def shortToyOne(ds, newpath="/rg"):
dat = ds.getData()[:,0]
fs = ds.fs()
start = ds.start()
h = {'SampleType':'timeseries', 'SamplesPerSecond':fs, 'StartTime':start}
k = array([0,0,0,0,-1,1,-1,1,0,0,0,0])
ds.createSubData('/k', k, h, delete=True)
g = cal._applyFilterWithTR(dat, k)
g = shift(g, 6)
g = g/g.max()
ds.createSubData(newpath, g, h, delete=True)
def Filter(ds, select=(None, None, None), selectFilter=('/pca', [0], None), eventOffset=-.02, newpath='/dp'):
'''Apply the data specified by selectFilter as a filter on the data specified by select. Store the resulting signal in newpath.'''
filt=getSelection(ds, selectFilter)[:,0]
#filt = reverseArray(filt[:,0])
dat=getSelection(ds, select)[:,0]
dat = cal._applyFilterWithTR(dat, filt)
h=getSelectionHeader(ds, select)
fs = h['SamplesPerSecond']
fl = filt.shape[0] / fs
os = (fl / 2) + eventOffset
h['StartTime']+=os
ds.createSubData(newpath, data=dat, head=h, delete=True)
return ds
def shortToyTwo(ds, newpath="/rg"):
dat = ds.getData()[:,0]
fs = ds.fs()
start = ds.start()
h = {'SampleType':'timeseries', 'SamplesPerSecond':fs, 'StartTime':start}
k2 = array([0,0,0,0,-1,1,-1,1,0,0,0,0])
k1 = array([-1,1,-1,1,0,0,0,0,-1,1,-1,1])
g = cal._applyFilterWithTR(dat, k1)
g = shift(g, 6)
g = g - g.min()
g = g/g.max()
g2 = -1*sc.match(dat, k2)
g2 = g2 - g2.min()
g2 = g2/g2.max()
g = g + 3*g2
ds.createSubData('/k', column_stack([k1, k2]), h, delete=True)
ds.createSubData(newpath, g, h, delete=True)
def compoundToy(ds, klen = 200, newpath="/rg"):
dat = ds.getData()[:,0]
fs = ds.fs()
start = ds.start()
h = {'SampleType':'timeseries', 'SamplesPerSecond':fs, 'StartTime':start}
x=arange(klen)/fs
k2=sin( 2*pi*100*x)
k1=sin( 2*pi*400*x)
g = cal._applyFilterWithTR(dat, k1)
g = shift(g, int(klen/2))
g = g - g.min()
g = g/g.max()
g2 = -1*sc.match(dat, k2)
g2 = g2 - g2.min()
g2 = g2/g2.max()
g = g + g2
ds.createSubData('/k', column_stack([k1, k2]), h, delete=True)
ds.createSubData(newpath, g, h, delete=True)
def toyWithInvariance(ds, klen = 200, noise=.05, newpath="/spikes", returnG=False):
dat = ds.getData()[:,0]
fs = ds.fs()
start = ds.start()
h = {'SampleType':'timeseries', 'SamplesPerSecond':fs, 'StartTime':start}
q = int(klen/4.0)
k = zeros(klen)
k[:2*q] = -1
k[:q] = 1
ds.createSubData('/k', k, h, delete=True)
g = cal._applyFilterWithTR(dat, k)
g = shift(g, int(klen/2))
g = g/g.max()
# g = zeros(ds.data.shape[0])
# for i in range(klen, g.shape[0]):
# g[i] = dat[i-2*third:i-third].mean() - dat[i-3*third:i-2*third].mean()
if returnG:
ds.createSubData(newpath, g, h, delete=True)
else:
h['SampleType']='events'
evts = _gtoSpikes(g, noise, fs)
ds.createSubData(newpath, evts, h, delete=True)
