Various pieces of code that help me make nice figures.

helpful_figure_stuff.jl

# && Exporting to movie:
framerate = 5
anim = `ffmpeg -y -framerate $(framerate) -start_number 1 -i $(savename)_%d.png
-c:v libx264 -pix_fmt yuv420p -preset veryslow -profile:v baseline -level 3.0 $(savename).mp4`
run(anim)

# To reduce movie size, you can add the option:
`-b:v 2048k` 
# lower number = lower size = lower quality


if deletefigs
    for i in 1:N
        rm(savename*"_$(i).png")
    end
end

# Add nice textbox, similar to legend:
bbox = Dict(:boxstyle => "round,pad=0.3", :facecolor=>"white", :alpha => 0.75)
text(0.05, 0.90, "text", bbox=bbox, transform = gca()[:transAxes], size = 22)




# && Many PyPlot axis with dedicated colorbars and no ticks:
fig = figure(figsize = (10.0, 10.0))
minorticks_off()

ax1 = subplot(2,2,1)
title("true field \$ $(target) \$")
ax2 = subplot(2,2,2, sharey = ax1)
title("true field \$ $(source) \$")
ax3 = subplot(2,2,3, sharex = ax1)
title("cross prediction \$ $(target) \$")
ax4 = subplot(2,2,4, sharey = ax1)
title("error")

# Plot frame n. 1
im1 = ax1[:imshow](Utest[N+1], cmap = "BuPu_r")
im2 = ax2[:imshow](Vtest[N+1], cmap = "viridis")
im3 = ax3[:imshow](Upred[1], cmap = "viridis")
im4 = ax4[:imshow](err[1], cmap = "inferno")

for (im, ax) in zip([im1,im2,im3,im4], [ax1,ax2,ax3,ax4])
    ax[:get_xaxis]()[:set_ticks]([])
    ax[:get_yaxis]()[:set_ticks]([])
    colorbar(im, ax = ax, fraction=0.046, pad=0.04)
    # ax[:minorticks_off]()
end

# && Adding title to an entire figure that is composed of subplots
figtitle = "whole title"
suptitle(figtitle, fontsize = 20)
subplots_adjust(top=0.9)


# && Adding colorbars same size as axes (Python)
# There are two ways. A total hack one:
for (im, ax) in zip([im1,im2,im3,im4], [ax1,ax2,ax3,ax4])
    ax[:get_xaxis]()[:set_ticks]([])
    ax[:get_yaxis]()[:set_ticks]([])
    colorbar(im, ax = ax, fraction=0.046, pad=0.04)
    # ax[:minorticks_off]()
end

# And a valid one, taken from http://joseph-long.com/writing/colorbars/
from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, (ax1, ax2) = plt.subplots(ncols=2)

img1 = ax1.imshow(data)
divider = make_axes_locatable(ax1)
cax1 = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(img1, cax=cax1)

img2 = ax2.imshow(-data)
divider = make_axes_locatable(ax2)
cax2 = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(img2, cax=cax2)

# or as a function:
def proper_colorbar(mappable):
    ax = mappable.axes
    fig = ax.figure
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    return fig.colorbar(mappable, cax=cax)




# && Create a "Distribution plot" With the central distrb. and the marginals on the side
# Create some normally distributed data
mean = [0, 0]
cov = [[1, 1], [1, 2]]
x, y = np.random.multivariate_normal(mean, cov, 3000).T

# Set up the axes with gridspec
fig = plt.figure(figsize=(6, 6))
grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)
main_ax = fig.add_subplot(grid[:-1, 1:])
y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)
x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)

# scatter points on the main axes
main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)

# histogram on the attached axes
x_hist.hist(x, 40, histtype='stepfilled',
            orientation='vertical', color='gray')
x_hist.invert_yaxis()

y_hist.hist(y, 40, histtype='stepfilled',
            orientation='horizontal', color='gray')
y_hist.invert_xaxis()





#### ---- Plot colorbar and grayscale equivalent


from matplotlib.colors import LinearSegmentedColormap

def grayscale_cmap(cmap):
    """Return a grayscale version of the given colormap"""
    cmap = plt.cm.get_cmap(cmap)
    colors = cmap(np.arange(cmap.N))
    
    # convert RGBA to perceived grayscale luminance
    # cf. http://alienryderflex.com/hsp.html
    RGB_weight = [0.299, 0.587, 0.114]
    luminance = np.sqrt(np.dot(colors[:, :3] ** 2, RGB_weight))
    colors[:, :3] = luminance[:, np.newaxis]
        
    return LinearSegmentedColormap.from_list(cmap.name + "_gray", colors, cmap.N)
    

def view_colormap(cmap):
    """Plot a colormap with its grayscale equivalent"""
    cmap = plt.cm.get_cmap(cmap)
    colors = cmap(np.arange(cmap.N))
    
    cmap = grayscale_cmap(cmap)
    grayscale = cmap(np.arange(cmap.N))
    
    fig, ax = plt.subplots(2, figsize=(6, 2),
                           subplot_kw=dict(xticks=[], yticks=[]))
    ax[0].imshow([colors], extent=[0, 10, 0, 1])
    ax[1].imshow([grayscale], extent=[0, 10, 0, 1])

view_colormap("viridis")




### ------ Hide tick labels but keep ticks themselves
figure()
ax1 = subplot(2, 2, 1)
ax2 = subplot(2, 2, 2)
ax3 = subplot(2, 2, 3)
ax4 = subplot(2, 2, 4)
# warning: this does not work if you sharex or sharey
for ax in (ax1, ax2); ax[:get_xaxis]()[:set_ticklabels]([]); end
for ax in (ax2, ax4); ax[:get_yaxis]()[:set_ticklabels]([]); end


#### __-_---___ Get specific color from a colormap
import matplotlib

cmap = matplotlib.cm.get_cmap('Spectral')

rgba = cmap(0.5)
print(rgba) # (0.99807766255210428, 0.99923106502084169, 0.74602077638401709, 1.0)

Put a label over many x-axis (i.e. put the label on the figure as text)

gcf()[:text](0.5, 0.04, "\$\\phi\$", ha="center", va="center", size = LABEL)
subplots_adjust(wspace=0.05, hspace = 0.1, bottom = 0.12)

Remove ticks from a sharedx axis without changing all axis:

sca(c2)
setp(c2[:get_xticklabels](), visible=false)
tick_params(axis="x", which="both", length=0)

Excellent subplot letter for all axes immediatelly:

bbox = Dict(:boxstyle => "round,pad=0.3", :facecolor=>"white", :alpha => 1.0)

for (l, ax) in zip('a':'e', gcf().o.get_axes())
    sca(ax)
    ax.text(0.95, 0.95, string(l), size = SUBPLOT,
    transform = ax[:transAxes], bbox = bbox, ha = "center", va = "center", zorder =5)
end