def get_Xc_yc(fname,p_smooth,unit_num,binsize):
varlist = ['M', 'F', 'TH', 'PHIE']
blk = neoUtils.get_blk(fname)
blk_smooth = GLM.get_blk_smooth(fname,p_smooth)
cbool = neoUtils.get_Cbool(blk)
X = GLM.create_design_matrix(blk,varlist)
Xdot = GLM.get_deriv(blk,blk_smooth,varlist,[0,5,9]) #maybe only want one derivative?
X = np.concatenate([X,Xdot],axis=1)
X = neoUtils.replace_NaNs(X,'pchip')
X = neoUtils.replace_NaNs(X,'interp')
Xbin = GLM.bin_design_matrix(X,binsize=binsize)
scaler = sklearn.preprocessing.StandardScaler(with_mean=False)
Xbin = scaler.fit_transform(Xbin)
cbool_bin= GLM.bin_design_matrix(cbool[:,np.newaxis],binsize=binsize).ravel()
y = neoUtils.concatenate_sp(blk)['cell_{}'.format(unit_num)]
ybin = elephant.conversion.BinnedSpikeTrain(y,binsize=binsize*pq.ms).to_array().T.astype('f8')
Xbin = Xbin[:ybin.shape[0],:]
cbool_bin = cbool_bin[:ybin.shape[0]]
yhat = np.zeros(ybin.shape[0])
Xc = Xbin[cbool_bin,:]
yc = ybin[cbool_bin,:]
return(Xc,yc,cbool_bin,yhat)
def create_design_matrix(blk,varlist,window=1,binsize=1,deriv_tgl=False,bases=None):
'''
Takes a list of variables and turns it into a matrix.
Sets the non-contact mechanics to zero, but keeps all the kinematics as NaN
You can append the derivative or apply the pillow bases, or both.
Scales, but does not center the output
'''
X = []
if type(window)==pq.quantity.Quantity:
window = int(window)
if type(binsize)==pq.quantity.Quantity:
binsize = int(binsize)
Cbool = neoUtils.get_Cbool(blk,-1)
use_flags = neoUtils.concatenate_epochs(blk)
# ================================ #
# GET THE CONCATENATED DESIGN MATRIX OF REQUESTED VARS
# ================================ #
for varname in varlist:
if varname in ['MB','FB']:
var = neoUtils.get_var(blk,varname[0],keep_neo=False)[0]
var = neoUtils.get_MB_MD(var)[0]
var[np.invert(Cbool)]=0
elif varname in ['MD','FD']:
var = neoUtils.get_var(blk,varname[0],keep_neo=False)[0]
var = neoUtils.get_MB_MD(var)[1]
var[np.invert(Cbool)]=0
elif varname in ['ROT','ROTD']:
TH = neoUtils.get_var(blk,'TH',keep_neo=False)[0]
PH = neoUtils.get_var(blk,'PHIE',keep_neo=False)[0]
TH = neoUtils.center_var(TH,use_flags=use_flags)
PH = neoUtils.center_var(PH,use_flags=use_flags)
TH[np.invert(Cbool)] = 0
PH[np.invert(Cbool)] = 0
if varname=='ROT':
var = np.sqrt(TH**2+PH**2)
else:
var = np.arctan2(PH,TH)
else:
var = neoUtils.get_var(blk,varname, keep_neo=False)[0]
if varname in ['M','F']:
var[np.invert(Cbool),:]=0
if varname in ['TH','PHIE']:
var = neoUtils.center_var(var,use_flags)
var[np.invert(Cbool),:]=0
var = neoUtils.replace_NaNs(var,'pchip')
var = neoUtils.replace_NaNs(var,'interp')
X.append(var)
X = np.concatenate(X, axis=1)
return X
def get_X_y(fname, unit_num=0):
varlist = ['M', 'FX', 'FY', 'TH']
blk = neoUtils.get_blk(fname)
cbool = neoUtils.get_Cbool(blk)
X = GLM.create_design_matrix(blk, varlist)
Xdot, Xsmooth = GLM.get_deriv(blk, blk, varlist, [0, 5, 9])
X = np.concatenate([X, Xdot], axis=1)
X = neoUtils.replace_NaNs(X, 'pchip')
X = neoUtils.replace_NaNs(X, 'interp')
scaler = sklearn.preprocessing.StandardScaler(with_mean=False)
X = scaler.fit_transform(X)
y = neoUtils.get_rate_b(blk, unit_num)[1][:, np.newaxis]
yhat = np.zeros_like(y)
return (X, y, cbool)
def get_X_y(fname,p_smooth,unit_num=0):
varlist = ['M', 'F', 'TH', 'PHIE']
blk = neoUtils.get_blk(fname)
blk_smooth = GLM.get_blk_smooth(fname,p_smooth)
cbool = neoUtils.get_Cbool(blk)
X = GLM.create_design_matrix(blk,varlist)
Xdot,Xsmooth = GLM.get_deriv(blk,blk_smooth,varlist,[9])
X = np.concatenate([X,Xdot],axis=1)
X = neoUtils.replace_NaNs(X,'pchip')
X = neoUtils.replace_NaNs(X,'interp')
scaler = sklearn.preprocessing.StandardScaler(with_mean=False)
X = scaler.fit_transform(X)
y = neoUtils.get_rate_b(blk,unit_num)[1][:,np.newaxis]
y[np.invert(cbool)]=0
return(X,y,cbool)
def get_components(fname,p_smooth=None,smooth_idx=9):
''' Get the PCA comonents given a filename'''
varlist = ['M', 'F', 'TH', 'PHIE']
blk = neoUtils.get_blk(fname)
cbool = neoUtils.get_Cbool(blk)
root = neoUtils.get_root(blk,0)[:-2]
X = GLM.create_design_matrix(blk,varlist)
if p_smooth is not None:
blk_smooth = GLM.get_blk_smooth(fname,p_smooth)
Xdot = GLM.get_deriv(blk,blk_smooth,varlist,smoothing=[smooth_idx])[0]
X = np.concatenate([X,Xdot],axis=1)
X[np.invert(cbool),:]=0
X = neoUtils.replace_NaNs(X,'pchip')
X = neoUtils.replace_NaNs(X,'interp')
scaler = sklearn.preprocessing.StandardScaler(with_mean=False)
X[cbool,:] = scaler.fit_transform(X[cbool,:])
pca = sklearn.decomposition.PCA()
pca.fit_transform(X[cbool,:])
return(pca,root)
def get_X_y(fname, p_smooth, unit_num, pca_tgl=False, n_pcs=3):
varlist = ['M', 'F', 'TH', 'PHIE']
blk = neoUtils.get_blk(fname)
blk_smooth = get_blk_smooth(fname, p_smooth)
cbool = neoUtils.get_Cbool(blk)
X = GLM.create_design_matrix(blk, varlist)
Xdot, Xsmooth = GLM.get_deriv(blk, blk_smooth, varlist, [0, 5, 9])
# if using the PCA decomposition of the inputs:
if pca_tgl:
X = neoUtils.replace_NaNs(X, 'pchip')
X = neoUtils.replace_NaNs(X, 'interp')
Xsmooth = neoUtils.replace_NaNs(Xsmooth, 'pchip')
Xsmooth = neoUtils.replace_NaNs(Xsmooth, 'interp')
scaler = sklearn.preprocessing.StandardScaler(with_mean=False)
X = scaler.fit_transform(X)
scaler = sklearn.preprocessing.StandardScaler(with_mean=False)
Xsmooth = scaler.fit_transform(Xsmooth)
pca = sklearn.decomposition.PCA()
X_pc = pca.fit_transform(X)[:, :n_pcs]
pca = sklearn.decomposition.PCA()
Xs_pc = pca.fit_transform(Xsmooth)[:, :n_pcs]
zero_pad = np.zeros([1, n_pcs])
Xd_pc = np.diff(np.concatenate([zero_pad, Xs_pc], axis=0), axis=0)
X = np.concatenate([X_pc, Xd_pc], axis=1)
scaler = sklearn.preprocessing.StandardScaler(with_mean=False)
X = scaler.fit_transform(X)
else:
X = np.concatenate([X, Xdot], axis=1)
X = neoUtils.replace_NaNs(X, 'pchip')
X = neoUtils.replace_NaNs(X, 'interp')
scaler = sklearn.preprocessing.StandardScaler(with_mean=False)
X = scaler.fit_transform(X)
y = neoUtils.get_rate_b(blk, unit_num)[1][:, np.newaxis]
# Xc = X[cbool,:]
# yc = y[cbool]
yhat = np.zeros_like(y)
return (X, y, cbool)