HSTI 0.0.88

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-Pérot interferometer (FPI). This includes transmission simulations of the FPI itself.

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Key Features:

• Image importing

• Most common image analysis

• Fabry-Pérot simulation

Quick Start

1. Installation - Run pip3 install HSTI.

2. Adding Examples in a Jupyter notebook or .py file

    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

    # 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()
   
    #transform image to two-dimensional
    two_dim = np.reshape(HS_image,(HS_image.shape[0]*HS_image.shape[1],HS_image.shape[2]))
   
    #calculate and apply PCA
    PCA_object.calculate_pca(two_dim)
    PCA_two_dim_imgs = PCA_object.apply_pca(two_dim)
   
    #create three-dimensional datacube with PCA images
    pca_imgs = np.reshape(PCA_two_dim_imgs,(HS_image.shape[0],HS_image.shape[1],HS_image.shape[2]))
   

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-Pérot transmission

    X_min = 3.6 # µm
    X_max = 14  # µm
   
   
    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 [µm]")
   
    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 [µm]")
    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].