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:

• Image importing

• Most common image analysis

• Fabry-Perot 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-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.