def main(ps,
nbins,
nboots,
ignore_dur,
doPlotPmf,
doPlotElb,
outdir,
isLongDur,
nElbows,
enforceZeroSlope,
resample=None,
ignoreElbs=False):
firstElbow = (136.0, 171.0) if ignoreElbs else False
secondElbow = (
983.0, 1267.0
) if ignoreElbs else False # ignores data past these times in (2d, 3d)
df = load(ps, None, 'both' if isLongDur else False, nbins, secondElbow,
firstElbow)
if resample:
df = resample_by_grp(df, resample)
durmap = make_durmap(df)
rows1, rows2 = [], []
for dotmode, df_dotmode in df.groupby('dotmode'):
if ignore_dur:
r1, r2 = make_rows(df_dotmode, dotmode, durmap, 0, nboots)
rows1.extend(r1)
rows2.extend(r2)
else:
for di, df_durind in df_dotmode.groupby('duration_index'):
r1, r2 = make_rows(df_durind, dotmode, durmap, di, nboots)
rows1.extend(r1)
rows2.extend(r2)
df_pts = pd.DataFrame(
rows1, columns=['subj', 'dotmode', 'di', 'dur', 'x', 'y', 'ntrials'])
df_fts = pd.DataFrame(rows2,
columns=[
'subj', 'dotmode', 'di', 'dur', 'bi', 'thresh',
'loc', 'scale', 'lapse'
])
df_fts['dur'] = 1000 * df_fts['dur']
df_fts['sens'] = 1 / df_fts['thresh']
if not ignore_dur and nElbows >= 0:
df_elbs = find_elbows_per_boots(df_fts, nElbows, enforceZeroSlope)
else:
df_elbs = pd.DataFrame()
if doPlotPmf:
plot_logistics(df_pts, df_fts)
if doPlotElb and not ignore_dur:
plot_threshes(df_fts, df_elbs)
if outdir is not None:
subj = ps['subj'] if ps['subj'] is not None else 'ALL'
df_pts['subj'] = subj
df_fts['subj'] = subj
df_elbs['subj'] = subj
to_csv(nbins, nboots, subj, df_pts, df_fts, df_elbs, outdir,
ignore_dur)
def load(min_dur=0.3, max_dur=1.2, min_coh=0.03, max_coh=0.5):
fltrs = [
pd_io.make_gt_filter('real_duration', min_dur),
pd_io.make_lt_filter('real_duration', max_dur)
]
fltrs.extend([
pd_io.make_gt_filter('coherence', min_coh),
pd_io.make_lt_filter('coherence', max_coh)
])
return pd_io.load(None, fltrs, 'both')
def plot(args, isLongDur=False):
df = load(args, None, isLongDur)
subj = subj_label(df, '')
for dotmode, df_dotmode in df.groupby('dotmode'):
fig = plt.figure()
ax = plt.subplot(111)
title = '{0}, {1}'.format(dotmode, subj)
plot_inner(ax, df_dotmode)
plot_info(ax, title)
plt.show()
def plot(args, isLongDur=False):
df = load(args, None, isLongDur)
subj = subj_label(df, '')
for dotmode, df_dotmode in df.groupby('dotmode'):
fig = plt.figure()
ax = plt.subplot(111)
title = '{0}, {1}'.format(dotmode, subj)
plot_inner(ax, df_dotmode)
plot_info(ax, title)
plt.show()
def main(isLongDur=True, nbins=20, resample=5, nboots=1000, doPlot=False):
df0 = load({}, None, 'both' if isLongDur else False, nbins)
xss = np.zeros([nboots, nbins])
yss = np.zeros([nboots, nbins])
for i in xrange(nboots):
if resample:
df = resample_by_grp(df0, resample)
vals = regress(df, doPlot)
xs = np.array(vals['2d'])[:,1]
ys = np.array(vals['3d'])[:,1]
xss[i, :] = xs
yss[i, :] = ys
durinds_to_ignore_2d, ubs_2d = check_different(xss)
durinds_to_ignore_3d, ubs_3d = check_different(yss)
print ('2d', zip(durinds_to_ignore_2d, ubs_2d)), ('3d', zip(durinds_to_ignore_3d, ubs_3d))
def fit(args, outdir, nboots, collapseCoh=False, isLongDur=False, resample=None, plot=False):
df = load(args, None, 'both' if isLongDur else False)
if resample:
df = resample_by_grp(df, resample)
dur_rng = (min_dur, max_dur_longDur) if isLongDur else (min_dur, max_dur)
subj = subj_label(df)
durmap = durmap_fcn(df)
res = {}
for grp, df_dotmode in df.groupby('dotmode' if collapseCoh else ['dotmode', 'coherence']):
key = grp if not collapseCoh else (grp, None)
res = fit_df(df_dotmode, nboots, res, key, dur_rng, durmap)
df1, df2 = finish_res(res, subj)
if outdir:
write_csv(df1, df2, subj, collapseCoh, outdir)
if plot:
plot_fit(df1, df2, collapseCoh)
def main(isLongDur=True, nbins=20, resample=5, nboots=1000, doPlot=False):
df0 = load({}, None, 'both' if isLongDur else False, nbins)
xss = np.zeros([nboots, nbins])
yss = np.zeros([nboots, nbins])
for i in xrange(nboots):
if resample:
df = resample_by_grp(df0, resample)
vals = regress(df, doPlot)
xs = np.array(vals['2d'])[:, 1]
ys = np.array(vals['3d'])[:, 1]
xss[i, :] = xs
yss[i, :] = ys
durinds_to_ignore_2d, ubs_2d = check_different(xss)
durinds_to_ignore_3d, ubs_3d = check_different(yss)
print('2d', zip(durinds_to_ignore_2d,
ubs_2d)), ('3d', zip(durinds_to_ignore_3d, ubs_3d))
def main(args, outdir):
if outdir:
CURDIR = os.path.dirname(os.path.abspath(__file__))
BASEDIR = os.path.abspath(os.path.join(CURDIR, '..'))
OUTDIR = os.path.join(BASEDIR, 'res', 'sequential-effects', outdir)
outfile = os.path.join(OUTDIR, '{0}-{1}.png'.format(subj, '_'.join(conds)))
else:
outfile = None
df = load(args)
ds = parse(df)
print 'Loaded'
res = calculate(ds)
print 'Calculated'
subjs = df['subj'].unique()
plot(res, subjs[0] if len(subjs) == 1 else 'ALL', outfile)
def main():
def summary(df, key=['subj', 'dotmode', 'isLongDur']):
rows = []
for k, dg in df.groupby(key, as_index=False):
tm = dg['session_index']
nts = []
for k1, dh in dg.groupby('session_index'):
nts.append(len(set(dh['trial_index'])))
v = '{0} - {1}'.format(
min(nts), max(nts)) if min(nts) != max(nts) else str(min(nts))
row = list(k) + [len(set(tm.values)), len(tm), v]
rows.append(row)
return pd.DataFrame(rows,
columns=key +
['# sessions', '# trials', '# trials/session'])
# data actually collected
df1 = load_df()
df1['isLongDur'] = False
dfA = summary(df1)
df2 = load_df(SESSIONS_INFILE_2, TRIALS_INFILE_2)
df2['isLongDur'] = True
dft = summary(df2)
dfA = dfA.append(dft)
# data used in analysis
df3 = load({}, None, 'both')
dfB = summary(df3)
# combine
key = ['isLongDur', 'subj', 'dotmode']
dfA = dfA.sort(key).reset_index()
dfB = dfB.sort(key).reset_index()
dfA['# trials analyzed'] = dfB['# trials']
dfA['# trials/session analyzed'] = dfB['# trials/session']
keys = [
"# sessions", "# trials", "# trials analyzed", "# trials/session",
"# trials/session analyzed"
]
print dfA.set_index(key)[keys].to_csv()
print dfA.groupby('isLongDur')[keys].sum()
print dfA.groupby('dotmode')[keys].sum()
print resample_by_grp(df3)
def main():
def summary(df, key=['subj', 'dotmode', 'isLongDur']):
rows = []
for k, dg in df.groupby(key, as_index=False):
tm = dg['session_index']
nts = []
for k1, dh in dg.groupby('session_index'):
nts.append(len(set(dh['trial_index'])))
v = '{0} - {1}'.format(min(nts), max(nts)) if min(nts) != max(nts) else str(min(nts))
row = list(k) + [len(set(tm.values)), len(tm), v]
rows.append(row)
return pd.DataFrame(rows, columns=key + ['# sessions', '# trials', '# trials/session'])
# data actually collected
df1 = load_df()
df1['isLongDur'] = False
dfA = summary(df1)
df2 = load_df(SESSIONS_INFILE_2, TRIALS_INFILE_2)
df2['isLongDur'] = True
dft = summary(df2)
dfA = dfA.append(dft)
# data used in analysis
df3 = load({}, None, 'both')
dfB = summary(df3)
# combine
key = ['isLongDur', 'subj', 'dotmode']
dfA = dfA.sort(key).reset_index()
dfB = dfB.sort(key).reset_index()
dfA['# trials analyzed'] = dfB['# trials']
dfA['# trials/session analyzed'] = dfB['# trials/session']
keys = ["# sessions", "# trials", "# trials analyzed", "# trials/session", "# trials/session analyzed"]
print dfA.set_index(key)[keys].to_csv()
print dfA.groupby('isLongDur')[keys].sum()
print dfA.groupby('dotmode')[keys].sum()
print resample_by_grp(df3)
def fit(args,
outdir,
nboots,
collapseCoh=False,
isLongDur=False,
resample=None,
plot=False):
df = load(args, None, 'both' if isLongDur else False)
if resample:
df = resample_by_grp(df, resample)
dur_rng = (min_dur, max_dur_longDur) if isLongDur else (min_dur, max_dur)
subj = subj_label(df)
durmap = durmap_fcn(df)
res = {}
for grp, df_dotmode in df.groupby(
'dotmode' if collapseCoh else ['dotmode', 'coherence']):
key = grp if not collapseCoh else (grp, None)
res = fit_df(df_dotmode, nboots, res, key, dur_rng, durmap)
df1, df2 = finish_res(res, subj)
if outdir:
write_csv(df1, df2, subj, collapseCoh, outdir)
if plot:
plot_fit(df1, df2, collapseCoh)
def load(min_dur=0.3, max_dur=1.2, min_coh=0.03, max_coh=0.5):
fltrs = [pd_io.make_gt_filter('real_duration', min_dur), pd_io.make_lt_filter('real_duration', max_dur)]
fltrs.extend([pd_io.make_gt_filter('coherence', min_coh), pd_io.make_lt_filter('coherence', max_coh)])
return pd_io.load(None, fltrs, 'both')