fix unitt estr with sahpitng

Author: Jammy2211

autoarray/operators/transformer.py

@@ -273,62 +273,4 @@ def transform_mapping_matrix(self, mapping_matrix):
 
             transformed_mapping_matrix[:, source_pixel_1d_index] = visibilities
 
-        return transformed_mapping_matrix
-
-    def forward_lop(self, x):
-        """
-        Forward NUFFT on CPU
-        :param x: The input numpy array, with the size of Nd or Nd + (batch,)
-        :type: numpy array with the dtype of numpy.complex64
-        :return: y: The output numpy array, with the size of (M,) or (M, batch)
-        :rtype: numpy array with the dtype of numpy.complex64
-        """
-
-        warnings.filterwarnings("ignore")
-
-        x2d = array_2d_util.array_2d_native_complex_via_indexes_from(
-            array_2d_slim=x,
-            shape_native=self.real_space_mask.shape_native,
-            native_index_for_slim_index_2d=self.native_index_for_slim_index,
-        )[::-1, :]
-
-        y = self.k2y(self.xx2k(self.x2xx(x2d)))
-        return np.concatenate((y.real, y.imag), axis=0)
-
-    def adjoint_lop(self, y):
-        """
-        Adjoint NUFFT on CPU
-        :param y: The input numpy array, with the size of (M,) or (M, batch)
-        :type: numpy array with the dtype of numpy.complex64
-        :return: x: The output numpy array,
-                    with the size of Nd or Nd + (batch, )
-        :rtype: numpy array with the dtype of numpy.complex64
-        """
-
-        warnings.filterwarnings("ignore")
-
-        def a_complex_from(a_real, a_imag):
-            return a_real + 1j * a_imag
-
-        y = a_complex_from(
-            a_real=y[: int(self.shape[0] / 2.0)], a_imag=y[int(self.shape[0] / 2.0) :]
-        )
-
-        x2d = np.real(self.xx2x(self.k2xx(self.y2k(y))))
-
-        x = array_2d_util.array_2d_slim_complex_from(
-            array_2d_native=x2d[::-1, :],
-            mask=np.array(self.real_space_mask),
-        )
-        x = x.real  # NOTE:
-
-        # NOTE:
-        x *= self.adjoint_scaling
-
-        return x
-
-    def _matvec(self, x):
-        return self.forward_lop(x)
-
-    def _rmatvec(self, x):
-        return self.adjoint_lop(x)
+        return transformed_mapping_matrix

autoarray/operators/transformer_util.py

@@ -115,7 +115,7 @@ def visibilities_jit(image_1d, grid_radians, uv_wavelengths):
     return visibilities
 
 
-# @numba_util.jit()
+@numba_util.jit()
 def image_via_jit_from(n_pixels, grid_radians, uv_wavelengths, visibilities):
     image_1d = np.zeros(n_pixels)
 

test_autoarray/operators/test_transformer.py

@@ -27,8 +27,6 @@ def test__dft__visibilities_from(visibilities_7, uv_wavelengths_7x2, mask_2d_7x7
 
     visibilities = transformer.visibilities_from(image=image)
 
-    print(visibilities)
-
     assert visibilities[0:3] == pytest.approx(
         np.array(
             [