I want to make a plot with square root scale using Python:

However, I have no idea how to make it. Matplotlib allows to make log scale but in this case I need something like power function scale.

解决方案

You can make your own ScaleBase class to do this. I have modified the example from here (which made a square-scale, not a square-root-scale) for your purposes. Also, see the documentation here.

Note that to do this properly, you should probably also create your own custom tick locator; I haven't done that here though; I just manually set the major and minor ticks using ax.set_yticks().

import matplotlib.scale as mscale

import matplotlib.pyplot as plt

import matplotlib.transforms as mtransforms

import matplotlib.ticker as ticker

import numpy as np

class SquareRootScale(mscale.ScaleBase):

"""

ScaleBase class for generating square root scale.

"""

name = 'squareroot'

def __init__(self, axis, **kwargs):

# note in older versions of matplotlib (<3.1), this worked fine.

# mscale.ScaleBase.__init__(self)

# In newer versions (>=3.1), you also need to pass in `axis` as an arg

mscale.ScaleBase.__init__(self, axis)

def set_default_locators_and_formatters(self, axis):

axis.set_major_locator(ticker.AutoLocator())

axis.set_major_formatter(ticker.ScalarFormatter())

axis.set_minor_locator(ticker.NullLocator())

axis.set_minor_formatter(ticker.NullFormatter())

def limit_range_for_scale(self, vmin, vmax, minpos):

return max(0., vmin), vmax

class SquareRootTransform(mtransforms.Transform):

input_dims = 1

output_dims = 1

is_separable = True

def transform_non_affine(self, a):

return np.array(a)**0.5

def inverted(self):

return SquareRootScale.InvertedSquareRootTransform()

class InvertedSquareRootTransform(mtransforms.Transform):

input_dims = 1

output_dims = 1

is_separable = True

def transform(self, a):

return np.array(a)**2

def inverted(self):

return SquareRootScale.SquareRootTransform()

def get_transform(self):

return self.SquareRootTransform()

mscale.register_scale(SquareRootScale)

fig, ax = plt.subplots(1)

ax.plot(np.arange(0, 9)**2, label='$y=x^2$')

ax.legend()

ax.set_yscale('squareroot')

ax.set_yticks(np.arange(0,9,2)**2)

ax.set_yticks(np.arange(0,8.5,0.5)**2, minor=True)

plt.show()