[statistics] plots work for old and new matplotlib

plotstyle.py

@@ -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'

statistics/lecture/diehistograms.py

@@ -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')

statistics/lecture/displayunivariatedata.py

@@ -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)
@@ -67,7 +70,10 @@ 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)
+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)
 ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean+barstd, barmean+barstd], **lsMarker)

statistics/lecture/kerneldensity.py

@@ -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.yaxis.set_major_locator(ticker.MultipleLocator(0.2))
-#ax.plot(x, g, '-b', lw=2, zorder=-1)
+if mpl_major > 1:
-try:
     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.yaxis.set_major_locator(ticker.MultipleLocator(0.2))
-#ax.plot(x, g, '-b', lw=2, zorder=-1)
+if mpl_major > 1:
-try:
     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')
 

statistics/lecture/pdfhistogram.py

@@ -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)
 

statistics/lecture/pdfprobabilities.py

@@ -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',