We lifted 8 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sfu-cosmo/MagCosmoMC | pwc_unofficial | 3 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| lastTopComment | Ran | sfu-cosmo/MagCosmoMC/python/CMBlikes.py code served (permissive licence) · get_code("f85df2ee897f5d6c") |
| readTextCommentColumns | Ran | sfu-cosmo/MagCosmoMC/python/CMBlikes.py code served (permissive licence) · get_code("0d67adf422b83416") |
| readWithHeader | Ran | sfu-cosmo/MagCosmoMC/python/CMBlikes.py code served (permissive licence) · get_code("36004963d8da44b1") |
| fileMatches | Not yet run | sfu-cosmo/MagCosmoMC/python/copyGridFiles.py code served (permissive licence) · get_code("55892c55ec3e82b1") |
| fsizestr | Not yet run | sfu-cosmo/MagCosmoMC/python/cleanup.py code served (permissive licence) · get_code("ad7bf9a7a6125198") |
| getColorMap | Not yet run | sfu-cosmo/MagCosmoMC/python/chainDumpPlot.py code served (permissive licence) · get_code("2b2a0d6d135114cb") |
| get_camb_params | Not yet run | sfu-cosmo/MagCosmoMC/python/cosmomc_to_camb.py code served (permissive licence) · get_code("4ba058e12ce912ad") |
| truncate_colormap | Not yet run | sfu-cosmo/MagCosmoMC/python/chainDumpPlot.py code served (permissive licence) · get_code("de55e0c67b4e4488") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a fast Markov Chain Monte-Carlo exploration of cosmological parameter space. We perform a joint analysis of results from recent CMB experiments and provide parameter constraints, including sigma_8, from the CMB independent of other data. We next combine data from the CMB, HST Key Project, 2dF galaxy redshift survey, supernovae Ia and big-bang nucleosynthesis. The Monte Carlo method allows the rapid investigation of a large number of parameters, and we present results from 6 and 9 parameter analyses of flat models, and an 11 parameter analysis of non-flat models. Our results include constraints on the neutrino mass (m_nu < 0.3eV), equation of state of the dark energy, and the tensor amplitude, as well as demonstrating the effect of additional parameters on the base parameter constraints. In a series of appendices we describe the many uses of importance sampling, including computing results from new data and accuracy correction of results generated from an approximate method. We also discuss the different ways of converting parameter samples to parameter constraints, the effect of the prior, assess the goodness of fit and consistency, and describe the use of analytic marginalization over normalization parameters.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("astro-ph/0205436")
get_code_for_paper("astro-ph/0205436")
have("astro-ph/0205436")
Connect an agent — have() is free.