Python pyplot.clabel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 clabel [as 别名]
def test_collection():
    x, y = np.meshgrid(np.linspace(0, 10, 150), np.linspace(-5, 5, 100))
    data = np.sin(x) + np.cos(y)
    cs = plt.contour(data)
    pe = [path_effects.PathPatchEffect(edgecolor='black', facecolor='none',
                                       linewidth=12),
          path_effects.Stroke(linewidth=5)]

    for collection in cs.collections:
        collection.set_path_effects(pe)

    for text in plt.clabel(cs, colors='white'):
        text.set_path_effects([path_effects.withStroke(foreground='k',
                                                       linewidth=3)])
        text.set_bbox({'boxstyle': 'sawtooth', 'facecolor': 'none',
                       'edgecolor': 'blue'}) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def visualizeFit(X,mu,sigma2):
    x = np.arange(0, 36, 0.5) # 0-36，步长0.5
    y = np.arange(0, 36, 0.5)
    X1,X2 = np.meshgrid(x,y)  # 要画等高线，所以meshgird
    Z = multivariateGaussian(np.hstack((X1.reshape(-1,1),X2.reshape(-1,1))), mu, sigma2)  # 计算对应的高斯分布函数
    Z = Z.reshape(X1.shape)  # 调整形状
    plt.plot(X[:,0],X[:,1],'bx')
    
    if np.sum(np.isinf(Z).astype(float)) == 0:   # 如果计算的为无穷，就不用画了
        #plt.contourf(X1,X2,Z,10.**np.arange(-20, 0, 3),linewidth=.5)
        CS = plt.contour(X1,X2,Z,10.**np.arange(-20, 0, 3),color='black',linewidth=.5)   # 画等高线，Z的值在10.**np.arange(-20, 0, 3)
        #plt.clabel(CS)
            
    plt.show()

# 选择最优的epsilon，即：使F1Score最大 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def test_labels():
    # Adapted from pylab_examples example code: contour_demo.py
    # see issues #2475, #2843, and #2818 for explanation
    delta = 0.025
    x = np.arange(-3.0, 3.0, delta)
    y = np.arange(-2.0, 2.0, delta)
    X, Y = np.meshgrid(x, y)
    Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
    Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
    # difference of Gaussians
    Z = 10.0 * (Z2 - Z1)

    fig, ax = plt.subplots(1, 1)
    CS = ax.contour(X, Y, Z)
    disp_units = [(216, 177), (359, 290), (521, 406)]
    data_units = [(-2, .5), (0, -1.5), (2.8, 1)]

    CS.clabel()

    for x, y in data_units:
        CS.add_label_near(x, y, inline=True, transform=None)

    for x, y in disp_units:
        CS.add_label_near(x, y, inline=True, transform=False) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def Pcolor(xs, ys, zs, pcolor=True, contour=False, **options):
    """Makes a pseudocolor plot.
    
    xs:
    ys:
    zs:
    pcolor: boolean, whether to make a pseudocolor plot
    contour: boolean, whether to make a contour plot
    options: keyword args passed to plt.pcolor and/or plt.contour
    """
    _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues)

    X, Y = np.meshgrid(xs, ys)
    Z = zs

    x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
    axes = plt.gca()
    axes.xaxis.set_major_formatter(x_formatter)

    if pcolor:
        plt.pcolormesh(X, Y, Z, **options)

    if contour:
        cs = plt.contour(X, Y, Z, **options)
        plt.clabel(cs, inline=1, fontsize=10) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def test_contour_badlevel_fmt():
    # test funny edge case from
    # https://github.com/matplotlib/matplotlib/issues/9742
    # User supplied fmt for each level as a dictionary, but
    # MPL changed the level to the minimum data value because
    # no contours possible.
    # This would error out pre
    # https://github.com/matplotlib/matplotlib/pull/9743
    x = np.arange(9)
    z = np.zeros((9, 9))

    fig, ax = plt.subplots()
    fmt = {1.: '%1.2f'}
    with pytest.warns(UserWarning) as record:
        cs = ax.contour(x, x, z, levels=[1.])
        ax.clabel(cs, fmt=fmt)
    assert len(record) == 1 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def Pcolor(xs, ys, zs, pcolor=True, contour=False, **options):
    """Makes a pseudocolor plot.
    
    xs:
    ys:
    zs:
    pcolor: boolean, whether to make a pseudocolor plot
    contour: boolean, whether to make a contour plot
    options: keyword args passed to pyplot.pcolor and/or pyplot.contour
    """
    _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues)

    X, Y = np.meshgrid(xs, ys)
    Z = zs

    x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
    axes = pyplot.gca()
    axes.xaxis.set_major_formatter(x_formatter)

    if pcolor:
        pyplot.pcolormesh(X, Y, Z, **options)

    if contour:
        cs = pyplot.contour(X, Y, Z, **options)
        pyplot.clabel(cs, inline=1, fontsize=10) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def test_patheffect2():

    ax2 = plt.subplot(111)
    arr = np.arange(25).reshape((5, 5))
    ax2.imshow(arr)
    cntr = ax2.contour(arr, colors="k")

    plt.setp(cntr.collections,
             path_effects=[path_effects.withStroke(linewidth=3,
                                                   foreground="w")])

    clbls = ax2.clabel(cntr, fmt="%2.0f", use_clabeltext=True)
    plt.setp(clbls,
             path_effects=[path_effects.withStroke(linewidth=3,
                                                   foreground="w")]) 

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

    x, y = np.meshgrid(np.arange(0, 10), np.arange(0, 10))
    z = np.max(np.dstack([abs(x), abs(y)]), 2)

    plt.figure(figsize=(6, 2))
    cs = plt.contour(x, y, z)
    pts = np.array([(1.5, 3.0), (1.5, 4.4), (1.5, 6.0)])
    plt.clabel(cs, manual=pts, fontsize='small', colors=('r', 'g')) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def test_circular_contour_warning():
    # Check that almost circular contours don't throw a warning
    with pytest.warns(None) as record:
        x, y = np.meshgrid(np.linspace(-2, 2, 4), np.linspace(-2, 2, 4))
        r = np.sqrt(x ** 2 + y ** 2)

        plt.figure()
        cs = plt.contour(x, y, r)
        plt.clabel(cs)
    assert len(record) == 0 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def DrawContourAndMark(contour, x, y, z, level, clipborder, patch, m):

        # 是否绘制等值线 ------ 等值线和标注是一体的

        if contour.contour['visible']:

            matplotlib.rcParams['contour.negative_linestyle'] = 'dashed'
            if contour.contour['colorline']:
                CS1 = m.contour(x, y, z, levels=level, linewidths=contour.contour['linewidth'])
            else:
                CS1 = m.contour(x,
                                y,
                                z,
                                levels=level,
                                linewidths=contour.contour['linewidth'],
                                colors=contour.contour['linecolor'])

            # 是否绘制等值线标注
            CS2 = None
            if contour.contourlabel['visible']:
                CS2 = plt.clabel(CS1,
                                 inline=1,
                                 fmt=contour.contourlabel['fmt'],
                                 inline_spacing=contour.contourlabel['inlinespacing'],
                                 fontsize=contour.contourlabel['fontsize'],
                                 colors=contour.contourlabel['fontcolor'])

            # 用区域边界裁切等值线图
            if clipborder.path is not None and clipborder.using:
                for collection in CS1.collections:
                    # collection.set_clip_on(True)
                    collection.set_clip_path(patch)

                if CS2 is not None:
                    for text in CS2:
                        if not clipborder.path.contains_point(text.get_position()):
                            text.remove() 

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

    x, y = np.meshgrid(np.arange(0, 10), np.arange(0, 10))
    z = np.max(np.dstack([abs(x), abs(y)]), 2)

    plt.figure(figsize=(6, 2))
    cs = plt.contour(x, y, z)
    pts = np.array([(1.5, 3.0), (1.5, 4.4), (1.5, 6.0)])
    plt.clabel(cs, manual=pts) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def Contour(obj, pcolor=False, contour=True, imshow=False, **options):
    """Makes a contour plot.
    
    d: map from (x, y) to z, or object that provides GetDict
    pcolor: boolean, whether to make a pseudocolor plot
    contour: boolean, whether to make a contour plot
    imshow: boolean, whether to use plt.imshow
    options: keyword args passed to plt.pcolor and/or plt.contour
    """
    try:
        d = obj.GetDict()
    except AttributeError:
        d = obj

    _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues)

    xs, ys = zip(*d.keys())
    xs = sorted(set(xs))
    ys = sorted(set(ys))

    X, Y = np.meshgrid(xs, ys)
    func = lambda x, y: d.get((x, y), 0)
    func = np.vectorize(func)
    Z = func(X, Y)

    x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
    axes = plt.gca()
    axes.xaxis.set_major_formatter(x_formatter)

    if pcolor:
        plt.pcolormesh(X, Y, Z, **options)
    if contour:
        cs = plt.contour(X, Y, Z, **options)
        plt.clabel(cs, inline=1, fontsize=10)
    if imshow:
        extent = xs[0], xs[-1], ys[0], ys[-1]
        plt.imshow(Z, extent=extent, **options) 

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

    x, y = np.meshgrid(np.arange(0, 10), np.arange(0, 10))
    z = np.max(np.dstack([abs(x), abs(y)]), 2)

    plt.figure(figsize=(6, 2), dpi=200)
    cs = plt.contour(x, y, z)
    pts = np.array([(1.5, 3.0), (1.5, 4.4), (1.5, 6.0)])
    plt.clabel(cs, manual=pts) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def visualize_function(self, func, show_3d=True, show_3d_inv=True,
                         show_contour=True, num_levels=15, rng_x=(-5, 5),
                         rng_y=(-5, 5)):
    import matplotlib.pyplot as plt
    if self._dim == 1:
      xs = np.linspace(rng_x[0], rng_x[1], 100)
      plt.plot(xs, np.apply_along_axis(func, 0, xs[np.newaxis, :]))
    elif self._dim == 2:
      freq = 50
      x = np.linspace(rng_x[0], rng_x[1], freq)
      y = np.linspace(rng_y[0], rng_y[1], freq)
      Xs, Ys = np.meshgrid(x, y)
      xs = np.reshape(Xs, -1)
      ys = np.reshape(Ys, -1)
      zs = np.apply_along_axis(func, 0, np.vstack((xs, ys)))

      if show_3d:
        fig = plt.figure(figsize=(10, 10))
        ax = fig.gca(projection='3d')
        ax.plot_trisurf(xs, ys, zs, linewidth=0.2, antialiased=True)
        plt.show()
      if show_3d_inv:
        fig = plt.figure(figsize=(10, 10))
        ax = fig.gca(projection='3d')
        ax.invert_zaxis()
        ax.plot_trisurf(xs, ys, zs, linewidth=0.2, antialiased=True)
        plt.show()
      if show_contour:
        fig = plt.figure(figsize=(10, 10))
        cs = plt.contour(Xs, Ys, zs.reshape(freq, freq), num_levels)
        plt.clabel(cs, inline=1, fontsize=10)
        plt.show()
    else:
      raise ValueError("Only dim=1 or dim=2 are supported") 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import clabel [as 别名]
def visualize_distribution(log_densities, ax = None):
    if ax is None:
        ax = plt.gca()
    t = normalize_log_density(log_densities)
    img = ax.imshow(t, cmap=plt.cm.viridis)
    levels = levels=[0, 0.25, 0.5, 0.75, 1.0]
    cs = ax.contour(t, levels=levels, colors='black')
    #plt.clabel(cs)

    return img, cs