[plotstyle] set_[xyz]label() functions are set as members of axes

regression/lecture/cubicerrors.py

@@ -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')
-    set_xlabel(ax, 'Size x', 'm')
-    set_ylabel(ax, 'Weight y', 'kg')
+    ax.set_xlabel('Size x', 'm')
+    ax.set_ylabel('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')
-    set_xlabel(ax, 'Size x', 'm')
-    #set_ylabel(ax, 'Weight y', 'kg')
+    ax.set_xlabel('Size x', 'm')
+    #ax.set_ylabel('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')
-    set_xlabel(ax, 'Squared error')
-    set_ylabel(ax, 'Frequency')
+    ax.set_xlabel('Squared error')
+    ax.set_ylabel('Frequency')
     bins = np.arange(0.0, 1250.0, 100)
     ax.set_xlim(bins[0], bins[-1])
     #ax.set_ylim(0, 35)

regression/lecture/cubicfunc.py

@@ -23,8 +23,8 @@ if __name__ == "__main__":
         ax.plot(xx, cc*xx**3.0, color=colors['orange'], lw=2, zorder=5)
     
     show_spines(ax, 'lb')
-    set_xlabel(ax, 'Size x', 'm')
-    set_ylabel(ax, 'Weight y', 'kg')
+    ax.set_xlabel('Size x', 'm')
+    ax.set_ylabel('Weight y', 'kg')
     ax.set_xlim(2, 4)
     ax.set_ylim(0, 400)
     ax.set_xticks(np.arange(2.0, 4.1, 0.5))

regression/lecture/cubicmse.py

@@ -51,8 +51,8 @@ def plot_mse(ax, x, y, c, cs):
         
     
     show_spines(ax, 'lb')
-    set_xlabel(ax, 'c')
-    set_ylabel(ax, 'Mean squared error')
+    ax.set_xlabel('c')
+    ax.set_ylabel('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')
-    set_xlabel(ax, 'Iteration')
-    #set_ylabel(ax, 'Mean squared error')
+    ax.set_xlabel('Iteration')
+    #ax.set_ylabel('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))
@@ -75,7 +75,7 @@ if __name__ == "__main__":
     x, y, c = create_data()
     cs, mses = gradient_descent(x, y)
     fig, (ax1, ax2) = plt.subplots(1, 2)
-    fig.subplots_adjust(wspace=0.2, **adjust_fs(left=7.5, right=0.5))
+    fig.subplots_adjust(wspace=0.2, **adjust_fs(left=8.0, right=0.5))
     plot_mse(ax1, x, y, c, cs)
     plot_descent(ax2, cs, mses)
     fig.savefig("cubicmse.pdf")

regression/lecture/error_surface.py

@@ -16,9 +16,9 @@ def create_data():
     
 
 def plot_error_plane(ax, x, y, m, n):
-    set_xlabel(ax, 'Slope m')
-    set_ylabel(ax, 'Intercept b')
-    set_zlabel(ax, 'Mean squared error')
+    ax.set_xlabel('Slope m')
+    ax.set_ylabel('Intercept b')
+    ax.set_zlabel('Mean squared error')
     ax.set_xlim(-4.5, 5.0)
     ax.set_ylim(-60.0, -20.0)
     ax.set_zlim(0.0, 700.0)

regression/lecture/gradient.py

@@ -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)
-set_xlabel(ax, 'x')
-set_ylabel(ax, 'y', rotation='horizontal')
+ax.set_xlabel('x')
+ax.set_ylabel('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)

regression/lecture/lin_regress.py

@@ -16,8 +16,8 @@ def create_data():
 def plot_data(ax, x, y):
     ax.scatter(x, y, marker='o', color=colors['blue'], s=40)
     show_spines(ax, 'lb')
-    set_xlabel(ax, 'Input x')
-    set_ylabel(ax, 'Output y')
+    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))
@@ -30,8 +30,8 @@ def plot_data_slopes(ax, x, y, m, n):
     for i in np.linspace(0.3*m, 2.0*m, 5):
         ax.plot(xx, i*xx+n, color=colors['red'], lw=2)
     show_spines(ax, 'lb')
-    set_xlabel(ax, 'Input x')
-    #set_ylabel(ax, 'Output y')
+    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))
@@ -44,8 +44,8 @@ def plot_data_intercepts(ax, x, y, m, n):
     for i in np.linspace(n-1*n, n+1*n, 5):
         ax.plot(xx, m*xx + i, color=colors['red'], lw=2)
     show_spines(ax, 'lb')
-    set_xlabel(ax, 'Input x')
-    #set_ylabel(ax, 'Output y')
+    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))

regression/lecture/linear_least_squares.py

@@ -15,8 +15,8 @@ def create_data():
     
 def plot_data(ax, x, y, m, n):
     show_spines(ax, 'lb')
-    set_xlabel(ax, 'Input x')
-    set_ylabel(ax, 'Output y')
+    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))