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main_source_size_method_fixed_coordinates.py
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main_source_size_method_fixed_coordinates.py
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import matplotlib.pyplot as plt
import numpy as np
import basic_image_app
from lmfit.models import GaussianModel
import math
# note a background substraction method is added
# the initial guess is now the variable "coordinates" -> array with [y0, x0]
# crop-roi: is [y1,y2,x1,x2] that gives the ROI range (in order to accelerate the method -> choose ROI)
# width area: searching area (in px) for center of mass determination
# Attention: you might change the self.file_name borders of your file_name string
# Attention2: you might need to change the scaling (see below) and unit-name
# Attention3: in background function: MANUALLY set threshold for "hot pixel" correction
class SourceSize:
def __init__(self, file, img_background, width_area, crop_coordinates, coordinates):
self.file_name = file[0:-4]
self.image = basic_image_app.read_image(file)
self.image = basic_image_app.convert_32_bit(self.image)
self.back_ground = img_background
self.background_substraction_on_img()
self.crop_coordinates = crop_coordinates
self.crop_image()
self.index_y, self.index_x_axis = coordinates[0], coordinates[1]
self.width_area = width_area
self.center_of_intensity()
self.x_axis_vertical, self.line_out_vertical = self.vertical_line_out()
self.x_axis_horizontal, self.line_out_horizontal = self.horizontal_line_out()
self.line_out_horizontal = self.zero_offset(self.line_out_horizontal)
self.line_out_vertical = self.zero_offset(self.line_out_vertical)
self.sigma_temp = 0
self.amplitude_temp = 0
self.center_temp = 0
self.result = np.empty([1, 4])
def background_substraction_on_img(self):
if self.back_ground is None:
print("no background img")
return self.image
else:
roi_img = np.mean(self.image[0:250, 400:-1])
roi_back = np.mean(self.image[0:250, 400:-1])
scale = roi_img/roi_back
# ! Atttention ! if anything is <0 the value is at maximum of dynamic range
self.image[:,:] = self.image[:,:] - self.back_ground[:,:]*scale
self.image[self.image < 0] = 0
#threshhold
self.image[self.image > 65000] = 1
return self.image
def crop_image(self):
# 0 = x, 1=y
self.image = self.image[self.crop_coordinates[0]:self.crop_coordinates[1], self.crop_coordinates[2]:self.crop_coordinates[3]]
return self.image
def evaluate_index_of_maximum_in_picture(self):
# does not work with hot pixels
self.index_y, self.index_x_axis = np.where(self.image == np.amax(self.image))
# sometimes np.where has more than one coordinate:
self.index_y = self.index_y[0]
self.index_x_axis = self.index_x_axis[0]
def center_lineout_on_image(self):
plt.figure(12)
plt.imshow(self.image[int(self.index_y - self.width_area / 2): int(self.index_y + self.width_area / 2),
int(self.index_x_axis - self.width_area / 2): int(self.index_x_axis + self.width_area / 2)])
def center_of_intensity(self):
print('before', self.index_y, self.index_x_axis)
y_up = int(self.index_y - self.width_area / 2)
y_down = int(y_up + self.width_area)
x_left = int(self.index_x_axis - self.width_area / 2)
x_right = int(x_left + self.width_area)
sum_of_y_line = np.sum(self.image[y_up: y_down, x_left:x_right], axis=1)
sum_of_x_line = np.sum(self.image[y_up: y_down, x_left:x_right], axis=0)
plt.legend()
self.index_y = int(np.where(sum_of_y_line[:] >= np.amax(sum_of_y_line))[0] - self.width_area / 2 + self.index_y)
self.index_x_axis = int(
np.where(sum_of_x_line[:] >= np.amax(sum_of_x_line))[0] - self.width_area / 2 + self.index_x_axis)
print('after:', self.index_y, self.index_x_axis)
return self.index_y, self.index_x_axis
def integrate_intensity_in_range_width(self):
small_roi = self.image[int(self.index_y - self.width_area / 2):int(self.index_y + self.width_area / 2),
int(self.index_x_axis - self.width_area / 2): int(self.index_x_axis + self.width_area / 2)]
sum = np.sum(small_roi, axis = 0)
sum = np.sum(small_roi)
print("sum of center +/- ",self.width_area, sum)
return sum
def plot_results(self):
plt.figure(1)
plt.imshow(self.image)
plt.scatter(self.index_x_axis, self.index_y)
plt.vlines(x=self.index_x_axis, ymin=0, ymax=len(self.image), color="y")
plt.figure(2)
plt.plot(self.x_axis_vertical, self.line_out_vertical, color="b")
plt.figure(3)
plt.plot(self.x_axis_horizontal, self.line_out_horizontal, color="r")
def vertical_line_out(self):
# seems to be horizontal [x values for y]
self.line_out_vertical = self.image[:, self.index_x_axis]
self.x_axis_vertical = np.arange(0, len(self.line_out_vertical))
return self.x_axis_vertical, self.line_out_vertical
def horizontal_line_out(self):
self.line_out_horizontal = np.reshape(self.image[self.index_y, :], np.size(self.image[self.index_y, :]))
self.x_axis_horizontal = np.arange(0, len(self.line_out_horizontal))
return self.x_axis_horizontal, self.line_out_horizontal
def evaluate_horizontal_and_vertical(self):
self.fit_gaussian(self.x_axis_horizontal, self.line_out_horizontal, 3)
self.result[0, 0] = self.sigma_temp
self.result[0, 2] = self.center_temp
self.fit_gaussian(self.x_axis_vertical, self.line_out_vertical, 2)
self.result[0, 1] = self.sigma_temp
self.result[0, 3] = self.center_temp
return self.result
def fit_gaussian(self, array_x, array_y, figure_number):
mod = GaussianModel()
pars = mod.guess(array_y, x=array_x)
out = mod.fit(array_y, pars, x=array_x)
self.sigma_temp = out.params['sigma'].value
self.amplitude_temp = out.params['amplitude'].value
self.center_temp = out.params['center'].value
self.plot_fit_function(array_x, figure_number)
return self.sigma_temp, self.amplitude_temp, self.center_temp
def zero_offset(self, array):
offset = np.mean(array[0:1])
return array[:] - offset
def plot_fit_function(self, array_x, figure_number):
# IMPORTANT sigma corresponds to w(0) beamwaist = half beam aperture
yy = np.zeros([len(array_x), 1])
for counter, value in enumerate(array_x):
a = (self.amplitude_temp / (self.sigma_temp * ((2 * math.pi) ** 0.5)))
b = -(array_x[counter] - self.center_temp) ** 2
c = 2 * self.sigma_temp ** 2
yy[counter] = (a * math.exp(b / c))
plt.figure(figure_number)
plt.plot(array_x, yy)
if figure_number == 2:
plt.plot(self.x_axis_vertical, self.line_out_vertical)
else:
plt.plot(self.x_axis_horizontal, self.line_out_horizontal)
class BatchStack:
def __init__(self, path, avg_img_back, width, crop_coordinates, coordinates):
self.folder_name = path
self.file_list = basic_image_app.get_file_list(self.folder_name)
self.folder_result_sigma = np.empty([1, 4])
self.label = "px"
self.width = width
self.crop_coordinates = crop_coordinates
self.coordinates = coordinates
self.img_background = avg_img_back
self.integrated = np.zeros([len(self.file_list)])
def evaluate_folder(self):
for counter, x in enumerate(self.file_list):
print("file:", x)
SinglePicture = SourceSize(self.folder_name + x, self.img_background, self.width, self.crop_coordinates, self.coordinates)
result = SinglePicture.evaluate_horizontal_and_vertical()
self.folder_result_sigma = np.vstack((self.folder_result_sigma, result))
self.integrated[counter] = SinglePicture.integrate_intensity_in_range_width()
SinglePicture.plot_results()
#plt.show()
print('############')
print(self.folder_name)
self.folder_result_sigma = self.folder_result_sigma[1:]
self.pointing_vertical()
self.pointing_horizontal()
self.beamwaist_to_FWHM()
self.integrated_signal()
return self.folder_result_sigma, len(self.file_list)
def integrated_signal(self):
x = np.linspace(0,len(self.integrated), len(self.integrated))
print(len(x), "x")
print(len(self.integrated), "y")
avg = np.mean(self.integrated)
sigma = np.std(self.integrated)
plt.figure(10)
plt.scatter(x,self.integrated, label = " stability decimal (" + str(round(sigma/avg, 3)) +")" )
plt.xlabel("shot no")
plt.ylabel("integrated counts")
plt.legend()
plt.savefig( self.folder_name[5:-2] + "Intensity" + ".png", bbox_inches="tight", dpi=500)
def beamwaist_to_FWHM(self):
FWHM = np.empty([len(self.file_list), 2])
#[:,0] horizontal
#[:,1] vertical
FWHM[:, 0] = 2* self.folder_result_sigma[:, 0]*(2 * 0.69) ** 0.5
# sigma in the gaussian fit is w(z) beam-waist radius : w(z) = FWHM/(2*ln2)**0.5 (see wiki gaussian beams)
FWHM[:, 1] = ( 2* self.folder_result_sigma[:, 1]) * (2 * 0.69) ** 0.5
self.folder_result_sigma = np.hstack((self.folder_result_sigma, FWHM))
return self.folder_result_sigma
def scale_result(self, const, label):
self.label = label
self.folder_result_sigma[:, :] = self.folder_result_sigma[:, :] * const
return self.folder_result_sigma, self.label
def pointing_vertical(self):
mean_position = np.mean(self.folder_result_sigma[:, 2])
print("vertical")
self.statistics_pointing(self.folder_result_sigma[:, 2])
self.folder_result_sigma[:, 2] = self.folder_result_sigma[:, 2] - mean_position
return self.folder_result_sigma
def pointing_horizontal(self):
mean_position = np.mean(self.folder_result_sigma[:, 3])
print("horizontal")
self.statistics_pointing(self.folder_result_sigma[:, 3])
self.folder_result_sigma[:, 3] = self.folder_result_sigma[:, 3] - mean_position
return self.folder_result_sigma
def statistics_pointing(self, array):
print('pointing position 0 mean in px:', np.mean(array), 'std in px', np.std(array))
def statistics_divergence(self, array):
print("size mean i:" + self.label ,np.mean(array), 'std', np.std(array))
def prepare_header(self, description1):
# insert header line and change index
self.statistics_divergence(self.folder_result_sigma[:,0])
self.statistics_divergence(self.folder_result_sigma[:,1])
names = (['file', self.folder_name, description1, "unit: ", self.label, "..."])
parameter_info = (
["horizontal w(z)", " vertical w(z)", "pointing_horizontal", "pointing_vertical", "FWHM_h", "FWHM_v"])
return np.vstack((parameter_info, names, self.folder_result_sigma))
def save_data(self, description1, file_name):
result = self.prepare_header(description1)
print('...saving:', file_name)
plt.figure(5)
x_axis = np.arange(0, len(self.file_list))
plt.scatter(x_axis, self.folder_result_sigma[:, -1], label='vertical FWHM ' + file_name[-18:-5], color="y")
plt.scatter(x_axis, self.folder_result_sigma[:, -2], label='horizontal FWHM' + file_name[-18:-5], color="c")
plt.xlabel('shot number')
plt.ylabel(self.label)
#plt.ylim(10, 70)
plt.legend()
plt.savefig(file_name + description1 + ".png", bbox_inches="tight", dpi=500)
plt.figure(6)
plt.scatter(x_axis, self.folder_result_sigma[:, 3], label="pointing_vertical " + file_name[-18:-5])
plt.scatter(x_axis, self.folder_result_sigma[:, 2], label="pointing_horizontal " + file_name[-18:-5])
plt.xlabel("shot no")
plt.ylabel(self.label)
#plt.ylim(-100, 70)
plt.legend()
plt.savefig(file_name + "pointing" + ".png", bbox_inches="tight", dpi=500)
np.savetxt(file_name + description1 + ".txt", result, delimiter=' ',
header='string', comments='',
fmt='%s')
path = "data/38A_4x4_0.25s/"
path_back = "data/dark_4x4_0.25s/"
avg_background_image = basic_image_app.ImageStackMeanValue(basic_image_app.get_file_list(path_back), path_back)
avg_backround_img = avg_background_image.average_stack()
# requires, path, width_of area to calculate center of intensity (must be in row of 2)
#tofill: path, background_image or None, coordinates - in relation to crop-coordinates (see class 1)
#y1,y2, x1,x2
crop_coordinates = ([240, 450, 57,250])
fixed_coordinates = ([ 86, 76])
my_result = BatchStack(path, avg_backround_img,100, crop_coordinates, fixed_coordinates)
my_result.evaluate_folder()
# rescale is factor for e.g. px or magnification size, changes (float, str()) str is unit name
my_result.scale_result((15*4)*(82/1200), "um")
#my_result.scale_result(1000/2.625, "mrad")
my_result.save_data('LTXM', "20210720_sourcesize_38A_4x4_0.25s")
#plt.ylim(-60,60)
plt.show()