nwaylib package

nwaylib package#

Submodules#

nwaylib.bayesdistance module#

Probabilistic Cross-Identification of Astronomical Sources

nwaylib.bayesdistance.log_posterior(prior, log_bf)[source]#: Returns log10 posterior probability normalised against alternative hypothesis that the sources are unrelated (Budavari+08)

nwaylib.bayesdistance.posterior(prior, log_bf)[source]#: Returns posterior probability normalised against alternative hypothesis that the sources are unrelated (Budavari+08)

nwaylib.bayesdistance.unnormalised_log_posterior(prior, log_bf, ncat)[source]#: Returns posterior probability (without normalisation against alternatives)

nwaylib.bayesdistance.log_bf2(psi, s1, s2)[source]#: log10 of the 2-way Bayes factor, see eq.(16) psi separation s1 and s2=accuracy of coordinates

nwaylib.bayesdistance.log_bf3(p12, p23, p31, s1, s2, s3)[source]#: log10 of the 3-way Bayes factor, see eq.(17)

nwaylib.bayesdistance.log_bf(p, s)[source]#

log10 of the multi-way Bayes factor, see eq.(18)

p: separations matrix (NxN matrix of arrays) s: errors (list of N arrays)

nwaylib.bayesdistance.assert_possemdef(M)[source]#: Check that the 2x2 matrix M is positive semi-definite, vectorized

nwaylib.bayesdistance.matrix_add(A, B)[source]#: 2D matrix addition, vectorized

nwaylib.bayesdistance.matrix_multiply(A, B)[source]#: 2D matrix multiplication, vectorized

nwaylib.bayesdistance.matrix_invert(A)[source]#: invert a matrix A, vectorized

nwaylib.bayesdistance.matrix_det(A)[source]#: 2D matrix determinant, vectorized

nwaylib.bayesdistance.apply_vector_right(A, b)[source]#: multiply 2D vector with 2D matrix, vectorized

nwaylib.bayesdistance.apply_vector_left(a, B)[source]#: multiply 2D matrix with 2D vector, vectorized

nwaylib.bayesdistance.vector_multiply(a, b)[source]#: multiply two vectors, vectorized

nwaylib.bayesdistance.vector_normalised(v)[source]#

nwaylib.bayesdistance.apply_vABv(v, A, B)[source]#: compute v^T A B v, vectorized

nwaylib.bayesdistance.make_covmatrix(sigma_x, sigma_y, rho=0)[source]#: create covariance matrix from given standard deviations and normalised correlation rho, vectorized

nwaylib.bayesdistance.make_invcovmatrix(sigma_x, sigma_y, rho=0)[source]#: create inverse covariance matrix from given standard deviations and normalised correlation rho, vectorized

nwaylib.bayesdistance.convert_from_ellipse(a, b, phi)[source]#

create covariance parameters from ellipse major axis a, minor axis b and angle phi; vectorized

as in Pineau+16, eq 8-10, for example.

nwaylib.bayesdistance.log_bf_elliptical(separations_ra, separations_dec, pos_errors)[source]#

log10 of the multi-way Bayes factor, see eq.(18)

separations_ra: RA separations matrix (NxN matrix of arrays) separations_dec: DEC separations matrix (NxN matrix of arrays) pos_errors: errors (list of (N,3) arrays) giving sigma_RA, sigma_DEC, rho

nwaylib.checkupdates module#

nwaylib.checkupdates.checkupdates(current_version=None)[source]#

Check for program updates on PyPI

If you want to disable this, create a file I_will_check_for_NWAY_updates_myself_thank_you in your working directory.

nwaylib.fastskymatch module#

Functions for finding pairs within a certain search distance “err”.

Very fast method based on hashing.

nwaylib.fastskymatch.dist(apos, bpos)[source]#: Angular separation (in degrees) between two points on a sphere. http://en.wikipedia.org/wiki/Great-circle_distance

nwaylib.fastskymatch.dist3d(apos, bpos)[source]#: Angular separation in ra & dec between two points on a sphere.

nwaylib.fastskymatch.get_tablekeys(table, name, tablename='')[source]#

nwaylib.fastskymatch.get_healpix_resolution_degrees(nside)[source]#

nwaylib.fastskymatch.match_multiple(tables, table_names, err, fits_formats, logger, circular=True, pairwise_errs=[])[source]#

computes the cartesian product of all possible matches, limited to a maximum distance of err (in degrees).

tables: input FITS table table_names: names of the tables fits_formats: FITS data type of the columns of each table

returns results: cartesian product of all possible matches (smaller than err) cat_columns: table with separation distances in arcsec header: which columns were used in each table for RA/DEC

nwaylib.fastskymatch.wraptable2fits(cat_columns, extname)[source]#

nwaylib.fastskymatch.array2fits(table, extname)[source]#

nwaylib.logger module#

class nwaylib.logger.FakeProgressBar(*args)[source]#

Bases: object

start()[source]#

increment()[source]#

finish()[source]#

class nwaylib.logger.NullOutputLogger[source]#

Bases: object

log(*msg)[source]#

warn(msg)[source]#

progress(*args, **kwargs)[source]#

class nwaylib.logger.NormalLogger[source]#

Bases: object

log(msg)[source]#

warn(msg)[source]#

progress(ndigits=6, *args, **kwargs)[source]#

nwaylib.magnitudeweights module#

Computes adaptively binned histograms of two distributions.

Provides smooth biasing functions and plots them.

This is not specific for magnitudes, it can be any provided property.

nwaylib.magnitudeweights.ratio(hist_sel, hist_all)[source]#: ratio between the two histograms. Minor offsets to avoid zero and inf.

nwaylib.magnitudeweights.fraction(bin_mag, hist_sel, hist_all)[source]#: fraction of selected

nwaylib.magnitudeweights.plot_fit(bin_mag, hist_sel, hist_all, func, name)[source]#: Plotting

nwaylib.magnitudeweights.fitfunc_histogram(bin_mag, hist_sel, hist_all)[source]#: creates the biasing functions

nwaylib.magnitudeweights.adaptive_histograms(mag_all, mag_sel, weights=None)[source]#: creates the histograms for the two columns in an adaptive way (based on mag_sel) with the same binning.

nwaylib.progress module#

nwaylib.progress.bar(**kwargs)[source]#

Module contents#

Multiway association between astrometric catalogues

exception nwaylib.UndersampledException[source]#: Bases: Exception

exception nwaylib.EmptyResultException[source]#: Bases: Exception

nwaylib.nway_match(match_tables, match_radius, prior_completeness, mag_include_radius=None, mag_exclude_radius=None, magauto_post_single_minvalue=0.9, prob_ratio_secondary=0.5, min_prob=0.0, consider_unrelated_associations=True, store_mag_hists=True, logger=<nwaylib.logger.NormalLogger object>)[source]#

match_tables: list of catalogues, each a dict with entries:

name (short catalog name, no spaces, used in output columns)
ra (RA in degrees)
dec (dec in degrees)
error (positional error in arcsec)
area (sky area covered by the catalogue in square degrees)
mags (list of additional columns to consider as priors)
magnames (short name for each entry in mags, used in output columns)
maghists (list of prior information for each entry in mags) use None to automatically build a histogram of target/non-target sources. Otherwise, supply the histogram manually: bins_lo, bins_hi, hist_sel, hist_all.

match_radius: maximum radius in arcsec to consider.

More distant counterparts are cut off. Set to a very large value, e.g. 5 times the largest positional error. Setting to larger values does not change the result, but setting to smaller values improves performance.

prior_completeness: expected fraction of sources in the primary catalogue

that are expected to have a counterpart (e.g., 90%). If an array is passed, completeness for each catalog. First entry has to be 1.

mag_include_radius:

search radius for building the magnitude histogram of target sources. If None (default), the Bayesian posterior is used

mag_exclude_radius:

exclusion radius for building the magnitude histogram of field sources. If None (default), mag_include_radius is used

magauto_post_single_minvalue:

minimum posterior probability (default: 0.9) for the magnitude histogram of secure target sources. Used in the Bayesian procedure.

prob_ratio_secondary: for each primary catalogue entry, the most probable associations is marked with match_flag=1. Comparably good solutions receive match_flag=2 (all others are match_flag=0). The prob_ratio_secondary parameter controls the lowest probability ratio p(secondary)/p(primary) still considered comparable.

min_prob: Minimum probability for truncating the output table.: default: 0 (no truncation)
consider_unrelated_associations: Correction for missing sources in: partial assocations. Keep to True.

store_mag_hists: Write constructed mag hists to file (filename based on table name and mags).

logger: NormalLogger for stderr output and progress bars, NullOutputLogger if silent

nwaylib package

Contents

nwaylib package#

Submodules#

nwaylib.bayesdistance module#

nwaylib.checkupdates module#

nwaylib.fastskymatch module#

nwaylib.logger module#

nwaylib.magnitudeweights module#

nwaylib.progress module#

Module contents#