/usr/share/pyshared/spykeutils/stationarity.py is in python-spykeutils 0.4.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | import scipy as sp
import quantities as pq
from progress_indicator import ProgressIndicator
from . import SpykeException
def spike_amplitude_histogram(trains, num_bins, uniform_y_scale=True,
unit=pq.uV, progress=None):
""" Return a spike amplitude histogram.
The resulting is useful to assess the drift in spike amplitude over a
longer recording. It shows histograms (one for each ``trains`` entry,
e.g. segment) of maximum and minimum spike amplitudes.
:param list trains: A list of lists of :class:`neo.core.SpikeTrain`
objects. Each entry of the outer list will be one point on the
x-axis (they could correspond to segments), all amplitude occurences
of spikes contained in the inner list will be added up.
:param int num_bins: Number of bins for the histograms.
:param bool uniform_y_scale: If True, the histogram for each channel
will use the same bins. Otherwise, the minimum bin range is computed
separately for each channel.
:param Quantity unit: Unit of Y-Axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`.progress_indicator.ProgressIndicator`
:return: A tuple with three values:
* A three-dimensional histogram matrix, where the first dimension
corresponds to bins, the second dimension to the entries of
``trains`` (e.g. segments) and the third dimension to channels.
* A list of the minimum amplitude value for each channel (all values
will be equal if ``uniform_y_scale`` is true).
* A list of the maximum amplitude value for each channel (all values
will be equal if ``uniform_y_scale`` is true).
:rtype: (ndarray, list, list)
"""
if not progress:
progress = ProgressIndicator()
num_channels = 1
for t in trains:
if not t:
continue
num_channels = t[0].waveforms.shape[2]
break
progress.set_ticks(2*len(trains))
progress.set_status('Calculating Spike Amplitude Histogram')
# Find maximum and minimum amplitudes on all channels
up = [0] * num_channels
down = [0] * num_channels
for t in trains:
for s in t:
if s.waveforms is None:
continue
if s.waveforms.shape[2] != num_channels:
raise SpykeException('All spikes need to have the same ' +
'numer of channels for Spike Amplitude Histogram!')
a = sp.asarray(s.waveforms.rescale(unit))
u = a.max(1)
d = a.min(1)
for c in xrange(num_channels):
up[c] = max(up[c], sp.stats.mstats.mquantiles(
u[:,c], [0.999])[0])
down[c] = min(down[c], sp.stats.mstats.mquantiles(
d[:,c], [0.001])[0])
progress.step()
if uniform_y_scale:
up = [max(up)] * num_channels
down = [min(down)] * num_channels
# Create histogram
bins = [sp.linspace(down[c],up[c], num_bins+1)
for c in xrange(num_channels)]
hist = sp.zeros((num_bins, len(trains), num_channels))
for i, t in enumerate(trains):
for s in t:
if s.waveforms is None:
continue
a = sp.asarray(s.waveforms.rescale(unit))
upper = a.max(1)
lower = a.min(1)
for c in xrange(num_channels):
hist[:,i,c] += sp.histogram(upper[:,c], bins[c])[0]
hist[:,i,c] += sp.histogram(lower[:,c], bins[c])[0]
progress.step()
return hist, down, up
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