# 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
            idxs = np.arange(inter[0], inter[1])
            for i in range(inter[0], inter[1] - 1):
                plt.plot(mx[i:i + 2, 0], mx[i:i + 2, 1],
                         '-o', color=colors[k], ms=3, linewidth=1, zorder=101)
        # plt.xlabel(r'$x_{t, 1}$')
        # plt.ylabel(r'$x_{t, 2}$')
        plt.xlim([mins[0], maxs[0]])
        plt.ylim([mins[1], maxs[1]])
        plt.title(plot_title)
        plt.savefig('/tmp/hh_gpssm_dim_1' +
                    name_suffix + '_c_%.2f.pdf' % c_val)


def plot_posterior_linear(params_fname, fig_fname, control=False, 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]
    if control:
        x_control = xc
        no_panes = 5
    else:
        x_control = None
        no_panes = 4

# 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

            idxs = np.arange(inter[0], inter[1])
            for i in range(inter[0], inter[1] - 1):
                plt.plot(mx[i:i + 2, 0], mx[i:i + 2, 1],
                         '-o', color=colors[k], ms=3, linewidth=1, zorder=101)
        # plt.xlabel(r'$x_{t, 1}$')
        # plt.ylabel(r'$x_{t, 2}$')
        plt.xlim([mins[0], maxs[0]])
        plt.ylim([mins[1], maxs[1]])
        plt.title(plot_title)
        plt.savefig('/tmp/hh_gpssm_dim_1' +
                    name_suffix + '_c_%.2f.pdf' % c_val)


def plot_posterior_linear(params_fname, fig_fname, control=False, 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]
    if control:
        x_control = xc
        no_panes = 5
    else:
        x_control = None
        no_panes = 4

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