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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 -*-
# @file
# @section LICENSE
#
# Copyright (©) 2016-19 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 tamaas.Model
"""
from __future__ import print_function
from .. import ModelDumper, model_type
from ._helper import PeriodicHelper, step_dump, directory_dump
import numpy as np
class FieldDumper(ModelDumper):
"""Abstract dumper for python classes using fields"""
postfix = ''
extension = ''
name_format = "{basename}{postfix}.{extension}"
def __init__(self, basename, *fields, **kwargs):
"""Construct with desired fields"""
super(FieldDumper, self).__init__()
self.basename = basename
self.fields = fields
self.all_fields = kwargs.get('all_fields', False)
self.make_periodic = PeriodicHelper()
def add_field(self, field):
"""Add another field to the dump"""
if field not in self.fields:
self.fields.append(field)
def dump_to_file(self, fd, model):
"""Dump to a file (name or handle)"""
pass
def get_fields(self, model):
"""Get the desired fields"""
if not self.all_fields:
requested_fields = self.fields
else:
requested_fields = model.getFields()
return {field: model.getField(field) for field in requested_fields}
def get_attributes(self, model):
"""Get model attributes"""
return {
'model_type': str(model.type),
'system_size': model.getSystemSize(),
'discretization': model.getDiscretization()
}
def dump(self, model):
self.dump_to_file(self.file_path, model)
@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, fd, model):
"""Saving to compressed multi-field Numpy format"""
np.savez_compressed(fd, attrs=self.get_attributes(model),
**self.get_fields(model))
try:
import h5py
@directory_dump('hdf5')
@step_dump
class H5Dumper(FieldDumper):
"""Dumper to HDF5 file format"""
extension = 'h5'
def dump_to_file(self, fd, model):
"""Saving to HDF5 with metadata about the model"""
with h5py.File(fd, 'w') as fh:
# Writing data
for name, field in self.get_fields(model).items():
dset = fh.create_dataset(name, field.shape, field.dtype,
compression='gzip',
compression_opts=7)
dset[:] = field
# Writing metadata
for name, attr in self.get_attributes(model).items():
fh.attrs[name] = attr
except ImportError:
pass
try:
import uvw # noqa
@directory_dump('paraview')
@step_dump
class UVWDumper(FieldDumper):
"""Dumper to VTK files for elasto-plastic calculations"""
extension = 'vtr'
forbidden_fields = ['traction', 'gap'] # TODO make generic
def dump_to_file(self, fd, model):
"""Dump displacements, plastic deformations and stresses"""
discretization = model.getDiscretization().copy()
# Because we make fields periodic
discretization[1] += 1
discretization[2] += 1
# Space coordinates
coordinates = [np.linspace(0, L, N)
for L, N in zip(model.getSystemSize(),
discretization)]
# Correct order of coordinate dimensions
dimension_indices = [1, 2, 0]
# Creating rectilinear grid with correct order for components
grid = uvw.RectilinearGrid(fd, (coordinates[i]
for i in dimension_indices))
# Iterator over fields we want to dump
# Avoid 2D fields (TODO make generic)
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(np.array(self.make_periodic[name](field),
dtype=np.double),
dimension_indices, name)
grid.addPointData(array)
grid.write()
except ImportError as e:
print(e)
try:
from netCDF4 import Dataset
@directory_dump('netcdf')
@step_dump
class NetCDFDumper(FieldDumper):
"""Dumper to netCDF4 files"""
extension = "nc"
boundary_fields = ['traction', 'gap']
def dump_to_file(self, fd, model):
with Dataset(fd, 'w', format='NETCDF4_CLASSIC') as rootgrp:
model_dim = len(model.getDiscretization())
self._vec = rootgrp.createDimension('spatial', model_dim)
self._tens = rootgrp.createDimension('Voigt', 2*model_dim)
self._dump_boundary(rootgrp, model)
if model.type in {model_type.volume_1d, model_type.volume_2d}:
self._dump_volume(rootgrp, model)
def _dump_boundary(self, grp, model):
# Create boundary dimensions
it = zip("xy", model.getBoundaryDiscretization(),
model.getSystemSize())
for label, size, length in it:
grp.createDimension(label, size)
coord = grp.createVariable(label, 'f8', (label,))
coord[:] = np.linspace(0, length, size, endpoint=False)
self._dump_generic(grp, model,
lambda f: f[0] in self.boundary_fields,
'boundary',
model.getBoundaryDiscretization(),
"xy")
def _dump_volume(self, grp, model):
# Create volume dimension
size = model.getDiscretization()[0]
grp.createDimension("z", size)
coord = grp.createVariable("z", 'f8', ("z",))
coord[:] = np.linspace(0, model.getSystemSize()[0], size)
self._dump_generic(grp, model,
lambda f: f[0] not in self.boundary_fields,
'volume', model.getDiscretization(), "zxy")
def _dump_generic(self, grp, model, predicate,
group_name, shape, dimensions):
field_dim = len(shape)
fields = filter(predicate, self.get_fields(model).items())
dim_labels = list(dimensions[:field_dim])
for label, data in fields:
dims = dim_labels
# If we have an extra component
if data.ndim > field_dim:
if data.shape[-1] == self._tens.size:
dims.append(self._tens.name)
elif data.shape[-1] == self._vec.size:
dims.append(self._vec.name)
var = grp.createVariable(label, 'f8', dims)
var[:] = np.array(data, dtype=np.double).flatten()
except ImportError:
pass
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