def loc_auto_corr_down(self, ftimes, other_spatial, other_ftimes, **kwargs):
"""
Calculate auto correlation after downsampling.
Calculates the two-dimensional correlation of firing map which is the
map of the firing rate of the animal with respect to its location
Parameters
----------
ftimes : ndarray
Timestamps of the spiking activity of a unit
other_spatial : NSpatial
The spatial data to downsample to.
other_ftimes : list or ndarray
The firing times of the cell in other spatial.
**kwargs
Keyword arguments
Returns
-------
dict
Graphical data of the analysis
"""
graph_data = {}
minPixel = kwargs.get('minPixel', 20)
pixel = kwargs.get('pixel', 3)
if 'update' in kwargs.keys():
del kwargs['update']
placeData = self.downsample_place(
ftimes, other_spatial, other_ftimes, update=False, **kwargs)
fmap = placeData['smoothMap']
fmap[np.isnan(fmap)] = 0
leny, lenx = fmap.shape
xshift = np.arange(-(lenx - 1), lenx)
yshift = np.arange(-(leny - 1), leny)
corrMap = np.zeros((yshift.size, xshift.size))
for J, ysh in enumerate(yshift):
for I, xsh in enumerate(xshift):
if ysh >= 0:
map1YInd = np.arange(ysh, leny)
map2YInd = np.arange(leny - ysh)
elif ysh < 0:
map1YInd = np.arange(leny + ysh)
map2YInd = np.arange(-ysh, leny)
if xsh >= 0:
map1XInd = np.arange(xsh, lenx)
map2XInd = np.arange(lenx - xsh)
elif xsh < 0:
map1XInd = np.arange(lenx + xsh)
map2XInd = np.arange(-xsh, lenx)
map1 = fmap[tuple(np.meshgrid(map1YInd, map1XInd))]
map2 = fmap[tuple(np.meshgrid(map2YInd, map2XInd))]
if map1.size < minPixel:
corrMap[J, I] = -1
else:
corrMap[J, I] = corr_coeff(map1, map2)
graph_data['corrMap'] = corrMap
graph_data['xshift'] = xshift * pixel
graph_data['yshift'] = yshift * pixel
return graph_data
def grid_down(self, ftimes, other_spatial, other_ftimes, **kwargs):
"""
Perform grid cell analysis after downsampling.
Analysis of Grid cells characterised by formation of grid-like pattern
of high activity in the firing-rate map
Parameters
----------
ftimes : ndarray
Timestamps of the spiking activity of a unit
other_spatial : NSpatial
The spatial data to downsample to.
other_ftimes : list or ndarray
The firing times of the cell in other spatial.
**kwargs
Keyword arguments
Returns
-------
dict
Graphical data of the analysis
"""
_results = oDict()
tol = kwargs.get('angtol', 2)
binsize = kwargs.get('binsize', 3)
bins = np.arange(0, 360, binsize)
graph_data = self.loc_auto_corr_down(
ftimes, other_spatial, other_ftimes, update=False, **kwargs)
corrMap = graph_data['corrMap']
corrMap[np.isnan(corrMap)] = 0
xshift = graph_data['xshift']
yshift = graph_data['yshift']
pixel = np.int(np.diff(xshift).mean())
ny, nx = corrMap.shape
rpeaks = np.zeros(corrMap.shape, dtype=bool)
cpeaks = np.zeros(corrMap.shape, dtype=bool)
for j in np.arange(ny):
rpeaks[j, extrema(corrMap[j, :])[1]] = True
for i in np.arange(nx):
cpeaks[extrema(corrMap[:, i])[1], i] = True
ymax, xmax = find2d(np.logical_and(rpeaks, cpeaks))
peakDist = np.sqrt((ymax - find(yshift == 0))**2 +
(xmax - find(xshift == 0))**2)
sortInd = np.argsort(peakDist)
ymax, xmax, peakDist = ymax[sortInd], xmax[sortInd], peakDist[sortInd]
ymax, xmax, peakDist = (
ymax[1:7], xmax[1:7], peakDist[1:7]) if ymax.size >= 7 else ([], [], [])
theta = np.arctan2(yshift[ymax], xshift[xmax]) * 180 / np.pi
theta[theta < 0] += 360
sortInd = np.argsort(theta)
ymax, xmax, peakDist, theta = (
ymax[sortInd], xmax[sortInd], peakDist[sortInd], theta[sortInd])
graph_data['ymax'] = yshift[ymax]
graph_data['xmax'] = xshift[xmax]
meanDist = peakDist.mean()
X, Y = np.meshgrid(xshift, yshift)
distMat = np.sqrt(X**2 + Y**2) / pixel
maskInd = np.logical_and(
distMat > 0.5 * meanDist, distMat < 1.5 * meanDist)
rotCorr = np.array([corr_coeff(rot_2d(corrMap, theta)[
maskInd], corrMap[maskInd]) for k, theta in enumerate(bins)])
ramax, rimax, ramin, rimin = extrema(rotCorr)
mThetaPk, mThetaTr = (np.diff(bins[rimax]).mean(), np.diff(
bins[rimin]).mean()) if rimax.size and rimin.size else (None, None)
graph_data['rimax'] = rimax
graph_data['rimin'] = rimin
graph_data['anglemax'] = bins[rimax]
graph_data['anglemin'] = bins[rimin]
graph_data['rotAngle'] = bins
graph_data['rotCorr'] = rotCorr
if mThetaPk is not None and mThetaTr is not None:
isGrid = True if 60 - tol < mThetaPk < 60 + \
tol and 60 - tol < mThetaTr < 60 + tol else False
else:
isGrid = False
meanAlpha = np.diff(theta).mean()
psi = theta[np.array([2, 3, 4, 5, 0, 1])] - theta
psi[psi < 0] += 360
meanPsi = psi.mean()
_results["First Check"] = (len(ymax) == np.logical_and(
peakDist > 0.75 * meanDist, peakDist < 1.25 * meanDist).sum())
_results['Is Grid'] = isGrid and 120 - tol < meanPsi < 120 + \
tol and 60 - tol < meanAlpha < 60 + tol
_results['Grid Mean Alpha'] = meanAlpha
_results['Grid Mean Psi'] = meanPsi
_results['Grid Spacing'] = meanDist * pixel
# Difference between highest Pearson R at peaks and lowest at troughs
_results['Grid Score'] = rotCorr[rimax].max() - \
rotCorr[rimin].min()
_results['Grid Orientation'] = theta[0]
self.update_result(_results)
return graph_data
def always_grid(self, ftimes, **kwargs):
"""
This outputs grid cell statistics even if the cell is clearly not grid.
Recommended to use NeuroChaTs method, this was to test the values
in non grid situations.
Analysis of Grid cells characterised by formation of grid-like pattern
of high activity in the firing-rate map.
Parameters
----------
ftimes : ndarray
Timestamps of the spiking activity of a unit
**kwargs
Keyword arguments
Returns
-------
dict
Graphical data of the analysis
"""
_results = oDict()
tol = kwargs.get("angtol", 2)
binsize = kwargs.get("binsize", 3)
bins = np.arange(0, 360, binsize)
graph_data = self.loc_auto_corr(ftimes, update=False, **kwargs)
corrMap = graph_data["corrMap"]
corrMap[np.isnan(corrMap)] = 0
xshift = graph_data["xshift"]
yshift = graph_data["yshift"]
pixel = np.int(np.diff(xshift).mean())
ny, nx = corrMap.shape
rpeaks = np.zeros(corrMap.shape, dtype=bool)
cpeaks = np.zeros(corrMap.shape, dtype=bool)
for j in np.arange(ny):
rpeaks[j, extrema(corrMap[j, :])[1]] = True
for i in np.arange(nx):
cpeaks[extrema(corrMap[:, i])[1], i] = True
ymax, xmax = find2d(np.logical_and(rpeaks, cpeaks))
peakDist = np.sqrt((ymax - find(yshift == 0))**2 +
(xmax - find(xshift == 0))**2)
sortInd = np.argsort(peakDist)
ymax, xmax, peakDist = ymax[sortInd], xmax[sortInd], peakDist[sortInd]
ymax, xmax, peakDist = ((ymax[1:7], xmax[1:7],
peakDist[1:7]) if ymax.size >= 7 else
([], [], []))
theta = np.arctan2(yshift[ymax], xshift[xmax]) * 180 / np.pi
theta[theta < 0] += 360
sortInd = np.argsort(theta)
ymax, xmax, peakDist, theta = (
ymax[sortInd],
xmax[sortInd],
peakDist[sortInd],
theta[sortInd],
)
graph_data["ymax"] = yshift[ymax]
graph_data["xmax"] = xshift[xmax]
meanDist = peakDist.mean()
X, Y = np.meshgrid(xshift, yshift)
distMat = np.sqrt(X**2 + Y**2) / pixel
_results["First Check"] = (len(ymax) == np.logical_and(
peakDist > 0.75 * meanDist, peakDist < 1.25 * meanDist).sum())
maskInd = np.logical_and(distMat > 0.5 * meanDist,
distMat < 1.5 * meanDist)
rotCorr = np.array([
corr_coeff(rot_2d(corrMap, theta)[maskInd], corrMap[maskInd])
for k, theta in enumerate(bins)
])
ramax, rimax, ramin, rimin = extrema(rotCorr)
mThetaPk, mThetaTr = ((np.diff(bins[rimax]).mean(), np.diff(
bins[rimin]).mean()) if rimax.size and rimin.size else (None, None))
graph_data["rimax"] = rimax
graph_data["rimin"] = rimin
graph_data["anglemax"] = bins[rimax]
graph_data["anglemin"] = bins[rimin]
graph_data["rotAngle"] = bins
graph_data["rotCorr"] = rotCorr
if mThetaPk is not None and mThetaTr is not None:
isGrid = (True if 60 - tol < mThetaPk < 60 + tol
and 60 - tol < mThetaTr < 60 + tol else False)
else:
isGrid = False
meanAlpha = np.diff(theta).mean()
psi = theta[np.array([2, 3, 4, 5, 0, 1])] - theta
psi[psi < 0] += 360
meanPsi = psi.mean()
_results["Is Grid"] = (isGrid and 120 - tol < meanPsi < 120 + tol
and 60 - tol < meanAlpha < 60 + tol)
_results["Grid Mean Alpha"] = meanAlpha
_results["Grid Mean Psi"] = meanPsi
_results["Grid Spacing"] = meanDist * pixel
# Difference between highest Pearson R at peaks and lowest at troughs
_results["Grid Score"] = rotCorr[rimax].max() - rotCorr[rimin].min()
_results["Grid Orientation"] = theta[0]
self.update_result(_results)
return graph_data