_images/serpentTools-plot-cartMeshPlot-2.png

All values less than or equal to zero are excluded from the color normalization. The logColor argument works well for plotting sparse matrices, as the zero-valued indices can be identified without obscuring the trends presented in the non-zero data.

Alternatively, one can use the thresh argument to set a threshold for plotted data. Any value less than or equal to thresh will not be colored, and the colorbar will be updated to reflect this.

>>> from serpentTools.plot import cartMeshPlot
>>> from numpy import arange
>>> data = arange(100).reshape(10, 10)
>>> cartMeshPlot(data, thresh=50)

(Source code, png, hires.png, pdf)

../../_images/serpentTools-plot-cartMeshPlot-3.png

See also