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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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- ########################################
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+ ###############################################################################
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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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- ########################################
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+ ###############################################################################
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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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- ########################################
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+ ###############################################################################
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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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+ ###############################################################################
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#
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# .. admonition:: References
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#
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fig .tight_layout ()
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plt .show ()
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- ########################################
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+ ###############################################################################
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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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+ ###############################################################################
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#
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# .. admonition:: References
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#
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