Plots and images on A4 with gridspec
Question:
I would like to create a summary A4 page for the results of some computations I have done.
These include both images and plot in a layout like the one the code below produces. Unfortunately, matplotlib makes the images very small and the plots very wide.
How can I make everything align nicely in a 2×6 grid, as in images are displayed square and plots have shape 1×2 and save it in portrait orientation?
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
mpl.use('pdf')
img = np.random.standard_normal((20,20))
data = np.linspace(0,1,10000)
title_fontsize = 'x-small'
fig = plt.figure()
fig.figsize = (6*5, 2*5)
ax = np.zeros(8, dtype=object)
gs = fig.add_gridspec(8, 2, width_ratios=[1,1])
ax[0] = fig.add_subplot(gs[0, 0])
ax[1] = fig.add_subplot(gs[0, 1])
ax[2] = fig.add_subplot(gs[1:3, :])
ax[3] = fig.add_subplot(gs[3, :])
ax[4] = fig.add_subplot(gs[4, 0])
ax[5] = fig.add_subplot(gs[4, 1])
ax[6] = fig.add_subplot(gs[5, :])
ax[7] = fig.add_subplot(gs[6, :])
ax[0].imshow(img)
ax[0].set_title('Covariance Operator', fontsize = title_fontsize)
ax[1].imshow(img)
ax[1].set_title('Sample', fontsize = title_fontsize)
ax[2].imshow(img)
ax[2].set_title('Truth', fontsize = title_fontsize)
ax[3].plot(data)
ax[3].set_title('Measurement', fontsize = title_fontsize)
ax[4].imshow(img)
ax[4].set_title('MCMC Reconstruction', fontsize = title_fontsize)
ax[5].imshow(img)
ax[5].set_title('FBP Reconstruction', fontsize = title_fontsize)
ax[6].plot(data)
ax[6].set_title('Heightscale', fontsize = title_fontsize)
ax[7].plot(data)
ax[7].set_title('Jump Size', fontsize = title_fontsize)
for x in ax.flat:
for tick in x.xaxis.get_major_ticks():
tick.label.set_fontsize('xx-small')
for tick in x.yaxis.get_major_ticks():
tick.label.set_fontsize('xx-small')
plt.savefig('test.pdf')
For reference, this is how the output looks like now:
Answers:
So I changed the layout a little, but the solution was to set the figsize to that of A4 paper and adjusting the height ratios of gs. Also fig.set_size_inches takes width as first argument, then height.
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
mpl.use('pdf')
img = np.random.standard_normal((20,20))
data = np.random.standard_normal((10000,2))
theta = [1,2]
description = 'Size: %s, #Samples: %s, Computation Time: %ss'%(25, 13, int(12.5))
title_fontsize = 'x-small'
fig = plt.figure(dpi=300, tight_layout=True)
fig.set_size_inches(8.27, 11.69, forward=True)
plt.figtext(0.02, .99, description, fontsize = 'small')
ax = np.zeros(9, dtype=object)
gs = fig.add_gridspec(5, 3, height_ratios=[3,2,3,2,2])
ax[0] = fig.add_subplot(gs[0, 0])
ax[1] = fig.add_subplot(gs[0, 1])
ax[2] = fig.add_subplot(gs[0, 2])
ax[3] = fig.add_subplot(gs[1, :])
ax[4] = fig.add_subplot(gs[2, 0])
ax[5] = fig.add_subplot(gs[2, 1])
ax[6] = fig.add_subplot(gs[2, 2])
ax[7] = fig.add_subplot(gs[3, :])
ax[8] = fig.add_subplot(gs[4, :])
ax[0].imshow(img)
ax[0].set_title('Slice through Covariance Operator', fontsize = title_fontsize)
ax[1].imshow(img)
ax[1].set_title('Last Sample', fontsize = title_fontsize)
ax[2].imshow(img)
ax[2].set_title('Truth', fontsize = title_fontsize)
for i, d in enumerate(data.T):
ax[3].plot(d, label = '%s°'%int(theta[i]))
ax[3].legend(loc='upper right')
ax[3].set_title('Measurement (Sinogram)', fontsize = title_fontsize)
ax[4].imshow(img)
ax[4].set_title('MCMC Reconstruction (Sample Mean)', fontsize = title_fontsize)
ax[5].imshow(img)
ax[5].set_title('MCMC Sample Variance', fontsize = title_fontsize)
ax[6].imshow(img)
ax[6].set_title('FBP Reconstruction', fontsize = title_fontsize)
ax[7].plot(data)
ax[7].set_title('Heightscale', fontsize = title_fontsize)
ax[8].plot([b[0] for b in data], label='Layer 1')
ax[8].plot([b[1] for b in data], label='Layer 0')
ax[8].legend(loc='upper right')
ax[8].set_title('Jump Size', fontsize = title_fontsize)
for x in ax.flat:
for tick in x.xaxis.get_major_ticks():
tick.label.set_fontsize('xx-small')
for tick in x.yaxis.get_major_ticks():
tick.label.set_fontsize('xx-small')
plt.savefig('test.pdf')
And a picture for reference:
I would like to create a summary A4 page for the results of some computations I have done.
These include both images and plot in a layout like the one the code below produces. Unfortunately, matplotlib makes the images very small and the plots very wide.
How can I make everything align nicely in a 2×6 grid, as in images are displayed square and plots have shape 1×2 and save it in portrait orientation?
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
mpl.use('pdf')
img = np.random.standard_normal((20,20))
data = np.linspace(0,1,10000)
title_fontsize = 'x-small'
fig = plt.figure()
fig.figsize = (6*5, 2*5)
ax = np.zeros(8, dtype=object)
gs = fig.add_gridspec(8, 2, width_ratios=[1,1])
ax[0] = fig.add_subplot(gs[0, 0])
ax[1] = fig.add_subplot(gs[0, 1])
ax[2] = fig.add_subplot(gs[1:3, :])
ax[3] = fig.add_subplot(gs[3, :])
ax[4] = fig.add_subplot(gs[4, 0])
ax[5] = fig.add_subplot(gs[4, 1])
ax[6] = fig.add_subplot(gs[5, :])
ax[7] = fig.add_subplot(gs[6, :])
ax[0].imshow(img)
ax[0].set_title('Covariance Operator', fontsize = title_fontsize)
ax[1].imshow(img)
ax[1].set_title('Sample', fontsize = title_fontsize)
ax[2].imshow(img)
ax[2].set_title('Truth', fontsize = title_fontsize)
ax[3].plot(data)
ax[3].set_title('Measurement', fontsize = title_fontsize)
ax[4].imshow(img)
ax[4].set_title('MCMC Reconstruction', fontsize = title_fontsize)
ax[5].imshow(img)
ax[5].set_title('FBP Reconstruction', fontsize = title_fontsize)
ax[6].plot(data)
ax[6].set_title('Heightscale', fontsize = title_fontsize)
ax[7].plot(data)
ax[7].set_title('Jump Size', fontsize = title_fontsize)
for x in ax.flat:
for tick in x.xaxis.get_major_ticks():
tick.label.set_fontsize('xx-small')
for tick in x.yaxis.get_major_ticks():
tick.label.set_fontsize('xx-small')
plt.savefig('test.pdf')
For reference, this is how the output looks like now:
So I changed the layout a little, but the solution was to set the figsize to that of A4 paper and adjusting the height ratios of gs. Also fig.set_size_inches takes width as first argument, then height.
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
mpl.use('pdf')
img = np.random.standard_normal((20,20))
data = np.random.standard_normal((10000,2))
theta = [1,2]
description = 'Size: %s, #Samples: %s, Computation Time: %ss'%(25, 13, int(12.5))
title_fontsize = 'x-small'
fig = plt.figure(dpi=300, tight_layout=True)
fig.set_size_inches(8.27, 11.69, forward=True)
plt.figtext(0.02, .99, description, fontsize = 'small')
ax = np.zeros(9, dtype=object)
gs = fig.add_gridspec(5, 3, height_ratios=[3,2,3,2,2])
ax[0] = fig.add_subplot(gs[0, 0])
ax[1] = fig.add_subplot(gs[0, 1])
ax[2] = fig.add_subplot(gs[0, 2])
ax[3] = fig.add_subplot(gs[1, :])
ax[4] = fig.add_subplot(gs[2, 0])
ax[5] = fig.add_subplot(gs[2, 1])
ax[6] = fig.add_subplot(gs[2, 2])
ax[7] = fig.add_subplot(gs[3, :])
ax[8] = fig.add_subplot(gs[4, :])
ax[0].imshow(img)
ax[0].set_title('Slice through Covariance Operator', fontsize = title_fontsize)
ax[1].imshow(img)
ax[1].set_title('Last Sample', fontsize = title_fontsize)
ax[2].imshow(img)
ax[2].set_title('Truth', fontsize = title_fontsize)
for i, d in enumerate(data.T):
ax[3].plot(d, label = '%s°'%int(theta[i]))
ax[3].legend(loc='upper right')
ax[3].set_title('Measurement (Sinogram)', fontsize = title_fontsize)
ax[4].imshow(img)
ax[4].set_title('MCMC Reconstruction (Sample Mean)', fontsize = title_fontsize)
ax[5].imshow(img)
ax[5].set_title('MCMC Sample Variance', fontsize = title_fontsize)
ax[6].imshow(img)
ax[6].set_title('FBP Reconstruction', fontsize = title_fontsize)
ax[7].plot(data)
ax[7].set_title('Heightscale', fontsize = title_fontsize)
ax[8].plot([b[0] for b in data], label='Layer 1')
ax[8].plot([b[1] for b in data], label='Layer 0')
ax[8].legend(loc='upper right')
ax[8].set_title('Jump Size', fontsize = title_fontsize)
for x in ax.flat:
for tick in x.xaxis.get_major_ticks():
tick.label.set_fontsize('xx-small')
for tick in x.yaxis.get_major_ticks():
tick.label.set_fontsize('xx-small')
plt.savefig('test.pdf')
And a picture for reference: