Version2

spas/reconstruction_SPC1D.py

@@ -0,0 +1,82 @@
+# -*- coding: utf-8 -*-
+__author__ = 'Guilherme Beneti Martins'
+
+import numpy as np
+from spas.metadata_SPC1D import AcquisitionParameters
+
+def reconstruction_hadamard(acquisition_parameters: AcquisitionParameters,
+                            mode: str,
+                            Q: np.ndarray, 
+                            M: np.ndarray, 
+                            N: int = 64) -> np.ndarray:
+    """Reconstruct an image acquired with Hadamard patterns.
+
+    Args:
+        acquisition_parameters (AcquisitionParameters):
+            Object containing acquisition specifications
+        mode (str):
+            Select if reconstruction is based on MATLAB, fht or Walsh generated 
+            patterns.
+        Q (np.ndarray):
+            Acquisition matrix used to generate Hadamard patterns.
+        M (np.ndarray):
+            Spectral data matrix containing acquired spectra.
+        N (int, optional): 
+            Reconstructed image dimension. Defaults to 64.
+
+    Returns:
+        [np.ndarray]: 
+            Reconstructed matrix of size NxN pixels.
+    """
+
+    patterns = acquisition_parameters.patterns
+
+    if mode == 'matlab':
+        ind_opt = patterns[1::2]
+    if mode == 'fht' or mode == 'walsh':
+        ind_opt = patterns[0::2]
+
+    ind_opt = np.array(ind_opt)/2
+
+    if mode == 'matlab':
+        ind_opt = ind_opt - 1
+
+    ind_opt = ind_opt.astype('int')
+    M_breve = M[0::2,:] - M[1::2,:]
+    M_Had = np.zeros((N*N, M.shape[1]))
+    M_Had[ind_opt,:] = M_breve
+
+    f = np.matmul(Q,M_Had) # Q.T = Q
+    frames = np.reshape(f,(N,N,M.shape[1]))
+    frames /= N*N
+
+    mask_index = acquisition_parameters.mask_index
+    if len(mask_index) > 0:
+        x_mask_coord = acquisition_parameters.x_mask_coord
+        y_mask_coord = acquisition_parameters.y_mask_coord         
+        x_mask_length = x_mask_coord[1] - x_mask_coord[0]
+        y_mask_length = y_mask_coord[1] - y_mask_coord[0]
+
+        GTnew_vec = np.zeros((x_mask_length*y_mask_length, frames.shape[2]))
+        GT_vec = frames.reshape(-1, frames.shape[-1])
+
+        GTnew_vec[mask_index,:] = GT_vec[:len(mask_index),:]
+        frames = np.reshape(GTnew_vec, (y_mask_length, x_mask_length, frames.shape[2]))
+
+    return frames
+
+
+def reconstruction_raster(M: np.ndarray, N: int = 64) -> np.ndarray:    
+    """Reconstruct an image obtained via Raster scan.
+
+    Args:
+        M (np.ndarray): 
+             Spectral data matrix containing acquired spectra.
+        N (int, optional): 
+            Reconstructed image dimension. Defaults to 64.
+
+    Returns:
+        np.ndarray:
+            Reconstructed matrix of size NxN pixels.
+    """
+    return np.reshape(M,(N,N,M.shape[1]))

spas/reconstruction_SPC2D.py

@@ -0,0 +1,82 @@
+# -*- coding: utf-8 -*-
+__author__ = 'Guilherme Beneti Martins'
+
+import numpy as np
+from spas.metadata_SPC2D import AcquisitionParameters
+
+def reconstruction_hadamard(acquisition_parameters: AcquisitionParameters,
+                            mode: str,
+                            Q: np.ndarray, 
+                            M: np.ndarray, 
+                            N: int = 64) -> np.ndarray:
+    """Reconstruct an image acquired with Hadamard patterns.
+
+    Args:
+        acquisition_parameters (AcquisitionParameters):
+            Object containing acquisition specifications
+        mode (str):
+            Select if reconstruction is based on MATLAB, fht or Walsh generated 
+            patterns.
+        Q (np.ndarray):
+            Acquisition matrix used to generate Hadamard patterns.
+        M (np.ndarray):
+            Spectral data matrix containing acquired spectra.
+        N (int, optional): 
+            Reconstructed image dimension. Defaults to 64.
+
+    Returns:
+        [np.ndarray]: 
+            Reconstructed matrix of size NxN pixels.
+    """
+
+    patterns = acquisition_parameters.patterns
+
+    if mode == 'matlab':
+        ind_opt = patterns[1::2]
+    if mode == 'fht' or mode == 'walsh':
+        ind_opt = patterns[0::2]
+
+    ind_opt = np.array(ind_opt)/2
+
+    if mode == 'matlab':
+        ind_opt = ind_opt - 1
+
+    ind_opt = ind_opt.astype('int')
+    M_breve = M[0::2,:] - M[1::2,:]
+    M_Had = np.zeros((N*N, M.shape[1]))
+    M_Had[ind_opt,:] = M_breve
+
+    f = np.matmul(Q,M_Had) # Q.T = Q
+    frames = np.reshape(f,(N,N,M.shape[1]))
+    frames /= N*N
+
+    mask_index = acquisition_parameters.mask_index
+    if len(mask_index) > 0:
+        x_mask_coord = acquisition_parameters.x_mask_coord
+        y_mask_coord = acquisition_parameters.y_mask_coord         
+        x_mask_length = x_mask_coord[1] - x_mask_coord[0]
+        y_mask_length = y_mask_coord[1] - y_mask_coord[0]
+
+        GTnew_vec = np.zeros((x_mask_length*y_mask_length, frames.shape[2]))
+        GT_vec = frames.reshape(-1, frames.shape[-1])
+
+        GTnew_vec[mask_index,:] = GT_vec[:len(mask_index),:]
+        frames = np.reshape(GTnew_vec, (y_mask_length, x_mask_length, frames.shape[2]))
+
+    return frames
+
+
+def reconstruction_raster(M: np.ndarray, N: int = 64) -> np.ndarray:    
+    """Reconstruct an image obtained via Raster scan.
+
+    Args:
+        M (np.ndarray): 
+             Spectral data matrix containing acquired spectra.
+        N (int, optional): 
+            Reconstructed image dimension. Defaults to 64.
+
+    Returns:
+        np.ndarray:
+            Reconstructed matrix of size NxN pixels.
+    """
+    return np.reshape(M,(N,N,M.shape[1]))

spas/spectrograph.py

@@ -0,0 +1,12 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 28 10:08:56 2025
+
+@author: chiliaeva
+
+init
+piloting
+disconnect
+"""
+
+# def init_spectrograph :