HSTI 0.0.86

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
   

3. Importing a hyperspectral image

    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

    data_ac = np.loadtxt('NO.csv', delimiter=',')
   
    lam_ac = data_ac[:,0] #[µm]
    idx = ((lam_ac < 17) & (lam_ac > 7))
   
    lam_ac = lam_ac[idx]
    spec_ac = data_ac[idx,1]
   
    diff_lam_ac = np.diff(lam_ac)
    diff_lam_ac = np.append(diff_lam_ac, diff_lam_ac[-1])
   
    t = np.zeros(len(lam_ac)) #Transmittance of FPI
    r = np.zeros(len(lam_ac)) #Reflectance of FPI
    l = np.zeros(len(lam_ac)) #Numeric loss
   
    A_ac = np.zeros([len(mirror_sep_ac), len(lam_ac)])
   
   
   
    with open('interpolatedResponse.pkl', 'rb') as f:
    interpolate_resp = pickle.load(f)
   
   
   
    resp = np.zeros([len(lam_ac)])
    for i in range(len(lam_ac)):
    resp[i] = interpolate_resp(lam_ac[i]*1e-6)
   
   
   
    for i in range(len(mirror_sep_ac)):
    clear_output(wait=True)
    print('Progress: ' + str(np.round(100*(i+1)/len(mirror_sep_ac), decimals = 2)) + '%')
    for j in range(len(lam_ac)):
    t[j], r[j], l[j] = FPI_trans(mirror_sep_ac[i]*1e-6, lam_ac[j]/1e6, 297)
    t[j] = t[j]*(-diff_lam_ac[j])
    A_ac[i,:] = t*spec_ac
   
   
   
    sim_spec_ac = A_ac@resp
    sim_spec_ac = sim_spec_ac - np.min(sim_spec_ac)
    sim_spec_ac = sim_spec_ac/np.max(sim_spec_ac)
   
   
   
    fig8 = plt.figure()
    fig8.set_figheight(5) #set height of the entire figure
    fig8.set_figwidth(10) #set width of the entire figure
    gs = gridspec.GridSpec(1, 2) #set size ratio of the subfigures
    ax1 = fig8.add_subplot(gs[0,0])
    ax2 = fig8.add_subplot(gs[0,1])
   
   
   
    ax1.plot(mirror_sep_ac, sim_spec_ac, color = 'blue', label = 'Simulated')
    ax1.plot(mirror_sep_ac, meas_spec_ac, color = 'red', label = 'Measured')
    ax1.grid()
    ax1.legend()
    ax1.set_xlabel('Mirror separation [µm]')
    ax1.set_ylabel('Intensity [a.u.]')
   
   
   
    ax2.plot(lam_ac, spec_ac)
    ax2.grid()
    ax2.set_xlabel('Wavelength [µm]')
    ax2.set_ylabel('Intensity [a.u.]')
    fig8.tight_layout(pad = 2)
   
   
   
    sim_spec_ac_meth = sim_spec_ac
   

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Contact

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