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Begining of data reduction
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Yael-II
144
Source/FIDR.py
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144
Source/FIDR.py
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@@ -0,0 +1,144 @@
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import os
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import numpy as np
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import data_reduction_tools as tools
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import astroalign as aa
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TYPE = ["image", "spectroscopy"][1]
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target = "NGC_40"
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IN_DIR = "./Input/"
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OUT_DIR = "./Output/"
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LIGHT_DIR = IN_DIR + "Light/"
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DARK_DIR = IN_DIR + "Dark/"
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FLAT_DIR = IN_DIR + "Flat/"
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BIAS_DIR = IN_DIR + "Bias/"
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THAR_DIR = IN_DIR + "ThAr/"
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def spectro_reduction():
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# BIAS
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filelist = tools.list_fits(BIAS_DIR)
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tools.stack(filelist, BIAS_DIR, "master_bias.fits")
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# DARK
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#filelist = tools.list_fits(DARK_DIR)
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#tools.debias(filelist, DARK_DIR)
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#filelist = ["debiased_" + fname for fname in filelist \
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# if not "debiased_" in fname]
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#tools.stack(filelist, DARK_DIR, "master_debiased_dark.fits")
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# FLAT
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filelist = tools.list_fits(FLAT_DIR)
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tools.debias(filelist, FLAT_DIR)
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filelist = ["debiased_" + fname for fname in filelist \
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if not "debiased_" in fname]
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tools.normalize_spectrum_flat(filelist)
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filelist = ["normalized_" + fname for fname in filelist \
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if not "normalized_" in fname]
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tools.stack(filelist, FLAT_DIR, "master_normalized_debiased_flat.fits")
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# LIGHT
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filelist = tools.list_fits(LIGHT_DIR)
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tools.debias(filelist, LIGHT_DIR)
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filelist = ["debiased_" + fname for fname in filelist \
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if not "debiased_" in fname]
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tools.stack(filelist, LIGHT_DIR, "master_debiased_light.fits")
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tools.reduction_operation(target + ".fits",
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master_light = "master_debiased_light.fits",
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master_flat = "master_normalized_debiased_flat.fits")
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# MAIN
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# tools.plot_spectrum("master_debiased_flat.fits", FLAT_DIR)
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# tools.plot_spectrum("reduced_" + target + ".fits")
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return 0
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def spectro_calibration():
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# BIAS
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filelist = tools.list_fits(BIAS_DIR)
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tools.stack(filelist, BIAS_DIR, "master_bias.fits")
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# DARK
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#filelist = tools.list_fits(DARK_DIR)
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#tools.debias(filelist, DARK_DIR)
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#filelist = ["debiased_" + fname for fname in filelist \
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# if not "debiased_" in fname]
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#tools.stack(filelist, DARK_DIR, "master_debiased_dark.fits")
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# FLAT
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filelist = tools.list_fits(FLAT_DIR)
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tools.debias(filelist, FLAT_DIR)
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filelist = ["debiased_" + fname for fname in filelist \
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if not "debiased_" in fname]
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tools.normalize_spectrum_flat(filelist)
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filelist = ["normalized_" + fname for fname in filelist \
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if not "normalized_" in fname]
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tools.stack(filelist, FLAT_DIR, "master_normalized_debiased_flat.fits")
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# ThAr
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filelist = tools.list_fits(THAR_DIR)
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tools.debias(filelist, THAR_DIR)
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filelist = ["debiased_" + fname for fname in filelist \
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if not "debiased_" in fname]
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tools.stack(filelist, THAR_DIR, "master_debiased_ThAr.fits")
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tools.reduction_operation("ThAr.fits",
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light_directory = THAR_DIR,
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master_light = "master_debiased_ThAr.fits",
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master_flat = "master_normalized_debiased_flat.fits")
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# MAIN
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tools.plot_spectrum("reduced_ThAr.fits")
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return 0
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def image_reduction():
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# BIAS
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filelist = tools.list_fits(BIAS_DIR)
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tools.stack(filelist, BIAS_DIR, "master_bias.fits")
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# DARK
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filelist = tools.list_fits(DARK_DIR)
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tools.debias(filelist, DARK_DIR)
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filelist = ["debiased_" + fname for fname in filelist \
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if not "debiased_" in fname]
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tools.stack(filelist, DARK_DIR, "master_debiased_dark.fits")
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# FLAT
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filter_list = [d for d in os.listdir(LIGHT_DIR) if not d[0] == "."]
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for filter_ in filter_list:
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filter_dir = filter_ + "/"
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filelist = tools.list_fits(FLAT_DIR + filter_dir)
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tools.debias(filelist, FLAT_DIR + filter_dir)
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filelist = ["debiased_" + fname for fname in filelist \
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if not "debiased_" in fname]
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tools.normalize_image_flat(filelist, FLAT_DIR + filter_dir)
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filelist = ["normalized_" + fname for fname in filelist \
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if not "normalized_" in fname]
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tools.stack(filelist, FLAT_DIR + filter_dir,
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"master_normalized_debiased_flat_{}.fits".format(filter_))
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# LIGHT
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for filter_ in filter_list:
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filter_dir = filter_ + "/"
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filelist = tools.list_fits(LIGHT_DIR + filter_dir)
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tools.debias(filelist, LIGHT_DIR + filter_dir)
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filelist = ["debiased_" + fname for fname in filelist \
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if not "debiased_" in fname]
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tools.stack(filelist,
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LIGHT_DIR + filter_dir,
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"master_debiased_light_{}.fits".format(filter_))
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tools.reduction_operation(target + "_" + filter_ + ".fits",
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master_light = "master_debiased_light_{}.fits".format(filter_),
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light_directory = LIGHT_DIR + filter_dir,
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master_dark = "master_debiased_dark.fits",
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master_flat = "master_normalized_" \
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+ "debiased_flat_{}.fits".format(filter_),
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flat_directory = FLAT_DIR + filter_dir)
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return 0
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def main():
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if TYPE == "image":
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image_reduction()
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if TYPE == "spectroscopy":
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spectro_reduction()
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spectro_calibration()
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return 0
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if __name__ == "__main__":
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main()
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@@ -347,7 +347,7 @@ def add_simbad(table: Table,
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ra = coord.Angle(obj["ra"][i], unit=u.degree)
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dec = coord.Angle(obj["dec"][i], unit=u.degree)
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except KeyError:
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ra = coord.Angle(obj["RA"][i], unit=u.degree)
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ra = coord.Angle(obj["RA"][i], unit=u.hourangle)
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dec = coord.Angle(obj["DEC"][i], unit=u.degree)
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ra_str = ra.to_string(unit=u.hourangle,
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sep=":",
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@@ -727,6 +727,8 @@ def make_plot(table: Table,
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ra = coord.Angle(table["ra"][i], unit=u.hourangle).degree
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if ra > window_west:
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ra -= 360
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if ra < window_east:
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ra += 360
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ra_before = ra + a_before
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ra_after = ra - a_after
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@@ -748,12 +750,13 @@ def make_plot(table: Table,
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horizontalalignment="right", verticalalignment="center")
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ra_text = table["ra"][i]
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dec_text = table["dec"][i]
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ax.text(time_before, i, " {}{}".format(ra_text, dec_text))
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ax.text(time_before, i, " {}{}".format(ra_text, dec_text),
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horizontalalignment="left", verticalalignment="center")
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ax.set_xlabel("Observation date and time (UTC)")
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ax.set_yticks(range(N), table["seq"])
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plt.show(block=False)
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plt.show(block=True)
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# obs_time = np.arange(sun_set, sun_rise, dtype="datetime64[m]")
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return 0
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159
Source/data_reduction_tools.py
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159
Source/data_reduction_tools.py
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@@ -0,0 +1,159 @@
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from astropy.io import fits
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IN_DIR = "./Input/"
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OUT_DIR = "./Output/"
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LIGHT_DIR = IN_DIR + "Light/"
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DARK_DIR = IN_DIR + "Dark/"
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FLAT_DIR = IN_DIR + "Flat/"
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BIAS_DIR = IN_DIR + "Bias/"
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THAR_DIR = IN_DIR + "ThAr/"
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PLT_STYLE = "YII_light_1"
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def list_fits(directory: str = LIGHT_DIR):
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"""
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Returns the list of files in the directory
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@params:
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- directory: the directory containing the fits
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@output:
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- list of fits files in the directory
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"""
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filelist = [fname for fname in os.listdir(directory) if ".fit" in fname]
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# works for fit and fits
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return filelist
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def stack(filelist: list,
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in_directory: str,
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out_name: str,
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out_directory: str = None,
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method:str = "median"):
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in_data = []
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if out_directory == None:
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out_directory = in_directory
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N = len(filelist)
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for file in filelist:
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if not "master_" in file:
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in_data.append(fits.getdata(in_directory + file))
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in_data = np.array(in_data)
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if method == "mean":
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out_data = np.mean(in_data, axis=0)
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if method == "median":
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out_data = np.median(in_data, axis=0)
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header = fits.getheader(in_directory+file)
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header["history"] = "stacking with {} files".format(N)
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fits.writeto(out_directory \
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+ out_name, out_data, header, overwrite=True)
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return 0
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def debias(filelist: list,
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in_directory: str,
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master_bias: str = "master_bias.fits",
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bias_directory: str = BIAS_DIR,
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out_directory: str = None):
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if out_directory == None:
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out_directory = in_directory
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for file in filelist:
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if not "debiased_" in file:
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in_data = fits.getdata(in_directory + file)
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header = fits.getheader(in_directory + file)
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bias = fits.getdata(bias_directory+master_bias)
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out_data = in_data - bias
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header["history"] = "debiased with master bias"
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fits.writeto(out_directory + "debiased_" + file, \
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out_data, header, \
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overwrite=True)
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return 0
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def normalize_image_flat(filelist: str,
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in_directory: str = FLAT_DIR,
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out_directory: str = None):
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if out_directory == None:
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out_directory = in_directory
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for file in filelist:
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if not "normalized_" in file:
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data = fits.getdata(in_directory + file)
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head = fits.getheader(in_directory + file)
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normalized_data = data/np.median(data)
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fits.writeto(out_directory + "normalized_" + file,
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normalized_data, overwrite=True)
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return 0
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def normalize_spectrum_flat(filelist: str,
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in_directory: str = FLAT_DIR,
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out_directory: str = None,
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fit_degree: int = 3,
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d_lim: int = 10000):
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if out_directory == None:
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out_directory = in_directory
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poly = np.polynomial.chebyshev
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for file in filelist:
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if not "normalized_" in file:
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data = fits.getdata(in_directory + file).flatten()
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head = fits.getheader(in_directory + file)
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pixel = list(range(len(data)))
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mask = np.ones_like(data)
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mask[np.argwhere(data < d_lim)] = 0
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fit_coefs = poly.chebfit(pixel, data, fit_degree, w=mask)
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fit_data = poly.chebval(pixel, fit_coefs)
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normalized_data = data/fit_data
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normalized_data[np.argwhere(normalized_data<0.5)] = np.nan
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fits.writeto(out_directory + "normalized_" + file,
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normalized_data, overwrite=True)
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return 0
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def reduction_operation(target: str,
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master_light: list,
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light_directory: str = LIGHT_DIR,
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master_dark: str = None,
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dark_directory: str = DARK_DIR,
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master_flat: str = None,
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flat_directory: str = FLAT_DIR,
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out_directory: str = OUT_DIR):
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data = fits.getdata(light_directory + master_light)
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head = fits.getheader(light_directory + master_light)
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if master_dark != None:
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dark = fits.getdata(dark_directory + master_dark)
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data = data - dark
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head["history"] = "removed dark with {}".format(master_dark)
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if master_flat != None:
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flat = fits.getdata(flat_directory + master_flat)
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data = data / flat
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head["history"] = "flatten with {}".format(master_flat)
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fits.writeto(out_directory + "reduced_" + target,
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data, head, overwrite=True)
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return 0
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def plot_spectrum(reduced: str,
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directory: str = OUT_DIR):
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if PLT_STYLE in plt.style.available: plt.style.use(PLT_STYLE)
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data = fits.getdata(directory + reduced).flatten()
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head = fits.getheader(directory + reduced)
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pixel = list(range(len(data)))
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plt.plot(pixel, data)
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plt.show(block=True)
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return 0
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def find_peaks_highest(data: list,
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N: int = 10):
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"""Find the N highest peaks in the data"""
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peaks = []
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mask = np.
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for i in range(N):
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None
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return 0
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