Feature/jax interferometer

autoarray/dataset/interferometer/dataset.py

@@ -10,7 +10,7 @@
 from autoarray.dataset.interferometer.w_tilde import WTildeInterferometer
 from autoarray.dataset.grids import GridsDataset
 from autoarray.operators.transformer import TransformerNUFFT
-
+from autoarray.mask.mask_2d import Mask2D
 from autoarray.structures.visibilities import Visibilities
 from autoarray.structures.visibilities import VisibilitiesNoiseMap
 
@@ -25,8 +25,9 @@ def __init__(
         data: Visibilities,
         noise_map: VisibilitiesNoiseMap,
         uv_wavelengths: np.ndarray,
-        real_space_mask,
+        real_space_mask: Mask2D,
         transformer_class=TransformerNUFFT,
+        dft_preload_transform: bool = True,
         preprocessing_directory=None,
     ):
         """
@@ -73,6 +74,9 @@ def __init__(
         transformer_class
             The class of the Fourier Transform which maps images from real space to Fourier space visibilities and
             the uv-plane.
+        dft_preload_transform
+            If True, precomputes and stores the cosine and sine terms for the Fourier transform.
+            This accelerates repeated transforms but consumes additional memory (~1GB+ for large datasets).
         """
         self.real_space_mask = real_space_mask
 
@@ -86,7 +90,9 @@ def __init__(
         self.uv_wavelengths = uv_wavelengths
 
         self.transformer = transformer_class(
-            uv_wavelengths=uv_wavelengths, real_space_mask=real_space_mask
+            uv_wavelengths=uv_wavelengths,
+            real_space_mask=real_space_mask,
+            preload_transform=dft_preload_transform,
         )
 
         self.preprocessing_directory = (
@@ -114,6 +120,7 @@ def from_fits(
         noise_map_hdu=0,
         uv_wavelengths_hdu=0,
         transformer_class=TransformerNUFFT,
+        dft_preload_transform: bool = True,
     ):
         """
         Factory for loading the interferometer data_type from .fits files, as well as computing properties like the
@@ -139,6 +146,7 @@ def from_fits(
             noise_map=noise_map,
             uv_wavelengths=uv_wavelengths,
             transformer_class=transformer_class,
+            dft_preload_transform=dft_preload_transform,
         )
 
     def w_tilde_preprocessing(self):

autoarray/fit/fit_interferometer.py

@@ -113,7 +113,7 @@ def chi_squared(self) -> float:
         Returns the chi-squared terms of the model data's fit to an dataset, by summing the chi-squared-map.
         """
         return fit_util.chi_squared_complex_from(
-            chi_squared_map=self.chi_squared_map,
+            chi_squared_map=self.chi_squared_map.array,
         )
 
     @property

autoarray/fit/fit_util.py

@@ -158,8 +158,8 @@ def chi_squared_complex_from(*, chi_squared_map: jnp.ndarray) -> float:
     chi_squared_map
         The chi-squared-map of values of the model-data fit to the dataset.
     """
-    chi_squared_real = jnp.sum(np.array(chi_squared_map.real))
-    chi_squared_imag = jnp.sum(np.array(chi_squared_map.imag))
+    chi_squared_real = jnp.sum(chi_squared_map.real)
+    chi_squared_imag = jnp.sum(chi_squared_map.imag)
     return chi_squared_real + chi_squared_imag
 
 

autoarray/fit/plot/fit_interferometer_plotters.py

@@ -183,7 +183,7 @@ def figures_2d(
                 auto_labels=AutoLabels(
                     title="Model Visibilities", filename="model_data"
                 ),
-                color_array=np.real(self.fit.model_data),
+                color_array=np.real(self.fit.model_data.array),
             )
 
         if residual_map_real:

autoarray/inversion/inversion/abstract.py

@@ -287,7 +287,7 @@ def mapping_matrix(self) -> np.ndarray:
         If there are multiple linear objects, the mapping matrices are stacked such that their simultaneous linear
         equations are solved simultaneously. This property returns the stacked mapping matrix.
         """
-        return np.hstack(
+        return jnp.hstack(
             [linear_obj.mapping_matrix for linear_obj in self.linear_obj_list]
         )
 

autoarray/inversion/inversion/dataset_interface.py

@@ -36,6 +36,9 @@ def __init__(
         noise_map
             An array describing the RMS standard deviation error in each pixel used for computing quantities like the
             chi-squared in a fit (in PyAutoGalaxy and PyAutoLens the recommended units are electrons per second).
+        grids
+            The grids of (y,x) Cartesian coordinates that the image data is paired with, which are used for evaluting
+            light profiles and calculations associated with a pixelization.
         over_sampler
             Performs over-sampling whereby the masked image pixels are split into sub-pixels, which are all
             mapped via the mapper with sub-fractional values of flux.
@@ -50,9 +53,6 @@ def __init__(
         w_tilde
             The w_tilde matrix used by the w-tilde formalism to construct the data vector and
             curvature matrix during an inversion efficiently..
-        grids
-            The grids of (y,x) Cartesian coordinates that the image data is paired with, which are used for evaluting
-            light profiles and calculations associated with a pixelization.
         noise_covariance_matrix
             A noise-map covariance matrix representing the covariance between noise in every `data` value, which
             can be used via a bespoke fit to account for correlated noise in the data.

autoarray/inversion/inversion/interferometer/inversion_interferometer_util.py

@@ -387,7 +387,6 @@ def w_tilde_via_preload_from(w_tilde_preload, native_index_for_slim_index):
     return w_tilde_via_preload
 
 
-@numba_util.jit()
 def data_vector_via_transformed_mapping_matrix_from(
     transformed_mapping_matrix: np.ndarray,
     visibilities: np.ndarray,
@@ -406,31 +405,24 @@ def data_vector_via_transformed_mapping_matrix_from(
     noise_map
         Flattened 1D array of the noise-map used by the inversion during the fit.
     """
+    # Extract components
+    vis_real = visibilities.real
+    vis_imag = visibilities.imag
+    f_real = transformed_mapping_matrix.real
+    f_imag = transformed_mapping_matrix.imag
+    noise_real = noise_map.real
+    noise_imag = noise_map.imag
 
-    data_vector = np.zeros(transformed_mapping_matrix.shape[1])
-
-    visibilities_real = visibilities.real
-    visibilities_imag = visibilities.imag
-    transformed_mapping_matrix_real = transformed_mapping_matrix.real
-    transformed_mapping_matrix_imag = transformed_mapping_matrix.imag
-    noise_map_real = noise_map.real
-    noise_map_imag = noise_map.imag
-
-    for vis_1d_index in range(transformed_mapping_matrix.shape[0]):
-        for pix_1d_index in range(transformed_mapping_matrix.shape[1]):
-            real_value = (
-                visibilities_real[vis_1d_index]
-                * transformed_mapping_matrix_real[vis_1d_index, pix_1d_index]
-                / (noise_map_real[vis_1d_index] ** 2.0)
-            )
-            imag_value = (
-                visibilities_imag[vis_1d_index]
-                * transformed_mapping_matrix_imag[vis_1d_index, pix_1d_index]
-                / (noise_map_imag[vis_1d_index] ** 2.0)
-            )
-            data_vector[pix_1d_index] += real_value + imag_value
+    # Square noise components
+    inv_var_real = 1.0 / (noise_real**2)
+    inv_var_imag = 1.0 / (noise_imag**2)
 
-    return data_vector
+    # Real and imaginary contributions
+    weighted_real = (vis_real * inv_var_real)[:, None] * f_real
+    weighted_imag = (vis_imag * inv_var_imag)[:, None] * f_imag
+
+    # Sum over visibilities
+    return np.sum(weighted_real + weighted_imag, axis=0)
 
 
 @numba_util.jit()
@@ -512,7 +504,6 @@ def curvature_matrix_via_w_tilde_curvature_preload_interferometer_from(
     return curvature_matrix
 
 
-@numba_util.jit()
 def mapped_reconstructed_visibilities_from(
     transformed_mapping_matrix: np.ndarray, reconstruction: np.ndarray
 ) -> np.ndarray:
@@ -525,20 +516,7 @@ def mapped_reconstructed_visibilities_from(
         The matrix representing the blurred mappings between sub-grid pixels and pixelization pixels.
 
     """
-    mapped_reconstructed_visibilities = (0.0 + 0.0j) * np.zeros(
-        transformed_mapping_matrix.shape[0]
-    )
-
-    transformed_mapping_matrix_real = transformed_mapping_matrix.real
-    transformed_mapping_matrix_imag = transformed_mapping_matrix.imag
-
-    for i in range(transformed_mapping_matrix.shape[0]):
-        for j in range(reconstruction.shape[0]):
-            mapped_reconstructed_visibilities[i] += (
-                reconstruction[j] * transformed_mapping_matrix_real[i, j]
-            ) + 1.0j * (reconstruction[j] * transformed_mapping_matrix_imag[i, j])
-
-    return mapped_reconstructed_visibilities
+    return transformed_mapping_matrix @ reconstruction
 
 
 """

autoarray/inversion/inversion/interferometer/mapping.py

@@ -1,3 +1,4 @@
+import jax.numpy as jnp
 import numpy as np
 from typing import Dict, List, Optional, Union
 
@@ -76,8 +77,8 @@ def data_vector(self) -> np.ndarray:
         """
 
         return inversion_interferometer_util.data_vector_via_transformed_mapping_matrix_from(
-            transformed_mapping_matrix=np.array(self.operated_mapping_matrix),
-            visibilities=np.array(self.data),
+            transformed_mapping_matrix=self.operated_mapping_matrix,
+            visibilities=self.data,
             noise_map=np.array(self.noise_map),
         )
 
@@ -106,13 +107,13 @@ def curvature_matrix(self) -> np.ndarray:
             noise_map=self.noise_map.imag,
         )
 
-        curvature_matrix = np.add(real_curvature_matrix, imag_curvature_matrix)
+        curvature_matrix = jnp.add(real_curvature_matrix, imag_curvature_matrix)
 
         if len(self.no_regularization_index_list) > 0:
             curvature_matrix = inversion_util.curvature_matrix_with_added_to_diag_from(
                 curvature_matrix=curvature_matrix,
-                no_regularization_index_list=self.no_regularization_index_list,
                 value=self.settings.no_regularization_add_to_curvature_diag_value,
+                no_regularization_index_list=self.no_regularization_index_list,
             )
 
         return curvature_matrix
@@ -152,10 +153,8 @@ def mapped_reconstructed_data_dict(
 
             visibilities = (
                 inversion_interferometer_util.mapped_reconstructed_visibilities_from(
-                    transformed_mapping_matrix=np.array(
-                        operated_mapping_matrix_list[index]
-                    ),
-                    reconstruction=np.array(reconstruction),
+                    transformed_mapping_matrix=operated_mapping_matrix_list[index],
+                    reconstruction=reconstruction,
                 )
             )
 

autoarray/inversion/inversion/interferometer/w_tilde.py

@@ -85,9 +85,7 @@ def data_vector(self) -> np.ndarray:
 
         The calculation is described in more detail in `inversion_util.w_tilde_data_interferometer_from`.
         """
-        return np.dot(
-            self.linear_obj_list[0].mapping_matrix.T, self.w_tilde.dirty_image
-        )
+        return np.dot(self.mapping_matrix.T, self.w_tilde.dirty_image)
 
     @cached_property
     @profile_func