def make_time_frequency_plot(dtup, event_names, Tpre, Tpost, freqs, baseline_interval, thresh):
# get lfp data
print "Fetching data: " + str(dtup)
lfp = dbio.fetch_all_such_LFP(dbname, *dtup).censor()
# get events
evt = dbio.fetch(dbname, 'events', *dtup[:2])
times0 = evt[event_names[0]].dropna()
times1 = evt[event_names[1]].dropna()
nf = physutils.norm_by_mean(baseline_interval)
contr_tf, fig1 = lfp.contrast_time_frequency(dtup[2], [times0, times1], Tpre, Tpost, method='wav', normfun=nf, doplot=True, freqs=freqs)
mcontr, fig2 = lfp.significant_time_frequency(dtup[2], [times0, times1], Tpre, Tpost, thresh=thresh, niter=1000, method='wav', doplot=True, normfun=nf, freqs=freqs)
return fig1, fig2
import numpy as np
import pandas as pd
import physutils as phys
lfp = phys.LFPset(pd.DataFrame.from_csv("sample_lfp.csv"))
evt = pd.DataFrame.from_csv('sample_events.csv')
stops = evt['stop inflating'].dropna()
pops = evt['popped'].dropna()
Tpre = -1.5
Tpost = 0.5
baseline_interval = (-1.5, -1.35)
lothresh = -2.56
hithresh = 2.56
thresh = (lothresh, hithresh)
freqs = np.exp(np.linspace(np.log(2.5), np.log(50)))
series = lfp.dataframe['17']
nf = phys.norm_by_mean(baseline_interval)
lfp.significant_time_frequency('17', [stops, pops], Tpre, Tpost, thresh, niter=100, method='wav', doplot=False, normfun=nf, freqs=freqs)
def make_time_frequency_plot(dbname, dtup, event_name, Tpre, Tpost, freqs, baseline_interval):
# get lfp data
print "Fetching data: " + str(dtup)
lfp = dbio.fetch_all_such_LFP(dbname, *dtup).censor()
# get events
evt = dbio.fetch(dbname, 'events', *dtup[:2])
times = evt[event_name].dropna()
wav_normed, fig = lfp.avg_time_frequency(dtup[2], times, Tpre, Tpost, method='wav', normfun=physutils.norm_by_mean(baseline_interval))
return fig
