update

Source/FIDR.py

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

Source/data_reduction_tools.py

@@ -1,8 +1,10 @@
 import os
 import numpy as np
 import matplotlib.pyplot as plt
+import astroalign as aa
 from astropy.io import fits
-
+from scipy.signal import find_peaks
+from scipy.optimize import curve_fit
 
 IN_DIR = "./Input/"
 OUT_DIR = "./Output/"
@@ -13,6 +15,7 @@ BIAS_DIR = IN_DIR + "Bias/"
 THAR_DIR = IN_DIR + "ThAr/"
 
 PLT_STYLE = "YII_light_1"
+if PLT_STYLE in plt.style.available: plt.style.use(PLT_STYLE)
 
 def list_fits(directory: str = LIGHT_DIR):
     """
@@ -35,18 +38,19 @@ def stack(filelist: list,
     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)
+    if N > 0:
+        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, 
@@ -88,7 +92,7 @@ def normalize_spectrum_flat(filelist: str,
                             in_directory: str = FLAT_DIR,
                             out_directory: str = None,
                             fit_degree: int = 3, 
-                            d_lim: int = 10000):
+                            d_lim: int = 100):
     if out_directory == None:
         out_directory = in_directory
 
@@ -103,39 +107,40 @@ def normalize_spectrum_flat(filelist: str,
             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
+            #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)
-
-
+def reduction_individual(lightlist: 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 = None):
+    if out_directory == None:
+        out_directory = light_directory
+    for light in lightlist:
+        if not "reduced_" in light:
+            data = fits.getdata(light_directory + light)
+            head = fits.getheader(light_directory + light)
+            if master_dark != None:
+                dark_data = fits.getdata(dark_directory + master_dark)
+                dark_head = fits.getheader(dark_directory + master_dark)
+                exp = head["EXPTIME"]
+                dark_exp = dark_head["EXPTIME"]
+                data = data - dark_data * exp/dark_exp
+                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_" + light, 
+                         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)))
@@ -144,16 +149,44 @@ def plot_spectrum(reduced: str,
     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
+def align(filelist: str,
+          ref_file: str,
+          directory: str = LIGHT_DIR,
+          ref_dir: str = LIGHT_DIR):
+    data_ref = fits.getdata(ref_dir + ref_file)
+    good = []
+    if len(filelist) > 0:
+        for file in filelist:
+            data = fits.getdata(directory + file)
+            head = fits.getheader(directory + file)
+            try:
+                aligned_data, fp = aa.register(np.float32(data), np.float32(data_ref))
+                good.append(file)
+                head["history"] = "aligned with {}".format(ref_file)
+                fits.writeto(directory + "aligned_" + file,
+                         aligned_data, head,
+                         overwrite = True)
+            except:
+                print("alignement failed with {}".format(file))
+    return good
+
+def wavelength_calibration(file: str,
+                           out_name: str,
+                           directory: str = LIGHT_DIR,
+                           reference: str = "values.csv",
+                           ref_directory: str = IN_DIR,
+                           out_directory: str = OUT_DIR):
+    with open(ref_directory+reference) as ref_file:
+        vals = ref_file.readline().replace(" ", "").split(",")
+    a0 = float(vals[0]) 
+    a1 = float(vals[1])
+    in_data = fits.getdata(directory + file).flatten()
+    head = fits.getheader(directory + file)
+    pixel = np.array(range(len(in_data)))
+    lambda_ = a1 * pixel + a0
+    out_data = np.array([lambda_, in_data])
+    np.savetxt(out_directory + out_name, out_data)    
+    head["history"] = ("calibrated wavelength: "
+                       "lambda = {} + {} * pixel").format(a0, a1)
+    fits.writeto(out_directory + out_name.replace(".csv", ".fits"), out_data, head, overwrite=True)
     return 0
-
-
-
-
-

Source/get_calib.py

@@ -0,0 +1,171 @@
+# => makes the plot interactive
+# %matplotlib widget 
+# inline makes the plots static
+#%matplotlib inline 
+
+import numpy as np
+from astropy.io import fits
+import matplotlib.pyplot as plt
+from scipy.signal import find_peaks
+from scipy.optimize import curve_fit
+from numpy.polynomial import chebyshev
+import warnings
+# filter astropy warning on fits headers
+warnings.filterwarnings('ignore', category=UserWarning, append=True)
+from spectro_tools import *
+
+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 main(ref_file: str = "reduced_master_debiased_ThAr.fits",
+         ref_directory: str = THAR_DIR):
+
+    fits_file = ref_directory+ref_file
+    xaxis,data=read_raw_spectrum(fits_file)
+    spectrum=data
+
+    # Find peaks in the spectrum
+
+    # TODO THRESHOLD
+
+    peaks = find_peaks(data, height=5000.)[0]  # You can adjust the 'height' threshold
+    # NB: 'fiducial value': height=5000
+
+    # Get the centroid (x-value) of each peak
+    centroid_x_values = peaks
+    # Positions in pixels of the peaks
+
+    # Plot the spectrum and mark the centroids
+    if False:
+        plt.plot(data)
+        plt.plot(centroid_x_values, data[peaks], 'ro', label='Max')
+        plt.hlines(5000., 0, len(data), "r")
+        plt.hlines(np.quantile(data,0.95), 0, len(data), "g")
+        plt.xlabel('pixel')
+        plt.ylabel('Flux')
+        plt.title('Spectrum with peaks and lines')
+        plt.grid(True)
+
+        plt.show()
+        # Nice, now in order to improve the precision on the line centers,
+
+
+
+    # let's fit each detected peak with a gaussian to get a better centroid position
+    # generate first_guess for the fitting routine
+    # The method below just makes up credible values for a triplet (intensity, centre, width) for each line
+    # (~credible) using the peaks detected 
+    # and concatenates all that into a large vector first_guess
+    first_guess=generate_first_guess(peaks)
+    #print(first_guess)
+
+    # fit the lamp spectrum as a sum of gaussian lines using curve_fit and our first guess
+    params, covariance = curve_fit(gaussian, xaxis, data, p0=first_guess)
+    #print(np.shape(covariance))
+    # Reshape params into a 2D array (N, 3) for readability
+    num_peaks = len(params) // 3
+    params = np.array(params).reshape((num_peaks, 3))
+    allamps=params[:,0]
+    allcens=params[:,1] # => THIS ARRAY HAS THE FITTED GAUSSIAN CENTROILDS OF THE LINES
+    allwids=params[:,2]
+
+    if(0):
+        # remove the huge saturaed line at pixel 1987  & 6965 Angstrom
+        # well not 100% needed it seems we throw it away later
+        print(len(allcens))
+        ibad=np.argmin(np.abs(allcens-1987.))
+        print(ibad)
+        allcens=np.delete(allcens,ibad)
+        print(len(allcens))
+        allamps=np.delete(allamps,ibad)
+        allwids=np.delete(allwids,ibad)
+        print(allcens)
+
+        
+    # Now plot the spectrum again
+    if False:
+        plt.plot(data)
+        plt.plot(centroid_x_values, data[peaks], 'ro', label='Max')
+        plt.xlabel('Pixel')
+        plt.ylabel('Flux')
+        plt.title('Spectrum with peaks and lines')
+        # plot individual gaussian fit for each line, for check
+        for i in range(num_peaks):
+            fit_params = params[i]  # Extract parameters for each Gaussian
+            gau=gaussian(xaxis, *fit_params)
+            plt.plot(xaxis, gau)#, label=f'Gaussian {i+1}')
+            plt.text(allcens[i], np.max(gau)+3000, str(i), fontsize=12, ha='center', va='center', color='blue')
+
+        plt.legend()
+        plt.show()
+
+    fig, ax0 = plt.subplots(1)
+
+    #ax0 = axs[0]
+    #ax1 = axs[1]
+
+    ax0.plot(xaxis, data)
+    for i in range(num_peaks):
+        fit_params = params[i]  # Extract parameters for each Gaussian
+        gau=gaussian(xaxis, *fit_params)
+        ax0.text(allcens[i], np.max(gau)+3000, str(i), fontsize=10, 
+                 ha='center', 
+                 va='center', 
+                 color='C3')
+    #atlas = np.genfromtxt("Source/atlas_linelist.csv", usecols=(0,4), delimiter=",")
+    #atlas_val = atlas[:,1]
+    #atlas_l = atlas[:,0]
+    #w = np.argwhere(atlas_val > 0)
+    #atlas_val = atlas_val[w]
+    #atlas_l = atlas_l[w]
+    #ax1.plot(atlas_l, atlas_val)
+    plt.show(block=False)
+    ans = "!"
+    print("use: https://github.com/pocvirk/astronomical_data_reduction/blob/main/doc/line_atlas_ThAr.pdf")
+    print("[number] [wavelength (nm)] ; enter ok when done")
+    numbers = []
+    lambdas = []
+    while not ans == "ok":
+        ans = input("> ")
+        if ans not in ["ok", "", " "]:
+            try:
+                n, l = ans.split(" ")
+                numbers.append(int(n)), lambdas.append(float(l))
+                print("ok !")
+            except:
+                print("error")
+    pixel_lambda = []
+    for i in range(len(numbers)):
+        pixel_lambda.append([allcens[numbers[i]], lambdas[i]])
+    pixel_lambda = np.array(pixel_lambda)
+    plt.close()
+    # Now derive the full dispersion law as a polynomial fit through the points above
+    # Fit a Chebyshev polynomial of degree 1 (linear)
+    degree = 1
+    coeffs = chebyshev.chebfit(pixel_lambda[:,0], pixel_lambda[:,1], degree)
+    # Evaluate the Chebyshev polynomial across xaxis
+    y_fit = chebyshev.chebval(xaxis, coeffs)
+
+    print("{},{}".format(coeffs[0], coeffs[1]))
+    with open("./Input/values.csv", "w+") as values_file:
+        values_files.write("{},{}".format(coeffs[0], coeffs[1]))
+    # plot the fit with our calibration points:
+    plt.figure(figsize=(5,5))
+    plt.scatter(pixel_lambda[:,0],pixel_lambda[:,1])
+    plt.xlabel('pixel')
+    plt.ylabel('Angstrom')
+    plt.plot(xaxis, y_fit, label=f'Chebyshev Polynomial (Degree {degree})', color='red')
+    plt.show()
+
+    # thats a pretty good fit.
+    # to see how good it is, we will check the residuals in the next cell
+
+    return None
+
+if __name__ == "__main__":
+    main()

Source/spectro_tools.py

@@ -0,0 +1,147 @@
+# => makes the plot interactive
+#%matplotlib notebook 
+# inline makes the plots static
+#%matplotlib inline 
+
+import numpy as np
+from astropy.io import fits
+import matplotlib.pyplot as plt
+from scipy.signal import find_peaks
+import warnings
+# filter astropy warning on fits headers
+warnings.filterwarnings('ignore', category=UserWarning, append=True)
+
+def plot_spectrum_panels(xaxis,data,num_panels=2):
+    # not that useful, interactive plotting is better
+    spectrum_data=np.copy(data)
+
+    # Divide the spectrum into four panels
+    panel_height = len(spectrum_data) // num_panels
+
+    # Create subplots
+    fig, axs = plt.subplots(num_panels, 1, figsize=(15, 6*num_panels))
+
+    # Plot each panel
+    for i in range(num_panels):
+        start_idx = i * panel_height
+        end_idx = (i + 1) * panel_height
+        xaxis= list(range(len(spectrum_data)))
+        axs[i].plot(xaxis[start_idx:end_idx],spectrum_data[start_idx:end_idx])
+#        axs[i].set_xlabel('Wavelength (Angstroms)')
+        axs[i].set_ylabel('counts(ADU)')
+        axs[i].set_title(f'Panel {i + 1}')
+        axs[i].grid(True)
+
+
+def read_raw_spectrum(fits_file,get_header=0):
+#    print("yoyoyo")
+    # returns an index and the fluxes
+    # Open the FITS file
+    hdulist = fits.open(fits_file)
+    header = hdulist[0].header
+    # Assuming the spectrum data is in the first extension (HDU index 0)
+    data = hdulist[0].data[0][0]
+    xaxis= list(range(len(data)))
+    if(get_header==0):
+        return xaxis,data
+    if(get_header==1):
+        return xaxis,data,header
+
+def write_fits_spectrum(target_file,data,header=[]):
+    if(header==[]):
+        hdu=fits.hdu.hdulist.PrimaryHDU([[data]])
+    else:
+        hdu=fits.hdu.hdulist.PrimaryHDU([[data]],header)
+    hdul = fits.hdu.HDUList([hdu])
+    hdul.writeto(target_file,overwrite=True)
+    print('wrote ',target_file)
+        
+        
+def read_raw_spectrum_old(fits_file,get_header=0):
+    # returns an index and the fluxes
+    # Open the FITS file
+    hdulist = fits.open(fits_file)
+    header = hdulist[0].header
+    # Assuming the spectrum data is in the first extension (HDU index 0)
+    data = hdulist[0].data[0]
+    xaxis= list(range(len(data)))
+    if(get_header==0):
+        return xaxis,data
+    if(get_header==1):
+        return xaxis,data,header
+
+def write_fits_spectrum_old(target_file,data,header=[]):
+    if(header==[]):
+        hdu=fits.hdu.hdulist.PrimaryHDU([data])
+    else:
+        hdu=fits.hdu.hdulist.PrimaryHDU([data],header)
+    hdul = fits.hdu.HDUList([hdu])
+    hdul.writeto(target_file,overwrite=True)
+    print('wrote ',target_file)
+
+def read_calibrated_spectrum(fits_file):
+    # Open the FITS file
+    with fits.open(fits_file) as hdul:
+        # Extract the data and header from the primary HDU
+        data = hdul[0].data[0]
+        header = hdul[0].header
+
+        # Extract necessary header keywords for calibration
+        crval1 = header['CRVAL1']  # Reference value of the first pixel (wavelength)
+        cdelt1 = header['CDELT1']  # Pixel scale (wavelength per pixel)
+        naxis1 = header['NAXIS1']  # Number of pixels in the spectral axis
+
+        # Create an array of calibrated wavelengths
+        wavelengths = crval1 + np.arange(naxis1) * cdelt1
+    return wavelengths,data
+
+
+# Define the Gaussian function
+def gaussian(x, *params):
+    result= np.full(len(x),0.)
+    for i in range(0, len(params), 3):
+        amp, cen, wid = params[i:i+3]
+        result += amp * np.exp(-(x - cen) ** 2 / (2 * wid ** 2))
+    return result
+
+# Define the Gaussian function
+def gaussian_plus_bg_2(x, *params):
+    # just like the gaussian but with a constant background under ti
+    # should help fitting emission lines in objects
+    # this is bad because there are multiple backgrounds => degenerate
+    result= np.full(len(x),0.)
+    for i in range(0, len(params), 4):
+        amp, cen, wid , bg = params[i:i+4]
+        result += amp * np.exp(-(x - cen) ** 2 / (2 * wid ** 2)) + bg
+    return result
+
+def gaussian_plus_bg(x, *params):
+    # just like the gaussian but with a constant background under ti
+    # should help fitting emission lines in objects
+    result= np.full(len(x),0.)
+    bg=params[0]
+    result=result+bg
+    for i in range(1, len(params), 3):
+        amp, cen, wid = params[i:i+3]
+        result += amp * np.exp(-(x - cen) ** 2 / (2 * wid ** 2))
+    return result
+
+def generate_first_guess(peaks):
+#    npeaks=len(peaks[:3])
+    npeaks=len(peaks)
+    initial_cen=peaks
+    initial_amp=np.full(npeaks,20000.)
+    initial_wid=np.full(npeaks,1.)
+#    print(initial_cen)
+#    print(initial_amp)
+#    print(initial_wid)
+    first_guess=np.full((npeaks,3),0.)
+    for i in range(npeaks):
+        first_guess[i,0]=initial_amp[i]
+        first_guess[i,1]=initial_cen[i]
+        first_guess[i,2]=initial_wid[i]
+    first_guess=np.reshape(first_guess,3*npeaks)
+#    print(first_guess)
+    return first_guess
+
+

get_calib.sh

@@ -0,0 +1,2 @@
+source activate.sh
+python Source/get_calib.py

reduction.sh

@@ -0,0 +1,2 @@
+source activate.sh
+python Source/FIDR.py