def create_multisession_fet_files(self):
if not os.path.exists(self.clustersDir):
print 'Creating clusters directory: %s'%(self.clustersDir)
os.makedirs(self.clustersDir)
if self.samples is None:
self.load_all_waveforms()
self.featureValues = spikesorting.calculate_features(self.samples,self.featureNames)
spikesorting.write_fet_file(self.fetFilename, self.featureValues)
def create_multisession_fet_files(self):
if not os.path.exists(self.clustersDir):
print 'Creating clusters directory: %s'%(self.clustersDir)
os.makedirs(self.clustersDir)
if self.samples is None:
self.load_waveforms()
self.featureValues = spikesorting.calculate_features(self.samples,self.featureNames)
spikesorting.write_fet_file(self.fetFilename, self.featureValues)
def __init__(self, samples):
self.samples = samples
self.points = spikesorting.calculate_features(
self.samples, ["peak", "valley", "energy"])
self.fig = plt.figure()
self.cloudax = plt.subplot2grid((3, 4), (0, 0), rowspan=2, colspan=2)
self.wave0ax = plt.subplot2grid((3, 4), (0, 2), rowspan=1, colspan=1)
self.wave1ax = plt.subplot2grid((3, 4), (0, 3), rowspan=1, colspan=1)
self.wave2ax = plt.subplot2grid((3, 4), (1, 2), rowspan=1, colspan=1)
self.wave3ax = plt.subplot2grid((3, 4), (1, 3), rowspan=1, colspan=1)
self.wavaxes = [self.wave0ax, self.wave1ax, self.wave2ax, self.wave3ax]
self.loghistax = plt.subplot2grid((3, 4), (2, 0), rowspan=1, colspan=2)
self.timehistax = plt.subplot2grid((3, 4), (2, 2),
rowspan=1,
colspan=2)
self.numDims = self.points.shape[1]
self.combinations = [
c for c in itertools.combinations(range(self.numDims), 2)
]
self.maxDim = len(self.combinations) - 1
#Start with the first combination
self.dimNumber = 0
#All points start inside the cluster
self.inCluster = np.ones(len(self.points), dtype=bool)
self.outsideCluster = np.logical_not(self.inCluster)
#Preserve the last cluster state for undo
self.oldInsideCluster = self.inCluster
self.oldOutsideCluster = self.outsideCluster
#Make the fig and ax, and draw the initial plot
self.draw_dimension(self.dimNumber)
#Start the mouse handle.r and make an attribute to hold click pos
self.mpid = self.fig.canvas.mpl_connect('button_press_event',
self.on_click)
self.cloudMouseClickData = []
#Start the key press handler
self.kpid = self.fig.canvas.mpl_connect('key_press_event',
self.on_key_press)
#Start the handler to monitor the axis that the mouse is over
self.axiswatcher = self.fig.canvas.mpl_connect('axes_enter_event',
self.on_axis_enter)
#show the plot
plt.hold(True)
self.fig.show()
def __init__(self, samples):
self.samples = samples
self.points = spikesorting.calculate_features(self.samples, ["peak", "valley", "energy"])
self.fig = plt.figure()
self.cloudax = plt.subplot2grid((3, 4), (0, 0), rowspan=2, colspan=2)
self.wave0ax = plt.subplot2grid((3, 4), (0, 2), rowspan=1, colspan=1)
self.wave1ax = plt.subplot2grid((3, 4), (0, 3), rowspan=1, colspan=1)
self.wave2ax = plt.subplot2grid((3, 4), (1, 2), rowspan=1, colspan=1)
self.wave3ax = plt.subplot2grid((3, 4), (1, 3), rowspan=1, colspan=1)
self.wavaxes = [self.wave0ax, self.wave1ax, self.wave2ax, self.wave3ax]
self.loghistax = plt.subplot2grid((3, 4), (2, 0), rowspan=1, colspan=2)
self.timehistax = plt.subplot2grid((3, 4), (2, 2), rowspan=1, colspan=2)
self.numDims=self.points.shape[1]
self.combinations=[c for c in itertools.combinations(range(self.numDims), 2)]
self.maxDim=len(self.combinations)-1
#Start with the first combination
self.dimNumber=0
#All points start inside the cluster
self.inCluster=np.ones(len(self.points), dtype=bool)
self.outsideCluster=np.logical_not(self.inCluster)
#Preserve the last cluster state for undo
self.oldInsideCluster=self.inCluster
self.oldOutsideCluster=self.outsideCluster
#Make the fig and ax, and draw the initial plot
self.draw_dimension(self.dimNumber)
#Start the mouse handle.r and make an attribute to hold click pos
self.mpid=self.fig.canvas.mpl_connect('button_press_event', self.on_click)
self.cloudMouseClickData=[]
#Start the key press handler
self.kpid = self.fig.canvas.mpl_connect('key_press_event', self.on_key_press)
#Start the handler to monitor the axis that the mouse is over
self.axiswatcher = self.fig.canvas.mpl_connect('axes_enter_event', self.on_axis_enter)
#show the plot
plt.hold(True)
self.fig.show()
figure()
dataplotter.two_axis_heatmap(spikeTimes, eventOnsetTimes,
behavData['currentIntensity'],
behavData['currentFreq'])
title('All Spikes')
show()
# figure()
# spikesorting.plot_waveforms(spikeData.samples)
# title('all spikes')
# figure()
# dataplotter.plot_raster(spikeTimes, eventOnsetTimes)
# show()
fet = spikesorting.calculate_features(spikeData.samples,
['peak', 'valley', 'energy'])
# fet = spikesorting.calculate_features(spikeData.samples, ['peak'])
cw = clustercutting.AdvancedClusterCutter(spikeData.samples)
cont = raw_input("Press enter to continue")
while cont != '':
cont = raw_input("Press enter to continue")
figure()
dataplotter.two_axis_heatmap(spikeTimes[cw.inCluster], eventOnsetTimes,
behavData['currentIntensity'],
behavData['currentFreq'])
title('Selected Spikes')
show()
SAVE=True
# dbPath = os.path.join(settings.FIGURES_DATA_PATH, figparams.STUDY_NAME, 'celldatabase_ALLCELLS.h5')
dbPath = '/tmp/celldatabase_new_20180830.h5'
# dbPath = os.path.join(settings.FIGURES_DATA_PATH, figparams.STUDY_NAME, 'celldatabase_ALLCELLS_MODIFIED_CLU.h5')
db = pd.read_hdf(dbPath, key='dataframe')
cellsToRescue = db.query('autoTagged==1 and isiViolations>0.02 and isiViolations<0.04')
for indRow, dbRow in cellsToRescue.iterrows():
cell = ephyscore.Cell(dbRow, useModifiedClusters=False)
timestamps, samples, recordingNumber = cell.load_all_spikedata()
isiViolations = spikesorting.calculate_ISI_violations(timestamps)
print "isi violations: %{}".format(isiViolations*100)
print "nSpikes: {}".format(len(timestamps))
featuresMat = spikesorting.calculate_features(samples, ['peakFirstHalf', 'valleyFirstHalf', 'energy'])
#Sort by mahalanobis distance to the cluster centroid
try:
dM = spikesorting.distance_to_centroid(featuresMat)
# except LinAlgError, errMsg: #Singular matrix error
except: #FIXME: LinAlgError not defined??
continue
sortArray = np.argsort(dM)
spikesToRemove = 0
thisISIviolation = isiViolations #The isi violations including all the spikes
jumpBy = int(len(timestamps)*0.01) #Jump by 1% of spikes each time
if jumpBy==0:
jumpBy = 1 #Jump by at least 1 spike FIXME: This is probably a terrible cluster if this happens
