root = io.find_root_folder(folder)
# Load Camera objects
camera = io.load_camera(root)
print(f"Loaded Camera object: {camera}")
# Save location based on camera name
savefolder = camera.filename_analysis("bias", makefolders=True)
# Load the data
isos, means = io.load_means(folder, retrieve_value=io.split_iso)
print("Loaded data")
# Print statistics at each ISO
stats = analyse.statistics(means,
prefix_column=isos,
prefix_column_header="ISO")
print(stats)
# Range on the x axis for the histograms
xmin, xmax = analyse.symmetric_percentiles(means, percent=0.001)
# Loop over the data and make plots at each ISO value
for ISO, mean in zip(isos, means):
save_to_histogram = savefolder / f"bias_histogram_iso{ISO}.pdf"
save_to_maps = savefolder / f"bias_map_iso{ISO}.pdf"
camera.plot_histogram_RGB(mean,
xmin=xmin,
xmax=xmax,
xlabel="Bias (ADU)",
# Load Camera object
camera = io.load_camera(root)
print(f"Loaded Camera object: {camera}")
# Save locations
savefolder = camera.filename_analysis("readnoise", makefolders=True)
# Load the data
isos, stds = io.load_stds(folder, retrieve_value=io.split_iso)
# Normalise the data using the ISO look-up table
stds_normalised = camera.normalise_iso(isos, stds)
# Print statistics at each ISO
stats = analyse.statistics(stds_normalised,
prefix_column=isos,
prefix_column_header="ISO")
print(stats)
# Range on the x axis for the histograms
xmin, xmax = 0., analyse.symmetric_percentiles(stds_normalised)[1]
# Loop over the data and make plots at each ISO value
for ISO, std in zip(isos, stds_normalised):
save_to_histogram = savefolder / f"readnoise_normalised_histogram_iso{ISO}.pdf"
save_to_maps = savefolder / f"readnoise_normalised_map_iso{ISO}.pdf"
camera.plot_histogram_RGB(std,
xmin=xmin,
xmax=xmax,
xlabel="Read noise (norm. ADU)",
colorbar_here = plot.colorbar(img)
ax.set_xticks([])
ax.set_yticks([])
if ax is axs[1]:
colorbar_here.set_label("Pearson $r$")
colorbar_here.locator = plot.ticker.MaxNLocator(nbins=4)
colorbar_here.update_ticks()
plt.savefig(save_to_map_JPEG)
plt.close()
print(f"Saved map of JPEG Pearson r to '{save_to_map_JPEG}'")
# Print comparative statistics on the RAW and JPEG (R/G/B) Pearson r values
if jpeg_data_available:
r_all = np.stack([r_raw, *r_jpeg])
labels = ["RAW", "JPEG R", "JPEG G", "JPEG B"]
stats = analyse.statistics(r_all, prefix_column=labels, prefix_column_header="Channel")
print(stats)
# Make an RGB histogram of the RAW r values
xmax = 1.
camera.plot_histogram_RGB(r_raw, xmax=xmax, xlabel="Pearson $r$", saveto=save_to_histogram_RGB)
print(f"Saved RGB histogram to '{save_to_histogram_RGB}'")
# Make a histogram comparing RAW and JPEG r values
if jpeg_data_available:
bins = np.linspace(0.9, 1.0, 150)
plt.figure(tight_layout=True, figsize=(5,2))
plt.hist(r_raw.ravel(), bins=bins, color='k')
for j, c in enumerate(plot.rgb):
plt.hist(r_jpeg[j].ravel(), bins=bins, color=c, alpha=0.7)
plt.xlabel("Pearson $r$")
