# Path: examples/context.py



import matplotlib
import numpy as np
import scipy.stats
import matplotlib.pylab as plt
import pdb
from .context import aep, ep
    if control:
        plt.subplot(no_panes, 1, no_panes)
        plt.plot(t, x_control, '-', color='m')
        plt.ylabel('I')
        plt.yticks([])
    plt.xlabel('t')

    plt.savefig(fig_fname)

    # if control:
    # 	plot_model_with_control(model_aep, '', '_gp_with_control')
    # else:
    # 	plot_model_no_control(model_aep, '', '_gp_no_control')


def plot_prediction_gp(params_fname, fig_fname, M=20):
    # load dataset
    data = np.loadtxt('./sandbox/hh_data.txt')
    # use the voltage and potasisum current
    data = data / np.std(data, axis=0)
    y = data[:, :4]
    xc = data[:, [-1]]
    # init hypers
    Dlatent = 2
    Dobs = y.shape[1]
    T = y.shape[0]
    x_control = xc
    # x_control_test = np.flipud(x_control)
    x_control_test = x_control * 1.5
    no_panes = 5

# Path: examples/context.py



# Path: examples/gpssm_hodgkin_huxley.py
import matplotlib
import numpy as np
import scipy.stats
import matplotlib.pylab as plt
import pdb
from .context import aep, ep

    if control:
        plt.subplot(no_panes, 1, no_panes)
        plt.plot(t, x_control, '-', color='m')
        plt.ylabel('I')
        plt.yticks([])
    plt.xlabel('t')

    plt.savefig(fig_fname)

    # if control:
    # 	plot_model_with_control(model_aep, '', '_gp_with_control')
    # else:
    # 	plot_model_no_control(model_aep, '', '_gp_no_control')


def plot_prediction_gp(params_fname, fig_fname, M=20):
    # load dataset
    data = np.loadtxt('./sandbox/hh_data.txt')
    # use the voltage and potasisum current
    data = data / np.std(data, axis=0)
    y = data[:, :4]
    xc = data[:, [-1]]
    # init hypers
    Dlatent = 2
    Dobs = y.shape[1]
    T = y.shape[0]
    x_control = xc
    # x_control_test = np.flipud(x_control)
    x_control_test = x_control * 1.5
    no_panes = 5

    model_aep = aep.SGPSSM_GP(y, Dlatent, M,