def test_convert_neurovault_to_dataset(kwargs):
"""Test conversion of neurovault collection to a dataset."""
if 778 in kwargs["collection_ids"]:
with pytest.raises(ValueError) as excinfo:
dset = io.convert_neurovault_to_dataset(**kwargs)
assert "Collection 778 not found." in str(excinfo.value)
return
else:
dset = io.convert_neurovault_to_dataset(**kwargs)
# check if names are propagated into Dataset
if isinstance(kwargs.get("collection_ids"), dict):
study_ids = set(kwargs["collection_ids"].keys())
else:
study_ids = set(map(str, kwargs["collection_ids"]))
dset_ids = {id_.split("-")[1] for id_ in dset.ids}
assert study_ids == dset_ids
# check if images were downloaded and are unique
if kwargs.get("map_type_conversion"):
for img_type in kwargs.get("map_type_conversion").values():
assert not dset.images[img_type].empty
assert len(set(dset.images[img_type])) == len(
dset.images[img_type])
def create_neurovault_dataset(
collection_ids=NEUROVAULT_IDS,
contrasts=CONTRAST_OF_INTEREST,
img_dir=None,
map_type_conversion=None,
**dset_kwargs,
):
"""Download images from NeuroVault and use them to create a dataset.
.. versionadded:: 0.0.8
This function will also attempt to generate Z images for any contrasts
for which this is possible.
Parameters
----------
collection_ids : :obj:`list` of :obj:`int` or :obj:`dict`, optional
A list of collections on neurovault specified by their id.
The collection ids can accessed through the neurovault API
(i.e., https://neurovault.org/api/collections) or
their main website (i.e., https://neurovault.org/collections).
For example, in this URL https://neurovault.org/collections/8836/,
`8836` is the collection id.
collection_ids can also be a dictionary whose keys are the informative
study name and the values are collection ids to give the collections
human readable names in the dataset.
contrasts : :obj:`dict`, optional
Dictionary whose keys represent the name of the contrast in
the dataset and whose values represent a regular expression that would
match the names represented in NeuroVault.
For example, under the ``Name`` column in this URL
https://neurovault.org/collections/8836/,
a valid contrast could be "as-Animal", which will be called "animal" in the created
dataset if the contrasts argument is ``{'animal': "as-Animal"}``.
img_dir : :obj:`str` or None, optional
Base path to save all the downloaded images, by default the images
will be saved to a temporary directory with the prefix "neurovault"
map_type_conversion : :obj:`dict` or None, optional
Dictionary whose keys are what you expect the `map_type` name to
be in neurovault and the values are the name of the respective
statistic map in a nimare dataset. Default = None.
**dset_kwargs : keyword arguments passed to Dataset
Keyword arguments to pass in when creating the Dataset object.
see :obj:`~nimare.dataset.Dataset` for details.
Returns
-------
:obj:`~nimare.dataset.Dataset`
Dataset object containing experiment information from neurovault.
"""
dataset = convert_neurovault_to_dataset(collection_ids, contrasts, img_dir,
map_type_conversion, **dset_kwargs)
transformer = ImageTransformer(target="z")
dataset = transformer.transform(dataset)
return dataset
("Working memory load of 2 faces versus 1 face - NT2_Tstat|"
"t-value contrast 2-back minus 0-back|"
"Searchlight multivariate Decoding 2: visual working memory|"
"Context-dependent group-specific WM information|"
"WM working memory zstat1|"
"WM task over CRT task map|"
"tfMRI WM 2BK PLACE zstat1")
}
# Convert how the statistical maps on neurovault are represented
# in a NiMARE dataset.
map_type_conversion = {"Z map": "z", "T map": "t"}
dset = convert_neurovault_to_dataset(
collection_ids,
contrasts,
img_dir=None,
map_type_conversion=map_type_conversion,
)
###############################################################################
# Conversion of Statistical Maps
# ------------------------------
# Some of the statistical maps are T statistics and others are Z statistics.
# To perform a Fisher's meta analysis, we need all Z maps.
# Thoughtfully, NiMARE has a class named ``ImageTransformer`` that will
# help us.
from nimare.transforms import ImageTransformer
# Not all studies have Z maps!
print(dset.images["z"])