587 lines
27 KiB
Python
587 lines
27 KiB
Python
import numpy
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class Field3d:
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def __init__(self,data,origin,spacing):
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assert len(origin)==3, "'origin' must be of length 3"
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assert len(spacing)==3, "'spacing' must be of length 3"
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self.data = numpy.array(data)
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self.origin = tuple([float(x) for x in origin])
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self.spacing = tuple([float(x) for x in spacing])
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self.eps_collapse = 1e-12
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self._dim = None
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return
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def __str__(self):
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str = 'Field3d with\n'
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str+= ' dimension: {}, {}, {}\n'.format(*self.dim())
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str+= ' origin: {:G}, {:G}, {:G}\n'.format(*self.origin)
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str+= ' spacing: {:G}, {:G}, {:G}\n'.format(*self.spacing)
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str+= ' datatype: {}'.format(self.dtype())
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return str
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def __add__(self,other):
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if isinstance(other,Field3d):
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assert self.has_same_grid(other), "Grid mismatch."
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return Field3d(self.data+other.data,self.origin,self.spacing)
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else:
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return Field3d(self.data+other,self.origin,self.spacing)
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def __sub__(self,other):
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if isinstance(other,Field3d):
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assert self.has_same_grid(other), "Grid mismatch."
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return Field3d(self.data-other.data,self.origin,self.spacing)
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else:
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return Field3d(self.data-other,self.origin,self.spacing)
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def __mul__(self,other):
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if isinstance(other,Field3d):
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assert self.has_same_grid(other), "Grid mismatch."
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return Field3d(self.data*other.data,self.origin,self.spacing)
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else:
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return Field3d(self.data*other,self.origin,self.spacing)
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def __radd__(self,other):
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return Field3d(other+self.data,self.origin,self.spacing)
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def __rmul__(self,other):
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return Field3d(other*self.data,self.origin,self.spacing)
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def __pow__(self,other):
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return Field3d(self.data**other,self.origin,self.spacing)
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def __iadd__(self,other):
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if isinstance(other,Field3d):
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assert self.has_same_grid(other), "Grid mismatch."
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self.data += other.data
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else:
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self.data += other
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return self
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def __isub__(self,other):
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if isinstance(other,Field3d):
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assert self.has_same_grid(other), "Grid mismatch."
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self.data -= other.data
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else:
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self.data -= other
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return self
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def __imul__(self,other):
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if isinstance(other,Field3d):
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assert self.has_same_grid(other), "Grid mismatch."
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self.data *= other.data
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else:
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self.data *= other
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return self
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# TBD: this should return another Field3d object
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# def __getitem__(self,val):
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# assert isinstance(val,tuple) and len(val)==3, "Field3d must be indexed by [ii,jj,kk]."
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# sl = []
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# for x in val:
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# if isinstance(x,int):
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# lo,hi = x,x+1
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# hi = hi if hi!=0 else None
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# sl.append(slice(lo,hi))
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# elif isinstance(x,slice):
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# sl.append(x)
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# else:
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# raise TypeError("Trajectories can only be sliced by slice objects or integers.")
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# return self.data[sl[0],sl[1],sl[2]]
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@classmethod
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def from_chunk(cls,chunk,gridg):
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'''Initialize Field3d from chunk data and global grid.'''
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xg,yg,zg = gridg
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ib,jb,kb = chunk['ibeg']-1, chunk['jbeg']-1, chunk['kbeg']-1
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dx,dy,dz = xg[1]-xg[0], yg[1]-yg[0], zg[1]-zg[0]
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xo,yo,zo = xg[ib]-chunk['ighost']*dx, yg[jb]-chunk['ighost']*dy, zg[kb]-chunk['ighost']*dz
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nx,ny,nz = chunk['data'].shape
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assert (chunk['nxl']+2*chunk['ighost'])==nx, "Invalid chunk data: nxl != chunk['data'].shape[0]"
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assert (chunk['nyl']+2*chunk['ighost'])==ny, "Invalid chunk data: nyl != chunk['data'].shape[1]"
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assert (chunk['nzl']+2*chunk['ighost'])==nz, "Invalid chunk data: nzl != chunk['data'].shape[2]"
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return cls(chunk['data'],origin=(xo,yo,zo),spacing=(dx,dy,dz))
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@classmethod
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def allocate(cls,dim,origin,spacing,fill=None,dtype=numpy.float64,pseudo=False):
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'''Allocates an empty field, or a field filled with 'fill'.'''
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assert isinstance(dim,(tuple,list,numpy.ndarray)) and len(dim)==3,\
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"'dim' must be a tuple/list of length 3."
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assert isinstance(origin,(tuple,list,numpy.ndarray)) and len(origin)==3,\
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"'origin' must be a tuple/list of length 3."
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assert isinstance(spacing,(tuple,list,numpy.ndarray)) and len(spacing)==3,\
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"'spacing' must be a tuple/list of length 3."
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if pseudo:
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data = numpy.empty((0,0,0))
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r = cls(data,origin,spacing)
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r._dim = dim
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return r
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else:
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if fill is None:
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data = numpy.empty(dim,dtype=dtype)
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else:
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data = numpy.full(dim,fill,dtype=dtype)
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return cls(data,origin,spacing)
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def copy(self):
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return Field3d(self.data.copy(),self.origin,self.spacing)
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def insert_subfield(self,subfield):
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assert all([abs(subfield.spacing[ii]-self.spacing[ii])<self.eps_collapse
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for ii in range(3)]), "spacing differs."
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assert all([self.distance_to_nearest_gridpoint(subfield.origin[ii],axis=ii)<self.eps_collapse
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for ii in range(3)]), "subfield has shifted origin."
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assert all(self.is_within_bounds(subfield.origin,axis=None)), "subfield origin is out-of-bounds."
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assert all(self.is_within_bounds(subfield.endpoint(),axis=None)), "subfield endpoint is out-of-bounds."
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#ib,jb,kb = [int(round((subfield.origin[ii]-self.origin[ii])/self.spacing[ii])) for ii in range(3)]
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ib,jb,kb = self.nearest_gridpoint(subfield.origin,axis=None)
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nx,ny,nz = subfield.dim()
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self.data[ib:ib+nx,jb:jb+ny,kb:kb+nz] = subfield.data[:,:,:]
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return
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def extract_subfield(self,idx_origin,dim,stride=(1,1,1)):
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assert all(idx_origin[ii]>=0 and idx_origin[ii]<self.dim(axis=ii) for ii in range(3)),\
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"'origin' is out-of-bounds."
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assert all(idx_origin[ii]+stride[ii]*(dim[ii]-1)<self.dim(axis=ii) for ii in range(3)),\
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"endpoint is out-of-bounds."
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sl = tuple(slice(idx_origin[ii],idx_origin[ii]+stride[ii]*dim[ii],stride[ii]) for ii in range(3))
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origin = self.coordinate(idx_origin)
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spacing = tuple(self.spacing[ii]*stride[ii] for ii in range(3))
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data = self.data[sl].copy()
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return Field3d(data,origin,spacing)
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def coordinate(self,idx,axis=None):
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if axis is None:
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assert len(idx)==3, "If 'axis' is None, 'idx' must be a tuple/list of length 3."
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return tuple(self.coordinate(idx[ii],axis=ii) for ii in range(3))
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assert axis<3, "'axis' must be one of 0,1,2."
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assert idx<self.dim(axis=axis), "'idx' is out-of-bounds."
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return self.origin[axis]+idx*self.spacing[axis]
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def nearest_gridpoint(self,coord,axis=None,lower=False):
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if axis is None:
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assert len(coord)==3, "If 'axis' is None, 'coord' must be a tuple/list of length 3."
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return tuple(self.nearest_gridpoint(coord[ii],axis=ii,lower=lower) for ii in range(3))
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assert axis<3, "'axis' must be one of 0,1,2."
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if lower:
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return numpy.floor((coord+self.eps_collapse-self.origin[axis])/self.spacing[axis]).astype('int')
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else:
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return numpy.round((coord-self.origin[axis])/self.spacing[axis]).astype('int')
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def distance_to_nearest_gridpoint(self,coord,axis=None,lower=False):
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if axis is None:
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assert len(coord)==3, "If 'axis' is None, 'coord' must be a tuple/list of length 3."
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return tuple(self.distance_to_nearest_gridpoint(coord[ii],axis=ii,lower=lower) for ii in range(3))
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assert axis<3, "'axis' must be one of 0,1,2."
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val = numpy.remainder(coord+self.eps_collapse-self.origin[axis],self.spacing[axis])-self.eps_collapse
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if not lower and val>0.5*self.spacing[axis]:
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val = self.spacing[axis]-val
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return val
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def is_within_bounds(self,coord,axis=None):
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if axis is None:
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assert len(coord)==3, "If 'axis' is None, 'coord' must be a tuple/list of length 3."
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return tuple(self.is_within_bounds(coord[ii],axis=ii) for ii in range(3))
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assert axis<3, "'axis' must be one of 0,1,2."
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idx_nearest = self.nearest_gridpoint(coord,axis=axis)
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if idx_nearest>0 and idx_nearest<self.dim(axis=axis)-1:
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return True
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dist_nearest = self.distance_to_nearest_gridpoint(coord,axis=axis)
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if (idx_nearest==0 or idx_nearest==self.dim(axis=axis)-1) and abs(dist_nearest)<self.eps_collapse:
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return True
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else:
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return False
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def has_same_grid(self,other):
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if not self.has_same_origin(other): return False
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if not self.has_same_spacing(other): return False
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if not self.has_same_origin(other): return False
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return True
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def has_same_origin(self,other,axis=None):
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if axis is None:
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return all([self.has_same_origin(other,axis=ii) for ii in range(3)])
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origin = other.origin if isinstance(other,Field3d) else other
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return abs(origin[axis]-self.origin[axis])<self.eps_collapse
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def has_same_spacing(self,other,axis=None):
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if axis is None:
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return all([self.has_same_spacing(other,axis=ii) for ii in range(3)])
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spacing = other.spacing if isinstance(other,Field3d) else other
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return abs(spacing[axis]-self.spacing[axis])<self.eps_collapse
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def has_same_dim(self,other,axis=None):
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if axis is None:
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return all([self.has_same_dim(other,axis=ii) for ii in range(3)])
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dim = other.dim(axis=axis) if isinstance(other,Field3d) else other
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return dim==self.dim(axis=axis)
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def dim(self,axis=None):
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if axis is None:
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return tuple(self.dim(axis=ii) for ii in range(3))
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assert axis<3, "'axis' must be one of 0,1,2."
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if self._dim is not None:
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return self._dim[axis]
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else:
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return self.data.shape[axis]
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def endpoint(self,axis=None):
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if axis is None:
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return tuple(self.endpoint(axis=ii) for ii in range(3))
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assert axis<3, "'axis' must be one of 0,1,2."
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return self.origin[axis]+(self.dim(axis=axis)-1)*self.spacing[axis]
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def dtype(self):
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return self.data.dtype
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def convert_dtype(self,dtype):
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self.data = self.data.astype(dtype,copy=False)
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return
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def derivative(self,axis,only_keep_interior=False,shift_origin='before'):
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'''Computes derivative wrt to direction 'axis' with 2nd order finite differences
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centered between the origin grid points'''
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from scipy import ndimage
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assert axis<3, "'axis' must be one of 0,1,2."
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origin = list(self.origin)
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assert shift_origin in ('before','after'), "'shift_origin' must be one of {'before','after'}."
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if shift_origin=='left':
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corr_orig = 0
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origin[axis] -= 0.5*self.spacing[axis]
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else:
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corr_orig = -1
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origin[axis] += 0.5*self.spacing[axis]
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data = ndimage.correlate1d(self.data,numpy.array([-1.,1.])/self.spacing[axis],axis=axis,
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mode='constant',cval=numpy.nan,origin=corr_orig)
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if only_keep_interior:
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sl = 3*[slice(None)]
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if shift_origin=='before':
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sl[axis] = slice(-1,None)
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origin[axis] += self.spacing[axis]
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else:
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sl[axis] = slice(0,-1)
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data = data[tuple(sl)]
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return Field3d(data,origin,self.spacing)
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def gradient(self,axis,preserve_origin=False,only_keep_interior=False,add_border='before'):
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return [self.derivative(axis,preserve_origin=preserve_origin,
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only_keep_interior=only_keep_interior,add_border=add_border) for axis in range(0,3)]
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def laplacian(self,only_keep_interior=False):
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'''Computes the Laplacian of a field.'''
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from scipy import ndimage
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data = ndimage.correlate1d(self.data,numpy.array([1.,-2.,1.])/self.spacing[0]**2,axis=0,mode='constant',cval=numpy.nan,origin=0)
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data += ndimage.correlate1d(self.data,numpy.array([1.,-2.,1.])/self.spacing[1]**2,axis=1,mode='constant',cval=numpy.nan,origin=0)
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data += ndimage.correlate1d(self.data,numpy.array([1.,-2.,1.])/self.spacing[2]**2,axis=2,mode='constant',cval=numpy.nan,origin=0)
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origin = list(self.origin)
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if only_keep_interior:
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data = data[1:-1,1:-1,1:-1]
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for axis in range(3):
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origin[axis] = origin[axis]+self.spacing[axis]
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return Field3d(data,origin,self.spacing)
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def integral(self,integrate_axis,average=False,ignore_nan=False,return_weights=False,ufunc=None):
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'''Computes the integral or average along a given axis applying the
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function 'ufunc' to each node.'''
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assert isinstance(integrate_axis,(list,tuple,numpy.ndarray)) and len(integrate_axis)==3,\
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"'integrate_axis' must be a tuple/list of length 3."
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assert all([isinstance(integrate_axis[ii],(bool,int)) for ii in range(3)]),\
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"'integrate_axis' requires bool values."
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assert any(integrate_axis), "'integrate_axis' must contain at least one True."
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axes = []
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weight = 1.0
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for axis in range(3):
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if integrate_axis[axis]:
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axes.append(axis)
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if average:
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weight *= self.dim(axis=axis)
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else:
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weight *= self.spacing[axis]
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axes = tuple(axes)
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if ignore_nan:
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if average:
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weight = numpy.sum(~numpy.isnan(self.data),axis=axes,keepdims=True)
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func_sum = numpy.nansum
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else:
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func_sum = numpy.sum
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if ufunc is None:
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out = func_sum(self.data,axis=axes,keepdims=True)
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else:
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assert isinstance(ufunc,numpy.ufunc), "'ufunc' needs to be a numpy ufunc. "\
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"Check out https://numpy.org/doc/stable/reference/ufuncs.html for reference."
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assert ufunc.nin==1, "Only ufunc with single input argument are supported for now."
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out = func_sum(ufunc(self.data),axis=axes,keepdims=True)
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if return_weights:
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return (out,weight)
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else:
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return out/weight
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def gaussian_filter(self,sigma,truncate=4.0,only_keep_interior=False):
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'''Applies a gaussian filter: sigma is standard deviation for Gaussian kernel for each axis.'''
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from scipy import ndimage
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assert isinstance(sigma,(tuple,list,numpy.ndarray)) and len(sigma)==3,\
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"'sigma' must be a tuple/list of length 3"
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# Convert sigma from simulation length scales to grid points as required by ndimage
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sigma_img = tuple(sigma[ii]/self.spacing[ii] for ii in range(3))
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data = ndimage.gaussian_filter(self.data,sigma_img,truncate=truncate,mode='constant',cval=numpy.nan)
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origin = list(self.origin)
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if only_keep_interior:
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r = self.gaussian_filter_radius(sigma,truncate=truncate)
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data = data[r[0]:-r[0],r[1]:-r[1],r[2]:-r[2]]
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for axis in range(3):
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origin[axis] = origin[axis]+r[axis]*self.spacing[axis]
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return Field3d(data,origin,self.spacing)
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def gaussian_filter_radius(self,sigma,truncate=4.0):
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'''Radius of Gaussian filter. Stencil width is 2*radius+1.'''
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assert isinstance(sigma,(tuple,list,numpy.ndarray)) and len(sigma)==3,\
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"'sigma' must be a tuple/list of length 3"
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# Convert sigma from simulation length scales to grid points as required by ndimage
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sigma_img = tuple(sigma[ii]/self.spacing[ii] for ii in range(3))
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radius = []
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for ii in range(3):
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radius.append(int(truncate*sigma_img[ii]+0.5))
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return tuple(radius)
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# def shift_origin(self,rel_shift):
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# raise NotImplementedError("Routine has not been verified yet.")
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# #TBD: verify this routine
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# assert isinstance(rel_shift,(tuple,list,numpy.ndarray)) and len(rel_shift)==3,\
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# "'shift' must be tuple/list with length 3."
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# assert all([rel_shift[ii]>-1.0 and rel_shift[ii]<1.0 for ii in range(3)]),\
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# "'shift' must be in (-1.0,1.0)."
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# #data = numpy.full(self.dim,numpy.nan)
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# data = self.data.copy()
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# origin = list(self.origin)
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# for axis in range(3):
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# if abs(rel_shift[axis])<self.eps_collapse:
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# continue
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# origin[axis] += rel_shift[axis]*self.spacing[axis]
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# sl_hi = 3*[slice(None)]
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# sl_lo = 3*[slice(None)]
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# sl_out = 3*[slice(None)]
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# sl_clr = 3*[slice(None)]
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# sl_hi[axis] = slice(1,None)
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# sl_lo[axis] = slice(0,-1)
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# if rel_shift[ii]<0.0:
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# w = 1.0+rel_shift[axis]
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# sl_out[axis] = slice(1,None)
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# sl_clr[axis] = slice(0,1)
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# else:
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# w = rel_shift[axis]
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# sl_out[axis] = slice(0,-1)
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# sl_clr[axis] = slice(-1,None)
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# sl_hi = tuple(sl_hi)
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# sl_lo = tuple(sl_lo)
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# sl_out = tuple(sl_out)
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# sl_clr = tuple(sl_clr)
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# data[sl_out] = (1.0-w)*data[sl_lo] + w*data[sl_hi]
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# data[sl_clr] = numpy.nan
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# return Field3d(data,origin,self.spacing)
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def shift_origin(self,rel_shift,only_keep_interior=False):
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'''Shifts the origin of a field by multiple of spacing.'''
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from scipy import ndimage
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assert isinstance(rel_shift,(tuple,list,numpy.ndarray)) and len(rel_shift)==3,\
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"'shift' must be tuple/list with length 3."
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assert all([rel_shift[ii]>=-1.0 and rel_shift[ii]<=1.0 for ii in range(3)]),\
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"'shift' must be in (-1.0,1.0)."
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data = self.data.copy()
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origin = list(self.origin)
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sl = 3*[slice(None)]
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for axis in range(3):
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if abs(rel_shift[axis])<self.eps_collapse:
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continue
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elif rel_shift[axis]>0:
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weights = (1.0-rel_shift[axis],rel_shift[axis])
|
|
data = ndimage.correlate1d(data,weights,axis=axis,mode='constant',cval=numpy.nan,origin=-1)
|
|
origin[axis] += rel_shift[axis]*self.spacing[axis]
|
|
if only_keep_interior:
|
|
sl[axis] = slice(0,-1)
|
|
else:
|
|
weights = (-rel_shift[axis],1.0+rel_shift[axis])
|
|
data = ndimage.correlate1d(data,weights,axis=axis,mode='constant',cval=numpy.nan,origin=0)
|
|
origin[axis] += rel_shift[axis]*self.spacing[axis]
|
|
if only_keep_interior:
|
|
sl[axis] = slice(1,None)
|
|
origin[axis] += self.spacing[axis]
|
|
if only_keep_interior:
|
|
data = data[sl]
|
|
return Field3d(data,origin,self.spacing)
|
|
|
|
def relative_shift(self,field):
|
|
'''Compute the relative shift (in terms of spacing) to shift self onto field.'''
|
|
assert self.has_same_spacing(field), "spacing differs."
|
|
rel_shift = [0.0,0.0,0.0]
|
|
for axis in range(3):
|
|
dist = field.origin[axis]-self.origin[axis]
|
|
if abs(dist)>self.eps_collapse:
|
|
rel_shift[axis] = dist/self.spacing[axis]
|
|
return tuple(rel_shift)
|
|
|
|
def change_grid(self,origin,spacing,dim,padding=None,numpad=1):
|
|
assert all([origin[ii]>=self.origin[ii] for ii in range(0,3)]), "New origin is out of bounds."
|
|
endpoint = [origin[ii]+(dim[ii]-1)*spacing[ii] for ii in range(0,3)]
|
|
assert all([endpoint[ii]<=self.endpoint(ii) for ii in range(0,3)]), "New end point is out of bounds."
|
|
# Allocate (possibly padded array)
|
|
origin_pad,dim_pad,sl_out = padding(origin,spacing,dim,padding,numpad)
|
|
data = numpy.zeros(dim_pad,dtype=self.data.dtype)
|
|
# Trilinear interpolation
|
|
if numpy.allclose(spacing,self.spacing):
|
|
# spacing is the same: we can construct universal weights for the stencil
|
|
i0,j0,k0 = self.nearest_gridpoint(origin,axis=None,lower=True)
|
|
cx,cy,cz = [self.distance_to_nearest_gridpoint(origin[ii],axis=ii,lower=True)/self.spacing[ii]
|
|
for ii in range(3)]
|
|
c = self.weights_trilinear((cx,cy,cz))
|
|
for ii in range(0,2):
|
|
for jj in range(0,2):
|
|
for kk in range(0,2):
|
|
if c[ii,jj,kk]>self.eps_collapse:
|
|
data[sl_out] += c[ii,jj,kk]*self.data[
|
|
i0+ii:i0+ii+dim[0],
|
|
j0+jj:j0+jj+dim[1],
|
|
k0+kk:k0+kk+dim[2]]
|
|
else:
|
|
data_ref = data[sl_out]
|
|
for ii in range(0,dim[0]):
|
|
for jj in range(0,dim[1]):
|
|
for kk in range(0,dim[2]):
|
|
coord = (
|
|
origin[0]+ii*spacing[0],
|
|
origin[1]+jj*spacing[1],
|
|
origin[2]+kk*spacing[2])
|
|
data_ref[ii,jj,kk] = self.interpolate(coord)
|
|
return Field3d(data,origin_pad,spacing)
|
|
|
|
def interpolate(self,coord):
|
|
assert all([coord[ii]>=self.origin[ii] for ii in range(0,3)]), "'coord' is out of bounds."
|
|
assert all([coord[ii]<=self.endpoint(ii) for ii in range(0,3)]), "'coord' is out of bounds."
|
|
i0,j0,k0 = self.nearest_gridpoint(coord,axis=None,lower=True)
|
|
cx,cy,cz = [self.distance_to_nearest_gridpoint(coord[ii],axis=ii,lower=True)/self.spacing[ii]
|
|
for ii in range(3)]
|
|
c = self.weights_trilinear((cx,cy,cz))
|
|
val = 0.0
|
|
for ii in range(0,2):
|
|
for jj in range(0,2):
|
|
for kk in range(0,2):
|
|
if c[ii,jj,kk]>self.eps_collapse:
|
|
val += c[ii,jj,kk]*self.data[i0+ii,j0+jj,k0+kk]
|
|
return val
|
|
|
|
@staticmethod
|
|
def padding(origin,spacing,dim,padding,numpad):
|
|
if isinstance(numpad,int):
|
|
numpad = numpy.fill(3,numpad,dtype=int)
|
|
else:
|
|
numpad = numpy.array(numpad,dtype=int)
|
|
assert len(numpad)==3, "'numpad' must be either an integer or tuple/list of length 3."
|
|
origin_pad = numpy.array(origin)
|
|
dim_pad = numpy.array(dim)
|
|
sl_out = [slice(None),slice(None),slice(None)]
|
|
if padding is not None:
|
|
if padding=='before':
|
|
dim_pad += numpad
|
|
origin_pad -= numpad*spacing
|
|
for axis in range(3):
|
|
sl_out[axis] = slice(numpad[axis],None)
|
|
elif padding=='after':
|
|
dim_pad += numpad
|
|
for axis in range(3):
|
|
sl_out[axis] = slice(0,-numpad[axis])
|
|
elif padding=='both':
|
|
dim_pad += 2*numpad
|
|
origin_pad -= numpad*spacing
|
|
for axis in range(3):
|
|
sl_out[axis] = slice(numpad[axis],-numpad[axis])
|
|
else:
|
|
raise ValueError("'padding' must either be None or one of {'before','after','both'}.")
|
|
sl_out = tuple(sl_out)
|
|
origin_pad = tuple(origin_pad)
|
|
dim_pad = tuple(dim_pad)
|
|
return (origin_pad,dim_pad,sl_out)
|
|
|
|
def weights_trilinear(self,rel_dist):
|
|
assert len(rel_dist)==3, "len(rel_dist) must be 3."
|
|
cx,cy,cz = rel_dist
|
|
if cx<0.0 and cx>-self.eps_collapse: cx=0.0
|
|
if cy<0.0 and cy>-self.eps_collapse: cy=0.0
|
|
if cz<0.0 and cz>-self.eps_collapse: cz=0.0
|
|
if cx>1.0 and cx<1.0+self.eps_collapse: cx=1.0
|
|
if cy>1.0 and cy<1.0+self.eps_collapse: cy=1.0
|
|
if cz>1.0 and cz<1.0+self.eps_collapse: cz=1.0
|
|
assert cx>=0.0 and cy>=0.0 and cz>=0.0, "'rel_dist' must be >=0"
|
|
assert cx<=1.0 and cy<=1.0 and cz<=1.0, "'rel_dist' must be <=1"
|
|
c = numpy.zeros((2,2,2))
|
|
c[0,0,0] = 1.0-(cx+cy+cz)+(cx*cy+cx*cz+cy*cz)-(cx*cy*cz)
|
|
c[1,0,0] = cx-(cx*cy+cx*cz)+(cx*cy*cz)
|
|
c[0,1,0] = cy-(cx*cy+cy*cz)+(cx*cy*cz)
|
|
c[0,0,1] = cz-(cx*cz+cy*cz)+(cx*cy*cz)
|
|
c[1,1,0] = (cx*cy)-(cx*cy*cz)
|
|
c[1,0,1] = (cx*cz)-(cx*cy*cz)
|
|
c[0,1,1] = (cy*cz)-(cx*cy*cz)
|
|
c[1,1,1] = (cx*cy*cz)
|
|
return c
|
|
|
|
def set_writable(self,flag):
|
|
self.data.setflags(write=flag)
|
|
return
|
|
|
|
def to_vtk(self,deep=False):
|
|
import pyvista as pv
|
|
mesh = pv.UniformGrid()
|
|
mesh.dimensions = self.dim(axis=None)
|
|
mesh.origin = self.origin
|
|
mesh.spacing = self.spacing
|
|
# order needs to be F no matter how array is stored in memory
|
|
if deep:
|
|
mesh.point_arrays['data'] = self.data.flatten(order='F')
|
|
else:
|
|
mesh.point_arrays['data'] = self.data.ravel(order='F')
|
|
return mesh
|
|
|
|
def vtk_contour(self,val,deep=False):
|
|
if not isinstance(val,(tuple,list)):
|
|
val = [val]
|
|
return self.to_vtk(deep=deep).contour(val)
|
|
|
|
def vtk_slice(self,normal,origin,deep=False):
|
|
assert (normal in ('x','y','z') or (isinstance(normal,(tuple,list))
|
|
and len(normal)==3)), "'normal' must be 'x','y','z' or tuple of length 3."
|
|
assert isinstance(origin,(tuple,list)) and len(origin)==3,\
|
|
"'origin' must be tuple of length 3."
|
|
return self.to_vtk(deep=deep).slice(normal=normal,origin=origin)
|
|
|
|
def gaussian_filter_umean_channel(array,spacing,sigma,truncate=4.0):
|
|
'''Applies a Gaussian filter to a numpy array of dimension (1,ny,1) which
|
|
contains the mean streamwise velocity of the channel (or possibly sth else).
|
|
Since yu[0] = c and yu[-1] = d, we can use scipy's mirror settings and don't
|
|
need ghost cells.'''
|
|
from scipy import ndimage
|
|
assert array.ndim==3, "Expected an array with three dimensions/axes."
|
|
assert array.shape[0]==1 and array.shape[1]>1 and array.shape[2]==1,\
|
|
"Expected an array with shape (1,ny,1)."
|
|
sigma_img = sigma/spacing
|
|
array = ndimage.gaussian_filter1d(array,sigma_img,axis=1,truncate=truncate,mode='mirror')
|
|
return array
|
|
|
|
class ChunkIterator:
|
|
'''Iterates through all chunks. 'snapshot' must be an instance
|
|
of a class which returns a Field3d from the method call
|
|
snapshot.field_chunk(rank,key,keep_ghost=keep_ghost).
|
|
One example implementation is UCFSnapshot from suspendtools.ucf.'''
|
|
def __init__(self,snapshot,key,keep_ghost=True):
|
|
self.snapshot = snapshot
|
|
self.key = key
|
|
self.keep_ghost = keep_ghost
|
|
self.iter_rank = 0
|
|
def __iter__(self):
|
|
self.iter_rank = 0
|
|
return self
|
|
def __next__(self):
|
|
if self.iter_rank<self.snapshot.nproc():
|
|
field = self.snapshot.field_chunk(
|
|
self.iter_rank,self.key,keep_ghost=self.keep_ghost)
|
|
self.iter_rank += 1
|
|
return field
|
|
else:
|
|
raise StopIteration
|