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dataset.py
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dataset.py
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"""
Classes for representing datasets of images and/or coordinates.
"""
from __future__ import print_function
import json
import copy
import logging
import os.path as op
import numpy as np
import pandas as pd
import nibabel as nib
from .base import NiMAREBase
from .utils import (tal2mni, mni2tal, mm2vox, get_template, listify,
try_prepend, find_stem, get_masker)
LGR = logging.getLogger(__name__)
class Dataset(NiMAREBase):
"""
Storage container for a coordinate- and/or image-based meta-analytic
dataset/database.
Parameters
----------
source : :obj:`str`
JSON file containing dictionary with database information or the dict()
object
target : :obj:`str`
Desired coordinate space for coordinates. Names follow NIDM convention.
mask : `str`, `Nifti1Image`, or any nilearn `Masker`
Mask(er) to use. If None, uses the target space image, with all
non-zero voxels included in the mask.
"""
_id_cols = ['id', 'study_id', 'contrast_id']
def __init__(self, source, target='mni152_2mm', mask=None):
if isinstance(source, str):
with open(source, 'r') as f_obj:
self.data = json.load(f_obj)
elif isinstance(source, dict):
self.data = source
else:
raise Exception("`source` needs to be a file path or a dictionary")
# Datasets are organized by study, then experiment
# To generate unique IDs, we combine study ID with experiment ID
# build list of ids
id_columns = ['id', 'study_id', 'contrast_id']
all_ids = []
for pid in self.data.keys():
for expid in self.data[pid]['contrasts'].keys():
id_ = '{0}-{1}'.format(pid, expid)
all_ids.append([id_, pid, expid])
id_df = pd.DataFrame(columns=id_columns, data=all_ids)
id_df = id_df.set_index('id', drop=False)
self.ids = id_df.index.values
# Set up Masker
if mask is None:
mask = get_template(target, mask='brain')
self.masker = get_masker(mask)
self.space = target
self.annotations = self._load_data(id_df, key='labels')
self.metadata = self._load_data(id_df, key='metadata')
self.texts = self._load_data(id_df, key='text')
raw_image_df = self._load_data(id_df, key='images')
self.images = self._validate_images(raw_image_df)
self.coordinates = self._load_coordinates()
def slice(self, ids):
"""
Return a reduced dataset with only requested IDs.
Parameters
----------
ids : array_like
List of study IDs to include in new dataset
Returns
-------
new_dset : :obj:`nimare.dataset.Dataset`
Redcued Dataset containing only requested studies.
"""
new_dset = copy.deepcopy(self)
new_dset.ids = ids
new_dset.coordinates = new_dset.coordinates.loc[new_dset.coordinates['id'].isin(ids)]
new_dset.images = new_dset.images.loc[new_dset.images['id'].isin(ids)]
new_dset.annotations = new_dset.annotations.loc[new_dset.annotations['id'].isin(ids)]
new_dset.texts = new_dset.texts.loc[new_dset.texts['id'].isin(ids)]
temp_data = {}
for id_ in ids:
pid, expid = id_.split('-')
if pid not in temp_data.keys():
temp_data[pid] = self.data[pid].copy() # make sure to copy
temp_data[pid]['contrasts'] = {}
temp_data[pid]['contrasts'][expid] = self.data[pid]['contrasts'][expid]
new_dset.data = temp_data
return new_dset
def update_path(self, new_path):
"""
Update paths to images. Prepends new path to the relative path for
files in Dataset.images.
Parameters
----------
new_path : :obj:`str`
Path to prepend to relative paths of files in Dataset.images.
"""
relative_path_cols = [c for c in self.images if c.endswith('__relative')]
for col in relative_path_cols:
abs_col = col.replace('__relative', '')
if abs_col in self.images.columns:
LGR.info('Overwriting images column {}'.format(abs_col))
self.images[abs_col] = self.images[col].apply(try_prepend, prefix=new_path)
def _load_data(self, id_df, key='labels'):
"""
Load a given data type in Dataset into DataFrame.
Parameters
----------
id_df : :obj:`pandas.DataFrame`
DataFrame with columns for identifiers. Index is [studyid]-[expid].
key : {'labels', 'metadata', 'text', 'images'}
Which data type to load.
Returns
-------
df : :obj:`pandas.DataFrame`
DataFrame with id columns from id_df and new columns for the
requested data type.
"""
exp_dict = {}
for pid in self.data.keys():
for expid in self.data[pid]['contrasts'].keys():
exp = self.data[pid]['contrasts'][expid]
id_ = '{0}-{1}'.format(pid, expid)
if key not in self.data[pid]['contrasts'][expid].keys():
continue
exp_dict[id_] = exp[key]
temp_df = pd.DataFrame.from_dict(exp_dict, orient='index')
df = pd.merge(id_df, temp_df, left_index=True, right_index=True, how='outer')
df = df.reset_index(drop=True)
df = df.replace(to_replace='None', value=np.nan)
return df
def _validate_images(self, image_df):
"""
Check and update image paths in DataFrame.
"""
valid_suffices = ['.brik', '.head', '.nii', '.img', '.hed']
file_cols = []
for col in image_df.columns:
vals = [v for v in image_df[col].values if isinstance(v, str)]
fc = any([any([vs in v for vs in valid_suffices]) for v in vals])
if fc:
file_cols.append(col)
# Clean up image_df
# Find out which columns have full paths and which have relative paths
abs_cols = []
for col in file_cols:
files = image_df[col].tolist()
abspaths = [f == op.abspath(f) for f in files if isinstance(f, str)]
if all(abspaths):
abs_cols.append(col)
elif not any(abspaths):
image_df = image_df.rename(columns={col: col + '__relative'})
else:
raise ValueError('Mix of absolute and relative paths detected '
'for "{0}" images'.format(col))
# Set relative paths from absolute ones
if len(abs_cols):
all_files = list(np.ravel(image_df[abs_cols].values))
all_files = [f for f in all_files if isinstance(f, str)]
shared_path = find_stem(all_files)
LGR.info('Shared path detected: "{0}"'.format(shared_path))
for abs_col in abs_cols:
image_df[abs_col + '__relative'] = image_df[abs_col].apply(
lambda x: x.split(shared_path)[1] if isinstance(x, str) else x)
return image_df
def _load_coordinates(self):
"""
Load coordinates in Dataset into DataFrame.
"""
# Required columns
columns = ['id', 'study_id', 'contrast_id', 'x', 'y', 'z', 'n', 'space']
core_columns = columns[:] # Used in contrast for loop
all_dfs = []
for pid in self.data.keys():
for expid in self.data[pid]['contrasts'].keys():
if 'coords' not in self.data[pid]['contrasts'][expid].keys():
continue
exp_columns = core_columns[:]
exp = self.data[pid]['contrasts'][expid]
# Required info (ids, x, y, z, space)
n_coords = len(exp['coords']['x'])
rep_id = np.array([['{0}-{1}'.format(pid, expid), pid, expid]] * n_coords).T
# collect sample size if available
sample_size = exp['metadata'].get('sample_sizes', np.nan)
if not isinstance(sample_size, list):
sample_size = [sample_size]
sample_size = np.array([n for n in sample_size if n])
if len(sample_size):
sample_size = np.mean(sample_size)
sample_size = np.array([sample_size] * n_coords)
else:
sample_size = np.array([np.nan] * n_coords)
space = exp['coords'].get('space')
space = np.array([space] * n_coords)
temp_data = np.vstack((rep_id,
np.array(exp['coords']['x']),
np.array(exp['coords']['y']),
np.array(exp['coords']['z']),
sample_size,
space))
# Optional information
for k in list(set(exp['coords'].keys()) - set(columns)):
k_data = exp['coords'][k]
if not isinstance(k_data, list):
k_data = np.array([k_data] * n_coords)
exp_columns.append(k)
if k not in columns:
columns.append(k)
temp_data = np.vstack((temp_data, k_data))
# Place data in list of dataframes to merge
con_df = pd.DataFrame(temp_data.T, columns=exp_columns)
all_dfs.append(con_df)
df = pd.concat(all_dfs, axis=0, join='outer', sort=False)
df = df[columns].reset_index(drop=True)
df = df.replace(to_replace='None', value=np.nan)
df[['x', 'y', 'z']] = df[['x', 'y', 'z']].astype(float)
# Now to apply transformations!
if 'mni' in self.space.lower() or 'ale' in self.space.lower():
transform = {'MNI': None,
'TAL': tal2mni,
'Talairach': tal2mni,
}
elif 'tal' in self.space.lower():
transform = {'MNI': mni2tal,
'TAL': None,
'Talairach': None,
}
else:
raise ValueError('Unrecognized space: {0}'.format(self.space))
found_spaces = df['space'].unique()
for found_space in found_spaces:
if found_space not in transform.keys():
LGR.warning('Not applying transforms to coordinates in '
'unrecognized space "{0}"'.format(found_space))
alg = transform.get(found_space, None)
idx = df['space'] == found_space
if alg:
df.loc[idx, ['x', 'y', 'z']] = alg(df.loc[idx, ['x', 'y', 'z']].values)
df.loc[idx, 'space'] = self.space
xyz = df[['x', 'y', 'z']].values
ijk = pd.DataFrame(mm2vox(xyz, self.masker.mask_img.affine),
columns=['i', 'j', 'k'])
df = pd.concat([df, ijk], axis=1)
return df
def get(self, dict_):
"""
Retrieve files and/or metadata from the current Dataset.
Parameters
----------
dict_ : :obj:`dict`
Dictionary specifying images or metadata to collect
Returns
-------
results : :obj:`dict`
A dictionary of lists of requested data.
"""
results = {}
results['id'] = self.ids
keep_idx = np.arange(len(self.ids), dtype=int)
for k in dict_:
vals = dict_[k]
if vals[0] == 'image':
temp = self.get_images(imtype=vals[1])
elif vals[0] == 'metadata':
temp = self.get_metadata(field=vals[1])
else:
raise ValueError('Input "{}" not understood.'.format(vals[0]))
results[k] = temp
temp_keep_idx = np.where([t is not None for t in temp])[0]
keep_idx = np.intersect1d(keep_idx, temp_keep_idx)
# reduce
if len(keep_idx) != len(self.ids):
LGR.info('Retaining {0}/{1} studies'.format(len(keep_idx),
len(self.ids)))
for k in results:
results[k] = [results[k][i] for i in keep_idx]
return results
def get_labels(self, ids=None):
"""
Extract list of labels for which studies in Dataset have annotations.
Parameters
----------
ids : list, optional
A list of IDs in the Dataset for which to find labels. Default is
None, in which case all labels are returned.
Returns
-------
labels : list
List of labels for which there are annotations in the Dataset.
"""
if not isinstance(ids, list) and ids is not None:
ids = listify(ids)
result = [c for c in self.annotations.columns if c not in self._id_cols]
if ids is not None:
temp_annotations = self.annotations.loc[self.annotations['id'].isin(ids)]
res = temp_annotations[result].any(axis=0)
result = res.loc[res].index.tolist()
return result
def get_texts(self, ids=None, text_type='abstract'):
"""
Extract list of texts of a given type for selected IDs.
Parameters
----------
ids : list, optional
A list of IDs in the Dataset for which to find texts. Default is
None, in which case all texts of requested type are returned.
text_type : str, optional
Type of text to extract. Corresponds to column name in
Dataset.texts DataFrame. Default is 'abstract'.
Returns
-------
texts : list
List of texts of requested type for selected IDs.
"""
return_first = False
if not isinstance(ids, list) and ids is not None:
return_first = True
ids = listify(ids)
text_types = [c for c in self.texts.columns if c not in self._id_cols]
if text_type not in text_types:
raise ValueError('Text type "{0}" not found.\nAvailable types: '
'{1}'.format(text_type, ', '.join(text_types)))
if ids is not None:
result = self.texts[text_type].loc[self.texts['id'].isin(ids)]
else:
result = self.texts[text_type]
if return_first:
return result[0]
else:
return result
return result
def get_metadata(self, ids=None, field='sample_sizes'):
"""
Get metadata from Dataset.
Parameters
----------
ids : list, optional
A list of IDs in the Dataset for which to find texts. Default is
None, in which case all texts of requested type are returned.
field : str, optional
Metadata field to extract. Corresponds to column name in
Dataset.metadata DataFrame. Default is 'sample_sizes'.
Returns
-------
metadata : list
List of values of requested type for selected IDs.
"""
return_first = False
if not isinstance(ids, list) and ids is not None:
return_first = True
ids = listify(ids)
md_fields = [c for c in self.metadata.columns if c not in self._id_cols]
if field not in md_fields:
raise ValueError('Metadata field "{0}" not found.\nAvailable fields: '
'{1}'.format(field, ', '.join(md_fields)))
if ids is not None:
result = self.metadata[field].loc[self.metadata['id'].isin(ids)].tolist()
else:
result = self.metadata[field].tolist()
if return_first:
return result[0]
else:
return result
def get_images(self, ids=None, imtype='z'):
"""
Get images of a certain type for a subset of studies in the dataset.
Parameters
----------
ids : list, optional
A list of IDs in the Dataset for which to find texts. Default is
None, in which case all texts of requested type are returned.
imtype : str, optional
Type of image to extract. Corresponds to column name in
Dataset.images DataFrame. Default is 'z'.
Returns
-------
images : list
List of images of requested type for selected IDs.
"""
return_first = False
if not isinstance(ids, list) and ids is not None:
return_first = True
ids = listify(ids)
imtypes = [c for c in self.images.columns if c not in self._id_cols]
if imtype not in imtypes:
raise ValueError('Image type "{0}" not found.\nAvailable types: '
'{1}'.format(imtype, ', '.join(imtypes)))
if ids is not None:
result = self.images[imtype].loc[self.images['id'].isin(ids)].tolist()
else:
result = self.images[imtype].tolist()
if return_first:
return result[0]
else:
return result
def get_studies_by_label(self, labels=None, label_threshold=0.5):
"""
Extract list of studies with a given label.
Parameters
----------
labels : list, optional
List of labels to use to search Dataset. If a contrast has all of
the labels above the threshold, it will be returned.
Default is None.
label_threshold : float, optional
Default is 0.5.
Returns
-------
found_ids : list
A list of IDs from the Dataset found by the search criteria.
"""
if isinstance(labels, str):
labels = [labels]
elif labels is None:
# For now, labels are all we can search by.
return self.ids
elif not isinstance(labels, list):
raise ValueError('Argument "labels" cannot be {0}'.format(type(labels)))
found_labels = [l for l in labels if l in self.annotations.columns]
temp_annotations = self.annotations[self._id_cols + found_labels]
found_rows = (temp_annotations[found_labels] >= label_threshold).all(axis=1)
if any(found_rows):
found_ids = temp_annotations.loc[found_rows, 'id'].tolist()
else:
found_ids = []
return found_ids
def get_studies_by_mask(self, mask):
"""
Extract list of studies with at least one coordinate in mask.
Parameters
----------
mask : img_like
Mask across which to search for coordinates.
Returns
-------
found_ids : list
A list of IDs from the Dataset with at least one focus in the mask.
"""
from scipy.spatial.distance import cdist
if isinstance(mask, str):
mask = nib.load(mask)
curr_mask = self.masker.mask_img
if not np.array_equal(curr_mask.affine, mask.affine):
from nilearn.image import resample_to_img
mask = resample_to_img(mask, curr_mask)
mask_ijk = np.vstack(np.where(mask.get_data())).T
distances = cdist(mask_ijk, self.coordinates[['i', 'j', 'k']].values)
distances = np.any(distances == 0, axis=0)
found_ids = list(self.coordinates.loc[distances, 'id'].unique())
return found_ids
def get_studies_by_coordinate(self, xyz, r=20):
"""
Extract list of studies with at least one focus within radius r of
requested coordinates.
Parameters
----------
xyz : (X x 3) array_like
List of coordinates against which to find studies.
r : float, optional
Radius (in mm) within which to find studies. Default is 20mm.
Returns
-------
found_ids : list
A list of IDs from the Dataset with at least one focus within
radius r of requested coordinates.
"""
from scipy.spatial.distance import cdist
assert xyz.shape[1] == 3 and xyz.ndim == 2
distances = cdist(xyz, self.coordinates[['x', 'y', 'z']].values)
distances = np.any(distances <= r, axis=0)
found_ids = list(self.coordinates.loc[distances, 'id'].unique())
return found_ids