def test_sklearn(self):
if os.path.exists("./tests/out/repo2data_sklearn"):
shutil.rmtree("./tests/out/repo2data_sklearn")
repo2data = Repo2Data("./tests/in/sklearn.json")
repo2data.install()
self.assertEqual(dirhash("./tests/out/repo2data_sklearn"),
dirhash("./tests/out/sklearn/repo2data_sklearn"))
def test_url(self):
dir_path = "./data/repo2data_s3_binder"
if os.path.exists(dir_path):
shutil.rmtree(dir_path)
repo2data = Repo2Data("https://github.com/ltetrel/repo2data-caching-s3")
repo2data.install()
dirs = os.listdir(dir_path)
self.assertTrue(len(dirs) > 1)
def test_s3(self):
data_req_path = "./tests/in/s3.json"
with open(data_req_path, "r") as f:
data_req = json.load(f)
dir_path = os.path.join(data_req["dst"], data_req["projectName"])
if os.path.exists(dir_path):
shutil.rmtree(dir_path)
repo2data = Repo2Data(data_req_path)
repo2data.install()
dirs = os.listdir(dir_path)
self.assertTrue(len(dirs) > 1)
def test_multiple(self):
if os.path.exists("./tests/out/repo2data_multiple1"):
shutil.rmtree("./tests/out/repo2data_multiple1")
if os.path.exists("./tests/out/repo2data_multiple2"):
shutil.rmtree("./tests/out/repo2data_multiple2")
repo2data = Repo2Data("./tests/in/multiple.json")
repo2data.install()
self.assertTrue(
os.path.exists("./tests/out/repo2data_multiple1/AUTHORS"))
self.assertTrue(
os.path.exists(
"./tests/out/repo2data_multiple1/data_requirement.json"))
self.assertTrue(
os.path.exists("./tests/out/repo2data_multiple2/LICENSE"))
self.assertTrue(
os.path.exists(
"./tests/out/repo2data_multiple2/data_requirement.json"))
# First, import the necessary modules and functions
import os
from pprint import pprint
import matplotlib.pyplot as plt
import numpy as np
from myst_nb import glue
from nilearn import plotting
from repo2data.repo2data import Repo2Data
from nimare import dataset
# Install the data if running locally, or points to cached data if running on neurolibre
DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json")
repo2data = Repo2Data(DATA_REQ_FILE)
data_path = repo2data.install()
data_path = os.path.join(data_path[0], "data")
# Set an output directory for any files generated during the book building process
out_dir = os.path.abspath("../outputs/")
os.makedirs(out_dir, exist_ok=True)
# Now, load the Datasets we will use in this chapter
sleuth_dset1 = dataset.Dataset.load(
os.path.join(data_path, "sleuth_dset1.pkl.gz"))
sleuth_dset2 = dataset.Dataset.load(
os.path.join(data_path, "sleuth_dset2.pkl.gz"))
neurosynth_dset = dataset.Dataset.load(
os.path.join(data_path, "neurosynth_dataset.pkl.gz"))