Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
versions View all 3 versions
addClaim

Resampled Opacity Database for PICASO

Authors: Natasha Batalha; Richard Freedman; Ehsan Gharib-Nezhad; Roxana Lupu;

Resampled Opacity Database for PICASO

Abstract

Wavelength range of the opacities go from 0.3-15 microns. Opacities are resampled to R=60,000 from an original R=1e6 line by line calculation performed by Richard Freedman, Ehsan Gharib-Nezhad, and Roxana Lupu. This does not mean that these opacities are suitable for data at R=60k!!!! Instead, resampled opacities have to be convolved to data that are at a resolution of to 100x less than the original data. This tutorial shows the effects of under-sampling opacities. TLDR: In general, this opacity dataset are suitable for data that is no higher than R=3000, if using the 60k database and R=100 if using the 20k database. It is not suitable for high resolution cross correlation studies. File sizes too big? Please download version 1: https://zenodo.org/record/3759675#.YuN4E-zMLvU "But my data is less then R=3000, is it possible to get a lower sampling?" Yes! import picaso.justdoit as jdi #will get everything as is opa = jdi.opannection(filename_db = "all_opacities_0.6_6_R60000.db") #will compute spectra for only a subset of wavelength opa = jdi.opannection(filename_db = "all_opacities_0.6_6_R60000.db", wave_ranage=[0.6,1]) #will compute spectra for a smaller resolution sampling opa = jdi.opannection(filename_db = "all_opacities_0.6_6_R60000.db", resample=2) #resample =2 decreases the sampling by a factor of 2 (e.g. R=60000 -> R=30000) Using PICASO to Query the data A full tutorial on querying the database is available in the PICASO read the docs. Below is a brief example: import picaso.opacity_factory as opa db_filename = 'all_opacities_0.6_6_R60000.db' molecules, pt_pairs = opa.molecular_avail(db_filename) print(molecules) ['AlH', 'C2H2', 'C2H4', 'C2H6', 'CH4', 'CO', 'CO2', 'CaH', 'CrH', 'Cs', 'Fe', 'FeH', 'H2', 'H2O', 'H2S', 'H3+', 'HCN', 'K', 'Li', 'LiCl', 'LiF', 'LiH', 'MgH', 'N2', 'N2O', 'NH3', 'Na', 'O2', 'O3', 'OCS', 'PH3', 'Rb', 'SO2', 'SiO', 'TiH', 'TiO', 'VO'] pt_pairs[0:10] [(1, 1e-06, 75.0), (2, 3e-06, 75.0), (3, 1e-05, 75.0), (4, 3e-05, 75.0), (5, 0.0001, 75.0), (6, 0.0003, 75.0), (7, 0.001, 75.0), (8, 0.003, 75.0), (9, 0.01, 75.0), (10, 0.03, 75.0)] As you can see from the pt_pairs, our grid is computed on a specific pressure-temperature grid that has a total of 1460 points. NOTE: A full table of references and citations is currently being compiled for publication with the Sonora Grid (Marley+2020). PICASO Citation Tools PICASO can help you get out individual citations. If you do not want to look at code: you can find bibid's here for each molecule corresponding PICASO bib file here If you do want to look at code you can check out this tutorial. (code snippet below) import picaso.references as pref refs = pref.References() opa_latex, bibdb = refs.get_opa(molecules=['H2O','CO2']) print(opa_latex) \begin{table*} \centering \begin{tabular}{c|c} H2O & \citet{Polyansky2018H2O} \\ CO2 & \citet{HUANG2014reliable} \\ \end{tabular} \caption{Line lists used to make PICASO Opacities} \label{tab:opas} \end{table*} pref.create_bib(bibdb, 'molecule.bib')#creates bibtex file

{"references": ["Marley M.~S., Saumon D., Visscher C., Lupu R., Freedman R., Morley C., Fortney J.~J., et al., 2021, ApJ, 920, 85. doi:10.3847/1538-4357/ac141d"]}

Related Organizations
Keywords

exoplanets, radiative transfer, planetary atmospheres

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    13
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 259
    download downloads 1K
  • 259
    views
    1K
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
13
Top 10%
Average
Average
259
1K
Related to Research communities