Python pylab.hist方法代码示例

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def compute_class_prior(do_plot=False):
    categories_folder = 'data/instance-level_human_parsing/Training/Category_ids'
    names = [f for f in os.listdir(categories_folder) if f.lower().endswith('.png')]
    num_samples = len(names)
    prior_prob = np.zeros(num_classes)
    pb = ProgressBar(total=num_samples, prefix='Compute class prior', suffix='', decimals=3, length=50, fill='=')
    for i in range(num_samples):
        name = names[i]
        filename = os.path.join(categories_folder, name)
        category = np.ravel(cv.imread(filename, 0))
        counts = np.bincount(category)
        idxs = np.nonzero(counts)[0]
        prior_prob[idxs] += counts[idxs]
        pb.print_progress_bar(i + 1)

    prior_prob = prior_prob / (1.0 * np.sum(prior_prob))

    # Save
    np.save(os.path.join(data_dir, "prior_prob.npy"), prior_prob)

    if do_plot:
        plt.hist(prior_prob, bins=100)
        plt.yscale("log")
        plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def plotkde(covfact):
    gkde.reset_covfact(covfact)
    kdepdf = gkde.evaluate(ind)
    plt.figure()
    # plot histgram of sample
    plt.hist(xn, bins=20, normed=1)
    # plot estimated density
    plt.plot(ind, kdepdf, label='kde', color="g")
    # plot data generating density
    plt.plot(ind, alpha * stats.norm.pdf(ind, loc=mlow) +
                  (1-alpha) * stats.norm.pdf(ind, loc=mhigh),
                  color="r", label='DGP: normal mix')
    plt.title('Kernel Density Estimation - ' + str(gkde.covfact))
    plt.legend() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def plot_feat_hist(data_name_list, filename=None):
    pylab.clf()
    num_rows = 1 + (len(data_name_list) - 1) / 2
    num_cols = 1 if len(data_name_list) == 1 else 2
    pylab.figure(figsize=(5 * num_cols, 4 * num_rows))

    for i in range(num_rows):
        for j in range(num_cols):
            pylab.subplot(num_rows, num_cols, 1 + i * num_cols + j)
            x, name = data_name_list[i * num_cols + j]
            pylab.title(name)
            pylab.xlabel('Value')
            pylab.ylabel('Density')
            # the histogram of the data
            max_val = np.max(x)
            if max_val <= 1.0:
                bins = 50
            elif max_val > 50:
                bins = 50
            else:
                bins = max_val
            n, bins, patches = pylab.hist(
                x, bins=bins, normed=1, facecolor='green', alpha=0.75)

            pylab.grid(True)

    if not filename:
        filename = "feat_hist_%s.png" % name

    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight") 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def plot_feat_hist(data_name_list, filename=None):
    if len(data_name_list) > 1:
        assert filename is not None

    pylab.figure(num=None, figsize=(8, 6))
    num_rows = int(1 + (len(data_name_list) - 1) / 2)
    num_cols = int(1 if len(data_name_list) == 1 else 2)
    pylab.figure(figsize=(5 * num_cols, 4 * num_rows))

    for i in range(num_rows):
        for j in range(num_cols):
            pylab.subplot(num_rows, num_cols, 1 + i * num_cols + j)
            x, name = data_name_list[i * num_cols + j]
            pylab.title(name)
            pylab.xlabel('Value')
            pylab.ylabel('Fraction')
            # the histogram of the data
            max_val = np.max(x)
            if max_val <= 1.0:
                bins = 50
            elif max_val > 50:
                bins = 50
            else:
                bins = max_val
            n, bins, patches = pylab.hist(
                x, bins=bins, normed=1, alpha=0.75)

            pylab.grid(True)

    if not filename:
        filename = "feat_hist_%s.png" % name.replace(" ", "_")

    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight") 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def plotkde(covfact):
    gkde.reset_covfact(covfact)
    kdepdf = gkde.evaluate(ind)
    plt.figure()
    # plot histgram of sample
    plt.hist(xn, bins=20, normed=1)
    # plot estimated density
    plt.plot(ind, kdepdf, label='kde', color="g")
    # plot data generating density
    plt.plot(ind, alpha * stats.norm.pdf(ind, loc=mlow) + 
                  (1-alpha) * stats.norm.pdf(ind, loc=mhigh),
                  color="r", label='DGP: normal mix')
    plt.title('Kernel Density Estimation - ' + str(gkde.covfact))
    plt.legend() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def histogram(vals, variable, n, outFile):
    figFile = os.path.splitext(outFile)[0] + '_hist.png'
    M.clf()
    #    M.hist(vals, 47, (-2., 45.))
    M.hist(vals, 94)
    M.xlim(-5, 45)
    M.xlabel('SST in degrees Celsius')
    M.ylim(0, 300000)
    M.ylabel('Count')
    M.title('Histogram of %s %d-day Mean from %s' % (variable.upper(), n, outFile))
    M.show()
    print >> sys.stderr, 'Writing histogram plot to %s' % figFile
    M.savefig(figFile)
    return figFile 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def hist(x, bins, outFile=None, **options):
    if outFile: M.clf()
    M.hist(x, bins, **options)
    if outFile: M.savefig(outFile, **validCmdOptions(options, 'savefig')) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def histogram(vals, variable, n, outFile):
    figFile = os.path.splitext(outFile)[0] + '_hist.png'
    M.clf()
#    M.hist(vals, 47, (-2., 45.))
    M.hist(vals, 94)
    M.xlim(-5, 45)
    M.xlabel('SST in degrees Celsius')
    M.ylim(0, 300000)
    M.ylabel('Count')
    M.title('Histogram of %s %d-day Mean from %s' % (variable.upper(), n, outFile))
    M.show()
    print >>sys.stderr, 'Writing histogram plot to %s' % figFile
    M.savefig(figFile)
    return figFile 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def plot_null_distribution(stats, stats_null, p_value, data_name='',
                           stats_name='$MMD^2_u$', save_figure=True):
    """Plot the observed value for the test statistic, its null
    distribution and p-value.
    """
    fig = plt.figure()
    ax = fig.add_subplot(111)
    prob, bins, patches = plt.hist(stats_null, bins=50, normed=True)
    ax.plot(stats, prob.max()/30, 'w*', markersize=15,
            markeredgecolor='k', markeredgewidth=2,
            label="%s = %s" % (stats_name, stats))

    ax.annotate('p-value: %s' % (p_value),
                xy=(float(stats), prob.max()/9.),  xycoords='data',
                xytext=(-105, 30), textcoords='offset points',
                bbox=dict(boxstyle="round", fc="1."),
                arrowprops={"arrowstyle": "->",
                            "connectionstyle": "angle,angleA=0,angleB=90,rad=10"},
                )
    plt.xlabel(stats_name)
    plt.ylabel('p(%s)' % stats_name)
    plt.legend(numpoints=1)
    plt.title('Data: %s' % data_name)

    if save_figure:
        save_dir = 'figures'
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)

        stn = 'ktst' if stats_name == '$MMD^2_u$' else 'clf'
        fig_name = os.path.join(save_dir, '%s_%s.pdf' % (data_name, stn))
        fig.savefig(fig_name) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import hist [as 别名]
def compute_color_prior(size=64, do_plot=False):

    # Load the gamut points location
    q_ab = np.load(os.path.join(data_dir, "pts_in_hull.npy"))

    if do_plot:
        plt.figure(figsize=(15, 15))
        gs = gridspec.GridSpec(1, 1)
        ax = plt.subplot(gs[0])
        for i in range(q_ab.shape[0]):
            ax.scatter(q_ab[:, 0], q_ab[:, 1])
            ax.annotate(str(i), (q_ab[i, 0], q_ab[i, 1]), fontsize=6)
            ax.set_xlim([-110,110])
            ax.set_ylim([-110,110])

    with h5py.File(os.path.join(data_dir, "CelebA_%s_data.h5" % size), "a") as hf:
        # Compute the color prior over a subset of the training set
        # Otherwise it is quite long
        X_ab = hf["training_lab_data"][:100000][:, 1:, :, :]
        npts, c, h, w = X_ab.shape
        X_a = np.ravel(X_ab[:, 0, :, :])
        X_b = np.ravel(X_ab[:, 1, :, :])
        X_ab = np.vstack((X_a, X_b)).T

        if do_plot:
            plt.hist2d(X_ab[:, 0], X_ab[:, 1], bins=100, norm=LogNorm())
            plt.xlim([-110, 110])
            plt.ylim([-110, 110])
            plt.colorbar()
            plt.show()
            plt.clf()
            plt.close()

        # Create nearest neighbord instance with index = q_ab
        NN = 1
        nearest = nn.NearestNeighbors(n_neighbors=NN, algorithm='ball_tree').fit(q_ab)
        # Find index of nearest neighbor for X_ab
        dists, ind = nearest.kneighbors(X_ab)

        # We now count the number of occurrences of each color
        ind = np.ravel(ind)
        counts = np.bincount(ind)
        idxs = np.nonzero(counts)[0]
        prior_prob = np.zeros((q_ab.shape[0]))
        for i in range(q_ab.shape[0]):
            prior_prob[idxs] = counts[idxs]

        # We turn this into a color probability
        prior_prob = prior_prob / (1.0 * np.sum(prior_prob))

        # Save
        np.save(os.path.join(data_dir, "CelebA_%s_prior_prob.npy" % size), prior_prob)

        if do_plot:
            plt.hist(prior_prob, bins=100)
            plt.yscale("log")
            plt.show()