Data resampling
ocelot.cluster.resample_gaia_astrometry(data_gaia, n_resamples=1, suffixes=None, method='svd')
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Resample Gaia astrometric parameters for pmra, pmdec and parallax, given input best estimate means and covariance matrices.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data_gaia
|
DataFrame
|
data for the field, including keys 'astrometric_params_solved', 'pmra', 'pmdec', 'parallax', 'pmra_error', 'pmdec_error', 'parallax_error', 'pmra_pmdec_corr', 'parallax_pmra_corr', 'parallax_pmdec_corr', 'pseudocolour_error', 'pmra_pseudocolour_corr', 'pmdec_pseudocolour_corr', 'parallax_pseudocolour_corr' |
required |
n_resamples
|
int
|
Number of resamples to perform. Default: 1 |
1
|
suffixes
|
Iterable[str] | None
|
Suffixes to apply in the output dataframe's pmra, pmdec, and parallax columns per-resample. |
None
|
method
|
'svd', 'eigh', 'cholesky'
|
Method to use for matrix decompositions. From the numpy docs: "The cov input is used to compute a factor matrix A such that A @ A.T = cov. This argument is used to select the method used to compute the factor matrix A. The default method 'svd' is the slowest, while 'cholesky' is the fastest but less robust than the slowest method. The method eigh uses eigen decomposition to compute A and is faster than svd but slower than cholesky." Default: 'svd' |
'svd'
|
Returns:
| Type | Description |
|---|---|
DataFrame
|
Output dataframe with same length as input, including 3*n_resamples columns containing resampled astrometry. |
Notes
When using cholesky decomposition to speed this up, all covariance matrices must be positive definite. Basically, this means they should be symmetric and have no negative numbers. This is not always the case for Gaia covariance matrices; hence, the default method is the slower (but more robust) svd.
ocelot.cluster.generate_gaia_covariance_matrix(data_gaia, six_parameter_sources=False)
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Generates a covariance matrix for Gaia data. Currently only has pmra/pmdec/parallax(/color) covariance matrix support, but could be easily extended in the future to also/or re-sample more things.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data_gaia
|
DataFrame
|
Gaia data to make a covariance matrix for. Must contain the following keys: pmra_error, pmdec_error, parallax_error, pmra_pmdec_corr, parallax_pmra_corr, parallax_pmdec_corr For resampling for six parameter sources (i.e. six_parameter_sources=True), it must also contain: pseudocolour_error, pmra_pseudocolour_corr, pmdec_pseudocolour_corr, parallax_pseudocolour_corr see Gaia release notes for help. |
required |
six_parameter_sources
|
bool
|
whether or not ALL sources in data_gaia also depend on the estimated pseudocolour. If true, will return matrices of shape (n_samples, 4, 4) instead. Default: False |
False
|
Returns:
| Type | Description |
|---|---|
ndarray
|
Array of covariance matrices of shape (n_stars, n_params, n_params). n_params is 4 if six_parameter_sources is true, otherwise it is 3. |