def run():
# Quantities for the response function
# and randomization
#
#E_mod = 70 * 1e+9 # Pa
#sig_u = 1.25 * 1e+9 # Pa
D = 26 * 1.0e-6 # m
A = ( D / 2.0 ) ** 2 * pi
#xi_u = sig_u / E_mod
dict_var = {'E_mod':70.e9,
'xi':0.02,
'A':A,
'theta':0,
'lambd':0}
# random variable dictionary
n_int = 300
dict_rv = {
'xi':{'variable':'xi', 'distribution':'weibull_min', 'scale':0.02, 'shape':10., 'n_int':n_int},
'E_mod':{'variable':'E_mod', 'distribution':'uniform', 'loc':70e+9, 'scale':15e+9, 'n_int':n_int},
'theta':{'variable':'theta', 'distribution':'uniform', 'loc':0.0, 'scale':0.01, 'n_int':n_int},
'lambd':{'variable':'lambd', 'distribution':'uniform', 'loc':0.0, 'scale':.2, 'n_int':n_int},
'A':{'variable':'A', 'distribution':'uniform', 'loc':A * 0.3, 'scale':0.7 * A, 'n_int':n_int},
}
# list of all combinations of response function parameters
rv_comb_list = list( powerset( dict_rv ) )
# define a tables with the run configurations to start in a batch
run_list = [
# (
# {'cached_dG' : False,
# 'compiled_QdG_loop' : True,
# 'compiled_eps_loop' : True },
# 'bx-',
# '$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec'
# ),
# (
# {'cached_dG' : False,
# 'compiled_QdG_loop' : True,
# 'compiled_eps_loop' : False },
# 'r-2',
# '$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec',
# ),
(
{'cached_dG' : True,
'compiled_QdG_loop' : False,
'compiled_eps_loop' : False },
'y--',
'$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec'
),
# (
# {'cached_dG' : True,
# 'compiled_QdG_loop' : True,
# 'compiled_eps_loop' : True },
# 'go-',
# '$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec',
# ),
]
outfile = open( 'filament_comb_stat.dat', 'w' )
sp1_outfile = open( 'filament_comb_1.dat', 'w' )
sp2_outfile = open( 'filament_comb_2.dat', 'w' )
plot = False
for id, rv_comb in enumerate( rv_comb_list[15:25] ):#[0,-1]
for i in range( 0, 1 ):#2
num_rv = len( rv_comb ) # number of randomized variables
if i == 0:
sp1_outfile.write( '%i \t %i' % ( id, num_rv ) )
else:
sp2_outfile.write( '%i \t %i' % ( id, num_rv ) )
# construct a default response function
rf = Filament()
rf.set( **dict_var )
# construct the integrator and provide it with the response function.
s = SPIRRID( rf = rf,
min_eps = 0.00, max_eps = 0.04, n_eps = 20 ) # max_eps = 0.05
for rv in rv_comb:
s.add_rv( **dict_rv[rv] )
if plot == True:
legend = []
print 'Combination', rv_comb, ' -- ', i
outfile.write( "Combination %s -- %i\n" % ( rv_comb, i ) )
outfile.write( "%s \t %s\n" % ( "Run", "Time" ) )
for idx, run in enumerate( run_list ):
run_options, plot_options, legend_string = run
print 'run', idx,
s.set( **run_options )
if plot == True:
plt.figure( id )
s.mean_curve.plot( plt, plot_options )
legend.append( legend_string % s.exec_time )
print 'execution time', s.exec_time
outfile.write( "%s \t %.6f\n" % ( idx, s.exec_time ) )
if i == 0:
sp1_outfile.write( "\t %.6f" % ( s.exec_time ) )
else:
sp2_outfile.write( "\t %.6f" % ( s.exec_time ) )
outfile.write( "=================\n" )
outfile.flush()
if i == 0:
sp1_outfile.write( '\t %s\n' % str( rv_comb ) )
sp1_outfile.flush()
else:
sp2_outfile.write( '\t %s\n' % str( rv_comb ) )
sp2_outfile.flush()
if plot == True:
plt.xlabel( 'strain [-]' )
plt.ylabel( 'stress' )
plt.legend( legend )
plt.title( s.rf.title )
del s
outfile.close()
sp1_outfile.close()
sp2_outfile.close()
if plot == True:
plt.show()
def run():
# Quantities for the response function
# and randomization
# construct a default response function for a single filament
rf = ConstantFrictionFiniteFiber( fu = 1200e15, qf = 1200,
L = 0.02, A = 0.00000002,
E_mod = 210.e9, z = 0.004,
phi = 0.5, f = 0.01 )
# construct the integrator and provide it with the response function.
s = SPIRRID( rf = rf,
min_eps = 0.00, max_eps = 0.0008, n_eps = 80 )
# construct the random variables
n_int = 10
s.add_rv( 'E_mod', distribution = 'uniform', loc = 170.e9, scale = 250.e9, n_int = n_int )
s.add_rv( 'L', distribution = 'uniform', loc = 0.02, scale = 0.03, n_int = n_int )
s.add_rv( 'phi', distribution = 'cos_distr', loc = 0., scale = 1., n_int = n_int )
s.add_rv( 'z', distribution = 'uniform', loc = 0, scale = rf.L / 2., n_int = n_int )
#s.add_rv( 'fu', distribution='weibull_min', loc=1200.0e6, scale=200., n_int=n_int )
s.add_rv( 'qf', distribution = 'uniform', loc = 1500., scale = 100., n_int = n_int )
s.add_rv( 'A', distribution = 'uniform', loc = 5.30929158457e-10, scale = .03 * 5.30929158457e-10, n_int = n_int )
#s.add_rv( 'f', distribution='uniform', loc=0., scale=.03, n_int=n_int )
# define a tables with the run configurations to start in a batch
run_list = [
(
{'cached_qg' : False,
'compiled_qg_loop' : True,
'compiled_eps_loop' : True },
'bx-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec'
),
# (
# {'cached_qg' : False,
# 'compiled_qg_loop' : True,
# 'compiled_eps_loop' : False },
# 'r-2',
# '$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec',
# ),
## (
## {'cached_qg' : True,
## 'compiled_qg_loop' : True,
## 'compiled_eps_loop' : True },
## 'go-',
## '$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec',
## ),
# (
# {'cached_qg' : True,
# 'compiled_qg_loop' : False,
# 'compiled_eps_loop' : False },
# 'b--',
# '$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec'
# ),
]
# import cProfile
# cProfile.run( 'tloop.eval()', 'spirrid_prof' )
# print tloop.eval()
#import pstats
#p = pstats.Stats('tloop_prof')
#p.strip_dirs()
#print 'cumulative'
#p.sort_stats('cumulative').print_stats(20)
#print 'time'
#p.sort_stats('time').print_stats(20)
for idx, run in enumerate( run_list ):
run_options, plot_options, legend_string = run
print 'run', idx,
s.set( **run_options )
import time
start = time.time()
s.mean_curve.plot( plt, plot_options, linewidth = 2, label = legend_string % s.exec_time )
print 'execution time', s.exec_time, s.mu_q_peak
print time.time() - start
plt.xlabel( 'strain [-]' )
plt.ylabel( 'stress' )
plt.legend( loc = 'lower right' )
plt.title( s.rf.title )
plt.show()
