def data_aligned(container):
"""
Check if all signals are aligned at the same time line, regarding
(start, stop).
Parameters
----------
container : list, tuple, iterable, dict, neo container
The container for the neo objects.
tuple: list of AnalogSignal objects and list of SpikeTrain objects
A tuple consisting of two list with AnalogSignal and SpikeTrain
objects.
Returns
-------
sigs, sts: `neo.core.AnalogSignal` and `neo.core.SpikeTrain` objects
All time aligned objects regarding the same start and stop time
are returned.
"""
as_time_list = []
st_time_list = []
sts = []
sigs = []
try:
sts = nt.get_all_spiketrains(container)
sigs = nt.get_all_analogsignals(container)
except TypeError:
if sts:
sigs = []
sts = nt.get_all_spiketrains(container)
else:
sts = []
sigs = nt.get_all_analogsignals(container)
# Empty signals
if not (sts or sigs):
return []
for st in sts:
st_time_list.append((st.t_start, st.t_stop))
for sig in sigs:
as_time_list.append((sig.t_start, sig.t_stop))
# Check if start and stop times are equal
if len(as_time_list) == 0:
if st_time_list.count(st_time_list[0]) != len(
st_time_list):
return []
elif len(st_time_list) == 0:
if as_time_list.count(as_time_list[0]) != len(
as_time_list):
return []
else:
if np.equal(st_time_list, as_time_list).all() is False:
return []
if sigs:
sigs.extend(sts)
return sigs
else:
return sts
def contains_each_unit(container):
"""
Each `neo.core.SpikeTrain` object of given input has a link to each
`neo.core.Unit`.
Parameters
----------
container : list, tuple, iterable, dict, neo container
The container for the neo objects.
Returns
-------
sts : list of neo.core.SpikeTrain objects
List of `neo.core.SpikeTrain` objects with a link to each neo.core.Unit
"""
units = nt.get_all_units(container)
sts = nt.get_all_spiketrains(container)
return [spk for spk in
filter(lambda st: st.unit in units, sts)]
def each_train_exact_n_spikes(container, n):
"""
Each SpikeTrain of the trial must have `n` spikes.
Parameters
----------
container : list, tuple, iterable, dict, neo container
The container for the neo objects.
n: int
Number of spikes each SpikeTrain object must have.
Returns
-------
"""
if n < 1:
raise ValueError("Please provide a number greater than %d, "
"when setting the condition for an exact number "
"of Spikes in each SpikeTrain." % n)
sts = nt.get_all_spiketrains(container)
return [st for st in filter(lambda st: np.size(st) == n, sts)]
def at_least_n_trains(container, n):
"""
Given input is checked if it has at least `n` **neo.core.SpikeTrain**
objects.
Parameters
----------
container : list, tuple, iterable, dict, neo container
The container for the neo objects.
n: int
Number of SpikeTrain objects given input must have at least.
Returns
-------
list : list of `neo.core.SpikeTrain` objects.
"""
if n < 1:
raise ValueError(
"Please provide a number greater than %d, when setting "
"the condition for a minimal number of SpikeTrains." % n)
sts = nt.get_all_spiketrains(container)
return sts if len(sts) >= n else []
