def histogramFromData(angleData, numBins, **kwargs):
''' This function plots the histograms of angles with the number of bins
equal to numBins, assuming the angles are between 0 and 180.
'''
kwargs = {'title': 'Bobby', 'ylimit': 0.1}
binsRange = [((2.0 * i + 1) / 2) * (180.0 / numBins)
for i in xrange(-1, numBins)]
bins = np.array(binsRange)
hist1, bins = np.histogram(angleData, bins=bins)
widths = np.zeros(numBins) # @TODO not required. better way to look
center = (bins[:-1] + bins[1:]) / 2
widths = np.diff(bins)
histplt = hist1 / np.sum(hist1 * widths)
plt.bar(center, histplt, align='center', width=widths, facecolor='c')
plt.ylim(0, kwargs['ylimit'])
plt.title(kwargs['title'])
plt.show()
print('Total number of pixels on the centerline is = {}'.format(
len(angleData)))
mean = (0.5) * pcirc.mean(2 * np.array(angleData) * np.pi / 180)
var = pcirc.var(2 * np.array(angleData) * np.pi / 180)
print('The circular mean and variance are ' + str(mean * 180 / np.pi) +
' and ' + str(var) + ' respectively.')
#histogramFromData(angleData,numBins,title,ylimit,**kwargs)
def test_var():
data = np.array([
1.80044838, 2.02938314, 1.03534016, 4.84225057, 1.54256458, 5.19290675,
2.18474784, 4.77054777, 1.51736933, 0.72727580
])
s = pycircstat.var(data)
assert_allclose(0.65842, s, atol=0.001, rtol=0.001)
def histogramFromData(angleData,numBins,**kwargs):
''' This function plots the histograms of angles with the number of bins
equal to numBins, assuming the angles are between 0 and 180.
'''
kwargs = {'title': 'Bobby', 'ylimit': 0.1}
binsRange = [ ((2.0*i+1)/2)*(180.0/numBins) for i in xrange(-1,numBins)]
bins =np.array( binsRange)
hist1, bins = np.histogram(angleData,bins = bins)
widths = np.zeros(numBins) # @TODO not required. better way to look
center = (bins[:-1] + bins[1:]) / 2
widths = np.diff(bins)
histplt=hist1/np.sum(hist1*widths)
plt.bar(center, histplt, align='center', width=widths,facecolor='c')
plt.ylim(0,kwargs['ylimit'])
plt.title(kwargs['title'])
plt.show()
print('Total number of pixels on the centerline is = {}'.format(len(angleData)))
mean=(0.5)*pcirc.mean(2*np.array(angleData)*np.pi/180)
var=pcirc.var(2*np.array(angleData)*np.pi/180)
print('The circular mean and variance are ' + str(mean*180/np.pi)+
' and ' + str(var) +' respectively.' )
#histogramFromData(angleData,numBins,title,ylimit,**kwargs)
def drawHistograms(fileFactory, msMap, histRange):
'''
INPUT: histRange : (start, end) angle of histogram
msMap:
'''
numBands = msMap[0][0][0][0]
#@TODO 2,180 can be replace with histRange[1] - histRange[0] ? no use case now
binsRange = [((2.0 * i + 1) / 2) * (180.0 / numBands)
for i in xrange(-1, numBands)]
bins = np.array(binsRange) + histRange[0]
#widths = np.zeros(numBands) # @TODO not required. better way to look
center = (bins[:-1] + bins[1:]) / 2
# @TODO 1, review np.diff
widths = np.diff(bins)
maxYLimit = _estimateYLimit(msMap, histRange, bins, widths)
for (xSeg, name), (histCont, _) in itertools.izip(fileFactory, msMap):
#@TODO May be need tom kae color map a parameter
plt.imshow(xSeg, cmap=plt.get_cmap('gray'))
plt.title('mutliscale directional histogram for file {0}'.format(name))
plt.xticks([])
plt.yticks([])
plt.show()
for index, (nBands, mSize, newAngles) in enumerate(histCont):
# hist=msMap[index][0][0] # histogram information
# newAngles=msMap[index][0][1] # angle information between 0 and 180 degrees
## converting angles to angles between -90 and 90 degrees
if histRange == (-90, 90):
newAngles1 = [
-180 + angle for angle in newAngles if angle > 90
]
newAngles2 = [angle for angle in newAngles if angle <= 90]
newAngles = newAngles1 + newAngles2
#mSize=msMap[index][0][2]
hist1, bins = np.histogram(newAngles, bins=bins)
histplt = hist1 / np.sum(hist1 * widths)
plt.bar(center,
histplt,
align='center',
width=widths,
facecolor='c')
plt.ylim(0, maxYLimit)
plt.title('At Scale: ' + str(index) + '_msize_' + str(mSize) +
'Angles density')
plt.show()
print('Total number of pixels on the centerline is = {}'.format(
len(newAngles)))
mean = (0.5) * pcirc.mean(2 * np.array(newAngles) * np.pi / 180)
var = pcirc.var(2 * np.array(newAngles) * np.pi / 180)
print('The circular mean and variance are ' +
str(-180 + mean * 180 / np.pi) + ' and ' + str(var) +
' respectively.')
def direct_vector_strength_spectrum(event_times, frequencies):
"""
Computes the direct vector strength spectrum for the given frequencies.
:param event_times: event times in seconds
:param frequencies: locking frequencies in Hz
:return: vector strength spectrum
"""
ret = np.asarray([1-var( (event_times % (1./w) )*w*2*np.pi ) for w in frequencies])
return ret
def _calc_variances(true_values, true_counts, bootstrap_values,
bootstrap_counts):
true_variances = []
p_vals = []
bins = 2 * np.pi * np.arange(0, 1, 1. / len(true_values[0]))
for values, counts, shuffle_values, shuffle_counts in it.izip(
true_values, true_counts, bootstrap_values, bootstrap_counts):
true_value = var(bins, values / counts)
true_variances.append(true_value)
roi_shuffles = []
for shuffle in range(shuffle_values.shape[1]):
roi_shuffles.append(
var(bins,
shuffle_values[:, shuffle] / shuffle_counts[:, shuffle]))
p_vals.append(percentileofscore(roi_shuffles, true_value) / 100.)
return true_variances, p_vals
def direct_vector_strength_spectrum(event_times, frequencies):
"""
Computes the direct vector strength spectrum for the given frequencies.
:param event_times: event times in seconds
:param frequencies: locking frequencies in Hz
:return: vector strength spectrum
"""
ret = np.asarray([
1 - var((event_times % (1. / w)) * w * 2 * np.pi) for w in frequencies
])
return ret
def _make_tuples(self, key):
print('Processing', key['cell_id'])
sampling_rate, eod = (Baseline() & key).fetch1['samplingrate', 'eod']
dt = 1. / sampling_rate
trials = Baseline.LocalEODPeaksTroughs() * Baseline.SpikeTimes() & key
aggregated_spikes = np.hstack([s / 1000 - p[0] * dt for s, p in zip(*trials.fetch['times', 'peaks'])])
aggregated_spikes %= 1 / eod
aggregated_spikes *= eod * 2 * np.pi # normalize to 2*pi
key['base_var'], key['base_mean'], key['base_std'] = \
circ.var(aggregated_spikes), circ.mean(aggregated_spikes), circ.std(aggregated_spikes)
self.insert1(key)
def mean_var(self, restrictions):
"""
Computes the mean and variance of the baseline psth
:param restrictions: restriction that identify one baseline trial
:return: mean and variance
"""
rel = self & restrictions
spikes = (Baseline.SpikeTimes() & rel).fetch1['times']
eod = rel.fetch1['eod']
period = 1 / eod
factor = 2 * np.pi / period
t = (spikes % period)
mu = circ.mean(t * factor) / factor
sigma2 = circ.var(t * factor) / factor ** 2
return mu, sigma2
def mean_var(self, restrictions):
"""
Computes the mean and variance of the baseline psth
:param restrictions: restriction that identify one baseline trial
:return: mean and variance
"""
rel = self & restrictions
spikes = (Baseline.SpikeTimes() & rel).fetch1('times')
eod = rel.fetch1('eod')
period = 1 / eod
factor = 2 * np.pi / period
t = (spikes % period)
mu = circ.mean(t * factor) / factor
sigma2 = circ.var(t * factor) / factor**2
return mu, sigma2
def drawHistograms(fileFactory, msMap, histRange):
'''
INPUT: histRange : (start, end) angle of histogram
msMap:
'''
numBands=msMap[0][0][0][0]
#@TODO 2,180 can be replace with histRange[1] - histRange[0] ? no use case now
binsRange = [ ((2.0*i+1)/2)*(180.0/numBands) for i in xrange(-1,numBands)]
bins =np.array( binsRange) + histRange[0]
#widths = np.zeros(numBands) # @TODO not required. better way to look
center = (bins[:-1] + bins[1:]) / 2
# @TODO 1, review np.diff
widths = np.diff(bins)
maxYLimit = _estimateYLimit(msMap, histRange, bins, widths)
for (xSeg, name), (histCont, _) in itertools.izip(fileFactory,msMap):
#@TODO May be need tom kae color map a parameter
plt.imshow(xSeg, cmap = plt.get_cmap('gray'))
plt.title('mutliscale directional histogram for file {0}'.format(name))
plt.xticks([])
plt.yticks([])
plt.show()
for index, (nBands, mSize, newAngles) in enumerate(histCont):
# hist=msMap[index][0][0] # histogram information
# newAngles=msMap[index][0][1] # angle information between 0 and 180 degrees
## converting angles to angles between -90 and 90 degrees
if histRange == (-90, 90):
newAngles1=[-180+angle for angle in newAngles if angle>90]
newAngles2=[angle for angle in newAngles if angle<=90]
newAngles=newAngles1+newAngles2
#mSize=msMap[index][0][2]
hist1, bins = np.histogram(newAngles,bins = bins)
histplt=hist1/np.sum(hist1*widths)
plt.bar(center, histplt, align='center', width=widths,facecolor='c')
plt.ylim(0, maxYLimit)
plt.title('At Scale: '+ str(index)+'_msize_'+str(mSize) + 'Angles density')
plt.show()
print('Total number of pixels on the centerline is = {}'.format(len(newAngles)))
mean=(0.5)*pcirc.mean(2*np.array(newAngles)*np.pi/180)
var=pcirc.var(2*np.array(newAngles)*np.pi/180)
print('The circular mean and variance are ' + str(-180+mean*180/np.pi)+
' and ' + str(var) +' respectively.' )
def _make_tuples(self, key):
print('Processing', key['cell_id'])
sampling_rate, eod = (Baseline() & key).fetch1('samplingrate', 'eod')
dt = 1. / sampling_rate
trials = Baseline.LocalEODPeaksTroughs() * Baseline.SpikeTimes() & key
aggregated_spikes = np.hstack([
s / 1000 - p[0] * dt
for s, p in zip(*trials.fetch('times', 'peaks'))
])
aggregated_spikes %= 1 / eod
aggregated_spikes *= eod * 2 * np.pi # normalize to 2*pi
key['base_var'], key['base_mean'], key['base_std'] = \
circ.var(aggregated_spikes), circ.mean(aggregated_spikes), circ.std(aggregated_spikes)
self.insert1(key)
def _make_tuples(self, key):
key_sub = dict(key)
delta_f, eod, samplingrate = (Runs() & key).fetch1(
'delta_f', 'eod', 'samplingrate')
locking_frequency = (FirstOrderSignificantPeaks()
& key).fetch1('frequency')
if key['eod_coeff'] > 0:
# convert spikes to s and center on first peak of eod
# times, peaks = (Runs.SpikeTimes() * LocalEODPeaksTroughs() & key).fetch('times', 'peaks')
peaks = (Runs.GlobalEODPeaksTroughs() & key).fetch('peaks')
#
# spikes = np.hstack([s / 1000 - p[0] / samplingrate for s, p in zip(times, peaks)])
else:
# # convert spikes to s and center on first peak of stimulus
# times, peaks = (Runs.SpikeTimes() * GlobalEFieldPeaksTroughs() & key).fetch('times', 'peaks')
peaks = (Runs.GlobalEFieldPeaksTroughs() & key).fetch('peaks')
# spikes = np.hstack([s / 1000 - p[0] / samplingrate for s, p in zip(times, peaks)])
spikes = np.hstack(TrialAlign().load_trials(key))
key['peak_frequency'] = samplingrate / np.mean(
[np.diff(p).mean() for p in peaks])
key['locking_frequency'] = locking_frequency
cycle = 1 / locking_frequency
spikes %= cycle
key['spikes'] = spikes / cycle * 2 * np.pi
key['vector_strength'] = 1 - circ.var(key['spikes'])
self.insert1(key)
histograms = self.Histograms()
for n in SamplingPointsPerBin().fetch:
n = int(n[0])
bin_width_time = n / samplingrate
bin_width_radians = bin_width_time / cycle * np.pi * 2
bins = np.arange(0, cycle + bin_width_time, bin_width_time)
key_sub['n'] = n
key_sub['histogram'], _ = np.histogram(spikes, bins=bins)
key_sub['bin_width_time'] = bin_width_time
key_sub['bin_width_radians'] = bin_width_radians
histograms.insert1(key_sub)
def _make_tuples(self, key):
print('Processing', key['cell_id'], 'run', key['run_id'], )
if SecondOrderSignificantPeaks() & dict(key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0, refined=1):
eod, vs = (SecondOrderSignificantPeaks() & dict(key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0,
refined=1)).fetch1['frequency', 'vector_strength']
elif SecondOrderSignificantPeaks() & dict(key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0, refined=0):
eod, vs = (SecondOrderSignificantPeaks() & dict(key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0,
refined=0)).fetch1['frequency', 'vector_strength']
else:
eod = (Runs() & key).fetch1['eod']
aggregated_spikes = np.hstack(TrialAlign().load_trials(key))
aggregated_spikes %= 1 / eod
aggregated_spikes *= eod * 2 * np.pi # normalize to 2*pi
if len(aggregated_spikes) > 1:
key['stim_var'], key['stim_mean'], key['stim_std'] = \
circ.var(aggregated_spikes), circ.mean(aggregated_spikes), circ.std(aggregated_spikes)
self.insert1(key)
def _make_tuples(self, key):
key_sub = dict(key)
delta_f, eod, samplingrate = (Runs() & key).fetch1['delta_f', 'eod', 'samplingrate']
locking_frequency = (FirstOrderSignificantPeaks() & key).fetch1['frequency']
if key['eod_coeff'] > 0:
# convert spikes to s and center on first peak of eod
# times, peaks = (Runs.SpikeTimes() * LocalEODPeaksTroughs() & key).fetch['times', 'peaks']
peaks = (GlobalEODPeaksTroughs() & key).fetch['peaks']
#
# spikes = np.hstack([s / 1000 - p[0] / samplingrate for s, p in zip(times, peaks)])
else:
# # convert spikes to s and center on first peak of stimulus
# times, peaks = (Runs.SpikeTimes() * GlobalEFieldPeaksTroughs() & key).fetch['times', 'peaks']
peaks = (GlobalEFieldPeaksTroughs() & key).fetch['peaks']
# spikes = np.hstack([s / 1000 - p[0] / samplingrate for s, p in zip(times, peaks)])
spikes = np.hstack(TrialAlign().load_trials(key))
key['peak_frequency'] = samplingrate / np.mean([np.diff(p).mean() for p in peaks])
key['locking_frequency'] = locking_frequency
cycle = 1 / locking_frequency
spikes %= cycle
key['spikes'] = spikes / cycle * 2 * np.pi
key['vector_strength'] = 1 - circ.var(key['spikes'])
self.insert1(key)
histograms = self.Histograms()
for n in SamplingPointsPerBin().fetch:
n = int(n[0])
bin_width_time = n / samplingrate
bin_width_radians = bin_width_time / cycle * np.pi * 2
bins = np.arange(0, cycle + bin_width_time, bin_width_time)
key_sub['n'] = n
key_sub['histogram'], _ = np.histogram(spikes, bins=bins)
key_sub['bin_width_time'] = bin_width_time
key_sub['bin_width_radians'] = bin_width_radians
histograms.insert1(key_sub)
def _make_tuples(self, key):
print('Processing', key['cell_id'], 'run', key['run_id'])
if SecondOrderSignificantPeaks() & dict(
key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0,
refined=1):
eod, vs = (SecondOrderSignificantPeaks()
& dict(key,
eod_coeff=1,
stimulus_coeff=0,
baseline_coeff=0,
refined=1)).fetch1('frequency',
'vector_strength')
elif SecondOrderSignificantPeaks() & dict(
key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0,
refined=0):
eod, vs = (SecondOrderSignificantPeaks()
& dict(key,
eod_coeff=1,
stimulus_coeff=0,
baseline_coeff=0,
refined=0)).fetch1('frequency',
'vector_strength')
else:
eod = (Runs() & key).fetch1('eod')
aggregated_spikes = TrialAlign().load_trials(key)
if len(aggregated_spikes) == 0:
warn('TrialAlign returned no spikes. Skipping')
return
else:
aggregated_spikes = np.hstack(aggregated_spikes)
aggregated_spikes %= 1 / eod
aggregated_spikes *= eod * 2 * np.pi # normalize to 2*pi
if len(aggregated_spikes) > 1:
key['stim_var'], key['stim_mean'], key['stim_std'] = \
circ.var(aggregated_spikes), circ.mean(aggregated_spikes), circ.std(aggregated_spikes)
self.insert1(key)
def vector_strength_at(f, trial, alpha=None):
if alpha is None:
return 1 - circ.var((trial % (1. / f)) * f * 2 * np.pi)
else:
return 1 - circ.var((trial % (1. / f)) * f * 2 * np.pi), np.sqrt(
-np.log(alpha) / len(trial))
def _neg_vs_at(f, spikes):
return -np.mean(
[1 - circ.var((trial % (1. / f)) * f * 2 * np.pi) for trial in spikes])
def _neg_vs_at(f, spikes):
return -np.mean([1 - circ.var((trial % (1. / f)) * f * 2 * np.pi) for trial in spikes])
def vector_strength_at(f, trial, alpha=None):
if alpha is None:
return 1 - circ.var((trial % (1. / f)) * f * 2 * np.pi)
else:
return 1 - circ.var((trial % (1. / f)) * f * 2 * np.pi), np.sqrt(- np.log(alpha) / len(trial))
histInformation.append(collectrow)
binNum, lBin, uBin, freq = zip(*histInformation)
bins = (np.array(lBin) + np.array(uBin)) / 2
hist1 = np.array(freq)
widths = np.ones(len(bins)) * (bins[1] - bins[0])
histplt = hist1 / np.sum(hist1 * widths)
newAngles = [bins[i] * np.ones(freq[i]) for i in xrange(len(bins))]
angles = np.array(list(itertools.chain(*newAngles)))
mean = (0.5) * pcirc.mean(2 * np.array(angles) * np.pi / 180)
var = pcirc.var(2 * np.array(angles) * np.pi / 180)
meanIndegrees = mean * 180 / np.pi
if meanIndegrees > 90:
meanIndegrees = -180 + meanIndegrees
print("The circular mean and variance are " + str(meanIndegrees) + " and " + str(var) + " respectively.")
histName = (
"Histogram_Sigma_" + str(Sigma) + "_mean" + str(round(meanIndegrees, 2)) + "_var_" + str(round(var, 2)) + ".png"
)
fig = plt.figure()
plt.bar(bins, histplt, align="center", width=widths, facecolor="r")
plt.ylim(0, ylimit)
# plt.title('At Scale: '+ str(index)+'_msize_'+str(mSize) + 'Angles density')
def _vector_strength(param):
event_times, w = param
return 1 - var((event_times % (1. / w)) * w * 2 * np.pi)
def test_var():
data = np.array([1.80044838, 2.02938314, 1.03534016, 4.84225057,
1.54256458, 5.19290675, 2.18474784,
4.77054777, 1.51736933, 0.72727580])
s = pycircstat.var(data)
assert_allclose(0.65842, s, atol=0.001, rtol=0.001)
def _vector_strength(param):
event_times, w = param
return 1-var( (event_times % (1./w) )*w*2*np.pi )
