def torusPopulationVector(spikes, sheetSize, tstart=0, tend=-1, dt=0.02, winLen=1.0):
'''
This function is deprecated. Use the OO version instead
'''
log_warn('This function is deprecated')
N = sheetSize[0]*sheetSize[1]
F, tsteps = slidingFiringRateTuple(spikes, N, tstart, tend, dt, winLen)
return torusPopulationVectorFromRates((F, tsteps), sheetSize)
def aggregateType(sp, iterList, types, NTrials, ignoreNaNs=False, **kw):
'''
Automatically aggregate data according to the type of the data.
.. deprecated::
Use the object-oriented versions instead.
'''
type, subType = types
vars = ['analysis']
output_dtype = 'array'
funReduce = None
normalizeTicks = kw.pop('normalizeTicks', False)
if (type == 'gamma'):
# Gamma oscillation analyses
if (subType == 'acVal'):
# Autocorrelation first local maximum
vars += ['acVal']
elif (subType == 'freq'):
# Gamm frequency
vars += ['freq']
elif (subType == 'acVec'):
# All the autocorrelations
vars += ['acVec']
output_dtype = 'list'
else:
raise ValueError('Unknown gamma subtype: {0}'.format(subType))
trialNumList = np.arange(NTrials)
elif (type == 'bump'):
trialNumList = np.arange(NTrials)
if (subType == 'sigma'):
vars += ['bump_e', 'sigma']
elif (subType == 'rateMap_e'):
vars += ['bump_e', 'bump_e_rateMap']
output_dtype = 'list'
elif (subType == 'rateMap_i'):
vars += ['bump_i', 'bump_i_rateMap']
output_dtype = 'list'
else:
raise ValueError('Unknown bump subtype: {0}'.format(subType))
elif (type == 'bump_full'):
trialNumList = np.arange(NTrials)
if (subType == 'sigma'):
vars += ['bump_e_full', 'sigma']
elif (subType == 'rateMap_e'):
vars += ['bump_e_full', 'bump_e_rateMap']
output_dtype = 'list'
elif (subType == 'rateMap_i'):
vars += ['bump_i_full', 'bump_i_rateMap']
output_dtype = 'list'
else:
raise ValueError('Unknown bump_full subtype: {0}'.format(subType))
elif (type == 'velocity'):
if (subType == 'slope'):
vars += ['lineFitSlope']
elif (subType == 'fitErr'):
vars += ['lineFitErr']
funReduce = np.sum
else:
raise ValueError('Unknown velocity subtype: {0}'.format(subType))
trialNumList = 'all-at-once'
elif (type == 'grids'):
trialNumList = np.arange(NTrials)
if (subType == 'gridnessScore'):
vars += ['gridnessScore']
funReduce = None
else:
raise ValueError('Unknown grids subtype: {0}'.format(subType))
elif type == 'FR':
trialNumList = np.arange(NTrials)
if subType == 'E':
vars += ['FR_e', 'avg']
funReduce = None
elif subType == 'I_10': # user should be aware of 10 neurons limitation
vars += ['FR_i', 'all']
funReduce = None
else:
raise ValueError('Unknown FR subtype: {0}'.format(subType))
else:
raise ValueError('Unknown aggregation type: {0}'.format(type))
data = sp.aggregateData(vars,
trialNumList,
output_dtype=output_dtype,
loadData=True,
saveData=False,
funReduce=funReduce)
if (output_dtype != 'list'):
data = ma.MaskedArray(data)
if (ignoreNaNs):
log_warn('aggregateType', 'Ignoring NaNs')
nans = np.isnan(data)
data.mask = nans
if (type == 'velocity'):
Y, X = computeVelYX(sp, iterList, normalize=normalizeTicks, **kw)
else:
if (type == 'bump' or type == 'bump_full'):
if (subType == 'sigma'):
# bump sigma is a reciprocal
data = 1. / data
ignoreThreshold = 1.0
data.mask = np.logical_or(data.mask, data > ignoreThreshold)
data = np.mean(data,
axis=2) # TODO: fix the trials, stack them
else:
data = np.mean(data, axis=2) # TODO: fix the trials, stack them
Y, X = computeYX(sp, iterList, normalize=normalizeTicks, **kw)
return data, X, Y
def aggregateType(sp, iterList, types, NTrials, ignoreNaNs=False, **kw):
'''
Automatically aggregate data according to the type of the data.
.. deprecated::
Use the object-oriented versions instead.
'''
type, subType = types
vars = ['analysis']
output_dtype = 'array'
funReduce = None
normalizeTicks = kw.pop('normalizeTicks', False)
if (type == 'gamma'):
# Gamma oscillation analyses
if (subType == 'acVal'):
# Autocorrelation first local maximum
vars += ['acVal']
elif (subType == 'freq'):
# Gamm frequency
vars += ['freq']
elif (subType == 'acVec'):
# All the autocorrelations
vars += ['acVec']
output_dtype = 'list'
else:
raise ValueError('Unknown gamma subtype: {0}'.format(subType))
trialNumList = np.arange(NTrials)
elif (type == 'bump'):
trialNumList = np.arange(NTrials)
if (subType == 'sigma'):
vars += ['bump_e', 'sigma']
elif (subType == 'rateMap_e'):
vars += ['bump_e', 'bump_e_rateMap']
output_dtype = 'list'
elif (subType == 'rateMap_i'):
vars += ['bump_i', 'bump_i_rateMap']
output_dtype = 'list'
else:
raise ValueError('Unknown bump subtype: {0}'.format(subType))
elif (type == 'bump_full'):
trialNumList = np.arange(NTrials)
if (subType == 'sigma'):
vars += ['bump_e_full', 'sigma']
elif (subType == 'rateMap_e'):
vars += ['bump_e_full', 'bump_e_rateMap']
output_dtype = 'list'
elif (subType == 'rateMap_i'):
vars += ['bump_i_full', 'bump_i_rateMap']
output_dtype = 'list'
else:
raise ValueError('Unknown bump_full subtype: {0}'.format(subType))
elif (type == 'velocity'):
if (subType == 'slope'):
vars += ['lineFitSlope']
elif (subType == 'fitErr'):
vars += ['lineFitErr']
funReduce = np.sum
else:
raise ValueError('Unknown velocity subtype: {0}'.format(subType))
trialNumList = 'all-at-once'
elif (type == 'grids'):
trialNumList = np.arange(NTrials)
if (subType == 'gridnessScore'):
vars += ['gridnessScore']
funReduce = None
else:
raise ValueError('Unknown grids subtype: {0}'.format(subType))
elif type == 'FR':
trialNumList = np.arange(NTrials)
if subType == 'E':
vars += ['FR_e', 'avg']
funReduce = None
elif subType == 'I_10': # user should be aware of 10 neurons limitation
vars += ['FR_i', 'all']
funReduce = None
else:
raise ValueError('Unknown FR subtype: {0}'.format(subType))
else:
raise ValueError('Unknown aggregation type: {0}'.format(type))
data = sp.aggregateData(vars, trialNumList, output_dtype=output_dtype,
loadData=True, saveData=False, funReduce=funReduce)
if (output_dtype != 'list'):
data = ma.MaskedArray(data)
if (ignoreNaNs):
log_warn('aggregateType', 'Ignoring NaNs')
nans = np.isnan(data)
data.mask = nans
if (type == 'velocity'):
Y, X = computeVelYX(sp, iterList, normalize=normalizeTicks, **kw)
else:
if (type == 'bump' or type == 'bump_full'):
if (subType == 'sigma'):
# bump sigma is a reciprocal
data = 1./data
ignoreThreshold = 1.0
data.mask = np.logical_or(data.mask, data > ignoreThreshold)
data = np.mean(data, axis=2) # TODO: fix the trials, stack them
else:
data = np.mean(data, axis=2) # TODO: fix the trials, stack them
Y, X = computeYX(sp, iterList, normalize=normalizeTicks, **kw)
return data, X, Y
