def main():
parser = argparse.ArgumentParser(description='CT analysis - Exporter')
parser.add_argument('data', nargs='?', type=str, default='-')
args = parser.parse_args()
df = read_data([args.data])
bgs = sorted(np.unique(df['bg']))
if len(bgs) != 8:
print('bgs != 8', file=sys.stderr)
sys.exit(-1)
sizes = np.unique(df['size'])
if len(sizes) != 1:
print('size != 1', file=sys.stderr)
sys.exit(-1)
subjects = np.unique(df['subject'])
lst = []
res = {"*": lst}
for bg in bgs:
part = df.loc[df['bg'] == bg]
fgs = sorted(np.unique(part['fg']))
rdat = []
for fg in fgs:
row = part.loc[part['fg'] == fg]
rdat+= [[fg, float(row['shift']), float(row['err'])]]
lst += [rdat]
s = json.dumps(res)
print(s)
def main():
parser = argparse.ArgumentParser(description='CT - Analysis')
parser.add_argument('--data', nargs='+', type=str)
parser.add_argument('--exclude-files', dest='fnfilter', type=str)
parser.add_argument('experiment', nargs='?', type=str, default=None)
parser.add_argument('subjects', nargs='*', type=str, default=None)
args = parser.parse_args()
args_ok = check_args(args)
if not args_ok:
parser.print_help(sys.stderr)
sys.exit(-1)
if args.experiment:
exp = ct.Experiment.load_from_path(args.experiment)
subjects = filter(lambda s: len(s), args.subjects) or exp.subjects
print('[i] subjects: ' + ' '.join(subjects), file=sys.stderr)
dfs = map(lambda subject: exp.load_result_data(subject, args.fnfilter),
subjects)
df = pd.concat(dfs, ignore_index=True)
else:
df = read_data(args.data)
df['subject'] = 'data'
df.rename(columns={'fg': 'fg_abs'}, inplace=True)
df['shift'] = df['phi'].combine(df['fg_abs'], calc_angle_shift)
df['fg'] = df['fg_abs'].combine(df['bg'], calc_angle_shift)
df.to_csv(sys.stdout, index=False)
def main():
parser = argparse.ArgumentParser(description='CT analysis - Filter')
parser.add_argument('data', nargs='?', type=str, default='-')
parser.add_argument('--size', default=None)
parser.add_argument('--no-control',
action='store_true',
default=False,
dest='ctrl')
parser.add_argument('--fg', dest='fg', type=float, default=None)
parser.add_argument('-B', '--bg', dest='bg', type=float, default=None)
parser.add_argument('--fg-sign', dest='fg_sign', default=None)
parser.add_argument('--subject', dest='subject', default=None)
args = parser.parse_args()
df = read_data([args.data])
if args.size is not None:
df = filter_size(df, args.size)
if args.ctrl:
df = filter_control(df)
if args.fg:
df = filter_fg_range(df, args.fg)
if args.fg_sign:
df = filter_fg_sign(df, args.fg_sign)
if args.bg is not None:
df = filter_bg(df, args.bg)
if args.subject is not None:
df = df[df.subject == args.subject]
df.to_csv(sys.stdout, index=False)
def main():
parser = argparse.ArgumentParser(description='CT - Analysis')
parser.add_argument('data', type=str, nargs='?', default='-')
parser.add_argument('--sizerel', default=False, action='store_true')
parser.add_argument('--max-spread',
dest='maxspread',
default=False,
action='store_true')
parser.add_argument('--sl-rel',
dest='slrel',
choices={'40', 'upper', 'mean28'},
default=None)
args = parser.parse_args()
df = read_data([args.data])
gd = df.groupby(['bg', 'fg', 'subject'])
x = gd.apply(calc_spread)
x.reset_index(inplace=True)
if args.sizerel:
x = convert2sizerel(x, df)
x.to_csv(sys.stdout, ignore_index=True)
elif args.maxspread:
x = max_spread(x)
x.to_csv(sys.stdout, index=False)
elif args.slrel:
x = spread_slrel(x, df, args.slrel)
x.to_csv(sys.stdout, index=False)
else:
x.to_csv(sys.stdout, index=False)
return 0
def main():
parser = argparse.ArgumentParser(description='CT - Analysis')
parser.add_argument('data', type=str)
parser.add_argument('--combine', action='store_true', default=False)
parser.add_argument('--dbm', action='store_true', default=False)
args = parser.parse_args()
df = read_data([args.data])
groups = ['bg', 'fg', 'subject']
if args.combine:
groups.remove('subject')
gpd = df[['bg', 'fg', 'size', 'shift', 'subject']].groupby(groups,
as_index=False)
dfg = gpd.apply(make_calc_size_diff(args.dbm))
x = dfg.reset_index()
if args.combine:
subs = df['subject'].unique()
x['subject'] = '_'.join(map(lambda x: x[:2],
subs)) if len(subs) > 1 else subs[0]
x['size'] = 40 # FIXME
x.to_csv(sys.stdout, index=False)
def main():
parser = argparse.ArgumentParser(description='CT - Analysis')
parser.add_argument('data', nargs='?', type=str, default='-')
args = parser.parse_args()
df = read_data([args.data])
df = df[df.bg != -1]
dfg = df.groupby(['bg', 'fg'])
x = dfg.apply(calc_delta)
x = x.reset_index()
x.to_csv(sys.stdout, index=False)
def main():
parser = argparse.ArgumentParser(description='CT - Analysis]')
parser.add_argument('data', nargs='?', type=str, default='-')
parser.add_argument('--alpha', type=float, default=0.01)
args = parser.parse_args()
df = read_data([args.data])
groups = ['bg', 'subject']
gpd = df.groupby(groups)
dfg = gpd.apply(chi_squared_sizes)
dfg = dfg.reset_index()
dfg.rename(columns={'level_2': 'combination'}, inplace=True)
test_significance(dfg, args.alpha)
dfg.to_csv(sys.stdout, index=False)
def main():
parser = argparse.ArgumentParser(description='CT - Analysis [SizeRel]')
parser.add_argument('--data', type=str, default=['-'])
parser.add_argument('--mean', action='store_true', default=False)
args = parser.parse_args()
df = read_data(args.data)
df = df[df.bg != -1]
subjects = df['subject'].unique()
have_avg = len(subjects) == 1 and '_' in subjects[0]
if have_avg:
print('[I] sizerel: using average method!', file=sys.stderr)
x = calc_sizerel_avg(df, args)
else:
x = calc_sizerel(df, args)
x.to_csv(sys.stdout, ignore_index=have_avg)
def main():
parser = argparse.ArgumentParser(description='CT - Analysis')
parser.add_argument('data', type=str, nargs='?', default='-')
parser.add_argument('over', choices=['surrounds', 'size'])
parser.add_argument('--method',
choices=['mean', 'regress', 'last'],
default='regress')
parser.add_argument('--no-s',
dest='nos',
action='store_true',
default=False)
args = parser.parse_args()
df = read_data([args.data])
if args.over == 'surrounds':
slope_avg_surrounds(df, args)
else:
slope_avg_sizes(df, args)
def main():
parser = argparse.ArgumentParser(description='CT analysis - Filter')
parser.add_argument('--data', nargs='+', type=str, default=['-'])
subparsers = parser.add_subparsers(help='sub-command help')
sp_r2a = subparsers.add_parser('rel2abs', help='a help')
sp_r2a.add_argument('--no-extend',
action='store_false',
default=True,
dest='extend')
sp_r2a.set_defaults(dispatch=rel2abs)
as_r2a = subparsers.add_parser('abs-shift', help='a help')
as_r2a.set_defaults(dispatch=abs_shift)
args = parser.parse_args()
df = read_data(args.data)
df = args.dispatch(df, args)
df.to_csv(sys.stdout, index=False)
def main():
parser = argparse.ArgumentParser(description='CT - Analysis')
parser.add_argument('subject', type=str)
parser.add_argument('olddata', type=str)
parser.add_argument('data', nargs='?', type=str, default='-')
parser.add_argument('--inner', action='store_true', default=False)
parser.add_argument('--chi2', action='store_true', default=False)
parser.add_argument('--alpha', type=float, default=0.01)
args = parser.parse_args()
df = read_data([args.data])
if args.subject not in df.subject.unique():
print('Subject not in new data!', file=sys.stderr)
sys.exit(-1)
df = df[df.subject == args.subject]
df = df[df.size == 40]
old_df = pd.read_csv(args.olddata)
old_df['size'] = 40
old_df.columns = ['bg', 'fg', 'oshift', 'oerr', 'size']
dfi = df.set_index(['size', 'bg', 'fg'])
dfo = old_df.set_index(['size', 'bg', 'fg'])
kwargs = {}
if args.chi2 or args.inner:
kwargs['join'] = 'inner'
dfa = pd.concat([dfi, dfo], axis=1, **kwargs)
x = dfa.reset_index()
x.subject = args.subject
if args.chi2:
test_significance(x, alpha=args.alpha)
else:
x.to_csv(sys.stdout, index=False)
def main():
parser = argparse.ArgumentParser(description='CT - Analysis')
parser.add_argument('data', nargs='?', type=str, default='-')
args = parser.parse_args()
df = read_data([args.data])
gpd = df.groupby(['fg', 'bg', 'subject'])
x = gpd.apply(find_max)
x.reset_index(inplace=True)
foo = max_spread(x)
foo.to_csv(sys.stdout, index=False)
gx = x.groupby(['bg', 'subject'])
imax = x.ix[gx.upper.idxmax()]
imax.to_csv(sys.stdout, index=False)
foo.set_index(['bg', 'subject'])
imax.set_index(['bg', 'subject'])
foo['dfg'] = foo['fg'] - imax['fg']
foo.to_csv(sys.stdout, index=False)
def main():
parser = argparse.ArgumentParser(description='CT - Analysis')
parser.add_argument('data', type=str, nargs='?', default='-')
parser.add_argument('--single', action='store_true', default=False)
parser.add_argument('--style', nargs='*', type=str, default=['ck'])
parser.add_argument('--color', default='seq_bmr', type=str)
parser.add_argument('-S', '--save', dest='save', default=False, action='store_true')
parser.add_argument('--no-legend', dest='legend', action='store_false', default=True)
parser.add_argument('--no-title', dest='no_title', action='store_true', default=False)
parser.add_argument('--ylim', default=None, type=float)
parser.add_argument('--vertical', default=False, action='store_true')
parser.add_argument('-H', '--height', dest='height', type=float, default=13.7)
parser.add_argument('-W', '--width', dest='width', type=float, default=24.7)
parser.add_argument('-U', '--unit', dest='unit', type=str, default='cm')
parser.add_argument('-P', '--path', dest='path', type=str, default=None)
parser.add_argument('-F', '--filename', dest='filename', type=str, default=None)
parser.add_argument('--scale', default=1, type=float)
parser.add_argument('--daylight', default=False, action='store_true')
parser.add_argument('--annotate', default=None)
args = parser.parse_args()
df = read_data([args.data])
print(df, file=sys.stderr)
plt.style.use(args.style)
if 'shift' in df.columns and 'N' not in df.columns:
fig = plot_shifts_individual(df, args)
elif 'shift' in df.columns and 'bg' not in df.columns:
fig = plot_shifts_bgavg(df, args)
elif 'oshift' in df.columns:
plotter = CompareShiftPlotter(df, args)
fig = plotter()
elif 'shift' in df.columns:
plotter = ShiftPlotter.make(df, args)
if args.annotate:
data = read_data([args.annotate])
plotter.annotate_fgs(data)
fig = plotter()
elif 'delta' in df.columns:
fig = plot_delta_combined(df, args)
elif 'm_plus' in df.columns:
fig = plot_sizerel(df, args)
elif 'm_mean' in df.columns:
fig = plot_sizerel_combined(df, args)
elif 'duration' in df.columns:
plotter = ShiftPlotter.make(df, args, column='duration')
fig = plotter()
elif 'szdiff' in df.columns:
plotter = ShiftPlotter.make(df, args, column='szdiff')
fig = plotter()
elif 'spread' in df.columns and (len(df) > 10):
#plotter = ShiftPlotter.make(df, args, column='spread')
#fig = plotter()
#fig = plot_spread_polar(df, args)
fig = scatter_spread(df, args)
elif 'spread' in df.columns or 'slope_mean_abs' in df.columns:
fig = plot_spread_polar(df, args)
else:
raise ValueError('Unknown data set')
if args.save:
for f in fig:
ggsave(plot=f, filename=args.filename, path=args.path,
width=args.width, height=args.height, units=args.unit,
dpi=600, scale=args.scale)
else:
plt.show()
