Python matplotlib.patches() Examples

def construct_ball_trajectory(var, r=1., cmap='Blues', start_color=0.4, shape='c'):
    # https://matplotlib.org/examples/color/colormaps_reference.html
    patches = []
    for pos in var:
        if shape == 'c':
            patches.append(mpatches.Circle(pos, r))
        elif shape == 'r':
            patches.append(mpatches.RegularPolygon(pos, 4, r))
        elif shape == 's':
            patches.append(mpatches.RegularPolygon(pos, 6, r))

    colors = np.linspace(start_color, .9, len(patches))
    collection = PatchCollection(patches, cmap=cm.get_cmap(cmap), alpha=1.)
    collection.set_array(np.array(colors))
    collection.set_clim(0, 1)
    return collection 

def plot_filled_polygons(self,polygons, facecolour='green', edgecolour='black', linewidth=1, alpha=0.5):
        """
        This function plots a series of shapely polygons but fills them in

        Args:
            ax_list: list of axes
            polygons: list of shapely polygons

        Author: FJC
        """
        from shapely.geometry import Polygon
        from descartes import PolygonPatch
        from matplotlib.collections import PatchCollection

        print('Plotting the polygons...')

        #patches = []
        for key, poly in polygons.items():
            this_patch = PolygonPatch(poly, fc=facecolour, ec=edgecolour, alpha=alpha)
            self.ax_list[0].add_patch(this_patch) 

def read_JSON_file(f):
    
    with open(f, 'r') as g:
        d = json.loads(g.read())
    
    img_paths = [x['file_name'] for x in d['images']]
    
    rects = [{'bbox': x['segmentation'][0], 'class': x['category_id'], 'image_id': x['image_id']} for x in d['annotations']]
    
    annotations = defaultdict(list)
    
    for rect in rects:
        r = rect['bbox']
        x0, y0 = min(r[0], r[2], r[4], r[6]), min(r[1], r[3], r[5], r[7])
        x1, y1 = max(r[0], r[2], r[4], r[6]), max(r[1], r[3], r[5], r[7])
        
        r = patches.Rectangle((x0,y0),x1-x0,y1-y0,linewidth=1,edgecolor='g',facecolor='g', alpha=0.4)        
        
        annotations[img_paths[rect['image_id']]].append({'bbox': r, 'cls': d['classes'][rect['class']-1]})
        
    return d['classes'], img_paths, annotations 

def add_patch(self, p):
        """
        Add a :class:`~matplotlib.patches.Patch` *p* to the list of
        axes patches; the clipbox will be set to the Axes clipping
        box.  If the transform is not set, it will be set to
        :attr:`transData`.

        Returns the patch.
        """

        self._set_artist_props(p)
        if p.get_clip_path() is None:
            p.set_clip_path(self.patch)
        self._update_patch_limits(p)
        self.patches.append(p)
        p._remove_method = lambda h: self.patches.remove(h)
        return p 

def _update_patch_limits(self, patch):
        """update the data limits for patch *p*"""
        # hist can add zero height Rectangles, which is useful to keep
        # the bins, counts and patches lined up, but it throws off log
        # scaling.  We'll ignore rects with zero height or width in
        # the auto-scaling

        # cannot check for '==0' since unitized data may not compare to zero
        # issue #2150 - we update the limits if patch has non zero width
        # or height.
        if (isinstance(patch, mpatches.Rectangle) and
                ((not patch.get_width()) and (not patch.get_height()))):
            return
        vertices = patch.get_path().vertices
        if vertices.size > 0:
            xys = patch.get_patch_transform().transform(vertices)
            if patch.get_data_transform() != self.transData:
                patch_to_data = (patch.get_data_transform() -
                                    self.transData)
                xys = patch_to_data.transform(xys)

            updatex, updatey = patch.get_transform().\
                contains_branch_seperately(self.transData)
            self.update_datalim(xys, updatex=updatex,
                                updatey=updatey) 

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) 

def test_arc_ellipse():
    from matplotlib import patches
    xcenter, ycenter = 0.38, 0.52
    width, height = 1e-1, 3e-1
    angle = -30

    theta = np.arange(0.0, 360.0, 1.0)*np.pi/180.0
    x = width/2. * np.cos(theta)
    y = height/2. * np.sin(theta)

    rtheta = angle*np.pi/180.
    R = np.array([
        [np.cos(rtheta),  -np.sin(rtheta)],
        [np.sin(rtheta), np.cos(rtheta)],
        ])

    x, y = np.dot(R, np.array([x, y]))
    x += xcenter
    y += ycenter

    fig = plt.figure()
    ax = fig.add_subplot(211, aspect='auto')
    ax.fill(x, y, alpha=0.2, facecolor='yellow', edgecolor='yellow', linewidth=1, zorder=1)

    e1 = patches.Arc((xcenter, ycenter), width, height,
                     angle=angle, linewidth=2, fill=False, zorder=2)

    ax.add_patch(e1)

    ax = fig.add_subplot(212, aspect='equal')
    ax.fill(x, y, alpha=0.2, facecolor='green', edgecolor='green', zorder=1)
    e2 = patches.Arc((xcenter, ycenter), width, height,
                     angle=angle, linewidth=2, fill=False, zorder=2)

    ax.add_patch(e2) 

def add_patch(self, p):
        """
        Add a :class:`~matplotlib.patches.Patch` *p* to the list of
        axes patches; the clipbox will be set to the Axes clipping
        box.  If the transform is not set, it will be set to
        :attr:`transData`.

        Returns the patch.
        """

        self._set_artist_props(p)
        if p.get_clip_path() is None:
            p.set_clip_path(self.patch)
        self._update_patch_limits(p)
        self.patches.append(p)
        p._remove_method = self.patches.remove
        return p 

def _update_patch_limits(self, patch):
        """update the data limits for patch *p*"""
        # hist can add zero height Rectangles, which is useful to keep
        # the bins, counts and patches lined up, but it throws off log
        # scaling.  We'll ignore rects with zero height or width in
        # the auto-scaling

        # cannot check for '==0' since unitized data may not compare to zero
        # issue #2150 - we update the limits if patch has non zero width
        # or height.
        if (isinstance(patch, mpatches.Rectangle) and
                ((not patch.get_width()) and (not patch.get_height()))):
            return
        vertices = patch.get_path().vertices
        if vertices.size > 0:
            xys = patch.get_patch_transform().transform(vertices)
            if patch.get_data_transform() != self.transData:
                patch_to_data = (patch.get_data_transform() -
                                 self.transData)
                xys = patch_to_data.transform(xys)

            updatex, updatey = patch.get_transform().\
                contains_branch_seperately(self.transData)
            self.update_datalim(xys, updatex=updatex,
                                updatey=updatey) 

def get_children(self):
        """return a list of child artists"""
        children = []
        children.extend(self.collections)
        children.extend(self.patches)
        children.extend(self.lines)
        children.extend(self.texts)
        children.extend(self.artists)
        children.extend(self.spines.values())
        children.append(self.xaxis)
        children.append(self.yaxis)
        children.append(self.title)
        children.append(self._left_title)
        children.append(self._right_title)
        children.extend(self.tables)
        children.extend(self.images)
        children.extend(self.child_axes)

        if self.legend_ is not None:
            children.append(self.legend_)
        children.append(self.patch)

        return children 

def add_patch(self, p):
        """
        Add a :class:`~matplotlib.patches.Patch` *p* to the list of
        axes patches; the clipbox will be set to the Axes clipping
        box.  If the transform is not set, it will be set to
        :attr:`transData`.

        Returns the patch.
        """

        self._set_artist_props(p)
        if p.get_clip_path() is None:
            p.set_clip_path(self.patch)
        self._update_patch_limits(p)
        self.patches.append(p)
        p._remove_method = self.patches.remove
        return p 

def _update_patch_limits(self, patch):
        """update the data limits for patch *p*"""
        # hist can add zero height Rectangles, which is useful to keep
        # the bins, counts and patches lined up, but it throws off log
        # scaling.  We'll ignore rects with zero height or width in
        # the auto-scaling

        # cannot check for '==0' since unitized data may not compare to zero
        # issue #2150 - we update the limits if patch has non zero width
        # or height.
        if (isinstance(patch, mpatches.Rectangle) and
                ((not patch.get_width()) and (not patch.get_height()))):
            return
        vertices = patch.get_path().vertices
        if vertices.size > 0:
            xys = patch.get_patch_transform().transform(vertices)
            if patch.get_data_transform() != self.transData:
                patch_to_data = (patch.get_data_transform() -
                                 self.transData)
                xys = patch_to_data.transform(xys)

            updatex, updatey = patch.get_transform().\
                contains_branch_seperately(self.transData)
            self.update_datalim(xys, updatex=updatex,
                                updatey=updatey) 

def test_arc_angles():
    from matplotlib import patches
    # Ellipse parameters
    w = 2
    h = 1
    centre = (0.2, 0.5)
    scale = 2

    fig, axs = plt.subplots(3, 3)
    for i, ax in enumerate(axs.flat):
        theta2 = i * 360 / 9
        theta1 = theta2 - 45

        ax.add_patch(patches.Ellipse(centre, w, h, alpha=0.3))
        ax.add_patch(patches.Arc(centre, w, h, theta1=theta1, theta2=theta2))
        # Straight lines intersecting start and end of arc
        ax.plot([scale * np.cos(np.deg2rad(theta1)) + centre[0],
                 centre[0],
                 scale * np.cos(np.deg2rad(theta2)) + centre[0]],
                [scale * np.sin(np.deg2rad(theta1)) + centre[1],
                 centre[1],
                 scale * np.sin(np.deg2rad(theta2)) + centre[1]])

        ax.set_xlim(-scale, scale)
        ax.set_ylim(-scale, scale)

        # This looks the same, but it triggers a different code path when it
        # gets large enough.
        w *= 10
        h *= 10
        centre = (centre[0] * 10, centre[1] * 10)
        scale *= 10 

def test_arc_ellipse():
    from matplotlib import patches
    xcenter, ycenter = 0.38, 0.52
    width, height = 1e-1, 3e-1
    angle = -30

    theta = np.deg2rad(np.arange(360))
    x = width / 2. * np.cos(theta)
    y = height / 2. * np.sin(theta)

    rtheta = np.deg2rad(angle)
    R = np.array([
        [np.cos(rtheta), -np.sin(rtheta)],
        [np.sin(rtheta), np.cos(rtheta)]])

    x, y = np.dot(R, np.array([x, y]))
    x += xcenter
    y += ycenter

    fig = plt.figure()
    ax = fig.add_subplot(211, aspect='auto')
    ax.fill(x, y, alpha=0.2, facecolor='yellow', edgecolor='yellow',
            linewidth=1, zorder=1)

    e1 = patches.Arc((xcenter, ycenter), width, height,
                     angle=angle, linewidth=2, fill=False, zorder=2)

    ax.add_patch(e1)

    ax = fig.add_subplot(212, aspect='equal')
    ax.fill(x, y, alpha=0.2, facecolor='green', edgecolor='green', zorder=1)
    e2 = patches.Arc((xcenter, ycenter), width, height,
                     angle=angle, linewidth=2, fill=False, zorder=2)

    ax.add_patch(e2) 

def test_hist_step_filled():
    np.random.seed(0)
    x = np.random.randn(1000, 3)
    n_bins = 10

    kwargs = [{'fill': True}, {'fill': False}, {'fill': None}, {}]*2
    types = ['step']*4+['stepfilled']*4
    fig, axes = plt.subplots(nrows=2, ncols=4)
    axes = axes.flatten()

    for kg, _type, ax in zip(kwargs, types, axes):
        ax.hist(x, n_bins, histtype=_type, stacked=True, **kg)
        ax.set_title('%s/%s' % (kg, _type))
        ax.set_ylim(bottom=-50)

    patches = axes[0].patches
    assert all(p.get_facecolor() == p.get_edgecolor() for p in patches) 

def add_heatmap(self, title, x, tensor, drawbox=None):
        assert tensor.dim() == 3

        def plot_heatmap(ax, tensor, drawbox=None):
            ax.imshow(tensor.detach().cpu().numpy())
            ax.invert_yaxis()
            ax.label_outer()
            if drawbox is not None:
                rect = patches.Rectangle((0, 0), *drawbox.tolist(), linewidth=1, edgecolor='b', facecolor='none')
                ax.add_patch(rect)

        if tensor.size(0) == 1:
            fig, ax = plt.subplots()
            plot_heatmap(ax, tensor[0], drawbox)
        else:
            fig, axs = plt.subplots(tensor.size(0), sharex=True)
            for i, ax in enumerate(axs):
                plot_heatmap(ax, tensor[i], drawbox)
            fig.subplots_adjust(hspace=2)

        fig.patch.set_color('white')
        fig.tight_layout()
        self.writer.add_figure(title, fig, x) 

def lasso_onselect(self, verts):
        self.pathlasso = path.Path(verts)
        patch = patches.PathPatch(self.pathlasso, fill=True, alpha=.2, edgecolor=None)
        self.ax1.add_patch(patch)
        self.figure.canvas.draw()
        if self.mode == 1 or self.mode == 4 or self.mode == 7:
            onslice = 'Z %s' % (self.ind + 1)
        elif self.mode == 2 or self.mode == 5 or self.mode == 8:
            onslice = 'X %s' % (self.ind + 1)
        elif self.mode == 3 or self.mode == 6 or self.mode == 9:
            onslice = 'Y %s' % (self.ind + 1)
        self.df = pandas.read_csv('Markings/marking_records.csv')
        df_size = pandas.DataFrame.count(self.df)
        df_rows = df_size['artist']
        self.df.loc[df_rows, 'artist'] = patch
        plist = np.ndarray.tolist(self.pathlasso.vertices)
        plist = ', '.join(str(x) for x in plist)
        self.df.loc[df_rows, 'labelshape'] = 'lasso'
        self.df.loc[df_rows, 'slice'] = onslice
        self.df.loc[df_rows, 'path'] = plist
        self.df.loc[df_rows, 'status'] = 0
        self.df.to_csv('Markings/marking_records.csv', index=False) 

def add_patch(self, p):
        """
        Add a :class:`~matplotlib.patches.Patch` *p* to the list of
        axes patches; the clipbox will be set to the Axes clipping
        box.  If the transform is not set, it will be set to
        :attr:`transData`.

        Returns the patch.
        """

        self._set_artist_props(p)
        if p.get_clip_path() is None:
            p.set_clip_path(self.patch)
        self._update_patch_limits(p)
        self.patches.append(p)
        p._remove_method = lambda h: self.patches.remove(h)
        return p 

def _update_patch_limits(self, patch):
        """update the data limits for patch *p*"""
        # hist can add zero height Rectangles, which is useful to keep
        # the bins, counts and patches lined up, but it throws off log
        # scaling.  We'll ignore rects with zero height or width in
        # the auto-scaling

        # cannot check for '==0' since unitized data may not compare to zero
        # issue #2150 - we update the limits if patch has non zero width
        # or height.
        if (isinstance(patch, mpatches.Rectangle) and
                ((not patch.get_width()) and (not patch.get_height()))):
            return
        vertices = patch.get_path().vertices
        if vertices.size > 0:
            xys = patch.get_patch_transform().transform(vertices)
            if patch.get_data_transform() != self.transData:
                patch_to_data = (patch.get_data_transform() -
                                    self.transData)
                xys = patch_to_data.transform(xys)

            updatex, updatey = patch.get_transform().\
                contains_branch_seperately(self.transData)
            self.update_datalim(xys, updatex=updatex,
                                updatey=updatey) 

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) 

def plot_cloud(self, Y, facecolor='0.8', edgecolor='0.8', alpha=0.5, edgelinestyle='-'):
		### create patches:
		y0,y1       = Y
		x,y0,y1     = self.x.tolist(), y0.tolist(), y1.tolist()
		x           = [x[0]]  + x  + [x[-1]]
		y0          = [y0[0]] + y0 + [y0[-1]]
		y1          = [y1[0]] + y1 + [y1[-1]]
		y1.reverse()
		### concatenate:
		x1          = np.copy(x).tolist()
		x1.reverse()
		x,y         = x + x1, y0 + y1
		patches     = PatchCollection(   [  Polygon(  np.array([x,y]).T  )  ], edgecolors=None)
		### plot:
		self.ax.add_collection(patches)
		pyplot.setp(patches, facecolor=facecolor, edgecolor=edgecolor, alpha=alpha, linestyle=edgelinestyle)
		return patches 

def plot_polys(plist,scale=500.0):
        fig, ax = plt.subplots()
        patches = []
        for p in plist:
            poly = Polygon(np.array(p)/scale, True)
            patches.append(poly) 

def add_arrows_from_points(self, arrow_df, azimuth_header='azimuth', arrow_length=0.1, colour='black', linewidth=1, alpha = 1):
        """
        This function adds arrows at locations and azimuths specified by the
        pandas dataframe.  The X and Y locations should have column headers
        called 'X' and 'Y', the user specifies the column header of the azimuths
        column (default = 'azimuth')

        Args:
            arrow_df: pandas dataframe with the X and Y locations and the azimuths
            azimuth_header (str): the name of the column header with the azimuth locations
            colour: arrow colour, can pass in the name of one of the column headers and arrows will be coloured according to this.

        Author: FJC

        """
        import math
        import matplotlib.patches as patches

        # convert azimuths to radians
        azimuths = list(arrow_df[azimuth_header])
        az_radians = np.radians(azimuths)
        print(az_radians)

        # get points
        X = np.array(arrow_df['X'])
        Y = np.array(arrow_df['Y'])
        dx = np.array([ arrow_length*np.sin(i) for i in az_radians ])
        dy = np.array([ arrow_length*np.cos(i) for i in az_radians ])
        new_X = X - dx/2
        new_Y = Y - dy/2

        print((dx,dy))

        self.ax_list[0].quiver(new_X,new_Y,dx,dy,angles='xy',scale_units='xy',scale=1, width=0.002) 

def _draw_exons(self):
        for exon in self.ensembl_transcript.exon_intervals:

            if self.ensembl_transcript.strand == '+':
                start = exon[0] - self.ensembl_transcript.start
                end = exon[1] - self.ensembl_transcript.start
            else:

                # this is so the transcription direction is not plotted
                # in reverse for genes on minus strand

                start = -(exon[1] - self.ensembl_transcript.end)
                end = -(exon[0] - self.ensembl_transcript.end)

            exon_start = (int(start)/float(self.normalize))*0.9 + self.offset
            exon_end = (int(end)/float(self.normalize))*0.9 + self.offset
            exon_center = (exon_end-exon_start)/2. + exon_start

            self.ax.add_patch(
                patches.Rectangle(
                    (
                        exon_start,
                        0.45,
                    ),
                    exon_end-exon_start,
                    0.1,
                    color="black"
                )
            ) 

def _draw_main_body(self, name_symbols, name_isoform):
        """
        main protein frame
        """

        self.ax.add_patch(
            patches.Rectangle(
                (self.offset, self.vertical_offset),
                self.protein_frame_length,
                0.1,
                fill=False
            )
        )

        self.ax.text(
            0.5,
            0.95,
            name_symbols,
            horizontalalignment='center',
            fontsize=self.fontsize
        )

        self.ax.text(
            0.5,
            0.88,
            name_isoform,
            horizontalalignment='center',
            fontsize=self.fontsize-3
        ) 

def figlegend(handles, labels, loc, **kwargs):
    """
    Place a legend in the figure.

    *labels*
      a sequence of strings

    *handles*
      a sequence of :class:`~matplotlib.lines.Line2D` or
      :class:`~matplotlib.patches.Patch` instances

    *loc*
      can be a string or an integer specifying the legend
      location

    A :class:`matplotlib.legend.Legend` instance is returned.

    Example::

      figlegend( (line1, line2, line3),
                 ('label1', 'label2', 'label3'),
                 'upper right' )

    .. seealso::

       :func:`~matplotlib.pyplot.legend`

    """
    l = gcf().legend(handles, labels, loc, **kwargs)
    draw_if_interactive()
    return l


## Figure and Axes hybrid ## 

def visualize(self):
        if self.leaves is None or self.X.shape[1] != 2:
            print
            'error: x shape is wrong or leaves is none.'

        # visaulize 2d mondrians
        import matplotlib.pyplot as plt
        import matplotlib.patches as patches
        import matplotlib
        font = {'size': 20}
        matplotlib.rc('font', **font)
        mondrian_colors = np.array([[255, 240, 1], [48, 48, 58],
                                    [255, 1, 1], [1, 1, 253], [249, 249, 249]])
        mondrian_colors = mondrian_colors / 255.0
        fig = plt.figure()
        ax = fig.add_subplot(111, aspect='equal')
        print('number of leaves = {}'.format(len(self.leaves)))
        for node in self.leaves:
            xy = node.x_range[0]
            xylen = node.x_range[1] - node.x_range[0]
            c = mondrian_colors[4]
            p = patches.Rectangle(
                xy, xylen[0], xylen[1],
                facecolor=c,
                linewidth=1,
                edgecolor='k'
            )
            ax.add_patch(p)
        for x in self.X:
            c = '#fdbf6f'
            p = patches.Circle(
                x, 0.01,
                facecolor=c,
                linewidth=0
            )
            ax.add_patch(p)
        return ax, fig 

def figlegend(handles, labels, loc, **kwargs):
    """
    Place a legend in the figure.

    *labels*
      a sequence of strings

    *handles*
      a sequence of :class:`~matplotlib.lines.Line2D` or
      :class:`~matplotlib.patches.Patch` instances

    *loc*
      can be a string or an integer specifying the legend
      location

    A :class:`matplotlib.legend.Legend` instance is returned.

    Example::

      figlegend( (line1, line2, line3),
                 ('label1', 'label2', 'label3'),
                 'upper right' )

    .. seealso::

       :func:`~matplotlib.pyplot.legend`

    """
    l = gcf().legend(handles, labels, loc, **kwargs)
    draw_if_interactive()
    return l


## Figure and Axes hybrid ## 

def draw_box(box, image_np):
    #expand the box by 50%
    box += np.array([-(box[2] - box[0])/2, -(box[3] - box[1])/2, (box[2] - box[0])/2, (box[3] - box[1])/2]) 

    fig = plt.figure()
    ax = plt.Axes(fig, [0., 0., 1., 1.])
    fig.add_axes(ax)

    #draw blurred boxes around box
    ax.add_patch(patches.Rectangle((0,0),box[1]*image_np.shape[1], image_np.shape[0],linewidth=0,edgecolor='none',facecolor='w',alpha=0.8))
    ax.add_patch(patches.Rectangle((box[3]*image_np.shape[1],0),image_np.shape[1], image_np.shape[0],linewidth=0,edgecolor='none',facecolor='w',alpha=0.8))
    ax.add_patch(patches.Rectangle((box[1]*image_np.shape[1],0),(box[3]-box[1])*image_np.shape[1], box[0]*image_np.shape[0],linewidth=0,edgecolor='none',facecolor='w',alpha=0.8))
    ax.add_patch(patches.Rectangle((box[1]*image_np.shape[1],box[2]*image_np.shape[0]),(box[3]-box[1])*image_np.shape[1], image_np.shape[0],linewidth=0,edgecolor='none',facecolor='w',alpha=0.8))

    return fig, ax 

def __setstate__(self, state):
        self.__dict__ = state
        # put the _remove_method back on all artists contained within the axes
        for container_name in ['lines', 'collections', 'tables', 'patches',
                               'texts', 'images']:
            container = getattr(self, container_name)
            for artist in container:
                artist._remove_method = container.remove