Python pyplot.axes方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def _show_plot(x_values, y_values, x_labels=None, y_labels=None):
    try:
        import matplotlib.pyplot as plt
    except ImportError:
        raise ImportError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

    plt.locator_params(axis='y', nbins=3)
    axes = plt.axes()
    axes.yaxis.grid()
    plt.plot(x_values, y_values, 'ro', color='red')
    plt.ylim(ymin=-1.2, ymax=1.2)
    plt.tight_layout(pad=5)
    if x_labels:
        plt.xticks(x_values, x_labels, rotation='vertical')
    if y_labels:
        plt.yticks([-1, 0, 1], y_labels, rotation='horizontal')
    # Pad margins so that markers are not clipped by the axes
    plt.margins(0.2)
    plt.show()

#////////////////////////////////////////////////////////////
#{ Parsing and conversion functions
#//////////////////////////////////////////////////////////// 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def analyseSex(firends):
    sexs = list(map(lambda x:x['Sex'],friends[1:]))
    counts = Counter(sexs).items()
    counts = sorted(counts, key=lambda x:x[0], reverse=False)
    counts = list(map(lambda x:x[1],counts))
    labels = ['Unknow','Male','Female']
    colors = ['red','yellowgreen','lightskyblue']
    plt.figure(figsize=(8,5), dpi=80)
    plt.axes(aspect=1) 
    plt.pie(counts, 
            labels=labels, 
            colors=colors, 
            labeldistance = 1.1, 
            autopct = '%3.1f%%',
            shadow = False, 
            startangle = 90, 
            pctdistance = 0.6 
    )
    plt.legend(loc='upper right',)
    plt.title(u'%s的微信好友性別組成' % friends[0]['NickName'])
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def _plot_gaussian(mean, covariance, color, zorder=0):
    """Plots the mean and 2-std ellipse of a given Gaussian"""
    plt.plot(mean[0], mean[1], color[0] + ".", zorder=zorder)

    if covariance.ndim == 1:
        covariance = np.diag(covariance)

    radius = np.sqrt(5.991)
    eigvals, eigvecs = np.linalg.eig(covariance)
    axis = np.sqrt(eigvals) * radius
    slope = eigvecs[1][0] / eigvecs[1][1]
    angle = 180.0 * np.arctan(slope) / np.pi

    plt.axes().add_artist(pat.Ellipse(
        mean, 2 * axis[0], 2 * axis[1], angle=angle,
        fill=False, color=color, linewidth=1, zorder=zorder
    )) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def plot_fitted_data(points, c_means, c_variances):
    """Plots the data and given Gaussian components"""
    plt.plot(points[:, 0], points[:, 1], "b.", zorder=0)
    plt.plot(c_means[:, 0], c_means[:, 1], "r.", zorder=1)

    for i in range(c_means.shape[0]):
        std = np.sqrt(c_variances[i])
        plt.axes().add_artist(pat.Ellipse(
            c_means[i], 2 * std[0], 2 * std[1],
            fill=False, color="red", linewidth=2, zorder=1
        ))

    plt.show()


# PREPARING DATA

# generating DATA_POINTS points from a GMM with COMPONENTS components 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def ts_anim():
    # create a simple animation
    fig = plt.figure()
    ax = plt.axes(xlim=(0, 100), ylim=(-1, 1))
    Color = [ 1 ,0.498039, 0.313725];
    line, = ax.plot([], [], '*',color = Color)
    plt.xlabel("Time")
    plt.ylabel("Measurement")

    def init():
        line.set_data([], [])
        return line,

    def animate(i):
        x = np.linspace(0, i+1, i+1)
        ts = 5*np.cos(x * 0.02 * np.pi) * np.sin(np.cos(x)  * 0.02 * np.pi)
        line.set_data(x, ts)
        return line,

    return animation.FuncAnimation(fig, animate, init_func=init,
                                   frames=100, interval=200, blit=True) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def basic_animation(frames=100, interval=30):
    """Plot a basic sine wave with oscillating amplitude"""
    fig = plt.figure()
    ax = plt.axes(xlim=(0, 10), ylim=(-2, 2))
    line, = ax.plot([], [], lw=2)

    x = np.linspace(0, 10, 1000)

    def init():
        line.set_data([], [])
        return line,

    def animate(i):
        y = np.cos(i * 0.02 * np.pi) * np.sin(x - i * 0.02 * np.pi)
        line.set_data(x, y)
        return line,

    return animation.FuncAnimation(fig, animate, init_func=init,
                                   frames=frames, interval=interval) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def plot_DOY(dates, y, mpl_cmap):
    """ Create a DOY plot

    Args:
        dates (iterable): sequence of datetime
        y (np.ndarray): variable to plot
        mpl_cmap (colormap): matplotlib colormap
    """
    doy = np.array([d.timetuple().tm_yday for d in dates])
    year = np.array([d.year for d in dates])

    sp = plt.scatter(doy, y, c=year, cmap=mpl_cmap,
                     marker='o', edgecolors='none', s=35)
    plt.colorbar(sp)

    months = mpl.dates.MonthLocator()  # every month
    months_fmrt = mpl.dates.DateFormatter('%b')

    plt.tick_params(axis='x', which='minor', direction='in', pad=-10)
    plt.axes().xaxis.set_minor_locator(months)
    plt.axes().xaxis.set_minor_formatter(months_fmrt)

    plt.xlim(1, 366)
    plt.xlabel('Day of Year') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def _plot_spectra(outpath, typ, savefig=True):
    spec = alf.io.load_object(outpath, '_iblqc_ephysSpectralDensity' + typ.upper())

    sns.set_style("whitegrid")
    plt.figure(figsize=[9, 4.5])
    ax = plt.axes()
    ax.plot(spec['freqs'], 20 * np.log10(spec['power'] + 1e-14),
            linewidth=0.5, color=[0.5, 0.5, 0.5])
    ax.plot(spec['freqs'], 20 * np.log10(np.median(spec['power'] + 1e-14, axis=1)), label='median')
    ax.set_xlabel(r'Frequency (Hz)')
    ax.set_ylabel(r'dB rel to $V^2.$Hz$^{-1}$')
    if typ == 'ap':
        ax.set_ylim([-275, -125])
    elif typ == 'lf':
        ax.set_ylim([-260, -60])
    ax.legend()
    if savefig:
        plt.savefig(outpath / (typ + '_spec.png'), dpi=150) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def _plot_rmsmap(outfil, typ, savefig=True):
    rmsmap = alf.io.load_object(outpath, '_iblqc_ephysTimeRms' + typ.upper())
    plt.figure(figsize=[12, 4.5])
    axim = plt.axes([0.2, 0.1, 0.7, 0.8])
    axrms = plt.axes([0.05, 0.1, 0.15, 0.8])
    axcb = plt.axes([0.92, 0.1, 0.02, 0.8])

    axrms.plot(np.median(rmsmap['rms'], axis=0)[:-1] * 1e6, np.arange(1, rmsmap['rms'].shape[1]))
    axrms.set_ylim(0, rmsmap['rms'].shape[1])

    im = axim.imshow(20 * np.log10(rmsmap['rms'].T + 1e-15), aspect='auto', origin='lower',
                     extent=[rmsmap['timestamps'][0], rmsmap['timestamps'][-1],
                             0, rmsmap['rms'].shape[1]])
    axim.set_xlabel(r'Time (s)')
    axim.set_ylabel(r'Channel Number')
    plt.colorbar(im, cax=axcb)
    if typ == 'ap':
        im.set_clim(-110, -90)
        axrms.set_xlim(100, 0)
    elif typ == 'lf':
        im.set_clim(-100, -60)
        axrms.set_xlim(500, 0)
    axim.set_xlim(0, 4000)
    if savefig:
        plt.savefig(outpath / (typ + '_rms.png'), dpi=150) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def blank_axes(ax):
    """
    blank_axes:  blank the extraneous spines and tick marks for an axes

    Input:
    ax:  a matplotlib Axes object

    Output: None
    """

    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
    ax.tick_params(labelbottom='off', labeltop='off', labelleft='off', labelright='off', \
                   bottom='off', top='off', left='off', right='off')


# end blank_axes

#######################################################
# MAIN
####################################################### 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def vis(embed, vis_alg='PCA', pool_alg='REDUCE_MEAN'):
    plt.close()
    fig = plt.figure()
    plt.rcParams['figure.figsize'] = [21, 7]
    for idx, ebd in enumerate(embed):
        ax = plt.subplot(2, 6, idx + 1)
        vis_x = ebd[:, 0]
        vis_y = ebd[:, 1]
        plt.scatter(vis_x, vis_y, c=subset_label, cmap=ListedColormap(["blue", "green", "yellow", "red"]), marker='.',
                    alpha=0.7, s=2)
        ax.set_title('pool_layer=-%d' % (idx + 1))
    plt.tight_layout()
    plt.subplots_adjust(bottom=0.1, right=0.95, top=0.9)
    cax = plt.axes([0.96, 0.1, 0.01, 0.3])
    cbar = plt.colorbar(cax=cax, ticks=range(num_label))
    cbar.ax.get_yaxis().set_ticks([])
    for j, lab in enumerate(['ent.', 'bus.', 'sci.', 'heal.']):
        cbar.ax.text(.5, (2 * j + 1) / 8.0, lab, ha='center', va='center', rotation=270)
    fig.suptitle('%s visualization of BERT layers using "bert-as-service" (-pool_strategy=%s)' % (vis_alg, pool_alg),
                 fontsize=14)
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def plot_dterms(self, sites='all', label=None, legend=True, clist=ehc.SCOLORS,
                    rangex=False, rangey=False, markersize=2 * ehc.MARKERSIZE,
                    show=True, grid=True, export_pdf=""):

        # sites
        if sites in ['all' or 'All'] or sites == []:
            sites = list(self.data.keys())

        if not isinstance(sites, list):
            sites = [sites]

        keys = [self.tkey[site] for site in sites]

        axes = plot_tarr_dterms(self.tarr, keys=keys, label=label, legend=legend, clist=clist,
                                rangex=rangex, rangey=rangey, markersize=markersize,
                                show=show, grid=grid, export_pdf=export_pdf)

        return axes 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def plot_convergence(self, filename=None):
        yy = self.iter_values
        xx = range(len(yy))
        import matplotlib.pyplot as plt
        # Plot
        plt.ioff()
        fig = plt.figure()
        fig.set_size_inches(18.5, 10.5)
        font = {'size': 28}
        plt.title('Value over # evaluations')
        plt.xlabel('X', fontdict=font)
        plt.ylabel('Y', fontdict=font)
        plt.plot(xx, yy)
        plt.axes().set_yscale('log')
        if filename is None:
            filename = 'plots/iter.png'
        plt.savefig(filename, bbox_inches='tight')
        plt.close(fig)
        print('plotting convergence OK.. ' + filename) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def draw_chessboard_model(marker_size=marker_size):
    gird_coords = generate_grid_coords(x_res=marker_size[0], y_res=marker_size[1])
    grid_ls = [(p[0]).flatten()[:2] for p in gird_coords]
    corner_arr = np.transpose(np.array(grid_ls).reshape(marker_size[0], marker_size[1], 2)[1:, 1:], (1, 0, 2))
    c = np.zeros([corner_arr.shape[0], corner_arr.shape[1], 3]).reshape(
        corner_arr.shape[0] * corner_arr.shape[1], 3).astype(np.float32)
    c[0] = np.array([0, 0, 1])
    c[-1] = np.array([1, 0, 0])
    s = np.zeros(corner_arr[:, :, 0].flatten().shape[0]) + 20
    s[0] = 60
    s[-1] = 60

    plt.scatter(corner_arr[:, :, 0].flatten(), corner_arr[:, :, 1].flatten(), c=c, s=s)

    plt.plot(corner_arr[:, :, 0].flatten(), corner_arr[:, :, 1].flatten())

    plt.xlim(corner_arr[:, :, 0].min(), corner_arr[:, :, 0].max())
    plt.ylim(corner_arr[:, :, 1].min(), corner_arr[:, :, 1].max())
    plt.xlabel("x coordinates [cm]")
    plt.ylabel("y coordinates [cm]")
    # plt.axes().set_aspect('equal', 'datalim')
    plt.axis('equal')
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import axes [as 別名]
def view_polygons(polygons):
    """Display a collection of polygons for inspection.

    :param polygons: A dict keyed by colour, containing Polygon3D objects to show in that colour.
    """
    # create the figure and add the surfaces
    plt.figure()
    ax = plt.axes(projection="3d")

    collections = _make_collections(polygons, opacity=0.5)

    for c in collections:
        ax.add_collection3d(c)

    # calculate and set the axis limits
    limits = _get_limits(polygons=polygons)
    ax.set_xlim(limits["x"])
    ax.set_ylim(limits["y"])
    ax.set_zlim(limits["z"])

    plt.show()