Add installation instructions in README (#64)

* Add instructions on how to install

* Separate cloning commands

* Temporarily remove Windows from supported OS

* Separate source and PyPi sections

README.md

@@ -31,6 +31,29 @@ clust.cluster_plotter()
   <img width="380" height="380" src="https://raw.githubusercontent.com/cms-patatrack/CLUEstering/main/images/blobwithnoise.png">
 </p>
 
+## Installation
+### From source
+To install the library, first clone the repository recursively:
+```shell
+git clone --recursive https://github.com/cms-patatrack/CLUEstering.git
+```
+alternatively, clone and update the submodules manually:
+```shell
+git clone https://github.com/cms-patatrack/CLUEstering.git
+git submodule update --init --recursive
+```
+Then, inside the root directory install the library with pip:
+```shell
+pip install -v .
+```
+where the `-v` flag is optional but suggested because provides more details during the compilation process.
+
+### From PyPi
+The library is also available on the PyPi repository, and can be installed with:
+```shell
+pip install -v CLUEstering
+```
+
 ## Heterogeneous backend support with `Alpaka`
 Since version `2.0.0` the pybind module is compiled for all the supported backends using the `Alpaka` portability library (https://github.com/alpaka-group/alpaka).  
 Currently the supported backends include:
@@ -81,9 +104,9 @@ The class has several methods:
 Data is read with the `read_data` method.  
 For the data to be acceptable, it must contain the values of at least one coordinate for all the points, and their `weights`. The `weights` of the points represent their relative importance in the dataset, so in most cases they can all be set to 1. There are several accepted formats for providing the data:  
 * `string`, where the string contains the relative path to a `csv` file. The file must contain at least one column for the coordinates, which must be named as `x*` (`x0`, `x1`, ecc.) and one column for the `weight`
-* `pandas.DataFrame`, where the columns for the coordinates must be named `x*` (`x1`, `x2`, ecc.) and one column should contain the `weight`
+* `pandas.DataFrame`, where the columns for the coordinates must be named `x*` (`x0`, `x1`, ecc.) and one column should contain the `weight`
 * `list` or `np.ndarray`, where the coordinate data should be provided as a list of lists (or array of arrays), and the weights inserted in a second, separate list/array
-* `dictionary`, where the coordinate data must be contained in lists, each with key `x*` (``, ``, ecc.) and the weights in another list with key `weight`
+* `dictionary`, where the coordinate data must be contained in lists, each with key `x*` (`x0`, `x1`, ecc.) and the weights in another list with key `weight`
 
 ## Generating a test dataset with `test_blobs`
 If a user wants to test the library without using real data, they can easily do so using the `test_blobs` method.

setup.py

@@ -40,7 +40,6 @@
         'Intended Audience :: Developers',
         'Programming Language :: Python :: 3',
         'Operating System :: Unix',
-        'Operating System :: MacOS :: MacOS X',
-        'Operating System :: Microsoft :: Windows',
+        'Operating System :: MacOS :: MacOS X'
     ]
 )