Yael-II/Astrobs-Tools
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Yael-II
2025-01-02 22:43:21 +01:00
parent c8ae670ea7
commit ca4274fc55
7 changed files with 569 additions and 92 deletions
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137
Source/data_reduction_tools.py
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@@ -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