def to_method(self, config=None):
"""
Return the PHS numerical method associated with the PHS core for the
specified configuration.
Parameter
---------
config: dict or None
A dictionary of simulation parameters. If None, the standard
pyphs.config.simulations is used (the default is None).
keys and default values are
'fs': 48e3, # Sample rate (Hz)
'grad': 'discret', # In {'discret', 'theta', 'trapez'}
'theta': 0., # Theta-scheme for the structure
'split': False, # split implicit from explicit part
'maxit': 10, # Max number of iterations for NL solvers
'eps': 1e-16, # Global numerical tolerance
Output
------
method : pyphs.Method
The PHS numerical method associated with the PHS core for the
specified configuration.
"""
from pyphs import Method
return Method(self, config=config)
def to_simulation(self, config=None, inits=None, erase=True):
"""
Return a simulation associated with the PHS core for the
specified configuration.
Parameter
---------
config: dict or None
A dictionary of simulation parameters. If None, the standard
pyphs.config.simulations is used (the default is None).
keys and default values are
'fs': 48e3, # Sample rate (Hz)
'grad': 'discret', # In {'discret', 'theta', 'trapez'}
'theta': 0., # Theta-scheme for the structure
'split': False, # split implicit from explicit part
'maxit': 10, # Max number of iterations for NL solvers
'eps': 1e-16, # Global numerical tolerance
erase : bool (optional)
If True and a h5file exists with same path than simulation data,
it is erased. Else, it is used to initialize the data object. The
default is True.
Output
------
simulation : pyphs.Simulation
The simulation associated with the PHS core for the
specified configuration.
"""
from pyphs import Method
from pyphs.config import CONFIG_METHOD
config_method = CONFIG_METHOD.copy()
if config is None:
config = {}
for k in CONFIG_METHOD.keys():
if k in config.keys():
config_method.update({k: config[k]})
method = Method(self, config=config_method)
return method.to_simulation(config=config, inits=inits, erase=erase)
def to_simulation(self, config=None, inits=None):
"""
Return a simulation associated with the PHS core for the
specified configuration.
Parameter
---------
config: dict or None
A dictionary of simulation parameters. If None, the standard
pyphs.config.simulations is used (the default is None).
keys and default values are
'fs': 48e3, # Sample rate (Hz)
'grad': 'discret', # In {'discret', 'theta', 'trapez'}
'theta': 0., # Theta-scheme for the structure
'split': False, # split implicit from explicit part
'maxit': 10, # Max number of iterations for NL solvers
'eps': 1e-16, # Global numerical tolerance
Output
------
simulation : pyphs.Simulation
The simulation associated with the PHS core for the
specified configuration.
"""
from pyphs import Method
from pyphs.config import CONFIG_METHOD
config_method = CONFIG_METHOD.copy()
if config is None:
config = {}
for k in CONFIG_METHOD.keys():
if k in config.keys():
config_method.update({k: config[k]})
method = Method(self, config=config_method)
return method.to_simulation(config=config, inits=inits)
Jxx = numpy.array([[0., -1.], [1., 0.]])
core.set_Jxx(Jxx)
# Physical parameters
F0 = 100.
L_value = 5e-1 # 500mH
# f0 = (2*pi*sqrt(L*C))**-1
C_value = (2 * numpy.pi * F0)**-2 / L_value # ?F
# Dictionary with core.symbols as keys and parameters value as values
subs = {L: L_value, C: C_value}
core.subs.update(subs)
core.linear_nonlinear()
# instantiate a pyphs.Evaluation object associated with a pyphs.Core
method = Method(core)
#method.dxH = list(map(lambda g: g.subs(dict([(ex, ex+ 0.5*edx) for ex, edx in zip(core.x, core.dx())])), core.dxH()))
method.dxH = core.dxH()
# Explicit Euler update:
# dx = Mxx*dxH(x) + Mxy*u
# x = x + dx
# Mxx * dxH
op_MxxDotdxH = method.Operation('dot', ('Mxx', 'dxH'))
# Mxy * u
op_MxyDotU = method.Operation('dot', ('Mxy', 'u'))
# Mxx * dxH + Mxy * u