fix: only apply comp_prec for floating dtypes

[njzjz]

Fix #703.

Summary by CodeRabbit

• New Features

  • Improved handling of data types during the reshaping process, ensuring type conversion only occurs for floating-point data.

• Bug Fixes

  • Enhanced robustness of data processing by preventing unnecessary type casting for non-floating-point data types.

[coderabbitai]

Walkthrough

Walkthrough

The changes made to the dump function in both dpdata/deepmd/comp.py and dpdata/deepmd/hdf5.py involve the introduction of conditional checks for data type conversion. The reshaped data is now only converted to the specified precision type if it is determined to be a floating-point subtype, enhancing type handling and preventing unnecessary conversions.

Changes

Files	Change Summary
dpdata/deepmd/comp.py, dpdata/deepmd/hdf5.py	Introduced conditional checks to ensure type conversion to comp_prec only occurs for floating-point subtypes, improving type handling during data reshaping.

Assessment against linked issues

Objective	Addressed	Explanation
Support data type other than float for custom dtype in deepmd format (#703)	❌	The changes still enforce conversion to float, not supporting other data types like strings.

---

Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between f4d0adf and ac5e9ec.

Files selected for processing (2)

• dpdata/deepmd/comp.py (1 hunks)
• dpdata/deepmd/hdf5.py (1 hunks)

Additional comments not posted (2)

dpdata/deepmd/comp.py (1)

206-208: Conditional precision conversion for floating-point data types.

The changes introduce a type check before applying the comp_prec conversion, which aligns with the PR objectives.

This modification addresses the issue with custom data types (like string arrays) by only applying the precision conversion to floating-point data. It's a good solution that maintains compatibility with existing floating-point data while allowing for other data types.

To ensure this change doesn't introduce any unintended side effects, let's verify the usage of comp_prec throughout the codebase:

Consider adding a comment explaining the purpose of the type check for better code readability:

 ddata = np.reshape(data[dtype.name], [nframes, -1])
+# Only apply precision conversion for floating-point data types
 if np.issubdtype(ddata.dtype, np.floating):
     ddata = ddata.astype(comp_prec)

Verification successful

---

Verification successful: Conditional precision conversion is correctly implemented.

The usage of comp_prec is consistent across the codebase, and the conditional check for floating-point data types ensures that precision conversion is applied appropriately. This change aligns with the PR objectives and does not introduce any unintended side effects.

• comp_prec is used in multiple files, primarily for converting data to a specific precision.
• Floating-point type checks are present in dpdata/deepmd/comp.py and dpdata/deepmd/hdf5.py, ensuring precision conversion is conditionally applied.

Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Check usage of comp_prec in the codebase
# Expected: comp_prec should only be applied to floating-point data

# Search for comp_prec usage
echo "Searching for comp_prec usage:"
rg "comp_prec" --type python

# Search for float type checks similar to the one introduced
echo "\nSearching for float type checks:"
rg "np.issubdtype\(.*np.floating\)" --type python

Length of output: 292

---

Script:

#!/bin/bash
# Description: Check usage of comp_prec in the codebase
# Expected: comp_prec should only be applied to floating-point data

# Search for comp_prec usage
echo "Searching for comp_prec usage:"
rg "comp_prec"

# Search for float type checks similar to the one introduced
echo "\nSearching for float type checks:"
rg "np.issubdtype\(.*np.floating\)"

Length of output: 2029

dpdata/deepmd/hdf5.py (1)

255-258: Verify the impact of these changes across the codebase.

The changes look good and address the issue. However, it's important to ensure that this modification doesn't have unintended consequences elsewhere in the codebase.

Let's run a script to check for any other occurrences of comp_prec usage that might need similar treatment:

This script will help us identify if there are other places in the code that might benefit from similar changes or if there are any known issues related to data type conversion that we should address.

---

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 84.59%. Comparing base (f4d0adf) to head (ac5e9ec).

Additional details and impacted files

@@            Coverage Diff             @@
##            devel     #711      +/-   ##
==========================================
+ Coverage   84.58%   84.59%   +0.01%     
==========================================
  Files          81       81              
  Lines        7052     7057       +5     
==========================================
+ Hits         5965     5970       +5     
  Misses       1087     1087              

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

[codspeed-hq]

CodSpeed Performance Report

Merging #711 will not alter performance

_{Comparing njzjz:comp_prec (ac5e9ec) with devel (f4d0adf)}

Summary

✅ 2 untouched benchmarks

[coderabbitai]

Actionable comments posted: 0

Outside diff range, codebase verification and nitpick comments (1)

dpdata/deepmd/hdf5.py (1)

255-258: Approve changes with a minor suggestion for improvement.

The changes correctly address the issue of applying comp_prec only to floating-point data types. This fix aligns with the PR objectives and resolves the problem described in the linked issue #703.

Consider adding a brief comment explaining why we're only converting floating-point data. This would improve code readability and maintainability. Here's a suggested implementation:

 ddata = np.reshape(data[dt], prop["shape"])
+# Only apply precision conversion to floating-point data types
 if np.issubdtype(ddata.dtype, np.floating):
     ddata = ddata.astype(comp_prec)
 reshaped_data[dt] = ddata