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tamaas/python/tamaas/dumpers/__init__.py
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__init__.py
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# -*- mode:python; coding: utf-8 -*-
#
# Copyright (©) 2016-2022 EPFL (École Polytechnique Fédérale de Lausanne),
# Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
"""Dumpers for the class :py:class:`Model <tamaas._tamaas.Model>`."""
from pathlib import Path
from os import PathLike
import glob
import json
import io
import typing as ts
from collections.abc import Collection
import numpy as np
from .. import (
ModelDumper,
model_type,
mpi,
type_traits,
ModelFactory,
Model,
__version__,
)
from ._helper import (
step_dump,
directory_dump,
local_slice,
_is_surface_field,
_basic_types,
file_handler,
)
__all__ = [
"JSONDumper",
"FieldDumper",
"NumpyDumper",
]
FileType = ts.Union[str, PathLike, io.TextIOBase]
NameType = ts.Union[str, PathLike]
_reverse_trait_map = {
'model_type.' + t.__name__: mtype
for mtype, t in type_traits.items()
}
def _get_attributes(model: Model):
"""Get model attributes."""
return {
'model_type': str(model.type),
'system_size': model.system_size,
'discretization': model.global_shape,
'program': f"Tamaas {__version__}, DOI:10.21105/joss.02121",
}
def _create_model(attrs: ts.MutableMapping):
"""Create a model from attribute dictionary."""
mtype = _reverse_trait_map[attrs['model_type']]
# netcdf4 converts 1-lists attributes to numbers
for attr in ['system_size', 'discretization']:
if not isinstance(attrs[attr], Collection):
attrs[attr] = [attrs[attr]]
return ModelFactory.createModel(mtype, attrs['system_size'],
attrs['discretization'])
class MPIIncompatibilityError(RuntimeError):
"""Raised when code is not meant to be executed in MPI environment."""
class ModelError(ValueError):
"""Raised when unexpected model is passed to a dumper with a state."""
class ComponentsError(ValueError):
"""Raised when an unexpected number of components is encountred."""
class _ModelJSONEncoder(json.JSONEncoder):
"""Encode a model to JSON."""
def default(self, obj):
"""Encode model."""
if isinstance(obj, Model):
model = obj
attrs = _get_attributes(model)
model_dict = {
'attrs': attrs,
'fields': {},
'operators': [],
}
for field in model:
model_dict['fields'][field] = model[field].tolist()
for op in model.operators:
model_dict['operators'].append(op)
return model_dict
return json.JSONEncoder.default(self, obj)
class JSONDumper(ModelDumper):
"""Dumper to JSON."""
def __init__(self, file_descriptor: FileType):
"""Construct with file handle."""
super(JSONDumper, self).__init__()
self.fd = file_descriptor
@file_handler('w')
def _dump_to_file(self, fd: FileType, model: Model):
json.dump(model, fd, cls=_ModelJSONEncoder)
def dump(self, model: Model):
"""Dump model."""
self._dump_to_file(self.fd, model)
@file_handler('r')
def read(self, fd: FileType):
"""Read model from file."""
properties = json.load(fd)
model = _create_model(properties['attrs'])
for name, field in properties['fields'].items():
v = np.asarray(field)
if model.type in _basic_types:
v = v.reshape(list(v.shape) + [1])
model[name] = v
return model
class FieldDumper(ModelDumper):
"""Abstract dumper for python classes using fields."""
postfix = ""
extension = ""
name_format = "{basename}{postfix}.{extension}"
def __init__(self, basename: NameType, *fields, **kwargs):
"""Construct with desired fields."""
super(FieldDumper, self).__init__()
self.basename = basename
self.fields: ts.List[str] = list(fields)
self.all_fields: bool = kwargs.get('all_fields', False)
def add_field(self, field: str):
"""Add another field to the dump."""
if field not in self.fields:
self.fields.append(field)
def _dump_to_file(self, file_descriptor: FileType, model: Model):
"""Dump to a file (path-like or file handle)."""
raise NotImplementedError()
def get_fields(self, model: Model):
"""Get the desired fields."""
if not self.all_fields:
requested_fields = self.fields
else:
requested_fields = list(model)
return {field: model[field] for field in requested_fields}
def dump(self, model: Model):
"""Dump model."""
self._dump_to_file(self.file_path, model)
def read(self, file_descriptor: FileType):
"""Read model from file."""
raise NotImplementedError(
f'read() method not implemented in {type(self).__name__}')
def read_sequence(self, glob_pattern):
"""Read models from a file sequence."""
return map(self.read, glob.iglob(glob_pattern))
@property
def file_path(self):
"""Get the default filename."""
return self.name_format.format(basename=self.basename,
postfix=self.postfix,
extension=self.extension)
@directory_dump('numpys')
@step_dump
class NumpyDumper(FieldDumper):
"""Dumper to compressed numpy files."""
extension = 'npz'
def _dump_to_file(self, file_descriptor: FileType, model: Model):
"""Save to compressed multi-field Numpy format."""
if mpi.size() > 1:
raise MPIIncompatibilityError("NumpyDumper does not function "
"at all in parallel")
np.savez_compressed(file_descriptor,
attrs=json.dumps(_get_attributes(model)),
**self.get_fields(model))
def read(self, file_descriptor: FileType):
"""Create model from Numpy file."""
data = np.load(file_descriptor, mmap_mode='r')
model = _create_model(json.loads(str(data['attrs'])))
for k, v in filter(lambda k: k[0] != 'attrs', data.items()):
if model.type in _basic_types:
v = v.reshape(list(v.shape) + [1])
model[k] = v
return model
try:
import h5py
__all__.append("H5Dumper")
@directory_dump('hdf5')
@step_dump
class H5Dumper(FieldDumper):
"""Dumper to HDF5 file format."""
extension = 'h5'
def _hdf5_args(self):
if mpi.size() > 1:
from mpi4py import MPI # noqa
mpi_args = dict(driver='mpio', comm=MPI.COMM_WORLD)
comp_args = {} # compression does not work in parallel
else:
mpi_args = {}
comp_args = dict(compression='gzip', compression_opts=7)
return mpi_args, comp_args
def _dump_to_file(self, file_descriptor: FileType, model: Model):
"""Save to HDF5 with metadata about the model."""
# Setup for MPI
if not h5py.get_config().mpi and mpi.size() > 1:
raise MPIIncompatibilityError("HDF5 does not have MPI support")
mpi_args, comp_args = self._hdf5_args()
with h5py.File(file_descriptor, 'w', **mpi_args) as handle:
# Writing data
for name, field in self.get_fields(model).items():
shape = list(field.shape)
if mpi.size() > 1:
xdim = 0 if _is_surface_field(field, model) else 1
shape[xdim] = mpi_args['comm'].allreduce(shape[xdim])
dset = handle.create_dataset(name, shape, field.dtype,
**comp_args)
dset[local_slice(field, model)] = field
# Writing metadata
for name, attr in _get_attributes(model).items():
handle.attrs[name] = attr
def read(self, file_descriptor: FileType):
"""Create model from HDF5 file."""
mpi_args, _ = self._hdf5_args()
with h5py.File(file_descriptor, 'r', **mpi_args) as handle:
model = _create_model(handle.attrs)
for k, v in handle.items():
if model.type in _basic_types:
v = np.asarray(v).reshape(list(v.shape) + [1])
model[k] = v[local_slice(v, model)].copy()
return model
except ImportError:
pass
try:
import uvw # noqa
__all__ += [
"UVWDumper",
"UVWGroupDumper",
]
@directory_dump('paraview')
@step_dump
class UVWDumper(FieldDumper):
"""Dumper to VTK files for elasto-plastic calculations."""
extension = 'vtr'
forbidden_fields = ['traction', 'gap']
def _dump_to_file(self, file_descriptor: FileType, model: Model):
"""Dump displacements, plastic deformations and stresses."""
if mpi.size() > 1:
raise MPIIncompatibilityError("UVWDumper does not function "
"properly in parallel")
bdim = len(model.boundary_shape)
# Local MPI size
lsize = model.shape
gsize = mpi.global_shape(model.boundary_shape)
gshape = gsize
if len(lsize) > bdim:
gshape = [model.shape[0]] + gshape
# Space coordinates
coordinates = [
np.linspace(0, L, N, endpoint=False)
for L, N in zip(model.system_size, gshape)
]
# If model has subsurfce domain, z-coordinate is always first
dimension_indices = np.arange(bdim)
if len(lsize) > bdim:
dimension_indices += 1
dimension_indices = np.concatenate((dimension_indices, [0]))
coordinates[0] = \
np.linspace(0, model.system_size[0], gshape[0])
offset = np.zeros_like(dimension_indices)
offset[0] = mpi.local_offset(gsize)
rectgrid = uvw.RectilinearGrid if mpi.size() == 1 \
else uvw.parallel.PRectilinearGrid
# Creating rectilinear grid with correct order for components
coordlist = [
coordinates[i][o:o + lsize[i]]
for i, o in zip(dimension_indices, offset)
]
grid = rectgrid(
file_descriptor,
coordlist,
compression=True,
offsets=offset,
)
# Iterator over fields we want to dump
fields_it = filter(lambda t: t[0] not in self.forbidden_fields,
self.get_fields(model).items())
# We make fields periodic for visualization
for name, field in fields_it:
array = uvw.DataArray(field, dimension_indices, name)
grid.addPointData(array)
grid.write()
@directory_dump('paraview')
class UVWGroupDumper(FieldDumper):
"""Dumper to ParaViewData files."""
extension = 'pvd'
def __init__(self, basename: NameType, *fields, **kwargs):
"""Construct with desired fields."""
super(UVWGroupDumper, self).__init__(basename, *fields, **kwargs)
subdir = Path('paraview') / f'{basename}-VTR'
subdir.mkdir(parents=True, exist_ok=True)
self.uvw_dumper = UVWDumper(
Path(f'{basename}-VTR') / basename, *fields, **kwargs)
self.group = uvw.ParaViewData(self.file_path, compression=True)
def _dump_to_file(self, file_descriptor, model):
self.group.addFile(self.uvw_dumper.file_path.replace(
'paraview/', ''),
timestep=self.uvw_dumper.count)
self.group.write()
self.uvw_dumper.dump(model)
except ImportError:
pass
try:
from netCDF4 import Dataset
__all__.append("NetCDFDumper")
@directory_dump('netcdf')
class NetCDFDumper(FieldDumper):
"""Dumper to netCDF4 files."""
extension = "nc"
time_dim = 'frame'
def _file_setup(self, grp, model: Model):
grp.createDimension(self.time_dim, None)
# Attibutes
for k, v in _get_attributes(model).items():
grp.setncattr(k, v)
# Local dimensions
voigt_dim = type_traits[model.type].voigt
components = type_traits[model.type].components
self._vec = grp.createDimension('spatial', components)
self._tens = grp.createDimension('Voigt', voigt_dim)
self.model_info = model.global_shape, model.type
global_boundary_shape = mpi.global_shape(model.boundary_shape)
# Create boundary dimensions
for label, size, length in zip("xy", global_boundary_shape,
model.boundary_system_size):
grp.createDimension(label, size)
coord = grp.createVariable(label, 'f8', (label, ))
coord[:] = np.linspace(0, length, size, endpoint=False)
self._create_variables(grp, model,
lambda f: _is_surface_field(f[1], model),
global_boundary_shape, "xy")
# Create volume dimension
if model.type in {model_type.volume_1d, model_type.volume_2d}:
size = model.shape[0]
grp.createDimension("z", size)
coord = grp.createVariable("z", 'f8', ("z", ))
coord[:] = np.linspace(0, model.system_size[0], size)
self._create_variables(
grp, model, lambda f: not _is_surface_field(f[1], model),
model.global_shape, "zxy")
self.has_setup = True
def _set_collective(self, rootgrp):
if mpi.size() == 1:
return
for v in rootgrp.variables.values():
if self.time_dim in v.dimensions:
v.set_collective(True)
def _dump_to_file(self, file_descriptor: NameType, model: Model):
format = 'NETCDF4_CLASSIC'
mode = 'a' if Path(file_descriptor).is_file() \
and getattr(self, 'has_setup', False) else 'w'
with Dataset(file_descriptor,
mode,
format=format,
parallel=mpi.size() > 1) as rootgrp:
if rootgrp.dimensions == {}:
self._file_setup(rootgrp, model)
self._set_collective(rootgrp)
if self.model_info != (model.global_shape, model.type):
raise ModelError(f"Unexpected model {mode}")
self._dump_generic(rootgrp, model)
def _create_variables(self, grp, model, predicate, shape, dimensions):
field_dim = len(shape)
fields = list(filter(predicate, self.get_fields(model).items()))
dim_labels = list(dimensions[:field_dim])
for label, data in fields:
local_dim = []
# If we have an extra component
if data.ndim > field_dim:
if data.shape[-1] == self._tens.size:
local_dim = [self._tens.name]
elif data.shape[-1] == self._vec.size:
local_dim = [self._vec.name]
else:
raise ComponentsError(
f"{label} has unexpected number of components "
f"({data.shape[-1]})")
grp.createVariable(label,
'f8',
[self.time_dim] + dim_labels + local_dim,
zlib=mpi.size() == 0)
def _dump_generic(self, grp, model):
fields = self.get_fields(model).items()
new_frame = len(grp.dimensions[self.time_dim])
for label, data in fields:
var = grp[label]
slice_in_global = (new_frame, ) + local_slice(data, model)
var[slice_in_global] = np.array(data, dtype=np.double)
@staticmethod
def _open_read(fd):
return Dataset(fd,
'r',
format='NETCDF4_CLASSIC',
parallel=mpi.size() > 1)
@staticmethod
def _create_model(rootgrp):
attrs = {k: rootgrp.getncattr(k) for k in rootgrp.ncattrs()}
return _create_model(attrs)
@staticmethod
def _set_model_fields(rootgrp, model, frame):
dims = rootgrp.dimensions.keys()
for k, v in filter(lambda k: k[0] not in dims,
rootgrp.variables.items()):
v = v[frame, :]
if model.type in _basic_types:
v = np.asarray(v).reshape(list(v.shape) + [1])
model[k] = v[local_slice(v, model)].copy()
def read(self, file_descriptor: NameType):
"""Create model with last frame."""
with self._open_read(file_descriptor) as rootgrp:
model = self._create_model(rootgrp)
self._set_model_fields(rootgrp, model, -1)
return model
def read_sequence(self, file_descriptor: NameType):
with self._open_read(file_descriptor) as rootgrp:
model = self._create_model(rootgrp)
for frame in range(len(rootgrp.dimensions[self.time_dim])):
self._set_model_fields(rootgrp, model, frame)
yield model
except ImportError:
pass
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