def test_one_by_one(self):
df = pd.DataFrame(self.r.uniform(0,1,(500, 3)), columns=['x', 'y', 'strength'])
sup = SpatialSuppression(df, (1,1), k = 1)
self.assertRaises(ValueError, sup.suppress())
def test_point_overload(self):
df = pd.DataFrame(self.r.uniform(0,15,(500, 3)), columns=['x', 'y', 'strength'])
sup = SpatialSuppression(df, (15,15), k = 200)
sup.suppress()
self.assertEqual(len(df[sup.mask]), 70)
def test_normal_distribution(self):
df = pd.DataFrame(self.r.uniform(0,100,(500, 3)), columns=['x', 'y', 'strength'])
sup = SpatialSuppression(df, (100,100), k = 15)
sup.suppress()
self.assertEqual(len(df[sup.mask]), 17)
def test_min_max(self):
df = pd.DataFrame(self.r.uniform(0,2,(500, 3)), columns=['x', 'y', 'strength'])
sup = SpatialSuppression(df, (1.5,1.5), k = 1)
sup.suppress()
self.assertEqual(len(df[sup.mask]), 1)
def test_one_by_one(self):
df = pd.DataFrame(self.r.uniform(0, 1, (500, 3)),
columns=['x', 'y', 'strength'])
sup = SpatialSuppression(df, (1, 1), k=1)
self.assertRaises(ValueError, sup.suppress())
def test_normal_distribution(self):
df = pd.DataFrame(self.r.uniform(0, 100, (500, 3)),
columns=['x', 'y', 'strength'])
sup = SpatialSuppression(df, (100, 100), k=15)
sup.suppress()
self.assertEqual(len(df[sup.mask]), 17)
def test_point_overload(self):
df = pd.DataFrame(self.r.uniform(0, 15, (500, 3)),
columns=['x', 'y', 'strength'])
sup = SpatialSuppression(df, (15, 15), k=200)
sup.suppress()
self.assertEqual(len(df[sup.mask]), 70)
def test_min_max(self):
df = pd.DataFrame(self.r.uniform(0, 2, (500, 3)),
columns=['x', 'y', 'strength'])
sup = SpatialSuppression(df, (1.5, 1.5), k=1)
sup.suppress()
self.assertEqual(len(df[sup.mask]), 1)
