HSTI 0.0.103

The NEWTEC HSTI package contains fundamental functions for the data analysis of hyperspectral thermal images (HSTI).

This package contains functions used in data processing of hyperspectral images captured using a scanning Fabry-P��rot interferometer (FPI). This includes transmission simulations of the FPI itself.

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Key Features:

• Image importing

• Most common image analysis

• Fabry-P��rot simulation

The Quick Start below can be executed given that the following points are at hand:

• 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

1. Installation - Run pip3 install HSTI.

2. 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
   

3. 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)
   

4. 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
   

5. 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')
   

6. Simulating the Fabry-P��rot transmission based on an absorption spectrum

    X_min = 3.6 # ��m
    X_max = 14  # ��m
   
   
    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 [��m]")
   
    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 [��m]")
    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)
   

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Contact

For bug reports or other questions please contact [e-mail] or [e-mail].