diff --git a/bootstrap/lecture/bootstrapsem.py b/bootstrap/lecture/bootstrapsem.py
index 66154ce..d8b0346 100644
--- a/bootstrap/lecture/bootstrapsem.py
+++ b/bootstrap/lecture/bootstrapsem.py
@@ -1,8 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from plotstyle import *
 
-plt.xkcd()
-fig = plt.figure( figsize=(6,3.5) )
 rng = np.random.RandomState(637281)
 
 nsamples = 100
@@ -25,11 +24,8 @@ for i in range(nresamples) :
     musrs.append(np.mean(rng.randn(nsamples)))
 hmusrs, _ = np.histogram(musrs, bins, density=True)
 
-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')
+fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
+fig.subplots_adjust(**adjust_fs(left=4.0, bottom=2.7, right=1.5))
 ax.set_xlabel('Mean')
 ax.set_xlim(-0.4, 0.4)
 ax.set_ylabel('Probability density')
@@ -45,9 +41,7 @@ ax.annotate('bootstrap\ndistribution',
             xytext=(0.25, 4), textcoords='data',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
                 connectionstyle="angle3,angleA=20,angleB=60") )
-ax.bar(bins[:-1]-0.25*db, hmusrs, 0.5*db, color='r')
-ax.bar(bins[:-1]+0.25*db, hmus, 0.5*db, color='b')
+ax.bar(bins[:-1]-0.25*db, hmusrs, 0.5*db, **fsB)
+ax.bar(bins[:-1]+0.25*db, hmus, 0.5*db, **fsA)
 
-plt.tight_layout()
 plt.savefig('bootstrapsem.pdf')
-#plt.show();
diff --git a/bootstrap/lecture/permutecorrelation.py b/bootstrap/lecture/permutecorrelation.py
index b6bc1bd..2f10af8 100644
--- a/bootstrap/lecture/permutecorrelation.py
+++ b/bootstrap/lecture/permutecorrelation.py
@@ -1,9 +1,8 @@
 import numpy as np
 import scipy.stats as st
 import matplotlib.pyplot as plt
+from plotstyle import *
 
-plt.xkcd()
-fig = plt.figure( figsize=(6,3.5) )
 rng = np.random.RandomState(637281)
 
 # generate correlated data:
@@ -36,33 +35,28 @@ print('Measured correlation coefficient %.2f is at %.4f percentile of bootstrap'
 rp, ra = st.pearsonr(x, y)
 print('Measured correlation coefficient %.2f is at %.4f percentile of test' % (rp, ra))
 
-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')
+fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
+fig.subplots_adjust(**adjust_fs(left=4.0, bottom=2.7, right=0.5, top=1.0))
 ax.annotate('Measured\ncorrelation\nis significant!',
             xy=(rd, 1.1), xycoords='data',
             xytext=(rd, 2.2), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.2,0.0),
-            connectionstyle="angle3,angleA=10,angleB=80") )
+                connectionstyle="angle3,angleA=10,angleB=80") )
 ax.annotate('95% percentile',
             xy=(0.14, 0.9), xycoords='data',
             xytext=(0.2, 4.0), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.1,0.0),
-            connectionstyle="angle3,angleA=30,angleB=70") )
+                connectionstyle="angle3,angleA=30,angleB=70") )
 ax.annotate('Distribution of\nuncorrelated\nsamples',
             xy=(-0.08, 3.6), xycoords='data',
             xytext=(-0.22, 5.0), textcoords='data', ha='left',
             arrowprops=dict(arrowstyle="->", relpos=(0.5,0.0),
-            connectionstyle="angle3,angleA=150,angleB=100") )
-ax.bar(b[:-1], h, width=b[1]-b[0], color='#ffff66')
-ax.bar(b[:-1][b[:-1]>=rq], h[b[:-1]>=rq], width=b[1]-b[0], color='#ff9900')
-ax.plot( [rd, rd], [0, 1], 'b', linewidth=4 )
+                connectionstyle="angle3,angleA=150,angleB=100") )
+ax.bar(b[:-1], h, width=b[1]-b[0], **fsC)
+ax.bar(b[:-1][b[:-1]>=rq], h[b[:-1]>=rq], width=b[1]-b[0], **fsB)
+ax.plot( [rd, rd], [0, 1], **lsA)
 ax.set_xlim(-0.25, 0.35)
 ax.set_xlabel('Correlation coefficient')
 ax.set_ylabel('Probability density of H0')
 
-plt.tight_layout()
 plt.savefig('permutecorrelation.pdf')
-#plt.show();
diff --git a/likelihood/lecture/mlecoding.py b/likelihood/lecture/mlecoding.py
index 1a5f829..3edd5a5 100644
--- a/likelihood/lecture/mlecoding.py
+++ b/likelihood/lecture/mlecoding.py
@@ -1,24 +1,22 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
+import matplotlib.gridspec as gridspec
+from plotstyle import *
 
-plt.xkcd()
-fig = plt.figure( figsize=(6,6.8) )
 rng = np.random.RandomState(4637281)
 lmarg=0.1
 rmarg=0.1
 
-ax = fig.add_axes([lmarg, 0.75, 1.0-rmarg, 0.25])
-ax.spines['bottom'].set_position('zero')
-ax.spines['left'].set_visible(False)
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.xaxis.set_ticks_position('bottom')
-ax.get_yaxis().set_visible(False)
+fig = plt.figure(figsize=cm_size(figure_width, 2.8*figure_height))
+spec = gridspec.GridSpec(nrows=4, ncols=1, height_ratios=[4, 4, 1, 3], hspace=0.2,
+                         **adjust_fs(fig, left=4.0))
+ax = fig.add_subplot(spec[0, 0])
 ax.set_xlim(0.0, np.pi)
 ax.set_xticks(np.arange(0.125*np.pi, 1.*np.pi, 0.125*np.pi))
 ax.set_xticklabels([])
 ax.set_ylim(0.0, 3.5)
+ax.yaxis.set_major_locator(plt.NullLocator())
 ax.text(-0.2, 0.5*3.5, 'Activity', rotation='vertical', va='center')
 ax.annotate('Tuning curve',
             xy=(0.42*np.pi, 2.5), xycoords='data',
@@ -31,55 +29,49 @@ ax.annotate('',
             arrowprops=dict(arrowstyle="->", relpos=(0.5,0.5),
             connectionstyle="angle3,angleA=80,angleB=90") )
 ax.text(0.52*np.pi, 0.7, 'preferred\norientation')
-ax.plot([0, 0], [0.0, 3.5], 'k', zorder=10, clip_on=False)
 xx = np.arange(0.0, 2.0*np.pi, 0.01)
 pp = 0.5*np.pi
 yy = np.exp(np.cos(2.0*(xx+pp)))
-ax.fill_between(xx, yy+0.25*yy, yy-0.25*yy, color=cm.autumn(0.3, 1), alpha=0.5)
-ax.plot(xx, yy, color=cm.autumn(0.0, 1))
+ax.fill_between(xx, yy+0.25*yy, yy-0.25*yy, **fsBa)
+ax.plot(xx, yy, **lsB)
 
-ax = fig.add_axes([lmarg, 0.34, 1.0-rmarg, 0.38])
-ax.spines['bottom'].set_position('zero')
-ax.spines['left'].set_visible(False)
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.xaxis.set_ticks_position('bottom')
-ax.get_yaxis().set_visible(False)
+ax = fig.add_subplot(spec[1, 0])
 ax.set_xlim(0.0, np.pi)
 ax.set_xticks(np.arange(0.125*np.pi, 1.*np.pi, 0.125*np.pi))
 ax.set_xticklabels([])
-ax.set_ylim(-1.5, 3.0)
-ax.text(0.5*np.pi, -1.8, 'Orientation', ha='center')
+ax.set_ylim(0.0, 3.0)
+ax.yaxis.set_major_locator(plt.NullLocator())
 ax.text(-0.2, 0.5*3.5, 'Activity', rotation='vertical', va='center')
-ax.plot([0, 0], [0.0, 3.0], 'k', zorder=10, clip_on=False)
 xx = np.arange(0.0, 1.0*np.pi, 0.01)
 prefphases = np.arange(0.125*np.pi, 1.*np.pi, 0.125*np.pi)
 responses = []
 xresponse = 0.475*np.pi
-for pp in prefphases :
+for pp, ls, ps in zip(prefphases, [lsE, lsC, lsD, lsB, lsD, lsC, lsE],
+                      [psE, psC, psD, psB, psD, psC, psE]) :
     yy = np.exp(np.cos(2.0*(xx+pp)))
-    ax.plot(xx, yy, color=cm.autumn(2.0*np.abs(pp/np.pi-0.5), 1))
+    #ax.plot(xx, yy, color=cm.autumn(2.0*np.abs(pp/np.pi-0.5), 1))
+    ax.plot(xx, yy, **ls)
     y = np.exp(np.cos(2.0*(xresponse+pp)))
     responses.append(y + rng.randn()*0.25*y)
-    ax.plot(xresponse, y, '.', markersize=20, color=cm.autumn(2.0*np.abs(pp/np.pi-0.5), 1))
-    r=0.3
-    y=-0.8
-    ax.plot([pp-0.5*r*np.cos(pp), pp+0.5*r*np.cos(pp)], [y-r*np.sin(pp), y+r*np.sin(pp)], 'k', lw=6)
+    ax.plot(xresponse, y, **ps)
 responses = np.array(responses)
 
-ax = fig.add_axes([lmarg, 0.05, 1.0-rmarg, 0.22])
-ax.spines['left'].set_visible(False)
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.xaxis.set_ticks_position('bottom')
-ax.get_yaxis().set_visible(False)
+ax = fig.add_subplot(spec[2, 0])
+ax.show_spines('')
+r = 0.3
+ax.set_ylim(-1.1*r, 1.1*r)
+for pp in prefphases:
+    ax.plot([pp-0.5*r*np.cos(pp), pp+0.5*r*np.cos(pp)], [-r*np.sin(pp), r*np.sin(pp)],
+            colors['black'], lw=6, clip_on=False)
+
+ax = fig.add_subplot(spec[3, 0])
 ax.set_xlim(0.0, np.pi)
 ax.set_xticks(np.arange(0.125*np.pi, 1.*np.pi, 0.125*np.pi))
 ax.set_xticklabels([])
 ax.set_ylim(-1600, 0)
+ax.yaxis.set_major_locator(plt.NullLocator())
 ax.set_xlabel('Orientation')
 ax.text(-0.2, -800, 'Log-Likelihood', rotation='vertical', va='center')
-ax.plot([0, 0], [-1600, 0], 'k', zorder=10, clip_on=False)
 phases = np.linspace(0.0, 1.1*np.pi, 100)
 probs = np.zeros((len(responses), len(phases)))
 for k, (pp, r) in enumerate(zip(prefphases, responses)) :
@@ -95,7 +87,6 @@ ax.annotate('',
             arrowprops=dict(arrowstyle="->", relpos=(0.5,0.5),
             connectionstyle="angle3,angleA=80,angleB=90") )
 ax.text(maxp+0.05, -1100, 'most likely\norientation\ngiven the responses')
-ax.plot(phases, loglikelihood, '-b')
+ax.plot(phases, loglikelihood, **lsA)
 
 plt.savefig('mlecoding.pdf')
-#plt.show();
diff --git a/likelihood/lecture/mlemean.py b/likelihood/lecture/mlemean.py
index 5fd4932..83b1bab 100644
--- a/likelihood/lecture/mlemean.py
+++ b/likelihood/lecture/mlemean.py
@@ -1,8 +1,11 @@
 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from plotstyle import *
 
-plt.xkcd()
-fig = plt.figure( figsize=(6,5) )
+fig = plt.figure(figsize=cm_size(figure_width, 1.8*figure_height))
+spec = gridspec.GridSpec(nrows=2, ncols=2, hspace=0.6,
+                         **adjust_fs(fig, left=5.5))
 
 # the data:
 n = 40
@@ -11,21 +14,17 @@ sigma = 0.5
 rmu = 2.0
 xd = rng.randn(n)*sigma+rmu
 # and possible pdfs:
-x = np.arange( 0.0, 4.0, 0.01 )
+x = np.arange(0.0, 4.0, 0.01)
 mus = [1.5, 2.0, 2.5]
 g=np.zeros((len(x), len(mus)))
 for k, mu in enumerate(mus) :
     g[:,k] = np.exp(-0.5*((x-mu)/sigma)**2.0)/np.sqrt(2.0*np.pi)/sigma
 # plot it:
-ax = fig.add_subplot( 2, 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 = fig.add_subplot(spec[0, :])
 ax.set_xlim(0.5, 3.5)
 ax.set_ylim(-0.02, 0.85)
-ax.set_xticks( np.arange(0, 5))
-ax.set_yticks( np.arange(0, 0.9, 0.2))
+ax.set_xticks(np.arange(0, 5))
+ax.set_yticks(np.arange(0, 0.9, 0.2))
 ax.set_xlabel('x')
 ax.set_ylabel('Probability density')
 s = 1
@@ -36,14 +35,14 @@ for mu in mus :
     ax.annotate('', xy=(mu, 0.02), xycoords='data',
                 xytext=(mu, 0.75), textcoords='data',
                 arrowprops=dict(arrowstyle="->", relpos=(0.5,0.5),
-                connectionstyle=cs), zorder=1 )
+                connectionstyle=cs), zorder=1)
     if mu > rmu :
         ax.text(mu-0.1, 0.04, '?', zorder=1, ha='right')
     else :
         ax.text(mu+0.1, 0.04, '?', zorder=1)
-for k in range(len(mus)) :
-    ax.plot(x, g[:,k], zorder=5)
-ax.scatter(xd, 0.05*rng.rand(len(xd))+0.2, s=30, zorder=10)
+for k, ls in enumerate([lsCm, lsBm, lsDm]) :
+    ax.plot(x, g[:,k], zorder=5, **ls)
+ax.plot(xd, 0.05*rng.rand(len(xd))+0.2, zorder=10, **psAm)
 
 # likelihood:
 thetas=np.arange(1.5, 2.6, 0.01)
@@ -52,48 +51,38 @@ for i, theta in enumerate(thetas) :
     ps[:,i]=np.exp(-0.5*((xd-theta)/sigma)**2.0)/np.sqrt(2.0*np.pi)/sigma
 p=np.prod(ps,axis=0)
 # plot it:
-ax = fig.add_subplot( 2, 2, 3 )
-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 = fig.add_subplot(spec[1, 0])
 ax.set_xlabel(r'Parameter $\theta$')
 ax.set_ylabel('Likelihood')
-ax.set_xticks( np.arange(1.6, 2.5, 0.4))
+ax.set_xticks(np.arange(1.6, 2.5, 0.4))
 ax.annotate('Maximum',
             xy=(2.0, 5.5e-11), xycoords='data',
             xytext=(1.0, 1.1), textcoords='axes fraction', ha='right',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
-            connectionstyle="angle3,angleA=10,angleB=70") )
+            connectionstyle="angle3,angleA=10,angleB=70"))
 ax.annotate('',
             xy=(2.0, 0), xycoords='data',
             xytext=(2.0, 5e-11), textcoords='data',
             arrowprops=dict(arrowstyle="->", relpos=(0.5,0.5),
-            connectionstyle="angle3,angleA=90,angleB=80") )
-ax.plot(thetas,p)
+            connectionstyle="angle3,angleA=90,angleB=80"))
+ax.plot(thetas, p, **lsAm)
 
-ax = fig.add_subplot( 2, 2, 4 )
-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 = fig.add_subplot(spec[1, 1])
 ax.set_xlabel(r'Parameter $\theta$')
 ax.set_ylabel('Log-Likelihood')
 ax.set_ylim(-50,-20)
-ax.set_xticks( np.arange(1.6, 2.5, 0.4))
-ax.set_yticks( np.arange(-50, -19, 10.0))
+ax.set_xticks(np.arange(1.6, 2.5, 0.4))
+ax.set_yticks(np.arange(-50, -19, 10.0))
 ax.annotate('Maximum',
             xy=(2.0, -23), xycoords='data',
             xytext=(1.0, 1.1), textcoords='axes fraction', ha='right',
             arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
-            connectionstyle="angle3,angleA=10,angleB=70") )
+            connectionstyle="angle3,angleA=10,angleB=70"))
 ax.annotate('',
             xy=(2.0, -50), xycoords='data',
             xytext=(2.0, -26), textcoords='data',
             arrowprops=dict(arrowstyle="->", relpos=(0.5,0.5),
-            connectionstyle="angle3,angleA=80,angleB=100") )
-ax.plot(thetas,np.log(p))
+            connectionstyle="angle3,angleA=80,angleB=100"))
+ax.plot(thetas,np.log(p), **lsAm)
 
-plt.tight_layout();
 plt.savefig('mlemean.pdf')
-#plt.show();
diff --git a/likelihood/lecture/mlepdf.py b/likelihood/lecture/mlepdf.py
index 54ffd4c..ddbcb57 100644
--- a/likelihood/lecture/mlepdf.py
+++ b/likelihood/lecture/mlepdf.py
@@ -2,9 +2,10 @@ import numpy as np
 import scipy.stats as st
 import scipy.optimize as opt
 import matplotlib.pyplot as plt
+from plotstyle import *
 
-plt.xkcd()
-fig = plt.figure( figsize=(6,3) )
+fig, (ax1, ax2) = plt.subplots(1, 2)
+fig.subplots_adjust(**adjust_fs(fig, right=1.0))
 
 # the data:
 n = 100
@@ -23,27 +24,23 @@ a = st.gamma.fit(xd, 5.0)
 yf = st.gamma.pdf(xx, *a)
 
 # plot it:
-ax = fig.add_subplot( 1, 2, 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_xlim(0, 10.0)
-ax.set_ylim(0.0, 0.42)
-ax.set_xticks( np.arange(0, 11, 2))
-ax.set_yticks( np.arange(0, 0.42, 0.1))
-ax.set_xlabel('x')
-ax.set_ylabel('Probability density')
-ax.plot(xx, yy, '-', lw=5, color='#ff0000', label='pdf')
-ax.plot(xx, yf, '-', lw=2, color='#ffcc00', label='mle')
+ax1.set_xlim(0, 10.0)
+ax1.set_ylim(0.0, 0.42)
+ax1.set_xticks(np.arange(0, 11, 2))
+ax1.set_yticks(np.arange(0, 0.42, 0.1))
+ax1.set_xlabel('x')
+ax1.set_ylabel('Probability density')
+ax1.plot(xx, yy, label='pdf', **lsB)
+ax1.plot(xx, yf, label='mle', **lsCm)
 kernel = st.gaussian_kde(xd)
 x = kernel(xd)
 x /= np.max(x)
-ax.scatter(xd, 0.05*x*(rng.rand(len(xd))-0.5)+0.05, s=30, zorder=10)
-ax.legend(loc='upper right', frameon=False)
+sigma = 0.07
+ax1.plot(xd, sigma*x*(rng.rand(len(xd))-0.5)+sigma, zorder=10, **psAm)
+ax1.legend(loc='upper right')
 
 # histogram:
-h,b = np.histogram(xd, np.arange(0, 8.5, 1), density=True)
+h,b = np.histogram(xd, np.arange(0, 8.4, 0.5), density=True)
 
 # fit histogram:
 def gammapdf(x, n, l, s) :
@@ -52,22 +49,15 @@ popt, pcov = opt.curve_fit(gammapdf, b[:-1]+0.5*(b[1]-b[0]), h)
 yc = st.gamma.pdf(xx, *popt)
 
 # plot it:
-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, 10.0)
-ax.set_xticks( np.arange(0, 11, 2))
-ax.set_xlabel('x')
-ax.set_ylim(0.0, 0.42)
-ax.set_yticks( np.arange(0, 0.42, 0.1))
-ax.set_ylabel('Probability density')
-ax.plot(xx, yy, '-', lw=5, color='#ff0000', label='pdf')
-ax.plot(xx, yc, '-', lw=2, color='#ffcc00', label='fit')
-ax.bar(b[:-1], h, np.diff(b))
-ax.legend(loc='upper right', frameon=False)
+ax2.set_xlim(0, 10.0)
+ax2.set_xticks(np.arange(0, 11, 2))
+ax2.set_xlabel('x')
+ax2.set_ylim(0.0, 0.42)
+ax2.set_yticks(np.arange(0, 0.42, 0.1))
+ax2.set_ylabel('Probability density')
+ax2.plot(xx, yy, label='pdf', **lsB)
+ax2.plot(xx, yc, label='fit', **lsCm)
+ax2.bar(b[:-1], h, np.diff(b), **fsA)
+ax2.legend(loc='upper right')
 
-plt.tight_layout();
 plt.savefig('mlepdf.pdf')
-#plt.show();
diff --git a/likelihood/lecture/mlepropline.py b/likelihood/lecture/mlepropline.py
index c89310c..906a3f2 100644
--- a/likelihood/lecture/mlepropline.py
+++ b/likelihood/lecture/mlepropline.py
@@ -1,9 +1,14 @@
 import numpy as np
 import scipy.stats as st
 import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from plotstyle import *
 
-plt.xkcd()
-fig = plt.figure(figsize=(6, 3))
+fig = plt.figure()
+spec = gridspec.GridSpec(nrows=1, ncols=2, wspace=0.3,
+                         **adjust_fs(fig, left=5.5))
+spec1 = gridspec.GridSpecFromSubplotSpec(1, 2, spec[0, 0], width_ratios=[3, 1], wspace=0.0)
+spec2 = gridspec.GridSpecFromSubplotSpec(1, 2, spec[0, 1], width_ratios=[3, 1], wspace=0.0)
 
 # the line:
 slope = 2.0
@@ -20,71 +25,44 @@ slopef = np.sum(x*y)/np.sum(x*x)
 yf = slopef*xx
 
 # plot it:
-ax = fig.add_axes([0.09, 0.02, 0.33, 0.9])
-ax.spines['left'].set_position('zero')
-ax.spines['bottom'].set_position('zero')
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.get_xaxis().set_tick_params(direction='inout', length=10, width=2)
-ax.get_yaxis().set_tick_params(direction='inout', length=10, width=2)
-ax.yaxis.set_ticks_position('left')
-ax.xaxis.set_ticks_position('bottom')
+ax = fig.add_subplot(spec1[0, 0])
 ax.set_xticks(np.arange(0.0, 4.1))
 ax.set_xlim(0.0, 4.2)
 ax.set_ylim(-4.0, 12.0)
 ax.set_xlabel('x')
 ax.set_ylabel('y')
-ax.scatter(x, y, label='data', s=40, zorder=10)
-ax.plot(xx, yy, 'r', lw=5.0, color='#ff0000', label='original', zorder=5)
-ax.plot(xx, yf, '--', lw=1.0, color='#ffcc00', label='fit', zorder=7)
-ax.legend(loc='upper left', bbox_to_anchor=(0.0, 1.15), frameon=False)
+ax.plot(x, y, label='data', zorder=10, **psAm)
+ax.plot(xx, yy, label='original', zorder=5, **lsB)
+ax.plot(xx, yf, label='fit', zorder=7, **lsCm)
+ax.legend(loc='upper left', bbox_to_anchor=(0.0, 1.15))
 
-ax = fig.add_axes([0.42, 0.02, 0.07, 0.9])
-ax.spines['left'].set_position('zero')
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.get_yaxis().set_tick_params(direction='inout', length=10, width=2)
-ax.yaxis.set_ticks_position('left')
+ax = fig.add_subplot(spec1[0, 1])
+ax.show_spines('l')
 ax.set_xticks([])
 ax.set_ylim(-4.0, 12.0)
 ax.set_yticks([])
 bins = np.arange(-4.0, 12.1, 0.75)
-ax.hist(y, bins, orientation='horizontal', zorder=10)
+ax.hist(y, bins, orientation='horizontal', zorder=10, **fsA)
 
-ax = fig.add_axes([0.6, 0.02, 0.33, 0.9])
-ax.spines['left'].set_position('zero')
-ax.spines['bottom'].set_position('zero')
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.get_xaxis().set_tick_params(direction='inout', length=10, width=2)
-ax.get_yaxis().set_tick_params(direction='inout', length=10, width=2)
-ax.yaxis.set_ticks_position('left')
-ax.xaxis.set_ticks_position('bottom')
+ax = fig.add_subplot(spec2[0, 0])
 ax.set_xticks(np.arange(0.0, 4.1))
 ax.set_xlim(0.0, 4.2)
 ax.set_ylim(-4.0, 12.0)
 ax.set_xlabel('x')
 ax.set_ylabel('y - mx')
-ax.scatter(x, y - slopef*x, label='residuals', s=40, zorder=10)
-#ax.legend(loc='upper left', bbox_to_anchor=(0.0, 1.0), frameon=False)
+ax.plot(x, y - slopef*x, label='residuals', zorder=10, **psAm)
+#ax.legend(loc='upper left', bbox_to_anchor=(0.0, 1.0))
 
-ax = fig.add_axes([0.93, 0.02, 0.07, 0.9])
-ax.spines['left'].set_position('zero')
-ax.spines['right'].set_visible(False)
-ax.spines['top'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.get_yaxis().set_tick_params(direction='inout', length=10, width=2)
-ax.yaxis.set_ticks_position('left')
+ax = fig.add_subplot(spec2[0, 1])
+ax.show_spines('l')
 ax.set_xlim(0.0, 11.0)
 ax.set_xticks([])
 ax.set_ylim(-4.0, 12.0)
 ax.set_yticks([])
 r = y - slopef*x
-ax.hist(r, bins, orientation='horizontal', zorder=10)
+ax.hist(r, bins, orientation='horizontal', zorder=10, **fsA)
 gx = np.arange(-4.0, 12.1, 0.1)
 gy = st.norm.pdf(gx, np.mean(r), np.std(r))
-ax.plot(1.0+gy*29.0, gx, 'r', lw=2, zorder=5)
+ax.plot(1.0+gy*29.0, gx, zorder=5, **lsBm)
 
 plt.savefig('mlepropline.pdf')
-#plt.show();
diff --git a/plotstyle.py b/plotstyle.py
index f41fce7..8799339 100644
--- a/plotstyle.py
+++ b/plotstyle.py
@@ -13,19 +13,16 @@ ppi = 72.0
 
 # colors:
 colors = {}
-colors['red'] = '#CC0000'
+colors['red'] = '#DD1000'
 colors['orange'] = '#FF9900'
 colors['lightorange'] = '#FFCC00'
-colors['yellow'] = '#FFFF66'
+colors['yellow'] = '#FFF720'
 colors['green'] = '#99FF00'
 colors['blue'] = '#0010CC'
 colors['gray'] = '#A7A7A7'
 colors['black'] = '#000000'
 colors['white'] = '#FFFFFF'
 
-#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'
 
@@ -36,11 +33,14 @@ 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
+largelinepoints = {'linestyle': '-', 'linewidth': lwthick, 'marker': 'o', 'markersize': 10, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
+smalllinepoints = {'linestyle': '-', 'linewidth': lwthin, 'marker': 'o', 'markersize': 7, 'markeredgecolor': colors['white'], 'markeredgewidth': 1}
+filllw = 1
+fillec = colors['white']
+fillalpha = 0.4
 
 # helper lines:
-lsSpine = {'c': colors['black'], 'linestyle': '-', 'linewidth': 1}
+lsSpine = {'c': colors['black'], 'linestyle': '-', 'linewidth': 1, 'clip_on': False}
 lsGrid = {'c': colors['gray'], 'linestyle': '--', 'linewidth': 1}
 lsMarker = {'c': colors['black'], 'linestyle': '-', 'linewidth': 2}
 
@@ -52,9 +52,13 @@ lsMarker = {'c': colors['black'], 'linestyle': '-', 'linewidth': 2}
 # - 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).
+# Point (marker) styles come in two variants:
+# - plain style with large solid markers (e.g. psB), and
+# - minor style with smaller markers (e.g. psBm).
+
+# Linepoint styles (markers connected by lines) come in two variants:
+# - plain style with large solid markers (e.g. lpsA), and
+# - minor style with smaller markers (e.g. lpsAm).
 
 # Fill styles come in three variants:
 # - plain (e.g. fsB) for a solid fill color and a darker edge color,
@@ -65,13 +69,19 @@ 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)
+lpsA = dict({'color': colors['blue']}, **largelinepoints)
+lpsAm = dict({'color': colors['blue']}, **smalllinepoints)
+fsA = {'facecolor': colors['blue'], 'edgecolor': fillec, 'linewidth': filllw}
+fsAs = {'facecolor': colors['blue'], 'edgecolor': 'none'}
 fsAa = {'facecolor': colors['blue'], 'edgecolor': 'none', 'alpha': fillalpha}
 
 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}
+lpsB = dict({'color': colors['red']}, **largelinepoints)
+lpsBm = dict({'color': colors['red']}, **smalllinepoints)
+fsB = {'facecolor': colors['red'], 'edgecolor': fillec, 'linewidth': filllw}
 fsBs = {'facecolor': colors['red'], 'edgecolor': 'none'}
 fsBa = {'facecolor': colors['red'], 'edgecolor': 'none', 'alpha': fillalpha}
 
@@ -79,17 +89,25 @@ 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}
+fsC = {'facecolor': colors['lightorange'], 'edgecolor': fillec, 'linewidth': filllw}
 fsCs = {'facecolor': colors['lightorange'], 'edgecolor': 'none'}
 fsCa = {'facecolor': colors['lightorange'], 'edgecolor': 'none', 'alpha': fillalpha}
 
-fsD = {'facecolor': colors['orange'], 'edgecolor': colors['black'], 'linewidth': filllw}
+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}
 fsDs = {'facecolor': colors['orange'], 'edgecolor': 'none'}
 
-fsE = {'facecolor': colors['yellow'], 'edgecolor': colors['black'], 'linewidth': filllw}
+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}
 fsEs = {'facecolor': colors['yellow'], 'edgecolor': 'none'}
 
-fsF = {'facecolor': colors['green'], 'edgecolor': colors['black'], 'linewidth': filllw}
+fsF = {'facecolor': colors['green'], 'edgecolor': fillec, 'linewidth': filllw}
 fsFs = {'facecolor': colors['green'], 'edgecolor': 'none'}
 
 # factor for scaling widths of bars in a bar plot:
@@ -323,6 +341,7 @@ def common_format():
     mpl.rcParams['grid.color'] = lsGrid['c']
     mpl.rcParams['grid.linestyle'] = lsGrid['linestyle']
     mpl.rcParams['grid.linewidth'] = lsGrid['linewidth']
+    mpl.rcParams['legend.frameon'] = False
     mpl.rcParams['axes.facecolor'] = 'none'
     mpl.rcParams['axes.edgecolor'] = lsSpine['c']
     mpl.rcParams['axes.linewidth'] = lsSpine['linewidth']
diff --git a/regression/lecture/cubicerrors.py b/regression/lecture/cubicerrors.py
index 64a4671..e072d9e 100644
--- a/regression/lecture/cubicerrors.py
+++ b/regression/lecture/cubicerrors.py
@@ -16,11 +16,11 @@ def create_data():
 
     
 def plot_data(ax, x, y, c):
-    ax.scatter(x, y, marker='o', color=colors['blue'], s=40, zorder=10)
+    ax.plot(x, y, zorder=10, **psAm)
     xx = np.linspace(2.1, 3.9, 100)
-    ax.plot(xx, c*xx**3.0, color=colors['red'], lw=2, zorder=5)
+    ax.plot(xx, c*xx**3.0, zorder=5, **lsBm)
     for cc in [0.25*c, 0.5*c, 2.0*c, 4.0*c]:
-        ax.plot(xx, cc*xx**3.0, color=colors['orange'], lw=1.5, zorder=5)
+        ax.plot(xx, cc*xx**3.0, zorder=5, **lsDm)
     ax.set_xlabel('Size x', 'm')
     ax.set_ylabel('Weight y', 'kg')
     ax.set_xlim(2, 4)
@@ -42,15 +42,15 @@ def plot_data_errors(ax, x, y, c):
                 xytext=(3.4, 70), textcoords='data', ha='left',
                 arrowprops=dict(arrowstyle="->", relpos=(0.9,1.0),
                 connectionstyle="angle3,angleA=50,angleB=-30") )
-    ax.scatter(x[:40], y[:40], color=colors['blue'], s=10, zorder=0)
+    ax.plot(x[:40], y[:40], zorder=0, **psAm)
     inxs = [3, 10, 11, 17, 18, 21, 28, 30, 33]
-    ax.scatter(x[inxs], y[inxs], color=colors['blue'], s=40, zorder=10)
+    ax.plot(x[inxs], y[inxs], zorder=10, **psA)
     xx = np.linspace(2.1, 3.9, 100)
-    ax.plot(xx, c*xx**3.0, color=colors['red'], lw=2)
+    ax.plot(xx, c*xx**3.0, **lsBm)
     for i in inxs :
         xx = [x[i], x[i]]
         yy = [c*x[i]**3.0, y[i]]
-        ax.plot(xx, yy, color=colors['orange'], lw=2, zorder=5)
+        ax.plot(xx, yy, zorder=5, **lsDm)
 
 def plot_error_hist(ax, x, y, c):
     ax.set_xlabel('Squared error')
@@ -67,7 +67,7 @@ def plot_error_hist(ax, x, y, c):
                 xytext=(800, 3), textcoords='data', ha='left',
                 arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
                 connectionstyle="angle3,angleA=10,angleB=90") )
-    ax.hist(errors, bins, color=colors['orange'])
+    ax.hist(errors, bins, **fsC)
 
 
 
diff --git a/regression/lecture/cubicfunc.py b/regression/lecture/cubicfunc.py
index e08f21d..e8c0565 100644
--- a/regression/lecture/cubicfunc.py
+++ b/regression/lecture/cubicfunc.py
@@ -16,11 +16,11 @@ if __name__ == "__main__":
     fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.4*figure_height))
     fig.subplots_adjust(**adjust_fs(left=6.0, right=1.2))
     
-    ax.scatter(x, y, marker='o', color=colors['blue'], s=40, zorder=10)
+    ax.plot(x, y, zorder=10, **psA)
     xx = np.linspace(2.1, 3.9, 100)
-    ax.plot(xx, c*xx**3.0, color=colors['red'], lw=3, zorder=5)
+    ax.plot(xx, c*xx**3.0, zorder=5, **lsB)
     for cc in [0.25*c, 0.5*c, 2.0*c, 4.0*c]:
-        ax.plot(xx, cc*xx**3.0, color=colors['orange'], lw=2, zorder=5)
+        ax.plot(xx, cc*xx**3.0, zorder=5, **lsDm)
     ax.set_xlabel('Size x', 'm')
     ax.set_ylabel('Weight y', 'kg')
     ax.set_xlim(2, 4)
diff --git a/regression/lecture/cubicmse.py b/regression/lecture/cubicmse.py
index 2520824..f687885 100644
--- a/regression/lecture/cubicmse.py
+++ b/regression/lecture/cubicmse.py
@@ -39,9 +39,9 @@ def plot_mse(ax, x, y, c, cs):
     for i, cc in enumerate(ccs):
         mses[i] = np.mean((y-(cc*x**3.0))**2.0)
         
-    ax.plot(ccs, mses, colors['blue'], lw=2, zorder=10)
-    ax.scatter(cs, ms, color=colors['red'], s=40, zorder=20)
-    ax.scatter(cs[-1], ms[-1], color=colors['orange'], s=60, zorder=30)
+    ax.plot(ccs, mses, zorder=10, **lsAm)
+    ax.plot(cs[:12], ms[:12], zorder=20, **psB)
+    ax.plot(cs[-1], ms[-1], zorder=30, **psC)
     for i in range(4):
         ax.annotate('',
                     xy=(cs[i+1]+0.2, ms[i+1]), xycoords='data',
@@ -56,12 +56,12 @@ def plot_mse(ax, x, y, c, cs):
     ax.set_yticks(np.arange(0, 30001, 10000))
     
 def plot_descent(ax, cs, mses):
-    ax.plot(np.arange(len(mses))+1, mses, '-o', c=colors['red'], mew=0, ms=8)
+    ax.plot(np.arange(len(mses))+1, mses, **lpsBm)
     ax.set_xlabel('Iteration')
     #ax.set_ylabel('Mean squared error')
-    ax.set_xlim(0, 10.5)
+    ax.set_xlim(0, 12.5)
     ax.set_ylim(0, 25000)
-    ax.set_xticks(np.arange(0.0, 10.1, 2.0))
+    ax.set_xticks(np.arange(0.0, 12.1, 2.0))
     ax.set_yticks(np.arange(0, 30001, 10000))
     ax.set_yticklabels([])
 
diff --git a/regression/lecture/derivative.py b/regression/lecture/derivative.py
index a377109..62a9e39 100644
--- a/regression/lecture/derivative.py
+++ b/regression/lecture/derivative.py
@@ -4,8 +4,7 @@ from plotstyle import *
 
 plain_style()
 
-fig = plt.figure( figsize=(2.5,3.4) )
-ax = fig.add_subplot(1, 1, 1)
+fig, ax = plt.subplots(figsize=(2.5,3.4))
 
 # parabula:
 x1 = -0.2
@@ -14,7 +13,7 @@ x = np.linspace(x1, x2, 200)
 y = x*x
 ax.set_xlim(x1, x2)
 ax.set_ylim(-0.2, 0.7)
-ax.plot(x, y, c=colors['blue'], lw=4, zorder=0)
+ax.plot(x, y, zorder=0, **lsA)
 # secant:
 x = np.asarray([0.1, 0.7])
 y = x*x
@@ -22,33 +21,33 @@ ax.set_xticks(x)
 ax.set_yticks(y)
 ax.set_xticklabels(['$x$','$x+\Delta x$'])
 ax.set_yticklabels(['',''])
-ax.scatter(x, y, c=colors['red'], edgecolor='none', s=150, zorder=10)
+ax.plot(x, y, zorder=10, **psB)
 # function values:
-ax.plot([x[0], x[0], x1],[-0.2, y[0], y[0]], '--k', lw=1, zorder=6)
-ax.plot([x[1], x[1], x1],[-0.2, y[1], y[1]], '--k', lw=1, zorder=6)
+ax.plot([x[0], x[0], x1],[-0.2, y[0], y[0]], zorder=6, **lsGrid)
+ax.plot([x[1], x[1], x1],[-0.2, y[1], y[1]], zorder=6, **lsGrid)
 ax.text(x1+0.05, y[0]+0.05, '$f(x)$', zorder=6)
 ax.text(x1+0.05, y[1]+0.05, '$f(x+\Delta x)$', zorder=6)
 # slope triangle:
-ax.plot([x[0], x[1], x[1]],[y[0], y[0], y[1]], '-k', lw=2, zorder=7)
-ax.text(np.mean(x), y[0]-0.08, '$\Delta x$', ha='center', zorder=7)
+ax.plot([x[0], x[1], x[1]],[y[0], y[0], y[1]], zorder=7, **lsMarker)
+ax.text(np.mean(x), y[0]-0.07, '$\Delta x$', ha='center', zorder=7)
 ax.text(x[1]+0.05, np.mean(y), '$f(x+\Delta x)-f(x)$', va='center', rotation='vertical', zorder=7)
 # secant line:
 m = np.diff(y)/np.diff(x)
 xl = [x1, x2]
 yl = m*(xl-x[0])+y[0]
-ax.plot(xl, yl, c=colors['red'], lw=3, zorder=7)
+ax.plot(xl, yl, zorder=7, **lsBm)
 
 # derivative:
 md = 2.0*x[0]
 yl = md*(xl-x[0])+y[0]
-ax.plot(xl, yl, c=colors['yellow'], lw=3, zorder=5)
+ax.plot(xl, yl, zorder=5, **lsDm)
 
 # limit:
 for ml in np.linspace(md, m, 5)[1:] :
     yl = ml*(xl-x[0])+y[0]
     xs = 0.5*(ml+np.sqrt(ml*ml-4.0*(ml*x[0]-y[0])))
-    ax.scatter([xs], [xs*xs], c=colors['orange'], edgecolor='none', s=80, zorder=3)
-    ax.plot(xl, yl, c=colors['orange'], lw=2, zorder=3)
+    ax.plot([xs], [xs*xs], zorder=3, **psC)
+    ax.plot(xl, yl, zorder=3, **lsCm)
 
 fig.subplots_adjust(**adjust_fs(fig, 0.5, 0.5, 1.4, 0.5))
 plt.savefig('derivative.pdf')
diff --git a/regression/lecture/lin_regress.py b/regression/lecture/lin_regress.py
index 443bab9..ae854d5 100644
--- a/regression/lecture/lin_regress.py
+++ b/regression/lecture/lin_regress.py
@@ -14,7 +14,7 @@ def create_data():
 
     
 def plot_data(ax, x, y):
-    ax.scatter(x, y, marker='o', color=colors['blue'], s=40)
+    ax.plot(x, y, **psA)
     ax.set_xlabel('Input x')
     ax.set_ylabel('Output y')
     ax.set_xlim(0, 120)
@@ -24,10 +24,10 @@ def plot_data(ax, x, y):
     
 
 def plot_data_slopes(ax, x, y, m, n):
-    ax.scatter(x, y, marker='o', color=colors['blue'], s=40)
+    ax.plot(x, y, **psA)
     xx = np.asarray([2, 118])
     for i in np.linspace(0.3*m, 2.0*m, 5):
-        ax.plot(xx, i*xx+n, color=colors['red'], lw=2)
+        ax.plot(xx, i*xx+n, **lsBm)
     ax.set_xlabel('Input x')
     #ax.set_ylabel('Output y')
     ax.set_xlim(0, 120)
@@ -37,10 +37,10 @@ def plot_data_slopes(ax, x, y, m, n):
 
 
 def plot_data_intercepts(ax, x, y, m, n):
-    ax.scatter(x, y, marker='o', color=colors['blue'], s=40)
+    ax.plot(x, y, **psA)
     xx = np.asarray([2, 118])
     for i in np.linspace(n-1*n, n+1*n, 5):
-        ax.plot(xx, m*xx + i, color=colors['red'], lw=2)
+        ax.plot(xx, m*xx + i, **lsBm)
     ax.set_xlabel('Input x')
     #ax.set_ylabel('Output y')
     ax.set_xlim(0, 120)
diff --git a/regression/lecture/linear_least_squares.py b/regression/lecture/linear_least_squares.py
index 5b07e99..d36fd32 100644
--- a/regression/lecture/linear_least_squares.py
+++ b/regression/lecture/linear_least_squares.py
@@ -25,15 +25,15 @@ def plot_data(ax, x, y, m, n):
                 xytext=(80, -50), textcoords='data', ha='left',
                 arrowprops=dict(arrowstyle="->", relpos=(0.9,1.0),
                 connectionstyle="angle3,angleA=50,angleB=-30") )
-    ax.scatter(x[:40], y[:40], color=colors['blue'], s=10, zorder=0)
+    ax.plot(x[:40], y[:40], zorder=0, **psAm)
     inxs = [3, 13, 16, 19, 25, 34, 36]
-    ax.scatter(x[inxs], y[inxs], color=colors['blue'], s=40, zorder=10)
+    ax.plot(x[inxs], y[inxs], zorder=10, **psA)
     xx = np.asarray([2, 118])
-    ax.plot(xx, m*xx+n, color=colors['red'], lw=2)
+    ax.plot(xx, m*xx+n, **lsBm)
     for i in inxs :
         xx = [x[i], x[i]]
         yy = [m*x[i]+n, y[i]]
-        ax.plot(xx, yy, color=colors['orange'], lw=2, zorder=5)
+        ax.plot(xx, yy, zorder=5, **lsDm)
     
 
 def plot_error_hist(ax, x, y, m, n):
@@ -51,7 +51,7 @@ def plot_error_hist(ax, x, y, m, n):
                 xytext=(350, 20), textcoords='data', ha='left',
                 arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
                 connectionstyle="angle3,angleA=10,angleB=90") )
-    ax.hist(errors, bins, color=colors['orange'])
+    ax.hist(errors, bins, **fsD)
 
 
 
diff --git a/statistics/lecture/cumulative.py b/statistics/lecture/cumulative.py
index 014805e..0cc0102 100644
--- a/statistics/lecture/cumulative.py
+++ b/statistics/lecture/cumulative.py
@@ -24,17 +24,17 @@ ax.set_ylim(-0.05, 1.05)
 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], zorder=-5, **lsMarker)
+ax.plot([-3.2, med, med], [0.5, 0.5, 0.0], zorder=-5, **lsSpine)
 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], zorder=-5, **lsMarker)
+ax.plot([-3.2, q3, q3], [0.75, 0.75, 0.0], zorder=-5, **lsSpine)
 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], zorder=-5, **lsMarker)
+ax.plot([-3.2, -1.0, -1.0], [p, p, 0.0], zorder=-5, **lsSpine)
 ax.text(-2.8, 0.2, 'F=%.2f' % p)
 ax.text(-0.9, 0.05, '-1')
 
diff --git a/statistics/lecture/diehistograms.py b/statistics/lecture/diehistograms.py
index 57283a1..637d420 100644
--- a/statistics/lecture/diehistograms.py
+++ b/statistics/lecture/diehistograms.py
@@ -25,6 +25,6 @@ ax2.set_xticks(range(1, 7))
 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=1, **lsAm)
-ax2.hist([x2, x1], bins, normed=True, zorder=10, **fs)
+ax2.plot([0.2, 6.8], [1.0/6.0, 1.0/6.0], zorder=-10, **lsAm)
+ax2.hist([x2, x1], bins, normed=True, zorder=-5, **fs)
 fig.savefig('diehistograms.pdf')
diff --git a/statistics/lecture/displayunivariatedata.py b/statistics/lecture/displayunivariatedata.py
index da2c347..38b0df4 100644
--- a/statistics/lecture/displayunivariatedata.py
+++ b/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, 1.2*figure_height))
+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))
 
@@ -53,7 +53,7 @@ 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'])
+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)
 
@@ -85,7 +85,7 @@ ax = fig.add_subplot(spec[0, 1])
 ax.set_yticklabels([])
 ax.set_ylim( 0.0, 8.0)
 ax.set_xticks(np.arange(0.0, 0.4, 0.1))
-ax.set_xlabel('(4) p(x)')
+ax.set_xlabel('(4) pdf')
 bw = 0.75
 bins = np.arange(0, 8.0+bw, bw)
 h, b = np.histogram(data, bins)
diff --git a/statistics/lecture/kerneldensity.py b/statistics/lecture/kerneldensity.py
index 19e06b9..14f6147 100644
--- a/statistics/lecture/kerneldensity.py
+++ b/statistics/lecture/kerneldensity.py
@@ -60,7 +60,7 @@ 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.set_ylabel('Probab. density p(x)')
+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)
diff --git a/statistics/lecture/statistics.tex b/statistics/lecture/statistics.tex
index 59a3c0f..e6945d2 100644
--- a/statistics/lecture/statistics.tex
+++ b/statistics/lecture/statistics.tex
@@ -115,17 +115,9 @@ function \mcode{median()} computes the median.
   writing reliable code!
 \end{exercise}
 
-\begin{figure}[t]
-  \includegraphics[width=1\textwidth]{quartile}
-  \titlecaption{\label{quartilefig} Median and quartiles of a normal
-    distribution.}{ The interquartile range between the first and the
-    third quartile contains 50\,\% of the data and contains the
-    median.}
-\end{figure}
-
 The distribution of data can be further characterized by the position
 of its \entermde[quartile]{Quartil}{quartiles}. Neighboring quartiles are
-separated by 25\,\% of the data (\figref{quartilefig}).
+separated by 25\,\% of the data.% (\figref{quartilefig}).
 \entermde[percentile]{Perzentil}{Percentiles} allow to characterize the
 distribution of the data in more detail. The 3$^{\rm rd}$ quartile
 corresponds to the 75$^{\rm th}$ percentile, because 75\,\% of the
@@ -156,15 +148,13 @@ median that extends from the 1$^{\rm st}$ to the 3$^{\rm rd}$
 quartile. The whiskers mark the minimum and maximum value of the data
 set (\figref{displayunivariatedatafig} (3)).
 
-\begin{exercise}{univariatedata.m}{}
-  Generate 40 normally distributed random numbers with a mean of 2 and
-  illustrate their distribution in a box-whisker plot
-  (\code{boxplot()} function), with a bar and errorbar illustrating
-  the mean and standard deviation (\code{bar()}, \code{errorbar()}),
-  and the data themselves jittered randomly (as in
-  \figref{displayunivariatedatafig}).  How to interpret the different
-  plots?
-\end{exercise}
+% \begin{figure}[t]
+%   \includegraphics[width=1\textwidth]{quartile}
+%   \titlecaption{\label{quartilefig} Median and quartiles of a normal
+%     distribution.}{ The interquartile range between the first and the
+%     third quartile contains 50\,\% of the data and contains the
+%     median.}
+% \end{figure}
 
 % \begin{exercise}{boxwhisker.m}{}
 %   Generate a $40 \times 10$ matrix of random numbers and
@@ -201,6 +191,16 @@ Histograms are often used to estimate the
 \enterm[probability!distribution]{probability distribution}
 (\determ[Wahrscheinlichkeits!-verteilung]{Wahrscheinlichkeitsverteilung}) of the data values.
 
+\begin{exercise}{univariatedata.m}{}
+  Generate 40 normally distributed random numbers with a mean of 2 and
+  illustrate their distribution in a box-whisker plot
+  (\code{boxplot()} function), with a bar and errorbar illustrating
+  the mean and standard deviation (\code{bar()}, \code{errorbar()}),
+  and the data themselves jittered randomly (as in
+  \figref{displayunivariatedatafig}).  How to interpret the different
+  plots?
+\end{exercise}
+
 \subsection{Probabilities}
 In the frequentist interpretation of probability, the
 \enterm{probability} (\determ{Wahrscheinlichkeit}) of an event
@@ -252,7 +252,7 @@ real number like, e.g., 0.123456789 is zero, because there are
 uncountable many real numbers.
 
 We can only ask for the probability to get a measurement value in some
-range.  For example, we can ask for the probability $P(1.2<x<1.3)$ to
+range.  For example, we can ask for the probability $P(0<x<1)$ to
 get a measurement between 0 and 1 (\figref{pdfprobabilitiesfig}). More
 generally, we want to know the probability $P(x_0<x<x_1)$ to obtain a
 measurement between $x_0$ and $x_1$. If we define the width of the
@@ -280,7 +280,7 @@ inverse of the unit of the data values --- hence the name ``density''.
 \end{figure}
 
 The probability to get a value $x$ between $x_1$ and $x_2$ is 
-given by the integral of the probability density:
+given by an integral over the probability density:
 \[ P(x_1 < x < x2) = \int\limits_{x_1}^{x_2} p(x) \, dx \; . \]
 Because the probability to get any value $x$ is one, the integral of
 the probability density over the whole real axis must be one:
@@ -329,7 +329,7 @@ values fall within each bin (\figref{pdfhistogramfig} left).
   observe?
 \end{exercise}
 
-To turn such histograms to estimates of probability densities they
+To turn such histograms into estimates of probability densities they
 need to be normalized such that according to \eqnref{pdfnorm} their
 integral equals one. While histograms of categorical data are
 normalized such that their sum equals one, here we need to integrate
@@ -343,7 +343,7 @@ and the
 \[ p(x_i) = \frac{n_i}{A} = \frac{n_i}{\Delta x \sum_{i=1}^N n_i} =
    \frac{n_i}{N \Delta x} \; .\]
 A histogram needs to be divided by both the sum of the frequencies
-$n_i$ and the bin width $\Delta x$ to results in an estimate of the
+$n_i$ and the bin width $\Delta x$ to result in an estimate of the
 corresponding probability density. Only then can the distribution be
 compared with other distributions and in particular with theoretical
 probability density functions like the one of the normal distribution
@@ -371,19 +371,20 @@ probability density functions like the one of the normal distribution
 A problem of using histograms for estimating probability densities is
 that they have hard bin edges. Depending on where the bin edges are
 placed a data value falls in one or the other bin. As a result the
-shape histogram depends on the exact position of its bins
-(\figref{kerneldensityfig} left).
+shape of the resulting histogram depends on the exact position of its
+bins (\figref{kerneldensityfig} left).
 
 \begin{figure}[t]
   \includegraphics[width=1\textwidth]{kerneldensity}
-  \titlecaption{\label{kerneldensityfig} Kernel densities.}{Left: The
-    histogram-based estimation of the probability density is dependent
-    on the position of the bins. In the bottom plot the bins have
+  \titlecaption{\label{kerneldensityfig} Kernel densities.}{The
+    histogram-based estimation of the probability density depends on
+    the position of the bins (left). In the bottom plot the bins have
     been shifted by half a bin width (here $\Delta x=0.4$) and as a
     result details of the probability density look different. Look,
-    for example, at the height of the largest bin. Right: In contrast,
-    a kernel density is uniquely defined for a given kernel width
-    (here Gaussian kernels with standard deviation of $\sigma=0.2$).}
+    for example, at the height of the largest bin. In contrast, a
+    kernel density is uniquely defined for a given kernel width
+    (right, Gaussian kernels with standard deviation of
+    $\sigma=0.2$).}
 \end{figure}
 
 To avoid this problem so called \entermde[kernel
@@ -460,7 +461,6 @@ and percentiles can be determined from the inverse cumulative function.
   Use the estimate to compute the value of the 5\,\% percentile.
 \end{exercise}
 
-\newpage
 \section{Correlations}
 
 Until now we described properties of univariate data sets.  In