def test_copy_data_to_tmp_dir():
"""Test copy data to temp dir"""
tmp_dir_path = cft.make_tmp_dir()
copied_file_path1 = pjoin(tmp_dir_path,
"custom_feature_defs.py")
copied_file_path2 = pjoin(tmp_dir_path,
"features_already_known.pkl")
feats_known_dict = {"feat1": 0.215, "feat2": 0.311}
ts_datafile = pjoin(DATA_PATH, "dotastro_215153.dat")
t, m, e = ctt.parse_ts_data(ts_datafile)
feats_known_dict['t'] = t
feats_known_dict['m'] = m
feats_known_dict['e'] = e
for fpath in [copied_file_path1, copied_file_path2]:
if os.path.exists(fpath):
os.remove(fpath)
assert(not os.path.exists(copied_file_path1))
cft.copy_data_to_tmp_dir(tmp_dir_path, pjoin(DATA_PATH, "testfeature1.py"),
feats_known_dict)
assert(os.path.exists(copied_file_path1))
assert(os.path.exists(copied_file_path2))
with open(copied_file_path2, "rb") as f:
unpickled_dict = pickle.load(f)
npt.assert_equal(unpickled_dict, feats_known_dict)
shutil.rmtree(tmp_dir_path, ignore_errors=True)
def test_feature_generation():
"""Compare generated features to reference values."""
this_dir = os.path.join(os.path.dirname(__file__))
test_files = [
os.path.join(this_dir, 'data/257141.dat'),
os.path.join(this_dir, 'data/245486.dat'),
os.path.join(this_dir, 'data/247327.dat'),
]
features_extracted = None
values_computed = None
for i, ts_data_file_path in enumerate(test_files):
t, m, e = ctt.parse_ts_data(ts_data_file_path)
features = sft.generate_science_features(t, m, e, cfg.features_list_science)
sorted_features = sorted(features.items())
if features_extracted is None:
features_extracted = [f[0] for f in sorted_features]
values_computed = np.zeros((len(test_files),
len(features_extracted)))
values_computed[i,:] = [f[1] for f in sorted_features]
def features_from_csv(filename):
with open(filename) as f:
feature_names = f.readline().strip().split(",")
feature_values = np.loadtxt(f, delimiter=',')
return feature_names, feature_values
this_dir = os.path.join(os.path.dirname(__file__))
features_expected, values_expected = features_from_csv(
os.path.join(this_dir, "data/expected_features.csv"))
npt.assert_equal(features_extracted, features_expected)
npt.assert_array_almost_equal(values_computed, values_expected)
def test_docker_extract_features():
"""Test main Docker extract features method"""
script_fpath = pjoin(DATA_PATH, "testfeature1.py")
ts_datafile = pjoin(DATA_PATH, "dotastro_215153.dat")
t, m, e = ctt.parse_ts_data(ts_datafile)
feats_known_dict = {'t': t, 'm': m, 'e': e}
results = cft.docker_extract_features(script_fpath, feats_known_dict)
assert(isinstance(results, dict))
npt.assert_almost_equal(results["avg_mag"], 10.347417647058824)
def test_extract_feats_in_docker_container():
"""Test custom feature extraction in Docker container"""
tmp_dir_path = cft.make_tmp_dir()
feats_known_dict = {"feat1": 0.215, "feat2": 0.311}
ts_datafile = pjoin(DATA_PATH, "dotastro_215153.dat")
t, m, e = ctt.parse_ts_data(ts_datafile)
feats_known_dict['t'] = t
feats_known_dict['m'] = m
feats_known_dict['e'] = e
cft.copy_data_to_tmp_dir(tmp_dir_path,
pjoin(DATA_PATH, "testfeature1.py"),
feats_known_dict)
results = cft.extract_feats_in_docker_container("test", tmp_dir_path)
shutil.rmtree(tmp_dir_path, ignore_errors=True)
cft.remove_tmp_files(tmp_dir_path)
assert(isinstance(results, dict))
npt.assert_almost_equal(results["avg_mag"], 10.347417647058824)
def test_generate_custom_features():
"""Test main generate custom features function"""
t, m, e = ctt.parse_ts_data(pjoin(DATA_PATH, "dotastro_215153.dat"))
feats = cft.generate_custom_features(pjoin(DATA_PATH, "testfeature1.py"),
t, m, e)
npt.assert_almost_equal(feats["avg_mag"], 10.347417647058824)
