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import numpy as np
import scipy.interpolate
from gudhi.representations.vector_methods import BettiCurve
def test_betti_curve_is_irregular_betti_curve_followed_by_interpolation():
m = 10
n = 1000
pinf = 0.05
pzero = 0.05
res = 100
pds = []
for i in range(0, m):
pd = np.zeros((n, 2))
pd[:, 0] = np.random.uniform(0, 10, n)
pd[:, 1] = np.random.uniform(pd[:, 0], 10, n)
pd[np.random.uniform(0, 1, n) < pzero, 0] = 0
pd[np.random.uniform(0, 1, n) < pinf, 1] = np.inf
pds.append(pd)
bc = BettiCurve(None)
bc.fit(pds)
bettis = bc.transform(pds)
bc2 = BettiCurve(None)
bettis2 = bc2.fit_transform(pds)
assert((bc2.grid_ == bc.grid_).all())
assert((bettis2 == bettis).all())
for i in range(0, m):
grid = np.linspace(pds[i][np.isfinite(pds[i])].min(), pds[i][np.isfinite(pds[i])].max() + 1, res)
bc_gridded = BettiCurve(grid)
bc_gridded.fit([])
bettis_gridded = bc_gridded(pds[i])
interp = scipy.interpolate.interp1d(bc.grid_, bettis[i, :], kind="previous", fill_value="extrapolate")
bettis_interp = np.array(interp(grid), dtype=int)
assert((bettis_interp == bettis_gridded).all())
def test_empty_with_predefined_grid():
random_grid = np.sort(np.random.uniform(0, 1, 100))
bc = BettiCurve(random_grid)
bettis = bc.fit_transform([])
assert((bc.grid_ == random_grid).all())
assert((bettis == 0).all())
def test_empty():
bc = BettiCurve()
bettis = bc.fit_transform([])
assert(bc.grid_ == [-np.inf])
assert((bettis == 0).all())
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