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Commit 47b6395f authored by Marco Selig's avatar Marco Selig
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minor update due to feature request.

parent 8ae46d85
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nifty_core.py
View file @ 47b6395f
......@@ -486,7 +486,7 @@ class _about(object): ## nifty support class for global settings
"""
## version
self._version = "0.6.7"
self._version = "0.6.8"
## switches and notifications
self._errors = notification(default=True,ccode=notification._code)
......
nifty_explicit.py
View file @ 47b6395f
......@@ -55,7 +55,7 @@ class explicit_operator(operator):
"""
epsilon = 1E-12 ## absolute precision for comparisons to identity
def __init__(self,domain,matrix,bare=True,sym=None,uni=None,target=None):
def __init__(self,domain,matrix=None,bare=True,sym=None,uni=None,target=None): ## FIXME: None
"""
TODO: documentation
......@@ -63,28 +63,38 @@ class explicit_operator(operator):
## check domain
if(not isinstance(domain,space)):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
elif(np.size(matrix,axis=None)%domain.dim(split=False)!=0):
raise ValueError(about._errors.cstring("ERROR: dimension mismatch ( "+str(np.size(matrix,axis=None))+" <> "+str(domain.dim(split=False))+" )."))
self.domain = domain
## check shape
val = np.array(matrix).reshape((-1,self.domain.dim(split=False)))
## check matrix and target
if(matrix is None):
if(target is None):
val = np.zeros((self.domain.dim(split=False),self.domain.dim(split=False)),dtype=np.int,order='C')
target = self.domain
else:
if(not isinstance(target,space)):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
elif(target!=self.domain):
sym = False
uni = False
val = np.zeros((target.dim(split=False),self.domain.dim(split=False)),dtype=np.int,order='C')
elif(np.size(matrix,axis=None)%self.domain.dim(split=False)==0):
val = np.array(matrix).reshape((-1,self.domain.dim(split=False)))
if(target is not None):
if(not isinstance(target,space)):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
elif(val.shape[0]!=target.dim(split=False)):
raise ValueError(about._errors.cstring("ERROR: dimension mismatch ( "+str(val.shape[0])+" <> "+str(target.dim(split=False))+" )."))
elif(target!=self.domain):
sym = False
uni = False
elif(val.shape[0]==val.shape[1]):
target = self.domain
else:
raise TypeError(about._errors.cstring("ERROR: insufficient input."))
else:
raise ValueError(about._errors.cstring("ERROR: dimension mismatch ( "+str(np.size(matrix,axis=None))+" <> "+str(self.domain.dim(split=False))+" )."))
if(val.size>1048576):
about.infos.cprint("INFO: matrix size > 2 ** 20.")
## check target
if(target is not None):
if(not isinstance(target,space)):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
elif(val.shape[0]!=target.dim(split=False)):
raise ValueError(about._errors.cstring("ERROR: dimension mismatch ( "+str(val.shape[0])+" <> "+str(target.dim(split=False))+" )."))
elif(target!=self.domain):
sym = False
uni = False
elif(val.shape[0]==val.shape[1]):
target = self.domain
else:
raise TypeError(about._errors.cstring("ERROR: insufficient input."))
self.target = target
## check datatype
......@@ -106,30 +116,6 @@ class explicit_operator(operator):
else:
self.val = val.astype(datatype)
# if(np.iscomplexobj(val)):
# if(np.all(np.imag(val)==0)):
# val = np.real(val).astype(min(val.dtype,self.domain.vol.dtype,self.target.vol.dtype))
# else:
# val = val.astype(min(val.dtype,self.domain.datatype,self.target.datatype))
# else:
# val = val.astype(min(val.dtype,self.domain.vol.dtype,self.target.vol.dtype))
# ## weight if ... (given `domain` and `target`)
# if(isinstance(bare,tuple)):
# if(len(bare)!=2):
# raise ValueError(about._errors.cstring("ERROR: invalid input."))
# else:
# val = self._calc_weight_rows(val,-bool(bare[0]))
# val = self._calc_weight_cols(val,-bool(bare[1]))
# elif(not bare):
# val = self._calc_weight_rows(val,-1)
# self.val = val
## check hidden degrees of freedom
self._hidden = np.array([self.domain.dim(split=False)<self.domain.dof(),self.target.dim(split=False)<self.target.dof()],dtype=np.bool)
# if(np.any(self._hidden)):
......@@ -175,29 +161,35 @@ class explicit_operator(operator):
TODO: documentation
"""
## check shape
val = np.array(newmatrix).reshape((-1,self.domain.dim(split=False)))
if(val.size>1048576):
about.warnings.cprint("WARNING: matrix size > 2 ** 20.")
## check matrix
if(np.size(newmatrix,axis=None)==self.domain.dim(split=False)*self.target.dim(split=False)):
val = np.array(newmatrix).reshape((self.target.dim(split=False),self.domain.dim(split=False)))
if(self.target!=self.domain):
sym = False
uni = False
if(val.size>1048576):
about.infos.cprint("INFO: matrix size > 2 ** 20.")
else:
raise ValueError(about._errors.cstring("ERROR: dimension mismatch ( "+str(np.size(matrix,axis=None))+" <> "+str(self.nrow())+" x "+str(self.ncol())+" )."))
## check datatype
if(np.iscomplexobj(val)):
if(np.all(np.imag(val)==0)):
val = np.real(val).astype(min(val.dtype,self.domain.vol.dtype,self.target.vol.dtype))
else:
val = val.astype(min(val.dtype,self.domain.datatype,self.target.datatype))
if(np.any(np.iscomplex(val))):
datatype,purelyreal = max(min(val.dtype,self.domain.datatype),min(val.dtype,self.target.datatype)),False
else:
val = val.astype(min(val.dtype,self.domain.vol.dtype,self.target.vol.dtype))
datatype,purelyreal = max(min(val.dtype,self.domain.vol.dtype),min(val.dtype,self.target.vol.dtype)),True
## weight if ... (given `domain` and `target`)
if(isinstance(bare,tuple)):
if(len(bare)!=2):
raise ValueError(about._errors.cstring("ERROR: invalid input."))
else:
val = self._calc_weight_rows(val,power=-bool(bare[0]))
val = self._calc_weight_cols(val,power=-bool(bare[1]))
val = self._calc_weight_rows(val,-bool(bare[0]))
val = self._calc_weight_cols(val,-bool(bare[1]))
elif(not bare):
val = self._calc_weight_rows(val,-1)
self.val = val
if(purelyreal):
self.val = np.real(val).astype(datatype)
else:
self.val = val.astype(datatype)
## check flags
self.sym,self.uni = self._check_flags(sym=sym,uni=uni)
......
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