diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index acfc5b2f00..41fc46b6b5 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,5 +1,9 @@
 name: GalSim CI
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 on:
     push:
         branches:
diff --git a/setup.py b/setup.py
index 1bae8495c8..8ce1f0fcd8 100644
--- a/setup.py
+++ b/setup.py
@@ -244,7 +244,7 @@ def supports_gpu(compiler, cc_type):
     extra_cflags = copt[cc_type]
     extra_lflags = lopt[cc_type]
     return try_compile(cpp_code, compiler, extra_cflags, extra_lflags)
-    
+
 # Check for the fftw3 library in some likely places
 def find_fftw_lib(output=False):
     import distutils.sysconfig
@@ -434,29 +434,29 @@ def find_eigen_dir(output=False):
         if output:
             print("Downloaded %s.  Unpacking tarball."%fname)
         with tarfile.open(fname) as tar:
-            
+
             def is_within_directory(directory, target):
-                
+
                 abs_directory = os.path.abspath(directory)
                 abs_target = os.path.abspath(target)
-            
+
                 prefix = os.path.commonprefix([abs_directory, abs_target])
-                
+
                 return prefix == abs_directory
-            
+
             def safe_extract(tar, path=".", members=None, *, numeric_owner=False):
                 # Avoid security vulnerability in tar.extractall function.
                 # This bit of code was added by the Advanced Research Center at Trellix in PR #1188.
                 # For more information about the security vulnerability, see
                 # https://github.com/advisories/GHSA-gw9q-c7gh-j9vm
-            
+
                 for member in tar.getmembers():
                     member_path = os.path.join(path, member.name)
                     if not is_within_directory(path, member_path):
                         raise Exception("Attempted Path Traversal in Tar File")
-            
-                tar.extractall(path, members, numeric_owner=numeric_owner) 
-            
+
+                tar.extractall(path, members, numeric_owner=numeric_owner)
+
             safe_extract(tar, dir)
         os.remove(fname)
         # This actually extracts into a subdirectory with a name eigen-eigen-5a0156e40feb/
diff --git a/tests/galsim_test_helpers.py b/tests/galsim_test_helpers.py
index 128075c61e..d6c4fa0cf4 100644
--- a/tests/galsim_test_helpers.py
+++ b/tests/galsim_test_helpers.py
@@ -25,6 +25,31 @@
 from numpy.testing import assert_raises
 from numpy.testing import assert_warns
 
+__all__ = [
+    "default_params",
+    "gsobject_compare",
+    "printval",
+    "convertToShear",
+    "check_basic_x",
+    "check_basic_k",
+    "assert_floatlike",
+    "assert_intlike",
+    "check_basic",
+    "do_shoot",
+    "do_kvalue",
+    "radial_integrate",
+    "drawNoise",
+    "check_pickle",
+    "check_all_diff",
+    "timer",
+    "CaptureLog",
+    "assert_raises",
+    "assert_warns",
+    "Profile",
+    "galsim_backend",
+    "is_jax_galsim",
+]
+
 # This file has some helper functions that are used by tests from multiple files to help
 # avoid code duplication.
 
@@ -43,6 +68,18 @@
         integration_relerr = 1.e-6,
         integration_abserr = 1.e-8)
 
+
+def galsim_backend():
+    if "jax_galsim/__init__.py" in galsim.__file__:
+        return "jax_galsim"
+    else:
+        return "galsim"
+
+
+def is_jax_galsim():
+    return galsim_backend() == "jax_galsim"
+
+
 def gsobject_compare(obj1, obj2, conv=None, decimal=10):
     """Helper function to check that two GSObjects are equivalent
     """
@@ -133,20 +170,32 @@ def check_basic_x(prof, name, approx_maxsb=False, scale=None):
         np.testing.assert_allclose(
                 image(i,j), prof._xValue(galsim.PositionD(x,y)), rtol=1.e-5,
                 err_msg="%s profile sb image does not match _xValue at %d,%d"%(name,i,j))
-    assert prof.withFlux.__doc__ == galsim.GSObject.withFlux.__doc__
-    assert prof.__class__.withFlux.__doc__ == galsim.GSObject.withFlux.__doc__
+    if is_jax_galsim():
+        for line in galsim.GSObject.withFlux.__doc__.splitlines():
+            if line.strip() and "LAX" not in line:
+                assert line.strip() in prof.withFlux.__doc__, (
+                    prof.withFlux.__doc__, galsim.GSObject.withFlux.__doc__,
+                )
+        for line in galsim.GSObject.withFlux.__doc__.splitlines():
+            if line.strip() and "LAX" not in line:
+                assert line.strip() in prof.__class__.withFlux.__doc__, (
+                    prof.__class__.withFlux.__doc__, galsim.GSObject.withFlux.__doc__,
+                )
+    else:
+        assert prof.withFlux.__doc__ == galsim.GSObject.withFlux.__doc__
+        assert prof.__class__.withFlux.__doc__ == galsim.GSObject.withFlux.__doc__
 
     # Check negative flux:
     neg_image = prof.withFlux(-prof.flux).drawImage(method='sb', scale=scale, use_true_center=False)
-    np.testing.assert_almost_equal(neg_image.array/prof.flux, -image.array/prof.flux, 7,
-                                   '%s negative flux drawReal is not negative of +flux image'%name)
+    np.testing.assert_array_almost_equal(neg_image.array/prof.flux, -image.array/prof.flux, 7,
+                                         '%s negative flux drawReal is not negative of +flux image'%name)
 
     # Direct call to drawReal should also work and be equivalent to the above with scale = 1.
     prof.drawImage(image, method='sb', scale=1., use_true_center=False)
     image2 = image.copy()
     prof.drawReal(image2)
-    np.testing.assert_equal(image2.array, image.array,
-                            err_msg="%s drawReal not equivalent to drawImage"%name)
+    np.testing.assert_array_equal(image2.array, image.array,
+                                  err_msg="%s drawReal not equivalent to drawImage"%name)
 
     # If supposed to be axisymmetric, make sure it is.
     if prof.is_axisymmetric:
@@ -194,7 +243,7 @@ def check_basic_k(prof, name):
 
     # Check negative flux:
     neg_image = prof.withFlux(-prof.flux).drawKImage(kimage.copy())
-    np.testing.assert_almost_equal(neg_image.array/prof.flux, -kimage.array/prof.flux, 7,
+    np.testing.assert_array_almost_equal(neg_image.array/prof.flux, -kimage.array/prof.flux, 7,
                                    '%s negative flux drawK is not negative of +flux image'%name)
 
     # If supposed to be axisymmetric, make sure it is in the kValues.
@@ -206,6 +255,30 @@ def check_basic_k(prof, name):
             np.testing.assert_allclose(test_values, ref_value, rtol=1.e-5,
                                        err_msg="%s profile not axisymmetric in kValues"%name)
 
+def assert_floatlike(val):
+    assert (
+        isinstance(val, float)
+        or (
+            is_jax_galsim()
+            and hasattr(val, "shape")
+            and val.shape == ()
+            and hasattr(val, "dtype")
+            and val.dtype.name in ["float", "float32", "float64"]
+        )
+    ), "Value is not float-like: type(%r) = %r" % (val, type(val))
+
+def assert_intlike(val):
+    assert (
+        isinstance(val, int)
+        or (
+            is_jax_galsim()
+            and hasattr(val, "shape")
+            and val.shape == ()
+            and hasattr(val, "dtype")
+            and val.dtype.name in ["int", "int32", "int64"]
+        )
+    ), "Value is not int-like: type(%r) = %r" % (val, type(val))
+
 def check_basic(prof, name, approx_maxsb=False, scale=None, do_x=True, do_k=True):
     """Do some basic sanity checks that should work for all profiles.
     """
@@ -220,12 +293,12 @@ def check_basic(prof, name, approx_maxsb=False, scale=None, do_x=True, do_k=True
             prof.positive_flux - prof.negative_flux, prof.flux,
             err_msg="%s profile flux not equal to posflux + negflux"%name)
     assert isinstance(prof.centroid, galsim.PositionD)
-    assert isinstance(prof.flux, float)
-    assert isinstance(prof.positive_flux, float)
-    assert isinstance(prof.negative_flux, float)
-    assert isinstance(prof.max_sb, float)
-    assert isinstance(prof.stepk, float)
-    assert isinstance(prof.maxk, float)
+    assert_floatlike(prof.flux)
+    assert_floatlike(prof.positive_flux)
+    assert_floatlike(prof.negative_flux)
+    assert_floatlike(prof.max_sb)
+    assert_floatlike(prof.stepk)
+    assert_floatlike(prof.maxk)
     assert isinstance(prof.has_hard_edges, bool)
     assert isinstance(prof.is_axisymmetric, bool)
     assert isinstance(prof.is_analytic_x, bool)
@@ -298,6 +371,9 @@ def do_shoot(prof, img, name):
     print('nphot = ',nphot)
     img2 = img.copy()
 
+    if is_jax_galsim():
+        rtol *= 3
+
     # Use a deterministic random number generator so we don't fail tests because of rare flukes
     # in the random numbers.
     rng = galsim.UniformDeviate(12345)
diff --git a/tests/test_catalog.py b/tests/test_catalog.py
index 11623ec60f..331b495f88 100644
--- a/tests/test_catalog.py
+++ b/tests/test_catalog.py
@@ -16,6 +16,7 @@
 #    and/or other materials provided with the distribution.
 #
 
+import os
 import numpy as np
 
 import galsim
@@ -198,7 +199,7 @@ def test_basic_dict():
 def test_single_row():
     """Test that we can read catalogs with just one row (#394)
     """
-    filename = "output/test394.txt"
+    filename = os.path.join(os.path.dirname(__file__), "output/test394.txt")
     with open(filename, 'w') as f:
         f.write("3 4 5\n")
     cat = galsim.Catalog(filename, file_type='ascii')
diff --git a/tests/test_celestial.py b/tests/test_celestial_galsim.py
similarity index 96%
rename from tests/test_celestial.py
rename to tests/test_celestial_galsim.py
index 1a64f37283..3e89bf4eb9 100644
--- a/tests/test_celestial.py
+++ b/tests/test_celestial_galsim.py
@@ -17,6 +17,7 @@
 #
 
 import numpy
+import numpy as np
 import os
 import math
 
@@ -122,11 +123,11 @@ def test_angle():
 
     # Check invalid constructors
     assert_raises(TypeError,galsim.AngleUnit, galsim.degrees)
-    assert_raises(ValueError,galsim.AngleUnit, 'spam')
+    assert_raises((ValueError, TypeError), galsim.AngleUnit, 'spam')
     assert_raises(TypeError,galsim.AngleUnit, 1, 3)
     assert_raises(TypeError,galsim.Angle, 3.4)
     assert_raises(TypeError,galsim.Angle, theta1, galsim.degrees)
-    assert_raises(ValueError,galsim.Angle, 'spam', galsim.degrees)
+    assert_raises((ValueError, TypeError), galsim.Angle, 'spam')
     assert_raises(TypeError,galsim.Angle, 1, 3)
 
 
@@ -155,7 +156,7 @@ def test_celestialcoord_basic():
 
     x, y, z = c1.get_xyz()
     print('c1 is at x,y,z = ',x,y,z)
-    np.testing.assert_equal((x,y,z), (1,0,0))
+    np.testing.assert_array_equal((x,y,z), (1,0,0))
     assert c1 == galsim.CelestialCoord.from_xyz(x,y,z)
 
     x, y, z = c2.get_xyz()
@@ -343,9 +344,19 @@ def test_projection():
 
     # First the trivial case
     p0 = center.project(center, projection='lambert')
-    assert p0 == (0.0 * galsim.arcsec, 0.0 * galsim.arcsec)
+    np.testing.assert_allclose(
+        (p0[0].rad, p0[1].rad),
+        (0.0, 0.0),
+        rtol=0,
+        atol=1e-16,
+    )
     c0 = center.deproject(*p0, projection='lambert')
-    assert c0 == center
+    np.testing.assert_allclose(
+        c0.rad,
+        center.rad,
+        rtol=0,
+        atol=1e-16,
+    )
     np.testing.assert_almost_equal(center.jac_deproject(*p0, projection='lambert').ravel(),
                                    (1,0,0,1))
 
@@ -398,9 +409,19 @@ def test_projection():
 
     # First the trivial case
     p0 = center.project(center, projection='stereographic')
-    assert p0 == (0.0 * galsim.arcsec, 0.0 * galsim.arcsec)
+    np.testing.assert_allclose(
+        (p0[0].rad, p0[1].rad),
+        (0.0, 0.0),
+        rtol=0,
+        atol=1e-16,
+    )
     c0 = center.deproject(*p0, projection='stereographic')
-    assert c0 == center
+    np.testing.assert_allclose(
+        c0.rad,
+        center.rad,
+        rtol=0,
+        atol=1e-16,
+    )
     np.testing.assert_almost_equal(center.jac_deproject(*p0, projection='stereographic').ravel(),
                                    (1,0,0,1))
 
@@ -456,9 +477,19 @@ def test_projection():
 
     # First the trivial case
     p0 = center.project(center, projection='gnomonic')
-    assert p0 == (0.0 * galsim.arcsec, 0.0 * galsim.arcsec)
+    np.testing.assert_allclose(
+        (p0[0].rad, p0[1].rad),
+        (0.0, 0.0),
+        rtol=0,
+        atol=1e-16,
+    )
     c0 = center.deproject(*p0, projection='gnomonic')
-    assert c0 == center
+    np.testing.assert_allclose(
+        c0.rad,
+        center.rad,
+        rtol=0,
+        atol=1e-16,
+    )
     np.testing.assert_almost_equal(center.jac_deproject(*p0, projection='gnomonic').ravel(),
                                    (1,0,0,1))
 
@@ -510,9 +541,19 @@ def test_projection():
 
     # First the trivial case
     p0 = center.project(center, projection='postel')
-    assert p0 == (0.0 * galsim.arcsec, 0.0 * galsim.arcsec)
+    np.testing.assert_allclose(
+        (p0[0].rad, p0[1].rad),
+        (0.0, 0.0),
+        rtol=0,
+        atol=1e-16,
+    )
     c0 = center.deproject(*p0, projection='postel')
-    assert c0 == center
+    np.testing.assert_allclose(
+        c0.rad,
+        center.rad,
+        rtol=0,
+        atol=1e-16,
+    )
     np.testing.assert_almost_equal(center.jac_deproject(*p0, projection='postel').ravel(),
                                    (1,0,0,1))
 
diff --git a/tests/test_chromatic.py b/tests/test_chromatic.py
index 23afd9de46..af87ae8476 100644
--- a/tests/test_chromatic.py
+++ b/tests/test_chromatic.py
@@ -16,6 +16,7 @@
 #    and/or other materials provided with the distribution.
 #
 
+import copy
 import os
 import copy
 import numpy as np
diff --git a/tests/test_config_output.py b/tests/test_config_output.py
index c75ff49301..aed0c3bec5 100644
--- a/tests/test_config_output.py
+++ b/tests/test_config_output.py
@@ -1238,7 +1238,7 @@ def test_config():
     }
 
     # Test yaml
-    yaml_file_name = "output/test_config.yaml"
+    yaml_file_name = os.path.join(os.path.dirname(__file__), "output/test_config.yaml")
     with open(yaml_file_name, 'w') as fout:
         yaml.dump(config, fout, default_flow_style=True)
     # String None will be coverted to a real None.  Convert here in the comparison dict
@@ -1252,7 +1252,7 @@ def test_config():
     assert config == dict(config2)
 
     # Test json
-    json_file_name = "output/test_config.json"
+    json_file_name = os.path.join(os.path.dirname(__file__), "output/test_config.json")
     with open(json_file_name, 'w') as fout:
         json.dump(config, fout)
 
diff --git a/tests/test_convolve.py b/tests/test_convolve.py
index 53984c6223..3abe444ea0 100644
--- a/tests/test_convolve.py
+++ b/tests/test_convolve.py
@@ -22,8 +22,8 @@
 import galsim
 from galsim_test_helpers import *
 
-imgdir = os.path.join(".", "SBProfile_comparison_images") # Directory containing the reference
-                                                          # images.
+# Directory containing the reference images.
+imgdir = os.path.join(os.path.dirname(__file__), "SBProfile_comparison_images")
 
 @timer
 def test_convolve():
@@ -496,9 +496,9 @@ def test_deconvolve():
 
     cen = galsim.PositionD(0,0)
     np.testing.assert_equal(inv_psf.centroid, cen)
-    np.testing.assert_almost_equal(inv_psf.flux, 1./psf.flux)
+    np.testing.assert_array_almost_equal(inv_psf.flux, 1./psf.flux)
     # This doesn't really have any meaning, but this is what we've assigned to a deconvolve max_sb.
-    np.testing.assert_almost_equal(inv_psf.max_sb, -psf.max_sb / psf.flux**2)
+    np.testing.assert_array_almost_equal(inv_psf.max_sb, -psf.max_sb / psf.flux**2)
 
     check_basic(inv_psf, "Deconvolve(Moffat)", do_x=False)
 
diff --git a/tests/test_correlatednoise.py b/tests/test_correlatednoise.py
index 6848fc04c4..5e5c0a4bf0 100644
--- a/tests/test_correlatednoise.py
+++ b/tests/test_correlatednoise.py
@@ -16,6 +16,8 @@
 #    and/or other materials provided with the distribution.
 #
 
+import os
+import time
 import numpy as np
 import os
 
diff --git a/tests/test_deprecated.py b/tests/test_deprecated.py
index 0e562531ab..150654a77e 100644
--- a/tests/test_deprecated.py
+++ b/tests/test_deprecated.py
@@ -34,9 +34,12 @@ def check_dep(f, *args, **kwargs):
 
 @timer
 def test_gsparams():
-    check_dep(galsim.GSParams, allowed_flux_variation=0.90)
-    check_dep(galsim.GSParams, range_division_for_extrema=50)
-    check_dep(galsim.GSParams, small_fraction_of_flux=1.e-6)
+    if is_jax_galsim():
+        pass
+    else:
+        check_dep(galsim.GSParams, allowed_flux_variation=0.90)
+        check_dep(galsim.GSParams, range_division_for_extrema=50)
+        check_dep(galsim.GSParams, small_fraction_of_flux=1.e-6)
 
 
 @timer
@@ -540,75 +543,182 @@ def test_photon_array_depr():
 
     # Using the getter is allowed, but deprecated.
     photon_array = galsim.PhotonArray(nphotons)
-    dxdz = check_dep(getattr, photon_array, 'dxdz')
-    assert photon_array.hasAllocatedAngles()
-    assert photon_array.hasAllocatedAngles()
-    assert len(photon_array.dxdz) == nphotons
-    assert len(photon_array.dydz) == nphotons
-    dxdz[:] = 0.17
+    if is_jax_galsim():
+        # jax-galsim always sets these additional properties
+        # dxdz = check_dep(getattr, photon_array, 'dxdz')
+        # however jax-galsim sets them to NaN so they are not allocated
+        assert not photon_array.hasAllocatedAngles()
+        assert len(photon_array.dxdz) == nphotons
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.dxdz = 0.17
+        # non-nan means allocated for jax-galsim
+        assert photon_array.hasAllocatedAngles()
+    else:
+        dxdz = check_dep(getattr, photon_array, 'dxdz')
+        assert photon_array.hasAllocatedAngles()
+        assert photon_array.hasAllocatedAngles()
+        assert len(photon_array.dxdz) == nphotons
+        assert len(photon_array.dydz) == nphotons
+        dxdz[:] = 0.17
     np.testing.assert_array_equal(photon_array.dxdz, 0.17)
     np.testing.assert_array_equal(photon_array.dydz, 0.)
 
-    dydz = photon_array.dydz  # Allowed now.
-    dydz[:] = 0.59
+    if is_jax_galsim():
+        assert hasattr(photon_array, "dydz")
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.dydz = 0.59
+    else:
+        dydz = photon_array.dydz  # Allowed now.
+        dydz[:] = 0.59
     np.testing.assert_array_equal(photon_array.dydz, 0.59)
 
-    wave = check_dep(getattr, photon_array, 'wavelength')
-    assert photon_array.hasAllocatedWavelengths()
+    if is_jax_galsim():
+        # jax-galsim always sets these additional properties
+        # wave = check_dep(getattr, photon_array, 'wavelength')
+        # however jax-galsim sets them to NaN so they are not allocated
+        assert not photon_array.hasAllocatedWavelengths()
+    else:
+        wave = check_dep(getattr, photon_array, 'wavelength')
+        assert photon_array.hasAllocatedWavelengths()
     assert len(photon_array.wavelength) == nphotons
-    wave[:] = 500.
+    if is_jax_galsim():
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.wavelength = 500.0
+        # jax-galsim is allocated now
+        assert photon_array.hasAllocatedWavelengths()
+    else:
+        wave[:] = 500.
     np.testing.assert_array_equal(photon_array.wavelength, 500)
 
-    u = check_dep(getattr, photon_array, 'pupil_u')
-    assert photon_array.hasAllocatedPupil()
-    assert len(photon_array.pupil_u) == nphotons
+    if is_jax_galsim():
+        # jax-galsim always sets these additional properties
+        # u = check_dep(getattr, photon_array, "pupil_u")
+        # however jax-galsim sets them to NaN so they are not allocated
+        assert not photon_array.hasAllocatedPupil()
+    else:
+        u = check_dep(getattr, photon_array, 'pupil_u')
+        assert photon_array.hasAllocatedPupil()
+        assert len(photon_array.pupil_u) == nphotons
     assert len(photon_array.pupil_v) == nphotons
-    u[:] = 6.0
+    if is_jax_galsim():
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.pupil_u = 6.0
+        # jax-galsim is allocated now
+        assert photon_array.hasAllocatedPupil()
+    else:
+        u[:] = 6.0
     np.testing.assert_array_equal(photon_array.pupil_u, 6.0)
     np.testing.assert_array_equal(photon_array.pupil_v, 0.0)
-    v = photon_array.pupil_v
-    v[:] = 10.0
+    if is_jax_galsim():
+        assert hasattr(photon_array, "pupil_v")
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.pupil_v = 10.0
+    else:
+        v = photon_array.pupil_v
+        v[:] = 10.0
     np.testing.assert_array_equal(photon_array.pupil_v, 10.0)
 
-    t = check_dep(getattr, photon_array, 'time')
+    if is_jax_galsim():
+        # jax-galsim always sets these additional properties
+        # t = check_dep(getattr, photon_array, "time")
+        # however jax-galsim sets them to NaN so they are not allocated
+        assert not photon_array.hasAllocatedTimes()
+        # jax-galsim needs to set 0
+        photon_array.time = 0.0
+        # jax-galsim is allocated now
+        assert photon_array.hasAllocatedTimes()
+    else:
+        t = check_dep(getattr, photon_array, 'time')
     assert photon_array.hasAllocatedTimes()
     assert len(photon_array.time) == nphotons
     np.testing.assert_array_equal(photon_array.time, 0.0)
-    t[:] = 10
+    if is_jax_galsim():
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.time = 10
+    else:
+        t[:] = 10
     np.testing.assert_array_equal(photon_array.time, 10.0)
 
     # For coverage, also need to test the two pair ones in other order.
     photon_array = galsim.PhotonArray(nphotons)
-    dydz = check_dep(getattr, photon_array, 'dydz')
-    assert photon_array.hasAllocatedAngles()
-    assert photon_array.hasAllocatedAngles()
+    if is_jax_galsim():
+        # jax-galsim always sets these additional properties
+        # dydz = check_dep(getattr, photon_array, "dydz")
+        # however jax-galsim sets them to NaN so they are not allocated
+        assert not photon_array.hasAllocatedAngles()
+    else:
+        dydz = check_dep(getattr, photon_array, 'dydz')
+        assert photon_array.hasAllocatedAngles()
+        assert photon_array.hasAllocatedAngles()
     assert len(photon_array.dxdz) == nphotons
     assert len(photon_array.dydz) == nphotons
-    dydz[:] = 0.59
+    if is_jax_galsim():
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.dydz = 0.59
+        # non-nan means allocated for jax-galsim
+        assert photon_array.hasAllocatedAngles()
+    else:
+        dydz[:] = 0.59
     np.testing.assert_array_equal(photon_array.dxdz, 0.)
     np.testing.assert_array_equal(photon_array.dydz, 0.59)
 
-    dxdz = photon_array.dxdz  # Allowed now.
-    dxdz[:] = 0.17
+    if is_jax_galsim():
+        assert hasattr(photon_array, "dxdz")
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.dxdz = 0.17
+    else:
+        dxdz = photon_array.dxdz  # Allowed now.
+        dxdz[:] = 0.17
     np.testing.assert_array_equal(photon_array.dxdz, 0.17)
 
-    v = check_dep(getattr, photon_array, 'pupil_v')
-    assert photon_array.hasAllocatedPupil()
-    assert len(photon_array.pupil_u) == nphotons
+    if is_jax_galsim():
+        # jax-galsim always sets these additional properties
+        # v = check_dep(getattr, photon_array, "pupil_v")
+        # however jax-galsim sets them to NaN so they are not allocated
+        assert not photon_array.hasAllocatedPupil()
+    else:
+        v = check_dep(getattr, photon_array, 'pupil_v')
+        assert photon_array.hasAllocatedPupil()
+        assert len(photon_array.pupil_u) == nphotons
     assert len(photon_array.pupil_v) == nphotons
-    v[:] = 10.0
+    if is_jax_galsim():
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.pupil_v = 10.0
+        # jax-galsim is allocated now
+        assert photon_array.hasAllocatedPupil()
+    else:
+        v[:] = 10.0
     np.testing.assert_array_equal(photon_array.pupil_u, 0.0)
     np.testing.assert_array_equal(photon_array.pupil_v, 10.0)
-    u = photon_array.pupil_u
-    u[:] = 6.0
+    if is_jax_galsim():
+        assert hasattr(photon_array, "pupil_u")
+        # JAX-Galsim does not allow by reference setting - changed this
+        # to make tests below run
+        photon_array.pupil_u = 6.0
+    else:
+        u = photon_array.pupil_u
+        u[:] = 6.0
     np.testing.assert_array_equal(photon_array.pupil_u, 6.0)
 
     # Check assignAt
     pa1 = galsim.PhotonArray(50)
     pa1.x = photon_array.x[:50]
-    for i in range(50):
-        pa1.y[i] = photon_array.y[i]
-    pa1.flux[0:50] = photon_array.flux[:50]
+    if is_jax_galsim():
+        pa1.y = photon_array.y[:50]
+        pa1.flux.at[0:50].set(photon_array.flux[:50])
+    else:
+        for i in range(50):
+            pa1.y[i] = photon_array.y[i]
+        pa1.flux[0:50] = photon_array.flux[:50]
     pa1.dxdz = photon_array.dxdz[:50]
     pa1.dydz = photon_array.dydz[:50]
     pa1.pupil_u = photon_array.pupil_u[:50]
@@ -616,20 +726,20 @@ def test_photon_array_depr():
     pa2 = galsim.PhotonArray(100)
     check_dep(pa2.assignAt, 0, pa1)
     check_dep(pa2.assignAt, 50, pa1)
-    np.testing.assert_almost_equal(pa2.x[:50], pa1.x)
-    np.testing.assert_almost_equal(pa2.y[:50], pa1.y)
-    np.testing.assert_almost_equal(pa2.flux[:50], pa1.flux)
-    np.testing.assert_almost_equal(pa2.dxdz[:50], pa1.dxdz)
-    np.testing.assert_almost_equal(pa2.dydz[:50], pa1.dydz)
-    np.testing.assert_almost_equal(pa2.pupil_u[:50], pa1.pupil_u)
-    np.testing.assert_almost_equal(pa2.pupil_v[:50], pa1.pupil_v)
-    np.testing.assert_almost_equal(pa2.x[50:], pa1.x)
-    np.testing.assert_almost_equal(pa2.y[50:], pa1.y)
-    np.testing.assert_almost_equal(pa2.flux[50:], pa1.flux)
-    np.testing.assert_almost_equal(pa2.dxdz[50:], pa1.dxdz)
-    np.testing.assert_almost_equal(pa2.dydz[50:], pa1.dydz)
-    np.testing.assert_almost_equal(pa2.pupil_u[50:], pa1.pupil_u)
-    np.testing.assert_almost_equal(pa2.pupil_v[50:], pa1.pupil_v)
+    np.testing.assert_array_almost_equal(pa2.x[:50], pa1.x)
+    np.testing.assert_array_almost_equal(pa2.y[:50], pa1.y)
+    np.testing.assert_array_almost_equal(pa2.flux[:50], pa1.flux)
+    np.testing.assert_array_almost_equal(pa2.dxdz[:50], pa1.dxdz)
+    np.testing.assert_array_almost_equal(pa2.dydz[:50], pa1.dydz)
+    np.testing.assert_array_almost_equal(pa2.pupil_u[:50], pa1.pupil_u)
+    np.testing.assert_array_almost_equal(pa2.pupil_v[:50], pa1.pupil_v)
+    np.testing.assert_array_almost_equal(pa2.x[50:], pa1.x)
+    np.testing.assert_array_almost_equal(pa2.y[50:], pa1.y)
+    np.testing.assert_array_almost_equal(pa2.flux[50:], pa1.flux)
+    np.testing.assert_array_almost_equal(pa2.dxdz[50:], pa1.dxdz)
+    np.testing.assert_array_almost_equal(pa2.dydz[50:], pa1.dydz)
+    np.testing.assert_array_almost_equal(pa2.pupil_u[50:], pa1.pupil_u)
+    np.testing.assert_array_almost_equal(pa2.pupil_v[50:], pa1.pupil_v)
 
     # Error if it doesn't fit.
     with assert_raises(ValueError):
diff --git a/tests/test_des.py b/tests/test_des.py
index c6f6e72404..52dcebf075 100644
--- a/tests/test_des.py
+++ b/tests/test_des.py
@@ -491,7 +491,7 @@ def test_nan_fits():
     if not hasattr(pyfits, 'verify'): return
 
     # The problematic file:
-    file_name = "des_data/DECam_00158414_01.fits.fz"
+    file_name = os.path.join(os.path.dirname(__file__), "des_data/DECam_00158414_01.fits.fz")
 
     # These are the values we should be reading in:
     ref_bounds = galsim.BoundsI(xmin=1, xmax=2048, ymin=1, ymax=4096)
@@ -549,7 +549,7 @@ def test_nan_fits():
 def test_psf():
     """Test the two kinds of PSF files we have in DES.
     """
-    data_dir = 'des_data'
+    data_dir = os.path.join(os.path.dirname(__file__), 'des_data')
     psfex_file = "DECam_00154912_12_psfcat.psf"
     fitpsf_file = "DECam_00154912_12_fitpsf.fits"
     wcs_file = "DECam_00154912_12_header.fits"
diff --git a/tests/test_draw.py b/tests/test_draw.py
index 24e22bca78..e2077c5c73 100644
--- a/tests/test_draw.py
+++ b/tests/test_draw.py
@@ -16,6 +16,7 @@
 #    and/or other materials provided with the distribution.
 #
 
+import os
 import numpy as np
 
 import galsim
@@ -375,21 +376,28 @@ def test_drawImage():
     assert_raises(TypeError, obj.drawImage, bounds=bounds, scale=scale, wcs=galsim.PixelScale(3))
     assert_raises(TypeError, obj.drawImage, bounds=bounds, wcs=scale)
     assert_raises(TypeError, obj.drawImage, image=im10.array)
-    assert_raises(TypeError, obj.drawImage, wcs=galsim.FitsWCS('fits_files/tpv.fits'))
+    assert_raises(TypeError, obj.drawImage, wcs=galsim.FitsWCS(
+        os.path.join(os.path.dirname(__file__), 'fits_files/tpv.fits')))
 
     assert_raises(ValueError, obj.drawImage, bounds=galsim.BoundsI())
-    assert_raises(ValueError, obj.drawImage, image=im10, gain=0.)
-    assert_raises(ValueError, obj.drawImage, image=im10, gain=-1.)
-    assert_raises(ValueError, obj.drawImage, image=im10, area=0.)
-    assert_raises(ValueError, obj.drawImage, image=im10, area=-1.)
-    assert_raises(ValueError, obj.drawImage, image=im10, exptime=0.)
-    assert_raises(ValueError, obj.drawImage, image=im10, exptime=-1.)
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(ValueError, obj.drawImage, image=im10, gain=0.)
+        assert_raises(ValueError, obj.drawImage, image=im10, gain=-1.)
+        assert_raises(ValueError, obj.drawImage, image=im10, area=0.)
+        assert_raises(ValueError, obj.drawImage, image=im10, area=-1.)
+        assert_raises(ValueError, obj.drawImage, image=im10, exptime=0.)
+        assert_raises(ValueError, obj.drawImage, image=im10, exptime=-1.)
     assert_raises(ValueError, obj.drawImage, image=im10, method='invalid')
 
     # These options are invalid unless metho=phot
     assert_raises(TypeError, obj.drawImage, image=im10, n_photons=3)
     assert_raises(TypeError, obj.drawImage, rng=galsim.BaseDeviate(234))
-    assert_raises(TypeError, obj.drawImage, max_extra_noise=23)
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(TypeError, obj.drawImage, max_extra_noise=23)
     assert_raises(TypeError, obj.drawImage, poisson_flux=True)
     assert_raises(TypeError, obj.drawImage, maxN=10000)
     assert_raises(TypeError, obj.drawImage, save_photons=True)
@@ -519,6 +527,15 @@ def test_drawKImage():
     """Test the various optional parameters to the drawKImage function.
        In particular test the parameters image, and scale in various combinations.
     """
+    if is_jax_galsim():
+        maxk_threshold = 1.e-3
+        N = 880
+        Ns = 28
+    else:
+        maxk_threshold = 1.e-4
+        N = 1174
+        Ns = 37
+
     # We use a Moffat profile with beta = 1.5, since its real-space profile is
     #    flux / (2 pi rD^2) * (1 + (r/rD)^2)^3/2
     # and the 2-d Fourier transform of that is
@@ -526,14 +543,13 @@ def test_drawKImage():
     # So this should draw in Fourier space the same image as the Exponential drawn in
     # test_drawImage().
     obj = galsim.Moffat(flux=test_flux, beta=1.5, scale_radius=0.5)
-    obj = obj.withGSParams(maxk_threshold=1.e-4)
+    obj = obj.withGSParams(maxk_threshold=maxk_threshold)
 
     # First test drawKImage() with no kwargs.  It should:
     #   - create new images
     #   - return the new images
     #   - set the scale to 2pi/(N*obj.nyquist_scale)
     im1 = obj.drawKImage()
-    N = 1174
     np.testing.assert_equal(im1.bounds, galsim.BoundsI(-N/2,N/2,-N/2,N/2),
                             "obj.drawKImage() produced image with wrong bounds")
     stepk = obj.stepk
@@ -555,7 +571,7 @@ def test_drawKImage():
     #   - also return that image
     #   - set the scale to obj.stepk
     #   - zero out any existing data
-    im3 = galsim.ImageCD(1149,1149)
+    im3 = galsim.ImageCD(N-25,N-25)
     im4 = obj.drawKImage(im3)
     np.testing.assert_almost_equal(im3.scale, stepk, 9,
                                    "obj.drawKImage(im3) produced image with wrong scale")
@@ -603,7 +619,7 @@ def test_drawKImage():
     np.testing.assert_almost_equal(CalculateScale(im7), 2, 1,
                                    "Measured wrong scale after obj.drawKImage(dx)")
     # This image is smaller because not using nyquist scale for stepk
-    np.testing.assert_equal(im7.bounds, galsim.BoundsI(-37,37,-37,37),
+    np.testing.assert_equal(im7.bounds, galsim.BoundsI(-Ns,Ns,-Ns,Ns),
                             "obj.drawKImage(dx) produced image with wrong bounds")
 
     # Test if we provide an image with a defined scale.  It should:
@@ -758,7 +774,7 @@ def test_drawKImage():
     np.testing.assert_equal(
             im6.array.shape, (ny//4+1, nx//3+1),
             "obj.drawKImage(bounds,scale,recenter=False) produced image with wrong shape")
-    np.testing.assert_almost_equal(
+    np.testing.assert_array_almost_equal(
             im6.array, im4[bounds6].array, 9,
             "obj.drawKImage(recenter=False) produced different values than recenter=True")
 
@@ -768,7 +784,7 @@ def test_drawKImage():
     np.testing.assert_almost_equal(
             im6.scale, scale, 9,
             "obj.drawKImage(image,recenter=False) produced image with wrong scale")
-    np.testing.assert_almost_equal(
+    np.testing.assert_array_almost_equal(
             im6.array, im4[bounds6].array, 9,
             "obj.drawKImage(image,recenter=False) produced different values than recenter=True")
 
@@ -778,7 +794,7 @@ def test_drawKImage():
     np.testing.assert_almost_equal(
             im6.scale, scale, 9,
             "obj.drawKImage(image,add_to_image=True) produced image with wrong scale")
-    np.testing.assert_almost_equal(
+    np.testing.assert_array_almost_equal(
             im6.array, im4[bounds6].array, 9,
             "obj.drawKImage(image,add_to_image=True) produced different values than recenter=True")
 
@@ -790,7 +806,7 @@ def test_drawKImage():
     np.testing.assert_almost_equal(
             im7.scale, scale, 9,
             "obj.drawKImage(image,add_to_image=True) produced image with wrong scale")
-    np.testing.assert_almost_equal(
+    np.testing.assert_array_almost_equal(
             im7.array, im4.array, 9,
             "obj.drawKImage(image,add_to_image=True) produced different values than recenter=True")
 
@@ -951,7 +967,7 @@ def test_offset():
 
         # Can also use center to explicitly say we want to use the true_center.
         im3 = obj.drawImage(im.copy(), method='sb', center=im.true_center)
-        np.testing.assert_almost_equal(im3.array, im.array)
+        np.testing.assert_array_almost_equal(im3.array, im.array)
 
         # Test that a few pixel values match xValue.
         # Note: we don't expect the FFT drawn image to match the xValues precisely, since the
@@ -1039,14 +1055,14 @@ def test_offset():
             # Test that the center parameter can be used to do the same thing.
             center = galsim.PositionD(cenx + offx, ceny + offy)
             im3 = obj.drawImage(im.copy(), method='sb', center=center)
-            np.testing.assert_almost_equal(im3.array, im.array)
+            np.testing.assert_array_almost_equal(im3.array, im.array)
             assert im3.bounds == im.bounds
             assert im3.wcs == im.wcs
 
             # Can also use both offset and center
             im3 = obj.drawImage(im.copy(), method='sb',
                                 center=(cenx-1, ceny+1), offset=(offx+1, offy-1))
-            np.testing.assert_almost_equal(im3.array, im.array)
+            np.testing.assert_array_almost_equal(im3.array, im.array)
             assert im3.bounds == im.bounds
             assert im3.wcs == im.wcs
 
@@ -1097,19 +1113,31 @@ def test_shoot():
     # in exact arithmetic.  We had an assert there which blew up in a not very nice way.
     obj = galsim.Gaussian(sigma=0.2398318) + 0.1*galsim.Gaussian(sigma=0.47966352)
     obj = obj.withFlux(100001)
-    image1 = galsim.ImageF(32,32, init_value=100)
+    if is_jax_galsim():
+        # jax galsim needs double images here
+        image1 = galsim.ImageD(32,32, init_value=100)
+    else:
+        image1 = galsim.ImageF(32,32, init_value=100)
     rng = galsim.BaseDeviate(1234)
     obj.drawImage(image1, method='phot', poisson_flux=False, add_to_image=True, rng=rng,
                   maxN=100000)
 
     # The test here is really just that it doesn't crash.
     # But let's do something to check correctness.
-    image2 = galsim.ImageF(32,32)
+    if is_jax_galsim():
+        # jax galsim needs double images here
+        image2 = galsim.ImageD(32,32)
+    else:
+        image2 = galsim.ImageF(32,32)
     rng = galsim.BaseDeviate(1234)
     obj.drawImage(image2, method='phot', poisson_flux=False, add_to_image=False, rng=rng,
                   maxN=100000)
     image2 += 100
-    np.testing.assert_almost_equal(image2.array, image1.array, decimal=12)
+    if is_jax_galsim():
+        # jax galsim works not as well
+        np.testing.assert_array_almost_equal(image2.array, image1.array, decimal=10)
+    else:
+        np.testing.assert_array_almost_equal(image2.array, image1.array, decimal=12)
 
     # Also check that you get the same answer with a smaller maxN.
     image3 = galsim.ImageF(32,32, init_value=100)
@@ -1120,16 +1148,19 @@ def test_shoot():
 
     # Test that shooting with 0.0 flux makes a zero-photons image.
     image4 = (obj*0).drawImage(method='phot')
-    np.testing.assert_equal(image4.array, 0)
+    np.testing.assert_array_equal(image4.array, 0)
 
     # Warns if flux is 1 and n_photons not given.
     psf = galsim.Gaussian(sigma=3)
-    with assert_warns(galsim.GalSimWarning):
-        psf.drawImage(method='phot')
-    with assert_warns(galsim.GalSimWarning):
-        psf.drawPhot(image4)
-    with assert_warns(galsim.GalSimWarning):
-        psf.makePhot()
+    if is_jax_galsim():
+        pass
+    else:
+        with assert_warns(galsim.GalSimWarning):
+            psf.drawImage(method='phot')
+        with assert_warns(galsim.GalSimWarning):
+            psf.drawPhot(image4)
+        with assert_warns(galsim.GalSimWarning):
+            psf.makePhot()
     # With n_photons=1, it's fine.
     psf.drawImage(method='phot', n_photons=1)
     psf.drawPhot(image4, n_photons=1)
@@ -1188,19 +1219,28 @@ def test_drawImage_area_exptime():
 
     # Shooting with flux=1 raises a warning.
     obj1 = obj.withFlux(1)
-    with assert_warns(galsim.GalSimWarning):
-        obj1.drawImage(method='phot')
+    if is_jax_galsim():
+        pass
+    else:
+        with assert_warns(galsim.GalSimWarning):
+            obj1.drawImage(method='phot')
     # But not if we explicitly tell it to shoot 1 photon
     with assert_raises(AssertionError):
         assert_warns(galsim.GalSimWarning, obj1.drawImage, method='phot', n_photons=1)
     # Likewise for makePhot
-    with assert_warns(galsim.GalSimWarning):
-        obj1.makePhot()
+    if is_jax_galsim():
+        pass
+    else:
+        with assert_warns(galsim.GalSimWarning):
+            obj1.makePhot()
     with assert_raises(AssertionError):
         assert_warns(galsim.GalSimWarning, obj1.makePhot, n_photons=1)
     # And drawPhot
-    with assert_warns(galsim.GalSimWarning):
-        obj1.drawPhot(im1)
+    if is_jax_galsim():
+        pass
+    else:
+        with assert_warns(galsim.GalSimWarning):
+            obj1.drawPhot(im1)
     with assert_raises(AssertionError):
         assert_warns(galsim.GalSimWarning, obj1.drawPhot, im1, n_photons=1)
 
@@ -1219,9 +1259,15 @@ def test_fft():
                              [4,6,8,4],
                              [2,4,6,6] ],
                            xmin=-2, ymin=-2, dtype=dt, scale=0.1)
-        kim = xim.calculate_fft()
-        xim2 = kim.calculate_inverse_fft()
-        np.testing.assert_almost_equal(xim.array, xim2.array)
+        if is_jax_galsim():
+            if dt not in [np.complex128, complex]:
+                kim = xim.calculate_fft()
+                xim2 = kim.calculate_inverse_fft()
+                np.testing.assert_array_almost_equal(xim.array, xim2.array)
+        else:
+            kim = xim.calculate_fft()
+            xim2 = kim.calculate_inverse_fft()
+            np.testing.assert_array_almost_equal(xim.array, xim2.array)
 
         # Now the other way, starting with a (real) k-space image.
         kim = galsim.Image([ [4,2,0],
@@ -1231,7 +1277,7 @@ def test_fft():
                            xmin=0, ymin=-2, dtype=dt, scale=0.1)
         xim = kim.calculate_inverse_fft()
         kim2 = xim.calculate_fft()
-        np.testing.assert_almost_equal(kim.array, kim2.array)
+        np.testing.assert_array_almost_equal(kim.array, kim2.array)
 
         # Test starting with a larger image that gets wrapped.
         kim3 = galsim.Image([ [0,1,2,1,0],
@@ -1242,7 +1288,7 @@ def test_fft():
                             xmin=-2, ymin=-2, dtype=dt, scale=0.1)
         xim = kim3.calculate_inverse_fft()
         kim2 = xim.calculate_fft()
-        np.testing.assert_almost_equal(kim.array, kim2.array)
+        np.testing.assert_array_almost_equal(kim.array, kim2.array)
 
         # Test padding X Image with zeros
         xim = galsim.Image([ [0,0,0,0],
@@ -1253,9 +1299,15 @@ def test_fft():
         xim2 = galsim.Image([ [2,4,6],
                               [4,6,8] ],
                             xmin=-2, ymin=-1, dtype=dt, scale=0.1)
-        kim = xim.calculate_fft()
-        kim2 = xim2.calculate_fft()
-        np.testing.assert_almost_equal(kim.array, kim2.array)
+        if is_jax_galsim():
+            if dt not in [np.complex128, complex]:
+                kim = xim.calculate_fft()
+                kim2 = xim2.calculate_fft()
+                np.testing.assert_array_almost_equal(kim.array, kim2.array)
+        else:
+            kim = xim.calculate_fft()
+            kim2 = xim2.calculate_fft()
+            np.testing.assert_array_almost_equal(kim.array, kim2.array)
 
         # Test padding K Image with zeros
         kim = galsim.Image([ [4,2,0],
@@ -1270,14 +1322,22 @@ def test_fft():
                            xmin=0, ymin=-1, dtype=dt, scale=0.1)
         xim = kim.calculate_inverse_fft()
         xim2 = kim2.calculate_inverse_fft()
-        np.testing.assert_almost_equal(xim.array, xim2.array)
+        np.testing.assert_array_almost_equal(xim.array, xim2.array)
 
     # Now use drawKImage (as above in test_drawKImage) to get a more realistic k-space image
+    # NB. It is useful to have this come out not a multiple of 4, since some of the
+    #     calculation needs to be different when N/2 is odd.
+    if is_jax_galsim():
+        maxk_threshold = 0.78e-3
+        N = 912
+        Nfft = 1024
+    else:
+        maxk_threshold = 1.e-4
+        N = 1174
+        Nfft = 1536
     obj = galsim.Moffat(flux=test_flux, beta=1.5, scale_radius=0.5)
-    obj = obj.withGSParams(maxk_threshold=1.e-4)
+    obj = obj.withGSParams(maxk_threshold=maxk_threshold)
     im1 = obj.drawKImage()
-    N = 1174  # NB. It is useful to have this come out not a multiple of 4, since some of the
-              #     calculation needs to be different when N/2 is odd.
     np.testing.assert_equal(im1.bounds, galsim.BoundsI(-N/2,N/2,-N/2,N/2),
                             "obj.drawKImage() produced image with wrong bounds")
     nyq_scale = obj.nyquist_scale
@@ -1297,14 +1357,14 @@ def test_fft():
     np.testing.assert_almost_equal(
             im1_real.scale, im1_alt_real.scale, 3,
             "inverse_fft produce a different scale than obj2.drawImage(method='sb')")
-    np.testing.assert_almost_equal(
+    np.testing.assert_array_almost_equal(
             im1_real.array, im1_alt_real.array, 3,
             "inverse_fft produce a different array than obj2.drawImage(method='sb')")
 
     # If we give both a good size to use and match up the scales, then they should produce the
     # same thing.
     N = galsim.Image.good_fft_size(N)
-    assert N == 1536 == 3 * 2**9
+    assert N == Nfft
     kscale = 2.*np.pi / (N * nyq_scale)
     im2 = obj.drawKImage(nx=N+1, ny=N+1, scale=kscale)
     im2_real = im2.calculate_inverse_fft()
@@ -1316,7 +1376,7 @@ def test_fft():
     np.testing.assert_almost_equal(
             im2_real.scale, im2_alt_real.scale, 9,
             "inverse_fft produce a different scale than obj2.drawImage(nx,ny,method='sb')")
-    np.testing.assert_almost_equal(
+    np.testing.assert_array_almost_equal(
             im2_real.array, im2_alt_real.array, 9,
             "inverse_fft produce a different array than obj2.drawImage(nx,ny,method='sb')")
 
@@ -1524,8 +1584,12 @@ def test_np_fft():
 def round_cast(array, dt):
     # array.astype(dt) doesn't round to the nearest for integer types.
     # This rounds first if dt is integer and then casts.
-    if dt(0.5) != 0.5:
-        array = np.around(array)
+    if is_jax_galsim():
+        # NOTE JAX doesn't round to the nearest int when drawing
+        pass
+    else:
+        if dt(0.5) != 0.5:
+            array = np.around(array)
     return array.astype(dt)
 
 @timer
@@ -1551,13 +1615,13 @@ def test_types():
                                     "wrong scale when drawing onto dt=%s"%dt)
             np.testing.assert_equal(im.bounds, ref_im.bounds,
                                     "wrong bounds when drawing onto dt=%s"%dt)
-            np.testing.assert_almost_equal(im.array, round_cast(ref_im.array, dt), 6,
+            np.testing.assert_array_almost_equal(im.array, round_cast(ref_im.array, dt), 6,
                                            "wrong array when drawing onto dt=%s"%dt)
 
             if method == 'phot':
                 rng.reset(1234)
             obj.drawImage(im, method=method, add_to_image=True, rng=rng)
-            np.testing.assert_almost_equal(im.array, round_cast(ref_im.array, dt) * 2, 6,
+            np.testing.assert_array_almost_equal(im.array, round_cast(ref_im.array, dt) * 2, 6,
                                            "wrong array when adding to image with dt=%s"%dt)
 
 @timer
@@ -1597,13 +1661,13 @@ def test_direct_scale():
     obj.dilate(1.0).drawReal(im4)
     obj.rotate(0.3*galsim.radians).drawReal(im5)
     print('no_pixel: max diff = ',np.max(np.abs(im1.array - im2.array)))
-    np.testing.assert_almost_equal(im1.array, im2.array, 15,
+    np.testing.assert_array_almost_equal(im1.array, im2.array, 15,
                                    "drawReal made different image than method='no_pixel'")
-    np.testing.assert_almost_equal(im3.array, im2[im3.bounds].array, 15,
+    np.testing.assert_array_almost_equal(im3.array, im2[im3.bounds].array, 15,
                                    "drawReal made different image when off-center")
-    np.testing.assert_almost_equal(im4.array, im2[im3.bounds].array, 15,
+    np.testing.assert_array_almost_equal(im4.array, im2[im3.bounds].array, 15,
                                    "drawReal made different image when jac is not None")
-    np.testing.assert_almost_equal(im5.array, im2[im3.bounds].array, 15,
+    np.testing.assert_array_almost_equal(im5.array, im2[im3.bounds].array, 15,
                                    "drawReal made different image when jac is not diagonal")
 
     obj.drawImage(im1, method='sb')
@@ -1612,13 +1676,13 @@ def test_direct_scale():
     obj_sb.dilate(1.0).drawReal(im4)
     obj_sb.rotate(0.3*galsim.radians).drawReal(im5)
     print('sb: max diff = ',np.max(np.abs(im1.array - im2.array)))
-    np.testing.assert_almost_equal(im1.array, im2.array, 15,
+    np.testing.assert_array_almost_equal(im1.array, im2.array, 14,
                                    "drawReal made different image than method='sb'")
-    np.testing.assert_almost_equal(im3.array, im2[im3.bounds].array, 15,
+    np.testing.assert_array_almost_equal(im3.array, im2[im3.bounds].array, 15,
                                    "drawReal made different image when off-center")
-    np.testing.assert_almost_equal(im4.array, im2[im3.bounds].array, 15,
+    np.testing.assert_array_almost_equal(im4.array, im2[im3.bounds].array, 15,
                                    "drawReal made different image when jac is not None")
-    np.testing.assert_almost_equal(im5.array, im2[im3.bounds].array, 14,
+    np.testing.assert_array_almost_equal(im5.array, im2[im3.bounds].array, 14,
                                    "drawReal made different image when jac is not diagonal")
 
     obj.drawImage(im1, method='fft')
@@ -1627,13 +1691,13 @@ def test_direct_scale():
     obj_with_pixel.dilate(1.0).drawFFT(im4)
     obj_with_pixel.rotate(90 * galsim.degrees).drawFFT(im5)
     print('fft: max diff = ',np.max(np.abs(im1.array - im2.array)))
-    np.testing.assert_almost_equal(im1.array, im2.array, 15,
+    np.testing.assert_array_almost_equal(im1.array, im2.array, 15,
                                    "drawFFT made different image than method='fft'")
-    np.testing.assert_almost_equal(im3.array, im2[im3.bounds].array, 15,
+    np.testing.assert_array_almost_equal(im3.array, im2[im3.bounds].array, 15,
                                    "drawFFT made different image when off-center")
-    np.testing.assert_almost_equal(im4.array, im2[im3.bounds].array, 15,
+    np.testing.assert_array_almost_equal(im4.array, im2[im3.bounds].array, 15,
                                    "drawFFT made different image when jac is not None")
-    np.testing.assert_almost_equal(im5.array, im2[im3.bounds].array, 14,
+    np.testing.assert_array_almost_equal(im5.array, im2[im3.bounds].array, 14,
                                    "drawFFT made different image when jac is not diagonal")
 
     obj.drawImage(im1, method='real_space')
@@ -1644,13 +1708,13 @@ def test_direct_scale():
     print('real_space: max diff = ',np.max(np.abs(im1.array - im2.array)))
     # I'm not sure why this one comes out a bit less precisely equal.  But 12 digits is still
     # plenty accurate enough.
-    np.testing.assert_almost_equal(im1.array, im2.array, 12,
+    np.testing.assert_array_almost_equal(im1.array, im2.array, 12,
                                    "drawReal made different image than method='real_space'")
-    np.testing.assert_almost_equal(im3.array, im2[im3.bounds].array, 14,
+    np.testing.assert_array_almost_equal(im3.array, im2[im3.bounds].array, 14,
                                    "drawReal made different image when off-center")
-    np.testing.assert_almost_equal(im4.array, im2[im3.bounds].array, 14,
+    np.testing.assert_array_almost_equal(im4.array, im2[im3.bounds].array, 14,
                                    "drawReal made different image when jac is not None")
-    np.testing.assert_almost_equal(im5.array, im2[im3.bounds].array, 14,
+    np.testing.assert_array_almost_equal(im5.array, im2[im3.bounds].array, 14,
                                    "drawReal made different image when jac is not diagonal")
 
     obj.drawImage(im1, method='phot', rng=rng.duplicate())
@@ -1660,18 +1724,18 @@ def test_direct_scale():
     phot3.scaleXY(1./scale)
     phot4 = im3.wcs.toImage(obj).makePhot(rng=rng.duplicate())
     print('phot: max diff = ',np.max(np.abs(im1.array - im2.array)))
-    np.testing.assert_almost_equal(im1.array, im2.array, 15,
+    np.testing.assert_array_almost_equal(im1.array, im2.array, 15,
                                    "drawPhot made different image than method='phot'")
-    np.testing.assert_almost_equal(im3.array, im2[im3.bounds].array, 15,
+    np.testing.assert_array_almost_equal(im3.array, im2[im3.bounds].array, 15,
                                    "drawPhot made different image when off-center")
     assert phot2 == phot1, "drawPhot made different photons than method='phot'"
     assert phot3 == phot1, "makePhot made different photons than method='phot'"
     # phot4 has a different order of operations for the math, so it doesn't come out exact.
-    np.testing.assert_almost_equal(phot4.x, phot3.x, 15,
+    np.testing.assert_array_almost_equal(phot4.x, phot3.x, 15,
                                    "two ways to have makePhot apply scale have different x")
-    np.testing.assert_almost_equal(phot4.y, phot3.y, 15,
+    np.testing.assert_array_almost_equal(phot4.y, phot3.y, 15,
                                    "two ways to have makePhot apply scale have different y")
-    np.testing.assert_almost_equal(phot4.flux, phot3.flux, 15,
+    np.testing.assert_array_almost_equal(phot4.flux, phot3.flux, 15,
                                    "two ways to have makePhot apply scale have different flux")
 
     # Check images with invalid wcs raise ValueError
diff --git a/tests/test_errors.py b/tests/test_errors.py
index c655b52ea0..ade275c968 100644
--- a/tests/test_errors.py
+++ b/tests/test_errors.py
@@ -16,6 +16,7 @@
 #    and/or other materials provided with the distribution.
 #
 
+import numpy as np
 import galsim
 from galsim_test_helpers import *
 
diff --git a/tests/test_exponential.py b/tests/test_exponential.py
index 8d3549d41e..f1ee579913 100644
--- a/tests/test_exponential.py
+++ b/tests/test_exponential.py
@@ -226,7 +226,12 @@ def test_exponential_shoot():
     assert np.isclose(added_flux, obj.flux)
     assert np.isclose(im.array.sum(), obj.flux)
     photons2 = obj.makePhot(poisson_flux=False, rng=rng)
-    assert photons2 == photons, "Exponential makePhot not equivalent to drawPhot"
+    if is_jax_galsim():
+        np.testing.assert_allclose(photons2.x, photons.x)
+        np.testing.assert_allclose(photons2.y, photons.y)
+        np.testing.assert_allclose(photons2.flux, photons.flux)
+    else:
+        assert photons2 == photons, "Exponential makePhot not equivalent to drawPhot"
 
 
 @timer
diff --git a/tests/test_fitsheader.py b/tests/test_fitsheader.py
index 13f9e08e9c..ea2f96af2f 100644
--- a/tests/test_fitsheader.py
+++ b/tests/test_fitsheader.py
@@ -49,7 +49,7 @@ def check_tpv(header):
         assert 54384.18627436 in header.itervalues()
 
     file_name = 'tpv.fits'
-    dir = 'fits_files'
+    dir = os.path.join(os.path.dirname(__file__), 'fits_files')
     # First option: give a file_name
     header = galsim.FitsHeader(file_name=os.path.join(dir,file_name))
     check_tpv(header)
@@ -189,7 +189,7 @@ def check_tpv(header):
 def test_scamp():
     """Test that we can read in a SCamp .head file correctly
     """
-    dir = 'fits_files'
+    dir = os.path.join(os.path.dirname(__file__), 'fits_files')
     file_name = 'scamp.head'
 
     header = galsim.FitsHeader(file_name=file_name, dir=dir, text_file=True)
diff --git a/tests/test_hsm.py b/tests/test_hsm.py
index 1d06284ff0..28e1c7121a 100644
--- a/tests/test_hsm.py
+++ b/tests/test_hsm.py
@@ -45,7 +45,7 @@
 test_timing = False
 
 # define inputs and expected results for tests that use real SDSS galaxies
-img_dir = os.path.join(".","HSM_precomputed")
+img_dir = os.path.join(os.path.dirname(__file__), "HSM_precomputed")
 gal_file_prefix = "image."
 psf_file_prefix = "psf."
 img_suff = ".fits"
diff --git a/tests/test_image.py b/tests/test_image.py
index 2d6e1a7b33..4e6302508c 100644
--- a/tests/test_image.py
+++ b/tests/test_image.py
@@ -81,7 +81,7 @@
     # it helps speed things up.
     nimages = 3
 
-datadir = os.path.join(".", "Image_comparison_images")
+datadir = os.path.join(os.path.dirname(__file__), "Image_comparison_images")
 
 
 @timer
@@ -103,8 +103,12 @@ def test_Image_basic():
         np.testing.assert_array_equal(im1.array, 0.)
         assert im1.array.shape == (nrow,ncol)
         assert im1.array.dtype.type == np_array_type
-        assert im1.array.flags.writeable == True
-        assert im1.array.flags.c_contiguous == True
+        if is_jax_galsim():
+            pass
+        else:
+            # jax arrays do not have flags
+            assert im1.array.flags.writeable == True
+            assert im1.array.flags.c_contiguous == True
         assert im1.dtype == np_array_type
         assert im1.ncol == ncol
         assert im1.nrow == nrow
@@ -201,16 +205,32 @@ def test_Image_basic():
                 assert im1.view()(x,y) == value
                 assert im1.view()(galsim.PositionI(x,y)) == value
                 assert im1.view(make_const=True)(x,y) == value
-                assert im2(x,y) == value
+                if is_jax_galsim():
+                    # no real views in jax
+                    assert im2(x,y) != value
+                else:
+                    assert im2(x,y) == value
                 assert im2_view(x,y) == value
-                assert im2_cview(x,y) == value
+                if is_jax_galsim():
+                    # no real views in jax
+                    assert im2_cview(x,y) != value
+                else:
+                    assert im2_cview(x,y) == value
                 assert im1.conjugate(x,y) == value
                 if tchar[i][0] == 'C':
                     # complex conjugate is not a view into the original.
                     assert im2_conj(x,y) == 23
-                    assert im2.conjugate(x,y) == value
+                    if is_jax_galsim():
+                        # no real views in jax
+                        assert im2.conjugate(x,y) != value
+                    else:
+                        assert im2.conjugate(x,y) == value
                 else:
-                    assert im2_conj(x,y) == value
+                    if is_jax_galsim():
+                        # no real views in jax
+                        assert im2_conj(x,y) != value
+                    else:
+                        assert im2_conj(x,y) == value
 
                 value2 = 53 + 12*x - 19*y
                 if tchar[i] in ['US', 'UI']:
@@ -220,16 +240,32 @@ def test_Image_basic():
                 assert im1.getValue(x,y) == value2
                 assert im1.view().getValue(x=x, y=y) == value2
                 assert im1.view(make_const=True).getValue(x,y) == value2
-                assert im2.getValue(x=x, y=y) == value2
+                if is_jax_galsim():
+                    # no real views in jax
+                    assert im2.getValue(x=x, y=y) != value2
+                else:
+                    assert im2.getValue(x=x, y=y) == value2
                 assert im2_view.getValue(x,y) == value2
-                assert im2_cview._getValue(x,y) == value2
+                if is_jax_galsim():
+                    # no real views in jax
+                    assert im2_cview._getValue(x,y) != value2
+                else:
+                    assert im2_cview._getValue(x,y) == value2
 
                 assert im1.real(x,y) == value2
                 assert im1.view().real(x,y) == value2
                 assert im1.view(make_const=True).real(x,y) == value2.real
-                assert im2.real(x,y) == value2.real
+                if is_jax_galsim():
+                    # no real views in jax
+                    assert im2.real(x,y) != value2.real
+                else:
+                    assert im2.real(x,y) == value2.real
                 assert im2_view.real(x,y) == value2.real
-                assert im2_cview.real(x,y) == value2.real
+                if is_jax_galsim():
+                    # no real views in jax
+                    assert im2_cview.real(x,y) != value2.real
+                else:
+                    assert im2_cview.real(x,y) == value2.real
                 assert im1.imag(x,y) == 0
                 assert im1.view().imag(x,y) == 0
                 assert im1.view(make_const=True).imag(x,y) == 0
@@ -237,15 +273,26 @@ def test_Image_basic():
                 assert im2_view.imag(x,y) == 0
                 assert im2_cview.imag(x,y) == 0
 
-                value3 = 10*x + y
+                if is_jax_galsim():
+                    value3 = 10*x + y + 111
+                else:
+                    value3 = 10*x + y
                 im1.addValue(x,y, np.int64(value3-value2))
                 im2_view[x,y] += np.int64(value3-value2)
                 assert im1[galsim.PositionI(x,y)] == value3
                 assert im1.view()[x,y] == value3
                 assert im1.view(make_const=True)[galsim.PositionI(x,y)] == value3
-                assert im2[x,y] == value3
+                if is_jax_galsim():
+                    # no real views in jax
+                    assert im2[x,y] != value3
+                else:
+                    assert im2[x,y] == value3
                 assert im2_view[galsim.PositionI(x,y)] == value3
-                assert im2_cview[x,y] == value3
+                if is_jax_galsim():
+                    # no real views in jax
+                    assert im2_cview[x,y] != value3
+                else:
+                    assert im2_cview[x,y] == value3
 
         # Setting or getting the value outside the bounds should throw an exception.
         assert_raises(galsim.GalSimBoundsError,im1.setValue,0,0,1)
@@ -356,11 +403,19 @@ def test_Image_basic():
         assert im2.bounds == bounds
         for y in range(1,nrow+1):
             for x in range(1,ncol+1):
-                value3 = 10*x+y
+                if is_jax_galsim():
+                    value3 = 10*x+y + 111
+                else:
+                    value3 = 10*x+y
                 assert im1(x+dx,y+dy) == value3
                 assert im1_view(x,y) == value3
-                assert im2(x,y) == value3
+                if is_jax_galsim():
+                    assert im2(x,y) != value3
+                else:
+                    assert im2(x,y) == value3
                 assert im2_view(x+dx,y+dy) == value3
+                if is_jax_galsim():
+                    value3 = 10*x+y
                 assert im3_view(x+dx,y+dy) == value3
 
         assert_raises(TypeError, im1.shift, dx)
@@ -690,7 +745,7 @@ def test_Image_FITS_IO():
         assert_raises(OSError, galsim.fits.read, test_file, compression='none')
 
     # Check a file with no WCS information
-    nowcs_file = 'fits_files/blankimg.fits'
+    nowcs_file = os.path.join(os.path.dirname(__file__), 'fits_files/blankimg.fits')
     im = galsim.fits.read(nowcs_file)
     assert im.wcs == galsim.PixelScale(1.0)
 
@@ -1016,7 +1071,7 @@ def test_Image_MultiFITS_IO():
         assert_raises(OSError, galsim.fits.readMulti, test_multi_file, compression='none')
 
     # Check a file with no WCS information
-    nowcs_file = 'fits_files/blankimg.fits'
+    nowcs_file = os.path.join(os.path.dirname(__file__), 'fits_files/blankimg.fits')
     ims = galsim.fits.readMulti(nowcs_file)
     assert ims[0].wcs == galsim.PixelScale(1.0)
 
@@ -1346,7 +1401,7 @@ def test_Image_CubeFITS_IO():
         assert_raises(OSError, galsim.fits.readCube, test_cube_file, compression='none')
 
     # Check a file with no WCS information
-    nowcs_file = 'fits_files/blankimg.fits'
+    nowcs_file = os.path.join(os.path.dirname(__file__), 'fits_files/blankimg.fits')
     ims = galsim.fits.readCube(nowcs_file)
     assert ims[0].wcs == galsim.PixelScale(1.0)
 
@@ -2133,7 +2188,7 @@ def test_subImage_persistence():
     """Test that a subimage is properly accessible even if the original image has gone out
     of scope.
     """
-    file_name = os.path.join('fits_files','tpv.fits')
+    file_name = os.path.join(os.path.dirname(__file__), os.path.join('fits_files','tpv.fits'))
     bounds = galsim.BoundsI(123, 133, 45, 55)  # Something random
 
     # In this case, the original image has gone out of scope.  At least on some systems,
@@ -2415,7 +2470,11 @@ def test_Image_view():
     assert imv.bounds == im.bounds
     imv.setValue(11,19, 20)
     assert imv(11,19) == 20
-    assert im(11,19) == 20
+    if is_jax_galsim():
+        # jax-galsim does not support views
+        assert im(11,19) != 20
+    else:
+        assert im(11,19) == 20
     check_pickle(im)
     check_pickle(imv)
 
@@ -2427,7 +2486,11 @@ def test_Image_view():
     assert imv.bounds == galsim.BoundsI(0,24,0,24)
     imv.setValue(10,18, 30)
     assert imv(10,18) == 30
-    assert im(11,19) == 30
+    if is_jax_galsim():
+        # jax-galsim does not support views
+        assert im(11,19) != 20
+    else:
+        assert im(11,19) == 30
     imv2 = im.view()
     imv2.setOrigin(0,0)
     assert imv.bounds == imv2.bounds
@@ -2443,7 +2506,11 @@ def test_Image_view():
     assert imv.bounds == galsim.BoundsI(-12,12,-12,12)
     imv.setValue(-2,6, 40)
     assert imv(-2,6) == 40
-    assert im(11,19) == 40
+    if is_jax_galsim():
+        # jax-galsim does not support views
+        assert im(11,19) != 40
+    else:
+        assert im(11,19) == 40
     imv2 = im.view()
     imv2.setCenter(0,0)
     assert imv.bounds == imv2.bounds
@@ -2460,7 +2527,11 @@ def test_Image_view():
     assert imv.bounds == im.bounds
     imv.setValue(11,19, 50)
     assert imv(11,19) == 50
-    assert im(11,19) == 50
+    if is_jax_galsim():
+        # jax-galsim does not support views
+        assert im(11,19) != 50
+    else:
+        assert im(11,19) == 50
     imv2 = im.view()
     with assert_raises(galsim.GalSimError):
         imv2.scale = 0.17   # Invalid if wcs is not PixelScale
@@ -2478,7 +2549,11 @@ def test_Image_view():
     assert imv.bounds == im.bounds
     imv.setValue(11,19, 60)
     assert imv(11,19) == 60
-    assert im(11,19) == 60
+    if is_jax_galsim():
+        # jax-galsim does not support views
+        assert im(11,19) != 60
+    else:
+        assert im(11,19) == 60
     imv2 = im.view()
     imv2.wcs = galsim.JacobianWCS(0.,0.23,-0.23,0.)
     assert imv.bounds == imv2.bounds
@@ -2569,13 +2644,17 @@ def test_copy():
     assert im(3,8) != 11.
 
     # If copy=False is specified, then it shares the same array
-    im3b = galsim.Image(im, copy=False)
-    assert im3b.wcs == im.wcs
-    assert im3b.bounds == im.bounds
-    np.testing.assert_array_equal(im3b.array, im.array)
-    im3b.setValue(2,3,2.)
-    assert im3b(2,3) == 2.
-    assert im(2,3) == 2.
+    if is_jax_galsim():
+        # jax-galsim does not support references
+        pass
+    else:
+        im3b = galsim.Image(im, copy=False)
+        assert im3b.wcs == im.wcs
+        assert im3b.bounds == im.bounds
+        np.testing.assert_array_equal(im3b.array, im.array)
+        im3b.setValue(2,3,2.)
+        assert im3b(2,3) == 2.
+        assert im(2,3) == 2.
 
     # Constructor can change the wcs
     im4 = galsim.Image(im, scale=0.6)
@@ -2626,13 +2705,17 @@ def test_copy():
     assert im_slice(2,3) != 11.
 
     # Can also copy by giving the array and specify copy=True
-    im10 = galsim.Image(im.array, bounds=im.bounds, wcs=im.wcs, copy=False)
-    assert im10.wcs == im.wcs
-    assert im10.bounds == im.bounds
-    np.testing.assert_array_equal(im10.array, im.array)
-    im10[2,3] = 17
-    assert im10(2,3) == 17.
-    assert im(2,3) == 17.
+    if is_jax_galsim():
+        # jax-galsim does not support references
+        pass
+    else:
+        im10 = galsim.Image(im.array, bounds=im.bounds, wcs=im.wcs, copy=False)
+        assert im10.wcs == im.wcs
+        assert im10.bounds == im.bounds
+        np.testing.assert_array_equal(im10.array, im.array)
+        im10[2,3] = 17
+        assert im10(2,3) == 17.
+        assert im(2,3) == 17.
 
     im10b = galsim.Image(im.array, bounds=im.bounds, wcs=im.wcs, copy=True)
     assert im10b.wcs == im.wcs
@@ -2683,39 +2766,82 @@ def test_complex_image():
                 assert im1(x,y) == value
                 assert im1.view()(x,y) == value
                 assert im1.view(make_const=True)(x,y) == value
-                assert im2(x,y) == value
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2(x,y) != value
+                else:
+                    assert im2(x,y) == value
                 assert im2_view(x,y) == value
-                assert im2_cview(x,y) == value
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2_cview(x,y) != value
+                else:
+                    assert im2_cview(x,y) == value
                 assert im1.conjugate(x,y) == np.conjugate(value)
 
                 # complex conjugate is not a view into the original.
                 assert im2_conj(x,y) == 23
-                assert im2.conjugate(x,y) == np.conjugate(value)
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2.conjugate(x,y) != np.conjugate(value)
+                else:
+                    assert im2.conjugate(x,y) == np.conjugate(value)
 
-                value2 = 10*x + y + 20j*x + 2j*y
+                if is_jax_galsim():
+                    value2 = 400000 + 10*x + y + 20j*x + 2j*y
+                else:
+                    value2 = 10*x + y + 20j*x + 2j*y
                 im1.setValue(x,y, value2)
                 im2_view.setValue(x=x, y=y, value=value2)
                 assert im1(x,y) == value2
                 assert im1.view()(x,y) == value2
                 assert im1.view(make_const=True)(x,y) == value2
-                assert im2(x,y) == value2
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2(x,y) != value2
+                else:
+                    assert im2(x,y) == value2
                 assert im2_view(x,y) == value2
-                assert im2_cview(x,y) == value2
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2_cview(x,y) != value2
+                else:
+                    assert im2_cview(x,y) == value2
 
                 assert im1.real(x,y) == value2.real
                 assert im1.view().real(x,y) == value2.real
                 assert im1.view(make_const=True).real(x,y) == value2.real
-                assert im2.real(x,y) == value2.real
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2.real(x,y) != value2.real
+                else:
+                    assert im2.real(x,y) == value2.real
                 assert im2_view.real(x,y) == value2.real
-                assert im2_cview.real(x,y) == value2.real
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2_cview.real(x,y) != value2.real
+                else:
+                    assert im2_cview.real(x,y) == value2.real
                 assert im1.imag(x,y) == value2.imag
                 assert im1.view().imag(x,y) == value2.imag
                 assert im1.view(make_const=True).imag(x,y) == value2.imag
-                assert im2.imag(x,y) == value2.imag
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2.imag(x,y) != value2.imag
+                else:
+                    assert im2.imag(x,y) == value2.imag
                 assert im2_view.imag(x,y) == value2.imag
-                assert im2_cview.imag(x,y) == value2.imag
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2_cview.imag(x,y) != value2.imag
+                else:
+                    assert im2_cview.imag(x,y) == value2.imag
                 assert im1.conjugate(x,y) == np.conjugate(value2)
-                assert im2.conjugate(x,y) == np.conjugate(value2)
+                if is_jax_galsim():
+                    # jax galsim does not support views
+                    assert im2.conjugate(x,y) != np.conjugate(value2)
+                else:
+                    assert im2.conjugate(x,y) == np.conjugate(value2)
 
                 rvalue3 = 12*x + y
                 ivalue3 = x + 21*y
@@ -2724,14 +2850,25 @@ def test_complex_image():
                 im1.imag.setValue(x,y, ivalue3)
                 im2_view.real.setValue(x,y, rvalue3)
                 im2_view.imag.setValue(x,y, ivalue3)
-                assert im1(x,y) == value3
-                assert im1.view()(x,y) == value3
-                assert im1.view(make_const=True)(x,y) == value3
-                assert im2(x,y) == value3
-                assert im2_view(x,y) == value3
-                assert im2_cview(x,y) == value3
-                assert im1.conjugate(x,y) == np.conjugate(value3)
-                assert im2.conjugate(x,y) == np.conjugate(value3)
+                # jax galsim does not support views
+                if is_jax_galsim():
+                    assert im1(x,y) != value3
+                    assert im1.view()(x,y) != value3
+                    assert im1.view(make_const=True)(x,y) != value3
+                    assert im2(x,y) != value3
+                    assert im2_view(x,y) != value3
+                    assert im2_cview(x,y) != value3
+                    assert im1.conjugate(x,y) != np.conjugate(value3)
+                    assert im2.conjugate(x,y) != np.conjugate(value3)
+                else:
+                    assert im1(x,y) == value3
+                    assert im1.view()(x,y) == value3
+                    assert im1.view(make_const=True)(x,y) == value3
+                    assert im2(x,y) == value3
+                    assert im2_view(x,y) == value3
+                    assert im2_cview(x,y) == value3
+                    assert im1.conjugate(x,y) == np.conjugate(value3)
+                    assert im2.conjugate(x,y) == np.conjugate(value3)
 
         # Check view of given data
         im3_view = galsim.Image((1+2j)*ref_array.astype(complex))
@@ -2768,7 +2905,7 @@ def test_complex_image_arith():
     np.testing.assert_array_equal(image2.array, ref_array * (2+5j),
             err_msg="ImageD * complex is not correct")
     image2 = image1 / (2+5j)
-    np.testing.assert_array_equal(image2.array, ref_array / (2+5j),
+    np.testing.assert_allclose(image2.array, ref_array / (2+5j),
             err_msg="ImageD / complex is not correct")
 
     # Binary complex scalar op ImageD
@@ -2782,7 +2919,7 @@ def test_complex_image_arith():
     np.testing.assert_array_equal(image2.array, ref_array * (2+5j),
             err_msg="complex * ImageD is not correct")
     image2 = (2+5j) / image1
-    np.testing.assert_array_equal(image2.array, (2+5j) / ref_array.astype(float),
+    np.testing.assert_allclose(image2.array, (2+5j) / ref_array.astype(float),
             err_msg="complex / ImageD is not correct")
 
     image2 = image1 * (3+1j)
@@ -2798,7 +2935,7 @@ def test_complex_image_arith():
     np.testing.assert_array_equal(image3.array, (3+1j)*ref_array * (2+5j),
             err_msg="ImageCD * complex is not correct")
     image3 = image2 / (2+5j)
-    np.testing.assert_array_equal(image3.array, (3+1j)*ref_array / (2+5j),
+    np.testing.assert_allclose(image3.array, (3+1j)*ref_array / (2+5j),
             err_msg="ImageCD / complex is not correct")
 
     # Binary complex scalar op ImageCD
@@ -2812,7 +2949,7 @@ def test_complex_image_arith():
     np.testing.assert_array_equal(image3.array, (3+1j)*ref_array * (2+5j),
             err_msg="complex * ImageCD is not correct")
     image3 = (2+5j) / image2
-    np.testing.assert_array_equal(image3.array, (2+5j) / ((3+1j)*ref_array),
+    np.testing.assert_allclose(image3.array, (2+5j) / ((3+1j)*ref_array),
             err_msg="complex / ImageCD is not correct")
 
     # Binary ImageD op ImageCD
@@ -2826,7 +2963,7 @@ def test_complex_image_arith():
     np.testing.assert_array_equal(image3.array, (3+1j)*ref_array**2,
             err_msg="ImageD * ImageCD is not correct")
     image3 = image1 / image2
-    np.testing.assert_almost_equal(image3.array, 1./(3+1j), decimal=12,
+    np.testing.assert_array_almost_equal(image3.array, 1./(3+1j), decimal=12,
             err_msg="ImageD / ImageCD is not correct")
 
     # Binary ImageCD op ImageD
@@ -2840,7 +2977,7 @@ def test_complex_image_arith():
     np.testing.assert_array_equal(image3.array, (3+1j)*ref_array**2,
             err_msg="ImageD * ImageCD is not correct")
     image3 = image2 / image1
-    np.testing.assert_almost_equal(image3.array, (3+1j), decimal=12,
+    np.testing.assert_array_almost_equal(image3.array, (3+1j), decimal=12,
             err_msg="ImageD / ImageCD is not correct")
 
     # Binary ImageCD op ImageCD
@@ -2855,7 +2992,7 @@ def test_complex_image_arith():
     np.testing.assert_array_equal(image4.array, (15-5j)*ref_array**2,
             err_msg="ImageCD * ImageCD is not correct")
     image4 = image2 / image3
-    np.testing.assert_almost_equal(image4.array, (9+13j)/25., decimal=12,
+    np.testing.assert_array_almost_equal(image4.array, (9+13j)/25., decimal=12,
             err_msg="ImageCD / ImageCD is not correct")
 
     # In place ImageCD op complex scalar
@@ -2873,7 +3010,7 @@ def test_complex_image_arith():
             err_msg="ImageCD * complex is not correct")
     image4 = image2.copy()
     image4 /= (2+5j)
-    np.testing.assert_array_equal(image4.array, (3+1j)*ref_array / (2+5j),
+    np.testing.assert_allclose(image4.array, (3+1j)*ref_array / (2+5j),
             err_msg="ImageCD / complex is not correct")
 
     # In place ImageCD op ImageD
@@ -2891,7 +3028,7 @@ def test_complex_image_arith():
             err_msg="ImageD * ImageCD is not correct")
     image4 = image2.copy()
     image4 /= image1
-    np.testing.assert_almost_equal(image4.array, (3+1j), decimal=12,
+    np.testing.assert_array_almost_equal(image4.array, (3+1j), decimal=12,
             err_msg="ImageD / ImageCD is not correct")
 
     # In place ImageCD op ImageCD
@@ -2909,7 +3046,7 @@ def test_complex_image_arith():
             err_msg="ImageCD * ImageCD is not correct")
     image4 = image2.copy()
     image4 /= image3
-    np.testing.assert_almost_equal(image4.array, (9+13j)/25., decimal=12,
+    np.testing.assert_array_almost_equal(image4.array, (9+13j)/25., decimal=12,
             err_msg="ImageCD / ImageCD is not correct")
 
 @timer
@@ -3258,7 +3395,7 @@ def test_wrap():
     b = galsim.BoundsI(1,4,1,4)
     im_quad = im_orig[b]
     im_wrap = im.wrap(b)
-    np.testing.assert_almost_equal(im_wrap.array, 4.*im_quad.array, 12,
+    np.testing.assert_array_almost_equal(im_wrap.array, 4.*im_quad.array, 12,
                                    "image.wrap() into first quadrant did not match expectation")
 
     # The same thing should work no matter where the lower left corner is:
@@ -3267,7 +3404,7 @@ def test_wrap():
         im_quad = im_orig[b]
         im = im_orig.copy()
         im_wrap = im.wrap(b)
-        np.testing.assert_almost_equal(im_wrap.array, 4.*im_quad.array, 12,
+        np.testing.assert_array_almost_equal(im_wrap.array, 4.*im_quad.array, 12,
                                        "image.wrap(%s) did not match expectation"%b)
         np.testing.assert_array_equal(im_wrap.array, im[b].array,
                                       "image.wrap(%s) did not return the right subimage")
@@ -3289,7 +3426,7 @@ def test_wrap():
             jj = (j-b.ymin) % (b.ymax-b.ymin+1) + b.ymin
             im_test.addValue(ii,jj,val)
     im_wrap = im.wrap(b)
-    np.testing.assert_almost_equal(im_wrap.array, im_test.array, 12,
+    np.testing.assert_array_almost_equal(im_wrap.array, im_test.array, 12,
                                    "image.wrap(%s) did not match expectation"%b)
     np.testing.assert_array_equal(im_wrap.array, im[b].array,
                                   "image.wrap(%s) did not return the right subimage")
@@ -3336,7 +3473,7 @@ def test_wrap():
 
     im_wrap = im.wrap(b)
     #print("im_wrap = ",im_wrap.array)
-    np.testing.assert_almost_equal(im_wrap.array, im_test.array, 12,
+    np.testing.assert_array_almost_equal(im_wrap.array, im_test.array, 12,
                                    "image.wrap(%s) did not match expectation"%b)
     np.testing.assert_array_equal(im_wrap.array, im[b].array,
                                   "image.wrap(%s) did not return the right subimage")
@@ -3347,7 +3484,7 @@ def test_wrap():
     #print('im_test = ',im_test[b2].array)
     #print('im2_wrap = ',im2_wrap.array)
     #print('diff = ',im2_wrap.array-im_test[b2].array)
-    np.testing.assert_almost_equal(im2_wrap.array, im_test[b2].array, 12,
+    np.testing.assert_array_almost_equal(im2_wrap.array, im_test[b2].array, 12,
                                    "image.wrap(%s) did not match expectation"%b)
     np.testing.assert_array_equal(im2_wrap.array, im2[b2].array,
                                   "image.wrap(%s) did not return the right subimage")
@@ -3358,7 +3495,7 @@ def test_wrap():
     #print('im_test = ',im_test[b3].array)
     #print('im3_wrap = ',im3_wrap.array)
     #print('diff = ',im3_wrap.array-im_test[b3].array)
-    np.testing.assert_almost_equal(im3_wrap.array, im_test[b3].array, 12,
+    np.testing.assert_array_almost_equal(im3_wrap.array, im_test[b3].array, 12,
                                    "image.wrap(%s) did not match expectation"%b)
     np.testing.assert_array_equal(im3_wrap.array, im3[b3].array,
                                   "image.wrap(%s) did not return the right subimage")
@@ -3536,7 +3673,7 @@ def test_fpack():
     """Test the functionality that we advertise as being equivalent to fpack/funpack
     """
     from astropy.io import fits
-    file_name0 = os.path.join('des_data','DECam_00158414_01.fits.fz')
+    file_name0 = os.path.join(os.path.dirname(__file__), 'des_data','DECam_00158414_01.fits.fz')
     hdulist = fits.open(file_name0)
 
     # Remove a few invalid header keys in the DECam fits file
diff --git a/tests/test_inclined.py b/tests/test_inclined.py
index 7fcd83e325..a7a04b70d3 100644
--- a/tests/test_inclined.py
+++ b/tests/test_inclined.py
@@ -16,6 +16,7 @@
 #    and/or other materials provided with the distribution.
 #
 
+import os
 from copy import deepcopy
 import numpy as np
 
@@ -28,7 +29,7 @@
 
 # set up any necessary info for tests
 # Note that changes here should match changes to test image files
-image_dir = './inclined_exponential_images'
+image_dir = os.path.join(os.path.dirname(__file__), './inclined_exponential_images')
 
 # Values here are strings, so the filenames will be sure to work (without truncating zeros)
 
diff --git a/tests/test_integ.py b/tests/test_integ.py
index 537b5c72b1..c89785c20f 100644
--- a/tests/test_integ.py
+++ b/tests/test_integ.py
@@ -183,8 +183,11 @@ def test_func(x): return x**-2
         test_integral, true_result, decimal=test_decimal, verbose=True,
         err_msg="x^(-2) integral failed across interval [1, inf].")
 
-    with assert_raises(galsim.GalSimError):
-        galsim.integ.int1d(test_func, 0., 1., test_rel_err, test_abs_err)
+    if is_jax_galsim():
+        assert np.isnan(galsim.integ.int1d(test_func, 0., 1., test_rel_err, test_abs_err))
+    else:
+        with assert_raises(galsim.GalSimError):
+            galsim.integ.int1d(test_func, 0., 1., test_rel_err, test_abs_err)
 
 
 @timer
diff --git a/tests/test_interpolatedimage.py b/tests/test_interpolatedimage.py
index 281b3de568..f3e97891e6 100644
--- a/tests/test_interpolatedimage.py
+++ b/tests/test_interpolatedimage.py
@@ -369,8 +369,12 @@ def test_interpolant():
                      -(vm+1) * sici(np.pi*(vm+1))[0]
                      -(vp-1) * sici(np.pi*(vp-1))[0]
                      +(vp+1) * sici(np.pi*(vp+1))[0] ) / (2*np.pi)
-        np.testing.assert_allclose(ln.kval(x), true_kval, rtol=1.e-4, atol=1.e-8)
-        assert np.isclose(ln.kval(x[12]), true_kval[12])
+        if is_jax_galsim():
+            np.testing.assert_allclose(ln.kval(x), true_kval, rtol=3.0e-4, atol=3.0e-6)
+            np.testing.assert_allclose(ln.kval(x[12]), true_kval[12], rtol=3.0e-4, atol=3.0e-6)
+        else:
+            np.testing.assert_allclose(ln.kval(x), true_kval, rtol=1.e-4, atol=1.e-8)
+            assert np.isclose(ln.kval(x[12]), true_kval[12])
 
     # Base class is invalid.
     assert_raises(NotImplementedError, galsim.Interpolant)
@@ -400,12 +404,18 @@ def test_unit_integrals():
         print(str(interp))
         # Compute directly with int1d
         n = interp.ixrange//2 + 1
+        if is_jax_galsim():
+            # jax galsim is slow when doing direct integration
+            _n_do = min(n, 100)
+        else:
+            _n_do = n
+
         direct_integrals = np.zeros(n)
         if isinstance(interp, galsim.Delta):
             # int1d doesn't handle this well.
             direct_integrals[0] = 1
         else:
-            for k in range(n):
+            for k in range(_n_do):
                 direct_integrals[k] = galsim.integ.int1d(interp.xval, k-0.5, k+0.5)
         print('direct: ',direct_integrals)
 
@@ -414,7 +424,7 @@ def test_unit_integrals():
         print('integrals: ',len(integrals),integrals)
 
         assert len(integrals) == n
-        np.testing.assert_allclose(integrals, direct_integrals, atol=1.e-12)
+        np.testing.assert_allclose(integrals[:_n_do], direct_integrals[:_n_do], atol=1.e-12)
 
         if n > 10:
             print('n>10 for ',repr(interp))
@@ -451,8 +461,8 @@ def test_fluxnorm():
     # First, make some Image with some total flux value (sum of pixel values) and scale
     im = galsim.ImageF(im_lin_scale, im_lin_scale, scale=im_scale, init_value=im_fill_value)
     total_flux = im_fill_value*(im_lin_scale**2)
-    np.testing.assert_equal(total_flux, im.array.sum(),
-                            err_msg='Created array with wrong total flux')
+    np.testing.assert_array_equal(total_flux, im.array.sum(),
+                                  err_msg='Created array with wrong total flux')
 
     # Check that if we make an InterpolatedImage with flux normalization, it keeps that flux
     interp = galsim.InterpolatedImage(im) # note, flux normalization is the default
@@ -486,8 +496,8 @@ def test_fluxnorm():
     # Finally make an InterpolatedImage but give it some other flux value
     interp_flux = galsim.InterpolatedImage(im, flux=test_flux)
     # Check that it has that flux
-    np.testing.assert_equal(test_flux, interp_flux.flux,
-                            err_msg = 'InterpolatedImage did not use flux keyword')
+    np.testing.assert_array_equal(test_flux, interp_flux.flux,
+                                  err_msg = 'InterpolatedImage did not use flux keyword')
     # Check that this is preserved when drawing
     im5 = interp_flux.drawImage(scale = im_scale, method='no_pixel')
     np.testing.assert_almost_equal(test_flux/im5.array.sum(), 1.0, decimal=6,
@@ -509,12 +519,18 @@ def test_exceptions():
         galsim.InterpolatedImage(image=galsim.ImageF(5, 5))
 
     # Image must be real type (F or D)
-    with assert_raises(galsim.GalSimValueError):
-        galsim.InterpolatedImage(image=galsim.ImageI(5, 5, scale=1))
+    if is_jax_galsim():
+        pass
+    else:
+        with assert_raises(galsim.GalSimValueError):
+            galsim.InterpolatedImage(image=galsim.ImageI(5, 5, scale=1))
 
-    # Image must have non-zero flux
-    with assert_raises(galsim.GalSimValueError):
-        galsim.InterpolatedImage(image=galsim.ImageF(5, 5, scale=1, init_value=0.))
+    if is_jax_galsim():
+        pass
+    else:
+        # Image must have non-zero flux
+        with assert_raises(galsim.GalSimValueError):
+            galsim.InterpolatedImage(image=galsim.ImageF(5, 5, scale=1, init_value=0.))
 
     # Can't shoot II with SincInterpolant
     ii = galsim.InterpolatedImage(image=galsim.ImageF(5, 5, scale=1, init_value=1.),
@@ -742,8 +758,11 @@ def test_operations():
     test_decimal = 3
 
     # Make some nontrivial image
-    im = galsim.fits.read('./real_comparison_images/test_images.fits') # read in first real galaxy
-                                                                       # in test catalog
+    im_path = os.path.join(
+        os.path.dirname(__file__), "real_comparison_images/test_images.fits"
+    )
+    im = galsim.fits.read(im_path) # read in first real galaxy
+                                   # in test catalog
     int_im = galsim.InterpolatedImage(im)
     orig_mom = im.FindAdaptiveMom()
 
@@ -952,7 +971,7 @@ def test_corr_padding():
     # Set up some defaults for tests.
     decimal_precise=4
     decimal_coarse=2
-    imgfile = 'fits_files/blankimg.fits'
+    imgfile = os.path.join(os.path.dirname(__file__), 'fits_files/blankimg.fits')
     orig_nx = 187
     orig_ny = 164
     big_nx = 319
@@ -1588,9 +1607,9 @@ def test_ii_shoot():
     else:
         flux = 1.e4
     for interp in interp_list:
+        print('interp = ',interp)
         obj = galsim.InterpolatedImage(image_in, x_interpolant=interp, scale=3.3, flux=flux)
         added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng.duplicate())
-        print('interp = ',interp)
         print('obj.flux = ',obj.flux)
         print('added_flux = ',added_flux)
         print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
@@ -1608,7 +1627,12 @@ def test_ii_shoot():
         assert np.isclose(added_flux, obj.flux, rtol=rtol)
         assert np.isclose(im.array.sum(), obj.flux, rtol=rtol)
         photons2 = obj.makePhot(poisson_flux=False, rng=rng.duplicate())
-        assert photons2 == photons, "InterpolatedImage makePhot not equivalent to drawPhot"
+        if is_jax_galsim():
+            np.testing.assert_allclose(photons2.x, photons.x)
+            np.testing.assert_allclose(photons2.y, photons.y)
+            np.testing.assert_allclose(photons2.flux, photons.flux)
+        else:
+            assert photons2 == photons, "InterpolatedImage makePhot not equivalent to drawPhot"
 
         # Can treat as a convolution of a delta function and put it in a photon_ops list.
         delta = galsim.DeltaFunction(flux=flux)
@@ -1630,7 +1654,10 @@ def test_ne():
     # Copy ref_image and perturb it slightly in the middle, away from where the InterpolatedImage
     # repr string will report.
     perturb_image = ref_image.copy()
-    perturb_image.array[64, 64] *= 1000
+    if is_jax_galsim():
+        perturb_image._array = perturb_image._array.at[64, 64].set(perturb_image._array[64, 64] * 100)
+    else:
+        perturb_image.array[64, 64] *= 100
     obj2 = galsim.InterpolatedImage(perturb_image, flux=20, calculate_maxk=False, calculate_stepk=False)
 
     with galsim.utilities.printoptions(threshold=128*128):
@@ -1696,7 +1723,7 @@ def test_quintic_glagn():
     """This is code that was giving a seg fault.  cf. Issue 1079.
     """
 
-    fname = os.path.join('fits_files','GLAGN_host_427_0_disk.fits')
+    fname = os.path.join(os.path.dirname(__file__), 'fits_files','GLAGN_host_427_0_disk.fits')
     for interpolant in 'linear cubic quintic'.split():
         print(interpolant)
         fits_image = galsim.InterpolatedImage(fname, scale=0.04, x_interpolant=interpolant)
@@ -1845,33 +1872,39 @@ def test_depixelize():
 def test_drawreal_seg_fault():
     """Test to reproduce bug report in Issue #1164 that was causing seg faults
     """
-
-    import pickle
-
-    prof_file = 'input/test_interpolatedimage_seg_fault_prof.pkl'
-    with open(prof_file, 'rb') as f:
-        prof = pickle.load(f)
-    print(repr(prof))
-
-    image = galsim.Image(
-        galsim.BoundsI(
-            xmin=-12,
-            xmax=12,
-            ymin=-12,
-            ymax=12
-        ),
-        dtype=float,
-        scale=1
-    )
-
-    image.fill(3)
-    prof.drawReal(image)
-
-    # The problem was that the object is shifted fully off the target image and that was leading
-    # to an attempt to create a stack of length -1, which caused the seg fault.
-    # So mostly this test just confirms that this runs without seg faulting.
-    # But we can check that the image is now correctly all zeros.
-    np.testing.assert_array_equal(image.array, 0)
+    # this test only runs with real galsim
+    if is_jax_galsim():
+        pass
+    else:
+        import pickle
+
+        prof_file = os.path.join(
+            os.path.dirname(__file__),
+            'input/test_interpolatedimage_seg_fault_prof.pkl'
+        )
+        with open(prof_file, 'rb') as f:
+            prof = pickle.load(f)
+        print(repr(prof))
+
+        image = galsim.Image(
+            galsim.BoundsI(
+                xmin=-12,
+                xmax=12,
+                ymin=-12,
+                ymax=12
+            ),
+            dtype=float,
+            scale=1
+        )
+
+        image.fill(3)
+        prof.drawReal(image)
+
+        # The problem was that the object is shifted fully off the target image and that was leading
+        # to an attempt to create a stack of length -1, which caused the seg fault.
+        # So mostly this test just confirms that this runs without seg faulting.
+        # But we can check that the image is now correctly all zeros.
+        np.testing.assert_array_equal(image.array, 0)
 
 
 
diff --git a/tests/test_lensing.py b/tests/test_lensing.py
index 04c9ea0025..c2acc41395 100644
--- a/tests/test_lensing.py
+++ b/tests/test_lensing.py
@@ -24,7 +24,7 @@
 from galsim_test_helpers import *
 
 
-refdir = os.path.join(".", "lensing_reference_data") # Directory containing the reference
+refdir = os.path.join(os.path.dirname(__file__), ".", "lensing_reference_data") # Directory containing the reference
 
 klim_test = 0.00175 # Value of klim for flat (up to klim, then zero beyond) power spectrum test
 tolerance_var = 0.03 # fractional error allowed in the variance of shear - calculation is not exact
diff --git a/tests/test_moffat.py b/tests/test_moffat.py
index e98bf1bcbf..f19b5c352f 100644
--- a/tests/test_moffat.py
+++ b/tests/test_moffat.py
@@ -23,8 +23,9 @@
 from galsim_test_helpers import *
 
 path, filename = os.path.split(__file__)
-imgdir = os.path.join(path, "SBProfile_comparison_images") # Directory containing the reference
-                                                           # images.
+# Directory containing the reference images.
+imgdir = os.path.join(path, "SBProfile_comparison_images")
+
 
 @timer
 def test_moffat():
@@ -135,36 +136,42 @@ def test_moffat_properties():
     cen = galsim.PositionD(0, 0)
     np.testing.assert_equal(psf.centroid, cen)
     # Check Fourier properties
-    np.testing.assert_almost_equal(psf.maxk, 11.634597424960159)
-    np.testing.assert_almost_equal(psf.stepk, 0.62831853071795873)
-    np.testing.assert_almost_equal(psf.kValue(cen), test_flux+0j)
-    np.testing.assert_almost_equal(psf.half_light_radius, 1.0)
-    np.testing.assert_almost_equal(psf.fwhm, fwhm_backwards_compatible)
-    np.testing.assert_almost_equal(psf.xValue(cen), 0.50654651638242509)
-    np.testing.assert_almost_equal(psf.kValue(cen), (1+0j) * test_flux)
-    np.testing.assert_almost_equal(psf.flux, test_flux)
-    np.testing.assert_almost_equal(psf.xValue(cen), psf.max_sb)
+    if is_jax_galsim():
+        np.testing.assert_allclose(psf.maxk, 11.634597424960159, atol=0, rtol=0.2)
+    else:
+        np.testing.assert_array_almost_equal(psf.maxk, 11.634597424960159)
+    np.testing.assert_array_almost_equal(psf.stepk, 0.62831853071795873)
+    np.testing.assert_array_almost_equal(psf.kValue(cen), test_flux+0j)
+    np.testing.assert_array_almost_equal(psf.half_light_radius, 1.0)
+    np.testing.assert_array_almost_equal(psf.fwhm, fwhm_backwards_compatible)
+    np.testing.assert_array_almost_equal(psf.xValue(cen), 0.50654651638242509)
+    np.testing.assert_array_almost_equal(psf.kValue(cen), (1+0j) * test_flux)
+    np.testing.assert_array_almost_equal(psf.flux, test_flux)
+    np.testing.assert_array_almost_equal(psf.xValue(cen), psf.max_sb)
 
     # Now create the same profile using the half_light_radius:
     psf = galsim.Moffat(beta=2.0, half_light_radius=1.,
                         trunc=2*fwhm_backwards_compatible, flux=test_flux)
     np.testing.assert_equal(psf.centroid, cen)
-    np.testing.assert_almost_equal(psf.maxk, 11.634597426100862)
-    np.testing.assert_almost_equal(psf.stepk, 0.62831853071795862)
-    np.testing.assert_almost_equal(psf.kValue(cen), test_flux+0j)
-    np.testing.assert_almost_equal(psf.half_light_radius, 1.0)
-    np.testing.assert_almost_equal(psf.fwhm, fwhm_backwards_compatible)
-    np.testing.assert_almost_equal(psf.xValue(cen), 0.50654651638242509)
-    np.testing.assert_almost_equal(psf.kValue(cen), (1+0j) * test_flux)
-    np.testing.assert_almost_equal(psf.flux, test_flux)
-    np.testing.assert_almost_equal(psf.xValue(cen), psf.max_sb)
+    if is_jax_galsim():
+        np.testing.assert_allclose(psf.maxk, 11.634597424960159, atol=0, rtol=0.2)
+    else:
+        np.testing.assert_array_almost_equal(psf.maxk, 11.634597424960159)
+    np.testing.assert_array_almost_equal(psf.stepk, 0.62831853071795862)
+    np.testing.assert_array_almost_equal(psf.kValue(cen), test_flux+0j)
+    np.testing.assert_array_almost_equal(psf.half_light_radius, 1.0)
+    np.testing.assert_array_almost_equal(psf.fwhm, fwhm_backwards_compatible)
+    np.testing.assert_array_almost_equal(psf.xValue(cen), 0.50654651638242509)
+    np.testing.assert_array_almost_equal(psf.kValue(cen), (1+0j) * test_flux)
+    np.testing.assert_array_almost_equal(psf.flux, test_flux)
+    np.testing.assert_array_almost_equal(psf.xValue(cen), psf.max_sb)
 
     # Check input flux vs output flux
     for inFlux in np.logspace(-2, 2, 10):
         psfFlux = galsim.Moffat(2.0, fwhm=fwhm_backwards_compatible,
                                 trunc=2*fwhm_backwards_compatible, flux=inFlux)
         outFlux = psfFlux.flux
-        np.testing.assert_almost_equal(outFlux, inFlux)
+        np.testing.assert_array_almost_equal(outFlux, inFlux)
 
 @timer
 def test_moffat_maxk():
@@ -190,13 +197,16 @@ def test_moffat_maxk():
         galsim.Moffat(beta=12.9, scale_radius=11, flux=23, trunc=1000),
     ]
     threshs = [1.e-3, 1.e-4, 0.03]
-    print('beta \t trunc \t thresh \t kValue(maxk)')
+    print('beta \t trunc \t thresh \t kValue(maxk) \t maxk')
     for psf in psfs:
         for thresh in threshs:
             psf = psf.withGSParams(maxk_threshold=thresh)
-            rtol = 1.e-7 if psf.trunc == 0 else 3.e-3
+            if is_jax_galsim():
+                rtol = 5e-3
+            else:
+                rtol = 1.e-7 if psf.trunc == 0 else 3.e-3
             fk = psf.kValue(psf.maxk,0).real/psf.flux
-            print(f'{psf.beta} \t {int(psf.trunc)} \t {thresh:.1e} \t {fk:.3e}')
+            print(f'{psf.beta} \t {int(psf.trunc)} \t {thresh:.1e} \t {fk:.3e} \t {psf.maxk:.3e}')
             np.testing.assert_allclose(abs(psf.kValue(psf.maxk,0).real)/psf.flux, thresh, rtol=rtol)
 
 
@@ -217,7 +227,7 @@ def test_moffat_radii():
     np.testing.assert_almost_equal(
             hlr_sum, 0.5, decimal=4,
             err_msg="Error in Moffat constructor with half-light radius")
-    np.testing.assert_equal(
+    np.testing.assert_array_equal(
             test_gal.half_light_radius, test_hlr,
             err_msg="Moffat half_light_radius returned wrong value")
 
@@ -282,7 +292,7 @@ def test_moffat_radii():
     np.testing.assert_almost_equal(
             ratio, 0.5, decimal=4,
             err_msg="Error in Moffat constructor with fwhm")
-    np.testing.assert_equal(
+    np.testing.assert_array_equal(
             test_gal.fwhm, test_fwhm,
             err_msg="Moffat fwhm returned wrong value")
 
@@ -312,7 +322,7 @@ def test_moffat_radii():
     np.testing.assert_almost_equal(
             hlr_sum, 0.5, decimal=4,
             err_msg="Error in Moffat constructor with half-light radius")
-    np.testing.assert_equal(
+    np.testing.assert_allclose(
             test_gal.half_light_radius, test_hlr,
             err_msg="Moffat hlr incorrect")
 
diff --git a/tests/test_noise.py b/tests/test_noise.py
index df788ca004..faaaefad0c 100644
--- a/tests/test_noise.py
+++ b/tests/test_noise.py
@@ -37,7 +37,11 @@ def test_deviate_noise():
     """
     u = galsim.UniformDeviate(testseed)
     uResult = np.empty((10,10))
-    u.generate(uResult)
+    # jax-galsim cannot fill arrays so it returns
+    if is_jax_galsim():
+        uResult = u.generate(uResult)
+    else:
+        u.generate(uResult)
 
     noise = galsim.DeviateNoise(galsim.UniformDeviate(testseed))
 
@@ -100,7 +104,11 @@ def test_gaussian_noise():
     gSigma = 17.23
     g = galsim.GaussianDeviate(testseed, sigma=gSigma)
     gResult = np.empty((10,10))
-    g.generate(gResult)
+    # jax-galsim cannot fill arrays so it returns
+    if is_jax_galsim():
+        gResult = g.generate(gResult)
+    else:
+        g.generate(gResult)
     noise = galsim.DeviateNoise(g)
 
     # Test filling an image
@@ -276,13 +284,22 @@ def test_variable_gaussian_noise():
     gSigma2 = 28.55
     var_image = galsim.ImageD(galsim.BoundsI(0,9,0,9))
     coords = np.ogrid[0:10, 0:10]
-    var_image.array[ (coords[0] + coords[1]) % 2 == 1 ] = gSigma1**2
-    var_image.array[ (coords[0] + coords[1]) % 2 == 0 ] = gSigma2**2
+    # jax does not support item assignment
+    if is_jax_galsim():
+        var_image._array = var_image.array.at[(coords[0] + coords[1]) % 2 == 1].set(gSigma1**2)
+        var_image._array = var_image.array.at[(coords[0] + coords[1]) % 2 == 0].set(gSigma2**2)
+    else:
+        var_image.array[ (coords[0] + coords[1]) % 2 == 1 ] = gSigma1**2
+        var_image.array[ (coords[0] + coords[1]) % 2 == 0 ] = gSigma2**2
     print('var_image.array = ',var_image.array)
 
     g = galsim.GaussianDeviate(testseed, sigma=1.)
     vgResult = np.empty((10,10))
-    g.generate(vgResult)
+    # jax-galsim cannot fill arrays so it returns
+    if is_jax_galsim():
+        vgResult = g.generate(vgResult)
+    else:
+        g.generate(vgResult)
     vgResult *= np.sqrt(var_image.array)
 
     # Test filling an image
@@ -302,7 +319,7 @@ def test_variable_gaussian_noise():
             err_msg="Wrong VariableGaussian noise generated for Fortran-ordered Image")
 
     # Check var_image property
-    np.testing.assert_almost_equal(
+    np.testing.assert_array_almost_equal(
             vgn.var_image.array, var_image.array, precision,
             err_msg="VariableGaussianNoise var_image returns wrong var_image")
 
@@ -311,8 +328,13 @@ def test_variable_gaussian_noise():
     big_coords = np.ogrid[0:2048, 0:2048]
     mask1 = (big_coords[0] + big_coords[1]) % 2 == 0
     mask2 = (big_coords[0] + big_coords[1]) % 2 == 1
-    big_var_image.array[mask1] = gSigma1**2
-    big_var_image.array[mask2] = gSigma2**2
+    # jax does not support item assignment
+    if is_jax_galsim():
+        big_var_image._array = big_var_image.array.at[mask1].set(gSigma1**2)
+        big_var_image._array = big_var_image.array.at[mask2].set(gSigma2**2)
+    else:
+        big_var_image.array[mask1] = gSigma1**2
+        big_var_image.array[mask2] = gSigma2**2
     big_vgn = galsim.VariableGaussianNoise(galsim.BaseDeviate(testseed), big_var_image)
 
     big_im = galsim.Image(2048,2048,dtype=float)
@@ -320,8 +342,13 @@ def test_variable_gaussian_noise():
     var = np.var(big_im.array)
     print('variance = ',var)
     print('getVar = ',big_vgn.var_image.array.mean())
+    if is_jax_galsim():
+        # jax galsim has a different RNG
+        decimal = 0
+    else:
+        decimal = 1
     np.testing.assert_almost_equal(
-            var, big_vgn.var_image.array.mean(), 1,
+            var, big_vgn.var_image.array.mean(), decimal,
             err_msg='Realized variance for VariableGaussianNoise did not match var_image')
 
     # Check realized variance in each mask
@@ -337,8 +364,13 @@ def test_variable_gaussian_noise():
     big_im.addNoise(big_vgn)
     gal.withFlux(-1.e4).drawImage(image=big_im, add_to_image=True)
     var = np.var(big_im.array)
+    if is_jax_galsim():
+        # jax galsim has a different RNG
+        decimal = 0
+    else:
+        decimal = 1
     np.testing.assert_almost_equal(
-            var, big_vgn.var_image.array.mean(), 1,
+            var, big_vgn.var_image.array.mean(), decimal,
             err_msg='VariableGaussianNoise wrong when already an object drawn on the image')
 
     # Check picklability
@@ -376,7 +408,11 @@ def test_poisson_noise():
     pMean = 17
     p = galsim.PoissonDeviate(testseed, mean=pMean)
     pResult = np.empty((10,10))
-    p.generate(pResult)
+    # jax does not support item assignment
+    if is_jax_galsim():
+        pResult = p.generate(pResult)
+    else:
+        p.generate(pResult)
     noise = galsim.DeviateNoise(p)
 
     # Test filling an image
@@ -545,11 +581,24 @@ def test_ccdnoise():
     sky = 50
 
     # Tabulated results for the above settings and testseed value.
-    cResultS = np.array([[44, 47], [50, 49]], dtype=np.int16)
-    cResultI = np.array([[44, 47], [50, 49]], dtype=np.int32)
-    cResultF = np.array([[44.45332718, 47.79725266], [50.67744064, 49.58272934]], dtype=np.float32)
-    cResultD = np.array([[44.453328440057618, 47.797254142519577],
-                        [50.677442088335162, 49.582730949808081]],dtype=np.float64)
+    if is_jax_galsim():
+        # jax-galsim has a different RNG
+        cResultS = np.array([[42, 52], [49, 45]], dtype=np.int16)  # noqa: F841
+        cResultI = np.array([[42, 52], [49, 45]], dtype=np.int32)  # noqa: F841
+        cResultF = np.array([  # noqa: F841
+            [42.4286994934082, 52.42875671386719],
+            [49.016048431396484, 45.61003875732422]
+        ], dtype=np.float32)
+        cResultD = np.array([  # noqa: F841
+            [42.42870031326479, 52.42875718917211],
+            [49.016050296441094, 45.61003745208172]
+        ], dtype=np.float64)
+    else:
+        cResultS = np.array([[44, 47], [50, 49]], dtype=np.int16)
+        cResultI = np.array([[44, 47], [50, 49]], dtype=np.int32)
+        cResultF = np.array([[44.45332718, 47.79725266], [50.67744064, 49.58272934]], dtype=np.float32)
+        cResultD = np.array([[44.453328440057618, 47.797254142519577],
+                            [50.677442088335162, 49.582730949808081]],dtype=np.float64)
 
     for i in range(4):
         prec = eval("precision"+typestrings[i])
diff --git a/tests/test_optics.py b/tests/test_optics.py
index 6c2398c757..c73bc9d55f 100644
--- a/tests/test_optics.py
+++ b/tests/test_optics.py
@@ -22,7 +22,7 @@
 import galsim
 from galsim_test_helpers import *
 
-imgdir = os.path.join(".", "Optics_comparison_images") # Directory containing the reference images.
+imgdir = os.path.join(os.path.dirname(__file__), "Optics_comparison_images") # Directory containing the reference images.
 
 
 testshape = (512, 512)  # shape of image arrays for all tests
@@ -745,7 +745,11 @@ def test_OpticalPSF_pupil_plane_size():
     im = galsim.Image(512, 512)
     x = y = np.arange(512) - 256
     y, x = np.meshgrid(y, x)
-    im.array[x**2+y**2 < 230**2] = 1.0
+    if is_jax_galsim():
+        # no refs in jax-galsim
+        im._array = im.array.at[x**2+y**2 < 230**2].set(1.0)
+    else:
+        im.array[x**2+y**2 < 230**2] = 1.0
     # The following still fails (uses deprecated optics framework):
     # galsim.optics.OpticalPSF(aberrations=[0,0,0,0,0.5], diam=4.0, lam=700.0, pupil_plane_im=im)
     # But using the new framework, should work.
diff --git a/tests/test_phase_psf.py b/tests/test_phase_psf.py
index e2fdf247b1..c4dfb923e6 100644
--- a/tests/test_phase_psf.py
+++ b/tests/test_phase_psf.py
@@ -24,7 +24,7 @@
 from galsim_test_helpers import *
 
 
-imgdir = os.path.join(".", "Optics_comparison_images") # Directory containing the reference images.
+imgdir = os.path.join(os.path.dirname(__file__), "Optics_comparison_images") # Directory containing the reference images.
 pp_file = 'sample_pupil_rolled.fits'
 
 theta0 = (0*galsim.arcmin, 0*galsim.arcmin)
diff --git a/tests/test_photon_array.py b/tests/test_photon_array.py
index f17cf409ea..acb3aada8b 100644
--- a/tests/test_photon_array.py
+++ b/tests/test_photon_array.py
@@ -60,12 +60,16 @@ def test_photon_array():
     check_pickle(photon_array)
 
     # Check assignment via numpy [:]
-    photon_array.x[:] = 5
-    photon_array.y[:] = 17
-    photon_array.flux[:] = 23
-    np.testing.assert_array_equal(photon_array.x, 5.)
-    np.testing.assert_array_equal(photon_array.y, 17.)
-    np.testing.assert_array_equal(photon_array.flux, 23.)
+    # jax does not support direct assignment
+    if is_jax_galsim():
+        pass
+    else:
+        photon_array.x[:] = 5
+        photon_array.y[:] = 17
+        photon_array.flux[:] = 23
+        np.testing.assert_array_equal(photon_array.x, 5.)
+        np.testing.assert_array_equal(photon_array.y, 17.)
+        np.testing.assert_array_equal(photon_array.flux, 23.)
 
     # Check assignment directly to the attributes
     photon_array.x = 25
@@ -94,9 +98,9 @@ def test_photon_array():
     photon_array.x *= 5
     photon_array.y += 17
     photon_array.flux /= 23
-    np.testing.assert_almost_equal(photon_array.x, orig_x * 5.)
-    np.testing.assert_almost_equal(photon_array.y, orig_y + 17.)
-    np.testing.assert_almost_equal(photon_array.flux, orig_flux / 23.)
+    np.testing.assert_array_almost_equal(photon_array.x, orig_x * 5.)
+    np.testing.assert_array_almost_equal(photon_array.y, orig_y + 17.)
+    np.testing.assert_array_almost_equal(photon_array.flux, orig_flux / 23.)
 
     # Check picklability again with non-zero values
     check_pickle(photon_array)
@@ -181,30 +185,36 @@ def test_photon_array():
     x = photon_array.x.copy()
     y = photon_array.y.copy()
     photon_array.scaleXY(1.9)
-    np.testing.assert_almost_equal(photon_array.x, 1.9*x)
-    np.testing.assert_almost_equal(photon_array.y, 1.9*y)
+    np.testing.assert_array_almost_equal(photon_array.x, 1.9*x)
+    np.testing.assert_array_almost_equal(photon_array.y, 1.9*y)
 
     # Check ways to assign to photons
     pa1 = galsim.PhotonArray(50)
     pa1.x = photon_array.x[:50]
-    for i in range(50):
-        pa1.y[i] = photon_array.y[i]
-    pa1.flux[0:50] = photon_array.flux[:50]
+    if is_jax_galsim():
+        pa1.y = photon_array.y[:50]
+    else:
+        for i in range(50):
+            pa1.y[i] = photon_array.y[i]
+    if is_jax_galsim():
+        pa1.flux = photon_array.flux[:50]
+    else:
+        pa1.flux[0:50] = photon_array.flux[:50]
     pa1.dxdz = photon_array.dxdz[:50]
     pa1.dydz = photon_array.dydz[:50]
     pa1.wavelength = photon_array.wavelength[:50]
     pa1.pupil_u = photon_array.pupil_u[:50]
     pa1.pupil_v = photon_array.pupil_v[:50]
     pa1.time = photon_array.time[:50]
-    np.testing.assert_almost_equal(pa1.x, photon_array.x[:50])
-    np.testing.assert_almost_equal(pa1.y, photon_array.y[:50])
-    np.testing.assert_almost_equal(pa1.flux, photon_array.flux[:50])
-    np.testing.assert_almost_equal(pa1.dxdz, photon_array.dxdz[:50])
-    np.testing.assert_almost_equal(pa1.dydz, photon_array.dydz[:50])
-    np.testing.assert_almost_equal(pa1.wavelength, photon_array.wavelength[:50])
-    np.testing.assert_almost_equal(pa1.pupil_u, photon_array.pupil_u[:50])
-    np.testing.assert_almost_equal(pa1.pupil_v, photon_array.pupil_v[:50])
-    np.testing.assert_almost_equal(pa1.time, photon_array.time[:50])
+    np.testing.assert_array_almost_equal(pa1.x, photon_array.x[:50])
+    np.testing.assert_array_almost_equal(pa1.y, photon_array.y[:50])
+    np.testing.assert_array_almost_equal(pa1.flux, photon_array.flux[:50])
+    np.testing.assert_array_almost_equal(pa1.dxdz, photon_array.dxdz[:50])
+    np.testing.assert_array_almost_equal(pa1.dydz, photon_array.dydz[:50])
+    np.testing.assert_array_almost_equal(pa1.wavelength, photon_array.wavelength[:50])
+    np.testing.assert_array_almost_equal(pa1.pupil_u, photon_array.pupil_u[:50])
+    np.testing.assert_array_almost_equal(pa1.pupil_v, photon_array.pupil_v[:50])
+    np.testing.assert_array_almost_equal(pa1.time, photon_array.time[:50])
 
     # Check copyFrom
     pa2 = galsim.PhotonArray(100)
@@ -236,7 +246,10 @@ def test_photon_array():
     assert pa2.time[17] == pa1.time[20]
 
     # Can choose not to copy flux
-    pa2.flux[27] = -1
+    if is_jax_galsim():
+        pa2._flux = pa2._flux.at[27].set(-1)
+    else:
+        pa2.flux[27] = -1
     pa2.copyFrom(pa1, 27, 10, do_flux=False)
     assert pa2.flux[27] != pa1.flux[10]
     assert pa2.x[27] == pa1.x[10]
@@ -250,8 +263,16 @@ def test_photon_array():
     assert pa2.time[37] == pa1.time[8]
 
     # ... or the other arrays
-    pa2.dxdz[47] = pa2.dydz[47] = pa2.wavelength[47] = -1
-    pa2.pupil_u[47] = pa2.pupil_v[47] = pa2.time[47] = -1
+    if is_jax_galsim():
+        pa2._dxdz = pa2._dxdz.at[47].set(-1)
+        pa2._dydz = pa2._dydz.at[47].set(-1)
+        pa2._wave = pa2._wave.at[47].set(-1)
+        pa2._pupil_u = pa2._pupil_u.at[47].set(-1)
+        pa2._pupil_v = pa2._pupil_v.at[47].set(-1)
+        pa2._time = pa2._time.at[47].set(-1)
+    else:
+        pa2.dxdz[47] = pa2.dydz[47] = pa2.wavelength[47] = -1
+        pa2.pupil_u[47] = pa2.pupil_v[47] = pa2.time[47] = -1
     pa2.copyFrom(pa1, 47, 18, do_other=False)
     assert pa2.flux[47] == pa1.flux[18]
     assert pa2.x[47] == pa1.x[18]
@@ -276,7 +297,10 @@ def test_photon_array():
 
     # Error if indices are invalid
     assert_raises(ValueError, pa2.copyFrom, pa1, slice(50,None), slice(50,None))
-    assert_raises(ValueError, pa2.copyFrom, pa1, 100, 0)
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(ValueError, pa2.copyFrom, pa1, 100, 0)
     assert_raises(ValueError, pa2.copyFrom, pa1, 0, slice(None))
     assert_raises(ValueError, pa2.copyFrom, pa1)
     assert_raises(ValueError, pa2.copyFrom, pa1, slice(None), pa1.x<0)
@@ -293,13 +317,13 @@ def test_photon_array():
     photons = galsim.PhotonArray.makeFromImage(ones)
     print('photons = ',photons)
     assert len(photons) == 16
-    np.testing.assert_almost_equal(photons.flux, 1.)
+    np.testing.assert_array_almost_equal(photons.flux, 1.)
 
     tens = galsim.Image(4,4,init_value=8)
     photons = galsim.PhotonArray.makeFromImage(tens, max_flux=5.)
     print('photons = ',photons)
     assert len(photons) == 32
-    np.testing.assert_almost_equal(photons.flux, 4.)
+    np.testing.assert_array_almost_equal(photons.flux, 4.)
 
     assert_raises(ValueError, galsim.PhotonArray.makeFromImage, zero, max_flux=0.)
     assert_raises(ValueError, galsim.PhotonArray.makeFromImage, zero, max_flux=-2)
@@ -1435,9 +1459,15 @@ def test_fromArrays():
         flux[Nsplit:]
     )
 
-    assert pa_batch.x is x
-    assert pa_batch.y is y
-    assert pa_batch.flux is flux
+    if is_jax_galsim():
+        # jax-galsim never copies
+        assert pa_batch.x is not x
+        assert pa_batch.y is not y
+        assert pa_batch.flux is not flux
+    else:
+        assert pa_batch.x is x
+        assert pa_batch.y is y
+        assert pa_batch.flux is flux
     np.testing.assert_array_equal(pa_batch.x, x)
     np.testing.assert_array_equal(pa_batch.y, y)
     np.testing.assert_array_equal(pa_batch.flux, flux)
diff --git a/tests/test_random.py b/tests/test_random.py
index 429137600b..5fbd90b43f 100644
--- a/tests/test_random.py
+++ b/tests/test_random.py
@@ -47,42 +47,80 @@
 testseed = 1000 # seed used for UniformDeviate for all tests
 # Warning! If you change testseed, then all of the *Result variables below must change as well.
 
-# the right answer for the first three uniform deviates produced from testseed
-uResult = (0.11860922840423882, 0.21456799632869661, 0.43088198406621814)
-
-# mean, sigma to use for Gaussian tests
-gMean = 4.7
-gSigma = 3.2
-# the right answer for the first three Gaussian deviates produced from testseed
-gResult = (6.3344979808161215, 6.2082355273987861, -0.069894693358302007)
-
-# N, p to use for binomial tests
-bN = 10
-bp = 0.7
-# the right answer for the first three binomial deviates produced from testseed
-bResult = (9, 8, 7)
-
-# mean to use for Poisson tests
-pMean = 7
-# the right answer for the first three Poisson deviates produced from testseed
-pResult = (4, 5, 6)
-
-# a & b to use for Weibull tests
-wA = 4.
-wB = 9.
-# Tabulated results for Weibull
-wResult = (5.3648053017485591, 6.3093033550873878, 7.7982696798921074)
-
-# k & theta to use for Gamma tests
-gammaK = 1.5
-gammaTheta = 4.5
-# Tabulated results for Gamma
-gammaResult = (4.7375613139927157, 15.272973580418618, 21.485016362839747)
-
-# n to use for Chi2 tests
-chi2N = 30
-# Tabulated results for Chi2
-chi2Result = (32.209933900954049, 50.040002656028513, 24.301442486313896)
+if is_jax_galsim():
+    # the right answer for the first three uniform deviates produced from testseed
+    uResult = (0.0160653916, 0.228817832, 0.1609966951)
+
+    # mean, sigma to use for Gaussian tests
+    gMean = 4.7
+    gSigma = 3.2
+    # the right answer for the first three Gaussian deviates produced from testseed
+    gResult = (-2.1568953985, 2.3232138032, 1.5308165692)
+
+    # N, p to use for binomial tests
+    bN = 10
+    bp = 0.7
+    # the right answer for the first three binomial deviates produced from testseed
+    bResult = (5, 8, 7)
+
+    # mean to use for Poisson tests
+    pMean = 7
+    # the right answer for the first three Poisson deviates produced from testseed
+    pResult = (6, 11, 4)
+
+    # a & b to use for Weibull tests
+    wA = 4.0
+    wB = 9.0
+    # Tabulated results for Weibull
+    wResult = (3.2106530102, 6.4256210259, 5.8255498741)
+
+    # k & theta to use for Gamma tests
+    gammaK = 1.5
+    gammaTheta = 4.5
+    # Tabulated results for Gamma
+    gammaResult = (10.9318881415, 7.6074550007, 2.0526795529)
+
+    # n to use for Chi2 tests
+    chi2N = 30
+    # Tabulated results for Chi2
+    chi2Result = (36.7583415337, 32.7223187231, 23.1555198334)
+else:
+    # the right answer for the first three uniform deviates produced from testseed
+    uResult = (0.11860922840423882, 0.21456799632869661, 0.43088198406621814)
+
+    # mean, sigma to use for Gaussian tests
+    gMean = 4.7
+    gSigma = 3.2
+    # the right answer for the first three Gaussian deviates produced from testseed
+    gResult = (6.3344979808161215, 6.2082355273987861, -0.069894693358302007)
+
+    # N, p to use for binomial tests
+    bN = 10
+    bp = 0.7
+    # the right answer for the first three binomial deviates produced from testseed
+    bResult = (9, 8, 7)
+
+    # mean to use for Poisson tests
+    pMean = 7
+    # the right answer for the first three Poisson deviates produced from testseed
+    pResult = (4, 5, 6)
+
+    # a & b to use for Weibull tests
+    wA = 4.
+    wB = 9.
+    # Tabulated results for Weibull
+    wResult = (5.3648053017485591, 6.3093033550873878, 7.7982696798921074)
+
+    # k & theta to use for Gamma tests
+    gammaK = 1.5
+    gammaTheta = 4.5
+    # Tabulated results for Gamma
+    gammaResult = (4.7375613139927157, 15.272973580418618, 21.485016362839747)
+
+    # n to use for Chi2 tests
+    chi2N = 30
+    # Tabulated results for Chi2
+    chi2Result = (32.209933900954049, 50.040002656028513, 24.301442486313896)
 
 #function and min&max to use for DistDeviate function call tests
 dmin=0.0
@@ -214,14 +252,20 @@ def test_uniform():
     # Test generate
     u.seed(testseed)
     test_array = np.empty(3)
-    u.generate(test_array)
+    if is_jax_galsim():
+        test_array = u.generate(test_array)
+    else:
+        u.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(uResult), precision,
             err_msg='Wrong uniform random number sequence from generate.')
 
     # Test add_generate
     u.seed(testseed)
-    u.add_generate(test_array)
+    if is_jax_galsim():
+        test_array = u.add_generate(test_array)
+    else:
+        u.add_generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, 2.*np.array(uResult), precision,
             err_msg='Wrong uniform random number sequence from generate.')
@@ -229,14 +273,20 @@ def test_uniform():
     # Test generate with a float32 array
     u.seed(testseed)
     test_array = np.empty(3, dtype=np.float32)
-    u.generate(test_array)
+    if is_jax_galsim():
+        test_array = u.generate(test_array)
+    else:
+        u.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(uResult), precisionF,
             err_msg='Wrong uniform random number sequence from generate.')
 
     # Test add_generate
     u.seed(testseed)
-    u.add_generate(test_array)
+    if is_jax_galsim():
+        test_array = u.add_generate(test_array)
+    else:
+        u.add_generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, 2.*np.array(uResult), precisionF,
             err_msg='Wrong uniform random number sequence from generate.')
@@ -247,14 +297,26 @@ def test_uniform():
     v1 = np.empty(555)
     v2 = np.empty(555)
     with single_threaded():
-        u1.generate(v1)
+        if is_jax_galsim():
+            v1 = u1.generate(v1)
+        else:
+            u1.generate(v1)
     with single_threaded(num_threads=10):
-        u2.generate(v2)
+        if is_jax_galsim():
+            v2 = u2.generate(v2)
+        else:
+            u2.generate(v2)
     np.testing.assert_array_equal(v1, v2)
     with single_threaded():
-        u1.add_generate(v1)
+        if is_jax_galsim():
+            v1 = u1.add_generate(v1)
+        else:
+            u1.add_generate(v1)
     with single_threaded(num_threads=10):
-        u2.add_generate(v2)
+        if is_jax_galsim():
+            v2 = u2.add_generate(v2)
+        else:
+            u2.add_generate(v2)
     np.testing.assert_array_equal(v1, v2)
 
     # Check picklability
@@ -275,12 +337,16 @@ def test_uniform():
     assert u1 != u2, "Consecutive UniformDeviate(None) compared equal!"
     # We shouldn't be able to construct a UniformDeviate from anything but a BaseDeviate, int, str,
     # or None.
-    assert_raises(TypeError, galsim.UniformDeviate, dict())
-    assert_raises(TypeError, galsim.UniformDeviate, list())
-    assert_raises(TypeError, galsim.UniformDeviate, set())
+    if is_jax_galsim():
+        # jax galsim doesn't test this
+        pass
+    else:
+        assert_raises(TypeError, galsim.UniformDeviate, dict())
+        assert_raises(TypeError, galsim.UniformDeviate, list())
+        assert_raises(TypeError, galsim.UniformDeviate, set())
 
-    assert_raises(TypeError, u.seed, '123')
-    assert_raises(TypeError, u.seed, 12.3)
+        assert_raises(TypeError, u.seed, '123')
+        assert_raises(TypeError, u.seed, 12.3)
 
 
 @timer
@@ -323,19 +389,28 @@ def test_gaussian():
     v1,v2 = g(),g2()
     print('after %d vals, next one is %s, %s'%(nvals,v1,v2))
     assert v1 == v2
-    # Note: For Gaussian, this only works if nvals is even.
-    g2 = galsim.GaussianDeviate(testseed, mean=gMean, sigma=gSigma)
-    g2.discard(nvals+1, suppress_warnings=True)
-    v1,v2 = g(),g2()
-    print('after %d vals, next one is %s, %s'%(nvals+1,v1,v2))
-    assert v1 != v2
-    assert g.has_reliable_discard
-    assert g.generates_in_pairs
+    if is_jax_galsim():
+        # jax doesn't have this issue
+        assert g.has_reliable_discard
+        assert not g.generates_in_pairs
+    else:
+        # Note: For Gaussian, this only works if nvals is even.
+        g2 = galsim.GaussianDeviate(testseed, mean=gMean, sigma=gSigma)
+        g2.discard(nvals+1, suppress_warnings=True)
+        v1,v2 = g(),g2()
+        print('after %d vals, next one is %s, %s'%(nvals+1,v1,v2))
+        assert v1 != v2
+        assert g.has_reliable_discard
+        assert g.generates_in_pairs
 
     # If don't explicitly suppress the warning, then a warning is emitted when n is odd.
     g2 = galsim.GaussianDeviate(testseed, mean=gMean, sigma=gSigma)
-    with assert_warns(galsim.GalSimWarning):
-        g2.discard(nvals+1)
+    if is_jax_galsim():
+        pass
+    else:
+        # jax doesn't do this
+        with assert_warns(galsim.GalSimWarning):
+            g2.discard(nvals+1)
 
     # Check seed, reset
     g.seed(testseed)
@@ -405,7 +480,10 @@ def test_gaussian():
     # Test generate
     g.seed(testseed)
     test_array = np.empty(3)
-    g.generate(test_array)
+    if is_jax_galsim():
+        test_array = g.generate(test_array)
+    else:
+        g.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(gResult), precision,
             err_msg='Wrong Gaussian random number sequence from generate.')
@@ -413,29 +491,43 @@ def test_gaussian():
     # Test generate_from_variance.
     g2 = galsim.GaussianDeviate(testseed, mean=5, sigma=0.3)
     g3 = galsim.GaussianDeviate(testseed, mean=5, sigma=0.3)
+    test_array = np.empty(3)
     test_array.fill(gSigma**2)
-    g2.generate_from_variance(test_array)
+    if is_jax_galsim():
+        test_array = g2.generate_from_variance(test_array)
+    else:
+        g2.generate_from_variance(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(gResult)-gMean, precision,
             err_msg='Wrong Gaussian random number sequence from generate_from_variance.')
     # After running generate_from_variance, it should be back to using the specified mean, sigma.
     # Note: need to round up to even number for discard, since gd generates 2 at a time.
-    g3.discard((len(test_array)+1)//2 * 2)
+    if is_jax_galsim():
+        g3.discard(len(test_array))
+    else:
+        g3.discard((len(test_array)+1)//2 * 2)
     print('g2,g3 = ',g2(),g3())
     assert g2() == g3()
 
     # Test generate with a float32 array.
     g.seed(testseed)
     test_array = np.empty(3, dtype=np.float32)
-    g.generate(test_array)
+    if is_jax_galsim():
+        test_array = g.generate(test_array)
+    else:
+        g.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(gResult), precisionF,
             err_msg='Wrong Gaussian random number sequence from generate.')
 
     # Test generate_from_variance.
     g2.seed(testseed)
+    test_array = np.empty(3, dtype=np.float32)
     test_array.fill(gSigma**2)
-    g2.generate_from_variance(test_array)
+    if is_jax_galsim():
+        test_array = g2.generate_from_variance(test_array)
+    else:
+        g2.generate_from_variance(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(gResult)-gMean, precisionF,
             err_msg='Wrong Gaussian random number sequence from generate_from_variance.')
@@ -446,23 +538,45 @@ def test_gaussian():
     v1 = np.empty(555)
     v2 = np.empty(555)
     with single_threaded():
-        g1.generate(v1)
+        if is_jax_galsim():
+            v1 = g1.generate(v1)
+        else:
+            g1.generate(v1)
     with single_threaded(num_threads=10):
-        g2.generate(v2)
+        if is_jax_galsim():
+            v2 = g2.generate(v2)
+        else:
+            g2.generate(v2)
     np.testing.assert_array_equal(v1, v2)
     with single_threaded():
-        g1.add_generate(v1)
+        if is_jax_galsim():
+            v1 = g1.add_generate(v1)
+        else:
+            g1.add_generate(v1)
     with single_threaded(num_threads=10):
-        g2.add_generate(v2)
+        if is_jax_galsim():
+            v2 = g2.add_generate(v2)
+        else:
+            g2.add_generate(v2)
     np.testing.assert_array_equal(v1, v2)
     ud = galsim.UniformDeviate(testseed + 3)
     ud.generate(v1)
     v1 += 6.7
-    v2[:] = v1
+    if is_jax_galsim():
+        # jax galsim makes a copy
+        v2 = v1.copy()
+    else:
+        v2[:] = v1
     with single_threaded():
-        g1.generate_from_variance(v1)
+        if is_jax_galsim():
+            v1 = g1.generate_from_variance(v1)
+        else:
+            g1.generate_from_variance(v1)
     with single_threaded(num_threads=10):
-        g2.generate_from_variance(v2)
+        if is_jax_galsim():
+            v2 = g2.generate_from_variance(v2)
+        else:
+            g2.generate_from_variance(v2)
     np.testing.assert_array_equal(v1, v2)
 
     # Check picklability
@@ -480,11 +594,15 @@ def test_gaussian():
     assert g1 != g2, "Consecutive GaussianDeviate(None) compared equal!"
     # We shouldn't be able to construct a GaussianDeviate from anything but a BaseDeviate, int, str,
     # or None.
-    assert_raises(TypeError, galsim.GaussianDeviate, dict())
-    assert_raises(TypeError, galsim.GaussianDeviate, list())
-    assert_raises(TypeError, galsim.GaussianDeviate, set())
+    if is_jax_galsim():
+        pass
+    else:
+        # jax-galsim doesn't test for these things
+        assert_raises(TypeError, galsim.GaussianDeviate, dict())
+        assert_raises(TypeError, galsim.GaussianDeviate, list())
+        assert_raises(TypeError, galsim.GaussianDeviate, set())
 
-    assert_raises(ValueError, galsim.GaussianDeviate, testseed, mean=1, sigma=-1)
+        assert_raises(ValueError, galsim.GaussianDeviate, testseed, mean=1, sigma=-1)
 
 
 @timer
@@ -597,7 +715,10 @@ def test_binomial():
     # Test generate
     b.seed(testseed)
     test_array = np.empty(3)
-    b.generate(test_array)
+    if is_jax_galsim():
+        test_array = b.generate(test_array)
+    else:
+        b.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(bResult), precision,
             err_msg='Wrong binomial random number sequence from generate.')
@@ -605,7 +726,10 @@ def test_binomial():
     # Test generate with an int array
     b.seed(testseed)
     test_array = np.empty(3, dtype=int)
-    b.generate(test_array)
+    if is_jax_galsim():
+        test_array = b.generate(test_array)
+    else:
+        b.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(bResult), precisionI,
             err_msg='Wrong binomial random number sequence from generate.')
@@ -616,14 +740,26 @@ def test_binomial():
     v1 = np.empty(555)
     v2 = np.empty(555)
     with single_threaded():
-        b1.generate(v1)
+        if is_jax_galsim():
+            v1 = b1.generate(v1)
+        else:
+            b1.generate(v1)
     with single_threaded(num_threads=10):
-        b2.generate(v2)
+        if is_jax_galsim():
+            v2 = b2.generate(v2)
+        else:
+            b2.generate(v2)
     np.testing.assert_array_equal(v1, v2)
     with single_threaded():
-        b1.add_generate(v1)
+        if is_jax_galsim():
+            v1 = b1.add_generate(v1)
+        else:
+            b1.add_generate(v1)
     with single_threaded(num_threads=10):
-        b2.add_generate(v2)
+        if is_jax_galsim():
+            v2 = b2.add_generate(v2)
+        else:
+            b2.add_generate(v2)
     np.testing.assert_array_equal(v1, v2)
 
     # Check picklability
@@ -641,9 +777,13 @@ def test_binomial():
     assert b1 != b2, "Consecutive BinomialDeviate(None) compared equal!"
     # We shouldn't be able to construct a BinomialDeviate from anything but a BaseDeviate, int, str,
     # or None.
-    assert_raises(TypeError, galsim.BinomialDeviate, dict())
-    assert_raises(TypeError, galsim.BinomialDeviate, list())
-    assert_raises(TypeError, galsim.BinomialDeviate, set())
+    if is_jax_galsim():
+        pass
+    else:
+        # jax does not raise for this
+        assert_raises(TypeError, galsim.BinomialDeviate, dict())
+        assert_raises(TypeError, galsim.BinomialDeviate, list())
+        assert_raises(TypeError, galsim.BinomialDeviate, set())
 
 
 @timer
@@ -697,14 +837,23 @@ def test_poisson():
     p2.discard(nvals, suppress_warnings=True)
     v1,v2 = p(),p2()
     print('With mean = %d, after %d vals, next one is %s, %s'%(high_mean,nvals,v1,v2))
-    assert v1 != v2
-    assert not p.has_reliable_discard
+    if is_jax_galsim():
+        # jax always discards reliably
+        assert v1 == v2
+        assert p.has_reliable_discard
+    else:
+        assert v1 != v2
+        assert not p.has_reliable_discard
     assert not p.generates_in_pairs
 
     # Discard normally emits a warning for Poisson
     p2 = galsim.PoissonDeviate(testseed, mean=pMean)
-    with assert_warns(galsim.GalSimWarning):
+    if is_jax_galsim():
+        # jax always discards reliably
         p2.discard(nvals)
+    else:
+        with assert_warns(galsim.GalSimWarning):
+            p2.discard(nvals)
 
     # Check seed, reset
     p = galsim.PoissonDeviate(testseed, mean=pMean)
@@ -774,7 +923,10 @@ def test_poisson():
     # Test generate
     p.seed(testseed)
     test_array = np.empty(3)
-    p.generate(test_array)
+    if is_jax_galsim():
+        test_array = p.generate(test_array)
+    else:
+        p.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(pResult), precision,
             err_msg='Wrong poisson random number sequence from generate.')
@@ -782,7 +934,10 @@ def test_poisson():
     # Test generate with an int array
     p.seed(testseed)
     test_array = np.empty(3, dtype=int)
-    p.generate(test_array)
+    if is_jax_galsim():
+        test_array = p.generate(test_array)
+    else:
+        p.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(pResult), precisionI,
             err_msg='Wrong poisson random number sequence from generate.')
@@ -790,7 +945,10 @@ def test_poisson():
     # Test generate_from_expectation
     p2 = galsim.PoissonDeviate(testseed, mean=77)
     test_array = np.array([pMean]*3, dtype=int)
-    p2.generate_from_expectation(test_array)
+    if is_jax_galsim():
+        test_array = p2.generate_from_expectation(test_array)
+    else:
+        p2.generate_from_expectation(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(pResult), precisionI,
             err_msg='Wrong poisson random number sequence from generate_from_expectation.')
@@ -807,14 +965,26 @@ def test_poisson():
     v1 = np.empty(555)
     v2 = np.empty(555)
     with single_threaded():
-        p1.generate(v1)
+        if is_jax_galsim():
+            v1 = p1.generate(v1)
+        else:
+            p1.generate(v1)
     with single_threaded(num_threads=10):
-        p2.generate(v2)
+        if is_jax_galsim():
+            v2 = p2.generate(v2)
+        else:
+            p2.generate(v2)
     np.testing.assert_array_equal(v1, v2)
     with single_threaded():
-        p1.add_generate(v1)
+        if is_jax_galsim():
+            v1 = p1.add_generate(v1)
+        else:
+            p1.add_generate(v1)
     with single_threaded(num_threads=10):
-        p2.add_generate(v2)
+        if is_jax_galsim():
+            v2 = p2.add_generate(v2)
+        else:
+            p2.add_generate(v2)
     np.testing.assert_array_equal(v1, v2)
 
     # Check picklability
@@ -832,9 +1002,12 @@ def test_poisson():
     assert p1 != p2, "Consecutive PoissonDeviate(None) compared equal!"
     # We shouldn't be able to construct a PoissonDeviate from anything but a BaseDeviate, int, str,
     # or None.
-    assert_raises(TypeError, galsim.PoissonDeviate, dict())
-    assert_raises(TypeError, galsim.PoissonDeviate, list())
-    assert_raises(TypeError, galsim.PoissonDeviate, set())
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(TypeError, galsim.PoissonDeviate, dict())
+        assert_raises(TypeError, galsim.PoissonDeviate, list())
+        assert_raises(TypeError, galsim.PoissonDeviate, set())
 
 
 @timer
@@ -966,11 +1139,20 @@ def test_poisson_zeromean():
 
     # Test generate
     test_array = np.empty(3, dtype=int)
-    p.generate(test_array)
+    if is_jax_galsim():
+        test_array = p.generate(test_array)
+    else:
+        p.generate(test_array)
     np.testing.assert_array_equal(test_array, 0)
-    p2.generate(test_array)
+    if is_jax_galsim():
+        test_array = p2.generate(test_array)
+    else:
+        p2.generate(test_array)
     np.testing.assert_array_equal(test_array, 0)
-    p3.generate(test_array)
+    if is_jax_galsim():
+        test_array = p3.generate(test_array)
+    else:
+        p3.generate(test_array)
     np.testing.assert_array_equal(test_array, 0)
 
     # Test generate_from_expectation
@@ -982,16 +1164,20 @@ def test_poisson_zeromean():
     assert test_array[2] != 0
 
     # Error raised if mean<0
-    with assert_raises(ValueError):
-        p = galsim.PoissonDeviate(testseed, mean=-0.1)
-    with assert_raises(ValueError):
-        p = galsim.PoissonDeviate(testseed, mean=-10)
-    test_array = np.array([-1,1,4])
-    with assert_raises(ValueError):
-        p.generate_from_expectation(test_array)
-    test_array = np.array([1,-1,-4])
-    with assert_raises(ValueError):
-        p.generate_from_expectation(test_array)
+    # jax doesn't raise here
+    if is_jax_galsim():
+        pass
+    else:
+        with assert_raises(ValueError):
+            p = galsim.PoissonDeviate(testseed, mean=-0.1)
+        with assert_raises(ValueError):
+            p = galsim.PoissonDeviate(testseed, mean=-10)
+        test_array = np.array([-1,1,4])
+        with assert_raises(ValueError):
+            p.generate_from_expectation(test_array)
+        test_array = np.array([1,-1,-4])
+        with assert_raises(ValueError):
+            p.generate_from_expectation(test_array)
 
 @timer
 def test_weibull():
@@ -1103,7 +1289,10 @@ def test_weibull():
     # Test generate
     w.seed(testseed)
     test_array = np.empty(3)
-    w.generate(test_array)
+    if is_jax_galsim():
+        test_array = w.generate(test_array)
+    else:
+        w.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(wResult), precision,
             err_msg='Wrong weibull random number sequence from generate.')
@@ -1111,7 +1300,10 @@ def test_weibull():
     # Test generate with a float32 array
     w.seed(testseed)
     test_array = np.empty(3, dtype=np.float32)
-    w.generate(test_array)
+    if is_jax_galsim():
+        test_array = w.generate(test_array)
+    else:
+        w.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(wResult), precisionF,
             err_msg='Wrong weibull random number sequence from generate.')
@@ -1122,14 +1314,26 @@ def test_weibull():
     v1 = np.empty(555)
     v2 = np.empty(555)
     with single_threaded():
-        w1.generate(v1)
+        if is_jax_galsim():
+            v1 = w1.generate(v1)
+        else:
+            w1.generate(v1)
     with single_threaded(num_threads=10):
-        w2.generate(v2)
+        if is_jax_galsim():
+            v2 = w2.generate(v2)
+        else:
+            w2.generate(v2)
     np.testing.assert_array_equal(v1, v2)
     with single_threaded():
-        w1.add_generate(v1)
+        if is_jax_galsim():
+            v1 = w1.add_generate(v1)
+        else:
+            w1.add_generate(v1)
     with single_threaded(num_threads=10):
-        w2.add_generate(v2)
+        if is_jax_galsim():
+            v2 = w2.add_generate(v2)
+        else:
+            w2.add_generate(v2)
     np.testing.assert_array_equal(v1, v2)
 
     # Check picklability
@@ -1147,9 +1351,12 @@ def test_weibull():
     assert w1 != w2, "Consecutive WeibullDeviate(None) compared equal!"
     # We shouldn't be able to construct a WeibullDeviate from anything but a BaseDeviate, int, str,
     # or None.
-    assert_raises(TypeError, galsim.WeibullDeviate, dict())
-    assert_raises(TypeError, galsim.WeibullDeviate, list())
-    assert_raises(TypeError, galsim.WeibullDeviate, set())
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(TypeError, galsim.WeibullDeviate, dict())
+        assert_raises(TypeError, galsim.WeibullDeviate, list())
+        assert_raises(TypeError, galsim.WeibullDeviate, set())
 
 
 @timer
@@ -1192,14 +1399,22 @@ def test_gamma():
     v1,v2 = g(),g2()
     print('after %d vals, next one is %s, %s'%(nvals,v1,v2))
     # Gamma uses at least 2 rngs per value, but can use arbitrarily more than this.
-    assert v1 != v2
-    assert not g.has_reliable_discard
+    if is_jax_galsim():
+        assert v1 == v2
+        assert g.has_reliable_discard
+    else:
+        assert v1 != v2
+        assert not g.has_reliable_discard
     assert not g.generates_in_pairs
 
     # Discard normally emits a warning for Gamma
     g2 = galsim.GammaDeviate(testseed, k=gammaK, theta=gammaTheta)
-    with assert_warns(galsim.GalSimWarning):
+    if is_jax_galsim():
+        # jax always discards reliably
         g2.discard(nvals)
+    else:
+        with assert_warns(galsim.GalSimWarning):
+            g2.discard(nvals)
 
     # Check seed, reset
     g.seed(testseed)
@@ -1266,7 +1481,10 @@ def test_gamma():
     # Test generate
     g.seed(testseed)
     test_array = np.empty(3)
-    g.generate(test_array)
+    if is_jax_galsim():
+        test_array = g.generate(test_array)
+    else:
+        g.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(gammaResult), precision,
             err_msg='Wrong gamma random number sequence from generate.')
@@ -1274,7 +1492,10 @@ def test_gamma():
     # Test generate with a float32 array
     g.seed(testseed)
     test_array = np.empty(3, dtype=np.float32)
-    g.generate(test_array)
+    if is_jax_galsim():
+        test_array = g.generate(test_array)
+    else:
+        g.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(gammaResult), precisionF,
             err_msg='Wrong gamma random number sequence from generate.')
@@ -1294,9 +1515,12 @@ def test_gamma():
     assert g1 != g2, "Consecutive GammaDeviate(None) compared equal!"
     # We shouldn't be able to construct a GammaDeviate from anything but a BaseDeviate, int, str,
     # or None.
-    assert_raises(TypeError, galsim.GammaDeviate, dict())
-    assert_raises(TypeError, galsim.GammaDeviate, list())
-    assert_raises(TypeError, galsim.GammaDeviate, set())
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(TypeError, galsim.GammaDeviate, dict())
+        assert_raises(TypeError, galsim.GammaDeviate, list())
+        assert_raises(TypeError, galsim.GammaDeviate, set())
 
 
 @timer
@@ -1339,14 +1563,22 @@ def test_chi2():
     v1,v2 = c(),c2()
     print('after %d vals, next one is %s, %s'%(nvals,v1,v2))
     # Chi2 uses at least 2 rngs per value, but can use arbitrarily more than this.
-    assert v1 != v2
-    assert not c.has_reliable_discard
+    if is_jax_galsim():
+        assert v1 == v2
+        assert c.has_reliable_discard
+    else:
+        assert v1 != v2
+        assert not c.has_reliable_discard
     assert not c.generates_in_pairs
 
     # Discard normally emits a warning for Chi2
     c2 = galsim.Chi2Deviate(testseed, n=chi2N)
-    with assert_warns(galsim.GalSimWarning):
+    if is_jax_galsim():
+        # jax always discards reliably
         c2.discard(nvals)
+    else:
+        with assert_warns(galsim.GalSimWarning):
+            c2.discard(nvals)
 
     # Check seed, reset
     c.seed(testseed)
@@ -1413,7 +1645,10 @@ def test_chi2():
     # Test generate
     c.seed(testseed)
     test_array = np.empty(3)
-    c.generate(test_array)
+    if is_jax_galsim():
+        test_array = c.generate(test_array)
+    else:
+        c.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(chi2Result), precision,
             err_msg='Wrong Chi^2 random number sequence from generate.')
@@ -1421,7 +1656,10 @@ def test_chi2():
     # Test generate with a float32 array
     c.seed(testseed)
     test_array = np.empty(3, dtype=np.float32)
-    c.generate(test_array)
+    if is_jax_galsim():
+        test_array = c.generate(test_array)
+    else:
+        c.generate(test_array)
     np.testing.assert_array_almost_equal(
             test_array, np.array(chi2Result), precisionF,
             err_msg='Wrong Chi^2 random number sequence from generate.')
@@ -1441,9 +1679,12 @@ def test_chi2():
     assert c1 != c2, "Consecutive Chi2Deviate(None) compared equal!"
     # We shouldn't be able to construct a Chi2Deviate from anything but a BaseDeviate, int, str,
     # or None.
-    assert_raises(TypeError, galsim.Chi2Deviate, dict())
-    assert_raises(TypeError, galsim.Chi2Deviate, list())
-    assert_raises(TypeError, galsim.Chi2Deviate, set())
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(TypeError, galsim.Chi2Deviate, dict())
+        assert_raises(TypeError, galsim.Chi2Deviate, list())
+        assert_raises(TypeError, galsim.Chi2Deviate, set())
 
 
 @timer
@@ -1927,7 +2168,11 @@ def test_permute():
     ind_list = list(range(n_list))
 
     # Permute both at the same time.
-    galsim.random.permute(312, my_list, ind_list)
+    if is_jax_galsim():
+        # jax requires arrays
+        galsim.random.permute(312, np.array(my_list), np.array(ind_list))
+    else:
+        galsim.random.permute(312, my_list, ind_list)
 
     # Make sure that everything is sensible
     for ind in range(n_list):
@@ -1935,13 +2180,20 @@ def test_permute():
 
     # Repeat with same seed, should do same permutation.
     my_list = copy.deepcopy(my_list_copy)
-    galsim.random.permute(312, my_list)
+    if is_jax_galsim():
+        galsim.random.permute(312, np.array(my_list))
+    else:
+        galsim.random.permute(312, my_list)
     for ind in range(n_list):
         assert my_list_copy[ind_list[ind]] == my_list[ind]
 
     # permute with no lists should raise TypeError
-    with assert_raises(TypeError):
-        galsim.random.permute(312)
+    # jax galsim does not raise
+    if is_jax_galsim():
+        pass
+    else:
+        with assert_raises(TypeError):
+            galsim.random.permute(312)
 
 
 @timer
@@ -1949,10 +2201,16 @@ def test_ne():
     """ Check that inequality works as expected for corner cases where the reprs of two
     unequal BaseDeviates may be the same due to truncation.
     """
-    a = galsim.BaseDeviate(seed='1 2 3 4 5 6 7 8 9 10')
-    b = galsim.BaseDeviate(seed='1 2 3 7 6 5 4 8 9 10')
-    assert repr(a) == repr(b)
-    assert a != b
+    if is_jax_galsim():
+        a = galsim.BaseDeviate(seed="(0, 10)")
+        b = galsim.BaseDeviate(seed="(0, 11)")
+        assert repr(a) != repr(b)
+        assert a != b
+    else:
+        a = galsim.BaseDeviate(seed='1 2 3 4 5 6 7 8 9 10')
+        b = galsim.BaseDeviate(seed='1 2 3 7 6 5 4 8 9 10')
+        assert repr(a) == repr(b)
+        assert a != b
 
     # Check DistDeviate separately, since it overrides __repr__ and __eq__
     d1 = galsim.DistDeviate(seed=a, function=galsim.LookupTable([1, 2, 3], [4, 5, 6]))
diff --git a/tests/test_real.py b/tests/test_real.py
index eef6312b92..c95a2d73a3 100644
--- a/tests/test_real.py
+++ b/tests/test_real.py
@@ -29,7 +29,7 @@
 # set up any necessary info for tests
 ### Note: changes to either of the tests below might require regeneration of the catalog and image
 ### files that are saved here.  Modify with care!!!
-image_dir = './real_comparison_images'
+image_dir = os.path.join(os.path.dirname(__file__), './real_comparison_images')
 catalog_file = 'test_catalog.fits'
 
 # some helper functions
@@ -77,8 +77,8 @@ def test_real_galaxy_catalog():
 
     # Test some values that are lazy evaluated:
     assert rgc.ident[0] == '100533'
-    assert rgc.gal_file_name[0] == './real_comparison_images/test_images.fits'
-    assert rgc.psf_file_name[0] == './real_comparison_images/test_images.fits'
+    assert rgc.gal_file_name[0].split("/")[-2:] == ['real_comparison_images', 'test_images.fits']
+    assert rgc.psf_file_name[0].split("/")[-2:] == ['real_comparison_images', 'test_images.fits']
     assert rgc.noise_file_name is None
     np.testing.assert_array_equal(rgc.gal_hdu, [0,1])
     np.testing.assert_array_equal(rgc.psf_hdu, [2,3])
@@ -962,7 +962,7 @@ def test_sys_share_dir():
         import galsim
         print(galsim.meta_data.share_dir)
         """)
-    script_file = os.path.join('scratch_space', 'sys_share_dir.py')
+    script_file = os.path.join(os.path.dirname(__file__), os.path.join('scratch_space', 'sys_share_dir.py'))
     with open(script_file, 'w') as f:
         f.write(script)
     env = os.environ.copy()
diff --git a/tests/test_roman.py b/tests/test_roman.py
index fa3e7db8e6..6371819227 100644
--- a/tests/test_roman.py
+++ b/tests/test_roman.py
@@ -16,9 +16,9 @@
 #    and/or other materials provided with the distribution.
 #
 
+import sys
 import logging
 import os
-import sys
 import numpy as np
 import datetime
 from unittest import mock
@@ -148,7 +148,7 @@ def test_roman_wcs():
     # we compare that with the GalSim routines for finding SCAs.
     import datetime
     date = datetime.datetime(2025, 1, 12)
-    test_data_file = os.path.join('roman_files','chris_comparison.txt')
+    test_data_file = os.path.join(os.path.dirname(__file__), os.path.join('roman_files','chris_comparison.txt'))
     test_data = np.loadtxt(test_data_file).transpose()
 
     ra_cen = test_data[0,:]
diff --git a/tests/test_shear.py b/tests/test_shear.py
index 6efd9f1423..26546cb6a6 100644
--- a/tests/test_shear.py
+++ b/tests/test_shear.py
@@ -116,7 +116,7 @@ def test_shear_initialization():
     vec_ideal = np.zeros(len(vec))
     np.testing.assert_array_almost_equal(vec, vec_ideal, decimal = decimal,
                                          err_msg = "Incorrectly initialized empty shear")
-    np.testing.assert_equal(s.q, 1.)
+    np.testing.assert_array_equal(s.q, 1.)
     # now loop over shear values and ways of initializing
     for ind in range(n_shear):
         # initialize with reduced shear components
@@ -176,17 +176,23 @@ def test_shear_initialization():
     assert_raises(TypeError,galsim.Shear,g1=0.3,e2=0.2)
     assert_raises(TypeError,galsim.Shear,eta1=0.3,beta=0.*galsim.degrees)
     assert_raises(TypeError,galsim.Shear,q=0.3)
-    assert_raises(galsim.GalSimRangeError,galsim.Shear,q=1.3,beta=0.*galsim.degrees)
-    assert_raises(galsim.GalSimRangeError,galsim.Shear,g1=0.9,g2=0.6)
-    assert_raises(galsim.GalSimRangeError,galsim.Shear,e=-1.3,beta=0.*galsim.radians)
-    assert_raises(galsim.GalSimRangeError,galsim.Shear,e=1.3,beta=0.*galsim.radians)
-    assert_raises(galsim.GalSimRangeError,galsim.Shear,e1=0.7,e2=0.9)
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(galsim.GalSimRangeError,galsim.Shear,q=1.3,beta=0.*galsim.degrees)
+        assert_raises(galsim.GalSimRangeError,galsim.Shear,g1=0.9,g2=0.6)
+        assert_raises(galsim.GalSimRangeError,galsim.Shear,e=-1.3,beta=0.*galsim.radians)
+        assert_raises(galsim.GalSimRangeError,galsim.Shear,e=1.3,beta=0.*galsim.radians)
+        assert_raises(galsim.GalSimRangeError,galsim.Shear,e1=0.7,e2=0.9)
     assert_raises(TypeError,galsim.Shear,g=0.5)
     assert_raises(TypeError,galsim.Shear,e=0.5)
     assert_raises(TypeError,galsim.Shear,eta=0.5)
-    assert_raises(galsim.GalSimRangeError,galsim.Shear,eta=-0.5,beta=0.*galsim.radians)
-    assert_raises(galsim.GalSimRangeError,galsim.Shear,g=1.3,beta=0.*galsim.radians)
-    assert_raises(galsim.GalSimRangeError,galsim.Shear,g=-0.3,beta=0.*galsim.radians)
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(galsim.GalSimRangeError,galsim.Shear,eta=-0.5,beta=0.*galsim.radians)
+        assert_raises(galsim.GalSimRangeError,galsim.Shear,g=1.3,beta=0.*galsim.radians)
+        assert_raises(galsim.GalSimRangeError,galsim.Shear,g=-0.3,beta=0.*galsim.radians)
     assert_raises(TypeError,galsim.Shear,e=0.3,beta=0.)
     assert_raises(TypeError,galsim.Shear,eta=0.3,beta=0.)
     assert_raises(TypeError,galsim.Shear,randomkwarg=0.1)
diff --git a/tests/test_shear_position.py b/tests/test_shear_position.py
index 8b2689a98e..bb18d4bd99 100644
--- a/tests/test_shear_position.py
+++ b/tests/test_shear_position.py
@@ -88,7 +88,7 @@ def test_shear_position_image_integration_pixelwcs():
     )
 
     print("err:", np.max(np.abs(im1.array - im2.array)))
-    assert np.allclose(im1.array, im2.array, rtol=0, atol=5e-8)
+    np.testing.assert_allclose(im1.array, im2.array, rtol=0, atol=5e-8)
 
 
 @timer
@@ -119,9 +119,7 @@ def test_shear_position_image_integration_offsetwcs():
     )
 
     print("err:", np.max(np.abs(im1.array - im2.array)))
-    assert np.allclose(im1.array, im2.array, rtol=0, atol=2e-7), (
-        np.max(np.abs(im1.array - im2.array))
-    )
+    np.testing.assert_allclose(im1.array, im2.array, rtol=0, atol=2e-7)
 
 
 if __name__ == "__main__":
diff --git a/tests/test_sum.py b/tests/test_sum.py
index bd9b12ea55..1447d348d1 100644
--- a/tests/test_sum.py
+++ b/tests/test_sum.py
@@ -22,8 +22,8 @@
 import galsim
 from galsim_test_helpers import *
 
-imgdir = os.path.join(".", "SBProfile_comparison_images") # Directory containing the reference
-                                                          # images.
+# Directory containing the reference images.
+imgdir = os.path.join(os.path.dirname(__file__), "SBProfile_comparison_images")
 
 @timer
 def test_add():
@@ -259,12 +259,12 @@ def test_sum_transform():
         rgal2_im = rgal2.drawImage(nx=64, ny=64, scale=0.2)
 
         # Check that the objects are equivalent, even if they may be written differently.
-        np.testing.assert_almost_equal(gal1_im.array, sgal1_im.array, decimal=8)
-        np.testing.assert_almost_equal(gal1_im.array, rgal1_im.array, decimal=8)
+        np.testing.assert_array_almost_equal(gal1_im.array, sgal1_im.array, decimal=8)
+        np.testing.assert_array_almost_equal(gal1_im.array, rgal1_im.array, decimal=8)
 
         # These two used to fail.
-        np.testing.assert_almost_equal(gal2_im.array, sgal2_im.array, decimal=8)
-        np.testing.assert_almost_equal(gal2_im.array, rgal2_im.array, decimal=8)
+        np.testing.assert_array_almost_equal(gal2_im.array, sgal2_im.array, decimal=8)
+        np.testing.assert_array_almost_equal(gal2_im.array, rgal2_im.array, decimal=8)
 
         check_pickle(gal0)
         check_pickle(gal1)
diff --git a/tests/test_table.py b/tests/test_table.py
index cd1e419f08..d6b6bc09bb 100644
--- a/tests/test_table.py
+++ b/tests/test_table.py
@@ -16,7 +16,7 @@
 #    and/or other materials provided with the distribution.
 #
 
-
+import sys
 import os
 import sys
 import numpy as np
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index 0d4a2c8276..63376c9513 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -22,8 +22,8 @@
 import galsim
 from galsim_test_helpers import *
 
-imgdir = os.path.join(".", "SBProfile_comparison_images") # Directory containing the reference
-                                                          # images.
+# Directory containing the reference images.
+imgdir = os.path.join(os.path.dirname(__file__), "SBProfile_comparison_images")
 
 # for flux normalization tests
 test_flux = 1.8
@@ -947,9 +947,9 @@ def test_compound():
     gal5.drawImage(image=im5_f, method='sb', scale=0.2)
     np.testing.assert_almost_equal(im3_f[1,1], gal3.xValue(-0.7,-0.7), decimal=4)
     np.testing.assert_almost_equal(im5_f[1,1], gal3.xValue(-0.7,-0.7), decimal=4)
-    np.testing.assert_almost_equal(im3_f.array, im5_f.array, decimal=4)
-    np.testing.assert_almost_equal(im3_f.array, im3_d.array, decimal=4)
-    np.testing.assert_almost_equal(im5_f.array, im5_d.array, decimal=4)
+    np.testing.assert_array_almost_equal(im3_f.array, im5_f.array, decimal=4)
+    np.testing.assert_array_almost_equal(im3_f.array, im3_d.array, decimal=4)
+    np.testing.assert_array_almost_equal(im5_f.array, im5_d.array, decimal=4)
 
     gal3.drawKImage(image=im3_cd, scale=0.5)
     gal5.drawKImage(image=im5_cd, scale=0.5)
diff --git a/tests/test_utilities.py b/tests/test_utilities.py
index bfbd039f60..41bec8252c 100644
--- a/tests/test_utilities.py
+++ b/tests/test_utilities.py
@@ -35,7 +35,10 @@ def test_pos():
     assert pi1.y == 23
     assert isinstance(pi1.x, int)
     assert isinstance(pi1.y, int)
-    assert isinstance(pi1._p, galsim._galsim.PositionI)
+    if is_jax_galsim():
+        pass
+    else:
+        assert isinstance(pi1._p, galsim._galsim.PositionI)
 
     pi2 = galsim.PositionI((11,23))
     pi3 = galsim.PositionI(x=11.0, y=23.0)
@@ -43,7 +46,10 @@ def test_pos():
     pi5 = galsim.PositionI(galsim.PositionD(11.0,23.0))
     pi6 = galsim.PositionD(11.3,23.4).round()
     pi7 = pi2.round()
-    pi8 = galsim._PositionI(11,23)
+    if is_jax_galsim():
+        pi8 = galsim.PositionI(11,23)
+    else:
+        pi8 = galsim._PositionI(11,23)
     assert pi2 == pi1
     assert pi3 == pi1
     assert pi4 == pi1
@@ -61,14 +67,20 @@ def test_pos():
     assert pd1.y == 23.
     assert isinstance(pd1.x, float)
     assert isinstance(pd1.y, float)
-    assert isinstance(pd1._p, galsim._galsim.PositionD)
+    if is_jax_galsim():
+        pass
+    else:
+        assert isinstance(pd1._p, galsim._galsim.PositionD)
 
     pd2 = galsim.PositionD((11,23))
     pd3 = galsim.PositionD(x=11.0, y=23.0)
     pd4 = galsim.PositionD(pd1)
     pd5 = galsim.PositionD(pi1)
     pd6 = galsim.PositionD(galsim.PositionD(11.3,23.4).round())
-    pd7 = galsim._PositionD(11.0,23.0)
+    if is_jax_galsim():
+        pd7 = galsim.PositionD(11.0,23.0)
+    else:
+        pd7 = galsim._PositionD(11.0,23.0)
     assert pd2 == pd1
     assert pd3 == pd1
     assert pd4 == pd1
@@ -86,7 +98,10 @@ def test_pos():
     assert_raises(TypeError, galsim.PositionI, x=11)
     assert_raises(TypeError, galsim.PositionD, x=11, y=23, z=17)
     assert_raises(TypeError, galsim.PositionI, 11, 23, x=13, z=21)
-    assert_raises(TypeError, galsim.PositionI, 11, 23.5)
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(TypeError, galsim.PositionI, 11, 23.5)
 
     assert_raises(TypeError, galsim.PositionD, 11)
     assert_raises(TypeError, galsim.PositionD, 11, 23, 9)
@@ -94,7 +109,7 @@ def test_pos():
     assert_raises(TypeError, galsim.PositionD, x=11)
     assert_raises(TypeError, galsim.PositionD, x=11, y=23, z=17)
     assert_raises(TypeError, galsim.PositionD, 11, 23, x=13, z=21)
-    assert_raises(ValueError, galsim.PositionD, 11, "blue")
+    assert_raises((ValueError, TypeError), galsim.PositionD, 11, "blue")
 
     # Can't use base class directly.
     assert_raises(TypeError, galsim.Position, 11, 23)
@@ -170,7 +185,10 @@ def test_bounds():
     assert isinstance(bi1.xmax, int)
     assert isinstance(bi1.ymin, int)
     assert isinstance(bi1.ymax, int)
-    assert isinstance(bi1._b, galsim._galsim.BoundsI)
+    if is_jax_galsim():
+        pass
+    else:
+        assert isinstance(bi1._b, galsim._galsim.BoundsI)
 
     bi2 = galsim.BoundsI(galsim.PositionI(11,17), galsim.PositionI(23,50))
     bi3 = galsim.BoundsI(galsim.PositionD(11.,50.), galsim.PositionD(23.,17.))
@@ -183,17 +201,20 @@ def test_bounds():
     bi10 = galsim.BoundsI() + galsim.PositionI(11,17) + galsim.PositionI(23,50)
     bi11 = galsim.BoundsI(galsim.BoundsD(11.,23.,17.,50.))
     bi12 = galsim.BoundsI(xmin=11,ymin=17,xmax=23,ymax=50)
-    bi13 = galsim._BoundsI(11,23,17,50)
+    if is_jax_galsim():
+        bi13 = galsim.BoundsI(11,23,17,50)
+    else:
+        bi13 = galsim._BoundsI(11,23,17,50)
     bi14 = galsim.BoundsI()
     bi14 += galsim.PositionI(11,17)
     bi14 += galsim.PositionI(23,50)
     for b in [bi1, bi2, bi3, bi4, bi5, bi6, bi7, bi8, bi9, bi10, bi11, bi12, bi13, bi14]:
         assert b.isDefined()
         assert b == bi1
-        assert isinstance(b.xmin, int)
-        assert isinstance(b.xmax, int)
-        assert isinstance(b.ymin, int)
-        assert isinstance(b.ymax, int)
+        assert_intlike(b.xmin)
+        assert_intlike(b.xmax)
+        assert_intlike(b.ymin)
+        assert_intlike(b.ymax)
         assert b.origin == galsim.PositionI(11, 17)
         assert b.center == galsim.PositionI(17, 34)
         assert b.true_center == galsim.PositionD(17, 33.5)
@@ -203,11 +224,14 @@ def test_bounds():
     assert bd1.xmax == bd1.getXMax() == 23.
     assert bd1.ymin == bd1.getYMin() == 17.
     assert bd1.ymax == bd1.getYMax() == 50.
-    assert isinstance(bd1.xmin, float)
-    assert isinstance(bd1.xmax, float)
-    assert isinstance(bd1.ymin, float)
-    assert isinstance(bd1.ymax, float)
-    assert isinstance(bd1._b, galsim._galsim.BoundsD)
+    assert_floatlike(bd1.xmin)
+    assert_floatlike(bd1.xmax)
+    assert_floatlike(bd1.ymin)
+    assert_floatlike(bd1.ymax)
+    if is_jax_galsim():
+        pass
+    else:
+        assert isinstance(bd1._b, galsim._galsim.BoundsD)
 
     bd2 = galsim.BoundsD(galsim.PositionI(11,17), galsim.PositionI(23,50))
     bd3 = galsim.BoundsD(galsim.PositionD(11.,50.), galsim.PositionD(23.,17.))
@@ -220,17 +244,20 @@ def test_bounds():
     bd10 = galsim.BoundsD() + galsim.PositionD(11,17) + galsim.PositionD(23,50)
     bd11 = galsim.BoundsD(galsim.BoundsI(11,23,17,50))
     bd12 = galsim.BoundsD(xmin=11.0,ymin=17.0,xmax=23.0,ymax=50.0)
-    bd13 = galsim._BoundsD(11,23,17,50)
+    if is_jax_galsim():
+        bd13 = galsim.BoundsD(11,23,17,50)
+    else:
+        bd13 = galsim._BoundsD(11,23,17,50)
     bd14 = galsim.BoundsD()
     bd14 += galsim.PositionD(11.,17.)
     bd14 += galsim.PositionD(23,50)
     for b in [bd1, bd2, bd3, bd4, bd5, bd6, bd7, bd8, bd9, bd10, bd11, bd12, bd13, bd14]:
         assert b.isDefined()
         assert b == bd1
-        assert isinstance(b.xmin, float)
-        assert isinstance(b.xmax, float)
-        assert isinstance(b.ymin, float)
-        assert isinstance(b.ymax, float)
+        assert_floatlike(b.xmin)
+        assert_floatlike(b.xmax)
+        assert_floatlike(b.ymin)
+        assert_floatlike(b.ymax)
         assert b.origin == galsim.PositionD(11, 17)
         assert b.center == galsim.PositionD(17, 33.5)
         assert b.true_center == galsim.PositionD(17, 33.5)
@@ -241,7 +268,10 @@ def test_bounds():
     assert_raises(TypeError, galsim.BoundsI, 11, 23, 9, 12, 59)
     assert_raises(TypeError, galsim.BoundsI, xmin=11, xmax=23, ymin=17, ymax=50, z=23)
     assert_raises(TypeError, galsim.BoundsI, xmin=11, xmax=50)
-    assert_raises(TypeError, galsim.BoundsI, 11, 23.5, 17, 50.9)
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(TypeError, galsim.BoundsI, 11, 23.5, 17, 50.9)
     assert_raises(TypeError, galsim.BoundsI, 11, 23, 9, 12, xmin=19, xmax=2)
     with assert_raises(TypeError):
         bi1 += (11,23)
@@ -252,7 +282,11 @@ def test_bounds():
     assert_raises(TypeError, galsim.BoundsD, 11, 23, 9, 12, 59)
     assert_raises(TypeError, galsim.BoundsD, xmin=11, xmax=23, ymin=17, ymax=50, z=23)
     assert_raises(TypeError, galsim.BoundsD, xmin=11, xmax=50)
-    assert_raises(ValueError, galsim.BoundsD, 11, 23, 17, "blue")
+    if is_jax_galsim():
+        # jax doesn't raise for this
+        pass
+    else:
+        assert_raises(ValueError, galsim.BoundsD, 11, 23, 17, "blue")
     assert_raises(TypeError, galsim.BoundsD, 11, 23, 9, 12, xmin=19, xmax=2)
     with assert_raises(TypeError):
         bd1 += (11,23)
@@ -373,15 +407,22 @@ def test_bounds():
     assert galsim.BoundsD() == galsim.BoundsD() + galsim.BoundsD()
     assert galsim.BoundsD().area() == 0
 
-    assert galsim.BoundsI(23, 11, 17, 50) == galsim.BoundsI()
-    assert galsim.BoundsI(11, 23, 50, 17) == galsim.BoundsI()
-    assert galsim.BoundsD(23, 11, 17, 50) == galsim.BoundsD()
-    assert galsim.BoundsD(11, 23, 50, 17) == galsim.BoundsD()
-
-    assert_raises(galsim.GalSimUndefinedBoundsError, getattr, galsim.BoundsI(), 'center')
-    assert_raises(galsim.GalSimUndefinedBoundsError, getattr, galsim.BoundsD(), 'center')
-    assert_raises(galsim.GalSimUndefinedBoundsError, getattr, galsim.BoundsI(), 'true_center')
-    assert_raises(galsim.GalSimUndefinedBoundsError, getattr, galsim.BoundsD(), 'true_center')
+    if is_jax_galsim():
+        pass
+    else:
+        assert galsim.BoundsI(23, 11, 17, 50) == galsim.BoundsI()
+        assert galsim.BoundsI(11, 23, 50, 17) == galsim.BoundsI()
+        assert galsim.BoundsD(23, 11, 17, 50) == galsim.BoundsD()
+        assert galsim.BoundsD(11, 23, 50, 17) == galsim.BoundsD()
+
+    if is_jax_galsim():
+        # jax doesn't raise for these things
+        pass
+    else:
+        assert_raises(galsim.GalSimUndefinedBoundsError, getattr, galsim.BoundsI(), 'center')
+        assert_raises(galsim.GalSimUndefinedBoundsError, getattr, galsim.BoundsD(), 'center')
+        assert_raises(galsim.GalSimUndefinedBoundsError, getattr, galsim.BoundsI(), 'true_center')
+        assert_raises(galsim.GalSimUndefinedBoundsError, getattr, galsim.BoundsD(), 'true_center')
 
     check_pickle(bi1)
     check_pickle(bd1)
@@ -501,15 +542,14 @@ def test_check_all_contiguous():
 
 @timer
 def test_deInterleaveImage():
-    from galsim.utilities import deInterleaveImage, interleaveImages
 
     np.random.seed(84) # for generating the same random instances
 
     # 1) Check compatability with interleaveImages
     img = galsim.Image(np.random.randn(64,64),scale=0.25)
     img.setOrigin(galsim.PositionI(5,7)) ## for non-trivial bounds
-    im_list, offsets = deInterleaveImage(img,8)
-    img1 = interleaveImages(im_list,8,offsets)
+    im_list, offsets = galsim.utilities.deInterleaveImage(img,8)
+    img1 = galsim.utilities.interleaveImages(im_list,8,offsets)
     np.testing.assert_array_equal(img1.array,img.array,
             err_msg = "interleaveImages cannot reproduce the input to deInterleaveImage for square "
                       "images")
@@ -519,8 +559,8 @@ def test_deInterleaveImage():
 
     img = galsim.Image(abs(np.random.randn(16*5,16*2)),scale=0.5)
     img.setCenter(0,0) ## for non-trivial bounds
-    im_list, offsets = deInterleaveImage(img,(2,5))
-    img1 = interleaveImages(im_list,(2,5),offsets)
+    im_list, offsets = galsim.utilities.deInterleaveImage(img,(2,5))
+    img1 = galsim.utilities.interleaveImages(im_list,(2,5),offsets)
     np.testing.assert_array_equal(img1.array,img.array,
             err_msg = "interleaveImages cannot reproduce the input to deInterleaveImage for "
                       "rectangular images")
@@ -530,7 +570,7 @@ def test_deInterleaveImage():
 
     # 2) Checking for offsets
     img = galsim.Image(np.random.randn(32,32),scale=2.0)
-    im_list, offsets = deInterleaveImage(img,(4,2))
+    im_list, offsets = galsim.utilities.deInterleaveImage(img,(4,2))
 
     ## Checking if offsets are centered around zero
     assert np.sum([offset.x for offset in offsets]) == 0.
@@ -547,7 +587,7 @@ def test_deInterleaveImage():
     img0 = galsim.Image(32,32)
     g0.drawImage(image=img0,method='no_pixel',scale=0.25)
 
-    im_list0, offsets0 = deInterleaveImage(img0,2,conserve_flux=True)
+    im_list0, offsets0 = galsim.utilities.deInterleaveImage(img0,2,conserve_flux=True)
 
     for n in range(len(im_list0)):
         im = galsim.Image(16,16)
@@ -570,8 +610,8 @@ def test_deInterleaveImage():
     g1.drawImage(image=img1,scale=0.5/n1,method='no_pixel')
     g2.drawImage(image=img2,scale=0.5/n2,method='no_pixel')
 
-    im_list1, offsets1 = deInterleaveImage(img1,(n1**2,1),conserve_flux=True)
-    im_list2, offsets2 = deInterleaveImage(img2,[1,n2**2],conserve_flux=False)
+    im_list1, offsets1 = galsim.utilities.deInterleaveImage(img1,(n1**2,1),conserve_flux=True)
+    im_list2, offsets2 = galsim.utilities.deInterleaveImage(img2,[1,n2**2],conserve_flux=False)
 
     for n in range(n1**2):
         im, offset = im_list1[n], offsets1[n]
@@ -588,26 +628,24 @@ def test_deInterleaveImage():
                      "horizontal direction")
         # im is scaled to account for flux not being conserved
 
-    assert_raises(TypeError, deInterleaveImage, image=img0.array, N=2)
-    assert_raises(TypeError, deInterleaveImage, image=img0, N=2.0)
-    assert_raises(TypeError, deInterleaveImage, image=img0, N=(2.0, 2.0))
-    assert_raises(TypeError, deInterleaveImage, image=img0, N=(2,2,3))
-    assert_raises(ValueError, deInterleaveImage, image=img0, N=7)
-    assert_raises(ValueError, deInterleaveImage, image=img0, N=(2,7))
-    assert_raises(ValueError, deInterleaveImage, image=img0, N=(7,2))
+    assert_raises(TypeError, galsim.utilities.deInterleaveImage, image=img0.array, N=2)
+    assert_raises(TypeError, galsim.utilities.deInterleaveImage, image=img0, N=2.0)
+    assert_raises(TypeError, galsim.utilities.deInterleaveImage, image=img0, N=(2.0, 2.0))
+    assert_raises(TypeError, galsim.utilities.deInterleaveImage, image=img0, N=(2,2,3))
+    assert_raises(ValueError, galsim.utilities.deInterleaveImage, image=img0, N=7)
+    assert_raises(ValueError, galsim.utilities.deInterleaveImage, image=img0, N=(2,7))
+    assert_raises(ValueError, galsim.utilities.deInterleaveImage, image=img0, N=(7,2))
 
     # It is legal to have the input image with wcs=None, but it emits a warning
     img0.wcs = None
     with assert_warns(galsim.GalSimWarning):
-        deInterleaveImage(img0, N=2)
+        galsim.utilities.deInterleaveImage(img0, N=2)
     # Unless suppress_warnings is True
-    deInterleaveImage(img0, N=2, suppress_warnings=True)
+    galsim.utilities.deInterleaveImage(img0, N=2, suppress_warnings=True)
 
 
 @timer
 def test_interleaveImages():
-    from galsim.utilities import interleaveImages, deInterleaveImage
-
     # 1a) With galsim Gaussian
     g = galsim.Gaussian(sigma=3.7,flux=1000.)
     gal = galsim.Convolve([g,galsim.Pixel(1.0)])
@@ -625,7 +663,7 @@ def test_interleaveImages():
     scale = im.scale
 
     # Input to N as an int
-    img = interleaveImages(im_list,n,offsets=offset_list)
+    img = galsim.utilities.interleaveImages(im_list,n,offsets=offset_list)
     im = galsim.Image(16*n*n,16*n*n)
     g = galsim.Gaussian(sigma=3.7,flux=1000.*n*n)
     gal = galsim.Convolve([g,galsim.Pixel(1.0)])
@@ -651,7 +689,7 @@ def test_interleaveImages():
     im_list_randperm = [im_list[idx] for idx in rand_idx]
     offset_list_randperm = [offset_list[idx] for idx in rand_idx]
     # Input to N as a tuple
-    img_randperm = interleaveImages(im_list_randperm,(n,n),offsets=offset_list_randperm)
+    img_randperm = galsim.utilities.interleaveImages(im_list_randperm,(n,n),offsets=offset_list_randperm)
 
     np.testing.assert_array_equal(img_randperm.array,img.array,
         err_msg="Interleaved images do not match when 'offsets' is supplied")
@@ -674,9 +712,9 @@ def test_interleaveImages():
 
     N = (n,n)
     with assert_raises(ValueError):
-        interleaveImages(im_list,N,offset_list)
+        galsim.utilities.interleaveImages(im_list,N,offset_list)
     # Can turn off the checks and just use these as they are with catch_offset_errors=False
-    interleaveImages(im_list,N,offset_list, catch_offset_errors=False)
+    galsim.utilities.interleaveImages(im_list,N,offset_list, catch_offset_errors=False)
 
     offset_list = []
     im_list = []
@@ -693,8 +731,8 @@ def test_interleaveImages():
 
     N = (n,n)
     with assert_raises(ValueError):
-        interleaveImages(im_list, N, offset_list)
-    interleaveImages(im_list, N, offset_list, catch_offset_errors=False)
+        galsim.utilities.interleaveImages(im_list, N, offset_list)
+    galsim.utilities.interleaveImages(im_list, N, offset_list, catch_offset_errors=False)
 
     # 2a) Increase resolution along one direction - square to rectangular images
     n = 2
@@ -713,8 +751,8 @@ def test_interleaveImages():
         gal1.drawImage(im,offset=offset,method='no_pixel',scale=2.0)
         im_list.append(im)
 
-    img = interleaveImages(im_list, N=[1,n**2], offsets=offset_list,
-                           add_flux=False, suppress_warnings=True)
+    img = galsim.utilities.interleaveImages(im_list, N=[1,n**2], offsets=offset_list,
+                                            add_flux=False, suppress_warnings=True)
     im = galsim.Image(16,16*n*n)
     # The interleaved image has the total flux averaged out since `add_flux = False'
     gal = galsim.Gaussian(sigma=3.7*n,flux=100.)
@@ -741,7 +779,7 @@ def test_interleaveImages():
         gal2.drawImage(im,offset=offset,method='no_pixel',scale=3.0)
         im_list.append(im)
 
-    img = interleaveImages(im_list, N=np.array([n**2,1]), offsets=offset_list,
+    img = galsim.utilities.interleaveImages(im_list, N=np.array([n**2,1]), offsets=offset_list,
                                             suppress_warnings=True)
     im = galsim.Image(16*n*n,16*n*n)
     gal = galsim.Gaussian(sigma=3.7,flux=100.*n*n)
@@ -770,8 +808,8 @@ def test_interleaveImages():
             im.setOrigin(3,3) # for non-trivial bounds
             im_list.append(im)
 
-    img = interleaveImages(im_list,N=n,offsets=offset_list)
-    im_list_1, offset_list_1 = deInterleaveImage(img, N=n)
+    img = galsim.utilities.interleaveImages(im_list,N=n,offsets=offset_list)
+    im_list_1, offset_list_1 = galsim.utilities.deInterleaveImage(img, N=n)
 
     for k in range(n**2):
         assert offset_list_1[k] == offset_list[k]
@@ -782,50 +820,50 @@ def test_interleaveImages():
         assert im_list[k].bounds == im_list_1[k].bounds
 
     # Checking for non-default flux option
-    img = interleaveImages(im_list,N=n,offsets=offset_list,add_flux=False)
-    im_list_2, offset_list_2 = deInterleaveImage(img,N=n,conserve_flux=True)
+    img = galsim.utilities.interleaveImages(im_list,N=n,offsets=offset_list,add_flux=False)
+    im_list_2, offset_list_2 = galsim.utilities.deInterleaveImage(img,N=n,conserve_flux=True)
 
     for k in range(n**2):
         assert offset_list_2[k] == offset_list[k]
         np.testing.assert_array_equal(im_list_2[k].array, im_list[k].array)
         assert im_list_2[k].wcs == im_list[k].wcs
 
-    assert_raises(TypeError, interleaveImages, im_list=img, N=n, offsets=offset_list)
-    assert_raises(ValueError, interleaveImages, [img], N=1, offsets=offset_list)
-    assert_raises(ValueError, interleaveImages, im_list, n, offset_list[:-1])
-    assert_raises(TypeError, interleaveImages, [im.array for im in im_list], n, offset_list)
-    assert_raises(TypeError, interleaveImages,
+    assert_raises(TypeError, galsim.utilities.interleaveImages, im_list=img, N=n, offsets=offset_list)
+    assert_raises(ValueError, galsim.utilities.interleaveImages, [img], N=1, offsets=offset_list)
+    assert_raises(ValueError, galsim.utilities.interleaveImages, im_list, n, offset_list[:-1])
+    assert_raises(TypeError, galsim.utilities.interleaveImages, [im.array for im in im_list], n, offset_list)
+    assert_raises(TypeError, galsim.utilities.interleaveImages,
                   [im_list[0]] + [im.array for im in im_list[1:]],
                   n, offset_list)
-    assert_raises(TypeError, interleaveImages,
+    assert_raises(TypeError, galsim.utilities.interleaveImages,
                   [galsim.Image(16+i,16+j,scale=1) for i in range(n) for j in range(n)],
                   n, offset_list)
-    assert_raises(TypeError, interleaveImages,
+    assert_raises(TypeError, galsim.utilities.interleaveImages,
                   [galsim.Image(16,16,scale=i) for i in range(n) for j in range(n)],
                   n, offset_list)
-    assert_raises(TypeError, interleaveImages, im_list, N=3.0, offsets=offset_list)
-    assert_raises(TypeError, interleaveImages, im_list, N=(3.0, 3.0), offsets=offset_list)
-    assert_raises(TypeError, interleaveImages, im_list, N=(3,3,3), offsets=offset_list)
-    assert_raises(ValueError, interleaveImages, im_list, N=7, offsets=offset_list)
-    assert_raises(ValueError, interleaveImages, im_list, N=(2,7), offsets=offset_list)
-    assert_raises(ValueError, interleaveImages, im_list, N=(7,2), offsets=offset_list)
-    assert_raises(TypeError, interleaveImages, im_list, N=n)
-    assert_raises(TypeError, interleaveImages, im_list, N=n, offsets=offset_list[0])
-    assert_raises(TypeError, interleaveImages, im_list, N=n, offsets=range(n*n))
+    assert_raises(TypeError, galsim.utilities.interleaveImages, im_list, N=3.0, offsets=offset_list)
+    assert_raises(TypeError, galsim.utilities.interleaveImages, im_list, N=(3.0, 3.0), offsets=offset_list)
+    assert_raises(TypeError, galsim.utilities.interleaveImages, im_list, N=(3,3,3), offsets=offset_list)
+    assert_raises(ValueError, galsim.utilities.interleaveImages, im_list, N=7, offsets=offset_list)
+    assert_raises(ValueError, galsim.utilities.interleaveImages, im_list, N=(2,7), offsets=offset_list)
+    assert_raises(ValueError, galsim.utilities.interleaveImages, im_list, N=(7,2), offsets=offset_list)
+    assert_raises(TypeError, galsim.utilities.interleaveImages, im_list, N=n)
+    assert_raises(TypeError, galsim.utilities.interleaveImages, im_list, N=n, offsets=offset_list[0])
+    assert_raises(TypeError, galsim.utilities.interleaveImages, im_list, N=n, offsets=range(n*n))
 
     # It is legal to have the input images with wcs=None, but it emits a warning
     for im in im_list:
         im.wcs = None
     with assert_warns(galsim.GalSimWarning):
-        interleaveImages(im_list, N=n, offsets=offset_list)
+        galsim.utilities.interleaveImages(im_list, N=n, offsets=offset_list)
     # Unless suppress_warnings is True
-    interleaveImages(im_list, N=n, offsets=offset_list, suppress_warnings=True)
+    galsim.utilities.interleaveImages(im_list, N=n, offsets=offset_list, suppress_warnings=True)
 
     # Also legal to have different origins
     im_list[0].setCenter(0,0)
     with assert_warns(galsim.GalSimWarning):
-        interleaveImages(im_list, N=n, offsets=offset_list)
-    interleaveImages(im_list, N=n, offsets=offset_list, suppress_warnings=True)
+        galsim.utilities.interleaveImages(im_list, N=n, offsets=offset_list)
+    galsim.utilities.interleaveImages(im_list, N=n, offsets=offset_list, suppress_warnings=True)
 
 
 @timer
@@ -1112,67 +1150,73 @@ def test_horner():
     # Make a random list of values to test
     x = np.empty(20)
     rng = galsim.UniformDeviate(1234)
-    rng.generate(x)
+    if is_jax_galsim():
+        x = rng.generate(x)
+    else:
+        rng.generate(x)
 
     # Check against the direct calculation
     truth = coef[0] + coef[1]*x + coef[2]*x**2 + coef[3]*x**3 + coef[4]*x**4
     result = galsim.utilities.horner(x, coef)
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
 
     # Also check against the (slower) numpy code
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval(x,coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval(x,coef))
 
     # Check that trailing zeros give the same answer
     result = galsim.utilities.horner(x, coef + [0]*3)
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
 
     # Check that leading zeros give the right answer
     result = galsim.utilities.horner(x, [0]*3 + coef)
-    np.testing.assert_almost_equal(result, truth*x**3)
+    np.testing.assert_array_almost_equal(result, truth*x**3)
 
     # Check using a different dtype
     result = galsim.utilities.horner(x, coef, dtype=complex)
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
 
     # Check that a single element coef gives the right answer
     result = galsim.utilities.horner([1,2,3], [17])
-    np.testing.assert_almost_equal(result, 17)
+    np.testing.assert_array_almost_equal(result, 17)
     result = galsim.utilities.horner(x, [17])
-    np.testing.assert_almost_equal(result, 17)
+    np.testing.assert_array_almost_equal(result, 17)
     result = galsim.utilities.horner([1,2,3], [17,0,0,0])
-    np.testing.assert_almost_equal(result, 17)
+    np.testing.assert_array_almost_equal(result, 17)
     result = galsim.utilities.horner(x, [17,0,0,0])
-    np.testing.assert_almost_equal(result, 17)
+    np.testing.assert_array_almost_equal(result, 17)
     result = galsim.utilities.horner([1,2,3], [0,0,0,0])
-    np.testing.assert_almost_equal(result, 0)
+    np.testing.assert_array_almost_equal(result, 0)
     result = galsim.utilities.horner(x, [0,0,0,0])
-    np.testing.assert_almost_equal(result, 0)
+    np.testing.assert_array_almost_equal(result, 0)
 
     # Check that x may be non-contiguous
     result = galsim.utilities.horner(x[::3], coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval(x[::3],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval(x[::3],coef))
 
     # Check that coef may be non-contiguous
     result = galsim.utilities.horner(x, coef[::-1])
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval(x,coef[::-1]))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval(x,coef[::-1]))
 
     # Check odd length
     result = galsim.utilities.horner(x[:15], coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval(x[:15],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval(x[:15],coef))
 
     # Check unaligned array
     result = galsim.utilities.horner(x[1:], coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval(x[1:],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval(x[1:],coef))
 
     # Check length > 64
     xx = np.empty(2000)
-    rng.generate(xx)
+    if is_jax_galsim():
+        xx = rng.generate(xx)
+    else:
+        rng.generate(xx)
     result = galsim.utilities.horner(xx, coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval(xx,coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval(xx,coef))
 
     # Check scalar x
     result = galsim.utilities.horner(3.9, coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval([3.9],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval([3.9],coef))
 
     # Check invalid arguments
     with assert_raises(galsim.GalSimValueError):
@@ -1190,78 +1234,86 @@ def test_horner2d():
     x = np.empty(20)
     y = np.empty(20)
     rng = galsim.UniformDeviate(1234)
-    rng.generate(x)
-    rng.generate(y)
+    if is_jax_galsim():
+        x = rng.generate(x)
+        y = rng.generate(y)
+    else:
+        rng.generate(x)
+        rng.generate(y)
 
     # Check against the direct calculation
     truth = coef[0,0] + coef[0,1]*y + coef[0,2]*y**2 + coef[0,3]*y**3 + coef[0,4]*y**4
     truth += (coef[1,0] + coef[1,1]*y + coef[1,2]*y**2 + coef[1,3]*y**3 + coef[1,4]*y**4)*x
     truth += (coef[2,0] + coef[2,1]*y + coef[2,2]*y**2 + coef[2,3]*y**3 + coef[2,4]*y**4)*x**2
     result = galsim.utilities.horner2d(x, y, coef)
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
 
     # Also check against the (slower) numpy code
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x,y,coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x,y,coef))
 
     # Check that trailing zeros give the same answer
     result = galsim.utilities.horner2d(x, y, np.hstack([coef, np.zeros((3,1))]))
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
     result = galsim.utilities.horner2d(x, y, np.hstack([coef, np.zeros((3,6))]))
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
     result = galsim.utilities.horner2d(x, y, np.vstack([coef, np.zeros((1,5))]))
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
     result = galsim.utilities.horner2d(x, y, np.vstack([coef, np.zeros((6,5))]))
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
 
     # Check that leading zeros give the right answer
     result = galsim.utilities.horner2d(x, y, np.hstack([np.zeros((3,1)), coef]))
-    np.testing.assert_almost_equal(result, truth*y)
+    np.testing.assert_array_almost_equal(result, truth*y)
     result = galsim.utilities.horner2d(x, y, np.hstack([np.zeros((3,6)), coef]))
-    np.testing.assert_almost_equal(result, truth*y**6)
+    np.testing.assert_array_almost_equal(result, truth*y**6)
     result = galsim.utilities.horner2d(x, y, np.vstack([np.zeros((1,5)), coef]))
-    np.testing.assert_almost_equal(result, truth*x)
+    np.testing.assert_array_almost_equal(result, truth*x)
     result = galsim.utilities.horner2d(x, y, np.vstack([np.zeros((6,5)), coef]))
-    np.testing.assert_almost_equal(result, truth*x**6)
+    np.testing.assert_array_almost_equal(result, truth*x**6)
 
     # Check using a different dtype
     result = galsim.utilities.horner2d(x, y, coef, dtype=complex)
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
 
     # Check that x,y may be non-contiguous
     result = galsim.utilities.horner2d(x[::3], y[:7], coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x[::3],y[:7],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x[::3],y[:7],coef))
     result = galsim.utilities.horner2d(x[:7], y[::-3], coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x[:7],y[::-3],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x[:7],y[::-3],coef))
 
     # Check that coef may be non-contiguous
     result = galsim.utilities.horner2d(x, y, coef[:,::-1])
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x,y,coef[:,::-1]))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x,y,coef[:,::-1]))
     result = galsim.utilities.horner2d(x, y, coef[::-1,:])
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x,y,coef[::-1,:]))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x,y,coef[::-1,:]))
 
     # Check odd length
     result = galsim.utilities.horner2d(x[:15], y[:15], coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x[:15],y[:15],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x[:15],y[:15],coef))
 
     # Check unaligned array
     result = galsim.utilities.horner2d(x[1:], y[1:], coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x[1:],y[1:],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x[1:],y[1:],coef))
     result = galsim.utilities.horner2d(x[1:], y[:-1], coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x[1:],y[:-1],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x[1:],y[:-1],coef))
     result = galsim.utilities.horner2d(x[:-1], y[1:], coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x[:-1],y[1:],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x[:-1],y[1:],coef))
 
     # Check length > 64
     xx = np.empty(2000)
     yy = np.empty(2000)
-    rng.generate(xx)
-    rng.generate(yy)
+    if is_jax_galsim():
+        xx = rng.generate(xx)
+        yy = rng.generate(yy)
+    else:
+        rng.generate(xx)
+        rng.generate(yy)
     result = galsim.utilities.horner2d(xx, yy, coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(xx,yy,coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(xx,yy,coef))
 
     # Check scalar x, y
     result = galsim.utilities.horner2d(3.9, 1.7, coef)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d([3.9],[1.7],coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d([3.9],[1.7],coef))
 
 
     # Check the triangle = True option
@@ -1275,13 +1327,13 @@ def test_horner2d():
     truth += coef[1,0]*x + coef[1,1]*x*y
     truth += coef[2,0]*x**2
     result = galsim.utilities.horner2d(x, y, coef)
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
     result = galsim.utilities.horner2d(x, y, coef, triangle=True)
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
 
     # Check using a different dtype
     result = galsim.utilities.horner2d(x, y, coef, dtype=complex, triangle=True)
-    np.testing.assert_almost_equal(result, truth)
+    np.testing.assert_array_almost_equal(result, truth)
 
     # Check invalid arguments
     with assert_raises(galsim.GalSimValueError):
@@ -1310,60 +1362,67 @@ def test_horner_complex():
     rx = np.empty(20)
     ry = np.empty(20)
     rng = galsim.UniformDeviate(1234)
-    rng.generate(rx)
-    rng.generate(ry)
+    if is_jax_galsim():
+        rx = rng.generate(rx)
+        ry = rng.generate(ry)
+    else:
+        rng.generate(rx)
+        rng.generate(ry)
 
     ix = np.empty(20)
     iy = np.empty(20)
     rng = galsim.UniformDeviate(1234)
-    rng.generate(ix)
-    rng.generate(iy)
-
+    if is_jax_galsim():
+        ix = rng.generate(ix)
+        iy = rng.generate(iy)
+    else:
+        rng.generate(ix)
+        rng.generate(iy)
     x = rx + 1j*ix
     y = ry + 1j*iy
 
     # Check all combinations of which things are complex and which are real.
     # First, just 1 of the three complex:
     result = galsim.utilities.horner2d(rx, ry, coef, dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(rx, ry, coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(rx, ry, coef))
 
     result = galsim.utilities.horner2d(rx, y, rcoef, dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(rx, y, rcoef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(rx, y, rcoef))
 
     result = galsim.utilities.horner2d(x, ry, rcoef, dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x, ry, rcoef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x, ry, rcoef))
 
     # Now two complex:
     result = galsim.utilities.horner2d(rx, y, coef, dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(rx, y, coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(rx, y, coef))
 
     result = galsim.utilities.horner2d(x, ry, coef, dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x, ry, coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x, ry, coef))
 
     result = galsim.utilities.horner2d(x, y, rcoef, dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x, y, rcoef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x, y, rcoef))
 
     # All three complex
     result = galsim.utilities.horner2d(x, y, coef, dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(x, y, coef))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(x, y, coef))
 
     # Check scalar complex x, y
     result = galsim.utilities.horner2d(3.9+2.1j, 1.7-0.9j, coef, dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval2d(
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval2d(
             [3.9+2.1j],[1.7-0.9j],coef))
 
     # Repeast for 1d
     result = galsim.utilities.horner(rx, coef[0], dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval(rx, coef[0]))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval(rx, coef[0]))
 
     result = galsim.utilities.horner(x, rcoef[0], dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval(x, rcoef[0]))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval(x, rcoef[0]))
 
     result = galsim.utilities.horner(x, coef[0], dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval(x, coef[0]))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval(x, coef[0]))
 
     result = galsim.utilities.horner(3.9+2.1j, coef[0], dtype=complex)
-    np.testing.assert_almost_equal(result, np.polynomial.polynomial.polyval([3.9+2.1j],coef[0]))
+    np.testing.assert_array_almost_equal(result, np.polynomial.polynomial.polyval([3.9+2.1j],coef[0]))
 
 @timer
 def test_merge_sorted():
diff --git a/tests/test_wcs.py b/tests/test_wcs.py
index ff5699060e..f945e35598 100644
--- a/tests/test_wcs.py
+++ b/tests/test_wcs.py
@@ -24,6 +24,7 @@
 import coord
 from unittest import mock
 
+import coord
 import galsim
 from galsim_test_helpers import *
 
@@ -499,7 +500,7 @@ def do_local_wcs(wcs, ufunc, vfunc, name):
     wcs2 = wcs.local()
     assert wcs == wcs2, name+' local() is not == the original'
     new_origin = galsim.PositionI(123,321)
-    wcs3 = wcs.withOrigin(new_origin)
+    wcs3 = wcs.shiftOrigin(new_origin)
     assert wcs != wcs3, name+' is not != wcs.withOrigin(pos)'
     assert wcs3 != wcs, name+' is not != wcs.withOrigin(pos) (reverse)'
     wcs2 = wcs3.local()
@@ -513,7 +514,7 @@ def do_local_wcs(wcs, ufunc, vfunc, name):
             world_pos2.y, world_pos1.y, digits,
             'withOrigin(new_origin) returned wrong world position')
     new_world_origin = galsim.PositionD(5352.7, 9234.3)
-    wcs4 = wcs.withOrigin(new_origin, new_world_origin)
+    wcs4 = wcs.shiftOrigin(new_origin, new_world_origin)
     world_pos3 = wcs4.toWorld(new_origin)
     np.testing.assert_almost_equal(
             world_pos3.x, new_world_origin.x, digits,
@@ -1181,7 +1182,10 @@ def test_pixelscale():
     assert wcs.world_origin == galsim.PositionD(0,0)
 
     assert_raises(TypeError, galsim.PixelScale)
-    assert_raises(TypeError, galsim.PixelScale, scale=galsim.PixelScale(scale))
+    if is_jax_galsim():
+        pass
+    else:
+        assert_raises(TypeError, galsim.PixelScale, scale=galsim.PixelScale(scale))
     assert_raises(TypeError, galsim.PixelScale, scale=scale, origin=galsim.PositionD(0,0))
     assert_raises(TypeError, galsim.PixelScale, scale=scale, world_origin=galsim.PositionD(0,0))
 
@@ -2370,7 +2374,7 @@ def test_gsfitswcs():
     # And it's required to get (relatively) complete test coverage.
     test_tags = [ 'TAN', 'STG', 'ZEA', 'ARC', 'TPV', 'TAN-PV', 'TAN-FLIP', 'TNX', 'SIP', 'ZTF' ]
 
-    dir = 'fits_files'
+    dir = os.path.join(os.path.dirname(__file__), 'fits_files')
 
     for tag in test_tags:
         file_name, ref_list = references[tag]
@@ -2463,32 +2467,44 @@ def test_inverseab_convergence():
     # Now one that should fail, since it's well outside the applicable area for the SIP polynomials.
     ra = 2.1
     dec = -0.45
-    with assert_raises(galsim.GalSimError):
+    if is_jax_galsim():
         x, y = wcs.radecToxy(ra, dec, units="radians")
-    try:
-        x, y = wcs.radecToxy(ra, dec, units="radians")
-    except galsim.GalSimError as e:
-        print('Error message is\n',e)
-        assert "[0,]" in str(e)
+        assert np.all(np.isnan(x))
+        assert np.all(np.isnan(y))
+    else:
+        with assert_raises(galsim.GalSimError):
+            x, y = wcs.radecToxy(ra, dec, units="radians")
+        try:
+            x, y = wcs.radecToxy(ra, dec, units="radians")
+        except galsim.GalSimError as e:
+            print('Error message is\n',e)
+            assert "[0,]" in str(e) or "[0]" in str(e)
 
     # Check as part of a longer list (longer than 256 is important)
-    ra = np.random.uniform(2.185, 2.186, 1000)
-    dec = np.random.uniform(-0.501, -0.499, 1000)
+    rng = np.random.RandomState(1234)
+    ra = rng.uniform(2.185, 2.186, 1000)
+    dec = rng.uniform(-0.501, -0.499, 1000)
     ra = np.append(ra, [2.1, 2.9])
     dec = np.append(dec, [-0.45, 0.2])
     print('ra = ',ra)
     print('dec = ',dec)
-    with assert_raises(galsim.GalSimError):
+    if is_jax_galsim():
         x, y = wcs.radecToxy(ra, dec, units="radians")
-    try:
-        x, y = wcs.radecToxy(ra, dec, units="radians")
-    except galsim.GalSimError as e:
-        print('Error message is\n',e)
-        assert "[1000,1001,]" in str(e)
-        # We don't currently do this for the user, but it's not too hard to get a python list
-        # of the bad indices.  Included here as an example for users who may need this.
-        bad = eval(str(e)[str(e).rfind('['):])
-        print('as a python list: ',bad)
+        assert np.sum(np.isnan(x)) >= 2
+        assert np.sum(np.isnan(y)) >= 2
+    else:
+        with assert_raises(galsim.GalSimError):
+            x, y = wcs.radecToxy(ra, dec, units="radians")
+        try:
+            x, y = wcs.radecToxy(ra, dec, units="radians")
+        except galsim.GalSimError as e:
+            print('Error message is\n',e)
+            assert "[1000,1001,]" in str(e) or "[1000, 1001]" in str(e)
+            # We don't currently do this for the user, but it's not too hard to get a python list
+            # of the bad indices.  Included here as an example for users who may need this.
+            bad = eval(str(e)[str(e).rfind('['):])
+            print('as a python list: ',bad)
+
 
 @timer
 def test_tanwcs():
@@ -2580,7 +2596,7 @@ def test_fitswcs():
         except:
             pass
 
-    dir = 'fits_files'
+    dir = os.path.join(os.path.dirname(__file__), 'fits_files')
 
     for tag in test_tags:
         file_name, ref_list = references[tag]
@@ -2688,7 +2704,7 @@ def test_fittedsipwcs():
         'ZTF': (0.1, 0.1),
     }
 
-    dir = 'fits_files'
+    dir = os.path.join(os.path.dirname(__file__), 'fits_files')
 
     if __name__ == "__main__":
         test_tags = all_tags
@@ -2917,7 +2933,7 @@ def test_fittedsipwcs():
 def test_scamp():
     """Test that we can read in a SCamp .head file correctly
     """
-    dir = 'fits_files'
+    dir = os.path.join(os.path.dirname(__file__), 'fits_files')
     file_name = 'scamp.head'
 
     wcs = galsim.FitsWCS(file_name, dir=dir, text_file=True)
@@ -3138,7 +3154,7 @@ def test_int_args():
 
     test_tags = all_tags
 
-    dir = 'fits_files'
+    dir = os.path.join(os.path.dirname(__file__), 'fits_files')
 
     for tag in test_tags:
         file_name, ref_list = references[tag]
@@ -3161,7 +3177,7 @@ def test_int_args():
     # Along the way, check issue #1024 where Erin noticed that reading the WCS from the
     # header of a compressed file was spending lots of time decompressing the data, which
     # is unnecessary.
-    dir = 'des_data'
+    dir = os.path.join(os.path.dirname(__file__), 'des_data')
     file_name = 'DECam_00158414_01.fits.fz'
     with Profile():
         t0 = time.time()
@@ -3211,7 +3227,7 @@ def test_razero():
         import astropy.wcs
         import scipy  # AstropyWCS constructor will do this, so check now.
 
-    dir = 'fits_files'
+    dir = os.path.join(os.path.dirname(__file__), 'fits_files')
     # This file is based in sipsample.fits, but with the CRVAL1 changed to 0.002322805429
     file_name = 'razero.fits'
     wcs = galsim.AstropyWCS(file_name, dir=dir)
diff --git a/tests/test_zernike.py b/tests/test_zernike.py
index 254aeaad0d..d53a4c3087 100644
--- a/tests/test_zernike.py
+++ b/tests/test_zernike.py
@@ -19,8 +19,7 @@
 import numpy as np
 
 import galsim
-from galsim.zernike import Zernike, DoubleZernike
-from galsim_test_helpers import *
+from galsim_test_helpers import timer, check_pickle, assert_raises, check_all_diff, is_jax_galsim
 
 
 @timer
@@ -41,10 +40,10 @@ def test_Zernike_orthonormality():
     y = y[w].ravel()
     area = np.pi*R_outer**2
     for j1 in range(1, jmax+1):
-        Z1 = Zernike([0]*(j1+1)+[1], R_outer=R_outer)
+        Z1 = galsim.zernike.Zernike([0]*(j1+1)+[1], R_outer=R_outer)
         val1 = Z1.evalCartesian(x, y)
         for j2 in range(j1, jmax+1):
-            Z2 = Zernike([0]*(j2+1)+[1], R_outer=R_outer)
+            Z2 = galsim.zernike.Zernike([0]*(j2+1)+[1], R_outer=R_outer)
             val2 = Z2.evalCartesian(x, y)
             integral = np.dot(val1, val2) * dx**2
             if j1 == j2:
@@ -73,10 +72,10 @@ def test_Zernike_orthonormality():
     y = y[w].ravel()
     area = np.pi*(R_outer**2 - R_inner**2)
     for j1 in range(1, jmax+1):
-        Z1 = Zernike([0]*(j1+1)+[1], R_outer=R_outer, R_inner=R_inner)
+        Z1 = galsim.zernike.Zernike([0]*(j1+1)+[1], R_outer=R_outer, R_inner=R_inner)
         val1 = Z1.evalCartesian(x, y)
         for j2 in range(j1, jmax+1):
-            Z2 = Zernike([0]*(j2+1)+[1], R_outer=R_outer, R_inner=R_inner)
+            Z2 = galsim.zernike.Zernike([0]*(j2+1)+[1], R_outer=R_outer, R_inner=R_inner)
             val2 = Z2.evalCartesian(x, y)
             integral = np.dot(val1, val2) * dx**2
             if j1 == j2:
@@ -93,7 +92,7 @@ def test_Zernike_orthonormality():
     check_pickle(Z1, lambda z: tuple(z.evalCartesian(x, y)))
 
     with assert_raises(ValueError):
-        Z1 = Zernike([0]*4 + [0.1]*7, R_outer=R_inner, R_inner=R_outer)
+        Z1 = galsim.zernike.Zernike([0]*4 + [0.1]*7, R_outer=R_inner, R_inner=R_outer)
         val1 = Z1.evalCartesian(x, y)
 
 
@@ -202,13 +201,13 @@ def test_Zernike_rotate():
         R_inner = R_outer*eps
 
         coefs = [u() for _ in range(jmax+1)]
-        Z = Zernike(coefs, R_outer=R_outer, R_inner=R_inner)
+        Z = galsim.zernike.Zernike(coefs, R_outer=R_outer, R_inner=R_inner)
         check_pickle(Z)
 
         for theta in [0.0, 0.1, 1.0, np.pi, 4.0]:
             R = galsim.zernike.zernikeRotMatrix(jmax, theta)
             rotCoefs = np.dot(R, coefs)
-            Zrot = Zernike(rotCoefs, R_outer=R_outer, R_inner=R_inner)
+            Zrot = galsim.zernike.Zernike(rotCoefs, R_outer=R_outer, R_inner=R_inner)
             print('j,theta: ',jmax,theta)
             print('Z: ',Z.evalPolar(rhos, thetas))
             print('Zrot: ',Zrot.evalPolar(rhos, thetas+theta))
@@ -236,7 +235,7 @@ def test_zernike_eval():
         np.ones(4, dtype=float),
         np.ones(4, dtype=np.float32)
     ]:
-        Z = Zernike(coef)
+        Z = galsim.zernike.Zernike(coef)
         assert Z.coef.dtype == np.float64
         assert Z(0.0, 0.0) == 1.0
         assert Z(0, 0) == 1.0
@@ -246,29 +245,29 @@ def test_zernike_eval():
         np.ones((4, 4), dtype=float),
         np.ones((4, 4), dtype=np.float32)
     ]:
-        dz = DoubleZernike(coefs)
+        dz = galsim.zernike.DoubleZernike(coefs)
         assert dz.coef.dtype == np.float64
         assert dz(0.0, 0.0) == dz(0, 0)
 
         # Make sure we cast to float in _from_uvxy
         uvxy = dz._coef_array_uvxy
-        dz2 = DoubleZernike._from_uvxy(uvxy.astype(int))
+        dz2 = galsim.zernike.DoubleZernike._from_uvxy(uvxy.astype(int))
         np.testing.assert_array_equal(dz2._coef_array_uvxy, dz._coef_array_uvxy)
 
 
 @timer
 def test_ne():
     objs = [
-        Zernike([0, 1, 2]),
-        Zernike([0, 1, 2, 3]),
-        Zernike([0, 1, 2, 3], R_outer=0.2),
-        Zernike([0, 1, 2, 3], R_outer=0.2, R_inner=0.1),
-        DoubleZernike(np.eye(3)),
-        DoubleZernike(np.ones((4, 4))),
-        DoubleZernike(np.ones((4, 4)), xy_outer=1.1),
-        DoubleZernike(np.ones((4, 4)), xy_outer=1.1, xy_inner=0.9),
-        DoubleZernike(np.ones((4, 4)), xy_outer=1.1, xy_inner=0.9, uv_outer=1.1),
-        DoubleZernike(np.ones((4, 4)), xy_outer=1.1, xy_inner=0.9, uv_outer=1.1, uv_inner=0.9)
+        galsim.zernike.Zernike([0, 1, 2]),
+        galsim.zernike.Zernike([0, 1, 2, 3]),
+        galsim.zernike.Zernike([0, 1, 2, 3], R_outer=0.2),
+        galsim.zernike.Zernike([0, 1, 2, 3], R_outer=0.2, R_inner=0.1),
+        galsim.zernike.DoubleZernike(np.eye(3)),
+        galsim.zernike.DoubleZernike(np.ones((4, 4))),
+        galsim.zernike.DoubleZernike(np.ones((4, 4)), xy_outer=1.1),
+        galsim.zernike.DoubleZernike(np.ones((4, 4)), xy_outer=1.1, xy_inner=0.9),
+        galsim.zernike.DoubleZernike(np.ones((4, 4)), xy_outer=1.1, xy_inner=0.9, uv_outer=1.1),
+        galsim.zernike.DoubleZernike(np.ones((4, 4)), xy_outer=1.1, xy_inner=0.9, uv_outer=1.1, uv_inner=0.9)
     ]
     check_all_diff(objs)
 
@@ -294,7 +293,7 @@ def test_Zernike_basis():
 
         # Compare to basis vectors generated one at a time
         for j in range(1, jmax):
-            Z = Zernike([0]*j+[1], R_outer=R_outer, R_inner=R_inner)
+            Z = galsim.zernike.Zernike([0]*j+[1], R_outer=R_outer, R_inner=R_inner)
             zBasis = Z.evalCartesian(x, y)
             np.testing.assert_allclose(
                     zBases[j],
@@ -326,11 +325,11 @@ def test_fit():
                            [u()-0.5,       0,       0,       0,       0]]
         z = galsim.utilities.horner2d(x, y, cartesian_coefs)
         z2 = galsim.utilities.horner2d(x, y, cartesian_coefs, triangle=True)
-        np.testing.assert_equal(z,z2)
+        np.testing.assert_array_equal(z,z2)
 
         basis = galsim.zernike.zernikeBasis(21, x, y, R_outer=R_outer, R_inner=R_inner)
         coefs, _, _, _ = np.linalg.lstsq(basis.T, z, rcond=-1.)
-        resids = (Zernike(coefs, R_outer=R_outer, R_inner=R_inner)
+        resids = (galsim.zernike.Zernike(coefs, R_outer=R_outer, R_inner=R_inner)
                   .evalCartesian(x, y)
                   - z)
         resids2 = np.dot(basis.T, coefs).T - z
@@ -379,7 +378,7 @@ def test_fit():
         assert basis.shape == (22, 25, 40)
         # lstsq doesn't handle the extra dimension though...
         coefs, _, _, _ = np.linalg.lstsq(basis.reshape(21+1, 1000).T, z.ravel(), rcond=-1.)
-        resids = (Zernike(coefs, R_outer=R_outer, R_inner=R_inner)
+        resids = (galsim.zernike.Zernike(coefs, R_outer=R_outer, R_inner=R_inner)
                   .evalCartesian(x, y)
                   - z)
         resids2 = np.dot(basis.T, coefs).T - z
@@ -395,7 +394,7 @@ def test_gradient():
     """
     # Start with a few that just quote the literature, e.g., Stephenson (2014).
 
-    Z11 = Zernike([0]*11+[1])
+    Z11 = galsim.zernike.Zernike([0]*11+[1])
 
     x = np.linspace(-1, 1, 100)
     x, y = np.meshgrid(x, x)
@@ -419,7 +418,7 @@ def Z11_grad(x, y):
 
     np.testing.assert_allclose(Z11.evalCartesianGrad(x, y), Z11_grad(x, y), rtol=1.e-12, atol=1e-12)
 
-    Z28 = Zernike([0]*28+[1])
+    Z28 = galsim.zernike.Zernike([0]*28+[1])
 
     def Z28_grad(x, y):
         # Z28 = sqrt(14) (x^6 - 15 x^4 y^2 + 15 x^2 y^4 - y^6)
@@ -444,7 +443,7 @@ def finite_difference_gradient(Z, x, y):
         nj = 1+int(u()*55)
         R_inner = 0.2+0.6*u()
         R_outer = R_inner + 0.2+0.6*u()
-        Z = Zernike([0]+[u() for _ in range(nj)], R_inner=R_inner, R_outer=R_outer)
+        Z = galsim.zernike.Zernike([0]+[u() for _ in range(nj)], R_inner=R_inner, R_outer=R_outer)
 
         np.testing.assert_allclose(
                 finite_difference_gradient(Z, x, y),
@@ -452,7 +451,7 @@ def finite_difference_gradient(Z, x, y):
                 rtol=1e-5, atol=1e-5)
 
     # Make sure the gradient of the zero-Zernike works
-    Z = Zernike([0])
+    Z = galsim.zernike.Zernike([0])
     assert Z == Z.gradX == Z.gradX.gradX == Z.gradY == Z.gradY.gradY
 
 
@@ -478,7 +477,7 @@ def test_gradient_bases():
 
         # Compare to basis vectors generated one at a time
         for j in range(1, jmax+1):
-            Z = Zernike([0]*j+[1], R_outer=R_outer, R_inner=R_inner)
+            Z = galsim.zernike.Zernike([0]*j+[1], R_outer=R_outer, R_inner=R_inner)
             ZX = Z.gradX
             ZY = Z.gradY
 
@@ -517,16 +516,22 @@ def test_sum():
         a2 = np.empty(n2, dtype=float)
         u.generate(a1)
         u.generate(a2)
-        z1 = Zernike(a1, R_outer=R_outer, R_inner=R_inner)
-        z2 = Zernike(a2, R_outer=R_outer, R_inner=R_inner)
+        z1 = galsim.zernike.Zernike(a1, R_outer=R_outer, R_inner=R_inner)
+        z2 = galsim.zernike.Zernike(a2, R_outer=R_outer, R_inner=R_inner)
 
         c1 = u()
         c2 = u()
 
         coefSum = c2*np.array(z2.coef)
-        coefSum[:len(z1.coef)] += c1*z1.coef
+        if is_jax_galsim():
+            coefSum = coefSum.at[:len(z1.coef)].add(c1*z1.coef)
+        else:
+            coefSum[:len(z1.coef)] += c1*z1.coef
         coefDiff = c2*np.array(z2.coef)
-        coefDiff[:len(z1.coef)] -= c1*z1.coef
+        if is_jax_galsim():
+            coefDiff = coefDiff.at[:len(z1.coef)].add(-c1*z1.coef)
+        else:
+            coefDiff[:len(z1.coef)] -= c1*z1.coef
         np.testing.assert_allclose(coefSum, (c1*z1 + c2*z2).coef)
         np.testing.assert_allclose(coefDiff, -(c1*z1 - c2*z2).coef)
 
@@ -553,13 +558,13 @@ def test_sum():
     with np.testing.assert_raises(TypeError):
         z1 - 3
     with np.testing.assert_raises(ValueError):
-        z1 + Zernike([0,1], R_outer=z1.R_outer*2)
+        z1 + galsim.zernike.Zernike([0,1], R_outer=z1.R_outer*2)
     with np.testing.assert_raises(ValueError):
-        z1 + Zernike([0,1], R_outer=z1.R_outer, R_inner=z1.R_inner*2)
+        z1 + galsim.zernike.Zernike([0,1], R_outer=z1.R_outer, R_inner=z1.R_inner*2)
 
     # Commutative with integer coefficients
-    z1 = Zernike([0,1,2,3,4])
-    z2 = Zernike([1,2,3,4,5,6])
+    z1 = galsim.zernike.Zernike([0,1,2,3,4])
+    z2 = galsim.zernike.Zernike([1,2,3,4,5,6])
     assert z1+z2 == z2+z1
     assert (z2-z1) == z2 + -z1 == -(z1-z2)
 
@@ -583,8 +588,8 @@ def test_product():
         a2 = np.empty(n2, dtype=float)
         u.generate(a1)
         u.generate(a2)
-        z1 = Zernike(a1, R_outer=R_outer, R_inner=R_inner)
-        z2 = Zernike(a2, R_outer=R_outer, R_inner=R_inner)
+        z1 = galsim.zernike.Zernike(a1, R_outer=R_outer, R_inner=R_inner)
+        z2 = galsim.zernike.Zernike(a2, R_outer=R_outer, R_inner=R_inner)
 
         np.testing.assert_allclose(
             z1(x, y) * z2(x, y),
@@ -627,15 +632,15 @@ def test_product():
     with np.testing.assert_raises(TypeError):
         z1 * galsim.Gaussian(fwhm=1)
     with np.testing.assert_raises(ValueError):
-        z1 * Zernike([0,1], R_outer=z1.R_outer*2)
+        z1 * galsim.zernike.Zernike([0,1], R_outer=z1.R_outer*2)
     with np.testing.assert_raises(ValueError):
-        z1 * Zernike([0,1], R_outer=z1.R_outer, R_inner=z1.R_inner*2)
+        z1 * galsim.zernike.Zernike([0,1], R_outer=z1.R_outer, R_inner=z1.R_inner*2)
     with np.testing.assert_raises(TypeError):
         z1 / z2
 
     # Commutative with integer coefficients
-    z1 = Zernike([0,1,2,3,4,5])
-    z2 = Zernike([1,2,3,4,5,6])
+    z1 = galsim.zernike.Zernike([0,1,2,3,4,5])
+    z2 = galsim.zernike.Zernike([1,2,3,4,5,6])
     assert z1*z2 == z2*z1
 
 
@@ -655,7 +660,7 @@ def test_laplacian():
         u.generate(a)
         R_outer = 1+0.1*u()
         R_inner = 0.1*u()
-        z = Zernike(a, R_outer=R_outer, R_inner=R_inner)
+        z = galsim.zernike.Zernike(a, R_outer=R_outer, R_inner=R_inner)
 
         np.testing.assert_allclose(
             z.laplacian(x, y),
@@ -676,7 +681,7 @@ def test_laplacian():
     # implies laplacian = 4 sqrt(3) + 4 sqrt(3) = 8 sqrt(3)
     # which is 8 sqrt(3) Z1
     np.testing.assert_allclose(
-        Zernike([0,0,0,0,1]).laplacian.coef,
+        galsim.zernike.Zernike([0,0,0,0,1]).laplacian.coef,
         np.array([0,8*np.sqrt(3)])
     )
 
@@ -686,7 +691,7 @@ def test_laplacian():
     # implies laplacian = 24 sqrt(8) y
     # which is 12*sqrt(8) * Z3 since Z3 = 2 y
     np.testing.assert_allclose(
-        Zernike([0,0,0,0,0,0,0,1]).laplacian.coef,
+        galsim.zernike.Zernike([0,0,0,0,0,0,0,1]).laplacian.coef,
         np.array([0,0,0,12*np.sqrt(8)])
     )
 
@@ -707,7 +712,7 @@ def test_hessian():
         u.generate(a)
         R_outer = 1+0.1*u()
         R_inner = 0.1*u()
-        z = Zernike(a, R_outer=R_outer, R_inner=R_inner)
+        z = galsim.zernike.Zernike(a, R_outer=R_outer, R_inner=R_inner)
 
         np.testing.assert_allclose(
             z.hessian(x, y),
@@ -728,7 +733,7 @@ def test_hessian():
     # implies hessian = 4 sqrt(3) * 4 sqrt(3) - 0 * 0 = 16*3 = 48
     # which is 48 Z1
     np.testing.assert_allclose(
-        Zernike([0,0,0,0,1]).hessian.coef,
+        galsim.zernike.Zernike([0,0,0,0,1]).hessian.coef,
         np.array([0,48])
     )
 
@@ -741,7 +746,7 @@ def test_hessian():
     # That's a little inconvenient to decompose into Zernikes by hand, but we can test against
     # an array of (x,y) values.
     np.testing.assert_allclose(
-        Zernike([0,0,0,0,0,0,0,1]).hessian(x, y),
+        galsim.zernike.Zernike([0,0,0,0,0,0,0,1]).hessian(x, y),
         864*y*y - 288*x*x
     )
 
@@ -783,7 +788,7 @@ def test_lazy_coef():
         zarr = [0]+[u() for i in range(jmax)]
         R_inner = u()*0.5+0.2
         R_outer = u()*2.0+2.0
-        Z = Zernike(zarr, R_outer=R_outer, R_inner=R_inner)
+        Z = galsim.zernike.Zernike(zarr, R_outer=R_outer, R_inner=R_inner)
         Z._coef_array_xy
         del Z.coef
         np.testing.assert_allclose(zarr, Z.coef, rtol=0, atol=1e-12)
@@ -793,7 +798,7 @@ def test_lazy_coef():
         zarr = [0]+[u() for i in range(jmax)]
         R_inner = u()*0.5+0.2
         R_outer = u()*2.0+2.0
-        Z = Zernike(zarr, R_outer=R_outer, R_inner=R_inner)
+        Z = galsim.zernike.Zernike(zarr, R_outer=R_outer, R_inner=R_inner)
         Z._coef_array_xy
         del Z.coef
         np.testing.assert_allclose(zarr, Z.coef[:len(zarr)], rtol=0, atol=1e-12)
@@ -812,7 +817,7 @@ def test_dz_val():
         uv_outer = rng.uniform(1.3, 1.7)
         xy_inner = rng.uniform(0.4, 0.7)
         xy_outer = rng.uniform(1.3, 1.7)
-        dz = DoubleZernike(
+        dz = galsim.zernike.DoubleZernike(
             coef,
             uv_inner=uv_inner,
             uv_outer=uv_outer,
@@ -836,9 +841,9 @@ def test_dz_val():
         check_pickle(dz, lambda dz_: tuple(dz_(*uv_vector, *xy_vector)))
 
         # If you don't specify xy, then get (list of) Zernike out.
-        assert isinstance(dz(*uv_scalar), Zernike)
+        assert isinstance(dz(*uv_scalar), galsim.zernike.Zernike)
         assert isinstance(dz(*uv_vector), list)
-        assert all(isinstance(z, Zernike) for z in dz(*uv_vector))
+        assert all(isinstance(z, galsim.zernike.Zernike) for z in dz(*uv_vector))
 
         # If uv scalar and xy scalar, then get scalar out.
         assert np.ndim(dz(*uv_scalar, *xy_scalar)) == 0
@@ -891,7 +896,7 @@ def test_dz_val():
             dz([0.0, 1.0], [0.0, 1.0], x=[1.0], y=[1.0])
 
     # Try pickle/repr with default domain
-    dz = DoubleZernike(coef)
+    dz = galsim.zernike.DoubleZernike(coef)
     check_pickle(dz)
 
 
@@ -908,7 +913,7 @@ def test_dz_coef_uvxy():
         uv_outer = rng.uniform(1.3, 1.7)
         xy_inner = rng.uniform(0.4, 0.7)
         xy_outer = rng.uniform(1.3, 1.7)
-        dz = DoubleZernike(
+        dz = galsim.zernike.DoubleZernike(
             coef,
             uv_inner=uv_inner,
             uv_outer=uv_outer,
@@ -988,12 +993,12 @@ def test_dz_sum():
         coef2[0] = 0.0
         coef2[:, 0] = 0.0
 
-        dz1 = DoubleZernike(
+        dz1 = galsim.zernike.DoubleZernike(
             coef1,
             uv_inner=uv_inner, uv_outer=uv_outer,
             xy_inner=xy_inner, xy_outer=xy_outer
         )
-        dz2 = DoubleZernike(
+        dz2 = galsim.zernike.DoubleZernike(
             coef2,
             uv_inner=uv_inner, uv_outer=uv_outer,
             xy_inner=xy_inner, xy_outer=xy_outer
@@ -1048,25 +1053,25 @@ def test_dz_sum():
         with np.testing.assert_raises(TypeError):
             dz1 - 3
         with np.testing.assert_raises(ValueError):
-            dz1 + DoubleZernike(
+            dz1 + galsim.zernike.DoubleZernike(
                 coef1, uv_outer=2*uv_outer, uv_inner=uv_inner, xy_outer=xy_outer, xy_inner=xy_inner
             )
         with np.testing.assert_raises(ValueError):
-            dz1 + DoubleZernike(
+            dz1 + galsim.zernike.DoubleZernike(
                 coef1, uv_outer=uv_outer, uv_inner=2*uv_inner, xy_outer=xy_outer, xy_inner=xy_inner
             )
         with np.testing.assert_raises(ValueError):
-            dz1 + DoubleZernike(
+            dz1 + galsim.zernike.DoubleZernike(
                 coef1, uv_outer=uv_outer, uv_inner=uv_inner, xy_outer=2*xy_outer, xy_inner=xy_inner
             )
         with np.testing.assert_raises(ValueError):
-            dz1 + DoubleZernike(
+            dz1 + galsim.zernike.DoubleZernike(
                 coef1, uv_outer=uv_outer, uv_inner=uv_inner, xy_outer=xy_outer, xy_inner=2*xy_inner
             )
 
         # Commutative with integer coefficients
-        dz1 = DoubleZernike(np.eye(3, dtype=int))
-        dz2 = DoubleZernike(np.ones((4, 4), dtype=int))
+        dz1 = galsim.zernike.DoubleZernike(np.eye(3, dtype=int))
+        dz2 = galsim.zernike.DoubleZernike(np.ones((4, 4), dtype=int))
         assert dz1 + dz2 == dz2 + dz1
         assert (dz2 - dz1) == dz2 + (-dz1) == -(dz1 - dz2)
 
@@ -1105,12 +1110,12 @@ def test_dz_product():
         coef2[0] = 0.0
         coef2[:, 0] = 0.0
 
-        dz1 = DoubleZernike(
+        dz1 = galsim.zernike.DoubleZernike(
             coef1,
             uv_inner=uv_inner, uv_outer=uv_outer,
             xy_inner=xy_inner, xy_outer=xy_outer
         )
-        dz2 = DoubleZernike(
+        dz2 = galsim.zernike.DoubleZernike(
             coef2,
             uv_inner=uv_inner, uv_outer=uv_outer,
             xy_inner=xy_inner, xy_outer=xy_outer
@@ -1155,27 +1160,27 @@ def test_dz_product():
     with np.testing.assert_raises(TypeError):
         dz1 * galsim.Gaussian(sigma=1.0)
     with np.testing.assert_raises(ValueError):
-        dz1 * DoubleZernike(
+        dz1 * galsim.zernike.DoubleZernike(
             coef1, uv_outer=2*uv_outer, uv_inner=uv_inner, xy_outer=xy_outer, xy_inner=xy_inner
         )
     with np.testing.assert_raises(ValueError):
-        dz1 * DoubleZernike(
+        dz1 * galsim.zernike.DoubleZernike(
             coef1, uv_outer=uv_outer, uv_inner=2*uv_inner, xy_outer=xy_outer, xy_inner=xy_inner
         )
     with np.testing.assert_raises(ValueError):
-        dz1 * DoubleZernike(
+        dz1 * galsim.zernike.DoubleZernike(
             coef1, uv_outer=uv_outer, uv_inner=uv_inner, xy_outer=2*xy_outer, xy_inner=xy_inner
         )
     with np.testing.assert_raises(ValueError):
-        dz1 * DoubleZernike(
+        dz1 * galsim.zernike.DoubleZernike(
             coef1, uv_outer=uv_outer, uv_inner=uv_inner, xy_outer=xy_outer, xy_inner=2*xy_inner
         )
     with np.testing.assert_raises(TypeError):
         dz1 / dz2
 
     # Commutative with integer coefficients
-    dz1 = DoubleZernike(np.eye(3, dtype=int))
-    dz2 = DoubleZernike(np.ones((4, 4), dtype=int))
+    dz1 = galsim.zernike.DoubleZernike(np.eye(3, dtype=int))
+    dz2 = galsim.zernike.DoubleZernike(np.ones((4, 4), dtype=int))
     assert dz1 * dz2 == dz2 * dz1
     assert (dz2 * 3) == (3 * dz2)
 
@@ -1203,7 +1208,7 @@ def test_dz_grad():
         coef[0] = 0.0
         coef[:, 0] = 0.0
 
-        dz = DoubleZernike(
+        dz = galsim.zernike.DoubleZernike(
             coef,
             uv_inner=uv_inner, uv_outer=uv_outer,
             xy_inner=xy_inner, xy_outer=xy_outer
@@ -1221,7 +1226,7 @@ def test_dz_grad():
         # U and V are trickier, since we aren't including a way to turn a DZ evaluated
         # at (x, y) into a single Zernike of (u, v).  We can mock that though up by
         # transposing the DZ coefficients and swapping the domain parameters.
-        dz_xyuv = DoubleZernike(
+        dz_xyuv = galsim.zernike.DoubleZernike(
             np.transpose(coef, axes=(1, 0)),
             uv_inner=xy_inner, uv_outer=xy_outer,
             xy_inner=uv_inner, xy_outer=uv_outer
@@ -1284,7 +1289,7 @@ def test_dz_to_T():
         coef[0] = 0.0
         coef[:, 0] = 0.0
 
-        W = DoubleZernike(
+        W = galsim.zernike.DoubleZernike(
             coef,
             uv_inner=uv_inner, uv_outer=uv_outer,  # field
             xy_inner=xy_inner, xy_outer=xy_outer   # pupil
@@ -1391,7 +1396,7 @@ def test_dz_rotate():
         coef[0] = 0.0
         coef[:, 0] = 0.0
 
-        dz = DoubleZernike(
+        dz = galsim.zernike.DoubleZernike(
             coef,
             uv_inner=uv_inner, uv_outer=uv_outer,  # field
             xy_inner=xy_inner, xy_outer=xy_outer   # pupil
@@ -1449,7 +1454,7 @@ def test_dz_basis():
                 for k in range(1, k1):
                     coef = np.zeros((k1, j1))
                     coef[k, j] = 1.0
-                    DZ = DoubleZernike(
+                    DZ = galsim.zernike.DoubleZernike(
                         coef,
                         uv_inner=uv_inner, uv_outer=uv_outer,
                         xy_inner=xy_inner, xy_outer=xy_outer
@@ -1479,7 +1484,7 @@ def test_dz_mean():
         coef[0] = 0.0
         coef[:, 0] = 0.0
 
-        dz = DoubleZernike(
+        dz = galsim.zernike.DoubleZernike(
             coef,
             uv_inner=uv_inner, uv_outer=uv_outer,
             xy_inner=xy_inner, xy_outer=xy_outer
@@ -1487,7 +1492,7 @@ def test_dz_mean():
         # We don't have a function that returns a Zernike over uv at a given xy
         # point, but we can mimic that by transposing xy an uv in a new
         # DoubleZernike object.
-        dzT = DoubleZernike(
+        dzT = galsim.zernike.DoubleZernike(
             coef.T,
             uv_inner=xy_inner, uv_outer=xy_outer,
             xy_inner=uv_inner, xy_outer=uv_outer