from astropy.io import fits
import numpy as np
from kai import instruments
import copy
[docs]
def lin_correction(file, instrument=instruments.default_inst):
"""
Perform linearity correction on input file, as defined below
x = (FITS_orig) / (No. of coadds)
Normalization is defined as a polynomial in the following way:
norm = coeffs[0] + (coeffs[1] * x) + (coeffs[2] * x^2) + ... + (coeffs[n] * x^n)
FITS_corrected = FITS_orig / norm
Parameters
----------
file : str
File path of FITS file to perform linearity correction
instrument : instruments object, optional
Instrument of data. Default is `instruments.default_inst`
"""
# Extract header and image data
hdul = fits.open(file, mode='update', ignore_missing_end=True)
im_header = hdul[0].header
im_data = hdul[0].data
# Determine if we have linearity correction coefficients to apply.
coeffs = instrument.get_linearity_correction_coeffs(im_header)
if coeffs is None:
return
# Perform correction
num_coadds = im_header['COADDS']
x = im_data / num_coadds
# Determine order of polynomial correction
norm_poly_order = len(coeffs)
# Construct normalization from polynomial
norm = coeffs[0]
for cur_poly_order in range(1, norm_poly_order):
norm = norm + (coeffs[cur_poly_order] * (x ** float(cur_poly_order)))
# Perform correction
# Only want to perform correction on positive pixel values
negative_filter = np.where(im_data < 0)
# Perform correction
im_data_corrected = copy.deepcopy(im_data)
im_data_corrected =\
im_data_corrected / norm
# Set back values for negative pixels back to original value
im_data_corrected[negative_filter] = im_data[negative_filter]
# Write out corrected image data to file
hdul[0].data = im_data_corrected
hdul.flush(output_verify='ignore')
hdul.close(output_verify='ignore')
return