resamp¶
- sofia_redux.toolkit.resampling.resamp(coordinates, data, *locations, error=None, mask=None, window=None, order=1, fix_order=True, robust=None, negthresh=None, window_estimate_bins=10, window_estimate_percentile=50, window_estimate_oversample=2.0, leaf_size=40, large_data=False, smoothing=0.0, relative_smooth=False, adaptive_threshold=None, adaptive_algorithm='scaled', fit_threshold=0.0, cval=nan, edge_threshold=0.0, edge_algorithm='distribution', order_algorithm='bounded', error_weighting=True, estimate_covariance=False, is_covar=False, jobs=None, get_error=False, get_counts=False, get_weights=False, get_distance_weights=False, get_rchi2=False, get_cross_derivatives=False, get_offset_variance=False, **distance_kwargs)[source]¶
ResamplePolynomial data using local polynomial fitting.
Initializes and then calls the
ResamplePolynomialclass. For further details on all available parameters, please seeResamplePolynomial.__init__()andResamplePolynomial.__call__().- Parameters:
- coordinates
- data
- locations
- error
- mask
- window
- order
- fix_order
- robust
- negthresh
- window_estimate_bins
- window_estimate_percentile
- window_estimate_oversample
- leaf_size
- large_data
- smoothing
- relative_smooth
- adaptive_threshold
- adaptive_algorithm
- fit_threshold
- cval
- edge_threshold
- edge_algorithm
- order_algorithm
- error_weighting
- estimate_covariance
- is_covar
- jobs
- get_error
- get_counts
- get_weights
- get_distance_weights
- get_rchi2
- get_cross_derivatives
- get_offset_variance
- distance_kwargs
- Returns:
- resultsfloat or numpy.ndarray or n-tuple of (float or numpy.ndarray)
If a fit is performed at a single location, the output will consist of int or float scalar values. Multiple fits result in numpy arrays. The exact output shape depends on the number of data sets, number of fitted points, dimensions of the fit locations. Assuming that all get_* keywords are set to
True, the output order is:results[0] = fitted values results[1] = error on the fit results[2] = sample counts for each fit results[3] = total weight of all samples in fit results[4] = total distance weight sum of all samples in fit results[5] = reduced chi-squared statistic of the fit results[6] = derivative mean squared cross products results[7] = offset variance of fit from sample distribution