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Update Download Datasets #431

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432f69a
updated new nilearn nv download
ljchang May 1, 2024
85067c8
added deprecation warning
ljchang May 2, 2024
41219c5
Pairwise distance metric keyword for stats.isc (#442)
srken32 Aug 1, 2024
fb504d9
updated new nilearn nv download
ljchang May 1, 2024
2e24674
added deprecation warning
ljchang May 2, 2024
3900ff9
Merge remote-tracking branch 'refs/remotes/origin/fetch_data' into fe…
ejolly May 21, 2025
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48 changes: 38 additions & 10 deletions nltools/datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,15 @@

import os
import pandas as pd
import warnings
from nltools.data import Brain_Data
from nilearn.datasets.utils import _get_dataset_dir, _fetch_file
import pkg_resources

if pkg_resources.get_distribution("nilearn").version >= "0.10.4":
from nilearn.datasets import fetch_neurovault_ids
from nilearn.datasets.utils import get_data_dirs
else:
from nilearn.datasets.utils import _get_dataset_dir, _fetch_file
from pynv import Client

# Optional dependencies
Expand Down Expand Up @@ -59,7 +66,9 @@ def get_collection_image_metadata(collection=None, data_dir=None, limit=10):
Returns:
pd.DataFrame: Dataframe with full image metadata from collection
"""

warnings.warn(
"This function is deprecated and will be removed in a future version. Please use fetch_neurovault_ids instead."
)
if os.path.isfile(os.path.join(data_dir, "metadata.csv")):
dat = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
else:
Expand Down Expand Up @@ -96,6 +105,9 @@ def download_collection(
Returns:
(pd.DataFrame, list): (DataFrame of image metadata, list of files from downloaded collection)
"""
warnings.warn(
"This function is deprecated and will be removed in a future version. Please use fetch_neurovault_ids instead."
)

if data_dir is None:
data_dir = _get_dataset_dir(str(collection), data_dir=data_dir, verbose=verbose)
Expand Down Expand Up @@ -128,10 +140,18 @@ def fetch_pain(data_dir=None, resume=True, verbose=1):

collection = 504
dataset_name = "chang2015_pain"
data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)
metadata, files = download_collection(
collection=collection, data_dir=data_dir, resume=resume, verbose=verbose
)

if pkg_resources.get_distribution("nilearn").version >= "0.10.4":
nv_data = fetch_neurovault_ids(
collection_ids=[collection], data_dir=data_dir, verbose=verbose
)
files = nv_data["images"]
metadata = pd.DataFrame(nv_data["images_meta"])
else:
data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)
metadata, files = download_collection(
collection=collection, data_dir=data_dir, resume=resume, verbose=verbose
)
return Brain_Data(data=files, X=metadata)


Expand All @@ -148,8 +168,16 @@ def fetch_emotion_ratings(data_dir=None, resume=True, verbose=1):

collection = 1964
dataset_name = "chang2015_emotion_ratings"
data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)
metadata, files = download_collection(
collection=collection, data_dir=data_dir, resume=resume, verbose=verbose
)

if pkg_resources.get_distribution("nilearn").version >= "0.10.4":
nv_data = fetch_neurovault_ids(
collection_ids=[collection], data_dir=data_dir, verbose=verbose
)
files = nv_data["images"]
metadata = pd.DataFrame(nv_data["images_meta"])
else:
data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir, verbose=verbose)
metadata, files = download_collection(
collection=collection, data_dir=data_dir, resume=resume, verbose=verbose
)
return Brain_Data(data=files, X=metadata)
4 changes: 3 additions & 1 deletion nltools/stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1943,6 +1943,7 @@ def isc(
tail=2,
n_jobs=-1,
random_state=None,
sim_metric="correlation",
):
"""Compute pairwise intersubject correlation from observations by subjects array.

Expand Down Expand Up @@ -1981,6 +1982,7 @@ def isc(
tail: (int) either 1 for one-tail or 2 for two-tailed test (default: 2)
n_jobs: (int) The number of CPUs to use to do the computation. -1 means all CPUs.
return_null: (bool) Return the permutation distribution along with the p-value; default False
sim_metric: (str) pairwise distance metric. See sklearn's pairwise_distances for valid inputs (default: correlation)

Returns:
stats: (dict) dictionary of permutation results ['correlation','p']
Expand All @@ -2000,7 +2002,7 @@ def isc(
stats = {"isc": _compute_isc(data, metric=metric)}

similarity = Adjacency(
1 - pairwise_distances(data.T, metric="correlation"), matrix_type="similarity"
1 - pairwise_distances(data.T, metric=sim_metric), matrix_type="similarity"
)

if method == "bootstrap":
Expand Down