diff --git a/CHANGES.rst b/CHANGES.rst
index e5aa718b..eb4c3b97 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -20,7 +20,7 @@ Version 0.0.6   (unreleased)
 • Rename ``scico.flax.save_weights`` and ``scico.flax.load_weights`` to
   ``scico.flax.save_variables`` and ``scico.flax.load_variables``
   respectively.
-• Support ``jaxlib`` and ``jax`` versions 0.4.3 to 0.4.33.
+• Support ``jaxlib`` and ``jax`` versions 0.4.3 to 0.4.34.
 • Support ``flax`` versions 0.8.0 to 0.9.0.
 
 
diff --git a/data b/data
index c1233896..1ceadbbb 160000
--- a/data
+++ b/data
@@ -1 +1 @@
-Subproject commit c12338966b1b9f92554066743b1a8b664c7b7e24
+Subproject commit 1ceadbbbe6bef9f364fd76dbda44ff6e185a7d10
diff --git a/docs/source/examples.rst b/docs/source/examples.rst
index 58ba847f..b97a52bd 100644
--- a/docs/source/examples.rst
+++ b/docs/source/examples.rst
@@ -34,12 +34,11 @@ Computed Tomography
    examples/ct_svmbir_ppp_bm3d_admm_cg
    examples/ct_svmbir_ppp_bm3d_admm_prox
    examples/ct_fan_svmbir_ppp_bm3d_admm_prox
-   examples/ct_astra_modl_train_foam2
-   examples/ct_astra_odp_train_foam2
-   examples/ct_astra_unet_train_foam2
+   examples/ct_modl_train_foam2
+   examples/ct_odp_train_foam2
+   examples/ct_unet_train_foam2
    examples/ct_projector_comparison_2d
    examples/ct_projector_comparison_3d
-   examples/ct_multi_cs_tv_admm
    examples/ct_multi_tv_admm
 
 Deconvolution
@@ -96,7 +95,7 @@ Miscellaneous
    examples/denoise_dncnn_universal
    examples/diffusercam_tv_admm
    examples/video_rpca_admm
-   examples/ct_astra_datagen_foam2
+   examples/ct_datagen_foam2
    examples/deconv_datagen_bsds
    examples/deconv_datagen_foam1
    examples/denoise_datagen_bsds
@@ -181,10 +180,10 @@ Machine Learning
 .. toctree::
    :maxdepth: 1
 
-   examples/ct_astra_datagen_foam2
-   examples/ct_astra_modl_train_foam2
-   examples/ct_astra_odp_train_foam2
-   examples/ct_astra_unet_train_foam2
+   examples/ct_datagen_foam2
+   examples/ct_modl_train_foam2
+   examples/ct_odp_train_foam2
+   examples/ct_unet_train_foam2
    examples/deconv_datagen_bsds
    examples/deconv_datagen_foam1
    examples/deconv_modl_train_foam1
diff --git a/examples/jnb.py b/examples/jnb.py
index 9135add1..f51c3f9a 100644
--- a/examples/jnb.py
+++ b/examples/jnb.py
@@ -60,10 +60,10 @@ def py_file_to_string(src):
 
         # Process remainder of source file
         for line in srcfile:
-            if re.match("^input\(", line):  # end processing when input statement encountered
+            if re.match(r"^input\(", line):  # end processing when input statement encountered
                 break
             line = re.sub('^r"""', '"""', line)  # remove r from r"""
-            line = re.sub(":cite:\`([^`]+)\`", r'<cite data-cite="\1"/>', line)  # fix cite format
+            line = re.sub(r":cite:\`([^`]+)\`", r'<cite data-cite="\1"/>', line)  # fix cite format
             lines.append(line)
 
         # Backtrack through list of lines to remove trailing newlines
diff --git a/examples/scripts/README.rst b/examples/scripts/README.rst
index 446186a7..95e11f7c 100644
--- a/examples/scripts/README.rst
+++ b/examples/scripts/README.rst
@@ -33,11 +33,11 @@ Computed Tomography
       PPP (with BM3D) CT Reconstruction (ADMM with Fast SVMBIR Prox)
    `ct_fan_svmbir_ppp_bm3d_admm_prox.py <ct_fan_svmbir_ppp_bm3d_admm_prox.py>`_
       PPP (with BM3D) Fan-Beam CT Reconstruction
-   `ct_astra_modl_train_foam2.py <ct_astra_modl_train_foam2.py>`_
-      CT Training and Reconstructions with MoDL
-   `ct_astra_odp_train_foam2.py <ct_astra_odp_train_foam2.py>`_
-      CT Training and Reconstructions with ODP
-   `ct_astra_unet_train_foam2.py <ct_astra_unet_train_foam2.py>`_
+   `ct_modl_train_foam2.py <ct_modl_train_foam2.py>`_
+      CT Training and Reconstruction with MoDL
+   `ct_odp_train_foam2.py <ct_odp_train_foam2.py>`_
+      CT Training and Reconstruction with ODP
+   `ct_unet_train_foam2.py <ct_unet_train_foam2.py>`_
       CT Training and Reconstructions with UNet
    `ct_projector_comparison_2d.py <ct_projector_comparison_2d.py>`_
       2D X-ray Transform Comparison
@@ -123,7 +123,7 @@ Miscellaneous
       TV-Regularized 3D DiffuserCam Reconstruction
    `video_rpca_admm.py <video_rpca_admm.py>`_
       Video Decomposition via Robust PCA
-   `ct_astra_datagen_foam2.py <ct_astra_datagen_foam2.py>`_
+   `ct_datagen_foam2.py <ct_datagen_foam2.py>`_
       CT Data Generation for NN Training
    `deconv_datagen_bsds.py <deconv_datagen_bsds.py>`_
       Blurred Data Generation (Natural Images) for NN Training
@@ -239,13 +239,13 @@ Sparsity
 Machine Learning
 ^^^^^^^^^^^^^^^^
 
-   `ct_astra_datagen_foam2.py <ct_astra_datagen_foam2.py>`_
+   `ct_datagen_foam2.py <ct_datagen_foam2.py>`_
       CT Data Generation for NN Training
-   `ct_astra_modl_train_foam2.py <ct_astra_modl_train_foam2.py>`_
-      CT Training and Reconstructions with MoDL
-   `ct_astra_odp_train_foam2.py <ct_astra_odp_train_foam2.py>`_
-      CT Training and Reconstructions with ODP
-   `ct_astra_unet_train_foam2.py <ct_astra_unet_train_foam2.py>`_
+   `ct_modl_train_foam2.py <ct_modl_train_foam2.py>`_
+      CT Training and Reconstruction with MoDL
+   `ct_odp_train_foam2.py <ct_odp_train_foam2.py>`_
+      CT Training and Reconstruction with ODP
+   `ct_unet_train_foam2.py <ct_unet_train_foam2.py>`_
       CT Training and Reconstructions with UNet
    `deconv_datagen_bsds.py <deconv_datagen_bsds.py>`_
       Blurred Data Generation (Natural Images) for NN Training
diff --git a/examples/scripts/ct_astra_datagen_foam2.py b/examples/scripts/ct_datagen_foam2.py
similarity index 93%
rename from examples/scripts/ct_astra_datagen_foam2.py
rename to examples/scripts/ct_datagen_foam2.py
index 4e6fb97c..2fc9be59 100644
--- a/examples/scripts/ct_astra_datagen_foam2.py
+++ b/examples/scripts/ct_datagen_foam2.py
@@ -14,6 +14,7 @@
 """
 
 # isort: off
+import os
 import numpy as np
 
 import logging
@@ -21,6 +22,9 @@
 
 ray.init(logging_level=logging.ERROR)  # need to call init before jax import: ray-project/ray#44087
 
+# Set an arbitrary processor count (only applies if GPU is not available).
+os.environ["XLA_FLAGS"] = "--xla_force_host_platform_device_count=8"
+
 from scico import plot
 from scico.flax.examples import load_ct_data
 
diff --git a/examples/scripts/ct_astra_modl_train_foam2.py b/examples/scripts/ct_modl_train_foam2.py
similarity index 96%
rename from examples/scripts/ct_astra_modl_train_foam2.py
rename to examples/scripts/ct_modl_train_foam2.py
index a06d7b81..19a3d810 100644
--- a/examples/scripts/ct_astra_modl_train_foam2.py
+++ b/examples/scripts/ct_modl_train_foam2.py
@@ -5,8 +5,8 @@
 # with the package.
 
 r"""
-CT Training and Reconstructions with MoDL
-=========================================
+CT Training and Reconstruction with MoDL
+========================================
 
 This example demonstrates the training and application of a
 model-based deep learning (MoDL) architecture described in
@@ -65,7 +65,7 @@
 from scico import metric, plot
 from scico.flax.examples import load_ct_data
 from scico.flax.train.traversals import clip_positive, construct_traversal
-from scico.linop.xray.astra import XRayTransform2D
+from scico.linop.xray import XRayTransform2D
 
 """
 Prepare parallel processing. Set an arbitrary processor count (only
@@ -89,16 +89,17 @@
 
 
 """
-Build CT projection operator.
+Build CT projection operator. Parameters are chosen so that the operator
+is equivalent to the one used to generate the training data.
 """
 angles = np.linspace(0, np.pi, n_projection)  # evenly spaced projection angles
 A = XRayTransform2D(
     input_shape=(N, N),
-    det_spacing=1,
-    det_count=N,
     angles=angles,
-)  # CT projection operator
-A = (1.0 / N) * A  # normalized
+    det_count=int(N * 1.05 / np.sqrt(2.0)),
+    dx=1.0 / np.sqrt(2),
+)
+A = (1.0 / N) * A  # normalize projection operator
 
 
 """
diff --git a/examples/scripts/ct_multi_tv_admm.py b/examples/scripts/ct_multi_tv_admm.py
index 95711679..f2f13fd8 100644
--- a/examples/scripts/ct_multi_tv_admm.py
+++ b/examples/scripts/ct_multi_tv_admm.py
@@ -38,7 +38,7 @@
 np.random.seed(1234)
 x_gt = snp.array(discrete_phantom(Foam(size_range=[0.075, 0.0025], gap=1e-3, porosity=1), size=N))
 
-det_count = N
+det_count = int(N * 1.05 / np.sqrt(2.0))
 det_spacing = np.sqrt(2)
 
 
diff --git a/examples/scripts/ct_astra_odp_train_foam2.py b/examples/scripts/ct_odp_train_foam2.py
similarity index 94%
rename from examples/scripts/ct_astra_odp_train_foam2.py
rename to examples/scripts/ct_odp_train_foam2.py
index 03753c1b..e5cd58ae 100644
--- a/examples/scripts/ct_astra_odp_train_foam2.py
+++ b/examples/scripts/ct_odp_train_foam2.py
@@ -5,8 +5,8 @@
 # with the package.
 
 r"""
-CT Training and Reconstructions with ODP
-========================================
+CT Training and Reconstruction with ODP
+=======================================
 
 This example demonstrates the training of the unrolled optimization with
 deep priors (ODP) gradient descent architecture described in
@@ -72,7 +72,7 @@
 from scico import metric, plot
 from scico.flax.examples import load_ct_data
 from scico.flax.train.traversals import clip_positive, construct_traversal
-from scico.linop.xray.astra import XRayTransform2D
+from scico.linop.xray import XRayTransform2D
 
 
 platform = get_backend().platform
@@ -92,21 +92,22 @@
 
 
 """
-Build CT projection operator.
+Build CT projection operator. Parameters are chosen so that the operator
+is equivalent to the one used to generate the training data.
 """
 angles = np.linspace(0, np.pi, n_projection)  # evenly spaced projection angles
 A = XRayTransform2D(
     input_shape=(N, N),
-    det_spacing=1,
-    det_count=N,
     angles=angles,
-)  # CT projection operator
-A = (1.0 / N) * A  # normalized
+    det_count=int(N * 1.05 / np.sqrt(2.0)),
+    dx=1.0 / np.sqrt(2),
+)
+A = (1.0 / N) * A  # normalize projection operator
 
 
 """
 Build training and testing structures. Inputs are the sinograms and
-outpus are the original generated foams. Keep training and testing
+outputs are the original generated foams. Keep training and testing
 partitions.
 """
 numtr = 320
diff --git a/examples/scripts/ct_projector_comparison_2d.py b/examples/scripts/ct_projector_comparison_2d.py
index 2e5d02d3..0a47c7b0 100644
--- a/examples/scripts/ct_projector_comparison_2d.py
+++ b/examples/scripts/ct_projector_comparison_2d.py
@@ -29,9 +29,6 @@
 Create a ground truth image.
 """
 N = 512
-
-det_count = int(jnp.ceil(jnp.sqrt(2 * N**2)))
-
 x_gt = discrete_phantom(Foam(size_range=[0.075, 0.0025], gap=1e-3, porosity=1), size=N)
 x_gt = jnp.array(x_gt)
 
@@ -41,17 +38,18 @@
 """
 num_angles = 500
 angles = jnp.linspace(0, jnp.pi, num=num_angles, endpoint=False)
+det_count = int(N * 1.02 / jnp.sqrt(2.0))
 
 timer = Timer()
 
 projectors = {}
 timer.start("scico_init")
-projectors["scico"] = XRayTransform2D((N, N), angles)
+projectors["scico"] = XRayTransform2D((N, N), angles, det_count=det_count)
 timer.stop("scico_init")
 
 timer.start("astra_init")
 projectors["astra"] = astra.XRayTransform2D(
-    (N, N), det_count=det_count, det_spacing=1.0, angles=angles - jnp.pi / 2.0
+    (N, N), det_count=det_count, det_spacing=np.sqrt(2), angles=angles - jnp.pi / 2.0
 )
 timer.stop("astra_init")
 
diff --git a/examples/scripts/ct_astra_unet_train_foam2.py b/examples/scripts/ct_unet_train_foam2.py
similarity index 100%
rename from examples/scripts/ct_astra_unet_train_foam2.py
rename to examples/scripts/ct_unet_train_foam2.py
diff --git a/examples/scripts/index.rst b/examples/scripts/index.rst
index 4f05ba2f..e36e9fbd 100644
--- a/examples/scripts/index.rst
+++ b/examples/scripts/index.rst
@@ -21,9 +21,9 @@ Computed Tomography
    - ct_svmbir_ppp_bm3d_admm_cg.py
    - ct_svmbir_ppp_bm3d_admm_prox.py
    - ct_fan_svmbir_ppp_bm3d_admm_prox.py
-   - ct_astra_modl_train_foam2.py
-   - ct_astra_odp_train_foam2.py
-   - ct_astra_unet_train_foam2.py
+   - ct_modl_train_foam2.py
+   - ct_odp_train_foam2.py
+   - ct_unet_train_foam2.py
    - ct_projector_comparison_2d.py
    - ct_projector_comparison_3d.py
    - ct_multi_tv_admm.py
@@ -73,7 +73,7 @@ Miscellaneous
    - denoise_dncnn_universal.py
    - diffusercam_tv_admm.py
    - video_rpca_admm.py
-   - ct_astra_datagen_foam2.py
+   - ct_datagen_foam2.py
    - deconv_datagen_bsds.py
    - deconv_datagen_foam1.py
    - denoise_datagen_bsds.py
@@ -143,10 +143,10 @@ Sparsity
 Machine Learning
 ^^^^^^^^^^^^^^^^
 
-   - ct_astra_datagen_foam2.py
-   - ct_astra_modl_train_foam2.py
-   - ct_astra_odp_train_foam2.py
-   - ct_astra_unet_train_foam2.py
+   - ct_datagen_foam2.py
+   - ct_modl_train_foam2.py
+   - ct_odp_train_foam2.py
+   - ct_unet_train_foam2.py
    - deconv_datagen_bsds.py
    - deconv_datagen_foam1.py
    - deconv_modl_train_foam1.py
diff --git a/misc/conda/install_conda.sh b/misc/conda/install_conda.sh
index 73defcaa..744d1836 100755
--- a/misc/conda/install_conda.sh
+++ b/misc/conda/install_conda.sh
@@ -97,7 +97,6 @@ rm -f /tmp/miniconda.sh
 export PATH="$CONDAHOME/bin:$PATH"
 hash -r
 conda config --set always_yes yes
-conda install mamba -n base -c conda-forge
 conda update -q conda
 conda info -a
 
diff --git a/misc/conda/make_conda_env.sh b/misc/conda/make_conda_env.sh
index b5aa4411..cab2b7e2 100755
--- a/misc/conda/make_conda_env.sh
+++ b/misc/conda/make_conda_env.sh
@@ -50,7 +50,7 @@ EOF
 )
 # Requirements that cannot be installed via conda (i.e. have to use pip)
 NOCONDA=$(cat <<-EOF
-flax bm3d bm4d py2jn colour_demosaicing hyperopt ray[tune,train]
+flax orbax-checkpoint bm3d bm4d py2jn colour_demosaicing hyperopt ray[tune,train]
 EOF
 )
 
@@ -217,19 +217,16 @@ eval "$(conda shell.bash hook)"  # required to avoid errors re: `conda init`
 conda activate $ENVNM  # Q: why not `source activate`? A: not always in the path
 
 # Add conda-forge channel
-conda config --env --append channels conda-forge
-
-# Install mamba
-conda install mamba -n base -c conda-forge
+conda config --append channels conda-forge
 
 # Install required conda packages (and extra useful packages)
-mamba install $CONDA_FLAGS $CONDAREQ ipython
+conda install $CONDA_FLAGS $CONDAREQ ipython
 
 # Utility ffmpeg is required by imageio for reading mp4 video files
 # it can also be installed via the system package manager, .e.g.
 #    sudo apt install ffmpeg
 if [ "$(which ffmpeg)" = '' ]; then
-    mamba install $CONDA_FLAGS ffmpeg
+    conda install $CONDA_FLAGS ffmpeg
 fi
 
 # Install jaxlib and jax
diff --git a/requirements.txt b/requirements.txt
index 1b0e5359..feab852c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,8 +4,8 @@ scipy>=1.6.0
 imageio>=2.17
 tifffile
 matplotlib
-jaxlib>=0.4.3,<=0.4.33
-jax>=0.4.3,<=0.4.33
+jaxlib>=0.4.3,<=0.4.34
+jax>=0.4.3,<=0.4.34
 orbax-checkpoint>=0.5.0
 flax>=0.8.0,<=0.9.0
 pyabel>=0.9.0
diff --git a/scico/flax/examples/data_generation.py b/scico/flax/examples/data_generation.py
index e7a99030..e89f10cf 100644
--- a/scico/flax/examples/data_generation.py
+++ b/scico/flax/examples/data_generation.py
@@ -51,16 +51,9 @@ class UnitCircle:
     from jax.lib.xla_bridge import get_backend
 
 from scico.linop import CircularConvolve
+from scico.linop.xray import XRayTransform2D
 from scico.numpy import Array
 
-try:
-    import astra  # noqa: F401
-except ImportError:
-    have_astra = False
-else:
-    have_astra = True
-    from scico.linop.xray.astra import XRayTransform2D
-
 
 class Foam2(UnitCircle):
     """Foam-like material with two attenuations.
@@ -218,10 +211,8 @@ def generate_ct_data(
            - **sino** : (:class:`jax.Array`): Corresponding sinograms.
            - **fbp** : (:class:`jax.Array`) Corresponding filtered back projections.
     """
-    if not (have_ray and have_xdesign and have_astra):
-        raise RuntimeError(
-            "Packages ray, xdesign, and astra are required for use of this function."
-        )
+    if not (have_ray and have_xdesign):
+        raise RuntimeError("Packages ray and xdesign are required for use of this function.")
 
     # Generate input data.
     start_time = time()
@@ -234,17 +225,17 @@ def generate_ct_data(
 
     # Configure a CT projection operator to generate synthetic measurements.
     angles = np.linspace(0, jnp.pi, nproj)  # evenly spaced projection angles
-    gt_sh = (size, size)
-    detector_spacing = 1.0
-    A = XRayTransform2D(gt_sh, size, detector_spacing, angles)  # X-ray transform operator
-
+    gt_shape = (size, size)
+    dx = 1.0 / np.sqrt(2)
+    det_count = int(size * 1.05 / np.sqrt(2.0))
+    A = XRayTransform2D(gt_shape, angles, dx=dx, det_count=det_count)
     # Compute sinograms in parallel.
     start_time = time()
     if nproc > 1:
         # shard array
         imgshd = img.reshape((nproc, -1, size, size, 1))
         sinoshd = batched_f(A, imgshd)
-        sino = sinoshd.reshape((-1, nproj, size, 1))
+        sino = sinoshd.reshape((-1, nproj, sinoshd.shape[-2], 1))
     else:
         sino = vector_f(A, img)
 
@@ -261,8 +252,8 @@ def generate_ct_data(
 
     # Normalize sinogram.
     sino = sino / size
-    # Shift FBP to [0,1] range.
-    fbp = (fbp - fbp.min()) / (fbp.max() - fbp.min())
+    # Clip FBP to [0,1] range.
+    fbp = np.clip(fbp, 0, 1)
 
     if verbose:  # pragma: no cover
         platform = get_backend().platform
diff --git a/scico/functional/_functional.py b/scico/functional/_functional.py
index 256791a7..1cbef6fc 100644
--- a/scico/functional/_functional.py
+++ b/scico/functional/_functional.py
@@ -117,7 +117,7 @@ def conj_prox(
         return v - lam * self.prox(v / lam, 1.0 / lam, **kwargs)
 
     def grad(self, x: Union[Array, BlockArray]):
-        r"""Evaluates the gradient of this functional at :math:`\mb{x}`.
+        r"""Evaluate the gradient of this functional at :math:`\mb{x}`.
 
         Args:
             x: Point at which to evaluate gradient.
diff --git a/scico/linop/xray/_xray.py b/scico/linop/xray/_xray.py
index 83bd8462..9d459db5 100644
--- a/scico/linop/xray/_xray.py
+++ b/scico/linop/xray/_xray.py
@@ -9,7 +9,7 @@
 
 
 from functools import partial
-from typing import Optional
+from typing import Optional, Tuple
 from warnings import warn
 
 import numpy as np
@@ -273,14 +273,14 @@ def _back_project(
             y[jnp.arange(len(angles)).reshape(-1, 1, 1), inds + 1] * weights_valid, axis=0
         )
 
-        return HTy
+        return HTy.astype(jnp.float32)
 
     @staticmethod
     @partial(jax.jit, static_argnames=["nx"])
     @partial(jax.vmap, in_axes=(None, None, None, 0, None))
     def _calc_weights(
-        x0: ArrayLike, dx: ArrayLike, nx: Shape, angle: float, y0: float
-    ) -> snp.Array:
+        x0: ArrayLike, dx: ArrayLike, nx: Shape, angles: ArrayLike, y0: float
+    ) -> Tuple[snp.Array, snp.Array]:
         """
 
         Args:
@@ -288,12 +288,12 @@ def _calc_weights(
             dx: Pixel side length in x- and y-direction. Units are such
                 that the detector bins have length 1.0.
             nx: Input image shape.
-            angle: (num_angles,) array of angles in radians. Pixels are
+            angles: (num_angles,) array of angles in radians. Pixels are
                 projected onto units vectors pointing in these directions.
                 (This argument is `vmap`ed.)
             y0: Location of the edge of the first detector bin.
         """
-        u = [jnp.cos(angle), jnp.sin(angle)]
+        u = [jnp.cos(angles), jnp.sin(angles)]
         Px0 = x0[0] * u[0] + x0[1] * u[1] - y0
         Pdx = [dx[0] * u[0], dx[1] * u[1]]
         Pxmin = jnp.min(jnp.array([Px0, Px0 + Pdx[0], Px0 + Pdx[1], Px0 + Pdx[0] + Pdx[1]]))
diff --git a/scico/plot.py b/scico/plot.py
index 6c0375be..72ae4962 100644
--- a/scico/plot.py
+++ b/scico/plot.py
@@ -13,6 +13,7 @@
 
 # This module is copied from https://github.com/bwohlberg/sporco
 
+import os
 import sys
 
 import numpy as np
@@ -820,7 +821,8 @@ def config_notebook_plotting():
     Configure plotting functions for inline plotting within a Jupyter
     Notebook shell. This function has no effect when not within a
     notebook shell, and may therefore be used within a normal python
-    script.
+    script. If environment variable ``MATPLOTLIB_IPYNB_BACKEND`` is set,
+    the matplotlib backend is explicitly set to the specified value.
     """
 
     # Check whether running within a notebook shell and have
@@ -828,8 +830,9 @@ def config_notebook_plotting():
     module = sys.modules[__name__]
     if _in_notebook() and module.plot.__name__ == "plot":
 
-        # Set inline backend (i.e. %matplotlib inline) if in a notebook shell
-        set_notebook_plot_backend()
+        # Set backend if specified by environment variable
+        if "MATPLOTLIB_IPYNB_BACKEND" in os.environ:
+            set_notebook_plot_backend(os.environ["MATPLOTLIB_IPYNB_BACKEND"])
 
         # Replace plot function with a wrapper function that discards
         # its return value (within a notebook with inline plotting, plots
diff --git a/scico/test/flax/test_examples_flax.py b/scico/test/flax/test_examples_flax.py
index 72c084dd..ce265d05 100644
--- a/scico/test/flax/test_examples_flax.py
+++ b/scico/test/flax/test_examples_flax.py
@@ -12,7 +12,6 @@
     generate_ct_data,
     generate_foam1_images,
     generate_foam2_images,
-    have_astra,
     have_ray,
     have_xdesign,
 )
@@ -75,8 +74,8 @@ def random_data_gen(seed, N, ndata):
 
 
 @pytest.mark.skipif(
-    not have_astra or not have_ray or not have_xdesign,
-    reason="astra, ray, or xdesign package not installed",
+    not have_ray or not have_xdesign,
+    reason="ray or xdesign package not installed",
 )
 def test_ct_data_generation():
     N = 32
@@ -90,7 +89,7 @@ def random_img_gen(seed, size, ndata):
 
     img, sino, fbp = generate_ct_data(nimg, N, nproj, imgfunc=random_img_gen)
     assert img.shape == (nimg, N, N, 1)
-    assert sino.shape == (nimg, nproj, N, 1)
+    assert sino.shape == (nimg, nproj, sino.shape[2], 1)
     assert fbp.shape == (nimg, N, N, 1)
 
 
diff --git a/scico/test/flax/test_inv.py b/scico/test/flax/test_inv.py
index b63a88a8..d49df64b 100644
--- a/scico/test/flax/test_inv.py
+++ b/scico/test/flax/test_inv.py
@@ -6,18 +6,12 @@
 import jax.numpy as jnp
 from jax import lax
 
-import pytest
-
 from scico import flax as sflax
 from scico import random
 from scico.flax.examples import PaddedCircularConvolve, build_blur_kernel
-from scico.flax.examples.data_generation import have_astra
 from scico.flax.train.traversals import clip_positive, clip_range, construct_traversal
 from scico.linop import CircularConvolve, Identity
-
-if have_astra:
-    from scico.linop.xray.astra import XRayTransform2D
-
+from scico.linop.xray import XRayTransform2D
 
 os.environ["XLA_FLAGS"] = "--xla_force_host_platform_device_count=8"
 
@@ -153,7 +147,6 @@ def test_train_odpdcnv_default(self):
             np.testing.assert_array_less(1e-2 * np.ones(alphaval.shape), alphaval)
 
 
-@pytest.mark.skipif(not have_astra, reason="astra package not installed")
 class TestCT:
     def setup_method(self, method):
         self.N = 32  # signal size
@@ -162,11 +155,10 @@ def setup_method(self, method):
         xt, key = random.randn((2 * self.bsize, self.N, self.N, self.chn), seed=4321)
 
         self.nproj = 60  # number of projections
-        angles = np.linspace(0, np.pi, self.nproj)  # evenly spaced projection angles
+        angles = np.linspace(0, np.pi, self.nproj, endpoint=False, dtype=np.float32)
         self.opCT = XRayTransform2D(
             input_shape=(self.N, self.N),
             det_count=self.N,
-            det_spacing=1.0,
             angles=angles,
         )  # Radon transform operator
         a_f = lambda v: jnp.atleast_3d(self.opCT(v.squeeze()))