semvec/pypi-packages huggingface dataset:
dash-callbackmanager 2.0.0
Dash callback manager, group callbacks easier within code.
License: MIT
Requires Python: >=3.8,<3.11.0
Keywords: dash,callback manager

# dash-callbackmanager

As your dash application grows the management of callbacks becomes a bit of an overhead. The 
callback manager allows you to bundle collections of dash callbacks together allowing you to easily keep your
`app.py` clean.

## Installation:
```shell
pip install dash-callbackmanager
```

## Usage:
The callback manager allows you to easily slip out the callbacks into separate files.

```python
# callbacks.py

from dash_callbackmanager import CallbackManager

manager = CallbackManager()

@manger.callback()
def my_callback(Output("element", "children"), Input("other-element", "value")):
    ...
```

```python 
# app.py
from dash import Dash
from .callbacks import manager

app = Dash(__name__)

manager.register(app)
```
scipy-psdm 0.2.2
transform an ill-conditioned quadratic matrix to a positive semidefinite matrix
License: MIT
Requires Python: >=3.6

|PyPI version| |PyPi downloads|

scipy-psdm
==========

Transform an ill-conditioned quadratic matrix into a positive
semi-definite matrix.

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

The ``scipy-psdm`` `git repo <http://github.com/ulf1/scipy-psdm>`__ is
available as `PyPi package <https://pypi.org/project/scipy-psdm>`__

.. code:: bash

   pip install scipy-psdm

Usage
-----

Lurie-Goldberg Algorithm to transform an ill-conditioned quadratic
matrix into a positive semi-definite matrix.

.. code:: python

   import scipy_psdm as psdm
   import numpy as np

   # A matrix with subjectively set correlations
   mat = [[ 1.   , -0.948,  0.099, -0.129],
          [-0.948,  1.   , -0.591,  0.239],
          [ 0.099, -0.591,  1.   ,  0.058],
          [-0.129,  0.239,  0.058,  1.   ]]
   mat = np.array(mat)

   # Convert to a positive semi-definite matrix
   rho = psdm.approximate_correlation_matrix(mat)
   print(rho.round(3))

Generate correlated random numbers

.. code:: python

   import scipy_psdm as psdm
   X, rho = psdm.randcorr(n_obs=100, n_vars=5, random_state=42)

   # compare
   import numpy as np
   print(rho.round(3))
   print(np.corrcoef(X, rowvar=False).round(3))

Check the
`examples <https://github.com/ulf1/scipy-psdm/tree/master/examples>`__
folder for notebooks.

Appendix
--------

Install a virtual environment
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: bash

   python3 -m venv .venv
   source .venv/bin/activate
   pip3 install -r requirements.txt
   pip3 install -r requirements-dev.txt
   pip3 install -r requirements-demo.txt

(If your git repo is stored in a folder with whitespaces, then don’t use
the subfolder ``.venv``. Use an absolute path without whitespaces.)

Python commands
~~~~~~~~~~~~~~~

-  Start virtual env: ``source .venv/bin/activate``
-  Jupyter for the examples: ``jupyter lab``
-  Check syntax:
   ``flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')``
-  Run Unit Tests: ``pytest -v``

Publish

::

   pandoc README.md --from markdown --to rst -s -o README.rst
   python setup.py sdist 
   twine upload -r pypi dist/*

Clean up
~~~~~~~~

.. code:: bash

   find . -type f -name "*.pyc" | xargs rm
   find . -type d -name "__pycache__" | xargs rm -r
   rm -r .pytest_cache
   rm -r .venv

Support
~~~~~~~

Please `open an issue <https://github.com/ulf1/scipy-psdm/issues/new>`__
for support.

Contributing
~~~~~~~~~~~~

Please contribute using `Github
Flow <https://guides.github.com/introduction/flow/>`__. Create a branch,
add commits, and `open a pull
request <https://github.com/ulf1/scipy-psdm/compare/>`__.

Contributers
------------

-  `@KikeM Enrique Millán Valbuena <https://github.com/KikeM>`__

.. |PyPI version| image:: https://badge.fury.io/py/scipy-psdm.svg
   :target: https://badge.fury.io/py/scipy-psdm
.. |PyPi downloads| image:: https://img.shields.io/pypi/dm/scipy-psdm
   :target: https://img.shields.io/pypi/dm/scipy-psdm