def add_individuals(target_context, children):
target_entity = target_context['__entity__']
id_to_rownum = target_entity.id_to_rownum
array = target_entity.array
num_rows = len(array)
num_birth = len(children)
print "%d new %s(s) (%d -> %d)" % (num_birth, target_entity.name,
num_rows, num_rows + num_birth),
target_entity.array = np.concatenate((array, children))
temp_variables = target_entity.temp_variables
for name, temp_value in temp_variables.iteritems():
#FIXME: OUCH, this is getting ugly, I'll need a better way to
# differentiate nd-arrays from "entity" variables
# I guess having the context contain all entities and a separate
# globals namespace should fix this problem
if (isinstance(temp_value, np.ndarray) and
temp_value.shape == (num_rows,)):
extra = get_missing_vector(num_birth, temp_value.dtype)
temp_variables[name] = np.concatenate((temp_value, extra))
extra_variables = target_context.extra
for name, temp_value in extra_variables.iteritems():
if name == '__globals__':
continue
if isinstance(temp_value, np.ndarray) and temp_value.shape:
extra = get_missing_vector(num_birth, temp_value.dtype)
extra_variables[name] = np.concatenate((temp_value, extra))
id_to_rownum_tail = np.arange(num_rows, num_rows + num_birth)
target_entity.id_to_rownum = np.concatenate((id_to_rownum,
id_to_rownum_tail))
def add_individuals(target_context, children):
target_entity = target_context['__entity__']
id_to_rownum = target_entity.id_to_rownum
array = target_entity.array
num_rows = len(array)
num_birth = len(children)
print "%d new %s(s) (%d -> %d)" % (num_birth, target_entity.name,
num_rows, num_rows + num_birth),
target_entity.array = np.concatenate((array, children))
temp_variables = target_entity.temp_variables
for name, temp_value in temp_variables.iteritems():
if isinstance(temp_value, np.ndarray) and temp_value.shape:
extra = get_missing_vector(num_birth, temp_value.dtype)
temp_variables[name] = np.concatenate((temp_value, extra))
extra_variables = target_context.extra
for name, temp_value in extra_variables.iteritems():
if name == '__globals__':
continue
if isinstance(temp_value, np.ndarray) and temp_value.shape:
extra = get_missing_vector(num_birth, temp_value.dtype)
extra_variables[name] = np.concatenate((temp_value, extra))
id_to_rownum_tail = np.arange(num_rows, num_rows + num_birth)
target_entity.id_to_rownum = np.concatenate((id_to_rownum,
id_to_rownum_tail))
def add_individuals(target_context, children):
target_entity = target_context.entity
id_to_rownum = target_entity.id_to_rownum
array = target_entity.array
num_rows = len(array)
num_birth = len(children)
if config.log_level == "processes":
print("%d new %s(s) (%d -> %d)" % (num_birth, target_entity.name,
num_rows, num_rows + num_birth),
end=' ')
target_entity.array.append(children)
temp_variables = target_entity.temp_variables
for name, temp_value in temp_variables.iteritems():
#FIXME: OUCH, this is getting ugly, I'll need a better way to
# differentiate nd-arrays from "entity" variables
# I guess having the context contain all entities and a separate
# globals namespace should fix this problem. Well, no it would not
# fix the problem by itself, as this would only move the problem
# to the "store" part of Assignment processes which would need to be
# able to differentiate between an "entity temp" and a global temp.
# I think this can be done by inspecting the expressions that generate
# them: no non-aggregated entity var => global temp. It would be nice
# to further distinguish between aggregated entity var and other global
# temporaries to store them in the entity somewhere, but I am unsure
# whether it is possible.
if (isinstance(temp_value, np.ndarray) and temp_value.shape == (
num_rows,)):
extra = get_missing_vector(num_birth, temp_value.dtype)
temp_variables[name] = np.concatenate((temp_value, extra))
extra_variables = target_context.entity_data.extra
for name, temp_value in extra_variables.iteritems():
if name == '__globals__':
continue
if isinstance(temp_value, np.ndarray) and temp_value.shape:
extra = get_missing_vector(num_birth, temp_value.dtype)
extra_variables[name] = np.concatenate((temp_value, extra))
id_to_rownum_tail = np.arange(num_rows, num_rows + num_birth)
target_entity.id_to_rownum = np.concatenate(
(id_to_rownum, id_to_rownum_tail))
def add_and_drop_fields(self, output_fields):
"""modify inplace"""
output_dtype = np.dtype(output_fields)
output_names = set(output_dtype.names)
input_names = set(self.dtype.names)
length = len(self)
# add missing fields
for name in output_names - input_names:
self[name] = get_missing_vector(length, output_dtype[name])
# delete extra fields
for name in input_names - output_names:
del self[name]
def add_and_drop_fields(self, output_fields):
'''modify inplace'''
output_dtype = np.dtype(output_fields)
output_names = set(output_dtype.names)
input_names = set(self.dtype.names)
default_values = self.dval
length = len(self)
# add missing fields
for name in output_names - input_names:
if name in default_values:
self[name] = np.empty(length, dtype=output_dtype[name])
self[name].fill(default_values[name])
else:
self[name] = get_missing_vector(length, output_dtype[name])
# delete extra fields
for name in input_names - output_names:
del self[name]