The PyPHS.Simulation class

In this tutorial, we cover the pyphs.Simulation object for the numerical simulation of pyphs.Core objects. The core object from the previous tutorial on pyphs.Core is used as an example.

The corresponding Python script can be found in the tutorials at \pyphs\tutorials\

In [1]:
# Plot appears in the notebook
%matplotlib inline
In [2]:
# Support for Python 2.x and Python 3.x
from __future__ import division

# Disable warnings 
import warnings; 

# import of external packages
import numpy                     # numerical tools
import matplotlib.pyplot as plt  # plot tools
In [3]:
from pyphs.tutorials.core import core 
from pyphs import Simulation
Remove Inverse of Parameters...
In [4]:
# Define the simulation parameters
config = {'fs': 48e3,               # Sample rate (Hz)
          'grad': 'discret',    # gradient evaluation in {'discret', 'theta', 'trapez'}
          'theta': 0.,             # theta for evaluation of the structure
          'split': True,            # Split and presolve the explicit equations
          'maxit': 100,              # Max number of iterations for NL solvers
          'eps': 1e-11,          # Global numerical tolerance
          'path': None,             # Path to saved results. If None, /data/ is created.
          'pbar': False,     # Display a progress bar
          'timer': False            # Display minimal timing infos

# Instantiate a pyphs.Simulation object associated with a given core PHS

simu = core.to_simulation(config = config)
Build method my_core...
    Init Method...
    Split Linear/Nonlinear...
    Build numerical structure...
    Split Implicit/Resolved...
    Re-Build numerical structure...
    Init update actions...
    Init arguments...
        Build x
        Build dx
        Build w
        Build u
        Build p
        Build vl
        Build o
    Init functions...
        Build z
        Build ud_o
        Build dxH
        Build jactempFll
        Build Gl
        Build y
Build numeric my_core...
    Build numerical evaluation of x
    Build numerical evaluation of ud_x
    Build numerical evaluation of o
    Build numerical evaluation of ud_o
    Build numerical evaluation of jactempFll
    Build numerical evaluation of ijactempFll
    Build numerical evaluation of Gl
    Build numerical evaluation of vl
    Build numerical evaluation of ud_vl
    Build numerical evaluation of dxH
    Build numerical evaluation of z
    Build numerical evaluation of y
    Build numerical evaluation of dx
    Build numerical evaluation of w
    Build numerical evaluation of u
    Build numerical evaluation of p
Build data i/o...
In [5]:
# def simulation time
tmax = 0.005
nmax = int(tmax*simu.config['fs'])
t = [n/simu.config['fs'] for n in range(nmax)]

# def input signal
def sig(t, mode='sin'):
    F = 1000.
    A = 50.
    if mode == 'sin':
        out = A*numpy.sin(2*numpy.pi*F*t)
    elif mode == 'impact':
        dur = 0.5*1e-3
        start = 0.001
        out = A if start <= t < start + dur else 0.
    elif mode == 'none':
        out = 0.
    return numpy.array([out])

# def generator for sequence of inputs to feed in the PHSSimulation object
def sequ(mode='sin'):
    for tn in t:
        yield sig(tn, mode)
In [6]:
# Initialize the simulation
simu.init(u=sequ(), nt=len(t))

# Proceed
Simulation: Process...
Simulation: Done
In [7]:
[xL, xK, xM]
In [8]:
# The simulation results are stored in the object
t =       # a generator of time value at each time step
x =['x']       # a generator of value for vector x at each time step
x1 =['x', :, 0]    # a generator of value for scalar x component 1

plt.plot(t, x1)
plt.xlabel('$t \; \mathrm{(s)}$')
plt.ylabel('$x_L \; \mathrm{(Wb)}$')
<matplotlib.text.Text at 0x11c999390>
In [9]:
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(x[:,0], x[:,1], x[:,2], label='State space')
<matplotlib.legend.Legend at 0x11c6c54e0>


In [10]:
fig, axes =[('u', 0), ('x', 0), ('dtx', 0), ('y', 0)])
In [11]:
fig, ax ='single')
Build numerical evaluations...
Build numerical evaluations...
Build numerical evaluations...
Build numerical evaluations...
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