Python Ellipse.set_facecolor方法代码示例

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def plot_qudratic_confidence_region_2D_ellipsoid( \
    config, center, ellipsoid, no_grid, rows, cols, fig):
    subell = ellipsoid[numpy.ix_([rows,cols],[rows,cols])]
    eigenvals, eigenvecs = numpy.linalg.eig(subell)
    # sign eigenvals
    lambdaa = numpy.sqrt(eigenvals)

    plot_no = no_grid*cols+rows+1
    ax = fig.add_subplot(no_grid, no_grid, plot_no)
    ell = Ellipse(xy     = [center[rows],center[cols]], \
                  width  = lambdaa[0]*2, \
                  height = lambdaa[1]*2, \
                  angle  = numpy.rad2deg(numpy.arccos(eigenvecs[0,0])))
    ax.add_artist(ell)
    ell.set_clip_box(ax.bbox)
    
    sf = 1.0
    height = numpy.sqrt(subell[0,0]) * sf
    width = numpy.sqrt(subell[1,1]) * sf
    ax.set_xlim(center[rows]-height, center[rows]+height)
    ax.set_ylim(center[cols]-width, center[cols]+width)
    squared = True
    if squared:
        x0,x1 = ax.get_xlim()
        y0,y1 = ax.get_ylim()
        ax.set_aspect(abs(x1-x0)/abs(y1-y0))
    ax.plot([0],[0], 'b+')
    ell.set_color('b')
    ell.set_facecolor('none')
    ax.set_xticks(config.axes[cols].get_major_ticks())
    ax.set_yticks(config.axes[rows].get_major_ticks())
    handle_axes_labels(config, ax, rows, cols)

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
    def calculate_ellipse(center_x, center_y, covariance_matrix, n_std_dev=3):
        values, vectors = np.linalg.eigh(covariance_matrix)
        order = values.argsort()[::-1]
        values = values[order]
        vectors = vectors[:, order]

        theta = np.degrees(np.arctan2(*vectors[:, 0][::-1]))

        # make all angles positive
        if theta < 0:
            theta += 360

        # Width and height are "full" widths, not radius
        width, height = 2 * n_std_dev * np.sqrt(values)

        ellipse = Ellipse(
            xy=[center_x, center_y],
            width=width,
            height=height,
            angle=theta
        )

        ellipse.set_alpha(0.3)
        ellipse.set_facecolor((1, 0, 0))

        return ellipse

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def scatter(title="title", xlab="x", ylab="y", data=None):
    if data == None:
        r = random.random
        data = [(r() * 10, r() * 10, r(), r(), r(), r(), r()) for i in range(100)]
    figure = Figure()
    figure.set_facecolor("white")
    axes = figure.add_subplot(111)
    if title:
        axes.set_title(title)
    if xlab:
        axes.set_xlabel(xlab)
    if ylab:
        axes.set_ylabel(ylab)
    for i, p in enumerate(data):
        p = list(p)
        while len(p) < 4:
            p.append(0.01)
        e = Ellipse(xy=p[:2], width=p[2], height=p[3])
        axes.add_artist(e)
        e.set_clip_box(axes.bbox)
        e.set_alpha(0.5)
        if len(p) == 7:
            e.set_facecolor(p[4:])
        data[i] = p
    axes.set_xlim(min(p[0] - p[2] for p in data), max(p[0] + p[2] for p in data))
    axes.set_ylim(min(p[1] - p[3] for p in data), max(p[1] + p[3] for p in data))
    canvas = FigureCanvas(figure)
    stream = cStringIO.StringIO()
    canvas.print_png(stream)
    return stream.getvalue()

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def plot_ellipse(x, y, ax, color):
	(xy, width, height, angle) = get_ellipse(x, y)
	e = Ellipse(xy, width, height, angle)
	ax.add_artist(e)
	#e.set_clip_box(ax.bbox)
	e.set_alpha(0.2)
	e.set_facecolor(color)

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def show_minimum_bounding_ellipsoids(Xr, with_sampled = False):
    clustellip = Clustered_Sampler(active_samples = Xr,likelihood_constraint=0.0, enlargement= 1.0,no=1)
    ellipsoids = clustellip.ellipsoid_set
    X0 = [i[0] for i in Xr]
    Y0 = [i[1] for i in Xr] 
    plt.figure()
    ax = plt.gca()
    points = []
    #print len(ellipsoids)
    for i in range(len(ellipsoids)):
        if ellipsoids[i]!=None:
            a, b = np.linalg.eig(ellipsoids[i].covariance_matrix)
            c = np.dot(b, np.diag(np.sqrt(a)))
            width = np.sqrt(np.sum(c[:,1]**2)) * 2.
            height = np.sqrt(np.sum(c[:,0]**2)) * 2.
            angle = math.atan(c[1,1] / c[0,1]) * 180./math.pi
            points = ellipsoids[i].sample(n_points = 50)
            ellipse = Ellipse(ellipsoids[i].centroid, width, height, angle)
            ellipse.set_facecolor('None')
            ax.add_patch(ellipse)
    X = [i[0] for i in points]
    Y = [i[1] for i in points]
    if with_sampled == True:
        plt.plot(X, Y, 'bo')
    plt.plot(X0, Y0, 'ro')
    plt.show()

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def plot_data(data0, label0, title, subtitle, *args):
    
    colors = iter(cm.rainbow(np.linspace(0,1,5)))
    plt.clf()
    fig = plt.figure()
    ax = plt.gca()
    if (len(args) == 2):
        means, covmats = args
    elif (len(args) == 4):
        means, covmats, traindata, trainlabel = args
        trainlabel = trainlabel.reshape(trainlabel.size)
    for i in range(1,6):
        cl = next(colors)
        plt.scatter(data0[(label0==i).reshape((label0==i).size),0],
        data0[(label0==i).reshape((label0==i).size),1],color=cl)
        # Draw ellipse with 1 standard deviation
        if (len(args) >= 2):
            # Compute eigen vectors and value for covmat to plot an ellipse
            lambda_, v = np.linalg.eig(covmats[i-1])
            lambda_ = np.sqrt(lambda_)
            ell = Ellipse(xy=(means[0,i-1], means[1,i-1]),
                        width=lambda_[0]*2, height=lambda_[1]*2,
                        angle=np.rad2deg(np.arccos(v[0, 0])))
            ell.set_facecolor('none')
            ax.add_artist(ell)
            #Add mean points
            plt.scatter(means[0,i-1], means[1,i-1],c='black',s=30);
            if (len(args) == 4):
                plt.scatter(traindata[trainlabel==i,0],
                traindata[trainlabel==i,1],color=cl,
                edgecolor='black')
    fig.suptitle(title)
    ax.set_title(subtitle,fontsize='10')
    fig.show()
    return

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def plotCovariance(center, matrix, color):
    eigvalue, eigvector = lng.eig(matrix)
    theta = atan2(eigvector[0][1], eigvector[0][0])
    ellipse = Ellipse([center[0], center[1]], 2*pow(eigvalue[0], 0.5), 2*pow(eigvalue[1], 0.5), center[2])
    ellipse.set_facecolor(color)
    fig = plt.gcf()
    fig.gca().add_artist(ellipse)

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def visualizer(data, x, y, lengths):
    fig = plt.figure()
    ax = fig.add_subplot(111)
    fig.suptitle("Clustered Results")
    colors = plt.get_cmap("gist_rainbow")
    norm = col.Normalize(vmin = 0, vmax = len(lengths))
    color_map = cm.ScalarMappable(cmap = colors, norm = norm)
    index = 0
    for i in range(len(lengths)):
        plt.scatter(data[index:index + lengths[i], x], data[index:index + lengths[i], y], c = color_map.to_rgba(i))
        if lengths[i] != 1:
            cov = numpy.cov(data[index:index + lengths[i], x], data[index:index + lengths[i], y], ddof = 0)
        else:
            cov = numpy.asarray([[0, 0], [0,0]])
        values, vectors = numpy.linalg.eig(cov)
        angle = numpy.arctan((vectors[0,1] - vectors[1,1])/(vectors[0,0] - vectors[1,0]))
        ellipse = Ellipse(xy = (numpy.mean(data[index:index + lengths[i], x]), numpy.mean(data[index:index + lengths[i], y])), width = 2 * 2 * numpy.sqrt(values[0]),
                          height = 2 * 2 * numpy.sqrt(values[1]), angle = numpy.rad2deg(angle))
        ellipse.set_edgecolor(color_map.to_rgba(i))
        ellipse.set_facecolor("none")
        ax.add_artist(ellipse)
        plt.scatter(numpy.mean(data[index:index + lengths[i], x]), numpy.mean(data[index:index + lengths[i], y]), c = color_map.to_rgba(i), marker = 'x')
        index += lengths[i]
    plt.show()
    plt.close()

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def drawDef(dfeat,dy,dx,mindef=0.001,distr="father"):
    """
        auxiliary funtion to draw recursive levels of deformation
    """
    from matplotlib.patches import Ellipse
    pylab.ioff()
    if distr=="father":
        py=[0,0,2,2];px=[0,2,0,2]
    if distr=="child":
        py=[0,1,1,2];px=[1,2,0,1]
    ordy=[0,0,1,1];ordx=[0,1,0,1]
    x1=-0.5+dx;x2=2.5+dx
    y1=-0.5+dy;y2=2.5+dy
    if distr=="father":       
        pylab.fill([x1,x1,x2,x2,x1],[y1,y2,y2,y1,y1],"r", alpha=0.15, edgecolor="b",lw=1)    
    for l in range(len(py)):
        aux=dfeat[ordy[l],ordx[l],:].clip(-1,-mindef)
        wh=numpy.exp(-mindef/aux[0])/numpy.exp(1);hh=numpy.exp(-mindef/aux[1])/numpy.exp(1)
        e=Ellipse(xy=[(px[l]+dx),(py[l]+dy)], width=wh, height=hh, alpha=0.35)
        x1=-0.75+dx+px[l];x2=0.75+dx+px[l]
        y1=-0.76+dy+py[l];y2=0.75+dy+py[l]
        col=numpy.array([wh*hh]*3).clip(0,1)
        if distr=="father":
            col[0]=0       
        e.set_facecolor(col)
        pylab.gca().add_artist(e)
        if distr=="father":       
            pylab.fill([x1,x1,x2,x2,x1],[y1,y2,y2,y1,y1],"b", alpha=0.15, edgecolor="b",lw=1)            

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def graficaGrilla(dataGrilla,name,framesNumber,colour,xPixels,yPixels):	
	from matplotlib.patches import Ellipse
	from pylab import figure, show, savefig

	fig = figure()
	ax = fig.add_subplot(111, aspect='equal')
	# Each row of dataGrilla contains 
	# N == "framesNumbers" , signal
	# A radius of the RF ellipse
	# B radius of the RF ellipse
	# Angle of the RF ellipse
	# X coordinate of the RF ellipse
	# Y coordinate of the RF ellipse

	ax = fig.add_subplot(111, aspect='equal')
	for unit in range(dataGrilla.shape[0]):
		eWidth = dataGrilla[unit][framesNumber-1+1]
		eHeight = dataGrilla[unit][framesNumber-1+2]
		eAngle = dataGrilla[unit][framesNumber-1+3]
		eXY = [dataGrilla[unit][framesNumber-1+4],  dataGrilla[unit][framesNumber-1+5]]
		e = Ellipse(xy=eXY, width=eWidth, height=eHeight, angle=eAngle)
		ax.add_artist(e)
		e.set_alpha(0.2)
		e.set_facecolor(colour)
	
	ax.set_xlim(0, xPixels)
	ax.set_ylim(0, yPixels)
	savefig(name, dpi=None, bbox_inches='tight')
	return 0

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def UVellipse(u,v,w,a,b,v0):
    fig=plt.figure(0)
    
    e1=Ellipse(xy=np.array([0,v0]),width=2*a,height=2*b,angle=0)
    e2=Ellipse(xy=np.array([0,-v0]),width=2*a,height=2*b,angle=0)

    ax=fig.add_subplot(111,aspect="equal")

    ax.plot([0],[v0],"go")
    ax.plot([0],[-v0],"go")
    ax.plot(u[0],v[0],"bo")
    ax.plot(u[-1],v[-1],"bo")

    ax.plot(-u[0],-v[0],"ro")
    ax.plot(-u[-1],-v[-1],"ro")

    ax.add_artist(e1)
    e1.set_lw(1)
    e1.set_ls("--")
    e1.set_facecolor("w")
    e1.set_edgecolor("b")
    e1.set_alpha(0.5)
    ax.add_artist(e2)

    e2.set_lw(1)
    e2.set_ls("--")
    e2.set_facecolor("w")
    e2.set_edgecolor("r")
    e2.set_alpha(0.5)
    ax.plot(u,v,"b")
    ax.plot(-u,-v,"r")
    ax.hold('on')

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def plot(data, mus=None, sigmas=None, colors='black', figname='fig.png'):
    # Create figure
    fig = plt.figure()

    # Plot data
    x = data[:, 0]
    y = data[:, 1]
    plt.scatter(x, y, 24, c=colors)

    # Plot cluster centers
    if mus is not None:
        x = [float(m[0]) for m in mus]
        y = [float(m[1]) for m in mus]
        plt.scatter(x, y, 99, c='red')

    # Plot ellipses for each cluster
    if sigmas is not None:
        for sig_ix in range(K):
            ax = fig.gca()
            cov = np.array(sigmas[sig_ix])
            lam, v = np.linalg.eig(cov)
            lam = np.sqrt(lam)
            ell = Ellipse(xy=(x[sig_ix], y[sig_ix]),
                          width=lam[0]*4, height=lam[1]*4,
                          angle=np.rad2deg(np.arccos(v[0, 0])),
                          color='blue')
            ell.set_facecolor('none')
            ax.add_artist(ell)

    # Save figure
    fig.savefig(figname)

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def plotCovEllipse(cov, pos, nstd=2, ax=None, with_line=False, **kwargs):
    """
    Plots an `nstd` sigma error ellipse based on the specified covariance
    matrix (`cov`). Additional keyword arguments are passed on to the
    ellipse patch artist.
    Parameters
    ----------
        cov : The 2x2 covariance matrix to base the ellipse on
        pos : The location of the center of the ellipse. Expects a 2-element
            sequence of [x0, y0].
        nstd : The radius of the ellipse in numbers of standard deviations.
            Defaults to 2 standard deviations.
        ax : The axis that the ellipse will be plotted on. Defaults to the
            current axis.
        Additional keyword arguments are pass on to the ellipse patch.
    Returns
    -------
        A matplotlib ellipse artist
    """

    def eigsorted(cov):
        vals, vecs = np.linalg.eigh(cov)
        order = vals.argsort()[::-1]
        return vals[order], vecs[:, order]

    if ax is None:
        ax = plt.gca()

    # largest eigenvalue is first
    vals, vecs = eigsorted(cov)
    theta = np.degrees(np.arctan2(*vecs[:, 0][::-1]))

    # Width and height are "full" widths, not radius
    width, height = 2 * nstd * np.sqrt(vals)
    ellip = Ellipse(xy=pos, width=width, height=height, angle=theta, **kwargs)

    if 'alpha' not in kwargs.keys():
        ellip.set_alpha(0.3)
    if 'color' not in kwargs.keys():# and 'c' not in kwargs.keys():
        ellip.set_facecolor('red')

    ax.add_patch(ellip)

    # THEN just f***ing plot an invisible line across the ellipse.
    if with_line:
        # brute forcing axes limits so they contain ellipse patch
        # maybe a cleaner way of doing this, but I couldn't work it out
        x_extent = 0.5*(abs(width*np.cos(np.radians(theta))) +
                        abs(height*np.sin(np.radians(theta))))
        y_extent = 0.5*(abs(width*np.sin(np.radians(theta))) +
                        abs(height*np.cos(np.radians(theta))))

        lx = pos[0] - x_extent
        ux = pos[0] + x_extent
        ly = pos[1] - y_extent
        uy = pos[1] + y_extent
        ax.plot((lx, ux), (ly, uy), alpha=0.)

    return ellip

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
 def draw(self, axes, figure, color):
     mycirc = Ellipse(self.center, self.width, self.height, facecolor='none', edgecolor=color)
     mycirc.set_linewidth(1)
     mycirc.set_alpha(1)
     mycirc.set_facecolor('none')
     mycirc.set_hatch('//')
     circ = axes.add_artist(mycirc)
     figure.canvas.draw()
     return circ

# 需要导入模块: from matplotlib.patches import Ellipse [as 别名]
# 或者: from matplotlib.patches.Ellipse import set_facecolor [as 别名]
def plot_ellipse(w,h,b):
    fig = plt.figure(0)
    fig.clf()
    ax = fig.add_subplot(111, aspect='equal')
    e = Ellipse((0.,0.),w,h)
    e.set_facecolor((0.,0.,b))
    ax.add_artist(e)
    ax.set_xlim(-1., 1.)
    ax.set_ylim(-1., 1.)