gh-478: glass.core.array -> glass.arraytools (#524)

glass/core/array.py → glass/arraytools.py

@@ -1,4 +1,4 @@
-"""Module for array utilities."""  # noqa: A005
+"""Module for array utilities."""
 
 from __future__ import annotations
 

glass/galaxies.py

@@ -26,7 +26,7 @@
 import numpy as np
 
 import glass
-import glass.core.array
+import glass.arraytools
 
 if TYPE_CHECKING:
     from numpy.typing import NDArray
@@ -117,7 +117,7 @@ def redshifts_from_nz(
         rng = np.random.default_rng()
 
     # bring inputs' leading axes into common shape
-    dims, *rest = glass.core.array.broadcast_leading_axes((count, 0), (z, 1), (nz, 1))
+    dims, *rest = glass.arraytools.broadcast_leading_axes((count, 0), (z, 1), (nz, 1))
     count_out, z_out, nz_out = rest
 
     # list of results for all dimensions
@@ -129,7 +129,7 @@ def redshifts_from_nz(
     # go through extra dimensions; also works if dims is empty
     for k in np.ndindex(dims):
         # compute the CDF of each galaxy population
-        cdf = glass.core.array.cumulative_trapezoid(nz_out[k], z_out[k], dtype=float)
+        cdf = glass.arraytools.cumulative_trapezoid(nz_out[k], z_out[k], dtype=float)
         cdf /= cdf[-1]
 
         # sample redshifts and store result

glass/observations.py

@@ -34,7 +34,7 @@
 import healpy as hp
 import numpy as np
 
-import glass.core.array
+import glass.arraytools
 
 if TYPE_CHECKING:
     from numpy.typing import NDArray
@@ -261,7 +261,7 @@ def equal_dens_zbins(
     # first compute the cumulative integral (by trapezoidal rule)
     # then normalise: the first z is at CDF = 0, the last z at CDF = 1
     # interpolate to find the z values at CDF = i/nbins for i = 0, ..., nbins
-    cuml_nz = glass.core.array.cumulative_trapezoid(nz, z)
+    cuml_nz = glass.arraytools.cumulative_trapezoid(nz, z)
     cuml_nz /= cuml_nz[[-1]]
     zbinedges = np.interp(np.linspace(0, 1, nbins + 1), cuml_nz, z)
 

glass/points.py

@@ -37,7 +37,7 @@
 import numpy as np
 
 import glass
-import glass.core.array
+import glass.arraytools
 
 if TYPE_CHECKING:
     from collections.abc import Callable, Generator
@@ -85,7 +85,7 @@ def effective_bias(
 
     """
     norm = np.trapezoid(w.wa, w.za)
-    return glass.core.array.trapezoid_product((z, bz), (w.za, w.wa)) / norm
+    return glass.arraytools.trapezoid_product((z, bz), (w.za, w.wa)) / norm
 
 
 def linear_bias(
@@ -232,7 +232,7 @@ def positions_from_delta(  # noqa: PLR0912, PLR0913, PLR0915
         inputs.append((bias, 0))
     if vis is not None:
         inputs.append((vis, 1))
-    dims, *rest = glass.core.array.broadcast_leading_axes(*inputs)
+    dims, *rest = glass.arraytools.broadcast_leading_axes(*inputs)
     ngal, delta, *rest = rest
     if bias is not None:
         bias, *rest = rest
@@ -409,7 +409,7 @@ def position_weights(
     """
     # bring densities and bias into the same shape
     if bias is not None:
-        densities, bias = glass.core.array.broadcast_first(densities, bias)
+        densities, bias = glass.arraytools.broadcast_first(densities, bias)
     # normalise densities after shape has been fixed
     densities = densities / np.sum(densities, axis=0)
     # apply bias after normalisation

glass/shells.py

@@ -52,7 +52,7 @@
 import numpy as np
 
 import glass.algorithm
-import glass.core.array
+import glass.arraytools
 
 if TYPE_CHECKING:
     from collections.abc import Callable, Sequence
@@ -411,9 +411,9 @@ def restrict(
     """
     z_ = np.compress(np.greater(z, w.za[0]) & np.less(z, w.za[-1]), z)
     zr = np.union1d(w.za, z_)
-    fr = glass.core.array.ndinterp(
+    fr = glass.arraytools.ndinterp(
         zr, z, f, left=0.0, right=0.0
-    ) * glass.core.array.ndinterp(zr, w.za, w.wa)
+    ) * glass.arraytools.ndinterp(zr, w.za, w.wa)
     return zr, fr
 
 
@@ -583,7 +583,7 @@ def partition_lstsq(
     a = a * dz
 
     # create the target vector of distribution values
-    b = glass.core.array.ndinterp(zp, z, fz, left=0.0, right=0.0)
+    b = glass.arraytools.ndinterp(zp, z, fz, left=0.0, right=0.0)
     b = b * dz
 
     # append a constraint for the integral
@@ -658,7 +658,7 @@ def partition_nnls(
     a = a * dz
 
     # create the target vector of distribution values
-    b = glass.core.array.ndinterp(zp, z, fz, left=0.0, right=0.0)
+    b = glass.arraytools.ndinterp(zp, z, fz, left=0.0, right=0.0)
     b = b * dz
 
     # append a constraint for the integral

tests/core/test_array.py → tests/test_arraytools.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-import glass.core.array
+import glass.arraytools
 
 if TYPE_CHECKING:
     from numpy.typing import NDArray
@@ -15,7 +15,7 @@ def test_broadcast_first() -> None:
 
     # arrays with shape ((3, 4, 2)) and ((1, 2)) are passed
     # to np.broadcast_arrays; hence it works
-    a_a, b_a = glass.core.array.broadcast_first(a, b)
+    a_a, b_a = glass.arraytools.broadcast_first(a, b)
     assert a_a.shape == (2, 3, 4)
     assert b_a.shape == (2, 3, 4)
 
@@ -29,7 +29,7 @@ def test_broadcast_first() -> None:
     b = np.ones((5, 6))
 
     with pytest.raises(ValueError, match="shape mismatch"):
-        glass.core.array.broadcast_first(a, b)
+        glass.arraytools.broadcast_first(a, b)
 
     # plain np.broadcast_arrays will work
     a_a, b_a = np.broadcast_arrays(a, b)
@@ -43,7 +43,7 @@ def test_broadcast_leading_axes() -> None:
     b_in = np.zeros((4, 10))
     c_in = np.zeros((3, 1, 5, 6))
 
-    dims, *rest = glass.core.array.broadcast_leading_axes(
+    dims, *rest = glass.arraytools.broadcast_leading_axes(
         (a_in, 0), (b_in, 1), (c_in, 2)
     )
     a_out, b_out, c_out = rest
@@ -61,17 +61,17 @@ def test_ndinterp() -> None:
     yp = np.array([1.1, 1.2, 1.3, 1.4, 1.5])
 
     x: float | NDArray[np.float64] = 0.5
-    y = glass.core.array.ndinterp(x, xp, yp)
+    y = glass.arraytools.ndinterp(x, xp, yp)
     assert np.shape(y) == ()
     np.testing.assert_allclose(y, 1.15, atol=1e-15)
 
     x = np.array([0.5, 1.5, 2.5])
-    y = glass.core.array.ndinterp(x, xp, yp)
+    y = glass.arraytools.ndinterp(x, xp, yp)
     assert np.shape(y) == (3,)
     np.testing.assert_allclose(y, [1.15, 1.25, 1.35], atol=1e-15)
 
     x = np.array([[0.5, 1.5], [2.5, 3.5]])
-    y = glass.core.array.ndinterp(x, xp, yp)
+    y = glass.arraytools.ndinterp(x, xp, yp)
     assert np.shape(y) == (2, 2)
     np.testing.assert_allclose(y, [[1.15, 1.25], [1.35, 1.45]], atol=1e-15)
 
@@ -80,17 +80,17 @@ def test_ndinterp() -> None:
     yp = np.array([[1.1, 1.2, 1.3, 1.4, 1.5], [2.1, 2.2, 2.3, 2.4, 2.5]])
 
     x = 0.5
-    y = glass.core.array.ndinterp(x, xp, yp)
+    y = glass.arraytools.ndinterp(x, xp, yp)
     assert np.shape(y) == (2,)
     np.testing.assert_allclose(y, [1.15, 2.15], atol=1e-15)
 
     x = np.array([0.5, 1.5, 2.5])
-    y = glass.core.array.ndinterp(x, xp, yp)
+    y = glass.arraytools.ndinterp(x, xp, yp)
     assert np.shape(y) == (2, 3)
     np.testing.assert_allclose(y, [[1.15, 1.25, 1.35], [2.15, 2.25, 2.35]], atol=1e-15)
 
     x = np.array([[0.5, 1.5], [2.5, 3.5]])
-    y = glass.core.array.ndinterp(x, xp, yp)
+    y = glass.arraytools.ndinterp(x, xp, yp)
     assert np.shape(y) == (2, 2, 2)
     np.testing.assert_allclose(
         y,
@@ -105,12 +105,12 @@ def test_ndinterp() -> None:
     )
 
     x = 0.5
-    y = glass.core.array.ndinterp(x, xp, yp, axis=1)
+    y = glass.arraytools.ndinterp(x, xp, yp, axis=1)
     assert np.shape(y) == (2, 1)
     np.testing.assert_allclose(y, [[1.15], [2.15]], atol=1e-15)
 
     x = np.array([0.5, 1.5, 2.5])
-    y = glass.core.array.ndinterp(x, xp, yp, axis=1)
+    y = glass.arraytools.ndinterp(x, xp, yp, axis=1)
     assert np.shape(y) == (2, 3, 1)
     np.testing.assert_allclose(
         y,
@@ -119,7 +119,7 @@ def test_ndinterp() -> None:
     )
 
     x = np.array([[0.5, 1.5, 2.5, 3.5], [3.5, 2.5, 1.5, 0.5], [0.5, 3.5, 1.5, 2.5]])
-    y = glass.core.array.ndinterp(x, xp, yp, axis=1)
+    y = glass.arraytools.ndinterp(x, xp, yp, axis=1)
     assert np.shape(y) == (2, 3, 4, 1)
     np.testing.assert_allclose(
         y,
@@ -146,7 +146,7 @@ def test_trapezoid_product() -> None:
     x2 = np.linspace(1, 2, 10)
     f2 = np.full_like(x2, 0.5)
 
-    s = glass.core.array.trapezoid_product((x1, f1), (x2, f2))
+    s = glass.arraytools.trapezoid_product((x1, f1), (x2, f2))
 
     np.testing.assert_allclose(s, 1.0)
 
@@ -159,18 +159,18 @@ def test_cumulative_trapezoid() -> None:
 
     # default dtype (int)
 
-    ct = glass.core.array.cumulative_trapezoid(f, x)
+    ct = glass.arraytools.cumulative_trapezoid(f, x)
     np.testing.assert_allclose(ct, np.array([0, 1, 4, 7]))
 
     # explicit dtype (float)
 
-    ct = glass.core.array.cumulative_trapezoid(f, x, dtype=float)
+    ct = glass.arraytools.cumulative_trapezoid(f, x, dtype=float)
     np.testing.assert_allclose(ct, np.array([0.0, 1.5, 4.0, 7.5]))
 
     # explicit return array
 
     out = np.zeros((4,))
-    ct = glass.core.array.cumulative_trapezoid(f, x, dtype=float, out=out)
+    ct = glass.arraytools.cumulative_trapezoid(f, x, dtype=float, out=out)
     np.testing.assert_equal(ct, out)
 
     # 2D f and 1D x
@@ -180,18 +180,18 @@ def test_cumulative_trapezoid() -> None:
 
     # default dtype (int)
 
-    ct = glass.core.array.cumulative_trapezoid(f, x)
+    ct = glass.arraytools.cumulative_trapezoid(f, x)
     np.testing.assert_allclose(ct, np.array([[0, 2, 12, 31], [0, 2, 8, 17]]))
 
     # explicit dtype (float)
 
-    ct = glass.core.array.cumulative_trapezoid(f, x, dtype=float)
+    ct = glass.arraytools.cumulative_trapezoid(f, x, dtype=float)
     np.testing.assert_allclose(
         ct, np.array([[0.0, 2.5, 12.25, 31.0], [0.0, 2.5, 8.5, 17.5]])
     )
 
     # explicit return array
 
     out = np.zeros((2, 4))
-    ct = glass.core.array.cumulative_trapezoid(f, x, dtype=float, out=out)
+    ct = glass.arraytools.cumulative_trapezoid(f, x, dtype=float, out=out)
     np.testing.assert_equal(ct, out)