Getting-started.md

 In this section we will describe how to quickly run an *Exo-REM* simulation. We will use the `inputs/example.nml` coming with all [distributed versions](https://gitlab.obspm.fr/dblain/exorem/-/tree/master/dist) of *Exo-REM* as a starting point.
 
+Summary:
+- [Prerequisites](#prerequisites)
+- [First run](#first-run)
+    - [Setup](#setup)
+    - [Running](#running)
+    - [Plotting](#plotting)
+- [More precision](#more-precision)
+    - Setup
+    - Running
+    - Plotting
+    - [Better figures](#better-figures)
+
 # Prerequisites
 If you downloaded the archive in the [_dist_](https://gitlab.obspm.fr/dblain/exorem/-/tree/master/dist) directory, you should have everything you need, except a stellar spectrum (see point 4 below). Otherwise, check the following:
 
@@ -10,6 +22,11 @@ If you downloaded the archive in the [_dist_](https://gitlab.obspm.fr/dblain/exo
 5. Verify if all the condensates and gases thermochemical tables are in the _data/thermochemical_tables_ directory. If you want to add more species to the chemical model, respect the same format and use "speciesName.tct.dat" as file name (e.g. "H2O.tct.dat").
 6. Put a temperature profile as a priori inside the _inputs/atmospheres/temperature_profiles_ directory. The *Exo-REM* data format must be respected. You should have received an example of such a file with your *Exo-REM* distribution.
 
+To plot the figures using the provided plot functions, you will need Python3, and the following Python packages:
+- numpy
+- scipy
+- matplotlib
+
 # First run
 ## Setup
 In this example, we will simulate the atmosphere of CoRoT-4 b, a well studied planet. A good source of planetary information can be found [here](https://exoplanetarchive.ipac.caltech.edu/). We will use the parameters from Moutou et al. 2008.
@@ -104,7 +121,7 @@ The transmission spectrum should look like this:
 
 This is nice, but the resolution is quite low.
 
-# More precision !
+# More precision
 This time, our goal will be to have more precise results. We will use our calculated temperature profile as input, and a higher resolution power. We will also add a stellar spectrum, and use an advanced mode to calculate the eddy diffusion coefficient. To keep it simple, we will consider only KCl and Na2S clouds.
 
 ## Setup
@@ -190,14 +207,18 @@ And the transmission spectrum should look like this:
 ![transmission_spectrum_corot-4b_R500](uploads/51da1babc3b2219569a0d861ae645c73/transmission_spectrum_corot-4b_R500.png)
 
 ## Better figures
-Not happy with the figures you get ? What if for example you wanted to see the contributions of everything but clouds between 0.5 and 1.5 µm ? To do that, open inside the *Exo-REM* main directory a python console:
+Not happy with the figures you get ? What if for example you wanted to see the contributions of everything but clouds between 0.5 and 1.5 µm ? 
+
+1. To do that, open inside the *Exo-REM* main directory a python console:
     ```bash
     python
     ```
-
-Then, simply do:
+2. Import the *Exo-REM* plot functions:
+    ```python
+    from src.python.plot_figures import *
+    ```
+3. Then, simply do:
     ```python
-from src.python.plot_figures import *  # import everything from plot_figures
     plot_contribution_transmission_spectra(
         './outputs/exorem/spectra_corot-4b_R500.dat', 
         wvn2wvl=True, 
@@ -206,3 +227,16 @@ plot_contribution_transmission_spectra(
         exclude=['clouds']
     )
     ```
+4. If you want to see the effect of the clouds on the transmission spectrum, you can try:
+    ```python
+    plot_transmission_spectrum('./outputs/exorem/spectra_corot-4b_R500.dat', wvn2wvl=True, star_radius=814e6, cloud_coverage=0, color='r', label='no cloud')
+    plot_transmission_spectrum('./outputs/exorem/spectra_corot-4b_R500.dat', wvn2wvl=True, star_radius=814e6, cloud_coverage=0.5, color='g', label='50% cover')
+    plot_transmission_spectrum('./outputs/exorem/spectra_corot-4b_R500.dat', wvn2wvl=True, star_radius=814e6, cloud_coverage=1, color='b', label='full cover')
+    plt.xlim([0.3e-6, 2e-6])
+    plt.gca().set_xscale('log')
+    plt.legend()
+    ```
+
+There is much other things you can do. Do not hesitate to check the _src/python/plot_figures.py_ file to look at the docstrings of the functions.
+
+Enjoy Exo-REM !
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