Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Improve dist array tests and protect against segfault. #179

Merged
merged 3 commits into from
Oct 27, 2024
Merged

Improve dist array tests and protect against segfault. #179

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
456153e
protect from seg and add more tests
hmacdope Oct 24, 2024
50ce92f
fixify
hmacdope Oct 24, 2024
1cadcb3
improve tests
hmacdope Oct 24, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 12 additions & 0 deletions distopia/pydistopia.pyx
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -771,6 +771,9 @@ def calc_self_distance_array_no_box(

dims[0] = <ssize_t > final_size

# return early, will seg
if nvals0 == 0:
return np.array([])

if results is None:
if floating is float:
Expand Down Expand Up @@ -823,6 +826,11 @@ def calc_self_distance_array_ortho(

dims[0] = <ssize_t > final_size


# return early, will seg
if nvals0 == 0:
return np.array([])

if results is None:
if floating is float:
results = cnp.PyArray_EMPTY(1, dims, cnp.NPY_FLOAT32, 0)
Expand Down Expand Up @@ -874,6 +882,10 @@ def calc_self_distance_array_triclinic(
dims[0] = <ssize_t > final_size


# return early, will seg
if nvals0 == 0:
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

very nitpicky but this probably should be typed either to f32/64 based on the input

return np.array([])

if results is None:
if floating is float:
results = cnp.PyArray_EMPTY(1, dims, cnp.NPY_FLOAT32, 0)
Expand Down
119 changes: 98 additions & 21 deletions distopia/tests/test_distopia.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -65,8 +65,17 @@ def test_calc_bonds_triclinic_all_zero(self, N, use_result_buffer, dtype):
box = np.asarray([[30, 0, 0], [-2.6146722, 29.885841, 0], [-10.260604, 9.402112, 26.576687]], dtype=dtype)
result = distopia.calc_bonds_triclinic(c0, c1, box, results=result_buffer)
assert_allclose(result, np.zeros(N))
# check dtype of result
assert result.dtype == dtype

def test_calc_bonds_inplace_results(self):
N = 100
dtype = np.float32
c0 = self.arange_input(N, dtype)
c1 = self.arange_input(N, dtype) + 1
result_buffer = np.empty(N, dtype=dtype)
result = distopia.calc_bonds_no_box(c0, c1, results=result_buffer)
assert_allclose(result, result_buffer)
assert_allclose(result, np.linalg.norm(c0 - c1, axis=1))




Expand Down Expand Up @@ -193,6 +202,12 @@ def test_triclinic_bad_result_or_input_shape(self):

class TestDistanceArray:

def result_shim(self, make_arr, X, Y, dtype):
if not make_arr:
return None
else:
return np.empty((X, Y), dtype=dtype)


def test_no_box_bad_result(self):
c0 = np.zeros(6, dtype=np.float32).reshape(2, 3)
Expand All @@ -216,21 +231,59 @@ def test_triclinic_bad_result(self):
distopia.calc_distance_array_triclinic(c0, c1, box, results=np.empty((1,1), dtype=np.float32))


def test_distance_array_arange(self):
c0 = np.ones(9, dtype=np.float32).reshape(3, 3)
c1 = np.ones(9, dtype=np.float32).reshape(3, 3)
results = distopia.calc_distance_array_no_box(c0, c1)
assert_almost_equal(results, np.zeros((3,3), dtype=np.float32))
@pytest.mark.parametrize("X, Y", ((0, 0), (10, 20), (1000, 100), (200, 1000)))
@pytest.mark.parametrize("dtype", (np.float32, np.float64))
@pytest.mark.parametrize("use_result_buffer", (True, False))
def test_distance_array_const(self, X, Y, dtype, use_result_buffer):
result_buffer = self.result_shim(use_result_buffer, X, Y, dtype)
c0 = np.ones(3 * X, dtype=dtype).reshape(X, 3) * 2
c1 = np.ones(3 * Y, dtype=dtype).reshape(Y, 3) * 3
results = distopia.calc_distance_array_no_box(c0, c1, results=result_buffer)
# equilateral triangle, edge is 3**(1/2)
expected = np.ones((X, Y), dtype=dtype) * 3**(1/2)
assert_almost_equal(results, expected)

def test_distance_array_results(self):
c0 = np.ones(9, dtype=np.float32).reshape(3, 3)
c1 = np.ones(9, dtype=np.float32).reshape(3, 3)
results = distopia.calc_distance_array_no_box(c0, c1, results=np.empty((3,3), dtype=np.float32))
assert_almost_equal(results, np.zeros((3,3), dtype=np.float32))

@pytest.mark.parametrize("X, Y", ((0, 0), (10, 20), (1000, 100), (200, 1000)))
@pytest.mark.parametrize("dtype", (np.float32, np.float64))
@pytest.mark.parametrize("use_result_buffer", (True, False))
def test_distance_array_const_ortho(self, X, Y, dtype, use_result_buffer):
result_buffer = self.result_shim(use_result_buffer, X, Y, dtype)
c0 = np.ones(3 * X, dtype=dtype).reshape(X, 3) * 2
c1 = np.ones(3 * Y, dtype=dtype).reshape(Y, 3) * 3
box = np.array([2.5, 2.5, 2.5], dtype=dtype)
results = distopia.calc_distance_array_ortho(c0, c1, box=box, results=result_buffer)
# equilateral triangle, edge is 3**(1/2)
expected = np.ones((X, Y), dtype=dtype) * 3**(1/2)
assert_almost_equal(results, expected)



@pytest.mark.parametrize("X, Y", ((0, 0), (10, 20), (1000, 100), (200, 1000)))
@pytest.mark.parametrize("dtype", (np.float32, np.float64))
@pytest.mark.parametrize("use_result_buffer", (True, False))
def test_distance_const_tric(self, X, Y, dtype, use_result_buffer):
result_buffer = self.result_shim(use_result_buffer, X, Y, dtype)
c0 = np.ones(3 * X, dtype=dtype).reshape(X, 3) * 2
c1 = np.ones(3 * Y, dtype=dtype).reshape(Y, 3) * 3
box = np.array([[2.5, 0, 0], [0, 2.5, 0], [0, 0, 2.5]], dtype=dtype)
results = distopia.calc_distance_array_triclinic(c0, c1, box=box, results=result_buffer)
# equilateral triangle, edge is 3**(1/2)
expected = np.ones((X, Y), dtype=dtype) * 3**(1/2)
assert_almost_equal(results, expected)


class TestSelfDistanceArray:


def result_shim(self, make_arr, N, dtype):
if not make_arr:
return None
else:
size = N * (N - 1) // 2 # reduced triangular matrix
return np.empty(size, dtype=dtype)


def test_no_box_bad_result(self):
c0 = np.zeros(12, dtype=np.float32).reshape(4, 3)
with pytest.raises(ValueError, match="results must be"):
Expand All @@ -248,17 +301,41 @@ def test_triclinic_bad_result(self):
with pytest.raises(ValueError, match="results must be"):
distopia.calc_self_distance_array_triclinic(c0, box, results=np.empty(1, dtype=np.float32))

def test_self_distance_ones(self):
c0 = np.ones(9, dtype=np.float32).reshape(3, 3)
results = distopia.calc_self_distance_array_no_box(c0)
assert_almost_equal(results, np.zeros(3, dtype=np.float32))

@pytest.mark.parametrize("N", (0, 10, 1000, 10000))
@pytest.mark.parametrize("dtype", (np.float32, np.float64))
@pytest.mark.parametrize("use_result_buffer", (True, False))
def test_self_distance_const(self, N, dtype, use_result_buffer):
result_buffer = self.result_shim(use_result_buffer, N, dtype)
c0 = np.ones(3 * N, dtype=dtype).reshape(N, 3)
expected_size = N * (N - 1) // 2
results = distopia.calc_self_distance_array_no_box(c0, results=result_buffer)
assert_almost_equal(results, np.zeros(expected_size, dtype=dtype))



def test_self_distance_ones_result(self):
c0 = np.ones(9, dtype=np.float32).reshape(3, 3)
# n(n-1) //2
results = distopia.calc_self_distance_array_no_box(c0, results=np.empty(3, dtype=np.float32))
assert_almost_equal(results, np.zeros(3, dtype=np.float32))
@pytest.mark.parametrize("N", (0, 10, 1000, 10000))
@pytest.mark.parametrize("dtype", (np.float32, np.float64))
@pytest.mark.parametrize("use_result_buffer", (True, False))
def test_self_distance_const_ortho(self, N, dtype, use_result_buffer):
result_buffer = self.result_shim(use_result_buffer, N, dtype)
c0 = np.ones(3 * N, dtype=dtype).reshape(N, 3)
expected_size = N * (N - 1) // 2
box = np.array([10, 10, 10], dtype=dtype)
results = distopia.calc_self_distance_array_ortho(c0, box=box, results=result_buffer)
assert_almost_equal(results, np.zeros(expected_size, dtype=dtype))


@pytest.mark.parametrize("N", (0, 10, 1000, 10000))
@pytest.mark.parametrize("dtype", (np.float32, np.float64))
@pytest.mark.parametrize("use_result_buffer", (True, False))
def test_self_distance_const_tric(self, N, dtype, use_result_buffer):
result_buffer = self.result_shim(use_result_buffer, N, dtype)
c0 = np.ones(3 * N, dtype=dtype).reshape(N, 3)
expected_size = N * (N - 1) // 2
box = np.array([[10, 0, 0], [0, 10, 0], [0, 0, 10]], dtype=dtype)
results = distopia.calc_self_distance_array_triclinic(c0, box=box, results=result_buffer)
assert_almost_equal(results, np.zeros(expected_size, dtype=dtype))

class TestMDA:
"""
Expand Down
Loading