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tamaas/python/tamaas/dumpers/netcdf_dumper.py
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netcdf_dumper.py
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# @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/>.
from ._helper import FieldDumper
import numpy as np
from netCDF4 import Dataset
from ._helper import step_dump
__all__ = ['NetCDFDumper']
@step_dump
class NetCDFDumper(FieldDumper):
"""Dumper to NetCDF format files"""
def __init__(self, basename, *fields):
default_fields = ['traction', 'displacement',
'stress', 'plastic_strain']
super().__init__(basename, default_fields, *fields)
self.rootgrp = Dataset("{}.nc".format(self.basename),
"w", format="NETCDF4_CLASSIC")
def __del__(self):
self.rootgrp.close()
def dump_boundary(self, model):
group = self.rootgrp
shape = model.getDiscretization()
tags = ["Z", "X", "Y"]
# Creating dimensions
group.createDimension("T", None)
for tag, dim in zip(tags, shape):
group.createDimension(tag, dim)
# Dimension variables
for dim, L in zip(group.dimensions.values(),
model.getSystemSize()):
tag = dim.name
var = group.createVariable(tag, 'f8', (tag,))
var[...] = np.linspace(0, L, dim.size)
# Field variables
for field in self.fields:
var = group.createVariable(field, 'f8', ['T'] + tags)
var[self.count, ...] = model.getField(field)[..., 2]
def dump_volume(self, model):
pass
def dump(self, model):
self.dump_boundary(model)
self.rootgrp.model_type = str(model.type)
self.rootgrp.sync()
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