Python Dataset.get_feature_names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 get_feature_names [as 别名]
from neurosynth.base.dataset import Dataset
from neurosynth.analysis import meta
import os
dataset = Dataset('database.txt')
dataset.add_features('features.txt')
print dataset.get_feature_names()
ids = dataset.get_ids_by_features('emo*', threshold=0.001)
print len(ids)
ma = meta.MetaAnalysis(dataset, ids)
ma.save_results('emotion')

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import get_feature_names [as 别名]
class NeurosynthMerge:
    def __init__(self, thesaurus, npath, outdir, test_mode=False):
        """
        Generates a new set of images using the neurosynth repository combining 
        across terms in a thesarus.

        Args:
            - thesaurus: A list of tuples where:[('term that will be the name 
                of the file', 'the other term', 'expression combining the 
                terms')]
                    - the last expression is alphanumeric and separated by: 
                    (& for and) (&~ for andnot) (| for or) 
            - npath: directory where the neurosynth git repository is locally 
            on your machine (https://github.com/neurosynth/neurosynth)
            - outdir: directory where the generated images will be saved
            - test_mode: when true, the code will run an abridged version for 
            test purposes (as implemented by test.Neurosynth.py)
        """
        self.thesaurus = thesaurus
        self.npath = npath
        self.outdir = outdir

        self.import_neurosynth_git()
        from neurosynth.analysis import meta

        # Take out first two terms from the feature_list and insert the third 
        # term from the tuple.
        for triplet in thesaurus:
            self.feature_list = [feature for feature in self.feature_list \
            if feature not in triplet]
            self.feature_list.append(triplet[-1])

        # This makes an abridged version of feature_list for testing purposes. 
        if test_mode:
            self.feature_list = [triplet[-1] for triplet in thesaurus]

        # Run metanalyses on the new features set and save the results to the 
            #outdir.
        for feature in self.feature_list:
            self.ids = self.dataset.get_ids_by_expression(feature, 
                threshold=0.001)
            ma = meta.MetaAnalysis(self.dataset, self.ids)

            # Parse the feature name (to avoid conflicts with illegal 
                #characters as file names)
            regex = re.compile('\W+')
            split = re.split(regex, feature)
            feat_fname = split[0] 

            # Save the results (many different types of files)
            ma.save_results(self.outdir+os.sep+feat_fname)

    def import_neurosynth_git(self):
        # Add the appropriate neurosynth git folder to the python path. 
        sys.path.append(self.npath)
        from neurosynth.base.dataset import Dataset
        from neurosynth.analysis import meta

        # Try to load a pickle if it exists. Create a new dataset instance 
        # if it doesn't.
        try:
            self.dataset = cPickle.load(
                open(self.npath+os.sep+'data/dataset.pkl', 'rb'))
        except IOError:
        # Create Dataset instance from a database file.
            self.dataset = Dataset(self.npath+os.sep+'data/database.txt')

        # Load features from file
        self.dataset.add_features(self.npath+os.sep+'data/features.txt')

        # Get names of features. 
        self.feature_list = self.dataset.get_feature_names()

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import get_feature_names [as 别名]
resource_dir = path.join(path.pardir, 'resources')

# make sure we have the data
dataset_dir = path.join(path.expanduser('~'), 'Documents', 'neurosynth-data')
database_path = path.join(dataset_dir, 'database_bregma.txt')
neurosynth_data_url = 'https://github.com/wmpauli/neurosynth-data'
if not path.exists(database_path):
    print("Please download dataset from %s and store it in %s" % (neurosynth_data_url, dataset_dir))

# load dataset, both image table and feature table
r = 1.0 # 1mm smoothing kernel
transform = {'BREGMA': transformations.bregma_to_whs()}
target = 'WHS'
masker_filename = path.join(resource_dir, 'WHS_SD_rat_brainmask_sm_v2.nii.gz')
dataset = Dataset(path.join(dataset_dir, 'database_bregma.txt'), masker=masker_filename, r=r, transform=transform, target=target)
dataset.feature_table = FeatureTable(dataset)
dataset.add_features(path.join(dataset_dir, "features_bregma.txt")) # add features
fn = dataset.get_feature_names()

# get the ids of studies where this feature occurs
ids = dataset.get_ids_by_features(('%s*' % feature), threshold=0.1)
ma = meta.MetaAnalysis(dataset, ids)
results_path = path.join('results', 'meta', feature)
if not path.exists(results_path):
    makedirs(results_path)

    print("saving results to: %s" % results_path)
ma.save_results(results_path)

# note, figure 2 of manuscript was used by plotting the z-score statistical maps for forward inference (pAgF_z.nii.gz) and reverse inference (pFgA_z.nii.gz)