Python transforms.ScaledTranslation类代码示例

    def _set_lim_and_transforms(self):
        self.transAxes = BboxTransformTo(self.bbox)

        # Transforms the x and y axis separately by a scale factor
        # It is assumed that this part will have non-linear components
        self.transScale = TransformWrapper(IdentityTransform())

        # A (possibly non-linear) projection on the (already scaled)
        # data.  This one is aware of rmin
        self.transProjection = self.PolarTransform(self)

        # This one is not aware of rmin
        self.transPureProjection = self.PolarTransform(self, use_rmin=False)

        # An affine transformation on the data, generally to limit the
        # range of the axes
        self.transProjectionAffine = self.PolarAffine(self.transScale, self.viewLim)

        # The complete data transformation stack -- from data all the
        # way to display coordinates
        self.transData = self.transScale + self.transProjection + (self.transProjectionAffine + self.transAxes)

        # This is the transform for theta-axis ticks.  It is
        # equivalent to transData, except it always puts r == 1.0 at
        # the edge of the axis circle.
        self._xaxis_transform = (
            self.transPureProjection + self.PolarAffine(IdentityTransform(), Bbox.unit()) + self.transAxes
        )
        # The theta labels are moved from radius == 0.0 to radius == 1.1
        self._theta_label1_position = Affine2D().translate(0.0, 1.1)
        self._xaxis_text1_transform = self._theta_label1_position + self._xaxis_transform
        self._theta_label2_position = Affine2D().translate(0.0, 1.0 / 1.1)
        self._xaxis_text2_transform = self._theta_label2_position + self._xaxis_transform

        # This is the transform for r-axis ticks.  It scales the theta
        # axis so the gridlines from 0.0 to 1.0, now go from 0.0 to
        # 2pi.
        self._yaxis_transform = Affine2D().scale(np.pi * 2.0, 1.0) + self.transData
        # The r-axis labels are put at an angle and padded in the r-direction
        self._r_label1_position = ScaledTranslation(
            22.5, self._rpad, blended_transform_factory(Affine2D(), BboxTransformToMaxOnly(self.viewLim))
        )
        self._yaxis_text1_transform = (
            self._r_label1_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform
        )
        self._r_label2_position = ScaledTranslation(
            22.5, -self._rpad, blended_transform_factory(Affine2D(), BboxTransformToMaxOnly(self.viewLim))
        )
        self._yaxis_text2_transform = (
            self._r_label2_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform
        )

#.........这里部分代码省略.........
        # An affine transformation on the data, generally to limit the
        # range of the axes
        self.transProjectionAffine = self.PolarAffine(self.transScale, self.viewLim)

        # The complete data transformation stack -- from data all the
        # way to display coordinates
        self.transData = self.transScale + self.transProjection + \
            (self.transProjectionAffine + self.transAxes)

        # This is the transform for theta-axis ticks.  It is
        # equivalent to transData, except it always puts r == 1.0 at
        # the edge of the axis circle.
        self._xaxis_transform = (
            self.transPureProjection +
            self.PolarAffine(IdentityTransform(), Bbox.unit()) +
            self.transAxes)
        # The theta labels are moved from radius == 0.0 to radius == 1.1
        self._theta_label1_position = Affine2D().translate(0.0, 1.1)
        self._xaxis_text1_transform = (
            self._theta_label1_position +
            self._xaxis_transform)
        self._theta_label2_position = Affine2D().translate(0.0, 1.0 / 1.1)
        self._xaxis_text2_transform = (
            self._theta_label2_position +
            self._xaxis_transform)

        # This is the transform for r-axis ticks.  It scales the theta
        # axis so the gridlines from 0.0 to 1.0, now go from 0.0 to
        # 2pi.
        self._yaxis_transform = (
            Affine2D().scale(np.pi * 2.0, 1.0) +
            self.transData)
        # The r-axis labels are put at an angle and padded in the r-direction
        self._r_label_position = ScaledTranslation(
            22.5, 0.0, Affine2D())
        self._yaxis_text_transform = (
            self._r_label_position +
            Affine2D().scale(1.0 / 360.0, 1.0) +
            self._yaxis_transform
            )

    def get_xaxis_transform(self,which='grid'):
        assert which in ['tick1','tick2','grid']
        return self._xaxis_transform

    def get_xaxis_text1_transform(self, pad):
        return self._xaxis_text1_transform, 'center', 'center'

    def get_xaxis_text2_transform(self, pad):
        return self._xaxis_text2_transform, 'center', 'center'

    def get_yaxis_transform(self,which='grid'):
        assert which in ['tick1','tick2','grid']
        return self._yaxis_transform

    def get_yaxis_text1_transform(self, pad):
        angle = self._r_label_position.to_values()[4]
        if angle < 90.:
            return self._yaxis_text_transform, 'bottom', 'left'
        elif angle < 180.:
            return self._yaxis_text_transform, 'bottom', 'right'
        elif angle < 270.:
            return self._yaxis_text_transform, 'top', 'right'
        else:
            return self._yaxis_text_transform, 'top', 'left'

#.........这里部分代码省略.........
        # An affine transformation on the data, generally to limit the
        # range of the axes
        self.transProjectionAffine = self.PolarAffine(self.transScale, self.viewLim)

        # The complete data transformation stack -- from data all the
        # way to display coordinates
        self.transData = self.transScale + self.transProjection + \
            (self.transProjectionAffine + self.transAxes)

        # This is the transform for theta-axis ticks.  It is
        # equivalent to transData, except it always puts r == 1.0 at
        # the edge of the axis circle.
        self._xaxis_transform = (
            self.transPureProjection +
            self.PolarAffine(IdentityTransform(), Bbox.unit()) +
            self.transAxes)
        # The theta labels are moved from radius == 0.0 to radius == 1.1
        self._theta_label1_position = Affine2D().translate(0.0, 1.1)
        self._xaxis_text1_transform = (
            self._theta_label1_position +
            self._xaxis_transform)
        self._theta_label2_position = Affine2D().translate(0.0, 1.0 / 1.1)
        self._xaxis_text2_transform = (
            self._theta_label2_position +
            self._xaxis_transform)

        # This is the transform for r-axis ticks.  It scales the theta
        # axis so the gridlines from 0.0 to 1.0, now go from 0.0 to
        # 2pi.
        self._yaxis_transform = (
            Affine2D().scale(np.pi * 2.0, 1.0) +
            self.transData)
        # The r-axis labels are put at an angle and padded in the r-direction
        self._r_label_position = ScaledTranslation(
            22.5, 0.0, Affine2D())
        self._yaxis_text_transform = (
            self._r_label_position +
            Affine2D().scale(1.0 / 360.0, 1.0) +
            self._yaxis_transform
            )

    def get_xaxis_transform(self,which='grid'):
        assert which in ['tick1','tick2','grid']
        return self._xaxis_transform

    def get_xaxis_text1_transform(self, pad):
        return self._xaxis_text1_transform, 'center', 'center'

    def get_xaxis_text2_transform(self, pad):
        return self._xaxis_text2_transform, 'center', 'center'

    def get_yaxis_transform(self,which='grid'):
        assert which in ['tick1','tick2','grid']
        return self._yaxis_transform

    def get_yaxis_text1_transform(self, pad):
        angle = self._r_label_position.to_values()[4]
        if angle < 90.:
            return self._yaxis_text_transform, 'bottom', 'left'
        elif angle < 180.:
            return self._yaxis_text_transform, 'bottom', 'right'
        elif angle < 270.:
            return self._yaxis_text_transform, 'top', 'right'
        else:
            return self._yaxis_text_transform, 'top', 'left'