def test_get_start_stop(self):
a = self.spiketrain_a
b = neo.SpikeTrain([-0.1, -0.7, 1.2, 2.2, 4.3, 5.5, 8.0] * pq.s,
t_start=-1 * pq.s,
t_stop=8 * pq.s)
start, stop = cv._get_start_stop_from_input(a)
self.assertEqual(start, a.t_start)
self.assertEqual(stop, a.t_stop)
start, stop = cv._get_start_stop_from_input([a, b])
self.assertEqual(start, a.t_start)
self.assertEqual(stop, b.t_stop)
def test_get_start_stop(self):
a = self.spiketrain_a
b = neo.SpikeTrain(
[-0.1, -0.7, 1.2, 2.2, 4.3, 5.5, 8.0] * pq.s,
t_start=-1 * pq.s, t_stop=8 * pq.s)
start, stop = cv._get_start_stop_from_input(a)
self.assertEqual(start, a.t_start)
self.assertEqual(stop, a.t_stop)
start, stop = cv._get_start_stop_from_input([a, b])
self.assertEqual(start, a.t_start)
self.assertEqual(stop, b.t_stop)
def time_histogram(spiketrains,
binsize,
t_start=None,
t_stop=None,
output='counts',
binary=False):
"""
Time Histogram of a list of :attr:`neo.SpikeTrain` objects.
Parameters
----------
spiketrains : List of neo.SpikeTrain objects
Spiketrains with a common time axis (same `t_start` and `t_stop`)
binsize : quantities.Quantity
Width of the histogram's time bins.
t_start, t_stop : Quantity (optional)
Start and stop time of the histogram. Only events in the input
`spiketrains` falling between `t_start` and `t_stop` (both included)
are considered in the histogram. If `t_start` and/or `t_stop` are not
specified, the maximum `t_start` of all :attr:spiketrains is used as
`t_start`, and the minimum `t_stop` is used as `t_stop`.
Default: t_start = t_stop = None
output : str (optional)
Normalization of the histogram. Can be one of:
* `counts`'`: spike counts at each bin (as integer numbers)
* `mean`: mean spike counts per spike train
* `rate`: mean spike rate per spike train. Like 'mean', but the
counts are additionally normalized by the bin width.
binary : bool (optional)
If **True**, indicates whether all spiketrain objects should first
binned to a binary representation (using the `BinnedSpikeTrain` class
in the `conversion` module) and the calculation of the histogram is
based on this representation.
Note that the output is not binary, but a histogram of the converted,
binary representation.
Default: False
Returns
-------
time_hist : neo.AnalogSignal
A neo.AnalogSignal object containing the histogram values.
`AnalogSignal[j]` is the histogram computed between
`t_start + j * binsize` and `t_start + (j + 1) * binsize`.
See also
--------
elephant.conversion.BinnedSpikeTrain
"""
min_tstop = 0
if t_start is None:
# Find the internal range for t_start, where all spike trains are
# defined; cut all spike trains taking that time range only
max_tstart, min_tstop = conv._get_start_stop_from_input(spiketrains)
t_start = max_tstart
if not all([max_tstart == t.t_start for t in spiketrains]):
warnings.warn("Spiketrains have different t_start values -- "
"using maximum t_start as t_start.")
if t_stop is None:
# Find the internal range for t_stop
if min_tstop:
t_stop = min_tstop
if not all([min_tstop == t.t_stop for t in spiketrains]):
warnings.warn("Spiketrains have different t_stop values -- "
"using minimum t_stop as t_stop.")
else:
min_tstop = conv._get_start_stop_from_input(spiketrains)[1]
t_stop = min_tstop
if not all([min_tstop == t.t_stop for t in spiketrains]):
warnings.warn("Spiketrains have different t_stop values -- "
"using minimum t_stop as t_stop.")
sts_cut = [
st.time_slice(t_start=t_start, t_stop=t_stop) for st in spiketrains
]
# Bin the spike trains and sum across columns
bs = conv.BinnedSpikeTrain(sts_cut,
t_start=t_start,
t_stop=t_stop,
binsize=binsize)
if binary:
bin_hist = bs.to_sparse_bool_array().sum(axis=0)
else:
bin_hist = bs.to_sparse_array().sum(axis=0)
# Flatten array
bin_hist = np.ravel(bin_hist)
# Renormalise the histogram
if output == 'counts':
# Raw
bin_hist = bin_hist * pq.dimensionless
elif output == 'mean':
# Divide by number of input spike trains
bin_hist = bin_hist * 1. / len(spiketrains) * pq.dimensionless
elif output == 'rate':
# Divide by number of input spike trains and bin width
bin_hist = bin_hist * 1. / len(spiketrains) / binsize
else:
raise ValueError('Parameter output is not valid.')
return neo.AnalogSignal(signal=bin_hist.reshape(bin_hist.size, 1),
sampling_period=binsize,
units=bin_hist.units,
t_start=t_start)
def time_histogram(spiketrains, binsize, t_start=None, t_stop=None,
output='counts', binary=False):
"""
Time Histogram of a list of :attr:`neo.SpikeTrain` objects.
Parameters
----------
spiketrains : List of neo.SpikeTrain objects
Spiketrains with a common time axis (same `t_start` and `t_stop`)
binsize : quantities.Quantity
Width of the histogram's time bins.
t_start, t_stop : Quantity (optional)
Start and stop time of the histogram. Only events in the input
`spiketrains` falling between `t_start` and `t_stop` (both included)
are considered in the histogram. If `t_start` and/or `t_stop` are not
specified, the maximum `t_start` of all :attr:spiketrains is used as
`t_start`, and the minimum `t_stop` is used as `t_stop`.
Default: t_start = t_stop = None
output : str (optional)
Normalization of the histogram. Can be one of:
* `counts`'`: spike counts at each bin (as integer numbers)
* `mean`: mean spike counts per spike train
* `rate`: mean spike rate per spike train. Like 'mean', but the
counts are additionally normalized by the bin width.
binary : bool (optional)
If **True**, indicates whether all spiketrain objects should first
binned to a binary representation (using the `BinnedSpikeTrain` class
in the `conversion` module) and the calculation of the histogram is
based on this representation.
Note that the output is not binary, but a histogram of the converted,
binary representation.
Default: False
Returns
-------
time_hist : neo.AnalogSignal
A neo.AnalogSignal object containing the histogram values.
`AnalogSignal[j]` is the histogram computed between
`t_start + j * binsize` and `t_start + (j + 1) * binsize`.
See also
--------
elephant.conversion.BinnedSpikeTrain
"""
min_tstop = 0
if t_start is None:
# Find the internal range for t_start, where all spike trains are
# defined; cut all spike trains taking that time range only
max_tstart, min_tstop = conv._get_start_stop_from_input(spiketrains)
t_start = max_tstart
if not all([max_tstart == t.t_start for t in spiketrains]):
warnings.warn(
"Spiketrains have different t_start values -- "
"using maximum t_start as t_start.")
if t_stop is None:
# Find the internal range for t_stop
if min_tstop:
t_stop = min_tstop
if not all([min_tstop == t.t_stop for t in spiketrains]):
warnings.warn(
"Spiketrains have different t_stop values -- "
"using minimum t_stop as t_stop.")
else:
min_tstop = conv._get_start_stop_from_input(spiketrains)[1]
t_stop = min_tstop
if not all([min_tstop == t.t_stop for t in spiketrains]):
warnings.warn(
"Spiketrains have different t_stop values -- "
"using minimum t_stop as t_stop.")
sts_cut = [st.time_slice(t_start=t_start, t_stop=t_stop) for st in
spiketrains]
# Bin the spike trains and sum across columns
bs = conv.BinnedSpikeTrain(sts_cut, t_start=t_start, t_stop=t_stop,
binsize=binsize)
if binary:
bin_hist = bs.to_sparse_bool_array().sum(axis=0)
else:
bin_hist = bs.to_sparse_array().sum(axis=0)
# Flatten array
bin_hist = np.ravel(bin_hist)
# Renormalise the histogram
if output == 'counts':
# Raw
bin_hist = bin_hist * pq.dimensionless
elif output == 'mean':
# Divide by number of input spike trains
bin_hist = bin_hist * 1. / len(spiketrains) * pq.dimensionless
elif output == 'rate':
# Divide by number of input spike trains and bin width
bin_hist = bin_hist * 1. / len(spiketrains) / binsize
else:
raise ValueError('Parameter output is not valid.')
return neo.AnalogSignal(signal=bin_hist.reshape(bin_hist.size, 1),
sampling_period=binsize, units=bin_hist.units,
t_start=t_start)
def time_histogram(spiketrains, bin_size, t_start=None, t_stop=None,
output='counts', binary=False):
"""
Time Histogram of a list of `neo.SpikeTrain` objects.
Parameters
----------
spiketrains : list of neo.SpikeTrain
`neo.SpikeTrain`s with a common time axis (same `t_start` and `t_stop`)
bin_size : pq.Quantity
Width of the histogram's time bins.
t_start : pq.Quantity, optional
Start time of the histogram. Only events in `spiketrains` falling
between `t_start` and `t_stop` (both included) are considered in the
histogram.
If None, the maximum `t_start` of all `neo.SpikeTrain`s is used as
`t_start`.
Default: None.
t_stop : pq.Quantity, optional
Stop time of the histogram. Only events in `spiketrains` falling
between `t_start` and `t_stop` (both included) are considered in the
histogram.
If None, the minimum `t_stop` of all `neo.SpikeTrain`s is used as
`t_stop`.
Default: None.
output : {'counts', 'mean', 'rate'}, optional
Normalization of the histogram. Can be one of:
* 'counts': spike counts at each bin (as integer numbers)
* 'mean': mean spike counts per spike train
* 'rate': mean spike rate per spike train. Like 'mean', but the
counts are additionally normalized by the bin width.
Default: 'counts'.
binary : bool, optional
If True, indicates whether all `neo.SpikeTrain` objects should first
be binned to a binary representation (using the
`conversion.BinnedSpikeTrain` class) and the calculation of the
histogram is based on this representation.
Note that the output is not binary, but a histogram of the converted,
binary representation.
Default: False.
Returns
-------
neo.AnalogSignal
A `neo.AnalogSignal` object containing the histogram values.
`neo.AnalogSignal[j]` is the histogram computed between
`t_start + j * bin_size` and `t_start + (j + 1) * bin_size`.
Raises
------
ValueError
If `output` is not 'counts', 'mean' or 'rate'.
Warns
-----
UserWarning
If `t_start` is None and the objects in `spiketrains` have different
`t_start` values.
If `t_stop` is None and the objects in `spiketrains` have different
`t_stop` values.
See also
--------
elephant.conversion.BinnedSpikeTrain
"""
min_tstop = 0
if t_start is None:
# Find the internal range for t_start, where all spike trains are
# defined; cut all spike trains taking that time range only
max_tstart, min_tstop = conv._get_start_stop_from_input(spiketrains)
t_start = max_tstart
if not all([max_tstart == t.t_start for t in spiketrains]):
warnings.warn(
"Spiketrains have different t_start values -- "
"using maximum t_start as t_start.")
if t_stop is None:
# Find the internal range for t_stop
if not min_tstop:
min_tstop = conv._get_start_stop_from_input(spiketrains)[1]
t_stop = min_tstop
if not all([min_tstop == t.t_stop for t in spiketrains]):
warnings.warn(
"Spiketrains have different t_stop values -- "
"using minimum t_stop as t_stop.")
sts_cut = [st.time_slice(t_start=t_start, t_stop=t_stop) for st in
spiketrains]
# Bin the spike trains and sum across columns
bs = conv.BinnedSpikeTrain(sts_cut, t_start=t_start, t_stop=t_stop,
bin_size=bin_size)
if binary:
bin_hist = bs.to_sparse_bool_array().sum(axis=0)
else:
bin_hist = bs.to_sparse_array().sum(axis=0)
# Flatten array
bin_hist = np.ravel(bin_hist)
# Renormalise the histogram
if output == 'counts':
# Raw
bin_hist = bin_hist * pq.dimensionless
elif output == 'mean':
# Divide by number of input spike trains
bin_hist = bin_hist * 1. / len(spiketrains) * pq.dimensionless
elif output == 'rate':
# Divide by number of input spike trains and bin width
bin_hist = bin_hist * 1. / len(spiketrains) / bin_size
else:
raise ValueError('Parameter output is not valid.')
return neo.AnalogSignal(signal=np.expand_dims(bin_hist, axis=1),
sampling_period=bin_size, units=bin_hist.units,
t_start=t_start)
