| Name | nbsync JSON |
| Version |
0.3.7
JSON |
| download |
| home_page | None |
| Summary | A core library to synchronize Jupyter notebooks and Markdown documents, enabling seamless integration and dynamic content execution |
| upload_time | 2025-07-26 23:51:31 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2025 Daizu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
documentation
dynamic-execution
jupyter
markdown
notebook
python
real-time-sync
visualization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# nbsync
[![PyPI Version][pypi-v-image]][pypi-v-link]
[![Python Version][python-v-image]][python-v-link]
[![Build Status][GHAction-image]][GHAction-link]
[![Coverage Status][codecov-image]][codecov-link]
<strong>Connect Jupyter notebooks and Markdown documents</strong>
nbsync is a core library that seamlessly bridges Jupyter notebooks and Markdown documents, enabling dynamic content synchronization and execution.
## Why Use nbsync?
### The Documentation Challenge
Data scientists, researchers, and technical writers face a common dilemma:
- **Development happens in notebooks** - ideal for experimentation and visualization
- **Documentation lives in markdown** - perfect for narrative and explanation
- **Connecting the two is painful** - screenshots break, exports get outdated
### Our Solution
nbsync creates a live bridge between your notebooks and markdown documents by:
- **Keeping environments separate** - work in the tool best suited for each task
- **Maintaining connections** - reference specific figures from notebooks
- **Automating updates** - changes to notebooks reflect in documentation
## Key Benefits
- **True Separation of Concerns**:
Develop visualizations in Jupyter notebooks and write documentation
in markdown files, with each tool optimized for its purpose.
- **Intuitive Markdown Syntax**:
Use standard image syntax with a simple extension to reference
notebook figures: `{#figure-id}`
- **Automatic Updates**:
When you modify your notebooks, your documentation updates
automatically.
- **Clean Source Documents**:
Your markdown remains readable and focused on content, without
code distractions or complex embedding techniques.
- **Enhanced Development Experience**:
Take advantage of IDE features like code completion and syntax
highlighting in the appropriate environment.
## Quick Start
### 1. Installation
```bash
pip install nbsync
```
### 2. Basic Usage
```python
from nbsync.sync import Synchronizer
from nbstore import Store
# Initialize with a notebook store
store = Store("path/to/notebooks")
sync = Synchronizer(store)
# Process markdown with notebook references
markdown_text = """
# My Document
{#my-figure}
"""
# Convert markdown with notebook references to final output
for element in sync.convert(markdown_text):
# Process each element (string or Cell objects)
print(element)
```
### 3. Mark Figures in Your Notebook
In your Jupyter notebook, identify figures with a comment:
```python
# #my-figure
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(8, 4))
ax.plot([1, 2, 3, 4], [10, 20, 25, 30])
```
### 4. Reference in Markdown
Use standard Markdown image syntax with the figure identifier:
```markdown
{#my-figure}
```
## The Power of Separation
Creating documentation and developing visualizations involve different
workflows and timeframes. When building visualizations in Jupyter notebooks,
you need rapid cycles of execution, verification, and modification.
nbsync is designed specifically to address these separation of
concerns, allowing you to:
- **Focus on code** in notebooks without documentation distractions
- **Focus on narrative** in markdown without code interruptions
- **Maintain powerful connections** between both environments
Each environment is optimized for its purpose, while nbsync
handles the integration automatically.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request.
## License
This project is licensed under the MIT License.
<!-- Badges -->
[pypi-v-image]: https://img.shields.io/pypi/v/nbsync.svg
[pypi-v-link]: https://pypi.org/project/nbsync/
[python-v-image]: https://img.shields.io/pypi/pyversions/nbsync.svg
[python-v-link]: https://pypi.org/project/nbsync
[GHAction-image]: https://github.com/daizutabi/nbsync/actions/workflows/ci.yaml/badge.svg?branch=main&event=push
[GHAction-link]: https://github.com/daizutabi/nbsync/actions?query=event%3Apush+branch%3Amain
[codecov-image]: https://codecov.io/github/daizutabi/nbsync/coverage.svg?branch=main
[codecov-link]: https://codecov.io/github/daizutabi/nbsync?branch=main
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"description": "# nbsync\n\n[![PyPI Version][pypi-v-image]][pypi-v-link]\n[![Python Version][python-v-image]][python-v-link]\n[![Build Status][GHAction-image]][GHAction-link]\n[![Coverage Status][codecov-image]][codecov-link]\n\n<strong>Connect Jupyter notebooks and Markdown documents</strong>\n\nnbsync is a core library that seamlessly bridges Jupyter notebooks and Markdown documents, enabling dynamic content synchronization and execution.\n\n## Why Use nbsync?\n\n### The Documentation Challenge\n\nData scientists, researchers, and technical writers face a common dilemma:\n\n- **Development happens in notebooks** - ideal for experimentation and visualization\n- **Documentation lives in markdown** - perfect for narrative and explanation\n- **Connecting the two is painful** - screenshots break, exports get outdated\n\n### Our Solution\n\nnbsync creates a live bridge between your notebooks and markdown documents by:\n\n- **Keeping environments separate** - work in the tool best suited for each task\n- **Maintaining connections** - reference specific figures from notebooks\n- **Automating updates** - changes to notebooks reflect in documentation\n\n## Key Benefits\n\n- **True Separation of Concerns**:\n Develop visualizations in Jupyter notebooks and write documentation\n in markdown files, with each tool optimized for its purpose.\n\n- **Intuitive Markdown Syntax**:\n Use standard image syntax with a simple extension to reference\n notebook figures: `{#figure-id}`\n\n- **Automatic Updates**:\n When you modify your notebooks, your documentation updates\n automatically.\n\n- **Clean Source Documents**:\n Your markdown remains readable and focused on content, without\n code distractions or complex embedding techniques.\n\n- **Enhanced Development Experience**:\n Take advantage of IDE features like code completion and syntax\n highlighting in the appropriate environment.\n\n## Quick Start\n\n### 1. Installation\n\n```bash\npip install nbsync\n```\n\n### 2. Basic Usage\n\n```python\nfrom nbsync.sync import Synchronizer\nfrom nbstore import Store\n\n# Initialize with a notebook store\nstore = Store(\"path/to/notebooks\")\nsync = Synchronizer(store)\n\n# Process markdown with notebook references\nmarkdown_text = \"\"\"\n# My Document\n\n{#my-figure}\n\"\"\"\n\n# Convert markdown with notebook references to final output\nfor element in sync.convert(markdown_text):\n # Process each element (string or Cell objects)\n print(element)\n```\n\n### 3. Mark Figures in Your Notebook\n\nIn your Jupyter notebook, identify figures with a comment:\n\n```python\n# #my-figure\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(8, 4))\nax.plot([1, 2, 3, 4], [10, 20, 25, 30])\n```\n\n### 4. Reference in Markdown\n\nUse standard Markdown image syntax with the figure identifier:\n\n```markdown\n{#my-figure}\n```\n\n## The Power of Separation\n\nCreating documentation and developing visualizations involve different\nworkflows and timeframes. When building visualizations in Jupyter notebooks,\nyou need rapid cycles of execution, verification, and modification.\n\nnbsync is designed specifically to address these separation of\nconcerns, allowing you to:\n\n- **Focus on code** in notebooks without documentation distractions\n- **Focus on narrative** in markdown without code interruptions\n- **Maintain powerful connections** between both environments\n\nEach environment is optimized for its purpose, while nbsync\nhandles the integration automatically.\n\n## Contributing\n\nContributions are welcome! Please open an issue or submit a pull request.\n\n## License\n\nThis project is licensed under the MIT License.\n\n<!-- Badges -->\n[pypi-v-image]: https://img.shields.io/pypi/v/nbsync.svg\n[pypi-v-link]: https://pypi.org/project/nbsync/\n[python-v-image]: https://img.shields.io/pypi/pyversions/nbsync.svg\n[python-v-link]: https://pypi.org/project/nbsync\n[GHAction-image]: https://github.com/daizutabi/nbsync/actions/workflows/ci.yaml/badge.svg?branch=main&event=push\n[GHAction-link]: https://github.com/daizutabi/nbsync/actions?query=event%3Apush+branch%3Amain\n[codecov-image]: https://codecov.io/github/daizutabi/nbsync/coverage.svg?branch=main\n[codecov-link]: https://codecov.io/github/daizutabi/nbsync?branch=main\n",
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