def select_column_from_mindboggle_tables(subjects, hemi, index, tables_dir,
table_name, is_surface_table=True, write_table=True, output_table=''):
"""
Select column from Mindboggle shape tables and make a new table.
For example, extract the median travel depth column for the label regions
across a set of subjects, and make a new table.
Expects::
<tables_dir>/<subject>/tables/['left','right']_cortical_surface/<table_name>
Parameters
----------
subjects : list of strings
names of subjects processed by Mindboggle
hemi : string
hemisphere in {'left', 'right}
index : integer
index for column to select
tables_dir : string
name of Mindboggle tables directory
table_name : string
name of Mindboggle table file
is_surface_table : Boolean
if True, use path to surface tables
write_table : Boolean
write output table?
output_table : string
output table file name
Returns
-------
tables : list of strings
input table files (full paths)
columns : list of lists of floats or integers
columns of data
output_table : string
output table file name
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import select_column_from_mindboggle_tables
>>> subjects = ['Twins-2-1', 'Colin27-1']
>>> hemi = 'left'
>>> index = 2
>>> tables_dir = os.path.join(os.environ['HOME'], 'mindboggled')
>>> table_name = "label_shapes.csv"
>>> label_name = 'Label name'
>>> is_surface_table = True
>>> write_table = True
>>> output_table = ''
>>> select_column_from_mindboggle_tables(subjects, hemi, index, tables_dir,
>>> table_name, is_surface_table, write_table, output_table)
"""
import os
from mindboggle.mio.tables import select_column_from_tables
#-------------------------------------------------------------------------
# Construct list of Mindboggle shape table file names:
#-------------------------------------------------------------------------
tables = []
for subject in subjects:
if is_surface_table:
table = os.path.join(tables_dir, subject, 'tables',
hemi+'_cortical_surface', table_name)
else:
table = os.path.join(tables_dir, subject, 'tables', table_name)
tables.append(table)
#-------------------------------------------------------------------------
# Extract columns and construct new table:
#-------------------------------------------------------------------------
tables, columns, output_table = select_column_from_tables(tables, index,
write_table, output_table)
return tables, columns, output_table
def select_column_from_mindboggle_tables(subjects,
hemi,
index,
tables_dir,
table_name,
is_surface_table=True,
write_table=True,
output_table=''):
"""
Select column from Mindboggle shape tables and make a new table.
For example, extract the median travel depth column for the label regions
across a set of subjects, and make a new table.
Expects::
<tables_dir>/<subject>/tables/['left','right']_cortical_surface/<table_name>
Parameters
----------
subjects : list of strings
names of subjects processed by Mindboggle
hemi : string
hemisphere in {'left', 'right}
index : integer
index for column to select
tables_dir : string
name of Mindboggle tables directory
table_name : string
name of Mindboggle table file
is_surface_table : Boolean
if True, use path to surface tables
write_table : Boolean
write output table?
output_table : string
output table file name
Returns
-------
tables : list of strings
input table files (full paths)
columns : list of lists of floats or integers
columns of data
output_table : string
output table file name
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import select_column_from_mindboggle_tables
>>> subjects = ['Twins-2-1', 'Colin27-1']
>>> hemi = 'left'
>>> index = 2
>>> tables_dir = os.path.join(os.environ['HOME'], 'mindboggled')
>>> table_name = "label_shapes.csv"
>>> label_name = 'Label name'
>>> is_surface_table = True
>>> write_table = True
>>> output_table = ''
>>> select_column_from_mindboggle_tables(subjects, hemi, index, tables_dir,
>>> table_name, is_surface_table, write_table, output_table)
"""
import os
from mindboggle.mio.tables import select_column_from_tables
#-------------------------------------------------------------------------
# Construct list of Mindboggle shape table file names:
#-------------------------------------------------------------------------
tables = []
for subject in subjects:
if is_surface_table:
table = os.path.join(tables_dir, subject, 'tables',
hemi + '_cortical_surface', table_name)
else:
table = os.path.join(tables_dir, subject, 'tables', table_name)
tables.append(table)
#-------------------------------------------------------------------------
# Extract columns and construct new table:
#-------------------------------------------------------------------------
tables, columns, output_table = select_column_from_tables(
tables, index, write_table, output_table)
return tables, columns, output_table
def select_column_from_mindboggle_tables(subjects,
hemi,
index,
tables_dir,
table_name,
is_surface_table=True,
write_table=True,
output_table=''):
"""
Select column from Mindboggle shape tables and make a new table.
For example, extract the median travel depth column for the label regions
across a set of subjects, and make a new table.
Expects::
<tables_dir>/<subject>/tables/['left','right']_cortical_surface/<table_name>
Parameters
----------
subjects : list of strings
names of subjects processed by Mindboggle
hemi : string
hemisphere in {'left', 'right}
index : integer
index for column to select
tables_dir : string
name of Mindboggle tables directory
table_name : string
name of Mindboggle table file
is_surface_table : bool
if True, use path to surface tables
write_table : bool
write output table?
output_table : string
output table file name
Returns
-------
tables : list of strings
input table files (full paths)
columns : list of lists of floats or integers
columns of data
output_table : string
output table file name
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import select_column_from_mindboggle_tables
>>> path = os.environ['MINDBOGGLE_DATA'] # doctest: +SKIP
>>> subject1 = os.path.basename(path) # doctest: +SKIP
>>> subject2 = os.path.basename(path) # doctest: +SKIP
>>> subjects = [subject1, subject2] # doctest: +SKIP
>>> hemi = 'left'
>>> index = 2
>>> tables_dir = os.path.dirname(path) # doctest: +SKIP
>>> table_name = "label_shapes.csv"
>>> label_name = 'Label name'
>>> is_surface_table = True
>>> write_table = True
>>> output_table = ''
>>> tables, cols, output = select_column_from_mindboggle_tables(subjects,
... hemi, index, tables_dir, table_name, is_surface_table,
... write_table, output_table) # doctest: +SKIP
>>> cols[0][0] # doctest: +SKIP
878.03969839999979
>>> cols[0][1] # doctest: +SKIP
3085.6236725000008
>>> cols[0][2] # doctest: +SKIP
1761.2330760000002
"""
import os
from mindboggle.mio.tables import select_column_from_tables
# ------------------------------------------------------------------------
# Construct list of Mindboggle shape table file names:
# ------------------------------------------------------------------------
tables = []
for subject in subjects:
if is_surface_table:
table = os.path.join(tables_dir, subject, 'tables',
hemi + '_cortical_surface', table_name)
else:
table = os.path.join(tables_dir, subject, 'tables', table_name)
tables.append(table)
# ------------------------------------------------------------------------
# Extract columns and construct new table:
# ------------------------------------------------------------------------
tables, columns, output_table = select_column_from_tables(
tables, index, write_table, output_table)
return tables, columns, output_table
def select_column_from_mindboggle_tables(subjects, hemi, index, tables_dir,
table_name, is_surface_table=True, write_table=True, output_table=''):
"""
Select column from Mindboggle shape tables and make a new table.
For example, extract the median travel depth column for the label regions
across a set of subjects, and make a new table.
Expects::
<tables_dir>/<subject>/tables/['left','right']_cortical_surface/<table_name>
Parameters
----------
subjects : list of strings
names of subjects processed by Mindboggle
hemi : string
hemisphere in {'left', 'right}
index : integer
index for column to select
tables_dir : string
name of Mindboggle tables directory
table_name : string
name of Mindboggle table file
is_surface_table : bool
if True, use path to surface tables
write_table : bool
write output table?
output_table : string
output table file name
Returns
-------
tables : list of strings
input table files (full paths)
columns : list of lists of floats or integers
columns of data
output_table : string
output table file name
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import select_column_from_mindboggle_tables
>>> path = os.environ['MINDBOGGLE_DATA'] # doctest: +SKIP
>>> subject1 = os.path.basename(path) # doctest: +SKIP
>>> subject2 = os.path.basename(path) # doctest: +SKIP
>>> subjects = [subject1, subject2] # doctest: +SKIP
>>> hemi = 'left'
>>> index = 2
>>> tables_dir = os.path.dirname(path) # doctest: +SKIP
>>> table_name = "label_shapes.csv"
>>> label_name = 'Label name'
>>> is_surface_table = True
>>> write_table = True
>>> output_table = ''
>>> tables, cols, output = select_column_from_mindboggle_tables(subjects,
... hemi, index, tables_dir, table_name, is_surface_table,
... write_table, output_table) # doctest: +SKIP
>>> cols[0][0] # doctest: +SKIP
878.03969839999979
>>> cols[0][1] # doctest: +SKIP
3085.6236725000008
>>> cols[0][2] # doctest: +SKIP
1761.2330760000002
"""
import os
from mindboggle.mio.tables import select_column_from_tables
#-------------------------------------------------------------------------
# Construct list of Mindboggle shape table file names:
#-------------------------------------------------------------------------
tables = []
for subject in subjects:
if is_surface_table:
table = os.path.join(tables_dir, subject, 'tables',
hemi+'_cortical_surface', table_name)
else:
table = os.path.join(tables_dir, subject, 'tables', table_name)
tables.append(table)
#-------------------------------------------------------------------------
# Extract columns and construct new table:
#-------------------------------------------------------------------------
tables, columns, output_table = select_column_from_tables(tables, index,
write_table, output_table)
return tables, columns, output_table
