def test_reconstruction():
assert np.all(
pypsr.reconstruct(np.arange(10), 1, 2) == np.vstack((np.arange(9), np.arange(1, 10))).transpose()
)
assert np.all(
pypsr.reconstruct(np.arange(10), 2, 3)
== np.vstack((np.arange(6), np.arange(2, 8), np.arange(4, 10))).transpose()
)
return 0
input_file = r'/home/mty/conflictNUM20181024.csv'
df = pd.read_csv(input_file)
origin_info = list(df.iloc[:, 1])
origin_time = list(df.iloc[:, 2])
new_info = []
new_time = []
for index, row in enumerate(origin_info):
if index % 2 == 0:
new_info.append(row)
new_time.append(int(origin_time[index]))
warnings.filterwarnings("error")
reconstruct_list = pypsr.reconstruct(new_info, 12, 6)
threshold = 0.92
node_couple_set = []
for index1, vector1 in enumerate(reconstruct_list):
tem_list = reconstruct_list[index1:, :]
if len(tem_list) > 1:
for index2, vector2 in enumerate(tem_list):
try:
correlation_coefficient = np.corrcoef(vector1, vector2)
D = heaviside(correlation_coefficient[0][1], threshold)
if D == 1:
node_couple_set.append((index1, index2 + index1 + 1))
except:
pass
node_set = []
for node_couple in node_couple_set:
import warnings
def heaviside(value, threshold=0.7):
if value >= threshold:
return 1
else:
return 0
input_file = r'/home/mty/conflictNUM.csv'
df = pd.read_csv(input_file)
origin_info = list(df.iloc[:, 1])
warnings.filterwarnings("error")
reconstruct_list = pypsr.reconstruct(origin_info, 12, 6)
average_clustering = []
for i in np.arange(0.80, 1., 0.01):
threshold = round(i, 2)
print(threshold)
node_couple_set = []
for index1, vector1 in enumerate(reconstruct_list):
tem_list = reconstruct_list[index1:, :]
if len(tem_list) > 1:
for index2, vector2 in enumerate(tem_list):
try:
correlation_coefficient = np.corrcoef(vector1, vector2)
D = heaviside(correlation_coefficient[0][1], threshold)
if D == 1:
node_couple_set.append((index1, index2 + index1 + 1))
except:
def test_reconstruction_too_long_lag():
with pytest.raises(ValueError):
pypsr.reconstruct(np.ones(10), 5, 2)
with pytest.raises(ValueError):
pypsr.reconstruct(np.ones(10), 2, 5)
def test_reconstruction_wrong_dimension_input():
with pytest.raises(ValueError):
pypsr.reconstruct(np.ones((10, 10)), 1, 2)