MAINT: Move to new numpy.random API in test_*.py

.ruff.toml

@@ -95,9 +95,6 @@ lint.ignore = [
     "N816",  # mixed-case-variable-in-global-scope
     "N818",  # error-suffix-on-exception-name
 
-    # NumPy-specific rules (NPY)
-    "NPY002", # Replace legacy `np.random.rand` call with `np.random.Generator`  (2023-05-03)
-
     # Perflint (PERF)
     "PERF203",  # `try`-`except` within a loop incurs performance overhead
     "PERF401",  # Use a list comprehension to create a transformed list

astropy/convolution/tests/test_convolve.py

@@ -1171,15 +1171,14 @@ def test_asymmetric_kernel(boundary):
 
 @pytest.mark.parametrize("ndims", (1, 2, 3))
 def test_convolution_consistency(ndims):
-    np.random.seed(0)
-    array = np.random.randn(*([3] * ndims))
-    np.random.seed(0)
-    kernel = np.random.rand(*([3] * ndims))
+    rng = np.random.default_rng(0)
+    array = rng.standard_normal([3] * ndims)
+    kernel = rng.random([3] * ndims)
 
     conv_f = convolve_fft(array, kernel, boundary="fill")
     conv_d = convolve(array, kernel, boundary="fill")
 
-    assert_array_almost_equal_nulp(conv_f, conv_d, 30)
+    assert_array_almost_equal_nulp(conv_f, conv_d, 40)
 
 
 def test_astropy_convolution_against_numpy():

astropy/convolution/tests/test_convolve_kernels.py

@@ -91,7 +91,8 @@ def test_random_makekernel(self, kernel):
 
         shape = kernel.array.shape
 
-        x = np.random.randn(*shape)
+        rng = np.random.default_rng()
+        x = rng.standard_normal(shape)
 
         c2 = convolve_fft(x, kernel, boundary="fill")
         c1 = convolve(x, kernel, boundary="fill")

astropy/convolution/tests/test_convolve_models.py

@@ -9,7 +9,6 @@
 from astropy.convolution.convolve import convolve, convolve_fft, convolve_models
 from astropy.modeling import fitting, models
 from astropy.utils.compat.optional_deps import HAS_SCIPY
-from astropy.utils.misc import NumpyRNGContext
 
 
 class TestConvolve1DModels:
@@ -87,8 +86,8 @@ def test_fitting_convolve_models(self, mode):
 
         x = np.linspace(-5, 5, 99)
         fake_model = models.Gaussian1D(amplitude=10)
-        with NumpyRNGContext(123):
-            fake_data = fake_model(x) + np.random.normal(size=len(x))
+        rng = np.random.default_rng(0)
+        fake_data = fake_model(x) + rng.normal(size=len(x))
 
         init_model = convolve_models(b1, g1, mode=mode, normalize_kernel=False)
         fitter = fitting.LevMarLSQFitter()

astropy/convolution/tests/test_kernel_class.py

@@ -57,8 +57,9 @@
 delta_pulse_2D = np.zeros((81, 81))
 delta_pulse_2D[40, 40] = 1
 
-random_data_1D = np.random.rand(61)
-random_data_2D = np.random.rand(61, 61)
+rng = np.random.default_rng()
+random_data_1D = rng.random(61)
+random_data_2D = rng.random((61, 61))
 
 
 class TestKernels:

astropy/convolution/tests/test_pickle.py

@@ -6,11 +6,13 @@
 from astropy import convolution as conv
 from astropy.tests.helper import check_pickling_recovery, pickle_protocol  # noqa: F401
 
+rng = np.random.default_rng()
+
 
 @pytest.mark.parametrize(
     ("name", "args", "kwargs", "xfail"),
     [
-        (conv.CustomKernel, [], {"array": np.random.rand(15)}, False),
+        (conv.CustomKernel, [], {"array": rng.random(15)}, False),
         (conv.Gaussian1DKernel, [1.0], {"x_size": 5}, True),
         (conv.Gaussian2DKernel, [1.0], {"x_size": 5, "y_size": 5}, True),
     ],

astropy/coordinates/tests/accuracy/generate_ref_ast.py

@@ -18,18 +18,18 @@ def ref_fk4_no_e_fk4(fnout="fk4_no_e_fk4.csv"):
     """
     import starlink.Ast as Ast
 
-    np.random.seed(12345)
+    rng = np.random.default_rng(12345)
 
     N = 200
 
     # Sample uniformly on the unit sphere. These will be either the FK4
     # coordinates for the transformation to FK5, or the FK5 coordinates for the
     # transformation to FK4.
-    ra = np.random.uniform(0.0, 360.0, N)
-    dec = np.degrees(np.arcsin(np.random.uniform(-1.0, 1.0, N)))
+    ra = rng.uniform(0.0, 360.0, N)
+    dec = np.degrees(np.arcsin(rng.uniform(-1.0, 1.0, N)))
 
     # Generate random observation epoch and equinoxes
-    obstime = [f"B{x:7.2f}" for x in np.random.uniform(1950.0, 2000.0, N)]
+    obstime = [f"B{x:7.2f}" for x in rng.uniform(1950.0, 2000.0, N)]
 
     ra_fk4ne, dec_fk4ne = [], []
     ra_fk4, dec_fk4 = [], []
@@ -75,20 +75,20 @@ def ref_fk4_no_e_fk5(fnout="fk4_no_e_fk5.csv"):
     """
     import starlink.Ast as Ast
 
-    np.random.seed(12345)
+    rng = np.random.default_rng(12345)
 
     N = 200
 
     # Sample uniformly on the unit sphere. These will be either the FK4
     # coordinates for the transformation to FK5, or the FK5 coordinates for the
     # transformation to FK4.
-    ra = np.random.uniform(0.0, 360.0, N)
-    dec = np.degrees(np.arcsin(np.random.uniform(-1.0, 1.0, N)))
+    ra = rng.uniform(0.0, 360.0, N)
+    dec = np.degrees(np.arcsin(rng.uniform(-1.0, 1.0, N)))
 
     # Generate random observation epoch and equinoxes
-    obstime = [f"B{x:7.2f}" for x in np.random.uniform(1950.0, 2000.0, N)]
-    equinox_fk4 = [f"B{x:7.2f}" for x in np.random.uniform(1925.0, 1975.0, N)]
-    equinox_fk5 = [f"J{x:7.2f}" for x in np.random.uniform(1975.0, 2025.0, N)]
+    obstime = [f"B{x:7.2f}" for x in rng.uniform(1950.0, 2000.0, N)]
+    equinox_fk4 = [f"B{x:7.2f}" for x in rng.uniform(1925.0, 1975.0, N)]
+    equinox_fk5 = [f"J{x:7.2f}" for x in rng.uniform(1975.0, 2025.0, N)]
 
     ra_fk4, dec_fk4 = [], []
     ra_fk5, dec_fk5 = [], []
@@ -140,19 +140,19 @@ def ref_galactic_fk4(fnout="galactic_fk4.csv"):
     """
     import starlink.Ast as Ast
 
-    np.random.seed(12345)
+    rng = np.random.default_rng(12345)
 
     N = 200
 
     # Sample uniformly on the unit sphere. These will be either the ICRS
     # coordinates for the transformation to FK5, or the FK5 coordinates for the
     # transformation to ICRS.
-    lon = np.random.uniform(0.0, 360.0, N)
-    lat = np.degrees(np.arcsin(np.random.uniform(-1.0, 1.0, N)))
+    lon = rng.uniform(0.0, 360.0, N)
+    lat = np.degrees(np.arcsin(rng.uniform(-1.0, 1.0, N)))
 
     # Generate random observation epoch and equinoxes
-    obstime = [f"B{x:7.2f}" for x in np.random.uniform(1950.0, 2000.0, N)]
-    equinox_fk4 = [f"J{x:7.2f}" for x in np.random.uniform(1975.0, 2025.0, N)]
+    obstime = [f"B{x:7.2f}" for x in rng.uniform(1950.0, 2000.0, N)]
+    equinox_fk4 = [f"J{x:7.2f}" for x in rng.uniform(1975.0, 2025.0, N)]
 
     lon_gal, lat_gal = [], []
     ra_fk4, dec_fk4 = [], []
@@ -201,19 +201,18 @@ def ref_icrs_fk5(fnout="icrs_fk5.csv"):
     """
     import starlink.Ast as Ast
 
-    np.random.seed(12345)
-
+    rng = np.random.default_rng(12345)
     N = 200
 
     # Sample uniformly on the unit sphere. These will be either the ICRS
     # coordinates for the transformation to FK5, or the FK5 coordinates for the
     # transformation to ICRS.
-    ra = np.random.uniform(0.0, 360.0, N)
-    dec = np.degrees(np.arcsin(np.random.uniform(-1.0, 1.0, N)))
+    ra = rng.uniform(0.0, 360.0, N)
+    dec = np.degrees(np.arcsin(rng.uniform(-1.0, 1.0, N)))
 
     # Generate random observation epoch and equinoxes
-    obstime = [f"B{x:7.2f}" for x in np.random.uniform(1950.0, 2000.0, N)]
-    equinox_fk5 = [f"J{x:7.2f}" for x in np.random.uniform(1975.0, 2025.0, N)]
+    obstime = [f"B{x:7.2f}" for x in rng.uniform(1950.0, 2000.0, N)]
+    equinox_fk5 = [f"J{x:7.2f}" for x in rng.uniform(1975.0, 2025.0, N)]
 
     ra_icrs, dec_icrs = [], []
     ra_fk5, dec_fk5 = [], []

astropy/coordinates/tests/accuracy/generate_spectralcoord_ref.py

@@ -15,21 +15,21 @@
     from astropy.table import QTable
     from astropy.time import Time
 
-    np.random.seed(12345)
+    rng = np.random.default_rng(12345)
 
     N = 100
 
     tab = QTable()
-    target_lon = np.random.uniform(0, 360, N) * u.deg
-    target_lat = np.degrees(np.arcsin(np.random.uniform(-1, 1, N))) * u.deg
+    target_lon = rng.uniform(0, 360, N) * u.deg
+    target_lat = np.degrees(np.arcsin(rng.uniform(-1, 1, N))) * u.deg
     tab["target"] = SkyCoord(target_lon, target_lat, frame="fk5")
     tab["obstime"] = Time(
-        np.random.uniform(Time("1997-01-01").mjd, Time("2017-12-31").mjd, N),
+        rng.uniform(Time("1997-01-01").mjd, Time("2017-12-31").mjd, N),
         format="mjd",
         scale="utc",
     )
-    tab["obslon"] = Angle(np.random.uniform(-180, 180, N) * u.deg)
-    tab["obslat"] = Angle(np.arcsin(np.random.uniform(-1, 1, N)) * u.deg)
+    tab["obslon"] = Angle(rng.uniform(-180, 180, N) * u.deg)
+    tab["obslat"] = Angle(np.arcsin(rng.uniform(-1, 1, N)) * u.deg)
     tab["geocent"] = 0.0
     tab["heliocent"] = 0.0
     tab["lsrk"] = 0.0

astropy/coordinates/tests/accuracy/test_fk4_no_e_fk4.py

@@ -26,7 +26,7 @@ def test_fk4_no_e_fk4():
     if N_ACCURACY_TESTS >= len(t):
         idxs = range(len(t))
     else:
-        idxs = np.random.randint(len(t), size=N_ACCURACY_TESTS)
+        idxs = np.random.default_rng().integers(len(t), size=N_ACCURACY_TESTS)
 
     diffarcsec1 = []
     diffarcsec2 = []

astropy/coordinates/tests/accuracy/test_fk4_no_e_fk5.py

@@ -23,7 +23,7 @@ def test_fk4_no_e_fk5():
     if N_ACCURACY_TESTS >= len(t):
         idxs = range(len(t))
     else:
-        idxs = np.random.randint(len(t), size=N_ACCURACY_TESTS)
+        idxs = np.random.default_rng().integers(len(t), size=N_ACCURACY_TESTS)
 
     diffarcsec1 = []
     diffarcsec2 = []

astropy/coordinates/tests/accuracy/test_galactic_fk4.py

@@ -23,7 +23,7 @@ def test_galactic_fk4():
     if N_ACCURACY_TESTS >= len(t):
         idxs = range(len(t))
     else:
-        idxs = np.random.randint(len(t), size=N_ACCURACY_TESTS)
+        idxs = np.random.default_rng().integers(len(t), size=N_ACCURACY_TESTS)
 
     diffarcsec1 = []
     diffarcsec2 = []

astropy/coordinates/tests/accuracy/test_icrs_fk5.py

@@ -23,7 +23,7 @@ def test_icrs_fk5():
     if N_ACCURACY_TESTS >= len(t):
         idxs = range(len(t))
     else:
-        idxs = np.random.randint(len(t), size=N_ACCURACY_TESTS)
+        idxs = np.random.default_rng().integers(len(t), size=N_ACCURACY_TESTS)
 
     diffarcsec1 = []
     diffarcsec2 = []

astropy/coordinates/tests/test_api_ape5.py

@@ -124,22 +124,23 @@ def test_representations_api():
     # physics convention, as it is one of the most common cases.
     _ = PhysicsSphericalRepresentation(phi=120 * u.deg, theta=85 * u.deg, r=3 * u.kpc)
 
+    rng = np.random.default_rng()
     # first dimension must be length-3 if a lone `Quantity` is passed in.
-    c1 = CartesianRepresentation(np.random.randn(3, 100) * u.kpc)
+    c1 = CartesianRepresentation(rng.standard_normal((3, 100)) * u.kpc)
     assert c1.xyz.shape[0] == 3
     assert c1.xyz.unit == u.kpc
     assert c1.x.shape[0] == 100
     assert c1.y.shape[0] == 100
     assert c1.z.shape[0] == 100
     # can also give each as separate keywords
     CartesianRepresentation(
-        x=np.random.randn(100) * u.kpc,
-        y=np.random.randn(100) * u.kpc,
-        z=np.random.randn(100) * u.kpc,
+        x=rng.standard_normal(100) * u.kpc,
+        y=rng.standard_normal(100) * u.kpc,
+        z=rng.standard_normal(100) * u.kpc,
     )
     # if the units don't match but are all distances, they will automatically be
     # converted to match `x`
-    xarr, yarr, zarr = np.random.randn(3, 100)
+    xarr, yarr, zarr = rng.standard_normal((3, 100))
     c1 = CartesianRepresentation(x=xarr * u.kpc, y=yarr * u.kpc, z=zarr * u.kpc)
     c2 = CartesianRepresentation(x=xarr * u.kpc, y=yarr * u.kpc, z=zarr * u.pc)
     assert c1.xyz.unit == c2.xyz.unit == u.kpc

astropy/coordinates/tests/test_frames_with_velocity.py

@@ -263,14 +263,15 @@ def test_shorthand_attributes():
     # Check that attribute access works
 
     # for array data:
+    rng = np.random.default_rng()
     n = 4
     icrs1 = ICRS(
-        ra=np.random.uniform(0, 360, n) * u.deg,
-        dec=np.random.uniform(-90, 90, n) * u.deg,
+        ra=rng.uniform(0, 360, n) * u.deg,
+        dec=rng.uniform(-90, 90, n) * u.deg,
         distance=100 * u.pc,
-        pm_ra_cosdec=np.random.normal(0, 100, n) * u.mas / u.yr,
-        pm_dec=np.random.normal(0, 100, n) * u.mas / u.yr,
-        radial_velocity=np.random.normal(0, 100, n) * u.km / u.s,
+        pm_ra_cosdec=rng.normal(0, 100, n) * u.mas / u.yr,
+        pm_dec=rng.normal(0, 100, n) * u.mas / u.yr,
+        radial_velocity=rng.normal(0, 100, n) * u.km / u.s,
     )
     v = icrs1.velocity
     pm = icrs1.proper_motion

astropy/coordinates/tests/test_matching.py

@@ -137,17 +137,16 @@ def test_python_kdtree(monkeypatch):
 def test_matching_method():
     from astropy.coordinates import ICRS, SkyCoord
     from astropy.coordinates.matching import match_coordinates_3d, match_coordinates_sky
-    from astropy.utils import NumpyRNGContext
 
-    with NumpyRNGContext(987654321):
-        cmatch = ICRS(
-            np.random.rand(20) * 360.0 * u.degree,
-            (np.random.rand(20) * 180.0 - 90.0) * u.degree,
-        )
-        ccatalog = ICRS(
-            np.random.rand(100) * 360.0 * u.degree,
-            (np.random.rand(100) * 180.0 - 90.0) * u.degree,
-        )
+    rng = np.random.default_rng(987654321)
+    cmatch = ICRS(
+        rng.random(20) * 360.0 * u.degree,
+        (rng.random(20) * 180.0 - 90.0) * u.degree,
+    )
+    ccatalog = ICRS(
+        rng.random(100) * 360.0 * u.degree,
+        (rng.random(100) * 180.0 - 90.0) * u.degree,
+    )
 
     idx1, d2d1, d3d1 = SkyCoord(cmatch).match_to_catalog_3d(ccatalog)
     idx2, d2d2, d3d2 = match_coordinates_3d(cmatch, ccatalog)

astropy/coordinates/tests/test_regression.py

@@ -339,10 +339,10 @@ def test_regression_5209():
 
 def test_regression_5133():
     N = 1000
-    np.random.seed(12345)
-    lon = np.random.uniform(-10, 10, N) * u.deg
-    lat = np.random.uniform(50, 52, N) * u.deg
-    alt = np.random.uniform(0, 10.0, N) * u.km
+    rng = np.random.default_rng(12345)
+    lon = rng.uniform(-10, 10, N) * u.deg
+    lat = rng.uniform(50, 52, N) * u.deg
+    alt = rng.uniform(0, 10.0, N) * u.km
 
     time = Time("2010-1-1")
 

astropy/coordinates/tests/test_shape_manipulation.py

@@ -51,7 +51,8 @@ def setup_class(cls):
         cls.pressure = 1000 * u.hPa
         # As well as an array.
         cls.temperature = (
-            np.random.uniform(0.0, 20.0, size=(lon.size, lat.size)) * u.deg_C
+            np.random.default_rng().uniform(0.0, 20.0, size=(lon.size, lat.size))
+            * u.deg_C
         )
         cls.s1 = AltAz(
             az=lon[:, np.newaxis],