55
66Illustrate the scale transformations applied to axes, e.g. log, symlog, logit.
77
8- The last two examples are examples ofusing the ``'function'`` scale by
9- supplying forward and inverse functions for the scale transformation .
8+ See `matplotlib.scale` for a full list ofbuilt-in scales, and
9+ :doc:`/gallery/scales/custom_scale` for how to create your own scale.
1010"""
1111
1212import matplotlib .pyplot as plt
1313import numpy as np
1414
15- from matplotlib .ticker import FixedLocator ,NullFormatter
15+ x = np .arange (400 )
16+ y = np .linspace (0.002 ,1 ,400 )
1617
17- # Fixing random state for reproducibility
18- np .random .seed (19680801 )
19-
20- # make up some data in the interval ]0, 1[
21- y = np .random .normal (loc = 0.5 ,scale = 0.4 ,size = 1000 )
22- y = y [(y > 0 )& (y < 1 )]
23- y .sort ()
24- x = np .arange (len (y ))
25-
26- # plot with various axes scales
2718fig ,axs = plt .subplots (3 ,2 ,figsize = (6 ,8 ),layout = 'constrained' )
2819
29- # linear
30- ax = axs [0 ,0 ]
31- ax .plot (x ,y )
32- ax .set_yscale ('linear' )
33- ax .set_title ('linear' )
34- ax .grid (True )
35-
20+ axs [0 ,0 ].plot (x ,y )
21+ axs [0 ,0 ].set_yscale ('linear' )
22+ axs [0 ,0 ].set_title ('linear' )
23+ axs [0 ,0 ].grid (True )
3624
37- # log
38- ax = axs [0 ,1 ]
39- ax .plot (x ,y )
40- ax .set_yscale ('log' )
41- ax .set_title ('log' )
42- ax .grid (True )
25+ axs [0 ,1 ].plot (x ,y )
26+ axs [0 ,1 ].set_yscale ('log' )
27+ axs [0 ,1 ].set_title ('log' )
28+ axs [0 ,1 ].grid (True )
4329
30+ axs [1 ,0 ].plot (x ,y - y .mean ())
31+ axs [1 ,0 ].set_yscale ('symlog' ,linthresh = 0.02 )
32+ axs [1 ,0 ].set_title ('symlog' )
33+ axs [1 ,0 ].grid (True )
4434
45- # symmetric log
46- ax = axs [1 ,1 ]
47- ax .plot (x ,y - y .mean ())
48- ax .set_yscale ('symlog' ,linthresh = 0.02 )
49- ax .set_title ('symlog' )
50- ax .grid (True )
35+ axs [1 ,1 ].plot (x ,y )
36+ axs [1 ,1 ].set_yscale ('logit' )
37+ axs [1 ,1 ].set_title ('logit' )
38+ axs [1 ,1 ].grid (True )
5139
52- # logit
53- ax = axs [1 ,0 ]
54- ax .plot (x ,y )
55- ax .set_yscale ('logit' )
56- ax .set_title ('logit' )
57- ax .grid (True )
40+ axs [2 ,0 ].plot (x ,y - y .mean ())
41+ axs [2 ,0 ].set_yscale ('asinh' ,linear_width = 0.01 )
42+ axs [2 ,0 ].set_title ('asinh' )
43+ axs [2 ,0 ].grid (True )
5844
5945
6046# Function x**(1/2)
@@ -66,38 +52,11 @@ def inverse(x):
6652return x ** 2
6753
6854
69- ax = axs [2 ,0 ]
70- ax .plot (x ,y )
71- ax .set_yscale ('function' ,functions = (forward ,inverse ))
72- ax .set_title ('function: $x^{1/2}$' )
73- ax .grid (True )
74- ax .yaxis .set_major_locator (FixedLocator (np .arange (0 ,1 ,0.2 )** 2 ))
75- ax .yaxis .set_major_locator (FixedLocator (np .arange (0 ,1 ,0.2 )))
76-
77-
78- # Function Mercator transform
79- def forward (a ):
80- a = np .deg2rad (a )
81- return np .rad2deg (np .log (np .abs (np .tan (a )+ 1.0 / np .cos (a ))))
82-
83-
84- def inverse (a ):
85- a = np .deg2rad (a )
86- return np .rad2deg (np .arctan (np .sinh (a )))
87-
88- ax = axs [2 ,1 ]
89-
90- t = np .arange (0 ,170.0 ,0.1 )
91- s = t / 2.
92-
93- ax .plot (t ,s ,'-' ,lw = 2 )
94-
95- ax .set_yscale ('function' ,functions = (forward ,inverse ))
96- ax .set_title ('function: Mercator' )
97- ax .grid (True )
98- ax .set_xlim ([0 ,180 ])
99- ax .yaxis .set_minor_formatter (NullFormatter ())
100- ax .yaxis .set_major_locator (FixedLocator (np .arange (0 ,90 ,10 )))
55+ axs [2 ,1 ].plot (x ,y )
56+ axs [2 ,1 ].set_yscale ('function' ,functions = (forward ,inverse ))
57+ axs [2 ,1 ].set_title ('function: $x^{1/2}$' )
58+ axs [2 ,1 ].grid (True )
59+ axs [2 ,1 ].set_yticks (np .arange (0 ,1.2 ,0.2 ))
10160
10261plt .show ()
10362
@@ -110,7 +69,6 @@ def inverse(a):
11069#
11170# - `matplotlib.axes.Axes.set_xscale`
11271# - `matplotlib.axes.Axes.set_yscale`
113- # - `matplotlib.axis.Axis.set_major_locator`
11472# - `matplotlib.scale.LinearScale`
11573# - `matplotlib.scale.LogScale`
11674# - `matplotlib.scale.SymmetricalLogScale`