def test_timeseries():
from earlywarningsignals.earlywarningsignals import check_time_series
# Test 1: Trying a known value
input_1a = np.arange(10) * 2
input_1b = np.arange(10)
input_2a = np.array([[1, 6], [3, 5], [4, 4], [6, 1]])
input_2aa = np.array([['a', 'b'], ['b', 'x'], ['l', 'i'], ['g', 'z']])
input_2b = np.arange(4)
input_2bb = np.random.randint(low=0, high=14, size=(4, 1))
input_3a = pd.DataFrame(input_1a)
input_3b = pd.DataFrame(input_1b)
input_4a = pd.DataFrame(input_2a)
input_4b = pd.DataFrame(input_2b)
[output_1a, output_1b] = check_time_series(input_1a)
#still have to write asserts
assert (isinstance(output_1a, pd.DataFrame))
#Check if evenly spaced check works
with pt.raises(ValueError):
check_time_series(input_2a, input_2bb)
#Check if timeseries and timeindex have same length
with pt.raises(ValueError):
check_time_series(input_3a, input_4b)
def test_check_timeseries_dataframe():
from earlywarningsignals.earlywarningsignals import check_time_series
N = 10
input_vector = np.arange(N)
input_df = pd.DataFrame(input_vector)
[ts, indices] = check_time_series(input_df)
# The indices are correct
assert (np.min(indices) == 0)
assert (np.max(indices) == N - 1)
# The number of elements is correct
assert (np.size(indices) == N)
assert (np.size(ts) == N)
def test_check_timeseries_customtimes():
from earlywarningsignals.earlywarningsignals import check_time_series
N = 10
input_vector = np.arange(N)
input_times = np.linspace(0, 0.9, N) # 0, 0.1, 0.2, ..., 0.9
input_df = pd.DataFrame(input_vector, input_times)
[ts, indices] = check_time_series(input_df)
# The indices are correct
assert (indices.all() == input_times.all())
# The number of elements is correct
assert (np.size(indices) == N)
assert (np.size(ts) == N)
def test_check_timeseries_array():
from earlywarningsignals.earlywarningsignals import check_time_series
input_array = np.array([[1, 6], [3, 5], [4, 4], [6, 1]])
[ts, indices] = check_time_series(input_array)
# The indices are correct
assert (np.min(indices) == 0)
assert (np.max(indices) == 4 - 1)
# The number of elements is correct
assert (np.size(indices) == 4)
# The output is a dataframe
assert (isinstance(ts, pd.DataFrame))