def build_generator(self, usymbs, psymbs, fs, time):
usigs = []
for u in usymbs:
usignal = self.signals[u]
delta = time - usignal.data['time']
if delta > 0:
usignal.data['tend'] += delta
usignal.data['fs'] = fs
usigs.append(signalgenerator(**usignal.data)())
def u():
i = 0
while i < time * fs:
si = [next(usig) for usig in usigs]
i += 1
yield numpy.array(si)
psigs = []
for p in psymbs:
psignal = self.signals[p]
delta = time - psignal.data['time']
if delta > 0:
psignal.data['tend'] += delta
psignal.data['fs'] = fs
psigs.append(signalgenerator(**psignal.data)())
def p():
i = 0
while i < time * fs:
si = [next(sig) for sig in psigs]
i += 1
yield numpy.array(si)
return u, p
def signal_synthesis():
sigs = list()
# test without parameters
sigs.append(signalgenerator())
# test for all synthesis methods
for w in sig.synthesis.names:
kwargs = {'which': w}
sigs.append(list(signalgenerator(**kwargs)()))
return True
def signal_synthesis():
sigs = list()
# test without parameters
sigs.append(signalgenerator())
# test for all synthesis methods
for w in sig.synthesis.names:
kwargs = {'which': w}
sigs.append(signalgenerator(**kwargs))
return True
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_without_split():
rlc = core.__deepcopy__()
# Define the simulation parameters
config = {
'fs': 48e3, # Sample rate
'gradient': '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
'numtol': 1e-16, # Global numerical tolerance
'path': None, # Path to the results folder
'progressbar': True, # Display a progress bar
'timer': True, # Display minimal timing infos
'language': 'python', # in {'python', 'c++'}
'cpp_build_and_run_script': None, # compile and exec binary
'eigen_path': None, # path to Eigen library
}
simu = PHSSimulation(rlc, config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.fs)
def sequ():
for el in u():
yield (el, )
simu.init(sequ=sequ(), nt=int(dur * simu.fs))
simu.process()
return True
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_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 plot(self):
import matplotlib.pyplot as plt
self.update_data()
data = self.data.copy()
data.pop('time')
sig = list(signalgenerator(**data)())
nt = len(sig)
t = [n*self.data['fs']**-1 for n in range(nt)]
plt.plot(t, sig)
plt.title(self.label)
plt.show()
def plot_rlc_with_split():
# Define the simulation parameters
config = {
'fs': 48e3, # Sample rate
'gradient': '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
'numtol': 1e-16, # Global numerical tolerance
'path': None, # Path to the results folder
'progressbar': True, # Display a progress bar
'timer': True, # Display minimal timing infos
'language': 'python', # in {'python', 'c++'}
'cpp_build_and_run_script': None, # compile and exec binary
'eigen_path': None, # path to Eigen library
}
# retrieve the pyphs.PHSCore of a linear RLC from the examples
from pyphs.examples.rlc.rlc import core as rlc
print(rlc.w)
print(rlc.z)
simu = PHSSimulation(rlc, config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.fs)
def sequ():
for el in u():
yield (el, )
simu.init(sequ=sequ(), nt=int(dur * simu.fs))
simu.process()
print(simu.nums.method.core.w)
print(simu.nums.method.core.z)
dims = simu.nums.method.core.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)
return True
def signal_waves():
"""
Write and read a random sample in wav format
"""
path = os.path.join(here, 'test.wav')
s = signalgenerator()
s /= np.max(np.abs(s))
fs = 1000
sig.waves.wavwrite(list(s), fs, path, normalize=False, timefades=0.)
fss, ss = sig.waves.wavread(path)
os.remove(path)
ss /= max(abs(ss))
return max(abs(ss - s)) < 1e-4 and fss - fs < 1e-4
def signal_waves():
"""
Write and read a random sample in wav format
"""
path = os.path.join(here, 'test.wav')
s = np.array(list(signalgenerator()()))
s /= np.max(np.abs(s))
fs = 1000
sig.waves.wavwrite(list(s), fs, path, normalize=False, timefades=0.)
fss, ss = sig.waves.wavread(path)
os.remove(path)
ss /= max(abs(ss))
return max(abs(ss-s)) < 1e-4 and fss - fs < 1e-4
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 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
def plot_power_balance_rlc_with_split():
# Define the simulation parameters
config = {
'fs': 48e3, # Sample rate
'gradient': '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
'numtol': 1e-16, # Global numerical tolerance
'path': None, # Path to the results folder
'progressbar': True, # Display a progress bar
'timer': True, # Display minimal timing infos
'language': 'python', # in {'python', 'c++'}
'cpp_build_and_run_script': None, # compile and exec binary
'eigen_path': None, # path to Eigen library
}
# retrieve the pyphs.PHSCore of a linear RLC from the examples
from pyphs.examples.rlc.rlc import core as rlc
rlc_ = rlc.__copy__()
rlc_.build_R()
simu = PHSSimulation(rlc_, config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.fs)
def sequ():
for el in u():
yield (el, )
simu.init(sequ=sequ(), nt=int(dur * simu.fs))
simu.process()
simu.data.plot_powerbal(mode='single', show=False)
simu.data.plot_powerbal(mode='multi', show=False)
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
graph = PHSGraph(netlist=netlist)
core = graph.buildCore()
if __name__ == '__main__':
config = {
'fs': 48e3,
'split': True,
'pbar': True,
'timer': True,
'path': path
}
simu = PHSSimulation(core, config=config)
dur = 0.01
u = signalgenerator(which='sin', f0=800., tsig=dur, fs=simu.fs)
def sequ():
for el in u():
yield (el, )
simu.init(sequ=sequ(), nt=int(dur * simu.fs))
simu.process()
simu.data.plot_powerbal(mode='multi')
simu.data.plot([('u', 0), ('x', 1), ('x', 0), ('dtx', 0), ('dxH', 2),
('y', 0)])
}
simu = core.to_simulation(config=config)
# Signal parameters
A = 100. # Signal amplitude (V)
f0 = 100. # Signal frequency (Hz)
nP = 5 # Sumber of periods (d.u.)
tsig = 5/f0 # Signal duration (s)
# signal generator
vout = signalgenerator(which="sin", # Sinusoidal signal
A=A, # Amplitude
f0=f0, # Frequency
tsig=tsig, # Number of time-steps
fs=config['fs'], # Samplerate
ramp_on=True, # Linear increase
)
u = list(([el, ] for el in vout))
# Init simulation
simu.init(u=u)
# Process simulation
simu.process()
# In[26]:
}
simu = core.to_simulation(config=config)
# Signal parameters
A = 100. # Signal amplitude (V)
f0 = 100. # Signal frequency (Hz)
nP = 5 # Sumber of periods (d.u.)
tsig = 5/f0 # Signal duration (s)
# signal generator
vout = signalgenerator(which="sin", # Sinusoidal signal
A=A, # Amplitude
f0=f0, # Frequency
tsig=tsig, # Number of time-steps
fs=config['fs'], # Samplerate
ramp_on=True, # Linear increase
)
u = list(([el, ] for el in vout()))
# Init simulation
simu.init(u=u)
# Process simulation
simu.process()
# In[26]:
'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')
simu.data.plot(['u', 'x', 'y'])
# clean: delete folders 'data' and 'figures'
shutil.rmtree(os.path.join(here, 'data'))
netlist = PHSNetlist(netlist_filename)
graph = PHSGraph(netlist=netlist)
core = graph.buildCore()
if __name__ == '__main__':
tsig = 0.01
fs = 48000.
config = {
'fs': 48e3,
'progressbar': True,
}
simu = PHSSimulation(core, config)
sig = signalgenerator(which='sin', f0=500., A=200., tsig=tsig, fs=fs)
def sequ():
for u in sig():
yield np.array([
u,
])
nt = int(fs * tsig)
simu.init(sequ=sequ(), nt=nt)
simu.process()
simu.data.plot_powerbal()
