def characterize(sesss = sesss, experiment = 'Fmr1_RR', pplot = True, verbose = False):
if type(sesss) == str:
sesss = [sesss]
# set up figure
figsize = (12, 12)
fig = plt.figure(figsize = figsize)
# loop through sesss
for sess in sesss:
DB = np.empty(0, dtype = dtype)
print '%s\n%s\n\n' % (sess, '-'*50)
# build the output directory path
savedir = os.path.join(basedir, experiment, 'Sessions', sess, 'analysis')
if not os.path.exists(savedir):
os.mkdir(savedir)
# WT or KO / CTL or EXP
gen, exp, date = sess.split('_')
# find the RF blocks
pens = glob.glob(os.path.join(basedir, experiment, 'Sessions', sess, 'fileconversion', 'RF*.h5'))
# load the cfs for this sess
cfs = np.loadtxt(os.path.join(basedir, experiment, 'Sessions', sess, 'cfs.txt'), ndmin = 1)
# loop through blocks in this sess
for pen in pens:
absol, relat = os.path.split(pen)
blockname = os.path.splitext(relat)[0]
# get unit number from filename
unitnum = np.int32(p.findall(relat))[0] # unit number
ix = cfs[:, 0] == unitnum
if ix.sum() > 0:
cf_man = cfs[ix, 1][0]
if verbose:
print pen
# load the RF block
f = h5py.File(pen, 'r')
spktimes = f['spktimes'].value
# if not np.isnan(spktimes[0]):
'''--------RF--------'''
# load the RF block to get the RF
rf = f['rf'].value; rast = f['rast'].value; spktimes = f['spktimes'].value; stimparams = f['stimID'].value; spktrials = f['spktrials'].value; coord = f['coord'].value; ntrials = f['rast'].shape[0]; f.close()
# calculate the psth (spk/s*trial, normalized by number of trials
psth = Spikes.calc_psth(rast, normed = True) # spk/s
# baseline firing rate
base_mean = psth[:stim_on].mean() # spk/s
# response onset/offset
psth_smoo = Spikes.exp_smoo(psth, tau = 0.003)
resp_on, resp_off = Spikes.calc_on_off(psth_smoo, stim_on = stim_on)
# rewindowed RF
rf_rewin = RF.calc_rf(rast, stimparams, resp_on = resp_on + stim_on - 3, resp_off = resp_off + stim_on + 3, normed = True)
# thresholded RF
rf_thresh = rf_rewin.copy()
rf_threshold = np.percentile(rf_thresh, 66)# upper quartile
rf_peak = rf_rewin.max()
rf_thresh[rf_thresh < rf_threshold] = 0
# find maximum RF cluster
(rf_clust, clust_sizes) = RF.findmaxcluster(rf_thresh, cf = cf_man, include_diagonal = False)
# if clust_sizes.max() < 10: # if it's a tiny RF, set it to nans
# rf_clust = np.empty(rf_clust.shape) * np.nan
rf_mask = rf_clust > 0
# find evoked psth
ev_psth = RF.calc_evoked_psth(rast, stimparams, rf_mask)
ev_psth_smoo = Spikes.exp_smoo(ev_psth, tau = 0.003)
ev_resp_on, ev_resp_off = Spikes.calc_on_off(ev_psth_smoo, stim_on = stim_on)
ev_mean = ev_psth[ev_resp_on : ev_resp_off].mean()
# bandwidth and threshold
bw, bw_lr, _, thresh = RF.calc_bw_cf_thresh(rf_mask)
# center of mass
com = RF.calc_rf_com(rf_clust)
tip_top = np.max([thresh-2, 0])
tip_bottom = thresh
com_tip = RF.calc_rf_com(rf_clust[tip_top:tip_bottom, :])
'''PLOT'''
if pplot:
rf1_ax = fig.add_subplot(221)
rf2_ax = fig.add_subplot(222)
psth1_ax = fig.add_subplot(223)
psth2_ax = fig.add_subplot(224)
RF.plot_RF(rf, bw_lr = bw_lr, thresh = thresh, cf = cf_man, ax = rf1_ax)
RF.plot_RF(rf_clust, bw_lr = bw_lr, thresh = thresh, cf = cf_man, ax = rf2_ax)
rf2_ax.axvline(com, color = 'g', ls = '--')
rf2_ax.axvline(com_tip, color = 'b', ls = '--')
psth1_ax.plot(psth_smoo)
psth2_ax.plot(ev_psth_smoo)
psth1_ax.axvline(resp_on+stim_on, color = 'r', ls = '--')
psth1_ax.axvline(resp_off+stim_on, color = 'r', ls = '--')
figpath = os.path.join(savedir, blockname + '.png')
fig.savefig(figpath);
fig.clf()
'''PLOT'''
DB.resize(DB.size + 1)
DB[-1] = np.array((gen, exp, sess, unitnum, \
psth[:333], ev_psth[:333], \
rf, rf_clust, \
cf_man, com, com_tip, \
bw, bw_lr, thresh, coord, \
resp_on, resp_off, ev_resp_on, ev_resp_off, \
base_mean, ev_mean), dtype = dtype)
# end unit loop
np.savez(os.path.join(basedir, experiment, 'Sessions', sess, sess + '_RF.npz'), DB = DB)
if verbose:
print '\n'*4
