Python Dataset.load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 load [as 别名]
 def test_dataset_save_and_load(self):
     # smoke test of saving and loading
     t = tempfile.mktemp()
     self.dataset.save(t, keep_mappables=True)
     self.assertTrue(os.path.exists(t))
     dataset = Dataset.load(t)
     self.assertIsNotNone(dataset)
     self.assertIsNotNone(dataset.mappables)
     self.assertEqual(len(dataset.mappables), 5)
     # Now with the mappables deleted
     dataset.save(t)
     self.assertTrue(os.path.exists(t))
     dataset = Dataset.load(t)
     self.assertEqual(len(dataset.mappables), 0)
     os.unlink(t)

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import load [as 别名]
def create_voxel_x_feature_matrix(path_to_dataset, path_to_image_files):
        dataset = Dataset.load(path_to_dataset)
	feature_list = dataset.get_feature_names()
	vox_feat_matrix = zeros((dataset.volume.num_vox_in_mask, len(feature_list)), dtype=int16)
	for (i,feature) in enumerate(feature_list):
		image_path = path_to_image_files + feature + '_pFgA_z.nii.gz'
		vox_feat_matrix[:,i] = dataset.volume.mask(image_path)
	return vox_feat_matrix

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import load [as 别名]
 def test_dataset_save_and_load(self):
     # smoke test of saving and loading
     t = tempfile.mktemp()
     self.dataset.save(t)
     self.assertTrue(os.path.exists(t))
     dataset = Dataset.load(t)
     self.assertIsNotNone(dataset)
     self.assertEqual(len(dataset.image_table.ids), 5)
     os.unlink(t)

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import load [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 load [as 别名]
    def __init__(
        self,
        metric="emd",
        image_type="pAgF",
        name=None,
        multi=True,
        image_transform="block_reduce",
        downsample=8,
        auto_save=True,
    ):
        self.image_type = image_type
        self.multi = multi
        self.downsample = downsample
        self.auto_save = auto_save

        if callable(metric):
            self.metric = metric
        elif metric == "emd":
            self.metric = euclidean_emd
        else:
            raise ValueError("{metric} is not a valid metric".format(**locals()))

        if callable(image_transform):
            self.image_transform = image_transform
        elif image_transform == "block_reduce":
            from functools import partial

            self.image_transform = partial(block_reduce, factor=downsample)
            # def block_reduce_transform(image):
            # """The default transformation."""
            # return block_reduce(image, downsample, blur)
            # self.image_transform = block_reduce_transform
        else:
            raise ValueError(("{image_transform} is not a valid" "transform function").format(**locals()))
        self.name = name if name else time.strftime("analysis_from_%m-%d_%H-%M-%S")

        try:
            self.data = Dataset.load("data/dataset.pkl")
        except FileNotFoundError:
            self.data = _getdata()

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import load [as 别名]
###
# This script shuffle the classification labels and reruns classification many times to get data to calculate a confidence interval around the null hypothesis

from sklearn.linear_model import RidgeClassifier
from base.classifiers import OnevsallClassifier
from neurosynth.base.dataset import Dataset
from sklearn.metrics import roc_auc_score
import pickle
from random import shuffle

def shuffle_data(classifier):
	for region in classifier.c_data:
		shuffle(region[1])


d_abs_topics_filt = Dataset.load('../data/datasets/abs_topics_filt_july.pkl')

results = []

clf = OnevsallClassifier(d_abs_topics_filt, '../masks/Ward/50.nii.gz', cv='4-Fold',
	 thresh=10, thresh_low=0, memsave=True, classifier=RidgeClassifier())
clf.load_data(None, None)
clf.initalize_containers(None, None, None)


for i in range(0, 500):
	shuffle_data(clf)
	clf.classify(scoring=roc_auc_score, processes=8, class_weight=None)
	results = list(clf.class_score) + results
	print(i),

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import load [as 别名]
from neurosynth.analysis.cluster import magic
from neurosynth.base.dataset import Dataset

# mydir = "/home/delavega/projects/permutation_clustering/"

mydir = "../data/"

dataset = Dataset.load(mydir + '/datasets/abs_60topics_filt_jul.pkl')

roi_mask = mydir + '../masks/new_medial_fc_30.nii.gz'
global_mask = mydir +  "../masks/MNI152_T1_2mm_brain.nii.gz"

magic(dataset, 10, method='coactivation', features=['topic57', 'topic32', 'topic39', 'topic44'], output_dir='../results/ef_cluster/all_cluster/', min_studies_per_voxel=100, filename='okay')

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import load [as 别名]
t = dlmread('/Users/lukechang/Dropbox/Github/toolbox/Python/ImageProcessing/tal2icbm_fsl.mat');
dat.volInfo.mat = inv(t)*dat.volInfo.mat;
dat.fullpath = '/Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/Friend.nii';
write(dat)

# 2) Reorient using FSL - Unix
fslreorient2std Friend Friend_Or

# 3) Coregister to 2mm MNI space - Unix
/usr/local/fsl/bin/flirt -in /Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/Friend_Or.nii.gz -ref /usr/local/fsl/data/standard/MNI152_T1_2mm_brain -out /Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/Friend_Or_Mni.nii.gz -omat /Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/Friend_Or_Mni.mat -bins 256 -cost corratio -searchrx -90 90 -searchry -90 90 -searchrz -90 90 -dof 12  -interp trilinear

# 4) Decode - Python
DATASET_FILE = '/Users/lukechang/Dropbox/Github/neurosynth/topics.pkl'
PREFIX = '/Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/'
INFILE = 'Friend_Or_Mni.nii.gz'
dataset = Dataset.load(DATASET_FILE)
decoder = decode.Decoder(dataset) #takes awhile to load, should only do this once.
img = imageutils.load_imgs(PREFIX + INFILE, decoder.mask)
result = decoder.decode(img)
np.savetxt(PREFIX + 'Friend_Decoded.txt', result)

# 5) Threshold at .001 - unix
fslmaths Friend_Or_Mni -thr 3 Friend_Or_Mni_001

# 6) Decode thresholded map - python
DATASET_FILE = '/Users/lukechang/Dropbox/Github/neurosynth/topics.pkl'
PREFIX = '/Users/lukechang/Research/Trust_Friend/Analyses/NeurosynthDecode/'
INFILE = 'Friend_Or_Mni_001.nii.gz'
dataset = Dataset.load(DATASET_FILE)
decoder = decode.Decoder(dataset) #takes awhile to load, should only do this once.
img = imageutils.load_imgs(PREFIX + INFILE, decoder.mask)

# 需要导入模块: from neurosynth.base.dataset import Dataset [as 别名]
# 或者: from neurosynth.base.dataset.Dataset import load [as 别名]
# -*- coding: utf-8 -*-

# Here I use Yeo to test Neurosynth's classify functions
from neurosynth.base.dataset import Dataset
from neurosynth.analysis import classify
import os
import itertools
import re
import numpy as np
import pdb
import sys
from nipype.interfaces import fsl
from sklearn.ensemble import GradientBoostingClassifier


dataset = Dataset.load('../data/pickled.pkl')

masklist = ['7Networks_Liberal_1.nii.gz', '7Networks_Liberal_2.nii.gz',
            '7Networks_Liberal_3.nii.gz', '7Networks_Liberal_4.nii.gz',
            '7Networks_Liberal_5.nii.gz', '7Networks_Liberal_6.nii.gz',
            '7Networks_Liberal_7.nii.gz']

rootdir = '../masks/Yeo_JNeurophysiol11_MNI152/standardized/'


class maskClassifier:
    def __init__(self, classifier=GradientBoostingClassifier(), param_grid={'max_features': np.arange(2, 140, 44), 'n_estimators': np.arange(5, 141, 50),
          'learning_rate': np.arange(0.05, 1, 0.1)}, thresh = 0.08)


diffs = {}