fix(cp2k): extract scalar from numpy array for math.isclose() comparison

[Copilot]

math.isclose() requires scalar arguments but was receiving 1D numpy arrays, causing TypeError: only 0-dimensional arrays can be converted to Python scalars in CP2K AIMD output parsing.

Changes

• Extract scalar values from energies arrays using [0] indexing before comparison in Cp2kSystems.__next__()

# Before
assert math.isclose(
    log_info_dict["energies"], xyz_info_dict["energies"], abs_tol=1.0e-6
)

# After  
assert math.isclose(
    log_info_dict["energies"][0], xyz_info_dict["energies"][0], abs_tol=1.0e-6
)

Both log_info_dict["energies"] and xyz_info_dict["energies"] are shape (1,) arrays created by np.asarray([energy]), so [0] extracts the scalar energy value for comparison while preserving the original arrays in the assertion message for debugging.

Original prompt

---

This section details on the original issue you should resolve

<issue_title>[BUG] cp2k: TypeError: only 0-dimensional arrays can be converted to Python scalars</issue_title>
<issue_description>### Bug summary

The recent CI runs reported an error on CP2K tests: TypeError: only 0-dimensional arrays can be converted to Python scalars. See the log below.

dpdata Version

devel

Input Files, Running Commands, Error Log, etc.

======================================================================
ERROR: test_nframs (test_cp2k_aimd_output.TestCp2kAimdStressOutput.test_nframs)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/runner/work/dpdata/dpdata/tests/test_cp2k_aimd_output.py", line 23, in setUp
    self.system_1 = dpdata.LabeledSystem("cp2k/aimd_stress", fmt="cp2k/aimd_output")
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/dpdata/dpdata/dpdata/system.py", line 197, in __init__
    self.from_fmt(
  File "/home/runner/work/dpdata/dpdata/dpdata/system.py", line 234, in from_fmt
    return self.from_fmt_obj(load_format(fmt), file_name, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/dpdata/dpdata/dpdata/system.py", line 1224, in from_fmt_obj
    data = fmtobj.from_labeled_system(file_name, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/dpdata/dpdata/dpdata/plugins/cp2k.py", line 25, in from_labeled_system
    return tuple(Cp2kSystems(log_file, xyz_file, restart))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/runner/work/dpdata/dpdata/dpdata/cp2k/output.py", line 67, in __next__
    assert math.isclose(
           ^^^^^^^^^^^^^
TypeError: only 0-dimensional arrays can be converted to Python scalars

Steps to Reproduce

Run the UT.

Further Information, Files, and Links

No response</issue_description>

Comments on the Issue (you are @copilot in this section)

• Fixes [BUG] cp2k: TypeError: only 0-dimensional arrays can be converted to Python scalars #922

---

💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more Copilot coding agent tips in the docs.

[codspeed-hq]

CodSpeed Performance Report

Merging this PR will improve performance by 17.32%

_{Comparing copilot/fix-cp2k-typeerror-bug (83b82bd) with devel (6edbd09)}

⚠️ Unknown Walltime execution environment detected

Using the Walltime instrument on standard Hosted Runners will lead to inconsistent data.

For the most accurate results, we recommend using CodSpeed Macro Runners: bare-metal machines fine-tuned for performance measurement consistency.

Summary

⚡ 2 improved benchmarks
⏩ 2 skipped benchmarks¹

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
⚡	WallTime	test_import	11.2 ms	9.6 ms	+17.32%
⚡	WallTime	test_cli	280.3 ms	253 ms	+10.78%

Footnotes

1. 2 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 86.06%. Comparing base (6edbd09) to head (83b82bd).
⚠️ Report is 1 commits behind head on devel.

Additional details and impacted files

@@           Coverage Diff           @@
##            devel     #924   +/-   ##
=======================================
  Coverage   86.06%   86.06%           
=======================================
  Files          83       83           
  Lines        7886     7886           
=======================================
  Hits         6787     6787           
  Misses       1099     1099           

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[Copilot]

Pull request overview

This PR fixes a TypeError that occurred when math.isclose() received 1D numpy arrays instead of scalar values during CP2K AIMD output parsing.

Changes:

• Extract scalar values from energies arrays using [0] indexing before passing to math.isclose() in the Cp2kSystems.__next__() method

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.