Python LineCollection.set_alpha方法代码示例

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
def plot(ax, x, y, time, sim_type):
	assert(len(x) == len(y) == len(time))

	l = len(time)
	if use_hf_coloration:
		time_to_grayscale = 0.8 / 23.6 # for HF coloring
	else:
		time_to_grayscale = 0.8 / time[l-1]
	colors = []
	for i in range(l-1):
		if use_hf_coloration:
			color = get_hf_color(time[i])  # time[] is really HF
		else:
			g = 0.8 - (time[i] * time_to_grayscale)**2.0
			if sim_type == 'driven':
				color = (g, 1.0, g, 0.8)
			else:
				color = (g, g, 1.0, 1.0)
		colors.append(color)
	
	points = zip(x,y)
	segments = zip(points[:-1], points[1:])
	lc = LineCollection(segments, colors=colors)
	lc.set_alpha(1.0)
	lc.set_linewidth(1.0)
	lc.set_antialiased(True)
	ax.add_collection(lc)
	if use_hf_coloration:
		end_points.append((x[l-1], y[l-1], get_hf_color(time[l-1])))
	else:
		end_points.append((x[l-1], y[l-1], COLOR[sim_type]))

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
    def __plot_all(self):
        total = len(self.data)
        count = 0.0
        for timeStamp in self.data:
            if len(self.data[timeStamp]) < 2:
                self.parent.threadPlot = None
                return None, None

            if self.fade:
                alpha = (total - count) / total
            else:
                alpha = 1

            data = self.data[timeStamp].items()
            peakF, peakL = self.extent.get_peak_fl()

            segments, levels = self.__create_segments(data)
            lc = LineCollection(segments)
            lc.set_array(numpy.array(levels))
            lc.set_norm(self.__get_norm(self.autoL, self.extent))
            lc.set_cmap(self.colourMap)
            lc.set_linewidth(self.lineWidth)
            lc.set_gid('plot')
            lc.set_alpha(alpha)
            self.axes.add_collection(lc)
            count += 1

        return peakF, peakL

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
    def __plot_all(self, spectrum):
        total = len(spectrum)
        count = 0.0
        for timeStamp in spectrum:
            if self.settings.fadeScans:
                alpha = (total - count) / total
            else:
                alpha = 1

            data = spectrum[timeStamp].items()
            peakF, peakL = self.extent.get_peak_fl()

            segments, levels = self.__create_segments(data)
            if segments is not None:
                lc = LineCollection(segments)
                lc.set_array(numpy.array(levels))
                lc.set_norm(self.__get_norm(self.settings.autoL, self.extent))
                lc.set_cmap(self.colourMap)
                lc.set_linewidth(self.lineWidth)
                lc.set_gid('plot')
                lc.set_alpha(alpha)
                self.axes.add_collection(lc)
                count += 1

        return peakF, peakL

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
def _plotpartial(ax, partial, downsample=1, cmap='inferno', exp=1, linewidth=1, avg=True):
    # columns: time, freq, amp, phase, bw
    segments, Z = _segmentsZ(partial, downsample=downsample, exp=exp, avg=avg)
    lc = LineCollection(segments, cmap=cmap)
    # Set the values used for colormapping
    lc.set_array(Z)
    lc.set_linewidth(linewidth)
    lc.set_alpha(None)
    ax.add_collection(lc, autolim=True)

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
def plot_trajectory_ellipse(frame, varx="attr_VARX", vary="attr_VARY", covxy="attr_COVXY", opacity_factor=1):
    """
    Draw the trajectory and uncertainty ellipses around teach point.
    1) Scatter of points 
    2) Trajectory lines
    3) Ellipses 
    :param frame: Trajectory
    :param opacity_factor: all opacity values are multiplied by this. Useful when used to plot multiple Trajectories in
     an overlay plot.
    :return: axis
    """
    ellipses = []    
    segments = []
    start_point = None

    for i, pnt in frame.iterrows():  
        # The ellipse
        U, s, V = np.linalg.svd(np.array([[pnt[varx], pnt[covxy]], 
                                          [pnt[covxy], pnt[vary]]]), full_matrices=True)
        w, h = s**.5 
        theta = np.arctan(V[1][0]/V[0][0])   # == np.arccos(-V[0][0])              
        ellipse = {"xy":pnt[list(frame.geo_cols)].values, "width":w, "height":h, "angle":theta}
        ellipses.append(Ellipse(**ellipse))
        
        # The line segment
        x, y = pnt[list(frame.geo_cols)][:2]
        if start_point:           
            segments.append([start_point, (x, y)])
        start_point = (x, y)

    ax = plt.gca()
    ellipses = PatchCollection(ellipses)
    ellipses.set_facecolor('none')
    ellipses.set_color("green")
    ellipses.set_linewidth(2)
    ellipses.set_alpha(.4*opacity_factor)
    ax.add_collection(ellipses)

    frame.plot(kind="scatter", x=frame.geo_cols[0], y=frame.geo_cols[1], marker=".", ax=plt.gca(), alpha=opacity_factor)

    lines = LineCollection(segments)
    lines.set_color("gray")
    lines.set_linewidth(1)
    lines.set_alpha(.2*opacity_factor)
    ax.add_collection(lines)
    return ax

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
def algo (region,output, word):
    reg = [0 for i in range(0,23)]
    total = 0
    for line in region: #reg_num number
        items = line.rstrip('\n').split('\t')
        reg[int(items[0])]=int(items[1])
        total = total+int(items[1])
    reg=np.array(reg)
    max_percent=np.max(reg)
    plt.figure(figsize=(15,15))
    ax = plt.subplot(111)
    m = Basemap(projection='merc', lat_0=45, lon_0=0,
                resolution = 'h', area_thresh = 10.0,
                llcrnrlat=41.33, llcrnrlon=-5,   
                urcrnrlat=51.5, urcrnrlon=9.7) 
    m.drawcoastlines()
    #m.drawcountries()  # french border does not fit with region ones
    m.fillcontinents(color='lightgrey',lake_color='white')
    m.drawmapboundary(fill_color='white')

    sf = shapefile.Reader("./geodata/FRA_adm1")
    shapes = sf.shapes()
    records = sf.records()
    for record, shape in zip(records,shapes):
        lons,lats = zip(*shape.points)
        data = np.array(m(lons, lats)).T
        if len(shape.parts) == 1:
            segs = [data,]
        else:
            segs = []
            for i in range(1,len(shape.parts)):
                index = shape.parts[i-1]
                index2 = shape.parts[i]
                segs.append(data[index:index2])
            segs.append(data[index2:])
        
        lines = LineCollection(segs,antialiaseds=(1,))
        lines.set_edgecolors('k')
        lines.set_linewidth(0.5)
        lines.set_facecolors('brown')
        lines.set_alpha(float(reg[record[3]])/max_percent) #record[3] est le numero de region
        ax.add_collection(lines)   

    plt.savefig(word+'-'+str(total)+'.png',dpi=300)

    return 0

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
    def plot(self, ax=None, cmapname=None, cmap=None, linewidth=1,
             edgecolor='grey', facecolor=None, alpha=1):
        """Plot the geometries on the basemap using the defined colors

        Parameters:
        -----------
        ax : matplotlib.axis object
            An axis object for plots. Overwrites the self.ax attribute.
        cmapname : string
            Name of the color map from matplotlib (LINK!) (default: 'seismic')
        cmap : matplotlib colormap
            You can create you own colormap and pass it to the plot.
        linewidth : float
            Width of the lines.
        edgecolor : string, float or iterable
            Definition of the edge color. Can be an iterable with a color
            definition for each geometry, a string with one color for
            all geometries or a float to define one color for all geometries
            from the cmap.
        facecolor : string, float or iterable
            Definition of the face color. See edgecolor.
        alpha : float
            Level of transparency.
        """
        if ax is not None:
            self.ax = ax
        n = 0
        if facecolor is None:
            facecolor = self.color
        if edgecolor is None:
            edgecolor = self.color
        if cmapname is not None:
            self.cmapname = cmapname
        if self.data is not None:
            self.data = np.array(self.data)
        if cmap is None:
            cmap = plt.get_cmap(self.cmapname)
        for geo in self.geometries:
            vectors = self.get_vectors_from_postgis_map(geo)
            lines = LineCollection(vectors, antialiaseds=(1, ))
            lines.set_facecolors(self.select_color(facecolor, cmap, n))
            lines.set_edgecolors(self.select_color(edgecolor, cmap, n))
            lines.set_linewidth(linewidth)
            lines.set_alpha(alpha)
            self.ax.add_collection(lines)
            n += 1

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
def plot_sparse_trajectory(trajectory, r=50, opacity_factor=1, plot_text=True,
                           num_col="segment_sparcify_n", min_static=100):
    """
    Plots a sparsified trajectory as circles with the number of points they represent as a number inside.
    :param trajectory: Trajectory object
    :param r: the radius of circles
    :param num_col: where to find the number to put in the circles
    :param min_static: minimum count to change color of circle
    :param plot_text: put the text with num of points in the circle?
    :return: ax
    """
    ax = plt.gca()
    trajectory.plot(kind="scatter", x=trajectory.geo_cols[0], y=trajectory.geo_cols[1], marker=".",
                    ax=plt.gca(), alpha=0.0*opacity_factor)

    circles = []
    segments = []
    start_point = None

    for i, pnt in trajectory.iterrows():
        circles.append(Circle(pnt[list(trajectory.geo_cols)].values, radius=r))

        if plot_text:
            plt.text(*pnt[list(trajectory.geo_cols)], s=str(int(pnt[num_col])), fontsize=12)

        x, y = pnt[list(trajectory.geo_cols)][:2]
        if start_point:
            segments.append([start_point, (x, y)])
        start_point = (x, y)

    circles = PatchCollection(circles)
    circles.set_facecolor(['none' if cnt < min_static else 'red' for cnt in trajectory[num_col].values])
    circles.set_alpha(.5*opacity_factor)
    ax.add_collection(circles)

    lines = LineCollection(segments)
    lines.set_color("gray")
    lines.set_alpha(.2*opacity_factor)
    ax.add_collection(lines)

    return ax

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
 def drawcountry(self,
                 ax,
                 base_map,
                 iso2,
                 color,
                 alpha = 1):
     if iso2 not in self.countries:
         raise ValueError, "Where is that country ?"
     vertices = self.countries[iso2]
     shape = []
     for vertex in vertices:
         longs, lats = zip(*vertex)
         # conversion to plot coordinates
         x,y = base_map(longs, lats)
         shape.append(zip(x,y))
     lines = LineCollection(shape,antialiaseds=(1,))
     lines.set_facecolors(cm.hot(np.array([color,])))
     lines.set_edgecolors('white')
     lines.set_linewidth(0.5)
     lines.set_alpha(alpha)
     ax.add_collection(lines)

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
def odometry_error_bar(fig_num, info, pred_mean, sampling_time='1s', color_bar='Reds'):
    ########################
    # Plot Bar 
    ########################
    matplotlib.rcParams.update({'font.size': 15, 'font.weight': 'bold'})
    fig, ax = plt.subplots()

    resample_info = info.resample(resampling_time).mean()

    x = resample_info.index.to_pydatetime()
    x[:] = [(i-x[0]).total_seconds() for i in x]
    y = info.inliers_matches_ratio_th

    # Display Odometry error information
    from numpy import linalg as la
    y  = np.ones(len(x))
    sd = la.norm(pred_mean, axis=1)
    points = np.array([x, y]).T.reshape(-1, 1, 2)
    segments = np.concatenate([points[:-1], points[1:]], axis=1)

    from matplotlib.collections import LineCollection
    from matplotlib.colors import ListedColormap, BoundaryNorm
    from matplotlib.colors import LinearSegmentedColormap as lscm

    cmap = plt.get_cmap(color_bar)

    norm = plt.Normalize(0.00, 0.0634491701615)
    lc = LineCollection(segments, cmap=cmap, norm=norm)
    lc.set_array(sd)
    lc.set_linewidth(100)
    lc.set_alpha(0.8)
    plt.gca().add_collection(lc)

    ax.tick_params('y', colors='k')
    ax.set_xlabel(r'Time [$s$]', fontsize=25, fontweight='bold')
    ax.tick_params('x', colors='k')
    ax.set_ylim([0.5, 1.5])
    ax.set_xlim([x[0], x[len(x)-1]])
    plt.show(block=True)

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
def arl_dem_figure(fig_num, dem_file, trajectory, pred_mean, kf_trajectory, frames_trajectory, color_bar='Reds'):

    ########################
    # Load Terrain DEM
    ########################
    import os
    plydata = PlyData.read(open(os.path.expanduser(dem_file)))

    vertex = plydata['vertex'].data

    [dem_px, dem_py, dem_pz] = (vertex[t] for t in ('x', 'y', 'z'))

    # define grid.
    npts=100
    dem_xi = np.linspace(min(dem_px), max(dem_px), npts)
    dem_yi = np.linspace(min(dem_py), max(dem_py), npts)

    # grid the data.
    dem_zi = griddata(dem_px, dem_py, dem_pz, dem_xi, dem_yi, interp='linear')


    matplotlib.rcParams.update({'font.size': 30, 'font.weight': 'bold'})
    fig = plt.figure(fig_num, figsize=(28, 16), dpi=120, facecolor='w', edgecolor='k')
    ax = fig.add_subplot(111)
    #fig, ax = plt.subplots()

    # Display the DEM
    plt.rc('text', usetex=False)# activate latex text rendering
    CS = plt.contour(dem_xi, dem_yi, dem_zi, 15, linewidths=0.5, colors='k')
    CS = plt.contourf(dem_xi, dem_yi, dem_zi, 15, cmap=plt.cm.gray, vmax=abs(dem_zi).max(), vmin=-abs(dem_zi).max())

    # plot data points.
    plt.xlim(min(dem_px), max(dem_xi))
    plt.ylim(min(dem_py), max(dem_yi))

    # Display Ground Truth trajectory
    from numpy import linalg as la
    x = trajectory[1:trajectory.shape[0]-1,0]
    y = trajectory[1:trajectory.shape[0]-1,1]
    sd = la.norm(pred_mean, axis=1)
    points = np.array([x, y]).T.reshape(-1, 1, 2)
    segments = np.concatenate([points[:-1], points[1:]], axis=1)

    from matplotlib.collections import LineCollection
    from matplotlib.colors import ListedColormap, BoundaryNorm
    from matplotlib.colors import LinearSegmentedColormap as lscm

    cmap = plt.get_cmap(color_bar)

    norm = plt.Normalize(0.00, 0.0634491701615)
    lc = LineCollection(segments, cmap=cmap, norm=norm)
    lc.set_array(sd)
    lc.set_linewidth(40)
    lc.set_alpha(0.8)
    plt.gca().add_collection(lc)


    #color bar of the covarianve
    #h_cbar = plt.colorbar(lc)#, orientation='horizontal')
    #h_cbar.ax.set_ylabel(r' odometry error[$m/s$]',  fontsize=25, fontweight='bold')

    # Color bar of the dem
    #cbar = plt.colorbar()  # draw colorbar
    #cbar.ax.set_ylabel(r' terrain elevation[$m$]', fontsize=25, fontweight='bold')

    # Plot all the image frames line
    fr_x = frames_trajectory[:,0]
    fr_y = frames_trajectory[:,1]
    ax.plot(fr_x, fr_y,
            #marker='s',
            linestyle='-', lw=2, alpha=0.5, color=[0.0, 0.3, 1.0],
            label='slam trajectory',
            zorder=98)
    # Plot all the image frames
    ax.scatter(fr_x, fr_y, marker='s', facecolor=[0.0,0.3,1.0], edgecolor='b',
            label='image frames', s=300, alpha=0.2, zorder=99)


    # Plot the key frames
    kf_x = kf_trajectory[:,0]
    kf_y = kf_trajectory[:,1]
    ax.scatter(kf_x, kf_y, marker='D', facecolor=[0.2,1.0,0.0], edgecolor='b',
            label='keyframes', s=250, alpha=0.8, zorder=100)

    import os
    from matplotlib.cbook import get_sample_data
    from matplotlib._png import read_png
    import matplotlib.image as image
    from scipy import ndimage
    from matplotlib.offsetbox import OffsetImage, AnnotationBbox
    fn = get_sample_data(os.getcwd()+"/data/img/exoter.png", asfileobj=False)
    exoter = image.imread(fn)
    exoter = ndimage.rotate(exoter, 100)
    imexoter = OffsetImage(exoter, zoom=0.5)


    ab = AnnotationBbox(imexoter, xy=(x[0], y[0]),
                    xybox=None,
                    xycoords='data',
                    boxcoords="offset points",
#.........这里部分代码省略.........

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
def plotter(choice, numNeurons, numTimeSteps, maxNeurons, maxTimeSteps, reverse, numInputNeurons, colorNeurons, redStart, redEnd, greenStart, greenEnd, blueStart, blueEnd, eatNeuron, mateNeuron, fightNeuron, moveNeuron, yawNeuron, label, behaviorLabels, inputLabels):
	rhythm_file = open_file(choice, "r")
	line = next_line(rhythm_file)
	line = next_line(rhythm_file)
	figwidth = 12.0
	figheight = 8.0

	fig = pylab.figure(figsize=(figwidth,figheight))

	ax = fig.add_subplot(111)
	ax.set_xlim(0.5, maxTimeSteps+0.5)
	ax.set_ylim(maxNeurons-0.5, -0.5)
	pylab.title(label)
	pylab.ylabel('Neuron Index')
	pylab.xlabel('Time Step')

	linewidth = 0.715 * fig.get_figwidth() * fig.get_dpi() / maxTimeSteps

	for time in range(numTimeSteps):
		x = []
		y = []
		colors = []
		for neuron in range(numNeurons):
			activ = line.split()
			x.append(time+1)			  
			y.append(neuron-0.5)
			activation = float(activ[1])
			if reverse:
				activation = 1.0 - activation
			if colorNeurons:
				if neuron in range(redStart, redEnd+1):
					colors.append((activation, activation*ALT_COLOR_MAX, activation*ALT_COLOR_MAX, 1.0))
				elif neuron in range(greenStart, greenEnd+1):
					colors.append((activation*ALT_COLOR_MAX, activation, activation*ALT_COLOR_MAX, 1.0))
				elif neuron in range(blueStart, blueEnd+1):
					colors.append((activation*ALT_COLOR_MAX, activation*ALT_COLOR_MAX, activation, 1.0))
				elif neuron == eatNeuron:
					colors.append((activation*ALT_COLOR_MAX, activation, activation*ALT_COLOR_MAX, 1.0))
				elif neuron == mateNeuron:
					colors.append((activation*ALT_COLOR_MAX, activation*ALT_COLOR_MAX, activation, 1.0))
				elif neuron == fightNeuron:
					colors.append((activation, activation*ALT_COLOR_MAX, activation*ALT_COLOR_MAX, 1.0))
				elif neuron == yawNeuron:
					colors.append((activation, activation, activation*ALT_COLOR_MAX, 1.0))
				else:
					colors.append((activation, activation, activation, 1.0))
			else:
				colors.append((activation, activation, activation, 1.0))
			line = next_line(rhythm_file)
		x.append(time+1)
		y.append(neuron+0.5)
		colors.append((activation, activation, activation, 1.0))
		points = zip(x, y)
		segments = zip(points[:-1], points[1:])
		lc = LineCollection(segments, colors=colors)
		lc.set_alpha(1.0)
		lc.set_linewidth(linewidth)
		lc.set_antialiased(False)
		ax.add_collection(lc)

	rhythm_file.close()

	if behaviorLabels:
		matplotlib.pyplot.text(maxTimeSteps+1.5, eatNeuron, "Eat", weight="ultralight", size="small", va="center")
		matplotlib.pyplot.text(maxTimeSteps+1.5, mateNeuron, "Mate", weight="ultralight", size="small", va="center")
		matplotlib.pyplot.text(maxTimeSteps+1.5, fightNeuron, "Fight", weight="ultralight", size="small", va="center")
		matplotlib.pyplot.text(maxTimeSteps+1.5, moveNeuron, "Move", weight="ultralight", size="small", va="center")
		matplotlib.pyplot.text(maxTimeSteps+1.5, yawNeuron, "Turn", weight="ultralight", size="small", va="center")
		matplotlib.pyplot.text(maxTimeSteps+1.5, yawNeuron+1, "Light", weight="ultralight", size="small", va="center")
		matplotlib.pyplot.text(maxTimeSteps+1.5, yawNeuron+2, "Focus", weight="ultralight", size="small", va="center")
	
	if inputLabels:
		matplotlib.pyplot.text(maxTimeSteps+1.5, 0, "Random", weight="ultralight", size="small", va="center")
		matplotlib.pyplot.text(maxTimeSteps+1.5, 1, "Health", weight="ultralight", size="small", va="center")
		for neuron in range(redStart, redEnd+1):
			matplotlib.pyplot.text(maxTimeSteps+1.5, neuron, "R", weight="ultralight", size="small", va="center")
		for neuron in range(greenStart, greenEnd+1):
			matplotlib.pyplot.text(maxTimeSteps+1.5, neuron, "G", weight="ultralight", size="small", va="center")
		for neuron in range(blueStart, blueEnd+1):
			matplotlib.pyplot.text(maxTimeSteps+1.5, neuron, "B", weight="ultralight", size="small", va="center")

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]

#.........这里部分代码省略.........
            # If color specs are given as (rgb) or (rgba) tuples, we're OK
            if numpy.alltrue([cb.iterable(c) and len(c) in (3, 4)
                             for c in edge_color]):
                edge_colors = tuple(edge_color)
            else:
                # numbers (which are going to be mapped with a colormap)
                edge_colors = None
        else:
            raise ValueError('edge_color must consist of either color names or numbers')
    else:
        if cb.is_string_like(edge_color) or len(edge_color) == 1:
            edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
        else:
            raise ValueError('edge_color must be a single color or list of exactly m colors where m is the number or edges')

    edge_collection = LineCollection(edge_pos,
                                     colors=edge_colors,
                                     linewidths=lw,
                                     antialiaseds=(1,),
                                     linestyle=style,
                                     transOffset = ax.transData,
                                     )

    edge_collection.set_zorder(1)  # edges go behind nodes
    edge_collection.set_label(label)
    ax.add_collection(edge_collection)

    # Note: there was a bug in mpl regarding the handling of alpha values for
    # each line in a LineCollection.  It was fixed in matplotlib in r7184 and
    # r7189 (June 6 2009).  We should then not set the alpha value globally,
    # since the user can instead provide per-edge alphas now.  Only set it
    # globally if provided as a scalar.
    if cb.is_numlike(alpha):
        edge_collection.set_alpha(alpha)

    if edge_colors is None:
        if edge_cmap is not None:
            assert(isinstance(edge_cmap, Colormap))
        edge_collection.set_array(numpy.asarray(edge_color))
        edge_collection.set_cmap(edge_cmap)
        if edge_vmin is not None or edge_vmax is not None:
            edge_collection.set_clim(edge_vmin, edge_vmax)
        else:
            edge_collection.autoscale()

    arrow_collection = None

    if G.is_directed() and arrows:

        # a directed graph hack
        # draw thick line segments at head end of edge
        # waiting for someone else to implement arrows that will work
        arrow_colors = edge_colors
        a_pos = []
        p = 1.0-0.25  # make head segment 25 percent of edge length
        for src, dst in edge_pos:
            x1, y1 = src
            x2, y2 = dst
            dx = x2-x1   # x offset
            dy = y2-y1   # y offset
            d = numpy.sqrt(float(dx**2 + dy**2))  # length of edge
            if d == 0:   # source and target at same position
                continue
            if dx == 0:  # vertical edge
                xa = x2
                ya = dy*p+y1

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]
dbf = dbflib.open('../states/statesp020')

for npoly in range(shp.info()[0]):
    # draw colored polygons on the map
    shpseqs = []
    shp_object = shp.read_object(npoly)
    verts = shp_object.vertices()
    rings = len(verts)
    for ring in range(rings):
        lons, lats = zip(*verts[ring])
        x, y = m(lons,lats)
        shpsegs.append(zip(x,y))
        if ring == 0:
            shapedict = dbf.read_record(npoly)
        name = shapedict['STATE']
    lines = LineCollection(shpsegs,antialiased=(1,))

    # state_to_code dict, e.g. 'ALASKA' -> 'AK', omitted
    try:
        per = obama[state_to_code[name.upper()]]
    except KeyError:
        continue

    lines.set_facecolors('k')
    lines.set_alpha(0.75 * per) # shrink the percentage a bit
    lines.set_edgecolors('k')
    lines.set_linewidth(0.3)
    ax.add_collection(lines)

plt.show()

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_alpha [as 别名]

#.........这里部分代码省略.........
            edge_colors = tuple([colorConverter.to_rgba(c,alpha) 
                                 for c in edge_color])
        elif np.alltrue([not cb.is_string_like(c) 
                           for c in edge_color]):
            # If color specs are given as (rgb) or (rgba) tuples, we're OK
            if np.alltrue([cb.iterable(c) and len(c) in (3,4)
                             for c in edge_color]):
                edge_colors = tuple(edge_color)
                alpha=None
            else:
                # numbers (which are going to be mapped with a colormap)
                edge_colors = None
        else:
            raise ValueError('edge_color must consist of either color names or numbers')
    else:
        if len(edge_color)==1:
            edge_colors = ( colorConverter.to_rgba(edge_color, alpha), )
        else:
            raise ValueError('edge_color must be a single color or list of exactly m colors where m is the number or edges')
    edge_collection = LineCollection(edge_pos,
                                     colors       = edge_colors,
                                     linewidths   = lw,
                                     antialiaseds = (1,),
                                     linestyle    = style,     
                                     transOffset = ax.transData,             
                                     )

    # Note: there was a bug in mpl regarding the handling of alpha values for
    # each line in a LineCollection.  It was fixed in matplotlib in r7184 and
    # r7189 (June 6 2009).  We should then not set the alpha value globally,
    # since the user can instead provide per-edge alphas now.  Only set it
    # globally if provided as a scalar.
    if cb.is_numlike(alpha):
        edge_collection.set_alpha(alpha)

    # need 0.87.7 or greater for edge colormaps.  No checks done, this will
    # just not work with an older mpl
    if edge_colors is None:
        if edge_cmap is not None: assert(isinstance(edge_cmap, Colormap))
        edge_collection.set_array(np.asarray(edge_color))
        edge_collection.set_cmap(edge_cmap)
        if edge_vmin is not None or edge_vmax is not None:
            edge_collection.set_clim(edge_vmin, edge_vmax)
        else:
            edge_collection.autoscale()
        pylab.sci(edge_collection)

    arrow_collection=None

    if G.is_directed() and arrows:

        # a directed graph hack
        # draw thick line segments at head end of edge
        # waiting for someone else to implement arrows that will work 
        arrow_colors = ( colorConverter.to_rgba('k', alpha), )
        a_pos=[]
        p=1.0-0.25 # make head segment 25 percent of edge length
        for src,dst in edge_pos:
            x1,y1=src
            x2,y2=dst
            dx=x2-x1 # x offset
            dy=y2-y1 # y offset
            d=np.sqrt(float(dx**2+dy**2)) # length of edge
            if d==0: # source and target at same position
                continue
            if dx==0: # vertical edge