simulation.py

# coding: utf-8

# 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`](https://afalaize.github.io/pyphs/posts/pyphs-core/) is used as an example.
#
# <!-- TEASER_END -->
#
# The corresponding Python script `pyphs-simulation.py` can be found in the tutorials at `\pyphs\tutorials\`

# In[1]:

# Support for Python 2.x and Python 3.x
from __future__ import division

# Disable warnings
import warnings;
warnings.simplefilter('ignore')

# import of external packages
import numpy                     # numerical tools
import matplotlib.pyplot as plt  # plot tools


# In[2]:

# Uncomment to activate inline plots in notebooks
#get_ipython().magic('matplotlib inline')


# In[3]:

from pyphs.tutorials.core import core
from pyphs import Simulation


# 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)


# In[5]:

# def simulation time
tmax = 0.005
nmax = int(tmax*simu.config['fs'])
t = numpy.array([n/simu.config['fs'] for n in range(nmax)])

# def input signal
F = 1000.
A = 50.
sig = A*numpy.sin(2*numpy.pi*F*t)


# def generator for sequence of inputs to feed in the PHSSimulation object
u = sig[:, numpy.newaxis]


# In[6]:

# Initialize the simulation
simu.init(u=u)

# Proceed
simu.process()


# In[7]:

simu.data.method.x


# In[8]:

# The simulation results are stored in the simu.data object
t = simu.data.t()       # a generator of time value at each time step
x = simu.data['x']       # a generator of value for vector x at each time step
x1 = simu.data['x', :, 0]    # a generator of value for scalar x component 1

plt.figure()
plt.plot(t, x1)
plt.xlabel('$t \; \mathrm{(s)}$')
plt.ylabel('$x_L \; \mathrm{(Wb)}$')


# 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')
ax.legend()


# ### plt.figure()

# In[10]:

fig, axes = simu.data.plot([('u', 0), ('x', 0), ('dtx', 0), ('y', 0)])


# In[11]:

fig, ax = simu.data.plot_powerbal(mode='single')


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