Python pyplot.hold方法代码示例

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def plot_histograms(file_name, candidate_data_multiple_bands,
                    reference_data_multiple_bands=None,
                    # Default is for Blue-Green-Red-NIR:
                    colour_order=['b', 'g', 'r', 'y'],
                    x_limits=None, y_limits=None):
    logging.info('Display: Creating histogram plot - {}'.format(file_name))
    fig = plt.figure()
    plt.hold(True)
    for colour, c_band in zip(colour_order, candidate_data_multiple_bands):
        c_bh, c_bins = numpy.histogram(c_band, bins=256)
        plt.plot(c_bins[:-1], c_bh, color=colour, linestyle='-', linewidth=2)
    if reference_data_multiple_bands:
        for colour, r_band in zip(colour_order, reference_data_multiple_bands):
            r_bh, r_bins = numpy.histogram(r_band, bins=256)
            plt.plot(
                r_bins[:-1], r_bh, color=colour, linestyle='--', linewidth=2)
    plt.xlabel('DN')
    plt.ylabel('Number of pixels')
    if x_limits:
        plt.xlim(x_limits)
    if y_limits:
        plt.ylim(y_limits)
    fig.savefig(file_name, bbox_inches='tight')
    plt.close(fig) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def getBarycentricCoords(A, B, C, X, checkValidity = True):
    T = np.array( [ [A.x - C.x, B.x - C.x ], [A.y - C.y, B.y - C.y] ] )
    y = np.array( [ [X.x - C.x], [X.y - C.y] ] )
    lambdas = linalg.solve(T, y)
    lambdas = lambdas.flatten()
    lambdas = np.append(lambdas, 1 - (lambdas[0] + lambdas[1]))
    if checkValidity:
        if (lambdas[0] < 0 or lambdas[1] < 0 or lambdas[2] < 0):
            print "ERROR: Not a convex combination; lambda = %s"%lambdas
            print "pointInsideConvexPolygon2D = %s"%pointInsideConvexPolygon2D([A, B, C], X, 0)
            plt.plot([A.x, B.x, C.x, A.x], [A.y, B.y, C.y, A.y], 'r')
            plt.hold(True)
            plt.plot([X.x], [X.y], 'b.')
            plt.show()
        assert (lambdas[0] >= 0 and lambdas[1] >= 0 and lambdas[2] >= 0)
    else:
        lambdas[0] = max(lambdas[0], 0)
        lambdas[1] = max(lambdas[1], 0)
        lambdas[2] = max(lambdas[2], 0)
    return lambdas 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def plot_traj(w, w_true, fname=None):
    """ Plot the trajectory of the filter coefficients

    Inputs:
        w: matrix of filter weights versus time
        w_true: vector of true filter weights 
        fname: what filename to export the figure to. If None, then doesn't
        export

    """
    plt.figure()
    n = np.arange(w.shape[0])
    n_ones = np.ones(w.shape[0])
    plt.hold(True)
    # NOTE: This construction places a limit of 4 on the filter order
    plt_colors = ['b', 'r', 'g', 'k']
    for p in xrange(w.shape[1]):
        plt.plot(n, w[:,p], '{}-'.format(plt_colors[p]), label='w({})'.format(p))
        plt.plot(n, w_true[p] * n_ones, '{}--'.format(plt_colors[p]))
    plt.xlabel('Iteration')
    plt.ylabel('Coefficients')
    plt.legend()
    if fname:
        plt.savefig(fname)
        plt.close() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def hist(data):
    """Convenience method to do all the plotting gymnastics to get a resonable
    looking histogram plot.

    Input: data - numpy array in gdal format - (bands, x, y)

    Returns:  matplotlib figure handle

    Adapted from:
    http://nbviewer.jupyter.org/github/HyperionAnalytics/PyDataNYC2014/blob/master/color_image_processing.ipynb
    """

    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    plt.hold(True)
    for x in xrange(len(data[:,0,0])):
        counts, edges = np.histogram(data[x,:,:],bins=100)
        centers = [(edges[i]+edges[i+1])/2.0 for i,v in enumerate(edges[:-1])]
        ax1.plot(centers,counts)
    plt.hold(False)

    plt.show(block=False)

    # return fig 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def animate_series(fk, M, N, xmin, xmax, ymin, ymax, n, exact, exactname):
    x = np.linspace(xmin, xmax, n)
    s = np.zeros(len(x))
    counter = 1
    for k in range(M, N + 1):
        s += fk(x, k)
        plt.plot(x, s, linewidth=2, color='#67001f')
        plt.hold(1)
        plt.plot(x, exact(x), '--', linewidth=2, color='#053061')
        plt.hold(0)
        plt.xlim(xmin, xmax)
        plt.ylim(ymin, ymax)
        plt.legend(['Taylor series approximation - %d terms' %
                   counter, exactname])
        plt.savefig('tmp_%04d.png' % counter)
        counter += 1 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def animate_orbit(a, b, omega, n):
    tlist = np.linspace(0, 2 * np.pi / omega, n)
    xorbit, yorbit = orbit_path(tlist, a, b, omega)
    counter = 0
    for t in tlist:
        x, y = orbit_path(t, a, b, omega)
        plt.plot(xorbit, yorbit, '--', color='#67001f', linewidth=2)
        plt.hold(1)
        plt.plot(x, y, 'ro', markerfacecolor='#2166ac',
                 markeredgecolor='#053061', markeredgewidth=2, markersize=20)
        plt.hold(0)
        plt.xlim([xorbit.min() * 1.1, xorbit.max() * 1.1])
        plt.ylim([yorbit.min() * 1.1, yorbit.max() * 1.1])
        plt.xlabel('x')
        plt.ylabel('y')
        plt.title('Instantaneous velocity = %4f' % inst_vel(t, a, b, omega))
        plt.savefig('tmp_%03d.png' % counter)
        counter += 1 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def animate_series(fk, N, tmax, n, exact, exactname):
    t = np.linspace(0, tmax, n)
    s = np.zeros(len(t))
    counter = 1
    for k in range(0, N + 1):
        s = S(t, k, tmax)
        plt.plot(t, s, linewidth=2, color='#67001f')
        plt.hold(1)
        plt.plot(t, exact(t, tmax), '--', linewidth=2, color='#053061')
        plt.hold(0)
        plt.xlim(0, tmax)
        plt.ylim(-1.3, 1.3)
        plt.legend(['Sine sum - %d terms' %
                   counter, exactname])
        plt.savefig('tmp_%04d.png' % counter)
        counter += 1 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def viz_textbb(fignum,text_im, bb_list,alpha=1.0):
    """
    text_im : image containing text
    bb_list : list of 2x4xn_i boundinb-box matrices
    """
    plt.close(fignum)
    plt.figure(fignum)
    plt.imshow(text_im)
    plt.hold(True)
    H,W = text_im.shape[:2]
    for i in xrange(len(bb_list)):
        bbs = bb_list[i]
        ni = bbs.shape[-1]
        for j in xrange(ni):
            bb = bbs[:,:,j]
            bb = np.c_[bb,bb[:,0]]
            plt.plot(bb[0,:], bb[1,:], 'r', linewidth=2, alpha=alpha)
    plt.gca().set_xlim([0,W-1])
    plt.gca().set_ylim([H-1,0])
    plt.show(block=False) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def fig_memory_usage():

    # FAST memory
    x = [1,3,7,14,30,90,180]
    y_fast = [0.653,1.44,2.94,4.97,9.05,19.9,35.2]
    # ConvNetQuake
    y_convnet = [6.8*1e-5]*7
    # Create figure
    plt.loglog(x,y_fast,"o-")
    plt.hold('on')
    plt.loglog(x,y_convnet,"o-")
    # plot markers
    plt.loglog(x,[1e-5,1e-5,1e-5,1e-5,1e-5,1e-5,1e-5],'o')
    plt.ylabel("Memory usage (GB)")
    plt.xlabel("Continous data duration (days)")
    plt.xlim(1,180)
    plt.grid("on")
    plt.savefig("./figures/memoryusage.eps")
    plt.close() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def fig_run_time():
    # fast run time
    x_fast = [1,3,7,14,30,90,180]
    y_fast = [289,1.13*1e3,2.48*1e3,5.41*1e3,1.56*1e4,
              6.61*1e4,1.98*1e5]
    x_auto = [1,3]
    y_auto = [1.54*1e4, 8.06*1e5]
    x_convnet = [1,3,7,14,30]
    y_convnet = [9,27,61,144,291]
    # create figure
    plt.loglog(x_auto,y_auto,"o-")
    plt.hold('on')
    plt.loglog(x_fast[0:5],y_fast[0:5],"o-")
    plt.loglog(x_convnet,y_convnet,"o-")
    # plot x markers
    plt.loglog(x_convnet,[1e0]*len(x_convnet),'o')
    # plot y markers
    y_markers = [1,60,3600,3600*24]
    plt.plot([1]*4,y_markers,'ko')
    plt.ylabel("run time (s)")
    plt.xlabel("continous data duration (days)")
    plt.xlim(1,35)
    plt.grid("on")
    plt.savefig("./figures/runtimes.eps") 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def drawSamples(self, nsamples):
        import matplotlib.pyplot as plt
        plt.figure(1)
        plt.clf()
        plt.hold(True)
        k = ConvexHull(self.bot_pts.T).vertices
        k = np.hstack((k, k[0]))
        n = self.planar_polyhedron.generators.shape[0]
        plt.plot(self.planar_polyhedron.generators.T[0,list(range(n)) + [0]],
                 self.planar_polyhedron.generators.T[1,list(range(n)) + [0]], 'r.-')
        samples = sample_convex_polytope(self.c_space_polyhedron.A,
                                         self.c_space_polyhedron.b,
                                         500)
        for i in range(samples.shape[1]):
            R = np.array([[np.cos(samples[2,i]), -np.sin(samples[2,i])],
                          [np.sin(samples[2,i]), np.cos(samples[2,i])]])
            V = R.dot(self.bot_pts[:,k])
            V = V + samples[:2, i].reshape((2,1))
            plt.plot(V[0,:], V[1,:], 'k-')
        plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def make_plots(xs,ys,labels,title=None,x_name=None,y_name=None,y_bounds=None,save_to=None):
    colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k')
    handles = []
    plt.figure()
    plt.hold(True)
    for i in range(len(labels)):
        plot, = make_plot(xs[i],ys[i],color=colors[i%len(colors)],new_fig=False)
        handles.append(plot)
    plt.legend(handles,labels)
    if title is not None:
        plt.title(title)
    if x_name is not None:
        plt.xlabel(x_name)
    if y_name is not None:
        plt.ylabel(y_name)
    if y_bounds is not None:
        plt.ylim(y_bounds)
    if save_to is not None:
        plt.savefig(save_to,bbox_inches='tight')
    plt.hold(False) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def doit(fn, col1, col2, tag):
  train = []
  val = []
  with open(fn) as f:
    for l in f:
      if "finished" in l:
        sp = l.split()
        #clip to 5
        # tr = min(float(sp[col1]), 5.0)
        # va = min(float(sp[col2]), 5.0)
        tr = float(sp[col1])
        va = float(sp[col2])
        # if tr>1: tr = 1/tr
        # if va>1: va = 1/va

        train.append(tr)
        val.append(va)
  plt.plot(train, label="train")
  plt.hold(True)
  plt.plot(val, label="val")
  plt.legend()
  plt.title(fn + " " + tag)
  #plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def draw_minutiae(image, minutiae, fname, saveimage= False, r=15, drawScore=False):
    image = np.squeeze(image)
    fig = plt.figure()
    

    plt.imshow(image,cmap='gray')
    plt.hold(True)
    # Check if no minutiae
    if minutiae.shape[0] > 0:
        plt.plot(minutiae[:, 0], minutiae[:, 1], 'rs', fillstyle='none', linewidth=1)
        for x, y, o, s in minutiae:
            plt.plot([x, x+r*np.cos(o)], [y, y+r*np.sin(o)], 'r-')
            if drawScore == True:
                plt.text(x - 10, y - 10, '%.2f' % s, color='yellow', fontsize=4)

    plt.axis([0,image.shape[1],image.shape[0],0])
    plt.axis('off')
    if saveimage:
        plt.savefig(fname, dpi=500, bbox_inches='tight', pad_inches = 0)
        plt.close(fig)
    else:
        plt.show()
    return 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import hold [as 别名]
def plot_correlation(flname, title, x_label, y_label, dataset):
    """
    Scatter plot with line of best fit

    dataset - tuple of (x_values, y_values)
    """
    plt.clf()
    plt.hold(True)
    plt.scatter(dataset[0], dataset[1], alpha=0.7, color="k")
    xs = np.array(dataset[0])
    ys = np.array(dataset[1])
    A = np.vstack([xs, np.ones(len(xs))]).T
    m, c = np.linalg.lstsq(A, ys)[0]
    plt.plot(xs, m*xs + c, "c-")
    plt.xlabel(x_label, fontsize=16)
    plt.ylabel(y_label, fontsize=16)
    plt.xlim([0.25, max(dataset[0])])
    plt.ylim([10., max(dataset[1])])
    plt.title(title, fontsize=18)
    plt.savefig(flname, DPI=200)