from src.hyperspectral_imaging import get_hyperspectral_image
import os
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as sts
white_directory = '../Data/Xylene_Diluted_PDMS/white_light'
image_directory = '../Data/Xylene_Diluted_PDMS/image'
white_files = os.listdir(white_directory)
print(white_files)
wavelengths = np.arange(810, 940.5, 0.5)
print(wavelengths)
image = get_hyperspectral_image('xylene_diluted_pdms_hyperspectral.npy',
image_directory)
wave = np.linspace(498.6174, 1103.161, 3648)
raw_background = os.path.join(white_directory, white_files[0])
white_water = os.path.join(white_directory, white_files[-1])
fig, ax = plt.subplots()
fig2, ax2 = plt.subplots()
wavelength, mirror = np.genfromtxt(raw_background, unpack=True, delimiter=',')
wavelength, grating_water = np.genfromtxt(white_water,
unpack=True,
delimiter=',')
normalised_grating_reflectance = np.divide(grating_water, mirror)
ax.plot(wave, normalised_grating_reflectance)
ax.set_xlim([500, 1000])
ax.set_ylim([0, 1])
mean = list()
std =list()
images = list()
image_subdirectories = os.listdir(image_data_directory)
results_subdirectories = os.listdir(image_results_directory)
image_subdirectories.remove('.DS_Store')
sorted_image_directories = sorted(image_subdirectories, key = natural_key)
#initial = get_hyperspectral_image(os.path.join(image_results_directory,sorted_image_directories[0]),os.path.join(image_data_directory,sorted_image_directories[0]), hyperspectral_wavelengths)
results_filenames = os.listdir(image_results_directory)
sorted_results_file = sorted(results_filenames,key=natural_key)
print('Results Files')
print(results_filenames)
initial= get_hyperspectral_image(os.path.join(image_results_directory,sorted_results_file[0]),os.path.join(image_data_directory,sorted_results_file[0]), hyperspectral_wavelengths)
for directory in sorted_results_file[1:]:
print(directory)
results_file = directory
results_file_path = os.path.join(image_results_directory,results_file)
hyperspec = get_hyperspectral_image(results_file_path,os.path.join(image_data_directory,directory), hyperspectral_wavelengths)
hyperspec = np.subtract(hyperspec,initial)
data_slice = slice(200,-200)
side_slice = slice (0,-700)
mean.append(hyperspec[data_slice, side_slice].mean())
std.append(hyperspec[data_slice, side_slice].std())
images.append([image_plot.imshow(hyperspec[data_slice, side_slice],interpolation='nearest', cmap='hot', animated=True)])
colorbar = image_plot.imshow(hyperspec[data_slice, side_slice],interpolation='nearest', cmap='hot', animated=True)
image_average.errorbar(np.arange(0,len(sorted_results_file[1:]),1),mean,fmt='o')
cbar = fig.colorbar(colorbar)
except ValueError:
pass
sorted_image_directories = sorted(image_subdirectories, key=natural_key)
#initial = get_hyperspectral_image(os.path.join(image_results_directory,sorted_image_directories[0]),os.path.join(image_data_directory,sorted_image_directories[0]), hyperspectral_wavelengths)
results_filenames = os.listdir(image_results_directory)
sorted_results_file = sorted(results_filenames, key=natural_key)
print('Results Files')
print(results_filenames)
#initial= get_hyperspectral_image(os.path.join(image_results_directory,sorted_results_file[0]),os.path.join(image_data_directory,sorted_results_file[0]), hyperspectral_wavelengths)
for directory in sorted_results_file:
print(directory)
results_file = directory
results_file_path = os.path.join(image_results_directory, results_file)
hyperspec = get_hyperspectral_image(
results_file_path, os.path.join(image_data_directory, directory),
hyperspectral_wavelengths)
#hyperspec = np.subtract(hyperspec,initial)
data_slice = slice(0, -1)
side_slice = slice(0, -1)
mean.append(hyperspec[data_slice, side_slice].mean())
std.append(hyperspec[data_slice, side_slice].std())
images.append([
image_plot.imshow(hyperspec[data_slice, side_slice],
interpolation='nearest',
cmap='hot',
animated=True)
])
colorbar = image_plot.imshow(hyperspec[data_slice, side_slice],
interpolation='nearest',
cmap='hot',
average_fig,ax2 = plt.subplots()
fig2, (ax3,ax4) = plt.subplots(1,2)
fig,ax = plt.subplots()
sub_directories = os.listdir(data_directory)
sub_directories.remove('.DS_Store')
wavelengths = np.arange(820, 870.5, 0.5)
spectra_wavelengths = np.linspace(498.6174, 1103.161, 3648)
results_files = os.listdir(results_directory)
sorted_files = sorted(results_files, key=natural_key)
images = list()
mean = list()
std =list()
initial = get_hyperspectral_image(os.path.join(results_directory,sorted_files[1]),os.path.join(data_directory,sorted_files[1]), wavelengths)
for file in sorted_files[2:]:
if '.npy' in file:
hyperspec = get_hyperspectral_image(os.path.join(results_directory,file),os.path.join(data_directory,file), wavelengths)
hyperspec = np.subtract(hyperspec,initial)
data_slice = slice(350,-150)
side_slice = slice (350,-300)
mean.append(hyperspec[data_slice, side_slice].mean())
std.append(hyperspec[data_slice, side_slice].std())
images.append([ax.imshow(hyperspec[data_slice, side_slice],interpolation='nearest', cmap='hot', animated=True)])
image_file = file.split('.')[0] + '.png'
#plt.savefig(os.path.join(results_directory,image_file))
colorbar = ax.imshow(hyperspec[data_slice, side_slice],interpolation='nearest', cmap='hot', animated=True)
cbar = fig.colorbar(colorbar)
#Electrochemistry performed used 1mM MB in 100mM PB ph 7.2 and 100umM MB 100mM PB pH 7.2
import scipy.stats as sts
white_directory = '../Data/Xylene_Dilution_Imprint_Post_Anneal_ITO/Photonics/White_light'
image_directory = '../Data/Xylene_Dilution_Imprint_Post_Anneal_ITO/Photonics/image'
eis_directory = '../Data/Xylene_Dilution_Imprint_Post_Anneal_ITO/Electrochemistry/EIS'
uM_1_cv_directory = '../Data/Xylene_Dilution_Imprint_Post_Anneal_ITO/Electrochemistry/CV/100uM'
mM_1_cv_directory = '../Data/Xylene_Dilution_Imprint_Post_Anneal_ITO/Electrochemistry/CV/1mM'
white_files = os.listdir(white_directory)
print(white_files)
wavelengths = np.arange(810, 940.5, 0.5)
print(wavelengths)
image = get_hyperspectral_image('Xylene_Dilution_Imprint_Post_Anneal_ITO.npy',
image_directory)
wave = np.linspace(498.6174, 1103.161, 3648)
raw_background = os.path.join(white_directory, white_files[0])
white_water = os.path.join(white_directory, white_files[-1])
fig, ax = plt.subplots()
fig2, ax2 = plt.subplots()
wavelength, mirror = np.genfromtxt(raw_background, unpack=True, delimiter=',')
wavelength, grating_water = np.genfromtxt(white_water,
unpack=True,
delimiter=',')
normalised_grating_reflectance = np.divide(grating_water, mirror)
ax.plot(wave, normalised_grating_reflectance)
ax.set_xlim([820, 920])
ax.set_ylim([0, 1])
from matplotlib.colors import LogNorm
import numpy as np
pre_photoresist_directory = '../Data/Pre_resist_22_1_18/image'
post_photoresist_directory = '../Data/Nanoimp2cm_22-01-18_postdevelop/image'
post_photoresist_directory_opt1 = '../Data/PostDevelop_7secondexposure_1_30develop/image'
post_photoresist_directory_opt2 = '../Data/PostDevelop_7secondexposure_1_30develop_2/image'
fig, ax = plt.subplots()
fig2, ax2 = plt.subplots()
fig3, ax3 = plt.subplots()
fig4, ax4 = plt.subplots()
wavelengths = np.arange(850, 920.5, 0.5)
pre_resist_image = get_hyperspectral_image('pre_resist.npy',
pre_photoresist_directory)
post_resist_image = get_hyperspectral_image('post_resist.npy',
post_photoresist_directory)
post_resist_image_optimised = get_hyperspectral_image(
'post_resist_opt1.npy', post_photoresist_directory_opt1)
post_resist_image_optimised_2 = get_hyperspectral_image(
'post_resist_opt2.npy', post_photoresist_directory_opt2)
pre_resist_wavelengths = wavelengths[pre_resist_image]
post_resist_wavelengths = wavelengths[post_resist_image]
post_resist_wavelengths_optimised = wavelengths[post_resist_image_optimised]
post_resist_wavelengths_optimised_2 = wavelengths[
post_resist_image_optimised_2]
colorbar = ax.imshow(pre_resist_wavelengths,
cmap='hot',
interpolation='nearest')
try:
base = os.path.splitext(file)[0]
numpy_filename = base + '.npy'
image = np.load(os.path.join(directory_path, numpy_filename))
if '.npy' in file:
pixel_intensity.append(image[fit_slice_horizontal,
fit_slice_vertical])
except FileNotFoundError:
print(file + ' Numpy File Not Found, Generating Numpy File')
image = np.genfromtxt(os.path.join(directory_path, file),
delimiter=',')
np.save(os.path.join(directory_path, base), image)
print(file + ' Numpy file saved')
image = get_hyperspectral_image(
os.path.join(results_image_path, directory), directory_path,
wavelengths)
mean.append(np.mean(image[vertical_slice, horizontal_slice]))
std.append(np.std(image[vertical_slice, horizontal_slice]))
print(image)
images.append([
image_plot.imshow(image,
interpolation='nearest',
cmap='hot',
animated=True)
])
ani = animation.ArtistAnimation(fig2,
images,
interval=250,
blit=True,
repeat_delay=0)
for directory in sorted_photonics_sub_directories:
spectra_directory = os.path.join(directory, 'spectra')
results_files = os.path.join(photonics_results_directory, 'spectra')
data_path = os.path.join(photonics_directory, spectra_directory)
max_wavelength = plot_spectra(data_path, spectra_wavelengths,
spectra_plot)
max_wavelengths.append(max_wavelength)
np.save(os.path.join(results_files, 'spectra_results'), max_wavelengths)
for directory in sorted_results_files:
image_results_path = os.path.join(photonics_results_directory, 'image')
results_file = os.path.join(image_results_path, directory)
image_directory = os.path.join(directory, 'image')
data_path = os.path.join(photonics_directory, image_directory)
image = get_hyperspectral_image(results_file, data_path,
hyperspectral_wavelengths)
images.append([
image_plot.imshow(image[vertical_slice, horizontal_slice],
interpolation='nearest',
cmap='hot',
animated=True)
])
mean.append(np.mean(image[vertical_slice, horizontal_slice]))
std.append(np.std(image[vertical_slice, horizontal_slice]))
print(len(mean))
print(len(std))
range = np.arange(0, len(mean), 1)
print(len(range))
mean_plot.errorbar(range, mean, std, fmt='o')
#mean_plot.set_ylim([865,872])
#
#
#
#
# except OSError:
# print('Not a TIFF File')
#
# image_plots.append(images)
# peak_plot.plot(peak_positions,'o')
#
#
# except NotADirectoryError:
# print('Not A Directory')
# # ani = animation.ArtistAnimation(fig_images, image_plots, interval=100, blit=True,
# # repeat_delay=0)
# image_file = os.listdir(os.path.join(chirp_image_data_directory, '2salt/2salt_1'))[1]
# image = np.asarray(Image.open(os.path.join(os.path.join(chirp_image_data_directory, '2salt/2salt_1'),image_file)))
# image_plot.imshow(image[salt_2_vertical_slice,salt_2_horizontal_slice])
#HyperSpectral Imaging
hyperspectral_wavelengths = np.arange(850, 900.5, 0.5)
hyperspectral_results_file = '../Results/ChirpedGMR/Hyperspectral/chirpGMR.npy'
hyperspectral_image = get_hyperspectral_image(hyperspectral_results_file,
hyperspectral_data_directory,
hyperspectral_wavelengths)
hyperspectral_plot.imshow(hyperspectral_image,
interpolation='nearest',
cmap='hot')
plt.show()
from src.hyperspectral_imaging import get_hyperspectral_image
import matplotlib.pyplot as plt
import numpy as np
water_directory = '../Data/NanoImpITO_Hyperspec/image'
ipa_directory = '../Data/NanoImpITO_IPA_Hyperspec/image'
fig, (ax,ax2) = plt.subplots(1, 2)
wavelengths = np.arange(840, 910.5, 0.5)
water_image = get_hyperspectral_image('sputtered_ito_water_hyperspec.npy',water_directory)
ipa_image = get_hyperspectral_image('sputtered_ito_ipa_hyperspec.npy',ipa_directory)
ax.imshow(wavelengths[water_image], cmap='hot', interpolation='nearest')
ax2.imshow(wavelengths[ipa_image],cmap='hot', interpolation='nearest')
plt.show()
photonics_subdirectories = os.listdir(photonics_images_directory)
hyperspectral_wavelengths = np.arange(830, 890.5, 0.5)
sorted_photonics_subdirectories = sorted(photonics_subdirectories,
key=natural_key)
photonics_results_files = os.listdir(photonics_results_directory)
sorted_photonics_results_files = sorted(photonics_results_files,
key=natural_key)
flat_images = list()
images = list()
mean = list()
std = list()
for file in sorted_photonics_results_files:
image = get_hyperspectral_image(
os.path.join(photonics_results_directory, file),
os.path.join(photonics_images_directory, file),
hyperspectral_wavelengths)
flat_images.append(image.flatten())
v_slice = slice(500, 600)
images.append([
image_plot.imshow(image[v_slice, slice(100, 300)],
interpolation='nearest',
cmap='hot',
animated=True)
])
mean.append(np.mean(image[v_slice, slice(200, 300)]))
std.append(np.std(image[v_slice, slice(200, 300)]))
print(len(mean))
print(len(std))
range = np.arange(0, len(mean), 1)
pixel_plot.plot(wavelengths,
grating_fit(wavelengths, *popt), 'o')
resonance_wavelength = popt[-2]
fitted_resonance.append(resonance_wavelength)
except RuntimeError:
print('Fit Failed')
mean_resonance_fit.append(np.mean(resonance_wavelength))
std_resonance_fit.append(np.std(resonance_wavelength))
for sub_directory in sorted_results_file_paths[6:]:
sub_directory_image_path = os.path.join(
photonics_directory, os.path.join(sub_directory, 'image'))
results_image_path = os.path.join(image_results_directory,
sub_directory)
image = get_hyperspectral_image(results_image_path,
sub_directory_image_path, wavelengths)
images.append([
image_plot.imshow(image[vertical_slice, horizontal_slice],
interpolation='nearest',
cmap='hot',
animated=True)
])
mean.append(np.mean(image[vertical_slice, horizontal_slice]))
std.append(np.std(image[vertical_slice, horizontal_slice]))
except ValueError:
pass
#pixel_plot.plot(wavelengths,grating_fit(wavelengths,*popt))
time_points = np.arange(0, len(mean_resonance_fit), 1)
mean_plot.errorbar(time_points, mean_resonance_fit, std_resonance_fit, fmt='o')
ani = animation.ArtistAnimation(fig2,
