Begining of data reduction

Source/FIDR.py

@@ -0,0 +1,144 @@
+import os
+import numpy as np
+import data_reduction_tools as tools
+import astroalign as aa
+
+TYPE = ["image", "spectroscopy"][1]
+target = "NGC_40"
+IN_DIR = "./Input/"
+OUT_DIR = "./Output/"
+LIGHT_DIR = IN_DIR + "Light/"
+DARK_DIR = IN_DIR + "Dark/"
+FLAT_DIR = IN_DIR + "Flat/"
+BIAS_DIR = IN_DIR + "Bias/"
+THAR_DIR = IN_DIR + "ThAr/"
+
+def spectro_reduction():
+    # BIAS
+    filelist = tools.list_fits(BIAS_DIR)
+    tools.stack(filelist, BIAS_DIR, "master_bias.fits")
+
+    # DARK
+    #filelist = tools.list_fits(DARK_DIR)
+    #tools.debias(filelist, DARK_DIR)
+    #filelist = ["debiased_" + fname for fname in filelist \
+    #        if not "debiased_" in fname]
+    #tools.stack(filelist, DARK_DIR, "master_debiased_dark.fits")
+
+    # FLAT
+    filelist = tools.list_fits(FLAT_DIR)
+    tools.debias(filelist, FLAT_DIR)
+    filelist = ["debiased_" + fname for fname in filelist \
+            if not "debiased_" in fname]
+    tools.normalize_spectrum_flat(filelist)
+    filelist = ["normalized_" + fname for fname in filelist \
+            if not "normalized_" in fname]
+    tools.stack(filelist, FLAT_DIR, "master_normalized_debiased_flat.fits")
+
+    # LIGHT
+    filelist = tools.list_fits(LIGHT_DIR)
+    tools.debias(filelist, LIGHT_DIR)
+    filelist = ["debiased_" + fname for fname in filelist \
+            if not "debiased_" in fname]
+    tools.stack(filelist, LIGHT_DIR, "master_debiased_light.fits")
+    tools.reduction_operation(target + ".fits", 
+                        master_light = "master_debiased_light.fits",
+                        master_flat = "master_normalized_debiased_flat.fits")
+    # MAIN
+    # tools.plot_spectrum("master_debiased_flat.fits", FLAT_DIR)
+    # tools.plot_spectrum("reduced_" + target + ".fits")
+    return 0
+        
+def spectro_calibration():
+    # BIAS
+    filelist = tools.list_fits(BIAS_DIR)
+    tools.stack(filelist, BIAS_DIR, "master_bias.fits")
+
+    # DARK
+    #filelist = tools.list_fits(DARK_DIR)
+    #tools.debias(filelist, DARK_DIR)
+    #filelist = ["debiased_" + fname for fname in filelist \
+    #        if not "debiased_" in fname]
+    #tools.stack(filelist, DARK_DIR, "master_debiased_dark.fits")
+
+    # FLAT
+    filelist = tools.list_fits(FLAT_DIR)
+    tools.debias(filelist, FLAT_DIR)
+    filelist = ["debiased_" + fname for fname in filelist \
+            if not "debiased_" in fname]
+    tools.normalize_spectrum_flat(filelist)
+    filelist = ["normalized_" + fname for fname in filelist \
+            if not "normalized_" in fname]
+    tools.stack(filelist, FLAT_DIR, "master_normalized_debiased_flat.fits")
+
+    # ThAr
+    filelist = tools.list_fits(THAR_DIR)
+    tools.debias(filelist, THAR_DIR)
+    filelist = ["debiased_" + fname for fname in filelist \
+            if not "debiased_" in fname]
+    tools.stack(filelist, THAR_DIR, "master_debiased_ThAr.fits")
+    tools.reduction_operation("ThAr.fits", 
+                        light_directory = THAR_DIR,
+                        master_light = "master_debiased_ThAr.fits",
+                        master_flat = "master_normalized_debiased_flat.fits")
+    # MAIN
+    tools.plot_spectrum("reduced_ThAr.fits")
+    return 0
+
+def image_reduction():
+    # BIAS
+    filelist = tools.list_fits(BIAS_DIR)
+    tools.stack(filelist, BIAS_DIR, "master_bias.fits")
+
+    # DARK
+    filelist = tools.list_fits(DARK_DIR)
+    tools.debias(filelist, DARK_DIR)
+    filelist = ["debiased_" + fname for fname in filelist \
+            if not "debiased_" in fname]
+    tools.stack(filelist, DARK_DIR, "master_debiased_dark.fits")
+
+    # FLAT
+    filter_list = [d for d in os.listdir(LIGHT_DIR) if not d[0] == "."]
+    for filter_ in filter_list:
+        filter_dir = filter_ + "/"
+        filelist = tools.list_fits(FLAT_DIR + filter_dir)
+        tools.debias(filelist, FLAT_DIR + filter_dir)
+        filelist = ["debiased_" + fname for fname in filelist \
+                if not "debiased_" in fname]
+        tools.normalize_image_flat(filelist, FLAT_DIR + filter_dir)
+        filelist = ["normalized_" + fname for fname in filelist \
+                if not "normalized_" in fname]
+        tools.stack(filelist, FLAT_DIR + filter_dir, 
+                    "master_normalized_debiased_flat_{}.fits".format(filter_))
+
+    # LIGHT
+    for filter_ in filter_list:
+        filter_dir = filter_ + "/"
+        filelist = tools.list_fits(LIGHT_DIR + filter_dir)
+        tools.debias(filelist, LIGHT_DIR + filter_dir)
+        filelist = ["debiased_" + fname for fname in filelist \
+                if not "debiased_" in fname]
+        tools.stack(filelist, 
+                    LIGHT_DIR + filter_dir, 
+                    "master_debiased_light_{}.fits".format(filter_))
+        tools.reduction_operation(target + "_" + filter_ + ".fits", 
+            master_light = "master_debiased_light_{}.fits".format(filter_),
+            light_directory = LIGHT_DIR + filter_dir,
+            master_dark = "master_debiased_dark.fits",
+            master_flat = "master_normalized_" \
+                          + "debiased_flat_{}.fits".format(filter_),
+            flat_directory = FLAT_DIR + filter_dir)
+
+
+    return 0
+
+def main():
+    if TYPE == "image":
+        image_reduction()
+    if TYPE == "spectroscopy":
+        spectro_reduction()
+        spectro_calibration()
+    return 0
+
+if __name__ == "__main__":
+    main()

Source/astrobs_v1_toolbox.py

@@ -347,7 +347,7 @@ def add_simbad(table: Table,
             ra = coord.Angle(obj["ra"][i], unit=u.degree)
             dec = coord.Angle(obj["dec"][i], unit=u.degree)
         except KeyError:
-            ra = coord.Angle(obj["RA"][i], unit=u.degree)
+            ra = coord.Angle(obj["RA"][i], unit=u.hourangle)
             dec = coord.Angle(obj["DEC"][i], unit=u.degree)
         ra_str = ra.to_string(unit=u.hourangle, 
                               sep=":", 
@@ -727,6 +727,8 @@ def make_plot(table: Table,
         ra = coord.Angle(table["ra"][i], unit=u.hourangle).degree
         if ra > window_west:
             ra -= 360
+        if ra < window_east:
+            ra += 360
         ra_before = ra + a_before
         ra_after = ra - a_after
 
@@ -748,12 +750,13 @@ def make_plot(table: Table,
                 horizontalalignment="right", verticalalignment="center")
         ra_text = table["ra"][i]
         dec_text = table["dec"][i]
-        ax.text(time_before, i, "  {}{}".format(ra_text, dec_text))
+        ax.text(time_before, i, "  {}{}".format(ra_text, dec_text),
+                horizontalalignment="left", verticalalignment="center")
 
     ax.set_xlabel("Observation date and time (UTC)")
     ax.set_yticks(range(N), table["seq"])
 
-    plt.show(block=False)
+    plt.show(block=True)
 
     # obs_time = np.arange(sun_set, sun_rise, dtype="datetime64[m]")
     return 0

Source/data_reduction_tools.py

@@ -0,0 +1,159 @@
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from astropy.io import fits
+
+
+IN_DIR = "./Input/"
+OUT_DIR = "./Output/"
+LIGHT_DIR = IN_DIR + "Light/"
+DARK_DIR = IN_DIR + "Dark/"
+FLAT_DIR = IN_DIR + "Flat/"
+BIAS_DIR = IN_DIR + "Bias/"
+THAR_DIR = IN_DIR + "ThAr/"
+
+PLT_STYLE = "YII_light_1"
+
+def list_fits(directory: str = LIGHT_DIR):
+    """
+    Returns the list of files in the directory
+    @params:    
+        - directory: the directory containing the fits
+    @output:
+        - list of fits files in the directory
+    """
+    filelist = [fname for fname in os.listdir(directory) if ".fit" in fname] 
+    # works for fit and fits
+    return filelist
+
+def stack(filelist: list, 
+          in_directory: str, 
+          out_name: str,
+          out_directory: str = None,
+          method:str = "median"):
+    in_data = []
+    if out_directory == None:
+        out_directory = in_directory
+    N = len(filelist)
+    for file in filelist:
+        if not "master_" in file:
+            in_data.append(fits.getdata(in_directory + file))
+    in_data = np.array(in_data)
+    if method == "mean":
+        out_data = np.mean(in_data, axis=0)
+    if method == "median":
+        out_data = np.median(in_data, axis=0)
+    header = fits.getheader(in_directory+file)
+    header["history"] = "stacking with {} files".format(N)
+    fits.writeto(out_directory \
+            + out_name, out_data, header, overwrite=True)
+    return 0
+
+def debias(filelist: list, 
+           in_directory: str, 
+           master_bias: str = "master_bias.fits",
+           bias_directory: str = BIAS_DIR,
+           out_directory: str = None):
+    if out_directory == None:
+        out_directory = in_directory
+    for file in filelist:
+        if not "debiased_" in file:
+            in_data = fits.getdata(in_directory + file)
+            header = fits.getheader(in_directory + file)
+            bias = fits.getdata(bias_directory+master_bias)
+            out_data = in_data - bias
+            header["history"] = "debiased with master bias"
+            fits.writeto(out_directory + "debiased_" + file, \
+                         out_data, header, \
+                         overwrite=True)
+    return 0
+
+def normalize_image_flat(filelist: str, 
+                         in_directory: str = FLAT_DIR,
+                         out_directory: str = None):
+    if out_directory == None:
+        out_directory = in_directory
+
+    for file in filelist:
+        if not "normalized_" in file:
+            data = fits.getdata(in_directory + file)
+            head = fits.getheader(in_directory + file)
+            normalized_data = data/np.median(data)
+            fits.writeto(out_directory + "normalized_" + file, 
+                         normalized_data, overwrite=True)
+    return 0
+
+
+def normalize_spectrum_flat(filelist: str, 
+                            in_directory: str = FLAT_DIR,
+                            out_directory: str = None,
+                            fit_degree: int = 3, 
+                            d_lim: int = 10000):
+    if out_directory == None:
+        out_directory = in_directory
+
+    poly = np.polynomial.chebyshev
+    for file in filelist:
+        if not "normalized_" in file:
+            data = fits.getdata(in_directory + file).flatten()
+            head = fits.getheader(in_directory + file)
+            pixel = list(range(len(data)))
+            mask = np.ones_like(data)
+            mask[np.argwhere(data < d_lim)] = 0
+            fit_coefs = poly.chebfit(pixel, data, fit_degree, w=mask)
+            fit_data = poly.chebval(pixel, fit_coefs)
+            normalized_data = data/fit_data
+            normalized_data[np.argwhere(normalized_data<0.5)] = np.nan
+            fits.writeto(out_directory + "normalized_" + file, 
+                         normalized_data, overwrite=True)
+    return 0
+
+def reduction_operation(target: str,
+                        master_light: list,
+                        light_directory: str = LIGHT_DIR,
+                        master_dark: str = None,
+                        dark_directory: str = DARK_DIR,
+                        master_flat: str = None,
+                        flat_directory: str = FLAT_DIR,
+                        out_directory: str = OUT_DIR):
+
+    data = fits.getdata(light_directory + master_light)
+    head = fits.getheader(light_directory + master_light)
+    if master_dark != None:
+        dark = fits.getdata(dark_directory + master_dark)
+        data = data - dark
+        head["history"] = "removed dark with {}".format(master_dark)
+    if master_flat != None:
+        flat = fits.getdata(flat_directory + master_flat)
+        data = data / flat
+        head["history"] = "flatten with {}".format(master_flat)
+    fits.writeto(out_directory + "reduced_" + target, 
+                 data, head, overwrite=True)
+
+
+    return 0
+
+def plot_spectrum(reduced: str,
+                  directory: str = OUT_DIR):
+    if PLT_STYLE in plt.style.available: plt.style.use(PLT_STYLE)
+    data = fits.getdata(directory + reduced).flatten()
+    head = fits.getheader(directory + reduced)
+    pixel = list(range(len(data)))
+
+    plt.plot(pixel, data)
+    plt.show(block=True)
+    return 0
+
+def find_peaks_highest(data: list,
+                       N: int = 10):
+    """Find the N highest peaks in the data"""
+    peaks = []
+    mask = np.
+    for i in range(N):
+        None
+    return 0
+
+
+
+
+

requirements.txt

@@ -1,3 +1,4 @@
+astroalign==2.6.1
 astropy==7.0.0
 astropy-iers-data==0.2024.11.25.0.34.48
 astroquery==0.4.7
@@ -9,13 +10,16 @@ cycler==0.12.1
 fonttools==4.55.0
 html5lib==1.1
 idna==3.10
+imageio==2.36.1
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
 jaraco.functools==4.1.0
 keyring==25.5.0
 kiwisolver==1.4.7
+lazy_loader==0.4
 matplotlib==3.9.3
 more-itertools==10.5.0
+networkx==3.4.2
 numpy==2.1.3
 packaging==24.2
 pillow==11.0.0
@@ -25,7 +29,11 @@ python-dateutil==2.9.0.post0
 pyvo==1.6
 PyYAML==6.0.2
 requests==2.32.3
+scikit-image==0.24.0
+scipy==1.14.1
+sep-pjw==1.3.7
 six==1.16.0
 soupsieve==2.6
+tifffile==2024.9.20
 urllib3==2.2.3
 webencodings==0.5.1