def _calc_indices(self, item):
'''
Convert the neuron indices to indices into the stored values. For example, if neurons [0, 5, 10] have been
recorded, [5, 10] is converted to [1, 2].
'''
dtype = get_dtype(item)
# scalar value
if np.issubdtype(
dtype, np.signedinteger) and not isinstance(item, np.ndarray):
indices = np.nonzero(self.monitor.record == item)[0]
if len(indices) == 0:
raise IndexError('Index number %d has not been recorded' %
item)
return indices[0]
if self.monitor.record_all:
return item
indices = []
for index in item:
if index in self.monitor.record:
indices.append(np.nonzero(self.monitor.record == index)[0][0])
else:
raise IndexError('Index number %d has not been recorded' %
index)
return np.array(indices)
def __getitem__(self, item):
dtype = get_dtype(item)
if np.issubdtype(dtype, np.int):
return StateMonitorView(self, item)
elif isinstance(item, collections.Sequence):
index_array = np.array(item)
if not np.issubdtype(index_array.dtype, np.int):
raise TypeError('Index has to be an integer or a sequence '
'of integers')
return StateMonitorView(self, item)
else:
raise TypeError('Cannot use object of type %s as an index'
% type(item))
def __getitem__(self, item):
dtype = get_dtype(item)
if np.issubdtype(dtype, np.int):
return StateMonitorView(self, item)
elif isinstance(item, collections.Sequence):
index_array = np.array(item)
if not np.issubdtype(index_array.dtype, np.int):
raise TypeError('Index has to be an integer or a sequence '
'of integers')
return StateMonitorView(self, item)
else:
raise TypeError('Cannot use object of type %s as an index' %
type(item))
def __getitem__(self, item):
dtype = get_dtype(item)
if np.issubdtype(dtype, np.signedinteger):
return StateMonitorView(self, item)
elif isinstance(item, Sequence):
index_array = np.array(item)
if not np.issubdtype(index_array.dtype, np.signedinteger):
raise TypeError("Index has to be an integer or a sequence "
"of integers")
return StateMonitorView(self, item)
elif hasattr(item, '_indices'):
# objects that support the indexing interface will return absolute
# indices but here we need relative ones
# TODO: How to we prevent the use of completely unrelated objects here?
source_offset = getattr(self.source, '_offset', 0)
return StateMonitorView(self, item._indices() - source_offset)
else:
raise TypeError(f'Cannot use object of type {type(item)} as an index')
def __getitem__(self, item):
dtype = get_dtype(item)
if np.issubdtype(dtype, np.int):
return StateMonitorView(self, item)
elif isinstance(item, collections.Sequence):
index_array = np.array(item)
if not np.issubdtype(index_array.dtype, np.int):
raise TypeError('Index has to be an integer or a sequence '
'of integers')
return StateMonitorView(self, item)
elif hasattr(item, '_indices'):
# objects that support the indexing interface will return absolute
# indices but here we need relative ones
# TODO: How to we prevent the use of completely unrelated objects here?
source_offset = getattr(self.source, '_offset', 0)
return StateMonitorView(self, item._indices() - source_offset)
else:
raise TypeError('Cannot use object of type %s as an index'
% type(item))
def _calc_indices(self, item):
'''
Convert the neuron indices to indices into the stored values. For example, if neurons [0, 5, 10] have been
recorded, [5, 10] is converted to [1, 2].
'''
dtype = get_dtype(item)
# scalar value
if np.issubdtype(dtype, np.int) and not isinstance(item, np.ndarray):
indices = np.nonzero(self.monitor.record == item)[0]
if len(indices) == 0:
raise IndexError('Index number %d has not been recorded' % item)
return indices[0]
if self.monitor.record_all:
return item
indices = []
for index in item:
if index in self.monitor.record:
indices.append(np.nonzero(self.monitor.record == index)[0][0])
else:
raise IndexError('Index number %d has not been recorded' % index)
return np.array(indices)
