apply_mask_to_set_arrays (mask, data, phi, ...)
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Set certain arrays to a fixed size based on a mask array. |
array_sum (mask)
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Return the sum of an array. |
calculate_adaptive_distance_weights_scaled (...)
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Returns distance weights based on offsets and scaled adaptive weighting. |
calculate_adaptive_distance_weights_shaped (...)
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Returns distance weights based on offsets and shaped adaptive weighting. |
calculate_distance_weights (coordinates, ...)
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Returns a distance weighting based on coordinate offsets. |
calculate_distance_weights_from_matrix (...)
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Returns distance weights based on coordinate offsets and matrix operation. |
calculate_fitting_weights (errors, weights[, ...])
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Calculate the final weighting factor based on errors and other weights. |
check_edge_with_box (coordinates, reference, ...)
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Defines a hyperrectangle edge around a coordinate distribution. |
check_edge_with_distribution (coordinates, ...)
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Defines an edge based on statistical deviation from a sample distribution. |
check_edge_with_ellipsoid (coordinates, ...)
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Defines an ellipsoid edge around a coordinate distribution. |
check_edge_with_range (coordinates, ...)
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Defines an edge based on the range of coordinates in each dimension. |
check_edges (coordinates, reference, mask, ...)
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Determine whether a reference position is within a distribution "edge". |
check_orders (orders, coordinates, reference)
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Checks the sample distribution is suitable for a polynomial fit order. |
check_orders_with_bounds (orders, ...[, ...])
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Checks maximum order for sample coordinates bounding a reference. |
check_orders_with_counts (orders, counts[, ...])
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Checks maximum order based only on the number of samples. |
check_orders_without_bounds (orders, coordinates)
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Checks maximum order based on unique samples, irrespective of reference. |
clean_image (image[, error, mask, window, ...])
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Uses ResamplePolynomial to correct NaNs in image and/or supplied in mask. |
convert_to_numba_list (thing)
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Converts a Python iterable to a Numba list for use in jitted functions. |
coordinate_covariance (coordinates[, mean, ...])
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Calculate the covariance of a distribution. |
coordinate_mean (coordinates[, mask])
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Returns the mean coordinate of a distribution. |
covariance_matrix_inverse (amat, phi, error, ...)
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Calculates the inverse covariance matrix inverse of the fit coefficients. |
derivative_mscp (coefficients, phi_samples, ...)
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Return the weighted mean-square-cross-product (mscp) of sample derivatives. |
distribution_variances (coordinates[, mean, ...])
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Return variance at each coordinate based on coordinate distribution. |
estimated_covariance_matrix_inverse (phi, ...)
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Calculates covariance matrix inverse of fit coefficients from mean error. |
evaluate_derivative (coefficients, phi_point, ...)
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Calculates the derivative of a polynomial at a single point. |
evaluate_derivatives (coefficients, ...)
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Calculates the derivative of a polynomial at multiple points. |
fasttrapz (y, x)
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Fast 1-D integration using Trapezium method. |
fit_phi_value (phi, coefficients)
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Returns the dot product of phi and coefficients. |
fit_phi_variance (phi, inv_covariance)
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Calculates variance given the polynomial terms of a coordinate. |
fit_residual (data, phi, coefficients)
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Calculates the residual of a polynomial fit to data. |
half_max_sigmoid (x[, x_half, k, a, c, q, b, v])
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Evaluate a special case of the logistic function where f(x0) = 0.5. |
logistic_curve (x[, x0, k, a, c, q, b, v])
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Evaluate the generalized logistic function. |
multiple_polynomial_terms (coordinates, exponents)
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Derive polynomial terms for a coordinate set given polynomial exponents. |
multivariate_gaussian (covariance, coordinates)
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Return values of a multivariate Gaussian in K-dimensional coordinates. |
no_fit_solution (set_index, point_index, ...)
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Fill output arrays with set values on fit failure. |
offset_variance (coordinates, reference[, ...])
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Variance at reference coordinate derived from distribution uncertainty. |
polynomial_derivative_map (exponents)
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Creates a mapping from polynomial exponents to derivatives. |
polynomial_exponents (order[, ndim, ...])
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Define a set of polynomial exponents. |
polynomial_terms (coordinates, exponents)
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Derive polynomial terms given coordinates and polynomial exponents. |
relative_density (sigma, counts, weight_sum)
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Returns the relative density of samples compared to a uniform distribution. |
resamp (coordinates, data, *locations[, ...])
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ResamplePolynomial data using local polynomial fitting. |
resampler (coordinates, data, *locations[, ...])
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ResamplePolynomial data using local polynomial fitting. |
scale_coordinates (coordinates, scale, offset)
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Apply scaling factors and offsets to N-dimensional data. |
scale_forward_scalar (coordinate, scale, offset)
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Applies the function f(x) = (x - offset) / scale to a single coordinate. |
scale_forward_vector (coordinates, scale, offset)
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Applies the function f(x) = (x - offset) / scale to a coordinate array. |
scale_reverse_scalar (coordinate, scale, offset)
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Applies the function f(x) = (x * scale) + offset to a single coordinate. |
scale_reverse_vector (coordinates, scale, offset)
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Applies the function f(x) = (x * scale) + offset to a coordinate array. |
scaled_adaptive_weight_matrices (sigma, ...)
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Wrapper for scaled_adaptive_weight_matrix over multiple values. |
scaled_adaptive_weight_matrix (sigma, rchi2)
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Scales a Gaussian weighting kernel based on a prior fit. |
shaped_adaptive_weight_matrices (sigma, ...)
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Wrapper for shaped_adaptive_weight_matrix over multiple values. |
shaped_adaptive_weight_matrix (sigma, rchi2, ...)
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Shape and scale the weighting kernel based on a prior fit. |
sigmoid (x[, factor, offset])
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Evaluate a scaled and shifted logistic function. |
single_polynomial_terms (coordinate, exponents)
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Derive polynomial terms for a single coordinate given polynomial exponents. |
solve_amat_beta (phi, data, weights)
|
Convenience function returning matrices suitable for linear algebra. |
solve_coefficients (amat, beta)
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Find least squares solution of Ax=B and rank of A. |
solve_fit (window_coordinates, window_phi, ...)
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Solve for a fit at a single coordinate. |
solve_fits (sample_indices, ...[, is_covar, ...])
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Solve all fits within one intersection block. |
solve_inverse_covariance_matrices (phi, ...)
|
Inverse covariance matrices on fit coefficients from errors and residuals. |
solve_mean_fit (data, error, weight[, ...])
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Return the weighted mean of data, variance, and reduced chi-squared. |
solve_polynomial_fit (phi_samples, phi_point, ...)
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Derive a polynomial fit from samples, then calculate fit at single point. |
solve_rchi2_from_error (residuals, weights, ...)
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Return the reduced chi-squared given residuals and sample errors. |
solve_rchi2_from_variance (residuals, ...[, ...])
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Return the reduced chi-squared given residuals and constant variance. |
sscp (matrix[, weight, normalize])
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Calculate the sum-of-squares-and-cross-products of a matrix. |
stretch_correction (rchi2, density, ...)
|
A sigmoid function used by the "shaped" adaptive resampling algorithm. |
update_mask (weights, mask)
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Updates a mask, setting False values where weights are zero or non-finite. |
variance_from_offsets (offsets, covariance[, ...])
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Determine the variance given offsets from the expected value. |
weighted_fit_variance (residuals, weights[, ...])
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Calculate variance of a fit from the residuals of the fit to data. |
weighted_mean (data, weights[, weightsum])
|
Calculate the weighted mean of a data set. |
weighted_mean_variance (variance, weights[, ...])
|
Calculated mean weighted variance. |
weighted_variance (error, weights[, weightsum])
|
Utility function to calculate the biased weighted variance. |