%matplotlib inline
from io import BytesIO
import numpy as np
from matplotlib.figure import Figure

# Random Number Generator.
rng = np.random.default_rng()

# Constants.
figsize = (10, 6)
ncols = 3
nrows = 10
size = 100
size_total = ncols * nrows * size

# Figure with many subplots.
fig_many = Figure(figsize=figsize)
axs_many = fig_many.subplots(ncols=ncols, nrows=nrows)

# Figure with many subplots and sharex='col'.
fig_many_sharex = Figure(figsize=figsize)
axs_many_sharex = fig_many_sharex.subplots(ncols=ncols, nrows=nrows, sharex='col')

# Figure with a single axes.
fig_single = Figure(figsize=figsize)
ax_single = fig_single.subplots()

# Helper-function: Generate random line-plots in the many subplots.
def generate_fig_many(axs):
    for row in range(nrows):
        for col in range(ncols):
            ax = axs[row, col]
            x = rng.normal(loc=row+1, scale=col+1, size=size)
            y = rng.normal(loc=col+1, scale=row+1, size=size)
            x = np.sort(x)
            ax.plot(x, y);
            ax.set_yticks([])

# Generate fig_many 
generate_fig_many(axs=axs_many)
fig_many.tight_layout()

# Generate fig_many_sharex
generate_fig_many(axs=axs_many_sharex)
fig_many_sharex.tight_layout()

# Generate fig_single
x = rng.normal(size=size_total)
y = rng.normal(size=size_total)
x = np.sort(x)
ax_single.plot(x, y);
fig_single.tight_layout()

# The following code-chunks were run in individual Jupyter cells.

%%timeit
stream = BytesIO()
fig_single.savefig(stream, format='svg')
s = stream.getvalue()
# 29.2 ms ± 168 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

%%timeit
stream = BytesIO()
fig_single.savefig(stream, format='svg', bbox_inches='tight')
s = stream.getvalue()
# 102 ms ± 6.03 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
stream = BytesIO()
fig_many.savefig(stream, format='svg')
s = stream.getvalue()
# 374 ms ± 4.17 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
stream = BytesIO()
fig_many.savefig(stream, format='svg', bbox_inches='tight')
s = stream.getvalue()
# 1.4 s ± 12.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
stream = BytesIO()
fig_many_sharex.savefig(stream, format='svg')
s = stream.getvalue()
# 565 ms ± 5.58 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
stream = BytesIO()
fig_many_sharex.savefig(stream, format='svg', bbox_inches='tight')
s = stream.getvalue()
# 2.22 s ± 20.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
stream = BytesIO()
fig_many_sharex.savefig(stream, format='jpg', bbox_inches='tight')
s = stream.getvalue()
# 2.17 s ± 21 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
stream = BytesIO()
fig_many_sharex.savefig(stream, format='png', bbox_inches='tight')
s = stream.getvalue()
# 2.19 s ± 31.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)