The field contains classes and function exported from the waLBerla C++ field module. fields are the central data structure in waLBerla where all simulation data is stored. A field is basically a four dimensional array, where three dimensions are used to index cells in three dimensional space and the fourth dimension for storing multiple values per cell. The field module supports two different layouts: array of structures (zyxf) and structure of arrays (fzyx). For details have a look at the C++ documentation: walberla::field::Field
Fields and numpy¶
waLBerla fields can be easily converted to an
numpy.ndarray using the array functionality of pybind11. This means
that a numpy array and a field can share the same memory such that fields can be manipulated using all the
power of numpy:
>>> import waLBerla >>> import numpy >>> field = waLBerla.field.createField( [3,3,3,1], float ) >>> field[0,0,0,0] 0.0 >>> npArr = numpy.asarray( field ) >>> npArr = waLBerla.field.toArray( field, True ) # convenience function, same as above (True includes ghostlayers) >>> npArr[:] = 42.0 >>> field[0,0,0,0] 42.0
A new field is created which is by default initialized with zero. Then a numpy array is created which shares the same data. After modifying the numpy array also the field values have changed.
A common source of error is to forget that some numpy functions create a copy of the data. The copy is of course not shared with the field anymore:
>>> npArr = numpy.roll( npArr, 1, axis=0 ) >>> npArr[:] = 5 >>> field[0,0,0] 42.0
When during the array manipulation a copy was created the result has to be copied back into the
field again. Here the function
numpy.copyto() is helpful::
>>> numpy.copyto( numpy.asarray( field ), npArr ) >>> field = waLBerla.field.toArray( field ) # convenience function, equivalent to above >>> field[0,0,0] 5.0
- class Field¶
Exported from C++ class walberla::field::Field
To modify or access a field class, the most convenient way is to create a numpy.ndarray view on it.
The returned object implements pybind11::array and can be used for example to create a numpy.ndarray that shares the same data:
numpy.asarray( field )
With this function all ghostlayers are included in the view on the array. If the fourth dimension is one (this means only one value per cell). The returned numpy array has only 3 dimensions.
Swaps the data of two fields. Only works if sizes, allocSizes and layout of the two fields are identical. The content of numpy arrays that have been created using the buffer interface are NOT swapped.
The following attributes are read-only and provide information about sizes and memory layout
4-tuple with sizes of (x,y,z,f) coordinates not counting ghost layers
The actual number of allocated elements for each coordinate. Differences of size and allocSize are due to ghost layers and/or padding and depend on the chosen C++ walberla::field::FieldAllocator
How many elements have to be skipped over in memory when incrementing the (x,y,z,f) dimension by one.
How many elements to skip over in memory from allocation begin to element (0,0,0,0)
Either zyxf (Array-of-Structures) or fzyx (Structure-of-Arrays)
- createField(size, type, ghostLayers=1, layout=field.zyxf)¶
Creates a new GhostLayerField
size – List of length 4 specifying x,y,z,f size of the field.
- Type of the field elements. Valid types are the python types as well as some numpy types:
Integer types: int, numpy.int[8,16,32,64]
Unsigned types: numpy.uint[8,16,32,64]
Float types : float, numpy.float32, numpy.float64
Bool types : numpy.bool
The type mapping is done via the C++ template trait
walberla::python_coupling::isCppEqualToPythonTypesuch that custom C++ types can be exported as well.
ghostLayers – number of ghost layers of new field
layout – Either array-of-structures
- addToStorage(blocks, name, dtype, fSize=1, layout=field.fzyx, ghostLayers=1, initValue=0.0, alignment=0)¶
Adds a GhostLayerField to the given blockStorage
blocks – the structured blockstorage where the field should be added to
name – name of block data, is used to retrieve the created field later on
dtype – data type of the field
fSize – number of values per cell
layout – field.fzyx (SoA) or field.zyxf(AoS)
ghostLayers – number of ghost layers of the field
initValue – initial value for all cells, if None the types are default initialized (for most types zero)
alignment – alignment in bytes of the field vector
- gather(blocks, blockDataName, slice, targetRank=0)¶
Gathers part of the complete simulation domain (which is distributed to multiple processes) to one process.
blocks – the blockstorage where the field is stored
blockDataName – the name of the block data where the field is stored
slice – a slice object describing the region that should be collected in global coordinates
targetRank – world rank of process where the data should be gathered to
Returns None on all processes except on process with rank targetRank. Here the collected field is returned.
- Slice gather example::
>>> field.gather( blocks, 'density', makeSlice[ :,:,2 ] )
- createPackInfo(blocks, blockDataName)¶
Creates a walberla::field::communication::PackInfo for a field. For details see tutorial on communication.
- createMPIDatatypeInfo(blocks, blockDataName)¶
Creates a walberla::field::communication::UniformMPIDatatypeInfo for a field. For details see tutorial on communication.