Metadata-Version: 2.1
Name: exojax
Version: 0.5.3
Summary: Auto-differentiable line-by-line spectral modeling of exoplanets/brown dwarfs using JAX.
Home-page: http://secondearths.sakura.ne.jp/exojax/
Author: Hajime Kawahara and collaborators
Author-email: divrot@gmail.com
Maintainer: Hajime Kawahara and collaborators
Maintainer-email: divrot@gmail.com
License: MIT
Description: # exojax
         [![License](https://img.shields.io/github/license/HajimeKawahara/exojax)](https://github.com/HajimeKawahara/exojax/blob/develop/LICENSE)
         [![Docs](https://img.shields.io/badge/docs-exojax-brightgreen)](http://secondearths.sakura.ne.jp/exojax/)
         [![arxiv](https://img.shields.io/badge/arxiv-2105.14782-blue)](http://arxiv.org/abs/2105.14782)
         
        Auto-differentiable line-by-line spectral modeling of exoplanets/brown dwarfs using JAX. Read [the docs](http://secondearths.sakura.ne.jp/exojax) 🐕. 
        
        <img src="https://user-images.githubusercontent.com/15956904/119144463-a5cba180-ba83-11eb-8a26-687075d43883.png" Titie="exojax" Width=850px>
         
        ## Functions
        
        <details open><summary>Voigt Profile :heavy_check_mark: </summary>
        
        ```python3
        from exojax.spec import voigt
        nu=numpy.linspace(-10,10,100)
        voigt(nu,1.0,2.0) #sigma_D=1.0, gamma_L=2.0
        ```
        
        </details>
        
        <details><summary>Cross Section using HITRAN/HITEMP/ExoMol :heavy_check_mark: </summary>
         
        ```python
        from exojax.spec import AutoXS
        nus=numpy.linspace(1900.0,2300.0,40000,dtype=numpy.float64) #wavenumber (cm-1)
        autoxs=AutoXS(nus,"ExoMol","CO") #using ExoMol CO (12C-16O). HITRAN and HITEMP are also supported.  
        xsv=autoxs.xsection(1000.0,1.0) #cross section for 1000K, 1bar (cm2)
        ```
        
         <img src="https://user-images.githubusercontent.com/15956904/111430765-2eedf180-873e-11eb-9740-9e1a313d590c.png" Titie="exojax auto cross section" Width=850px> 
         
        <details><summary> Do you just want to plot the line strength? </summary>
        
        ```python
        ls=autoxs.linest(1000.0,1.0) #line strength for 1000K, 1bar (cm)
        plt.plot(autoxs.mdb.nu_lines,ls,".")
        ```
        
        autoxs.mdb is the [moldb.MdbExomol class](http://secondearths.sakura.ne.jp/exojax/exojax/exojax.spec.html#exojax.spec.moldb.MdbExomol) for molecular database. Here is a entrance to a deeper level. exojax is more flexible in the way it calculates the molecular lines. 🐈 Go to [the docs](http://secondearths.sakura.ne.jp/exojax) for the deeper level.  
        
        </details>
         
         </details>
        
        <details><summary>Emission Spectrum :heavy_check_mark: </summary>
        
        ```python
        from exojax.spec.rtransfer import nugrid
        from exojax.spec import AutoRT
        nus,wav,res=nugrid(1900.0,2300.0,40000,"cm-1")
        Parr=numpy.logspace(-8,2,100) #100 layers from 10^-8 bar to 10^2 bar
        Tarr = 500.*(Parr/Parr[-1])**0.02    
        autort=AutoRT(nus,1.e5,2.33,Tarr,Parr) #g=1.e5 cm/s2, mmw=2.33
        autort.addcia("H2-H2",0.74,0.74)       #CIA, mmr(H)=0.74
        autort.addcia("H2-He",0.74,0.25)       #CIA, mmr(He)=0.25
        autort.addmol("ExoMol","CO",0.01)      #CO line, mmr(CO)=0.01
        F=autort.rtrun()
        ```
        
        <img src="https://user-images.githubusercontent.com/15956904/116488770-286ea000-a8ce-11eb-982d-7884b423592c.png" Titie="exojax auto \emission spectrum" Width=850px> 
        
        <details><summary>Are you an observer? </summary>
         
        ```python
        nusobs=numpy.linspace(1900.0,2300.0,10000,dtype=numpy.float64) #observation wavenumber bin (cm-1)
        F=autort.spectrum(nusobs,100000.0,20.0,0.0) #R=100000, vsini=10km/s, RV=0km/s
        ```
         
          <img src="https://user-images.githubusercontent.com/15956904/116488769-273d7300-a8ce-11eb-8da1-661b23215c26.png" Titie="exojax auto \emission spectrum for observers" Width=850px> 
        
         </details>
        
        If you want to customize the model, see [here](http://secondearths.sakura.ne.jp/exojax/tutorials/forward_modeling.html).
        
        </details>
        
        <details><summary>HMC-NUTS of Emission Spectra :heavy_check_mark: </summary>
        
        To fit a spectrum model to real data, you need to know a little more about exojax. See [here](http://secondearths.sakura.ne.jp/exojax/tutorials/reverse_modeling.html).
        
         
          <img src="https://github.com/HajimeKawahara/exojax/blob/master/documents/tutorials/results.png">
        
        🥥 HMC-NUTS modeling of a brown dwarf, [Luhman 16 A](https://en.wikipedia.org/wiki/Luhman_16) using exojax.  See [here](http://secondearths.sakura.ne.jp/exojax/tutorials/fitbd.html) for an example of the Bayes inference using the real spectrum.
         
        </details>
        
        <details><summary>HMC-NUTS of Transmission Spectra :x: </summary>Not supported yet. </details>
        
        <details><summary>Cloud modeling :x: </summary> Not supported yet. </details>
        
        
        
        ## Installation
        
        ```
        pip install exojax
        ```
        
        or
        
        ```
        python setup.py install
        ```
        
        <details><summary> Note on installation w/ GPU support</summary>
        
        :books: You need to install CUDA, NumPyro, JAX w/ NVIDIA GPU support, and cuDNN. 
        
        - NumPyro
        
        exojax supports NumPyro >0.5.0, which enables [the forward differentiation of HMC-NUTS](http://num.pyro.ai/en/latest/mcmc.html#numpyro.infer.hmc.NUTS). Please check the required JAX version by NumPyro. In May 2021, it seems the recent version of [NumPyro](https://github.com/pyro-ppl/numpyro) requires jaxlib>=0.1.62 (see [setup.py](https://github.com/pyro-ppl/numpyro/blob/master/setup.py) of NumPyro for instance). 
        
        - JAX
        
        Check you cuda version:
        
        ```
        nvcc -V
        ```
        
        Install such as
        
        ```
        pip install --upgrade jax jaxlib==0.1.62+cuda112  -f https://storage.googleapis.com/jax-releases/jax_releases.html
        ```
        
        In this case, jaxlib version is 0.1.62 and cuda version is 11.2. You can check which cuda version is avaiable at [here](https://storage.googleapis.com/jax-releases/jax_releases.html)
        
        Visit [here](https://github.com/google/jax) for the details.
        
        - cuDNN
        
        For instance, get .deb from NVIDIA and install such as
        
        ```
        sudo dpkg -i libcudnn8_8.2.0.53-1+cuda11.3_amd64.deb
        ```
        
        cuDNN is used for to compute the astronomical/instrumental response for the large number of wave number grid (exojax.spec.response). Otherwise, we do not use it. 
        
        </details>
        
        ## References
        
        - Kawahara, Kawashima, Masuda, and Crossfield (2021) under review: [arXiv:2105.14782](http://arxiv.org/abs/2105.14782)
        
        ## License
        
        🐈 Copyright 2020-2021 [Hajime Kawahara](http://secondearths.sakura.ne.jp/en/index.html). exojax is publicly available under the MIT license. Under development since Dec. 2020.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
