def prepare_calibration_files(self, electron_calibration_file,
quantum_efficiency_calibration_file):
calibration_energy = basic_file_app.load_1d_array(
quantum_efficiency_calibration_file, 0, 0)
calibration_quantum = basic_file_app.load_1d_array(
quantum_efficiency_calibration_file, 1, 0)
self.quantum_efficiency = basic_file_app.stack_arrays(
calibration_energy, calibration_quantum, axis=1)
self.calibration_photon_number = basic_file_app.load_1d_array(
electron_calibration_file, 0, 0)
return self.calibration_photon_number, self.quantum_efficiency
self.reference_points[:, 1], self.order)
def give_fit(self):
return self.poly_coefficients
def compare_fit(self):
#x_axis = np.linspace(np.min(self.reference_points[:, 1]), np.max(self.reference_points[:, 1]),2548)
x_axis = np.linspace(0, 2048, 2048)
#fit_y = np.linspace(np.min(self.reference_points[:, 1]), np.max(self.reference_points[:, 1]), 2048)
fit_y = np.linspace(0, 2048, 2048)
for counter, value in enumerate(x_axis):
fit_y[counter] = self.poly_coefficients[
-1] + self.poly_coefficients[-2] * x_axis[counter]
plt.figure(1)
plt.scatter(self.reference_points[:, 1], self.reference_points[:, 0])
plt.plot(x_axis, fit_y)
plt.plot()
reference_path = "data/S3_20210412_reference.txt"
reference_eV = basic_file_app.load_1d_array(reference_path, 0, 2)
reference_px = basic_file_app.load_1d_array(reference_path, 1, 2)
reference_points = basic_file_app.stack_arrays(reference_eV, reference_px, 1)
print(reference_points)
Test_Ni = CalibrationFit(reference_points, 1, "S3_Ni_20210412")
Test_Ni.fit_reciproce()
Test_Ni.compare_fit()
print(Test_Ni.give_fit())
plt.show()
plt.xlabel('eV')
plt.ylabel('Nphoton/ s*sr @0.1% bw')
plt.savefig(file_name + 'Nphoton' + ".png",
bbox_inches="tight",
dpi=500)
np.savetxt(file_name + 'Nphoton' + ".txt",
result,
delimiter=' ',
header='string',
comments='',
fmt='%s')
data_file = "Intensity_evaluation/data_intensity/S1_W_per_s/210205_PM043550_calibrated_analytical.txt"
data = basic_file_app.stack_arrays(
basic_file_app.load_1d_array(data_file, 1, 5),
basic_file_app.load_1d_array(data_file, 2, 5),
axis=1)
data_below_1000ev = data
calibration_number_e_per_photon = "Intensity_evaluation/electrons_per_photon_poly_fit.txt"
calibration_q_a = "Intensity_evaluation/QE_greateyesGE_BI_interpolation_bin_size_0.01.txt"
al_filter_file = "Intensity_evaluation/Al_500nm_eV_interpolation_bin_size_0.05.txt"
al_filter = basic_file_app.stack_arrays(
basic_file_app.load_1d_array(al_filter_file, 0, 0),
basic_file_app.load_1d_array(al_filter_file, 1, 0),
axis=1)
mylar_filter_file = "Intensity_evaluation/Mylar_900nm_eV_interpolation_bin_size_0.05.txt"
mylar_filter = basic_file_app.stack_arrays(
return np.vstack((parameter_info, names, header_names, result))
def save_data(self, description1):
result = self.prepare_header(description1)
print('...saving:', description1)
plt.figure(1)
plt.savefig(description1 + ".png", bbox_inches="tight", dpi=500)
np.savetxt(description1 + '_optical_density' + ".txt", result, delimiter=' ',
header='string', comments='',
fmt='%s')
reference_path = "data/stack_105ms/calibrated/SiN single_wo_calibration.txt"
reference_x = basic_file_app.load_1d_array(reference_path, 0, 4)
reference_y = basic_file_app.load_1d_array(reference_path, 1, 4)
reference = basic_file_app.stack_arrays(reference_x, reference_y, 1)
sample_path = "data/stack_105ms/calibrated/NiO single var 1_calibration.txt"
sample_x = basic_file_app.load_1d_array(sample_path, 0, 4)
sample_y = basic_file_app.load_1d_array(sample_path, 1, 4)
sample = basic_file_app.stack_arrays(sample_x, sample_y, 1)
Test = OpticalDensity(reference, sample, "SiN", "NiO")
Test.process_optical_density()
Test.plot_result()
plt.ylim(-0.2, 2.5)
plt.xlim(850, 890)
Test.save_data("S2_SiN_NiO_px_corrected_var1_with_wo")
plt.legend()
def scale_y(self, scale_factor):
self.array[:,1] = self.array[:,1] * scale_factor
return self.array
def substract_y(self, factor):
self.array[:,1] = self.array[:,1] - factor
return self.array
file_1_path = "data/20210426/210426_S2_mylar_high_98_stack_processing_Mylar.txt"
file_1_x = basic_file_app.load_1d_array(file_1_path, 0, 4)
file_1_y = basic_file_app.load_1d_array(file_1_path,1,4)
file_1 = basic_file_app.stack_arrays(file_1_x, file_1_y, 1)
file_2_path = "data/reference_2021041_stack_pre_processing.txt"
file_1_x = basic_file_app.load_1d_array(file_2_path, 0, 4)
file_1_y = basic_file_app.load_1d_array(file_2_path,1,4)
file_2 = basic_file_app.stack_arrays(file_1_x, file_1_y, 1)
scale_1 = np.amax(file_1[:,1])
scale_2 = np.amax(file_2[:,1])
def load_data(self):
x = basic_file_app.load_1d_array(self.path + '/' + self.filename, 0, 2)
y = basic_file_app.load_1d_array(self.path + '/' + self.filename, 1, 2)
return basic_file_app.stack_arrays(x, y, 1)
def open_file(file_name, path):
spectral = basic_file_app.load_1d_array(path + '/' + file_name, 0, 4)
counts = basic_file_app.load_1d_array(path + '/' + file_name, 2, 4)
return basic_file_app.stack_arrays(spectral, counts, 1)
def prepare_file(self, skip_row):
# ToDo: test if not empty, otherwise raise error
array_x = basic_file_app.load_1d_array(self.file, 1, skip_row)
array_y = basic_file_app.load_1d_array(self.file, 0, skip_row)
return basic_file_app.stack_arrays(array_x, array_y, 1)