def run():
logging.basicConfig(level=logging.INFO)
good_trials = try_cache('Good trials')
animal_sess_combs = [(animal,session) for animal in [66,70]
for session in good_trials[animal]]
_, good_clusters = get_good_clusters(0)
for animal, session in animal_sess_combs:
fn, trigger_tm = load_mux(animal, session)
vl = load_vl(animal,fn)
cls = {tetrode:load_cl(animal,fn,tetrode) for tetrode in range(1,17)}
for tetrode,cl in cls.items():
if tetrode not in good_clusters:
import pdb; pdb.set_trace()
continue
for cell in good_clusters[tetrode]:
logging.info('Finding spike locations for animal %i, session %i, tetrode %i, cell %i',animal, session, tetrode,cell)
cache_key = (cl['Label'][::10],vl['xs'][::10],trigger_tm,cell)
spk_i = spike_loc(cl, vl, trigger_tm, cell, key=None)
if spk_i is np.NAN: break
store_in_cache(cache_key,spk_i)
def run():
logging.basicConfig(level=logging.INFO)
good_trials = try_cache("Good trials")
animal_sess_combs = [(animal, session) for animal in [66, 70] for session in good_trials[animal]]
_, good_clusters = get_good_clusters(0)
for animal, session in animal_sess_combs:
fn, trigger_tm = load_mux(animal, session)
vl = load_vl(animal, fn)
cls = {tetrode: load_cl(animal, fn, tetrode) for tetrode in range(1, 17)}
for tetrode, cl in cls.items():
if tetrode not in good_clusters:
import pdb
pdb.set_trace()
continue
for cell in good_clusters[tetrode]:
logging.info(
"Finding spike locations for animal %i, session %i, tetrode %i, cell %i",
animal,
session,
tetrode,
cell,
)
cache_key = (cl["Label"][::10], vl["xs"][::10], trigger_tm, cell)
spk_i = spike_loc(cl, vl, trigger_tm, cell, key=None)
if spk_i is np.NAN:
break
store_in_cache(cache_key, spk_i)
def cluster_graphs():
animal = 70
session = 8
# Filenames (fn) are named descriptively:
# session 18:14:04 on day 10/25/2013
# load virmenLog75\20131025T181404.cmb.mat
#for tetrode in range(1,17):
for tetrode in [13]:
for context in [1, -1]:
global clrs
clrs = ['b', 'g', 'r', 'c', 'm', 'k', 'b', 'g', 'r', 'c', 'm', 'k']
fn, trigger_tm = load_mux(animal, session)
cl = load_cl(animal, fn, tetrode)
vl = load_vl(animal, fn)
spk_is = []
#for cell in range(2,100):
for cell in [3]:
cache_key = (cl['Label'][::10], vl['xs'][::10], trigger_tm,
cell)
spk_i = spike_loc(cl, vl, trigger_tm, cell, cache_key)
if spk_i is np.NAN: break
cntx_is = np.nonzero(vl['Task'] == context)[0]
spk_i = np.intersect1d(cntx_is, spk_i)
spk_is.append(spk_i)
tot_spks = len(spk_is)
if tot_spks == 0: continue
subp_x, subp_y = get_subplot_size(tot_spks)
#plt.figure()
for spk_i, i in zip(spk_is, range(tot_spks)):
plt.subplot(subp_x, subp_y, i + 1)
if context == 1:
plt.plot(vl['xs'], vl['ys'], zorder=1, color='k')
plt.scatter(vl['xs'][spk_i],
vl['ys'][spk_i],
zorder=2,
color='b',
label='Clockwise')
else:
plt.scatter(vl['xs'][spk_i],
vl['ys'][spk_i],
zorder=2,
color='r',
label='Counterclockwise')
#plot_spks(vl, spk_i, i+2)
#plt.suptitle('Animal %i, Tetrode %i, Session %i, Context:%i'%(animal,tetrode,session,context))
plt.xlim([-60, 60])
plt.ylim([-60, 60])
plt.xlabel('Position (in)')
plt.ylabel('Position (in)')
plt.show()
def count_cells(vl,cls,trigger_tm,good_clusters):
t_cells = {}
for tetrode,cl in cls.items():
if tetrode not in good_clusters: continue
for cell in good_clusters[tetrode]:
logging.info('Finding spike locations for tetrode %i, cell %i',tetrode,cell)
cache_key = (cl['Label'][::10],vl['xs'][::10],trigger_tm,cell)
spk_i = spike_loc(cl, vl, trigger_tm, cell, cache_key)
if spk_i is np.NAN: break
t_cells[(tetrode,cell)] = spk_i
return t_cells
def count_cells(vl, cls, trigger_tm, good_clusters):
t_cells = {}
for tetrode, cl in cls.items():
if tetrode not in good_clusters: continue
for cell in good_clusters[tetrode]:
logging.info('Finding spike locations for tetrode %i, cell %i',
tetrode, cell)
cache_key = (cl['Label'][::10], vl['xs'][::10], trigger_tm, cell)
spk_i = spike_loc(cl, vl, trigger_tm, cell, cache_key)
if spk_i is np.NAN: break
t_cells[(tetrode, cell)] = spk_i
return t_cells
def cluster_graphs():
animal = 70
session = 8
# Filenames (fn) are named descriptively:
# session 18:14:04 on day 10/25/2013
# load virmenLog75\20131025T181404.cmb.mat
#for tetrode in range(1,17):
for tetrode in [13]:
for context in [1,-1]:
global clrs
clrs = ['b','g','r','c','m','k','b','g','r','c','m','k']
fn, trigger_tm = load_mux(animal, session)
cl = load_cl(animal,fn,tetrode)
vl = load_vl(animal,fn)
spk_is = []
#for cell in range(2,100):
for cell in [3]:
cache_key = (cl['Label'][::10],vl['xs'][::10],trigger_tm,cell)
spk_i = spike_loc(cl, vl, trigger_tm, cell,cache_key)
if spk_i is np.NAN: break
cntx_is = np.nonzero(vl['Task']==context)[0]
spk_i = np.intersect1d(cntx_is, spk_i)
spk_is.append(spk_i)
tot_spks = len(spk_is)
if tot_spks == 0: continue
subp_x, subp_y = get_subplot_size(tot_spks)
#plt.figure()
for spk_i, i in zip(spk_is, range(tot_spks)):
plt.subplot(subp_x,subp_y, i+1)
if context==1:
plt.plot(vl['xs'],vl['ys'],zorder=1,color='k')
plt.scatter(vl['xs'][spk_i],vl['ys'][spk_i],zorder=2,color='b',
label='Clockwise')
else:
plt.scatter(vl['xs'][spk_i],vl['ys'][spk_i],zorder=2,color='r',
label='Counterclockwise')
#plot_spks(vl, spk_i, i+2)
#plt.suptitle('Animal %i, Tetrode %i, Session %i, Context:%i'%(animal,tetrode,session,context))
plt.xlim([-60,60])
plt.ylim([-60,60])
plt.xlabel('Position (in)')
plt.ylabel('Position (in)')
plt.show()
def count_cells(vl,cls,trigger_tm,good_clusters):
''' Returns a dictionary of tracked cells of the form
t_cells[(tetrode,cell)] = indices of spikes of this cell '''
t_cells = {}
for tetrode,cl in cls.items():
if tetrode not in good_clusters: continue
for cell in good_clusters[tetrode]:
logging.info('Finding spike locations for tetrode %i, cell %i',tetrode,cell)
cache_key = (cl['Label'][::10],vl['xs'][::10],trigger_tm,cell)
spk_i = spike_loc(cl, vl, trigger_tm, cell, cache_key)
if spk_i is np.NAN: break
t_cells[(tetrode,cell)] = spk_i
if len(t_cells) == 0: raise Exception('No cells found')
return t_cells
def count_cells(vl, cls, trigger_tm, good_clusters):
''' Returns a dictionary of tracked cells of the form
t_cells[(tetrode,cell)] = indices of spikes of this cell '''
t_cells = {}
for tetrode, cl in cls.items():
if tetrode not in good_clusters: continue
for cell in good_clusters[tetrode]:
logging.info('Finding spike locations for tetrode %i, cell %i',
tetrode, cell)
cache_key = (cl['Label'][::10], vl['xs'][::10], trigger_tm, cell)
spk_i = spike_loc(cl, vl, trigger_tm, cell, cache_key)
if spk_i is np.NAN: break
t_cells[(tetrode, cell)] = spk_i
if len(t_cells) == 0: raise Exception('No cells found')
return t_cells
