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Fix #1141 - Help about Managing Data between Python and Nelson #1150

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Mar 31, 2024
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Fix #1141 - Help about Managing Data between Python and Nelson #1150

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4 changes: 4 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

## 1.4.0 (UNRELEASED)

### Added

- [#1141](http://github.com/nelson-lang/nelson/issues/1141) Help about Managing Data between Python and Nelson.

### Changed

- [#1142](http://github.com/nelson-lang/nelson/issues/1142) Github Actions updated.
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53 changes: 53 additions & 0 deletions modules/python_engine/help/en_US/xml/1_The_power_of_Python.xml
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<?xml version="1.0" encoding="UTF-8" ?>
<xmldoc>
<copyright>SAME AS NELSON SOFTWARE</copyright>

<language>en_US</language>
<keyword>The power of calling Python from Nelson</keyword>

<description>
<p
>Python is a dominant force in various fields, including machine learning, data science, and general programming.</p>
<p
>Its extensive libraries, such as NumPy, SciPy and Pandas, provide an arsenal of tools for scientific computing and data manipulation.</p>
<p
>Combining the strengths of Nelson’s digital capabilities with Python’s vast ecosystem opens up a world of possibilities.</p>
<p />
<p>Why call Python from Nelson?</p>
<p
>- Access to a rich ecosystem: Python has a vast collection of libraries for various tasks, from advanced statistical analysis to machine learning and beyond.</p>
<p
>By calling Python from Nelson, users can access this rich ecosystem without sacrificing Nelson's core functionality.</p>
<p
>- Seamless integration: Nelson's ability to call external functions allows for seamless integration with Python.</p>
<p
>This means Nelson users can leverage Python capabilities within their familiar Nelson environment, improving productivity and workflow efficiency.</p>
<p />
<p
>- Specialized Libraries: Although Nelson offers a wide range of built-in functions, there are some niche tasks that Python libraries excel at.</p>
<p
>For example, deep learning tasks can be handled efficiently using Python libraries such as TensorFlow or PyTorch, seamlessly integrated into Nelson workflows.</p>
<p />
<p
>- Rapid prototyping and development: Python is often preferred for rapid prototyping and development due to its concise syntax and extensive library support.</p>
<p
>By leveraging Python from Nelson, users can leverage Python's rapid prototyping capabilities while maintaining Nelson's digital computing environment for production-level work.</p>
<p />
<p
>- Community Collaboration: By integrating Python into Nelson workflows, users can leverage the collective wisdom of both communities, fostering collaboration and innovation.</p>
<p />
<p
>In conclusion, calling Python from Nelson offers a powerful synergy that combines the strengths of both platforms.</p>
<p
>Whether accessing specialized libraries, prototyping algorithms, or fostering collaboration, integrating Python into Nelson workflows can improve productivity and open up new possibilities for scientific computing and data analysis.</p>
</description>

<see_also>
<see_also_item>
<link linkend="${python_engine}pyrun">pyrun</link>
</see_also_item>
<see_also_item>
<link linkend="${python_engine}pyenv">pyenv</link>
</see_also_item>
</see_also>
</xmldoc>
175 changes: 175 additions & 0 deletions modules/python_engine/help/en_US/xml/python_types.xml
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<?xml version="1.0" encoding="UTF-8" ?>
<xmldoc>
<copyright>SAME AS NELSON SOFTWARE</copyright>

<language>en_US</language>
<keyword>Python Nelson types</keyword>
<short_description
>Managing Data between Python and Nelson.</short_description>

<description>
<p><b>Managing data returned by Python functions:</b></p>
<table style="width:100%">
<tr>
<th>Python return type, as shown in Python</th>
<th>Corresponding Nelson type (scalar)</th>
</tr>
<tr>
<td>bool</td><td>logical</td>
</tr>
<tr>
<td>complex</td><td>double (complex)</td>
</tr>
<tr>
<td>float</td><td>double</td>
</tr>
</table>
<p />
<p><b>Convert Python types to Nelson type explicitly:</b></p>
<p />
<table style="width:100%">
<tr>
<th>Python return types or protocols shown in Nelson</th>
<th>Nelson conversion methods</th>
</tr>
<tr>
<td>py.str</td><td>char, string</td>
</tr>
<tr>
<td>py.int</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64</td>
</tr>
<tr>
<td>py.long</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64</td>
</tr>
<tr>
<td>py.float</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64</td>
</tr>
<tr>
<td>py.bool</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64, logical</td>
</tr>
<tr>
<td>py.bytes</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64, logical</td>
</tr>
<tr>
<td>py.array.array</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64</td>
</tr>
<tr>
<td>py.memoryview</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64</td>
</tr>
<tr>
<td>py.numpy.ndarray</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64</td>
</tr>
<tr>
<td>py.list</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64, logical, string, cell</td>
</tr>
<tr>
<td>py.tuple</td><td
>double, single, int8, uint8, int16, uint16, int32, uint32, int64, uint64, logical, string, cell</td>
</tr>
<tr>
<td>py.dict</td><td>struct</td>
</tr>
</table>

<p />
<p><b>Pass scalar Nelson type to Python:</b></p>
<p />
<table style="width:100%">
<tr>
<th>Nelson scalar input argument type</th>
<th>Python type</th>
</tr>
<tr><td>NaN</td><td>float("nan")</td></tr>
<tr><td>Inf</td><td>float("inf")</td></tr>
<tr><td>double (real)</td><td>py.float</td></tr>
<tr><td>single (real)</td><td>py.float</td></tr>
<tr><td>double (complex)</td><td>py.complex</td></tr>
<tr><td>single (complex)</td><td>py.complex</td></tr>
<tr><td>int8</td><td>py.int</td></tr>
<tr><td>uint8</td><td>py.int</td></tr>
<tr><td>int16</td><td>py.int</td></tr>
<tr><td>uint16</td><td>py.int</td></tr>
<tr><td>int32</td><td>py.int</td></tr>
<tr><td>uint32</td><td>py.int</td></tr>
<tr><td>int64</td><td>py.int</td></tr>
<tr><td>uint64</td><td>py.int</td></tr>
<tr><td>string scalar</td><td>py.str</td></tr>
<tr><td>char vector</td><td>py.str</td></tr>
<tr><td>logical</td><td>py.bool</td></tr>
<tr><td>struct</td><td>py.dict</td></tr>
</table>
<p />
<p><b>Pass 1-by-N Vector Nelson type to Python:</b></p>
<p />
<table style="width:100%">
<tr>
<th>Nelson 1-by-N Vector input argument type</th>
<th>Python type</th>
</tr>
<tr><td>double (real)</td><td>array.array('d')</td></tr>
<tr><td>single (real)</td><td>array.array('f')</td></tr>
<tr><td>int8</td><td>array.array('b')</td></tr>
<tr><td>uint8</td><td>array.array('B')</td></tr>
<tr><td>int16</td><td>array.array('h')</td></tr>
<tr><td>uint16</td><td>array.array('H')</td></tr>
<tr><td>int32</td><td>array.array('i')</td></tr>
<tr><td>uint32</td><td>array.array('I')</td></tr>
<tr><td>int64</td><td>array.array('q')</td></tr>
<tr><td>uint64</td><td>array.array('Q')</td></tr>
<tr><td>double</td><td>memoryview</td></tr>
<tr><td>single</td><td>memoryview</td></tr>
<tr><td>logical</td><td>memoryview</td></tr>
<tr><td>char vector</td><td>str</td></tr>
<tr><td>string scalar</td><td>str</td></tr>
<tr><td>cell vector</td><td>tuple</td></tr>
</table>

<p />
<p><b>Pass 2D Matrices and ND Arrays to Python:</b></p>
<p
>The Python language offers a protocol for accessing memory buffers, akin to the data stored in Nelson arrays.</p>
<p>Nelson incorporates this Python buffer protocol for its arrays.</p>


</description>

<used_function />
<bibliography />

<examples>

<example_item>
<example_item_type>nelson</example_item_type>
<example_item_description />
<example_item_data><![CDATA[R = pyrun('', "A", 'A', magic(3))
R.double()]]>
</example_item_data>
</example_item>
</examples>

<see_also>
<see_also_item>
<link linkend="${python_engine}pyrun">pyrun</link>
</see_also_item>
</see_also>

<history>
<history_item>
<history_version>1.4.0</history_version>
<history_description>initial version</history_description>
</history_item>
</history>

<authors>
<author_item>Allan CORNET</author_item>
</authors>
</xmldoc>
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