update repo links post migration vais-ral => TomographicImaging

Author: casperdcl

Installation.md

@@ -5,7 +5,7 @@
 In order to compile C/C++ sources and additional wrappers from source code for numpy 1.24 and python 3.10, the recommended way is:
 
 ```sh
-git clone https://github.com/vais-ral/CCPi-Regularisation-Toolkit
+git clone https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit
 cd CCPi-Regularisation-Toolkit
 export CCPI_BUILD_ARGS="--numpy 1.24 --python 3.10"
 build/jenkins-build.sh
@@ -36,7 +36,7 @@ Flags used during configuration
 Here an example of build on Linux (see also `run.sh` for additional info):
 
 ```sh
-git clone https://github.com/vais-ral/CCPi-Regularisation-Toolkit
+git clone https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit
 cd CCPi-Regularisation-Toolkit
 cmake -S . -B ./build_proj -DBUILD_MATLAB_WRAPPER=ON -DBUILD_PYTHON_WRAPPER=ON -DBUILD_CUDA=ON -DCMAKE_INSTALL_PREFIX=./install
 cmake --build ./build_proj --target install
@@ -69,7 +69,7 @@ python demo_gpu_regularisers.py # to run GPU demo
 One can also use some of the GPU modules directly (i.e. without the need of building the package) by using [CuPy](https://docs.cupy.dev) implementations.
 
 ```sh
-pip install git+https://github.com/vais-ral/CCPi-Regularisation-Toolkit
+pip install git+https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit
 ```
 
 > [!WARNING]

Readme.md

@@ -37,7 +37,7 @@ Iterative image reconstruction (IIR) methods frequently require regularisation t
 
 ## Installation
 
-The package comes as a [CMake](https://cmake.org) project and additional wrappers for Python and Matlab. Please see more detailed [Installation](https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/Installation.md) information.
+The package comes as a [CMake](https://cmake.org) project and additional wrappers for Python and Matlab. Please see more detailed [Installation](./Installation.md) information.
 
 ### Python binaries
 
@@ -55,7 +55,7 @@ One can also use some of the GPU modules with the provided [CuPy](https://docs.c
 conda install -c httomo ccpi-regularisation-cupy
 ```
 
-Once installed please see [Demos](https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/demos/demo_gpu_regularisers3D_CuPy.py). Please note that not all modules are yet supported as this is an ongoing development. One can install both CuPy-driven and the `ccpi-regulariser` packge in one environment, but please be aware that the functions carry the identical names.
+Once installed please see [Demos](./demos/demo_gpu_regularisers3D_CuPy.py). Please note that not all modules are yet supported as this is an ongoing development. One can install both CuPy-driven and the `ccpi-regulariser` package in one environment, but please be aware that the functions carry the identical names.
 
 ## References
 

demos/SoftwareX_supp/Readme.md

@@ -1,7 +1,7 @@
 # Software X journal publication supporting files
 
 ## Description:
-The scripts support [publication](https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/demos/SoftwareX_supp/paper/1-s2.0-S2352711018301912-main.pdf) in Software X journal [1] to ensure reproducibility of the research. The scripts linked with the data which is shared at [Zenodo](https://doi.org/10.5281/zenodo.2578893). 
+The scripts support [publication](./paper/1-s2.0-S2352711018301912-main.pdf) in Software X journal [1] to ensure reproducibility of the research. The scripts linked with the data which is shared at [Zenodo](https://doi.org/10.5281/zenodo.2578893).
 
 ## Data:
 Data is shared at Zenodo [here](https://doi.org/10.5281/zenodo.2578893)
@@ -11,15 +11,14 @@ Data is shared at Zenodo [here](https://doi.org/10.5281/zenodo.2578893)
 2. [ToMoBAR](https://github.com/dkazanc/ToMoBAR): `conda install -c dkazanc tomobar`
 3. [Tomophantom](https://github.com/dkazanc/TomoPhantom): `conda install tomophantom -c ccpi`
 
-## Files description: 
+## Files description:
 - `Demo_SimulData_SX.py` - simulates 3D projection data using [Tomophantom](https://github.com/dkazanc/TomoPhantom) software. One can skip this module if the data is taken from [Zenodo](https://doi.org/10.5281/zenodo.2578893)
-- `Demo_SimulData_ParOptimis_SX.py` - runs computationally extensive calculations for optimal regularisation parameters, the result are saved into directory `optim_param`. This script can be also skipped. 
+- `Demo_SimulData_ParOptimis_SX.py` - runs computationally extensive calculations for optimal regularisation parameters, the result are saved into directory `optim_param`. This script can be also skipped.
 - `Demo_SimulData_Recon_SX.py` - using established regularisation parameters, one runs iterative reconstruction
-- `Demo_RealData_Recon_SX.py` - runs real data reconstructions. Can be quite intense on memory so reduce the size of the reconstructed volume if needed. 
+- `Demo_RealData_Recon_SX.py` - runs real data reconstructions. Can be quite intense on memory so reduce the size of the reconstructed volume if needed.
 
 ### References:
-[1] [Kazantsev, D., Pasca, E., Turner, M.J. and Withers, P.J., 2019. CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX, 9, pp.317-323.](https://www.sciencedirect.com/science/article/pii/S2352711018301912) 
+[1] [Kazantsev, D., Pasca, E., Turner, M.J. and Withers, P.J., 2019. CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX, 9, pp.317-323.](https://www.sciencedirect.com/science/article/pii/S2352711018301912)
 
 ### Acknowledgments:
 CCPi-RGL software is a product of the [CCPi](https://www.ccpi.ac.uk/) group, STFC SCD software developers and Diamond Light Source (DLS). Any relevant questions/comments can be e-mailed to Daniil Kazantsev at dkazanc@hotmail.com
-