Merge pull request #120 from DiamondLightSource/notebooks

Notebooks updates, GA token

.scripts/conda_upload.sh

@@ -19,7 +19,7 @@ export CONDA_BLD_PATH=~/conda-bld
 $CONDA/bin/conda install conda-build
 $CONDA/bin/conda install -c anaconda anaconda-client
 
-$CONDA/bin/conda build . -c conda-forge -c httomo -c astra-toolbox --no-test
+$CONDA/bin/conda build . -c conda-forge -c httomo -c astra-toolbox -c rapidsai --no-test
 
 # upload packages to conda
 find $CONDA_BLD_PATH/$OS -name *.tar.bz2 | while read file

README.rst

@@ -3,8 +3,11 @@ HTTomolibGPU is a library of GPU accelerated methods for tomography
 
 **HTTomolibGPU** is a collection of image processing methods in Python for computed tomography.
 The methods are GPU-accelerated with the open-source Python library `CuPy <https://cupy.dev/>`_. Most of the 
-methods migrated from `TomoPy <https://tomopy.readthedocs.io/en/stable/>`_ and `Savu <https://savu.readthedocs.io/en/latest/>`_ software packages. They have been
-optimised to ensure computational efficiency and high-throughput.
+methods migrated from `TomoPy <https://tomopy.readthedocs.io/en/stable/>`_ and `Savu <https://savu.readthedocs.io/en/latest/>`_ software packages.
+They have been optimised to ensure higher computational efficiency.
+
+Purpose of HTTomolibGPU
+=======================
 
 **HTTomolibGPU** can be used as a stand-alone library, but it has been specifically developed to 
 work together with the `HTTomo <https://diamondlightsource.github.io/httomo/>`_ package.
@@ -15,7 +18,7 @@ Install HTTomolibGPU as a pre-built conda Python package
 .. code-block:: console
 
    $ conda create --name httomolibgpu # create a fresh conda environment
-   $ conda install -c httomo httomolibgpu
+   $ conda install httomo::httomolibgpu
 
 Setup the development environment:
 ==================================
@@ -25,32 +28,16 @@ Setup the development environment:
    $ git clone [email protected]:DiamondLightSource/httomolibgpu.git # clone the repo
    $ conda env create --name httomolibgpu --file conda/environment.yml # install dependencies
    $ conda activate httomolibgpu # activate the environment
-   $ pip install .[dev] # development mode
+   $ pip install -e .[dev] # editable/development mode
 
 Build HTTomolibGPU as a conda Python package
-=============================================
+============================================
 
 .. code-block:: console
 
-   $ conda build conda/recipe/ -c conda-forge -c httomo -c astra-toolbox
-
-An example of using the API
-===========================
-* The file :code:`examples/normalize-data.py` shows how to apply dark-flat field correction to the :code:`tests/test_data/tomo_standard.npz` data.
-* The file :code:`examples/fresnel-filter.py` shows how to apply Fresnel filtering to the :code:`tests/test_data/tomo_standard.npz` data.
-
-Input data for methods
-======================
+   $ conda build conda/recipe/ -c conda-forge -c httomo -c astra-toolbox -c rapidsai
 
-* We load the projection data from the file :code:`tests/test_data/tomo_standard.npz` using :code:`numpy.load`, which returns a dictionary-like object that can be indexed using the keys :code:`'data'` (to get :code:`host_data`), :code:`'flats'`, and :code:`'darks'`.
-* The dataset :code:`/data` in :code:`tests/test_data/normalized-projs.h5` is the input for methods in :code:`httomolibgpu.prep.stripe`
-* The dataset :code:`/data` in :code:`tests/test_data/removed-stripes.h5` is the input for methods in :code:`httomolibgpu.recon.rotation`
-
-Run tests
+Examples
 =========
-* Run all tests with :code:`$ pytest`. To increase verbosity, use :code:`$ pytest -v`.
-* Run GPU tests separately with :code:`$ pytest -v -m gpu`.
-* Run CPU tests separately with :code:`$ pytest -v -m "not gpu"`.
-* Run performance tests (only) with :code:`$ pytest --performance`
-  (note that performance tests always fail - they report the execution time in an assertion
-  to see them in the summary easily)
+
+There are series of Jupyter Notebooks located in :code:`examples/*` to demonstrate how some of the functions can be used.

conda/recipe/conda_build_config.yaml

@@ -3,3 +3,4 @@ python:
   - 3.10
 numpy:
   - 1.24
+  - 1.25

httomolibgpu/misc/corr.py

@@ -29,9 +29,6 @@
 import nvtx
 from cupy import float32
 
-from cucim.skimage.filters import median
-from cucim.skimage.morphology import disk
-
 from httomolibgpu.cuda_kernels import load_cuda_module
 
 __all__ = [
@@ -72,6 +69,9 @@ def median_filter(
     ValueError
         If the input array is not three dimensional.
     """
+    from cucim.skimage.filters import median
+    from cucim.skimage.morphology import disk
+
     input_type = data.dtype
 
     if input_type not in ["float32", "uint16"]: