Source code for mangadap.util.modeling

Module with generic functions used when modeling data.


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import numpy
from .filter import BoxcarFilter

[docs]def reject_residuals_1d(resid, lo=3.0, hi=3.0, boxcar=None): r""" Reject fit residuals. Args: resid (`numpy.ndarray`_,`_): Weighted fit residuals. If input as a `numpy.ndarray`_, all data is included in the calculation of the local or global standard deviation. To exclude values from this calculation, the input *must* be a masked array. Shape must be either :math:`(N_x,)` or :math:`(N_{\rm vec},N_x})`. If 2D, the rejection is performed separately for each *row*. lo (:obj:`float`, optional): Sigma rejection for low values. hi (:obj:`float`, optional): Sigma rejection for high values. boxcar (:obj:`int`, optional): Size of a boxcar to use if using local sigma rejection. If None, rejection is based on the global standard deviation. Returns: `numpy.ndarray`_: Boolean array flagging values to reject. """ if boxcar is None: mean =, axis=-1) std =, axis=-1) # NOTE: Transposes avoid the need for numpy.newaxis usage. return (( < mean - lo * std) \ | ( > mean + hi * std)).T \ & numpy.logical_not( bf = BoxcarFilter(boxcar, lo=lo, hi=hi, niter=1, y=resid, local_sigma=True) return numpy.squeeze(bf.output_mask) & numpy.logical_not(
# TODO: Should maybe change the behavior of this so that if rng=None, # the range is set to x[[0,-1]].
[docs]def scaled_coordinates(x, rng=None): r""" Scale the provided coordinates. The coordinates are scaled such that over the provided range the coordinates are rescaled to the range :math:`[-1, 1]`. .. warning:: If ``rng`` is None, the returned object is identical to ``x`` (i.e., it's not a copy of it). Args: x (`numpy.ndarray`_): Coordinate array rng (array-like, optional): The range over which to rescale to [-1,1]. If None, the input coordinates are simply returned. Returns: `numpy.ndarray`_: The rescaled coordinates. """ return x if rng is None else 2 * (x - rng[0]) / (rng[1] - rng[0]) - 1
[docs]class FitResiduals: def __init__(self, y, model, gpm=None): self.y = y self.model = model self.gpm = gpm def __call__(self, a): resid = (self.y-self.model(a)) return resid if self.gpm is None else resid[self.gpm]
[docs]class FitChiSquare: def __init__(self, y, e, model, gpm=None): self.y = y self.e = e self.model = model _gpm = self.e > 0 self.gpm = _gpm if gpm is None else gpm & _gpm def __call__(self, a): return (self.y[self.gpm]-self.model(a)[self.gpm])/self.e[self.gpm]