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Clarity update
  • Loading branch information
Sparks29032 committed Dec 16, 2025
commit 97383a0b7cce3c8172fd6b393b482a0e69ed727b
10 changes: 3 additions & 7 deletions src/diffpy/morph/morph_io.py
Original file line number Diff line number Diff line change
Expand Up @@ -447,17 +447,13 @@ def handle_extrapolation_warnings(morph):

def handle_check_increase_warning(squeeze_morph):
if squeeze_morph is not None:
if squeeze_morph.strictly_increasing:
wmsg = None
else:
if not squeeze_morph.strictly_increasing:
wmsg = (
"Warning: The squeeze morph has interpolated your morphed "
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sbillinge marked this conversation as resolved.
"function from a non-monotonically increasing grid. "
"This can result in strange behavior in the non-unique "
"grid regions. To disable this setting, "
"please enable --check-increasing."
"This can result in strange behavior in certain "
"grid regions."
)
if wmsg:
warnings.warn(
wmsg,
UserWarning,
Expand Down
1 change: 0 additions & 1 deletion src/diffpy/morph/morphpy.py
Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,6 @@ def __get_morph_opts__(parser, scale, stretch, smear, plot, **kwargs):
"reverse",
"diff",
"get-diff",
"check-increase",
]
opts_to_ignore = ["multiple-morphs", "multiple-targets"]
for opt in opts_storing_values:
Expand Down
18 changes: 9 additions & 9 deletions src/diffpy/morph/morphs/morphsqueeze.py
Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ class MorphSqueeze(Morph):
def __init__(self, config=None):
super().__init__(config)

def _ensure_strictly_increase(self, x, x_sorted):
def _check_strictly_increasing(self, x, x_sorted):
if list(x) != list(x_sorted):
self.strictly_increasing = False
else:
Expand All @@ -83,20 +83,20 @@ def _sort_squeeze(self, x, y):
"""Sort x,y according to the value of x."""
xy = list(zip(x, y))
xy_sorted = sorted(xy, key=lambda pair: pair[0])
x_sorted, y_sorted = list(zip(*xy_sorted))
x_sorted, y_sorted = numpy.array(list(zip(*xy_sorted)))
return x_sorted, y_sorted

def _handle_duplicates(self, x, y):
"""Remove duplicated x and use the mean value of y corresponded
to the duplicated x."""
unq_x, unq_inv = numpy.unique(x, return_inverse=True)
if len(unq_x) == len(x):
x_unique, inv = numpy.unique(x, return_inverse=True)
if len(x_unique) == len(x):
return x, y
else:
y_avg = numpy.zeros_like(unq_x)
for i in range(len(unq_x)):
y_avg[i] = numpy.array(y)[unq_inv == i].mean()
return unq_x, y_avg
y_avg = numpy.zeros_like(x_unique)
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Outdated
for idx, _ in enumerate(x_unique):
y_avg[idx] = y[inv == idx].mean()
return x_unique, y_avg

def morph(self, x_morph, y_morph, x_target, y_target):
"""Apply a polynomial to squeeze the morph function.
Expand All @@ -112,7 +112,7 @@ def morph(self, x_morph, y_morph, x_target, y_target):
x_squeezed_sorted, y_morph_sorted = self._sort_squeeze(
x_squeezed, self.y_morph_in
)
self._ensure_strictly_increase(x_squeezed, x_squeezed_sorted)
self._check_strictly_increasing(x_squeezed, x_squeezed_sorted)
x_squeezed_sorted, y_morph_sorted = self._handle_duplicates(
x_squeezed_sorted, y_morph_sorted
)
Expand Down
112 changes: 44 additions & 68 deletions tests/test_morphsqueeze.py
Original file line number Diff line number Diff line change
Expand Up @@ -172,80 +172,56 @@ def test_morphsqueeze_extrapolate(user_filesystem, squeeze_coeffs, wmsg_gen):
single_morph(parser, opts, pargs, stdout_flag=False)


@pytest.mark.parametrize(
"squeeze_coeffs, x_morph",
[
({"a0": 0.01, "a1": 0.01, "a2": -0.1}, np.linspace(0, 10, 101)),
],
)
def test_non_strictly_increasing_squeeze(squeeze_coeffs, x_morph):
x_target = x_morph
y_target = np.sin(x_target)
def test_non_unique_grid():
squeeze_coeffs = {"a0": 0.01, "a1": 0.01, "a2": -0.1}
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x_grid = np.linspace(0, 10, 101)

coeffs = [squeeze_coeffs[f"a{i}"] for i in range(len(squeeze_coeffs))]
squeeze_polynomial = Polynomial(coeffs)
x_squeezed = x_morph + squeeze_polynomial(x_morph)
# non-strictly-increasing
assert not np.all(np.sign(np.diff(x_squeezed)) > 0)
y_morph = np.sin(x_squeezed)
# all zero initial guess
morph_results = morphpy.morph_arrays(
x_morph = x_grid + squeeze_polynomial(x_grid)
x_gradient = np.diff(x_morph)
x_gradient_sign = np.sign(x_gradient)
# non-strictly increasing means the gradient becomes 0 or negative
assert not np.all(x_gradient_sign > 0)

x_target = np.linspace(min(x_morph), max(x_morph), len(x_morph))
y_target = np.sin(x_target)

y_morph = np.sin(x_morph)
# apply no squeeze, but the morph should sort the function
_, table = morphpy.morph_arrays(
np.array([x_morph, y_morph]).T,
np.array([x_target, y_target]).T,
squeeze=[0, 0, 0],
squeeze=[0, 0, 0.0001],
apply=True,
)
_, y_morph_actual = morph_results[1].T # noqa: F841
y_morph_expected = np.sin(x_morph) # noqa: F841
# squeeze morph extrapolates.
# Need to extract extrap_index from morph_results to examine
# the convergence.
# assert np.allclose(y_morph_actual, y_morph_expected, atol=1e-3)
# Raise warning when called without --check-increase
with pytest.warns() as w:
morph_results = morphpy.morph_arrays(
np.array([x_morph, y_morph]).T,
np.array([x_target, y_target]).T,
squeeze=[0.01, 0.01, -0.1],
apply=True,
)
assert w[0].category is UserWarning
actual_wmsg = " ".join([str(w[i].message) for i in range(len(w))])
expected_wmsg = (
"Warning: The squeeze morph has interpolated your morphed "
"function from a non-monotonically increasing grid. "
)
assert expected_wmsg in actual_wmsg
_, y_morph_actual = morph_results[1].T # noqa: F841
y_morph_expected = np.sin(x_morph) # noqa: F841
# squeeze morph extrapolates.
# Need to extract extrap_index from morph_results to examine
# the convergence.
# assert np.allclose(y_morph_actual, y_morph_expected, atol=1e-3)
# System exits when called with --check-increase
with pytest.raises(SystemExit) as excinfo:
morphpy.morph_arrays(
np.array([x_morph, y_morph]).T,
np.array([x_target, y_target]).T,
squeeze=[0.01, 0.009, -0.1],
check_increase=True,
)
actual_emsg = str(excinfo.value)
expected_emsg = "2"
assert expected_emsg == actual_emsg
x_refined, y_refined = table[:, 0], table[:, 1]

# grid should be properly sorted
assert np.allclose(x_refined, x_target)

print(y_refined, y_target)
x_sorted = x_morph.copy()
x_sorted.sort()
print(x_morph)
print(x_sorted)
# function values
assert np.allclose(y_refined, y_target)


@pytest.mark.parametrize(
"squeeze_coeffs, x_morph",
[
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# The following squeezes make the function non-monotonic

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# The following squeezes make the function non-monotonic. Expect code to work but issue the correct warning.

({"a0": -1, "a1": -1, "a2": 2}, np.linspace(-1, 1, 101)),
(
{"a0": -1, "a1": -1, "a2": 0, "a3": 0, "a4": 2},
np.linspace(-1, 1, 101),
),
],
)
def test_sort_squeeze_bad(user_filesystem, squeeze_coeffs, x_morph):
# call in .py without --check-increase
def test_squeeze_warnings(user_filesystem, squeeze_coeffs, x_morph):
# call in .py
x_target = x_morph
y_target = np.sin(x_target)
coeffs = [squeeze_coeffs[f"a{i}"] for i in range(len(squeeze_coeffs))]

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is there a reason you don't just give as "squeeze_coeffs" inputs [-1, -1, 0, 0, 2] and remove this line? I think it would be easier to read. Less cognifive overload for the poor reviewer....

Expand All @@ -267,10 +243,12 @@ def test_sort_squeeze_bad(user_filesystem, squeeze_coeffs, x_morph):
expected_wmsg = (
"Warning: The squeeze morph has interpolated your morphed "
"function from a non-monotonically increasing grid. "
"This can result in strange behavior in certain "
"grid regions."
)
assert expected_wmsg in actual_wmsg

# call in CLI without --check-increase
# call in CLI
morph_file, target_file = create_morph_data_file(
user_filesystem / "cwd_dir", x_morph, y_morph, x_target, y_target
)
Expand All @@ -293,18 +271,16 @@ def test_sort_squeeze_bad(user_filesystem, squeeze_coeffs, x_morph):


def test_handle_duplicates():
unq_x = np.linspace(0, 11, 10)
x_choices = np.linspace(0, 10, 11)
iter = 10
morph = MorphSqueeze()
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for i in range(iter):
actual_x = np.random.choice(unq_x, size=20)
actual_y = np.sin(actual_x)
actual_handled_x, actual_handled_y = morph._handle_duplicates(
actual_x, actual_y
)
expected_handled_x = np.unique(actual_x)
expected_handled_y = np.array(
[actual_y[actual_x == x].mean() for x in expected_handled_x]
x_sampled = np.random.choice(x_choices, size=20)
y_sampled = np.sin(x_sampled)
x_handled, y_handled = morph._handle_duplicates(x_sampled, y_sampled)
x_target = np.unique(x_sampled)
y_target = np.array(
[y_sampled[x_sampled == x].mean() for x in x_target]
)
assert np.allclose(actual_handled_x, expected_handled_x)
assert np.allclose(actual_handled_y, expected_handled_y)
assert np.allclose(x_handled, x_target)
assert np.allclose(y_handled, y_target)