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https://codeberg.org/Yael-II/Astrobs-Tools.git
synced 2026-03-15 03:16:27 +01:00
update
This commit is contained in:
Yael-II
202
Source/FIDR.py
202
Source/FIDR.py
@@ -4,7 +4,6 @@ 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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@@ -13,17 +12,20 @@ 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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def spectro_reduction(target: str):
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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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filelist = tools.list_fits(DARK_DIR)
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has_dark = False
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if len(filelist) > 0:
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has_dark = True
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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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@@ -41,25 +43,33 @@ def spectro_reduction():
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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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if has_dark:
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tools.reduction_individual(["master_debiased_light.fits"],
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master_dark = "master_debiased_dark.fits",
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master_flat = "master_normalized_debiased_flat.fits")
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else:
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tools.reduction_individual(["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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def spectro_calibration(target: str):
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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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filelist = tools.list_fits(DARK_DIR)
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has_dark = False
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if len(filelist) > 0:
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has_dark = True
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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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@@ -77,28 +87,55 @@ def spectro_calibration():
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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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if has_dark:
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tools.reduction_individual(["master_debiased_ThAr.fits"],
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light_directory = THAR_DIR,
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master_dark = "master_debiased_dark.fits",
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master_flat = "master_normalized_debiased_flat.fits")
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else:
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tools.reduction_individual(["master_debiased_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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filename = "reduced_master_debiased_light.fits"
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tools.wavelength_calibration(filename,
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"calibrated_" + target + ".csv")
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return 0
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def image_reduction():
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def image_reduction(target: str):
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filter_list = [d for d in os.listdir(LIGHT_DIR) if not d[0] == "."]
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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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dark_list = [d for d in os.listdir(DARK_DIR) if not d[0] == "."]
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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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has_dark = False
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has_filter_dark = False
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if np.array_equal(np.sort(dark_list), np.sort(filter_list)):
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has_dark = True
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has_filter_dark = True
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for filter_ in filter_list:
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filter_dir = filter_ + "/"
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filelist = tools.list_fits(DARK_DIR + filter_dir)
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tools.debias(filelist, DARK_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, DARK_DIR + filter_dir,
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"master_debiased_dark_{}.fits".format(filter_))
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elif len(filelist) > 0:
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has_dark = True
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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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@@ -112,32 +149,117 @@ def image_reduction():
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"master_normalized_debiased_flat_{}.fits".format(filter_))
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# LIGHT
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ref_dir = filter_list[0] + "/"
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ref_file = tools.list_fits(LIGHT_DIR + ref_dir)[0]
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ref_file = "reduced_debiased_" + ref_file
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good = []
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bad = []
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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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if has_dark:
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if has_filter_dark:
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tools.reduction_individual(filelist,
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light_directory = LIGHT_DIR + filter_dir,
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master_dark = "master_debiased_dark_{}.fits".format(filter_),
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master_flat = "master_normalized_" \
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+ "debiased_flat_{}.fits".format(filter_),
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dark_directory = DARK_DIR + filter_dir,
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flat_directory = FLAT_DIR + filter_dir)
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else:
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tools.reduction_individual(filelist,
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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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else:
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tools.reduction_individual(filelist,
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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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filelist = ["reduced_" + fname for fname in filelist \
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if not "reduced_" in fname]
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align = tools.align(filelist,
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ref_file,
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LIGHT_DIR + filter_dir,
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LIGHT_DIR + ref_dir)
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if len(align) > 1:
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good.append(filter_)
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filelist = ["aligned_" + fname for fname in align \
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if not "aligned_" 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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"master_aligned_reduced_debiased_light_{}_{}.fits".format(
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target, filter_), out_directory = OUT_DIR)
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else:
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bad.append(filter_)
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tools.stack(filelist,
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LIGHT_DIR + filter_dir,
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"master_reduced_debiased_light_{}_{}.fits".format(
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target, filter_), out_directory = OUT_DIR)
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if len(good) > 0 and len(bad) > 0:
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filter_ref = good[0]
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filter_ref_file = [fname for fname in tools.list_fits(OUT_DIR)
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if "aligned_" in fname][0]
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print(filter_ref, filter_ref_file)
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print(good)
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print(bad)
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for filter_ in bad:
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tools.align(["master_reduced_debiased_light_{}_{}.fits".format(
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target, filter_)],
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filter_ref_file,
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OUT_DIR,
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OUT_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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print("Select the type of reduction")
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print("\t1. Images")
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print("\t2. Spectra")
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i = int(input("Choice [1 or 2]: "))
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if i == 1:
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print(("IMPORTANT: Please put raw files in the Input/ directory "
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"(with any name)"))
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print("Input/")
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print(" ├ Bias/")
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print(" │ └ bias_1.fits, ...")
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print(" ├ Dark/")
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print(" │ └ dark_1.fits, ...")
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print(" ├ Flat/")
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print(" │ ├ B/")
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print(" │ │ └ flat_B_1.fits, ...")
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print(" │ └ V/...")
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print(" └ Light/")
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print(" ├ B/")
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print(" │ └ raw_B_1.fits")
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print(" └ V/ ...")
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if i == 2:
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print(("IMPORTANT: Please put raw files in the Input/ directory "
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"(with any name)"))
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print("Input/")
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print(" ├ Bias/")
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print(" │ └ bias1.fits, ...")
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print(" ├ Dark/ (optional)")
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print(" │ └ dark_1.fits, ...")
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print(" ├ Flat/")
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print(" │ └ flat_1.fits")
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print(" ├ Light/")
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print(" │ └ raw_1.fits")
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print(" └ ThAr/")
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print(" └ thar_raw_1.fits")
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name = input("Target name: ")
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if i == 1:
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image_reduction(name)
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if i == 2:
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spectro_reduction(name)
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spectro_calibration(name)
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return 0
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if __name__ == "__main__":
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