def read_segment(
self,
cascade=True,
lazy=False,
sampling_rate=1. * pq.Hz,
t_start=0. * pq.s,
unit=pq.V,
nbchannel=1,
bytesoffset=0,
dtype='f4',
rangemin=-10,
rangemax=10,
):
"""
Reading signal in a raw binary interleaved compact file.
Arguments:
sampling_rate : sample rate
t_start : time of the first sample sample of each channel
unit: unit of AnalogSignal can be a str or directly a Quantities
nbchannel : number of channel
bytesoffset : nb of bytes offset at the start of file
dtype : dtype of the data
rangemin , rangemax : if the dtype is integer, range can give in volt the min and the max of the range
"""
seg = Segment(file_origin=os.path.basename(self.filename))
if not cascade:
return seg
dtype = np.dtype(dtype)
if type(sampling_rate) == float or type(sampling_rate) == int:
# if not quantitities Hz by default
sampling_rate = sampling_rate * pq.Hz
if type(t_start) == float or type(t_start) == int:
# if not quantitities s by default
t_start = t_start * pq.s
unit = pq.Quantity(1, unit)
if lazy:
sig = []
else:
sig = np.memmap(self.filename,
dtype=dtype,
mode='r',
offset=bytesoffset)
if sig.size % nbchannel != 0:
sig = sig[:-sig.size % nbchannel]
sig = sig.reshape((sig.size / nbchannel, nbchannel))
if dtype.kind == 'i':
sig = sig.astype('f')
sig /= 2**(8 * dtype.itemsize)
sig *= (rangemax - rangemin)
sig += (rangemax + rangemin) / 2.
elif dtype.kind == 'u':
sig = sig.astype('f')
sig /= 2**(8 * dtype.itemsize)
sig *= (rangemax - rangemin)
sig += rangemin
anaSig = AnalogSignal(sig,
units=unit,
sampling_rate=sampling_rate,
t_start=t_start,
copy=False)
if lazy:
# TODO
anaSig.lazy_shape = None
seg.analogsignals.append(anaSig)
seg.create_many_to_one_relationship()
return seg
def spike_train_timescale(binned_spiketrain, max_tau):
r"""
Calculates the auto-correlation time of a binned spike train; uses the
definition of the auto-correlation time proposed in
:cite:`correlation-Wieland2015_040901` (Eq. 6):
.. math::
\tau_\mathrm{corr} = \int_{-\tau_\mathrm{max}}^{\tau_\mathrm{max}}\
\left[ \frac{\hat{C}(\tau)}{\hat{C}(0)} \right]^2 d\tau
where :math:`\hat{C}(\tau) = C(\tau)-\nu\delta(\tau)` denotes
the auto-correlation function excluding the Dirac delta at zero timelag.
Parameters
----------
binned_spiketrain : elephant.conversion.BinnedSpikeTrain
A binned spike train containing the spike train to be evaluated.
max_tau : pq.Quantity
Maximal integration time :math:`\tau_{max}` of the auto-correlation
function. It needs to be a multiple of the `bin_size` of
`binned_spiketrain`.
Returns
-------
timescale : pq.Quantity
The auto-correlation time of the binned spiketrain with the same units
as in the input. If `binned_spiketrain` has less than 2 spikes, a
warning is raised and `np.nan` is returned.
Notes
-----
* :math:`\tau_\mathrm{max}` is a critical parameter: numerical estimates
of the auto-correlation functions are inherently noisy. Due to the
square in the definition above, this noise is integrated. Thus, it is
necessary to introduce a cutoff for the numerical integration - this
cutoff should be neither smaller than the true auto-correlation time
nor much bigger.
* The bin size of `binned_spiketrain` is another critical parameter as it
defines the discretization of the integral :math:`d\tau`. If it is too
big, the numerical approximation of the integral is inaccurate.
Examples
--------
>>> import neo
>>> import numpy as np
>>> import quantities as pq
>>> from elephant.spike_train_correlation import spike_train_timescale
>>> from elephant.conversion import BinnedSpikeTrain
>>> spiketrain = neo.SpikeTrain([1, 5, 7, 8], units='ms', t_stop=10*pq.ms)
>>> bst = BinnedSpikeTrain(spiketrain, bin_size=1 * pq.ms)
>>> spike_train_timescale(bst, max_tau=5 * pq.ms)
array(14.11111111) * ms
"""
if binned_spiketrain.get_num_of_spikes() < 2:
warnings.warn("Spike train contains less than 2 spikes! "
"np.nan will be returned.")
return np.nan
bin_size = binned_spiketrain._bin_size
try:
max_tau = max_tau.rescale(binned_spiketrain.units).item()
except (AttributeError, ValueError):
raise ValueError("max_tau needs units of time")
# safe casting of max_tau/bin_size to integer
max_tau_bins = int(round(max_tau / bin_size))
if not np.isclose(max_tau, max_tau_bins * bin_size):
raise ValueError("max_tau has to be a multiple of the bin_size")
cch_window = [-max_tau_bins, max_tau_bins]
corrfct, bin_ids = cross_correlation_histogram(
binned_spiketrain,
binned_spiketrain,
window=cch_window,
cross_correlation_coefficient=True)
# Take only t > 0 values, in particular neglecting the delta peak.
start_id = corrfct.time_index((bin_size / 2) * binned_spiketrain.units)
corrfct = corrfct.magnitude.squeeze()[start_id:]
# Calculate the timescale using trapezoidal integration
integr = (corrfct / corrfct[0])**2
timescale = 2 * integrate.trapz(integr, dx=bin_size)
return pq.Quantity(timescale, units=binned_spiketrain.units, copy=False)
def load(self,
time_slice=None,
strict_slicing=True,
magnitude_mode='rescaled',
load_waveforms=False):
'''
*Args*:
:time_slice: None or tuple of the time slice expressed with quantities.
None is the entire signal.
:strict_slicing: True by default.
Control if an error is raise or not when one of time_slice
member (t_start or t_stop) is outside the real time range of the segment.
:magnitude_mode: 'rescaled' or 'raw'.
:load_waveforms: bool load waveforms or not.
'''
t_start, t_stop = consolidate_time_slice(time_slice, self.t_start,
self.t_stop, strict_slicing)
_t_start, _t_stop = prepare_time_slice(time_slice)
spike_timestamps = self._rawio.get_spike_timestamps(
block_index=self._block_index,
seg_index=self._seg_index,
unit_index=self._unit_index,
t_start=_t_start,
t_stop=_t_stop)
if magnitude_mode == 'raw':
# we must modify a bit the neo.rawio interface to also read the spike_timestamps
# underlying clock wich is not always same as sigs
raise (NotImplementedError)
elif magnitude_mode == 'rescaled':
dtype = 'float64'
spike_times = self._rawio.rescale_spike_timestamp(spike_timestamps,
dtype=dtype)
units = 's'
if load_waveforms:
assert self.sampling_rate is not None, 'Do not have waveforms'
raw_wfs = self._rawio.get_spike_raw_waveforms(
block_index=self._block_index,
seg_index=self._seg_index,
unit_index=self._unit_index,
t_start=_t_start,
t_stop=_t_stop)
if magnitude_mode == 'rescaled':
float_wfs = self._rawio.rescale_waveforms_to_float(
raw_wfs, dtype='float32', unit_index=self._unit_index)
waveforms = pq.Quantity(float_wfs,
units=self._wf_units,
dtype='float32',
copy=False)
elif magnitude_mode == 'raw':
# could code also CompundUnit here but it is over killed
# so we used dimentionless
waveforms = pq.Quantity(raw_wfs,
units='',
dtype=raw_wfs.dtype,
copy=False)
else:
waveforms = None
sptr = SpikeTrain(spike_times,
t_stop,
units=units,
dtype=dtype,
t_start=t_start,
copy=False,
sampling_rate=self.sampling_rate,
waveforms=waveforms,
left_sweep=self.left_sweep,
name=self.name,
file_origin=self.file_origin,
description=self.description,
**self.annotations)
return sptr
def test_lcc25_dwell():
with expected_protocol(ik.thorlabs.LCC25, ["dwell?", "dwell=10"],
["dwell?", "20", ">dwell=10", ">"],
sep="\r") as lcc:
unit_eq(lcc.dwell, pq.Quantity(20, "ms"))
lcc.dwell = 10
def test_lcc25_frequency():
with expected_protocol(ik.thorlabs.LCC25, ["freq?", "freq=10.0"],
["freq?", "20", ">freq=10.0", ">"],
sep="\r") as lcc:
unit_eq(lcc.frequency, pq.Quantity(20, "Hz"))
lcc.frequency = 10.0
def read_one_channel_continuous(self,
fid,
channel_num,
header,
take_ideal_sampling_rate,
lazy=True):
# read AnalogSignal
channelHeader = header.channelHeaders[channel_num]
# data type
if channelHeader.kind == 1:
dt = np.dtype('i2')
elif channelHeader.kind == 9:
dt = np.dtype('f4')
# sample rate
if take_ideal_sampling_rate:
sampling_rate = channelHeader.ideal_rate * pq.Hz
else:
if header.system_id in [1, 2, 3, 4, 5]: # Before version 5
#~ print channel_num, channelHeader.divide, \
#~ header.us_per_time, header.time_per_adc
sample_interval = (channelHeader.divide * header.us_per_time *
header.time_per_adc) * 1e-6
else:
sample_interval = (channelHeader.l_chan_dvd *
header.us_per_time * header.dtime_base)
sampling_rate = (1. / sample_interval) * pq.Hz
# read blocks header to preallocate memory by jumping block to block
if channelHeader.blocks == 0:
return []
fid.seek(channelHeader.firstblock)
blocksize = [0]
starttimes = []
for b in range(channelHeader.blocks):
blockHeader = HeaderReader(fid, np.dtype(blockHeaderDesciption))
if len(blocksize) > len(starttimes):
starttimes.append(blockHeader.start_time)
blocksize[-1] += blockHeader.items
if blockHeader.succ_block > 0:
# ugly but CED does not guarantee continuity in AnalogSignal
fid.seek(blockHeader.succ_block)
nextBlockHeader = HeaderReader(fid,
np.dtype(blockHeaderDesciption))
sample_interval = (blockHeader.end_time -
blockHeader.start_time) / \
(blockHeader.items - 1)
interval_with_next = nextBlockHeader.start_time - \
blockHeader.end_time
if interval_with_next > sample_interval:
blocksize.append(0)
fid.seek(blockHeader.succ_block)
ana_sigs = []
if channelHeader.unit in unit_convert:
unit = pq.Quantity(1, unit_convert[channelHeader.unit])
else:
# print channelHeader.unit
try:
unit = pq.Quantity(1, channelHeader.unit)
except:
unit = pq.Quantity(1, '')
for b, bs in enumerate(blocksize):
if lazy:
signal = [] * unit
else:
signal = pq.Quantity(np.empty(bs, dtype='f4'), units=unit)
ana_sig = AnalogSignal(
signal,
sampling_rate=sampling_rate,
t_start=(starttimes[b] * header.us_per_time *
header.dtime_base * pq.s),
channel_index=channel_num)
ana_sigs.append(ana_sig)
if lazy:
for s, ana_sig in enumerate(ana_sigs):
ana_sig.lazy_shape = blocksize[s]
else:
# read data by jumping block to block
fid.seek(channelHeader.firstblock)
pos = 0
numblock = 0
for b in range(channelHeader.blocks):
blockHeader = HeaderReader(fid,
np.dtype(blockHeaderDesciption))
# read data
sig = np.fromstring(fid.read(blockHeader.items * dt.itemsize),
dtype=dt)
ana_sigs[numblock][pos:pos + sig.size] = \
sig.reshape(-1, 1).astype('f4') * unit
pos += sig.size
if pos >= blocksize[numblock]:
numblock += 1
pos = 0
# jump to next block
if blockHeader.succ_block > 0:
fid.seek(blockHeader.succ_block)
# convert for int16
if dt.kind == 'i':
for ana_sig in ana_sigs:
ana_sig *= channelHeader.scale / 6553.6
ana_sig += channelHeader.offset * unit
return ana_sigs
def test_lcc25_voltage1():
with expected_protocol(ik.thorlabs.LCC25, ["volt1?", "volt1=10.0"],
["volt1?", "20", ">volt1=10.0", ">"],
sep="\r") as lcc:
unit_eq(lcc.voltage1, pq.Quantity(20, "V"))
lcc.voltage1 = 10.0
def __new__(cls,
times,
t_stop,
units=None,
dtype=None,
copy=True,
sampling_rate=1.0 * pq.Hz,
t_start=0.0 * pq.s,
waveforms=None,
left_sweep=None,
name=None,
file_origin=None,
description=None,
**annotations):
'''
Constructs a new :clas:`Spiketrain` instance from data.
This is called whenever a new :class:`SpikeTrain` is created from the
constructor, but not when slicing.
'''
if len(times) != 0 and waveforms is not None and len(
times
) != waveforms.shape[
0]: #len(times)!=0 has been used to workaround a bug occuring during neo import)
raise ValueError(
"the number of waveforms should be equal to the number of spikes"
)
# Make sure units are consistent
# also get the dimensionality now since it is much faster to feed
# that to Quantity rather than a unit
if units is None:
# No keyword units, so get from `times`
try:
dim = times.units.dimensionality
except AttributeError:
raise ValueError('you must specify units')
else:
if hasattr(units, 'dimensionality'):
dim = units.dimensionality
else:
dim = pq.quantity.validate_dimensionality(units)
if hasattr(times, 'dimensionality'):
if times.dimensionality.items() == dim.items():
units = None # units will be taken from times, avoids copying
else:
if not copy:
raise ValueError("cannot rescale and return view")
else:
# this is needed because of a bug in python-quantities
# see issue # 65 in python-quantities github
# remove this if it is fixed
times = times.rescale(dim)
if dtype is None:
if not hasattr(times, 'dtype'):
dtype = np.float
elif hasattr(times, 'dtype') and times.dtype != dtype:
if not copy:
raise ValueError("cannot change dtype and return view")
# if t_start.dtype or t_stop.dtype != times.dtype != dtype,
# _check_time_in_range can have problems, so we set the t_start
# and t_stop dtypes to be the same as times before converting them
# to dtype below
# see ticket #38
if hasattr(t_start, 'dtype') and t_start.dtype != times.dtype:
t_start = t_start.astype(times.dtype)
if hasattr(t_stop, 'dtype') and t_stop.dtype != times.dtype:
t_stop = t_stop.astype(times.dtype)
# check to make sure the units are time
# this approach is orders of magnitude faster than comparing the
# reference dimensionality
if (len(dim) != 1 or list(dim.values())[0] != 1
or not isinstance(list(dim.keys())[0], pq.UnitTime)):
ValueError("Unit has dimensions %s, not [time]" % dim.simplified)
# Construct Quantity from data
obj = pq.Quantity(times, units=units, dtype=dtype, copy=copy).view(cls)
# if the dtype and units match, just copy the values here instead
# of doing the much more expensive creation of a new Quantity
# using items() is orders of magnitude faster
if (hasattr(t_start, 'dtype') and t_start.dtype == obj.dtype
and hasattr(t_start, 'dimensionality')
and t_start.dimensionality.items() == dim.items()):
obj.t_start = t_start.copy()
else:
obj.t_start = pq.Quantity(t_start, units=dim, dtype=obj.dtype)
if (hasattr(t_stop, 'dtype') and t_stop.dtype == obj.dtype
and hasattr(t_stop, 'dimensionality')
and t_stop.dimensionality.items() == dim.items()):
obj.t_stop = t_stop.copy()
else:
obj.t_stop = pq.Quantity(t_stop, units=dim, dtype=obj.dtype)
# Store attributes
obj.waveforms = waveforms
obj.left_sweep = left_sweep
obj.sampling_rate = sampling_rate
# parents
obj.segment = None
obj.unit = None
# Error checking (do earlier?)
_check_time_in_range(obj, obj.t_start, obj.t_stop, view=True)
return obj
def __setslice__(self, i, j, value):
if not hasattr(value, "units"):
value = pq.Quantity(value, units=self.units)
_check_time_in_range(value, self.t_start, self.t_stop)
super(SpikeTrain, self).__setslice__(i, j, value)
def test_cv2_with_quantities(self):
seq = pq.Quantity(self.test_seq, units='ms')
assert_array_almost_equal(statistics.cv2(seq), self.target, decimal=9)
def test_isi_with_quantities_1d(self):
st = pq.Quantity(self.test_array_1d, units='ms')
target = pq.Quantity(self.targ_array_1d, 'ms')
res = statistics.isi(st)
assert_array_almost_equal(res, target, decimal=9)
def test_mean_firing_rate_with_quantities_1d(self):
st = pq.Quantity(self.test_array_1d, units='ms')
target = pq.Quantity(self.targ_array_1d / self.max_array_1d, '1/ms')
res = statistics.mean_firing_rate(st)
assert_array_almost_equal(res, target, decimal=9)
def test_mean_firing_rate_with_spiketrain(self):
st = neo.SpikeTrain(self.test_array_1d, units='ms', t_stop=10.0)
target = pq.Quantity(self.targ_array_1d / 10., '1/ms')
res = statistics.mean_firing_rate(st)
assert_array_almost_equal(res, target, decimal=9)
#~ mapperInfo = open_db(url, myglobals = globals(), )
mapperInfo = open_db(url,
myglobals=globals(),
numpy_storage_engine='hdf5',
hdf5_filename=hdf5_filename)
seg = Segment(name='first seg')
seg.save()
for i in range(3):
ana = AnalogSignal()
seg.analogsignals.append(ana)
ana.save()
ana.name = 'from attr {}'.format(i)
ana.signal = pq.Quantity([1., 2., 3.], units='mV')
ana.t_start = 50.23 * pq.s
ana.sampling_rate = 1000. * pq.Hz
ana.save()
for i in range(1):
ana = AnalogSignal(
signal=pq.Quantity([1., 2., 3.], units='mV'),
t_start=15.654654 * pq.s,
sampling_rate=10 * pq.Hz,
name='ini init {}'.format(i),
)
ana.signal[1] = 4 * pq.mV
seg.analogsignals.append(ana)
for i in range(2):
def test___get_sampling_rate__period_rate_not_equivalent_ValueError(self):
sampling_rate = pq.Quantity(10., units=pq.Hz)
sampling_period = pq.Quantity(10, units=pq.s)
self.assertRaises(ValueError, _get_sampling_rate,
sampling_rate, sampling_period)
def format_data(self, data):
"""
This accepts data input in the form:
***** (observation) *****
{ "AA":{
"SO": {"mean": "X0"},
"SP": {"mean": "X1"},
"SR": {"mean": "X2"},
"SLM":{"mean": "X3"}
},
"BP": {...},
"BS": {...},
"CCKBC":{...},
"Ivy":{...},
"OLM":{...},
"PC":{...},
"PPA":{...},
"SCA":{...},
"Tri":{...}
}
***** (prediction) *****
{ "AA":{
"SO": {"value": "X0"},
"SP": {"value": "X1"},
"SR": {"value": "X2"},
"SLM":{"value": "X3"},
"OUT":{"value": "X4"}
},
"BP": {...},
"BS": {...},
"CCKBC":{...},
"Ivy":{...},
"OLM":{...},
"PC":{...},
"PPA":{...},
"SCA":{...},
"Tri":{...}
}
Returns a new dictionary of the form
{ "AA":[X0, X1, X2, X3, X4], "BP":[...] , "BS":[...], "CCKBC":[...], "Ivy":[...], "OLM":[...],
"PC":[...], "PPA":[...], "SCA":[...], "Tri":[...] }
"""
data_new_dict = dict()
for key0, dict0 in data.items(): # dict0: a dictionary containing the synapses fraction in each of the
# Hippocampus CA1 layers (SO, SP, SR, SLM) and OUT (for prediction data only)
# for each m-type cell (AA, BP, BS, CCKBC, Ivy, OLM, PC, PPA, SCA, Tri)
data_list_1 = list()
for dict1 in dict0.values(): # dict1: a dictionary of the form
# {"mean": "X0"} (observation) or {"value": "X"} (prediction)
try:
synapses_fraction = float(dict1.values()[0])
assert(synapses_fraction <= 1.0)
data_list_1.extend([synapses_fraction])
except:
raise sciunit.Error("Values not in appropriate format. Synapses fraction of an m-type cell"
"must be dimensionless and not larger than 1.0")
if "out" not in [x.lower() for x in dict0.keys()]: data_list_1.extend([0.0]) # observation data
data_list_1_q = quantities.Quantity(data_list_1, self.units)
data_new_dict[key0] = data_list_1_q
return data_new_dict
def times(self):
return pq.Quantity(self)
def read_block(
self,
lazy=False,
cascade=True,
):
bl = Block(file_origin=os.path.basename(self.filename), )
if not cascade:
return bl
fid = open(self.filename, 'rb')
headertext = fid.read(1024)
if PY3K:
headertext = headertext.decode('ascii')
header = {}
for line in headertext.split('\r\n'):
if '=' not in line: continue
#print '#' , line , '#'
key, val = line.split('=')
if key in [
'NC',
'NR',
'NBH',
'NBA',
'NBD',
'ADCMAX',
'NP',
'NZ',
]:
val = int(val)
elif key in [
'AD',
'DT',
]:
val = val.replace(',', '.')
val = float(val)
header[key] = val
#print header
SECTORSIZE = 512
# loop for record number
for i in range(header['NR']):
#print 'record ',i
offset = 1024 + i * (SECTORSIZE * header['NBD'] + 1024)
# read analysis zone
analysisHeader = HeaderReader(
fid, AnalysisDescription).read_f(offset=offset)
#print analysisHeader
# read data
NP = (SECTORSIZE * header['NBD']) / 2
NP = NP - NP % header['NC']
NP = NP / header['NC']
if not lazy:
data = np.memmap(
self.filename,
np.dtype('i2'),
'r',
#shape = (header['NC'], header['NP']) ,
shape=(
NP,
header['NC'],
),
offset=offset + header['NBA'] * SECTORSIZE)
# create a segment
seg = Segment()
bl.segments.append(seg)
for c in range(header['NC']):
unit = header['YU%d' % c]
try:
unit = pq.Quantity(1., unit)
except:
unit = pq.Quantity(1., '')
if lazy:
signal = [] * unit
else:
YG = float(header['YG%d' % c].replace(',', '.'))
ADCMAX = header['ADCMAX']
VMax = analysisHeader['VMax'][c]
signal = data[:, header['YO%d' % c]].astype(
'f4') * VMax / ADCMAX / YG * unit
anaSig = AnalogSignal(
signal,
sampling_rate=pq.Hz / analysisHeader['SamplingInterval'],
t_start=analysisHeader['TimeRecorded'] * pq.s,
name=header['YN%d' % c],
channel_index=c)
if lazy:
anaSig.lazy_shape = NP
seg.analogsignals.append(anaSig)
fid.close()
bl.create_many_to_one_relationship()
return bl
def read_one_channel_event_or_spike(self,
fid,
channel_num,
header,
lazy=True):
# return SPikeTrain or Event
channelHeader = header.channelHeaders[channel_num]
if channelHeader.firstblock < 0:
return
if channelHeader.kind not in [2, 3, 4, 5, 6, 7, 8]:
return
# # Step 1 : type of blocks
if channelHeader.kind in [2, 3, 4]:
# Event data
fmt = [('tick', 'i4')]
elif channelHeader.kind in [5]:
# Marker data
fmt = [('tick', 'i4'), ('marker', 'i4')]
elif channelHeader.kind in [6]:
# AdcMark data
fmt = [('tick', 'i4'), ('marker', 'i4'),
('adc', 'S%d' % channelHeader.n_extra)]
elif channelHeader.kind in [7]:
# RealMark data
fmt = [('tick', 'i4'), ('marker', 'i4'),
('real', 'S%d' % channelHeader.n_extra)]
elif channelHeader.kind in [8]:
# TextMark data
fmt = [('tick', 'i4'), ('marker', 'i4'),
('label', 'S%d' % channelHeader.n_extra)]
dt = np.dtype(fmt)
## Step 2 : first read for allocating mem
fid.seek(channelHeader.firstblock)
totalitems = 0
for _ in range(channelHeader.blocks):
blockHeader = HeaderReader(fid, np.dtype(blockHeaderDesciption))
totalitems += blockHeader.items
if blockHeader.succ_block > 0:
fid.seek(blockHeader.succ_block)
#~ print 'totalitems' , totalitems
if lazy:
if channelHeader.kind in [2, 3, 4, 5, 8]:
ea = Event()
ea.annotate(channel_index=channel_num)
ea.lazy_shape = totalitems
return ea
elif channelHeader.kind in [6, 7]:
# correct value for t_stop to be put in later
sptr = SpikeTrain([] * pq.s, t_stop=1e99)
sptr.annotate(channel_index=channel_num, ced_unit=0)
sptr.lazy_shape = totalitems
return sptr
else:
alltrigs = np.zeros(totalitems, dtype=dt)
## Step 3 : read
fid.seek(channelHeader.firstblock)
pos = 0
for _ in range(channelHeader.blocks):
blockHeader = HeaderReader(fid,
np.dtype(blockHeaderDesciption))
# read all events in block
trigs = np.fromstring(fid.read(blockHeader.items *
dt.itemsize),
dtype=dt)
alltrigs[pos:pos + trigs.size] = trigs
pos += trigs.size
if blockHeader.succ_block > 0:
fid.seek(blockHeader.succ_block)
## Step 3 convert in neo standard class: eventarrays or spiketrains
alltimes = alltrigs['tick'].astype(
'f') * header.us_per_time * header.dtime_base * pq.s
if channelHeader.kind in [2, 3, 4, 5, 8]:
#events
ea = Event(alltimes)
ea.annotate(channel_index=channel_num)
if channelHeader.kind >= 5:
# Spike2 marker is closer to label sens of neo
ea.labels = alltrigs['marker'].astype('S32')
if channelHeader.kind == 8:
ea.annotate(extra_labels=alltrigs['label'])
return ea
elif channelHeader.kind in [6, 7]:
# spiketrains
# waveforms
if channelHeader.kind == 6:
waveforms = np.fromstring(alltrigs['adc'].tostring(),
dtype='i2')
waveforms = waveforms.astype(
'f4') * channelHeader.scale / 6553.6 + \
channelHeader.offset
elif channelHeader.kind == 7:
waveforms = np.fromstring(alltrigs['real'].tostring(),
dtype='f4')
if header.system_id >= 6 and channelHeader.interleave > 1:
waveforms = waveforms.reshape(
(alltimes.size, -1, channelHeader.interleave))
waveforms = waveforms.swapaxes(1, 2)
else:
waveforms = waveforms.reshape((alltimes.size, 1, -1))
if header.system_id in [1, 2, 3, 4, 5]:
sample_interval = (channelHeader.divide *
header.us_per_time *
header.time_per_adc) * 1e-6
else:
sample_interval = (channelHeader.l_chan_dvd *
header.us_per_time * header.dtime_base)
if channelHeader.unit in unit_convert:
unit = pq.Quantity(1, unit_convert[channelHeader.unit])
else:
#print channelHeader.unit
try:
unit = pq.Quantity(1, channelHeader.unit)
except:
unit = pq.Quantity(1, '')
if len(alltimes) > 0:
# can get better value from associated AnalogSignal(s) ?
t_stop = alltimes.max()
else:
t_stop = 0.0
if not self.ced_units:
sptr = SpikeTrain(alltimes,
waveforms=waveforms * unit,
sampling_rate=(1. / sample_interval) *
pq.Hz,
t_stop=t_stop)
sptr.annotate(channel_index=channel_num, ced_unit=0)
return [sptr]
sptrs = []
for i in set(alltrigs['marker'] & 255):
sptr = SpikeTrain(
alltimes[alltrigs['marker'] == i],
waveforms=waveforms[alltrigs['marker'] == i] * unit,
sampling_rate=(1. / sample_interval) * pq.Hz,
t_stop=t_stop)
sptr.annotate(channel_index=channel_num, ced_unit=i)
sptrs.append(sptr)
return sptrs
def test_quantities_array_uint(self):
'''test to make sure uint type quantites arrays are accepted'''
value = pq.Quantity([1, 2, 3, 4, 5], dtype=np.uint, units=pq.meter)
self.base.annotate(data=value)
result = {'data': value}
self.assertDictEqual(result, self.base.annotations)
def test_lcc25_maxvoltage():
with expected_protocol(ik.thorlabs.LCC25, ["max?", "max=10.0"],
["max?", "20", ">max=10.0", ">"],
sep="\r") as lcc:
unit_eq(lcc.max_voltage, pq.Quantity(20, "V"))
lcc.max_voltage = 10.0
def test_quantities_scalar_str(self):
'''test to make sure str type quantites scalars are accepted'''
value = pq.Quantity(99, dtype=np.str, units=pq.meter)
self.base.annotate(data=value)
result = {'data': value}
self.assertDictEqual(result, self.base.annotations)
def test_lcc25_increment():
with expected_protocol(ik.thorlabs.LCC25, ["increment?", "increment=10.0"],
["increment?", "20", ">increment=10.0", ">"],
sep="\r") as lcc:
unit_eq(lcc.increment, pq.Quantity(20, "V"))
lcc.increment = 10.0
def test___get_sampling_rate__period_quant_rate_none(self):
sampling_rate = None
sampling_period = pq.Quantity(10., units=pq.s)
targ_rate = 1/sampling_period
out_rate = _get_sampling_rate(sampling_rate, sampling_period)
self.assertEqual(targ_rate, out_rate)
def cross_correlation_histogram(binned_spiketrain_i,
binned_spiketrain_j,
window='full',
border_correction=False,
binary=False,
kernel=None,
method='speed',
cross_correlation_coefficient=False):
"""
Computes the cross-correlation histogram (CCH) between two binned spike
trains `binned_spiketrain_i` and `binned_spiketrain_j`.
Visualization of this function is covered in Viziphant:
:func:`viziphant.spike_train_correlation.plot_cross_correlation_histogram`.
Parameters
----------
binned_spiketrain_i, binned_spiketrain_j : BinnedSpikeTrain
Binned spike trains of lengths N and M to cross-correlate - the output
of :class:`elephant.conversion.BinnedSpikeTrain`. The input
spike trains can have any `t_start` and `t_stop`.
window : {'valid', 'full'} or list of int, optional
‘full’: This returns the cross-correlation at each point of overlap,
with an output shape of (N+M-1,). At the end-points of the
cross-correlogram, the signals do not overlap completely, and
boundary effects may be seen.
‘valid’: Mode valid returns output of length max(M, N) - min(M, N) + 1.
The cross-correlation product is only given for points where
the signals overlap completely.
Values outside the signal boundary have no effect.
List of integers (min_lag, max_lag):
The entries of window are two integers representing the left and
right extremes (expressed as number of bins) where the
cross-correlation is computed.
Default: 'full'
border_correction : bool, optional
whether to correct for the border effect. If True, the value of the
CCH at bin :math:`b` (for :math:`b=-H,-H+1, ...,H`, where :math:`H` is
the CCH half-length) is multiplied by the correction factor:
.. math::
(H+1)/(H+1-|b|),
which linearly corrects for loss of bins at the edges.
Default: False
binary : bool, optional
If True, spikes falling in the same bin are counted as a single spike;
otherwise they are counted as different spikes.
Default: False
kernel : np.ndarray or None, optional
A one dimensional array containing a smoothing kernel applied
to the resulting CCH. The length N of the kernel indicates the
smoothing window. The smoothing window cannot be larger than the
maximum lag of the CCH. The kernel is normalized to unit area before
being applied to the resulting CCH. Popular choices for the kernel are
* normalized boxcar kernel: `numpy.ones(N)`
* hamming: `numpy.hamming(N)`
* hanning: `numpy.hanning(N)`
* bartlett: `numpy.bartlett(N)`
If None, the CCH is not smoothed.
Default: None
method : {'speed', 'memory'}, optional
Defines the algorithm to use. "speed" uses `numpy.correlate` to
calculate the correlation between two binned spike trains using a
non-sparse data representation. Due to various optimizations, it is the
fastest realization. In contrast, the option "memory" uses an own
implementation to calculate the correlation based on sparse matrices,
which is more memory efficient but slower than the "speed" option.
Default: "speed"
cross_correlation_coefficient : bool, optional
If True, a normalization is applied to the CCH to obtain the
cross-correlation coefficient function ranging from -1 to 1 according
to Equation (5.10) in :cite:`correlation-Eggermont2010_77`. See Notes.
Default: False
Returns
-------
cch_result : neo.AnalogSignal
Containing the cross-correlation histogram between
`binned_spiketrain_i` and `binned_spiketrain_j`.
Offset bins correspond to correlations at delays equivalent
to the differences between the spike times of `binned_spiketrain_i` and
those of `binned_spiketrain_j`: an entry at positive lag corresponds to
a spike in `binned_spiketrain_j` following a spike in
`binned_spiketrain_i` bins to the right, and an entry at negative lag
corresponds to a spike in `binned_spiketrain_i` following a spike in
`binned_spiketrain_j`.
To illustrate this definition, consider two spike trains with the same
`t_start` and `t_stop`:
`binned_spiketrain_i` ('reference neuron') : 0 0 0 0 1 0 0 0 0 0 0
`binned_spiketrain_j` ('target neuron') : 0 0 0 0 0 0 0 1 0 0 0
Here, the CCH will have an entry of `1` at `lag=+3`.
Consistent with the definition of `neo.AnalogSignals`, the time axis
represents the left bin borders of each histogram bin. For example,
the time axis might be:
`np.array([-2.5 -1.5 -0.5 0.5 1.5]) * ms`
lags : np.ndarray
Contains the IDs of the individual histogram bins, where the central
bin has ID 0, bins to the left have negative IDs and bins to the right
have positive IDs, e.g.,:
`np.array([-3, -2, -1, 0, 1, 2, 3])`
Notes
-----
1. The Eq. (5.10) in :cite:`correlation-Eggermont2010_77` is valid for
binned spike trains with at most one spike per bin. For a general case,
refer to the implementation of `_covariance_sparse()`.
2. Alias: `cch`
Examples
--------
Plot the cross-correlation histogram between two Poisson spike trains
>>> import elephant
>>> import quantities as pq
>>> import numpy as np
>>> from elephant.conversion import BinnedSpikeTrain
>>> from elephant.spike_train_generation import homogeneous_poisson_process
>>> from elephant.spike_train_correlation import \
... cross_correlation_histogram
>>> np.random.seed(1)
>>> binned_spiketrain_i = BinnedSpikeTrain(
... homogeneous_poisson_process(
... 10. * pq.Hz, t_start=0 * pq.ms, t_stop=5000 * pq.ms),
... bin_size=5. * pq.ms)
>>> binned_spiketrain_j = BinnedSpikeTrain(
... homogeneous_poisson_process(
... 10. * pq.Hz, t_start=0 * pq.ms, t_stop=5000 * pq.ms),
... bin_size=5. * pq.ms)
>>> cc_hist, lags = cross_correlation_histogram(
... binned_spiketrain_i, binned_spiketrain_j, window=[-10, 10],
... border_correction=False,
... binary=False, kernel=None)
>>> print(cc_hist.flatten())
[ 5. 3. 3. 2. 4. 0. 1. 5. 3. 4. 2. 2. 2. 5.
1. 2. 4. 2. -0. 3. 3.] dimensionless
>>> lags
array([-10, -9, -8, -7, -6, -5, -4, -3, -2, -1,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10], dtype=int32)
"""
# Check that the spike trains are binned with the same temporal
# resolution
if binned_spiketrain_i.shape[0] != 1 or \
binned_spiketrain_j.shape[0] != 1:
raise ValueError("Spike trains must be one dimensional")
# rescale to the common units
# this does not change the data - only its representation
binned_spiketrain_j.rescale(binned_spiketrain_i.units)
if not np.isclose(binned_spiketrain_i._bin_size,
binned_spiketrain_j._bin_size):
raise ValueError("Bin sizes must be equal")
bin_size = binned_spiketrain_i._bin_size
left_edge_min = -binned_spiketrain_i.n_bins + 1
right_edge_max = binned_spiketrain_j.n_bins - 1
t_lags_shift = (binned_spiketrain_j._t_start -
binned_spiketrain_i._t_start) / bin_size
if not np.isclose(t_lags_shift, round(t_lags_shift)):
# For example, if bin_size=1 ms, binned_spiketrain_i.t_start=0 ms, and
# binned_spiketrain_j.t_start=0.5 ms then there is a global shift in
# the binning of the spike trains.
raise ValueError(
"Binned spiketrains time shift is not multiple of bin_size")
t_lags_shift = int(round(t_lags_shift))
# In the examples below we fix st2 and "move" st1.
# Zero-lag is equal to `max(st1.t_start, st2.t_start)`.
# Binned spiketrains (t_start and t_stop) with bin_size=1ms:
# 1) st1=[3, 8] ms, st2=[1, 13] ms
# t_start_shift = -2 ms
# zero-lag is at 3 ms
# 2) st1=[1, 7] ms, st2=[2, 9] ms
# t_start_shift = 1 ms
# zero-lag is at 2 ms
# 3) st1=[1, 7] ms, st2=[4, 6] ms
# t_start_shift = 3 ms
# zero-lag is at 4 ms
# Find left and right edges of unaligned (time-dropped) time signals
if len(window) == 2 and np.issubdtype(type(window[0]), np.integer) \
and np.issubdtype(type(window[1]), np.integer):
# ex. 1) lags range: [w[0] - 2, w[1] - 2] ms
# ex. 2) lags range: [w[0] + 1, w[1] + 1] ms
# ex. 3) lags range: [w[0] + 3, w[0] + 3] ms
if window[0] >= window[1]:
raise ValueError(
"Window's left edge ({left}) must be lower than the right "
"edge ({right})".format(left=window[0], right=window[1]))
left_edge, right_edge = np.subtract(window, t_lags_shift)
if left_edge < left_edge_min or right_edge > right_edge_max:
raise ValueError(
"The window exceeds the length of the spike trains")
lags = np.arange(window[0], window[1] + 1, dtype=np.int32)
cch_mode = 'pad'
elif window == 'full':
# cch computed for all the possible entries
# ex. 1) lags range: [-6, 9] ms
# ex. 2) lags range: [-4, 7] ms
# ex. 3) lags range: [-2, 4] ms
left_edge = left_edge_min
right_edge = right_edge_max
lags = np.arange(left_edge + t_lags_shift,
right_edge + 1 + t_lags_shift,
dtype=np.int32)
cch_mode = window
elif window == 'valid':
lags = _CrossCorrHist.get_valid_lags(binned_spiketrain_i,
binned_spiketrain_j)
left_edge, right_edge = lags[(0, -1), ]
cch_mode = window
else:
raise ValueError("Invalid window parameter")
if binary:
binned_spiketrain_i = binned_spiketrain_i.binarize()
binned_spiketrain_j = binned_spiketrain_j.binarize()
cch_builder = _CrossCorrHist(binned_spiketrain_i,
binned_spiketrain_j,
window=(left_edge, right_edge))
if method == 'memory':
cross_corr = cch_builder.correlate_memory(cch_mode=cch_mode)
else:
cross_corr = cch_builder.correlate_speed(cch_mode=cch_mode)
if border_correction:
if window == 'valid':
warnings.warn(
"Border correction does not have any effect in "
"'valid' window mode since there are no border effects!")
else:
cross_corr = cch_builder.border_correction(cross_corr)
if kernel is not None:
cross_corr = cch_builder.kernel_smoothing(cross_corr, kernel=kernel)
if cross_correlation_coefficient:
cross_corr = cch_builder.cross_correlation_coefficient(cross_corr)
normalization = 'normalized' if cross_correlation_coefficient else 'counts'
annotations = dict(window=window,
border_correction=border_correction,
binary=binary,
kernel=kernel is not None,
normalization=normalization)
annotations = dict(cch_parameters=annotations)
# Transform the array count into an AnalogSignal
t_start = pq.Quantity((lags[0] - 0.5) * bin_size,
units=binned_spiketrain_i.units,
copy=False)
cch_result = neo.AnalogSignal(signal=np.expand_dims(cross_corr, axis=1),
units=pq.dimensionless,
t_start=t_start,
sampling_period=binned_spiketrain_i.bin_size,
copy=False,
**annotations)
return cch_result, lags
def test___get_sampling_rate__period_none_rate_quant(self):
sampling_rate = pq.Quantity(10., units=pq.Hz)
sampling_period = None
targ_rate = sampling_rate
out_rate = _get_sampling_rate(sampling_rate, sampling_period)
self.assertEqual(targ_rate, out_rate)
def zscore(signal, inplace=True):
'''
Apply a z-score operation to one or several AnalogSignalArray objects.
The z-score operation subtracts the mean :math:`\\mu` of the signal, and
divides by its standard deviation :math:`\\sigma`:
.. math::
Z(x(t))= \\frac{x(t)-\\mu}{\\sigma}
If an AnalogSignalArray containing multiple signals is provided, the
z-transform is always calculated for each signal individually.
If a list of AnalogSignalArray objects is supplied, the mean and standard
deviation are calculated across all objects of the list. Thus, all list
elements are z-transformed by the same values of :math:`\\mu` and
:math:`\\sigma`. For AnalogSignalArrays, each signal of the array is
treated separately across list elements. Therefore, the number of signals
must be identical for each AnalogSignalArray of the list.
Parameters
----------
signal : neo.AnalogSignalArray or list of neo.AnalogSignalArray
Signals for which to calculate the z-score.
inplace : bool
If True, the contents of the input signal(s) is replaced by the
z-transformed signal. Otherwise, a copy of the original
AnalogSignalArray(s) is returned. Default: True
Returns
-------
neo.AnalogSignalArray or list of neo.AnalogSignalArray
The output format matches the input format: for each supplied
AnalogSignalArray object a corresponding object is returned containing
the z-transformed signal with the unit dimensionless.
Use Case
--------
You may supply a list of AnalogSignalArray objects, where each object in
the list contains the data of one trial of the experiment, and each signal
of the AnalogSignalArray corresponds to the recordings from one specific
electrode in a particular trial. In this scenario, you will z-transform the
signal of each electrode separately, but transform all trials of a given
electrode in the same way.
Examples
--------
>>> a = neo.AnalogSignalArray(
... np.array([1, 2, 3, 4, 5, 6]).reshape(-1,1)*mV,
... t_start=0*s, sampling_rate=1000*Hz)
>>> b = neo.AnalogSignalArray(
... np.transpose([[1, 2, 3, 4, 5, 6], [11, 12, 13, 14, 15, 16]])*mV,
... t_start=0*s, sampling_rate=1000*Hz)
>>> c = neo.AnalogSignalArray(
... np.transpose([[21, 22, 23, 24, 25, 26], [31, 32, 33, 34, 35, 36]])*mV,
... t_start=0*s, sampling_rate=1000*Hz)
>>> print zscore(a)
[[-1.46385011]
[-0.87831007]
[-0.29277002]
[ 0.29277002]
[ 0.87831007]
[ 1.46385011]] dimensionless
>>> print zscore(b)
[[-1.46385011 -1.46385011]
[-0.87831007 -0.87831007]
[-0.29277002 -0.29277002]
[ 0.29277002 0.29277002]
[ 0.87831007 0.87831007]
[ 1.46385011 1.46385011]] dimensionless
>>> print zscore([b,c])
[<AnalogSignalArray(array([[-1.11669108, -1.08361877],
[-1.0672076 , -1.04878252],
[-1.01772411, -1.01394628],
[-0.96824063, -0.97911003],
[-0.91875714, -0.94427378],
[-0.86927366, -0.90943753]]) * dimensionless, [0.0 s, 0.006 s],
sampling rate: 1000.0 Hz)>,
<AnalogSignalArray(array([[ 0.78170952, 0.84779261],
[ 0.86621866, 0.90728682],
[ 0.9507278 , 0.96678104],
[ 1.03523694, 1.02627526],
[ 1.11974608, 1.08576948],
[ 1.20425521, 1.1452637 ]]) * dimensionless, [0.0 s, 0.006 s],
sampling rate: 1000.0 Hz)>]
'''
# Transform input to a list
if type(signal) is not list:
signal = [signal]
# Calculate mean and standard deviation
m = np.mean(np.concatenate(signal), axis=0, keepdims=True)
s = np.std(np.concatenate(signal), axis=0, keepdims=True)
if not inplace:
# Create new signal instance
result = []
for sig in signal:
sig_dimless = sig.duplicate_with_new_array(
(sig.magnitude - m.magnitude) / s.magnitude) / sig.units
result.append(sig_dimless)
else:
result = []
# Overwrite signal
for sig in signal:
sig[:] = pq.Quantity(
(sig.magnitude - m.magnitude) / s.magnitude,
units=sig.units)
sig_dimless = sig / sig.units
result.append(sig_dimless)
# Return single object, or list of objects
if len(result) == 1:
return result[0]
else:
return result
def test___get_sampling_rate__period_rate_equivalent(self):
sampling_rate = pq.Quantity(10., units=pq.Hz)
sampling_period = pq.Quantity(0.1, units=pq.s)
targ_rate = sampling_rate
out_rate = _get_sampling_rate(sampling_rate, sampling_period)
self.assertEqual(targ_rate, out_rate)
def unitIsValid(unit):
try:
pq.Quantity(1, unit)
except:
return False
return True
def _read_analogsignal_t_start(self, attrs, data_group):
t_start = float(data_group.attrs['tstart']) * pq.Quantity(
1, data_group.attrs['tunit'])
t_start = t_start.rescale(attrs['t_start_unit'])
return t_start