update main script for pylsp
This commit is contained in:
@@ -1,200 +1,199 @@
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#!/usr/bin/python3
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#-*- coding:utf-8 -*-
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# !/usr/bin/python3
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# -*- coding:utf-8 -*-
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"""
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Main script where are progressively added the steps for the FOC pipeline reduction.
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"""
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#Project libraries
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# Project libraries
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import numpy as np
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from copy import deepcopy
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import lib.fits as proj_fits #Functions to handle fits files
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import lib.reduction as proj_red #Functions used in reduction pipeline
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import lib.plots as proj_plots #Functions for plotting data
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from lib.deconvolve import from_file_psf
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import lib.fits as proj_fits # Functions to handle fits files
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import lib.reduction as proj_red # Functions used in reduction pipeline
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import lib.plots as proj_plots # Functions for plotting data
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from lib.query import retrieve_products, path_exists, system
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from matplotlib.colors import LogNorm
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def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=0, interactive=0):
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## Reduction parameters
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# Deconvolution
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# Deconvolution
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deconvolve = False
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if deconvolve:
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psf = 'gaussian' #Can be user-defined as well
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#psf = from_file_psf(data_folder+psf_file)
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# from lib.deconvolve import from_file_psf
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psf = 'gaussian' # Can be user-defined as well
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# psf = from_file_psf(data_folder+psf_file)
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psf_FWHM = 0.15
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psf_scale = 'arcsec'
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psf_shape=(25,25)
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psf_shape = (25, 25)
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iterations = 5
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algo="richardson"
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# Initial crop
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algo = "richardson"
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# Initial crop
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display_crop = False
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# Background estimation
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error_sub_type = 'freedman-diaconis' #sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51,51))
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# Background estimation
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error_sub_type = 'freedman-diaconis' # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
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subtract_error = 1.00
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display_error = False
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# Data binning
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# Data binning
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rebin = True
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pxsize = 0.10
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px_scale = 'arcsec' #pixel, arcsec or full
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rebin_operation = 'sum' #sum or average
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# Alignement
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align_center = 'center' #If None will not align the images
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px_scale = 'arcsec' # pixel, arcsec or full
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rebin_operation = 'sum' # sum or average
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# Alignement
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align_center = 'center' # If None will not align the images
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display_bkg = False
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display_align = False
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display_data = False
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# Smoothing
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smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
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smoothing_FWHM = 0.10 #If None, no smoothing is done
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smoothing_scale = 'arcsec' #pixel or arcsec
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# Rotation
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rotate_data = False #rotation to North convention can give erroneous results
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# Smoothing
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smoothing_function = 'combine' # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
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smoothing_FWHM = 0.10 # If None, no smoothing is done
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smoothing_scale = 'arcsec' # pixel or arcsec
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# Rotation
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rotate_data = False # rotation to North convention can give erroneous results
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rotate_stokes = True
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# Final crop
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#crop = False #Crop to desired ROI
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#interactive = False #Whether to output to intercative analysis tool
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# Polarization map output
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SNRp_cut = 3. #P measurments with SNR>3
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SNRi_cut = 30. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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flux_lim = None #lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
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# Final crop
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# crop = False #Crop to desired ROI
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# interactive = False #Whether to output to intercative analysis tool
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# Polarization map output
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SNRp_cut = 3. # P measurments with SNR>3
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SNRi_cut = 30. # I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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flux_lim = None # lowest and highest flux displayed on plot, defaults to bkg and maximum in cut if None
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vec_scale = 3
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step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
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# if step_vec = 0 then all vectors are displayed at full length
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step_vec = 1 # plot all vectors in the array. if step_vec = 2, then every other vector will be plotted if step_vec = 0 then all vectors are displayed at full length
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##### Pipeline start
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## Step 1:
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# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
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# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
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if not infiles is None:
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prod = np.array([["/".join(filepath.split('/')[:-1]),filepath.split('/')[-1]] for filepath in infiles],dtype=str)
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prod = np.array([["/".join(filepath.split('/')[:-1]), filepath.split('/')[-1]] for filepath in infiles], dtype=str)
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obs_dir = "/".join(infiles[0].split("/")[:-1])
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if not path_exists(obs_dir):
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system("mkdir -p {0:s} {1:s}".format(obs_dir,obs_dir.replace("data","plots")))
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system("mkdir -p {0:s} {1:s}".format(obs_dir, obs_dir.replace("data", "plots")))
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if target is None:
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target = input("Target name:\n>")
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else:
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target, products = retrieve_products(target,proposal_id,output_dir=output_dir)
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target, products = retrieve_products(target, proposal_id, output_dir=output_dir)
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prod = products.pop()
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for prods in products:
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main(target=target,infiles=["/".join(pr) for pr in prods],output_dir=output_dir)
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main(target=target, infiles=["/".join(pr) for pr in prods], output_dir=output_dir)
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data_folder = prod[0][0]
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try:
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plots_folder = data_folder.replace("data","plots")
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except:
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plots_folder = data_folder.replace("data", "plots")
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except ValueError:
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plots_folder = "."
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if not path_exists(plots_folder):
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system("mkdir -p {0:s} ".format(plots_folder))
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infiles = [p[1] for p in prod]
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data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
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figname = "_".join([target,"FOC"])
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figname = "_".join([target, "FOC"])
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if rebin:
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if not px_scale in ['full']:
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figtype = "".join(["b","{0:.2f}".format(pxsize),px_scale]) #additionnal informations
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if px_scale not in ['full']:
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figtype = "".join(["b", "{0:.2f}".format(pxsize), px_scale]) # additionnal informations
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else:
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figtype = "full"
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if not smoothing_FWHM is None:
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figtype += "_"+"".join(["".join([s[0] for s in smoothing_function.split("_")]),"{0:.2f}".format(smoothing_FWHM),smoothing_scale]) #additionnal informations
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if smoothing_FWHM is not None:
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figtype += "_"+"".join(["".join([s[0] for s in smoothing_function.split("_")]), "{0:.2f}".format(smoothing_FWHM), smoothing_scale]) # additionnal informations
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if align_center is None:
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figtype += "_not_aligned"
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# Crop data to remove outside blank margins.
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# Crop data to remove outside blank margins.
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data_array, error_array, headers = proj_red.crop_array(data_array, headers, step=5, null_val=0., inside=True, display=display_crop, savename=figname, plots_folder=plots_folder)
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# Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
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# Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
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if deconvolve:
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data_array = proj_red.deconvolve_array(data_array, headers, psf=psf, FWHM=psf_FWHM, scale=psf_scale, shape=psf_shape, iterations=iterations, algo=algo)
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# Estimate error from data background, estimated from sub-image of desired sub_shape.
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# Estimate error from data background, estimated from sub-image of desired sub_shape.
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background = None
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data_array, error_array, headers, background = proj_red.get_error(data_array, headers, error_array, sub_type=error_sub_type, subtract_error=subtract_error, display=display_error, savename="_".join([figname,"errors"]), plots_folder=plots_folder, return_background=True)
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data_array, error_array, headers, background = proj_red.get_error(data_array, headers, error_array, sub_type=error_sub_type, subtract_error=subtract_error, display=display_error, savename="_".join([figname, "errors"]), plots_folder=plots_folder, return_background=True)
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if display_bkg:
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min()*headers[0]['photflam'], vmax=data_array[data_array>0.].max()*headers[0]['photflam'], savename="_".join([figname,"bkg"]), plots_folder=plots_folder)
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "bkg"]), plots_folder=plots_folder)
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# Align and rescale images with oversampling.
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# Align and rescale images with oversampling.
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data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array=error_array, background=background, upsample_factor=10, ref_center=align_center, return_shifts=False)
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if display_align:
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min()*headers[0]['photflam'], vmax=data_array[data_array>0.].max()*headers[0]['photflam'], savename="_".join([figname,str(align_center)]), plots_folder=plots_folder)
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, str(align_center)]), plots_folder=plots_folder)
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# Rebin data to desired pixel size.
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# Rebin data to desired pixel size.
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if rebin:
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data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation, data_mask=data_mask)
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# Rotate data to have North up
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# Rotate data to have North up
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if rotate_data:
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data_mask = np.ones(data_array.shape[1:]).astype(bool)
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alpha = headers[0]['orientat']
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data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -alpha)
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#Plot array for checking output
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if display_data and px_scale.lower() not in ['full','integrate']:
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min()*headers[0]['photflam'], vmax=data_array[data_array>0.].max()*headers[0]['photflam'], savename="_".join([figname,"rebin"]), plots_folder=plots_folder)
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# Plot array for checking output
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if display_data and px_scale.lower() not in ['full', 'integrate']:
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proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array > 0.].min()*headers[0]['photflam'], vmax=data_array[data_array > 0.].max()*headers[0]['photflam'], savename="_".join([figname, "rebin"]), plots_folder=plots_folder)
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background = np.array([np.array(bkg).reshape(1,1) for bkg in background])
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background_error = np.array([np.array(np.sqrt((bkg-background[np.array([h['filtnam1']==head['filtnam1'] for h in headers],dtype=bool)].mean())**2/np.sum([h['filtnam1']==head['filtnam1'] for h in headers]))).reshape(1,1) for bkg,head in zip(background,headers)])
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background = np.array([np.array(bkg).reshape(1, 1) for bkg in background])
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background_error = np.array([np.array(np.sqrt((bkg-background[np.array([h['filtnam1']==head['filtnam1'] for h in headers], dtype=bool)].mean())**2/np.sum([h['filtnam1']==head['filtnam1'] for h in headers]))).reshape(1, 1) for bkg, head in zip(background, headers)])
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## Step 2:
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# Compute Stokes I, Q, U with smoothed polarized images
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# SMOOTHING DISCUSSION :
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# FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
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# see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
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# Bibcode : 1995chst.conf...10J
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I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function,transmitcorr=False)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg = proj_red.compute_Stokes(background, background_error, np.array(True).reshape(1,1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function,transmitcorr=False)
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# Compute Stokes I, Q, U with smoothed polarized images
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# SMOOTHING DISCUSSION :
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# FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
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# see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
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# Bibcode : 1995chst.conf...10J
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I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg = proj_red.compute_Stokes(background, background_error, np.array(True).reshape(1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False)
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## Step 3:
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# Rotate images to have North up
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# Rotate images to have North up
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if rotate_stokes:
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I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, SNRi_cut=None)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg, _, _ = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1,1), headers, SNRi_cut=None)
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I_bkg, Q_bkg, U_bkg, S_cov_bkg, _, _ = proj_red.rotate_Stokes(I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), headers, SNRi_cut=None)
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# Compute polarimetric parameters (polarisation degree and angle).
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# Compute polarimetric parameters (polarisation degree and angle).
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P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers)
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P_bkg, debiased_P_bkg, s_P_bkg, s_P_P_bkg, PA_bkg, s_PA_bkg, s_PA_P_bkg = proj_red.compute_pol(I_bkg, Q_bkg, U_bkg, S_cov_bkg, headers)
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## Step 4:
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# Save image to FITS.
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Stokes_test = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, data_mask, "_".join([figname,figtype]), data_folder=data_folder, return_hdul=True)
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# Save image to FITS.
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Stokes_test = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, data_mask, "_".join([figname, figtype]), data_folder=data_folder, return_hdul=True)
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data_mask = Stokes_test[-1].data.astype(bool)
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## Step 5:
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# crop to desired region of interest (roi)
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# crop to desired region of interest (roi)
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if crop:
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figtype += "_crop"
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stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test),norm=LogNorm())
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stokescrop = proj_plots.crop_Stokes(deepcopy(Stokes_test), norm=LogNorm())
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stokescrop.crop()
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stokescrop.writeto("/".join([data_folder,"_".join([figname,figtype+".fits"])]))
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stokescrop.writeto("/".join([data_folder, "_".join([figname, figtype+".fits"])]))
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Stokes_test, data_mask, headers = stokescrop.hdul_crop, stokescrop.data_mask, [dataset.header for dataset in stokescrop.hdul_crop]
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print("F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'],*proj_plots.sci_not(Stokes_test[0].data[data_mask].sum()*headers[0]['photflam'],np.sqrt(Stokes_test[3].data[0,0][data_mask].sum())*headers[0]['photflam'],2,out=int)))
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print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]['p_int']*100.,np.ceil(headers[0]['p_int_err']*1000.)/10.))
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print("PA_int = {0:.1f} ±t {1:.1f} °".format(headers[0]['pa_int'],np.ceil(headers[0]['pa_int_err']*10.)/10.))
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# Background values
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print("F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'],*proj_plots.sci_not(I_bkg[0,0]*headers[0]['photflam'],np.sqrt(S_cov_bkg[0,0][0,0])*headers[0]['photflam'],2,out=int)))
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print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0,0]*100.,np.ceil(s_P_bkg[0,0]*1000.)/10.))
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print("PA_bkg = {0:.1f} ± {1:.1f} °".format(PA_bkg[0,0],np.ceil(s_PA_bkg[0,0]*10.)/10.))
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# Plot polarisation map (Background is either total Flux, Polarization degree or Polarization degree error).
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if px_scale.lower() not in ['full','integrate'] and not interactive:
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype]), plots_folder=plots_folder)
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"I"]), plots_folder=plots_folder, display='Intensity')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"P_flux"]), plots_folder=plots_folder, display='Pol_Flux')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"P"]), plots_folder=plots_folder, display='Pol_deg')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"PA"]), plots_folder=plots_folder, display='Pol_ang')
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proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"I_err"]), plots_folder=plots_folder, display='I_err')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"P_err"]), plots_folder=plots_folder, display='Pol_deg_err')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"SNRi"]), plots_folder=plots_folder, display='SNRi')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname,figtype,"SNRp"]), plots_folder=plots_folder, display='SNRp')
|
||||
print("F_int({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *proj_plots.sci_not(Stokes_test[0].data[data_mask].sum()*headers[0]['photflam'], np.sqrt(Stokes_test[3].data[0, 0][data_mask].sum())*headers[0]['photflam'], 2, out=int)))
|
||||
print("P_int = {0:.1f} ± {1:.1f} %".format(headers[0]['p_int']*100., np.ceil(headers[0]['p_int_err']*1000.)/10.))
|
||||
print("PA_int = {0:.1f} ±t {1:.1f} °".format(headers[0]['pa_int'], np.ceil(headers[0]['pa_int_err']*10.)/10.))
|
||||
# Background values
|
||||
print("F_bkg({0:.0f} Angs) = ({1} ± {2})e{3} ergs.cm^-2.s^-1.Angs^-1".format(headers[0]['photplam'], *proj_plots.sci_not(I_bkg[0, 0]*headers[0]['photflam'], np.sqrt(S_cov_bkg[0, 0][0, 0])*headers[0]['photflam'], 2, out=int)))
|
||||
print("P_bkg = {0:.1f} ± {1:.1f} %".format(debiased_P_bkg[0, 0]*100., np.ceil(s_P_bkg[0, 0]*1000.)/10.))
|
||||
print("PA_bkg = {0:.1f} ± {1:.1f} °".format(PA_bkg[0, 0], np.ceil(s_PA_bkg[0, 0]*10.)/10.))
|
||||
# Plot polarisation map (Background is either total Flux, Polarization degree or Polarization degree error).
|
||||
if px_scale.lower() not in ['full', 'integrate'] and not interactive:
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype]), plots_folder=plots_folder)
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "I"]), plots_folder=plots_folder, display='Intensity')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "P_flux"]), plots_folder=plots_folder, display='Pol_Flux')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "P"]), plots_folder=plots_folder, display='Pol_deg')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "PA"]), plots_folder=plots_folder, display='Pol_ang')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "I_err"]), plots_folder=plots_folder, display='I_err')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "P_err"]), plots_folder=plots_folder, display='Pol_deg_err')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "SNRi"]), plots_folder=plots_folder, display='SNRi')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim, step_vec=step_vec, vec_scale=vec_scale, savename="_".join([figname, figtype, "SNRp"]), plots_folder=plots_folder, display='SNRp')
|
||||
elif not interactive:
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename="_".join([figname,figtype]), plots_folder=plots_folder, display='integrate')
|
||||
proj_plots.polarisation_map(deepcopy(Stokes_test), data_mask, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename="_".join([figname, figtype]), plots_folder=plots_folder, display='integrate')
|
||||
elif px_scale.lower() not in ['full', 'integrate']:
|
||||
pol_map = proj_plots.pol_map(Stokes_test, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, flux_lim=flux_lim)
|
||||
|
||||
@@ -205,18 +204,18 @@ if __name__ == "__main__":
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description='Query MAST for target products')
|
||||
parser.add_argument('-t','--target', metavar='targetname', required=False,
|
||||
parser.add_argument('-t', '--target', metavar='targetname', required=False,
|
||||
help='the name of the target', type=str, default=None)
|
||||
parser.add_argument('-p','--proposal_id', metavar='proposal_id', required=False,
|
||||
parser.add_argument('-p', '--proposal_id', metavar='proposal_id', required=False,
|
||||
help='the proposal id of the data products', type=int, default=None)
|
||||
parser.add_argument('-f','--files', metavar='path', required=False, nargs='*',
|
||||
parser.add_argument('-f', '--files', metavar='path', required=False, nargs='*',
|
||||
help='the full or relative path to the data products', default=None)
|
||||
parser.add_argument('-o','--output_dir', metavar='directory_path', required=False,
|
||||
parser.add_argument('-o', '--output_dir', metavar='directory_path', required=False,
|
||||
help='output directory path for the data products', type=str, default="./data")
|
||||
parser.add_argument('-c','--crop', metavar='crop_boolean', required=False,
|
||||
parser.add_argument('-c', '--crop', metavar='crop_boolean', required=False,
|
||||
help='whether to crop the analysis region', type=int, default=0)
|
||||
parser.add_argument('-i','--interactive', metavar='interactive_boolean', required=False,
|
||||
parser.add_argument('-i', '--interactive', metavar='interactive_boolean', required=False,
|
||||
help='whether to output to the interactive analysis tool', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
exitcode = main(target=args.target, proposal_id=args.proposal_id, infiles=args.files, output_dir=args.output_dir, crop=args.crop, interactive=args.interactive)
|
||||
print("Finished with ExitCode: ",exitcode)
|
||||
print("Finished with ExitCode: ", exitcode)
|
||||
|
||||
Reference in New Issue
Block a user