def w_x_res(self, w_arr, ll, lr):
self.CB_model_view.model.Ll = ll
self.CB_model_view.model.Lr = lr
cb_epsm_interpolators_lst = [
MFnLineArray(xdata=np.linspace(-1e5, 1e5, 5), ydata=np.zeros(5))
]
cb_epsf_interpolators_lst = [
MFnLineArray(xdata=np.linspace(-1e5, 1e5, 5), ydata=np.zeros(5))
]
sigma_c_lst = [0.0]
w_lst = [0.0]
for w in w_arr:
self.CB_model_view.model.w = w
if self.CB_model_view.sigma_c > sigma_c_lst[-1]:
w_lst.append(w)
sigma_c_lst.append(self.CB_model_view.sigma_c)
x_i = np.hstack((-self.length - 1e-1, self.CB_model_view.x_arr,
self.length + 1e-1))
epsm_i = np.hstack((self.CB_model_view.epsm_arr[0],
self.CB_model_view.epsm_arr,
self.CB_model_view.epsm_arr[-1]))
epsf_i = np.hstack((self.CB_model_view.epsf_arr[0],
self.CB_model_view.epsf_arr,
self.CB_model_view.epsf_arr[-1]))
cb_epsm_interpolators_lst.append(
MFnLineArray(xdata=x_i, ydata=epsm_i))
cb_epsf_interpolators_lst.append(
MFnLineArray(xdata=x_i, ydata=epsf_i))
w_interpolator = MFnLineArray(xdata=np.array(sigma_c_lst),
ydata=np.array(w_lst))
return w_interpolator, [sigma_c_lst, cb_epsm_interpolators_lst
], [sigma_c_lst, cb_epsf_interpolators_lst]
def montecarlo(self,N):
ppf = MFnLineArray(xdata=self.hui_cdf, ydata=self.x)
fragment_lengths = []
for i in range(N):
fragment_length = ppf.get_value(np.random.rand(1)) + ppf.get_value(np.random.rand(1))
fragment_lengths.append(fragment_length / 2.)
return np.array(fragment_lengths)
def strains( self ):
mfn = MFnLineArray()
mfn.xdata, mfn.ydata = self.values
strains_fn = frompyfunc( mfn.get_diff, 1, 1 )
strains = strains_fn( mfn.xdata )
strains[0] = strains[1]
strains[-2] = strains[-1]
return strains
def montecarlo(self, N):
ppf = MFnLineArray(xdata=self.hui_cdf, ydata=self.x)
fragment_lengths = []
for i in range(N):
fragment_length = ppf.get_value(np.random.rand(1)) + ppf.get_value(
np.random.rand(1))
fragment_lengths.append(fragment_length / 2.)
return np.array(fragment_lengths)
def ppf( self, x ):
self.check()
if len( self.cdf_values ) != 0:
xx, cdf = self.x_values, self.cdf_values
else:
xx, cdf = self.x_values, self.cdf( self.x_values )
ppf_mfn_line = MFnLineArray( xdata = cdf, ydata = xx )
return ppf_mfn_line.get_values( x )
def _get_cached_pdf( self ):
if len( self.pdf_values ) == 0:
cdf_line = MFnLineArray( xdata = self.x_values, ydata = self.cdf_values )
pdf = []
for x in self.x_values:
pdf.append( cdf_line.get_diff( x ) )
return MFnLineArray( xdata = self.x_values, ydata = pdf )
else:
return MFnLineArray( xdata = self.x_values, ydata = self.pdf_values )
def get_epsm_x(self, w):
"""
evaluates the matrix strain profile at given load
"""
self.CB_model.w = w
if self.CB_model.Ll > self.CB_model.Lr:
epsm_interp = MFnLineArray(xdata=-self.CB_model._x_arr[::-1], ydata=self.CB_model._epsm_arr[::-1])
else:
epsm_interp = MFnLineArray(xdata=self.CB_model._x_arr, ydata=self.CB_model._epsm_arr)
return epsm_interp.get_values(self.x)
def get_epsf_x(self, w):
'''
evaluates the mean fiber strain profile at given load
'''
self.CB_model.w = w
if self.CB_model.Ll > self.CB_model.Lr:
epsf_interp = MFnLineArray(xdata=-self.CB_model._x_arr[::-1], ydata=self.CB_model._epsf_arr[::-1])
else:
epsf_interp = MFnLineArray(xdata=self.CB_model._x_arr, ydata=self.CB_model._epsf_arr)
return epsf_interp.get_values(self.x)
def get_epsf_x(self, w):
'''
evaluates the mean fiber strain profile at given load
'''
self.CB_model.w = w
if self.CB_model.Ll > self.CB_model.Lr:
epsf_interp = MFnLineArray(xdata=-self.CB_model._x_arr[::-1],
ydata=self.CB_model._epsf_arr[::-1])
else:
epsf_interp = MFnLineArray(xdata=self.CB_model._x_arr,
ydata=self.CB_model._epsf_arr)
return epsf_interp.get_values(self.x)
def w_x_results( self, w_arr, x ):
epsm = np.zeros( ( len( w_arr ), len( x ) ) )
mu_epsf = np.zeros( ( len( w_arr ), len( x ) ) )
sigma_c = []
for i, w in enumerate( w_arr ):
self.model.w = w
epsm_line = MFnLineArray( xdata = self.x_arr, ydata = self.epsm_arr )
mu_epsf_line = MFnLineArray( xdata = self.x_arr, ydata = self.mu_epsf_arr )
epsm[i, :] = epsm_line.get_values( x )
mu_epsf[i, :] = mu_epsf_line.get_values( x )
sigma_c.append( self.sigma_c )
return epsm, mu_epsf, np.array( sigma_c )
def _get__epsm_arr(self):
if len(self.sorted_reinf_lst[0]) != 0 and len(self.sorted_reinf_lst[1]) != 0:
epsm_cont_interp = MFnLineArray(xdata=self.cont_fibers.x_arr, ydata=self.cont_fibers.epsm_arr)
epsm_short_interp = MFnLineArray(xdata=self.short_fibers.x_arr, ydata=self.short_fibers.epsm_arr)
added_epsm_cont = self.cont_fibers.epsm_arr + epsm_short_interp.get_values(self.cont_fibers.x_arr)
added_epsm_short = self.short_fibers.epsm_arr + epsm_cont_interp.get_values(self.short_fibers.x_arr)
sorted_unique_idx = np.unique(np.hstack((self.cont_fibers.x_arr, self.short_fibers.x_arr)), return_index=True)[1]
return np.hstack((added_epsm_cont, added_epsm_short))[sorted_unique_idx]
elif len(self.sorted_reinf_lst[0]) != 0:
return self.cont_fibers.epsm_arr
elif len(self.sorted_reinf_lst[1]) != 0:
self.short_fibers.w = self.w
return self.short_fibers.epsm_arr
def _get_pdf_array( self ):
# get the normed histogram implemented in the base class YMBHist
h, b = self.normed_hist
b_cen = ( b[1:] + b[:-1] ) / 2.
mask = ( h != 0. )
h = h[mask]
b_cen = b_cen[mask]
b = hstack( [min( b ), b_cen, max( b )] )
h = hstack( [0, h, 0] )
h = h / trapz( h, b )
p = MFnLineArray( xdata=b, ydata=h )
return p.get_values( self.x_array )
def _get_psi_line(self):
try:
psi = open('psi_Curtin', 'r')
line = pickle.load(psi)
except:
eta_psi_vect = np.vectorize(self.intt)
psi = np.linspace(1e-3, 4.99, 300)
eta_psi_func = eta_psi_vect(psi)
eta2 = np.linspace(0.7476 - e**(-2. * 0.577216) / 5, 0.74759, 500)
psi2 = e**(-2. * 0.577216) / (0.7476 - eta2)
line = MFnLineArray(xdata=np.hstack((eta_psi_func, eta2)),
ydata=np.hstack((psi, psi2)))
return line
def _get_U_line(self):
'''work done by external force - mfn_line'''
w_arr = self.w_arr_energy
u_lst = []
F_lst = []
for w in w_arr:
self.model.w = w
u_lst.append(self.u_evaluated)
F_lst.append(self.sigma_c)
u_arr = np.array(u_lst)
F_arr = np.array(F_lst)
U_line = MFnLineArray(xdata=w_arr, ydata=np.hstack((0, cumtrapz(F_arr, u_arr))))
return U_line
def p_x(self, x_arr, P=0.2, order=1, **kw):
'''
returns p for a range of x
'''
kw['x'] = 0.0
kw['w'] = 0.0
w = newton(self.force_residuum, 1e-3, args=(order, P, kw))
kw['w'] = w
#x_arr = linspace( -kw['Ll'], kw['Lr'], np )
p_arr = []
for i, x in enumerate(x_arr):
kw['x'] = x
p_arr.append(self.mean_e_P(order=order, **kw).flatten())
return MFnLineArray(xdata=x_arr, ydata=array(p_arr).flatten())
def get_df_average( self, n_points ):
'''derive the average phi-function based on all entries
in damage_function_list
'''
def get_y_average( self, x_average ):
'''get the y-values from the mfn-functions in df_list for
'x_average' and return the average.
Note that the shape of 'mfn.xdata' does not necessarily needs to be equal in all
'DamageFunctionEntries' as the number of steps used for calibration or the adaptive
refinement in 'tloop' might have been different for each case.
'''
y_list = [ self.damage_function_list[i].damage_function.get_value( x_average ) \
for i in range(len( self.damage_function_list )) ]
return sum(y_list) / len(y_list)
get_y_average_vectorized = frompyfunc( get_y_average, 2, 1 )
mfn = MFnLineArray()
# take the smallest value of the strains for the average function. Beyond this value
# the average does not make sense anymore because it depends on the arbitrary number
# of entries in the df_list
#
xdata_min = min( self.damage_function_list[i].damage_function.xdata[-1] \
for i in range( len( self.damage_function_list ) ) )
# number of sampling point used for the average phi function
#
mfn.xdata = linspace( 0., xdata_min, num = n_points )
# get the corresponding average ydata values
#
mfn.ydata = self.get_y_average_vectorized( mfn.xdata )
return mfn
def get_df_average(self, n_points):
'''derive the average phi-function based on all entries
in damage_function_list
'''
def get_y_average(self, x_average):
'''get the y-values from the mfn-functions in df_list for
'x_average' and return the average.
Note that the shape of 'mfn.xdata' does not necessarily needs to be equal in all
'DamageFunctionEntries' as the number of steps used for calibration or the adaptive
refinement in 'tloop' might have been different for each case.
'''
y_list = [ self.damage_function_list[i].damage_function.get_value(x_average) \
for i in range(len(self.damage_function_list)) ]
return sum(y_list) / len(y_list)
get_y_average_vectorized = frompyfunc(get_y_average, 2, 1)
mfn = MFnLineArray()
# take the smallest value of the strains for the average function. Beyond this value
# the average does not make sense anymore because it depends on the arbitrary number
# of entries in the df_list
#
xdata_min = min(self.damage_function_list[i].damage_function.xdata[-1] \
for i in range(len(self.damage_function_list)))
# number of sampling point used for the average phi function
#
mfn.xdata = linspace(0., xdata_min, num=n_points)
# get the corresponding average ydata values
#
mfn.ydata = self.get_y_average_vectorized(mfn.xdata)
return mfn
def w_x_results(self, w_arr, x):
epsm = np.zeros((len(w_arr), len(x)))
mu_epsf = np.zeros((len(w_arr), len(x)))
sigma_c = []
for i, w in enumerate(w_arr):
self.model.w = w
epsm_line = MFnLineArray(xdata=self.x_arr, ydata=self.epsm_arr)
mu_epsf_line = MFnLineArray(xdata=self.x_arr,
ydata=self.mu_epsf_arr)
epsm[i, :] = epsm_line.get_values(x)
mu_epsf[i, :] = mu_epsf_line.get_values(x)
sigma_c.append(self.sigma_c)
return epsm, mu_epsf, np.array(sigma_c)
def _friction_fets_default(self):
width = self.specimen_width
G = self.g * width
T_max = self.t_max * width
ifopen_law = MFnLineArray(ydata=[-1.0e+1, 0., 1.0e+10],
xdata=[-1., 0., 1.])
mats_ifopen = MATS1DElastic(stress_strain_curve=ifopen_law)
mats = MATS1D5Bond(mats_phase1=MATS1DElastic(E=0),
mats_phase2=MATS1DElastic(E=0),
mats_ifslip=MATS1DPlastic(E=G,
sigma_y=T_max,
K_bar=0.,
H_bar=0.),
mats_ifopen=mats_ifopen)
fets = FETS1D52L6ULRH(mats_eval=mats) #quadratic
fets.vtk_r *= self.vtk_r
return fets
def _get__epsm_arr(self):
if len(self.sorted_reinf_lst[0]) != 0 and len(
self.sorted_reinf_lst[1]) != 0:
epsm_cont_interp = MFnLineArray(xdata=self.cont_fibers.x_arr,
ydata=self.cont_fibers.epsm_arr)
epsm_short_interp = MFnLineArray(xdata=self.short_fibers.x_arr,
ydata=self.short_fibers.epsm_arr)
added_epsm_cont = self.cont_fibers.epsm_arr + epsm_short_interp.get_values(
self.cont_fibers.x_arr)
added_epsm_short = self.short_fibers.epsm_arr + epsm_cont_interp.get_values(
self.short_fibers.x_arr)
sorted_unique_idx = np.unique(np.hstack(
(self.cont_fibers.x_arr, self.short_fibers.x_arr)),
return_index=True)[1]
return np.hstack(
(added_epsm_cont, added_epsm_short))[sorted_unique_idx]
elif len(self.sorted_reinf_lst[0]) != 0:
return self.cont_fibers.epsm_arr
elif len(self.sorted_reinf_lst[1]) != 0:
self.short_fibers.w = self.w
return self.short_fibers.epsm_arr
3.75724285, 3.84216067, 3.92147416, 3.997584 , 4.07213255, 4.1462654,
4.22079085, 4.29628189, 4.37314373, 4.4516601 , 4.53202557, 4.61436863,
4.69876847, 4.78526735, 4.87387975, 4.96459931, 5.0574041 , 5.15226056,
5.24912666, 5.3479542 , 5.44869074, 5.55128092, 5.65566766, 5.76179299,
5.86959871, 5.97902693, 6.09002044, 0.74043954, 0.74661105, 0.75304179,
0.75972494, 0.7666537 , 0.77382139, 0.7812214 , 0.78884722, 0.79669249,
0.80475096, 0.81301651, 0.82148317, 0.83014509, 0.83899658, 0.84803208,
0.85724617, 0.86663356, 0.87618913, 0.88590785, 0.89578486, 0.90581541,
0.91599489, 0.9263188 , 0.93678277, 0.94738257, 0.95811405, 0.9689732,
0.9689732 ])
# interpolation function for fitting data
mfn_line_array_fit = MFnLineArray()
mfn_line_array_fit.set( xdata = xdata_fit, ydata = ydata_fit )
mfn_line_array_fit.data_changed = True
# get the current strain:
eps_app_tn1 = copy( fitter.tloop.U_k )
print 'eps_app_tn1', eps_app_tn1
# get the current sctx:
sctx = fitter.tloop.tstepper.sctx
print 'sctx', sctx
# for fitting use default method 'get_value' of 'MFnLineArray' as 'phi_fn':
fitter.mats2D_eval.polar_fn.phi_fn.mfn.get_value = MFnLineArray().get_value
def _get_mean_curve( self ):
'''Mean response curve.
'''
return MFnLineArray( xdata = self.eps_arr, ydata = self.mu_q_arr )
def get_L(self, Ll, Lr):
self.result_values
BC_line = MFnLineArray(xdata=self.BC_range,
ydata=self.BC_range,
extrapolate='constant')
return BC_line.get_values([Ll, Lr])
def pdf(self, e, r, L):
cdf_line = MFnLineArray(xdata=e, ydata=self.cdf(e, r, L))
return cdf_line.get_diffs(e)
def _damage_law_default(self):
return MFnLineArray(xdata=[0.0, 1.0],
ydata=[0.0, 0.0],
plot_diff=False)
def _mfn_default( self ):
return MFnLineArray()
def pdf(self, e, depsf, r, al, ar):
cdf_line = MFnLineArray(xdata=e, ydata=self.cdf(e, depsf, r, al, ar))
return cdf_line.get_diffs(e)
def _get_ctrl_indices(self):
interp_indices_list = []
for i, value in enumerate(self.ctrl_list):
interp_array = MFnLineArray(xdata=value, ydata=arange(len(value)))
interp_indices_list.append(interp_array)
return interp_indices_list
def __stress_strain_curve_default(self):
return MFnLineArray(ydata=[0., self.E], xdata=[0., 1.])
def Pw(self, w):
mask = self.cached_pdfs[3] < w
dx2 = (self.x[-1]-self.x[-2])**2
return np.sum(self.cached_pdfs[2] * mask * dx2)
def CDF_w(self, w_arr):
cdfs = []
for w in w_arr:
cdfs.append(self.Pw(w))
return cdfs
if __name__ == '__main__':
ac = AnalyticalCracks(l0=1., d=0.007, tau=0.1, sigma0=2200., s=2.0,
rho=5.0, c=1.0, x=np.linspace(0.0, 5.0, 500))
for s in [1., 3., 5.0]:
ac.s = s
pdf_x = ac.p_x(s, ac.x)
pdf_x = pdf_x / np.trapz(pdf_x, ac.x)
cdf_x = np.hstack((0., cumtrapz(pdf_x, ac.x)))
sf = MFnLineArray(xdata=cdf_x + 1e-12 * ac.x, ydata=ac.x)
rand_vals1 = sf.get_values(np.random.rand(10000))
rand_vals2 = sf.get_values(np.random.rand(10000))
cracks = ac.w_func(rand_vals1, rand_vals2)
plt.hist(cracks, bins=40, normed=True, label=str(s), cumulative=True)
w_arr = np.linspace(0.0,7.0,100)
cdf_w = ac.CDF_w(w_arr)
plt.plot(w_arr, cdf_w, color='black', lw=2)
plt.ylim(0,1.2)
plt.legend()
plt.show()
def reset_stress_strain_curve(self):
self._stress_strain_curve = MFnLineArray(ydata=[0., self.E],
xdata=[0., 1.])
def eta(self, psi):
psi_line = MFnLineArray(xdata=self.psi_line.ydata,
ydata=self.psi_line.xdata)
return psi_line.get_values(psi, k=1)
def pdf(self, e, r, L):
cdf_line = MFnLineArray(xdata=e, ydata=self.cdf(e, r, L))
return cdf_line.get_diffs(e)
def _mfn_line_array_target_default(self):
mfn = MFnLineArray( xdata = self.xdata_target, ydata = self.ydata_target )
mfn.data_changed = True
return mfn
def _get_tloop(self):
#fets_eval.mats_eval.nu = self.nu
specmn = self.specmn_fe_grid
if False:
elstmr = self.elstmr_fe_grid
supprt = self.supprt_fe_grid
self.fe_domain.n_dofs
self.center_top_dofs = specmn[0, 0, -1, 0, 0, -1].dofs
center_bottom_dofs = specmn[0, 0, 0, 0, 0, 0].dofs
support_elem = self.shape_y - 4
#--------------------------------------------------------------
# boundary conditions for the symmetry and the single support
#--------------------------------------------------------------
bc_symplane_yz = BCSlice(var='u',
value=0.,
dims=[0],
slice=specmn[0, :, :, 0, :, :])
bc_symplane_xz = BCSlice(var='u',
value=0.,
dims=[1],
slice=specmn[:, 0, :, :, 0, :])
#--------------------------------------------------------------
# boundary conditions for the symmetry and the single support
#--------------------------------------------------------------
support_slice = specmn[-1, support_elem, :, -1, 0, :]
support_000 = BCSlice(var='u',
value=0.,
dims=[0, 2],
slice=support_slice)
#--------------------------------------------------------------
# loading
#--------------------------------------------------------------
# w_max = center displacement:
w_max = -0.07 # [m]
time_function = MFnLineArray(xdata=[0.0, 0.2, 0.4, 1.0],
ydata=[0.0, 0.2, 0.75, 1.0])
bc_el_w = BCSlice(
var='u',
value=w_max,
dims=[2], #time_function = time_function.get_value,
slice=specmn[0, 0, 0, 0, 0, 0])
p_max = -0.0042 / (0.2 * 0.2)
p_slice = specmn[:, 0, -1, :, :, -1]
bc_el_w = BCSlice(
var='f',
value=p_max,
dims=[2],
integ_domain='local', #time_function = time_function.get_value,
slice=p_slice)
#--------------------------------------------------------------
# ts
#--------------------------------------------------------------
center_dof = center_bottom_dofs[0, 0, 2]
# center_top_line_dofs
#
#ctl_dofs = elstmr[:, :, -1, :, :, -1].dofs[:, :, 2].flatten()
# force-displacement-diagram
#
self.f_w_diagram_center = RTraceGraph(
name='displacement (center) - reaction 2',
var_x='U_k',
idx_x=center_dof,
# elastomer load
var_y='F_int',
idx_y_arr=support_slice.dofs[:, :, 2].flatten(),
record_on='update',
transform_x='-x * 1000', # %g * x' % ( fabs( w_max ),),
# due to symmetry the total force sums up from four parts of the beam (2 symmetry axis):
#
transform_y='-2 * 1000. * y')
bcond_list = [
bc_symplane_yz,
bc_symplane_xz,
support_000,
# var 1:
bc_el_w,
# bc_center_w,
# # var 2:
# bc_center_w_elem,
# # var 3:
# bc_center_w_xline, bc_center_w_yline
]
rtrace_list = [
self.f_w_diagram_center,
RTraceDomainListField(name='Displacement',
var='u',
idx=0,
warp=True),
# RTraceDomainListField(name = 'Stress' ,
# var = 'sig_app', idx = 0, warp = True,
# record_on = 'update'),
# RTraceDomainListField(name = 'Strain' ,
# var = 'eps_app', idx = 0, warp = True,
# record_on = 'update'),
RTraceDomainListField(name='Damage',
var='omega_mtx',
idx=0,
warp=True,
record_on='update'),
RTraceDomainListField(
name='max principle stress',
idx=0,
var='max_principle_sig',
warp=True,
# position = 'int_pnts',
record_on='update',
)
# RTraceDomainListField(name = 'IStress' ,
# position = 'int_pnts',
# var = 'sig_app', idx = 0,
# record_on = 'update'),
# RTraceDomainListField(name = 'IStrain' ,
# position = 'int_pnts',
# var = 'eps_app', idx = 0,
# record_on = 'update'),
]
ts = TS(sdomain=self.fe_domain,
bcond_list=bcond_list,
rtrace_list=rtrace_list)
print 'tstep', self.tstep
# Add the time-loop control
tloop = TLoop(
tstepper=ts,
# # allow only a low tolerance
# #
# KMAX = 50,
# tolerance = 5e-4,
# allow a high tolerance
#
KMAX=100,
tolerance=0.001,
# # allow a very high tolerance
# #
# KMAX = 50,
# tolerance = 0.01,
RESETMAX=0,
debug=False,
tline=TLine(min=0.0, step=self.tstep, max=1))
return tloop
def get_L( self, Ll, Lr ):
self.result_values
BC_line = MFnLineArray( xdata = self.BC_range, ydata = self.BC_range, extrapolate = 'constant' )
return BC_line.get_values( [Ll, Lr] )
from mathkit.mfn.mfn_line.mfn_line import MFnLineArray from numpy import linspace, frompyfunc, array from math import sin, cos sigma_max = 0.4 xdata = linspace( 0., 1., 10000 ) fnydata = lambda x: 1.0+0.0001*sin(19.0*x)*cos(200*x) * sigma_max * 10000 fnydata_vec = frompyfunc( fnydata, 1, 1 ) ydata = array( fnydata_vec( xdata ), dtype = float ) #print ydata mfn = MFnLineArray( xdata = xdata, ydata = ydata ) mfn.configure_traits()
def get_sigma_m_x_input( self, sigma ):
self.apply_load( sigma )
line = MFnLineArray( xdata = self.x_arr,
ydata = self.epsm_arr )
return line.get_values( self.x_input )
for w in w_arr:
cdfs.append(self.Pw(w))
return cdfs
if __name__ == '__main__':
ac = AnalyticalCracks(l0=1.,
d=0.007,
tau=0.1,
sigma0=2200.,
s=2.0,
rho=5.0,
c=1.0,
x=np.linspace(0.0, 5.0, 500))
for s in [1., 3., 5.0]:
ac.s = s
pdf_x = ac.p_x(s, ac.x)
pdf_x = pdf_x / np.trapz(pdf_x, ac.x)
cdf_x = np.hstack((0., cumtrapz(pdf_x, ac.x)))
sf = MFnLineArray(xdata=cdf_x + 1e-12 * ac.x, ydata=ac.x)
rand_vals1 = sf.get_values(np.random.rand(10000))
rand_vals2 = sf.get_values(np.random.rand(10000))
cracks = ac.w_func(rand_vals1, rand_vals2)
plt.hist(cracks, bins=40, normed=True, label=str(s), cumulative=True)
w_arr = np.linspace(0.0, 7.0, 100)
cdf_w = ac.CDF_w(w_arr)
plt.plot(w_arr, cdf_w, color='black', lw=2)
plt.ylim(0, 1.2)
plt.legend()
plt.show()
def pdf(self, e, depsf, r, al, ar):
cdf_line = MFnLineArray(xdata=e, ydata=self.cdf(e, depsf, r, al, ar))
return cdf_line.get_diffs(e)
def _get_matrix_strength(self):
# evaluates a random field
# realization and creates a spline reprezentation
rf = self.random_field.random_field
rf_spline = MFnLineArray(xdata=self.random_field.xgrid, ydata=rf)
return rf_spline.get_values(self.x_arr)
def _get_matrix_strength(self):
# evaluates a random field
# realization and creates a spline representation
rf = self.random_field.random_field
rf_spline = MFnLineArray(xdata=self.random_field.xgrid, ydata=rf)
return rf_spline.get_values(self.x_arr)
def eta(self, psi):
psi_line = MFnLineArray(xdata=self.psi_line.ydata,
ydata=self.psi_line.xdata)
return psi_line.get_values(psi, k=1)
def _get_var_curve( self ):
'''variance of q at eps'''
return MFnLineArray( xdata = self.eps_arr, ydata = self.var_q_arr )
def get_sigma_m_x_input(self, sigma):
self.apply_load(sigma)
line = MFnLineArray(xdata=self.x_arr,
ydata=self.epsm_arr)
return line.get_values(self.x_input)