Jupyter Notebook: Output image in previous line

Question

I want to plot some image side by side in my jupyter notebook. So it can save some space for display. For example

This is done through

fig = plt.figure(figsize=(14,3))
ax1 = fig.add_subplot(1,3,1,projection = '3d')
ax2 = fig.add_subplot(1,3,2)
ax3 = fig.add_subplot(1,3,3)

And this makes them in one .png file. However, later on in writing the paper, I may only want part of the image. For example, the 2nd or the 3rd in previous plot. And this requires me to crop the image manually.

One way I can think of, is to make each subplot seperately, but display them in same line. In Python/Jupyter Notebook, the string output can achieve this by adding a comma at the end of previous line:

print 5, 
print 6
# returns 5, 6
# instead of 
# 5 
# 6

I'm wondering if there is anything similar in Jupyter Nobebook, that can do something like

plot fig1,
plot fig2
# Out put [fig1],[fig2]
# instead of 
# fig1 
# fig2

Output fig1, fig2 in the same line, but in seperate .png file?

Answer 1

use the following align_figures():

def align_figures():
    import matplotlib
    from matplotlib._pylab_helpers import Gcf
    from IPython.display import display_html
    import base64
    from ipykernel.pylab.backend_inline import show

    images = []
    for figure_manager in Gcf.get_all_fig_managers():
        fig = figure_manager.canvas.figure
        png = get_ipython().display_formatter.format(fig)[0]['image/png']
        src = base64.encodebytes(png).decode()
        images.append('<img style="margin:0" align="left" src="data:image/png;base64,{}"/>'.format(src))

    html = "<div>{}</div>".format("".join(images))
    show._draw_called = False
    matplotlib.pyplot.close('all')
    display_html(html, raw=True)

Here is a test:

fig1, ax1 = pl.subplots(figsize=(4, 3))
fig2, ax2 = pl.subplots(figsize=(4, 3))
fig3, ax3 = pl.subplots(figsize=(4, 3))
align_figures()

The code assumes that the output format is PNG image.

Answer 2

first let me recommend you use a colormap other than the jet colormap for the reasons detailed in A better colormap for matplotlib.

As to what you want to do you can achieve this with a modified code from: https://stackoverflow.com/a/26432947/835607

I've extended that function to handle the zaxis of 3d plots as well as the colorbars you are using.

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.transforms import Bbox
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import LinearLocator, FormatStrFormatter

def full_extent(ax, xpad=0.0, ypad=0.0, cbar=None):
    """Modified from https://stackoverflow.com/a/26432947/835607

    Get the full extent of an axes, including axes labels, tick labels, and
    titles.
    You may need to pad the x or y dimension in order to not get slightly chopped off labels

    For text objects, we need to draw the figure first, otherwise the extents
    are undefined. These draws can be eliminated by calling plt.show() prior 
    to calling this function."""

    ax.figure.canvas.draw()
    items = ax.get_xticklabels() + ax.get_yticklabels() 
    items += [ax, ax.title, ax.xaxis.label, ax.yaxis.label]
    if '3D' in str(type(ax)):  
        items += ax.get_zticklabels() +[ax.zaxis.label]
    if cbar:
        items+=cbar.ax.get_yticklabels()
        bbox = Bbox.union([cbar.ax.get_window_extent()]+[item.get_window_extent() for item in items])
    else:
         bbox = Bbox.union([item.get_window_extent() for item in items])
    return bbox.expanded(1.0 + xpad, 1.0 + ypad)

Now for an example I plot 3 subplots and save them all to separate files. Note that the full_extent function has cbar, xpad, and ypad as arguments. For the plots that have colorbars make sure to pass the colorbar axes object to the function. You may also need to play around with the padding to get the best results.

# Make an example plot with 3 subplots...
fig = plt.figure(figsize=(9,4))

#3D Plot
ax1 = fig.add_subplot(1,3,1,projection='3d')
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
surf = ax1.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='viridis',
                       linewidth=0, antialiased=False)
ax1.set_zlim(-1.01, 1.01)
ax1.zaxis.set_major_locator(LinearLocator(10))
ax1.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))

# This plot has a colorbar that we'll need to pass to extent
ax2 = fig.add_subplot(1,3,2)
data = np.clip(np.random.randn(250, 250), -1, 1)
cax = ax2.imshow(data, interpolation='nearest', cmap='viridis')
ax2.set_title('Gaussian noise')
cbar = fig.colorbar(cax)
ax2.set_xlabel('asdf')
ax2.set_ylabel('Some Cool Data')


#3rd plot for fun
ax3 = fig.add_subplot(1,3,3)
ax3.plot([1,4,5,7,7],[3,5,7,8,3],'ko--')
ax3.set_ylabel('adsf')
ax3.set_title('a title')


plt.tight_layout() #no overlapping labels
plt.show()  #show in notebook also give text an extent
fig.savefig('full_figure.png') #just in case

# Save just the portion _inside_ the boundaries of each axis
extent1 = full_extent(ax1).transformed(fig.dpi_scale_trans.inverted())
fig.savefig('ax1_figure.png', bbox_inches=extent1)

extent2 = full_extent(ax2,.05,.1,cbar).transformed(fig.dpi_scale_trans.inverted())
fig.savefig('ax2_figure.png', bbox_inches=extent2)

extent3 = full_extent(ax3).transformed(fig.dpi_scale_trans.inverted())
fig.savefig('ax3_figure.png', bbox_inches=extent3)

This plots the three plots on one line as you wanted and creates cropped output images such as this one: