Note

Quickstart for Research

If you are student using PICASO for classrooms go to Quickstart for Students and Learning. Otherwise, here is the quickstart to getting up and running with PICASO using python to set environment variables and get_data to get everything you need.

Be sure to have followed the installation instructions and follow the steps below depending on how you installed PICASO.

This quickstart for research is organized as follows:

Setup environment variable, if needed
Download required data
Run PICASO environment checker
Optional Data

1. Create `picaso_refdata` environment variable

We give different ways of setting environment variables here. Setting them with os is perfectly fine though some users like setting them system wide so that they do not have to constantly set paths.

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import os
print(os.environ['picaso_refdata'] ) #should return a path

#does it not?? Lets make sure you have it set in your environment:
os.environ['picaso_refdata'] = 'YOUR_PATH/picaso/reference/'

#set path for optional stellar data if you plan to model irradiated objects
os.environ['PYSYN_CDBS'] = os.path.join(os.environ['picaso_refdata'],'stellar_grids') #or your own path to stellar grids if you have them elsewhere

Note what we did above sets the environment variable which is totally okay but this way you will need to add this to the top of all your future notebooks before you import picaso if you haven’t set it in your bash file

2. Download Required Data

Required data:

This basic directory: https://github.com/natashabatalha/picaso/tree/master/reference (If you cloned on git you already have this!!!!!!)
Resampled opacities (7 Gb)

We can get both through picaso.data

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import picaso.data as data

1) Req’d Data: Reference Directory

If you installed through pip or conda then you will need to do this step. If you did a git clone then you should already have this and just need to point picaso_refdata to the directory reference.

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data.get_reference(os.environ['picaso_refdata']) #only ever need to do one time

2) Req’d Data: Resampled opacity file

get_data will auto put this files here (where $picaso_refdata points to your ref directory).

$picaso_refdata/opacities

Note: This is ~ 7GB file so please make sure that you have a reliable internet connection before trying to download this file, otherwise you might encounter a timeout error.

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data.get_data(category_download='resampled_opacity',target_download='default')

Issues with this large file and get_data?? Do it manually:

Download from Zenodo (see url below)
Unpack
place .db file here $picaso_refdata/opacities

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#url for manual download
data.get_data_config()[1]['resampled_opacity']['default']['url']

3. Run PICASO Environment Checker

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data.check_environ()

4. Optional reference data you may want for PICASO

Download the stellar grids needed for exoplanet modeling (optional)

If you want to use these stellar files they will need to be accessed by stsynphot package which checks for them here:

$PYSYN_CDBS/grid

You will be asked if you want to download phoenix or ck04models. we recommend ck04models as a default for reflected light spectra, but phoenix for mid-IR spectra and climate modeling.

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#now let's make sure we have the pysynphot environment variable set
data.get_data(category_download='stellar_grids')

Want anything else? Use `get_data` function

PICASO relies on lots of different kinds of data. However you might not need all of it depending on what you are working on. For example, if you are only working on substellar objects, you do not need to download stellar spectra.

Data Type	Req?	What it is primarily used for	Where it should go
Reference	Yes	everything	$picaso_refdata
Resampled Opacities	Yes	Spectroscopic modeling	$picaso_refdata/opacities/opacities*.db
Stellar Database	No	Exoplanet modeling	$PYSYN_CDBS/grid \| \| Pre-weighted correlated-K Tables \| No \| Chemical equilibrium climate \| Your choice (default=$picaso_refdata/opacities/preweighted)
By molecule correlated-K Tables	No	Disequilibrium climate	Your choice (default=$picaso_refdata/opacities/resortrebin) \| \| Sonora grid models \| No \| Initial guess/grid fitting \| Your choice (default=$picaso_refdata/sonora_grids)
Virga Mieff files	No	Virga cloud modeling	Your choice (default=$picaso_refdata/virga)

Examples using get data in interactive mode

data.get_data()
What data can I help you download? Options include:
['resampled_opacity', 'stellar_grids', 'sonora_grids', 'ck_tables']
>> sonora_grids
Great. I found these options for sonora_grids. Select one:
0 - 'elfowl-Ytype': The models between Teff of 275 to 550 K (applicable to Y-type objects). Total: ~40 Gb.
1 - 'elfowl-Ttype': The models for Teff between 575 to 1200 K (applicable for T-type objects). Total: ~40 Gb.
2 - 'elfowl-Ltype': Models for Teff between 1300 to 2400 K (applicable for L-type objects). Total: ~40 Gb.
3 - 'bobcat': Sonora bobcat pressure-temperature profiles
4 - 'diamondback':
>> elfowl-Ytype
No destination has been specified. Let me help put this in the right place.
When running the code you will have to point to this directory. Therefore, keep it somewhere you will remember. My suggestion would be something like /Users/myaccount/Documents/data/picaso_data/sonora_grids. Please enter a path:
/Users/nbatalh1/Documents/data/sonora_grids/elfowl

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data.get_data()

Quickstart for Students & Learning

Here is the quickstart to getting up and running with PICASO using python to set environment variables and get_data to get everything you need for PICASO-lite

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#install the code
!pip install picaso

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import picaso.data as d

#pick a path to download all the reference data
d.os.environ['picaso_refdata'] = '/data/reference_data/picaso/reftest'

#get all needed data
d.get_data(category_download='picaso-lite', target_download='tutorial_sagan23',final_destination_dir=d.os.environ['picaso_refdata'] )


#add this to the top of any picaso notebook that models exoplanets
d.os.environ['PYSYN_CDBS'] = d.os.path.join(d.os.environ['picaso_refdata'],'stellar_grids')


#use this for any virga related notebook
mieff_dir = d.os.path.join(d.os.environ['picaso_refdata'],'virga')
#use this for any climate modeling (this must includes a few metallicities and c/o ratios so you can get an idea of how to do climate modeling)
ck_dir = d.os.path.join(d.os.environ['picaso_refdata'],'opacities', 'preweighted')