This package contains functions used in data processing of hyperspectral images captured using a scanning Fabry-Perot interferometer (FPI). This includes transmission simulations of the FPI itself.
Key Features:
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Image importing
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Most common image analysis
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Fabry-Perot simulation
The Quick Start below can be executed given that the following points are at hand:
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A hyperspectral long wavelength infrared image with the following tree structure:
- images/ - capture/ - RGB.pnm - step4.pnm - step13.pnm . . . - step1327.pnm - output.txt - properties.json -
An example of an absorption spectrum (which may be acquired from an FTIR spectrometer)
#The quick start below uses a .csv file which contains a transmission measurement of ethylene gas. - Ethylene.csv -
A sensor response function in the form of a pickle file
#The quick start below also uses a pickle file containing the sensor response of the camera. - sensor_response.pkl
Quick Start
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Installation - Run
pip3 install HSTI. -
Importing the HSTI package in a e.g. Jupyter notebook or .py file along with other relevant packages
import HSTI # packages required for running the code blocks below import matplotlib.pyplot as plt import numpy as np from scipy.interpolate import interp1d import pickle -
Importing a hyperspectral image from an experiment directory
# The path below should be changed to the specific path used on the local PC path = '/home/user/experiments/experiment_1' HS_image = HSTI.import_data_cube(path) -
Performing a PCA of the hyperspectral image
PCA_object = HSTI.PCA(hsti.flatten(HS_image)) #Perform pca pca_img = PCA_object.scores.reshape(HS_image.shape) #reshape scores into same data structure as the original HS image -
Visualising the principal components
#import string for labelling images import string fig,ax = plt.subplots(4,4,figsize=(14,16.0)) newtec_cm = HSTI.import_cm() plt.rc('xtick', labelsize=8) plt.rc('ytick', labelsize=8) plt.rc('axes', labelsize=10) plt.rc('lines', linewidth=2) plt.rc('legend', fontsize=8) plt.rc('figure', titlesize=10) plt.rc('axes', titlesize=10) axs = ax.flat for idx,ax in enumerate(axs): _std = np.std(pca_imgs[:,:,idx]) _mean = np.mean(pca_imgs[:,:,idx]) if idx < 16: im = ax.imshow(pca_imgs[:,:,idx],vmin = _mean-2*_std,vmax = _mean+2*_std, cmap=newtec_cm) ax.text(0.5, 0.92, 'PC' + str(idx+1), transform=ax.transAxes, size=12, weight='bold', horizontalalignment='center',color='white') ax.text(0.02, 0.92,'(' + string.ascii_uppercase[idx] + ')', transform=ax.transAxes, size=10, weight='bold',color='white') if idx == 0 or idx == 4 or idx == 8 or idx == 12: ax.set_ylabel('Y [y$_j$]') if idx > 11: ax.set_xlabel('X [x$_i$]') plt.tight_layout() plt.savefig('experiment_1' + '_PCA' + '.png', dpi=100, bbox_inches='tight') -
Simulating the Fabry-Perot transmission based on an absorption spectrum
X_min = 3.6 # um X_max = 14 # um lam = np.linspace(X_min,X_max,150) wvls = np.linspace(7.5,16,1000) sys_matrix, R_matrix = HSTI.fpi_gmm(lam*10**-6, wvls*10**-6, n_points = 9) with open('sensor_response.pkl', 'rb') as file: sensor_response = pickle.load(file) C2H4 = np.loadtxt("Ethylene.csv", delimiter=",") wvls_C2H4 = 1/(C2H4[:,0]*100) f = interp1d(wvls_C2H4*10**6, C2H4[:,1]) C2H4_sim = [] BB = [] for i in range(sys_matrix.shape[0]): BB.append(np.sum(sys_matrix[i,:]*sensor_response(1/(wvls*10**-6))*np.ones(len(wvls)))) C2H4_sim.append(np.sum(sys_matrix[i,:]*sensor_response(1/(wvls*10**-6))*f(wvls))) BB = np.array(BB) C2H4_sim = np.array(C2H4_sim) fig,(ax1,ax2) = plt.subplots(1,2,figsize=(12,4)) ax1.set_title("Simulation of raw spectra") ax1.plot(lam,C2H4_sim-C2H4_sim[0],label = "Ethylene") ax1.plot(lam,BB-BB[0],label = "Blackbody") ax1.set_ylabel("Intensity [a.u.]") ax1.set_xlabel("Mirror Separation [um]") ax1.ticklabel_format(axis="y",style="sci",scilimits=(0,0)) ax1.legend() ax2.set_title("Simulated spectra with BB as a reference") ax2.set_ylabel("Intensity [a.u.]") ax2.set_xlabel("Mirror Separation [um]") ax2.plot(lam,(BB-BB[0])-(C2H4_sim-C2H4_sim[0]),label = "Blackbody - Ethylene") ax2.plot(lam,(BB-BB[0])-(BB-BB[0]),label = "Blackbody - Blackbody") ax2.ticklabel_format(axis="y",style="sci",scilimits=(0,0)) ax2.legend() plt.tight_layout() plt.savefig("Simulated_Ethylene.png", dpi=600)
Contact
For bug reports or other questions please contact mani@newtec.dk or alj@newtec.dk.
Release files for HSTI 0.0.106
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| hsti-0.0.106.tar.gz | 91.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hsti-0.0.106-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 192.2 kB
Release files / hsti-0.0.106.tar.gz
| Download URL | hsti-0.0.106.tar.gz |
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| Size | 91.9 kB |
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