def simulation_rlc_without_split():
rlc = core.__copy__()
# Define the simulation parameters
config = {'fs': 48e3, # Sample rate
'grad': 'discret', # in {'discret', 'theta', 'trapez'}
'theta': 0.5, # theta-scheme for the structure
'split': False, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'eps': 1e-16, # Global numerical tolerance
'path': path, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': True, # Display minimal timing infos
'lang': 'python', # in {'python', 'c++'}
}
simu = Simulation(rlc.to_method(), config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.config['fs'])
def sequ():
for el in u():
yield (el, )
simu.init(u=sequ(), nt=int(dur*simu.config['fs']))
simu.process()
if not os.name.lower().startswith('nt'):
shutil.rmtree(path)
return True
def simulation_rlc_cpp():
rlc = core.__copy__()
# Define the simulation parameters
config = {'fs': 48e3, # Sample rate
'grad': 'discret', # in {'discret', 'theta', 'trapez'}
'theta': 0.5, # theta-scheme for the structure
'split': True, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'eps': 1e-16, # Global numerical tolerance
'path': path, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': True, # Display minimal timing infos
'lang': 'c++', # in {'python', 'c++'}
}
simu = Simulation(rlc.to_method(), config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.config['fs'])
def sequ():
for el in u():
yield (el, )
simu.init(u=sequ(), nt=int(dur*simu.config['fs']))
simu2cpp(simu)
if not os.name.lower().startswith('nt'):
shutil.rmtree(path)
return True
def plot_rlc_with_split():
# Define the simulation parameters
config = {'fs': 48e3, # Sample rate
'grad': 'discret', # in {'discret', 'theta', 'trapez'}
'theta': 0.5, # theta-scheme for the structure
'split': False, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'eps': 1e-16, # Global numerical tolerance
'path': path, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': True, # Display minimal timing infos
'lang': 'python', # in {'python', 'c++'}
}
# retrieve the pyphs.Core of a linear RLC from the examples
from pyphs.examples.rlc.rlc import core as rlc
print(rlc.w)
print(rlc.z)
simu = Simulation(rlc.to_method(), config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.config['fs'])
def sequ():
for el in u():
yield (el, )
simu.init(u=sequ(), nt=int(dur*simu.config['fs']))
simu.process()
print(simu.nums.method.w)
print(simu.nums.method.z)
dims = simu.nums.method.dims
# plot u, y
simu.data.plot([('u', i) for i in range(dims.y())] +
[('y', i) for i in range(dims.y())], show=False)
# plot w, z
simu.data.plot([('w', 0), ('z', 0)], show=False)
simu.data.plot([('dtx', i) for i in range(dims.x())] +
[('dxH', i) for i in range(dims.x())], show=False)
if not os.name.lower().startswith('nt'):
shutil.rmtree(path)
return True
def to_simulation(self, config=None, inits=None, erase=True):
"""
Return a Numeric object for the evaluation of the PHS numerical method.
Parameter
---------
inits : dict or None (optional)
Dictionary with variable name as keys and initialization values
as value. E.g: inits = {'x': [0, 0, 1]} to initalize state x
with dim(x) = 3, x[0] = x[1] = 0 and x[2] = 1.
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
------
numeric : pyphs.Simulation
Object for the numerical evaluation of the PHS numerical method.
"""
from pyphs import Simulation
return Simulation(self, inits=inits, config=config, erase=erase)
def plot_power_balance_rlc_with_split():
# Define the simulation parameters
config = {
'fs': 48e3, # Sample rate
'grad': 'discret', # in {'discret', 'theta', 'trapez'}
'theta': 0.5, # theta-scheme for the structure
'split': False, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'path': path, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': True, # Display minimal timing infos
'lang': 'python', # in {'python', 'c++'}
}
# retrieve the pyphs.Core of a linear RLC from the examples
from pyphs.examples.rlc.rlc import core as rlc
rlc_ = rlc.__copy__()
rlc_.reduce_z()
simu = Simulation(rlc_.to_method(), config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.config['fs'])
sequ = u[:, np.newaxis]
simu.init(u=sequ)
simu.process()
simu.data.plot_powerbal(mode='single', show=False)
simu.data.plot_powerbal(mode='multi', show=False)
if not os.name.lower().startswith('nt'):
shutil.rmtree(path)
return True
def plot_power_balance_nlcore_with_split():
# Define the simulation parameters
config = {'fs': 48e3, # Sample rate
'grad': 'discret', # in {'discret', 'theta', 'trapez'}
'theta': 0.5, # theta-scheme for the structure
'split': True, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'eps': 1e-16, # Global numerical tolerance
'path': path, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': True, # Display minimal timing infos
'lang': 'python', # in {'python', 'c++'}
}
# retrieve the pyphs.Core of a nonlinear RLC from
# the tutorial on Core
from pyphs.tutorials.core import core as nlcore
nlcore.reduce_z()
simu = Simulation(nlcore.to_method(), config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.config['fs'])
def sequ():
for el in u():
yield (el, )
simu.init(u=sequ(), nt=int(dur*simu.config['fs']))
simu.process()
simu.data.plot_powerbal(mode='single', show=False)
simu.data.plot_powerbal(mode='multi', show=False)
if not os.name.lower().startswith('nt'):
shutil.rmtree(path)
return True
def plot_rlc_with_split():
# Define the simulation parameters
config = {
'fs': 48e3, # Sample rate
'grad': 'discret', # in {'discret', 'theta', 'trapez'}
'theta': 0.5, # theta-scheme for the structure
'split': False, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'eps': 1e-16, # Global numerical tolerance
'path': path, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': True, # Display minimal timing infos
'lang': 'python', # in {'python', 'c++'}
'eigen': None, # path to Eigen library
}
# retrieve the pyphs.Core of a linear RLC from the examples
from pyphs.examples.rlc.rlc import core as rlc
print(rlc.w)
print(rlc.z)
simu = Simulation(rlc, config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.config['fs'])
def sequ():
for el in u():
yield (el, )
simu.init(u=sequ(), nt=int(dur * simu.config['fs']))
simu.process()
print(simu.nums.method.w)
print(simu.nums.method.z)
dims = simu.nums.method.dims
# plot u, y
simu.data.plot([('u', i)
for i in range(dims.y())] + [('y', i)
for i in range(dims.y())],
show=False)
# plot w, z
simu.data.plot([('w', 0), ('z', 0)], show=False)
simu.data.plot([('dtx', i)
for i in range(dims.x())] + [('dxH', i)
for i in range(dims.x())],
show=False)
if not os.name.lower().startswith('nt'):
shutil.rmtree(path)
return True
def to_simulation(self, config=None, inits=None):
"""
Return a Numeric object for the evaluation of the PHS numerical method.
Parameter
---------
inits : dict or None (optional)
Dictionary with variable name as keys and initialization values
as value. E.g: inits = {'x': [0, 0, 1]} to initalize state x
with dim(x) = 3, x[0] = x[1] = 0 and x[2] = 1.
Return
------
numeric : pyphs.Simulation
Object for the numerical evaluation of the PHS numerical method.
"""
from pyphs import Simulation
return Simulation(self, inits=inits, config=config)
def dataH5File():
# Define the simulation parameters
config = {
'fs': 48e3, # Sample rate
'grad': 'discret', # in {'discret', 'theta', 'trapez'}
'theta': 0.5, # theta-scheme for the structure
'split': True, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'path': path, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': True, # Display minimal timing infos
'lang': 'python', # in {'python', 'c++'}
}
# retrieve the pyphs.Core of a nonlinear RLC from
# the tutorial on Core
from pyphs.tutorials.core import core as nlcore
nlcore.reduce_z()
simu = Simulation(nlcore.to_method(), config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.config['fs'])
sequ = u[:, np.newaxis]
simu.init(u=sequ)
simu.process()
simu.data.plot_powerbal(mode='single', show=False)
simu.data.plot_powerbal(mode='multi', show=False)
simu2 = Simulation(nlcore.to_method(), config=config, erase=False)
start = int(simu.data.nt / 10.)
stop = int(9 * simu.data.nt / 10.)
step = 3
simu2.data.start = start
simu2.data.stop = stop
simu2.data.step = step
test = len(range(start, stop, step)) == len(simu2.data['x', :, 0])
return test
def plot_power_balance_nlcore_with_split():
# Define the simulation parameters
config = {
'fs': 48e3, # Sample rate
'grad': 'discret', # in {'discret', 'theta', 'trapez'}
'theta': 0.5, # theta-scheme for the structure
'split': True, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'eps': 1e-16, # Global numerical tolerance
'path': path, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': True, # Display minimal timing infos
'lang': 'python', # in {'python', 'c++'}
'eigen': None, # path to Eigen library
}
# retrieve the pyphs.Core of a nonlinear RLC from
# the tutorial on Core
from pyphs.tutorials.core import core as nlcore
nlcore.reduce_z()
simu = Simulation(nlcore, config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.config['fs'])
def sequ():
for el in u():
yield (el, )
simu.init(u=sequ(), nt=int(dur * simu.config['fs']))
simu.process()
simu.data.plot_powerbal(mode='single', show=False)
simu.data.plot_powerbal(mode='multi', show=False)
if not os.name.lower().startswith('nt'):
shutil.rmtree(path)
return True
netlist = Netlist(netlist_filename)
graph = Graph(netlist=netlist)
core = graph.to_core()
# %% ------------------------------ SIMULATION ------------------------------ #
# UNCOMMENT BELOW FOR SIMULATION AND PLOTS
if __name__ == '__main__':
from pyphs import Netlist, Graph, Simulation, signalgenerator
import numpy as np
tsig = 0.01
fs = 48000.
config = {'fs': 48e3,
'pbar': True,
}
simu = Simulation(core.to_method(), config)
u = signalgenerator(which='sin', f0=500., A=200., tsig=tsig, fs=fs)
simu.init(u=u[:, np.newaxis])
simu.process()
simu.data.plot_powerbal()
# to index imax, rendering one element out of the number decim
'load': {
'imin': 0,
'imax': None,
'decim': 1
}
}
# state initialization
# !!! must be numpy array with shape (core.dims.x(), )
x0 = list(map(sympy.sympify, [
0.,
] * core.dims.x()))
# Instantiate a pyphs.Simulation object associated with a given core
simu = Simulation(core.to_method(), config=config, inits={'x': x0})
# def simulation time
tmax = 1e-2
nmax = int(tmax * simu.config['fs'])
t = [n / simu.config['fs'] for n in range(nmax)]
nt = len(t)
# def input signal
def sig(tn, mode='sin'):
freq = 300.
amp = 1.
if mode == 'sin':
pi = numpy.pi
sin = numpy.sin
'split': False, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'eps': 1e-30, # Global numerical tolerance
'path': None, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': False, # Display minimal timing infos
'lang': 'python', # in {'python', 'c++'}
'theano': False
}
# x0 = np.zeros(core.dims.x())
# x0[0] = 1
# x0_expr = list(map(sp.sympify, x0))
# inits = {'x': x0_expr}
# simu = Simulation(core, config=config, inits=inits)
simu = Simulation(core, config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.config['fs'])
def sequ():
for el in u():
yield (el, )
simu.init(u=sequ(), nt=int(dur * simu.config['fs']))
# Run the simulation
simu.process()
# Plots
simu.data.plot_powerbal(mode='single')
def simulation_nlcore_full():
# Define the simulation parameters
config = {'fs': 48e3, # Sample rate
'grad': 'discret', # in {'discret', 'theta', 'trapez'}
'theta': 0.5, # theta-scheme for the structure
'split': False, # apply core.split_linear() beforehand
'maxit': 10, # Max iteration for NL solvers
'eps': 1e-16, # Global numerical tolerance
'path': path, # Path to the results folder
'pbar': False, # Display a progress bar
'timer': True, # Display minimal timing infos
'lang': 'python', # in {'python', 'c++'}
}
# state initialization
# !!! must be array with shape (core.dims.x(), )
x0 = list(map(sympy.sympify, (0., 0., 0.)))
# Instantiate a pyphs.Simulation object associated with a given core
simu = Simulation(nlcore.to_method(), config=config, inits={'x': x0})
# def simulation time
tmax = 0.02
nmax = int(tmax*simu.config['fs'])
t = [n/simu.config['fs'] for n in range(nmax)]
nt = len(t)
# def input signal
def sig(tn, mode='impact'):
freq = 1000.
amp = 1000.
if mode == 'sin':
pi = numpy.pi
sin = numpy.sin
out = amp * sin(2*pi*freq*tn)
elif mode == 'impact':
dur = 0.5*1e-3 # duration: 0.5ms
start = 0.001 # start at 1ms
out = amp if start <= tn < start + dur else 0.
elif mode == 'const':
out = 1.
return out
# def generator for sequence of inputs to feed in the Simulation object
def sequ():
"""
generator of input sequence for Simulation
"""
for tn in t:
u1 = sig(tn)
# !!! must be array with shape (core.dims.u(), )
yield numpy.array([u1, ]) # numpy.array([u1, u2, ...])
# Initialize the simulation
simu.init(u=sequ(), nt=nt)
# Proceed
simu.process()
if not os.name.lower().startswith('nt'):
shutil.rmtree(path)
return True
