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rename x to dz in mud_calculator.py and decrease error tolerance for tests #147

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Dec 28, 2024
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rename x to dz in mud_calculator.py and decrease error tolerance for tests #147

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rename x to dz, decrease error range for tests
yucongalicechen Dec 28, 2024
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yucongalicechen Dec 28, 2024
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23 changes: 23 additions & 0 deletions news/muD2.rst
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@@ -0,0 +1,23 @@
**Added:**

* no news added - rename variables in `mud_calculator.py` and edit tests

**Changed:**

* <news item>

**Deprecated:**

* <news item>

**Removed:**

* <news item>

**Fixed:**

* <news item>

**Security:**

* <news item>
14 changes: 7 additions & 7 deletions src/diffpy/labpdfproc/mud_calculator.py
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Expand Up @@ -15,19 +15,19 @@ def _top_hat(z, half_slit_width):
def _model_function(z, diameter, z0, I0, mud, slope):
"""
Compute the model function with the following steps:
1. Recenter z to x by subtracting z0 (so that the circle is centered at 0 and it is easier to compute length l)
1. Let dz = z-z0, so that dz is centered at 0
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2. Compute length l that is the effective length for computing intensity I = I0 * e^{-mu * l}:
- For x within the diameter range, l is the chord length of the circle at position x
- For x outside this range, l = 0
- For dz within the capillary diameter, l is the chord length of the circle at position dz
- For dz outside this range, l = 0
3. Apply a linear adjustment to I0 by taking I0 as I0 - slope * z
"""
min_radius = -diameter / 2
max_radius = diameter / 2
x = z - z0
dz = z - z0
length = np.piecewise(
x,
[x < min_radius, (min_radius <= x) & (x <= max_radius), x > max_radius],
[0, lambda x: 2 * np.sqrt((diameter / 2) ** 2 - x**2), 0],
dz,
[dz < min_radius, (min_radius <= dz) & (dz <= max_radius), dz > max_radius],
[0, lambda dz: 2 * np.sqrt((diameter / 2) ** 2 - dz**2), 0],
)
return (I0 - slope * z) * np.exp(-mud / diameter * length)

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2 changes: 1 addition & 1 deletion tests/test_mud_calculator.py
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Expand Up @@ -19,4 +19,4 @@ def test_compute_mud(tmp_path):

expected_mud = 3
actual_mud = compute_mud(file)
assert actual_mud == pytest.approx(expected_mud, rel=0.01, abs=0.1)
assert actual_mud == pytest.approx(expected_mud, rel=1e-4, abs=1e-3)
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decrease the error tolerance. I think previously I changed this to 0.01 and 0.1 because I thought about the edge cases like HfO2 wire data. But for the other better data (pretty much all other real data we ran) the error is much lower. So we can have a smaller tolerance here.

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@sbillinge sbillinge Dec 28, 2024 •
edited
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This is a test, not code, so the logic is that we set the tolerance we want for the test, not some worst case scenario I think, right?

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