# Core functionality for managing and accessing data
from neurosynth import Dataset
# Analysis tools for meta-analysis, image decoding, and coactivation analysis
from neurosynth import meta, decode, network

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

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

dataset.save('dataset.pkl')

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

dataset.get_feature_names()

ids = dataset.get_studies(features='emo*', frequency_threshold=0.05)

len(ids)

# Run a meta-analysis on emotion
ids = dataset.get_ids_by_features('emo*', threshold=0.05)
ma = meta.MetaAnalysis(dataset, ids)
ma.save_results('.', 'emotion')

ids = dataset.get_studies(expression='emo* &~ (reward* | pain*)', frequency_threshold=0.05)
ma = meta.MetaAnalysis(dataset, ids)
ma.save_results('.', 'emotion_without_reward_or_pain')
print "Found %d studies." % len(ids)

# Get the recognition studies and print some info...
recog_ids = dataset.get_ids_by_features('recognition', threshold=0.05)
print "We found %d studies of recognition" % len(recog_ids)

# Repeat for recollection studies
recoll_ids = dataset.get_ids_by_features('recollection', threshold=0.05)
print "We found %d studies of recollection" % len(recoll_ids)

# Run the meta-analysis
ma = meta.MetaAnalysis(dataset, recog_ids, recoll_ids)
ma.save_results('.', 'recognition_vs_recollection')

# Seed-based coactivation
network.coactivation(dataset, [[0, 20, 28]], threshold=0.1, r=10, output_dir='.', prefix='acc_seed')

# Decode images
decoder = decode.Decoder(dataset, features=['taste', 'disgust', 'emotion', 'auditory', 'pain', 'somatosensory', 'conflict', 'switching', 'inhibition'])
data = decoder.decode(['pIns.nii.gz', 'vIns.nii.gz', 'dIns.nii.gz'], save='decoding_results.txt')