Python pyplot.subplot函数代码示例

def test_tripcolor():
    x = np.asarray([0, 0.5, 1, 0,   0.5, 1,   0, 0.5, 1, 0.75])
    y = np.asarray([0, 0,   0, 0.5, 0.5, 0.5, 1, 1,   1, 0.75])
    triangles = np.asarray([
        [0, 1, 3], [1, 4, 3],
        [1, 2, 4], [2, 5, 4],
        [3, 4, 6], [4, 7, 6],
        [4, 5, 9], [7, 4, 9], [8, 7, 9], [5, 8, 9]])

    # Triangulation with same number of points and triangles.
    triang = mtri.Triangulation(x, y, triangles)

    Cpoints = x + 0.5*y

    xmid = x[triang.triangles].mean(axis=1)
    ymid = y[triang.triangles].mean(axis=1)
    Cfaces = 0.5*xmid + ymid

    plt.subplot(121)
    plt.tripcolor(triang, Cpoints, edgecolors='k')
    plt.title('point colors')

    plt.subplot(122)
    plt.tripcolor(triang, facecolors=Cfaces, edgecolors='k')
    plt.title('facecolors')

def plot_feature_comparison(title, trajectory, features):
    plotCount = features.shape[1]
    pointCount = features.shape[0]
    fig = plt.figure()
    plt.title(title)
    #plt.ion()
    #plt.show()
    max = np.amax(features)
    min = np.amin(features)
    print "min=" + str(min) + ", max=" + str(max)
    for i in range(plotCount):
        plt.subplot(plotCount/2, 2, 1+i)
        f = features[:,i]
        
        for k in range(pointCount):
            color = ''
            if(f[k] > max * 0.6):
                color = 'r'
            elif(f[k] > max * 0.3):
                color = 'y'
            elif(f[k] < min * 0.3):
                color = 'b'
            elif(f[k] < min * 0.6):
                color = 'g'
                
            if (color != ''):
                plt.plot(trajectory[k,0], trajectory[k,1], color+'.', markersize=20)
                #plt.draw()
        plt.plot(trajectory[:,0], trajectory[:,1], 'k')
    plt.show()
    #raw_input()
    return

def plot_data(kx,omega,F,F_R,F_L,K,O):
    #plt.figure(4)
    #plt.imshow(K,extent=[omega[0],omega[-1],kx[0],kx[-1]],\
    #        interpolation = "nearest", aspect = "auto")
    #plt.xlabel('KX')
    #plt.colorbar()
    
    #plt.figure(5)
    #plt.imshow(O,extent =[omega[0],omega[-1],kx[0],kx[-1]],interpolation="nearest", aspect="auto")
    #plt.xlabel('omega')
    #plt.colorbar()
    
    plt.figure(6)
    pylab.subplot(1,2,1)
    plt.imshow(abs(F_R), extent= [omega[0],omega[-1],kx[0],kx[-1]], interpolation= "nearest", aspect = "auto")
    plt.xlabel('abs FFT_R')
    plt.colorbar()
    plt.subplot(1,2,2)
    plt.imshow(abs(F_L), extent= [omega[0],omega[-1],kx[0],kx[-1]], interpolation= "nearest", aspect = "auto")
    plt.xlabel('abs FFT_L')
    plt.colorbar()
    
    
    plt.figure(7)
    plt.subplot(2,1,1)
    plt.imshow(abs(F_L+F_R),extent=[omega[0],omega[-1],kx[0],kx[-1]],interpolation= "nearest", aspect = "auto")
    plt.xlabel('abs(F_L+F_R)  reconstructed')
    plt.colorbar()
    pylab.subplot(2,1,2)
    plt.imshow(abs(F),extent=[omega[0],omega[-1],kx[0],kx[-1]],interpolation ="nearest",aspect = "auto")
    plt.xlabel('FFT of the original data')
    plt.colorbar()

    #plt.show()
    return

def plot_images(data_list, data_shape="auto", fig_shape="auto"):
    """
    plotting data on current plt object.
    In default,data_shape and fig_shape are auto.
    It means considered the data as a sqare structure.
    """
    n_data = len(data_list)
    if data_shape == "auto":
        sqr = int(n_data ** 0.5)
        if sqr * sqr != n_data:
            data_shape = (sqr + 1, sqr + 1)
        else:
            data_shape = (sqr, sqr)
    plt.figure(figsize=data_shape)

    for i, data in enumerate(data_list):
        plt.subplot(data_shape[0], data_shape[1], i + 1)
        plt.gray()
        if fig_shape == "auto":
            fig_size = int(len(data) ** 0.5)
            if fig_size ** 2 != len(data):
                fig_shape = (fig_size + 1, fig_size + 1)
            else:
                fig_shape = (fig_size, fig_size)
        Z = data.reshape(fig_shape[0], fig_shape[1])
        plt.imshow(Z, interpolation="nearest")
        plt.tick_params(labelleft="off", labelbottom="off")
        plt.tick_params(axis="both", which="both", left="off", bottom="off", right="off", top="off")
        plt.subplots_adjust(hspace=0.05)
        plt.subplots_adjust(wspace=0.05)

def test_clipping():
    exterior = mpath.Path.unit_rectangle().deepcopy()
    exterior.vertices *= 4
    exterior.vertices -= 2
    interior = mpath.Path.unit_circle().deepcopy()
    interior.vertices = interior.vertices[::-1]
    clip_path = mpath.Path(vertices=np.concatenate([exterior.vertices,
                                                    interior.vertices]),
                           codes=np.concatenate([exterior.codes,
                                                 interior.codes]))

    star = mpath.Path.unit_regular_star(6).deepcopy()
    star.vertices *= 2.6

    ax1 = plt.subplot(121)
    col = mcollections.PathCollection([star], lw=5, edgecolor='blue',
                                      facecolor='red', alpha=0.7, hatch='*')
    col.set_clip_path(clip_path, ax1.transData)
    ax1.add_collection(col)

    ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
    patch = mpatches.PathPatch(star, lw=5, edgecolor='blue', facecolor='red',
                               alpha=0.7, hatch='*')
    patch.set_clip_path(clip_path, ax2.transData)
    ax2.add_patch(patch)

    ax1.set_xlim([-3, 3])
    ax1.set_ylim([-3, 3])

def plot_logpdf_Gaussian():
    y2 = -6
    logvar = np.arange(-4,4,0.05)
    logprior = np.array([lp_gaussian(lv, 0, 1) for lv in logvar])
    def plot_logvar(mu):
        lp = np.array([lp_gaussian(y2, mu, np.exp(lv)) for lv in logvar])
        plt.plot(logvar, lp+logprior, label='mu = {}'.format(mu))
        plt.ylim(-20,3)
    plt.clf()
    plt.subplot(2,1,1)
    plt.plot(logvar, logprior, label='prior', lw=5)
    plot_logvar(0)
    plot_logvar(-6)
    plt.xlabel('logvar')
    plt.legend()
    mu = np.arange(-8,8,0.05)
    logprior = np.array([lp_gaussian(m,0,1) for m in mu])
    def plot_mu(var):
        lp = np.array([lp_gaussian(y2, m, var) for m in mu])
        lp = lp + logprior
        plt.plot(mu, lp, label='var = {}'.format(var))
        plt.ylim(-30,0)
    plt.subplot(2,1,2)
    plt.plot(mu, logprior, label='prior', lw=5)
    plot_mu(0.1)
    plot_mu(1.0)
    plot_mu(10)
    plt.xlabel('mu')
    plt.legend()

def template_matching():
    img = cv2.imread('messi.jpg',0)
    img2 = img.copy()
    template = cv2.imread('face.png',0)
    w, h = template.shape[::-1]

    # All the 6 methods for comparison in a list
    methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
            'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

    for meth in methods:
        img = img2.copy()
        method = eval(meth)

        # Apply template Matching
        res = cv2.matchTemplate(img,template,method)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

        # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
        if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
            top_left = min_loc
        else:
            top_left = max_loc
        bottom_right = (top_left[0] + w, top_left[1] + h)

        cv2.rectangle(img,top_left, bottom_right, 255, 2)

        plt.subplot(121),plt.imshow(res,cmap = 'gray')
        plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
        plt.subplot(122),plt.imshow(img,cmap = 'gray')
        plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
        plt.suptitle(meth)

        plt.show()

	def __call__(self,u,w,bx,by,bz,b2,t):
		q = 8

		map = cm.red_blue()
		if self.x == None:
			nx = u.shape[2]
			nz = u.shape[0]
			self.x,self.y = np.meshgrid(range(nx),range(nz))
		x,y = self.x,self.y

		avgu = np.average(u,1)
		avgw = np.average(w,1)
		avgbx = np.average(bx,1)
		avgby = np.average(by,1)
		avgbz = np.average(bz,1)
		avgb2 = np.average(b2,1)
		avgt = np.average(t,1)

		plt.subplot(121)
		plt.imshow(avgt,cmap=map,origin='lower')
		plt.colorbar()
		plt.quiver(x[::q,::q],y[::q,::q],avgu[::q,::q],avgw[::q,::q])
		plt.title('Tracer-Vel')
		plt.axis("tight")

		plt.subplot(122)
		plt.imshow(avgby,cmap=map,origin='lower')
		plt.colorbar()
		plt.quiver(x[::q,::q],y[::q,::q],avgbx[::q,::q],avgbz[::q,::q])
		plt.title('By-Twist')
		plt.axis("tight")

    def subplot(self,names,title=None,style=None):
        assert isinstance(names,list)

        fig = plt.figure()
        if title is None:
            if isinstance(names,str):
                title = names
            else:
                assert isinstance(names,list)
                if len(names) == 1:
                    title = names[0]
                else:
                    title = str(names)
        fig.canvas.set_window_title(str(title))

        plt.clf()
        n = len(names)
        if style is None:
            style = [None]*n
        for k,name in enumerate(names):
            plt.subplot(n,1,k+1)
            if k==0:
                self._plot(name,title,style=style[k])
            else:
                self._plot(name,None,style=style[k])

def ReleaseMemoryPlot2(mincut = 0.9, maxcut = 1, N = 100):
    step = (maxcut - mincut)/N
    cuts = [mincut + step*i for i in range(0, N+1)]
    
    released_memory = []
    good_memory = []
    part_of_good_memory = []
    
    all_memory = signal_test2.get_data(['DiskSize']).values[:,0].sum() + bck_test2.get_data(['DiskSize']).values[:,0].sum()
    memory_can_be_free = signal_test2.get_data(['DiskSize']).values[:,0].sum()
    
    for i in cuts:
        rm, gm, pm = ReleaseMemory2(cut = i)
        released_memory.append(rm)
        good_memory.append(gm)
        part_of_good_memory.append(pm)
    
    print 'all_memory = ', all_memory
    print 'memory_can_be_free = ', memory_can_be_free
    
    plt.subplot(1,1,1)
    plt.plot(cuts, released_memory, 'b', label = 'released memory')
    plt.plot(cuts, good_memory, 'r', label = 'good memory')
    plt.legend(loc = 'best')
    plt.show()
    
    plt.subplot(1,1,1)
    plt.plot(cuts, part_of_good_memory, 'r', label = 'part of good memory')
    plt.legend(loc = 'best')
    plt.show()

def make_intergenerational_figure(data, lowerbound, upperbound, rows, title):
    plt.figure(figsize=(10,10))
    plt.suptitle(title,fontsize=20)
    for index in range(4):
        plt.subplot(2,2,index+1)    
        #simulation distribution
        plt.hist(accepted[:,rows[index]], normed=True, bins = range(0,100,5), color = col)
        #simulation values
        value = np.mean(accepted[:,rows[index]])
        std = 2*np.std(accepted[:,rows[index]])
        plt.errorbar((value,), (red_marker_location-0.02), xerr=((std,),(std,)),
                     color=col, fmt='o', linewidth=2, capsize=5, mec = col)
        #survey values
        value = data[index]
        lb = lowerbound[index]
        ub = upperbound[index]
        plt.errorbar((value,), (red_marker_location,), xerr=((value-lb,),(ub-value,)),
                     color='r', fmt='o', linewidth=2, capsize=5, mec = 'r')
        #labeling    
        plt.ylim(0,ylimit)
        plt.xlim(0,100)
    #make subplots pretty
    plt.subplot(2,2,1)
    plt.title("Males")
    plt.ylabel("'05\nFrequency")
    plt.subplot(2,2,2)
    plt.title("Females")
    plt.subplot(2,2,3)
    plt.ylabel("'08\nFrequency")
    plt.xlabel("Percent Responding Affirmatively")
    plt.subplot(2,2,4)
    plt.xlabel("Percent Responding Affirmatively")

def visualize_singular_values(args):
    param_values = load_parameter_values(args.load_path)
    for d in range(args.layers):
        if args.rnn_type == 'lstm':
            ws = param_values["/recurrentstack/lstm_" + str(d) + ".W_state"]
            w_rec = ws[:, 3 * args.state_dim:]
        elif args.rnn_type == 'simple':
            w_rec = param_values["/recurrentstack/simplerecurrent_" + str(d) +
                                 ".W_state"]
        else:
            raise NotImplementedError
        U, s, V = np.linalg.svd(w_rec, full_matrices=True)
        plt.subplot(2, 1, 1)
        plt.plot(np.arange(s.shape[0]), s, label='Layer_' + str(d))
        plt.grid(True)
        plt.legend(loc='upper right')
        plt.title("Singular_values_of_recurrent_weights")
        plt.subplot(2, 1, 2)
        plt.plot(np.arange(s.shape[0]), np.log(s + 1E-15),
                 label='Layer_' + str(d))
        plt.grid(True)
        plt.title("Log_singular_values_of_recurrent_weights")
    plt.tight_layout()

    plt.savefig(args.save_path + "/visualize_singular_values.png")
    logger.info("Figure \"visualize_singular_values"
                ".png\" saved at directory: " + args.save_path)

def plotMultiGameTaskResults(directory, game, numTasks = 2):
    filename = directory + game
    fullShareResultsFilename = filename + "_fullShare.csv"
    layerShareResultsFilename = filename + "_layerShare.csv"
    repShareResultsFilename = filename + "_repShare.csv"
 
    games = game.split(",")
    if len(games) != numTasks:
        print "The number of games is not equal to the number of tasks - not plotting"
        return
 
    fullResults = getResultsFromFile(fullShareResultsFilename, numTasks)
    layerResults = getResultsFromFile(layerShareResultsFilename, numTasks)
    repResults = getResultsFromFile(repShareResultsFilename, numTasks)
 
   
    # figure = plt.figure()
    # subplot = figure.add_subplot(111)
    # plots = []
    # for i in xrange(numTasks):
    #     plots.append(subplot.plot(fullResults[0][0:len(fullResults[1][i])], fullResults[1][i], label="FullShare: " + str(i)))
 
    # figure.suptitle(title)
   
    for i in xrange(len(games)):
        plt.figure(i + 1)
        plt.subplot(111)
        plt.plot(fullResults[0][0:len(fullResults[1][i])], fullResults[1][i], label="Full Share")
        plt.plot(layerResults[0][0:len(layerResults[1][i])], layerResults[1][i], label="Layer Share")
        plt.plot(repResults[0][0:len(repResults[1][i])], repResults[1][i], label="Rep Share")
        plt.xlabel('epochs')
        plt.ylabel('Average Reward')
        plt.title(games[i])
        L = plt.legend()
        L.draggable(state=True)

def calc_snr(plot = False):
    tx = fromfile(open('tx_sym.32fc'), dtype=complex64)
    rx = fromfile(open('rx_sym.32fc'), dtype=complex64)

    if (len(tx) == 0 or len(rx) == 0):
        print 'Not valid data'
        print '\tPlease run gnuradio simulation first'
        exit(-1)

    size = min([len(tx), len(rx)]) - 1
    rx = rx[0:size]
    tx = tx[0:size]

    tx_power = sum([abs(tx[i])**2 for i in range(size)])
    rx_power = sum([abs(rx[i])**2 for i in range(size)])
    noise_power = sum([abs(tx[i] - rx[i])**2 for i in range(size)])

    SNR = 1.0*tx_power/noise_power
    SNR_dB = 10*log10(SNR)

    if plot:
        init = 0
        end = 100
        p.subplot(211)
        p.plot(list(real(tx[init:end])), '-o')
        p.plot(list(imag(tx[init:end])), '-o')
        p.subplot(212)
        p.plot(list(real(rx[init:end])), '-o')
        p.plot(list(imag(rx[init:end])), '-o')
        p.show()

    return SNR_dB

    def plot_main_seeds(self, qname, radio=False, checkbox=False, 
                        numerical=False, array=False):
        """ Plot the responses separately for each seed group in main_seeds. """
        
        assert sum([radio, checkbox, numerical, array]) == 1

        for seed in self.main_seeds:
            responses_seed = self.filter_rows_by_seed(seed, self.responses)
            responses_seed_question = self.filter_columns_by_name(qname, responses_seed)

            plt.subplot(int("22" + str(self.main_seeds.index(seed))))
            plt.title("Seed " + seed)

            if radio:
                self.plot_convergence_radio(qname, responses_seed_question)
            elif checkbox:
                self.plot_convergence_checkbox(responses_seed_question)
            elif numerical:
                self.plot_convergence_numerical(responses_seed_question)
            elif array:
                self.plot_array_boxes(qname, responses_seed_question)

        qtext = self.get_qtext_from_qname(qname)
        plt.suptitle(qtext)
        plt.tight_layout()
        plt.show()