def test_kappa():
df1 = tctc(data, 2, 0.5, 2, 1, output='df')
df2 = tctc(data, 2, 0.5, 2, 0, output='df')
r = 0
for _, d in df1.iterrows():
if [0, 1] == d['community']:
r += 1
r2 = 0
for _, d in df2.iterrows():
if [0, 1] == d['community']:
r2 += 1
if r != 1 or r2 != 2:
raise AssertionError()
rt = []
for _, d in df1.iterrows():
if set(d['community']).issuperset([0, 1]):
rt += list(np.arange(d['start'], d['end']))
rt = np.unique(rt)
rt2 = []
for _, d in df2.iterrows():
if set(d['community']).issuperset([0, 1]):
rt2 += list(np.arange(d['start'], d['end']))
rt2 = np.unique(rt2)
if len(rt) != 13:
raise AssertionError()
# The two time points which shouldnt be included if kappa = 0
if 7 in rt2 or 10 in rt2:
raise AssertionError()
def test_sigma():
df1 = tctc(data, 3, 0.5, 2, 1, output='df')
df2 = tctc(data, 3, 0.5, 3, 1, output='df')
if df1['size'].min() != 2:
raise AssertionError()
if df2['size'].min() != 3:
raise AssertionError()
def test_tau():
df1 = tctc(data, 3, 0.5, 2, 1, output='df')
df2 = tctc(data, 5, 0.5, 2, 1, output='df')
if df1['length'].min() < 3:
raise AssertionError()
if df2['length'].min() < 5:
raise AssertionError()
df3 = tctc(data, 13, 0.5, 2, 1, output='df')
if df3.shape[0] != 1:
raise AssertionError()
def test_output_simularity():
array = tctc(data, 3, 0.5, 2, 1)
df = tctc(data, 3, 0.5, 2, 1, output='df')
array2 = df_to_array(df, (4, 4, 13))
if np.sum(array == array2) != np.prod(array2.shape):
raise AssertionError()
array = tctc(data, 3, 0.5, 2, 0)
df = tctc(data, 3, 0.5, 2, 0, output='df')
array2 = df_to_array(df, (4, 4, 13))
if np.sum(array == array2) != np.prod(array2.shape):
raise AssertionError()
def test_epsilon():
# Test goes through and makes sure that the epsilon parameter is working correctly by using input data
epsilon = np.sort(np.abs(data[:, 0]-data[:, 1]))
result = []
for e in epsilon:
array = tctc(data[:, :2], 1, e, 2, 0, output='array')
result.append(np.sum(array[0, 1, :]))
if not all(result == np.arange(1, 14)):
raise AssertionError()