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[statistics] plots work for old and new matplotlib

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This commit is contained in:
Jan Benda 2020-10-27 23:28:30 +01:00
parent ca11914956
commit d1c6174ae2
6 changed files with 55 additions and 42 deletions
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31
plotstyle.py
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@ -2,8 +2,12 @@ import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Set to true if you want xkcd-type sketchy plots:
xkcd_style = True
# matplotlib major version:
mpl_major = int(mpl.__version__.split('.')[0])
# default size of figure:
figure_width = 15.0 # cm, should be set according to \textwidth in the latex document
figure_height = 6.0 # cm, for a 1 x 2 figure
@ -26,7 +30,7 @@ colors['white'] = '#FFFFFF'
#colors_bendalab_vivid['green'] = '#30D700'
#colors_bendalab_vivid['blue'] = '#0020C0'
# line styles for plot():
# general settings for plot styles:
lwthick = 3.0
lwthin = 1.8
mainline = {'linestyle': '-', 'linewidth': lwthick}
@ -35,9 +39,12 @@ largemarker = {'marker': 'o', 'markersize': 9, 'markeredgecolor': colors['white'
smallmarker = {'marker': 'o', 'markersize': 6, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
largelinepoints = {'linestyle': '-', 'linewidth': lwthick, 'marker': 'o', 'markersize': 10, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
smalllinepoints = {'linestyle': '-', 'linewidth': 1.4, 'marker': 'o', 'markersize': 7, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
filllw = 1
filllw = 1.0
fillec = colors['white']
fillalpha = 0.4
filledge = {'linewidth': filllw, 'joinstyle': 'round'}
if int(mpl.__version__.split('.')[0]) < 2:
del filledge['joinstyle']
# helper lines:
lsSpine = {'c': colors['black'], 'linestyle': '-', 'linewidth': 1, 'clip_on': False}
@ -71,7 +78,7 @@ psA = dict({'color': colors['blue'], 'linestyle': 'none'}, **largemarker)
psAm = dict({'color': colors['blue'], 'linestyle': 'none'}, **smallmarker)
lpsA = dict({'color': colors['blue']}, **largelinepoints)
lpsAm = dict({'color': colors['blue']}, **smalllinepoints)
fsA = {'facecolor': colors['blue'], 'edgecolor': fillec, 'linewidth': filllw}
fsA = dict({'facecolor': colors['blue'], 'edgecolor': fillec}, **filledge)
fsAs = {'facecolor': colors['blue'], 'edgecolor': 'none'}
fsAa = {'facecolor': colors['blue'], 'edgecolor': 'none', 'alpha': fillalpha}
@ -81,7 +88,7 @@ psB = dict({'color': colors['red'], 'linestyle': 'none'}, **largemarker)
psBm = dict({'color': colors['red'], 'linestyle': 'none'}, **smallmarker)
lpsB = dict({'color': colors['red']}, **largelinepoints)
lpsBm = dict({'color': colors['red']}, **smalllinepoints)
fsB = {'facecolor': colors['red'], 'edgecolor': fillec, 'linewidth': filllw}
fsB = dict({'facecolor': colors['red'], 'edgecolor': fillec}, **filledge)
fsBs = {'facecolor': colors['red'], 'edgecolor': 'none'}
fsBa = {'facecolor': colors['red'], 'edgecolor': 'none', 'alpha': fillalpha}
@ -89,7 +96,7 @@ lsC = dict({'color': colors['lightorange']}, **mainline)
lsCm = dict({'color': colors['lightorange']}, **minorline)
psC = dict({'color': colors['lightorange'], 'linestyle': 'none'}, **largemarker)
psCm = dict({'color': colors['lightorange'], 'linestyle': 'none'}, **smallmarker)
fsC = {'facecolor': colors['lightorange'], 'edgecolor': fillec, 'linewidth': filllw}
fsC = dict({'facecolor': colors['lightorange'], 'edgecolor': fillec}, **filledge)
fsCs = {'facecolor': colors['lightorange'], 'edgecolor': 'none'}
fsCa = {'facecolor': colors['lightorange'], 'edgecolor': 'none', 'alpha': fillalpha}
@ -97,17 +104,17 @@ lsD = dict({'color': colors['orange']}, **mainline)
lsDm = dict({'color': colors['orange']}, **minorline)
psD = dict({'color': colors['orange'], 'linestyle': 'none'}, **largemarker)
psDm = dict({'color': colors['orange'], 'linestyle': 'none'}, **smallmarker)
fsD = {'facecolor': colors['orange'], 'edgecolor': fillec, 'linewidth': filllw}
fsD = dict({'facecolor': colors['orange'], 'edgecolor': fillec}, **filledge)
fsDs = {'facecolor': colors['orange'], 'edgecolor': 'none'}
lsE = dict({'color': colors['yellow']}, **mainline)
lsEm = dict({'color': colors['yellow']}, **minorline)
psE = dict({'color': colors['yellow'], 'linestyle': 'none'}, **largemarker)
psEm = dict({'color': colors['yellow'], 'linestyle': 'none'}, **smallmarker)
fsE = {'facecolor': colors['yellow'], 'edgecolor': fillec, 'linewidth': filllw}
fsE = dict({'facecolor': colors['yellow'], 'edgecolor': fillec}, **filledge)
fsEs = {'facecolor': colors['yellow'], 'edgecolor': 'none'}
fsF = {'facecolor': colors['green'], 'edgecolor': fillec, 'linewidth': filllw}
fsF = dict({'facecolor': colors['green'], 'edgecolor': fillec}, **filledge)
fsFs = {'facecolor': colors['green'], 'edgecolor': 'none'}
# factor for scaling widths of bars in a bar plot:
@ -326,7 +333,7 @@ def set_zlabel(ax, label, unit=None, **kwargs):
ax.set_zlabel_orig(axis_label(label, unit), **kwargs)
def common_format():
def common_style():
""" Set some rc parameter.
"""
mpl.rcParams['figure.figsize'] = cm_size(figure_width, figure_height)
@ -337,6 +344,8 @@ def common_format():
mpl.rcParams['figure.subplot.wspace'] = 0.4
mpl.rcParams['figure.subplot.hspace'] = 0.6
mpl.rcParams['figure.facecolor'] = 'white'
mpl.rcParams['figure.edgecolor'] = 'none'
mpl.rcParams['figure.frameon'] = False
mpl.rcParams['xtick.direction'] = 'out'
mpl.rcParams['ytick.direction'] = 'out'
mpl.rcParams['xtick.major.width'] = 1.25
@ -380,7 +389,7 @@ def sketch_style():
global bar_fac
bar_fac = 0.9
plt.xkcd()
common_format()
common_style()
mpl.rcParams['legend.fontsize'] = 'medium'
mpl.rcParams['xtick.labelsize'] = 'medium'
mpl.rcParams['ytick.labelsize'] = 'medium'
@ -394,7 +403,7 @@ def plain_style():
global bar_fac
bar_fac = 1.0
plt.rcdefaults()
common_format()
common_style()
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['legend.fontsize'] = 'x-small'
mpl.rcParams['xtick.labelsize'] = 'small'

5
statistics/lecture/diehistograms.py
View File

@ -2,7 +2,6 @@ import numpy as np
import matplotlib.pyplot as plt
from plotstyle import *
sketch_style()
# roll the die:
rng = np.random.RandomState(57281)
x1 = rng.randint(1, 7, 100)
@ -27,9 +26,9 @@ ax2.set_xlabel('x')
ax2.set_ylim(0, 0.23)
ax2.set_ylabel('Probability')
ax2.plot([0.2, 6.8], [1.0/6.0, 1.0/6.0], zorder=-10, **lsAm)
try:
if mpl_major > 1:
ax2.hist([x2, x1], bins, density=True, zorder=-5, **fs)
except AttributeError:
else:
ax2.hist([x2, x1], bins, normed=True, zorder=-5, **fs)
fig.subplots_adjust(left=0.125)
fig.savefig('diehistograms.pdf')

26
statistics/lecture/displayunivariatedata.py
View File

@ -4,7 +4,6 @@ import matplotlib.gridspec as gridspec
from scipy.stats import gaussian_kde
from plotstyle import *
#sketch_style()
#rng = np.random.RandomState(981)
#data = rng.randn(40, 1) + 4.0
rng = np.random.RandomState(1981)
@ -19,9 +18,11 @@ fig = plt.figure(figsize=cm_size(figure_width, 1.1*figure_height))
spec = gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[3, 1], wspace=0.1,
**adjust_fs(fig, left=4.0))
ax = fig.add_subplot(spec[0, 0])
ax = fig.add_subplot(spec[0, 0], label='1')
wh = ax.boxplot( data, positions=[boxpos], widths=[barwidth], whis=100.0, patch_artist=True)
mediancolor = 'k'
wh['medians'][0].set_linewidth(4)
wh['medians'][0].set_color(mediancolor)
wh['whiskers'][0].set_linewidth(2)
wh['whiskers'][1].set_linewidth(2)
wh['whiskers'][0].set_linestyle('-')
@ -34,6 +35,16 @@ wh['caps'][1].set_color(whiskercolor)
wh['boxes'][0].set_facecolor('#99ff00')
ax.set_xlim(0.0, 4.8)
ax.set_ylim( 0.0, 8.0)
ax.set_xticklabels([])
ax = fig.add_subplot(spec[0, 0], label='2')
ax.set_xlim(0.0, 4.8)
ax.set_xticks([scatterpos, barpos, boxpos])
ax.set_xticklabels(['(1) data', '(2) bar\n plot', '(3) box-\nwhisker'], fontsize='medium')
ax.set_ylabel('x')
ax.set_ylim( 0.0, 8.0)
# annotate box whisker:
ax.annotate('maximum',
xy=(boxpos, 6.5), xycoords='data',
xytext=(boxpos-1*barwidth, 7.6), textcoords='data', ha='left',
@ -49,14 +60,6 @@ ax.annotate('median',
xytext=(boxpos+0.1*barwidth, 4.2), textcoords='data', ha='left',
arrowprops=dict(arrowstyle="->", relpos=(0.8,0.0),
connectionstyle="angle3,angleA=-60,angleB=20") )
ax.set_xticklabels([])
ax = fig.add_subplot(spec[0, 0])
ax.set_xlim(0.0, 4.8)
ax.set_xticks([scatterpos, barpos, boxpos])
ax.set_xticklabels(['(1) data', '(2) bar\n plot', '(3) box-\nwhisker'], fontsize='medium')
ax.set_ylabel('x')
ax.set_ylim( 0.0, 8.0)
# scatter data points according to their density:
kernel = gaussian_kde(data)
@ -67,6 +70,9 @@ ax.plot(scatterpos+barwidth*x*(rng.rand(len(data))-0.5), data, **psA)
barmean = np.mean(data)
barstd = np.std(data)
ew = 0.2
if mpl_major > 1:
ax.bar([barpos], [barmean], barwidth, **fsC)
else:
ax.bar([barpos-0.5*barwidth], [barmean], barwidth, **fsC)
ax.plot([barpos, barpos], [barmean-barstd, barmean+barstd], **lsMarker)
ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean-barstd, barmean-barstd], **lsMarker)

25
statistics/lecture/kerneldensity.py
View File

@ -1,6 +1,7 @@
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.ticker as ticker
from plotstyle import *
# normal distribution:
@ -37,41 +38,39 @@ spec = gridspec.GridSpec(nrows=2, ncols=2, wspace=0.35, hspace=0.3,
**adjust_fs(fig, left=5.5, top=0.2, bottom=2.7))
ax = fig.add_subplot(spec[0, 0])
ax.set_xlabel('x')
ax.set_xlim(-3.2, 3.2)
ax.set_xticks(np.arange(-3.0, 3.1, 1.0))
ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.set_ylabel('p(x)')
ax.set_ylim(0.0, 0.49)
ax.set_yticks(np.arange(0.0, 0.41, 0.1))
#ax.plot(x, g, '-b', lw=2, zorder=-1)
try:
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.2))
if mpl_major > 1:
ax.hist(r, np.arange(-4.1, 4, 0.4), density=True, zorder=-5, **fsC)
except AttributeError:
else:
ax.hist(r, np.arange(-4.1, 4, 0.4), normed=True, zorder=-5, **fsC)
ax = fig.add_subplot(spec[1, 0])
ax.set_xlabel('x')
ax.set_xlim(-3.2, 3.2)
ax.set_xticks(np.arange(-3.0, 3.1, 1.0))
ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
ax.set_ylabel('p(x)')
ax.set_ylim(0.0, 0.49)
ax.set_yticks(np.arange(0.0, 0.41, 0.1))
#ax.plot(x, g, '-b', lw=2, zorder=-1)
try:
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.2))
if mpl_major > 1:
ax.hist(r, np.arange(-4.3, 4, 0.4), density=True, zorder=-5, **fsC)
except AttributeError:
else:
ax.hist(r, np.arange(-4.3, 4, 0.4), normed=True, zorder=-5, **fsC)
ax = fig.add_subplot(spec[:, 1])
ax.set_xlabel('x')
ax.set_xlim(-3.2, 3.2)
ax.set_xticks(np.arange(-3.0, 3.1, 1.0))
ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))
ax.set_ylabel('Prob. density p(x)')
ax.set_ylim(0.0, 0.49)
ax.set_yticks(np.arange(0.0, 0.41, 0.1))
kd, xx = kerneldensity(r, -3.2, 3.2, 0.2)
ax.fill_between(xx, 0.0, kd, zorder=-5, **fsDs)
ax.plot(xx, kd, '-', zorder=-1, **lsB)
fig.subplots_adjust(left=0.15)
fig.savefig('kerneldensity.pdf')

6
statistics/lecture/pdfhistogram.py
View File

@ -8,7 +8,7 @@ x = np.arange(-4.0, 4.0, 0.01)
g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
r = rng.randn(100)
fig, (ax1, ax2) = plt.subplots(1, 2)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=cm_size(figure_width, 1.1*figure_height))
ax1.set_xlabel('x')
ax1.set_xlim(-3.2, 3.2)
ax1.set_xticks(np.arange(-3.0, 3.1, 1.0))
@ -24,10 +24,10 @@ ax2.set_ylabel('Probab. density p(x)')
ax2.set_ylim(0.0, 0.44)
ax2.set_yticks(np.arange(0.0, 0.41, 0.1))
ax2.plot(x, g, zorder=-1, **lsA)
try:
if mpl_major > 1:
ax2.hist(r, 5, density=True, zorder=-10, **fsB)
ax2.hist(r, 20, density=True, zorder=-5, **fsC)
except AttributeError:
else:
ax2.hist(r, 5, normed=True, zorder=-10, **fsB)
ax2.hist(r, 20, normed=True, zorder=-5, **fsC)

2
statistics/lecture/pdfprobabilities.py
View File

@ -10,7 +10,7 @@ x2=1.0
fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
ax.set_xlabel('x')
ax.set_ylabel('Probability density p(x)')
ax.set_ylabel('Prob. density p(x)')
ax.set_ylim(0.0, 0.46)
ax.set_yticks(np.arange(0.0, 0.45, 0.1))
ax.annotate('Gaussian',