semvec/pypi-packages huggingface dataset:
conduction 0.1
Python package for solving implicit heat conduction
Requires Python: >=2.7, >=3.5

# Conduction

Implicit heat conduction solver on a structured grid written in Python. It interfaces with PETSc to provide highly scalable meshes and solve the steady-state heat equation using direct or iterative methods.

## Dependencies

- Python 2.7 and above
- Numpy 1.9 and above
- Scipy 0.14 and above
- [mpi4py](http://pythonhosted.org/mpi4py/usrman/index.html)
- [petsc4py](https://pythonhosted.org/petsc4py/usrman/install.html)
- [h5py](http://docs.h5py.org/en/latest/mpi.html#building-against-parallel-hdf5) (optional - for saving parallel data)
- Matplotlib (optional - for visualisation)

### PETSc installation

PETSc is used extensively via the Python frontend, petsc4py. It is required that PETSc be configured and installed on your local machine prior to using this module. You can use pip to install petsc4py and its dependencies.

```shell
pip install [--user] numpy mpi4py
pip install [--user] petsc petsc4py
```

If that fails, you must [compile](https://git.dias.ie/itherc/conduction/blob/master/COMPILE.md) these dependencies manually.

## Usage

All of the scripts in the *tests* directory can be run in parallel, e.g.

```
mpirun -np 4 python conduction3d_benchmark.py
```

where the number after the `np` flag specifies the number of processors.

### API

A `ConductionND` object can be defined based on the extent of the domain and the number of cells. The simplified use-case below outlines the following tasks:

1. Define a mesh on which to solve the heat equation
2. Populate the thermal conductivity and heat production fields
3. Set boundary conditions on the top and bottom walls
4. Solve for temperature

```python
from conduction import ConductionND

# define the mesh
minX, minY, minZ = 0.0, 0.0, 0.0
maxX, maxY, maxZ = 1.0, 1.0, 1.0
resX, resY, resZ = 10, 10, 10

mesh = ConductionND((minX, minY, minZ), (maxX, maxY, maxZ), (resX, resY, resZ))

# populate thermal conductivity and heat production fields
n = resX*resY*resZ
k = np.ones(n)
H = np.ones(n)

mesh.update_properties(k, H)

# set boundary conditions
mesh.boundary_condition("maxZ", 0.0, flux=False) # Dirichlet BC
mesh.boundary_condition("minZ", 1.0, flux=True) # Neumann BC

# solve temperature
T = mesh.solve()
```

More complex examples can be found in the `Examples` directory.
elm-kernel 0.21.1
Jupyter kernel for executing Elm code

This kernel adds support for Elm to [Jupyter](http://jupyter.org/) notebooks.

While basic functionality is in place, this is still very much a work in
progress. I'm still figuring it all out. Any help, ideas, etc. would be great.

# Requirements
- Python 3.6+

# Installation

Either install from a repository using `pip`:

```
pip install elm_kernel
```

or install the package from source:

```
pip install -e .
```

Then install the kernel spec:
```
python -m elm_kernel.install
```

# Usage

Run `jupyter notebook` and select the Elm kernel for a new notebook.

## Multi-cell code examples

By default, when you execute a code cell with the Elm kernel the code will *not*
be compiled. Instead, the kernel simply queues up code cells. This way you can
break longer examples over multiple cells, interleaving the code cells with
supporting Markdown cells.

In order to ask the kernel to actually compile your code, you need to terminate
a code cell with the line:

```
-- compile-code
```

When the kernel sees a cell like this it contatenates, in cell-execution order,
all of the executed but uncompiled code cells (i.e. everything since the start
of the kernel or the last `-- compile-code` cell). It then compiles the
concatenated code, returning the result to the notebook.

For a concrete example of this, see
[`examples/the-elm-architecture.ipynb`](https://github.com/abingham/jupyter-elm-kernel/blob/master/examples/the-elm-architecture.ipynb).

This is a bit hacky, and we're actively searching for a better alternative.
Ideas are welcome!

# Examples

The `examples` directory contains a few examples of how to use this kernel. Just
go to that directory and run `jupyter notebook` to see them.
xtractr 0.0.1
A mechanism to improve extraction
Requires Python: >=3.6

# xTractr

### Purpose
xTractr is a repository of all extraction-related functions that I've had to use for varying projects - generalized to be able to be useful across any range of tasks. Some of the extraction mechanisms are genuinely involved and are useful, others are simply 2 line functions meant to more clearly represent abstractions provided by packages and services like Python-Docx, Selenium, and AWS. The goal of xTractr is to make a variety of disparate 'extraction-related' tasks much more human-readable and simpler to reference in an IDE with autocomplete than having to lookup a method everytime you want to do something. 

### Future Work
Right now, xTractr is kind of barebones - I plan on diversifying some of its functionality and continuing to experiment with effective ways to communicate the purpose of varying packages. Let me know if there's a type of job that I missed as I expand its scope.
GPM-Playlist-Generator 1.2.1
Google Play Music - Playlist Generator
Keywords: google music,google play music,playlist,playlist generator

Google Play Music - Playlist Generator
======================================

|build status|

This is a command-line tool to generate a set of standard playlists for
Google Play Music libraries.

It's currently in beta, and some knowledge of Python and comfort with
the command line is required. Depending on interest, I will make it more
user friendly. The author is using the tool and care is being taken not
to make a mess in the library manipulated (I care about mine too), but
USE AT YOUR OWN RISK.

Installation
------------

You can either do a:

.. code::

    pip install GPM-Playlist-Generator

or if you've cloned this repo:

.. code::

    python setup.py install

Running the tool
----------------

Simply run:

.. code::

    gpm-playlistgen.py config.yaml

What it does
------------

The default configuration captured in ``config.sample.yaml`` will log
prompt you to log into your account via OAuth, download the list of tracks
and playlists in your library, and do the following as per the ``playlist``
section in the configuration:

-  Generate a set of playlists grouping your tracks by month in which
   they've been added to your library (``monthly_added``)
-  Generate a sorted list of your most frequently played tracks
   (``most_played``)

The playlist generated will all have a name starting with "[PG]". This
is configurable.

If you rerun the tool, it will only regenerate the playlists that are
needed (new tracks in monthly added, and most played).

I need help
-----------

Try:

.. code::

    gpm-playlistgen.py config.yaml

I want to report a problem or I have a cool idea for this tool
--------------------------------------------------------------

Please use the `issue
tracker <https://gitlab.com/hugoh/gpm-playlistgen/issues>`__.

FAQ
---

I don't trust this tool
~~~~~~~~~~~~~~~~~~~~~~~

"No problem". You can run it with the ``--dry-run`` option. It will not
write anything to your account, simply show you what it would do.

I want to get rid of all those generated playlists
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can run:

.. code::

    gpm-playlistgen.py --delete-all-playlists config.yaml

The description field is used to check that this is a generated list,
but I strongly advise to the the following first to see what it's going
to do:

.. code::

    gpm-playlistgen.py --delete-all-playlists config.yaml --dry-run

Credits
-------

The heavy lifting is done by `Simon Weber's
gmusicapi <https://github.com/simon-weber/gmusicapi>`__.

.. |build status| image:: https://gitlab.com/hugoh/gpm-playlistgen/badges/master/pipeline.svg
   :target: https://gitlab.com/hugoh/gpm-playlistgen/commits/master