def test_single_cond_multi_subjs(self):
params = OrderedDict([('loc', 0), ('scale', 1)])
subjs = 3
size = 100
# generate test data
seed = 31337
data, params_subjs = gen_rand_data(gen_func_df,
params,
size=size,
subjs=subjs,
seed=seed)
# test subj present
np.testing.assert_array_equal(np.unique(data['subj_idx']),
list(range(subjs)))
# test for correct length
np.testing.assert_array_equal(len(data), subjs * size)
# generate truth
np.random.seed(seed)
for i in range(subjs):
new_params = _add_noise({'test': params})['test']
print("check", new_params)
truth = gen_func_df(size=size, **new_params)
np.testing.assert_array_equal(data[data['subj_idx'] == i]['data'],
truth['data'])
self.assertEqual(params_subjs[i], new_params)
def test_multiple_cond_no_subj(self):
size = 100
params = OrderedDict([('cond1', {
'loc': 0,
'scale': 1
}), ('cond2', {
'loc': 100,
'scale': 10
})])
seed = 31337
data, subj_params = gen_rand_data(gen_func_df,
params,
size=size,
seed=seed)
# test whether conditions are present
np.testing.assert_array_equal(np.unique(data['condition'].values),
['cond1', 'cond2'])
self.assertEqual(list(subj_params.keys()), ['cond1', 'cond2'])
# test for correct length
np.testing.assert_array_equal(len(data), 2 * size)
# generate truth
np.random.seed(31337)
truth = gen_func_df(size=100, **params['cond1'])
np.testing.assert_array_equal(
data[data['condition'] == 'cond1']['data'], truth['data'])
truth = gen_func_df(size=100, **params['cond2'])
np.testing.assert_array_equal(
data[data['condition'] == 'cond2']['data'], truth['data'])
def test_single_cond_no_subj(self):
params = {'loc': 0, 'scale': 1}
seed = 31337
data, params_return = gen_rand_data(gen_func_df, params, size=100, seed=seed)
np.random.seed(seed)
truth = gen_func_df(size=100, **params)
np.testing.assert_array_equal(data['data'], truth['data'])
self.assertEqual(params, params_return)
def test_single_cond_no_subj(self):
params = {"loc": 0, "scale": 1}
np.random.seed(31337)
data, params_return = gen_rand_data(normal_like, params, samples=100)
np.random.seed(31337)
truth = np.float64(normal_like.rv.random(size=100, **params))
np.testing.assert_array_equal(data["data"], truth)
self.assertEqual(params, params_return)
def test_column_name(self):
params = OrderedDict([('loc', 0), ('scale', 1)])
subjs = 100
size = 100
# generate test data
np.random.seed(31337)
data, params_subjs = gen_rand_data(gen_func_df, params, size=size, subjs=subjs, exclude_params=('scale',))
self.assertIn('data', data.columns)
def test_column_name(self):
params = OrderedDict([('loc', 0), ('scale', 1)])
subjs = 100
samples = 100
# generate test data
np.random.seed(31337)
data, params_subjs = gen_rand_data(normal_like, params, samples=samples, subjs=subjs, exclude_params=('scale',), column_name='test')
self.assertIn('test', data.dtype.names)
def test_single_cond_no_subj(self):
params = {'loc': 0, 'scale': 1}
seed = 31337
data, params_return = gen_rand_data(gen_func_df,
params,
size=100,
seed=seed)
np.random.seed(seed)
truth = gen_func_df(size=100, **params)
np.testing.assert_array_equal(data['data'], truth['data'])
self.assertEqual(params, params_return)
def test_column_name(self):
params = OrderedDict([("loc", 0), ("scale", 1)])
subjs = 100
samples = 100
# generate test data
np.random.seed(31337)
data, params_subjs = gen_rand_data(
normal_like, params, samples=samples, subjs=subjs, exclude_params=("scale",), column_name="test"
)
self.assertIn("test", data.dtype.names)
def test_column_name(self):
params = OrderedDict([('loc', 0), ('scale', 1)])
subjs = 100
size = 100
# generate test data
np.random.seed(31337)
data, params_subjs = gen_rand_data(gen_func_df,
params,
size=size,
subjs=subjs,
exclude_params=('scale', ))
self.assertIn('data', data.columns)
def test_mulltiple_cond_no_subj(self):
samples = 100
params = OrderedDict([("cond1", {"loc": 0, "scale": 1}), ("cond2", {"loc": 100, "scale": 10})])
np.random.seed(31337)
data, subj_params = gen_rand_data(normal_like, params, samples=samples)
# test whether conditions are present
np.testing.assert_array_equal(np.unique(data["condition"]), ["cond1", "cond2"])
self.assertEqual(subj_params.keys(), ["cond1", "cond2"])
# test for correct length
np.testing.assert_array_equal(len(data), 2 * samples)
# generate truth
np.random.seed(31337)
truth = np.float64(normal_like.rv.random(size=100, **params["cond1"]))
np.testing.assert_array_equal(data[data["condition"] == "cond1"]["data"], truth)
truth = np.float64(normal_like.rv.random(size=100, **params["cond2"]))
np.testing.assert_array_equal(data[data["condition"] == "cond2"]["data"], truth)
def test_mulltiple_cond_no_subj(self):
size = 100
params = OrderedDict([('cond1', {'loc': 0, 'scale': 1}), ('cond2', {'loc': 100, 'scale': 10})])
seed = 31337
data, subj_params = gen_rand_data(gen_func_df, params, size=size, seed=seed)
# test whether conditions are present
np.testing.assert_array_equal(np.unique(data['condition']).values, ['cond1', 'cond2'])
self.assertEqual(subj_params.keys(), ['cond1', 'cond2'])
# test for correct length
np.testing.assert_array_equal(len(data), 2*size)
# generate truth
np.random.seed(31337)
truth = gen_func_df(size=100, **params['cond1'])
np.testing.assert_array_equal(data[data['condition'] == 'cond1']['data'], truth['data'])
truth = gen_func_df(size=100, **params['cond2'])
np.testing.assert_array_equal(data[data['condition'] == 'cond2']['data'], truth['data'])
def test_mulltiple_cond_no_subj(self):
samples = 100
params = OrderedDict([('cond1', {'loc': 0, 'scale': 1}), ('cond2', {'loc': 100, 'scale': 10})])
np.random.seed(31337)
data, subj_params = gen_rand_data(normal_like, params, samples=samples)
# test whether conditions are present
np.testing.assert_array_equal(np.unique(data['condition']), ['cond1', 'cond2'])
self.assertEqual(subj_params.keys(), ['cond1', 'cond2'])
# test for correct length
np.testing.assert_array_equal(len(data), 2*samples)
# generate truth
np.random.seed(31337)
truth = np.float64(normal_like.rv.random(size=100, **params['cond1']))
np.testing.assert_array_equal(data[data['condition'] == 'cond1']['data'], truth)
truth = np.float64(normal_like.rv.random(size=100, **params['cond2']))
np.testing.assert_array_equal(data[data['condition'] == 'cond2']['data'], truth)
def test_single_cond_multi_subjs_exclude(self):
params = OrderedDict([("loc", 0), ("scale", 1)])
subjs = 100
samples = 100
# generate test data
np.random.seed(31337)
data, params_subjs = gen_rand_data(normal_like, params, samples=samples, subjs=subjs, exclude_params=("scale",))
# test subj present
np.testing.assert_array_equal(np.unique(data["subj_idx"]), range(subjs))
# test for correct length
np.testing.assert_array_equal(len(data), subjs * samples)
# generate truth
np.random.seed(31337)
for i in range(subjs):
new_params = _add_noise(params, exclude_params=("scale",))
truth = np.float64(normal_like.rv.random(size=samples, **new_params))
np.testing.assert_array_equal(data[data["subj_idx"] == i]["data"], truth)
self.assertEqual(params_subjs[i], new_params)
def test_single_cond_multi_subjs_exclude(self):
params = OrderedDict([('loc', 0), ('scale', 1)])
subjs = 3
size = 100
# generate test data
seed = 31337
data, params_subjs = gen_rand_data(gen_func_df, params, size=size, subjs=subjs,
exclude_params=('scale',), seed=seed)
# test subj present
np.testing.assert_array_equal(np.unique(data['subj_idx']), range(subjs))
# test for correct length
np.testing.assert_array_equal(len(data), subjs*size)
# generate truth
np.random.seed(seed)
for i in range(subjs):
new_params = _add_noise({'test': params}, exclude_params=('scale',))['test']
truth = gen_func_df(size=size, **new_params)
np.testing.assert_array_equal(data[data['subj_idx'] == i]['data'], truth['data'])
self.assertEqual(params_subjs[i], new_params)