bugfix

meent/on_numpy/emsolver/convolution_matrix.py

@@ -220,8 +220,8 @@ def to_conv_mat_discrete(ucell, fourier_order_x, fourier_order_y, device=None, t
         for i, layer in enumerate(ucell_pmt):
             n = minimum_pattern_size // layer.shape[1]
             layer = np.repeat(layer, n + 1, axis=1)
-            f_coeffs = np.fft.fftshift(np.fft.fft(layer / layer.size))
-            o_f_coeffs = np.fft.fftshift(np.fft.fft(1/layer / layer.size))
+            f_coeffs = np.fft.fftshift(np.fft.fft(layer / layer.size).astype(type_complex))
+            o_f_coeffs = np.fft.fftshift(np.fft.fft(1/layer / layer.size).astype(type_complex))
             # FFT scaling:
             # https://kr.mathworks.com/matlabcentral/answers/15770-scaling-the-fft-and-the-ifft?s_tid=srchtitle
 
@@ -257,8 +257,8 @@ def to_conv_mat_discrete(ucell, fourier_order_x, fourier_order_y, device=None, t
                 n = minimum_pattern_size_x // layer.shape[1]
                 layer = np.repeat(layer, n + 1, axis=1)
 
-            f_coeffs = np.fft.fftshift(np.fft.fft2(layer / layer.size))
-            o_f_coeffs = np.fft.fftshift(np.fft.fft2(1/layer / layer.size))
+            f_coeffs = np.fft.fftshift(np.fft.fft2(layer / layer.size).astype(type_complex))
+            o_f_coeffs = np.fft.fftshift(np.fft.fft2(1/layer / layer.size).astype(type_complex))
             center = np.array(f_coeffs.shape) // 2
 
             conv_idx_y = np.arange(-ff_y + 1, ff_y, 1)

meent/on_torch/emsolver/convolution_matrix.py

@@ -364,8 +364,8 @@ def to_conv_mat_raster_discrete(ucell, fourier_order_x, fourier_order_y, device=
         for i, layer in enumerate(ucell_pmt):
             n = minimum_pattern_size // layer.shape[1]
             layer = layer.repeat_interleave(n + 1, axis=1)
-            f_coeffs = torch.fft.fftshift(torch.fft.fft(layer / layer.numel()))
-            o_f_coeffs = torch.fft.fftshift(torch.fft.fft(1/layer / layer.numel()))
+            f_coeffs = torch.fft.fftshift(torch.fft.fft(layer / layer.numel()).type(type_complex))
+            o_f_coeffs = torch.fft.fftshift(torch.fft.fft(1/layer / layer.numel()).type(type_complex))
             center = torch.tensor(f_coeffs.shape, device=device) // 2
             # center = torch.div(torch.tensor(f_coeffs.shape, device=device), 2, rounding_mode='trunc')
             # center = torch.tensor(center, device=device)

meent/on_torch/mee.py

@@ -35,6 +35,6 @@ def __init__(self, device=0, type_complex=0, *args, **kwargs):
         self.device = device
         self.type_complex = type_complex
 
-        ModelingTorch.__init__(self, *args, **kwargs)
-        RCWATorch.__init__(self, *args, **kwargs)
-        OptimizerTorch.__init__(self, *args, **kwargs)
+        ModelingTorch.__init__(self, device=device, type_complex=type_complex, *args, **kwargs)
+        RCWATorch.__init__(self, device=device, type_complex=type_complex, *args, **kwargs)
+        OptimizerTorch.__init__(self, device=device, type_complex=type_complex, *args, **kwargs)