https://github.com/infinitode/funcprofiler
An open-source Python library for identifying bottlenecks in code. It includes function profiling, data exports, logging, and line-by-line profiling for more granular control.
https://github.com/infinitode/funcprofiler
csv data-export functions html json logging minimal profiling profiling-library python
Last synced: about 1 month ago
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An open-source Python library for identifying bottlenecks in code. It includes function profiling, data exports, logging, and line-by-line profiling for more granular control.
- Host: GitHub
- URL: https://github.com/infinitode/funcprofiler
- Owner: Infinitode
- License: other
- Created: 2024-09-22T18:48:11.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-11-16T12:38:51.000Z (4 months ago)
- Last Synced: 2026-02-16T07:46:38.084Z (about 1 month ago)
- Topics: csv, data-export, functions, html, json, logging, minimal, profiling, profiling-library, python
- Language: Python
- Homepage: https://infinitode.netlify.app
- Size: 22.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# FuncProfiler
[](https://github.com/infinitode/funcprofiler)
An open-source Python library for identifying bottlenecks in code. It includes function profiling, data exports, logging, and line-by-line profiling for more granular control.
## Changelog (v.1.1.0):
- Added support for 2 new export formats: `yaml` and `toml`.
- Exports now include more information: peak memory usage, timestamp, arguments, return value, filepath, line number, and docstring.
- Added `enabled` and `log_level` options to the decorators.
- Improved export formats for better readability.
## Installation
You can install FuncProfiler using pip:
```bash
pip install funcprofiler
```
## Supported Python Versions
FuncProfiler supports Python 3.6 and later.
## Features
- **Function Profiling**: Monitor a function's memory usage and execution time.
- **Line-by-Line Profiling**: Get execution time and memory usage for each line of a function.
- **Shared Logging**: Log profiler outputs to a `.txt` file.
- **File Exports**: Export profiling data in various formats.
- **New Options**:
- `enabled`: A boolean to enable or disable profiling.
- `log_level`: Set the logging level to "info" or "debug".
## Export Formats
| Format | `function_profile` | `line_by_line_profile` |
|--------|--------------------|------------------------|
| `txt` | ✅ | ❌ |
| `json` | ✅ | ✅ |
| `csv` | ✅ | ✅ |
| `html` | ✅ | ✅ |
| `xml` | ✅ | ✅ |
| `md` | ✅ | ✅ |
| `yaml` | ✅ | ✅ |
| `toml` | ✅ | ✅ |
## Usage
### Function Profiling
```python
from funcprofiler import function_profile
@function_profile(export_format="html", shared_log=True, log_level="debug")
def some_function():
return "Hello World."
message = some_function()
```
### Line-by-Line Profiling
```python
from funcprofiler import line_by_line_profile
@line_by_line_profile(shared_log=True, enabled=True)
def some_complicated_function(n):
total = 0
for i in range(n):
for j in range(i):
total += (i * j) ** 0.5
return total
total = some_complicated_function(1000)
```
## Contributing
Contributions are welcome! Please open an issue or submit a pull request on [GitHub](https://github.com/infinitode/funcprofiler).
## License
FuncProfiler is released under the **MIT License (Modified)**. See the [LICENSE](https://github.com/infinitode/funcprofiler/blob/main/LICENSE) file for details.