def plt_traj(hdf, save=False):
rad = hdf.root.task.attrs.target_radius
cmap_list = ['maroon', 'orangered', 'darkgoldenrod', 'olivedrab', 'teal', 'steelblue', 'midnightblue', 'darkmagenta', 'darkgray']
f, ax =plt.subplots()
tg = hdf.root.task[:]['target']
tg_ix = pa.get_target_ix(tg[:, [0, 2]])
for it, t in enumerate(np.unique(tg_ix)):
ix = np.nonzero(tg_ix==t)[0][0]
tmp_circ = plt.Circle(tg[ix,[0,2]], rad, color=cmap_list[it], alpha=.5)
ax.add_artist(tmp_circ)
curs = hdf.root.task[:]['cursor']
epoch_ix = np.array([ [hdf.root.task_msgs[j-3]['time'], i['time']]
for j, i in enumerate(hdf.root.task_msgs[:]) if i['msg']=='reward'])
for i, (g, r) in enumerate(epoch_ix):
ax.plot(curs[g:r,0], curs[g:r,2], color='lightgrey')
ax.set_ylim([-14, 14])
ax.set_xlim([-14, 14])
if save:
f.savefig(hdf.filename[:-4]+'_traj.png', format='png')
def jerk_squared(R, input1, input2):
hdf = R.hdf
#Go indices
go_ix = np.array([hdf.root.task_msgs[it-3][1]
for it, t in enumerate(hdf.root.task_msgs[:])
if t[0] == 'reward'])
sub_ix = R.update_bmi_ix
go_ix = np.hstack((go_ix, len(R.cursor_vel) ))
jerk_sq = dict(priv=np.zeros((len(go_ix)-1, )), shar=np.zeros((len(go_ix)-1, )), norm=np.zeros((len(go_ix)-1, )))
jerk_sq_traj = dict(priv=dict(), shar=dict(), norm=dict())
targ_ix = pa.get_target_ix(R.target[:, [0,2]])
for ig, go in enumerate(go_ix[:-1]):
if go < len(R.cursor_vel):
ix_corr = set(R.update_bmi_ix)
ix_ = np.sort(np.array(list(ix_corr.intersection(np.arange(go,go_ix[ig+1])))))
priv_traj = np.squeeze(R.decoded_vel[input1][ix_,:]).copy()
shar_traj = np.squeeze(R.decoded_vel[input2][ix_,:]).copy()
norm_traj = np.squeeze(R.cursor_vel[ix_,:]).copy()
jerk_sq['priv'][ig], jerk_sq_traj['priv'][ig] = _get_jk_squ(priv_traj)
jerk_sq['shar'][ig], jerk_sq_traj['shar'][ig] = _get_jk_squ(shar_traj)
jerk_sq['norm'][ig], jerk_sq_traj['norm'][ig] = _get_jk_squ(norm_traj)
return jerk_sq, jerk_sq_traj, targ_ix[go_ix[:-1]]
def get_epoch(hdf_list, hold_min=0., set_window=None, epoch='hold'):
#Set window and hold_min are in seconds
binned_counts = []
trl_ix = []
trl_ix_start = 0
t2t_arr = []
target_info = []
cursor = []
for hdf_nm in hdf_list:
hdf = tables.openFile(os.path.expandvars('$FA_GROM_DATA/'+hdf_nm))
spike_counts = hdf.root.task[:]['spike_counts']
curs_vel = hdf.root.task[:]['internal_decoder_state'][:, [3, 5], 0]
if epoch=='hold':
start_tab = 2
end_tab = 1
elif epoch=='reach':
start_tab = 3
end_tab = 2
start_hold = np.array([hdf.root.task_msgs[i-start_tab]['time'] for i, j in enumerate(hdf.root.task_msgs[:]) if j['msg'] == 'reward'])
act_end_hold = np.array([hdf.root.task_msgs[i-end_tab]['time'] for i, j in enumerate(hdf.root.task_msgs[:]) if j['msg'] == 'reward'])
if set_window is None:
end_hold = act_end_hold.copy()
else:
end_hold = start_hold + (set_window*60)
t2t = (act_end_hold - start_hold)/60.
t2t_ix = t2t>=hold_min
t2t_arr.append(t2t[t2t_ix])
start_hold = start_hold[t2t_ix]
end_hold = end_hold[t2t_ix]
internal_state = hdf.root.task[:]['internal_decoder_state']
update_bmi_ix = np.nonzero(np.diff(np.squeeze(internal_state[:, 3, 0])))[0]+1
for i, (s, e) in enumerate(zip(start_hold, end_hold)):
sc = hdf.root.task[s:e]['spike_counts']
c = []
s_e = np.nonzero(update_bmi_ix>=s)[0]
s_e = update_bmi_ix[s_e[0]]
trl = []
for j, bin_edge in enumerate(np.arange(s_e, e, 6)):
trl.append(np.sum(spike_counts[bin_edge:bin_edge+6, :, 0], axis=0))
trl_ix.append(i+trl_ix_start)
c.append(curs_vel[bin_edge, :])
target_info.append(hdf.root.task[s+10]['target'][[0, 2]])
binned_counts.append(np.vstack((trl)))
cursor.append(np.vstack((c)))
trl_ix_start += i + 1
target_index = pa.get_target_ix(np.vstack(target_info))
return np.array(trl_ix), np.vstack((binned_counts)), np.hstack((t2t_arr)), np.vstack((cursor)), target_index
def perf_mets(hdf, save=False, hdf_fname=None):
epoch_ix = np.array([ [hdf.root.task_msgs[j-3]['time'], i['time']]
for j, i in enumerate(hdf.root.task_msgs[:]) if i['msg']=='reward'])
#Get targ ix:
tg = hdf.root.task[:]['target']
targ_ix = pa.get_target_ix(tg[:, [0, 2]])
targ_ix = targ_ix[epoch_ix[:,0]+3]
#Get time2targ:
time2targ = (epoch_ix[:,1] - epoch_ix[:,0])/60.
cursor = hdf.root.task[:]['cursor'][:, [0, 2]]
metrics = dict()
metrics['path_length'] = []
metrics['path_error'] = []
metrics['avg_speed'] = []
metrics['targ_ix'] = []
metrics['time2targ'] = []
#Path error:
for i, (g, r) in enumerate(epoch_ix):
path = cursor[g:r, :]
targ_loc = tg[g+5,[0, 2]]
path_length, path_error, avg_speed = bha.path_metrics(path, targ_loc)
metrics['path_length'].append(path_length)
metrics['path_error'].append(path_error)
metrics['avg_speed'].append(avg_speed)
metrics['targ_ix'].append(targ_ix[i])
metrics['time2targ'].append(time2targ[i])
#Add output SOT to metrics:
if hdf_fname is None:
print 'Need hdf_fname for sot calc'
else:
metrics['output_sot'] = get_output_sot(hdf, hdf_fname)
if save:
sio.savemat(hdf.filename[:-4]+'_metrics.mat', metrics)
def decompose_inputs(fa_dict, bin_spk_i, hdf, dec, task='bmi_resetting', process_to_ix=None, use_main = True):
#Main shared:
T = bin_spk_i.shape[0]
mn = np.tile(fa_dict['fa_mu'], [1, T])
dmn = bin_spk_i.T - mn
ReSim = trbt.RerunDecoding(hdf, dec, task='bmi_resetting')
if use_main:
shar = (fa_dict['fa_main_shared'] * dmn)
priv = (dmn - shar)
ReSim.main= True
else:
shar = (fa_dict['fa_sharL'] * dmn)
priv = (dmn - shar)
ReSim.main = False
main_shar_spks = sdf.rebin_spks(shar + mn)
main_priv_spks = sdf.rebin_spks(priv + mn)
if process_to_ix is None:
proc_ix = main_shar_spks.shape[0]
else:
proc_ix = process_to_ix
ReSim.add_input(ReSim.spike_counts[:proc_ix, :, :], 'all')
ReSim.add_input(main_shar_spks[:proc_ix, :, :], 'main_shar')
ReSim.add_input(main_priv_spks[:proc_ix, :, :], 'main_priv')
#Now plot trials:
gr_ix = np.array([(hdf.root.task_msgs[j-3]['time'], i['time']) for j, i
in enumerate(hdf.root.task_msgs) if i['msg'] == 'reward'])
targ_pos = hdf.root.task[gr_ix[:50,1]]['target']
targ_ix = pa.get_target_ix(targ_pos[:,[0, 2]])
ReSim.target_ix = targ_ix
ReSim.targ_pos_trunc = targ_pos
return ReSim, gr_ix, proc_ix
def psth(Recoder,bins_after_go=60*4, input_type='all'):
hdf = Recoder.hdf
#Go indices
go_ix = np.array([hdf.root.task_msgs[it-3][1] for it, t in enumerate(hdf.root.task_msgs[:]) if t[0] == 'reward'])
go_ix = np.hstack((go_ix, len(Recoder.decoded_pos[input_type]) ))
spks = sdf.rebin_spks(Recoder.dec_spk_cnt_bin[input_type])
targ_ix = pa.get_target_ix(Recoder.target[:, [0,2]])
psth = dict()
for ig, go in enumerate(go_ix[:-1]):
ti = targ_ix[go]
if go+bins_after_go < len(Recoder.decoded_pos[input_type]):
sp = np.squeeze(spks[go:go+bins_after_go, : , 0])
if int(ti) in psth.keys():
psth[int(ti)] = np.dstack((psth[int(ti)], sp))
else:
psth[int(ti)] = sp
return psth
def process_targets(te_list, new_hdf_name):
'''
Summary: Yields an array of trial information in
pytables with columns seen above in Trial_Metrics table class
Input param: hdf: hdf for either fa_bmi or bmi_resetting
Output param: trial dictionary
'''
h5file = tables.openFile(new_hdf_name, mode="w", title='FA Trial Analysis')
trial_mets_table = h5file.createTable("/", 'trial_metrics', Trial_Metrics, "Trial Mets")
meta_table = h5file.createTable("/", 'meta_metrics', Meta_Metrics, "Meta Mets")
row_cnt = -1
for te in te_list:
te_obj = dbfn.TaskEntry(te)
task_entry = te
hdf = te_obj.hdf
#Extract trials ignoring assist:
rew_ix, rew_per_min = pa.get_trials_per_min(hdf, nmin=2, rew_per_min_cutoff=0,
ignore_assist=True, return_rpm=True)
#Go backwards 3 steps (rew --> targ trans --> hold --> target (onset))
go_ix = np.array([hdf.root.task_msgs[it-3][1] for it, t in enumerate(hdf.root.task_msgs[:]) if
scipy.logical_and(t[0] == 'reward', t[1] in rew_ix)])
assert len(rew_ix) == len(go_ix)#: raise Exception("Rew ix and Go ix are unequal")
#Time to target is diff b/w target onset and reward time
time2targs = (rew_ix - go_ix)/60.
#Add buffer of 5 task steps (80 ms)
target_locs = hdf.root.task[go_ix.astype(int) + 5]['target'][:, [0, 2]]
try:
obstacle_sz = hdf.root.task[go_ix.astype(int) + 5]['obstacle_size'][:, 0]
except:
obstacle_sz = np.zeros_like(go_ix)
#Targ ix -- should be 8
targ_ixs = pa.get_target_ix(target_locs)
assert len(np.unique(targ_ixs)) == 8#: raise Exception("Target Ix has more or less than 8 targets :/ ")
#Input type:
try:
input_types = hdf.root.task[go_ix.astype(int)+5]['fa_input']
except:
input_types = ['all']*len(go_ix)
rew_time_2_trial = {}
for ri, r_ix in enumerate(rew_ix):
trl = trial_mets_table.row
row_cnt += 1
trl['trial_number'] = ri
trl['task_entry'] = task_entry
trl['rew_per_min'] = rew_per_min[ri]
trl['target_index'] = targ_ixs[ri]
trl['target_loc'] = target_locs[ri,:]
trl['start_time'] = go_ix[ri]/60. # In seconds
trl['input_type'] = input_types[ri]
trl['time2targ'] = time2targs[ri]
rew_time_2_trial[r_ix] = time2targs[ri]
trl['obstacle_size'] = obstacle_sz[ri]
path = hdf.root.task[int(go_ix[ri]):int(r_ix)]['cursor'][:, [0, 2]]
path_length, path_error, avg_speed = path_metrics(path, target_locs[ri, :])
trl['path_length'] = path_length
trl['path_error'] = path_error
trl['avg_speed'] = avg_speed
trl['timeout_time'] = hdf.root.task.attrs.timeout_time
trl['timeout_penalty_time'] = hdf.root.task.attrs.timeout_penalty_time
trl.append()
trial_mets_table.flush()
#Meta metrics for hdf:
block_len = hdf.root.task.shape[0]
wind_len = 5*60*60
wind_step = 2.5*60*60
meta_ix = np.arange(0, block_len - wind_len, wind_step)
trial_ix = np.array([i for i in hdf.root.task_msgs[:] if
i['msg'] in ['reward','timeout_penalty','hold_penalty','obstacle_penalty'] ], dtype=hdf.root.task_msgs.dtype)
for m in meta_ix:
meta_trl = meta_table.row
meta_trl['task_entry'] = te
end_meta_ix = m + wind_len
trial_ix_time = trial_ix[:,]
msg_ix = np.nonzero(np.logical_and(trial_ix['time']<=end_meta_ix, trial_ix['time']>m))[0]
targ = hdf.root.task[trial_ix[msg_ix]['time'].astype(int)]['target'][:,[0,2]]
targ_ix = pa.get_target_ix(targ)
#Only mark as correct if it's the first correct trial
targ_ix_ix = [0]
tg_prev = targ_ix[0]
for ii, tg_ix in enumerate(targ_ix[1:]):
if tg_prev != tg_ix:
targ_ix_ix.append(ii+1)
tg_prev = tg_ix
targ_ix_mod = targ_ix[targ_ix_ix]
msg_ix_mod = msg_ix[targ_ix_ix]
targ_percent_success = np.zeros((8, )) - 1
all_perc_succ_lte10sec = np.zeros((8, )) - 1
#time2targs for rew_ix:
rew_ix_meta = np.array([i['time'] for i in trial_ix[msg_ix] if i['msg']=='reward'])
for t in range(8):
t_ix = np.nonzero(targ_ix_mod==t)[0]
msgs = trial_ix[msg_ix_mod[t_ix]]
if len(msgs) > 0:
targ_percent_success[t] = len(np.nonzero(msgs['msg']=='reward')[0]) / float(len(msgs))
msgs_copy = msgs.copy()
for mc in msgs_copy:
if mc['msg'] == 'reward':
r_tm = mc['time']
if r_tm in rew_time_2_trial.keys():
if rew_time_2_trial[r_tm] > 10.:
msgs_copy['msg'] = 'not_reward'
print 'not reward: ', m, t, mc
else:
print 'asissted: ', m, t, mc
msgs_copy['msg'] = 'not_rewarded'
all_perc_succ_lte10sec[t] = len(np.nonzero(msgs_copy['msg'] == 'reward')[0]) / float(len(msgs_copy))
if all_perc_succ_lte10sec[t] < 0:
print 'error: ', m, t, mc
else: print 'no msgs for this epoch: ', m
meta_trl['targ_percent_success'] = targ_percent_success
meta_trl['all_perc_succ_lte10sec'] = all_perc_succ_lte10sec
all_msg = trial_ix[msg_ix_mod]
meta_trl['all_percent_success'] = len(np.nonzero(all_msg['msg']=='reward')[0]) / float(len(all_msg))
meta_trl.append()
meta_table.flush()
#How many 5 min segments in 2.5 min steps
h5file.close()
return new_hdf_name
def shared_vs_priv_vel_activ(Recoder, input_types=['private', 'shared']):
tiny = 1e-12
hdf = Recoder.hdf
#Go indices
go_ix = np.array([hdf.root.task_msgs[it-3][1]
for it, t in enumerate(hdf.root.task_msgs[:])
if t[0] == 'reward'])
#Rew indices
rew_ix = np.array([t[1]
for it, t in enumerate(hdf.root.task_msgs[:])
if t[0]=='reward'])
go_ix = np.hstack((go_ix, len(Recoder.decoded_pos) ))
targ_ix = pa.get_target_ix(Recoder.target[go_ix+5, [0,2]])
sot_start = dict()
sot_end = dict()
f, ax_start = plt.subplots(nrows=4, ncols=2)
f, ax_end = plt.subplots(nrows=4, ncols=2)
tg_key = dict()
for i_t, t in enumerate(np.unique(targ_ix[go_ix])):
sot_start[str(int(t))] = []
sot_end[str(int(t))] = []
tg_key[t] = i_t
for ig, go in enumerate(go_ix[:-1]):
if go < len(R_priv.decoded_pos):
#Speed calc:
traj_priv = np.sqrt(np.sum((R_priv.decoded_vel[go:go_ix[ig+1], [0, 2]].copy())**2, axis=1))
traj_shar = np.sqrt(np.sum((R_shar.decoded_vel[go:go_ix[ig+1], [0, 2]].copy())**2, axis=1))
tg_ix = targ_ix[go]
tmp = traj_shar / (tiny + traj_shar + traj_priv)
sot_start[str(int(tg_ix))].append(tmp[:120])
sot_end[str(int(tg_ix))].append(tmp[-120:])
for i_t, t in enumerate(np.unique(targ_ix[go_ix[:-1]])):
ky = str(int(t))
axi = ax_start[i_t%4, i_t/4]
axj = ax_end[i_t%4, i_t/4]
tmp_start = np.vstack(( sot_start[ky] ))
tmp_end = np.vstack(( sot_end[ky] ))
x = np.arange(120)/60.
sem_start = np.std(tmp_start, axis=0)/np.sqrt(tmp_start.shape[0])
sem_end = np.std(tmp_end, axis=0)/np.sqrt(tmp_end.shape[0])
axi.plot(x, np.mean(tmp_start, axis=0), 'b-')
axi.fill_between(x, np.mean(tmp_start, axis=0) - sem_start,
np.mean(tmp_start, axis=0) + sem_start, alpha=.5, color='b')
axj.plot(x, np.mean(tmp_end, axis=0), 'r-')
axj.fill_between(x, np.mean(tmp_end, axis=0) - sem_end,
np.mean(tmp_end, axis=0) + sem_end, alpha=.5, color='r')
axi.set_ylim([.5, .75])
axj.set_ylim([.5, .75])
def PSTH(fa_hdf,task_tbl_met = 'spike_counts', plot_by_neur=False, save_f=False):
epoch_ix = np.array([ [fa_hdf.root.task_msgs[j-3]['time'], i['time']]
for j, i in enumerate(fa_hdf.root.task_msgs[:]) if i['msg']=='reward'])
cursor_pos = fa_hdf.root.task[:]['cursor'][:, [0, 2]]
try:
spike_counts = fa_hdf.root.task[:][task_tbl_met][:,:,0] #Time x units
except:
spike_counts = fa_hdf.root.task[:]['spike_counts'][:,:,0]
print 'USING SPIKE COUNTS from HDF FILE'
st = np.nonzero(np.sum(spike_counts, axis=1))[0][0]
st = st % 6
update_ix = np.arange(st, spike_counts.shape[0], 6)
ixx = []
trl = []
tg = []
for i, (g,r) in enumerate(epoch_ix):
u_ix = np.nonzero(np.logical_and(update_ix<=r, update_ix>g))[0]
ixx.append(update_ix[u_ix])
if u_ix[0] >=4:
trl.append(spike_counts[update_ix[u_ix[0]-4:u_ix[0]+12], :])
tg.append(fa_hdf.root.task[update_ix[u_ix[0]+3]]['target'][[0, 2]])
IX = np.hstack((ixx))
if len(trl) > 0:
TRL = np.dstack((trl))
TG_IX = pa.get_target_ix(np.vstack((tg)))
x_ax = np.arange(-.4, 1.2, .1)
n_neurons = TRL.shape[1]
cnt = -1
if plot_by_neur:
u_col = get_cmap(len(np.unique(TG_IX)))
for n in range(n_neurons):
print n
if not (n % 9):
try:
plt.tight_layout()
if save_f:
f.savefig(fa_hdf.filename[:-4]+'_PSTH_by_neur_'+str(cnt)+'.png', format='png')
except:
pass
print n
f, ax = plt.subplots(nrows=3, ncols=3)
cnt += 1
axi = ax[(n%9)/3, (n%9)%3]
if not ((n%9)%3):
axi.set_ylabel('FR')
if (n%9)/3 == 2:
axi.set_xlabel('Sec w.r.t. Target Onset')
for ii, it in enumerate(np.unique(TG_IX)):
ix = np.nonzero(TG_IX==it)[0]
fr = TRL[:, n, ix].T
mn = np.mean(fr, axis=0)
axi.plot(x_ax, mn, color=u_col[ii])
axi.set_title('Unit '+str(n))
else:
u_col = get_cmap(n_neurons)
f, ax = plt.subplots(nrows = 3, ncols = 3)
for ii, it in enumerate(np.unique(TG_IX)):
axix = bha.targ_ix_to_3x3_subplot(it)
axi = ax[axix[0], axix[1]]
ix = np.nonzero(TG_IX==it)[0]
fr = TRL[:, :, ix].T #Trials x units x time
for u in range(n_neurons):
mn = np.mean(fr[:, u, :], axis=0)
axi.plot(x_ax, mn, color=u_col[ii])
def get_files(te_num=None, get_psth=False):
if te_num is None or te_num == 4048:
#Good centerout:
te = 4048
hdf = tables.openFile(os.path.expandvars('$FA_GROM_DATA/grom20160201_04_te4048.hdf'))
dec = pickle.load(open(os.path.expandvars('$FA_GROM_DATA/grom20160201_01_RMLC02011515.pkl')))
elif te_num == 4526:
#Learning obstacles:
hdf = tables.openFile(os.path.expandvars('$FA_GROM_DATA/grom20160317_11_te4526.hdf'))
dec = pickle.load(open(os.path.expandvars('$FA_GROM_DATA/grom20160317_03_test03171536.pkl')))
elif te_num == 2991:
#Suraj's BMI holding
hdf = tables.openFile(os.path.expandvars('$FA_GROM_DATA/grom20150414_04.hdf'))
dec = pickle.load(open(os.path.expandvars('$FA_GROM_DATA/grom20150414_02_RMLC04141528.pkl')))
elif te_num == 4549:
#Better obstacles
hdf = tables.openFile(os.path.expandvars('$FA_GROM_DATA/grom20160319_11_te4549.hdf'))
dec = pickle.load(open(os.path.expandvars('$FA_GROM_DATA/grom20160319_09_test03191611.pkl')))
else:
from db import dbfunctions
te = dbfunctions.TaskEntry(te_num)
hdf = te.hdf
dec = te.decoder
#Use reward to calc fa_dict:
fa_dict = factor_analysis_tasks.FactorBMIBase.generate_FA_matrices(None, hdf=hdf, dec=dec)
#Calculate spikes to transform:
drives_neurons_ix0 = 3
internal_state = hdf.root.task[:]['internal_decoder_state']
update_bmi_ix = np.nonzero(np.diff(np.squeeze(internal_state[:, drives_neurons_ix0, 0])))[0]+1
spike_i = hdf.root.task[:]['spike_counts'][:,:,0]
bin_spk_i = np.zeros((len(update_bmi_ix), spike_i.shape[1]))
if get_psth:
n_units = dec.n_units
go_ix = np.array([hdf.root.task_msgs[i-3]['time'] for i, j in enumerate(hdf.root.task_msgs) if j['msg'] == 'reward'])
rew_ix = np.array([j['time'] for i, j in enumerate(hdf.root.task_msgs) if j['msg'] == 'reward'])
targ = hdf.root.task[go_ix+10]['target'][:,[0, 2]]
targ_ix = pa.get_target_ix(targ)
d = {}
psth = {}
psth_max = {}
psth_shar = {}
psth_priv = {}
for t in np.unique(targ_ix):
t_ix = np.nonzero(targ_ix==t)[0]
min_len = 10e10
max_len = 0
for i_t, trl in enumerate(t_ix):
bin = []
g = go_ix[trl]
r = rew_ix[trl]
start_bin = np.nonzero(update_bmi_ix>=g)[0][0]
end_bin = np.nonzero(update_bmi_ix<=r)[0][-1]
for ib, i_ix in enumerate(update_bmi_ix[start_bin:end_bin+1]):
bin.append(np.sum(spike_i[i_ix-5:i_ix+1, :], axis=0))
min_len = np.min([min_len, len(bin)])
max_len = np.max([max_len, len(bin)])
if i_t == 0:
d[t] = [np.vstack(bin)]
else:
d[t].append(np.vstack(bin))
tmp = []
tmp_max = []
for b in d[t]:
#Share b:
tmp.append(b[:min_len, :])
mx = np.zeros((max_len, n_units))
mx[:,:] = np.nan
mx[:b.shape[0], :] = b
tmp_max.append(mx)
#Bins x neurons x trials
spk = np.dstack((tmp))
dmn = spk - np.tile(fa_dict['fa_mu'][np.newaxis, :, :], [spk.shape[0], 1, spk.shape[2]])
dmn_resh = dmn.reshape(dmn.shape[1], dmn.shape[0]*dmn.shape[2])
shar_resh = np.array((fa_dict['fa_main_shared'] * dmn_resh))
shar = shar_resh.reshape(dmn.shape[0], dmn.shape[1], dmn.shape[2])
priv = dmn - shar
spk_max = np.dstack((tmp_max))
psth[t] = np.mean(spk, axis=2)[:, :, np.newaxis]
psth_max[t] = np.nanmean(np.dstack((tmp_max)), axis=2)[:, :, np.newaxis]
psth_shar[t] = np.mean(shar, axis=2)[:, :, np.newaxis]
psth_priv[t] = np.mean(priv, axis=2)[:, :, np.newaxis]
return psth, psth_max, psth_shar, psth_priv, hdf, dec, targ, targ_ix
else:
#binned spikes:
for ib, i_ix in enumerate(update_bmi_ix):
bin_spk_i[ib, :]= np.sum(spike_i[i_ix-5:i_ix+1, :], axis=0)
return fa_dict, bin_spk_i, hdf, dec
def plot_traj(R, plot_pos=1, plot_vel=1, plot_force=0, it_cutoff=20000,
min_it_cutoff=0, input_type='all'):
rew_ix = pa.get_trials_per_min(R.hdf,nmin=2)
go_ix = np.array([R.hdf.root.task_msgs[it-3][1] for it, t in enumerate(R.hdf.root.task_msgs[:]) if t[0] == 'reward'])
#Make sure no assist is used
try:
zero_assist_start = np.nonzero(R.hdf.root.task[:]['assist_level']==0)[0][0]
except:
print 'ignoring assist'
zero_assist_start = 0
keep_ix = scipy.logical_and(go_ix>np.max([min_it_cutoff, zero_assist_start+(60*60)]), go_ix<it_cutoff)
go_ix = go_ix[keep_ix]
rew_ix = rew_ix[keep_ix]
targ_pos = R.hdf.root.task[go_ix.astype(int)+5]['target']
targ_ix = pa.get_target_ix(targ_pos[:,[0, 2]])
if plot_pos == 1:
f, ax = plt.subplots(nrows=4, ncols=2)
f2, ax2 = plt.subplots(nrows=4, ncols=2)
f0, ax0 = plt.subplots()
if plot_vel == 1:
f3, ax3 = plt.subplots(nrows=4, ncols=2)
f4, ax4 = plt.subplots(nrows=4, ncols=2)
if plot_force ==1:
f5, ax5 = plt.subplots(nrows=4, ncols=2)
f6, ax6 = plt.subplots(nrows=4, ncols=2)
#R = pickle.load(open(pkl_name))
for i, (g,r) in enumerate(zip(go_ix, rew_ix)):
#Choose axis:
targ = targ_ix[i]
if plot_pos == 1:
axi = ax[targ%4, targ/4]
axi2 = ax2[targ%4, targ/4]
axi.plot(R.cursor_pos[g:r, 0], 'k-')
axi2.plot(R.cursor_pos[g:r, 2], 'k-')
axi.plot(R.decoded_pos[input_type][g:r, 0], 'b-')
axi2.plot(R.decoded_pos[input_type][g:r, 2], 'b-')
ax0.plot(R.cursor_pos[g:r,0], R.cursor_pos[g:r, 2], '-', color='k')
ax0.plot(R.decoded_pos[input_type][g:r,0], R.decoded_pos[input_type][g:r, 2], '--', color=cmap_list[int(targ)])
if i==0:
axi.set_title('Position X')
axi2.set_title('Position Z')
if plot_vel == 1:
axi3 = ax3[targ%4, targ/4]
axi4 = ax4[targ%4, targ/4]
axi3.plot(R.cursor_vel[g:r, 0], 'k-')
axi4.plot(R.cursor_vel[g:r, 2], 'k-')
axi3.plot(R.decoded_vel[input_type][g:r, 0], 'b-')
axi4.plot(R.decoded_vel[input_type][g:r, 2], 'b-')
if i==0:
axi3.set_title('Vel X')
axi4.set_title('Vel Z')
if plot_force ==1:
axi5 = ax5[targ%4, targ/4]
axi6 = ax6[targ%4, targ/4]
axi5.plot(R.dec_state_mn[input_type][g-1:r-1, 9], 'b-')
axi6.plot(R.dec_state_mn[input_type][g-1:r-1, 11], 'b-')
axi5.plot(hdf.root.task[g:r]['internal_decoder_state'][:,9], 'k-')
axi6.plot(hdf.root.task[g:r]['internal_decoder_state'][:,11], 'k-')
if i==0:
axi5.set_title('Acc X')
axi6.set_title('Acc Z')
plt.tight_layout()
def traj_sem_plot(R, input_type_root, fa_by_targ=False, it_start=0, it_cutoff=20000, ax=None,
rm_assist=True, extra_title_text=''):
rew_ix = pa.get_trials_per_min(R.hdf,nmin=2)
go_ix = np.array([R.hdf.root.task_msgs[it-3][1] for it, t in enumerate(R.hdf.root.task_msgs[:]) if t[0] == 'reward'])
if rm_assist:
zero_assist_start = np.nonzero(R.hdf.root.task[:]['assist_level']==0)[0]
if len(zero_assist_start) > 0:
zero_assist_start = zero_assist_start[0]
else:
Exception('Assist is never == 0!')
else:
zero_assist_start = 0
start_ix = np.max([zero_assist_start, it_start])
keep_ix = scipy.logical_and(go_ix>start_ix, go_ix<it_cutoff)
go_ix = go_ix[keep_ix]
rew_ix = rew_ix[keep_ix]
t2targ = rew_ix - go_ix
targ_pos = R.hdf.root.task[go_ix.astype(int)+5]['target']
targ_ix = pa.get_target_ix(targ_pos[:,[0, 2]])
#For each target, make array of trials x time (min length for reward trial)
trial_arr = dict()
if ax is None:
f, ax = plt.subplots()
#For each target, concat trials:
for it, t in enumerate(np.unique(targ_ix)):
if fa_by_targ:
input_type = input_type_root + '_'+str(t)
else:
input_type = input_type_root
print input_type, R.decoded_pos[input_type].shape
t_ix = np.nonzero(targ_ix==t)[0]
trial_arr[str(t)] = np.zeros((len(t_ix), np.max(t2targ[t_ix]), 2))
trial_arr[str(t)][:,:] = np.nan
cix = 0
for i, ti in enumerate(t_ix):
if rew_ix[ti]<= R.decoded_pos[input_type].shape[0]:
cix = go_ix[ti].copy()
trial_arr[str(t)][i,:t2targ[ti], :] = R.decoded_pos[input_type][go_ix[ti]:rew_ix[ti],[0,2]]
ax.plot(trial_arr[str(t)][i,:,0], trial_arr[str(t)][i,:,1], '-', color='lightgrey')
#Plot Mean, sem:
mean = np.nanmean(trial_arr[str(t)], axis=0)
sem = np.nanstd(trial_arr[str(t)], axis=0)/np.sqrt(trial_arr[str(t)].shape[0])
ax.plot(mean[:,0], mean[:,1], '-',color=cmap_list[it])
ax.plot(mean[:,0] - sem[:,0], mean[:,1] - sem[:,1], '--',color=cmap_list[it])
ax.plot(mean[:,0] + sem[:,0], mean[:,1] + sem[:,1], '--',color=cmap_list[it])
tmp_circ = plt.Circle(targ_pos[t_ix[0],[0,2]], R.target_rad, color=cmap_list[it], alpha=.5)
ax.add_artist(tmp_circ)
ax.set_title(input_type_root + extra_title_text)
ax.set_xlim([-14, 14])
ax.set_ylim([-12, 12])
return ax
