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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.
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Summary:
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- [Prerequisites](#prerequisites)
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- [First run](#first-run)
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- [Setup](#setup)
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- [Running](#running)
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- [Plotting](#plotting)
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- [More precision](#more-precision)
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- Setup
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- Running
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- Plotting
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- [Better figures](#better-figures)
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# Prerequisites
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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:
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| ... | ... | @@ -10,6 +22,11 @@ If you downloaded the archive in the [_dist_](https://gitlab.obspm.fr/dblain/exo |
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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").
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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.
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To plot the figures using the provided plot functions, you will need Python3, and the following Python packages:
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- numpy
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- scipy
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- matplotlib
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# First run
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## Setup
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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.
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| ... | ... | @@ -104,7 +121,7 @@ The transmission spectrum should look like this: |
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This is nice, but the resolution is quite low.
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# More precision !
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# More precision
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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.
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## Setup
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| ... | ... | @@ -190,19 +207,36 @@ And the transmission spectrum should look like this: |
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## Better figures
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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:
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```bash
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python
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```
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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 ?
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Then, simply do:
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```python
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from src.python.plot_figures import * # import everything from plot_figures
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plot_contribution_transmission_spectra(
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'./outputs/exorem/spectra_corot-4b_R500.dat',
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wvn2wvl=True,
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xmin=0.5e-6,
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xmax=1.5e-6,
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exclude=['clouds']
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)
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``` |
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\ No newline at end of file |
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1. To do that, open inside the *Exo-REM* main directory a python console:
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```bash
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python
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```
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2. Import the *Exo-REM* plot functions:
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```python
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from src.python.plot_figures import *
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```
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3. Then, simply do:
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```python
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plot_contribution_transmission_spectra(
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'./outputs/exorem/spectra_corot-4b_R500.dat',
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wvn2wvl=True,
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xmin=0.5e-6,
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xmax=1.5e-6,
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exclude=['clouds']
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)
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```
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4. If you want to see the effect of the clouds on the transmission spectrum, you can try:
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```python
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plot_transmission_spectrum('./outputs/exorem/spectra_corot-4b_R500.dat', wvn2wvl=True, star_radius=814e6, cloud_coverage=0, color='r', label='no cloud')
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plot_transmission_spectrum('./outputs/exorem/spectra_corot-4b_R500.dat', wvn2wvl=True, star_radius=814e6, cloud_coverage=0.5, color='g', label='50% cover')
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plot_transmission_spectrum('./outputs/exorem/spectra_corot-4b_R500.dat', wvn2wvl=True, star_radius=814e6, cloud_coverage=1, color='b', label='full cover')
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plt.xlim([0.3e-6, 2e-6])
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plt.gca().set_xscale('log')
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plt.legend()
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```
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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.
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Enjoy Exo-REM ! |
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\ No newline at end of file |
