Observation Model¶
Basics¶
beast.observationmodel.observations Module¶
Defines a generic interface to observation catalog
Functions¶
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Generate simulated observations using the physics and observation grids. |
Classes¶
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A generic class that interfaces observation catalog in a standardized way |
Class Inheritance Diagram¶
digraph inheritance262dc9aea3 { bgcolor=transparent; rankdir=LR; size="8.0, 12.0"; "Observations" [URL="api/beast.observationmodel.observations.Observations.html#beast.observationmodel.observations.Observations",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="A generic class that interfaces observation catalog in a standardized way"]; }beast.observationmodel.phot Module¶
Photometric package¶
Defines a Filter class and associated functions to extract photometry.
This also include functions to keep libraries up to date
Note
integrations are done using trapz()
Why not Simpsons? Simpsons principle is to take sequence of 3 points to
make a quadratic interpolation. Which in the end, when filters have sharp
edges, the error due to this “interpolation” are extremely large in
comparison to the uncertainties induced by trapeze integration.
Functions¶
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load all filters from the library |
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load a limited set of filters |
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load a limited set of filters |
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Extract seds from a one single spectrum |
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Extract seds from a grid |
Convert an ST magnitude to erg/s/cm2/AA (Flambda) |
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Convert to ST magnitude from erg/s/cm2/AA (Flambda) |
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Return the magnitudes from flux values |
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Return the magnitudes and associated errors from fluxes and flux error values |
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Return the flux from magnitude values |
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Return the flux and associated errors from magnitude and mag error values |
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Edit the filter catalog and append a new one given by its transfer function |
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Add filter properties to the Vega library |
Classes¶
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Class filter Define a filter by its name, wavelength and transmission |
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Class filter |
Class Inheritance Diagram¶
digraph inheritance1831c56643 { bgcolor=transparent; rankdir=LR; size="8.0, 12.0"; "Filter" [URL="api/beast.observationmodel.phot.Filter.html#beast.observationmodel.phot.Filter",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Class filter"]; "IntegrationFilter" [URL="api/beast.observationmodel.phot.IntegrationFilter.html#beast.observationmodel.phot.IntegrationFilter",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Class filter"]; }beast.observationmodel.vega Module¶
Handle vega spec/mags/fluxes manipulations
Functions¶
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function decorator that transforms vega magnitudes to fluxes (without vega reference) |
Classes¶
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Class that handles vega spectrum and references. |
Class Inheritance Diagram¶
digraph inheritance97ca36de2c { bgcolor=transparent; rankdir=LR; size="8.0, 12.0"; "Vega" [URL="api/beast.observationmodel.vega.Vega.html#beast.observationmodel.vega.Vega",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Class that handles vega spectrum and references."]; }Noise Model¶
beast.observationmodel.noisemodel.noisemodel Module¶
Classes¶
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Initial class of noise models |
Class Inheritance Diagram¶
digraph inheritance249cb39b02 { bgcolor=transparent; rankdir=LR; size="8.0, 12.0"; "NoiseModel" [URL="api/beast.observationmodel.noisemodel.noisemodel.NoiseModel.html#beast.observationmodel.noisemodel.noisemodel.NoiseModel",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Initial class of noise models"]; }beast.observationmodel.noisemodel.splinter Module¶
splinter noise model assumes that every photometric band is independent from the others and has a fractional flux uncertainty and no bias.
Method¶
Create a noise model that has sigmas that are frac_unc times sed_flux and zeros for the bias terms.
Functions¶
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Splinter noise model assumes that every filter is independent with any other. |
beast.observationmodel.noisemodel.toothpick Module¶
Classes¶
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A noise model for based Artificial Star Tests (ASTs) that are provided as one single table. |
Class Inheritance Diagram¶
digraph inheritance9da68672ae { bgcolor=transparent; rankdir=LR; size="8.0, 12.0"; "MultiFilterASTs" [URL="api/beast.observationmodel.noisemodel.toothpick.MultiFilterASTs.html#beast.observationmodel.noisemodel.toothpick.MultiFilterASTs",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="A noise model for based Artificial Star Tests (ASTs) that are provided"]; "NoiseModel" -> "MultiFilterASTs" [arrowsize=0.5,style="setlinewidth(0.5)"]; "NoiseModel" [URL="api/beast.observationmodel.noisemodel.noisemodel.NoiseModel.html#beast.observationmodel.noisemodel.noisemodel.NoiseModel",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Initial class of noise models"]; }beast.observationmodel.noisemodel.trunchen Module¶
Trunchen version of noisemodel Goal is to compute the full n-band covariance matrix for each model
Classes¶
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Implement a noise model where the ASTs are provided as a single table |
Class Inheritance Diagram¶
digraph inheritance6b5d66272d { bgcolor=transparent; rankdir=LR; size="8.0, 12.0"; "MultiFilterASTs" [URL="api/beast.observationmodel.noisemodel.trunchen.MultiFilterASTs.html#beast.observationmodel.noisemodel.trunchen.MultiFilterASTs",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Implement a noise model where the ASTs are provided as a single table"]; "NoiseModel" -> "MultiFilterASTs" [arrowsize=0.5,style="setlinewidth(0.5)"]; "NoiseModel" [URL="api/beast.observationmodel.noisemodel.noisemodel.NoiseModel.html#beast.observationmodel.noisemodel.noisemodel.NoiseModel",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="Initial class of noise models"]; }beast.observationmodel.noisemodel.generic_noisemodel Module¶
Generates a generic noise model from artifical star tests (ASTs) results using the toothpick method. Using ASTs results in a noise model that includes contributions from measurement (photon) noise and crowding noise.
Toothpick assumes that all bands are independent - no covariance. This is a conservative assumption. If there is true covariance more accurate results with smaller uncertainties on fit parameters can be achieved using the trunchen method. The trunchen method requires significantly more complicated ASTs and many more of them.
Functions¶
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toothpick noise model assumes that every filter is independent with any other. |
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returns the noise model |