In this notebook we will explore the time series model of GWTSA
In [1]:
from GWTSA import *
In [ ]:
X1 = Parameters()
# (Name, Value, Vary, Min, Max, Expr)
X1.add_many(('A', 3.4, True, None, None, None),
('a', 2.0, True, None, None, None),
('b', 0.0, False, None, None, None),
('n', 1.5, True, None, None, None),
('alpha',2.0, True, None, 3.2, None),
('Srmax', -0.55, False, None, None, None),
('Kp', -3.25, True, None, -1.0, None),
('beta', 1.0, True, None, None, None),
('gamma', 1.0, True, None, None, None),
('f', 1.0, True, None, None, None),
('imax', 1e-3, False, None, None, None))
InputData = [range(0,10000), P, E, 1]
TFN_Model.percolation(X1, InputData)