Python transforms.Bbox类代码示例

 def _find_best_position(self, width, height, renderer, consider=None):
     """
     Determine the best location to place the legend.
     `consider` is a list of (x, y) pairs to consider as a potential
     lower-left corner of the legend. All are display coords.
     """
     assert self.isaxes # should always hold because function is only called internally
     verts, bboxes, lines = self._auto_legend_data()
     bbox = Bbox.from_bounds(0, 0, width, height)
     consider = [self._get_anchored_bbox(x, bbox, self.get_bbox_to_anchor(),
                                         renderer) for x in range(1, len(self.codes))]
     candidates = []
     for l, b in consider:
         legendBox = Bbox.from_bounds(l, b, width, height)
         badness = 0
         badness = legendBox.count_contains(verts)
         badness += legendBox.count_overlaps(bboxes)
         for line in lines:
             if line.intersects_bbox(legendBox):
                 badness += 1
         ox, oy = l, b
         if badness == 0:
             return ox, oy
         candidates.append((badness, (l, b)))
     minCandidate = candidates[0]
     for candidate in candidates:
         if candidate[0] < minCandidate[0]:
             minCandidate = candidate
     ox, oy = minCandidate[1]
     return ox, oy

def get_tight_bbox(fig, bbox_extra_artists=[], pad=None):
    """
    Compute a tight bounding box around all the artists in the figure.
    """
    renderer = fig.canvas.get_renderer()
    bbox_inches = fig.get_tightbbox(renderer)
    bbox_artists = bbox_extra_artists[:]
    bbox_artists += fig.get_default_bbox_extra_artists()
    bbox_filtered = []
    for a in bbox_artists:
        bbox = a.get_window_extent(renderer)
        if isinstance(bbox, tuple):
            continue
        if a.get_clip_on():
            clip_box = a.get_clip_box()
            if clip_box is not None:
                bbox = Bbox.intersection(bbox, clip_box)
            clip_path = a.get_clip_path()
            if clip_path is not None and bbox is not None:
                clip_path = clip_path.get_fully_transformed_path()
                bbox = Bbox.intersection(bbox,
                                         clip_path.get_extents())
        if bbox is not None and (bbox.width != 0 or
                                 bbox.height != 0):
            bbox_filtered.append(bbox)
    if bbox_filtered:
        _bbox = Bbox.union(bbox_filtered)
        trans = Affine2D().scale(1.0 / fig.dpi)
        bbox_extra = TransformedBbox(_bbox, trans)
        bbox_inches = Bbox.union([bbox_inches, bbox_extra])
    return bbox_inches.padded(pad) if pad else bbox_inches

def adjust_bbox_pdf(fig, bbox_inches):
    """
    adjust_bbox for pdf & eps format
    """

    if fig._cachedRenderer.__class__.__name__ == "RendererPgf":
        tr = Affine2D().scale(fig.dpi)
        f = 1.
    else:
        tr = Affine2D().scale(72)
        f = 72. / fig.dpi

    _bbox = TransformedBbox(bbox_inches, tr)

    fig.bbox_inches = Bbox.from_bounds(0, 0,
                                       bbox_inches.width,
                                       bbox_inches.height)
    x0, y0 = _bbox.x0, _bbox.y0
    w1, h1 = fig.bbox.width * f, fig.bbox.height * f
    fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0,
                                                       w1, h1)
    fig.transFigure.invalidate()

    fig.bbox = TransformedBbox(fig.bbox_inches, tr)

    fig.patch.set_bounds(x0 / w1, y0 / h1,
                         fig.bbox.width / w1, fig.bbox.height / h1)

    def _find_best_position(self, width, height, renderer, consider=None):
        """
        Determine the best location to place the legend.

        `consider` is a list of (x, y) pairs to consider as a potential
        lower-left corner of the legend. All are display coords.
        """
        # should always hold because function is only called internally
        assert self.isaxes

        verts, bboxes, lines, offsets = self._auto_legend_data()

        bbox = Bbox.from_bounds(0, 0, width, height)
        if consider is None:
            consider = [self._get_anchored_bbox(x, bbox,
                                                self.get_bbox_to_anchor(),
                                                renderer)
                        for x in range(1, len(self.codes))]

#       tx, ty = self.legendPatch.get_x(), self.legendPatch.get_y()

        candidates = []
        for l, b in consider:
            legendBox = Bbox.from_bounds(l, b, width, height)
            badness = 0
            # XXX TODO: If markers are present, it would be good to
            # take their into account when checking vertex overlaps in
            # the next line.
            badness = legendBox.count_contains(verts)
            badness += legendBox.count_contains(offsets)
            badness += legendBox.count_overlaps(bboxes)
            for line in lines:
                # FIXME: the following line is ill-suited for lines
                # that 'spiral' around the center, because the bbox
                # may intersect with the legend even if the line
                # itself doesn't. One solution would be to break up
                # the line into its straight-segment components, but
                # this may (or may not) result in a significant
                # slowdown if lines with many vertices are present.
                if line.intersects_bbox(legendBox):
                    badness += 1

            ox, oy = l, b
            if badness == 0:
                return ox, oy

            candidates.append((badness, (l, b)))

        # rather than use min() or list.sort(), do this so that we are assured
        # that in the case of two equal badnesses, the one first considered is
        # returned.
        # NOTE: list.sort() is stable.But leave as it is for now. -JJL
        minCandidate = candidates[0]
        for candidate in candidates:
            if candidate[0] < minCandidate[0]:
                minCandidate = candidate

        ox, oy = minCandidate[1]

        return ox, oy

    def draw_plot(self):
                if self._doRePlot:
                    self._resizeCreateContent()
                if self.background is None:
                    self.background = self.canvas.copy_from_bbox(self.ax.bbox)
                self.foo += 1
                #self.y = numpy.cos(numpy.arange(0.0,1.0,0.1)+self.foo*0.1)
                # Optimization on the blitting: we compute the box where the changes happen
                changes_box = None

                for i in range(len(self.lines)):
                    data=self.channels[i].getNext()
                    
                    if len(data[1])>0:
                        if self.autolim:
                            print self.autolim[0], data[1], self.autolim[1]
                            self.autolim = [ min(self.autolim[0], min(data[1])), \
                                max(self.autolim[1], max(data[1])) ]
                        else:
                            self.autolim = [ min(data[1]), min(data[1]) ]
                        
                        if changes_box is None:
                            changes_box = Bbox.unit()
                        print '>>>>>>>>'
                        print data[0], data[1]
                        changes_box.update_from_data(numpy.array(data[0]), \
                                numpy.array(data[1]), ignore=changes_box.is_unit())
                        
                        if not self._doRePlot and len(data[0]) > 0 :
                            end = data[0][-1]
                            
                            if end > self.begin+self.span:
                                self.begin += self.span
                                self._doRePlot = True
                                print 'do replot'
                        self.lines[i].set_data(data[0], data[1])
                    else:
                        self.lines[i].set_data([], [])
                
                if not changes_box:
                    return
                #self.canvas.restore_region(self.background)
                for line in self.lines:
                    self.ax.draw_artist(line)
                    #print line.get_transform()
                    tr = line.get_transform()
                    
                changes_box_inframe = changes_box.transformed(tr)
                
                box_padding = 5
                (x,y,l,w) = changes_box_inframe.bounds
                changes_box_inframe = Bbox.from_bounds(x-box_padding, \
                    y-box_padding, l+2*box_padding, w+2*box_padding)
                
                #print 
                t0 = time.time()
                self.canvas.blit(None)
                #self.canvas.blit(changes_box_inframe)
                self.blit_time += time.time() - t0

 def _calculate_bbox(self):
     r = self.renderer
     bboxes = self.xaxis.get_window_extent(r), self.yaxis.get_window_extent(r), self.subplot.bbox
     all_bbox = Bbox.union(bboxes)
     (x0, y0), (x1, y1) = all_bbox.get_points()
     w = x1 - x0
     h = y1 - y0
     all_bbox = Bbox.from_bounds(x0, y0, w * 1.02, h * 1.02)
     return all_bbox

def plotIToEBrokenAxis(sp, gIdx, neuronIdx, trialNum=0, axBoundaries=None,
                       axesProportions=(0.5, 0.5), bottomLimits=None,
                       topLimits=None, **kw):
    if axBoundaries is None:
        axBoundaries = [0, 0, 1, 1]
    left, bottom, right, top = axBoundaries
    title = kw.pop('title', 'E cell')
    fig   = kw.pop('fig', plt.gcf())
    h = top - bottom
    w = right - left
    hBottom = h*axesProportions[0]
    hTop = h*axesProportions[1]

    axBottom = fig.add_axes(Bbox.from_extents(left, bottom, right, bottom +
                                              hBottom))
    axTop = fig.add_axes(Bbox.from_extents(left, top - hTop, right, top),
                         sharex=axBottom)

    _, gI = aggr.computeYX(sp, iterList)
    M      = sp[0][gIdx][trialNum].data['g_EI']
    conns  = M[neuronIdx, :]

    pconn.plotConnHistogram(conns, title=title, ax=axBottom, **kw)
    kw['ylabel'] = ''
    pconn.plotConnHistogram(conns, title=title, ax=axTop, **kw)
    annG = gI[0, gIdx]
    if annG - int(annG) == 0:
        annG = int(annG)
    #ann = '$g_I$ = {0} nS'.format(annG)
    #fig.text(left+0.95*w, bottom+0.9*h, ann, ha='right', va='bottom',
    #        fontsize='x-small')

    axBottom.set_xlim([0, annG])
    axBottom.set_xticks([0, annG])
    axBottom.xaxis.set_ticklabels([0, '$g_I$'])
    axBottom.set_ylim(bottomLimits)
    axBottom.set_yticks(bottomLimits)
    axBottom.yaxis.set_minor_locator(ti.NullLocator())
    axTop.set_ylim(topLimits)
    axTop.set_yticks([topLimits[1]])
    axTop.xaxis.set_visible(False)
    axTop.spines['bottom'].set_visible(False)

    divLen = 0.07
    d = .015
    kwargs = dict(transform=fig.transFigure, color='k', clip_on=False)
    axBottom.plot((left-divLen*w, left+divLen*w), (bottom+hBottom + d,
                                                   bottom+hBottom - d),
                  **kwargs)
    axTop.plot((left-divLen*w, left+divLen*w), (top-hTop + d, top-hTop - d),
               **kwargs)

    return axBottom, axTop

def zoom_effect(ax_zoomed, ax_origin, xlims = None, orientation='below', **kwargs):
    """
    ax_zoomed : zoomed axes
    ax_origin:  the main axes
    (xmin,xmax) : the limits of the colored area in both plot axes.

    connect ax1 & ax2. The x-range of (xmin, xmax) in both axes will
    be marked.  The keywords parameters will be used ti create
    patches.

    """
    if xlims is None:
        tt = ax_zoomed.transScale + (ax_zoomed.transLimits + ax_origin.transAxes)
        transform = blended_transform_factory(ax_origin.transData, tt)

        bbox_zoomed=ax_zoomed.bbox
        bbox_origin=TransformedBbox(ax_zoomed.viewLim, transform)
    else:
        transform_zoomed=blended_transform_factory(ax_zoomed.transData, ax_zoomed.transAxes)
        transform_origin=blended_transform_factory(ax_origin.transData, ax_origin.transAxes)
    
        bbox_zoomed=TransformedBbox(Bbox.from_extents(xlims[0], 0, xlims[1], 1), transform_zoomed)
        bbox_origin=TransformedBbox(Bbox.from_extents(xlims[0], 0, xlims[1], 1), transform_origin)

    prop_patches = kwargs.copy()
    prop_patches["ec"] = "none"
    prop_patches["alpha"] = 0.2

    if orientation=='below':
        loc1a=2
        loc2a=3
        loc1b=1
        loc2b=4
    elif orientation=='above':
        loc1a=3
        loc2a=2
        loc1b=4
        loc2b=1
    else:
        raise Exception("orientation '%s' not recognized" % orientation)

    c1, c2, bbox_zoomed_patch, bbox_origin_patch, p = \
        connect_bbox(bbox_zoomed, bbox_origin,
                     loc1a=loc1a, loc2a=loc2a, loc1b=loc1b, loc2b=loc2b,
                     prop_lines=kwargs, prop_patches=prop_patches)

    ax_zoomed.add_patch(bbox_zoomed_patch)
    ax_origin.add_patch(bbox_origin_patch)
    ax_origin.add_patch(c1)
    ax_origin.add_patch(c2)
    ax_origin.add_patch(p)

    return c1, c2, bbox_zoomed_patch, bbox_origin_patch, p

    def connect_bbox(bbox1, bbox2, loc1, loc2=None):
        """
        Helper function to obtain a Path from one bbox to another.

        Parameters
        ----------
        bbox1, bbox2 : `matplotlib.transforms.Bbox`
            Bounding boxes to connect.

        loc1 : {1, 2, 3, 4}
            Corner of *bbox1* to use. Valid values are::

                'upper right'  : 1,
                'upper left'   : 2,
                'lower left'   : 3,
                'lower right'  : 4

        loc2 : {1, 2, 3, 4}, optional
            Corner of *bbox2* to use. If None, defaults to *loc1*.
            Valid values are::

                'upper right'  : 1,
                'upper left'   : 2,
                'lower left'   : 3,
                'lower right'  : 4

        Returns
        -------
        path : `matplotlib.path.Path`
            A line segment from the *loc1* corner of *bbox1* to the *loc2*
            corner of *bbox2*.
        """
        if isinstance(bbox1, Rectangle):
            transform = bbox1.get_transform()
            bbox1 = Bbox.from_bounds(0, 0, 1, 1)
            bbox1 = TransformedBbox(bbox1, transform)

        if isinstance(bbox2, Rectangle):
            transform = bbox2.get_transform()
            bbox2 = Bbox.from_bounds(0, 0, 1, 1)
            bbox2 = TransformedBbox(bbox2, transform)

        if loc2 is None:
            loc2 = loc1

        x1, y1 = BboxConnector.get_bbox_edge_pos(bbox1, loc1)
        x2, y2 = BboxConnector.get_bbox_edge_pos(bbox2, loc2)

        verts = [[x1, y1], [x2, y2]]
        codes = [Path.MOVETO, Path.LINETO]

        return Path(verts, codes)

    def plot(self, *args, **kwargs):
        ps = self.env.ps

        output_dir = self.config['output_dir']

        rateLeft    = rasterLeft
        rateBottom  = 0.2
        rateRight   = rasterRight
        rateTop     = self.myc['rateTop']

        for idx, noise_sigma in enumerate(ps.noise_sigmas):
            # E cells
            fig = self._get_final_fig(self.myc['fig_size'])
            ax = fig.add_axes(Bbox.from_extents(rateLeft, rateBottom, rateRight,
                rateTop))
            kw = {}
            if (idx != 0):
                kw['ylabel'] = ''

            rasters.plotAvgFiringRate(ps.bumpGamma[idx],
                    spaceType='bump',
                    noise_sigma=ps.noise_sigmas[idx],
                    popType='E',
                    r=rasterRC[idx][0], c=rasterRC[idx][1],
                    ylabelPos=self.myc['ylabelPos'],
                    color='red',
                    tLimits=tLimits,
                    ax=ax, **kw)
            fname = output_dir + "/bumps_rate_e{0}.pdf".format(noise_sigma)
            fig.savefig(fname, dpi=300, transparent=transparent)
            plt.close()

            # I cells
            fig = self._get_final_fig(self.myc['fig_size'])
            ax = fig.add_axes(Bbox.from_extents(rateLeft, rateBottom, rateRight,
                rateTop))
            kw = {}
            if (idx != 0):
                kw['ylabel'] = ''

            rasters.plotAvgFiringRate(ps.bumpGamma[idx],
                    spaceType='bump',
                    noise_sigma=ps.noise_sigmas[idx],
                    popType='I',
                    r=rasterRC[idx][0], c=rasterRC[idx][1],
                    ylabelPos=self.myc['ylabelPos'],
                    color='blue',
                    tLimits=tLimits,
                    ax=ax, **kw)
            fname = output_dir + "/bumps_rate_i{0}.pdf".format(noise_sigma)
            fig.savefig(fname, dpi=300, transparent=transparent)
            plt.close()

    def plot(self, *args, **kwargs):
        ps = self.env.ps
        output_dir = self.config['output_dir']
        transparent = self.myc['transparent']

        for idx, noise_sigma in enumerate(ps.noise_sigmas):
            # E cells
            fig = self._get_final_fig(self.myc['fig_size'])
            l, b, r, t = self.myc['bbox']
            ax = fig.add_axes(Bbox.from_extents(l, b, r, t))
            kw = {}
            if (idx != 0):
                kw['ylabel'] = ''

            rasters.plotAvgFiringRate(ps.v[idx],
                    spaceType='velocity',
                    noise_sigma=noise_sigma,
                    popType='E',
                    r=rasterRC[idx][0], c=rasterRC[idx][1],
                    color='red',
                    ylabelPos=self.config['vel_rasters']['ylabelPos'],
                    tLimits=self.config['vel_rasters']['tLimits'],
                    trialNum=self.config['vel_rasters']['trialNum'],
                    sigmaTitle=False,
                    ax=ax, **kw)
            fname = output_dir + "/velocity_rate_e{0}.pdf".format(noise_sigma)
            fig.savefig(fname, dpi=300, transparent=transparent)
            plt.close()

            # I cells
            fig = self._get_final_fig(self.myc['fig_size'])
            ax = fig.add_axes(Bbox.from_extents(l, b, r, t))
            kw = {}
            if (idx != 0):
                kw['ylabel'] = ''

            rasters.plotAvgFiringRate(ps.v[idx],
                    spaceType='velocity',
                    noise_sigma=noise_sigma,
                    popType='I',
                    r=rasterRC[idx][0], c=rasterRC[idx][1],
                    color='blue',
                    ylabelPos=self.config['vel_rasters']['ylabelPos'],
                    tLimits=self.config['vel_rasters']['tLimits'],
                    trialNum=self.config['vel_rasters']['trialNum'],
                    sigmaTitle=False,
                    ax=ax, **kw)
            fname = output_dir + "/velocity_rate_i{0}.pdf".format(noise_sigma)
            fig.savefig(fname, dpi=300, transparent=transparent)
            plt.close()

    def _compute_bbox(self, fig, kw):
        """
        Compute the tight bounding box for each figure once, reducing
        number of required canvas draw calls from N*2 to N+1 as a
        function of the number of frames.

        Tight bounding box computing code here mirrors:
        matplotlib.backend_bases.FigureCanvasBase.print_figure
        as it hasn't been factored out as a function.
        """
        fig_id = id(fig)
        if kw['bbox_inches'] == 'tight':
            if not fig_id in MPLRenderer.drawn:
                fig.set_dpi(self.dpi)
                fig.canvas.draw()
                renderer = fig._cachedRenderer
                bbox_inches = fig.get_tightbbox(renderer)
                bbox_artists = kw.pop("bbox_extra_artists", [])
                bbox_artists += fig.get_default_bbox_extra_artists()
                bbox_filtered = []
                for a in bbox_artists:
                    bbox = a.get_window_extent(renderer)
                    if isinstance(bbox, tuple):
                        continue
                    if a.get_clip_on():
                        clip_box = a.get_clip_box()
                        if clip_box is not None:
                            bbox = Bbox.intersection(bbox, clip_box)
                        clip_path = a.get_clip_path()
                        if clip_path is not None and bbox is not None:
                            clip_path = clip_path.get_fully_transformed_path()
                            bbox = Bbox.intersection(bbox,
                                                     clip_path.get_extents())
                    if bbox is not None and (bbox.width != 0 or
                                             bbox.height != 0):
                        bbox_filtered.append(bbox)
                if bbox_filtered:
                    _bbox = Bbox.union(bbox_filtered)
                    trans = Affine2D().scale(1.0 / self.dpi)
                    bbox_extra = TransformedBbox(_bbox, trans)
                    bbox_inches = Bbox.union([bbox_inches, bbox_extra])
                pad = plt.rcParams['savefig.pad_inches']
                bbox_inches = bbox_inches.padded(pad)
                MPLRenderer.drawn[fig_id] = bbox_inches
                kw['bbox_inches'] = bbox_inches
            else:
                kw['bbox_inches'] = MPLRenderer.drawn[fig_id]
        return kw

    def _find_best_position(self, width, height, renderer, consider=None):
        """
        Determine the best location to place the legend.

        `consider` is a list of (x, y) pairs to consider as a potential
        lower-left corner of the legend. All are display coords.
        """
        # should always hold because function is only called internally
        assert self.isaxes

        verts, bboxes, lines = self._auto_legend_data()

        bbox = Bbox.from_bounds(0, 0, width, height)
        consider = [self._get_anchored_bbox(x, bbox, self.get_bbox_to_anchor(),
                                            renderer)
                    for x
                    in range(1, len(self.codes))]

        #tx, ty = self.legendPatch.get_x(), self.legendPatch.get_y()

        candidates = []
        for l, b in consider:
            legendBox = Bbox.from_bounds(l, b, width, height)
            badness = 0
            badness = legendBox.count_contains(verts)
            badness += legendBox.count_overlaps(bboxes)
            for line in lines:
                if line.intersects_bbox(legendBox):
                    badness += 1

            ox, oy = l, b
            if badness == 0:
                return ox, oy

            candidates.append((badness, (l, b)))

        # rather than use min() or list.sort(), do this so that we are assured
        # that in the case of two equal badnesses, the one first considered is
        # returned.
        # NOTE: list.sort() is stable.But leave as it is for now. -JJL
        minCandidate = candidates[0]
        for candidate in candidates:
            if candidate[0] < minCandidate[0]:
                minCandidate = candidate

        ox, oy = minCandidate[1]

        return ox, oy

def test_text_with_arrow_annotation_get_window_extent():
    headwidth = 21
    fig, ax = plt.subplots(dpi=100)
    txt = ax.text(s='test', x=0, y=0)
    ann = ax.annotate(
        'test',
        xy=(0.0, 50.0),
        xytext=(50.0, 50.0), xycoords='figure pixels',
        arrowprops={
            'facecolor': 'black', 'width': 2,
            'headwidth': headwidth, 'shrink': 0.0})

    plt.draw()
    renderer = fig.canvas.renderer
    # bounding box of text
    text_bbox = txt.get_window_extent(renderer=renderer)
    # bounding box of annotation (text + arrow)
    bbox = ann.get_window_extent(renderer=renderer)
    # bounding box of arrow
    arrow_bbox = ann.arrow.get_window_extent(renderer)
    # bounding box of annotation text
    ann_txt_bbox = Text.get_window_extent(ann)

    # make sure annotation with in 50 px wider than
    # just the text
    eq_(bbox.width, text_bbox.width + 50.0)
    # make sure the annotation text bounding box is same size
    # as the bounding box of the same string as a Text object
    eq_(ann_txt_bbox.height, text_bbox.height)
    eq_(ann_txt_bbox.width, text_bbox.width)
    # compute the expected bounding box of arrow + text
    expected_bbox = Bbox.union([ann_txt_bbox, arrow_bbox])
    assert_almost_equal(bbox.height, expected_bbox.height)

        def _set_lim_and_transforms(self):
            PolarAxes._set_lim_and_transforms(self)
            try:
                theta_position = self._theta_label1_position
            except AttributeError:
                theta_position = self.get_theta_offset()

            self.transProjection = self.GlobeCrossSectionTransform()
            self.transData = (
                self.transScale +
                self.transProjection +
                (self.transProjectionAffine + self.transAxes))
            self._xaxis_transform = (
                self.transProjection +
                self.PolarAffine(IdentityTransform(), Bbox.unit()) +
                self.transAxes)
            self._xaxis_text1_transform = (
                theta_position +
                self._xaxis_transform)
            self._yaxis_transform = (
                Affine2D().scale(num.pi * 2.0, 1.0) +
                self.transData)

            try:
                rlp = getattr(self, '_r_label1_position')
            except AttributeError:
                rlp = getattr(self, '_r_label_position')

            self._yaxis_text1_transform = (
                rlp +
                Affine2D().scale(1.0 / 360.0, 1.0) +
                self._yaxis_transform)