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Patched various 79-character section breaks in documentation
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examples/scales/asinh_demo.py

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# Prepare sample values for variations on y=x graph:
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x = np.linspace(-3, 6, 500)
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# Compare "symlog" and "asinh" behaviour on sample y=x graph,
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# where there is a discontinuous gradient in "symlog" near y=2:
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fig1 = plt.figure()
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ax1.set_title('asinh')
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# Compare "asinh" graphs with different scale parameter "linear_width":
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fig2 = plt.figure(constrained_layout=True)
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axs = fig2.subplots(1, 3, sharex=True)
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ax.legend(loc='best', fontsize='small')
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# Compare "symlog" and "asinh" scalings
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# on 2D Cauchy-distributed random numbers,
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# where one may be able to see more subtle artifacts near y=2
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plt.show()
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#
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# .. admonition:: References
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#

examples/scales/symlog_demo.py

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fig.tight_layout()
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plt.show()
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# It should be noted that the coordinate transform used by ``symlog``
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# has a discontinuous gradient at the transition between its linear
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# and logarithmic regions. The ``asinh`` axis scale is an alternative
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# technique that may avoid visual artifacts caused by these disconinuities.
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#
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# .. admonition:: References
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#

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