Apply ax.colorbar() to examples

Author: timhoffm

galleries/examples/color/colorbar_basics.py

@@ -22,26 +22,20 @@
 
 fig, (ax1, ax2, ax3) = plt.subplots(figsize=(13, 3), ncols=3)
 
-# plot just the positive data and save the
-# color "mappable" object returned by ax1.imshow
-pos = ax1.imshow(Zpos, cmap='Blues', interpolation='none')
-
-# add the colorbar using the figure's method,
-# telling which mappable we're talking about and
-# which Axes object it should be near
-fig.colorbar(pos, ax=ax1)
+# plot just the positive data and add a colorbar
+ax1.imshow(Zpos, cmap='Blues', interpolation='none')
+ax1.colorbar()
 
 # repeat everything above for the negative data
 # you can specify location, anchor and shrink the colorbar
-neg = ax2.imshow(Zneg, cmap='Reds_r', interpolation='none')
-fig.colorbar(neg, ax=ax2, location='right', anchor=(0, 0.3), shrink=0.7)
+ax2.imshow(Zneg, cmap='Reds_r', interpolation='none')
+ax2.colorbar(location='right', anchor=(0, 0.3), shrink=0.7)
 
 # Plot both positive and negative values between +/- 1.2
-pos_neg_clipped = ax3.imshow(Z, cmap='RdBu', vmin=-1.2, vmax=1.2,
-                             interpolation='none')
+ax3.imshow(Z, cmap='RdBu', vmin=-1.2, vmax=1.2, interpolation='none')
 # Add minorticks on the colorbar to make it easy to read the
 # values off the colorbar.
-cbar = fig.colorbar(pos_neg_clipped, ax=ax3, extend='both')
+cbar = ax3.colorbar(extend='both')
 cbar.minorticks_on()
 plt.show()
 

galleries/examples/color/custom_cmap.py

@@ -122,9 +122,9 @@
     # Create the colormap
     cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bin)
     # Fewer bins will result in "coarser" colomap interpolation
-    im = ax.imshow(Z, origin='lower', cmap=cmap)
+    ax.imshow(Z, origin='lower', cmap=cmap)
+    ax.colorbar()
     ax.set_title("N bins: %s" % n_bin)
-    fig.colorbar(im, ax=ax)
 
 
 # %%
@@ -226,20 +226,20 @@
 fig, axs = plt.subplots(2, 2, figsize=(6, 9))
 fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)
 
-im1 = axs[0, 0].imshow(Z, cmap=blue_red1)
-fig.colorbar(im1, ax=axs[0, 0])
+axs[0, 0].imshow(Z, cmap=blue_red1)
+axs[0, 0].colorbar()
 
-im2 = axs[1, 0].imshow(Z, cmap='BlueRed2')
-fig.colorbar(im2, ax=axs[1, 0])
+axs[1, 0].imshow(Z, cmap='BlueRed2')
+axs[1, 0].colorbar()
 
 # Now we will set the third cmap as the default.  One would
 # not normally do this in the middle of a script like this;
 # it is done here just to illustrate the method.
 
 plt.rcParams['image.cmap'] = 'BlueRed3'
 
-im3 = axs[0, 1].imshow(Z)
-fig.colorbar(im3, ax=axs[0, 1])
+axs[0, 1].imshow(Z)
+axs[0, 1].colorbar()
 axs[0, 1].set_title("Alpha = 1")
 
 # Or as yet another variation, we can replace the rcParams
@@ -252,12 +252,12 @@
 # Draw a line with low zorder so it will be behind the image.
 axs[1, 1].plot([0, 10 * np.pi], [0, 20 * np.pi], color='c', lw=20, zorder=-1)
 
-im4 = axs[1, 1].imshow(Z)
-fig.colorbar(im4, ax=axs[1, 1])
+im = axs[1, 1].imshow(Z)
+axs[1, 1].colorbar()
 
 # Here it is: changing the colormap for the current image and its
 # colorbar after they have been plotted.
-im4.set_cmap('BlueRedAlpha')
+im.set_cmap('BlueRedAlpha')
 axs[1, 1].set_title("Varying alpha")
 
 fig.suptitle('Custom Blue-Red colormaps', fontsize=16)

galleries/examples/images_contours_and_fields/colormap_interactive_adjustment.py

@@ -24,10 +24,9 @@
 data2d = np.sin(t)[:, np.newaxis] * np.cos(t)[np.newaxis, :]
 
 fig, ax = plt.subplots()
-im = ax.imshow(data2d)
+ax.imshow(data2d)
+ax.colorbar(label='Interactive colorbar')
 ax.set_title('Pan on the colorbar to shift the color mapping\n'
              'Zoom on the colorbar to scale the color mapping')
 
-fig.colorbar(im, ax=ax, label='Interactive colorbar')
-
 plt.show()

galleries/examples/images_contours_and_fields/colormap_normalizations.py

@@ -32,12 +32,12 @@
 
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolor(X, Y, Z, cmap='PuBu_r', shading='nearest')
-fig.colorbar(pcm, ax=ax[0], extend='max', label='linear scaling')
+ax[0].pcolor(X, Y, Z, cmap='PuBu_r', shading='nearest')
+ax[0].colorbar(extend='max', label='linear scaling')
 
-pcm = ax[1].pcolor(X, Y, Z, cmap='PuBu_r', shading='nearest',
-                   norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()))
-fig.colorbar(pcm, ax=ax[1], extend='max', label='LogNorm')
+ax[1].pcolor(X, Y, Z, cmap='PuBu_r', shading='nearest',
+             norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()))
+ax[1].colorbar(extend='max', label='LogNorm')
 
 # %%
 # PowerNorm
@@ -53,12 +53,12 @@
 
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z, cmap='PuBu_r', shading='nearest')
-fig.colorbar(pcm, ax=ax[0], extend='max', label='linear scaling')
+ax[0].pcolormesh(X, Y, Z, cmap='PuBu_r', shading='nearest')
+ax[0].colorbar(extend='max', label='linear scaling')
 
-pcm = ax[1].pcolormesh(X, Y, Z, cmap='PuBu_r', shading='nearest',
-                       norm=colors.PowerNorm(gamma=0.5))
-fig.colorbar(pcm, ax=ax[1], extend='max', label='PowerNorm')
+ax[1].pcolormesh(X, Y, Z, cmap='PuBu_r', shading='nearest',
+                 norm=colors.PowerNorm(gamma=0.5))
+ax[1].colorbar(extend='max', label='PowerNorm')
 
 # %%
 # SymLogNorm
@@ -79,14 +79,13 @@
 
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
-                       vmin=-np.max(Z))
-fig.colorbar(pcm, ax=ax[0], extend='both', label='linear scaling')
+ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest', vmin=-np.max(Z))
+ax[0].colorbar(extend='both', label='linear scaling')
 
-pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
-                       norm=colors.SymLogNorm(linthresh=0.015,
-                                              vmin=-10.0, vmax=10.0, base=10))
-fig.colorbar(pcm, ax=ax[1], extend='both', label='SymLogNorm')
+ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
+                 norm=colors.SymLogNorm(linthresh=0.015,
+                                        vmin=-10.0, vmax=10.0, base=10))
+ax[1].colorbar(extend='both', label='SymLogNorm')
 
 # %%
 # Custom Norm
@@ -112,13 +111,12 @@ def __call__(self, value, clip=None):
 # %%
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
-                       vmin=-np.max(Z))
-fig.colorbar(pcm, ax=ax[0], extend='both', label='linear scaling')
+ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest', vmin=-np.max(Z))
+ax[0].colorbar(extend='both', label='linear scaling')
 
-pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
-                       norm=MidpointNormalize(midpoint=0))
-fig.colorbar(pcm, ax=ax[1], extend='both', label='Custom norm')
+ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
+                 norm=MidpointNormalize(midpoint=0))
+ax[1].colorbar(extend='both', label='Custom norm')
 
 # %%
 # BoundaryNorm
@@ -129,25 +127,22 @@ def __call__(self, value, clip=None):
 
 fig, ax = plt.subplots(3, 1, layout='constrained')
 
-pcm = ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
-                       vmin=-np.max(Z))
-fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical',
-             label='linear scaling')
+ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest', vmin=-np.max(Z))
+ax[0].colorbar(extend='both', orientation='vertical', label='linear scaling')
 
 # Evenly-spaced bounds gives a contour-like effect.
 bounds = np.linspace(-2, 2, 11)
 norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
+ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
                        norm=norm)
-fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical',
-             label='BoundaryNorm\nlinspace(-2, 2, 11)')
+ax[1].colorbar(extend='both', orientation='vertical',
+               label='BoundaryNorm\nlinspace(-2, 2, 11)')
 
 # Unevenly-spaced bounds changes the colormapping.
 bounds = np.array([-1, -0.5, 0, 2.5, 5])
 norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest',
-                       norm=norm)
-fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical',
-             label='BoundaryNorm\n[-1, -0.5, 0, 2.5, 5]')
+ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', shading='nearest', norm=norm)
+ax[2].colorbar(extend='both', orientation='vertical',
+               label='BoundaryNorm\n[-1, -0.5, 0, 2.5, 5]')
 
 plt.show()

galleries/examples/images_contours_and_fields/colormap_normalizations_symlognorm.py

@@ -40,16 +40,14 @@ def rbf(x, y):
 
 fig, ax = plt.subplots(2, 1, sharex=True, sharey=True)
 
-pcm = ax[0].pcolormesh(X, Y, Z,
-                       norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
-                                              vmin=-gain, vmax=gain, base=10),
-                       **shadeopts)
-fig.colorbar(pcm, ax=ax[0], extend='both')
+ax[0].pcolormesh(X, Y, Z, norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
+                                                 vmin=-gain, vmax=gain, base=10),
+                 **shadeopts)
+ax[0].colorbar(extend='both')
 ax[0].text(-2.5, 1.5, 'symlog')
 
-pcm = ax[1].pcolormesh(X, Y, Z, vmin=-gain, vmax=gain,
-                       **shadeopts)
-fig.colorbar(pcm, ax=ax[1], extend='both')
+ax[1].pcolormesh(X, Y, Z, vmin=-gain, vmax=gain, **shadeopts)
+ax[1].colorbar(extend='both')
 ax[1].text(-2.5, 1.5, 'linear')
 
 
@@ -67,18 +65,17 @@ def rbf(x, y):
 
 fig, ax = plt.subplots(2, 1, sharex=True, sharey=True)
 
-pcm = ax[0].pcolormesh(X, Y, Z,
-                       norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
-                                              vmin=-gain, vmax=gain, base=10),
-                       **shadeopts)
-fig.colorbar(pcm, ax=ax[0], extend='both')
+ax[0].pcolormesh(X, Y, Z,
+                 norm=colors.SymLogNorm(linthresh=lnrwidth, linscale=1,
+                                        vmin=-gain, vmax=gain, base=10),
+                 **shadeopts)
+ax[0].colorbar(extend='both')
 ax[0].text(-2.5, 1.5, 'symlog')
 
-pcm = ax[1].pcolormesh(X, Y, Z,
-                       norm=colors.AsinhNorm(linear_width=lnrwidth,
-                                             vmin=-gain, vmax=gain),
-                       **shadeopts)
-fig.colorbar(pcm, ax=ax[1], extend='both')
+ax[1].pcolormesh(X, Y, Z,
+                 norm=colors.AsinhNorm(linear_width=lnrwidth, vmin=-gain, vmax=gain),
+                 **shadeopts)
+ax[1].colorbar(extend='both')
 ax[1].text(-2.5, 1.5, 'asinh')
 
 

galleries/examples/images_contours_and_fields/contour_image.py

@@ -65,8 +65,7 @@
 
 cset3 = axs[0].contour(X, Y, Z, (0,), colors='g', linewidths=2)
 axs[0].set_title('Filled contours')
-fig.colorbar(cset1, ax=axs[0])
-
+axs[0].colorbar(cset1)
 
 axs[1].imshow(Z, extent=extent, cmap=cmap, norm=norm)
 axs[1].contour(Z, levels, colors='k', origin='upper', extent=extent)
@@ -82,13 +81,12 @@
 # This is intentional. The Z values are defined at the center of each
 # image pixel (each color block on the following subplot), so the
 # domain that is contoured does not extend beyond these pixel centers.
-im = axs[3].imshow(Z, interpolation='nearest', extent=extent,
-                   cmap=cmap, norm=norm)
+im = axs[3].imshow(Z, interpolation='nearest', extent=extent, cmap=cmap, norm=norm)
 axs[3].contour(Z, levels, colors='k', origin='image', extent=extent)
 ylim = axs[3].get_ylim()
 axs[3].set_ylim(ylim[::-1])
 axs[3].set_title("Origin from rc, reversed y-axis")
-fig.colorbar(im, ax=axs[3])
+axs[3].colorbar(im)
 
 fig.tight_layout()
 plt.show()

galleries/examples/images_contours_and_fields/contourf_demo.py

@@ -96,8 +96,8 @@
 fig, axs = plt.subplots(2, 2, layout="constrained")
 
 for ax, extend in zip(axs.flat, extends):
-    cs = ax.contourf(X, Y, Z, levels, cmap=cmap, extend=extend)
-    fig.colorbar(cs, ax=ax, shrink=0.9)
+    ax.contourf(X, Y, Z, levels, cmap=cmap, extend=extend)
+    ax.colorbar(shrink=0.9)
     ax.set_title("extend = %s" % extend)
     ax.locator_params(nbins=4)
 

galleries/examples/images_contours_and_fields/contourf_log.py

@@ -36,16 +36,16 @@
 # Automatic selection of levels works; setting the
 # log locator tells contourf to use a log scale:
 fig, ax = plt.subplots()
-cs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap="PuBu_r")
+ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap="PuBu_r")
 
 # Alternatively, you can manually set the levels
 # and the norm:
 # lev_exp = np.arange(np.floor(np.log10(z.min())-1),
 #                    np.ceil(np.log10(z.max())+1))
 # levs = np.power(10, lev_exp)
-# cs = ax.contourf(X, Y, z, levs, norm=colors.LogNorm())
+# ax.contourf(X, Y, z, levs, norm=colors.LogNorm())
 
-cbar = fig.colorbar(cs)
+ax.colorbar()
 
 plt.show()
 

galleries/examples/images_contours_and_fields/image_masked.py

@@ -42,29 +42,26 @@
 fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 5.4))
 
 # plot using 'continuous' colormap
-im = ax1.imshow(Zm, interpolation='bilinear',
-                cmap=palette,
-                norm=colors.Normalize(vmin=-1.0, vmax=1.0),
-                aspect='auto',
-                origin='lower',
-                extent=[x0, x1, y0, y1])
+ax1.imshow(Zm, interpolation='bilinear',
+           cmap=palette,
+           norm=colors.Normalize(vmin=-1.0, vmax=1.0),
+           aspect='auto',
+           origin='lower',
+           extent=[x0, x1, y0, y1])
+ax1.colorbar(extend='both', shrink=0.9, label='uniform')
 ax1.set_title('Green=low, Red=high, Blue=masked')
-cbar = fig.colorbar(im, extend='both', shrink=0.9, ax=ax1)
-cbar.set_label('uniform')
 ax1.tick_params(axis='x', labelbottom=False)
 
 # Plot using a small number of colors, with unevenly spaced boundaries.
-im = ax2.imshow(Zm, interpolation='nearest',
-                cmap=palette,
-                norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
-                                         ncolors=palette.N),
-                aspect='auto',
-                origin='lower',
-                extent=[x0, x1, y0, y1])
+ax2.imshow(Zm, interpolation='nearest',
+           cmap=palette,
+           norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
+                                    ncolors=palette.N),
+           aspect='auto',
+           origin='lower',
+           extent=[x0, x1, y0, y1])
+ax2.colorbar(extend='both', spacing='proportional', shrink=0.9, label='proportional')
 ax2.set_title('With BoundaryNorm')
-cbar = fig.colorbar(im, extend='both', spacing='proportional',
-                    shrink=0.9, ax=ax2)
-cbar.set_label('proportional')
 
 fig.suptitle('imshow, with out-of-range and masked data')
 plt.show()

galleries/examples/images_contours_and_fields/irregulardatagrid.py

@@ -59,7 +59,7 @@
 ax1.contour(xi, yi, zi, levels=14, linewidths=0.5, colors='k')
 cntr1 = ax1.contourf(xi, yi, zi, levels=14, cmap="RdBu_r")
 
-fig.colorbar(cntr1, ax=ax1)
+ax1.colorbar(cntr1)
 ax1.plot(x, y, 'ko', ms=3)
 ax1.set(xlim=(-2, 2), ylim=(-2, 2))
 ax1.set_title('grid and contour (%d points, %d grid points)' %
@@ -74,7 +74,7 @@
 ax2.tricontour(x, y, z, levels=14, linewidths=0.5, colors='k')
 cntr2 = ax2.tricontourf(x, y, z, levels=14, cmap="RdBu_r")
 
-fig.colorbar(cntr2, ax=ax2)
+ax2.colorbar(cntr2)
 ax2.plot(x, y, 'ko', ms=3)
 ax2.set(xlim=(-2, 2), ylim=(-2, 2))
 ax2.set_title('tricontour (%d points)' % npts)