def get_deriv(blk,blk_smooth,varlist,smoothing=range(10)):
"""
:param blk:
:param blk_smooth:
:param varlist:
:param smoothing: A list of indices of which smoothing parameter to use. Default is all 10
:return: Xdot, X
"""
use_flags = neoUtils.concatenate_epochs(blk)
Cbool = neoUtils.get_Cbool(blk)
X =[]
for varname in varlist:
var = neoUtils.get_var(blk_smooth, varname+'_smoothed', keep_neo=False)[0]
if varname in ['M', 'F']:
var[np.invert(Cbool), :, :] = 0
if varname in ['TH', 'PHIE']:
for ii in smoothing:
var[:, :, ii] = neoUtils.center_var(var[:,:,ii], use_flags)
var[np.invert(Cbool), :, :] = 0
var = var[:, :, smoothing]
# var = neoUtils.replace_NaNs(var, 'pchip')
# var = neoUtils.replace_NaNs(var, 'interp')
X.append(var)
X = np.concatenate(X, axis=1)
zero_pad = np.zeros([1,X.shape[1],X.shape[2]])
Xdot = np.diff(np.concatenate([zero_pad,X],axis=0),axis=0)
Xdot = np.reshape(Xdot,[Xdot.shape[0],Xdot.shape[1]*Xdot.shape[2]])
X = np.reshape(X,[X.shape[0],X.shape[1]*X.shape[2]])
return(Xdot,X)
def manifold_fit(blk,sub_samp=4,n_components=2,method='ltsa'):
'''
Fit the input data to a LLE manifold
:param blk:
:return:
'''
X = get_X(blk)
cbool=neoUtils.get_Cbool(blk)
X[np.invert(cbool),:]=np.nan
idx = np.all(np.isfinite(X),axis=1)
scaler = sklearn.preprocessing.StandardScaler(with_mean=False)
X[idx,:] = scaler.fit_transform(X[idx,:])
LLE = sklearn.manifold.LocallyLinearEmbedding(n_neighbors=10,method=method,n_jobs=-1,n_components=n_components,eigen_solver='dense')
X_sub = X[idx,:]
# samp = np.random.choice(X_sub.shape[0],n_pts,replace=False)
samp = np.arange(0,X_sub.shape[0],sub_samp)
X_sub = X_sub[samp,:]
LLE.fit(X_sub)
Y = np.empty([X.shape[0],n_components],dtype='f8')
Y[:] = np.nan
Y[idx,:] = LLE.transform(X[idx,:])
return(LLE,Y)
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_pc(blk):
'''
apply PCA to the input data
:param blk:
:return: principal components structure
'''
cbool = neoUtils.get_Cbool(blk)
X = get_X(blk)
pc = neoUtils.applyPCA(X, cbool)[1]
return(pc)
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(blk):
use_flags = neoUtils.concatenate_epochs(blk)
cbool = neoUtils.get_Cbool(blk)
M = neoUtils.get_var(blk, 'M').magnitude
F = neoUtils.get_var(blk, 'F').magnitude
TH = neoUtils.get_var(blk, 'TH').magnitude
PH = neoUtils.get_var(blk, 'PHIE').magnitude
# center angles
deltaTH = neoUtils.center_var(TH, use_flags)
deltaPH = neoUtils.center_var(PH, use_flags)
deltaTH[np.invert(cbool)] = np.nan
deltaPH[np.invert(cbool)] = np.nan
X = np.concatenate([M, F, deltaTH, deltaPH], axis=1)
return(X)
def calc_MSE(fname, p_smooth, unit_num):
blk = neoUtils.get_blk(fname)
blk_smooth = GLM.get_blk_smooth(fname, p_smooth)
varlist = ['M', 'F', 'TH', 'PHIE']
root = neoUtils.get_root(blk, unit_num)
print('Working on {}'.format(root))
Xdot = GLM.get_deriv(blk, blk_smooth, varlist)[0]
Xdot = np.reshape(Xdot, [-1, 8, 10])
sp = neoUtils.concatenate_sp(blk)['cell_{}'.format(0)]
cbool = neoUtils.get_Cbool(blk)
mse = []
for ii in range(Xdot.shape[1]):
var_in = Xdot[:, ii, :].copy()
mse.append(tuning_curve_MSE(var_in, sp, cbool, bins=50))
return (mse)
def smoothed_best():
df = pd.read_csv(min_entropy, index_col='id')
smooth_vals = np.arange(5, 100, 10).tolist()
best_smooth = df.mode(axis=1)[0]
best_idx = [smooth_vals.index(x) for x in best_smooth]
best_idx = pd.DataFrame({'idx': best_idx}, index=best_smooth.index)
for f in glob.glob(os.path.join(p_load, '*NEO.h5')):
try:
blk = neoUtils.get_blk(f)
blk_smooth = GLM.get_blk_smooth(f, p_smooth)
num_units = len(blk.channel_indexes[-1].units)
for unit_num in range(num_units):
varlist = ['M', 'F', 'TH', 'PHIE']
root = neoUtils.get_root(blk, unit_num)
print('Working on {}'.format(root))
if root not in best_idx.index:
print('{} not found in best smoothing derivative data'.
format(root))
continue
outname = os.path.join(
p_save,
'best_smoothing_deriv\\{}_best_smooth_pillowX.mat'.format(
root))
X = GLM.create_design_matrix(blk, varlist)
smoothing_to_use = best_idx.loc[root][0]
Xdot = GLM.get_deriv(blk,
blk_smooth,
varlist,
smoothing=[smoothing_to_use])[0]
X = np.concatenate([X, Xdot], axis=1)
y = neoUtils.get_rate_b(blk, unit_num)[1]
cbool = neoUtils.get_Cbool(blk)
arclengths = get_arclength_bool(blk, unit_num)
sio.savemat(
outname, {
'X': X,
'y': y,
'cbool': cbool,
'smooth': best_smooth.loc[root],
'arclengths': arclengths
})
except Exception as ex:
print('Problem with {}:{}'.format(os.path.basename(f), ex))
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)
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 mymz_space(blk,unit_num,bin_stretch=False,save_tgl=False,p_save=None,im_ext='png',dpi_res=300):
root = neoUtils.get_root(blk,unit_num)
use_flags = neoUtils.get_Cbool(blk)
M = neoUtils.get_var(blk).magnitude
sp = neoUtils.concatenate_sp(blk)['cell_{}'.format(unit_num)]
idx = np.all(np.isfinite(M),axis=1)
if bin_stretch:
MY = np.empty(M.shape[0])
MZ = np.empty(M.shape[0])
MY[idx], logit_y = nl(M[idx, 1],90)
MZ[idx], logit_z = nl(M[idx, 2],90)
else:
MY = M[:,1]*1e-6
MZ = M[:,2]*1e-6
response, var1_edges,var2_edges = varTuning.joint_response_hist(MY,MZ,sp,use_flags,bins = 100,min_obs=15)
if bin_stretch:
var1_edges = logit_y(var1_edges)
var2_edges = logit_z(var2_edges)
else:
pass
ax = varTuning.plot_joint_response(response,var1_edges,var2_edges,contour=False)
ax.axvline(color='k',linewidth=1)
ax.axhline(color='k',linewidth=1)
ax.patch.set_color([0.6,0.6,0.6])
mask = response.mask.__invert__()
if not mask.all():
ax.set_ylim(var2_edges[np.where(mask)[0].min()], var2_edges[np.where(mask)[0].max()])
ax.set_xlim(var1_edges[np.where(mask)[1].min()], var1_edges[np.where(mask)[1].max()])
ax.set_xlabel('M$_y$ ($\mu$N-m)')
ax.set_ylabel('M$_z$ ($\mu$N-m)')
plt.draw()
plt.tight_layout()
if save_tgl:
if p_save is None:
raise ValueError("figure save location is required")
else:
plt.savefig(os.path.join(p_save,'{}_mymz.{}'.format(root,im_ext)),dpi=dpi_res)
plt.close('all')
def smoothed_mechanics():
"""
use this function to grab the data from the smoothed mechanics and the
derivative of the same
"""
f_arclength = '/projects/p30144/_VG3D/deflections/direction_arclength_FR_group_data.csv'
f_list = glob.glob(os.path.join(p_load, '*NEO.h5'))
f_list.sort()
for f in f_list:
try:
blk = neoUtils.get_blk(f)
blk_smooth = GLM.get_blk_smooth(f, p_smooth)
num_units = len(blk.channel_indexes[-1].units)
for unit_num in range(num_units):
varlist = ['M', 'F', 'TH', 'PHIE']
root = neoUtils.get_root(blk, unit_num)
print('Working on {}'.format(root))
outname = os.path.join(p_save,
'{}_smooth_mechanicsX.mat'.format(root))
Xdot, X = GLM.get_deriv(blk,
blk_smooth,
varlist,
smoothing=[5])
X = np.concatenate([X, Xdot], axis=1)
y = neoUtils.get_rate_b(blk, unit_num)[1]
cbool = neoUtils.get_Cbool(blk)
arclengths = get_arclength_bool(blk,
unit_num,
fname=f_arclength)
sio.savemat(
outname, {
'X': X,
'y': y,
'cbool': cbool,
'smooth': 55,
'arclengths': arclengths
})
except Exception as ex:
print('Problem with {}:{}'.format(os.path.basename(f), ex))
def MB_curve(blk,unit_num,save_tgl=False,im_ext='svg',dpi_res=300):
root = neoUtils.get_root(blk, unit_num)
M = neoUtils.get_var(blk)
use_flags = neoUtils.get_Cbool(blk)
MB = mechanics.get_MB_MD(M)[0].magnitude.ravel()
MB[np.invert(use_flags)]=0
sp = neoUtils.concatenate_sp(blk)['cell_{}'.format(unit_num)]
r, b = neoUtils.get_rate_b(blk, unit_num, sigma=5 * pq.ms)
MB_bayes,edges = varTuning.stim_response_hist(MB*1e6,r,use_flags,nbins=100,min_obs=5)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(edges[:-1],MB_bayes,'o',color='k')
ax.set_ylabel('Spike Rate (sp/s)')
ax.set_xlabel('Bending Moment ($\mu$N-m)')
plt.tight_layout()
if save_tgl:
plt.savefig('./figs/{}_MB_tuning.{}'.format(root,im_ext),dpi=dpi_res)
plt.close('all')
def get_arclength_bool(blk, unit_num, fname=None):
# fname is the name of the csv file with arclength groupings
if fname is None:
if 'BOX_PATH' in os.environ:
fname = os.path.join(
os.environ['BOX_PATH'],
r'__VG3D\_deflection_trials\_NEO\results\direction_arclength_FR_group_data.csv'
)
else:
fname = os.path.join(
'/projects/p30144/_VG3D/deflections/direction_arclength_FR_group_data.csv'
)
df = pd.read_csv(fname)
id = neoUtils.get_root(blk, unit_num)
sub_df = df[df.id == id]
arclength_list = sub_df.Arclength.tolist()
use_flags = neoUtils.concatenate_epochs(blk)
if len(sub_df) != len(use_flags):
raise ValueError(
'The number of contacts in the block {} do not match the number of contacts in the csv {}'
.format(len(use_flags), len(sub_df)))
cbool = neoUtils.get_Cbool(blk)
distal_cbool = np.zeros_like(cbool)
medial_cbool = np.zeros_like(cbool)
proximal_cbool = np.zeros_like(cbool)
# loop through each contact and set the appropriate arclength boolean
for ii in range(len(use_flags)):
start = use_flags[ii].magnitude.astype('int')
dur = use_flags.durations[ii].magnitude.astype('int')
if arclength_list[ii] == 'Proximal':
proximal_cbool[start:start + dur] = 1
elif arclength_list[ii] == 'Distal':
distal_cbool[start:start + dur] = 1
elif arclength_list[ii] == 'Medial':
medial_cbool[start:start + dur] = 1
arclengths = {
'Distal': distal_cbool,
'Medial': medial_cbool,
'Proximal': proximal_cbool
}
return (arclengths)
def FX_plots(blk,unit_num,save_tgl=False,im_ext='svg',dpi_res=300):
root = neoUtils.get_root(blk, unit_num)
F = neoUtils.get_var(blk,'F')
Fx = F.magnitude[:,0]
use_flags = neoUtils.get_Cbool(blk)
sp = neoUtils.concatenate_sp(blk)['cell_{}'.format(unit_num)]
r, b = neoUtils.get_rate_b(blk, unit_num, sigma=5 * pq.ms)
Fx[np.invert(use_flags)] = 0
Fx_bayes, edges = varTuning.stim_response_hist(Fx * 1e6, r, use_flags, nbins=50, min_obs=5)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(edges[:-1], Fx_bayes*1000, 'o', color='k')
ax.set_ylabel('Spike Rate (sp/s)')
ax.set_xlabel('Axial Force ($\mu$N-m)')
plt.tight_layout()
if save_tgl:
plt.savefig('./figs/{}_Fx_tuning.{}'.format(root,im_ext), dpi=dpi_res)
plt.close('all')
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 smoothed(smooth_idx=9):
smooth_vals = np.arange(5, 100, 10)
sub_p_save = os.path.join(
p_save, '{}ms_smoothing_deriv'.format(smooth_vals[smooth_idx]))
if not os.path.isdir(sub_p_save):
os.mkdir(sub_p_save)
for f in glob.glob(os.path.join(p_load, '*NEO.h5')):
try:
blk = neoUtils.get_blk(f)
blk_smooth = GLM.get_blk_smooth(f, p_smooth)
num_units = len(blk.channel_indexes[-1].units)
for unit_num in range(num_units):
varlist = ['M', 'F', 'TH', 'PHIE']
root = neoUtils.get_root(blk, unit_num)
print('Working on {}'.format(root))
outname = os.path.join(
sub_p_save,
'{}ms_{}_pillowX.mat'.format(smooth_vals[smooth_idx],
root))
X = GLM.create_design_matrix(blk, varlist)
Xdot = GLM.get_deriv(blk, blk_smooth, varlist, [smooth_idx])[0]
X = np.concatenate([X, Xdot], axis=1)
sp = neoUtils.concatenate_sp(blk)['cell_{}'.format(unit_num)]
y = neoUtils.get_rate_b(blk, unit_num)[1]
cbool = neoUtils.get_Cbool(blk)
arclengths = get_arclength_bool(blk, unit_num)
sio.savemat(outname, {
'X': X,
'y': y,
'cbool': cbool,
'arclengths': arclengths
})
except Exception as ex:
print('Problem with {}:{}'.format(os.path.basename(f), ex))
def calc_corr(fname, p_smooth, unit_num):
blk = neoUtils.get_blk(fname)
blk_smooth = GLM.get_blk_smooth(fname, p_smooth)
varlist = ['M', 'F', 'TH', 'PHIE']
component_list = [
'{}_dot'.format(x)
for x in ['Mx', 'My', 'Mz', 'Fx', 'Fy', 'Fz', 'TH', 'PHI']
]
root = neoUtils.get_root(blk, unit_num)
Xdot = GLM.get_deriv(blk, blk_smooth, varlist)[0]
Xdot = np.reshape(Xdot, [-1, 8, 10])
windows = np.arange(5, 100, 10)
sp = neoUtils.concatenate_sp(blk)['cell_{}'.format(0)]
cbool = neoUtils.get_Cbool(blk)
corr = []
R = []
# loop over variables
for ii in range(Xdot.shape[1]):
var_in = Xdot[:, ii, :].copy()
# loop over smoothing
r = []
for jj in range(var_in.shape[1]):
kernel = elephant.kernels.GaussianKernel(pq.ms * windows[jj])
FR = elephant.statistics.instantaneous_rate(sp,
pq.ms,
kernel=kernel)
idx = np.isfinite(var_in[:, jj])
r.append(
scipy.corrcoef(var_in[:, jj].ravel()[idx],
FR.magnitude.ravel()[idx])[0, 1])
R.append(r)
R = np.array(R)
df = pd.DataFrame(data=R, columns=['{}ms'.format(x) for x in windows])
df.index = component_list
return (df)
def calc_world_geom_hist(p_load,p_save,n_bins=100):
"""
Since calculation takes so long on getting the histograms (mostly loading of data)
we want to calculate them once and save the data.
This calculates the Geometry.
:param p_load: Location where all the neo h5 files live
:param p_save: Location to save the output data files
:param n_bins: Number of bins in with which to split the data
:return None: Saves a 'world_geom_hists.npz' file.
"""
# init
ID = []
all_S_bayes = []
all_TH_bayes = []
all_PHIE_bayes = []
all_ZETA_bayes = []
all_S_edges = []
all_TH_edges = []
all_PHIE_edges = []
all_ZETA_edges = []
# loop files
for f in glob.glob(os.path.join(p_load,'rat*.h5')):
# load in
print(os.path.basename(f))
blk = neoUtils.get_blk(f)
# get contact
Cbool = neoUtils.get_Cbool(blk)
use_flags = neoUtils.concatenate_epochs(blk)
# get vars
S = neoUtils.get_var(blk, 'S').magnitude
TH = neoUtils.get_var(blk, 'TH').magnitude
neoUtils.center_var(TH, use_flags)
PHIE = neoUtils.get_var(blk, 'PHIE').magnitude
neoUtils.center_var(PHIE, use_flags)
ZETA = neoUtils.get_var(blk, 'ZETA').magnitude
neoUtils.center_var(ZETA, use_flags)
# loop units
for unit in blk.channel_indexes[-1].units:
# get unit info
unit_num = int(unit.name[-1])
r, b = neoUtils.get_rate_b(blk, unit_num, sigma=5 * pq.ms)
sp = neoUtils.concatenate_sp(blk)['cell_{}'.format(unit_num)]
root = neoUtils.get_root(blk,unit_num)
ID.append(root)
# Create hists
S_bayes, S_edges = varTuning.stim_response_hist(S.ravel(), r, Cbool, nbins=n_bins, min_obs=5)
TH_bayes, TH_edges = varTuning.stim_response_hist(TH.ravel(), r, Cbool, nbins=n_bins, min_obs=5)
PHIE_bayes, PHIE_edges = varTuning.stim_response_hist(PHIE.ravel(), r, Cbool, nbins=n_bins,min_obs=5)
ZETA_bayes, ZETA_edges = varTuning.stim_response_hist(ZETA.ravel(), r, Cbool, nbins=n_bins,min_obs=5)
# append outputs
plt.close('all')
all_S_bayes.append(S_bayes)
all_TH_bayes.append(TH_bayes)
all_PHIE_bayes.append(PHIE_bayes)
all_ZETA_bayes.append(ZETA_bayes)
all_S_edges.append(S_edges)
all_TH_edges.append(TH_edges)
all_PHIE_edges.append(PHIE_edges)
all_ZETA_edges.append(ZETA_edges)
np.savez(os.path.join(p_save, 'world_geom_hists.npz'),
all_S_bayes=all_S_bayes,
all_TH_bayes=all_TH_bayes,
all_PHIE_bayes=all_PHIE_bayes,
all_ZETA_bayes=all_ZETA_bayes,
all_S_edges=all_S_edges,
all_TH_edges=all_TH_edges,
all_PHIE_edges=all_PHIE_edges,
all_ZETA_edges=all_ZETA_edges,
ID=ID
)
def calc_all_mech_hists(p_load,p_save,n_bins=100):
"""
Since calculation takes so long on getting the histograms (mostly loading of data)
we want to calculate them once and save the data.
This calculates the mechanics.
:param p_load: Location where all the neo h5 files live
:param p_save: Location to save the output data files
:param n_bins: Number of bins in with which to split the data
:return None: Saves a 'mech_histograms.npz' file.
"""
# TODO: This is currently pretty gross, it is really too hardcoded (I wrote it in a car). Do better.
# TODO: Combine with geometry
# Case in point:
all_F_edges = []
all_M_edges = []
all_F_bayes = []
all_M_bayes = []
all_MB_edges = []
all_MD_edges = []
all_MD_bayes = []
all_MB_bayes = []
ID = []
# Loop all neo files
for f in glob.glob(os.path.join(p_load,'rat*.h5')):
print(os.path.basename(f))
blk = neoUtils.get_blk(f)
Cbool = neoUtils.get_Cbool(blk)
# Loop all units
for unit in blk.channel_indexes[-1].units:
unit_num = int(unit.name[-1])
# grab needed variables
r, b = neoUtils.get_rate_b(blk, unit_num, sigma=5 * pq.ms)
sp = neoUtils.concatenate_sp(blk)['cell_{}'.format(unit_num)]
root = neoUtils.get_root(blk,unit_num)
M = neoUtils.get_var(blk).magnitude
F = neoUtils.get_var(blk,'F').magnitude
MB, MD = neoUtils.get_MB_MD(M)
# init histograms
M_bayes = np.empty([n_bins,3])
F_bayes = np.empty([n_bins, 3])
M_edges = np.empty([n_bins+1, 3])
F_edges = np.empty([n_bins+1, 3])
#calculate tuning curves (seperately on each dimension)
for ii in range(3):
F_bayes[:, ii], F_edges[:, ii] = varTuning.stim_response_hist(F[:, ii] * 1e6, r, Cbool, nbins=n_bins, min_obs=5)
M_bayes[:, ii], M_edges[:, ii] = varTuning.stim_response_hist(M[:, ii] * 1e6, r, Cbool, nbins=n_bins, min_obs=5)
MB_bayes, MB_edges = varTuning.stim_response_hist(MB.squeeze() * 1e6, r, Cbool, nbins=n_bins, min_obs=5)
MD_bayes, MD_edges,_,_ = varTuning.angular_response_hist(MD.squeeze(), r, Cbool, nbins=n_bins)
plt.close('all')
# append to output lists
all_F_edges.append(F_edges)
all_M_edges.append(M_edges)
all_MB_edges.append(MB_edges)
all_MD_edges.append(MD_edges)
all_F_bayes.append(F_bayes)
all_M_bayes.append(M_bayes)
all_MB_bayes.append(MB_bayes)
all_MD_bayes.append(MD_bayes)
ID.append(root)
# save
np.savez(os.path.join(p_save,'mech_histograms.npz'),
all_F_bayes=all_F_bayes,
all_F_edges=all_F_edges,
all_M_bayes=all_M_bayes,
all_M_edges=all_M_edges,
all_MB_bayes=all_MB_bayes,
all_MB_edges=all_MB_edges,
all_MD_bayes=all_MD_bayes,
all_MD_edges=all_MD_edges,
ID=ID
)
import sys
import neoUtils
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set()
sns.set_style('ticks')
blk = neoUtils.get_blk(sys.argv[1])
M = neoUtils.get_var(blk).magnitude
sp = neoUtils.concatenate_sp(blk)
cc = neoUtils.concatenate_epochs(blk, -1)
Cbool = neoUtils.get_Cbool(blk)
c_idx = np.where(Cbool)[0]
# M[np.invert(Cbool),:] = 0
ymax = np.nanmax(M) / 4
ymin = np.nanmin(M) / 4
def shadeVector(cc, color='k'):
ax = plt.gca()
ylim = ax.get_ylim()
for start, dur in zip(cc.times.magnitude, cc.durations.magnitude):
ax.fill([start, start, start + dur, start + dur],
[ylim[0], ylim[1], ylim[1], ylim[0]],
color,
alpha=0.1)
for ii in xrange(len(sp)):
def phase_plots(blk,unit_num,save_tgl=False,bin_stretch=False,p_save=None,im_ext='png',dpi_res=300):
''' Plot Phase planes for My and Mz'''
root = neoUtils.get_root(blk, unit_num)
M = neoUtils.get_var(blk).magnitude
sp = neoUtils.concatenate_sp(blk)['cell_{}'.format(unit_num)]
r, b = neoUtils.get_rate_b(blk, unit_num, sigma=5 * pq.ms)
use_flags = neoUtils.get_Cbool(blk)
Mdot = mechanics.get_deriv(M)
if bin_stretch:
raise Exception('Not finished with use_flags')
# MY, logit_y = nl(M[idx, 1], 90)
# MZ, logit_z = nl(M[idx, 2], 90)
# MY_dot, logit_ydot = nl(Mdot[idx, 1], 95)
# MZ_dot, logit_zdot = nl(Mdot[idx, 2], 95)
else:
MY = M[:, 1] * 1e-6
MZ = M[:, 2] * 1e-6
MY_dot = Mdot[:, 1] * 1e-6
MZ_dot = Mdot[:, 2] * 1e-6
My_response,My_edges,Mydot_edges = varTuning.joint_response_hist(MY, MY_dot, r, use_flags, [100,30],min_obs=15)
Mz_response,Mz_edges,Mzdot_edges = varTuning.joint_response_hist(MZ, MZ_dot, r, use_flags, [100,30],min_obs=15)
if bin_stretch:
My_edges = logit_y(My_edges)
Mz_edges = logit_z(Mz_edges)
Mydot_edges = logit_ydot(Mydot_edges)
Mzdot_edges = logit_zdot(Mzdot_edges)
else:
pass
axy = varTuning.plot_joint_response(My_response,My_edges,Mydot_edges,contour=False)
axz = varTuning.plot_joint_response(Mz_response,Mz_edges,Mzdot_edges,contour=False)
# Set bounds
y_mask = My_response.mask.__invert__()
if not y_mask.all():
axy.set_ylim(Mydot_edges[np.where(y_mask)[0].min()], Mydot_edges[np.where(y_mask)[0].max()])
axy.set_xlim(My_edges[np.where(y_mask)[1].min()], My_edges[np.where(y_mask)[1].max()])
z_mask = Mz_response.mask.__invert__()
if not z_mask.all():
axz.set_ylim(Mzdot_edges[np.where(z_mask)[0].min()], Mzdot_edges[np.where(z_mask)[0].max()])
axz.set_xlim(Mz_edges[np.where(z_mask)[1].min()], Mz_edges[np.where(z_mask)[1].max()])
# other annotations
axy.set_title('M$_y$ Phase Plane')
axz.set_title('M$_z$ Phase Plane')
axy.set_xlabel('M$_y$ ($\mu$N-m)')
axy.set_ylabel('M$_\dot{y}$ ($\mu$N-m/ms)')
axz.set_xlabel('M$_z$ ($\mu$N-m)')
axz.set_ylabel('M$_\dot{z}$ ($\mu$N-m/ms)')
axy.grid('off')
axy.set_facecolor([0.6, 0.6, 0.6])
axy.axvline(color='k',linewidth=1)
axy.axhline(color='k',linewidth=1)
axz.grid('off')
axz.set_facecolor([0.6, 0.6, 0.6])
axz.axvline(color='k', linewidth=1)
axz.axhline(color='k', linewidth=1)
plt.sca(axy)
plt.tight_layout()
if save_tgl:
if p_save is None:
raise ValueError("figure save location is required")
else:
plt.savefig(os.path.join(p_save,'{}_My_phaseplane.{}'.format(root,im_ext)),dpi=dpi_res)
plt.sca(axz)
plt.tight_layout()
if save_tgl:
if p_save is None:
raise ValueError("figure save location is required")
else:
plt.savefig(os.path.join(p_save,'{}_Mz_phaseplane.{}'.format(root,im_ext)),dpi=dpi_res)
plt.close('all')
def plot_smooth_hists(blk,blk_smooth,unit_num=0,p_save=None,nbins=75):
DPI_RES=600
id = neoUtils.get_root(blk, unit_num)
fig_name = os.path.join(p_save, '{}_derivative_smoothing_compare.png'.format(id))
if os.path.isfile(fig_name):
print('{} found, skipping...'.format(fig_name))
return(None)
smoothing_windows = range(5,101,10)
use_flags = neoUtils.concatenate_epochs(blk)
cbool = neoUtils.get_Cbool(blk)
r,b =neoUtils.get_rate_b(blk,unit_num,2*pq.ms)
# catch empty smoothed data
if len(blk_smooth.segments)==0 or len(blk_smooth.segments[0].analogsignals)==0:
print('Smoothed data not found in {}'.format(id))
return(-1)
# get vars
M = neoUtils.get_var(blk_smooth,'M_smoothed').magnitude
M[np.invert(cbool),:]=np.nan
Mdot = neoUtils.get_deriv(M)
F = neoUtils.get_var(blk_smooth,'F_smoothed').magnitude
F[np.invert(cbool),:]=np.nan
Fdot = neoUtils.get_deriv(F)
PHI = neoUtils.get_var(blk_smooth,'PHIE_smoothed').magnitude
PHI = neoUtils.center_var(PHI.squeeze(),use_flags)
PHI[np.invert(cbool),:]=np.nan
PHIdot = neoUtils.get_deriv(PHI)
TH = neoUtils.get_var(blk_smooth,'TH_smoothed').magnitude
TH = neoUtils.center_var(TH.squeeze(),use_flags)
TH[np.invert(cbool),:]=np.nan
THdot = neoUtils.get_deriv(TH)
# ROT = np.sqrt(np.add(np.power(PHI,2),np.power(TH,2)))
# ROTdot = neoUtils.get_deriv(ROT)
# calculate histograms
R_Mdot, bins_Mdot, edgesx_Mdot, edgesy_Mdot = mult_join_plots(Mdot[:, 1, :], Mdot[:, 2, :], r, cbool, bins=nbins)
newbins =[np.linspace(bins_Mdot[0][edgesx_Mdot][0],bins_Mdot[0][edgesx_Mdot][1],nbins),
np.linspace(bins_Mdot[1][edgesy_Mdot][0], bins_Mdot[1][edgesy_Mdot][1], nbins)]
R_Mdot, bins_Mdot, edgesx_Mdot, edgesy_Mdot = mult_join_plots(Mdot[:, 1, :], Mdot[:, 2, :], r, cbool, bins=newbins)
R_Fdot, bins_Fdot, edgesx_Fdot, edgesy_Fdot = mult_join_plots(Fdot[:, 1, :], Fdot[:, 2, :], r, cbool,bins=nbins)
newbins = [np.linspace(bins_Fdot[0][edgesx_Fdot][0], bins_Fdot[0][edgesx_Fdot][1], nbins),
np.linspace(bins_Fdot[1][edgesy_Fdot][0], bins_Fdot[1][edgesy_Fdot][1], nbins)]
R_Fdot, bins_Fdot, edgesx_Fdot, edgesy_Fdot = mult_join_plots(Fdot[:, 1, :], Fdot[:, 2, :], r, cbool, bins=newbins)
R_ROTdot, bins_ROTdot, edgesx_ROTdot, edgesy_ROTdot = mult_join_plots(THdot, PHIdot, r, cbool,bins=nbins)
newbins = [np.linspace(bins_ROTdot[0][edgesx_ROTdot][0], bins_ROTdot[0][edgesx_ROTdot][1], nbins),
np.linspace(bins_ROTdot[1][edgesy_ROTdot][0], bins_ROTdot[1][edgesy_ROTdot][1], nbins)]
R_ROTdot, bins_ROTdot, edgesx_ROTdot, edgesy_ROTdot = mult_join_plots(THdot, PHIdot, r, cbool, bins=newbins)
FR = []
FR.append(np.nanmax([x.max() for x in R_Mdot.values()]))
FR.append(np.nanmax([x.max() for x in R_Fdot.values()]))
FR.append(np.nanmax([x.max() for x in R_ROTdot.values()]))
colormax = np.nanmax(FR)
# Plots
f = plt.figure()
figManager = plt.get_current_fig_manager()
figManager.window.showMaximized()
# hardcoded for 5 smoothing steps
for loc,ii in enumerate(range(0,10,2)):
ax = f.add_subplot(3,5,loc+1)
ax.pcolormesh(bins_Mdot[0],bins_Mdot[1],R_Mdot[ii], cmap='OrRd', edgecolors='None',vmin=0,vmax=colormax)
ax.set_xlim(bins_Mdot[0][edgesx_Mdot])
ax.set_ylim(bins_Mdot[1][edgesy_Mdot])
ax.set_title('Smoothing window = {}ms'.format(smoothing_windows[ii]))
ax.axvline(color='k',linewidth=1)
ax.axhline(color='k',linewidth=1)
if ii==0:
ax.set_ylabel('$\\dot{M_y}$ vs $\\dot{M_z}$',rotation=0,labelpad=20)
for loc,ii in enumerate(range(0,10,2)):
ax = f.add_subplot(3, 5, loc + 1+5)
ax.pcolormesh(bins_Fdot[0], bins_Fdot[1], R_Fdot[ii], cmap='OrRd', edgecolors='None', vmin=0, vmax=colormax)
ax.set_xlim(bins_Fdot[0][edgesx_Fdot])
ax.set_ylim(bins_Fdot[1][edgesy_Fdot])
ax.axvline(color='k', linewidth=1)
ax.axhline(color='k', linewidth=1)
if ii==0:
ax.set_ylabel('$\\dot{F_y}$ vs $\\dot{F_z}$',rotation=0,labelpad=20)
for loc,ii in enumerate(range(0,10,2)):
ax = f.add_subplot(3, 5, loc + 1+10)
h=ax.pcolormesh(bins_ROTdot[0], bins_ROTdot[1], R_ROTdot[ii], cmap='OrRd', edgecolors='None', vmin=0, vmax=colormax)
ax.set_xlim(bins_ROTdot[0][edgesx_ROTdot])
ax.set_ylim(bins_ROTdot[1][edgesy_ROTdot])
ax.axvline(color='k', linewidth=1)
ax.axhline(color='k', linewidth=1)
if ii==0:
ax.set_ylabel('$\\dot{\\theta}$ vs $\\dot{\\phi}$',rotation=0,labelpad=20)
plt.suptitle('{}'.format(id))
plt.colorbar(h)
plt.pause(0.1)
if p_save is not None:
plt.savefig(fig_name,dpi=DPI_RES)
plt.close('all')
return(None)
