Python Dataset.save方法代码示例

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import save [as 别名]
    def __init__(self, db, dataset=None, studies=None, features=None,
                 reset_db=False, reset_dataset=False, download_data=True):
        """
        Initialize instance from a pickled Neurosynth Dataset instance or a
        pair of study and analysis .txt files.

        Args:
            db: the SQLAlchemy database connection to use.
            dataset: an optional filename of a pickled neurosynth Dataset
                instance.
                Note that the Dataset must contain the list of Mappables (i.e.,
                    save() must have been called with keep_mappables set to
                    True).
            studies: name of file containing activation data. If passed, a new
                Dataset instance will be constructed.
            features: name of file containing feature data.
            reset_db: if True, will drop and re-create all database tables
                before adding new content. If False (default), will add content
                incrementally.
            reset_dataset: if True, will regenerate the pickled Neurosynth
                dataset.
            download_data: if True, ignores any existing files and downloads
                the latest Neurosynth data files from GitHub.
        """

        if (studies is not None and not os.path.exists(studies)) \
                or settings.RESET_ASSETS:
            print "WARNING: RESETTING ALL NEUROSYNTH ASSETS!"
            self.reset_assets(download_data)

        # Load or create Neurosynth Dataset instance
        if dataset is None or reset_dataset or (isinstance(dataset, basestring) and not os.path.exists(dataset)):

            print "\tInitializing a new Dataset..."
            if (studies is None) or (features is None):
                raise ValueError(
                    "To generate a new Dataset instance, both studies and "
                    "analyses must be provided.")
            dataset = Dataset(studies)
            dataset.add_features(features)
            dataset.save(settings.PICKLE_DATABASE, keep_mappables=True)
        else:
            print "\tLoading existing Dataset..."
            dataset = Dataset.load(dataset)
            if features is not None:
                dataset.add_features(features)

        self.dataset = dataset
        self.db = db

        if reset_db:
            print "WARNING: RESETTING DATABASE!!!"
            self.reset_database()

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import save [as 别名]
def _getdata():
    """Downloads data from neurosynth and returns it as a Dataset.

    Also pickles the dataset for future use."""
    LOG.warning("Downloading and processing Neurosynth database")

    os.makedirs("data", exist_ok=True)
    from neurosynth.base.dataset import download

    download(path="data", unpack=True)

    data = Dataset("data/database.txt")
    data.add_features("data/features.txt")
    data.save("data/dataset.pkl")
    return data

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import save [as 别名]
# <codecell>

# Create a new Dataset instance
dataset = Dataset('data/database.txt')

# Add some features
dataset.add_features('data/features.txt')

# <markdowncell>

# Because this takes a while, we'll save our Dataset object to disk. That way, the next time we want to use it, we won't have to sit through the whole creation operation again:

# <codecell>

dataset.save('dataset.pkl')

# <markdowncell>

# Now in future, instead of waiting, we could just load the dataset from file:

# <codecell>

dataset = Dataset.load('dataset.pkl')   # Note the capital D in the second Dataset--load() is a class method

# <markdowncell>

# ## Doing stuff with Neurosynth
# Now that our Dataset has both activation data and some features, we're ready to start doing some analyses! By design, Neurosynth focuses on facilitating simple, fast, and modestly useful analyses. This means you probably won't break any new ground using Neurosynth, but you should be able to supplement results you've generated using other approaches with a bunch of nifty analyses that take just 2 - 3 lines of code.
# 
# ### Simple feature-based meta-analyses

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import save [as 别名]
def create_dataset(database_location, feature_location):
	dataset = Dataset(database_location)
	dataset.add_features(feature_location)
	dataset.save('neurosynth-dataset.pkl')
	return dataset

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import save [as 别名]
def create_dataset(database_location, feature_location):
	dataset = Dataset(database_location)
	dataset.add_features(feature_location)
	dataset.save('dataset-old.pkl')
        print 'created dataset'
	return dataset

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import save [as 别名]
xfm2vol.inputs.identity = 'fsaverage4'
xfm2vol.inputs.hemi = 'lh'
xfm2vol.inputs.transformed_file = volume_file
xfm2vol.inputs.template_file = template
xfm2vol.run()

#make masks to input into neurosynth
def cluster2masks(clusterfile):
    clustermap = nb.load(clusterfile).get_data()
    for x in range(1,clustermap.max()+1):
        clustermask = (clustermap==x).astype(int)
        nImg = nb.Nifti1Image(clustermask, None)
        nb.save(nImg, os.path.abspath(clusterfile+'_clustermask'+str(x)+'.nii'))

cluster2masks(volume_file)

dataset_file = '/home/raid3/watanabe/neurosynth/data/dataset.pkl'
if not os.path.exists(dataset_file):
    dataset = Dataset('/home/raid3/watanabe/neurosynth/data/database.txt')
    dataset.add_features('/home/raid3/watanabe/neurosynth/data/features.txt')
    dataset.save(dataset_file)
else:
    dataset = cPickle.load(open(dataset_file,'rb'))

clustermask = volume_file+'_clustermask'+str(3)+'.nii'

ids = dataset.get_ids_by_mask(clustermask)
features = dataset.feature_table.get_features_by_ids(ids)

#mri_surf2vol --identity fsaverage4 --surfval /scr/ilz1/Data/attemptsurface.nii --hemi 'lh' --o /scr/ilz1/Data/results/surf2volume.nii --template /scr/ilz1/Data/freesurfer/fsaverage4/mri/orig.mgz