Use of swig and numpy to compute the evolution of internal energy in a vNR scheme



In [1]:

    
import os

We will use numpy extensively



In [2]:

    
import numpy as np

Import the module created by swig as a classical python module



In [3]:

    
import vnrinternalenergy as vnr_ext

Import classical numpy modules for performance comparison purpose



In [4]:

    
from xvof.solver.functionstosolve.vnrenergyevolutionforveformulation import VnrEnergyEvolutionForVolumeEnergyFormulation
from xvof.solver.newtonraphson import NewtonRaphson
from xvof.equationsofstate.miegruneisen import MieGruneisen

Creation of the data



In [5]:

    
pb_size = 100000



In [6]:

    
old_density = np.ndarray(pb_size, dtype=np.float64, order='c')



In [7]:

    
new_density = np.ndarray(pb_size, dtype=np.float64, order='c')



In [8]:

    
pressure= np.ndarray(pb_size, dtype=np.float64, order='c')



In [9]:

    
internal_energy = np.ndarray(pb_size, dtype=np.float64, order='c')



In [10]:

    
new_internal_energy = np.ndarray(pb_size, dtype=np.float64, order='c')



In [11]:

    
new_pressure = np.ndarray(pb_size, dtype=np.float64, order='c')



In [12]:

    
new_soundspeed = np.ndarray(pb_size, dtype=np.float64, order='c')

Simple test of the module

Initialization of the data



In [13]:

    
old_density[:] = 7500.



In [14]:

    
new_density[:] = 9500.



In [15]:

    
pressure[:] = 1e+09



In [16]:

    
internal_energy[:] = 1e+06



In [17]:

    
new_internal_energy[:] = 0.



In [18]:

    
new_pressure[:] = 0.



In [19]:

    
new_soundspeed[:] = 0.



In [20]:

    
print new_pressure[495:505], new_internal_energy[495:505], new_soundspeed[495:505]









    



[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.] [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.] [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]

Use the module created by swig as a classical python module



In [21]:

    
vnr_ext.launch_vnr_resolution(old_density, new_density, pressure, internal_energy, new_internal_energy, new_pressure, new_soundspeed)



In [22]:

    
print new_pressure[495:505], new_internal_energy[495:505], new_soundspeed[495:505]









    



[  5.10950604e+10   5.10950604e+10   5.10950604e+10   5.10950604e+10
   5.10950604e+10   5.10950604e+10   5.10950604e+10   5.10950604e+10
   5.10950604e+10   5.10950604e+10] [ 1731158.74241368  1731158.74241368  1731158.74241368  1731158.74241368
  1731158.74241368  1731158.74241368  1731158.74241368  1731158.74241368
  1731158.74241368  1731158.74241368] [ 5771.42229347  5771.42229347  5771.42229347  5771.42229347  5771.42229347
  5771.42229347  5771.42229347  5771.42229347  5771.42229347  5771.42229347]

Test of performance of swig made module



In [23]:

    
new_internal_energy[:] = 0.



In [24]:

    
new_pressure[:] = 0.



In [25]:

    
new_soundspeed[:] = 0.



In [26]:

    
print new_pressure[495:505], new_internal_energy[495:505], new_soundspeed[495:505]









    



[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.] [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.] [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]



In [27]:

    
%timeit vnr_ext.launch_vnr_resolution(old_density, new_density, pressure, internal_energy, new_internal_energy, new_pressure, new_soundspeed)









    



100 loops, best of 3: 9.43 ms per loop



In [28]:

    
print new_pressure[495:505], new_internal_energy[495:505], new_soundspeed[495:505]









    



[  5.10950604e+10   5.10950604e+10   5.10950604e+10   5.10950604e+10
   5.10950604e+10   5.10950604e+10   5.10950604e+10   5.10950604e+10
   5.10950604e+10   5.10950604e+10] [ 1731158.74241368  1731158.74241368  1731158.74241368  1731158.74241368
  1731158.74241368  1731158.74241368  1731158.74241368  1731158.74241368
  1731158.74241368  1731158.74241368] [ 5771.42229347  5771.42229347  5771.42229347  5771.42229347  5771.42229347
  5771.42229347  5771.42229347  5771.42229347  5771.42229347  5771.42229347]

Test of performance of classical numpy module



In [29]:

    
function_to_vanish = VnrEnergyEvolutionForVolumeEnergyFormulation()
solver = NewtonRaphson(function_to_vanish)



In [30]:

    
my_variables = {'EquationOfState': MieGruneisen(),
                'OldDensity': old_density,
                'NewDensity': new_density,
                'Pressure': pressure,
                'OldEnergy': internal_energy}



In [31]:

    
function_to_vanish.setVariables(my_variables)



In [32]:

    
%timeit reference_solution = solver.computeSolution(internal_energy)









    



10 loops, best of 3: 42.8 ms per loop



In [33]:

    
reference_solution = solver.computeSolution(internal_energy)
print reference_solution[495:505]









    



[ 1731158.74241368  1731158.74241368  1731158.74241368  1731158.74241368
  1731158.74241368  1731158.74241368  1731158.74241368  1731158.74241368
  1731158.74241368  1731158.74241368]

Test of behavior in case of wrong inputs



In [34]:

    
wrong_sized_density = np.ndarray(999, dtype=np.float64, order='c')



In [35]:

    
wrong_sized_density[:] = 9500.



In [36]:

    
new_internal_energy[:] = 0.



In [37]:

    
new_pressure[:] = 0.



In [38]:

    
new_soundspeed[:] = 0.



In [39]:

    
#print new_pressure[495:505], new_internal_energy[495:505], new_soundspeed[495:505]



In [40]:

    
#vnr_ext.launch_vnr_resolution(old_density, wrong_sized_density, pressure, internal_energy, new_internal_energy, new_pressure, new_soundspeed)