96 lines
3.6 KiB
Python
96 lines
3.6 KiB
Python
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.gridspec as gridspec
|
|
from scipy.stats import gaussian_kde
|
|
from plotstyle import *
|
|
|
|
#rng = np.random.RandomState(981)
|
|
#data = rng.randn(40, 1) + 4.0
|
|
rng = np.random.RandomState(1981)
|
|
data = rng.gamma(1.0, 1.5, 40) + 1.0
|
|
data = data[data<7.5]
|
|
barwidth = 0.8
|
|
scatterpos = 1.0
|
|
barpos = 2.5
|
|
boxpos = 4.0
|
|
|
|
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])
|
|
wh = ax.boxplot( data, positions=[boxpos], widths=[barwidth], whis=100.0, patch_artist=True)
|
|
wh['medians'][0].set_linewidth(4)
|
|
wh['whiskers'][0].set_linewidth(2)
|
|
wh['whiskers'][1].set_linewidth(2)
|
|
wh['whiskers'][0].set_linestyle('-')
|
|
wh['whiskers'][1].set_linestyle('-')
|
|
whiskercolor = 'k'
|
|
wh['whiskers'][0].set_color(whiskercolor)
|
|
wh['whiskers'][1].set_color(whiskercolor)
|
|
wh['caps'][0].set_color(whiskercolor)
|
|
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.annotate('maximum',
|
|
xy=(boxpos, 6.5), xycoords='data',
|
|
xytext=(boxpos-1*barwidth, 7.6), textcoords='data', ha='left',
|
|
arrowprops=dict(arrowstyle="->", relpos=(1.0,0.5),
|
|
connectionstyle="angle3,angleA=0,angleB=120") )
|
|
ax.annotate('3. quartile',
|
|
xy=(boxpos-0.3*barwidth, 3.7), xycoords='data',
|
|
xytext=(boxpos-0.1*barwidth, 5.5), textcoords='data', ha='right',
|
|
arrowprops=dict(arrowstyle="->", relpos=(0.4,0.0),
|
|
connectionstyle="angle3,angleA=0,angleB=120") )
|
|
ax.annotate('median',
|
|
xy=(boxpos+0.6*barwidth, 2.2), xycoords='data',
|
|
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)
|
|
x = kernel(data)
|
|
x /= np.max(x)
|
|
ax.plot(scatterpos+barwidth*x*(rng.rand(len(data))-0.5), data, **psA)
|
|
|
|
barmean = np.mean(data)
|
|
barstd = np.std(data)
|
|
ew = 0.2
|
|
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)
|
|
ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean+barstd, barmean+barstd], **lsMarker)
|
|
ax.annotate('mean',
|
|
xy=(barpos-0.4*barwidth, 2.7), xycoords='data',
|
|
xytext=(barpos-1*barwidth, 5.5), textcoords='data', ha='left',
|
|
arrowprops=dict(arrowstyle="->", relpos=(1.0,0.5),
|
|
connectionstyle="angle3,angleA=0,angleB=120") )
|
|
ax.annotate('mean plus\nstd. dev.',
|
|
xy=(barpos+0.05*barwidth, 4.2), xycoords='data',
|
|
xytext=(barpos-1*barwidth, 7.0), textcoords='data', ha='left',
|
|
arrowprops=dict(arrowstyle="->", relpos=(0.5,0.0),
|
|
connectionstyle="angle3,angleA=-60,angleB=80") )
|
|
|
|
ax = fig.add_subplot(spec[0, 1])
|
|
ax.set_yticklabels([])
|
|
ax.set_ylim( 0.0, 8.0)
|
|
ax.set_xticks(np.arange(0.0, 0.4, 0.1))
|
|
ax.set_xlabel('(4) pdf')
|
|
bw = 0.75
|
|
bins = np.arange(0, 8.0+bw, bw)
|
|
h, b = np.histogram(data, bins)
|
|
ax.barh(b[:-1], h/bw/np.sum(h), bw, **fsB)
|
|
|
|
plt.savefig('displayunivariatedata.pdf')
|
|
|