diff --git a/test/test_core.py b/test/test_core.py
index b0c89e95e2fcd2248118c99e711c8e465c09f9ca..5d17c83472660092817c8f848f7fe7e5550caf97 100644
--- a/test/test_core.py
+++ b/test/test_core.py
@@ -215,7 +215,17 @@ class Maps_Test(Base_Check, unittest.TestCase):
class Container_Check(AbstractNdarray_Check):
def demand_equal(self, other):
- raise NotImplementedError(self.__class__)
+ for i, item in enumerate(self._inst.items):
+ if issubclass(type(item), tfields.core.AbstractNdarray):
+ self.assertTrue(other.items[i].equal(item))
+ else:
+ self.assertEqual(other.items[i], item)
+ try:
+ self._inst.labels[i]
+ except (IndexError, TypeError):
+ pass
+ else:
+ self.assertEqual(other.labels[i], self._inst.labels[i])
def test_item(self):
if len(self._inst.items) > 0:
@@ -231,7 +241,7 @@ class Container_Test(Container_Check, unittest.TestCase):
(-np.pi / 2, np.pi / 2, 3),
coord_sys='spherical')
sphere2 = sphere.copy() * 3
- self._inst = tfields.Container([sphere, sphere2])
+ self._inst = tfields.Container([sphere, sphere2], labels=['test'])
if __name__ == '__main__':
diff --git a/tfields/core.py b/tfields/core.py
index da0f815c7640b4b248af55a9ba4ebf09ae3373d9..88f9107b9d91c1555eef8aa4e22aa5d176157c17 100644
--- a/tfields/core.py
+++ b/tfields/core.py
@@ -151,7 +151,7 @@ class AbstractObject(object):
[42]
"""
- content_dict = self._as_dict()
+ content_dict = self._as_new_dict()
np.savez(path, **content_dict)
@classmethod
@@ -164,7 +164,7 @@ class AbstractObject(object):
# wheter we could avoid pickling (potential security issue)
load_kwargs.setdefault('allow_pickle', True)
np_file = np.load(path, **load_kwargs)
- return cls._from_dict(**np_file)
+ return cls._from_new_dict(dict(np_file))
def _args(self) -> tuple:
return tuple()
@@ -218,17 +218,18 @@ class AbstractObject(object):
"""
for attr in here:
for key in here[attr]:
- if len(here[attr][key]) == 1:
- attr_value = here[attr][key].pop('')
- else:
+ if 'type' in here[attr][key]:
obj_type = here[attr][key].get("type")
- # obj_type = bulk_type.tolist() was necessary before. no clue
+ if isinstance(obj_type, np.ndarray): # happens on np.load
+ obj_type = obj_type.tolist()
if isinstance(obj_type, bytes):
# asthonishingly, this is not necessary under linux.
# Found under nt. ???
obj_type = obj_type.decode("UTF-8")
obj_type = getattr(tfields, obj_type)
attr_value = obj_type._from_new_dict(here[attr][key])
+ else: # if len(here[attr][key]) == 1:
+ attr_value = here[attr][key].pop('')
here[attr][key] = attr_value
'''
@@ -583,65 +584,6 @@ class AbstractNdarray(np.ndarray, AbstractObject):
return inst
- @classmethod
- def _from_dict(cls, **d):
- """
- legacy method
- Opposite of old _as_dict
- """
- list_dict = {}
- kwargs = {}
- """
- De-Flatten the first layer of lists
- """
- for key in sorted(list(d)):
- if "::" in key:
- splits = key.split("::")
- attr, _, end = key.partition("::")
- if attr not in list_dict:
- list_dict[attr] = {}
-
- index, _, end = end.partition("::")
- if not index.isdigit():
- raise ValueError("None digit index given")
- index = int(index)
- if index not in list_dict[attr]:
- list_dict[attr][index] = {}
- list_dict[attr][index][end] = d[key]
- else:
- kwargs[key] = d[key]
-
- """
- Build the lists (recursively)
- """
- for key in list(list_dict):
- sub_dict = list_dict[key]
- list_dict[key] = []
- for index in sorted(list(sub_dict)):
- bulk_type = sub_dict[index].get("bulk_type")
- bulk_type = bulk_type.tolist()
- if isinstance(bulk_type, bytes):
- # asthonishingly, this is not necessary under linux.
- # Found under nt. ???
- bulk_type = bulk_type.decode("UTF-8")
- bulk_type = getattr(tfields, bulk_type)
- list_dict[key].append(bulk_type._from_dict(**sub_dict[index]))
-
- with cls._bypass_setters('fields', demand_existence=False):
- '''
- Build the normal way
- '''
- bulk = kwargs.pop('bulk')
- bulk_type = kwargs.pop('bulk_type')
- obj = cls.__new__(cls, bulk, **kwargs)
-
- '''
- Set list attributes
- '''
- for attr, list_value in list_dict.items():
- setattr(obj, attr, list_value)
- return obj
-
class Tensors(AbstractNdarray):
"""
@@ -769,9 +711,11 @@ class Tensors(AbstractNdarray):
if issubclass(type(tensors), np.ndarray):
# np.empty
pass
+ elif hasattr(tensors, 'shape'):
+ dim = dim(tensors)
else:
raise ValueError(
- "Empty tensors need dimension " "parameter 'dim'."
+ "Empty tensors need dimension parameter 'dim'."
)
tensors = np.asarray(tensors, dtype=dtype, order=order)
@@ -828,13 +772,13 @@ class Tensors(AbstractNdarray):
def merged(cls, *objects, **kwargs):
"""
Factory method
- Merges all tensor inputs to one tensor
+ Merges all input arguments to one object
Args:
- **kwargs: passed to cls
- dim (int):
return_templates (bool): return the templates which can be used
together with cut to retrieve the original objects
+ dim (int):
+ **kwargs: passed to cls
Examples:
>>> import numpy as np
@@ -928,13 +872,10 @@ class Tensors(AbstractNdarray):
for i, obj in enumerate(remainingObjects):
tensors = np.append(tensors, obj, axis=0)
- if len(tensors) == 0 and 'dim' not in kwargs:
+ if len(tensors) == 0 and not kwargs.get('dim', None):
# if you can not determine the tensor dimension, search for the
# first object with some entries
- for obj in objects:
- if len(obj) != 0:
- kwargs['dim'] = dim(obj)
- break
+ kwargs['dim'] = dim(objects[0])
if not return_templates:
return cls.__new__(cls, tensors, **kwargs)
@@ -2089,7 +2030,7 @@ class Container(AbstractNdarray):
if issubclass(type(items), Container):
kwargs.setdefault('labels', items.labels)
items = items.items
- kwargs["items"] = items
+ kwargs["items"] = kwargs.pop('items_hack', items)
cls._update_slot_kwargs(kwargs)
empty = np.empty(0, int)
@@ -2105,6 +2046,11 @@ class Container(AbstractNdarray):
setattr(obj, attr, kwargs[attr])
return obj
+ def _kwargs(self):
+ d = super()._kwargs()
+ d['items_hack'] = d.pop('items') # hack for backwards compatibility
+ return d
+
def __getitem__(self, index):
return self.items[index]
@@ -2138,9 +2084,17 @@ class Maps(sortedcontainers.SortedDict, AbstractObject):
sortedcontainers.SortedItemsView):
args = tuple([v for k, v in args[0]])
elif len(args) == 1 and isinstance(args[0], list):
- if len(args[0]) > 0 and not issubclass(type(args[0][0]), tuple):
- # Maps([mp, mp2])- not Maps([]) and not Maps([(key, value)])
- args = tuple(args[0])
+ if args[0]:
+ # not not Maps([])
+ if issubclass(type(args[0][0]), tuple):
+ # Maps([(key, value), (key, value), ...])
+ args = tuple(v for k,v in args[0])
+ else:
+ # Maps([mp, mp, ...])
+ args = tuple(args[0])
+ else:
+ # Maps([]) -> Maps()
+ args = tuple()
elif len(args) == 1 and issubclass(type(args[0]), dict):
# Maps([]) - includes Maps i.e. copy
# dangerous because we run beefore super init
@@ -2380,7 +2334,7 @@ class TensorMaps(TensorFields):
cum_tensor_lengths = [sum(tensor_lengths[:i])
for i in range(len(objects))]
- return_value = super(TensorMaps, cls).merged(*objects, **kwargs)
+ return_value = super().merged(*objects, **kwargs)
return_templates = kwargs.get('return_templates', False)
if return_templates:
inst, templates = return_value
@@ -2392,15 +2346,15 @@ class TensorMaps(TensorFields):
for dimension, mp in obj.maps.items():
mp = mp + cum_tensor_lengths[i]
if dimension not in dim_maps_dict:
- dim_maps_dict[dimension] = []
- dim_maps_dict[dimension].append(mp)
+ dim_maps_dict[dimension] = {}
+ dim_maps_dict[dimension][i] = mp
maps = []
template_maps_list = [[] for i in range(len(objects))]
for dimension in sorted(dim_maps_dict):
# sort by object index
- obj_indices = sorted(dim_maps_dict[dimension].keys())
- dim_maps = [dim_maps_dict[dimension][i] for i in obj_indices]
+ dim_maps = [dim_maps_dict[dimension][i]
+ for i in range(len(objects))]
return_value = TensorFields.merged(
*dim_maps,
@@ -2408,14 +2362,14 @@ class TensorMaps(TensorFields):
)
if return_templates:
mp, dimension_map_templates = return_value
- for i in obj_indices:
+ for i in range(len(objects)):
template_maps_list[i].append(dimension_map_templates[i])
else:
mp = return_value
maps.append(mp)
- inst = cls.__new__(cls, inst, maps=maps)
- if 'return_templates' in kwargs:
+ inst.maps = maps
+ if return_templates:
for i, template_maps in enumerate(template_maps_list):
templates[i] = tfields.TensorMaps(
templates[i],
diff --git a/tfields/mesh3D.py b/tfields/mesh3D.py
index a35b631341a1cdea0eb6d5738caba9823ed54dbd..9747c29354ce78fe17001ed49b8d1ebb85b321e2 100644
--- a/tfields/mesh3D.py
+++ b/tfields/mesh3D.py
@@ -233,7 +233,7 @@ class Mesh3D(tfields.TensorMaps):
obj = super(Mesh3D, cls).__new__(cls, tensors, *fields, **kwargs)
if len(obj.maps) > 1:
raise ValueError("Mesh3D only allows one map")
- if obj.maps and obj.maps[0].dim != 3:
+ if obj.maps and (len(obj.maps) > 1 or obj.maps.keys()[0] != 3):
raise ValueError("Face dimension should be 3")
return obj