Python pylab.gca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 gca [as 别名]
def plot_feat_importance(feature_names, clf, name):
    pylab.clf()
    coef_ = clf.coef_
    important = np.argsort(np.absolute(coef_.ravel()))
    f_imp = feature_names[important]
    coef = coef_.ravel()[important]
    inds = np.argsort(coef)
    f_imp = f_imp[inds]
    coef = coef[inds]
    xpos = np.array(range(len(coef)))
    pylab.bar(xpos, coef, width=1)

    pylab.title('Feature importance for %s' % (name))
    ax = pylab.gca()
    ax.set_xticks(np.arange(len(coef)))
    labels = ax.set_xticklabels(f_imp)
    for label in labels:
        label.set_rotation(90)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(
        CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight") 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def plot_feat_importance(feature_names, clf, name):
    pylab.figure(num=None, figsize=(6, 5))
    coef_ = clf.coef_
    important = np.argsort(np.absolute(coef_.ravel()))
    f_imp = feature_names[important]
    coef = coef_.ravel()[important]
    inds = np.argsort(coef)
    f_imp = f_imp[inds]
    coef = coef[inds]
    xpos = np.array(list(range(len(coef))))
    pylab.bar(xpos, coef, width=1)

    pylab.title('Feature importance for %s' % (name))
    ax = pylab.gca()
    ax.set_xticks(np.arange(len(coef)))
    labels = ax.set_xticklabels(f_imp)
    for label in labels:
        label.set_rotation(90)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(
        CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight") 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def format_plot(X, epoch=None, title=None, figsize=(15, 10)):

    plt.figure(figsize=figsize)

    if X.shape[-1] == 1:
        plt.imshow(X[:, :, 0], cmap="gray")
    else:
        plt.imshow(X)

    plt.axis("off")
    plt.gca().xaxis.set_major_locator(mp.ticker.NullLocator())
    plt.gca().yaxis.set_major_locator(mp.ticker.NullLocator())

    if epoch is not None and title is None:
        save_path = os.path.join(FLAGS.fig_dir, "current_batch_%s.png" % epoch)
    elif epoch is not None and title is not None:
        save_path = os.path.join(FLAGS.fig_dir, "%s_%s.png" % (title, epoch))
    elif title is not None:
        save_path = os.path.join(FLAGS.fig_dir, "%s.png" % title)
    plt.savefig(save_path, bbox_inches='tight', pad_inches=0)
    plt.clf()
    plt.close() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def plot_correlations(corrs, errors=None, ax=None, **plot_kwargs):
    """
    Correlation vs CCA dimension

    :param corrs: correlation values for the CCA dimensions
    :type corrs: 1-D vector
    :param errors: error values
    :type shuffled: 1-D array of size len(corrs)
    :param ax: axis to plot on (default None)
    :type ax: matplotlib axis object
    :return: axis if specified, or plot if axis = None
    """
    # evaluate if np.arrays are passed
    assert type(corrs) is np.ndarray, "'corrs' is not a numpy array."
    if errors is not None:
        assert type(errors) is np.ndarray, "'errors' is not a numpy array."
    # create axis if no axis is passed
    if ax is None:
        ax = plt.gca()
    # get the data for the x and y axis
    y_data = corrs
    x_data = range(1, (len(corrs) + 1))
    # create the plot object
    ax.plot(x_data, y_data, **plot_kwargs)
    if errors is not None:
        ax.fill_between(x_data, y_data - errors, y_data + errors, **plot_kwargs, alpha=0.2)
    # change y and x labels and ticks
    ax.set_xticks(x_data)
    ax.set_ylabel("Correlation")
    ax.set_xlabel("CCA dimension")
    return ax 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def plot_dt(tri, colors=None):
    import matplotlib as mpl
    from matplotlib import pylab as pl
    if colors is None:
        colors = [(0, 0, 0, 0.2)]
    lc = mpl.collections.LineCollection(
        np.array([((tri.x[i], tri.y[i]), (tri.x[j], tri.y[j]))
                  for i, j in tri.edge_db]),
        colors=colors)
    ax = pl.gca()
    ax.add_collection(lc)
    pl.draw_if_interactive() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def plot_vo(tri, colors=None):
    import matplotlib as mpl
    from matplotlib import pylab as pl
    if colors is None:
        colors = [(0, 1, 0, 0.2)]
    lc = mpl.collections.LineCollection(np.array(
        [(tri.circumcenters[i], tri.circumcenters[j])
         for i in xrange(len(tri.circumcenters))
         for j in tri.triangle_neighbors[i] if j != -1]),
        colors=colors)
    ax = pl.gca()
    ax.add_collection(lc)
    pl.draw_if_interactive() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def plot_cc(tri, edgecolor=None):
    import matplotlib as mpl
    from matplotlib import pylab as pl
    if edgecolor is None:
        edgecolor = (0, 0, 1, 0.2)
    dxy = (np.array([(tri.x[i], tri.y[i]) for i, j, k in tri.triangle_nodes])
        - tri.circumcenters)
    r = np.hypot(dxy[:, 0], dxy[:, 1])
    ax = pl.gca()
    for i in xrange(len(r)):
        p = mpl.patches.Circle(tri.circumcenters[i], r[i],
                               resolution=100, edgecolor=edgecolor,
                               facecolor=(1, 1, 1, 0), linewidth=0.2)
        ax.add_patch(p)
    pl.draw_if_interactive() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def plot1D_mat(a, b, M, title=''):
    """ Plot matrix M  with the source and target 1D distribution

    Creates a subplot with the source distribution a on the left and
    target distribution b on the tot. The matrix M is shown in between.


    Parameters
    ----------
    a : ndarray, shape (na,)
        Source distribution
    b : ndarray, shape (nb,)
        Target distribution
    M : ndarray, shape (na, nb)
        Matrix to plot
    """
    na, nb = M.shape

    gs = gridspec.GridSpec(3, 3)

    xa = np.arange(na)
    xb = np.arange(nb)

    ax1 = pl.subplot(gs[0, 1:])
    pl.plot(xb, b, 'r', label='Target distribution')
    pl.yticks(())
    pl.title(title)

    ax2 = pl.subplot(gs[1:, 0])
    pl.plot(a, xa, 'b', label='Source distribution')
    pl.gca().invert_xaxis()
    pl.gca().invert_yaxis()
    pl.xticks(())

    pl.subplot(gs[1:, 1:], sharex=ax1, sharey=ax2)
    pl.imshow(M, interpolation='nearest')
    pl.axis('off')

    pl.xlim((0, nb))
    pl.tight_layout()
    pl.subplots_adjust(wspace=0., hspace=0.2) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def plot_learning_curves(self, hyperparameter):
        if hyperparameter == "TV":
            C = self.C_tv_history
        elif hyperparameter == "Group L1":
            C = self.C_group_l1_history
        else:
            raise ValueError("hyperparameter value should be either `TV` or"
                             " `Group L1`")
        x = np.log10(C)
        order = np.argsort(x)
        m = np.array(self.kfold_mean_train_scores)[order]
        sd = np.array(self.kfold_sd_train_scores)[order]
        fig = plt.figure()
        ax = plt.gca()
        p1 = ax.plot(x[order], m)
        p2 = ax.fill_between(x[order], m - sd, m + sd, alpha=.3)
        min_point_train = np.min(m - sd)
        m = np.array(self.kfold_mean_test_scores)[order]
        sd = np.array(self.kfold_sd_test_scores)[order]
        p3 = ax.plot(x[order], m)
        p4 = ax.fill_between(x[order], m - sd, m + sd, alpha=.3)
        min_point_test = np.min(m - sd)
        min_point = min(min_point_train, min_point_test)
        p5 = plt.scatter(np.log10(C), min_point * np.ones_like(C))

        ax.legend([(p1[0], p2), (p3[0], p4), p5],
                  ['train score', 'test score', 'tested hyperparameters'],
                  loc='lower right')
        ax.set_title('Learning curves')
        ax.set_xlabel('C %s (log scale)' % hyperparameter)
        ax.set_ylabel('Loss')
        return fig, ax 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def remove_values_along_axes():
    from matplotlib import pylab
    frame = pylab.gca()
    frame.axes.get_xaxis().set_ticks([])
    frame.axes.get_yaxis().set_ticks([]) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def newline(p1, p2, color):
    import matplotlib.pyplot as plt
    import matplotlib.lines as mlines
    ax = plt.gca()
    x_min = p1[0]
    x_max = p1[1]
    y_min = p2[0]
    y_max = p2[1]
    logging.info('{} {}'.format([x_min, x_max], [y_min, y_max]))
    l = mlines.Line2D([x_min, x_max], [y_min, y_max], color=color, linestyle='dashdot', linewidth=3)
    ax.add_line(l)
    return l


# plt.ion() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def plot_na(x,y,mode='stem'):
    pylab.figure(figsize=(5,2))
    frame1 = pylab.gca()
    if mode.lower() == 'stem':
         pylab.stem(x,y)
    else:
        pylab.plot(x,y)
    
    frame1.axes.get_xaxis().set_visible(False)
    frame1.axes.get_yaxis().set_visible(False) 
    pylab.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def remove_top_right_on_plot(ax=None):
    if ax==None:
        ax = plt.gca()
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def drawpoints(points, xmin=None, ymin=None, xmax=None, ymax=None):
    skips = np.hstack((np.array([0]),points[:,2]))
    xys = np.vstack((np.zeros((1,2)),np.cumsum(points[:,:2],axis=0)))
    xys[:,:2] -= xys[:,:2].mean(axis=0)
#     xys = points[:,:2]

    xs=[]
    ys=[]
    x=[]
    y=[]
    for xy,s in zip(xys, skips):
        if s:
            if len(x) > 1:
                xs.append(x)
                ys.append(y)
            x=[]
            y=[]
        else:
            x.append(xy[0])
            y.append(xy[1])

    for x,y in zip(xs, ys):
        pl.plot(x, y, 'k-')

    if xmin is None:
        xmin,ymin = xys.min(axis=0)
        xmax,ymax = xys.max(axis=0)
    ax = pl.gca()
    ax.set_xlim(xmin, xmax)
    ax.set_ylim(ymin, ymax)
    ax.invert_yaxis()
    ax.set_xticks([])
    ax.set_yticks([]) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import gca [as 别名]
def clean_ticks():
    ax = plt.gca()
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')