gh-998: Change all references to len to .shape[0] (#1032)

Author: paddyroddy

glass/fields.py

@@ -373,8 +373,7 @@ def _generate_grf(
         rng = _rng.rng_dispatcher(xp=xp)
 
     # number of gls and number of fields
-    ngls = len(gls)
-    ngrf = nfields_from_nspectra(ngls)
+    ngrf = nfields_from_nspectra(len(gls))
 
     # number of correlated fields if not specified
     if ncorr is None:
@@ -548,10 +547,10 @@ def getcl(
         i, j = j, i
     cl = cls[i * (i + 1) // 2 + i - j]
     if lmax is not None:
-        if cl.size > lmax + 1:
+        if cl.shape[0] > lmax + 1:
             cl = cl[: lmax + 1]
         else:
-            cl = xpx.pad(cl, (0, lmax + 1 - cl.size))
+            cl = xpx.pad(cl, (0, lmax + 1 - cl.shape[0]))
     return cl  # ty: ignore[invalid-return-type]
 
 
@@ -764,7 +763,7 @@ def compute_gaussian_spectra(
 
     gls = []
     for i, j, cl in enumerate_spectra(spectra):
-        gl = glass.grf.compute(cl, fields[i], fields[j]) if cl.size > 0 else 0 * cl
+        gl = glass.grf.compute(cl, fields[i], fields[j]) if cl.shape[0] > 0 else 0 * cl
         gls.append(gl)
     return gls
 
@@ -802,12 +801,12 @@ def solve_gaussian_spectra(
 
     gls = []
     for i, j, cl in enumerate_spectra(spectra):
-        if cl.size > 0:
+        if cl.shape[0] > 0:
             # transformation pair
             t1, t2 = fields[i], fields[j]
 
             # set zero-padding of solver to 2N
-            pad = 2 * cl.size
+            pad = 2 * cl.shape[0]
 
             # if the desired monopole is zero, that is most likely
             # and artefact of the theory spectra -- the variance of the
@@ -1008,7 +1007,7 @@ def cov_from_spectra(
     n = nfields_from_nspectra(len(spectra))
 
     # first case: maximum length in input spectra
-    k = max((cl.size for cl in spectra), default=0) if lmax is None else lmax + 1
+    k = max((cl.shape[0] for cl in spectra), default=0) if lmax is None else lmax + 1
 
     # this is the covariance matrix of the spectra
     # the leading dimension is k, then it is a n-by-n covariance matrix
@@ -1020,7 +1019,7 @@ def cov_from_spectra(
     # if the spectra are ragged, some entries at high ell may remain zero
     # only fill the lower triangular part, everything is symmetric
     for i, j, cl in enumerate_spectra(spectra):
-        size = min(k, cl.size)
+        size = min(k, cl.shape[0])
         cl_flat = xp.reshape(cl, (-1,))
         cov = xpx.at(cov)[:size, i, j].set(cl_flat[:size])
         cov = xpx.at(cov)[:size, j, i].set(cl_flat[:size])

glass/grf/_solver.py

@@ -89,7 +89,7 @@ def solve(  # noqa: PLR0912, PLR0913
     if t2 is None:
         t2 = t1
 
-    n = len(cl)
+    n = cl.shape[0]
     if pad < 0:
         msg = "pad must be a positive integer"
         raise ValueError(msg)
@@ -98,7 +98,7 @@ def solve(  # noqa: PLR0912, PLR0913
         gl = corrtocl(glass.grf.icorr(t1, t2, cltocorr(cl)))
     else:
         gl = np.zeros(n)
-        gl[: len(initial)] = initial[:n]
+        gl[: initial.shape[0]] = initial[:n]
 
     if monopole is not None:
         gl[0] = monopole

glass/healpix.py

@@ -122,7 +122,7 @@ def almxfl(
     alm
         The alm to multiply.
     fl
-        The function (at l=0..fl.size-1) by which alm must be multiplied.
+        The function (at l=0..fl.shape[0]-1) by which alm must be multiplied.
     inplace
         If True, modify the given alm, otherwise make a copy before multiplying.
 

glass/observations.py

@@ -332,7 +332,7 @@ def tomo_nz_gausserr(
     Returns
     -------
         The tomographic redshift bins convolved with a gaussian error.
-        Array has a shape (nbins, len(z))
+        Array has a shape (nbins, z.shape[0])
 
     See Also
     --------

glass/shells.py

@@ -260,7 +260,7 @@ def _calculate_zeff(self) -> float:
             The effective redshift depending on the size of ``za``.
 
         """
-        if self.za.size > 0:
+        if self.za.shape[0] > 0:
             return uxpx.trapezoid(
                 self.za * self.wa,
                 self.za,
@@ -313,7 +313,7 @@ def tophat_windows(
     if zbins.ndim != 1:
         msg = "zbins must be a 1D array"
         raise ValueError(msg)
-    if zbins.size < 2:
+    if zbins.shape[0] < 2:
         msg = "zbins must have at least two entries"
         raise ValueError(msg)
     if zbins[0] != 0:
@@ -381,7 +381,7 @@ def linear_windows(
     if zgrid.ndim != 1:
         msg = "zgrid must be a 1D array"
         raise ValueError(msg)
-    if zgrid.size < 3:
+    if zgrid.shape[0] < 3:
         msg = "nodes must have at least 3 entries"
         raise ValueError(msg)
     if zgrid[0] != 0:
@@ -451,7 +451,7 @@ def cubic_windows(
     if zgrid.ndim != 1:
         msg = "zgrid must be a 1D array"
         raise ValueError(msg)
-    if zgrid.size < 3:
+    if zgrid.shape[0] < 3:
         msg = "nodes must have at least 3 entries"
         raise ValueError(msg)
     if zgrid[0] != 0:
@@ -705,13 +705,13 @@ def partition_lstsq(
     a = xp.concat([a, mult * xp.ones((len(shells), 1))], axis=-1)
     b = xp.concat([b, mult * xp.reshape(uxpx.trapezoid(fz, z), (*dims, 1))], axis=-1)
 
-    # now a is a matrix of shape (len(shells), len(zp) + 1)
-    # and b is a matrix of shape (*dims, len(zp) + 1)
+    # now a is a matrix of shape (len(shells), zp.shape[0] + 1)
+    # and b is a matrix of shape (*dims, zp.shape[0] + 1)
     # need to find weights x such that b == x @ a over all axes of b
     # do the least-squares fit over partially flattened b, then reshape
     x = uxpx.linalg_lstsq(
         xp.matrix_transpose(a),
-        xp.matrix_transpose(xp.reshape(b, (-1, zp.size + 1))),
+        xp.matrix_transpose(xp.reshape(b, (-1, zp.shape[0] + 1))),
         rcond=None,
     )[0]
     x = xp.reshape(xp.matrix_transpose(x), (*dims, len(shells)))
@@ -777,8 +777,8 @@ def partition_nnls(
     a = xp.concat([a, mult * xp.ones((len(shells), 1))], axis=-1)
     b = xp.concat([b, mult * xp.reshape(uxpx.trapezoid(fz, z), (*dims, 1))], axis=-1)
 
-    # now a is a matrix of shape (len(shells), len(zp) + 1)
-    # and b is a matrix of shape (*dims, len(zp) + 1)
+    # now a is a matrix of shape (len(shells), zp.shape[0] + 1)
+    # and b is a matrix of shape (*dims, zp.shape[0] + 1)
     # for each dim, find non-negative weights x such that b == a.T @ x
 
     # reduce the dimensionality of the problem using a thin QR decomposition

glass/user.py

@@ -53,7 +53,7 @@ def save_cls(
         Angular matter power spectra in *GLASS* ordering.
 
     """
-    split = np.cumulative_sum([len(cl) for cl in cls[:-1]])
+    split = np.cumulative_sum([cl.shape[0] for cl in cls[:-1]])
     values = np.concatenate(cls)
     np.savez(filename, values=values, split=split)
 

tests/benchmarks/test_fields.py

@@ -244,7 +244,7 @@ def test_getcl_lmax_0(
         lmax=0,
     )
     expected = xpb.asarray([max(random_i, random_j)], dtype=xpb.float64)
-    assert result.size == 1
+    assert result.shape[0] == 1
     compare.assert_allclose(result, expected)
 
 
@@ -276,5 +276,5 @@ def test_getcl_lmax_larger_than_cls(
         lmax=lmax,
     )
     expected = xpb.zeros((lmax - 1,), dtype=xpb.float64)
-    assert result.size == lmax + 1
+    assert result.shape[0] == lmax + 1
     compare.assert_allclose(result[2:], expected)

tests/benchmarks/test_lensing.py

@@ -129,6 +129,6 @@ def multi_plane_weights_add_window(
         rounds=100,
     )
 
-    assert len(actual_convergence.kappa) == 10
+    assert actual_convergence.kappa.shape[0] == 10
     for x in actual_convergence.kappa:
         assert x is not None

tests/core/grf/test_solver.py

@@ -38,9 +38,9 @@ def test_pad(cl: FloatArray, rng: np.random.Generator) -> None:
     t = glass.grf.Lognormal(lam)
 
     # check that output size matches pad
-    _, cl_out, _ = glass.grf.solve(cl, t, pad=2 * cl.size)
+    _, cl_out, _ = glass.grf.solve(cl, t, pad=2 * cl.shape[0])
 
-    assert cl_out.size == 3 * cl.size
+    assert cl_out.shape[0] == 3 * cl.shape[0]
 
     with pytest.raises(ValueError, match="pad must be a positive integer"):
         glass.grf.solve(cl, t, pad=-1)
@@ -87,12 +87,12 @@ def test_lognormal(
 
     assert info > 0
 
-    compare.assert_allclose(cl_[1 : cl.size], cl[1:], atol=0.0, rtol=cltol)
+    compare.assert_allclose(cl_[1 : cl.shape[0]], cl[1:], atol=0.0, rtol=cltol)
 
     gl_ = glass.grf.compute(cl_, t1, t2)
 
     assert gl[0] == gl0
-    compare.assert_allclose(gl_[1 : gl.size], gl[1:])
+    compare.assert_allclose(gl_[1 : gl.shape[0]], gl[1:])
 
 
 def test_monopole(

tests/core/test_fields.py

@@ -475,13 +475,13 @@ def test_getcl(compare: type[Compare], xp: ModuleType) -> None:
             # check slicing
             result = glass.getcl(cls, i, j, lmax=0)
             expected = xp.asarray([max(i, j)], dtype=xp.float64)
-            assert result.size == 1
+            assert result.shape[0] == 1
             compare.assert_allclose(result, expected)
 
             # check padding
             result = glass.getcl(cls, i, j, lmax=50)
             expected = xp.zeros((49,), dtype=xp.float64)
-            assert result.size == 51
+            assert result.shape[0] == 51
             compare.assert_allclose(result[2:], expected)
 
 
@@ -596,7 +596,7 @@ def test_compute_gaussian_spectra_gh639(mocker: MockerFixture, xp: ModuleType) -
     assert mock.call_args_list[0] == mocker.call(spectra[0], fields[0], fields[0])
     assert mock.call_args_list[1] == mocker.call(spectra[1], fields[1], fields[1])
     assert gls[:2] == [mock.return_value, mock.return_value]
-    assert gls[2].size == 0
+    assert gls[2].shape[0] == 0
 
 
 def test_solve_gaussian_spectra(mocker: MockerFixture, xp: ModuleType) -> None: