Method transform

lib/matplotlib/transforms.py:1529–1564

Apply this transformation on the given array of *values*. Parameters ---------- values : array-like The input values as an array of length :attr:`~Transform.input_dims` or shape (N, :attr:`~Transform.input_dims`). Returns ---

(self, values)

        return self.get_affine().get_matrix()

    def transform(self, values):
        """
        Apply this transformation on the given array of *values*.

        Parameters
        ----------
        values : array-like
            The input values as an array of length :attr:`~Transform.input_dims` or
            shape (N, :attr:`~Transform.input_dims`).

        Returns
        -------
        array
            The output values as an array of length :attr:`~Transform.output_dims` or
            shape (N, :attr:`~Transform.output_dims`), depending on the input.
        """
        # Ensure that values is a 2d array (but remember whether
        # we started with a 1d or 2d array).
        values = np.asanyarray(values)
        ndim = values.ndim
        values = values.reshape((-1, self.input_dims))

        # Transform the values
        res = self.transform_affine(self.transform_non_affine(values))

        # Convert the result back to the shape of the input values.
        if ndim == 0:
            assert not np.ma.is_masked(res)  # just to be on the safe side
            return res[0, 0]
        if ndim == 1:
            return res.reshape(-1)
        elif ndim == 2:
            return res
        raise ValueError(
            "Input values must have shape (N, {dims}) or ({dims},)"
            .format(dims=self.input_dims))

    def transform_affine(self, values):
        """