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Condition to handle NaNs #44

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Condition to handle NaNs #44

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condition to handle NaNs
cadenmyers13 Oct 28, 2024
d064f83
news
cadenmyers13 Oct 28, 2024
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23 changes: 23 additions & 0 deletions news/NaNhandling.rst
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@@ -0,0 +1,23 @@
**Added:**

* <news item>

**Changed:**

* When a slice of all `NaN`s is encountered, the `NaN`s are now set to zero.

**Deprecated:**

* <news item>

**Removed:**

* <news item>

**Fixed:**

* <news item>

**Security:**

* <news item>
4 changes: 4 additions & 0 deletions src/diffpy/fourigui/fourigui.py
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Expand Up @@ -370,6 +370,10 @@ def intensity_upd_local(self):
plane = self.cube[:, self.plane_num.get(), :]
elif self.axis.get() == 2:
plane = self.cube[:, :, self.plane_num.get()]

if np.all(np.isnan(plane)):
plane = np.zeros_like(plane)

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@cadenmyers13 cadenmyers13 Oct 28, 2024

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condition setting all NaNs to zero

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@bobleesj bobleesj Oct 28, 2024

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If we do that, that means we are simply converting nan values to all 0s. but only for [nan, nan, ... , nan] but not when [nan, 0.1, 0.2, nan, ...]

so doesn't feel like a good solution to me - when we call np.nanmax, I think it will pick up 0 instead of nan and this might not be good since your code has a section where it calculates nan_ratio.

I am not sure why we use nan in the first place. Why nan instead of 0? Having nan around for an array where it should have the same type for the particular array normally, it feels quite odd.

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@sbillinge sbillinge Oct 29, 2024

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I think this is not the preferred behavior. Setting NaN's to 0 falls under the umbrella of "magic". Especially as scientists we want to avoid updating people's data.

Caden said "The goal of the test is to verify that the applycutoff method is working as expected for a given qmin and qmax." which of course is a tautology. That is what a test does. But the question is what do "we expect" that function to do. In other words, what is our desired behavior?

nan_ratio = np.count_nonzero(np.isnan(plane)) / plane.size
self.localmax["text"] = "{}".format(np.format_float_scientific(np.nanmax(plane), 1))
self.localmin["text"] = "{}".format(np.format_float_scientific(np.nanmin(plane), 1))
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