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POCreating a colour bar for a plot made with plt.fill
 

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  1. POCreating a colour bar for a plot made with plt.fill
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    <p>I'm new to Python (was an IDL user before hand) so I hope that I'm asking this in an understandable way. I've been trying to create a polar plot with x number of bins where the data in the bin is averaged and given a colour associated with that value. This seems to work fine while using the plt.fill command where I can define the bin and then the fill colour. The problem comes when I then try to make a colour bar to go with it. I keep getting errors that state AttributeError: 'Figure' object has no attribute 'autoscale_None'</p> <p>Any advice would be helpful thanks.</p> <pre><code>import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.cm as cm from matplotlib.pyplot import figure, show, rc, grid import pylab r = np.arange(50)/5. rstep = r[1] - r[0] theta = np.arange(50)/50.*2.*np.pi tstep = theta[1] - theta[0] colorv = np.arange(50)/50. # force square figure and square axes looks better for polar, IMO width, height = mpl.rcParams['figure.figsize'] size = min(width, height) # make a square figure fig = figure(figsize=(size, size)) ax = fig.add_axes([0.1, 0.1, .8, .8])#, polar=True) my_cmap = cm.jet for j in range(len(r)): rbox = np.array([r[j], r[j], r[j]+ rstep, r[j] + rstep]) for i in range(len(theta)): thetabox = np.array([theta[i], theta[i] + tstep, theta[i] + tstep, theta[i]]) x = rbox*np.cos(thetabox) y = rbox*np.sin(thetabox) plt.fill(x,y, facecolor = my_cmap(colorv[j])) # Add colorbar, make sure to specify tick locations to match desired ticklabels cbar = fig.colorbar(fig, ticks=[np.min(colorv), np.max(colorv)]) cb = plt.colorbar() plt.show() </code></pre> <p><strong>*</strong> here is a slightly better example of my real data, there are holes missing everywhere, so in this example I've just made a big one in a quarter of the circle. When I've tried meshing, the code seems to try to interpolate over these regions. </p> <pre><code>r = np.arange(50)/50.*7. + 3. rstep = r[1] - r[0] theta = np.arange(50)/50.*1.5*np.pi - np.pi tstep = theta[1] - theta[0] colorv = np.sin(r/10.*np.pi) # force square figure and square axes looks better for polar, IMO width, height = mpl.rcParams['figure.figsize'] size = min(width, height) # make a square figure fig = figure(figsize=(size, size)) ax = fig.add_axes([0.1, 0.1, .8, .8])#, polar=True) my_cmap = cm.jet for j in range(len(r)): rbox = np.array([r[j], r[j], r[j]+ rstep, r[j] + rstep]) for i in range(len(theta)): thetabox = np.array([theta[i], theta[i] + tstep, theta[i] + tstep, theta[i]]) x = rbox*np.cos(thetabox) y = rbox*np.sin(thetabox) plt.fill(x,y, facecolor = my_cmap(colorv[j])) # Add colorbar, make sure to specify tick locations to match desired ticklabels #cbar = fig.colorbar(fig, ticks=[np.min(colorv), np.max(colorv)]) #cb = plt.colorbar() plt.show() </code></pre> <p>And then with a meshing involved... </p> <p>from matplotlib.mlab import griddata</p> <pre><code>r = np.arange(50)/5. rstep = r[1] - r[0] theta = np.arange(50)/50.*1.5*np.pi - np.pi tstep = theta[1] - theta[0] colorv = np.sin(r/10.*np.pi) # force square figure and square axes looks better for polar, IMO width, height = mpl.rcParams['figure.figsize'] size = min(width, height) # make a square figure fig = figure(figsize=(size, size)) ax = fig.add_axes([0.1, 0.1, .8, .8])#, polar=True) my_cmap = cm.jet x = r*np.cos(theta) y = r*np.sin(theta) X,Y = np.meshgrid(x,y) data = griddata(x,y,colorv,X,Y) cax = plt.contourf(X,Y, data) plt.colorbar() # Add colorbar, make sure to specify tick locations to match desired ticklabels #cbar = fig.colorbar(fig, ticks=[np.min(colorv), np.max(colorv)]) #cb = plt.colorbar() plt.show() </code></pre>
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