How to use the probscale.transforms._ProbTransformMixin function in probscale

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github matplotlib / mpl-probscale / probscale / transforms.py View on Github external
"""

    def transform_non_affine(self, prob):
        with numpy.errstate(divide="ignore", invalid="ignore"):
            prob = self._handle_out_of_bounds(
                numpy.asarray(prob) / self.factor
            )
            q = self.dist.ppf(prob)
        return q

    def inverted(self):
        return QuantileTransform(self.dist, as_pct=self.as_pct,
                                 out_of_bounds=self.out_of_bounds)


class QuantileTransform(_ProbTransformMixin):
    """
    MPL axes transform class to convert probabilities or percents to
    quantiles.

    Parameters
    ----------
    dist : scipy.stats distribution
        The distribution whose ``cdf`` and ``pdf`` methods will set the
        scale of the axis.
    as_pct : bool, optional (True)
        Toggles the formatting of the probabilities associated with the
        tick labels as percentages (0 - 100) or fractions (0 - 1).
    out_of_bounds : string, optional ('mask' or 'clip')
        Determines how data outside the range of valid values is
        handled. The default behavior is to mask the data.
        Alternatively, the data can be clipped to values arbitrarily
github matplotlib / mpl-probscale / probscale / transforms.py View on Github external
self.as_pct = as_pct
        self.out_of_bounds = out_of_bounds
        if self.as_pct:
            self.factor = 100.0
        else:
            self.factor = 1.0

        if self.out_of_bounds == 'mask':
            self._handle_out_of_bounds = _mask_out_of_bounds
        elif self.out_of_bounds == 'clip':
            self._handle_out_of_bounds = _clip_out_of_bounds
        else:
            raise ValueError("`out_of_bounds` muse be either 'mask' or 'clip'")


class ProbTransform(_ProbTransformMixin):
    """
    MPL axes transform class to convert quantiles to probabilities
    or percents.

    Parameters
    ----------
    dist : scipy.stats distribution
        The distribution whose ``cdf`` and ``pdf`` methods will set the
        scale of the axis.
    as_pct : bool, optional (True)
        Toggles the formatting of the probabilities associated with the
        tick labels as percentages (0 - 100) or fractions (0 - 1).
    out_of_bounds : string, optional ('mask' or 'clip')
        Determines how data outside the range of valid values is
        handled. The default behavior is to mask the data.
        Alternatively, the data can be clipped to values arbitrarily