def single_run(filter, spk_src, bg_src, params):
import evaluate as eval
sp_win = params['sp_win']
spadd_win = params['sp_add_win']
pow_frac = params['pow_frac']
f_filter = params['f_filter']
type = params['sp_type']
thresh = params['thresh']
feats = params['features']
contacts = params['contacts']
n_pts = params['n_pts']
sp, stim, spt_real = eval.mix_cellbg(filter, spk_src, bg_src,
spadd_win, pow_frac)
sp = eval.filter_data(sp, f_filter)
spt, clust_idx, n_missing = eval.spike_clusters(sp, spt_real,
stim,
thresh,
type, sp_win)
features = eval.calc_features(sp, spt, sp_win, feats, contacts)
uni_metric = eval.univariate_metric(eval.mutual_information,
features, clust_idx)
multi_metric = eval.k_nearest(features, clust_idx, n_pts=n_pts)
n_total = len(spt_real['data'])
result_dict= {"cell" : spk_src,
"electrode" : bg_src,
"spikes_total" : n_total,
"spikes_missed" : n_missing,
"mutual_information" : uni_metric,
"k_nearest" : multi_metric}
result_dict.update(params)
import socket
#from sim_manager import get_version
result_dict['host'] = socket.gethostname()
#result_dict['dependencies'] ={'evaluate': get_version(eval),
# 'spike_sort': get_version(eval.sort)}
return result_dict
f_filter=None
thresh = 'auto'
type='max'
sp = h5filter.read_sp(dataset)
spt_orig = h5filter.read_spt(dataset+"/cell1_orig")
stim = h5filter.read_spt(dataset+"/stim")
sp = eval.filter_data(sp, f_filter)
spt, clust_idx, n_missing = eval.spike_clusters(sp, spt_orig,
stim,
thresh,
type, sp_win)
features = eval.calc_features(sp, spt, sp_win, ["P2P", "PCs"], [0,1])
single_metric, knearest_metric = calc_metrics(features, clust_idx)
n_total = len(spt_orig['data'])
print "Total n/o spikes:", n_total
print "Number of undetected spikes: %d (%f)" % (n_missing,
n_missing*1./n_total)
print "Univariate MI:", single_metric
print "K-nearest class. rate:", knearest_metric
spike_sort.ui.plotting.plot_features(features, clust_idx)
#plt.figure()
#spike_sort.ui.plotting.plot_spikes(sp_waves, clust_idx)
spike_sort.ui.plotting.show()