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Commit 716a1b8b authored by dboe's avatar dboe
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first iteration maps

parent d23dec96
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test/test_core.py
View file @ 716a1b8b
......@@ -7,7 +7,7 @@ import tfields
ATOL = 1e-8
class AbstractNdarray_Check(object):
class Base_Check(object):
def demand_equal(self, other):
raise NotImplementedError(self.__class__)
......@@ -21,6 +21,11 @@ class AbstractNdarray_Check(object):
self.demand_equal(reloaded)
def test_copy(self):
copy = type(self._inst)(self._inst)
self.demand_equal(copy)
self.assertIsNot(self._inst, copy)
def test_save_npz(self):
out_file = NamedTemporaryFile(suffix='.npz')
self._inst.save(out_file.name)
......@@ -39,6 +44,10 @@ class AbstractNdarray_Check(object):
del self._inst
class AbstractNdarray_Check(Base_Check):
pass
class Tensors_Check(AbstractNdarray_Check):
"""
Testing derivatives of Tensors
......@@ -60,6 +69,7 @@ class Tensors_Check(AbstractNdarray_Check):
transformer = self._inst.copy()
transformer.transform(tfields.bases.CYLINDER)
self.demand_equal(transformer, atol=True, transformed=True)
self.assertIs(self._inst, np.asarray(self._inst))
def test_cylinderTrafo(self):
# Test coordinate transformations in circle
......@@ -181,6 +191,14 @@ class TensorMaps_Copy_Test(TensorMaps_Empty_Test):
maps=self._maps)
class Maps_Test(Base_Check, unittest.TestCase):
def demand_equal(self, other):
self._inst.equal(other)
def setUp(self):
self._inst = tfields.Maps([[[1,2,3]]])
class Container_Check(AbstractNdarray_Check):
def demand_equal(self, other):
raise NotImplementedError(self.__class__)
......@@ -202,24 +220,24 @@ class Container_Test(Container_Check, unittest.TestCase):
self._inst = tfields.Container([sphere, sphere2])
class ContainerNoList_Test(Container_Check, unittest.TestCase):
def demand_equal_tensors(self, one, other, atol=False, transformed=False):
if atol:
self.assertTrue(one.equal(other, atol=ATOL))
else:
self.assertTrue(one.equal(other))
if not transformed:
self.assertEqual(one.coord_sys, other.coord_sys)
self.assertEqual(one.name, other.name)
def demand_equal(self, other):
self.demand_equal_tensors(self._inst.items.items[0], other)
self.demand_equal_tensors(self._inst.items.items[1], other)
def setUp(self):
t = tfields.TensorFields([[1, 2, 3]])
self._inst = tfields.Container(t)
print(self._inst.items)
# class ContainerNoList_Test(Container_Check, unittest.TestCase):
# def demand_equal_tensors(self, one, other, atol=False, transformed=False):
# if atol:
# self.assertTrue(one.equal(other, atol=ATOL))
# else:
# self.assertTrue(one.equal(other))
# if not transformed:
# self.assertEqual(one.coord_sys, other.coord_sys)
# self.assertEqual(one.name, other.name)
#
# def demand_equal(self, other):
# self.demand_equal_tensors(self._inst.items.items[0], other)
# self.demand_equal_tensors(self._inst.items.items[1], other)
#
# def setUp(self):
# t = tfields.TensorFields([[1, 2, 3]])
# self._inst = tfields.Container(t)
# print(self._inst.items)
if __name__ == '__main__':
......
tfields/__about__.py
View file @ 716a1b8b
......@@ -31,6 +31,7 @@ __dependencies__ = [
'pathlib2;python_version<"3.0"',
'pathlib;python_version>="3.0"',
'rna',
'sortedcontainers'
]
__classifiers__ = [
# find the full list of possible classifiers at https://pypi.org/classifiers/
......
tfields/__init__.py
View file @ 716a1b8b
......@@ -6,7 +6,7 @@ from . import lib
from .lib import *
# __all__ = ['core', 'points3D']
from .core import Tensors, TensorFields, TensorMaps, Container
from .core import Tensors, TensorFields, TensorMaps, Container, Maps
from .points3D import Points3D
from .mask import evalf
......
tfields/core.py
View file @ 716a1b8b
......@@ -12,16 +12,20 @@ Notes:
<https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/...
... numpy.lib.mixins.NDArrayOperatorsMixin.html>`_
"""
# builtin
import warnings
import pathlib
from six import string_types
from contextlib import contextmanager
from collections import Counter
# 3rd party
import numpy as np
import sympy
import scipy as sp
import sortedcontainers
import rna
import tfields.bases
np.seterr(all="warn", over="raise")
......@@ -31,6 +35,7 @@ def rank(tensor):
"""
Tensor rank
"""
tensor = np.asarray(tensor)
return len(tensor.shape) - 1
......@@ -38,6 +43,7 @@ def dim(tensor):
"""
Manifold dimension
"""
tensor = np.asarray(tensor)
if rank(tensor) == 0:
return 1
return tensor.shape[1]
......@@ -266,8 +272,7 @@ class AbstractNdarray(np.ndarray):
>>> import tfields
>>> m = tfields.TensorMaps(
... [[1,2,3], [3,3,3], [0,0,0], [5,6,7]],
... maps=[tfields.TensorFields([[0, 1, 2], [1, 2, 3]],
... [1, 2])])
... maps=[[[0, 1, 2], [1, 2, 3]], [1, 2])])
>>> mc = m.copy()
>>> mc is m
False
......@@ -484,8 +489,6 @@ class AbstractNdarray(np.ndarray):
'''
bulk = kwargs.pop('bulk')
bulk_type = kwargs.pop('bulk_type')
print("-"*100)
print(bulk, bulk_type, kwargs)
obj = cls.__new__(cls, bulk, **kwargs)
'''
......@@ -1953,8 +1956,8 @@ class Container(AbstractNdarray):
def __getitem__(self, index):
return self.items[index]
def __setitem__(self, index, value):
self.items[index] = value
def __setitem__(self, index, item):
self.items[index] = item
def __iter__(self):
return iter(self.items)
......@@ -1963,39 +1966,71 @@ class Container(AbstractNdarray):
return len(self.items)
class Maps(Container):
class Maps(sortedcontainers.SortedDict):
"""
A Maps object is a container for TensorFields sorted by dimension.
Indexing by dimension
"""
def __new__(cls, maps, **kwargs):
if issubclass(type(maps), Maps):
kwargs['labels'] = maps.labels
maps = maps.items
maps_cp = []
for mp in maps:
mp = TensorFields(mp, dtype=int)
if not mp.rank == 1:
raise ValueError(
"Incorrect map rank {mp.rank}".format(**locals())
)
maps_cp.append(mp)
maps = maps_cp
def __init__(self, maps):
if issubclass(type(maps), list):
maps = {dim(mp): mp for mp in maps}
if not issubclass(type(maps), dict):
raise TypeError("Could not interprete input {}".format(maps))
maps = {d: self.to_map(maps[d]) for d in maps.keys()}
super().__init__(maps)
@staticmethod
def to_map(mp):
mp = TensorFields(mp, dtype=int)
if not mp.rank == 1:
raise ValueError(
"Incorrect map rank {mp.rank}".format(**locals())
)
return mp
def __setitem__(self, dim, mp):
mp = self.to_map(mp)
if not dim == mp.dim:
raise KeyError(
"Incorrect map dimension {mp.dim} for index {dim}"
.format(**locals())
)
if dim == 0:
warnings.warn(
"Using map dimension {dim}"
.format(**locals())
)
obj = super().__new__(cls, maps, **kwargs)
# obj._update()
return obj
super().__setitem__(dim, mp)
def _update(self):
maps = self.items
dims = [dim(obj) for obj in maps]
dims, maps = tfields.lib.util.multi_sort(dims, maps)
self.items = maps
self.labels = dims
def __getitem__(self, dim):
if dim == 0:
warnings.warn(
"Using map dimension {dim}"
.format(**locals())
)
return super().__getitem__(dim)
# def __iter__(self):
# warnings.warn(
# "Deprecated: will bList like iteration"
# .format(**locals())
# )
# return iter([self[k] for k in super().__iter__()])
def equal(self, other, **kwargs):
"""
Test equality with other object.
Args:
**kwargs: passed to each item on equality check
"""
if not self.keys() == other.keys():
return False
for dim in self.keys():
if not self[dim].equal(other[dim], **kwargs):
return False
return True
@property
def dims(self):
return self.labels
class TensorMaps(TensorFields):
......@@ -2108,7 +2143,7 @@ class TensorMaps(TensorFields):
if isinstance(index, tuple):
index = index[0]
if len(item.maps) == 0:
item.maps = [mp.copy() for mp in item.maps]
item.maps = Maps(item.maps)
indices = np.array(range(len(self)))
keep_indices = indices[index]
if isinstance(keep_indices, (int, np.int64, np.int32)):
......@@ -2116,12 +2151,12 @@ class TensorMaps(TensorFields):
delete_indices = set(indices).difference(set(keep_indices))
# correct all maps that contain deleted indices
for mp_idx in range(len(self.maps)):
for map_dim in self.maps.keys():
# build mask, where the map should be deleted
map_delete_mask = np.full(
(len(self.maps[mp_idx]),), False, dtype=bool
(len(self.maps[map_dim]),), False, dtype=bool
)
for i, mp in enumerate(self.maps[mp_idx]):
for i, mp in enumerate(self.maps[map_dim]):
for index in mp:
if index in delete_indices:
map_delete_mask[i] = True
......@@ -2130,13 +2165,13 @@ class TensorMaps(TensorFields):
# build the correction counters
move_up_counter = np.zeros(
self.maps[mp_idx].shape, dtype=int
self.maps[map_dim].shape, dtype=int
)
for p in delete_indices:
move_up_counter[self.maps[mp_idx] > p] -= 1
move_up_counter[self.maps[map_dim] > p] -= 1
item.maps[mp_idx] = (
self.maps[mp_idx] + move_up_counter
item.maps[map_dim] = (
self.maps[map_dim] + move_up_counter
)[map_mask]
except IndexError as err:
warnings.warn(
......@@ -2166,23 +2201,18 @@ class TensorMaps(TensorFields):
else:
inst, templates = (return_value, None)
# save map_index in order to be able to recover the exact same
# order in the template later
dim_maps_dict = {} # {dim: {obj_index(i): (map_index(j), maps_field)}}
dim_maps_dict = {} # {dim: {i: mp}
for i, obj in enumerate(objects):
for j, map_field in enumerate(obj.maps):
map_field = map_field + cum_tensor_lengths[i]
if map_field.dim not in dim_maps_dict:
dim_maps_dict[map_field.dim] = {}
dim_maps_dict[map_field.dim][i] = (j, map_field)
for dimension, mp in obj.maps:
mp = mp + cum_tensor_lengths[i]
dim_maps_dict[mp.dim][i] = mp
maps = []
template_maps_list = [[] for i in range(len(objects))]
for dimension in sorted(dim_maps_dict.keys()):
for dimension in sorted(dim_maps_dict):
# sort by object index
obj_indices = sorted(dim_maps_dict[dimension].keys())
map_indices = [dim_maps_dict[dimension][i][0] for i in obj_indices]
dim_maps = [dim_maps_dict[dimension][i][1] for i in obj_indices]
dim_maps = [dim_maps_dict[dimension][i] for i in obj_indices]
return_value = TensorFields.merged(
*dim_maps,
......@@ -2191,11 +2221,7 @@ class TensorMaps(TensorFields):
if return_templates:
mp, dimension_map_templates = return_value
for i in obj_indices:
j = map_indices[i]
template_maps_list[i].append(
(j, dimension_map_templates[i])
)
template_maps_list[i].append(dimension_map_templates[i])
else:
mp = return_value
maps.append(mp)
......@@ -2205,8 +2231,7 @@ class TensorMaps(TensorFields):
for i, template_maps in enumerate(template_maps_list):
templates[i] = tfields.TensorMaps(
templates[i],
maps=[val[1] for val in sorted(template_maps,
key=lambda x: x[0])])
maps=template_maps)
return inst, templates
else:
return inst
......@@ -2242,11 +2267,7 @@ class TensorMaps(TensorFields):
with other.tmp_transform(self.coord_sys):
mask = super(TensorMaps, self).equal(other, **kwargs)
if issubclass(type(other), TensorMaps):
if len(self.maps) != len(other.maps):
mask &= False
else:
for i, mp in enumerate(self.maps):
mask &= mp.equal(other.maps[i], **kwargs)
mask &= self.maps.equal(other.maps, **kwargs)
return mask
def stale(self):
......@@ -2256,14 +2277,16 @@ class TensorMaps(TensorFields):
Examples:
>>> import tfields
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0], [4, 4, 4]])
>>> tm = tfields.TensorMaps(vectors, maps=[[[0, 1, 2], [0, 1, 2]],
... [[1, 1], [2, 2]]])
>>> vectors = tfields.Tensors(
... [[0, 0, 0], [0, 0, 1], [0, -1, 0], [4, 4, 4]])
>>> tm = tfields.TensorMaps(
... vectors,
... maps=[[[0, 1, 2], [0, 1, 2]], [[1, 1], [2, 2]]])
>>> assert np.array_equal(tm.stale(), [False, False, False, True])
"""
staleMask = np.full(self.shape[0], False, dtype=bool)
used = set([ind for mp in self.maps for ind in mp.flatten()])
used = set([ind for mp in self.maps.items() for ind in mp.flatten()])
for i in range(self.shape[0]):
if i not in used:
staleMask[i] = True
......@@ -2314,12 +2337,12 @@ class TensorMaps(TensorFields):
if duplicate_index != tensor_index:
stale_mask[tensor_index] = True
# redirect maps
for mp_idx in range(len(self.maps)):
for f in range(len(self.maps[mp_idx])):
mp = np.array(self.maps[mp_idx], dtype=int)
for map_dim in self.maps:
for f in range(len(self.maps[map_dim])): # face index
mp = np.array(self.maps[map_dim], dtype=int)
if tensor_index in mp[f]:
index = tfields.index(mp[f], tensor_index)
inst.maps[mp_idx][f][index] = duplicate_index
inst.maps[map_dim][f][index] = duplicate_index
return inst.removed(stale_mask)
......@@ -2354,14 +2377,14 @@ class TensorMaps(TensorFields):
# delete_indices = np.arange(self.shape[0])[remove_condition]
# face_keep_masks = self.to_maps_masks(~remove_condition)
# for mp_idx, face_keep_mask in enumerate(face_keep_masks):
# move_up_counter = np.zeros(self.maps[mp_idx].shape, dtype=int)
# for map_dim, face_keep_mask in enumerate(face_keep_masks):
# move_up_counter = np.zeros(self.maps[map_dim].shape, dtype=int)
# # correct map:
# for p in delete_indices:
# move_up_counter[self.maps[mp_idx] > p] -= 1
# move_up_counter[self.maps[map_dim] > p] -= 1
# inst.maps[mp_idx] = (self.maps[mp_idx] + move_up_counter)[face_keep_mask]
# inst.maps[map_dim] = (self.maps[map_dim] + move_up_counter)[face_keep_mask]
# return inst
return self[~remove_condition]
......@@ -2418,11 +2441,11 @@ class TensorMaps(TensorFields):
delete_indices = set(indices.flat).difference(set(keep_indices.flat))
masks = []
for mp_idx in range(len(self.maps)):
for map_dim in self.maps:
map_delete_mask = np.full(
(len(self.maps[mp_idx]),), False, dtype=bool
(len(self.maps[map_dim]),), False, dtype=bool
)
for i, mp in enumerate(self.maps[mp_idx]):
for i, mp in enumerate(self.maps[map_dim]):
for index in mp:
if index in delete_indices:
map_delete_mask[i] = True
......@@ -2434,8 +2457,8 @@ class TensorMaps(TensorFields):
"""
Args:
*map_descriptions (Tuple(int, List(List(int)))): tuples of
map_pos_idx (int): reference to map position
used like: self.maps[map_pos_idx]
map_dim (int): reference to map position
used like: self.maps[map_dim]
map_indices_list (List(List(int))): each int refers
to index in a map.
......@@ -2446,30 +2469,30 @@ class TensorMaps(TensorFields):
# raise ValueError(map_descriptions)
parts = []
for map_description in map_descriptions:
map_pos_idx, map_indices_list = map_description
map_dim, map_indices_list = map_description
for map_indices in map_indices_list:
obj = self.copy()
map_indices = set(map_indices) # for speed up
map_delete_mask = np.array(
[
True if i not in map_indices else False
for i in range(len(self.maps[map_pos_idx]))
for i in range(len(self.maps[map_dim]))
]
)
obj.maps[map_pos_idx] = obj.maps[map_pos_idx][~map_delete_mask]
obj.maps[map_dim] = obj.maps[map_dim][~map_delete_mask]
obj = obj.cleaned(duplicates=False)
parts.append(obj)
return parts
def disjoint_map(self, mp_idx):
def disjoint_map(self, map_dim):
"""
Find the disjoint sets of map = self.maps[mp_idx]
Find the disjoint sets of map = self.maps[map_dim]
As an example, this method is interesting for splitting a mesh
consisting of seperate parts
Args:
mp_idx (int): reference to map position
used like: self.maps[mp_idx]
map_dim (int): reference to map position
used like: self.maps[map_dim]
Returns:
Tuple(int, List(List(int))): map description(tuple): see self.parts
......@@ -2490,10 +2513,10 @@ class TensorMaps(TensorFields):
>>> assert ba.equal(b)
"""
maps_list = tfields.lib.sets.disjoint_group_indices(self.maps[mp_idx])
return (mp_idx, maps_list)
maps_list = tfields.lib.sets.disjoint_group_indices(self.maps[map_dim])
return (map_dim, maps_list)
def paths(self, mp_idx):
def paths(self, map_dim):
"""
Find the minimal amount of graphs building the original graph with
maximum of two links per node i.e.
......@@ -2510,6 +2533,7 @@ class TensorMaps(TensorFields):
o o o o
where 8 is a duplicated node (one has two links and one has only one.)
Examples:
>>> import tfields
>>> import numpy as np
......@@ -2540,7 +2564,7 @@ class TensorMaps(TensorFields):
"""
obj = self.cleaned()
flat_map = np.array(obj.maps[mp_idx].flat)
flat_map = np.array(obj.maps[map_dim].flat)
values, counts = np.unique(flat_map, return_counts=True)
counts = {v: n for v, n in zip(values, counts)}
......@@ -2571,22 +2595,22 @@ class TensorMaps(TensorFields):
if duplicat_indices:
duplicates = obj[duplicat_indices]
obj = type(obj).merged(obj, duplicates)
obj.maps = [tfields.Tensors(flat_map.reshape(-1, *obj.maps[mp_idx].shape[1:]))]
paths = obj.parts(obj.disjoint_map(0))
obj.maps = [flat_map.reshape(-1, *obj.maps[map_dim].shape[1:])]
paths = obj.parts(obj.disjoint_map(map_dim))
# paths = [paths[2]] # this path did not work previously - debugging # with vessel
# remove duplicate map entries and sort
sorted_paths = []
for path in paths:
# find start index
values, counts = np.unique(path.maps[0].flat, return_counts=True)
values, counts = np.unique(path.maps[map_dim].flat, return_counts=True)
first_node = None
for v, c in zip(values, counts):
if c == 1:
first_node = v
break
edges = [list(edge) for edge in path.maps[0]]
edges = [list(edge) for edge in path.maps[map_dim]]
if first_node is None:
first_node = 0 # edges[0][0]
path = path[list(range(len(path))) + [0]]
......
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