def test_NeuroVault_metadata():
from pyneurovault import api
# Test for all images
print "Checking metadata extraction for images..."
images = api.get_images()
check_df(df=images,size_min=7000,columns=["url","name","map_type"])
# Test for subset of images
images = api.get_images(pks=images.image_id[0:10].tolist())
check_df(df=images,size_min=10,columns=["url","name","map_type"])
# Test for collections
print "Checking metadata extraction for collections..."
collections = api.get_collections()
check_df(df=collections,size_min=300,columns=["used_smoothing","url","collection_id"])
# Test metadata from specific DOIs
dois = collections.DOI[collections.DOI.isnull()==False].tolist()[0:15]
results = api.collections_from_dois(dois)
check_df(df=results,size_min=len(dois),columns=["used_smoothing","url","collection_id"])
# Test get_images_and_collections
combined_df = api.get_images_with_collections(collection_pks=[877,437])
check_df(df=combined_df,size_min=50,columns=["url_image","collection_id","name_image","map_type","image_id"])
# Test metadata for subset of collections
collections = api.get_collections(pks=[877,437])
check_df(df=collections,size_min=1,columns=["used_smoothing","url","collection_id"])
# Test metadata of images from specific collections
images = api.get_images(collection_pks=[877,437])
check_df(df=images,size_min=50,columns=["url","name","map_type"])
def get_data(download_folder):
"""get_data
:param download_folder: download destination folder for nifti gz files
:returns brainmaps: list of nii gz images
"""
all_collections = api.get_collections()
collections_with_dois = all_collections[np.logical_not(all_collections.DOI.isnull())]
group_collections = collections_with_dois[collections_with_dois.group_comparison != True]
images = api.get_images(collection_pks=list(group_collections.collection_id))
images = api.filter(df=images, column_name="is_thresholded", field_value=False)
images = api.filter(df=images, column_name="not_mni", field_value=False)
images = api.filter(df=images, column_name="modality", field_value="fMRI-BOLD")
api.download_images(dest_dir=download_folder, images_df=images, resample=False)
return glob("%s/original/*.nii.gz" % (download_folder))
def get_data(download_folder):
'''get_data
:param download_folder: download destination folder for nifti gz files
:returns brainmaps: list of nii gz images
'''
all_collections = api.get_collections()
collections_with_dois = all_collections[np.logical_not(
all_collections.DOI.isnull())]
group_collections = collections_with_dois[
collections_with_dois.group_comparison != True]
images = api.get_images(
collection_pks=list(group_collections.collection_id))
images = api.filter(df=images,
column_name="is_thresholded",
field_value=False)
images = api.filter(df=images, column_name="not_mni", field_value=False)
images = api.filter(df=images,
column_name="modality",
field_value="fMRI-BOLD")
api.download_images(dest_dir=download_folder,
images_df=images,
resample=False)
return glob("%s/original/*.nii.gz" % (download_folder))
if not os.path.exists(results):
os.mkdir(results)
if not os.path.exists(data):
os.mkdir(data)
# Get all collections
collections = api.get_collections()
# Filter images to those that have a DOI
collections = collections[collections.DOI.isnull() == False]
# We use this file to create custom web interface
collections.to_csv("%s/collections_with_dois.tsv" % results, encoding="utf-8", sep="\t")
# Get image meta data for collections (N=1023)
images = api.get_images(collection_pks=collections.collection_id.tolist())
# Filter images to those with contrasts defined (N=98)
images = images[images.cognitive_contrast_cogatlas_id.isnull() == False]
# Not needed for future analyses, for documentation only
images.to_csv("%s/contrast_defined_images.tsv" % results, encoding="utf-8", sep="\t")
# Get rid of any not in MNI
images = images[images.not_mni == False]
images = images[images.analysis_level != "single-subject"]
# Get rid of thresholded images
images = images[images.is_thresholded == False]
# We can't use Rest or other/none
from pyneurovault import api import numpy as np all_collections = api.get_collections() collections_with_dois = all_collections[np.logical_not(all_collections.DOI.isnull())] images = api.get_images(collection_pks=list(collections_with_dois.collection_id)) # Remove images that are thresholded images = api.filter(df=images,column_name="is_thresholded",field_value=False) # Not in MNI images = api.filter(df=images,column_name="not_mni",field_value=False) # Just fMRI bold images = api.filter(df=images,column_name="modality",field_value="fMRI-BOLD") # Download all images to file, resample to target outfolder = "/home/vanessa/Documents/Work/NEUROVAULT/mosaic" # If you don't want to resample api.download_images(dest_dir = outfolder,images_df=images,resample=False)
from pyneurovault import api
import pandas as pd
from nilearn.image import resample_img, smooth_img
import nibabel as nb
import numpy as np
from scipy.ndimage import binary_fill_holes
from scipy import stats
from scipy.ndimage.measurements import labeled_comprehension
import sys
atlas_id = sys.argv[2]
data_location = sys.argv[1]
atlases = api.get_images(pks=[atlas_id])
api.download_images(data_location + "/atlases", atlases, resample=False)
standard = "/usr/share/fsl/data/standard/MNI152_T1_2mm_brain.nii.gz"
get_ipython().system('mkdir -p ' + data_location + '/atlases/resampled')
fname = atlas_id + ".nii.gz"
print "resampling " + fname
nii = nb.load(data_location + "atlases/original/" + fname)
target_nii = nb.load(standard)
resampled_nii = resample_img(nii,
target_affine=target_nii.get_affine(),
target_shape=target_nii.shape,
interpolation='nearest')
resampled_nii.to_filename(data_location + "atlases/resampled/" + fname)
folders = [data_directory, results_directory]
make_dirs(folders)
# Get all collections
collections = api.get_collections()
# Filter images to those that have a DOI
collections = collections[collections.DOI.isnull() == False]
# Useless, but might as well save it
collections.to_csv("%s/collections_with_dois.tsv" % (results_directory),
encoding="utf-8",
sep="\t")
# Get image meta data for collections
images = api.get_images(collection_pks=collections.collection_id.tolist())
# load list of included image IDs (curated by Poldracklab) and exclude others
curated_images = pandas.read_csv('%s/included_images.csv' % (data_directory),
header=None,
names=['image_id'])
images = images.loc[images['image_id'].isin(curated_images['image_id'])]
## the following won't really have any effect, since the filtering
## is done by the explicit list above, but I'm leaving them
## to make it clear how the filtering was initially done
## however, if any of these criteria change in the neurovault
## database (modality, MNI, thresholded description, cognitive atlas task)
## this list could be filtered further
# Get rid of any not in MNI
def download_combined_database(out_dir, overwrite=False):
"""
Download coordinates/annotations from brainspell and images/annotations
from Neurovault.
Currently, the largest barrier is the lack of links between experiments
(tables) in brainspell/NeuroSynth and those in NeuroVault. The closest we
have is overall papers, via DOIs.
Additional problems:
- Does NeuroVault have standard error maps?
- If so, I doubt there's any way to associate a given SE map and beta
map within a collection.
- How should space be handled?
- Should everything be resliced and transformed to the same space at
this stage or later on?
- How can we link a target template (for images) to a target space
(for coordinates)?
- Should we even allow custom targets? Maybe we just limit it to 2mm
and 1mm MNI templates.
Parameters
----------
out_dir : :obj:`str`
Folder in which to write out Dataset object and subfolders containing
images.
overwrite: :obj:`bool`, optional
Whether to overwrite existing database, if one exists in `out_dir`.
Defaults to False.
"""
# Download collections metadata from Neurovault
collections_file = op.join(out_dir, 'neurovault_collections.csv')
if overwrite or not op.isfile(collections_file):
colls_df = api.get_collections()
colls_df.to_csv(collections_file, index=False, encoding='utf-8')
else:
colls_df = pd.read_csv(collections_file, encoding='utf-8')
# Only include collections from published papers (or preprints)
papers_file = op.join(out_dir, 'neurovault_papers.csv')
if overwrite or not op.isfile(papers_file):
paper_df = colls_df.dropna(subset=['DOI'])
paper_df.to_csv(papers_file, index=False, encoding='utf-8')
else:
paper_df = pd.read_csv(papers_file, encoding='utf-8')
# Get metadata for individual images from valid collections
papers_metadata_file = op.join(out_dir, 'neurovault_papers_metadata.csv')
if overwrite or not op.isfile(papers_metadata_file):
valid_collections = sorted(paper_df['collection_id'].tolist())
# Sleep between get_images calls to avoid spamming Neurovault
image_dfs = []
for chunk in to_chunks(valid_collections, 500):
image_dfs.append(api.get_images(collection_pks=chunk))
time.sleep(10)
image_df = pd.concat(image_dfs)
image_df.to_csv(papers_metadata_file, index=False, encoding='utf-8')
else:
image_df = pd.read_csv(papers_metadata_file, encoding='utf-8')
# Reduce images database according to additional criteria
# Only keep unthresholded, MNI, group level fMRI maps
red_df = image_df.loc[image_df['modality'] == 'fMRI-BOLD']
red_df = red_df.loc[red_df['image_type'] == 'statistic_map']
red_df = red_df.loc[red_df['analysis_level'] == 'group']
red_df = red_df.loc[red_df['is_thresholded'] is False]
red_df = red_df.loc[red_df['not_mni'] is False]
# Look for relevant metadata
red_df = red_df.dropna(subset=['cognitive_paradigm_cogatlas'])
## MFX/FFX GLMs need contrast (beta) + standard error
mffx_df = red_df.loc[red_df['map_type'] == 'univariate-beta map']
## RFX GLMs need contrast (beta)
rfx_df = red_df.loc[red_df['map_type'] == 'univariate-beta map']
## Stouffer's, Stouffer's RFX, and Fisher's IBMAs can use Z maps.
# T and F maps can be transformed into Z maps, but T maps need sample size.
# Only keep test statistic maps
acc_map_types = ['Z map', 'T map', 'F map']
st_df = red_df.loc[red_df['map_type'].isin(acc_map_types)]
keep_idx = st_df['map_type'].isin(['Z map', 'F map'])
keep_idx2 = (st_df['map_type'] == 'T map') & ~pd.isnull(st_df['number_of_subjects'])
keep_idx = keep_idx | keep_idx2
st_df = st_df.loc[keep_idx]
## Weighted Stouffer's IBMAs need Z + sample size.
st_df['id_str'] = st_df['image_id'].astype(str).str.zfill(6)
if not op.isdir(out_dir):
mkdir(out_dir)
api.download_images(out_dir, red_df, target=None, resample=False)
elif overwrite:
# clear out out_dir
raise Exception('Currently not prepared to overwrite database.')
api.download_images(out_dir, red_df, target=None, resample=False)
def main():
base = "data/"
# Make a folder for mean images
if not os.path.exists("mr"):
os.mkdir("mr")
# Get Neurovault Images with defined cognitive atlas contrast
collections = get_collections()
# Filter images to those that have a DOI
collections = collections[collections.DOI.isnull() == False]
# Get image meta data for collections (N=1023)
images = get_images(collection_pks=collections.collection_id.tolist())
# Filter images to those with contrasts defined (N=98)
images = images[images.cognitive_contrast_cogatlas_id.isnull() == False]
# Get rid of any not in MNI
images = images[images.not_mni == False]
# Get rid of thresholded images
images = images[images.is_thresholded == False]
### Step 1: Load meta data sources
unique_contrasts = images.cognitive_contrast_cogatlas_id.unique().tolist()
# Images that do not match the correct identifier will not be used (eg, "Other")
expression = re.compile("cnt_*")
unique_contrasts = [u for u in unique_contrasts if expression.match(u)]
# Make sure exists in cognitive atlas
existing_contrasts = []
for u in unique_contrasts:
try:
tmp = get_concept(contrast_id=u, silent=True)
existing_contrasts.append(u)
except:
print "%s is defined in NeuroVault, does not exist in Cognitive Atlas" % u
image_lookup = dict()
for u in existing_contrasts:
image_lookup[u] = images.image_id[images.cognitive_contrast_cogatlas_id
== u].tolist()
# Create a data structure of tasks and contrasts for our analysis
relationship_table = concept_node_triples(image_dict=image_lookup,
save_to_file=False)
unique_nodes = relationship_table.id.unique().tolist()
# We will store a data frame of meta data
# Lookup for meta_data is the id of the node!
meta_data = {}
for node in unique_nodes:
meta_single = {}
# This is an image node
if re.search("node_", node):
print "Found image node!"
relationship_table_row = relationship_table[relationship_table.id
== node]
image_id = relationship_table_row.name.tolist()[0]
meta_single["category"] = ""
meta_single["type"] = "nii"
# NeuroVault metadata
concepts = relationship_table.parent[relationship_table.name ==
image_id]
concepts = [
relationship_table.name[relationship_table.id == c].tolist()[0]
for c in concepts
]
neurovault_row = images[images.image_id == int(image_id)]
collection_row = collections[collections.collection_id ==
neurovault_row.collection_id.tolist()
[0]]
collection_meta = {
"DOI":
collection_row["DOI"].tolist()[0],
"authors":
collection_row["authors"].tolist()[0],
"journal":
collection_row["journal_name"].tolist()[0],
"url":
collection_row["url"].tolist()[0],
"subjects":
collection_row["number_of_subjects"].tolist()[0],
"smoothing_fwhm":
str(collection_row["smoothing_fwhm"].tolist()[0]).encode(
"utf-8")
}
meta_single["url"] = neurovault_row["url"].tolist()[0]
meta_single["thumbnail"] = neurovault_row["thumbnail"].tolist()[0]
meta_single["images"] = neurovault_row["thumbnail"].tolist()
meta_single["task"] = neurovault_row[
"cognitive_paradigm_cogatlas"].tolist()[0]
meta_single["contrast"] = neurovault_row[
"cognitive_contrast_cogatlas"].tolist()[0]
meta_single["download"] = neurovault_row["file"].tolist()[0]
meta_single["concept"] = concepts
if neurovault_row["description"].tolist()[0]:
meta_single["description"] = str(
neurovault_row["description"].tolist()[0]).encode("utf-8")
else:
meta_single["description"] = ""
if len(meta_single["description"]) > 600:
meta_single["description"] = "%s..." % meta_single[
"description"][0:600]
else: # A concept node
if node != "1":
relationship_table_row = relationship_table[
relationship_table.id == node]
concept = get_concept(id=node, silent=True).json
children_nodes = [
relationship_table.name.tolist()[x]
for x in range(relationship_table.shape[0])
if relationship_table.parent.tolist()[x] == node
]
while len(
[x for x in children_nodes if not isinstance(x, int)]) > 0:
new_parent_nodes = [
x for x in children_nodes if not isinstance(x, int)
]
children_nodes = [
x for x in children_nodes if x not in new_parent_nodes
]
for new_parent in new_parent_nodes:
node_name = relationship_table.id[
relationship_table.name == new_parent].tolist()[0]
children_nodes = children_nodes + [
relationship_table.name.tolist()[x]
for x in range(relationship_table.shape[0]) if
relationship_table.parent.tolist()[x] == node_name
]
# Now only keep children that are images
meta_single["images"] = images["thumbnail"][
images.image_id.isin(children_nodes)].tolist()
# Cognitive Atlas meta data
meta_single[
"url"] = "http://www.cognitiveatlas.org/term/id/%s" % node
meta_single["type"] = "concept"
meta_single[
"thumbnail"] = "http://www.cognitiveatlas.org/images/logo-front.png"
meta_single["concept"] = [
relationship_table.name[relationship_table.id ==
node].tolist()[0]
]
meta_single["task"] = ""
meta_single["contrast"] = []
meta_single[
"download"] = "http://www.cognitiveatlas.org/rdf/id/%s" % node
if concept[0]["definition_text"]:
meta_single["description"] = concept[0][
"definition_text"].encode("utf-8")
else:
meta_single["description"] = ""
if len(meta_single["description"]) > 600:
meta_single["description"] = "%s..." % meta_single[
"description"][0:600]
meta_data[node] = meta_single
## STEP 2: VISUALIZATION WITH PYBRAINCOMPARE
from pybraincompare.ontology.tree import named_ontology_tree_from_tsv, make_ontology_tree_d3
# First let's look at the tree structure
# output_json = "%s/task_contrast_tree.json" % outfolder
tree = named_ontology_tree_from_tsv(relationship_table,
output_json=None,
meta_data=meta_data)
html_snippet = make_ontology_tree_d3(tree)
web_folder = base
make_analysis_web_folder(html_snippet, web_folder)
# To get a dump of just the tree (for use in more advanced custom web interface)
filey = open('%s/reverseinference.json' % base, 'wb')
filey.write(
json.dumps(tree, sort_keys=True, indent=4, separators=(',', ': ')))
filey.close()
## STEP 3: Export individual nodes
### Images
unique_images = images.image_id.unique().tolist()
# Images
for s in range(0, len(unique_images)):
image_id = unique_images[s]
meta_data = {}
meta_data["image_id"] = image_id
print "Parsing data for images %s of %s" % (s, len(unique_images))
concepts = relationship_table.parent[relationship_table.name == str(
image_id)].tolist()
concepts = [
relationship_table.name[relationship_table.id == c].tolist()[0]
for c in concepts
]
concepts_ids = [
relationship_table.id[relationship_table.id == c].tolist()[0]
for c in concepts
]
neurovault_row = images[images.image_id == int(image_id)]
collection_row = collections[collections.collection_id ==
neurovault_row.collection_id.tolist()[0]]
collection_meta = {
"DOI":
collection_row["DOI"].tolist()[0],
"authors":
collection_row["authors"].tolist()[0],
"journal":
collection_row["journal_name"].tolist()[0],
"url":
collection_row["url"].tolist()[0],
"subjects":
collection_row["number_of_subjects"].tolist()[0],
"smoothing_fwhm":
str(collection_row["smoothing_fwhm"].tolist()[0]).encode("utf-8"),
"title":
collection_row["name"].tolist()[0]
}
meta_data["collection"] = collection_meta
meta_data["url"] = neurovault_row["url"].tolist()[0]
meta_data["thumbnail"] = neurovault_row["thumbnail"].tolist()[0]
meta_data["images"] = neurovault_row["thumbnail"].tolist()
meta_data["task"] = neurovault_row[
"cognitive_paradigm_cogatlas"].tolist()[0]
meta_data["contrast"] = neurovault_row[
"cognitive_contrast_cogatlas"].tolist()[0]
meta_data["download"] = neurovault_row["file"].tolist()[0]
meta_data["concept"] = concepts
meta_data["concept_id"] = concepts_ids
if neurovault_row["description"].tolist()[0]:
try:
description = str(
neurovault_row["description"].tolist()[0]).encode("utf-8")
except:
description = ""
if description != "nan":
meta_data["description"] = description
else:
meta_data["description"] = ""
else:
meta_data["description"] = ""
if len(meta_data["description"]) > 600:
meta_data[
"description"] = "%s..." % meta_data["description"][0:600]
output_file = "%s/ri_%s.json" % (base, meta_data["image_id"])
filey = open(output_file, 'wb')
filey.write(
json.dumps(meta_data,
sort_keys=True,
indent=4,
separators=(',', ': ')))
filey.close()
### Concepts
for node in unique_nodes:
# This is a concept node
if not re.search("node_", node):
if node != "1":
relationship_table_row = relationship_table[
relationship_table.id == node]
concept = get_concept(id=node).json
meta_single = {}
children_nodes = [
relationship_table.name.tolist()[x]
for x in range(relationship_table.shape[0])
if relationship_table.parent.tolist()[x] == node
]
while len(
[x for x in children_nodes if not isinstance(x, int)]) > 0:
new_parent_nodes = [
x for x in children_nodes if not isinstance(x, int)
]
children_nodes = [
x for x in children_nodes if x not in new_parent_nodes
]
for new_parent in new_parent_nodes:
node_name = relationship_table.id[
relationship_table.name == new_parent].tolist()[0]
children_nodes = children_nodes + [
relationship_table.name.tolist()[x]
for x in range(relationship_table.shape[0]) if
relationship_table.parent.tolist()[x] == node_name
]
# Now only keep children that are images
meta_single["images"] = images["thumbnail"][
images.image_id.isin(children_nodes)].tolist()
meta_single["image_list"] = children_nodes
# Cognitive Atlas meta data
meta_single[
"url"] = "http://www.cognitiveatlas.org/term/id/%s" % node
meta_single["type"] = "concept"
meta_single[
"thumbnail"] = "http://www.cognitiveatlas.org/images/logo-front.png"
meta_single["concept"] = [
relationship_table.name[relationship_table.id ==
node].tolist()[0]
]
meta_single["task"] = ""
meta_single["contrast"] = []
meta_single[
"download"] = "http://www.cognitiveatlas.org/rdf/id/%s" % node
if concept[0]["definition_text"]:
meta_single["description"] = concept[0][
"definition_text"].encode("utf-8")
else:
meta_single["description"] = ""
if len(meta_single["description"]) > 600:
meta_single["description"] = "%s..." % meta_single[
"description"][0:600]
output_file = "%s/ri_%s.json" % (base, node)
filey = open(output_file, 'wb')
filey.write(
json.dumps(meta_single,
sort_keys=True,
indent=4,
separators=(',', ': ')))
filey.close()
from pyneurovault import api
import pandas as pd
from nilearn.image import resample_img, smooth_img
import nibabel as nb
import numpy as np
from scipy.ndimage import binary_fill_holes
from scipy import stats
from scipy.ndimage.measurements import labeled_comprehension
import sys
atlas_id = sys.argv[2]
data_location = sys.argv[1]
atlases = api.get_images(pks=[atlas_id])
api.download_images(data_location + "/atlases", atlases, resample=False)
standard = "/usr/share/fsl/data/standard/MNI152_T1_2mm_brain.nii.gz"
get_ipython().system('mkdir -p '+data_location+'/atlases/resampled')
fname = atlas_id+".nii.gz"
print "resampling " + fname
nii = nb.load(data_location+"atlases/original/" + fname)
target_nii = nb.load(standard)
resampled_nii = resample_img(nii,target_affine=target_nii.get_affine(),
target_shape=target_nii.shape, interpolation='nearest')
resampled_nii.to_filename(data_location+"atlases/resampled/" + fname)
def score_map(map_filename, atlas_data, labels):
map_data = nb.load(map_filename).get_data()
def main():
base = "data/"
# Make a folder for mean images
if not os.path.exists("mr"):
os.mkdir("mr")
# Get Neurovault Images with defined cognitive atlas contrast
collections = get_collections()
# Filter images to those that have a DOI
collections = collections[collections.DOI.isnull()==False]
# Get image meta data for collections (N=1023)
images = get_images(collection_pks=collections.collection_id.tolist())
# Filter images to those with contrasts defined (N=98)
images = images[images.cognitive_contrast_cogatlas_id.isnull()==False]
# Get rid of any not in MNI
images = images[images.not_mni == False]
# Get rid of thresholded images
images = images[images.is_thresholded == False]
### Step 1: Load meta data sources
unique_contrasts = images.cognitive_contrast_cogatlas_id.unique().tolist()
# Images that do not match the correct identifier will not be used (eg, "Other")
expression = re.compile("cnt_*")
unique_contrasts = [u for u in unique_contrasts if expression.match(u)]
# Make sure exists in cognitive atlas
existing_contrasts = []
for u in unique_contrasts:
try:
tmp = get_concept(contrast_id=u,silent=True)
existing_contrasts.append(u)
except:
print "%s is defined in NeuroVault, does not exist in Cognitive Atlas" %u
image_lookup = dict()
for u in existing_contrasts:
image_lookup[u] = images.image_id[images.cognitive_contrast_cogatlas_id==u].tolist()
# Create a data structure of tasks and contrasts for our analysis
relationship_table = concept_node_triples(image_dict=image_lookup,save_to_file=False)
unique_nodes = relationship_table.id.unique().tolist()
# We will store a data frame of meta data
# Lookup for meta_data is the id of the node!
meta_data = {}
for node in unique_nodes:
meta_single = {}
# This is an image node
if re.search("node_",node):
print "Found image node!"
relationship_table_row = relationship_table[relationship_table.id==node]
image_id = relationship_table_row.name.tolist()[0]
meta_single["category"] = ""
meta_single["type"] = "nii"
# NeuroVault metadata
concepts = relationship_table.parent[relationship_table.name == image_id]
concepts = [relationship_table.name[relationship_table.id==c].tolist()[0] for c in concepts]
neurovault_row = images[images.image_id == int(image_id)]
collection_row = collections[collections.collection_id == neurovault_row.collection_id.tolist()[0]]
collection_meta = {"DOI":collection_row["DOI"].tolist()[0],
"authors":collection_row["authors"].tolist()[0],
"journal":collection_row["journal_name"].tolist()[0],
"url":collection_row["url"].tolist()[0],
"subjects":collection_row["number_of_subjects"].tolist()[0],
"smoothing_fwhm":str(collection_row["smoothing_fwhm"].tolist()[0]).encode("utf-8")}
meta_single["url"] = neurovault_row["url"].tolist()[0]
meta_single["thumbnail"] = neurovault_row["thumbnail"].tolist()[0]
meta_single["images"] = neurovault_row["thumbnail"].tolist()
meta_single["task"] = neurovault_row["cognitive_paradigm_cogatlas"].tolist()[0]
meta_single["contrast"] = neurovault_row["cognitive_contrast_cogatlas"].tolist()[0]
meta_single["download"] = neurovault_row["file"].tolist()[0]
meta_single["concept"] = concepts
if neurovault_row["description"].tolist()[0]:
meta_single["description"] = str(neurovault_row["description"].tolist()[0]).encode("utf-8")
else:
meta_single["description"] = ""
if len(meta_single["description"]) > 600:
meta_single["description"] = "%s..." % meta_single["description"][0:600]
else: # A concept node
if node != "1":
relationship_table_row = relationship_table[relationship_table.id==node]
concept = get_concept(id=node,silent=True).json
children_nodes = [relationship_table.name.tolist()[x] for x in range(relationship_table.shape[0]) if relationship_table.parent.tolist()[x]==node]
while len([x for x in children_nodes if not isinstance(x,int)]) > 0:
new_parent_nodes = [x for x in children_nodes if not isinstance(x,int)]
children_nodes = [x for x in children_nodes if x not in new_parent_nodes]
for new_parent in new_parent_nodes:
node_name = relationship_table.id[relationship_table.name==new_parent].tolist()[0]
children_nodes = children_nodes + [relationship_table.name.tolist()[x] for x in range(relationship_table.shape[0]) if relationship_table.parent.tolist()[x]==node_name]
# Now only keep children that are images
meta_single["images"] = images["thumbnail"][images.image_id.isin(children_nodes)].tolist()
# Cognitive Atlas meta data
meta_single["url"] = "http://www.cognitiveatlas.org/term/id/%s" %node
meta_single["type"] = "concept"
meta_single["thumbnail"] = "http://www.cognitiveatlas.org/images/logo-front.png"
meta_single["concept"] = [relationship_table.name[relationship_table.id==node].tolist()[0]]
meta_single["task"] = ""
meta_single["contrast"] = []
meta_single["download"] = "http://www.cognitiveatlas.org/rdf/id/%s" %node
if concept[0]["definition_text"]:
meta_single["description"] = concept[0]["definition_text"].encode("utf-8")
else:
meta_single["description"] = ""
if len(meta_single["description"]) > 600:
meta_single["description"] = "%s..." % meta_single["description"][0:600]
meta_data[node] = meta_single
## STEP 2: VISUALIZATION WITH PYBRAINCOMPARE
from pybraincompare.ontology.tree import named_ontology_tree_from_tsv, make_ontology_tree_d3
# First let's look at the tree structure
# output_json = "%s/task_contrast_tree.json" % outfolder
tree = named_ontology_tree_from_tsv(relationship_table,output_json=None,meta_data=meta_data)
html_snippet = make_ontology_tree_d3(tree)
web_folder = base
make_analysis_web_folder(html_snippet,web_folder)
# To get a dump of just the tree (for use in more advanced custom web interface)
filey = open('%s/reverseinference.json' %base,'wb')
filey.write(json.dumps(tree, sort_keys=True,indent=4, separators=(',', ': ')))
filey.close()
## STEP 3: Export individual nodes
### Images
unique_images = images.image_id.unique().tolist()
# Images
for s in range(0,len(unique_images)):
image_id = unique_images[s]
meta_data = {}
meta_data["image_id"] = image_id
print "Parsing data for images %s of %s" %(s,len(unique_images))
concepts = relationship_table.parent[relationship_table.name == str(image_id)].tolist()
concepts = [relationship_table.name[relationship_table.id==c].tolist()[0] for c in concepts]
concepts_ids = [relationship_table.id[relationship_table.id==c].tolist()[0] for c in concepts]
neurovault_row = images[images.image_id == int(image_id)]
collection_row = collections[collections.collection_id == neurovault_row.collection_id.tolist()[0]]
collection_meta = {"DOI":collection_row["DOI"].tolist()[0],
"authors":collection_row["authors"].tolist()[0],
"journal":collection_row["journal_name"].tolist()[0],
"url":collection_row["url"].tolist()[0],
"subjects":collection_row["number_of_subjects"].tolist()[0],
"smoothing_fwhm":str(collection_row["smoothing_fwhm"].tolist()[0]).encode("utf-8"),
"title":collection_row["name"].tolist()[0]}
meta_data["collection"] = collection_meta
meta_data["url"] = neurovault_row["url"].tolist()[0]
meta_data["thumbnail"] = neurovault_row["thumbnail"].tolist()[0]
meta_data["images"] = neurovault_row["thumbnail"].tolist()
meta_data["task"] = neurovault_row["cognitive_paradigm_cogatlas"].tolist()[0]
meta_data["contrast"] = neurovault_row["cognitive_contrast_cogatlas"].tolist()[0]
meta_data["download"] = neurovault_row["file"].tolist()[0]
meta_data["concept"] = concepts
meta_data["concept_id"] = concepts_ids
if neurovault_row["description"].tolist()[0]:
try:
description = str(neurovault_row["description"].tolist()[0]).encode("utf-8")
except:
description = ""
if description != "nan":
meta_data["description"] = description
else:
meta_data["description"] = ""
else:
meta_data["description"] = ""
if len(meta_data["description"]) > 600:
meta_data["description"] = "%s..." % meta_data["description"][0:600]
output_file = "%s/ri_%s.json" %(base,meta_data["image_id"])
filey = open(output_file,'wb')
filey.write(json.dumps(meta_data, sort_keys=True,indent=4, separators=(',', ': ')))
filey.close()
### Concepts
for node in unique_nodes:
# This is a concept node
if not re.search("node_",node):
if node != "1":
relationship_table_row = relationship_table[relationship_table.id==node]
concept = get_concept(id=node).json
meta_single = {}
children_nodes = [relationship_table.name.tolist()[x] for x in range(relationship_table.shape[0]) if relationship_table.parent.tolist()[x]==node]
while len([x for x in children_nodes if not isinstance(x,int)]) > 0:
new_parent_nodes = [x for x in children_nodes if not isinstance(x,int)]
children_nodes = [x for x in children_nodes if x not in new_parent_nodes]
for new_parent in new_parent_nodes:
node_name = relationship_table.id[relationship_table.name==new_parent].tolist()[0]
children_nodes = children_nodes + [relationship_table.name.tolist()[x] for x in range(relationship_table.shape[0]) if relationship_table.parent.tolist()[x]==node_name]
# Now only keep children that are images
meta_single["images"] = images["thumbnail"][images.image_id.isin(children_nodes)].tolist()
meta_single["image_list"] = children_nodes
# Cognitive Atlas meta data
meta_single["url"] = "http://www.cognitiveatlas.org/term/id/%s" %node
meta_single["type"] = "concept"
meta_single["thumbnail"] = "http://www.cognitiveatlas.org/images/logo-front.png"
meta_single["concept"] = [relationship_table.name[relationship_table.id==node].tolist()[0]]
meta_single["task"] = ""
meta_single["contrast"] = []
meta_single["download"] = "http://www.cognitiveatlas.org/rdf/id/%s" %node
if concept[0]["definition_text"]:
meta_single["description"] = concept[0]["definition_text"].encode("utf-8")
else:
meta_single["description"] = ""
if len(meta_single["description"]) > 600:
meta_single["description"] = "%s..." % meta_single["description"][0:600]
output_file = "%s/ri_%s.json" %(base,node)
filey = open(output_file,'wb')
filey.write(json.dumps(meta_single, sort_keys=True,indent=4, separators=(',', ': ')))
filey.close()
# In case we want it, decode each of our original images
unique_images = images.image_id.unique().tolist()
decode = pandas.DataFrame(index=unique_images,columns=terms)
for unique_image in unique_images:
print "Decoding original image %s" %(unique_image)
ns = json.loads(get_url("http://neurosynth.org/decode/data/?neurovault=%s" %unique_image))
for term in ns["data"]:
decode.loc[unique_image,term["analysis"]] = term["r"]
decode_result_file = "%s/original_images_decoding.tsv" %results
decode.to_csv(decode_result_file,sep="\t")
# Now we will decode our collection of images!
rp_images = get_images(collection_pks=[neurovault_collection])
unique_rp_images = rp_images.image_id.unique().tolist()
rp_decode = pandas.DataFrame(index=unique_rp_images,columns=terms)
for unique_image in unique_rp_images:
print "Decoding regression parameter image %s" %(unique_image)
ns = json.loads(get_url("http://neurosynth.org/decode/data/?neurovault=%s" %unique_image))
for term in ns["data"]:
rp_decode.loc[unique_image,term["analysis"]] = term["r"]
# Now let's add some cognitive atlas meta data, so we don't have to look up later
image_urls = rp_images.file[rp_images.image_id.isin(unique_rp_images)]
rp_decode["0image_urls"] = image_urls.tolist()
concept_ids = [str(x.split("/")[-1].replace(".nii.gz","").replace("_regparam_z","")) for x in image_urls]
rp_decode["0cognitive_atlas_concept_id"] = concept_ids
#!/usr/bin/env python2
# This script will use the pyneurovault module to download meta information about images and collections from NeuroVault.
from pyneurovault import api
# Get a collection
collection = api.get_collections(pks=457)
# collection.collection_id is 457
# Get all images
images = api.get_images()
# Get all images meta data for a collection
images = api.get_images(collection_pks=457)
# Remove images that are thresholded
images = api.filter(df=images,column_name="is_thresholded",field_value=False)
# Not in MNI
images = api.filter(df=images,column_name="not_mni",field_value=False)
# Just fMRI bold
images = api.filter(df=images,column_name="modality",field_value="fMRI-BOLD")
# Download images, collections, or both
api.export_images_tsv("/home/vanessa/Desktop/images.tsv",images)
api.export_collections_tsv("/home/vanessa/Desktop/collections.tsv",collection)
# Download all images to file, resample to target
outfolder = "/home/vanessa/Desktop"