def calibrate_array(self):
self.file_list = basic_file_app.get_file_list(self.file_path)
my_calibration = calibration_analytical_from_array.CalibrateArray(
self.calibration_parameter, self.directory)
for x in self.file_list:
my_array = basic_file_app.load_2d_array(self.file_path + x, 0, 1,
2)
my_calibration.set_input_array(my_array, x)
my_calibration.main()
my_calibration.save_data(
"back: " + path_background + "RZP-structure:___" +
rzp_structure_name, roi_list)
def px_shift(self):
self.file_list = basic_file_app.get_file_list(self.new_dir)
print(len(self.file_list))
reference = basic_file_app.load_2d_array(
self.new_dir + '/' + self.file_list[0], 0, 1, 1)
#print("new file list", self.file_list)
for x in self.file_list[1:]:
image_array = basic_file_app.load_2d_array(self.new_dir + '/' + x,
0, 1, 1)
ShiftIt = px_shift_on_picture_array.PixelShift(
reference, self.reference_points)
corrected_array = ShiftIt.evaluate_shift_for_input_array(
image_array)
self.overwrite_original(x, corrected_array)
plt.close()
def plot_them_all(path, column_x, column_y, skip_rows):
file_list = basic_file_app.get_file_list(path)
for x in file_list[7:13]:
array = basic_file_app.load_2d_array(path + '/' + x, column_x,
column_y, skip_rows)
plt.figure(1)
plt.plot(array[:, 0],
-np.log(array[:, 1]) + 14,
marker=".",
markersize=3)
plt.xlabel("eV")
plt.ylabel("-log(signal) - const")
plt.legend()
plt.show()
def avg_of_stack(self):
self.file_list = basic_file_app.get_file_list(self.directory)
print("xxxxxxxxxxxxxx mean value of stack xxxxxxxxxxxxx")
print(self.directory, "path")
print(self.file_list)
my_avg = basic_file_app.StackMeanValue(self.file_list, self.directory,
1, 2, 4)
my_result = my_avg.get_result()
plt.figure(10)
plt.plot(my_result[:, 0],
-np.log(my_result[:, 1]) + 16,
label="my_avg")
save_pic = os.path.join(self.directory,
self.directory[4:] + "_mean" + ".png")
plt.savefig(save_pic, bbox_inches="tight", dpi=500)
return my_result
def batch_single_pictures_background():
for x in file_list_raws:
open_stack_raws = basic_image_app.SingleImageOpen(x, path_picture)
my_single_picture = open_stack_raws.return_single_image()
scaled_straylight_correction = process_raw_images_sum_then_back.ImagePreProcessing(
my_single_picture, x[:-4], my_background, "straylight", roi_list1)
scaled_straylight_correction.main()
scaled_straylight_correction.save_data(
"single_image_sum_and_then_back")
#batch_single_pictures_background()
path = "data/SiN_105ms_variant_2/"
binned_file_list = basic_file_app.get_file_list(path)
reference_points = (1128, 980)
def batch_pixel_correction():
reference_array = basic_file_app.load_2d_array(path + binned_file_list[0],
0, 1, 4)
PixelCorrection = px_shift_on_picture_array.PixelShift(
reference_array, reference_points)
my_result = reference_array
for x in binned_file_list[1:]:
my_array = basic_file_app.load_2d_array(path + x, 0, 1, 4)
corrected_array = PixelCorrection.evaluate_shift_for_input_array(
my_array)
my_result[:, 1] = my_result[:, 1] + corrected_array[:, 1]
def create_file_list(self):
return basic_file_app.get_file_list(self.path)
result = np.vstack((header, header2, result))
save_name = os.path.join(new_dir, file_name + 'avg' + ".txt")
np.savetxt(save_name,
result,
delimiter=' ',
header='string',
comments='',
fmt='%s')
my_path = "results_cal_5000ms_pos2/"
avg_path = "results_avg"
result_file_name = "20210628_5000ms_pos2_avg"
#os.mkdir(avg_path)
my_files = basic_file_app.get_file_list(my_path)
#class(path, scale, column) -> skiprows in the code
test = AvgOnStack1Column(my_path, 1., 2)
my_avg = test.integrate_over_stack()
#create x-axis from one of the files (path+name, column, skip-rows)
my_x = basic_file_app.load_1d_array(my_path + '/' + my_files[0], 1, 6)
my_error = test.standard_deviation()
test.save_statistics(avg_path, result_file_name)
#plt.scatter(my_x[:], my_avg[:])
plt.figure(1)
plt.errorbar(my_x[:],
my_avg[:],
yerr=my_error,
fmt="o",
label=result_file_name)