def Get_DRACS(filepath, chip):
"""Filepath needs to point object"""
filename = get_filenames(filepath, "CRIRE*.norm.comb.fits", "*_" + str(chip+1) + ".*")
print("Filename =", filepath + filename[0])
print("length filename",len(filename))
assert len(filename) is 1 # Check only one filename found
hdr = fits.getheader(filepath + filename[0])
data = fits.getdata(filepath + filename[0])
return hdr, data
def test_resampler():
""" Test reampler visually"""
import matplotlib.pyplot as plt
from astropy.io import fits
from Get_filenames import get_filenames
detector = 1
folder = "/home/jneal/Phd/data/Crires/BDs-DRACS/HD30501-1/Combined_Nods/"
file_names = get_filenames(folder, "C*_{}.nod.*".format(detector), "*wavecal.fits")
print("file names", file_names)
data = fits.getdata(file_names[0])
resampled_data = log_resampler(data["Wavelength"], data["Extracted_dracs"])
plt.plot(data["Wavelength"], data["Extracted_dracs"], label="org")
plt.plot(10 ** resampled_data[0], resampled_data[1], "o-", label="resampled")
plt.legend()
plt.show()
#path = "C:/Users/Jason/Dropbox/PhD/CriresReduction/{0}/".format(observation_name)
#intermediate_path = path + "Intermediate_steps/"
#combined_path = path + "Combined_Nods/"
#image_path = path + "quicklooks/"
# To run
dir_path = os.getcwd()
intermediate_path = dir_path + "/Intermediate_steps/"
combined_path = dir_path + "/Combined_Nods/"
image_path = dir_path + "/images/"
observation_name = os.path.split(dir_path)[-1]
for chip_num in range(1, 5):
combined_name = get_filenames(combined_path, 'CRIRE*.sum.fits', "*_{}.*".format(chip_num))
nod_names = get_filenames(intermediate_path, 'CRIRE*.ms.fits', "*_{}.*".format(chip_num))
norm_names = get_filenames(intermediate_path, 'CRIRE*.ms.norm.fits', "*_{}.*".format(chip_num))
combined_data = fits.getdata(combined_path + combined_name[0])
nod_data = [fits.getdata(name) for name in nod_names]
norm_data = [fits.getdata(name) for name in norm_names]
median_nod = np.median(norm_data, axis=0) # Median combine normalzied spectra
# Plot Reuslts
fig = plt.figure()
plt.suptitle("{0}, Chip-{1}".format(observation_name, chip_num), fontsize=16)
ax1 = plt.subplot(311)
for i, data in enumerate(nod_data):
ax1.plot(data, label=i+1)
