position = source.center
model = source.subtracted.fit_model(position, "Gaussian")
rel_center = center - Extent(source.x_min, source.y_min)
rel_model = fitting.shifted_model(model, -source.cutout.x_min, -source.cutout.y_min)
plotting.plot_peak_model(source.cutout, rel_center.x, rel_center.y, rel_model)
model_fwhm_pix = fitting.fwhm(model) * Unit("pix")
model_fwhm = (model_fwhm_pix * frame.average_pixelscale).to("arcsec")
print("Model FWHM: ", model_fwhm)
evaluated_model = source.cutout.evaluate_model(model)
all_residual = Frame(np.copy(model_residual))
all_residual[source.y_slice, source.x_slice] -= evaluated_model
all_residual.save(fs.join(residuals_path, "all_residual.fits"))
model = Gaussian2D(amplitude=0.0087509425805, x_mean=center.x, y_mean=center.y, x_stddev=sigma, y_stddev=sigma)
rel_model = fitting.shifted_model(model, -source.cutout.x_min, -source.cutout.y_min)
plotting.plot_peak_model(source.cutout, rel_center.x, rel_center.y, rel_model)
evaluated_model = source.cutout.evaluate_model(model)
all_residual2 = Frame(np.copy(model_residual))
all_residual2[source.y_slice, source.x_slice] -= evaluated_model
all_residual2.save(fs.join(residuals_path, "all_residual2.fits"))
# -----------------------------------------------------------------
# Create a mask of the pixels that are NaNs
nans = Mask.is_nan(frame)
# Set the NaN pixels to zero in the frame
frame[nans] = 0.0
# Interpolate the frame in the masked pixels
if arguments.method == "biharmonic": data = interpolation.inpaint_biharmonic(frame, mask)
elif arguments.method == "local_mean": data = interpolation.in_paint(frame, mask, method="localmean")
else: raise ValueError("Invalid interpolation method (should be 'biharmonic' or 'local_mean')")
new_frame = Frame(data)
# Set the original NaN pixels back to NaN
new_frame[nans] = float("nan")
# Inform the user
log.info("Saving the result ...")
# Save the result
path = fs.join(output_path, arguments.image)
new_frame.save(path, header=header)
# Write the mask
if arguments.mask:
path = fs.join(output_path, "mask.fits")
new_frame[mask] = float('nan')
new_frame.save(path, header=header)
# -----------------------------------------------------------------
# Interpolate the frame in the masked pixels
if arguments.method == "biharmonic":
data = interpolation.inpaint_biharmonic(frame, mask)
elif arguments.method == "local_mean":
data = interpolation.in_paint(frame, mask, method="localmean")
else:
raise ValueError(
"Invalid interpolation method (should be 'biharmonic' or 'local_mean')"
)
new_frame = Frame(data)
# Set the original NaN pixels back to NaN
new_frame[nans] = float("nan")
# Inform the user
log.info("Saving the result ...")
# Save the result
path = fs.join(output_path, arguments.image)
new_frame.save(path, header=header)
# Write the mask
if arguments.mask:
path = fs.join(output_path, "mask.fits")
new_frame[mask] = float('nan')
new_frame.save(path, header=header)
# -----------------------------------------------------------------
# -----------------------------------------------------------------
# Load the image
frame = Frame.from_file(arguments.image)
# Load the region
region_name = os.path.splitext(os.path.basename(arguments.region))[0]
region = Region.from_file(arguments.region)
# Create the mask
mask = Mask(region.get_mask(shape=frame.shape))
# Calculate the inverse, if requested
if arguments.invert: mask = mask.inverse()
# -----------------------------------------------------------------
if arguments.data:
new_frame = frame
frame[mask] = arguments.value
# Create a frame for the total mask
else:
new_frame = Frame(mask.astype(int))
# Write out the new frame
new_frame.save(region_name + ".fits")
# -----------------------------------------------------------------
# -----------------------------------------------------------------
# Load the image
frame = Frame.from_file(arguments.image)
# Load the region
region_name = os.path.splitext(os.path.basename(arguments.region))[0]
region = Region.from_file(arguments.region)
# Create the mask
mask = Mask(region.get_mask(shape=frame.shape))
# Calculate the inverse, if requested
if arguments.invert: mask = mask.inverse()
# -----------------------------------------------------------------
if arguments.data:
new_frame = frame
frame[mask] = arguments.value
# Create a frame for the total mask
else: new_frame = Frame(mask.astype(int))
# Write out the new frame
new_frame.save(region_name + ".fits")
# -----------------------------------------------------------------
frame *= conversion_factor
# Plot the difference
#plotting.plot_difference(frame, simulated[filter_name])
sum_simulated = np.sum(frame)
sum_observed = np.sum(observed[filter_name])
ratio = sum_simulated / sum_observed
wavelength = simulated[filter_name].filter.pivotwavelength()
x.append(wavelength)
y.append(ratio)
frame_path = fs.join(new_path, filter_name + ".fits")
frame.save(frame_path)
exit()
plt.plot(x, y)
plt.xscale('log')
plt.yscale('log')
plt.show()
# -----------------------------------------------------------------
