semvec/pypi-packages huggingface dataset:
django-valem 0.1.14
A collection of Django apps defining data models for managing chemical species, reactions and datasets.
Requires Python: >=3.6
Keywords: django,chemistry,formula,species,state,reaction

****************************
Introduction to Django-Valem
****************************


Django-Valem is a collection of Django apps defining data models for parsing,
validation, manipulation and interpretation of chemical reactions, formulas, and
quantum states.
The ``django-valem`` apps package is based around PyValem_.



Installation:
=============
The ``django-valem`` apps can be installed either from PyPI_ using pip

.. code-block:: bash

    python3 -m pip install django-valem


or from the source by running from the project source directory

.. code-block:: bash

    python3 -m pip install .


Configuration:
==============
The ``django-valem`` apps can be added to any Django project by adding the following
apps into the ``INSTALLED_APPS`` list in the ``settings.py`` of the project:

.. code-block:: python

    INSTALLED_APPS = [
        ...

        "rp",  # App handling species and their states
        "rxn",  # App handling chemical reactions between species (rp.RP instances)
        "ds",  # App handling datasets attached to rxn.Reaction instances
        "refs",  # App handling references for ds.ReactionDataSet subclasses instances
    ]


For Developers:
===============
It goes without saying that any development should be done in a clean virtual
environment.
After cloning or forking the project from its GitHub_ page, ``django-valem`` might be
installed into the virtual environment in editable mode with

.. code-block:: bash

    pip install -e .[dev]

or on zsh:

.. code-block:: zsh

   pip install -e .'[dev]'

The ``[dev]`` extra installs (apart from the package dependencies) also several
development-related packages, such as ``black``, ``ipython``, or ``django`` itself.
The tests can then be executed by running (from the project root directory)

.. code-block:: bash

    python runtests.py

The project does not have ``requirements.txt`` by design, all the package dependencies
are rather handled by ``setup.py``.
The package needs to be installed to run the tests, which grants the testing process
another layer of usefulness.

The project code is formatted by ``black``.
Always make sure to format your code before submitting a pull request, by running
``black`` on all your python files, or ``black .`` from the project source directory.

If the ``rp.models.py``, or ``rxn.models.py`` are changed, the ``makemigrations.py``
script needs to be run to make migrations for the apps, before the apps are pushed to
master, or published to PyPI.


.. _GitHub: https://github.com/xnx/django-valem
.. _PyPI: https://pypi.org/project/django-valem
.. _PyValem: https://github.com/xnx/pyvalem
electronconfig 0.1.0
Electron configuration
Requires Python: >=3.9.0

# Table of Contents <a name="table-of-contents"></a>
- [Table of Contents ](#table-of-contents-)
- [electronconfig ](#electronconfig-)
- [Installation ](#installation-)
- [Usage ](#usage-)

# electronconfig <a name="electronconfig"></a>
This package is used to find the electron configuration of an element. It is also used to do different operations on the electron configuration of an element. 

# Installation <a name="installation"></a>
To install this package, run the following command in the terminal:
```bash
pip install electronconfig
```
For developer installation, run the following command in the terminal:
```bash
git clone https://github.com/jako4295/electronconfig
pip install -e electronconfig
```

# Usage <a name="usage"></a>
To use this package, run the following command in the terminal:
```python
import electronconfig as ec

ec_obj = ec.ElectronConfig()
```
This will create an object of the ElectronConfig class. This object can be used to find the electron configuration of an element. All electron configuations are saved as attributes to the ```ec_obj``` object. For example, ```ec_obj.ec1``` gives the electron configuration of hydrogen. 

The following code can be used to remove electrons from a given element:
```python
ec_obj.remove_electrons(82, 54)
```
This will remove 54 electrons from the element with atomic number 82 (lead). To find the principle quantum number of outermost projectile electron ($n_0$) of an element, one can either look at the string returned from the code above or run the following code:
```python
ec_obj.n_0(82, 54)
```
This will return the principle quantum number of the outermost projectile electron of lead.