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####
#### MIT License
####
#### ParaMonte: plain powerful parallel Monte Carlo library.
####
#### Copyright (C) 2012-present, The Computational Data Science Lab
####
#### This file is part of the ParaMonte library.
####
#### Permission is hereby granted, free of charge, to any person obtaining a
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#### The above copyright notice and this permission notice shall be
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####
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import numpy as np
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#### colorbar
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[docs]def isColorBar(ax):
"""
Attempt to guess whether an input matplotlib
``Axes`` is home to a ``colorbar`` object.
**Parameters**
ax
An Axes instance.
**Returns**
A boolean value of ``True`` if the x xor y axis
satisfies all of the following and thus looks
like a ``colorbar`` object:
No ticks, no tick labels, no axis label
"""
xcb = (len(ax.get_xticks()) == 0) and (len(ax.get_xticklabels()) == 0) and (len(ax.get_xlabel()) == 0) #and (ax.get_xlim() == (0, 1))
ycb = (len(ax.get_yticks()) == 0) and (len(ax.get_yticklabels()) == 0) and (len(ax.get_ylabel()) == 0) #and (ax.get_ylim() == (0, 1))
return xcb != ycb
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#### isPresentColorBar
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[docs]def isPresentColorBar():
from matplotlib import pyplot as plt
return any([isColorBar(ax) for ax in np.atleast_1d(plt.gcf().axes).flatten()])