Python transforms.BboxTransformTo方法代码示例

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def _update_patch_transform(self):
        """NOTE: This cannot be called until after this has been added
                 to an Axes, otherwise unit conversion will fail. This
                 maxes it very important to call the accessor method and
                 not directly access the transformation member variable.
        """
        x = self.convert_xunits(self._x)
        y = self.convert_yunits(self._y)
        width = self.convert_xunits(self._width)
        height = self.convert_yunits(self._height)
        bbox = transforms.Bbox.from_bounds(x, y, width, height)
        rot_trans = transforms.Affine2D()
        rot_trans.rotate_deg_around(x, y, self._angle)
        self._rect_transform = transforms.BboxTransformTo(bbox)
        self._rect_transform += rot_trans 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def set_bbox_to_anchor(self, bbox, transform=None):
        """
        set the bbox that the legend will be anchored.

        *bbox* can be a BboxBase instance, a tuple of [left, bottom,
        width, height] in the given transform (normalized axes
        coordinate if None), or a tuple of [left, bottom] where the
        width and height will be assumed to be zero.
        """
        if bbox is None:
            self._bbox_to_anchor = None
            return
        elif isinstance(bbox, BboxBase):
            self._bbox_to_anchor = bbox
        else:
            try:
                l = len(bbox)
            except TypeError:
                raise ValueError("Invalid argument for bbox : %s" % str(bbox))

            if l == 2:
                bbox = [bbox[0], bbox[1], 0, 0]

            self._bbox_to_anchor = Bbox.from_bounds(*bbox)

        if transform is None:
            transform = BboxTransformTo(self.parent.bbox)

        self._bbox_to_anchor = TransformedBbox(self._bbox_to_anchor,
                                               transform) 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def __call__(self, ax, renderer):
       bbox_parent = self.parent.get_position(original=False)
       trans = mtrans.BboxTransformTo(bbox_parent)
       bbox_inset = mtrans.Bbox.from_bounds(*self.lbwh)
       bb = mtrans.TransformedBbox(bbox_inset, trans)
       return bb 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def set_bbox_to_anchor(self, bbox, transform=None):
        """
        Set the bbox that the legend will be anchored to.

        *bbox* can be

        - A `.BboxBase` instance
        - A tuple of ``(left, bottom, width, height)`` in the given transform
          (normalized axes coordinate if None)
        - A tuple of ``(left, bottom)`` where the width and height will be
          assumed to be zero.
        """
        if bbox is None:
            self._bbox_to_anchor = None
            return
        elif isinstance(bbox, BboxBase):
            self._bbox_to_anchor = bbox
        else:
            try:
                l = len(bbox)
            except TypeError:
                raise ValueError("Invalid argument for bbox : %s" % str(bbox))

            if l == 2:
                bbox = [bbox[0], bbox[1], 0, 0]

            self._bbox_to_anchor = Bbox.from_bounds(*bbox)

        if transform is None:
            transform = BboxTransformTo(self.parent.bbox)

        self._bbox_to_anchor = TransformedBbox(self._bbox_to_anchor,
                                               transform)
        self.stale = True 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def _set_lim_and_transforms(self):
        """
        Set the *_xaxis_transform*, *_yaxis_transform*, *transScale*,
        *transData*, *transLimits* and *transAxes* transformations.

        .. note::

            This method is primarily used by rectilinear projections of the
            `~matplotlib.axes.Axes` class, and is meant to be overridden by
            new kinds of projection axes that need different transformations
            and limits. (See `~matplotlib.projections.polar.PolarAxes` for an
            example.)
        """
        self.transAxes = mtransforms.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
        # (e.g., for a log scale).
        self.transScale = mtransforms.TransformWrapper(
            mtransforms.IdentityTransform())

        # An affine transformation on the data, generally to limit the
        # range of the axes
        self.transLimits = mtransforms.BboxTransformFrom(
            mtransforms.TransformedBbox(self.viewLim, self.transScale))

        # The parentheses are important for efficiency here -- they
        # group the last two (which are usually affines) separately
        # from the first (which, with log-scaling can be non-affine).
        self.transData = self.transScale + (self.transLimits + self.transAxes)

        self._xaxis_transform = mtransforms.blended_transform_factory(
            self.transData, self.transAxes)
        self._yaxis_transform = mtransforms.blended_transform_factory(
            self.transAxes, self.transData) 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def _set_lim_and_transforms(self):
        """
        set the *dataLim* and *viewLim*
        :class:`~matplotlib.transforms.Bbox` attributes and the
        *transScale*, *transData*, *transLimits* and *transAxes*
        transformations.

        .. note::

            This method is primarily used by rectilinear projections
            of the :class:`~matplotlib.axes.Axes` class, and is meant
            to be overridden by new kinds of projection axes that need
            different transformations and limits. (See
            :class:`~matplotlib.projections.polar.PolarAxes` for an
            example.

        """
        self.transAxes = mtransforms.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
        # (e.g., for a log scale).
        self.transScale = mtransforms.TransformWrapper(
            mtransforms.IdentityTransform())

        # An affine transformation on the data, generally to limit the
        # range of the axes
        self.transLimits = mtransforms.BboxTransformFrom(
            mtransforms.TransformedBbox(self.viewLim, self.transScale))

        # The parentheses are important for efficiency here -- they
        # group the last two (which are usually affines) separately
        # from the first (which, with log-scaling can be non-affine).
        self.transData = self.transScale + (self.transLimits + self.transAxes)

        self._xaxis_transform = mtransforms.blended_transform_factory(
            self.transData, self.transAxes)
        self._yaxis_transform = mtransforms.blended_transform_factory(
            self.transAxes, self.transData) 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def _set_lim_and_transforms(self):
        """
        set the *_xaxis_transform*, *_yaxis_transform*,
        *transScale*, *transData*, *transLimits* and *transAxes*
        transformations.

        .. note::

            This method is primarily used by rectilinear projections
            of the :class:`~matplotlib.axes.Axes` class, and is meant
            to be overridden by new kinds of projection axes that need
            different transformations and limits. (See
            :class:`~matplotlib.projections.polar.PolarAxes` for an
            example.

        """
        self.transAxes = mtransforms.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
        # (e.g., for a log scale).
        self.transScale = mtransforms.TransformWrapper(
            mtransforms.IdentityTransform())

        # An affine transformation on the data, generally to limit the
        # range of the axes
        self.transLimits = mtransforms.BboxTransformFrom(
            mtransforms.TransformedBbox(self.viewLim, self.transScale))

        # The parentheses are important for efficiency here -- they
        # group the last two (which are usually affines) separately
        # from the first (which, with log-scaling can be non-affine).
        self.transData = self.transScale + (self.transLimits + self.transAxes)

        self._xaxis_transform = mtransforms.blended_transform_factory(
            self.transData, self.transAxes)
        self._yaxis_transform = mtransforms.blended_transform_factory(
            self.transAxes, self.transData) 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def __call__(self, ax, renderer):
        bbox_parent = self.parent.get_position(original=False)
        trans = BboxTransformTo(bbox_parent)
        bbox_inset = Bbox.from_bounds(*self.lbwh)
        bb = TransformedBbox(bbox_inset, trans)
        return bb 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def convert_point(self,point_px, stop=False):
      '''
      given a touch point in the view space, compute the corresponding point in data coords. assumes linear scaling!
      TODO: support log scaling 
      
      there are basically two bbox transforms:  1) from figure coords to view coords, accounting for sign change in y. this then lets us compute axes box in view coords, and generate 2) transform from view to data coords.

      '''
      transFig=BboxTransformTo(Bbox([(0,self.height),(self.width,0)]))
      bbox_axes=Bbox(transFig.transform(plt.gca().get_position()))
      bbox_data=Bbox([(self.xlim[0],self.ylim[0]),(self.xlim[1],self.ylim[1])])
      transMPL=BboxTransform(bbox_axes,bbox_data)
      self.trans=transMPL
      ax_pt=transMPL.transform_point(point_px)      
      return ax_pt 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def compute_lims(self):
      '''compute new axes limits based on pan/zoom.
         basically, get limits in terms of bbox of size 1.  
         transform to bbox of size orig lims.       
      '''
      xlnorm=      self.hslider.barvalue+np.array([-.5,.5])*self.hslider.barwidth
      ylnorm=      self.vslider.barvalue+np.array([-.5,.5])*self.vslider.barwidth
      viewBbox=Bbox(zip(xlnorm,ylnorm))
      limBB=Bbox(zip(self.xlim,self.ylim))
      newlims=Bbox(BboxTransformTo(limBB).transform(viewBbox))
      return [(newlims.x0,newlims.x1),(newlims.y0,newlims.y1)] 

# 需要导入模块: from matplotlib import transforms [as 别名]
# 或者: from matplotlib.transforms import BboxTransformTo [as 别名]
def _set_lim_and_transforms(self):
        # A (possibly non-linear) projection on the (already scaled) data
        self.transProjection = self._get_core_transform(self.RESOLUTION)

        self.transAffine = self._get_affine_transform()

        self.transAxes = BboxTransformTo(self.bbox)

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

        # This is the transform for longitude ticks.
        self._xaxis_pretransform = \
            Affine2D() \
            .scale(1, self._longitude_cap * 2) \
            .translate(0, -self._longitude_cap)
        self._xaxis_transform = \
            self._xaxis_pretransform + \
            self.transData
        self._xaxis_text1_transform = \
            Affine2D().scale(1, 0) + \
            self.transData + \
            Affine2D().translate(0, 4)
        self._xaxis_text2_transform = \
            Affine2D().scale(1, 0) + \
            self.transData + \
            Affine2D().translate(0, -4)

        # This is the transform for latitude ticks.
        yaxis_stretch = Affine2D().scale(np.pi * 2, 1).translate(-np.pi, 0)
        yaxis_space = Affine2D().scale(1, 1.1)
        self._yaxis_transform = \
            yaxis_stretch + \
            self.transData
        yaxis_text_base = \
            yaxis_stretch + \
            self.transProjection + \
            (yaxis_space + \
             self.transAffine + \
             self.transAxes)
        self._yaxis_text1_transform = \
            yaxis_text_base + \
            Affine2D().translate(-8, 0)
        self._yaxis_text2_transform = \
            yaxis_text_base + \
            Affine2D().translate(8, 0)