Python transforms.Bbox方法代碼示例

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def __init__(self, meshWidth, meshHeight, coordinates,
                 antialiased=True, shading='flat', **kwargs):
        Collection.__init__(self, **kwargs)
        self._meshWidth = meshWidth
        self._meshHeight = meshHeight
        self._coordinates = coordinates
        self._antialiased = antialiased
        self._shading = shading

        self._bbox = transforms.Bbox.unit()
        self._bbox.update_from_data_xy(coordinates.reshape(
            ((meshWidth + 1) * (meshHeight + 1), 2)))

        # By converting to floats now, we can avoid that on every draw.
        self._coordinates = self._coordinates.reshape(
            (meshHeight + 1, meshWidth + 1, 2))
        self._coordinates = np.array(self._coordinates, np.float_) 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def set_figure(self, fig):
        """
        Set the `.Figure` for this `.Axes`.

        Parameters
        ----------
        fig : `.Figure`
        """
        martist.Artist.set_figure(self, fig)

        self.bbox = mtransforms.TransformedBbox(self._position,
                                                fig.transFigure)
        # these will be updated later as data is added
        self.dataLim = mtransforms.Bbox.null()
        self.viewLim = mtransforms.Bbox.unit()
        self.transScale = mtransforms.TransformWrapper(
            mtransforms.IdentityTransform())

        self._set_lim_and_transforms() 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def get_position(self, original=False):
        """
        Get a copy of the axes rectangle as a `.Bbox`.

        Parameters
        ----------
        original : bool
            If ``True``, return the original position. Otherwise return the
            active position. For an explanation of the positions see
            `.set_position`.

        Returns
        -------
        pos : `.Bbox`

        """
        if original:
            return self._originalPosition.frozen()
        else:
            locator = self.get_axes_locator()
            if not locator:
                self.apply_aspect()
            return self._position.frozen() 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def set_figure(self, fig):
        """
        Set the class:`~matplotlib.axes.Axes` figure

        accepts a class:`~matplotlib.figure.Figure` instance
        """
        martist.Artist.set_figure(self, fig)

        self.bbox = mtransforms.TransformedBbox(self._position,
                                                fig.transFigure)
        # these will be updated later as data is added
        self.dataLim = mtransforms.Bbox.null()
        self.viewLim = mtransforms.Bbox.unit()
        self.transScale = mtransforms.TransformWrapper(
            mtransforms.IdentityTransform())

        self._set_lim_and_transforms() 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def relim(self, visible_only=False):
        """
        Recompute the data limits based on current artists. If you want to
        exclude invisible artists from the calculation, set
        ``visible_only=True``

        At present, :class:`~matplotlib.collections.Collection`
        instances are not supported.
        """
        # Collections are deliberately not supported (yet); see
        # the TODO note in artists.py.
        self.dataLim.ignore(True)
        self.dataLim.set_points(mtransforms.Bbox.null().get_points())
        self.ignore_existing_data_limits = True

        for line in self.lines:
            if not visible_only or line.get_visible():
                self._update_line_limits(line)

        for p in self.patches:
            if not visible_only or p.get_visible():
                self._update_patch_limits(p) 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def set_position(self, pos, which='both'):
        """
        Set the axes position.

        Axes have two position attributes. The 'original' position is the
        position allocated for the Axes. The 'active' position is the
        position the Axes is actually drawn at. These positions are usually
        the same unless a fixed aspect is set to the Axes. See `.set_aspect`
        for details.

        Parameters
        ----------
        pos : [left, bottom, width, height] or `~matplotlib.transforms.Bbox`
            The new position of the in `.Figure` coordinates.

        which : {'both', 'active', 'original'}, optional
            Determines which position variables to change.

        """
        self._set_position(pos, which='both')
        # because this is being called externally to the library we
        # zero the constrained layout parts.
        self._layoutbox = None
        self._poslayoutbox = None 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def set_position(self, pos, which='both'):
        """
        Set the axes position.

        Axes have two position attributes. The 'original' position is the
        position allocated for the Axes. The 'active' position is the
        position the Axes is actually drawn at. These positions are usually
        the same unless a fixed aspect is set to the Axes. See `.set_aspect`
        for details.

        Parameters
        ----------
        pos : [left, bottom, width, height] or `~matplotlib.transforms.Bbox`
            The new position of the in `.Figure` coordinates.

        which : {'both', 'active', 'original'}, optional
            Determines which position variables to change.

        """
        self._set_position(pos, which=which)
        # because this is being called externally to the library we
        # zero the constrained layout parts.
        self._layoutbox = None
        self._poslayoutbox = None 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def test_bbox_intersection():
    bbox_from_ext = mtransforms.Bbox.from_extents
    inter = mtransforms.Bbox.intersection

    r1 = bbox_from_ext(0, 0, 1, 1)
    r2 = bbox_from_ext(0.5, 0.5, 1.5, 1.5)
    r3 = bbox_from_ext(0.5, 0, 0.75, 0.75)
    r4 = bbox_from_ext(0.5, 1.5, 1, 2.5)
    r5 = bbox_from_ext(1, 1, 2, 2)

    # self intersection -> no change
    assert_bbox_eq(inter(r1, r1), r1)
    # simple intersection
    assert_bbox_eq(inter(r1, r2), bbox_from_ext(0.5, 0.5, 1, 1))
    # r3 contains r2
    assert_bbox_eq(inter(r1, r3), r3)
    # no intersection
    assert inter(r1, r4) is None
    # single point
    assert_bbox_eq(inter(r1, r5), bbox_from_ext(1, 1, 1, 1)) 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def test_bbox_image_inverted():
    # This is just used to produce an image to feed to BboxImage
    image = np.arange(100).reshape((10, 10))

    fig, ax = plt.subplots()
    bbox_im = BboxImage(
        TransformedBbox(Bbox([[100, 100], [0, 0]]), ax.transData))
    bbox_im.set_data(image)
    bbox_im.set_clip_on(False)
    ax.set_xlim(0, 100)
    ax.set_ylim(0, 100)
    ax.add_artist(bbox_im)

    image = np.identity(10)

    bbox_im = BboxImage(TransformedBbox(Bbox([[0.1, 0.2], [0.3, 0.25]]),
                                        ax.figure.transFigure))
    bbox_im.set_data(image)
    bbox_im.set_clip_on(False)
    ax.add_artist(bbox_im) 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def imagesAsTickMarks(ax, images):
    TICKYPOS = -.6
    lowerCorner = ax.transData.transform((.8,TICKYPOS-.2))
    upperCorner = ax.transData.transform((1.2,TICKYPOS+.2))
    print(lowerCorner)
    print(upperCorner)
    bbox_image = BboxImage(Bbox([lowerCorner[0],
                                 lowerCorner[1],
                                 upperCorner[0],
                                 upperCorner[1],
                                 ]),
                           norm = None,
                           origin=None,
                           clip_on=False,
                           )

    image = imread(images[0])
    print('img loaded')
    bbox_image.set_data(image)
    ax.add_artist(bbox_image) 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def get_datalim(self, transData):
        transform = self.get_transform()
        transOffset = self.get_offset_transform()
        offsets = self._offsets
        paths = self.get_paths()

        if not transform.is_affine:
            paths = [transform.transform_path_non_affine(p) for p in paths]
            transform = transform.get_affine()
        if not transOffset.is_affine:
            offsets = transOffset.transform_non_affine(offsets)
            transOffset = transOffset.get_affine()

        offsets = np.asanyarray(offsets, np.float_)
        if np.ma.isMaskedArray(offsets):
            offsets = offsets.filled(np.nan)
            # get_path_collection_extents handles nan but not masked arrays
        offsets.shape = (-1, 2)                     # Make it Nx2

        if paths:
            result = mpath.get_path_collection_extents(
                transform.frozen(), paths, self.get_transforms(),
                offsets, transOffset.frozen())
            result = result.inverse_transformed(transData)
        else:
            result = transforms.Bbox([[0, 0], [0, 0]])
        return result 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def on_draw_event(self, widget, ctx):
        """ GtkDrawable draw event, like expose_event in GTK 2.X
        """
        allocation = self.get_allocation()
        w, h = allocation.width, allocation.height

        if not len(self._bbox_queue):
            if self._need_redraw:
                self._render_figure(w, h)
                bbox_queue = [transforms.Bbox([[0, 0], [w, h]])]
            else:
                return
        else:
            bbox_queue = self._bbox_queue

        for bbox in bbox_queue:
            area = self.copy_from_bbox(bbox)
            buf = np.fromstring(area.to_string_argb(), dtype='uint8')

            x = int(bbox.x0)
            y = h - int(bbox.y1)
            width = int(bbox.x1) - int(bbox.x0)
            height = int(bbox.y1) - int(bbox.y0)

            image = cairo.ImageSurface.create_for_data(
                buf, cairo.FORMAT_ARGB32, width, height)
            ctx.set_source_surface(image, x, y)
            ctx.paint()

        if len(self._bbox_queue):
            self._bbox_queue = []

        return False 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def unit_bbox():
    box = Bbox(np.array([[0, 0], [1, 1]]))
    return box 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def __init__(self, center, viewLim, originLim, **kwargs):
        mtransforms.Bbox.__init__(self,
                                  np.array([[0.0, 0.0], [1.0, 1.0]], np.float),
                                  **kwargs)
        self._center = center
        self._viewLim = viewLim
        self._originLim = originLim
        self.set_children(viewLim, originLim) 

# 需要導入模塊: from matplotlib import transforms [as 別名]
# 或者: from matplotlib.transforms import Bbox [as 別名]
def on_draw_event(self, widget, ctx):
        """GtkDrawable draw event, like expose_event in GTK 2.X.
        """
        allocation = self.get_allocation()
        w, h = allocation.width, allocation.height

        if not len(self._bbox_queue):
            Gtk.render_background(
                self.get_style_context(), ctx,
                allocation.x, allocation.y,
                allocation.width, allocation.height)
            bbox_queue = [transforms.Bbox([[0, 0], [w, h]])]
        else:
            bbox_queue = self._bbox_queue

        ctx = backend_cairo._to_context(ctx)

        for bbox in bbox_queue:
            x = int(bbox.x0)
            y = h - int(bbox.y1)
            width = int(bbox.x1) - int(bbox.x0)
            height = int(bbox.y1) - int(bbox.y0)

            buf = cbook._unmultiplied_rgba8888_to_premultiplied_argb32(
                np.asarray(self.copy_from_bbox(bbox)))
            image = cairo.ImageSurface.create_for_data(
                buf.ravel().data, cairo.FORMAT_ARGB32, width, height)
            ctx.set_source_surface(image, x, y)
            ctx.paint()

        if len(self._bbox_queue):
            self._bbox_queue = []

        return False