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data/neidMaster_LSFParameterCoefficients20230824HRSci_v1.txt
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,164 @@ | ||
| from astropy.io import fits | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
| from scipy.special import erf | ||
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| import sys | ||
| import os | ||
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| # Add the directory containing the script to sys.path | ||
| sys.path.append(os.path.abspath(os.path.dirname(__file__))) | ||
|
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| def tophat_gauss_kernel( x, mean, fwhm, box_width ): | ||
| """A function to return a top hat convolved Gaussian kernel provided the two width parameters. | ||
| Note that the kernel values are not normalized (needed for convolution). | ||
| Parameters | ||
| ---------- | ||
| x : array | ||
| An array of pixel locations to compute the kernel at. | ||
| mean : float | ||
| The mean of the kernel at the point in the observed spectrum to convolve at. | ||
| fwhm : float | ||
| The Gaussian FWHM of the kernel at the point in the observed spectrum to convolve at. | ||
| box_width : float | ||
| The top hat box width of the kernel at the point in the observed spectrum to convolve at. | ||
| Returns | ||
| ------- | ||
| kernel_values : array | ||
| The values of the LSF convolution kernel. | ||
| """ | ||
|
|
||
| # First convert the FWHM to a sigma | ||
| sigma = fwhm / ( 2 * np.sqrt( 2 * np.log( 2 ) ) ) | ||
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| # Generate the arguments for the two erf components. | ||
| erf_arg_1 = ( ( 2 * mean + box_width ) - 2 * x ) / ( 2 * np.sqrt( 2 ) * sigma ) | ||
| erf_arg_2 = ( (-2 * mean + box_width ) + 2 * x ) / ( 2 * np.sqrt( 2 ) * sigma ) | ||
|
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| # Generate the kernel values | ||
| kernel_values = erf( erf_arg_1 ) + erf( erf_arg_2 ) | ||
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| return kernel_values | ||
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| def read_lsf_masterfile(file_name): | ||
| """Reads in a text file (in form of IRAF beam trace file) as a dictionary. | ||
| Taken from the PRVextract.libs.beamtrace library file in the NEID DRP. | ||
| Parameters | ||
| ---------- | ||
| file_name : str | ||
| The path to the text file to read in. | ||
| Returns | ||
| ------- | ||
| file_dict : dict of dicts | ||
| The dictionary corresponding to the input file. | ||
| """ | ||
|
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| # Initialize output dictionary | ||
| file_dict = {} | ||
|
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| # Go through each of the lines in the file to append to output dictionary | ||
| current_aperture = None | ||
| for line in open(file_name): | ||
| line = line.split() | ||
|
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||
| # Skip empty lines | ||
| if not line: | ||
| continue | ||
|
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| # Read the line | ||
| key = line[0] | ||
| value = [parseValue(value) for value in line[1:]] | ||
|
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| # If only one value for line, turn from list to single value | ||
| if len(value) == 1: | ||
| value = value[0] | ||
|
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| # Insert the information into the dictionary | ||
| if key == 'APERTURE': | ||
| if current_aperture is not None: | ||
| file_dict[current_aperture['APERTURE']] = current_aperture | ||
| current_aperture = dict({'APERTURE': value}) | ||
| else: | ||
| current_aperture[key] = value | ||
|
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||
| if current_aperture is not None: | ||
| file_dict[current_aperture['APERTURE']] = current_aperture | ||
|
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| return file_dict | ||
|
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||
| def parseValue(value): | ||
| """Utility function for parsing a value when reading in beam trace file. | ||
| """ | ||
|
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||
| for parse_function in [int, float, str]: | ||
| try: | ||
| return parse_function(value) | ||
| except: | ||
| continue | ||
|
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| return value | ||
|
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| def get_variable_lsf_kernel_values(obs_pixel, kernel_pixel_arr, wavelength_spacing, order): | ||
| """Function to generate a 1D LSF kernel array given parameterization defined in the input masterfile. | ||
| The LSF profile has parameters that are fit with polynomials in pixel space across each order. | ||
| Parameters | ||
| ---------- | ||
| obs_pixel : float | ||
| The pixel location within spectral order to generate LSF at. | ||
| kernel_pixel_arr : array. | ||
| The pixel locations at which the kernel will be evaluated. | ||
| kernel_profile_information : dict | ||
| Dictionary with GLOBAL information about the LSF profile parameterization. | ||
| kernel_parameters : dict | ||
| The order-specific dictionary from the input LSF masterfile containing the kernel parameter fit information. | ||
| wavelength_spacing : float | ||
| The equal spacing of the wavelength grid being convolved, for normalizing the kernel. | ||
| Returns | ||
| ------- | ||
| lsf_kernel_values : array | ||
| The values of the LSF convolution kernel for the input kernel_pixel_arr. | ||
| """ | ||
| lsf_info = read_lsf_masterfile("data/neidMaster_LSFParameterCoefficients20230824HRSci_v1.txt") | ||
| kernel_profile_information = lsf_info['GLOBAL'] | ||
| kernel_parameters = lsf_info[order] | ||
| # Scale the input observation pixel value to be in the domain over which the LSF parameters are fit (turning [0,9215] to [-1,1]) | ||
| obs_pixel_m1top1 = scale_interval_m1top1(obs_pixel, *kernel_profile_information['DOMAIN']) | ||
|
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||
| ### Pick out the right line profile parameterization from the input master file. Currently just top hat gaussian | ||
| if kernel_profile_information['PROFILE'] == 'TOP_HAT_GAUSSIAN': | ||
|
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||
| # Take the polynomial coefficients describing the kernel parameters vs. pixel for this order | ||
| kernel_fwhm_poly = kernel_parameters['COEFF_1'] # Coefficient 1 is the Gaussian FWHM | ||
| kernel_boxwidth_poly = kernel_parameters['COEFF_2'] # Coefficient 2 is the top hat box width | ||
|
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||
| # Now apply the fits to the input observed pixel value | ||
| kernel_fwhm = np.polyval( kernel_fwhm_poly, obs_pixel_m1top1 ) | ||
| kernel_boxwidth = np.polyval( kernel_boxwidth_poly, obs_pixel_m1top1 ) | ||
|
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||
| # Generate the actual kernel values | ||
| lsf_kernel_values = tophat_gauss_kernel(kernel_pixel_arr, obs_pixel, kernel_fwhm, kernel_boxwidth) | ||
|
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| # Normalize the kernel values so that their integral is 1 (for flux conservation) | ||
| lsf_kernel_values /= np.trapz(lsf_kernel_values, dx = wavelength_spacing) | ||
|
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| return lsf_kernel_values | ||
|
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| def scale_interval_m1top1(x,a,b): | ||
| """Scales input x values over interval a to b onto the range of -1 to 1. | ||
| """ | ||
|
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||
| return (2.0 * x - (b + a)) / (b - a) |
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