├── MANIFEST.in
├── setup.cfg
├── .gitignore
├── ipynb_py_convert
    ├── __init__.py
    └── __main__.py
├── conda-forge
    ├── README.md
    └── meta.yaml
├── examples
    ├── jupyter.png
    ├── vscode.png
    ├── plot.py
    ├── plot.ipynb
    └── .ipynb_checkpoints
    │   └── plot-checkpoint.ipynb
├── upload
├── upload.txt
├── setup.py
├── LICENSE
└── README.md


/MANIFEST.in:
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1 | include LICENSE
2 | 


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/setup.cfg:
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1 | [metadata]
2 | description-file = README.md
3 | 


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/.gitignore:
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1 | build/
2 | dist/
3 | ipynb_py_convert.egg-info/
4 | *.pyc
5 | 


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/ipynb_py_convert/__init__.py:
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1 | from .__main__ import nb2py, py2nb, convert
2 | 


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/conda-forge/README.md:
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1 | [ipynb-py-convert](https://github.com/conda-forge/ipynb-py-convert-feedstock)
2 | 


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/examples/jupyter.png:
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https://raw.githubusercontent.com/kiwi0fruit/ipynb-py-convert/HEAD/examples/jupyter.png


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/examples/vscode.png:
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https://raw.githubusercontent.com/kiwi0fruit/ipynb-py-convert/HEAD/examples/vscode.png


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/upload:
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 1 | #!/bin/bash
 2 | cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 3 | 
 4 | python setup.py sdist
 5 | if [[ "$(uname -s)" == MINGW* ]]; then
 6 |     winpty twine upload dist/* --skip-existing
 7 | else
 8 |     twine upload dist/* --skip-existing; fi
 9 | 
10 | rm -rf ./dist
11 | 


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/examples/plot.py:
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 1 | # %%
 2 | '''
 3 | ## Matplot example
 4 | 
 5 | ** Run the cell below to import some packages and show a line plot **
 6 | '''
 7 | 
 8 | # %%
 9 | import matplotlib.pyplot as plt
10 | import numpy as np
11 | 
12 | x = np.linspace(0, 20, 100)
13 | plt.plot(x, np.sin(x))
14 | plt.show()
15 | 


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/upload.txt:
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 1 | Install to conda environment:
 2 | 
 3 | * twine
 4 | 
 5 |     conda install twine
 6 | 
 7 | * On Windows install Git together with Bash.
 8 | 
 9 | 
10 | Scripts:
11 | 
12 | To run the script open Bash terminal, then type <code>. </code> (dot with space), then
13 | drag and drop the script to the Bash terminal (the script will change CWD itself).
14 | 
15 | * open terminal with activated environment
16 |   * to upload package to pypi change version in `upload`, push changes, run `upload`
17 | 
18 | 
19 | Hint: Easily create shortcut to activated environment via Shortcutter:
20 | 
21 |     conda install -c defaults -c conda-forge shortcutter
22 |     # pip install shortcutter
23 |     shortcutter --terminal
24 | 
25 | * To start Bash on windows type `%b%` in the terminal created via Shortcutter
26 | 


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/setup.py:
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 1 | from setuptools import setup
 2 | from os import path
 3 | import io
 4 | 
 5 | here = path.abspath(path.dirname(__file__))
 6 | with io.open(path.join(here, 'README.md'), encoding='utf-8') as f:
 7 |     long_description = f.read()
 8 | 
 9 | setup(
10 |     name='ipynb-py-convert',
11 |     packages=['ipynb_py_convert'],
12 |     version='0.4.6',
13 |     description='Convert .py files runnable in VSCode/Python or Atom/Hydrogen to jupyter .ipynb notebooks and vice versa',
14 |     long_description=long_description,
15 |     long_description_content_type="text/markdown",
16 |     author='Noj Vek',
17 |     author_email='nojvek@gmail.com',
18 |     license='MIT',
19 |     url='https://github.com/kiwi0fruit/ipynb-py-convert',
20 |     keywords=['vscode', 'jupyter', 'convert', 'ipynb', 'py', 'atom', 'hydrogen'],
21 |     classifiers=[],
22 |     entry_points={
23 |         'console_scripts': [
24 |             'ipynb-py-convert=ipynb_py_convert.__main__:main',
25 |         ],
26 |     },
27 | )
28 | 


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/conda-forge/meta.yaml:
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 1 | {% set name = "ipynb-py-convert" %}
 2 | {% set version = "0.4.4" %}
 3 | 
 4 | package:
 5 |   name: {{ name|lower }}
 6 |   version: {{ version }}
 7 | 
 8 | source:
 9 |   url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
10 |   sha256: 45a1bb36ed3580b7c5f40d70ce104da375be89537925fba2643a9372cc002dc5
11 | 
12 | build:
13 |   noarch: python
14 |   number: 0
15 |   entry_points:
16 |     - ipynb-py-convert = ipynb_py_convert.__main__:main
17 |   script: "{{ PYTHON }} -m pip install . --no-deps -vv"
18 | 
19 | requirements:
20 |   host:
21 |     - python
22 |     - pip
23 |   run:
24 |     - python
25 | 
26 | test:
27 |   imports:
28 |     - ipynb_py_convert
29 | 
30 | about:
31 |   home: https://github.com/kiwi0fruit/ipynb-py-convert
32 |   license: MIT
33 |   license_family: MIT
34 |   license_file: LICENSE
35 |   summary: "Convert .py files runnable in VSCode/Python or Atom/Hydrogen to jupyter .ipynb notebooks and vice versa"
36 |   doc_url: https://github.com/kiwi0fruit/ipynb-py-convert
37 |   dev_url: https://github.com/kiwi0fruit/ipynb-py-convert
38 | 
39 | extra:
40 |   recipe-maintainers:
41 |     - kiwi0fruit
42 | 


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/LICENSE:
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 1 | The MIT License (MIT)
 2 | 
 3 | Copyright (c) 2018 Noj Vek nojvek@gmail.com
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 
23 | 


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/README.md:
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 1 | # ipynb-py-convert
 2 | 
 3 | Atom/Hydrogen or VSCode/Python allows creating a python files split into cells with `# %%` separators with the ability to run cells via backend Jupyter session and interactively show results back.
 4 | 
 5 | More examples: [Jupyter Python VSCode examples](https://github.com/DonJayamanne/pythonVSCode/wiki/Jupyter-Examples), [Atom/Hydrogen Getting Started](https://nteract.gitbooks.io/hydrogen/docs/Usage/GettingStarted.html).
 6 | 
 7 | [ipynb-py-convert](https://pypi.python.org/pypi/ipynb-py-convert) python module converts files: .ipynb to .py and .py to .ipynb.
 8 | 
 9 | **ipynb-py-convert** is a fork of the [vscode-ipynb-py-converter](https://github.com/nojvek/vscode-ipynb-py-converter).
10 | 
11 | 
12 | ## Install
13 | 
14 | ```bash
15 | conda install -c defaults -c conda-forge ipynb-py-convert
16 | ```
17 | or
18 | ```bash
19 | pip install ipynb-py-convert
20 | ```
21 | 
22 | 
23 | ## Troubleshooting
24 | 
25 | * If encoding problems on Windows try using `python>=3.7`, setting `set PYTHONUTF8=1` in Windows console and use `ipynb-py-convert` for UTF-8 files only. If using [Git-Bash on Windows](https://git-scm.com/download/win) setting:
26 | 
27 | ```bash
28 | export LANG=C.UTF-8
29 | export PYTHONIOENCODING=utf-8
30 | export PYTHONUTF8=1
31 | ```
32 | should be enough. Also try setting default Bash settings to UTF-8: [Options] - [Text] - [Locale / Character set] - [C / UTF-8]. It might affect all Bash runs so there would be no need to setting encoding every time. 
33 | 
34 | 
35 | ## Example
36 | 
37 | `ipynb-py-convert examples/plot.py examples/plot.ipynb`
38 | 
39 | or
40 | 
41 | `ipynb-py-convert examples/plot.ipynb examples/plot.py`
42 | 
43 | 
44 | **VSCode**
45 | 
46 | ![](https://github.com/kiwi0fruit/ipynb-py-convert/raw/master/examples/vscode.png)
47 | 
48 | Markdown cells are converted to python multiline strings `'''`. Code cells are left as is. `# %%` is used by vscode as the cell marker on which 'Run Cell' action is available.
49 | 
50 | 
51 | **Jupyter ipynb notebook**
52 | 
53 | ![](https://github.com/kiwi0fruit/ipynb-py-convert/raw/master/examples/jupyter.png)
54 | 


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/ipynb_py_convert/__main__.py:
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  1 | import json
  2 | import sys
  3 | from os import path
  4 | 
  5 | header_comment = '# %%\n'
  6 | 
  7 | 
  8 | def nb2py(notebook):
  9 |     result = []
 10 |     cells = notebook['cells']
 11 | 
 12 |     for cell in cells:
 13 |         cell_type = cell['cell_type']
 14 | 
 15 |         if cell_type == 'markdown':
 16 |             result.append('%s"""\n%s\n"""'%
 17 |                           (header_comment, ''.join(cell['source'])))
 18 | 
 19 |         if cell_type == 'code':
 20 |             result.append("%s%s" % (header_comment, ''.join(cell['source'])))
 21 | 
 22 |     return '\n\n'.join(result)
 23 | 
 24 | 
 25 | def py2nb(py_str):
 26 |     # remove leading header comment
 27 |     if py_str.startswith(header_comment):
 28 |         py_str = py_str[len(header_comment):]
 29 | 
 30 |     cells = []
 31 |     chunks = py_str.split('\n\n%s' % header_comment)
 32 | 
 33 |     for chunk in chunks:
 34 |         cell_type = 'code'
 35 |         if chunk.startswith("'''"):
 36 |             chunk = chunk.strip("'\n")
 37 |             cell_type = 'markdown'
 38 |         elif chunk.startswith('"""'):
 39 |             chunk = chunk.strip('"\n')
 40 |             cell_type = 'markdown'
 41 | 
 42 |         cell = {
 43 |             'cell_type': cell_type,
 44 |             'metadata': {},
 45 |             'source': chunk.splitlines(True),
 46 |         }
 47 | 
 48 |         if cell_type == 'code':
 49 |             cell.update({'outputs': [], 'execution_count': None})
 50 | 
 51 |         cells.append(cell)
 52 | 
 53 |     notebook = {
 54 |         'cells': cells,
 55 |         'metadata': {
 56 |             'anaconda-cloud': {},
 57 |             'kernelspec': {
 58 |                 'display_name': 'Python 3',
 59 |                 'language': 'python',
 60 |                 'name': 'python3'},
 61 |             'language_info': {
 62 |                 'codemirror_mode': {'name': 'ipython', 'version': 3},
 63 |                 'file_extension': '.py',
 64 |                 'mimetype': 'text/x-python',
 65 |                 'name': 'python',
 66 |                 'nbconvert_exporter': 'python',
 67 |                 'pygments_lexer': 'ipython3',
 68 |                 'version': '3.6.1'}},
 69 |         'nbformat': 4,
 70 |         'nbformat_minor': 4
 71 |     }
 72 | 
 73 |     return notebook
 74 | 
 75 | 
 76 | def convert(in_file, out_file):
 77 |     _, in_ext = path.splitext(in_file)
 78 |     _, out_ext = path.splitext(out_file)
 79 | 
 80 |     if in_ext == '.ipynb' and out_ext == '.py':
 81 |         with open(in_file, 'r', encoding='utf-8') as f:
 82 |             notebook = json.load(f)
 83 |         py_str = nb2py(notebook)
 84 |         with open(out_file, 'w', encoding='utf-8') as f:
 85 |             f.write(py_str)
 86 | 
 87 |     elif in_ext == '.py' and out_ext == '.ipynb':
 88 |         with open(in_file, 'r', encoding='utf-8') as f:
 89 |             py_str = f.read()
 90 |         notebook = py2nb(py_str)
 91 |         with open(out_file, 'w', encoding='utf-8') as f:
 92 |             json.dump(notebook, f, indent=2)
 93 | 
 94 |     else:
 95 |         raise(Exception('Extensions must be .ipynb and .py or vice versa'))
 96 | 
 97 | 
 98 | def main():
 99 |     argv = sys.argv
100 |     if len(argv) < 3:
101 |         print('Usage: ipynb-py-convert in.ipynb out.py')
102 |         print('or:    ipynb-py-convert in.py out.ipynb')
103 |         sys.exit(1)
104 | 
105 |     convert(in_file=argv[1], out_file=argv[2])
106 | 
107 | 
108 | if __name__ == '__main__':
109 |     main()
110 | 


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/examples/plot.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "## Matplot example\n",
 8 |     "\n",
 9 |     "** Run the cell below to import some packages and show a line plot **"
10 |    ]
11 |   },
12 |   {
13 |    "cell_type": "code",
14 |    "execution_count": 1,
15 |    "metadata": {},
16 |    "outputs": [
17 |     {
18 |      "data": {
19 |       "image/png": 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UKRH5nIhYWUZfBALAd5elpe4GDovIMeB54PNKqaQpBieWA4jG64rByR0TGO1/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\n",
20 |       "text/plain": [
21 |        "<matplotlib.figure.Figure at 0x10a91bfd0>"
22 |       ]
23 |      },
24 |      "metadata": {},
25 |      "output_type": "display_data"
26 |     }
27 |    ],
28 |    "source": [
29 |     "import matplotlib.pyplot as plt\n",
30 |     "import numpy as np\n",
31 |     "\n",
32 |     "x = np.linspace(0, 20, 100)\n",
33 |     "plt.plot(x, np.sin(x))\n",
34 |     "plt.show()\n"
35 |    ]
36 |   }
37 |  ],
38 |  "metadata": {
39 |   "anaconda-cloud": {},
40 |   "kernelspec": {
41 |    "display_name": "Python 3",
42 |    "language": "python",
43 |    "name": "python3"
44 |   },
45 |   "language_info": {
46 |    "codemirror_mode": {
47 |     "name": "ipython",
48 |     "version": 3
49 |    },
50 |    "file_extension": ".py",
51 |    "mimetype": "text/x-python",
52 |    "name": "python",
53 |    "nbconvert_exporter": "python",
54 |    "pygments_lexer": "ipython3",
55 |    "version": "3.6.1"
56 |   }
57 |  },
58 |  "nbformat": 4,
59 |  "nbformat_minor": 1
60 | }
61 | 


--------------------------------------------------------------------------------
/examples/.ipynb_checkpoints/plot-checkpoint.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "## Matplot example\n",
 8 |     "\n",
 9 |     "** Run the cell below to import some packages and show a line plot **"
10 |    ]
11 |   },
12 |   {
13 |    "cell_type": "code",
14 |    "execution_count": 1,
15 |    "metadata": {},
16 |    "outputs": [
17 |     {
18 |      "data": {
19 |       "image/png": 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UKRH5nIhYWUZfBALAd5elpe4GDovIMeB54PNKqaQpBieWA4jG64rByR0TGO1/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\n",
20 |       "text/plain": [
21 |        "<matplotlib.figure.Figure at 0x10a91bfd0>"
22 |       ]
23 |      },
24 |      "metadata": {},
25 |      "output_type": "display_data"
26 |     }
27 |    ],
28 |    "source": [
29 |     "import matplotlib.pyplot as plt\n",
30 |     "import numpy as np\n",
31 |     "\n",
32 |     "x = np.linspace(0, 20, 100)\n",
33 |     "plt.plot(x, np.sin(x))\n",
34 |     "plt.show()\n"
35 |    ]
36 |   }
37 |  ],
38 |  "metadata": {
39 |   "anaconda-cloud": {},
40 |   "kernelspec": {
41 |    "display_name": "Python 3",
42 |    "language": "python",
43 |    "name": "python3"
44 |   },
45 |   "language_info": {
46 |    "codemirror_mode": {
47 |     "name": "ipython",
48 |     "version": 3
49 |    },
50 |    "file_extension": ".py",
51 |    "mimetype": "text/x-python",
52 |    "name": "python",
53 |    "nbconvert_exporter": "python",
54 |    "pygments_lexer": "ipython3",
55 |    "version": "3.6.1"
56 |   }
57 |  },
58 |  "nbformat": 4,
59 |  "nbformat_minor": 1
60 | }
61 | 


--------------------------------------------------------------------------------