import numpy
class Field3d:
    def __init__(self,data,origin,spacing):
        assert len(origin)==3,  "'origin' must be of length 3"
        assert len(spacing)==3, "'spacing' must be of length 3"
        self.data      = numpy.array(data)
        self.numpoints = self.data.shape
        self.origin    = tuple([float(x) for x in origin])
        self.spacing   = tuple([float(x) for x in spacing])
        self.eps_collapse = 1e-12
        return

    def __str__(self):
        str = 'Field3d with\n'
        str+= ' dimension: {}, {}, {}\n'.format(*self.numpoints)
        str+= ' origin: {:G}, {:G}, {:G}\n'.format(*self.origin)
        str+= ' spacing: {:G}, {:G}, {:G}'.format(*self.spacing)
        return str

# TBD: this should return another Field3d object
#    def __getitem__(self,val):
#        assert isinstance(val,tuple) and len(val)==3, "Field3d must be indexed by [ii,jj,kk]."
#        sl = []
#        for x in val:
#            if isinstance(x,int):
#                lo,hi = x,x+1
#                hi = hi if hi!=0 else None
#                sl.append(slice(lo,hi))
#            elif isinstance(x,slice):
#                sl.append(x)
#            else:
#                raise TypeError("Trajectories can only be sliced by slice objects or integers.")
#        return self.data[sl[0],sl[1],sl[2]]

    @classmethod
    def from_chunk(cls,chunk,gridg):
        '''Initialize Field3d from chunk data and global grid.'''
        xg,yg,zg = gridg
        ib,jb,kb = chunk['ibeg']-1, chunk['jbeg']-1, chunk['kbeg']-1
        dx,dy,dz = xg[1]-xg[0], yg[1]-yg[0], zg[1]-zg[0]
        xo,yo,zo = xg[ib]-chunk['ighost']*dx, yg[jb]-chunk['ighost']*dy, zg[kb]-chunk['ighost']*dz
        nx,ny,nz = chunk['data'].shape
        assert (chunk['nxl']+2*chunk['ighost'])==nx, "Invalid chunk data: nxl != chunk['data'].shape[0]"
        assert (chunk['nyl']+2*chunk['ighost'])==ny, "Invalid chunk data: nyl != chunk['data'].shape[1]"
        assert (chunk['nzl']+2*chunk['ighost'])==nz, "Invalid chunk data: nzl != chunk['data'].shape[2]"
        return cls(chunk['data'],origin=(xo,yo,zo),spacing=(dx,dy,dz))

    def insert_subfield(self,subfield):
        assert all([abs(subfield.spacing[ii]-self.spacing[ii])<self.eps_collapse 
                        for ii in range(3)]), "spacing differs."
        assert all([self.distance_to_nearest_gridpoint(subfield.origin[ii],axis=ii)<self.eps_collapse 
                        for ii in range(3)]), "subfield has shifted origin."
        assert all(self.is_within_bounds(subfield.origin,axis=None)), "subfield origin is out-of-bounds."
        assert all(self.is_within_bounds(subfield.endpoint(),axis=None)), "subfield endpoint is out-of-bounds."
        #ib,jb,kb = [int(round((subfield.origin[ii]-self.origin[ii])/self.spacing[ii])) for ii in range(3)]
        ib,jb,kb = self.nearest_gridpoint(subfield.origin,axis=None)
        nx,ny,nz = subfield.numpoints
        self.data[ib:ib+nx,jb:jb+ny,kb:kb+nz] = subfield.data[:,:,:]
        return

    def extract_subfield(self,idx_origin,numpoints,stride=(1,1,1)):
        assert all(idx_origin[ii]>=0 and idx_origin[ii]<self.numpoints[ii] for ii in range(3)),\
                "'origin' is out-of-bounds."
        assert all(idx_origin[ii]+stride[ii]*(numpoints[ii]-1)<self.numpoints[ii] for ii in range(3)),\
                "endpoint is out-of-bounds."
        sl = tuple(slice(idx_origin[ii],idx_origin[ii]+stride[ii]*numpoints[ii],stride[ii]) for ii in range(3))
        origin  = self.coordinate(idx_origin)
        spacing = tuple(self.spacing[ii]*stride[ii] for ii in range(3))
        data = self.data[sl].copy()
        return Field3d(data,origin,spacing)

    def coordinate(self,idx,axis=None):
        if axis is None:
            assert len(idx)==3, "If 'axis' is None, 'idx' must be a tuple/list of length 3."
            return tuple(self.coordinate(idx[ii],axis=ii) for ii in range(3))
        assert axis<3, "'axis' must be one of 0,1,2."
        assert idx<self.numpoints[axis], "'idx' is out-of-bounds."
        return self.origin[axis]+idx*self.spacing[axis]

    def nearest_gridpoint(self,coord,axis=None,lower=False):
        if axis is None:
            assert len(coord)==3, "If 'axis' is None, 'coord' must be a tuple/list of length 3."
            return tuple(self.nearest_gridpoint(coord[ii],axis=ii,lower=lower) for ii in range(3))
        assert axis<3, "'axis' must be one of 0,1,2."
        if lower:
            return numpy.floor((coord+self.eps_collapse-self.origin[axis])/self.spacing[axis]).astype('int')
        else:
            return numpy.round((coord-self.origin[axis])/self.spacing[axis]).astype('int')

    def distance_to_nearest_gridpoint(self,coord,axis=None,lower=False):
        if axis is None:
            assert len(coord)==3, "If 'axis' is None, 'coord' must be a tuple/list of length 3."
            return tuple(self.distance_to_nearest_gridpoint(coord[ii],axis=ii,lower=lower) for ii in range(3))
        assert axis<3, "'axis' must be one of 0,1,2."
        val = numpy.remainder(coord+self.eps_collapse-self.origin[axis],self.spacing[axis])-self.eps_collapse
        if not lower and val>0.5*self.spacing[axis]:
            val = self.spacing[axis]-val
        return val

    def is_within_bounds(self,coord,axis=None):
        if axis is None:
            assert len(coord)==3, "If 'axis' is None, 'coord' must be a tuple/list of length 3."
            return tuple(self.is_within_bounds(coord[ii],axis=ii) for ii in range(3))
        assert axis<3, "'axis' must be one of 0,1,2."
        idx_nearest = self.nearest_gridpoint(coord,axis=axis)
        if idx_nearest>0 and idx_nearest<self.numpoints[axis]-1:
            return True 
        dist_nearest = self.distance_to_nearest_gridpoint(coord,axis=axis)
        if (idx_nearest==0 or idx_nearest==self.numpoints[axis]-1) and abs(dist_nearest)<self.eps_collapse:
            return True
        else:
            return False

    def dim(self,axis=None):
        if axis is None:
            return tuple(self.dim(axis=ii) for ii in range(3))
        assert axis<3, "'axis' must be one of 0,1,2."
        return self.numpoints[axis]

    def endpoint(self,axis=None):
        if axis is None:
            return tuple(self.endpoint(axis=ii) for ii in range(3))
        assert axis<3, "'axis' must be one of 0,1,2."
        return self.origin[axis]+(self.numpoints[axis]-1)*self.spacing[axis]

    def derivative(self,axis,preserve_grid=False,padding=None):
        if not preserve_grid: 
            origin = list(self.origin)
            if padding is None:
                data  = numpy.zeros(self.data.shape,dtype=self.data.dtype)
                slout = [slice(None),slice(None),slice(None)]
                origin[axis] += 0.5*self.spacing[axis]
            elif padding=='before':
                shape = numpy.array(self.data.shape)
                shape[axis] += 1
                data  = numpy.zeros(tuple(shape),dtype=self.data.dtype)
                slout = [slice(None),slice(None),slice(None)]
                slout[axis] = slice(1,None)
                origin[axis] -= 0.5*self.spacing[axis]
            elif padding=='after':
                shape = numpy.array(self.data.shape)
                shape[axis] += 1
                data  = numpy.zeros(tuple(shape),dtype=self.data.dtype)
                slout = [slice(None),slice(None),slice(None)]
                slout[axis] = slice(0,-1)
                origin[axis] += 0.5*self.spacing[axis]
            elif padding=='both':
                shape = numpy.array(self.data.shape)
                shape[axis] += 2
                data  = numpy.zeros(tuple(shape),dtype=self.data.dtype)
                slout = [slice(None),slice(None),slice(None)]
                slout[axis] = slice(1,-1)
                origin[axis] -= 0.5*self.spacing[axis]
            else:
                raise ValueError("'padding' must be one of 'none','before','after'.")
            slout = tuple(slout)
            # 2nd order FD with h = dx/2
            slhi = [slice(None),slice(None),slice(None)]
            sllo = [slice(None),slice(None),slice(None)]
            slhi[axis] = slice(1,None)
            sllo[axis] = slice(0,-1)
            slhi = tuple(slhi)
            sllo = tuple(sllo)
            data[slout] = (1./self.spacing[axis])*(self.data[slhi]-self.data[sllo])
        else:
            # 2nd order FD with h = dx, one-sided at boundaries
            # https://numpy.org/doc/stable/reference/generated/numpy.gradient.html
            origin = self.origin
            data = numpy.gradient(self.data,self.spacing[axis],axis=axis,edge_order=2)
        return Field3d(data,origin,self.spacing)

    def integral(self,integrate_axis,average=False,ignore_nan=False,return_weights=False,ufunc=None):
        '''Computes the integral or average along a given axis applying the 
        function 'ufunc' to each node.'''
        assert isinstance(integrate_axis,(list,tuple,numpy.ndarray)) and len(integrate_axis)==3,\
            "'integrate_axis' must be a tuple/list of length 3."
        assert all([isinstance(integrate_axis[ii],(bool,int)) for ii in range(3)]),\
            "'integrate_axis' requires bool values."
        assert any(integrate_axis), "'integrate_axis' must contain at least one True."
        axes = []
        weight = 1.0
        for axis in range(3):
            if integrate_axis[axis]: 
                axes.append(axis)
                if average:
                    weight *= self.numpoints[axis]
                else:
                    weight *= self.spacing[axis]                    
        axes = tuple(axes)
        if ignore_nan:
            if average:
                weight = numpy.sum(~numpy.isnan(self.data),axis=axes,keepdims=True)
            func_sum = numpy.nansum
        else:
            func_sum = numpy.sum
        if ufunc is None:
            out = func_sum(self.data,axis=axes,keepdims=True)
        else:
            assert isinstance(ufunc,numpy.ufunc), "'ufunc' needs to be a numpy ufunc. "\
                "Check out https://numpy.org/doc/stable/reference/ufuncs.html for reference."
            assert ufunc.nin==1, "Only ufunc with single input argument are supported for now."
            out = func_sum(ufunc(self.data),axis=axes,keepdims=True)
        if return_weights:
            return (out,weight)
        else:
            return out/weight
    
    def gradient(self,axis,preserve_origin=False,padding=None):
        return [self.derivative(axis,preserve_origin=preserve_origin,padding=padding) for axis in range(0,3)]

    def gaussian_filter(self,sigma,truncate=4.0,border='constant',const_val=numpy.nan):
        '''Applies a gaussian filter: sigma is standard deviation for Gaussian kernel for each axis.'''
        from scipy import ndimage
        assert isinstance(sigma,(tuple,list,numpy.ndarray)) and len(sigma)==3,\
                    "'sigma' must be a tuple/list of length 3"
        # Convert sigma from simulation length scales to grid points as required by ndimage
        sigma_img = tuple(sigma[ii]/self.spacing[ii] for ii in range(3))
        data = ndimage.gaussian_filter(self.data,sigma_img,truncate=truncate,mode=border,cval=const_val)
        print(data.shape,self.data.shape)
        print(numpy.linalg.norm(data-self.data))
        print(numpy.max(data))
        return Field3d(data,self.origin,self.spacing)

    def gaussian_filter_radius(self,sigma,truncate=4.0):
        '''Radius of Gaussian filter. Stencil width is 2*radius+1.'''
        assert isinstance(sigma,(tuple,list,numpy.ndarray)) and len(sigma)==3,\
                    "'sigma' must be a tuple/list of length 3"
        # Convert sigma from simulation length scales to grid points as required by ndimage
        sigma_img = tuple(sigma[ii]/self.spacing[ii] for ii in range(3))
        radius = []
        for ii in range(3):
            radius.append(int(truncate*sigma_img[ii]+0.5))
        return tuple(radius)

    def shift_origin(self,rel_shift):
        #TBD: verify this routine
        assert isinstance(rel_shift,(tuple,list,numpy.ndarray)) and len(rel_shift)==3,\
            "'shift' must be tuple/list with length 3."
        assert all([rel_shift[ii]>-1.0 and rel_shift[ii]<1.0 for ii in range(3)]),\
            "'shift' must be in (-1.0,1.0)."
        #data = numpy.full(self.numpoints,numpy.nan)
        data   = self.data.copy()
        origin = list(self.origin)
        for axis in range(3):
            if abs(rel_shift[axis])<self.eps_collapse:
                continue
            origin[axis] += rel_shift[axis]*self.spacing[axis]
            sl_hi  = 3*[slice(None)]
            sl_lo  = 3*[slice(None)]
            sl_out = 3*[slice(None)]
            sl_clr = 3*[slice(None)]
            sl_hi[axis] = slice(1,None)
            sl_lo[axis] = slice(0,-1)
            if rel_shift[ii]<0.0:
                w = 1.0+rel_shift[axis]
                sl_out[axis] = slice(1,None)
                sl_clr[axis] = slice(0,1)
            else:
                w = rel_shift[axis]
                sl_out[axis] = slice(0,-1)
                sl_clr[axis] = slice(-1,None)
            sl_hi  = tuple(sl_hi)
            sl_lo  = tuple(sl_lo)
            sl_out = tuple(sl_out)
            sl_clr = tuple(sl_clr)
            data[sl_out] = (1.0-w)*data[sl_lo] + w*data[sl_hi]
            data[sl_clr] = numpy.nan
        return Field3d(data,origin,self.spacing)

    def change_grid(self,origin,spacing,numpoints):
        assert all([origin[ii]>=self.origin[ii] for ii in range(0,3)]), "New origin is out of bounds."
        endpoint = [origin[ii]+(numpoints[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."
        data = numpy.zeros(numpoints)
        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 += c[ii,jj,kk]*self.data[
                                i0+ii:i0+ii+numpoints[0],
                                j0+jj:j0+jj+numpoints[1],
                                k0+kk:k0+kk+numpoints[2]]
        else:
            for ii in range(0,numpoints[0]):
                for jj in range(0,numpoints[1]):
                    for kk in range(0,numpoints[2]):
                        coord = (
                            origin[0]+ii*spacing[0],
                            origin[1]+jj*spacing[1],
                            origin[2]+kk*spacing[2])
                        data[ii,jj,kk] = self.interpolate(coord)
        return Field3d(data,origin,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

    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)

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