gridspec_kw={
"wspace": 0,
"hspace": 0
})
# Loop over the data and plot them
for path, gauss, axs_here in zip(files, gauss_all, axs):
# Loop over the Bayer RGBG2 channels
for j, (ax, D, c) in enumerate(zip(axs_here, gauss, plot.rgbg2)):
# Plot the image
img = ax.imshow(D, cmap=plot.cmaps[c + "r"], vmin=vmin, vmax=vmax)
# Remove the x- and y-axes
ax.set_xticks([])
ax.set_yticks([])
# For the lowest row only, add a colorbar
if ax is axs[-1, j]:
colorbar_here = plot.colorbar(img)
if ax is axs_here[1]:
colorbar_here.set_label(colorbar_label)
colorbar_here.locator = plot.ticker.MaxNLocator(nbins=3)
colorbar_here.update_ticks()
# Save the figure
fig.savefig(save_to)
plt.close()
print(f"Saved figure to '{save_to}'")
# Plot parameters
ax.set_xticks([])
ax.set_yticks([])
ax.set_title(label)
# Include a colorbar
# Left-most map has a colorbar on the left
if ax is axs[0]:
loc = "left"
# Right-most map has a colorbar on the right
elif ax is axs[-1]:
loc = "right"
# Any other maps have a colorbar on the bottom
else:
loc = "bottom"
cbar = plot.colorbar(im, location=loc, label="Gain (ADU/e$^-$)")
# Print the range of gain values found in this map
percentile_low, percentile_high = analyse.symmetric_percentiles(
data_RGBG)
print(label)
print(f"{c_label:>2}: {percentile_low:.2f} -- {percentile_high:.2f}")
# Save the figure
save_to_map_c = save_folder / f"gain_map_{c_label}.pdf"
fig.savefig(save_to_map_c)
plt.close()
print(f"Saved gain map for the {c_label} channel to '{save_to_map_c}'")
# Plot a histogram
fig, axs = plt.subplots(ncols=len(files),
