Getting-started.md

@@ -38,7 +38,7 @@ In this example, we will simulate the atmosphere of CoRoT-4 b, a well studied pl
 
 Copy and edit the file _inputs/example.nml_, rename it _corot-4b.nml_. An extended description of the input parameters is available [here](Documentation).
 
-1. Open _inputs/_corot-4b.nml_ with any notepad editor.
+1. Open _inputs/\_corot-4b.nml_ with any notepad editor.
 2. Edit the suffix of your future output files:
     ```text
     output_files_suffix = 'corot-4b'  ! suffix of the output files
@@ -136,7 +136,7 @@ This is nice, but the resolution is quite low.
 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
-1. Download the R500 compressed *k*-tables [here](https://gitlab.obspm.fr/dblain/exorem/-/tree/master/data/k_coefficients_tables).
+1. Download the R500 compressed *k*-tables [here](https://lesia.obspm.fr/exorem/ktables/default/xz/).
 2. Decompress them inside the _data/k_coefficients_tables_ directory executing e.g. `tar xJvf R500.tar.xz R500`.
 3. It is always better to use a stellar spectrum rather than a blackbody spectrum. Here we will download a [BT-Settl](http://svo2.cab.inta-csic.es/theory/newov2/index.php) spectrum model. Take T_eff = 6200 K, Log(g) = 4.5, and a metallicity of 0. Put the ASCII file (download it by marking the ASCII file then by clicking "retrieve") into the _data/stellar_spectra_ directory and rename it e.g. "spectrum_BTSettl_6200K_logg4.5_met0.dat" (mind the .dat extension). Replace the header of the file by the following:
     ```text
@@ -182,7 +182,6 @@ This time, our goal will be to have more precise results. We will use our calcul
     cloud_particle_radius = 5e-6, 5e-6  ! (m) mean radius of the cloud particles (fixed radius modes)
     sedimentation_parameter = 5, 5  ! sedimentation parameter of the clouds (fixed sedimentation mode)
     cloud_particle_density = 1980, 1860  ! (kg.m-3) density of the clouds particles
-    cloud_molar_mass = 74.5513e-3, 78.0452e-3  ! (kg.mol-1) molar mass of the clouds particles
     reference_wavenumber = 1e4, 1e4  ! (cm-1) [for diagnostic] wavenumber for cloud optical depth output
     ```
 10. Update the retrieval parameters, we will use our previously retrieved temperature profile, and a lower number of iterations since we should be close to a solution:
@@ -240,11 +239,11 @@ Not happy with the figures you get ? What if for example you wanted to see the c
     ```
 4. If you want to see the effect of the clouds on the transmission spectrum, you can try:
     ```python
-    plot_transmission_spectrum('./outputs/exorem/example_R50_beta8.h5', wvn2wvl=True, cloud_coverage=0,
+    plot_transmission_spectrum('./outputs/exorem/corot-4b_R500.h5', wvn2wvl=True, cloud_coverage=0,
                                color='r', label='no cloud')
-    plot_transmission_spectrum('./outputs/exorem/example_R50_beta8.h5', wvn2wvl=True, cloud_coverage=0.5,
+    plot_transmission_spectrum('./outputs/exorem/corot-4b_R500.h5', wvn2wvl=True, cloud_coverage=0.5,
                                color='g', label='50% cover')
-    plot_transmission_spectrum('./outputs/exorem/example_R50_beta8.h5', wvn2wvl=True, cloud_coverage=1,
+    plot_transmission_spectrum('./outputs/exorem/corot-4b_R500.h5', wvn2wvl=True, cloud_coverage=1,
                                color='b', label='full cover')
     plt.xlim([0.3e-6, 2e-6])
     plt.gca().set_xscale('log')