Python pyplot.vlines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 vlines [as 别名]
def time_ph(d, i=0, num_ph=1e4, ph_istart=0):
    """Plot 'num_ph' ph starting at 'ph_istart' marking burst start/end.
    TODO: Update to use the new matplotlib eventplot.
    """
    b = d.mburst[i]
    SLICE = slice(ph_istart, ph_istart+num_ph)
    ph_d = d.ph_times_m[i][SLICE][~d.A_em[i][SLICE]]
    ph_a = d.ph_times_m[i][SLICE][d.A_em[i][SLICE]]

    BSLICE = (b.stop < ph_a[-1])
    start, end = b[BSLICE].start, b[BSLICE].stop

    u = d.clk_p # time scale
    plt.vlines(ph_d*u, 0, 1, color='k', alpha=0.02)
    plt.vlines(ph_a*u, 0, 1, color='k', alpha=0.02)
    plt.vlines(start*u, -0.5, 1.5, lw=3, color=green, alpha=0.5)
    plt.vlines(end*u, -0.5, 1.5, lw=3, color=red, alpha=0.5)
    xlabel("Time (s)")


##
#  Histogram plots
# 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def test_scheduler(self):
        epochs = 10

        max_lr = 3e-3
        alpha = 1e-2

        steps_per_epoch = 1024 
        scheduler = optim.WarmupCosineDecayLRScheduler(
            max_lr,
            steps_per_epoch, 
            (steps_per_epoch * (epochs - 1)), alpha=alpha)

        lrs = [scheduler(i) for i in range(epochs * steps_per_epoch)]
        epoch_ends_at = [i * steps_per_epoch for i in range(epochs)]

        print('Last lr', lrs[-1])

        plt.plot(range(epochs * steps_per_epoch), lrs)
        plt.vlines(epoch_ends_at, 0, max_lr)
        plt.show(block=True) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def plot_1d(self,filename=None):
        """plot the 1d insertion representation of the matrix"""
        fig = plt.figure()
        xlim = len(self.one_d)/2
        plt.plot(range(-xlim,xlim+1),self.one_d)
        plt.vlines(-73,0,max(self.one_d)*1.1,linestyles='dashed')
        plt.vlines(73,0,max(self.one_d)*1.1,linestyles='dashed')
        plt.xlabel("Position relative to dyad")
        plt.ylabel("Insertion Frequency")
        if filename:
            fig.savefig(filename)
            plt.close(fig)
            #Also save text output!
            filename2 = ".".join(filename.split(".")[:-1]+['txt'])
            np.savetxt(filename2,self.one_d,delimiter="\t")
        else:
            fig.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def plotting_proc(self, g):
        """
        For internal use
        """
        survival = self.results[g][0]
        t = self.ts[g]
        e = (self.event)[g]
        if self.censoring != None:
            c = self.censorings[g]
            csurvival = survival[c != 0]
            ct = t[c != 0]
            if len(ct) != 0:
                plt.vlines(ct,csurvival+0.02,csurvival-0.02)
        x = np.repeat(t[e != 0], 2)
        y = np.repeat(survival[e != 0], 2)
        if self.ts[g][-1] in t[e != 0]:
            x = np.r_[0,x]
            y = np.r_[1,1,y[:-1]]
        else:
            x = np.r_[0,x,self.ts[g][-1]]
            y = np.r_[1,1,y]
        plt.plot(x,y) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def plot_knee(self, figsize: Optional[Tuple[int, int]] = None):
        """
        Plot the curve and the knee, if it exists

        :param figsize: Optional[Tuple[int, int]
            The figure size of the plot. Example (12, 8)
        :return: NoReturn
        """
        import matplotlib.pyplot as plt

        if figsize is None:
            figsize = (6, 6)

        plt.figure(figsize=figsize)
        plt.title("Knee Point")
        plt.plot(self.x, self.y, "b", label="data")
        plt.vlines(
            self.knee, plt.ylim()[0], plt.ylim()[1], linestyles="--", label="knee/elbow"
        )
        plt.legend(loc="best")

    # Niceties for users working with elbows rather than knees 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def draw_violin_sdr(json_folder):
    acc, voc = compute_mean_metrics(json_folder, compute_averages=False)
    acc = acc[~np.isnan(acc)]
    voc = voc[~np.isnan(voc)]
    data = [acc, voc]
    inds = [1,2]

    fig, ax = plt.subplots()
    ax.violinplot(data, showmeans=True, showmedians=False, showextrema=False, vert=False)
    ax.scatter(np.percentile(data, 50, axis=1),inds, marker="o", color="black")
    ax.set_title("Segment-wise SDR distribution")
    ax.vlines([np.min(acc), np.min(voc), np.max(acc), np.max(voc)], [0.8, 1.8, 0.8, 1.8], [1.2, 2.2, 1.2, 2.2], color="blue")
    ax.hlines(inds, [np.min(acc), np.min(voc)], [np.max(acc), np.max(voc)], color='black', linestyle='--', lw=1, alpha=0.5)

    ax.set_yticks([1,2])
    ax.set_yticklabels(["Accompaniment", "Vocals"])

    fig.set_size_inches(8, 3.)
    fig.savefig("sdr_histogram.pdf", bbox_inches='tight') 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def run_all_benchmarks(method='forward', order=4, x_values=(0.1, 0.5, 1.0, 5), n_max=11,
                       show_plot=True):

    epsilon = MinStepGenerator(num_steps=3, scale=None, step_nom=None)
    scales = {}
    for n in range(1, n_max):
        plt.figure(n)
        scale_n = scales.setdefault(n, [])
        # for (name, x) in itertools.product( function_names, x_values):
        for name in function_names:
            fun0, dfun = get_function(name, n)
            if dfun is None:
                continue
            fd = Derivative(fun0, step=epsilon, method=method, n=n, order=order)
            for x in x_values:
                r = benchmark(x=x, dfun=dfun, fd=fd, name=name, scales=None, show_plot=show_plot)
                print(r)
                scale = r['scale']
                if np.isfinite(scale):
                    scale_n.append(scale)

        plt.vlines(np.mean(scale_n), 1e-12, 1, 'r', linewidth=3)
        plt.vlines(np.median(scale_n), 1e-12, 1, 'b', linewidth=3)

    _print_summary(method, order, x_values, scales) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def plotSpectre(transitions, eneval, spectre):
    """ plot the UV-visible spectrum using matplotlib. Absissa are converted in nm. """

    # lambda in nm
    lambdaval = [cst.h * cst.c / (val * cst.e) * 1.e9 for val in eneval]

    # plot gaussian spectra
    plt.plot(lambdaval, spectre, "r-", label = "spectre")

    # plot transitions
    plt.vlines([val[1] for val in transitions], \
               0., \
               [val[2] for val in transitions], \
               color = "blue", \
               label = "transitions" )

    plt.xlabel("lambda   /   nm")
    plt.ylabel("Arbitrary unit")
    plt.title("UV-visible spectra")
    plt.grid()
    plt.legend(fancybox = True, shadow = True)
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def plot_knee(self, ):
        font1 = {'family' : 'STXihei',
                 'weight' : 'normal',
                 'size'   : 50,
                 }
        """Plot the curve and the knee, if it exists"""
        import matplotlib.pyplot as plt

        plt.figure(figsize=(8*3, 8*3))
        plt.plot(self.x, self.y,'ro-',label="建设用地聚类最大总数")
        plt.xlabel('聚类距离',font1)
#        plt.ylabel('POI独立点',font1)
        plt.ylabel('聚类最大总数',font1)
        plt.tick_params(labelsize=40)
        plt.legend(prop=font1)

#        plt.axis["right"].set_visible(False)
#        plt.axis["top"].set_visible(False)
        plt.vlines(self.knee, plt.ylim()[0], plt.ylim()[1],colors='black')

    # Niceties for users working with elbows rather than knees 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def plot_knee(self, ):
        font1 = {'family' : 'STXihei',
                 'weight' : 'normal',
                 'size'   : 50,
                 }
        """Plot the curve and the knee, if it exists"""
        import matplotlib.pyplot as plt

        plt.figure(figsize=(8*3, 8*3))
        plt.plot(self.x, self.y,'ro-',label="POI聚类总数")
        plt.xlabel('聚类距离',font1)
#        plt.ylabel('POI独立点',font1)
        plt.ylabel('聚类总数',font1)
        plt.tick_params(labelsize=40)
        plt.legend(prop=font1)

#        plt.axis["right"].set_visible(False)
#        plt.axis["top"].set_visible(False)
        plt.vlines(self.knee, plt.ylim()[0], plt.ylim()[1],colors='black')

    # Niceties for users working with elbows rather than knees 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def binom_pmf(n=1, p=0.1):
    """
    二项分布有两个参数
    https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.binom.html#scipy.stats.binom
    :param n:试验次数
    :param p:单次实验成功的概率
    :return:
    """
    binom_dis = stats.binom(n, p)
    x = np.arange(binom_dis.ppf(0.0001), binom_dis.ppf(0.9999))
    print(x)  # [ 0.  1.  2.  3.  4.]
    fig, ax = plt.subplots(1, 1)
    ax.plot(x, binom_dis.pmf(x), 'bo', label='binom pmf')
    ax.vlines(x, 0, binom_dis.pmf(x), colors='b', lw=5, alpha=0.5)
    ax.legend(loc='best', frameon=False)
    plt.ylabel('Probability')
    plt.title('PMF of binomial distribution(n={}, p={})'.format(n, p))
    plt.show()

# binom_pmf(n=20, p=0.6) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def poisson_pmf(mu=3):
    """
    泊松分布
    https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.poisson.html#scipy.stats.poisson
    :param mu: 单位时间（或单位面积）内随机事件的平均发生率
    :return:
    """
    poisson_dis = stats.poisson(mu)
    x = np.arange(poisson_dis.ppf(0.001), poisson_dis.ppf(0.999))
    print(x)
    fig, ax = plt.subplots(1, 1)
    ax.plot(x, poisson_dis.pmf(x), 'bo', ms=8, label='poisson pmf')
    ax.vlines(x, 0, poisson_dis.pmf(x), colors='b', lw=5, alpha=0.5)
    ax.legend(loc='best', frameon=False)
    plt.ylabel('Probability')
    plt.title('PMF of poisson distribution(mu={})'.format(mu))
    plt.show()

# poisson_pmf(mu=8) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def custom_made_discrete_dis_pmf():
    """
    https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_discrete.html
    :return:
    """
    xk = np.arange(7)  # 所有可能的取值
    print(xk)  # [0 1 2 3 4 5 6]
    pk = (0.1, 0.2, 0.3, 0.1, 0.1, 0.0, 0.2)  # 各个取值的概率
    custm = stats.rv_discrete(name='custm', values=(xk, pk))

    X = custm.rvs(size=20)
    print(X)

    fig, ax = plt.subplots(1, 1)
    ax.plot(xk, custm.pmf(xk), 'ro', ms=8, mec='r')
    ax.vlines(xk, 0, custm.pmf(xk), colors='r', linestyles='-', lw=2)
    plt.title('Custom made discrete distribution(PMF)')
    plt.ylabel('Probability')
    plt.show()

# custom_made_discrete_dis_pmf() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def plot_vertical_line(x, y, binEdges, linestyles="dashed", colors="k"):
    plt.vlines(x, 0, np.interp(x, binEdges, y), linestyles=linestyles, colors=colors) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import vlines [as 别名]
def Vlines(xs, y1, y2, **options):
    """Plots a set of vertical lines.

    Args:
      xs: sequence of x values
      y1: sequence of y values
      y2: sequence of y values
      options: keyword args passed to plt.vlines
    """
    options = _UnderrideColor(options)
    options = _Underride(options, linewidth=1, alpha=0.5)
    plt.vlines(xs, y1, y2, **options)