def loop_show_asymmetry(prefix,
grouping_keys=['Gender', 'FDH_23_Handedness_Prtcpnt'],
xaxis_key='Age_At_IMGExam',
plots='regressions'):
""" Loop over all properties to show asymmetry."""
data = get_all_data()
data.filter(lambda k, v: 'fuzzy' not in k) # Remove 'fuzzy'
data.filter([partial(lambda k, v, p: (k.startswith(p) or
k in grouping_keys or
k == xaxis_key),
p=p)
for p in prefix])
# Process & plot the data.
stats = []
regressions = []
group_samples = []
measure_keys = data.get_twohemi_keys()
for pi, key in enumerate(sorted(measure_keys)):
print("Comparing %d (%s)..." % (pi, key))
gn, ss, rv, gs = compare_group_asymmetry(data.data_dict, xaxis_key=xaxis_key,
yaxis_key=key, plots=plots,
grouping_keys=grouping_keys,
measure_key=key)
stats.append(ss)
regressions.append(rv)
group_samples.append(gs)
if 'regression_stats' in plots:
dump_regressions_csv(regressions,
group_names=gn,
measure_names=measure_keys)
plot_regressions_scatter(regressions,
group_names=gn,
measure_names=measure_keys)
if 'stat_distributions' in plots:
plot_stat_distributions(stats, group_names=gn)
plt.show()
"""
import sys
from functools import partial
from export import EXPORTED_PING_SPREADSHEET
from ping.apps import PINGSession
from ping.data import PINGData
from research.data import get_all_data
if len(sys.argv) > 1:
# Someone told us about an existing csv
csv_file = sys.argv[1]
else:
# We will build a csv on the fly.
csv_file = EXPORTED_PING_SPREADSHEET
filter_fns = [partial(lambda k, v: k.startswith(p), p=p)
for p in PINGData.IMAGING_PREFIX]
data = get_all_data(filter_fns=filter_fns)
data.export(out_file=csv_file)
for key in sorted(data.data_dict.keys()):
print(key)
# Upload the new spreadsheet.
sess = PINGSession()
sess.login()
sess.upload_user_spreadsheet(csv_file)
elif len(sys.argv) > 6:
do_usage(sys.argv, "Too many arguments.")
elif len(sys.argv) < 4:
do_usage(sys.argv, "Too few keys.")
else:
prefix, x_key, y_key = sys.argv[1:4]
prefix = prefix.split(',')
y_key = y_key.split(',')
size_key = None if len(sys.argv) < 5 else sys.argv[4].split(',')
color_key = None if len(sys.argv) < 6 else sys.argv[5].split(',')
# Get prefix
prefix_filter_fn = lambda k, v: np.any([k.startswith(p) for p in prefix])
# Load the data (should group, but ... later.)
data = get_all_data().filter(prefix_filter_fn)
size_label = ' Marker size indicates\n %s %s' % (
compute_scatter_label(size_key, part='key_type').lower(),
', '.join([prefix2text(p).lower() for p in prefix]))
ax = plot_scatter_4D(data, x_key=x_key, y_key=y_key, size_key=size_key, color_key=color_key,
size_label=size_label, add_marker_text=False)
#x_label='Asymmetry Index (mean)', y_label='Asymmetry Index (std)',
ax.get_figure().suptitle(', '.join([prefix2text(p) for p in prefix]), fontsize=24)
plt.show()