# Source code for pyFTS.data.rossler

    """
    O. E. Rössler, Phys. Lett. 57A, 397 (1976).

    dx/dt = -z - y
    dy/dt = x + ay
    dz/dt = b + z( x - c )

    """

    import numpy as np
    import pandas as pd

    def get_data(var, a = 0.2, b = 0.2, c = 5.7, dt = 0.01,
                      initial_values = [0.001, 0.001, 0.001], iterations=5000):
        """
        Get a simple univariate time series data.

        :param var: the dataset field name to extract
        :return: numpy array
        """
        return get_dataframe(a, b, c, dt, initial_values, iterations)[var].values

    def get_dataframe(a = 0.2, b = 0.2, c = 5.7, dt = 0.01,
                      initial_values = [0.001, 0.001, 0.001], iterations=5000):
        '''
        Return a dataframe with the multivariate Rössler Map time series (x, y, z).

        :param a: Equation coefficient. Default value: 0.2
        :param b: Equation coefficient. Default value: 0.2
        :param c: Equation coefficient. Default value: 5.7
        :param dt: Time differential for continuous time integration. Default value: 0.01
        :param initial_values: numpy array with the initial values of x,y and z. Default: [0.001, 0.001, 0.001]
        :param iterations: number of iterations. Default: 5000
        :return: Panda dataframe with the x, y and z values
        '''

        x = [initial_values[0]]
        y = [initial_values[1]]
        z = [initial_values[2]]

        for t in np.arange(0, iterations):
            dxdt = - (y[t] + z[t])
            dydt = x[t] + a * y[t]
            dzdt = b + z[t] * x[t] - z[t] * c
            x.append(x[t] + dt * dxdt)
            y.append(y[t] + dt * dydt)
            z.append(z[t] + dt * dzdt)

        return pd.DataFrame({'x': x, 'y':y, 'z': z})