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225 | # -*- coding: utf-8 -*-
# @Author: gviejo
# @Date: 2022-01-17 14:10:40
# @Last Modified by: gviejo
# @Last Modified time: 2022-01-18 17:40:08
import pynapple as nap
import pandas as pd
import numpy as np
def computePlaceFields(spikes, position, ep, nb_bins = 100, frequency = 120.0, smoothing = 2):
place_fields = {}
position_tsd = position.restrict(ep)
xpos = position_tsd.iloc[:,0]
ypos = position_tsd.iloc[:,1]
xbins = np.linspace(xpos.min(), xpos.max()+1e-6, nb_bins+1)
ybins = np.linspace(ypos.min(), ypos.max()+1e-6, nb_bins+1)
for n in spikes:
position_spike = position_tsd.realign(spikes[n].restrict(ep))
spike_count,_,_ = np.histogram2d(position_spike.iloc[:,1].values, position_spike.iloc[:,0].values, [ybins,xbins])
occupancy, _, _ = np.histogram2d(ypos, xpos, [ybins,xbins])
mean_spike_count = spike_count/(occupancy+1)
place_field = mean_spike_count*frequency
place_fields[n] = pd.DataFrame(index = ybins[0:-1][::-1],columns = xbins[0:-1], data = place_field)
extent = (xbins[0], xbins[-1], ybins[0], ybins[-1]) # USEFUL FOR MATPLOTLIB
return place_fields, extent
def computeOccupancy(position, ep, nb_bins = 100):
xpos = position.restrict(ep).iloc[:,0]
ypos = position.restrict(ep).iloc[:,1]
xbins = np.linspace(xpos.min(), xpos.max()+1e-6, nb_bins+1)
ybins = np.linspace(ypos.min(), ypos.max()+1e-6, nb_bins+1)
occupancy, _, _ = np.histogram2d(ypos, xpos, [ybins,xbins])
return occupancy
def findHDCells(tuning_curves, z = 50, p = 0.0001 , m = 1):
"""
Peak firing rate larger than 1
and Rayleigh test p<0.001 & z > 100
"""
cond1 = tuning_curves.max()>m
from pycircstat.tests import rayleigh
stat = pd.DataFrame(index = tuning_curves.columns, columns = ['pval', 'z'])
for k in tuning_curves:
stat.loc[k] = rayleigh(tuning_curves[k].index.values, tuning_curves[k].values)
cond2 = np.logical_and(stat['pval']<p,stat['z']>z)
tokeep = stat.index.values[np.where(np.logical_and(cond1, cond2))[0]]
hd = pd.Series(index = tuning_curves.columns, data = 0)
hd.loc[tokeep] = 1
return hd, stat
def computeAngularVelocityTuningCurves(spikes, angle, ep, nb_bins = 61, bin_size = 10000, norm=True):
tmp = pd.Series(index = angle.index.values, data = np.unwrap(angle.values))
tmp2 = tmp.rolling(window=100,win_type='gaussian',center=True,min_periods=1).mean(std=10.0)
time_bins = np.arange(tmp.index[0], tmp.index[-1]+bin_size, bin_size) # assuming microseconds
index = np.digitize(tmp2.index.values, time_bins)
tmp3 = tmp2.groupby(index).mean()
tmp3.index = time_bins[np.unique(index)-1]+bin_size/2
tmp3 = nap.Tsd(tmp3)
tmp4 = np.diff(tmp3.values)/np.diff(tmp3.as_units('s').index.values)
tmp2 = nap.Tsd(tmp2)
tmp4 = np.diff(tmp2.values)/np.diff(tmp2.as_units('s').index.values)
velocity = nap.Tsd(t=tmp2.index.values[1:], d = tmp4)
velocity = velocity.restrict(ep)
bins = np.linspace(-2*np.pi, 2*np.pi, nb_bins)
idx = bins[0:-1]+np.diff(bins)/2
velo_curves = pd.DataFrame(index = idx, columns = list(spikes.keys()))
for k in spikes:
spks = spikes[k]
spks = spks.restrict(ep)
speed_spike = velocity.realign(spks)
spike_count, bin_edges = np.histogram(speed_spike, bins)
occupancy, _ = np.histogram(velocity.restrict(ep), bins)
spike_count = spike_count/(occupancy+1)
velo_curves[k] = spike_count*(1/(bin_size*1e-6))
# normalizing by firing rate
if norm:
velo_curves[k] = velo_curves[k]/(len(spikes[k].restrict(ep))/ep.tot_length('s'))
return velo_curves
#seems there are 2 functions with the same name (see above)
def smoothAngularTuningCurves(tuning_curves, window = 20, deviation = 3.0):
for i in tuning_curves.columns:
tcurves = tuning_curves[i]
padded = pd.Series(index = np.hstack((tcurves.index.values-(2*np.pi),
tcurves.index.values,
tcurves.index.values+(2*np.pi))),
data = np.hstack((tcurves.values, tcurves.values, tcurves.values)))
smoothed = padded.rolling(window=window,win_type='gaussian',center=True,min_periods=1).mean(std=deviation)
tuning_curves[i] = smoothed[tcurves.index]
return tuning_curves
def smoothAngularTuningCurves(tuning_curves, window = 20, deviation = 3.0):
new_tuning_curves = {}
for i in tuning_curves.columns:
tcurves = tuning_curves[i]
offset = np.mean(np.diff(tcurves.index.values))
padded = pd.Series(index = np.hstack((tcurves.index.values-(2*np.pi)-offset,
tcurves.index.values,
tcurves.index.values+(2*np.pi)+offset)),
data = np.hstack((tcurves.values, tcurves.values, tcurves.values)))
smoothed = padded.rolling(window=window,win_type='gaussian',center=True,min_periods=1).mean(std=deviation)
new_tuning_curves[i] = smoothed.loc[tcurves.index]
new_tuning_curves = pd.DataFrame.from_dict(new_tuning_curves)
return new_tuning_curves
def computeSpeedTuningCurves(spikes, position, ep, bin_size = 0.1, nb_bins = 20, speed_max = 0.4):
time_bins = np.arange(position.index[0], position.index[-1]+bin_size*1e6, bin_size*1e6)
index = np.digitize(position.index.values, time_bins)
tmp = position.groupby(index).mean()
tmp.index = time_bins[np.unique(index)-1]+(bin_size*1e6)/2
distance = np.sqrt(np.power(np.diff(tmp['x']), 2) + np.power(np.diff(tmp['z']), 2))
speed = nap.Tsd(t = tmp.index.values[0:-1]+ bin_size/2, d = distance/bin_size)
speed = speed.restrict(ep)
bins = np.linspace(0, speed_max, nb_bins)
idx = bins[0:-1]+np.diff(bins)/2
speed_curves = pd.DataFrame(index = idx,columns = np.arange(len(spikes)))
for k in spikes:
spks = spikes[k]
spks = spks.restrict(ep)
speed_spike = speed.realign(spks)
spike_count, bin_edges = np.histogram(speed_spike, bins)
occupancy, _ = np.histogram(speed, bins)
spike_count = spike_count/(occupancy+1)
speed_curves[k] = spike_count/bin_size
return speed_curves
def computeAccelerationTuningCurves(spikes, position, ep, bin_size = 0.1, nb_bins = 40):
time_bins = np.arange(position.index[0], position.index[-1]+bin_size*1e6, bin_size*1e6)
index = np.digitize(position.index.values, time_bins)
tmp = position.groupby(index).mean()
tmp.index = time_bins[np.unique(index)-1]+(bin_size*1e6)/2
distance = np.sqrt(np.power(np.diff(tmp['x']), 2) + np.power(np.diff(tmp['z']), 2))
speed = nap.Tsd(t = tmp.index.values[0:-1]+ bin_size/2, d = distance/bin_size)
speed = speed.restrict(ep)
speed = speed.as_series()
speed2 = speed.rolling(window=10, win_type='gaussian', center= True, min_periods=1).mean(std = 1.0)
accel = nap.Tsd(t = speed2.index.values[0:-1] + np.diff(speed2.index.values)/2, d = np.diff(speed2.values))
bins = np.linspace(accel.min(), accel.max(), nb_bins)
idx = bins[0:-1]+np.diff(bins)/2
accel_curves = pd.DataFrame(index = idx,columns = np.arange(len(spikes)))
for k in spikes:
spks = spikes[k]
spks = spks.restrict(ep)
accel_spike = accel.realign(spks)
spike_count, bin_edges = np.histogram(accel_spike, bins)
occupancy, _ = np.histogram(accel, bins)
spike_count = spike_count/(occupancy+1)
accel_curves[k] = spike_count/bin_size
return accel_curves
def centerTuningCurves(tcurve):
"""
center tuning curves by peak
"""
#Added by Adrien on Dec 21st 2021
from pycircstat.descriptive import mean as circmean
peak = pd.Series(index=tcurve.columns,data = np.array([circmean(tcurve.index.values, tcurve[i].values) for i in tcurve.columns]))
new_tcurve = []
for p in tcurve.columns:
x = tcurve[p].index.values - tcurve[p].index[tcurve[p].index.get_loc(peak[p], method='nearest')]
x[x<-np.pi] += 2*np.pi
x[x>np.pi] -= 2*np.pi
tmp = pd.Series(index = x, data = tcurve[p].values).sort_index()
new_tcurve.append(tmp.values)
new_tcurve = pd.DataFrame(index = np.linspace(-np.pi, np.pi, tcurve.shape[0]+1)[0:-1], data = np.array(new_tcurve).T, columns = tcurve.columns)
return new_tcurve
# copied to neural_tuning
def offsetTuningCurves(tcurve, diffs):
"""
offseting tuning curves synced by diff
"""
new_tcurve = []
for p in tcurve.columns:
x = tcurve[p].index.values - tcurve[p].index[tcurve[p].index.get_loc(diffs[p], method='nearest')]
x[x<-np.pi] += 2*np.pi
x[x>np.pi] -= 2*np.pi
tmp = pd.Series(index = x, data = tcurve[p].values).sort_index()
new_tcurve.append(tmp.values)
new_tcurve = pd.DataFrame(index = np.linspace(-np.pi, np.pi, tcurve.shape[0]+1)[0:-1], data = np.array(new_tcurve).T, columns = tcurve.columns)
return new_tcurve
def computeSpeed(position, ep, bin_size = 0.1):
time_bins = np.arange(position.index[0], position.index[-1]+bin_size*1e6, bin_size*1e6)
index = np.digitize(position.index.values, time_bins)
tmp = position.groupby(index).mean()
tmp.index = time_bins[np.unique(index)-1]+(bin_size*1e6)/2
distance = np.sqrt(np.power(np.diff(tmp['x']), 2) + np.power(np.diff(tmp['z']), 2))
speed = nts.Tsd(t = tmp.index.values[0:-1]+ bin_size/2, d = distance/bin_size)
speed = speed.restrict(ep)
return speed
#########################################################
# INTERPOLATION
# ########################################################
def interpolate(z, x, y, inter, bbox = None):
import scipy.interpolate
xnew = np.arange(x.min(), x.max()+inter, inter)
ynew = np.arange(y.min(), y.max()+inter, inter)
if bbox == None:
f = scipy.interpolate.RectBivariateSpline(y, x, z)
else:
f = scipy.interpolate.RectBivariateSpline(y, x, z, bbox = bbox)
znew = f(ynew, xnew)
return (xnew, ynew, znew)
def filter_(z, n):
from scipy.ndimage import gaussian_filter
return gaussian_filter(z, n)
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