Python pylab.suptitle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 suptitle [as 别名]
def plot_valdata(x_val_cuda, knobs_val_cuda, y_val_cuda, y_val_hat_cuda, effect, \
	epoch, loss_val, file_prefix='val_data', num_plots=50, target_size=None):

	x_size = len(x_val_cuda.data.cpu().numpy()[0])
	if target_size is None:
		y_size = len(y_val_cuda.data.cpu().numpy()[0])
	else:
		y_size = target_size
	t_small = range(x_size-y_size, x_size)
	for plot_i in range(0, num_plots):
		x_val = x_val_cuda.data.cpu().numpy()
		knobs_w = effect.knobs_wc( knobs_val_cuda.data.cpu().numpy()[plot_i,:] )
		plt.figure(plot_i,figsize=(6,8))
		titlestr = f'{effect.name} Val data, epoch {epoch+1}, loss_val = {loss_val.item():.3e}\n'
		for i in range(len(effect.knob_names)):
		    titlestr += f'{effect.knob_names[i]} = {knobs_w[i]:.2f}'
		    if i < len(effect.knob_names)-1: titlestr += ', '
		plt.suptitle(titlestr)
		plt.subplot(3, 1, 1)
		plt.plot(x_val[plot_i, :], 'b', label='Input')
		plt.ylim(-1,1)
		plt.xlim(0,x_size)
		plt.legend()
		plt.subplot(3, 1, 2)
		y_val = y_val_cuda.data.cpu().numpy()
		plt.plot(t_small, y_val[plot_i, -y_size:], 'r', label='Target')
		plt.xlim(0,x_size)
		plt.ylim(-1,1)
		plt.legend()
		plt.subplot(3, 1, 3)
		plt.plot(t_small, y_val[plot_i, -y_size:], 'r', label='Target')
		y_val_hat = y_val_hat_cuda.data.cpu().numpy()
		plt.plot(t_small, y_val_hat[plot_i, -y_size:], c=(0,0.5,0,0.85), label='Predicted')
		plt.ylim(-1,1)
		plt.xlim(0,x_size)
		plt.legend()
		filename = file_prefix + '_' + str(plot_i) + '.png'
		savefig(filename)
	return 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import suptitle [as 别名]
def plot_intensities(self, figsize=(10, 6), sharex=False, sharey=False):
        """Plot intensities estimated by the penalized model. The intensities
        subfigures are plotted on two columns.

        Parameters
        ----------
        figsize : `tuple`, default=(10, 6)
        Size of the figure

        sharex : `bool`, default=False
        Constrain the x axes to have the same range.

        sharey : `bool`, default=False
        Constrain the y axes to have the same range.

        Returns
        -------
        fig : `matplotlib.figure.Figure`
        Figure to be plotted

        axarr : `numpy.ndarray`, `dtype=object`
        `matplotlib.axes._subplots.AxesSubplot` objects associated to each
        intensity subplot.
        """
        n_rows = int(np.ceil(self.n_features / 2))
        remove_last_plot = self.n_features % 2 != 0

        fig, axarr = plt.subplots(n_rows, 2, sharex=sharex, sharey=sharey,
                                  figsize=figsize)
        for i, c in enumerate(self.coeffs):
            self._plot_intensity(axarr[i // 2][i % 2], c, None, None)
        plt.suptitle('Estimated (penalized) relative risks')
        axarr[0][1].legend(loc='upper right')
        [ax[0].set_ylabel('Relative incidence') for ax in axarr]
        [ax.set_xlabel('Time after exposure start') for ax in axarr[-1]]
        if remove_last_plot:
            fig.delaxes(axarr[-1][-1])
        return fig, axarr 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import suptitle [as 别名]
def plot_tang(X, Y, Z, title, npts=None):

    fig = plt.figure()
    ax = fig.gca(projection='3d')

    # Plot the surface.
    surf = ax.plot_surface(X, Y, Z, cmap="viridis",
                           linewidth=0, antialiased=False)

    # Customize the z axis.
    ax.set_zlim(-100, 250)
    ax.zaxis.set_tick_params(pad=8)
    ax.zaxis.set_major_locator(LinearLocator(5))
    ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))

    # Add a color bar which maps values to colors.
    fig.colorbar(surf, shrink=0.5, aspect=5)

    if "teacher" in title:
        plt.suptitle("Teacher model")

    if "student" in title and "sobolev" not in title:
        assert (npts is not None)
        plt.suptitle("Student model %s training pts" % npts)

    if "sobolev" in title:
        assert (npts is not None)
        plt.suptitle("Student model %s training pts + Sobolev" % npts)

    else:
        plt.suptitle("Styblinski Tang function")

    plt.savefig(title) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import suptitle [as 别名]
def plot_confidence_intervals(self, figsize=(10, 6), sharex=False,
                                  sharey=False):
        """Plot intensities estimated by the penalized model. The intensities
        subfigures are plotted on two columns.

        Parameters
        ----------
        figsize : `tuple`, default=(10, 6)
        Size of the figure

        sharex : `bool`, default=False
        Constrain the x axes to have the same range.

        sharey : `bool`, default=False
        Constrain the y axes to have the same range.

        Returns
        -------
        fig : `matplotlib.figure.Figure`
        Figure to be plotted

        axarr : `numpy.ndarray`, `dtype=object`
        `matplotlib.axes._subplots.AxesSubplot` objects associated to each
        intensity subplot
        """
        n_rows = int(np.ceil(self.n_features / 2))
        remove_last_plot = (self.n_features % 2 != 0)

        fig, axarr = plt.subplots(n_rows, 2, sharex=sharex, sharey=sharey,
                                  figsize=figsize)
        ci = self.confidence_intervals
        coeffs = ci['refit_coeffs']
        lb = ci['lower_bound']
        ub = ci['upper_bound']
        for i, c in enumerate(coeffs):
            self._plot_intensity(axarr[i // 2][i % 2], c, lb[i], ub[i])
        plt.suptitle('Estimated relative risks with 95% confidence bands')
        axarr[0][1].legend(loc='best')
        [ax[0].set_ylabel('Relative incidence') for ax in axarr]
        [ax.set_xlabel('Time after exposure start') for ax in axarr[-1]]
        if remove_last_plot:
            fig.delaxes(axarr[-1][-1])
        return fig, axarr 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import suptitle [as 别名]
def plot_generated_toy_batch(X_real, generator_model, discriminator_model, noise_dim, gen_iter, noise_scale=0.5):

    # Generate images
    X_gen = sample_noise(noise_scale, 10000, noise_dim)
    X_gen = generator_model.predict(X_gen)

    # Get some toy data to plot KDE of real data
    data = load_toy(pts_per_mixture=200)
    x = data[:, 0]
    y = data[:, 1]
    xmin, xmax = -1.5, 1.5
    ymin, ymax = -1.5, 1.5

    # Peform the kernel density estimate
    xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
    positions = np.vstack([xx.ravel(), yy.ravel()])
    values = np.vstack([x, y])
    kernel = stats.gaussian_kde(values)
    f = np.reshape(kernel(positions).T, xx.shape)

    # Plot the contour
    fig = plt.figure(figsize=(10,10))
    plt.suptitle("Generator iteration %s" % gen_iter, fontweight="bold", fontsize=22)
    ax = fig.gca()
    ax.contourf(xx, yy, f, cmap='Blues', vmin=np.percentile(f,80), vmax=np.max(f), levels=np.linspace(0.25, 0.85, 30))

    # Also plot the contour of the discriminator
    delta = 0.025
    xmin, xmax = -1.5, 1.5
    ymin, ymax = -1.5, 1.5
    # Create mesh
    XX, YY = np.meshgrid(np.arange(xmin, xmax, delta), np.arange(ymin, ymax, delta))
    arr_pos = np.vstack((np.ravel(XX), np.ravel(YY))).T
    # Get Z = predictions
    ZZ = discriminator_model.predict(arr_pos)
    ZZ = ZZ.reshape(XX.shape)
    # Plot contour
    ax.contour(XX, YY, ZZ, cmap="Blues", levels=np.linspace(0.25, 0.85, 10))
    dy, dx = np.gradient(ZZ)
    # Add streamlines
    # plt.streamplot(XX, YY, dx, dy, linewidth=0.5, cmap="magma", density=1, arrowsize=1)
    # Scatter generated data
    plt.scatter(X_gen[:1000, 0], X_gen[:1000, 1], s=20, color="coral", marker="o")

    l_gen = plt.Line2D((0,1),(0,0), color='coral', marker='o', linestyle='', markersize=20)
    l_D = plt.Line2D((0,1),(0,0), color='steelblue', linewidth=3)
    l_real = plt.Rectangle((0, 0), 1, 1, fc="steelblue")

    # Create legend from custom artist/label lists
    # bbox_to_anchor = (0.4, 1)
    ax.legend([l_real, l_D, l_gen], ['Real data KDE', 'Discriminator contour',
                                     'Generated data'], fontsize=18, loc="upper left")
    ax.set_xlim(xmin, xmax)
    ax.set_ylim(ymin, ymax + 0.8)
    plt.savefig("../../figures/toy_dataset_iter%s.jpg" % gen_iter)
    plt.clf()
    plt.close()