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)