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_mesh.py
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_mesh.py
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import collections
import numpy
CellBlock = collections.namedtuple("CellBlock", ["type", "data"])
class Mesh:
def __init__(
self,
points,
cells,
point_data=None,
cell_data=None,
field_data=None,
point_sets=None,
cell_sets=None,
gmsh_periodic=None,
info=None,
):
self.points = points
if isinstance(cells, dict):
# Let's not deprecate this for now.
# import warnings
# warnings.warn(
# "cell dictionaries are deprecated, use list of tuples, e.g., "
# '[("triangle", [[0, 1, 2], ...])]',
# DeprecationWarning,
# )
# old dict, deprecated
self.cells = [
CellBlock(cell_type, data) for cell_type, data in cells.items()
]
else:
self.cells = [CellBlock(cell_type, data) for cell_type, data in cells]
self.point_data = {} if point_data is None else point_data
self.cell_data = {} if cell_data is None else cell_data
self.field_data = {} if field_data is None else field_data
self.point_sets = {} if point_sets is None else point_sets
self.cell_sets = {} if cell_sets is None else cell_sets
self.gmsh_periodic = gmsh_periodic
self.info = info
def __repr__(self):
lines = [
"<meshio mesh object>",
" Number of points: {}".format(len(self.points)),
]
if len(self.cells) > 0:
lines.append(" Number of cells:")
for tpe, elems in self.cells:
lines.append(" {}: {}".format(tpe, len(elems)))
else:
lines.append(" No cells.")
if self.point_sets:
lines.append(" Point sets: {}".format(", ".join(self.point_sets.keys())))
if self.cell_sets:
lines.append(" Cell sets: {}".format(", ".join(self.cell_sets.keys())))
if self.point_data:
lines.append(" Point data: {}".format(", ".join(self.point_data.keys())))
if self.cell_data:
lines.append(" Cell data: {}".format(", ".join(self.cell_data.keys())))
return "\n".join(lines)
def prune(self):
prune_list = ["vertex", "line", "line3"]
if any([c.type in ["tetra", "tetra10"] for c in self.cells]):
prune_list += ["triangle", "triangle6"]
new_cells = []
new_cell_data = {}
for c in self.cells:
if c.type not in prune_list:
new_cells.append(c)
for name, data in self.cell_data:
if name not in new_cell_data:
new_cell_data[name] = []
new_cell_data[name].append(data)
self.cells = new_cells
self.cell_data = new_cell_data
print("Pruned cell types: {}".format(", ".join(prune_list)))
# remove_orphaned_nodes.
# find which nodes are not mentioned in the cells and remove them
all_cells_flat = numpy.concatenate([c.data for c in self.cells]).flatten()
orphaned_nodes = numpy.setdiff1d(numpy.arange(len(self.points)), all_cells_flat)
self.points = numpy.delete(self.points, orphaned_nodes, axis=0)
# also adapt the point data
for key in self.point_data:
self.point_data[key] = numpy.delete(
self.point_data[key], orphaned_nodes, axis=0
)
# reset GLOBAL_ID
if "GLOBAL_ID" in self.point_data:
self.point_data["GLOBAL_ID"] = numpy.arange(1, len(self.points) + 1)
# We now need to adapt the cells too.
diff = numpy.zeros(len(all_cells_flat), dtype=all_cells_flat.dtype)
for orphan in orphaned_nodes:
diff[numpy.argwhere(all_cells_flat > orphan)] += 1
all_cells_flat -= diff
k = 0
for k, c in enumerate(self.cells):
s = c.data.shape
n = numpy.prod(s)
self.cells[k] = CellBlock(c.type, all_cells_flat[k : k + n].reshape(s))
k += n
def write(self, path_or_buf, file_format=None, **kwargs):
# avoid circular import
from ._helpers import write
write(path_or_buf, self, file_format, **kwargs)
def get_cells_type(self, cell_type):
return numpy.concatenate([c.data for c in self.cells if c.type == cell_type])
def get_cell_data(self, name, cell_type):
return numpy.concatenate(
[d for c, d in zip(self.cells, self.cell_data[name]) if c.type == cell_type]
)
@property
def cells_dict(self):
cells_dict = {}
for cell_type, data in self.cells:
if cell_type not in cells_dict:
cells_dict[cell_type] = []
cells_dict[cell_type].append(data)
# concatenate
for key, value in cells_dict.items():
cells_dict[key] = numpy.concatenate(value)
return cells_dict
@property
def cell_data_dict(self):
cell_data_dict = {}
for key, value_list in self.cell_data.items():
cell_data_dict[key] = {}
for value, (cell_type, _) in zip(value_list, self.cells):
if cell_type not in cell_data_dict[key]:
cell_data_dict[key][cell_type] = []
cell_data_dict[key][cell_type].append(value)
for cell_type, val in cell_data_dict[key].items():
cell_data_dict[key][cell_type] = numpy.concatenate(val)
return cell_data_dict
@property
def cell_sets_dict(self):
sets_dict = {}
for key, member_list in self.cell_sets.items():
sets_dict[key] = {}
offsets = {}
for members, cells in zip(member_list, self.cells):
if cells.type in offsets:
offset = offsets[cells.type]
offsets[cells.type] += cells.data.shape[0]
else:
offset = 0
offsets[cells.type] = cells.data.shape[0]
if cells.type in sets_dict[key]:
sets_dict[key][cells.type].append(members + offset)
else:
sets_dict[key][cells.type] = [members + offset]
return {
key: {
cell_type: numpy.concatenate(members)
for cell_type, members in sets.items()
if sum(map(numpy.size, members))
}
for key, sets in sets_dict.items()
}
@classmethod
def read(cls, path_or_buf, file_format=None):
# avoid circular import
from ._helpers import read
return read(path_or_buf, file_format)
def sets_to_int_data(self):
# If possible, convert cell sets to integer cell data. This is possible if all
# cells appear exactly in one group.
intfun = []
for c in zip(*self.cell_sets.values()):
# check if all numbers appear exactly once in the groups
d = numpy.sort(numpy.concatenate(c))
is_convertible = numpy.all(d[1:] == d[:-1] + 1) and len(d) == d[-1] + 1
if is_convertible:
intfun.append(numpy.zeros(len(d), dtype=int))
for k, cc in enumerate(c):
intfun[-1][cc] = k
data_name = "-".join(self.cell_sets.keys())
self.cell_data = {data_name: intfun}
self.cell_sets = {}
def int_data_to_sets(self):
"""Convert all int data to {point,cell}_sets, where possible.
defined by int-valued cell_data, keyed (like cell_sets_dict) by name (as found
in field_data or constructed from the int) and cell_type. The indices are into
the items of cells_dict[cell_type].
"""
keys = []
for key, data in self.cell_data.items():
# handle all int and uint data
if not numpy.all(v.dtype.kind in ["i", "u"] for v in data):
continue
keys.append(key)
# this call can be rather expensive
tags = numpy.unique(numpy.concatenate(data))
# try and get the names by splitting the key along "-" (this is how
# sets_to_int_data() forms the key
names = sorted(list(set(key.split("-"))))
if len(names) != len(tags):
# alternative names
names = ["set{}".format(tag) for tag in tags]
for name, tag in zip(names, tags):
self.cell_sets[name] = []
self.cell_sets[name] = [numpy.where(d == tag)[0] for d in data]
# remove the cell data
for key in keys:
del self.cell_data[key]