Class FixedLocator

lib/matplotlib/ticker.py:1840–1880

r""" Place ticks at a set of fixed values. If *nbins* is None ticks are placed at all values. Otherwise, the *locs* array of possible positions will be subsampled to keep the number of ticks :math:`\leq nbins + 1`. The subsampling will be done to include the smallest absolute va

class FixedLocator(Locator):
    r"""
    Place ticks at a set of fixed values.

    If *nbins* is None ticks are placed at all values. Otherwise, the *locs* array of
    possible positions will be subsampled to keep the number of ticks
    :math:`\leq nbins + 1`. The subsampling will be done to include the smallest
    absolute value; for example, if zero is included in the array of possibilities, then
    it will be included in the chosen ticks.
    """

    def __init__(self, locs, nbins=None):
        self.locs = np.asarray(locs)
        _api.check_shape((None,), locs=self.locs)
        self.nbins = max(nbins, 2) if nbins is not None else None

    def set_params(self, nbins=None):
        """Set parameters within this locator."""
        if nbins is not None:
            self.nbins = nbins

    def __call__(self):
        return self.tick_values(None, None)

    def tick_values(self, vmin, vmax):
        """
        Return the locations of the ticks.

        .. note::

            Because the values are fixed, *vmin* and *vmax* are not used.
        """
        if self.nbins is None:
            return self.locs
        step = max(int(np.ceil(len(self.locs) / self.nbins)), 1)
        ticks = self.locs[::step]
        for i in range(1, step):
            ticks1 = self.locs[i::step]
            if np.abs(ticks1).min() < np.abs(ticks).min():
                ticks = ticks1
        return self.raise_if_exceeds(ticks)


class NullLocator(Locator):