def generate_scatter_bokeh(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'plots')):
"""Various scatter plots"""
for atlas, measures in prefixes.items():
# Generate area vs. thickness plots
if atlas.lower() == 'destrieux':
continue
# Thickness vs. area
do_scatter(atlas=atlas, prefixes=[op.commonprefix(measures.values())],
x_key='%s:AI:mean' % measures['area'],
y_key='%s:AI:mean' % measures['thickness'],
title="Area vs. thickness",
data_dir=data_dir,
output_dir=output_dir,
output_format='bokeh')
for measure, prefix in measures.items():
# Generate scatter plot for given dataset / data point
do_scatter(atlas=atlas, prefixes=[prefix], x_key='AI:mean',
y_key='AI:std', size_key='LH_PLUS_RH:mean',
data_dir=data_dir,
output_dir=output_dir,
output_format='bokeh')
def generate_similarity_bokeh(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'plots',
'similarity')):
"""Asymmetry partial correlation matrix"""
for atlas, measures in prefixes.items():
if atlas == 'destrieux': # skip destrieux
continue
for measure, prefix in measures.items():
# Generate similarity matrix for given dataset / data point
do_similarity(atlas=atlas, prefixes=[prefix],
metric='partial-correlation',
measures=['Asymmetry Index'],
data_dir=data_dir,
output_dir=output_dir, output_format='bokeh')
def generate_multivariate(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'data')):
"""PCA overlay for roygbiv"""
from ping.ping.data import prefixes
from ping.scripts.multivariate import do_multivariate
for atlas, measures in prefixes.items():
if atlas == 'destrieux': # skip destrieux
continue
for measure, prefix in measures.items():
do_multivariate(prefixes=[prefix], atlas=atlas,
data_dir=data_dir,
output_dir=op.join(output_dir, 'fsaverage', atlas),
output_format='json',
verbose=0, pc_thresh=0.05)
def generate_similarity_json(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'data')):
"""Asymmetry partial correlation data as json overlay for roygbiv"""
from ping.ping.data import prefixes
from ping.scripts.similarity import do_similarity
for atlas, measures in prefixes.items():
if atlas == 'destrieux': # skip destrieux
continue
for measure, prefix in measures.items():
do_similarity(atlas=atlas, prefixes=[prefix],
metric='partial-correlation',
measures=['Asymmetry Index'],
data_dir=data_dir,
output_dir=op.join(output_dir, 'fsaverage', atlas),
output_format='json')
def generate_regressions(data_dir=op.join('generated', 'data'),
output_dir=op.join('generated', 'plots',
'regression')):
"""Regression between age and value, grouped by gender/handedness"""
from ping.ping.data import prefixes
from ping.scripts.grouping import do_grouping
for atlas, measures in prefixes.items():
if atlas == 'destrieux': # skip destrieux
continue
for measure, prefix in measures.items():
for grouping_key in ['Gender', 'FDH_23_Handedness_Prtcpnt']:
do_grouping(prefixes=[prefix], grouping_keys=[grouping_key],
xaxis_key='Age_At_IMGExam',
plots='regressions', atlas='desikan',
data_dir=data_dir,
output_dir=output_dir,
output_type='matplotlib')
