def process(self):
#print "process called."
if self.ctrl.disableChk.isChecked():
return
if self.data is None:
return
#print "calculating Probs"
fn.bendelsSpatialCorrelationAlgorithm(
self.data,
self.ctrl.radiusSpin.value(),
self.ctrl.spontSpin.value(),
self.ctrl.deltaTSpin.value(),
printProcess=False,
eventsKey=str(self.ctrl.eventCombo.currentText()))
#print "probs calculated"
self.data['prob'] = 1 - self.data[
'prob'] ## give probability that events are not spontaneous
if self.ctrl.probabilityRadio.isChecked():
self.emitOutputChanged(self.data)
elif self.ctrl.thresholdRadio.isChecked():
arr = self.data['prob']
arr[1 - arr < self.ctrl.thresholdSpin.value()] = 1
arr[(1 - arr > self.ctrl.thresholdSpin.value()) * (arr != 1)] = 0
self.data['prob'] = arr
self.emitOutputChanged(self.data)
def randomProbTest(sites, cellDir, n=10, getContours=False, **kwargs):
#prof = Profiler('randomProbTest', disabled=False)
keys={
'x':'modXPosCell',
'y':'percentDepth',
'probThreshold': 0.02,
'timeWindow':0.1,
'eventKey':'numOfPostEvents',
'spacing':5e-6,
'factor':1.11849
}
for k in kwargs:
keys[k] = kwargs[k]
tasks = range(n)
results = np.zeros((n), dtype=[('numOfSpots', int), ('contourInfo', object)])
data = sites[sites['CellDir']==cellDir]
spontRate = data['numOfPreEvents'].sum()/data['PreRegionLen'].sum()
#prof.mark('selected data, calculated spontRate')
d = afn.bendelsSpatialCorrelationAlgorithm(data, 90e-6, spontRate, keys['timeWindow'], eventsKey=keys['eventKey'])
actualNum = len(d[d['prob'] < keys['probThreshold']])
#prof.mark('calculated actual number of spots')
if getContours:
img, xmin, ymin = reserveImageArray(sites)
with Parallelize(tasks, workers=1, results=results)as tasker:
for task in tasker:
np.random.seed()
randomData = randomizeData(data, keys['eventKey'])
randomData = afn.bendelsSpatialCorrelationAlgorithm(randomData, 90e-6, spontRate, keys['timeWindow'], eventsKey=keys['eventKey'])
tasker.results[task]['numOfSpots'] = len(randomData[randomData['prob'] < keys['probThreshold']])
if getContours:
im = img.copy()
data = randomData[randomData['prob'] < probThreshold]
for i, s in enumerate(data):
x, y = (int((s[keys['x']]-xmin)/keys['spacing']), int((s[keys['y']]*keys['factor']-ymin)/keys['spacing']))
im[x,y] = 1
## convolution params are good for 5um grid spacing and 35um spaced samples -- need to generalize for other options
im = scipy.ndimage.gaussian_filter(im, 2, mode='constant')
im /= 0.039
im[im > 0.03] = 1
im[im <= 0.03] = 0
stats = getContourStats(im, spacing=keys['spacing'])
tasker.results[task]['contourInfo'] = stats
#prof.mark('calculated number of spots for %i random trials'%n)
#prof.finish()
return actualNum, results
def convolveCells_Xuying(sites, spacing=5e-6, probThreshold=0.02, probRadius=90e-6):
#avgCellX = np.array(list(set(sites['xPosCell']))).mean()
#avgCellY = np.array(list(set(sites['yPosCell']))).mean()
#xmin = (sites['xPos']-sites['xPosCell']).min() ## point furthest left of the cell
xmin = sites['xPosCell'].min()
#ymin = (sites['yPos']-sites['yPosCell']).min() ## point furthest above the cell
ymin = sites['yPosCell'].min()
#xmax = (sites['xPos']-sites['xPosCell']).max()
xmax = sites['xPosCell'].max()
#ymax = (sites['yPos']-sites['yPosCell']).max()
ymax = sites['yPosCell'].max()
xdim = int((xmax-xmin)/spacing)+10
ydim = int((ymax-ymin)/spacing)+10
#avgCellIndex = np.array([int((avgCellX-xmin)/spacing)+5, int((avgCellY-ymin)/spacing)+5])
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
results = []
#kernel = np.zeros((xdim, ydim))
#midx = xdim/2
#midy = ydim/2
#radius = int((sampleSpacing)/spacing)
#radius2 = radius**2
#for x in range(midx-radius, midx+radius):
#for y in range(midy - radius, midy+radius):
#r2 = (x-radius)**2 + (y-radius)**2
#if r2 <= radius2:
#kernel[x,y] = 1
results = []
for i, c in enumerate(cells):
data = sites[sites['CellDir']==c]
spontRate = data['numOfPreEvents'].sum()/data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data, probRadius, spontRate, data[0]['PostRegionLen'])
#data['prob'][data['prob'] < probThreshold] = 2.
#data['prob'][(data['prob'] >= probThreshold)*(data[i]['prob']!= 2.)] = 0.
#data['prob'][data['prob'] == 2.] = 1.
probs = np.zeros(len(data))
probs[data['prob'] < probThreshold] = 1.
for j, s in enumerate(data):
#trans1 = (data['xPosCell'][0] - avgCellX, data['yPosCell'][0]-avgCellY)
#trans2 = (avgCellX+xmin, avgCellY+ymin)
trans = (xmin, ymin)
#x, y = (int((s['xPos']-trans1[0]-trans2[0])/spacing), int((s['yPos']-trans1[1]-trans2[1])/spacing))
x, y = (int((s['xPosCell']-trans[0])/spacing), int((s['yPosCell'] - trans[1])/spacing))
#print i, x, y, j
arr[i, x, y] = probs[j]
results.append(arr[i].copy())
arr[i] = scipy.ndimage.gaussian_filter(arr[i], 2)
arr[i] = arr[i]/0.039
#arr[i][arr[i] > 0.04] = 2
#arr[i][(arr[i] > 0.02)*(arr[i] <=0.04)] = 1
arr[i][arr[i] > 0.02] = 1
arr[i][arr[i] <= 0.02] = 0
#arr[i] = scipy.ndimage.convolve(arr[i], kernel)
return arr, results
def interpolateCells(sites, spacing=5e-6, method='nearest', probThreshold=0.05):
avgCellX = np.array(list(set(sites['CellXPos']))).mean()
avgCellY = np.array(list(set(sites['CellYPos']))).mean()
xmin = (sites['xPos']-sites['CellXPos']).min() ## point furthest left of the cell
ymin = (sites['yPos']-sites['CellYPos']).min() ## point furthest above the cell
xmax = (sites['xPos']-sites['CellXPos']).max()
ymax = (sites['yPos']-sites['CellXPos']).max()
xdim = int((xmax-xmin)/spacing)+10
ydim = int((ymax-ymin)/spacing)+10
avgCellIndex = np.array([int((avgCellX-xmin)/spacing)+5, int((avgCellY-ymin)/spacing)+5])
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
results = []
print 'xdim:', xdim, 'ydim:', ydim
for i, c in enumerate(cells):
data = sites[sites['CellDir'] == c]
trans1 = (data['CellXPos'][0] - avgCellX, data['CellYPos'][0]-avgCellY)
trans2 = (avgCellX+xmin, avgCellY+ymin)
pts = np.array([data['xPos']-trans1[0]-trans2[0], data['yPos']-trans1[1]-trans2[1]], dtype=float)
#pts[0] = pts[0]+(avgCellX-xmin)
#pts[1] = pts[1]+(avgCellY-ymin)
xlimits = (int((data['xPos'].min()-trans1[0]-trans2[0])/spacing), int((data['xPos'].max()-trans1[0]-trans2[0])/spacing))
ylimits = (int((data['yPos'].min()-trans1[1]-trans2[1])/spacing), int((data['yPos'].max()-trans1[1]-trans2[1])/spacing))
print 'xlimits:', xlimits, ' ylimits:', ylimits
pts = pts.transpose()/spacing
xi = np.indices((xdim, ydim))
xi = xi.transpose(1,2,0)
spontRate = data['numOfPreEvents'].sum()/data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data, 90e-6, spontRate, data[0]['PostRegionLen'])
#print data['prob'].max()
data['prob'][data['prob'] < probThreshold] = 2.
# print data['prob'].max()
data['prob'][(data['prob'] >= probThreshold)*(data['prob']!= 2.)] = 0.
data['prob'][data['prob'] == 2.] = 1.
#print data['prob'].max()
#print "========= ", data['prob'].mean()
res = scipy.interpolate.griddata(pts, data['prob'], xi, method=method)
res[:xlimits[0], :] = 0
res[xlimits[1]+1:, :] = 0
res[:, :ylimits[0]] = 0
res[:, ylimits[1]+1:] = 0
arr[i] = res
results.append(res)
#arr[i][np.isnan(arr[i])] = .1
return arr, (xmin, ymin)
def process(self):
#print "process called."
if self.ctrl.disableChk.isChecked():
return
if self.data is None:
return
#print "calculating Probs"
fn.bendelsSpatialCorrelationAlgorithm(self.data, self.ctrl.radiusSpin.value(), self.ctrl.spontSpin.value(), self.ctrl.deltaTSpin.value(), printProcess=False, eventsKey=str(self.ctrl.eventCombo.currentText()))
#print "probs calculated"
self.data['prob'] = 1-self.data['prob'] ## give probability that events are not spontaneous
if self.ctrl.probabilityRadio.isChecked():
self.emitOutputChanged(self.data)
elif self.ctrl.thresholdRadio.isChecked():
arr = self.data['prob']
arr[1-arr < self.ctrl.thresholdSpin.value()] = 1
arr[(1-arr > self.ctrl.thresholdSpin.value())*(arr!=1)] = 0
self.data['prob'] = arr
self.emitOutputChanged(self.data)
def convolveCells(sites,
spacing=5e-6,
probThreshold=0.02,
probRadius=90e-6,
timeWindow=0.1,
eventKey='numOfPostEvents'):
#avgCellX = np.array(list(set(sites['xPosCell']))).mean()
#avgCellY = np.array(list(set(sites['yPosCell']))).mean()
#xmin = (sites['xPos']-sites['xPosCell']).min() ## point furthest left of the cell
xmin = sites['xPosCell'].min()
#ymin = (sites['yPos']-sites['yPosCell']).min() ## point furthest above the cell
ymin = sites['yPosCell'].min()
#xmax = (sites['xPos']-sites['xPosCell']).max()
xmax = sites['xPosCell'].max()
#ymax = (sites['yPos']-sites['yPosCell']).max()
ymax = sites['yPosCell'].max()
xdim = int((xmax - xmin) / spacing) + 10
ydim = int((ymax - ymin) / spacing) + 10
#avgCellIndex = np.array([int((avgCellX-xmin)/spacing)+5, int((avgCellY-ymin)/spacing)+5])
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
results = []
for i, c in enumerate(cells):
data = sites[sites['CellDir'] == c]
spontRate = data['numOfPreEvents'].sum() / data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data,
probRadius,
spontRate,
timeWindow=timeWindow,
eventKey=eventKey)
probs = np.zeros(len(data))
probs[data['prob'] < probThreshold] = 1.
for j, s in enumerate(data):
trans1 = (data['CellXPos'][0] - avgCellX,
data['CellYPos'][0] - avgCellY)
trans2 = (avgCellX + xmin, avgCellY + ymin)
x, y = (int((s['xPos'] - trans1[0] - trans2[0]) / spacing),
int((s['yPos'] - trans1[1] - trans2[1]) / spacing))
arr[i, x, y] = probs[j]
results.append(arr[i].copy())
arr[i] = scipy.ndimage.gaussian_filter(arr[i], 2)
arr[i] = arr[i] / 0.039
arr[i][arr[i] > 0.02] = 1
#arr[i][(arr[i] > 0.02)*(arr[i] <=0.04)] = 1
arr[i][arr[i] <= 0.02] = 0
return arr, results
def calculateProb(sites, spacing=5e-6, keys=None):
cells = set(sites['CellDir'])
arr = reserveArray(sites, spacing)
for i, c in enumerate(cells):
sites = data[data['CellDir']==c]
spontRate = sites['numOfPreEvents'].sum()/sites['PreRegionLen'].sum()
sites = afn.bendelsSpatialCorrelationAlgorithm(sites, 90e-6, spontRate, sites[0]['PostRegionLen'])
for s in sites:
x, y = (int((s['xPos']-xmin)/spacing), int((s['yPos']-ymin)/spacing))
arr[i, x, y] = s['prob']
return arr
def calculateProb(sites, spacing=5e-6, keys=None):
cells = set(sites['CellDir'])
arr = reserveArray(sites, spacing)
for i, c in enumerate(cells):
sites = data[data['CellDir'] == c]
spontRate = sites['numOfPreEvents'].sum() / sites['PreRegionLen'].sum()
sites = afn.bendelsSpatialCorrelationAlgorithm(
sites, 90e-6, spontRate, sites[0]['PostRegionLen'])
for s in sites:
x, y = (int(
(s['xPos'] - xmin) / spacing), int(
(s['yPos'] - ymin) / spacing))
arr[i, x, y] = s['prob']
return arr
def interpolateSlice(sites,
spacing=5e-6,
method='nearest',
probThreshold=0.05):
xmin = sites['xPos'].min()
ymin = sites['yPos'].min()
xdim = int((sites['xPos'].max() - xmin) / spacing) + 5
ydim = int((sites['yPos'].max() - ymin) / spacing) + 5
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
results = []
for i, c in enumerate(cells):
data = sites[sites['CellDir'] == c]
pts = np.array([data['xPos'], data['yPos']], dtype=float)
pts[0] = pts[0] - xmin
pts[1] = pts[1] - ymin
pts = pts.transpose() / spacing
xi = np.indices((xdim, ydim))
xi = xi.transpose(1, 2, 0)
spontRate = data['numOfPreEvents'].sum() / data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data, 90e-6, spontRate,
data[0]['PostRegionLen'])
#print data['prob'].max()
data['prob'][data['prob'] < probThreshold] = 2.
# print data['prob'].max()
data['prob'][(data['prob'] >= probThreshold) *
(data['prob'] != 2.)] = 0.
data['prob'][data['prob'] == 2.] = 1.
#print data['prob'].max()
#print "========= ", data['prob'].mean()
res = scipy.interpolate.griddata(pts, data['prob'], xi, method=method)
arr[i] = res
results.append(res)
#arr[i][np.isnan(arr[i])] = .1
return arr
def convolveCells(sites, spacing=5e-6, probThreshold=0.02, probRadius=90e-6, timeWindow=0.1, eventKey='numOfPostEvents'):
#avgCellX = np.array(list(set(sites['xPosCell']))).mean()
#avgCellY = np.array(list(set(sites['yPosCell']))).mean()
#xmin = (sites['xPos']-sites['xPosCell']).min() ## point furthest left of the cell
xmin = sites['xPosCell'].min()
#ymin = (sites['yPos']-sites['yPosCell']).min() ## point furthest above the cell
ymin = sites['yPosCell'].min()
#xmax = (sites['xPos']-sites['xPosCell']).max()
xmax = sites['xPosCell'].max()
#ymax = (sites['yPos']-sites['yPosCell']).max()
ymax = sites['yPosCell'].max()
xdim = int((xmax-xmin)/spacing)+10
ydim = int((ymax-ymin)/spacing)+10
#avgCellIndex = np.array([int((avgCellX-xmin)/spacing)+5, int((avgCellY-ymin)/spacing)+5])
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
results = []
for i, c in enumerate(cells):
data = sites[sites['CellDir']==c]
spontRate = data['numOfPreEvents'].sum()/data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data, probRadius, spontRate, timeWindow=timeWindow, eventKey=eventKey)
probs = np.zeros(len(data))
probs[data['prob'] < probThreshold] = 1.
for j, s in enumerate(data):
trans1 = (data['CellXPos'][0] - avgCellX, data['CellYPos'][0]-avgCellY)
trans2 = (avgCellX+xmin, avgCellY+ymin)
x, y = (int((s['xPos']-trans1[0]-trans2[0])/spacing), int((s['yPos']-trans1[1]-trans2[1])/spacing))
arr[i, x, y] = probs[j]
results.append(arr[i].copy())
arr[i] = scipy.ndimage.gaussian_filter(arr[i], 2)
arr[i] = arr[i]/0.039
arr[i][arr[i] > 0.02] = 1
#arr[i][(arr[i] > 0.02)*(arr[i] <=0.04)] = 1
arr[i][arr[i] <= 0.02] = 0
return arr, results
def interpolateSlice(sites, spacing=5e-6, method='nearest', probThreshold=0.05):
xmin = sites['xPos'].min()
ymin = sites['yPos'].min()
xdim = int((sites['xPos'].max()-xmin)/spacing)+5
ydim = int((sites['yPos'].max()-ymin)/spacing)+5
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
results = []
for i, c in enumerate(cells):
data = sites[sites['CellDir'] == c]
pts = np.array([data['xPos'], data['yPos']], dtype=float)
pts[0] = pts[0]-xmin
pts[1] = pts[1]-ymin
pts = pts.transpose()/spacing
xi = np.indices((xdim, ydim))
xi = xi.transpose(1,2,0)
spontRate = data['numOfPreEvents'].sum()/data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data, 90e-6, spontRate, data[0]['PostRegionLen'])
#print data['prob'].max()
data['prob'][data['prob'] < probThreshold] = 2.
# print data['prob'].max()
data['prob'][(data['prob'] >= probThreshold)*(data['prob']!= 2.)] = 0.
data['prob'][data['prob'] == 2.] = 1.
#print data['prob'].max()
#print "========= ", data['prob'].mean()
res = scipy.interpolate.griddata(pts, data['prob'], xi, method=method)
arr[i] = res
results.append(res)
#arr[i][np.isnan(arr[i])] = .1
return arr
def randomProbTest(sites, cellDir, n=10, getContours=False, **kwargs):
#prof = Profiler('randomProbTest', disabled=False)
keys = {
'x': 'modXPosCell',
'y': 'percentDepth',
'probThreshold': 0.02,
'timeWindow': 0.1,
'eventKey': 'numOfPostEvents',
'spacing': 5e-6,
'factor': 1.11849
}
for k in kwargs:
keys[k] = kwargs[k]
tasks = range(n)
results = np.zeros((n),
dtype=[('numOfSpots', int), ('contourInfo', object)])
data = sites[sites['CellDir'] == cellDir]
spontRate = data['numOfPreEvents'].sum() / data['PreRegionLen'].sum()
#prof.mark('selected data, calculated spontRate')
d = afn.bendelsSpatialCorrelationAlgorithm(data,
90e-6,
spontRate,
keys['timeWindow'],
eventsKey=keys['eventKey'])
actualNum = len(d[d['prob'] < keys['probThreshold']])
#prof.mark('calculated actual number of spots')
if getContours:
img, xmin, ymin = reserveImageArray(sites)
with Parallelize(tasks, workers=1, results=results) as tasker:
for task in tasker:
np.random.seed()
randomData = randomizeData(data, keys['eventKey'])
randomData = afn.bendelsSpatialCorrelationAlgorithm(
randomData,
90e-6,
spontRate,
keys['timeWindow'],
eventsKey=keys['eventKey'])
tasker.results[task]['numOfSpots'] = len(
randomData[randomData['prob'] < keys['probThreshold']])
if getContours:
im = img.copy()
data = randomData[randomData['prob'] < probThreshold]
for i, s in enumerate(data):
x, y = (int((s[keys['x']] - xmin) / keys['spacing']),
int((s[keys['y']] * keys['factor'] - ymin) /
keys['spacing']))
im[x, y] = 1
## convolution params are good for 5um grid spacing and 35um spaced samples -- need to generalize for other options
im = scipy.ndimage.gaussian_filter(im, 2, mode='constant')
im /= 0.039
im[im > 0.03] = 1
im[im <= 0.03] = 0
stats = getContourStats(im, spacing=keys['spacing'])
tasker.results[task]['contourInfo'] = stats
#prof.mark('calculated number of spots for %i random trials'%n)
#prof.finish()
return actualNum, results
def convolveCells_newAtlas(sites,
keys=None,
factor=1.11849,
spacing=5e-6,
probThreshold=0.02,
sampleSpacing=35e-6,
eventKey=None,
timeWindow=None):
if keys == None:
keys = {
'x': 'xPosCell',
'y': 'yPosCell',
'mappedX': 'modXPosCell',
'mappedY': 'percentDepth'
}
if eventKey == None:
eventKey = 'numOfPostEvents'
#if timeWindow == None:
# timeWindow = sites[0]['PostRgnLen']
#avgCellX = np.array(list(set(sites['CellXPos']))).mean()
#avgCellY = np.array(list(set(sites['CellYPos']))).mean()
#xmin = (sites['xPos']-sites['CellXPos']).min() ## point furthest left of the cell
xmin = sites[keys['mappedX']].min()
#ymin = (sites['yPos']-sites['CellYPos']).min() ## point furthest above the cell
ymin = (sites[keys['mappedY']].min()
if sites[keys['mappedY']].min() < 0 else 0.) * factor
#xmax = (sites['xPos']-sites['CellXPos']).max()
xmax = sites[keys['mappedX']].max()
#ymax = (sites['yPos']-sites['CellXPos']).max()
ymax = sites[keys['mappedY']].max() * factor
xdim = int((xmax - xmin) / spacing) + 10
ydim = int((ymax - ymin) / spacing) + 10
#avgCellIndex = np.array([int((avgCellX-xmin)/spacing)+5, int((avgCellY-ymin)/spacing)+5])
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
sampling = np.zeros((n, xdim, ydim), dtype=float)
#results = []
counts = []
for i, c in enumerate(cells):
print "index:", i, " = cell:", c
data = sites[sites['CellDir'] == c]
if timeWindow == None:
timeWindow = data[0]['PostRegionLen']
elif timeWindow == 'pre':
timeWindow = data[0]['PreRegionLen']
spontRate = data['numOfPreEvents'].sum() / data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data,
90e-6,
spontRate,
timeWindow,
eventsKey=eventKey)
probs = np.zeros(len(data))
probs[data['prob'] < probThreshold] = 1.
for j, s in enumerate(data):
#trans1 = (s[keys['mappedX']] - xmin, s[keys['mappedY']]-ymin)
#trans2 = (avgCellX+xmin, avgCellY+ymin)
x, y = (int((s[keys['mappedX']] - xmin) / spacing),
int((s[keys['mappedY']] * factor - ymin) / spacing))
arr[i, x, y] = probs[j]
sampling[i, x, y] = 1
counts.append(arr[i].sum())
#results.append(arr[i].copy())
arr[i] = scipy.ndimage.gaussian_filter(arr[i], 2, mode='constant')
arr[i] = arr[i] / 0.039
arr[i][arr[i] > 0.03] = 1
arr[i][arr[i] <= 0.03] = 0
sampling[i] = scipy.ndimage.gaussian_filter(sampling[i], 2)
sampling[i] = sampling[i] / 0.039
sampling[i][sampling[i] > 0.02] = 1
sampling[i][sampling[i] <= 0.02] = 0
### mark cell position
#xind = int(-xmin/spacing)
#yind = int(data['CellYPos'][0]/spacing)
##print "yind=", ymin, '*', factor, '/', spacing
##print arr.shape, xind, yind
#arr[i, xind-1:xind+2, yind-1:yind+2] = 2
##print arr.max()
### mark separation lines
#arr[:, int((-xmin-150e-6)/spacing), :] = 3
#arr[:, int((-xmin-450e-6)/spacing), :] = 3
#arr[:, int((-xmin+150e-6)/spacing), :] = 3
#arr[:, int((-xmin+450e-6)/spacing), :] = 3
#arr[:, :, int(130e-6*factor/spacing)] = 3
#arr[:, :, int(310e-6*factor/spacing)] = 3
#arr[:, :, int(450e-6*factor/spacing)] = 3
#arr[:, :, int(720e-6*factor/spacing)] = 3
return arr, counts
def convolveCells_Xuying(sites,
spacing=5e-6,
probThreshold=0.02,
probRadius=90e-6):
#avgCellX = np.array(list(set(sites['xPosCell']))).mean()
#avgCellY = np.array(list(set(sites['yPosCell']))).mean()
#xmin = (sites['xPos']-sites['xPosCell']).min() ## point furthest left of the cell
xmin = sites['xPosCell'].min()
#ymin = (sites['yPos']-sites['yPosCell']).min() ## point furthest above the cell
ymin = sites['yPosCell'].min()
#xmax = (sites['xPos']-sites['xPosCell']).max()
xmax = sites['xPosCell'].max()
#ymax = (sites['yPos']-sites['yPosCell']).max()
ymax = sites['yPosCell'].max()
xdim = int((xmax - xmin) / spacing) + 10
ydim = int((ymax - ymin) / spacing) + 10
#avgCellIndex = np.array([int((avgCellX-xmin)/spacing)+5, int((avgCellY-ymin)/spacing)+5])
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
results = []
#kernel = np.zeros((xdim, ydim))
#midx = xdim/2
#midy = ydim/2
#radius = int((sampleSpacing)/spacing)
#radius2 = radius**2
#for x in range(midx-radius, midx+radius):
#for y in range(midy - radius, midy+radius):
#r2 = (x-radius)**2 + (y-radius)**2
#if r2 <= radius2:
#kernel[x,y] = 1
results = []
for i, c in enumerate(cells):
data = sites[sites['CellDir'] == c]
spontRate = data['numOfPreEvents'].sum() / data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data, probRadius,
spontRate,
data[0]['PostRegionLen'])
#data['prob'][data['prob'] < probThreshold] = 2.
#data['prob'][(data['prob'] >= probThreshold)*(data[i]['prob']!= 2.)] = 0.
#data['prob'][data['prob'] == 2.] = 1.
probs = np.zeros(len(data))
probs[data['prob'] < probThreshold] = 1.
for j, s in enumerate(data):
#trans1 = (data['xPosCell'][0] - avgCellX, data['yPosCell'][0]-avgCellY)
#trans2 = (avgCellX+xmin, avgCellY+ymin)
trans = (xmin, ymin)
#x, y = (int((s['xPos']-trans1[0]-trans2[0])/spacing), int((s['yPos']-trans1[1]-trans2[1])/spacing))
x, y = (int((s['xPosCell'] - trans[0]) / spacing),
int((s['yPosCell'] - trans[1]) / spacing))
#print i, x, y, j
arr[i, x, y] = probs[j]
results.append(arr[i].copy())
arr[i] = scipy.ndimage.gaussian_filter(arr[i], 2)
arr[i] = arr[i] / 0.039
#arr[i][arr[i] > 0.04] = 2
#arr[i][(arr[i] > 0.02)*(arr[i] <=0.04)] = 1
arr[i][arr[i] > 0.02] = 1
arr[i][arr[i] <= 0.02] = 0
#arr[i] = scipy.ndimage.convolve(arr[i], kernel)
return arr, results
def interpolateCells(sites,
spacing=5e-6,
method='nearest',
probThreshold=0.05):
avgCellX = np.array(list(set(sites['CellXPos']))).mean()
avgCellY = np.array(list(set(sites['CellYPos']))).mean()
xmin = (sites['xPos'] -
sites['CellXPos']).min() ## point furthest left of the cell
ymin = (sites['yPos'] -
sites['CellYPos']).min() ## point furthest above the cell
xmax = (sites['xPos'] - sites['CellXPos']).max()
ymax = (sites['yPos'] - sites['CellXPos']).max()
xdim = int((xmax - xmin) / spacing) + 10
ydim = int((ymax - ymin) / spacing) + 10
avgCellIndex = np.array([
int((avgCellX - xmin) / spacing) + 5,
int((avgCellY - ymin) / spacing) + 5
])
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
results = []
print 'xdim:', xdim, 'ydim:', ydim
for i, c in enumerate(cells):
data = sites[sites['CellDir'] == c]
trans1 = (data['CellXPos'][0] - avgCellX,
data['CellYPos'][0] - avgCellY)
trans2 = (avgCellX + xmin, avgCellY + ymin)
pts = np.array([
data['xPos'] - trans1[0] - trans2[0],
data['yPos'] - trans1[1] - trans2[1]
],
dtype=float)
#pts[0] = pts[0]+(avgCellX-xmin)
#pts[1] = pts[1]+(avgCellY-ymin)
xlimits = (int((data['xPos'].min() - trans1[0] - trans2[0]) / spacing),
int((data['xPos'].max() - trans1[0] - trans2[0]) / spacing))
ylimits = (int((data['yPos'].min() - trans1[1] - trans2[1]) / spacing),
int((data['yPos'].max() - trans1[1] - trans2[1]) / spacing))
print 'xlimits:', xlimits, ' ylimits:', ylimits
pts = pts.transpose() / spacing
xi = np.indices((xdim, ydim))
xi = xi.transpose(1, 2, 0)
spontRate = data['numOfPreEvents'].sum() / data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data, 90e-6, spontRate,
data[0]['PostRegionLen'])
#print data['prob'].max()
data['prob'][data['prob'] < probThreshold] = 2.
# print data['prob'].max()
data['prob'][(data['prob'] >= probThreshold) *
(data['prob'] != 2.)] = 0.
data['prob'][data['prob'] == 2.] = 1.
#print data['prob'].max()
#print "========= ", data['prob'].mean()
res = scipy.interpolate.griddata(pts, data['prob'], xi, method=method)
res[:xlimits[0], :] = 0
res[xlimits[1] + 1:, :] = 0
res[:, :ylimits[0]] = 0
res[:, ylimits[1] + 1:] = 0
arr[i] = res
results.append(res)
#arr[i][np.isnan(arr[i])] = .1
return arr, (xmin, ymin)
def convolveCells_newAtlas(sites, keys=None, factor=1.11849, spacing=5e-6, probThreshold=0.02, sampleSpacing=35e-6, eventKey=None, timeWindow=None):
if keys == None:
keys = {
'x':'xPosCell',
'y':'yPosCell',
'mappedX': 'modXPosCell',
'mappedY': 'percentDepth'}
if eventKey == None:
eventKey = 'numOfPostEvents'
#if timeWindow == None:
# timeWindow = sites[0]['PostRgnLen']
#avgCellX = np.array(list(set(sites['CellXPos']))).mean()
#avgCellY = np.array(list(set(sites['CellYPos']))).mean()
#xmin = (sites['xPos']-sites['CellXPos']).min() ## point furthest left of the cell
xmin = sites[keys['mappedX']].min()
#ymin = (sites['yPos']-sites['CellYPos']).min() ## point furthest above the cell
ymin = (sites[keys['mappedY']].min() if sites[keys['mappedY']].min() < 0 else 0.) *factor
#xmax = (sites['xPos']-sites['CellXPos']).max()
xmax = sites[keys['mappedX']].max()
#ymax = (sites['yPos']-sites['CellXPos']).max()
ymax = sites[keys['mappedY']].max()*factor
xdim = int((xmax-xmin)/spacing)+10
ydim = int((ymax-ymin)/spacing)+10
#avgCellIndex = np.array([int((avgCellX-xmin)/spacing)+5, int((avgCellY-ymin)/spacing)+5])
cells = set(sites['CellDir'])
n = len(cells)
arr = np.zeros((n, xdim, ydim), dtype=float)
sampling = np.zeros((n, xdim, ydim), dtype=float)
#results = []
counts = []
for i, c in enumerate(cells):
print "index:", i," = cell:", c
data = sites[sites['CellDir']==c]
if timeWindow == None:
timeWindow = data[0]['PostRegionLen']
elif timeWindow == 'pre':
timeWindow = data[0]['PreRegionLen']
spontRate = data['numOfPreEvents'].sum()/data['PreRegionLen'].sum()
data = afn.bendelsSpatialCorrelationAlgorithm(data, 90e-6, spontRate, timeWindow, eventsKey=eventKey)
probs = np.zeros(len(data))
probs[data['prob'] < probThreshold] = 1.
for j, s in enumerate(data):
#trans1 = (s[keys['mappedX']] - xmin, s[keys['mappedY']]-ymin)
#trans2 = (avgCellX+xmin, avgCellY+ymin)
x, y = (int((s[keys['mappedX']]-xmin)/spacing), int((s[keys['mappedY']]*factor-ymin)/spacing))
arr[i, x, y] = probs[j]
sampling[i, x, y] = 1
counts.append(arr[i].sum())
#results.append(arr[i].copy())
arr[i] = scipy.ndimage.gaussian_filter(arr[i], 2, mode='constant')
arr[i] = arr[i]/0.039
arr[i][arr[i] > 0.03] = 1
arr[i][arr[i] <= 0.03] = 0
sampling[i] = scipy.ndimage.gaussian_filter(sampling[i], 2)
sampling[i] = sampling[i]/0.039
sampling[i][sampling[i] > 0.02] = 1
sampling[i][sampling[i] <= 0.02] = 0
### mark cell position
#xind = int(-xmin/spacing)
#yind = int(data['CellYPos'][0]/spacing)
##print "yind=", ymin, '*', factor, '/', spacing
##print arr.shape, xind, yind
#arr[i, xind-1:xind+2, yind-1:yind+2] = 2
##print arr.max()
### mark separation lines
#arr[:, int((-xmin-150e-6)/spacing), :] = 3
#arr[:, int((-xmin-450e-6)/spacing), :] = 3
#arr[:, int((-xmin+150e-6)/spacing), :] = 3
#arr[:, int((-xmin+450e-6)/spacing), :] = 3
#arr[:, :, int(130e-6*factor/spacing)] = 3
#arr[:, :, int(310e-6*factor/spacing)] = 3
#arr[:, :, int(450e-6*factor/spacing)] = 3
#arr[:, :, int(720e-6*factor/spacing)] = 3
return arr, counts
