[statistics] simplified plots, last part, added pointstyles

plotstyle.py

@@ -18,9 +18,10 @@ colors['orange'] = '#FF9900'
 colors['lightorange'] = '#FFCC00'
 colors['yellow'] = '#FFFF66'
 colors['green'] = '#99FF00'
-colors['blue'] = '#0000CC'
+colors['blue'] = '#0010CC'
 colors['gray'] = '#A7A7A7'
 colors['black'] = '#000000'
+colors['white'] = '#FFFFFF'
 
 #colors_bendalab_vivid['red'] = '#D71000'
 #colors_bendalab_vivid['orange'] = '#FF9000'
@@ -31,6 +32,11 @@ colors['black'] = '#000000'
 # line styles for plot():
 lwthick = 3.0
 lwthin = 1.8
+mainline = {'linestyle': '-', 'linewidth': lwthick}
+minorline = {'linestyle': '-', 'linewidth': lwthin}
+largemarker = {'marker': 'o', 'markersize': 9, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
+smallmarker = {'marker': 'o', 'markersize': 6, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
+filllw = 1.0
 fillalpha = 0.5
 
 # helper lines:
@@ -38,30 +44,52 @@ lsSpine = {'c': colors['black'], 'linestyle': '-', 'linewidth': 1}
 lsGrid = {'c': colors['gray'], 'linestyle': '--', 'linewidth': 1}
 lsMarker = {'c': colors['black'], 'linestyle': '-', 'linewidth': 2}
 
-# line styles and fill styles:
-lsA = {'color': colors['blue'], 'linestyle': '-', 'linewidth': lwthick}
-lsAm = {'color': colors['blue'], 'linestyle': '-', 'linewidth': lwthin}
+# line (ls), point (ps), and fill styles (fs).
+
+# Each style is derived from a main color as indicated by the capital letter.
+
+# Line styles come in two variants:
+# - plain style with a thick/solid line (e.g. lsA), and
+# - minor style with a thinner or dashed line (e.g. lsAm).
+
+# Point styles come in two variants:
+# - plain style with large solid markers (e.g. psA), and
+# - minor style with smaller markers (e.g. lsBm).
+
+# Fill styles come in three variants:
+# - plain (e.g. fsB) for a solid fill color and a darker edge color,
+# - solid (e.g. fsBs) for a solid fill color and without edge color, and
+# - alpha (e.g. fsBa) for a transparent fill color without edge color.
+
+lsA = dict({'color': colors['blue']}, **mainline)
+lsAm = dict({'color': colors['blue']}, **minorline)
+psA = dict({'color': colors['blue'], 'linestyle': 'none'}, **largemarker)
+psAm = dict({'color': colors['blue'], 'linestyle': 'none'}, **smallmarker)
 fsAa = {'facecolor': colors['blue'], 'edgecolor': 'none', 'alpha': fillalpha}
 
-lsB = {'color': colors['red'], 'linestyle': '-', 'linewidth': lwthick}
-lsBm = {'color': colors['red'], 'linestyle': '-', 'linewidth': lwthin}
-fsB = {'facecolor': colors['red'], 'edgecolor': colors['black'], 'linewidth': 1}
+lsB = dict({'color': colors['red']}, **mainline)
+lsBm = dict({'color': colors['red']}, **minorline)
+psB = dict({'color': colors['red'], 'linestyle': 'none'}, **largemarker)
+psBm = dict({'color': colors['red'], 'linestyle': 'none'}, **smallmarker)
+fsB = {'facecolor': colors['red'], 'edgecolor': colors['black'], 'linewidth': filllw}
 fsBs = {'facecolor': colors['red'], 'edgecolor': 'none'}
 fsBa = {'facecolor': colors['red'], 'edgecolor': 'none', 'alpha': fillalpha}
 
-lsC = {'color': colors['lightorange'], 'linestyle': '-', 'linewidth': lwthick}
-lsCm = {'color': colors['lightorange'], 'linestyle': '-', 'linewidth': lwthin}
-fsC = {'facecolor': colors['lightorange'], 'edgecolor': colors['black'], 'linewidth': 1}
+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': colors['black'], 'linewidth': filllw}
 fsCs = {'facecolor': colors['lightorange'], 'edgecolor': 'none'}
 fsCa = {'facecolor': colors['lightorange'], 'edgecolor': 'none', 'alpha': fillalpha}
 
-fsD = {'facecolor': colors['orange'], 'edgecolor': colors['black'], 'linewidth': 1}
+fsD = {'facecolor': colors['orange'], 'edgecolor': colors['black'], 'linewidth': filllw}
 fsDs = {'facecolor': colors['orange'], 'edgecolor': 'none'}
 
-fsE = {'facecolor': colors['yellow'], 'edgecolor': colors['black'], 'linewidth': 1}
+fsE = {'facecolor': colors['yellow'], 'edgecolor': colors['black'], 'linewidth': filllw}
 fsEs = {'facecolor': colors['yellow'], 'edgecolor': 'none'}
 
-fsF = {'facecolor': colors['green'], 'edgecolor': colors['black'], 'linewidth': 1}
+fsF = {'facecolor': colors['green'], 'edgecolor': colors['black'], 'linewidth': filllw}
 fsFs = {'facecolor': colors['green'], 'edgecolor': 'none'}
 
 # factor for scaling widths of bars in a bar plot:

statistics/lecture/correlation.py

@@ -3,8 +3,8 @@ import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 from plotstyle import *
 
-fig = plt.figure(figsize=cm_size(figure_width, 1.5*figure_height))
-spec = gridspec.GridSpec(nrows=2, ncols=2, wspace=0.35, hspace=0.35,
+fig = plt.figure(figsize=cm_size(figure_width, 2.0*figure_height))
+spec = gridspec.GridSpec(nrows=2, ncols=2, wspace=0.35, hspace=0.5,
                          **adjust_fs(fig, left=5.5, top=0.5, bottom=2.7))
 rng = np.random.RandomState(2981)
 n = 200
@@ -25,6 +25,6 @@ for k, r  in enumerate([ 1.0, 0.6, 0.0, -0.9 ]) :
     ax.set_ylabel('y')
     ax.set_xlim(-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.plot(x[(np.abs(x)<2.8)&(np.abs(y)<2.8)], y[(np.abs(x)<2.8)&(np.abs(y)<2.8)], **psAm)
 
 plt.savefig('correlation.pdf')

statistics/lecture/cumulative.py

@@ -15,7 +15,7 @@ gauss = np.exp(-0.5*xx*xx)/np.sqrt(2.0*np.pi)
 gausscdf = np.cumsum(gauss)*dx
 
 # plot:
-fig, ax = plt.subplots()
+fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
 ax.set_xlabel('x')
 ax.set_xlim(-3.2, 3.2)
 ax.set_xticks(np.arange(-3.0, 3.1, 1.0))

statistics/lecture/displayunivariatedata.py

@@ -14,7 +14,7 @@ scatterpos = 1.0
 barpos = 2.5
 boxpos = 4.0
 
-fig = plt.figure(figsize=cm_size(figure_width, 1.4*figure_height))
+fig = plt.figure(figsize=cm_size(figure_width, 1.2*figure_height))
 spec = gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[3, 1], wspace=0.1,
                          **adjust_fs(fig, left=4.0))
 
@@ -61,7 +61,7 @@ ax.set_ylim( 0.0, 8.0)
 kernel = gaussian_kde(data)
 x = kernel(data)
 x /= np.max(x)
-ax.scatter(scatterpos+barwidth*x*(rng.rand(len(data))-0.5), data, s=50)
+ax.plot(scatterpos+barwidth*x*(rng.rand(len(data))-0.5), data, **psA)
 
 barmean = np.mean(data)
 barstd = np.std(data)

statistics/lecture/nonlincorrelation.py

@@ -4,6 +4,8 @@ import matplotlib.gridspec as gridspec
 from plotstyle import *
 
 fig, (ax1, ax2) = plt.subplots(1, 2)
+fig.subplots_adjust(wspace=0.35, hspace=0.5,
+                    **adjust_fs(fig, left=4, top=0.5, bottom=2.7))
 
 n = 200
 rng = np.random.RandomState(3981)
@@ -18,7 +20,7 @@ ax1.set_xlabel('x')
 ax1.set_ylabel('y')
 ax1.set_xlim(-3.0, 3.0)
 ax1.set_ylim(-0.5, 6.0)
-ax1.scatter(x, z)
+ax1.plot(x, z, **psAm)
 
 z = 0.5*x*y
 r =np.corrcoef(x,z)[0,1]
@@ -28,6 +30,6 @@ ax2.set_xlabel('x')
 ax2.set_ylabel('y')
 ax2.set_xlim(-3.0, 3.0)
 ax2.set_ylim(-3.0, 3.0)
-ax2.scatter(x, z)
+ax2.plot(x, z, **psAm)
 
 plt.savefig('nonlincorrelation.pdf')

statistics/lecture/pdfprobabilities.py

@@ -8,7 +8,7 @@ g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
 x1=0.0
 x2=1.0
 
-fig, ax = plt.subplots()
+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_ylim(0.0, 0.46)

statistics/lecture/quartile.py

@@ -7,7 +7,7 @@ x = np.arange( -4.0, 4.0, 0.01 )
 g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
 q = [ -0.67488, 0.0, 0.67488 ]
 
-fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
+fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.0*figure_height))
 fig.subplots_adjust(**adjust_fs(bottom=2.7, top=0.1))
 ax.set_xlabel('x')
 ax.set_ylabel('Probability density p(x)')