[regression] updated plot formats

plotstyle.py

@@ -149,22 +149,31 @@ def show_spines(ax, spines):
             ax.yaxis.set_ticks_position('both')
 
 
-def set_xlabel(ax, label, unit=None):
+def set_xlabel(ax, label, unit=None, **kwargs):
     if not unit:
-        ax.set_xlabel(label)
+        ax.set_xlabel(label, **kwargs)
     elif xkcd_style:
-        ax.set_xlabel('%s / %s' % (label, unit))
+        ax.set_xlabel('%s / %s' % (label, unit), **kwargs)
     else:
-        ax.set_xlabel('%s [%s]' % (label, unit))
+        ax.set_xlabel('%s [%s]' % (label, unit), **kwargs)
 
         
-def set_ylabel(ax, label, unit=None):
+def set_ylabel(ax, label, unit=None, **kwargs):
     if not unit:
-        ax.set_ylabel(label)
+        ax.set_ylabel(label, **kwargs)
     elif xkcd_style:
-        ax.set_ylabel('%s / %s' % (label, unit))
+        ax.set_ylabel('%s / %s' % (label, unit), **kwargs)
     else:
-        ax.set_ylabel('%s [%s]' % (label, unit))
+        ax.set_ylabel('%s [%s]' % (label, unit), **kwargs)
+
+        
+def set_zlabel(ax, label, unit=None, **kwargs):
+    if not unit:
+        ax.set_zlabel(label, **kwargs)
+    elif xkcd_style:
+        ax.set_zlabel('%s / %s' % (label, unit), **kwargs)
+    else:
+        ax.set_zlabel('%s [%s]' % (label, unit), **kwargs)
 
             
 # initialization:

regression/lecture/cubicerrors.py

@@ -1,6 +1,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
-from plotstyle import colors, show_spines
+from plotstyle import *
 
 def create_data():
     # wikipedia:
@@ -23,8 +23,8 @@ def plot_data(ax, x, y, c):
         ax.plot(xx, cc*xx**3.0, color=colors['orange'], lw=1.5, zorder=5)
 
     show_spines(ax, 'lb')
-    ax.set_xlabel('Size x / m')
+    set_xlabel(ax, 'Size x', 'm')
-    ax.set_ylabel('Weight y / kg')
+    set_ylabel(ax, 'Weight y', 'kg')
     ax.set_xlim(2, 4)
     ax.set_ylim(0, 400)
     ax.set_xticks(np.arange(2.0, 4.1, 0.5))
@@ -33,8 +33,8 @@ def plot_data(ax, x, y, c):
     
 def plot_data_errors(ax, x, y, c):
     show_spines(ax, 'lb')
-    ax.set_xlabel('Size x / m')
+    set_xlabel(ax, 'Size x', 'm')
-    #ax.set_ylabel('Weight y / kg')
+    #set_ylabel(ax, 'Weight y', 'kg')
     ax.set_xlim(2, 4)
     ax.set_ylim(0, 400)
     ax.set_xticks(np.arange(2.0, 4.1, 0.5))
@@ -57,8 +57,8 @@ def plot_data_errors(ax, x, y, c):
 
 def plot_error_hist(ax, x, y, c):
     show_spines(ax, 'lb')
-    ax.set_xlabel('Squared error')
+    set_xlabel(ax, 'Squared error')
-    ax.set_ylabel('Frequency')
+    set_ylabel(ax, 'Frequency')
     bins = np.arange(0.0, 1250.0, 100)
     ax.set_xlim(bins[0], bins[-1])
     #ax.set_ylim(0, 35)
@@ -77,10 +77,10 @@ def plot_error_hist(ax, x, y, c):
 
 if __name__ == "__main__":
     x, y, c = create_data()
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(7., 2.6))
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    fig.subplots_adjust(wspace=0.2, **adjust_fs(left=6.0, right=1.2))
     plot_data(ax1, x, y, c)
     plot_data_errors(ax2, x, y, c)
     #plot_error_hist(ax2, x, y, c)
-    fig.tight_layout()
     fig.savefig("cubicerrors.pdf")
     plt.close()

regression/lecture/cubicfunc.py

@@ -1,6 +1,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
-from plotstyle import colors, show_spines
+from plotstyle import *
 
 if __name__ == "__main__":
     # wikipedia:
@@ -13,7 +13,8 @@ if __name__ == "__main__":
     noise = rng.randn(len(x))*50
     y += noise
     
-    fig, ax = plt.subplots(figsize=(7., 3.6))
+    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)
     xx = np.linspace(2.1, 3.9, 100)
@@ -22,13 +23,12 @@ if __name__ == "__main__":
         ax.plot(xx, cc*xx**3.0, color=colors['orange'], lw=2, zorder=5)
     
     show_spines(ax, 'lb')
-    ax.set_xlabel('Size x / m')
+    set_xlabel(ax, 'Size x', 'm')
-    ax.set_ylabel('Weight y / kg')
+    set_ylabel(ax, 'Weight y', 'kg')
     ax.set_xlim(2, 4)
     ax.set_ylim(0, 400)
     ax.set_xticks(np.arange(2.0, 4.1, 0.5))
     ax.set_yticks(np.arange(0, 401, 100))
     
-    fig.subplots_adjust(0.11, 0.16, 0.98, 0.97)
     fig.savefig("cubicfunc.pdf")
     plt.close()

regression/lecture/cubicmse.py

@@ -1,6 +1,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
-from plotstyle import colors, show_spines
+from plotstyle import *
 
 def create_data():
     # wikipedia:
@@ -51,8 +51,8 @@ def plot_mse(ax, x, y, c, cs):
         
     
     show_spines(ax, 'lb')
-    ax.set_xlabel('c')
+    set_xlabel(ax, 'c')
-    ax.set_ylabel('mean squared error')
+    set_ylabel(ax, 'Mean squared error')
     ax.set_xlim(0, 10)
     ax.set_ylim(0, 25000)
     ax.set_xticks(np.arange(0.0, 10.1, 2.0))
@@ -62,8 +62,8 @@ def plot_descent(ax, cs, mses):
     ax.plot(np.arange(len(mses))+1, mses, '-o', c=colors['red'], mew=0, ms=8)
     
     show_spines(ax, 'lb')
-    ax.set_xlabel('iteration')
+    set_xlabel(ax, 'Iteration')
-    #ax.set_ylabel('mean squared error')
+    #set_ylabel(ax, 'Mean squared error')
     ax.set_xlim(0, 10.5)
     ax.set_ylim(0, 25000)
     ax.set_xticks(np.arange(0.0, 10.1, 2.0))
@@ -74,9 +74,9 @@ def plot_descent(ax, cs, mses):
 if __name__ == "__main__":
     x, y, c = create_data()
     cs, mses = gradient_descent(x, y)
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(7., 2.6))
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    fig.subplots_adjust(wspace=0.2, **adjust_fs(left=7.5, right=0.5))
     plot_mse(ax1, x, y, c, cs)
     plot_descent(ax2, cs, mses)
-    fig.tight_layout()
     fig.savefig("cubicmse.pdf")
     plt.close()

regression/lecture/derivative.py

@@ -1,7 +1,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from plotstyle import *
 
-#plt.xkcd()
 fig = plt.figure( figsize=(2.5,3.4) )
 ax = fig.add_subplot(1, 1, 1)
 
@@ -12,7 +12,7 @@ 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='b', lw=4, zorder=0)
+ax.plot(x, y, c=colors['blue'], lw=4, zorder=0)
 # secant:
 x = np.asarray([0.1, 0.7])
 y = x*x
@@ -20,7 +20,7 @@ ax.set_xticks(x)
 ax.set_yticks(y)
 ax.set_xticklabels(['$x$','$x+\Delta x$'])
 ax.set_yticklabels(['',''])
-ax.scatter(x, y, c='#CC0000', edgecolor='none', s=150, zorder=10)
+ax.scatter(x, y, c=colors['red'], edgecolor='none', s=150, zorder=10)
 # 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)
@@ -34,20 +34,20 @@ ax.text(x[1]+0.05, np.mean(y), '$f(x+\Delta x)-f(x)$', va='center', rotation='ve
 m = np.diff(y)/np.diff(x)
 xl = [x1, x2]
 yl = m*(xl-x[0])+y[0]
-ax.plot(xl, yl, c='#CC0000', lw=3, zorder=7)
+ax.plot(xl, yl, c=colors['red'], lw=3, zorder=7)
 
 # derivative:
 md = 2.0*x[0]
 yl = md*(xl-x[0])+y[0]
-ax.plot(xl, yl, c='#FFFF66', lw=3, zorder=5)
+ax.plot(xl, yl, c=colors['yellow'], lw=3, zorder=5)
 
 # 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='#FFCC00', edgecolor='none', s=80, zorder=3)
+    ax.scatter([xs], [xs*xs], c=colors['orange'], edgecolor='none', s=80, zorder=3)
-    ax.plot(xl, yl, c='#FFCC00', lw=2, zorder=3)
+    ax.plot(xl, yl, c=colors['orange'], lw=2, zorder=3)
 
-plt.tight_layout();
+fig.subplots_adjust(**adjust_fs(fig, 0.5, 0.5, 1.4, 0.5))
 plt.savefig('derivative.pdf')
 #plt.show();

regression/lecture/error_surface.py

@@ -1,7 +1,8 @@
+import numpy as np
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
-import numpy as np
+from plotstyle import *
 
 def create_data():
     m = 0.75
@@ -15,9 +16,9 @@ def create_data():
     
 
 def plot_error_plane(ax, x, y, m, n):
-    ax.set_xlabel('Slope m')
+    set_xlabel(ax, 'Slope m')
-    ax.set_ylabel('Intercept b')
+    set_ylabel(ax, 'Intercept b')
-    ax.set_zlabel('Mean squared error')
+    set_zlabel(ax, 'Mean squared error')
     ax.set_xlim(-4.5, 5.0)
     ax.set_ylim(-60.0, -20.0)
     ax.set_zlim(0.0, 700.0)
@@ -46,12 +47,10 @@ def plot_error_plane(ax, x, y, m, n):
 
 if __name__ == "__main__":
     x, y, m, n = create_data()
-    plt.xkcd()
     fig = plt.figure()
     ax = fig.add_subplot(1, 1, 1, projection='3d')
     plot_error_plane(ax, x, y, m, n)
-    #fig.set_facecolor("white")
     fig.set_size_inches(7., 5.)
-    fig.tight_layout()
+    fig.subplots_adjust(**adjust_fs(fig, 1.0, 0.0, 0.0, 0.0))
     fig.savefig("error_surface.pdf")
     plt.close()

regression/lecture/gradient.py

@@ -1,9 +1,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
+from plotstyle import *
 
-#plt.xkcd()
+fig, ax = plt.subplots( figsize=(3.5,3.7) )
-fig = plt.figure( figsize=(3.5,3.7) )
+fig.subplots_adjust(**adjust_fs(fig, 4.0, 1.0, 2.8, 0.5))
-ax = fig.add_subplot(1, 1, 1)
 
 def gaussian(x, y) :
     return np.exp(-0.5*(x*x+y*y))
@@ -18,8 +18,8 @@ x = np.linspace(x1, x2, 200)
 y = np.linspace(x1, x2, 200)
 X, Y = np.meshgrid(x, y)
 Z = gaussian(X, Y)
-ax.set_xlabel('x')
+set_xlabel(ax, 'x')
-ax.set_ylabel('y', rotation='horizontal')
+set_ylabel(ax, 'y', rotation='horizontal')
 ax.set_xticks(np.arange(x1, x2+0.1, 1.0))
 ax.set_yticks(np.arange(x1, x2+0.1, 1.0))
 ax.contour(X, Y, Z, linewidths=2, zorder=0)
@@ -37,6 +37,4 @@ ax.text(-0.8, -1.5, '$\partial f/\partial x$', ha='center', zorder=20)
 ax.text(-1.55, -0.8, '$\partial f/\partial y$', va='center', rotation='vertical', zorder=20)
 ax.text(-0.4, -0.8, r'$\nabla f$', ha='left', zorder=20)
 
-plt.tight_layout();
 plt.savefig('gradient.pdf')
-#plt.show();

regression/lecture/lin_regress.py

@@ -1,5 +1,6 @@
-import matplotlib.pyplot as plt
 import numpy as np
+import matplotlib.pyplot as plt
+from plotstyle import *
 
 def create_data():
     m = 0.75
@@ -13,15 +14,10 @@ def create_data():
 
     
 def plot_data(ax, x, y):
-    ax.scatter(x, y, marker='o', color='b', s=40)
+    ax.scatter(x, y, marker='o', color=colors['blue'], s=40)
-    ax.spines["right"].set_visible(False)
+    show_spines(ax, 'lb')
-    ax.spines["top"].set_visible(False)
+    set_xlabel(ax, 'Input x')
-    ax.yaxis.set_ticks_position('left')
+    set_ylabel(ax, 'Output y')
-    ax.xaxis.set_ticks_position('bottom')
-    ax.tick_params(direction="out", width=1.25)
-    ax.tick_params(direction="out", width=1.25)
-    ax.set_xlabel('Input x')
-    ax.set_ylabel('Output y')
     ax.set_xlim(0, 120)
     ax.set_ylim(-80, 80)
     ax.set_xticks(np.arange(0,121, 40))
@@ -29,18 +25,13 @@ def plot_data(ax, x, y):
     
 
 def plot_data_slopes(ax, x, y, m, n):
-    ax.scatter(x, y, marker='o', color='b', s=40)
+    ax.scatter(x, y, marker='o', color=colors['blue'], s=40)
     xx = np.asarray([2, 118])
     for i in np.linspace(0.3*m, 2.0*m, 5):
-        ax.plot(xx, i*xx+n, color='#CC0000', lw=2)
+        ax.plot(xx, i*xx+n, color=colors['red'], lw=2)
-    ax.spines["right"].set_visible(False)
+    show_spines(ax, 'lb')
-    ax.spines["top"].set_visible(False)
+    set_xlabel(ax, 'Input x')
-    ax.yaxis.set_ticks_position('left')
+    #set_ylabel(ax, 'Output y')
-    ax.xaxis.set_ticks_position('bottom')
-    ax.tick_params(direction="out", width=1.25)
-    ax.tick_params(direction="out", width=1.25)
-    ax.set_xlabel('Input x')
-    #ax.set_ylabel('Output y')
     ax.set_xlim(0, 120)
     ax.set_ylim(-80, 80)
     ax.set_xticks(np.arange(0,121, 40))
@@ -48,18 +39,13 @@ 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='b', s=40)
+    ax.scatter(x, y, marker='o', color=colors['blue'], s=40)
     xx = np.asarray([2, 118])
     for i in np.linspace(n-1*n, n+1*n, 5):
-        ax.plot(xx, m*xx + i, color='#CC0000', lw=2)
+        ax.plot(xx, m*xx + i, color=colors['red'], lw=2)
-    ax.spines["right"].set_visible(False)
+    show_spines(ax, 'lb')
-    ax.spines["top"].set_visible(False)
+    set_xlabel(ax, 'Input x')
-    ax.yaxis.set_ticks_position('left')
+    #set_ylabel(ax, 'Output y')
-    ax.xaxis.set_ticks_position('bottom')
-    ax.tick_params(direction="out", width=1.25)
-    ax.tick_params(direction="out", width=1.25)
-    ax.set_xlabel('Input x')
-    #ax.set_ylabel('Output y')
     ax.set_xlim(0, 120)
     ax.set_ylim(-80, 80)
     ax.set_xticks(np.arange(0,121, 40))
@@ -68,16 +54,10 @@ def plot_data_intercepts(ax, x, y, m, n):
 
 if __name__ == "__main__":
     x, y, m, n = create_data()
-    plt.xkcd()
+    fig, axs = plt.subplots(1, 3)
-    fig = plt.figure()
+    fig.subplots_adjust(wspace=0.5, **adjust_fs(fig, left=6.0, right=1.5))
-    ax = fig.add_subplot(1, 3, 1)
+    plot_data(axs[0], x, y)
-    plot_data(ax, x, y)
+    plot_data_slopes(axs[1], x, y, m, n)
-    ax = fig.add_subplot(1, 3, 2)
+    plot_data_intercepts(axs[2], x, y, m, n)
-    plot_data_slopes(ax, x, y, m, n)
-    ax = fig.add_subplot(1, 3, 3)
-    plot_data_intercepts(ax, x, y, m, n)
-    fig.set_facecolor("white")
-    fig.set_size_inches(7., 2.6)
-    fig.tight_layout()
     fig.savefig("lin_regress.pdf")
     plt.close()

regression/lecture/linear_least_squares.py

@@ -1,5 +1,6 @@
-import matplotlib.pyplot as plt
 import numpy as np
+import matplotlib.pyplot as plt
+from plotstyle import *
 
 def create_data():
     m = 0.75
@@ -13,14 +14,9 @@ def create_data():
 
     
 def plot_data(ax, x, y, m, n):
-    ax.spines["right"].set_visible(False)
+    show_spines(ax, 'lb')
-    ax.spines["top"].set_visible(False)
+    set_xlabel(ax, 'Input x')
-    ax.yaxis.set_ticks_position('left')
+    set_ylabel(ax, 'Output y')
-    ax.xaxis.set_ticks_position('bottom')
-    ax.tick_params(direction="out", width=1.25)
-    ax.tick_params(direction="out", width=1.25)
-    ax.set_xlabel('Input x')
-    ax.set_ylabel('Output y')
     ax.set_xlim(0, 120)
     ax.set_ylim(-80, 80)
     ax.set_xticks(np.arange(0,121, 40))
@@ -30,24 +26,19 @@ 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='b', s=10, zorder=0)
+    ax.scatter(x[:40], y[:40], color=colors['blue'], s=10, zorder=0)
     inxs = [3, 13, 16, 19, 25, 34, 36]
-    ax.scatter(x[inxs], y[inxs], color='b', s=40, zorder=10)
+    ax.scatter(x[inxs], y[inxs], color=colors['blue'], s=40, zorder=10)
     xx = np.asarray([2, 118])
-    ax.plot(xx, m*xx+n, color='#CC0000', lw=2)
+    ax.plot(xx, m*xx+n, color=colors['red'], lw=2)
     for i in inxs :
         xx = [x[i], x[i]]
         yy = [m*x[i]+n, y[i]]
-        ax.plot(xx, yy, color='#FF9900', lw=2, zorder=5)
+        ax.plot(xx, yy, color=colors['orange'], lw=2, zorder=5)
     
 
 def plot_error_hist(ax, x, y, m, n):
-    ax.spines["right"].set_visible(False)
+    show_spines(ax, 'lb')
-    ax.spines["top"].set_visible(False)
-    ax.yaxis.set_ticks_position('left')
-    ax.xaxis.set_ticks_position('bottom')
-    ax.tick_params(direction="out", width=1.25)
-    ax.tick_params(direction="out", width=1.25)
     ax.set_xlabel('Squared error')
     ax.set_ylabel('Frequency')
     bins = np.arange(0.0, 602.0, 50.0)
@@ -62,20 +53,15 @@ 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='#FF9900')
+    ax.hist(errors, bins, color=colors['orange'])
 
 
 
 if __name__ == "__main__":
     x, y, m, n = create_data()
-    plt.xkcd()
+    fig, axs = plt.subplots(1, 2)
-    fig = plt.figure()
+    fig.subplots_adjust(**adjust_fs(fig, left=6.0))
-    ax = fig.add_subplot(1, 2, 1)
+    plot_data(axs[0], x, y, m, n)
-    plot_data(ax, x, y, m, n)
+    plot_error_hist(axs[1], x, y, m, n)
-    ax = fig.add_subplot(1, 2, 2)
-    plot_error_hist(ax, x, y, m, n)
-    fig.set_facecolor("white")
-    fig.set_size_inches(7., 2.6)
-    fig.tight_layout()
     fig.savefig("linear_least_squares.pdf")
     plt.close()