- Major API refactor, creating structures for arrays as well as data.

Merge branch 'release/0.3.0'

Author: Jammy2211

autoarray/__init__.py

@@ -1,40 +1,28 @@
-from autoarray.mask.mask import Mask as mask
-from autoarray.structures.arrays import Array as array, MaskedArray as masked_array
-from autoarray.structures.grids import (
-    Grid as grid,
-    MaskedGrid as masked_grid,
-    IrregularGrid as irregular_grid,
-)
-from autoarray.structures.kernel import Kernel as kernel
-from autoarray.structures.visibilities import Visibilities as visibilities
 from autoarray import conf
+from autoarray import exc
+from autoarray import masked
 from autoarray import plotters as plot
+from autoarray import simulator
 from autoarray import util
-from autoarray.data import data_converter
-from autoarray.data.imaging import Imaging as imaging
-from autoarray.data.imaging import SimulatedImaging
-from autoarray.data.interferometer import Interferometer as interferometer
-from autoarray.fit.fit import (
-    DataFit as fit,
-    ImagingFit as fit_imaging,
-    InterferometerFit as fit_interferometer,
-)
-from autoarray.fit.masked_data import (
-    MaskedImaging as masked_imaging,
-    MaskedInterferometer as masked_interferometer,
-)
+from autoarray.dataset import data_converter
+from autoarray.dataset.imaging import Imaging as imaging
+from autoarray.dataset.interferometer import Interferometer as interferometer
+from autoarray.fit.fit import fit
 from autoarray.mask.mask import Mask as mask
-from autoarray.operators.convolution import Convolver as convolver
-from autoarray.operators.fourier_transform import Transformer as transformer
-from autoarray.operators.inversion import (
-    pixelization_grids as pix_grid,
-    pixelizations as pix,
-    regularization as reg,
-    mappers,
-    inversions,
+from autoarray.operators.convolver import Convolver as convolver
+from autoarray.operators.inversion import pixelizations as pix, regularization as reg
+from autoarray.operators.inversion.inversions import inversion
+from autoarray.operators.inversion.mappers import mapper
+from autoarray.operators.transformer import Transformer as transformer
+from autoarray.structures.arrays import Array as array
+from autoarray.structures.grids import (
+    Grid as grid,
+    GridIrregular as grid_irregular,
+    GridRectangular as grid_rectangular,
+    GridVoronoi as grid_voronoi,
+    Positions as positions,
 )
-from autoarray.structures.arrays import Array as array, MaskedArray as masked_array
-from autoarray.structures.grids import Grid as grid, MaskedGrid as masked_grid
 from autoarray.structures.kernel import Kernel as kernel
+from autoarray.structures.visibilities import Visibilities as visibilities
 
-__version__ = '0.2.2'
+__version__ = '0.3.0'

autoarray/conf.py

@@ -3,6 +3,13 @@
 from copy import deepcopy
 
 
+def get_matplotlib_backend():
+    try:
+        return instance.visualize.get("figures", "backend", str)
+    except Exception:
+        return "TKAgg"
+
+
 def family(current_class):
     yield current_class
     for next_class in current_class.__bases__:

autoarray/data/__init__.py autoarray/dataset/__init__.pyautoarray/data/__init__.py renamed to autoarray/dataset/__init__.py

@@ -1,31 +1,30 @@
-import ast
 import numpy as np
 
 from autoarray.structures import arrays
 from autoarray import exc
 
 
-class AbstractData(object):
+class AbstractDataset(object):
     def __init__(self, data, noise_map, exposure_time_map=None):
         """A collection of abstract 2D for different data_type classes (an image, pixel-scale, noise-map, etc.)
 
         Parameters
         ----------
         data : arrays.Array
-            The array of the image data_type, in units of electrons per second.
+            The array of the image data_type, in unit_label of electrons per second.
         pixel_scales : float
             The size of each pixel in arc seconds.
         psf : PSF
             An array describing the PSF kernel of the image.
         noise_map : NoiseMap | float | ndarray
-            An array describing the RMS standard deviation error in each pixel, preferably in units of electrons per
+            An array describing the RMS standard deviation error in each pixel, preferably in unit_label of electrons per
             second.
         background_noise_map : NoiseMap
             An array describing the RMS standard deviation error in each pixel due to the background sky noise_map,
-            preferably in units of electrons per second.
+            preferably in unit_label of electrons per second.
         poisson_noise_map : NoiseMap
             An array describing the RMS standard deviation error in each pixel due to the Poisson counts of the source,
-            preferably in units of electrons per second.
+            preferably in unit_label of electrons per second.
         exposure_time_map : arrays.Array
             An array describing the effective exposure time in each imaging pixel.
         background_sky_map : aa.Scaled
@@ -82,7 +81,7 @@ def potential_chi_squared_max(self):
 
     def array_from_electrons_per_second_to_counts(self, array):
         """
-        For an array (in electrons per second) and an exposure time mappers, return an array in units counts.
+        For an array (in electrons per second) and an exposure time mappers, return an array in unit_label counts.
 
         Parameters
         ----------
@@ -93,7 +92,7 @@ def array_from_electrons_per_second_to_counts(self, array):
 
     def array_from_counts_to_electrons_per_second(self, array):
         """
-        For an array (in counts) and an exposure time mappers, convert the array to units electrons per second
+        For an array (in counts) and an exposure time mappers, convert the array to unit_label electrons per second
 
         Parameters
         ----------
@@ -107,7 +106,7 @@ def array_from_counts_to_electrons_per_second(self, array):
 
     def array_from_adus_to_electrons_per_second(self, array, gain):
         """
-        For an array (in counts) and an exposure time mappers, convert the array to units electrons per second
+        For an array (in counts) and an exposure time mappers, convert the array to unit_label electrons per second
 
         Parameters
         ----------
@@ -121,7 +120,7 @@ def array_from_adus_to_electrons_per_second(self, array, gain):
 
     @property
     def image_counts(self):
-        """The image in units of counts."""
+        """The image in unit_label of counts."""
         return self.array_from_electrons_per_second_to_counts(self.data)
 
 
@@ -209,54 +208,3 @@ def load_exposure_time_map(
             return ExposureTimeMap.from_exposure_time_and_inverse_noise_map(
                 exposure_time=exposure_time, inverse_noise_map=inverse_noise_map
             )
-
-
-def load_positions(positions_path):
-    """Load the positions of an image.
-
-    Positions correspond to a set of pixels in the lensed source galaxy that are anticipated to come from the same \
-    multiply-imaged region of the source-plane. Mass models which do not trace the pixels within a threshold value of \
-    one another are resampled during the non-linear search.
-
-    Positions are stored in a .dat file, where each line of the file gives a list of list of (y,x) positions which \
-    correspond to the same region of the source-plane. Thus, multiple source-plane regions can be input over multiple \
-    lines of the same positions file.
-
-    Parameters
-    ----------
-    positions_path : str
-        The path to the positions .dat file containing the positions (e.g. '/path/to/positions.dat')
-    """
-    with open(positions_path) as f:
-        position_string = f.readlines()
-
-    positions = []
-
-    for line in position_string:
-        position_list = ast.literal_eval(line)
-        positions.append(position_list)
-
-    return positions
-
-
-def output_positions(positions, positions_path):
-    """Output the positions of an image to a positions.dat file.
-
-    Positions correspond to a set of pixels in the lensed source galaxy that are anticipated to come from the same \
-    multiply-imaged region of the source-plane. Mass models which do not trace the pixels within a threshold value of \
-    one another are resampled during the non-linear search.
-
-    Positions are stored in a .dat file, where each line of the file gives a list of list of (y,x) positions which \
-    correspond to the same region of the source-plane. Thus, multiple source-plane regions can be input over multiple \
-    lines of the same positions file.
-
-    Parameters
-    ----------
-    positions : [[[]]]
-        The lists of positions (e.g. [[[1.0, 1.0], [2.0, 2.0]], [[3.0, 3.0], [4.0, 4.0]]])
-    positions_path : str
-        The path to the positions .dat file containing the positions (e.g. '/path/to/positions.dat')
-    """
-    with open(positions_path, "w") as f:
-        for position in positions:
-            f.write("%s\n" % position)