fix: fix load single peak in py3 support

[stevenhua0320]

@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

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 76.47%. Comparing base (d9e435d) to head (ed29b92).
⚠️ Report is 2 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]

@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.