fix: fix load single peak in py3 support by stevenhua0320 · Pull Request #178 · diffpy/diffpy.srmise

stevenhua0320 · 2026-04-08T02:16:03Z

@sbillinge ready to review. Note that there are a lot of py2 legacy in this package. (My bad, I did not clean it up comprehensively on 2024). Now it would give a plot when I run the command srmise docs/examples/data/Ag-nyquist-qmax30.gr --plot --range 2 3.5 --baseline "Polynomial(degree=1)" it would give me the plot below.
Also there are some messages for the fitting process:

The input PDF appears to be missing information: The sampling interval of the input PDF (0.10471976036036036) is larger than the Nyquist interval (0.10471975511965977) defined by qmax=30.0.  This information is irretrievable.
2.90030e+00 (2.30611e-03)  2.62623e-01 (5.94556e-03)  9.64654e+00 (2.24194e-01)
-3.87207e-01 (3.80378e-01)  -2.92455e-01 (1.41472e-01)
effective_dy: [0.2765272 0.3758883 0.3993384 0.4062425 0.4092704 0.4108721 0.4118211
 0.4124392 0.4128434 0.4131494 0.4133937 0.4135703 0.41365   0.4140212
 0.4138731 0.4139608 0.4140158 0.4140681 0.4140688 0.4141887 0.4141705
 0.4142069 0.4142    0.4142505 0.4142869 0.414276  0.414235  0.4143683
 0.4143157 0.4142939 0.4143335 0.4143717 0.4143361 0.4143605 0.4143777
 0.414216  0.4145213 0.4143604 0.4143672 0.414408  0.4143034 0.4145074
 0.4143658 0.4144454 0.4143957 0.4144191 0.41442   0.4144247 0.4143801
 0.4144333 0.4144703 0.4144277 0.414347  0.4144955 0.4144083 0.41444
 0.4143993 0.4144537 0.4144685 0.4143536 0.4145365 0.4144362 0.4143902
 0.4144907 0.4144324 0.4143906 0.4144803 0.4144227 0.4143587 0.414509
 0.4144201 0.4144356 0.4144505 0.4144687 0.414469  0.4144434 0.4144111
 0.4145219 0.4143796 0.4144955 0.4144445 0.414446  0.4144502 0.4143988
 0.4145152 0.4144222 0.4144654 0.4144388 0.4144548 0.4144297 0.4144823
 0.4144444 0.4144652 0.4144644 0.4144417 0.4144549 0.4144527 0.4144868
 0.4144561 0.4144608 0.4144841 0.4144248 0.4144966 0.4144239 0.4144633
 0.4144556 0.4144629 0.4144406 0.4144775 0.4144525 0.4144847 0.4144663
 0.4144317 0.4145045 0.4144493 0.414473  0.4144754 0.4144237 0.4144983
 0.4144333 0.414449  0.4144681 0.4144388 0.4144618 0.4144581 0.4144738
 0.4144686 0.4144742 0.4144435 0.414468  0.4144499 0.4144547 0.4144545
 0.4144739 0.4144597 0.4144296 0.4144873 0.4144529 0.4144721 0.4144343
 0.4144819 0.4144488 0.4144636 0.4144588 0.4144802 0.4144567 0.4144705
 0.4144523 0.4144734 0.4144571 0.4144744 0.4144514 0.4144552 0.414459
 0.4144734 0.414468  0.41448   0.4144769 0.4144631 0.4144793 0.414472
 0.4144708 0.4144635 0.4144717 0.4144609 0.4144541 0.4144635 0.4144642
 0.4144741 0.414465  0.4144646 0.4144802 0.4144664 0.4144843 0.4144721
 0.4144568 0.4144656 0.4144651 0.4144719 0.4144805 0.4144722 0.4144764
 0.4144649 0.4144721 0.4144765 0.4144656 0.4144695 0.4144606 0.4144579
 0.4144769 0.4144748 0.4144622 0.4144701 0.4144719 0.4144673 0.4144699
 0.4144735 0.4144827 0.4144778 0.4144763 0.4144695 0.4144653 0.4144726
 0.4144633 0.4144626 0.4144595 0.4144623 0.4144694 0.4144665 0.4144765
 0.4144772 0.4144741 0.4144802 0.4144756 0.4144667 0.4144744 0.414475
 0.4144755 0.4144746 0.4144769 0.4144813 0.4144749 0.4144805 0.4144797
 0.4144777 0.4144789 0.4144813 0.4144795 0.4144831 0.4144789 0.4144732
 0.414472  0.4144689 0.4144738 0.4144672 0.4144618 0.4144653 0.4144676
 0.4144723 0.4144764 0.4144796 0.4144753 0.4144743 0.4144714 0.4144631
 0.4144645 0.4144619 0.4144606 0.4144628 0.4144652 0.4144678 0.4144644
 0.4144702 0.4144717 0.4144741 0.4144759 0.4144742 0.414472  0.414475
 0.414476  0.4144714 0.4144684 0.4144675 0.4144691 0.4144731 0.4144762
 0.4144751 0.4144731 0.4144694 0.4144698 0.4144737 0.4144747 0.4144762
 0.414476  0.4144759 0.4144753 0.4144751 0.4144704 0.4144712 0.4144691
 0.4144694 0.4144679 0.4144692 0.4144697 0.4144709 0.4144724 0.4144726
 0.4144725 0.4144727 0.4144744 0.4144709 0.41447   0.4144686 0.4144733
 0.4144777 0.414476  0.4144723 0.414469  0.414467  0.4144685 0.414471
 0.4144731 0.4144743 0.4144733 0.4144693 0.4144691 0.4144692 0.4144668
 0.4144673 0.414468  0.4144688 0.4144693 0.4144716 0.414471  0.4144728
 0.4144734 0.4144724 0.4144769 0.4144768 0.4144725 0.4144718 0.4144688
 0.4144638 0.4144631 0.4144619 0.4144626 0.4144662 0.4144635 0.4144605
 0.4144651 0.4144694 0.414472  0.4144754 0.4144737]
rng: [2.0, 3.5]
pf: [<diffpy.srmise.peaks.gaussianoverr.GaussianOverR object at 0x109d7e900>]
initial_peaks: 
baseline: [-0.60176135  0.        ]
cres: 0.10471975511965977
error_method: <class 'diffpy.srmise.modelevaluators.aic.AIC'>
qmax: 30.0
supersample: 4.0
nyquist: True
scale: False
Extraction type: extract
--- Extracted ---
Slice: slice(None, 15, None)
Quality: 573.557582474722
Baseline: [-0.60176135  0.        ]
Peaks:
[ 2.89549619  0.54157962 18.57789434]


2.90030e+00 (2.30611e-03)  2.62623e-01 (5.94556e-03)  9.64654e+00 (2.24194e-01)
-3.87207e-01 (3.80378e-01)  -2.92455e-01 (1.41472e-01)

Closes #5 #147

codecov · 2026-04-08T02:18:15Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 76.47%. Comparing base (d9e435d) to head (36e6733).
⚠️ Report is 3 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #178   +/-   ##
=======================================
  Coverage   76.47%   76.47%           
=======================================
  Files           3        3           
  Lines          51       51           
=======================================
  Hits           39       39           
  Misses         12       12

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stevenhua0320 · 2026-04-08T02:20:05Z

@sbillinge I believe we have no tests to cover the issue here (since the tests only contain the easiest one written on 2024 summer by you and me), so it needs some human wisdom to decide if it is correct or not.

sbillinge · 2026-04-08T20:14:51Z

yah, something seems rather wrong here. The fit is not working. It should end up looking like the fit in @bobleesj from the other issue.

sbillinge · 2026-04-08T22:47:09Z

@stevenhua0320 let me know when you figure this out and want a review.

stevenhua0320 · 2026-04-09T04:18:04Z

@sbillinge ready to review. Finally, I figured out that it is because of the regularization of aic aicc in base is wrong, in the skpkg step in 2024 we changed the logic from checking true or false to if it is None. Therefore, we run into the issue. Now, with other py2 legacy cleanup, we managed to reproduce the correct result and the plot.

(diffpy.srmise_env) huarundong@Stevens-MacBook-Pro-405 diffpy.srmise % srmise docs/examples/data/Ag-nyquist-qmax30.gr --plot --range 2 3.5 --baseline "Polynomial(degree=1)"                                                                       
The input PDF appears to be missing information: The sampling interval of the input PDF (0.10471976036036036) is larger than the Nyquist interval (0.10471975511965977) defined by qmax=30.0.  This information is irretrievable.
2.90075e+00 (1.90429e-03)  2.67213e-01 (4.91303e-03)  9.84389e+00 (1.86615e-01)
-5.10705e-01 (3.12242e-01)  9.01918e-03 (1.16242e-01)
effective_dy: [0.2765272 0.3758883 0.3993384 0.4062425 0.4092704 0.4108721 0.4118211
 0.4124392 0.4128434 0.4131494 0.4133937 0.4135703 0.41365   0.4140212
 0.4138731 0.4139608 0.4140158 0.4140681 0.4140688 0.4141887 0.4141705
 0.4142069 0.4142    0.4142505 0.4142869 0.414276  0.414235  0.4143683
 0.4143157 0.4142939 0.4143335 0.4143717 0.4143361 0.4143605 0.4143777
 0.414216  0.4145213 0.4143604 0.4143672 0.414408  0.4143034 0.4145074
 0.4143658 0.4144454 0.4143957 0.4144191 0.41442   0.4144247 0.4143801
 0.4144333 0.4144703 0.4144277 0.414347  0.4144955 0.4144083 0.41444
 0.4143993 0.4144537 0.4144685 0.4143536 0.4145365 0.4144362 0.4143902
 0.4144907 0.4144324 0.4143906 0.4144803 0.4144227 0.4143587 0.414509
 0.4144201 0.4144356 0.4144505 0.4144687 0.414469  0.4144434 0.4144111
 0.4145219 0.4143796 0.4144955 0.4144445 0.414446  0.4144502 0.4143988
 0.4145152 0.4144222 0.4144654 0.4144388 0.4144548 0.4144297 0.4144823
 0.4144444 0.4144652 0.4144644 0.4144417 0.4144549 0.4144527 0.4144868
 0.4144561 0.4144608 0.4144841 0.4144248 0.4144966 0.4144239 0.4144633
 0.4144556 0.4144629 0.4144406 0.4144775 0.4144525 0.4144847 0.4144663
 0.4144317 0.4145045 0.4144493 0.414473  0.4144754 0.4144237 0.4144983
 0.4144333 0.414449  0.4144681 0.4144388 0.4144618 0.4144581 0.4144738
 0.4144686 0.4144742 0.4144435 0.414468  0.4144499 0.4144547 0.4144545
 0.4144739 0.4144597 0.4144296 0.4144873 0.4144529 0.4144721 0.4144343
 0.4144819 0.4144488 0.4144636 0.4144588 0.4144802 0.4144567 0.4144705
 0.4144523 0.4144734 0.4144571 0.4144744 0.4144514 0.4144552 0.414459
 0.4144734 0.414468  0.41448   0.4144769 0.4144631 0.4144793 0.414472
 0.4144708 0.4144635 0.4144717 0.4144609 0.4144541 0.4144635 0.4144642
 0.4144741 0.414465  0.4144646 0.4144802 0.4144664 0.4144843 0.4144721
 0.4144568 0.4144656 0.4144651 0.4144719 0.4144805 0.4144722 0.4144764
 0.4144649 0.4144721 0.4144765 0.4144656 0.4144695 0.4144606 0.4144579
 0.4144769 0.4144748 0.4144622 0.4144701 0.4144719 0.4144673 0.4144699
 0.4144735 0.4144827 0.4144778 0.4144763 0.4144695 0.4144653 0.4144726
 0.4144633 0.4144626 0.4144595 0.4144623 0.4144694 0.4144665 0.4144765
 0.4144772 0.4144741 0.4144802 0.4144756 0.4144667 0.4144744 0.414475
 0.4144755 0.4144746 0.4144769 0.4144813 0.4144749 0.4144805 0.4144797
 0.4144777 0.4144789 0.4144813 0.4144795 0.4144831 0.4144789 0.4144732
 0.414472  0.4144689 0.4144738 0.4144672 0.4144618 0.4144653 0.4144676
 0.4144723 0.4144764 0.4144796 0.4144753 0.4144743 0.4144714 0.4144631
 0.4144645 0.4144619 0.4144606 0.4144628 0.4144652 0.4144678 0.4144644
 0.4144702 0.4144717 0.4144741 0.4144759 0.4144742 0.414472  0.414475
 0.414476  0.4144714 0.4144684 0.4144675 0.4144691 0.4144731 0.4144762
 0.4144751 0.4144731 0.4144694 0.4144698 0.4144737 0.4144747 0.4144762
 0.414476  0.4144759 0.4144753 0.4144751 0.4144704 0.4144712 0.4144691
 0.4144694 0.4144679 0.4144692 0.4144697 0.4144709 0.4144724 0.4144726
 0.4144725 0.4144727 0.4144744 0.4144709 0.41447   0.4144686 0.4144733
 0.4144777 0.414476  0.4144723 0.414469  0.414467  0.4144685 0.414471
 0.4144731 0.4144743 0.4144733 0.4144693 0.4144691 0.4144692 0.4144668
 0.4144673 0.414468  0.4144688 0.4144693 0.4144716 0.414471  0.4144728
 0.4144734 0.4144724 0.4144769 0.4144768 0.4144725 0.4144718 0.4144688
 0.4144638 0.4144631 0.4144619 0.4144626 0.4144662 0.4144635 0.4144605
 0.4144651 0.4144694 0.414472  0.4144754 0.4144737]
rng: [2.0, 3.5]
pf: [<diffpy.srmise.peaks.gaussianoverr.GaussianOverR object at 0x1082bacf0>]
initial_peaks: 
baseline: [-0.60176135  0.        ]
cres: 0.10471975511965977
error_method: <class 'diffpy.srmise.modelevaluators.aic.AIC'>
qmax: 30.0
supersample: 4.0
nyquist: True
scale: False
Extraction type: extract
--- Extracted ---
Slice: slice(None, 15, None)
Quality: 8.142532170140099
Baseline: [-0.5107055   0.00901918]
Peaks:
[2.90074808 0.26721295 9.84388525]


2.90075e+00 (1.90429e-03)  2.67213e-01 (4.91303e-03)  9.84389e+00 (1.86615e-01)
-5.10705e-01 (3.12242e-01)  9.01918e-03 (1.16242e-01)

stevenhua0320 · 2026-04-09T04:19:24Z

src/diffpy/srmise/modelevaluators/aic.py

            self.chisq = self.chi_squared(fit.value(), fit.y_cluster, fit.error_cluster)

-        self.stat = self.chisq + self.parpenalty(k, n)
+        self.stat = self.chisq + self.parpenalty(k)


This is needed, otherwise, it would have a unneeded positional argument, Moreover, since we are doing AIC, we don't need n here by the AIC formula.

stevenhua0320 · 2026-04-09T04:20:00Z

src/diffpy/srmise/dataclusters.py

-                self.x[idx] - self.x[self.clusters[int(near_idx) - 1, 1]],
-                self.x[idx] - self.x[self.clusters[int(near_idx), 0]],
+                self.x[idx] - self.x[self.clusters[near_idx - 1, 1]],
+                self.x[idx] - self.x[self.clusters[near_idx, 0]],


This is to revert the incorrect fix done on summer 2024.

stevenhua0320 · 2026-04-09T04:20:27Z

src/diffpy/srmise/pdfpeakextraction.py

        # Enable "dg" as alias for "effective_dy"
        if "dg" in args and "effective_dy" not in args:
-            nargs.add("effective_dy")
+            nargs.append("effective_dy")


Now, we have nargs as a list, so we need append method here

stevenhua0320 · 2026-04-09T04:21:06Z

src/diffpy/srmise/pdfpeakextraction.py

    new_dr = (new_r[-1] - r[0]) / (len(new_r) - 1)

-    yfft = np.imag(np.fft.fft(new_y))[: len(new_y) / 2]
+    yfft = np.imag(np.fft.fft(new_y))[: len(new_y) // 2]


In py2 it doesn't matter but in py3, since the slicing number must be an integer, so it must be // to get an integer value.

sbillinge

Oh wow. Super! Great job!

stevenhua0320 and others added 2 commits April 7, 2026 22:07

fix: fix load single peak in py3 support

e46ac63

[pre-commit.ci] auto fixes from pre-commit hooks

ed29b92

stevenhua0320 and others added 5 commits April 8, 2026 16:33

fix:check what else we edited

e678645

fix: fix some unnecessary conversion to int

198d5a8

chore: revert CLI change

7761883

[pre-commit.ci] auto fixes from pre-commit hooks

0f3f789

chore: revert try except block

b127396

stevenhua0320 and others added 4 commits April 8, 2026 21:01

some proper fix to the code

7863722

[pre-commit.ci] auto fixes from pre-commit hooks

1fbfa0a

fix: fix fetch qmax

c3d9984

fix: correctly fix the single peak plot issue with correct plot

cd002f3

stevenhua0320 commented Apr 9, 2026

View reviewed changes

fix: fix namespace when we have --no-extract option

36e6733

stevenhua0320 mentioned this pull request Apr 9, 2026

Dataclusters iter() not functioning in PeakExtraction class #77

Closed

sbillinge approved these changes Apr 9, 2026

View reviewed changes

sbillinge merged commit 2a30b00 into diffpy:main Apr 9, 2026
7 checks passed

sbillinge mentioned this pull request Apr 9, 2026

Fix tutorial bug - sampling interval of the input PDF (0.1047...) is larger than the Nyquist interval (0.10471975...) #147

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fix: fix load single peak in py3 support#178

fix: fix load single peak in py3 support#178
sbillinge merged 12 commits intodiffpy:mainfrom
stevenhua0320:fix-single-peak

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