Update README.md

README.md

 # resite
-A framework for RES siting leveraging resource complementarity.
 
-Input and resource data available [here](https://dox.uliege.be/index.php/s/trys2xY7j9JsQ3z).
+This file provides the information required to set up the __resite__ tool. Also, it provides a minimal example for siting
+RES assets.  
 
-TO BE DETAILED SOON.
+# Setup
+
+This module runs in any 3.x version of Python. The Python packages required to run this module are listed 
+in the `requirements.yml` file included in the repository. Their installation can be easily achieved via the the following
+command that builds a separate environment for this module from the `yml` file:
+   
+    conda env create -f environment.yml
+    
+In addition, the tool also requires the installation of an MILP solver. At the time of writing, `gurobi` is the only
+solver this model supports. 
+
+At the time of writing, the model readily supports (given the availability of already downloaded data
+[here](https://dox.uliege.be/index.php/s/trys2xY7j9JsQ3z) and the set-up within `tools.py`) siting assessments in 
+Europe, North Africa, Middle East, Greenland and the USA.
+
+   
+# Example run
+
+The module structure includes five folders. The `input data` folder contains all data required at different stages within 
+the model. The `output_data` folder contains the result sub-folders. The `src_acquisition` folder contains the script
+used for reanalysis data download from the ECMWF ERA5 database. The `src` folder contains the scripts running the `resite`
+model. Finally, the `src_postprocessing` folder includes scripts used for analysing results.
+
+#### I. Resource data retrieval
+It should be noted that usable data (incl. resource data, land characteristics, etc.) are already available 
+[here](https://dox.uliege.be/index.php/s/trys2xY7j9JsQ3z). We recommend using this data to get a good understanding
+of the model architecture and cababilities. In case additional data is needed, the acquisition procedure detailed below
+should suffice. 
+1. Set up access to the CDS API by following instructions [here](https://cds.climate.copernicus.eu/api-how-to).
+2. Open the `data_retrieval.py` file in `src_acquisition` using any file editor or IDE available and adjust the
+download settings as required (e.g., area, time coverage, spatial resolution, variables). 
+3. Run `data_retrieval.py`. This will commence a connection to the CDS servers and data
+download will start. According to the amount of data requested, this process takes time.
+Downloaded files will be saved in the `input_data/resource_data` folder in a subfolder named after the spatial resolution chosen.
+                 
+#### II. Optimal deployment of generation sites
+1. Open the `parameters.yml` file using any available text editor or IDE and adjust the model parameters according to 
+the needs. Further comments on how to set these are provided in the file. Save the file.
+2. Run `main.py`. Upon completion, the output is saved in a dedicated sub-folder (named based on its creation time stamp) in `output_data`.
+
+#### III. Results assessment
+Run `output.py` in `src_postprocessing`. In console, define an instance of the `Output` class associated to a run, e.g.
+`20190101_123000`, as such:
+
+```
+instance = Output('20190101_123000')
+```
+
+The `instance` has the following attributes:
++ `return_numerics()`, which returns a series of scalar indicators associated with i) the optimal deployment scheme and
+ii) a couple other siting strategies. It should be called as:
+
+```
+results = instance.return_numerics(**kwargs)
+```
++ `plot_numerics()`, which provides a visual representation of the results obtained above. It is called as:
+```
+instance.plot_numerics(results, **kwargs)
+```
++ `optimal_location_plot()`, which plots the optimal sites on a map. It is called as:
+```
+instance.optimal_locations_plot()
+```
++ `optimal_locations_plot_heatmaps()`, which plots RES capacities per node (if capacities are considered)
+```
+instance.optimal_locations_plot_heatmaps()
+```
+    
+# Citing
+Please cite the following paper if you use this software in your research.
+
++ D. Radu, M. Berger, R. Fonteneau, A. Dubois, H. Pandžić, Y. Dvorkin, Q. Louveaux, D. Ernst, [Resource
+Complementarity as Criterion for Siting Renewable Energy Generation Assets](https://orbi.uliege.be/handle/2268/240014), pre-print, 2019.
+
+# License
+This software is released as free software under the [GPLv3](http://www.gnu.org/licenses/gpl-3.0.en.html).