Python pyplot.arrow函数代码示例

def phaseportrait(fs,inits,t=(-5,5),n=100, head_width = 0.13, head_length = 0.3, **kw):
    """
    plots phase portrait of the differential equation (\dot x,\dot y)=fs(x,y)

    f  -- must accept an array of X and t=0, and return a 2D array of \dot y and \dot x
    inits -- list of vectors representing inital conditions
    t -- time interval
    n -- number of points

    Example
    =======
    
    from itertools import product
    phaseportrait(lambda X, t=0: array([X[0],2*X[1]]), product(linspace(-4,4,15),linspace(-4,4,15)), [-2,0.3], n=20)
    """
    assert(t[0]<t[1] and t[1]>0)
    X=[]
    Y=[]
    for x0 in inits:
        x0=np.array(x0)
        if t[0]<0:
            segments=[np.linspace(0,t[0],n),np.linspace(0,t[1],n)]
        else:
            segments=[np.linspace(t[0],t[1],2*n)]
        for s in segments:
            points=integrate.odeint(fs,x0,s)
            for i,Z in enumerate([X,Y]):
                Z.extend(points[:,i])
                Z.append(None)
        direction = fs(x0)
        direction = direction / scipy.linalg.norm(direction) * 0.01
        plt.arrow(x0[0]-direction[0],x0[1]-direction[1],direction[0],direction[1],
              head_width=head_width, head_length=head_length, lw=0.0, **kw)
    plt.plot(X,Y,**kw)

    def draw_arrow(pair, alpha=alpha, ec=ec, labelcolor=labelcolor):
        # set the length of the arrow
        if display == 'length':
            length = max_head_length + data[pair]/sf*(max_arrow_length -
                                                      max_head_length)
        else:
            length = max_arrow_length
        # set the transparency of the arrow
        if display == 'alph':
            alpha = min(data[pair]/sf, alpha)
        else:
            alpha = alpha
        # set the width of the arrow
        if display == 'width':
            scale = data[pair]/sf
            width = max_arrow_width*scale
            head_width = max_head_width*scale
            head_length = max_head_length*scale
        else:
            width = max_arrow_width
            head_width = max_head_width
            head_length = max_head_length

        fc = colors[pair]
        ec = ec or fc

        x_scale, y_scale = deltas[pair]
        x_pos, y_pos = positions[pair]
        plt.arrow(x_pos, y_pos, x_scale*length, y_scale*length,
              fc=fc, ec=ec, alpha=alpha, width=width, head_width=head_width,
              head_length=head_length, **arrow_params)

        # figure out coordinates for text
        # if drawing relative to base: x and y are same as for arrow
        # dx and dy are one arrow width left and up
        # need to rotate based on direction of arrow, use x_scale and y_scale
        # as sin x and cos x?
        sx, cx = y_scale, x_scale

        where = label_positions[pair]
        if where == 'left':
            orig_position = 3*np.array([[max_arrow_width, max_arrow_width]])
        elif where == 'absolute':
            orig_position = np.array([[max_arrow_length/2.0, 3*max_arrow_width]])
        elif where == 'right':
            orig_position = np.array([[length - 3*max_arrow_width,
                                    3*max_arrow_width]])
        elif where == 'center':
            orig_position = np.array([[length/2.0, 3*max_arrow_width]])
        else:
            raise ValueError("Got unknown position parameter %s" % where)

        M = np.array([[cx, sx], [-sx, cx]])
        coords = np.dot(orig_position, M) + [[x_pos, y_pos]]
        x, y = np.ravel(coords)
        orig_label = rate_labels[pair]
        label = '$%s_{_{\mathrm{%s}}}$' % (orig_label[0], orig_label[1:])

        plt.text(x, y, label, size=label_text_size, ha='center', va='center',
             color=labelcolor or fc)

def plot_pick_and_place_stage(image_rgb, labeled_image, approximated_polygon, unfold_paths,
                              pick_point, place_point, to_file=None, show=True):
    plot_rgb(image_rgb, show=False)
    plt.imshow(labeled_image, cmap=plt.cm.RdGy, alpha=0.6)
    points = [tuple(point[0]) for point in approximated_polygon]
    # Plot polygon lines
    for (start_x, start_y), (end_x, end_y) in zip(points, points[1:]+points[0:1]):
        plt.plot( (start_x, end_x), (start_y, end_y), 'r-', linewidth=2.0 )
    # Plot polygon points
    for x, y in points:
        plt.plot(x, y, 'ro')
    #Plot unfold paths
    for path in unfold_paths:
        (start_x, start_y), (end_x, end_y) = path
        plt.plot( (start_x, end_x), (start_y, end_y), 'go-', linewidth=2.0 )
    # Plot arrow
    plt.arrow(pick_point[0], pick_point[1], place_point[0]-pick_point[0], place_point[1]-pick_point[1],
               head_width=15, head_length=15, fc='blue', ec='blue', lw=5, alpha=0.7)
    plt.axis('off')

    if to_file:
        plt.savefig(to_file, bbox_inches='tight')
        plt.close()
    elif show:
        plt.show()

def draw_experiment_data(experiment, viewport=None, estimate=True, camera=True, truth=True, cov=False):
    with open('/home/cristi/Dropbox/FIRM/Experiments/iser_trials/experiment_data_{}.txt'.format(experiment), 'r') as fin:
        for line in fin:
            line = line.strip()
            if line.startswith('#'):
                pass
            elif line:
                data = dict(eval(line))
                if data.has_key('opti') and data.has_key('filter'):
                    plot_data = []
                    if estimate:
                        plot_data.append((data['x'], (1.0, 0, 0)))
                    if camera:
                        plot_data.append((data['filter'], (1, 1, 0)))
                    if truth:
                        plot_data.append((data['opti'], (0, 1, 0))) # TODO: colors 'g'
                    
                    for (x, y, yaw,), col in plot_data:
                        plt.arrow(x, y, 0.1*np.cos(yaw), 0.1*np.sin(yaw),
                                  hold=True, color=col)
                    
                    if cov:
                        viewport.add_patch( Mission.covariance_ellipse(
                            data['x'][:2], np.diag(data['cov'][:2]), color='b',
                            fill=False, lw=1, zorder=0))

            def animate(generation):
                chromosome = ts_ga.generation_fittest[generation]
                ax.clear()

                x, y = [], []
                for city_id, point in city_points.items():
                    x.append(point[0])
                    y.append(point[1])
                ax.plot(x, y, marker='s', linestyle='', label='cities', alpha=0.6)

                # plot optimal route
                chrom_city_ids = ts_ga.translator.translate_chromosome(chromosome)
                dist = round(ts_ga.calc_distance(chromosome), 2)

                ax.set_title("generation " + str(generation) + "\ndistance = " + str(dist))

                for i, start_city_id in enumerate(chrom_city_ids):
                    end_city_idx = i + 1

                    if end_city_idx == num_cities:
                        # distance from last city to first
                        end_city_idx = 0

                    end_city_id = chrom_city_ids[end_city_idx]

                    x1, y1 = city_points[start_city_id]
                    x2, y2 = city_points[end_city_id]
                    mid_x = (x2 - x1) / 2 + x1
                    mid_y = (y2 - y1) / 2 + y1

                    plt.arrow(x1, y1, x2 - x1, y2 - y1, head_width=1.5, fc='k', ec='k', alpha=0.7, linestyle='dotted', length_includes_head=True)
                    plt.text(mid_x, mid_y, str(i + 1))

def Velocities_2D(n):
    dim = 2
    fig = plt.figure(dim, figsize=(8, 8), facecolor='white')
    fig.clf()
    xmin, xmax, ymin, ymax = 1000, -1000, 1000, -1000
    e = .5
    for k in range((2*n+1)**dim):
        v = pylbm.stencil.Velocity(dim = dim, num = k)
        x = v.vx
        y = v.vy
        xmin = min(xmin, x)
        xmax = max(xmax, x)
        ymin = min(ymin, y)
        ymax = max(ymax, y)
        couleur_texte = 0.
        couleur_trait = 0.5
        plt.text(x, y, str(v.num), color=[couleur_texte]*3,
                 horizontalalignment='center',verticalalignment='center',
                 fontsize=15)
    for x in range(xmin, xmax+1):
        plt.plot([x, x], [ymin, ymax], ':', color=[couleur_trait]*3)
    for y in range(ymin, ymax+1):
        plt.plot([xmin, xmax], [y, y], ':', color=[couleur_trait]*3)
    plt.text(0., ymax+2*e, "Velocities numbering {0:1d}D".format(dim),fontsize=20,
        verticalalignment='center', horizontalalignment='center', color='b')
    plt.arrow(xmin-e, ymin-e, 1, 0, head_width=0.05*dim, head_length=0.1, fc='b', ec='b')
    plt.arrow(xmin-e, ymin-e, 0, 1, head_width=0.05*dim, head_length=0.1, fc='b', ec='b')
    plt.text(xmin-e+.5, ymin-1.5*e, 'x', color='b',
        verticalalignment='center', horizontalalignment='center')
    plt.text(xmin-1.5*e, ymin-e+.5, 'y', color='b',
        verticalalignment='center', horizontalalignment='center')
    plt.axis('off')
    plt.xlim(xmin-2*e, xmax+2*e)
    plt.ylim(ymin-2*e, ymax+2*e)
    plt.draw()

    def plot_state(self, i, history):
        plt.clf()
        ax = plt.gca()
        w = float(self.width + 1)
        h = float(self.height + 1)

        minw = np.min(self.internal_weights)
        maxw = np.max(self.internal_weights)
        diffw = maxw - minw

        for n, neuron in enumerate(self.neurons):
            x, y = self.neuron_position(n)
            line_y = -history[i - 100:i:4, n]
            #line_y = history[max(i - 20, 0):i, n] + .25
            line_x = np.arange(len(line_y)) / 50.0
            #line_x = np.arange(len(line_y)) / 40.0
            line = mlines.Line2D((line_x + x + .75) / w, 1 - (line_y + y + 1) / h)
            ax.add_line(line)

            k = .2
            for dx, dy in DIRECTIONS:
                weight = self.internal_weights[self.neuron_index(x, y), self.neuron_index((x + dx) % self.width, (y + dy) % self.height)]

                plt.arrow(
                    (1 + x + (1 - k) / 2 * dx) / w,
                    1 - (1 + y + (1 - k) / 2 * dy) / h,
                    (k * dx) / w,
                    -(k * dy) / h,
                    width=.0002,
                    color=str(weight / maxw),
                )

        plt.draw()

def show(model):
    spiral_x = []
    spiral_y = []
    model.calculate_spiral(spiral_x, spiral_y)
    print("sp")

    figure = plot.figure(1)
    plot.subplot2grid((1, 3), (0, 2), colspan=1)
    plot.title('Комплексная амплитуда волны E')
    plot.xlabel('Re(E)')
    plot.ylabel('Im(E)')

    #d = sqrt((spiral_x[1] - spiral_x[0])**2 + (spiral_y[1] - spiral_y[0])**2)
    d = 0.01
    w = d / 3
    h_w = w * 2
    h_l = d / 3

    plot.plot(spiral_x, spiral_y, color='r', linewidth=2)
    #for i in arange(0, len(spiral_x) - 1, 1):
    #    plot.arrow(spiral_x[i], spiral_y[i], spiral_x[i + 1] - spiral_x[i], spiral_y[i + 1] - spiral_y[i],
    #    width=w, head_width=h_w, head_length=h_l, length_includes_head=True, fc='r', ec='k')

    plot.arrow(0, 0, spiral_x[-1], spiral_y[-1],
               width=w*1.5, head_width=h_w*1.5, head_length=h_l*1.5, length_includes_head=True, fc='g', ec='k')


    plot.grid(True)
    mx = max(spiral_x)
    my = max(spiral_y)
    #plot.xlim([-mx - d, mx + d])
    #plot.ylim([-d, my + d])

    model.draw(figure)

def visual_addition(vectors):
    x = 0
    y = 0
    for v in vectors:
        plt.arrow(x, y, v[0], v[1], head_width=0.1, head_length=0.1)
        x += v[0]
        y += v[1]

 def draw(self, show_normal=False, color='green'):
     for i, segment in enumerate(self.segments):
         plt.plot(segment[:, 0], segment[:, 1], color=color)
         if show_normal:
             normal = math2d.normal(segment)
             center = math2d.center(segment)
             plt.arrow(center[0], center[1], normal[0], normal[1], width=0.01, color=color)

    def draw(self,file=None):
        """
        Trace l'ensemble des lignes de champ passant par les points de départ 
        stockés dans Startpoints. Si un nom de fichier est donné, enregistre 
        la figure dans le fichier mais n'affiche rien à l'écran
        """

        def fun(P,t):
            B = self.B(P)
            Bx = B[0]
            By = B[1]
            B = np.sqrt(Bx*Bx+By*By)
            return [Bx/pow(B,4./3.),By/pow(B,4./3.)]

        t = np.linspace(0,self.k*self.maxint,self.numpoints/2)
        t2 = - t
        for P0 in self.startpoints:
            sol = odeint(fun,P0,t)
            x = sol[:,0]
            y = sol[:,1]
            pl.plot(x,y,'-',color='k')
            sol = odeint(fun,P0,t2)
            x = sol[1:,0]
            y = sol[1:,1]
            pl.plot(x,y,'-',color='k')
            pl.arrow(x[1],y[1],x[0]-x[1],y[0]-y[1],color='k')
        pl.title(self.title)
        pl.xlim([-self.size,self.size])
        pl.ylim([-self.size,self.size])
        if file:
            pl.savefig(file)
            pl.close()
        else:
            pl.show()

def open_spline_chain_main():
    #x = [0, 3, -20, -10]
    #y = [0, 30, -5, -3]
    #th = [np.pi/2., np.pi, -np.pi/2., 0.]
    x = np.cos(np.deg2rad(135.))*np.array([-10., 1., 50.])
    y = np.sin(np.deg2rad(135.))*np.array([-10., 1., 50.])
    th = [np.deg2rad(135.), np.deg2rad(135.), np.deg2rad(135.)]
    X = np.column_stack((x, y))
    # Random desired discretization along the splines for demonstration purposes
    Ns = np.random.random_integers(100., 200., (X.shape[0]-1,))  # one less spline than waypoints.
    sx, sy, sth, length, u, coeffs = PiazziSpline.splineOpenChain(X, th, Ns)

    plt.plot(sx, sy, 'k-', linewidth=2.0)
    #test_x = 3.0
    #test_y = 30.01
    test_x = 0.013
    test_y = 0.008
    u_star, closest, distance = closestPointOnSpline2D(length, sx, sy, test_x, test_y, u, coeffs, guess=None)
    print "closest X = {:.3f}, {:.3f},  u* = {}".format(closest[0], closest[1], u_star)

    # Figure out the LOS controller using this example
    tangent_th = np.interp(u_star, u, sth)
    print "tangent th = {:.3f} deg".format(tangent_th*180.0/np.pi)
    lookAhead = 0.1  # how far forward in u
    lookaheadState = splineToEuclidean2D(coeffs, min(u_star + lookAhead, u[-1]))
    print "lookahead X = {:.3f}, {:.3f}".format(lookaheadState[0], lookaheadState[1])
    dx_global = lookaheadState[0] - closest[0]
    dy_global = lookaheadState[1] - closest[1]
    print "dx_global = {:.3f}, dy_global = {:.3f}".format(dx_global, dy_global)
    dx_frenet = dx_global*math.cos(tangent_th) + dy_global*math.sin(tangent_th)
    dy_frenet = dx_global*math.sin(tangent_th) - dy_global*math.cos(tangent_th)
    print "y error = {:.3f}, dx_frenet = {:.3f}, dy_frenet = {:.3f}".format(distance, dx_frenet, dy_frenet)

    # need sign of distance to spline to change
    # look at sign of cross product to determine "handed-ness"
    angle_from_closest_to_test = math.atan2(closest[1] - test_y, closest[0] - test_x)
    print "angle from closest to test = {:.3f} deg".format(angle_from_closest_to_test*180./np.pi)
    sign_test = np.cross([math.cos(tangent_th), math.sin(tangent_th)], [math.cos(angle_from_closest_to_test), math.sin(angle_from_closest_to_test)])
    distance *= np.sign(sign_test)
    print "distance to spline after sign update = {:.3f}".format(distance)

    # resulting triangle
    relative_angle = math.atan2((distance - dy_frenet), dx_frenet)
    global_angle = tangent_th + relative_angle
    print "relative angle = {:.3f} deg, global angle = {:.3f} deg".format(relative_angle*180./np.pi, global_angle*180./np.pi)

    # plot
    plt.plot(test_x, test_y, 'r+', markersize=12., markeredgewidth=2.0)
    plt.plot(closest[0], closest[1], 'gx', markersize=12., markeredgewidth=2.0)
    plt.plot(lookaheadState[0], lookaheadState[1], 'go', markersize=12., markeredgewidth=2.0)
    result_line = np.array([[test_x, test_y], closest])
    lookAhead_line = np.array([closest, lookaheadState])
    plt.plot(result_line[:, 0], result_line[:, 1], 'b-')
    plt.plot(lookAhead_line[:, 0], lookAhead_line[:, 1], 'g-')
    plt.arrow(closest[0], closest[1], 10.*math.cos(tangent_th), 10.*math.sin(tangent_th), linestyle='dashed')
    d = math.sqrt(math.pow(lookaheadState[0] - test_x, 2) + math.pow(lookaheadState[1] - test_y, 2))
    plt.arrow(test_x, test_y, d*math.cos(global_angle), d*math.sin(global_angle), linestyle='dotted')
    plt.axis('equal')
    plt.title("length = {:.2f}, u = {:.4f}, distance = {:.2f}".format(length[-1], u_star, distance))
    plt.show()

def plotArrow(p0, p1, headWidth=.1):
    
    dx = p1[0] - p0[0]
    dy = p1[1] - p0[1]
    
    plt.arrow(p0[0], p0[1], dx, dy, head_width=headWidth, 
                  length_includes_head = True)

def matrix2png(matrixname, savedfolder, xlim, ylim, arrows):
    matrix = open(matrixname, 'r').readlines()
    matrix = [i.split() for i in matrix]
    matrix = [[float(j) for j in i] for i in matrix]

    matrix_t = transposed(matrix)

    y = []

    for col in range(0,len(matrix_t)):
        y.append(matrix_t[col])

    longitud = len(y)

    for i in range(0, len(y[1])):
        for j in range(0, longitud/2):                    
            plt.plot(y[j*2][i],y[j*2+1][i],'o')
        
        for a in arrows:
            ao = a[0] - 1
            ai = a[1] - 1
            plt.arrow(y[ao*2][i], y[ao*2+1][i], y[ai*2][i]-y[ao*2][i],y[ai*2+1][i]-y[ao*2+1][i])        

        #Limites de los ejes
        plt.xlim(xlim[0], xlim[1])
        plt.ylim(ylim[0], ylim[1])
        plt.savefig(savedfolder + str(i).zfill(5) + '.png')
        plt.clf()
        
    plt.close()

def plotarrows():
    COORDINATES = np.load(savefilename)
    plt.figure()
    plt.rc('text', usetex=True)
    ypmin = 0
    ypmax = 100
    xpmin = 0
    xpmax = 70
        
    plt.xlim(xmin=xpmin, xmax=xpmax)
    plt.ylim(ymin=ypmin, ymax=ypmax)
    
    for i in range(0,len(COORDINATES)-1):
        lw = 1+COORDINATES[i,2]*10.
        if i%2==0:
            plt.arrow(COORDINATES[i,0], COORDINATES[i,1], (COORDINATES[i+1,0] - COORDINATES[i,0]), (COORDINATES[i+1,1] - COORDINATES[i,1]), fc="k", ec="k", head_width=1.5, head_length=1, linewidth=lw)
        else:
            plt.arrow(COORDINATES[i,0], COORDINATES[i,1], (COORDINATES[i+1,0] - COORDINATES[i,0]), (COORDINATES[i+1,1] - COORDINATES[i,1]), linewidth=lw)
    
    plt.gca().set_aspect('equal')    
    plt.title('Trajectory over time: ' + str(len(COORDINATES)*100))
    plt.xlabel('$r/r_g$')
    plt.ylabel('$r/r_g$')
    plt.show()
    plt.savefig('/Users/Anton/Dropbox/Aleksander/Figures/simavg0070-0134/particles/particle_'+ str(gSTART_INDEX) +'_'+ str(gSTART_x)+'_'+str(gSTART_y), bbox_inches='tight')