def interp_psi(self,psi=None,iopt=1):
"""
iopt=0: interpolate along psi
iopt=1: interpolate along +- sin2psi
"""
psi = np.array(psi)
from numpy import sign
if iopt==0:
x = psi
x0 = self.psi
if iopt==1:
x = sign(psi) * sin(psi*np.pi/180.)**2
x0 = sign(self.psi) * sin(self.psi*np.pi/180.)**2
self.sf_old = self.sf.copy()
self.psi_old = self.psi.copy()
self.npsi # also need change
self.sf_new = np.zeros(
(self.nstp,self.nphi,len(x),self.nij))
for istp in xrange(self.nstp):
for iphi in xrange(self.nphi):
for k in xrange(self.nij):
y = self.sf_old[istp,iphi,:,k]
self.sf_new[istp,iphi,:,k] \
= rs.interpolate(x,x0,y)
self.sf = self.sf_new.copy()
self.psi = psi.copy()
self.npsi = len(self.psi)
def interp_psi(self, psi=None, iopt=1):
"""
iopt=0: interpolate along psi
iopt=1: interpolate along +- sin2psi
"""
psi = np.array(psi)
from numpy import sign
if iopt == 0:
x = psi
x0 = self.psi
if iopt == 1:
x = sign(psi) * sin(psi * np.pi / 180.)**2
x0 = sign(self.psi) * sin(self.psi * np.pi / 180.)**2
self.sf_old = self.sf.copy()
self.psi_old = self.psi.copy()
self.npsi # also need change
self.sf_new = np.zeros((self.nstp, self.nphi, len(x), self.nij))
for istp in xrange(self.nstp):
for iphi in xrange(self.nphi):
for k in xrange(self.nij):
y = self.sf_old[istp, iphi, :, k]
self.sf_new[istp,iphi,:,k] \
= rs.interpolate(x,x0,y)
self.sf = self.sf_new.copy()
self.psi = psi.copy()
self.npsi = len(self.psi)
def interp_strain(self,epsilon_vm,
iopt=0):
"""
Apply a certain interpolation method
on the SF with respect to the given VM strain
Arguments
=========
epsilon_vm = []
iopt=0: NN (piece-wise linear interpolation)
=1: Assign nearest data
=2: Cubic
=3: Quadratic
=4: Linear fit
=5: Poly2
=6: poly3
=7: Place-holder for power law fit
=8: zero
=9: slinear
"""
self.sf_old = self.sf.copy()
# self.flow.get_vm_strain()
# self.flow.epsilon_vm #
self.sf_new = np.zeros(
(len(epsilon_vm),self.nphi,self.npsi,self.nij))
for iphi in range(self.nphi):
for ipsi in range(self.npsi):
for k in range(self.nij):
y = self.sf_old[:,iphi,ipsi,k]
self.sf_new[:,iphi,ipsi,k] \
= rs.interpolate(
xs=epsilon_vm,
xp=self.flow.epsilon_vm,
fp=y,
iopt=iopt)
self.sf = self.sf_new
# Overwrite the flow? self.nstp also needs change
self.flow = fc()
self.flow.epsilon_vm = np.array(epsilon_vm)
self.nstp = len(self.flow.epsilon_vm)
def interp_strain(self,epsilon_vm,iopt=0):
"""
Apply a certain interpolation method
on the IG with respect to the given VM strain
Arguments
=========
epsilon_vm = []
iopt=0: NN (piece-wise linear interpolation)
=1: Assign nearest data
=2: Cubic
=3: Quadratic
=4: Linear fit
=5: Poly2
=6: poly3
=7: Place-holder for power law fit
=8: zero
=9: slinear
"""
self.ig_old = self.ig.copy()
self.flow.get_vm_strain()
self.flow.epsilon_vm #
self.ig_new = np.zeros(
(len(epsilon_vm),self.nphi,self.npsi))
xp = self.flow.epsilon_vm.copy()
for iphi in xrange(self.nphi):
for ipsi in xrange(self.npsi):
_y_ = self.ig_old[:,iphi,ipsi].copy()
## find non zero not (nan) values
inds = []
n_avail_dat = 0 ## in case that IG strain is all zeroes (Brian Lin's case)
if (_y_==0).all():
_y_ = _y_.copy()
_xp_ = xp.copy()
else:
for m in xrange(len(_y_)):
if _y_[m]!=0 and not(np.isnan(_y_[m])):
inds.append(m)
n_avail_dat = len(inds)
pass
if n_avail_dat==0 or n_avail_dat==1:
# print 'interpolation is not ',
# print 'feasible due to insufficient',
# print 'number of data'
self.ig_new[:,iphi,ipsi] = 0. # np.nan
elif n_avail_dat>=2:
_y_ = _y_[inds].copy()
_xp_ = xp[inds].copy()
try:
self.ig_new[:,iphi,ipsi]\
= rs.interpolate(
xs=epsilon_vm.copy(),
xp=_xp_,
fp=_y_,
iopt=iopt)
except:
print 'failed in ig interpolation'
try:
self.ig_new[:,iphi,ipsi]\
= rs.interpolate(
xs=epsilon_vm.copy(),
xp=_xp_,
fp=_y_,
iopt=1) ## if fail, try assign nearest data
except:
nans = np.zeros(len(epsilon_vm))
nans = np.nan * nans
self.ig_new[:,iphi,ipsi] = 0. #np.nan
else: raise IOError, 'Unexpected case'
self.ig = self.ig_new
# Overwrite the flow? self.nstp also needs change
self.flow = fc()
self.flow.epsilon_vm = np.array(epsilon_vm)
self.nstp = len(self.flow.epsilon_vm)
def interp_strain(self,epsilon_vm,
iopt=0):
"""
Apply a certain interpolation method
on the SF with respect to the given VM strain
Arguments
=========
epsilon_vm = []
iopt=0: NN (piece-wise linear interpolation)
=1: Assign nearest data
=2: Cubic
=3: Quadratic
=4: Linear fit
=5: Poly2
=6: poly3
=7: Place-holder for power law fit
=8: zero
=9: slinear
"""
print 'epsilon_vm:'
print epsilon_vm
import warnings
self.sf_old = self.sf.copy()
# self.flow.get_vm_strain()
# self.flow.epsilon_vm #
self.sf_new = np.zeros(
(len(epsilon_vm),self.nphi,self.npsi,self.nij))
xp = self.flow.epsilon_vm.copy()
print 'Strain reference at which sf was measured:'
print xp
for iphi in xrange(self.nphi):
for ipsi in xrange(self.npsi):
for k in xrange(self.nij):
_y_ = self.sf_old[:,iphi,ipsi,k].copy()
## find non zero not (nan) values
inds = []
if (_y_==0).all():
_y_ = _y_.copy()
_xp_ = xp.copy()
n_avail_dat = 0
else:
for m in xrange(len(_y_)):
if _y_[m]!=0 and not(np.isnan(_y_[m])):
inds.append(m)
n_avail_dat = len(inds)
pass
if n_avail_dat==0 or n_avail_dat==1:
if k<2:
print 'interpolation is not ',
print 'feasible due to insufficient',
print 'number of data'
self.sf_new[:,iphi,ipsi,k] = np.nan
elif n_avail_dat>=2:
_y_ = _y_[inds].copy()
_xp_ = xp[inds].copy()
try:
self.sf_new[:,iphi,ipsi,k] \
= rs.interpolate(
xs=epsilon_vm.copy(),
xp=_xp_,
fp=_y_,
iopt=iopt)
except:
print 'failed, thus using NN interp'
try:
self.sf_new[:,iphi,ipsi,k] \
= rs.interpolate(
xs=epsilon_vm.copy(),
xp=_xp_,
fp=_y_,
iopt=1) ## if fail, try assign nearest data
except:
nans = np.zeros(len(epsilon_vm))
nans = np.nan * nans
self.sf_new[:,iphi,ipsi] = nans
else: raise IOError, 'Unexpected case'
if self.sf_new.shape!=(len(epsilon_vm),self.nphi,self.npsi,self.nij):
raise IOError, 'something wrong'
self.sf = self.sf_new.copy()
# Overwrite the flow? self.nstp also needs change
self.flow = fc()
self.flow.epsilon_vm = np.array(epsilon_vm)
self.nstp = len(self.flow.epsilon_vm)
def interp_strain(self, epsilon_vm, iopt=0):
"""
Apply a certain interpolation method
on the IG with respect to the given VM strain
Arguments
=========
epsilon_vm = []
iopt=0: NN (piece-wise linear interpolation)
=1: Assign nearest data
=2: Cubic
=3: Quadratic
=4: Linear fit
=5: Poly2
=6: poly3
=7: Place-holder for power law fit
=8: zero
=9: slinear
"""
self.ig_old = self.ig.copy()
self.flow.get_vm_strain()
self.flow.epsilon_vm #
self.ig_new = np.zeros((len(epsilon_vm), self.nphi, self.npsi))
xp = self.flow.epsilon_vm.copy()
for iphi in xrange(self.nphi):
for ipsi in xrange(self.npsi):
_y_ = self.ig_old[:, iphi, ipsi].copy()
## find non zero not (nan) values
inds = []
n_avail_dat = 0 ## in case that IG strain is all zeroes (Brian Lin's case)
if (_y_ == 0).all():
_y_ = _y_.copy()
_xp_ = xp.copy()
else:
for m in xrange(len(_y_)):
if _y_[m] != 0 and not (np.isnan(_y_[m])):
inds.append(m)
n_avail_dat = len(inds)
pass
if n_avail_dat == 0 or n_avail_dat == 1:
# print 'interpolation is not ',
# print 'feasible due to insufficient',
# print 'number of data'
self.ig_new[:, iphi, ipsi] = 0. # np.nan
elif n_avail_dat >= 2:
_y_ = _y_[inds].copy()
_xp_ = xp[inds].copy()
try:
self.ig_new[:,iphi,ipsi]\
= rs.interpolate(
xs=epsilon_vm.copy(),
xp=_xp_,
fp=_y_,
iopt=iopt)
except:
print 'failed in ig interpolation'
try:
self.ig_new[:,iphi,ipsi]\
= rs.interpolate(
xs=epsilon_vm.copy(),
xp=_xp_,
fp=_y_,
iopt=1) ## if fail, try assign nearest data
except:
nans = np.zeros(len(epsilon_vm))
nans = np.nan * nans
self.ig_new[:, iphi, ipsi] = 0. #np.nan
else:
raise IOError, 'Unexpected case'
self.ig = self.ig_new
# Overwrite the flow? self.nstp also needs change
self.flow = fc()
self.flow.epsilon_vm = np.array(epsilon_vm)
self.nstp = len(self.flow.epsilon_vm)
def interp_strain(self, epsilon_vm, iopt=0):
"""
Apply a certain interpolation method
on the SF with respect to the given VM strain
Arguments
=========
epsilon_vm = []
iopt=0: NN (piece-wise linear interpolation)
=1: Assign nearest data
=2: Cubic
=3: Quadratic
=4: Linear fit
=5: Poly2
=6: poly3
=7: Place-holder for power law fit
=8: zero
=9: slinear
"""
print 'epsilon_vm:'
print epsilon_vm
import warnings
self.sf_old = self.sf.copy()
# self.flow.get_vm_strain()
# self.flow.epsilon_vm #
self.sf_new = np.zeros(
(len(epsilon_vm), self.nphi, self.npsi, self.nij))
xp = self.flow.epsilon_vm.copy()
print 'Strain reference at which sf was measured:'
print xp
for iphi in xrange(self.nphi):
for ipsi in xrange(self.npsi):
for k in xrange(self.nij):
_y_ = self.sf_old[:, iphi, ipsi, k].copy()
## find non zero not (nan) values
inds = []
if (_y_ == 0).all():
_y_ = _y_.copy()
_xp_ = xp.copy()
n_avail_dat = 0
else:
for m in xrange(len(_y_)):
if _y_[m] != 0 and not (np.isnan(_y_[m])):
inds.append(m)
n_avail_dat = len(inds)
pass
if n_avail_dat == 0 or n_avail_dat == 1:
if k < 2:
print 'interpolation is not ',
print 'feasible due to insufficient',
print 'number of data'
self.sf_new[:, iphi, ipsi, k] = np.nan
elif n_avail_dat >= 2:
_y_ = _y_[inds].copy()
_xp_ = xp[inds].copy()
try:
self.sf_new[:,iphi,ipsi,k] \
= rs.interpolate(
xs=epsilon_vm.copy(),
xp=_xp_,
fp=_y_,
iopt=iopt)
except:
print 'failed, thus using NN interp'
try:
self.sf_new[:,iphi,ipsi,k] \
= rs.interpolate(
xs=epsilon_vm.copy(),
xp=_xp_,
fp=_y_,
iopt=1) ## if fail, try assign nearest data
except:
nans = np.zeros(len(epsilon_vm))
nans = np.nan * nans
self.sf_new[:, iphi, ipsi] = nans
else:
raise IOError, 'Unexpected case'
if self.sf_new.shape != (len(epsilon_vm), self.nphi, self.npsi,
self.nij):
raise IOError, 'something wrong'
self.sf = self.sf_new.copy()
# Overwrite the flow? self.nstp also needs change
self.flow = fc()
self.flow.epsilon_vm = np.array(epsilon_vm)
self.nstp = len(self.flow.epsilon_vm)
