[statistics] simplified plots, part 2

plotstyle.py

@@ -29,8 +29,8 @@ colors['black'] = '#000000'
 #colors_bendalab_vivid['blue'] = '#0020C0'
 
 # line styles for plot():
-lwthick = 4.0
+lwthick = 3.0
-lwthin = 2.0
+lwthin = 1.8
 fillalpha = 0.5
 
 # helper lines:

statistics/lecture/boxwhisker.py

@@ -1,19 +1,14 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from plotstyle import *
 
 rng = np.random.RandomState(981)
 x = rng.randn( 40, 10 )
 
-plt.xkcd()
+fig, ax = plt.subplots()
-fig = plt.figure( figsize=(6,3.4) )
-ax = fig.add_subplot( 1, 1, 1 )
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.yaxis.set_ticks_position('left')
-ax.xaxis.set_ticks_position('bottom')
 ax.set_xlabel('Experiment')
 ax.set_ylabel('x')
-ax.set_ylim( -4.0, 4.0)
+ax.set_ylim(-4.0, 4.0)
 ax.annotate('Median',
             xy=(3.9, 0.0), xycoords='data',
             xytext=(3.5, -2.7), textcoords='data', ha='right',
@@ -39,8 +34,6 @@ ax.annotate('maximum',
             xytext=(4.9, 3.5), textcoords='data', ha='right',
             arrowprops=dict(arrowstyle="->", relpos=(1.0,0.5),
             connectionstyle="angle3,angleA=0,angleB=120") )
-ax.boxplot( x, whis=100.0 )
+ax.boxplot(x, whis=100.0)
-plt.tight_layout()
 plt.savefig('boxwhisker.pdf')
-#plt.show()
 

statistics/lecture/correlation.py

@@ -1,33 +1,30 @@
 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from plotstyle import *
 
-plt.xkcd()
+fig = plt.figure(figsize=cm_size(figure_width, 1.5*figure_height))
-fig = plt.figure( figsize=(6,4.6) )
+spec = gridspec.GridSpec(nrows=2, ncols=2, wspace=0.35, hspace=0.35,
+                         **adjust_fs(fig, left=5.5, top=0.5, bottom=2.7))
 rng = np.random.RandomState(2981)
 n = 200
-for k, r  in enumerate( [ 1.0, 0.6, 0.0, -0.9 ] ) :
+for k, r  in enumerate([ 1.0, 0.6, 0.0, -0.9 ]) :
-    x = rng.randn( n )
+    x = rng.randn(n)
-    y = r*x + np.sqrt(1.0-r*r)*rng.randn( n )
+    y = r*x + np.sqrt(1.0-r*r)*rng.randn(n)
-    ax = fig.add_subplot( 2, 2, k+1 )
+    ax = fig.add_subplot(spec[k//2, k%2])
-    ax.spines['right'].set_visible(False)
+    ax.text(-2, 2.5, 'r=%.1f' % r)
-    ax.spines['top'].set_visible(False)
-    ax.yaxis.set_ticks_position('left')
-    ax.xaxis.set_ticks_position('bottom')
-    ax.text( -2, 2.5, 'r=%.1f' % r )
     if k == 0 :
-        ax.text( 2.8, -2, 'positively\ncorrelated', ha='right' )
+        ax.text(2.8, -2.8, 'positively\ncorrelated', ha='right', va='bottom')
     elif k == 1 :
-        ax.text( 2.8, -2.5, 'weakly\ncorrelated', ha='right' )
+        ax.text(2.8, -2.8, 'weakly\ncorrelated', ha='right', va='bottom')
     elif k == 2 :
-        ax.text( 2.8, -2.5, 'not\ncorrelated', ha='right' )
+        ax.text(2.8, -2.8, 'not\ncorrelated', ha='right', va='bottom')
     elif k == 3 :
-        ax.text( -2.5, -2, 'negatively\ncorrelated', ha='left' )
+        ax.text(-2.8, -2.8, 'negatively\ncorrelated', ha='left', va='bottom')
     ax.set_xlabel('x')
     ax.set_ylabel('y')
-    ax.set_xlim( -3.0, 3.0)
+    ax.set_xlim(-3.0, 3.0)
-    ax.set_ylim( -3.0, 3.0)
+    ax.set_ylim(-3.0, 3.0)
-    ax.scatter( x[(np.abs(x)<2.8)&(np.abs(y)<2.8)], y[(np.abs(x)<2.8)&(np.abs(y)<2.8)] )
+    ax.scatter(x[(np.abs(x)<2.8)&(np.abs(y)<2.8)], y[(np.abs(x)<2.8)&(np.abs(y)<2.8)])
 
-plt.tight_layout()
 plt.savefig('correlation.pdf')
-#plt.show()

statistics/lecture/kerneldensity.py

@@ -1,9 +1,11 @@
 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from plotstyle import *
 
 # normal distribution:
 rng = np.random.RandomState(6281)
-x = np.arange( -4.0, 4.0, 0.01 )
+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)
 
@@ -29,55 +31,41 @@ def kerneldensity(data, xmin, xmax, sigma=1.0) :
         kd[k0:k1] += gauss[g0:g1]
     kd /= len(data)
     return kd, x
-    
 
-plt.xkcd()
+fig = plt.figure()
+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))
 
-fig = plt.figure( figsize=(6,3) )
+ax = fig.add_subplot(spec[0, 0])
-ax = fig.add_subplot(2, 2, 1)
+ax.set_xlabel('x')
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.yaxis.set_ticks_position('left')
-ax.xaxis.set_ticks_position('bottom')
-ax.set_xlabel( 'x' )
 ax.set_xlim(-3.2, 3.2)
-ax.set_xticks( np.arange( -3.0, 3.1, 1.0 ) )
+ax.set_xticks(np.arange(-3.0, 3.1, 1.0))
-ax.set_ylabel( 'p(x)' )
+ax.set_ylabel('p(x)')
 ax.set_ylim(0.0, 0.49)
-ax.set_yticks( np.arange( 0.0, 0.41, 0.1 ) )
+ax.set_yticks(np.arange(0.0, 0.41, 0.1))
 #ax.plot(x, g, '-b', lw=2, zorder=-1)
-ax.hist(r, np.arange(-4.1, 4, 0.4), normed=True, color='#FFCC00', zorder=-5)
+ax.hist(r, np.arange(-4.1, 4, 0.4), normed=True, zorder=-5, **fsC)
 
-ax = fig.add_subplot(2, 2, 3)
+ax = fig.add_subplot(spec[1, 0])
-ax.spines['right'].set_visible(False)
+ax.set_xlabel('x')
-ax.spines['top'].set_visible(False)
-ax.yaxis.set_ticks_position('left')
-ax.xaxis.set_ticks_position('bottom')
-ax.set_xlabel( 'x' )
 ax.set_xlim(-3.2, 3.2)
-ax.set_xticks( np.arange( -3.0, 3.1, 1.0 ) )
+ax.set_xticks(np.arange(-3.0, 3.1, 1.0))
-ax.set_ylabel( 'p(x)' )
+ax.set_ylabel('p(x)')
 ax.set_ylim(0.0, 0.49)
-ax.set_yticks( np.arange( 0.0, 0.41, 0.1 ) )
+ax.set_yticks(np.arange(0.0, 0.41, 0.1))
 #ax.plot(x, g, '-b', lw=2, zorder=-1)
-ax.hist(r, np.arange(-4.3, 4, 0.4), normed=True, color='#FFCC00', zorder=-5)
+ax.hist(r, np.arange(-4.3, 4, 0.4), normed=True, zorder=-5, **fsC)
 
-ax = fig.add_subplot(1, 2, 2)
+ax = fig.add_subplot(spec[:, 1])
-ax.spines['right'].set_visible(False)
+ax.set_xlabel('x')
-ax.spines['top'].set_visible(False)
-ax.yaxis.set_ticks_position('left')
-ax.xaxis.set_ticks_position('bottom')
-ax.set_xlabel( 'x' )
 ax.set_xlim(-3.2, 3.2)
-ax.set_xticks( np.arange( -3.0, 3.1, 1.0 ) )
+ax.set_xticks(np.arange(-3.0, 3.1, 1.0))
-ax.set_ylabel( 'Probab. density p(x)' )
+ax.set_ylabel('Probab. density p(x)')
 ax.set_ylim(0.0, 0.49)
-ax.set_yticks( np.arange( 0.0, 0.41, 0.1 ) )
+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, color='#FF9900', zorder=-5)
+ax.fill_between(xx, 0.0, kd, zorder=-5, **fsDs)
-ax.plot(xx, kd, '-', lw=3, color='#CC0000', zorder=-1)
+ax.plot(xx, kd, '-', zorder=-1, **lsB)
 
-plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.35, hspace=0.3)
+fig.savefig('kerneldensity.pdf')
-fig.savefig( 'kerneldensity.pdf' )
-#plt.show()
 

statistics/lecture/nonlincorrelation.py

@@ -1,42 +1,33 @@
 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from plotstyle import *
+
+fig, (ax1, ax2) = plt.subplots(1, 2)
 
-plt.xkcd()
-fig = plt.figure( figsize=(6,2.2) )
 n = 200
-x = np.random.randn( n )
+rng = np.random.RandomState(3981)
-y = np.random.randn( n )
+x = rng.randn(n)
+y = rng.randn(n)
 
 z = x*x+0.2*y
 r =np.corrcoef(x,z)[0,1]
-ax = fig.add_subplot( 1, 2, 1 )
+ax1.text(0, 4.0, 'r=%.1f' % r, ha='center')
-ax.spines['right'].set_visible(False)
+ax1.text(0, 5.6, r'$y = x^2+\xi/5$', ha='center')
-ax.spines['top'].set_visible(False)
+ax1.set_xlabel('x')
-ax.yaxis.set_ticks_position('left')
+ax1.set_ylabel('y')
-ax.xaxis.set_ticks_position('bottom')
+ax1.set_xlim(-3.0, 3.0)
-ax.text( 0, 4.0, 'r=%.1f' % r, ha='center' )
+ax1.set_ylim(-0.5, 6.0)
-ax.text( 0, 6, r'$y = x^2+\xi/5$', ha='center' )
+ax1.scatter(x, z)
-ax.set_xlabel('x')
-ax.set_ylabel('y')
-ax.set_xlim( -3.0, 3.0)
-ax.set_ylim( -0.5, 6.0)
-ax.scatter( x, z )
 
 z = 0.5*x*y
 r =np.corrcoef(x,z)[0,1]
-ax = fig.add_subplot( 1, 2, 2 )
+ax2.text(0, 1.5, 'r=%.1f' % r, ha='center')
-ax.spines['right'].set_visible(False)
+ax2.text(0, 2.8, r'$y = x \cdot \xi/2$', ha='center')
-ax.spines['top'].set_visible(False)
+ax2.set_xlabel('x')
-ax.yaxis.set_ticks_position('left')
+ax2.set_ylabel('y')
-ax.xaxis.set_ticks_position('bottom')
+ax2.set_xlim(-3.0, 3.0)
-ax.text( 0, 1.5, 'r=%.1f' % r, ha='center' )
+ax2.set_ylim(-3.0, 3.0)
-ax.text( 0, 3, r'$y = x \cdot \xi/2$', ha='center' )
+ax2.scatter(x, z)
-ax.set_xlabel('x')
-ax.set_ylabel('y')
-ax.set_xlim( -3.0, 3.0)
-ax.set_ylim( -3.0, 3.0)
-ax.scatter( x, z )
 
-plt.tight_layout()
 plt.savefig('nonlincorrelation.pdf')
-#plt.show()