def generate_ibsen_calibration_files(directory, reference):
# Extract Rasta specific raw data
cal_dict = sort_ibsen_by_int(directory)
cal_dict_tmp = copy.deepcopy(cal_dict)
bias_file = directory + 'assumed_bias/' + 'bias.txt'
flag = os.path.exists(bias_file)
noise_dict = get_noise(flag)(bias_file, cal_dict)
# Substract darkcurrent from measurements
nonlinear_config = {'max_lowest_int_time': 2323 , 'sigma': 100, 'index_start_spline_fit': 1500, 'gaussian_mean_steps':4}
#nonlinear_config = {'max_lowest_int_time': 1156, 'sigma': 50, 'index_start_spline_fit': 1400, 'gaussian_mean_steps':4}
nonlinear_correction_dict = generate_nonlinear_correction(cal_dict_tmp, nonlinear_config, noise_dict)
noise_dict['noise'] = np.zeros(len(noise_dict['channel'])) # Setting offset to zero
cal_dict_tmp = subtract_dark(cal_dict_tmp)
nonlinear_correction_dark = generate_nonlinear_correction(cal_dict_tmp, nonlinear_config, noise_dict)
check_nonlinearity(cal_dict, [nonlinear_correction_dict, nonlinear_correction_dark])
cal_dict = subtract_dark(cal_dict)
while raw_input('Change settings (y or n)') == 'y':
for key in nonlinear_config.keys():
nonlinear_config[key] = int(raw_input('%s' %key))
nonlinear_correctiony_dict = generate_nonlinear_correction(cal_dict, nonlinear_config)
check_nonlinearity(cal_dict, nonlinear_correction_dict)
#Nonlinear correction for ibsen response
for integration, spectra in cal_dict.items():
spectra['reference']['mean'] = spectra['reference']['mean'] / np.interp(spectra['reference']['mean'], nonlinear_correction_dict['DN'], nonlinear_correction_dict['nonlinear'])
spectra['reference']['mean'] = spectra['reference']['mean'] / integration
# Generate ibsen response factors for physical units
cal_dict, response_dict = generate_response_factors(cal_dict, reference)
import matplotlib.pyplot as plt
for integration, spectra in cal_dict.items():
spectra['reference']['mean'] = spectra['reference']['mean'] / np.interp(spectra['reference']['wave'], response_dict['wave'], response_dict['scale_factors'])
plt.plot(spectra['reference']['wave'], spectra['reference']['mean'])
plt.xlabel('Wavelength $\lambda$ [nm]', **hfont)
plt.ylabel(r'$\frac{mW}{nm m^2 sr}$', **hfont)
plt.show()
fix, ax1 = plt.subplots()
ax1.plot(response_dict['wave'], response_dict['intensity'], 'r+', label='Ibsen response')
ax1.set_xlabel('Wavelength $\lambda$ [nm]', **hfont)
ax1.set_ylabel('Signal [DN]')
ax1.legend(loc=2, ncol=1, prop = fontP,fancybox=True, shadow=False)
ax2 = ax1.twinx()
ax2.plot(response_dict['wave'], response_dict['halogen'], 'y+', label='Halogen lamp')
ax2.plot(response_dict['wave'], response_dict['intensity'] / response_dict['scale_factors'], 'b', label='Calibrated ibsen response')
ax2.set_ylabel(r'$\frac{mW}{nm m^2 sr}$', **hfont)
ax2.legend(loc=1, ncol=1, prop = fontP,fancybox=True, shadow=False)
plt.show()
plt.plot(response_dict['wave'], response_dict['intensity'], 'r+', label='Ibsen response')
plt.plot(response_dict['wave'], response_dict['halogen'], 'y+', label='Halogen lamp')
plt.plot(response_dict['wave'], response_dict['intensity'] / response_dict['scale_factors'], 'b', label='Calibrated ibsen response')
plt.xlabel('Wavelength $\lambda$ [nm]', **hfont)
plt.ylabel(r'$\frac{mW}{nm m^2 sr}$', **hfont)
plt.legend(loc=2, ncol=1, prop = fontP,fancybox=True, shadow=False,bbox_to_anchor=(1.0, 1.0))
plt.show()
def test_offset_subtraction():
""" testing of offset subtraction or darkcurrent subtraction is better"""
test_directory = '/home/joanna/DLR/Codes/calibration/test_nonlinearity/labor/ibsen_nonlinearity_verschoeben2/'
offset = 'offset_corrected_calibration/'
dark_ = 'darkcurrent_corrected_calibration/'
_file = 'nonlinearity_correction.txt'
nonlinear_correction_dict = dict()
nonlinear_correction_dict_dark = dict()
cal_dict = sort_ibsen_by_int(test_directory)
nonlinear_correction_dict['DN'], nonlinear_correction_dict['nonlinear'] = read_nonlinear_correction_file(offset + _file)
nonlinear_correction_dict_dark['DN'], nonlinear_correction_dict_dark['nonlinear'] = read_nonlinear_correction_file(dark_ + _file)
check_nonlinearity(cal_dict, [nonlinear_correction_dict,nonlinear_correction_dict_dark])
def test_offset_subtraction():
""" testing of offset subtraction or darkcurrent subtraction is better"""
test_directory = "/home/jana_jo/DLR/Codes/calibration/test_nonlinearity/labor/ibsen_nonlinearity_verschoeben2/"
offset = "offset_corrected_calibration/"
dark_ = "darkcurrent_corrected_calibration/"
_file = "nonlinearity_correction.txt"
nonlinear_correction_dict = dict()
nonlinear_correction_dict_dark = dict()
cal_dict = sort_ibsen_by_int(test_directory)
nonlinear_correction_dict["DN"], nonlinear_correction_dict["nonlinear"] = read_nonlinear_correction_file(
offset + _file
)
nonlinear_correction_dict_dark["DN"], nonlinear_correction_dict_dark["nonlinear"] = read_nonlinear_correction_file(
dark_ + _file
)
check_nonlinearity(cal_dict, [nonlinear_correction_dict, nonlinear_correction_dict_dark])
