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NelleVNelleV
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Merge pull request #7331 from phobson/MEP12-statistic-plots
MEP12 improvments for statistics plots
2 parents dc748b8 + 076b98c commit 3127815

15 files changed

Lines changed: 382 additions & 132 deletions

‎examples/statistics/boxplot_color_demo.py‎

Lines changed: 8 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1,10 +1,16 @@
1-
"""Box plots with custom fill colors.
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"""
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=================================
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Box plots with custom fill colors
4+
=================================
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This plot illustrates how to create two types of box plots
47
(rectangular and notched), and how to fill them with custom
58
colors by accessing the properties of the artists of the
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box plots. Additionally, the ``labels`` parameter is used to
710
provide x-tick labels for each sample.
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A good general reference on boxplots and their history can be found
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here: http://vita.had.co.nz/papers/boxplots.pdf
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"""
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import matplotlib.pyplot as plt
@@ -15,7 +21,7 @@
1521
all_data = [np.random.normal(0, std, size=100) for std in range(1, 4)]
1622
labels = ['x1', 'x2', 'x3']
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18-
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
24+
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))
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# rectangular box plot
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bplot1 = axes[0].boxplot(all_data,

‎examples/statistics/boxplot_demo.py‎

Lines changed: 19 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,19 @@
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"""
2-
Demo of the new boxplot functionality
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=========================================
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Demo of artist customization in box plots
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=========================================
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This example demonstrates how to use the various kwargs
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to fully customize box plots. The first figure demonstrates
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how to remove and add individual components (note that the
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mean is the only value not shown by default). The second
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figure demonstrates how the styles of the artists can
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be customized. It also demonstrates how to set the limit
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of the whiskers to specific percentiles (lower right axes)
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A good general reference on boxplots and their history can be found
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here: http://vita.had.co.nz/papers/boxplots.pdf
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317
"""
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519
import numpy as np
@@ -23,7 +37,8 @@
2337
axes[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs)
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2539
axes[1, 0].boxplot(data, labels=labels, showbox=False, showcaps=False)
26-
axes[1, 0].set_title('Tufte Style \n(showbox=False,\nshowcaps=False)', fontsize=fs)
40+
tufte_title = 'Tufte Style \n(showbox=False,\nshowcaps=False)'
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axes[1, 0].set_title(tufte_title, fontsize=fs)
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axes[1, 1].boxplot(data, labels=labels, notch=True, bootstrap=10000)
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axes[1, 1].set_title('notch=True,\nbootstrap=10000', fontsize=fs)
@@ -62,7 +77,8 @@
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showmeans=True)
6378
axes[1, 0].set_title('Custom mean\nas point', fontsize=fs)
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65-
axes[1, 1].boxplot(data, meanprops=meanlineprops, meanline=True, showmeans=True)
80+
axes[1, 1].boxplot(data, meanprops=meanlineprops, meanline=True,
81+
showmeans=True)
6682
axes[1, 1].set_title('Custom mean\nas line', fontsize=fs)
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axes[1, 2].boxplot(data, whis=[15, 85])

‎examples/statistics/boxplot_vs_violin_demo.py‎

Lines changed: 18 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -1,19 +1,29 @@
1-
"""Box plot - violin plot comparison.
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"""
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===================================
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Box plot vs. violin plot comparison
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===================================
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Note that although violin plots are closely related to Tukey's (1977)
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box plots, they add useful information such as the distribution of the
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sample data (density trace).
29
3-
Note that although violin plots are closely related to Tukey's (1977) box
4-
plots, they add useful information such as the distribution of the sample
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data (density trace).
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By default, box plots show data points outside 1.5 * the inter-quartile
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range as outliers above or below the whiskers whereas violin plots show
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the whole range of the data.
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By default, box plots show data points outside 1.5 x the inter-quartile range
8-
as outliers above or below the whiskers whereas violin plots show the whole
9-
range of the data.
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A good general reference on boxplots and their history can be found
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here: http://vita.had.co.nz/papers/boxplots.pdf
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Violin plots require matplotlib >= 1.4.
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For more information on violin plots, the scikit-learn docs have a great
20+
section: http://scikit-learn.org/stable/modules/density.html
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"""
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1323
import matplotlib.pyplot as plt
1424
import numpy as np
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16-
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
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fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))
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# generate some random test data
1929
all_data = [np.random.normal(0, std, 100) for std in range(6, 10)]

‎examples/statistics/bxp_demo.py‎

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Original file line numberDiff line numberDiff line change
@@ -1,5 +1,17 @@
11
"""
2-
Demo of the new boxplot drawer function
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===================================
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Demo of the boxplot drawer function
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===================================
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This example demonstrates how to pass pre-computed box plot
7+
statistics to the box plot drawer. The first figure demonstrates
8+
how to remove and add individual components (note that the
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mean is the only value not shown by default). The second
10+
figure demonstrates how the styles of the artists can
11+
be customized.
12+
13+
A good general reference on boxplots and their history can be found
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here: http://vita.had.co.nz/papers/boxplots.pdf
315
"""
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517
import numpy as np
@@ -21,6 +33,7 @@
2133
stats[n]['mean'] *= 2
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print(stats[0].keys())
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2437
fs = 10 # fontsize
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# demonstrate how to toggle the display of different elements:
@@ -35,7 +48,8 @@
3548
axes[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs)
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3750
axes[1, 0].bxp(stats, showbox=False, showcaps=False)
38-
axes[1, 0].set_title('Tufte Style\n(showbox=False,\nshowcaps=False)', fontsize=fs)
51+
tufte_title = 'Tufte Style\n(showbox=False,\nshowcaps=False)'
52+
axes[1, 0].set_title(tufte_title, fontsize=fs)
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4054
axes[1, 1].bxp(stats, shownotches=True)
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axes[1, 1].set_title('notch=True', fontsize=fs)
@@ -50,7 +64,6 @@
5064
fig.subplots_adjust(hspace=0.4)
5165
plt.show()
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53-
5467
# demonstrate how to customize the display different elements:
5568
boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')
5669
flierprops = dict(marker='o', markerfacecolor='green', markersize=12,
@@ -71,7 +84,8 @@
7184
showmeans=True)
7285
axes[1, 0].set_title('Custom mean\nas point', fontsize=fs)
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74-
axes[1, 1].bxp(stats, meanprops=meanlineprops, meanline=True, showmeans=True)
87+
axes[1, 1].bxp(stats, meanprops=meanlineprops, meanline=True,
88+
showmeans=True)
7589
axes[1, 1].set_title('Custom mean\nas line', fontsize=fs)
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7791
for ax in axes.flatten():

‎examples/statistics/customized_violin_demo.py‎

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Original file line numberDiff line numberDiff line change
@@ -1,6 +1,18 @@
1-
# Customizing violin plots
2-
#
3-
#
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"""
2+
=================================
3+
Demo of violin plot customization
4+
=================================
5+
6+
This example demonstrates how to fully customize violin plots.
7+
The first plot shows the default style by providing only
8+
the data. The second plot first limits what matplotlib draws
9+
with additional kwargs. Then a simplified representation of
10+
a box plot is drawn on top. Lastly, the styles of the artists
11+
of the violins are modified.
12+
13+
For more information on violin plots, the scikit-learn docs have a great
14+
section: http://scikit-learn.org/stable/modules/density.html
15+
"""
416

517
import matplotlib.pyplot as plt
618
import numpy as np
@@ -22,21 +34,28 @@ def percentile(vals, p):
2234
def adjacent_values(vals):
2335
q1 = percentile(vals, 0.25)
2436
q3 = percentile(vals, 0.75)
25-
uav = q3 + (q3-q1)*1.5
37+
iqr = q3 - q1 # inter-quartile range
38+
39+
# upper adjacent values
40+
uav = q3 + iqr * 1.5
2641
if uav > vals[-1]:
2742
uav = vals[-1]
2843
if uav < q3:
2944
uav = q3
30-
lav = q1 - (q3-q1)*1.5
45+
46+
# lower adjacent values
47+
lav = q1 - iqr * 1.5
3148
if lav < vals[0]:
3249
lav = vals[0]
3350
if lav > q1:
3451
lav = q1
3552
return [lav, uav]
3653

54+
3755
# create test data
38-
dat = [np.random.normal(0, std, 100) for std in range(6, 10)]
39-
lab = ['a', 'b', 'c', 'd'] # labels
56+
np.random.seed(123)
57+
dat = [np.random.normal(0, std, 100) for std in range(1, 5)]
58+
lab = ['A', 'B', 'C', 'D'] # labels
4059
med = [] # medians
4160
iqr = [] # inter-quantile ranges
4261
avs = [] # upper and lower adjacent values
@@ -47,31 +66,39 @@ def adjacent_values(vals):
4766
avs.append(adjacent_values(sarr))
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4968
# plot the violins
50-
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 5))
51-
parts = ax.violinplot(dat, showmeans=False, showmedians=False,
52-
showextrema=False)
69+
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(9, 4),
70+
sharey=True)
71+
_ = ax1.violinplot(dat)
72+
parts = ax2.violinplot(dat, showmeans=False, showmedians=False,
73+
showextrema=False)
74+
75+
ax1.set_title('Default violin plot')
76+
ax2.set_title('Customized violin plot')
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54-
# plot medians and averages
78+
# plot whiskers as thin lines, quartiles as fat lines,
79+
# and medians as points
5580
for i in range(len(med)):
56-
ax.plot([i+1, i+1], avs[i], '-', c='black', lw=1)
57-
ax.plot([i+1, i+1], iqr[i], '-', c='black', lw=5)
58-
ax.plot(i+1, med[i], 'o', mec='none', c='white', ms=6)
81+
# whiskers
82+
ax2.plot([i + 1, i + 1], avs[i], '-', color='black', linewidth=1)
83+
ax2.plot([i + 1, i + 1], iqr[i], '-', color='black', linewidth=5)
84+
ax2.plot(i + 1, med[i], 'o', color='white',
85+
markersize=6, markeredgecolor='none')
5986

6087
# customize colors
6188
for pc in parts['bodies']:
6289
pc.set_facecolor('#D43F3A')
6390
pc.set_edgecolor('black')
6491
pc.set_alpha(1)
6592

66-
ax.get_xaxis().set_tick_params(direction='out')
67-
ax.xaxis.set_ticks_position('bottom')
68-
ax.set_xticks([x+1 for x in range(len(lab))])
69-
ax.set_xticklabels(lab)
70-
ax.set_xlim(0.25, len(lab)+0.75)
71-
ax.set_ylabel('ylabel')
72-
ax.set_xlabel('xlabel')
73-
ax.set_title('customized violin plot')
93+
ax1.set_ylabel('Observed values')
94+
for ax in [ax1, ax2]:
95+
ax.get_xaxis().set_tick_params(direction='out')
96+
ax.xaxis.set_ticks_position('bottom')
97+
ax.set_xticks(np.arange(1, len(lab) + 1))
98+
ax.set_xticklabels(lab)
99+
ax.set_xlim(0.25, len(lab) + 0.75)
100+
ax.set_xlabel('Sample name')
74101

75-
plt.subplots_adjust(bottom=0.15)
102+
plt.subplots_adjust(bottom=0.15, wspace=0.05)
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77104
plt.show()

‎examples/statistics/errorbar_demo.py‎

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"""
2-
Demo of the errorbar function.
2+
=============================
3+
Demo of the errorbar function
4+
=============================
5+
6+
This exhibits the most basic use of the error bar method.
7+
In this case, constant values are provided for the error
8+
in both the x- and y-directions.
39
"""
10+
411
import numpy as np
512
import matplotlib.pyplot as plt
613

714
# example data
815
x = np.arange(0.1, 4, 0.5)
916
y = np.exp(-x)
1017

11-
plt.errorbar(x, y, xerr=0.2, yerr=0.4)
18+
fig, ax = plt.subplots()
19+
ax.errorbar(x, y, xerr=0.2, yerr=0.4)
1220
plt.show()

‎examples/statistics/errorbar_demo_features.py‎

Lines changed: 24 additions & 15 deletions
Original file line numberDiff line numberDiff line change
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11
"""
2-
Demo of errorbar function with different ways of specifying error bars.
2+
===================================================
3+
Demo of the different ways of specifying error bars
4+
===================================================
35
4-
Errors can be specified as a constant value (as shown in `errorbar_demo.py`),
5-
or as demonstrated in this example, they can be specified by an N x 1 or 2 x N,
6-
where N is the number of data points.
6+
Errors can be specified as a constant value (as shown in
7+
`errorbar_demo.py`). However, this example demonstrates
8+
how they vary by specifying arrays of error values.
79
8-
N x 1:
9-
Error varies for each point, but the error values are symmetric (i.e. the
10-
lower and upper values are equal).
10+
If the raw ``x`` and ``y`` data have length N, there are two options:
1111
12-
2 x N:
13-
Error varies for each point, and the lower and upper limits (in that order)
14-
are different (asymmetric case)
12+
Array of shape (N,):
13+
Error varies for each point, but the error values are
14+
symmetric (i.e. the lower and upper values are equal).
1515
16-
In addition, this example demonstrates how to use log scale with errorbar.
16+
Array of shape (2, N):
17+
Error varies for each point, and the lower and upper limits
18+
(in that order) are different (asymmetric case)
19+
20+
In addition, this example demonstrates how to use log
21+
scale with error bars.
1722
"""
23+
1824
import numpy as np
1925
import matplotlib.pyplot as plt
2026

2127
# example data
2228
x = np.arange(0.1, 4, 0.5)
2329
y = np.exp(-x)
30+
2431
# example error bar values that vary with x-position
2532
error = 0.1 + 0.2 * x
26-
# error bar values w/ different -/+ errors
27-
lower_error = 0.4 * error
28-
upper_error = error
29-
asymmetric_error = [lower_error, upper_error]
3033

3134
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)
3235
ax0.errorbar(x, y, yerr=error, fmt='-o')
3336
ax0.set_title('variable, symmetric error')
3437

38+
# error bar values w/ different -/+ errors that
39+
# also vary with the x-position
40+
lower_error = 0.4 * error
41+
upper_error = error
42+
asymmetric_error = [lower_error, upper_error]
43+
3544
ax1.errorbar(x, y, xerr=asymmetric_error, fmt='o')
3645
ax1.set_title('variable, asymmetric error')
3746
ax1.set_yscale('log')

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