Add Docker usage, update readme

README.md

@@ -7,7 +7,42 @@ The manual for the original code can be found here: [manual_vfisv](https://gitla
 
 This is an implementation of VFISV code to facilitate a seamless processing of the data provided by the SDC.
 
-## Requirements
+
+## Implementation
+
+The current implementation of the pipeline is a modified version of VFISV code to work with the GRIS data. The GRIS L1 header and Stokes data is extracted
+using a Python core module and sent to a bare VFISV via an MPI intercommunicator. The inversion is performed using VFISV
+and the buffer with the inversion results is communicated back to the Python module. The Python module propagates the
+keywords from L1 and packages the inversion results and outputs a FITS file (when used as a command-line interface) or
+returns an NDarray (when called within a script).
+
+## Install and usage
+
+You can either use Docker or install it natively.
+
+### 1. Using Docker
+
+Got to the directory where the data files (`level1split`) are located.
+
+```shell
+docker run -it --rm -v $PWD:/home/grisuser ghcr.io/vigeesh/sdc-grisinv
+```
+This is pull/run the latest image from the container repo.
+
+Now, within the Docker shell
+```shell
+mpiexec -n 1 vfisv \
+  --path='data/' \
+  --id=3 \
+  --line=15648.514 \
+  --numproc=20 \
+  --width=1.8 \
+  --out='output.fits'
+```
+
+### 2. Native installation
+
+#### Requirements
 
 It is highly recommended to install the pipeline in a new `conda` environment. <br>
 
@@ -31,7 +66,7 @@ conda create -n gris_env -c conda-forge mpi4py numpy scipy gfortran_linux-64 lap
 conda activate gris_env
 ```
 
-## Installation
+#### Installation
 You can install the pipeline code from the GitLab repo directly using `pip`
 ```
 pip install git+https://gitlab.leibniz-kis.de/sdc/grisinv.git
@@ -44,14 +79,11 @@ python setup.py install
 ```
 This builds the code and installs the command-line tool (`vfisv`) and in your `conda` environment.
 
+Set the `LD_LIBRARY_PATH` so that the MPI libraries provided by conda takes precedence.
+```shell
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH
+```
 
-## Implementation
-
-The current implementation of the pipeline is a modified version of VFISV code to work with the GRIS data. The GRIS L1 header and Stokes data is extracted
-using a Python core module and sent to a bare VFISV via an MPI intercommunicator. The inversion is performed using VFISV
-and the buffer with the inversion results is communicated back to the Python module. The Python module propagates the
-keywords from L1 and packages the inversion results and outputs a FITS file (when used as a command-line interface) or
-returns an NDarray (when called within a script).
 
 ## Help
 
@@ -73,11 +105,6 @@ Options:
 
 ### Command line utility
 
-First, set the `LD_LIBRARY_PATH` so that the MPI libraries provided by conda takes precedence.
-```shell
-export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH
-```
-
 To run the VFISIV inversion on multi-processor, you can either call `mpiexec` with one processor
 and set the `--numproc` to the required number of processors to run the inversion on.