Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/teaching/scientificComputing

plotstyle.py

@@ -12,125 +12,57 @@ figure_height = 6.0  # cm, for a 1 x 2 figure
 ppi = 72.0
 
 # colors:
+colors = {}
+colors['red'] = '#CC0000'
+colors['orange'] = '#FF9900'
+colors['lightorange'] = '#FFCC00'
+colors['yellow'] = '#FFFF66'
+colors['green'] = '#99FF00'
+colors['blue'] = '#0000CC'
+colors['gray'] = '#A7A7A7'
+colors['black'] = '#000000'
+
+#colors_bendalab_vivid['red'] = '#D71000'
+#colors_bendalab_vivid['orange'] = '#FF9000'
+#colors_bendalab_vivid['yellow'] = '#FFF700'
+#colors_bendalab_vivid['green'] = '#30D700'
+#colors_bendalab_vivid['blue'] = '#0020C0'
 
-def lighter(color, lightness):
+# line styles for plot():
-    """ Make a color lighter.
+lwthick = 4.0
+lwthin = 2.0
+fillalpha = 0.5
 
-    Parameters
+# helper lines:
-    ----------
+lsSpine = {'c': colors['black'], 'linestyle': '-', 'linewidth': 1}
-    color: string
+lsGrid = {'c': colors['gray'], 'linestyle': '--', 'linewidth': 1}
-        An RGB color as a hexadecimal string (e.g. '#rrggbb').
+lsMarker = {'c': colors['black'], 'linestyle': '-', 'linewidth': 2}
-    lightness: float
-        The smaller the lightness, the lighter the returned color.
-        A lightness of 1 leaves the color untouched.
-        A lightness of 0 returns white.
 
-    Returns
+# line styles and fill styles:
-    -------
+lsA = {'color': colors['blue'], 'linestyle': '-', 'linewidth': lwthick}
-    color: string
+lsAm = {'color': colors['blue'], 'linestyle': '-', 'linewidth': lwthin}
-        The lighter color as a hexadecimal RGB string (e.g. '#rrggbb').
+fsAa = {'facecolor': colors['blue'], 'edgecolor': 'none', 'alpha': fillalpha}
-    """
-    r = int(color[1:3], 16)
-    g = int(color[3:5], 16)
-    b = int(color[5:7], 16)
-    rl = r + (1.0-lightness)*(0xff - r)
-    gl = g + (1.0-lightness)*(0xff - g)
-    bl = b + (1.0-lightness)*(0xff - b)
-    return '#%02X%02X%02X' % (rl, gl, bl)
 
+lsB = {'color': colors['red'], 'linestyle': '-', 'linewidth': lwthick}
+lsBm = {'color': colors['red'], 'linestyle': '-', 'linewidth': lwthin}
+fsB = {'facecolor': colors['red'], 'edgecolor': colors['black'], 'linewidth': 1}
+fsBs = {'facecolor': colors['red'], 'edgecolor': 'none'}
+fsBa = {'facecolor': colors['red'], 'edgecolor': 'none', 'alpha': fillalpha}
 
-def darker(color, saturation):
+lsC = {'color': colors['lightorange'], 'linestyle': '-', 'linewidth': lwthick}
-    """ Make a color darker.
+lsCm = {'color': colors['lightorange'], 'linestyle': '-', 'linewidth': lwthin}
+fsC = {'facecolor': colors['lightorange'], 'edgecolor': colors['black'], 'linewidth': 1}
+fsCs = {'facecolor': colors['lightorange'], 'edgecolor': 'none'}
+fsCa = {'facecolor': colors['lightorange'], 'edgecolor': 'none', 'alpha': fillalpha}
 
-    Parameters
+fsD = {'facecolor': colors['orange'], 'edgecolor': colors['black'], 'linewidth': 1}
-    ----------
+fsDs = {'facecolor': colors['orange'], 'edgecolor': 'none'}
-    color: string
-        An RGB color as a hexadecimal string (e.g. '#rrggbb').
-    saturation: float
-        The smaller the saturation, the darker the returned color.
-        A saturation of 1 leaves the color untouched.
-        A saturation of 0 returns black.
-
-    Returns
-    -------
-    color: string
-        The darker color as a hexadecimal RGB string (e.g. '#rrggbb').
-    """
-    r = int(color[1:3], 16)
-    g = int(color[3:5], 16)
-    b = int(color[5:7], 16)
-    rd = r * saturation
-    gd = g * saturation
-    bd = b * saturation
-    return '#%02X%02X%02X' % (rd, gd, bd)
 
+fsE = {'facecolor': colors['yellow'], 'edgecolor': colors['black'], 'linewidth': 1}
+fsEs = {'facecolor': colors['yellow'], 'edgecolor': 'none'}
 
-# colors:
+fsF = {'facecolor': colors['green'], 'edgecolor': colors['black'], 'linewidth': 1}
-colors = {
+fsFs = {'facecolor': colors['green'], 'edgecolor': 'none'}
-    'red': '#CC0000',
-    'orange': '#FF9900',
-    'lightorange': '#FFCC00',
-    'yellow': '#FFFF66',
-    'green': '#99FF00',
-    'blue': '#0000CC'
-    }
-
-""" Muted colors used by the Benda-lab. """
-colors_bendalab = {}
-colors_bendalab['red'] = '#C02010'
-colors_bendalab['orange'] = '#F78010'
-colors_bendalab['yellow'] = '#F0D730'
-colors_bendalab['green'] = '#A0B717'
-colors_bendalab['cyan'] = '#40A787'
-colors_bendalab['blue'] = '#2757A0'
-colors_bendalab['purple'] = '#573790'
-colors_bendalab['pink'] = '#C72750'
-colors_bendalab['grey'] = '#A0A0A0'
-colors_bendalab['black'] = '#000000'
-
-""" Vivid colors used by the Benda-lab. """
-colors_bendalab_vivid = {}
-colors_bendalab_vivid['red'] = '#D71000'
-colors_bendalab_vivid['orange'] = '#FF9000'
-colors_bendalab_vivid['yellow'] = '#FFF700'
-colors_bendalab_vivid['green'] = '#30D700'
-colors_bendalab_vivid['cyan'] = '#00F0B0'
-colors_bendalab_vivid['blue'] = '#0020C0'
-colors_bendalab_vivid['purple'] = '#B000B0'
-colors_bendalab_vivid['pink'] = '#F00080'
-colors_bendalab_vivid['grey'] = '#A7A7A7'
-colors_bendalab_vivid['black'] = '#000000'
-
-# colors for the plots of the script:
-colors = colors_bendalab_vivid
-colors['lightorange'] = colors['yellow']
-#colors['yellow'] = lighter(colors['yellow'], 0.65)
-colors['yellow'] = '#FFFF55'
-
-# line styles for plot():
-lsSpine = {'c': colors['black'], 'linestyle': '-', 'linewidth': 1}
-lsGrid = {'c': colors['grey'], 'linestyle': '--', 'linewidth': 1}
-
-# 'B1': prominent line with first color and style from color group 'B'
-# 'C2m': minor line with second color and style from color group 'C'
-ls = {
-    'A1': {'c': colors['red'], 'linestyle': '-', 'linewidth': 3},
-    'A2': {'c': colors['orange'], 'linestyle': '-', 'linewidth': 3},
-    'A3': {'c': colors['lightorange'], 'linestyle': '-', 'linewidth': 3},
-    'B1': {'c': colors['orange'], 'linestyle': '-', 'linewidth': 3},
-    'B2': {'c': colors['lightorange'], 'linestyle': '-', 'linewidth': 3},
-    'B3': {'c': colors['yellow'], 'linestyle': '-', 'linewidth': 3},
-    'C1': {'c': colors['green'], 'linestyle': '-', 'linewidth': 3},
-    'D1': {'c': colors['blue'], 'linestyle': '-', 'linewidth': 3},
-    'A1m': {'c': colors['red'], 'linestyle': '-', 'linewidth': 2},
-    'A2m': {'c': colors['orange'], 'linestyle': '-', 'linewidth': 2},
-    'A3m': {'c': colors['lightorange'], 'linestyle': '-', 'linewidth': 2},
-    'B1m': {'c': colors['orange'], 'linestyle': '-', 'linewidth': 2},
-    'B2m': {'c': colors['lightorange'], 'linestyle': '-', 'linewidth': 2},
-    'B3m': {'c': colors['yellow'], 'linestyle': '-', 'linewidth': 2},
-    'C1m': {'c': colors['green'], 'linestyle': '-', 'linewidth': 2},
-    'D1m': {'c': colors['blue'], 'linestyle': '-', 'linewidth': 2},
-    }
 
 # factor for scaling widths of bars in a bar plot:
 bar_fac = 1.0
@@ -369,14 +301,12 @@ def common_format():
     if 'axes.prop_cycle' in mpl.rcParams:
         from cycler import cycler
         mpl.rcParams['axes.prop_cycle'] = cycler(color=[colors['blue'], colors['red'],
-                                                        colors['orange'], colors['green'],
+                                                        colors['lightorange'], colors['orange'],
-                                                        colors['purple'], colors['yellow'],
+                                                        colors['yellow'], colors['green']])
-                                                        colors['cyan'], colors['pink']])
     else:
         mpl.rcParams['axes.color_cycle'] = [colors['blue'], colors['red'],
-                                            colors['orange'], colors['green'],
+                                            colors['lightorange'], colors['orange'],
-                                            colors['purple'], colors['yellow'],
+                                            colors['yellow'], colors['green']]
-                                            colors['cyan'], colors['pink']]
     # overwrite axes constructor:
     if not hasattr(mpl.axes.Subplot, '__init__orig'):
         mpl.axes.Subplot.__init__orig = mpl.axes.Subplot.__init__

projects/README

@@ -1,94 +1,104 @@
+For new projects:
+Copy project_template/ and adapt according to your needs
+
 All projects:
 
 check for time information
 
 
+1) project_activation_curve
+medium
+Write questions
 
-project_adaptation_fit
+2) project_adaptation_fit
 OK, medium
 Add plotting of cost function
 
-project_eod
+3) project_eod
 OK, medium - difficult
 b_0 is not defined
 
-project_eyetracker
+4) project_eyetracker
 OK, difficult
 no statistics, but kmeans
 
-project_fano_slope
+5) project_face_selectivity
+medium-difficult
+(Marius monkey data)
+
+6) project_fano_slope
 OK, difficult
 
-project_fano_test
+7) project_fano_test
 OK -
 
-project_fano_time
+8) project_fano_time
 OK, medium-difficult
 
-project_ficurves
+9) project_ficurves
 OK, medium
 Maybe add correlation test or fit statistics
 
-project_input_resistance
+10) project_input_resistance
 medium
 What is the problem with this project? --> No difference between segments
 Improve questions
 
-project_isicorrelations
+11) project_isicorrelations
 medium-difficult
 Need to finish solution
 
-project_isipdffit
+12) project_isipdffit
 Too technical
 
-project_lif
+13) project_lif
 OK, difficult
 no statistics
 
-project_mutualinfo
+14) project_mutualinfo
 OK, medium
 
-project_noiseficurves
+15) project_noiseficurves
 OK, simple-medium
 no statistics
 
-project_numbers
+16) project_numbers
 simple
 We might add some more involved statistical analysis
 
-project_pca_natural_images
+17) project_pca_natural_images
 medium
 Make a solution (->Lukas)
 
-project_photoreceptor
+18) project_photoreceptor
 OK, simple
 
-project_populationvector
+19) project_populationvector
 difficult
 OK
 
-project_qvalues
+20) project_power_analysis
+medium
+
+21) project_qvalues
 -
 Interesting! But needs solution.
 
-project_random_walk
+22) project_random_walk
 simple-medium
 
-project_serialcorrelation
+23) project_serialcorrelation
 OK, simple-medium
 
-project_spectra
+24) project_shorttermpotentiation
+Write questions
+
+25) project_spectra
 -
 Needs improvements and a solution
 
-project_stimulus_reconstruction
+26) project_stimulus_reconstruction
 OK, difficult
 
-project_vector_strength
+27) project_vector_strength
 OK, medium-difficult
-
-project_power_analysis
-medium
-
-Marius monkey data:
-medium-difficult

projects/project_activation_curve/Makefile

@@ -0,0 +1,3 @@
+ZIPFILES=
+
+include ../project.mk

projects/project_shorttermpotentiation/Makefile

@@ -0,0 +1,3 @@
+ZIPFILES=
+
+include ../project.mk

projects/project_shorttermpotentiation/rawData/main.m

@@ -0,0 +1,34 @@
+clear all
+close all
+
+data = load('Username_2019-05-22_001.mat');
+
+IV_protnum = [25];
+STP_protnum = [26, 27, 28, 29, 30];
+
+IV = sortData(data, IV_protnum);
+STP = sortData(data, STP_protnum);
+IV_steps = -90:10:50;
+
+save 'IV.mat' IV
+save 'IV_steps.mat' IV_steps
+save 'STP.mat' STP
+
+% for i = 1:length(STP)
+%     for j = 1:length(STP{i})
+%         figure(j)
+%         hold on
+%         plot(STP{i}{j}(:,1),STP{i}{j}(:,2))
+%         hold off
+%     end
+% end
+% 
+% for i = 1:length(IV)
+%     for j = 1:length(IV{i})
+%         figure(j)
+%         hold on
+%         plot(IV{i}{j}(:,1),IV{i}{j}(:,2))
+%         hold off
+%     end
+% end
+

projects/project_shorttermpotentiation/rawData/sortData.m

@@ -0,0 +1,20 @@
+function C = sortData(data, protnums)
+
+names = fieldnames(data);
+C = cell(1,length(protnums));
+
+for i = 1:length(protnums)
+    idxvec = zeros(1,length(names));
+    for j = 1:length(names)
+        idxvec(j) = strcmp(names{j}(1:10), ['Trace_1_', num2str(protnums(i))]);
+    end
+    
+    c = cell(1,sum(idxvec));
+    idxvec = find(idxvec);
+    for j = 1:length(idxvec)
+        c{j} = data.(names{idxvec(j)});
+    end
+    C{i} = c;
+end
+
+

statistics/lecture/cumulative.py

@@ -1,5 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from plotstyle import *
 
 # data:
 rng = np.random.RandomState(981)
@@ -14,39 +15,31 @@ gauss = np.exp(-0.5*xx*xx)/np.sqrt(2.0*np.pi)
 gausscdf = np.cumsum(gauss)*dx
 
 # plot:
-plt.xkcd()
+fig, ax = plt.subplots()
-fig = plt.figure( figsize=(6, 2.4) )
+ax.set_xlabel('x')
-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( '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( 'F(x)' )
+ax.set_ylabel('F(x)')
 ax.set_ylim(-0.05, 1.05)
-ax.set_yticks( np.arange( 0.0, 1.1, 0.2 ) )
+ax.set_yticks(np.arange(0.0, 1.1, 0.2))
 
 med = xs[cdf>=0.5][0]
-ax.plot([-3.2, med, med], [0.5, 0.5, 0.0], 'k', lw=1, zorder=-5)
+ax.plot([-3.2, med, med], [0.5, 0.5, 0.0], zorder=-5, **lsMarker)
 ax.text(-2.8, 0.55, 'F=0.5')
 ax.text(0.15, 0.25, 'median at %.2f' % med)
 
 q3 = xs[cdf>=0.75][0]
-ax.plot([-3.2, q3, q3], [0.75, 0.75, 0.0], 'k', lw=1, zorder=-5)
+ax.plot([-3.2, q3, q3], [0.75, 0.75, 0.0], zorder=-5, **lsMarker)
 ax.text(-2.8, 0.8, 'F=0.75')
 ax.text(0.8, 0.5, '3. quartile at %.2f' % q3)
 
 p = cdf[xs>=-1.0][0]
-ax.plot([-3.2, -1.0, -1.0], [p, p, 0.0], 'k', lw=1, zorder=-5)
+ax.plot([-3.2, -1.0, -1.0], [p, p, 0.0], zorder=-5, **lsMarker)
 ax.text(-2.8, 0.2, 'F=%.2f' % p)
 ax.text(-0.9, 0.05, '-1')
 
-ax.plot(xx, gausscdf, '-', color='#0000ff', lw=2, zorder=-1)
+ax.plot(xx, gausscdf, zorder=-1, **lsAm)
-ax.plot(xs, cdf, '-', color='#cc0000', lw=4, zorder=-1)
+ax.plot(xs, cdf, zorder=-1, **lsB)
-ax.plot([-3.2, 3.2], [1.0, 1.0], '--', color='k', lw=2, zorder=-10)
+ax.plot([-3.2, 3.2], [1.0, 1.0], zorder=-10, **lsGrid)
 
-plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.35, hspace=0.3)
+fig.savefig('cumulative.pdf')
-fig.savefig( 'cumulative.pdf' )
-#plt.show()

statistics/lecture/diehistograms.py

@@ -1,39 +1,30 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from plotstyle import *
 
 # roll the die:
 rng = np.random.RandomState(57281)
-x1 = rng.random_integers( 1, 6, 100 )
+x1 = rng.random_integers(1, 6, 100)
-x2 = rng.random_integers( 1, 6, 500 )
+x2 = rng.random_integers(1, 6, 500)
 bins = np.arange(0.5, 7, 1.0)
 
-plt.xkcd()
+fig, (ax1, ax2) = plt.subplots(1, 2)
+fig.subplots_adjust(**adjust_fs(bottom=2.7, top=0.1))
+ax1.set_xlim(0, 7)
+ax1.set_xticks(range(1, 7))
+ax1.set_xlabel('x')
+ax1.set_ylim(0, 98)
+ax1.set_ylabel('Frequency')
+fs = fsC
+fs['color'] = [fsC['facecolor'], fsE['facecolor']]
+del fs['facecolor']
+ax1.hist([x2, x1], bins, **fs)
 
-fig = plt.figure( figsize=(6,3) )
+ax2.set_xlim(0, 7)
-ax = fig.add_subplot( 1, 2, 1 )
+ax2.set_xticks(range(1, 7))
-ax.spines['right'].set_visible(False)
+ax2.set_xlabel('x')
-ax.spines['top'].set_visible(False)
+ax2.set_ylim(0, 0.23)
-ax.yaxis.set_ticks_position('left')
+ax2.set_ylabel('Probability')
-ax.xaxis.set_ticks_position('bottom')
+ax2.plot([0.2, 6.8], [1.0/6.0, 1.0/6.0], zorder=1, **lsAm)
-ax.set_xlim(0, 7)
+ax2.hist([x2, x1], bins, normed=True, zorder=10, **fs)
-ax.set_xticks( range(1, 7) )
+fig.savefig('diehistograms.pdf')
-ax.set_xlabel( 'x' )
-ax.set_ylim(0, 98)
-ax.set_ylabel( 'Frequency' )
-ax.hist([x2, x1], bins, color=['#FFCC00', '#FFFF66' ])
-
-ax = fig.add_subplot( 1, 2, 2 )
-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_xlim(0, 7)
-ax.set_xticks( range(1, 7) )
-ax.set_xlabel( 'x' )
-ax.set_ylim(0, 0.23)
-ax.set_ylabel( 'Probability' )
-ax.plot([0.2, 6.8], [1.0/6.0, 1.0/6.0], '-b', lw=2, zorder=1)
-ax.hist([x2, x1], bins, normed=True, color=['#FFCC00', '#FFFF66' ], zorder=10)
-plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
-fig.savefig( 'diehistograms.pdf' )
-#plt.show()

statistics/lecture/displayunivariatedata.py

@@ -1,33 +1,24 @@
 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]
-xpos = 0.08
-ypos = 0.15
-width = 0.65
-height = 0.8
 barwidth = 0.8
 scatterpos = 1.0
 barpos = 2.5
 boxpos = 4.0
 
-plt.xkcd()
+fig = plt.figure(figsize=cm_size(figure_width, 1.4*figure_height))
-fig = plt.figure( figsize=(6,3.4) )
+spec = gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[3, 1], wspace=0.1,
+                         **adjust_fs(fig, left=4.0))
 
-ax = fig.add_axes([xpos, ypos, width, height])
+ax = fig.add_subplot(spec[0, 0])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-#ax.spines['left'].set_visible(False)
-#ax.spines['bottom'].set_visible(False)
-#ax.xaxis.set_ticks_position('none')
-#ax.yaxis.set_ticks_position('none')
-#ax.set_xticklabels([])
-#ax.set_yticklabels([])
 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)
@@ -49,7 +40,7 @@ ax.annotate('maximum',
             connectionstyle="angle3,angleA=0,angleB=120") )
 ax.annotate('3. quartile',
             xy=(boxpos-0.3*barwidth, 3.7), xycoords='data',
-            xytext=(boxpos-1.3*barwidth, 5.5), textcoords='data', ha='left',
+            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',
@@ -57,19 +48,14 @@ 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 = fig.add_axes([xpos, ypos, width, height])
-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_xticklabels([])
-ax.set_xlim(0.0, 4.8)
-ax.set_ylabel('x')
-ax.set_ylim( 0.0, 8.0)
 
+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'])
+ax.set_ylabel('x')
+ax.set_ylim( 0.0, 8.0)
 
 # scatter data points according to their density:
 kernel = gaussian_kde(data)
@@ -80,11 +66,10 @@ ax.scatter(scatterpos+barwidth*x*(rng.rand(len(data))-0.5), data, s=50)
 barmean = np.mean(data)
 barstd = np.std(data)
 ew = 0.2
-ax.bar([barpos-0.5*barwidth], [barmean], barwidth, color='#FFCC00')
+ax.bar([barpos-0.5*barwidth], [barmean], barwidth, **fsC)
-eargs = {'color': 'k', 'lw': 2}
+ax.plot([barpos, barpos], [barmean-barstd, barmean+barstd], **lsMarker)
-ax.plot([barpos, barpos], [barmean-barstd, barmean+barstd], **eargs)
+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], **eargs)
+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], **eargs)
 ax.annotate('mean',
             xy=(barpos-0.4*barwidth, 2.7), xycoords='data',
             xytext=(barpos-1*barwidth, 5.5), textcoords='data', ha='left',
@@ -96,11 +81,7 @@ ax.annotate('mean plus\nstd. dev.',
             arrowprops=dict(arrowstyle="->", relpos=(0.5,0.0),
             connectionstyle="angle3,angleA=-60,angleB=80") )
 
-ax = fig.add_axes([xpos+width+0.03, ypos, 0.98-(xpos+width+0.03), height])
+ax = fig.add_subplot(spec[0, 1])
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.xaxis.set_ticks_position('bottom')
-ax.yaxis.set_ticks_position('left')
 ax.set_yticklabels([])
 ax.set_ylim( 0.0, 8.0)
 ax.set_xticks(np.arange(0.0, 0.4, 0.1))
@@ -108,8 +89,7 @@ ax.set_xlabel('(4) p(x)')
 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, color='#CC0000')
+ax.barh(b[:-1], h/bw/np.sum(h), bw, **fsB)
 
 plt.savefig('displayunivariatedata.pdf')
-#plt.show()
 

statistics/lecture/median.py

@@ -1,78 +1,66 @@
 import numpy as np
 import scipy.stats as st
 import matplotlib.pyplot as plt
+from plotstyle import *
 
 # normal distribution:
-x = np.arange( -3.0, 3.0, 0.01 )
+x = np.arange(-3.0, 3.0, 0.01)
 g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
 
-plt.xkcd()
+fig, (ax1, ax2) = plt.subplots(1, 2)
-fig = plt.figure( figsize=(6, 2.8) )
+fig.subplots_adjust(**adjust_fs(bottom=2.7, top=0.1))
-ax = fig.add_subplot(1, 2, 1)
+ax1.set_xlabel('x')
-ax.spines['right'].set_visible(False)
+ax1.set_ylabel('Prob. density p(x)')
-ax.spines['top'].set_visible(False)
+ax1.set_ylim(0.0, 0.46)
-ax.yaxis.set_ticks_position('left')
+ax1.set_yticks(np.arange(0.0, 0.45, 0.1))
-ax.xaxis.set_ticks_position('bottom')
+ax1.text(-1.0, 0.06, '50%', ha='center')
-ax.set_xlabel( 'x' )
+ax1.text(+1.0, 0.06, '50%', ha='center')
-ax.set_ylabel( 'Prob. density p(x)' )
+ax1.annotate('Median\n= mean',
-ax.set_ylim( 0.0, 0.46 )
-ax.set_yticks( np.arange( 0.0, 0.45, 0.1 ) )
-ax.text(-1.0, 0.06, '50%', ha='center' )
-ax.text(+1.0, 0.06, '50%', ha='center' )
-ax.annotate('Median\n= mean',
             xy=(0.1, 0.3), xycoords='data',
             xytext=(1.2, 0.35), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
-            connectionstyle="angle3,angleA=10,angleB=40") )
+            connectionstyle="angle3,angleA=10,angleB=40"))
-ax.annotate('Mode',
+ax1.annotate('Mode',
             xy=(-0.1, 0.4), xycoords='data',
             xytext=(-2.5, 0.43), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
-            connectionstyle="angle3,angleA=10,angleB=120") )
+            connectionstyle="angle3,angleA=10,angleB=120"))
-ax.fill_between( x[x<0], 0.0, g[x<0], color='#ffcc00' )
+ax1.fill_between(x[x<0], 0.0, g[x<0], **fsCs)
-ax.fill_between( x[x>0], 0.0, g[x>0], color='#99ff00' )
+ax1.fill_between(x[x>0], 0.0, g[x>0], **fsFs)
-ax.plot(x, g, 'b', lw=4)
+ax1.plot(x, g, **lsA)
-ax.plot([0.0, 0.0], [0.0, 0.45], 'k', lw=2 )
+ax1.plot([0.0, 0.0], [0.0, 0.45], **lsMarker)
 
-# normal distribution:
+# gamma distribution:
-x = np.arange( 0.0, 6.0, 0.01 )
+x = np.arange(0.0, 6.0, 0.01)
 shape = 2.0
 g = st.gamma.pdf(x, shape)
 m = st.gamma.median(shape)
 gm = st.gamma.mean(shape)
-ax = fig.add_subplot(1, 2, 2)
+ax2.set_xlabel('x')
-ax.spines['right'].set_visible(False)
+ax2.set_ylabel('Prob. density p(x)')
-ax.spines['top'].set_visible(False)
+ax2.set_ylim(0.0, 0.46)
-ax.yaxis.set_ticks_position('left')
+ax2.set_yticks(np.arange(0.0, 0.45, 0.1))
-ax.xaxis.set_ticks_position('bottom')
+ax2.text(m-0.8, 0.06, '50%', ha='center')
-ax.set_xlabel( 'x' )
+ax2.text(m+1.2, 0.06, '50%', ha='center')
-ax.set_ylabel( 'Prob. density p(x)' )
+ax2.annotate('Median',
-ax.set_ylim( 0.0, 0.46 )
-ax.set_yticks( np.arange( 0.0, 0.45, 0.1 ) )
-ax.text(m-0.8, 0.06, '50%', ha='center' )
-ax.text(m+1.2, 0.06, '50%', ha='center' )
-ax.annotate('Median',
             xy=(m+0.1, 0.2), xycoords='data',
             xytext=(m+1.6, 0.25), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
-            connectionstyle="angle3,angleA=30,angleB=70") )
+            connectionstyle="angle3,angleA=30,angleB=70"))
-ax.annotate('Mean',
+ax2.annotate('Mean',
             xy=(gm, 0.01), xycoords='data',
             xytext=(gm+1.8, 0.15), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
-            connectionstyle="angle3,angleA=0,angleB=90") )
+            connectionstyle="angle3,angleA=0,angleB=90"))
-ax.annotate('Mode',
+ax2.annotate('Mode',
             xy=(1.0, 0.38), xycoords='data',
             xytext=(1.8, 0.42), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
-            connectionstyle="angle3,angleA=0,angleB=70") )
+            connectionstyle="angle3,angleA=0,angleB=70"))
-ax.fill_between( x[x<m], 0.0, g[x<m], color='#ffcc00' )
+ax2.fill_between(x[x<m], 0.0, g[x<m], **fsCs)
-ax.fill_between( x[x>m], 0.0, g[x>m], color='#99ff00' )
+ax2.fill_between(x[x>m], 0.0, g[x>m], **fsFs)
-ax.plot(x, g, 'b', lw=4)
+ax2.plot(x, g, **lsA)
-ax.plot([m, m], [0.0, 0.38], 'k', lw=2 )
+ax2.plot([m, m], [0.0, 0.38], **lsMarker)
-#ax.plot([gm, gm], [0.0, 0.42], 'k', lw=2 )
+#ax2.plot([gm, gm], [0.0, 0.38], **lsMarker)
 
-#plt.tight_layout()
+fig.savefig('median.pdf')
-plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
-fig.savefig( 'median.pdf' )
-#plt.show()

statistics/lecture/pdfhistogram.py

@@ -1,44 +1,31 @@
 import numpy as np
 import matplotlib.pyplot as plt
+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 )
+r = rng.randn(100)
 
-plt.xkcd()
+fig, (ax1, ax2) = plt.subplots(1, 2)
+ax1.set_xlabel('x')
+ax1.set_xlim(-3.2, 3.2)
+ax1.set_xticks(np.arange(-3.0, 3.1, 1.0))
+ax1.set_ylabel('Frequency')
+ax1.set_yticks(np.arange(0.0, 41.0, 10.0))
+ax1.hist(r, 5, zorder=-10, **fsB)
+ax1.hist(r, 20, zorder=-5, **fsC)
 
-fig = plt.figure( figsize=(6,3) )
+ax2.set_xlabel('x')
-ax = fig.add_subplot( 1, 2, 1 )
+ax2.set_xlim(-3.2, 3.2)
-ax.spines['right'].set_visible(False)
+ax2.set_xticks(np.arange(-3.0, 3.1, 1.0))
-ax.spines['top'].set_visible(False)
+ax2.set_ylabel('Probab. density p(x)')
-ax.yaxis.set_ticks_position('left')
+ax2.set_ylim(0.0, 0.44)
-ax.xaxis.set_ticks_position('bottom')
+ax2.set_yticks(np.arange(0.0, 0.41, 0.1))
-ax.set_xlabel( 'x' )
+ax2.plot(x, g, zorder=-1, **lsA)
-ax.set_xlim(-3.2, 3.2)
+ax2.hist(r, 5, normed=True, zorder=-10, **fsB)
-ax.set_xticks( np.arange( -3.0, 3.1, 1.0 ) )
+ax2.hist(r, 20, normed=True, zorder=-5, **fsC)
-ax.set_ylabel( 'Frequency' )
-ax.set_yticks( np.arange( 0.0, 41.0, 10.0 ) )
-ax.hist(r, 5, color='#CC0000')
-ax.hist(r, 20, color='#FFCC00')
 
-ax = fig.add_subplot( 1, 2, 2 )
+fig.savefig('pdfhistogram.pdf')
-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_ylabel( 'Probab. density p(x)' )
-ax.set_ylim(0.0, 0.44)
-ax.set_yticks( np.arange( 0.0, 0.41, 0.1 ) )
-ax.plot(x, g, '-b', lw=2, zorder=-1)
-ax.hist(r, 5, normed=True, color='#CC0000', zorder=-10)
-ax.hist(r, 20, normed=True, color='#FFCC00', zorder=-5)
-
-plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
-fig.savefig( 'pdfhistogram.pdf' )
-#plt.show()
 

statistics/lecture/pdfprobabilities.py

@@ -1,36 +1,28 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from plotstyle import *
 
 # normal distribution:
-x = np.arange( -3.0, 5.0, 0.01 )
+x = np.arange(-3.0, 5.0, 0.01)
 g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
 x1=0.0
 x2=1.0
 
-plt.xkcd()
+fig, ax = plt.subplots()
-fig = plt.figure( figsize=(6,3.4) )
+ax.set_xlabel('x')
-ax = fig.add_subplot( 1, 1, 1 )
+ax.set_ylabel('Probability density p(x)')
-ax.spines['right'].set_visible(False)
+ax.set_ylim(0.0, 0.46)
-ax.spines['top'].set_visible(False)
+ax.set_yticks(np.arange(0.0, 0.45, 0.1))
-ax.yaxis.set_ticks_position('left')
-ax.xaxis.set_ticks_position('bottom')
-ax.set_xlabel( 'x' )
-ax.set_ylabel( 'Probability density p(x)' )
-ax.set_ylim( 0.0, 0.46 )
-ax.set_yticks( np.arange( 0.0, 0.45, 0.1 ) )
 ax.annotate('Gaussian',
             xy=(-1.0, 0.28), xycoords='data',
             xytext=(-2.5, 0.35), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.5,0.0),
-            connectionstyle="angle3,angleA=10,angleB=110") )
+            connectionstyle="angle3,angleA=10,angleB=110"))
 ax.annotate('$P(0<x<1) = \int_0^1 p(x) \, dx$',
-            xy=(0.6, 0.28), xycoords='data',
+            xy=(0.5, 0.24), xycoords='data',
             xytext=(1.2, 0.4), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
-            connectionstyle="angle3,angleA=10,angleB=80") )
+            connectionstyle="angle3,angleA=10,angleB=80"))
-ax.fill_between( x[(x>x1)&(x<x2)], 0.0, g[(x>x1)&(x<x2)], color='#cc0000' )
+ax.fill_between(x[(x>x1)&(x<x2)], 0.0, g[(x>x1)&(x<x2)], **fsBs)
-ax.plot(x,g, 'b', lw=4)
+ax.plot(x,g, **lsA)
-#plt.tight_layout()
+fig.savefig('pdfprobabilities.pdf')
-plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
-fig.savefig( 'pdfprobabilities.pdf' )
-#plt.show()

statistics/lecture/quartile.py

@@ -1,22 +1,18 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from plotstyle import *
 
 # normal distribution:
 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 ]
 
-plt.xkcd()
+fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
-fig = plt.figure( figsize=(6,3.2) )
+fig.subplots_adjust(**adjust_fs(bottom=2.7, top=0.1))
-ax = fig.add_subplot( 1, 1, 1 )
+ax.set_xlabel('x')
-ax.spines['right'].set_visible(False)
+ax.set_ylabel('Probability density p(x)')
-ax.spines['top'].set_visible(False)
+ax.set_ylim(0.0, 0.46)
-ax.yaxis.set_ticks_position('left')
+ax.set_yticks(np.arange(0.0, 0.45, 0.1))
-ax.xaxis.set_ticks_position('bottom')
-ax.set_xlabel( 'x' )
-ax.set_ylabel( 'Probability density p(x)' )
-ax.set_ylim( 0.0, 0.46 )
-ax.set_yticks( np.arange( 0.0, 0.45, 0.1 ) )
 ax.text(-1.2, 0.1, '25%', ha='center' )
 ax.text(-0.35, 0.1, '25%', ha='center' )
 ax.text(+0.35, 0.1, '25%', ha='center' )
@@ -36,15 +32,12 @@ ax.annotate('Median',
             xytext=(1.6, 0.35), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
             connectionstyle="angle3,angleA=10,angleB=40") )
-ax.fill_between( x[x<q[0]], 0.0, g[x<q[0]], color='#ffcc00' )
+ax.fill_between( x[x<q[0]], 0.0, g[x<q[0]], **fsCs)
-ax.fill_between( x[(x>q[0])&(x<q[1])], 0.0, g[(x>q[0])&(x<q[1])], color='#ff0000' )
+ax.fill_between( x[(x>q[0])&(x<q[1])], 0.0, g[(x>q[0])&(x<q[1])], **fsBs)
-ax.fill_between( x[(x>q[1])&(x<q[2])], 0.0, g[(x>q[1])&(x<q[2])], color='#ff9900' )
+ax.fill_between( x[(x>q[1])&(x<q[2])], 0.0, g[(x>q[1])&(x<q[2])], **fsDs)
-ax.fill_between( x[x>q[2]], 0.0, g[x>q[2]], color='#ffff66' )
+ax.fill_between( x[x>q[2]], 0.0, g[x>q[2]], **fsEs)
-ax.plot(x,g, 'b', lw=4)
+ax.plot(x,g, **lsA)
-ax.plot([0.0, 0.0], [0.0, 0.45], 'k', lw=2 )
+ax.plot([0.0, 0.0], [0.0, 0.45], **lsMarker)
-ax.plot([q[0], q[0]], [0.0, 0.4], 'k', lw=2 )
+ax.plot([q[0], q[0]], [0.0, 0.4], **lsMarker)
-ax.plot([q[2], q[2]], [0.0, 0.4], 'k', lw=2 )
+ax.plot([q[2], q[2]], [0.0, 0.4], **lsMarker)
-plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
-#plt.tight_layout()
 fig.savefig( 'quartile.pdf' )
-#plt.show()