semvec/pypi-packages huggingface dataset:
configstore 0.9
Retrieve settings and secrets from different storages.
Requires Python: >= 3

~~~~~~~~~~~~~
 configstore
~~~~~~~~~~~~~

configstore is a small pluggable library that lets you retrieve settings
or secrets from a variety of storage systems to configure your app.

The main pain point this tries to solve is:

Assume you have your config in environment variables but you want to store your secrets in something else.
How do you mix having a secret backend for your secrets but still keep everything else in environment variables?
With this module you can use both. Let's say you use ``DATABASE_PASSWORD`` as your database password env variable.
This will first try to find this in your first backend (let's say docker secrets) and if it fails will fall back on the env variable.

See ``example.py`` for some example code.


Available backends
------------------

configstore.EnvVarBackend finds settings in environment variables.  This is the classic
12-factor approach, which main drawback is that it's easy for outside tools or sub-processes
to inspect the environment and access sensitive data.  This backend is still useful for
settings that are not secrets.

configstore.DotenvBackend lets you put settings in a key-value format file, using the
`dotenv module`_, which is useful for local development.
This backend requires an optional dependency, so use a requirement like ``configstore[dotenv]``
to get everything installed.

If you are using docker-compose, `another dotenv module`_ is already installed and
does not provide the same interface; use a requirement like `configstore[pydotenv]`
instead.  DotenvBackend is automatically compatible.

configstore.DockerSecretBackend can read `Docker secrets`_.
This is a secure storage with first-class support in the Docker runtime and related
tooling.

configstore.AwsSsmBackend connects to the Parameter Store service that
is part of `AWS Systems Manager`_.  This backend requires optional
dependencies (boto3), so use a requirement like ``configstore[awsssm]``
to get everything installed.

Finally, configstore.DictBackend is useful to define a bunch of settings
for testing or for defaults.

.. _docker secrets: https://docs.docker.com/engine/swarm/secrets/
.. _dotenv module: https://pypi.org/project/django-dotenv/
.. _another dotenv module: https://pypi.org/project/python-dotenv/
.. _aws systems manager: https://docs.aws.amazon.com/systems-manager/latest/APIReference/Welcome.html


Contributors
------------

Original author: Antoine Reversat @crevetor

Current maintainer: Éric Araujo @merwok

Project sponsored by Caravan Coop @caravancoop
geocovid 0.0.1
Analysis of COVID's effect on the relationship between geography and chronic health factors.
Requires Python: >=3.7

# GeoCOVID
Analysis of COVID's effect on the relationship between geography and chronic health factors.

# Graph Scaling Dimension

Here are a few examples of flooding graphs. This notion of scale dimension of a network is one of the aspects that makes this work relatively novel. It gives us an estimate of the instrinsic structure of the network. We expect road systems as networks to range numerically between 1-2 dimensions depending on their structure. Road systems that are closer to being one-dimensional may characterize less serviceabiliy and may be related to how rural a region is. Likewise, road systems that are closer to 2D will have many interconnecting paths as one would find in a city, so we expect higher-dimensional road networks to be more urban.

## Linear
[![Flood Linear Graph](https://img.youtube.com/vi/O4zynUlNEII/hqdefault.jpg)](https://www.youtube.com/watch?v=O4zynUlNEII)
## Cube
[![Flood Cube Graph](https://img.youtube.com/vi/J7syt9fq0-o/maxresdefault.jpg)](https://youtu.be/J7syt9fq0-o)
## Rectangular
[![Flood Rectangular Graph](https://img.youtube.com/vi/yKFxA5pDEHM/maxresdefault.jpg)](https://youtu.be/yKFxA5pDEHM)
## Lobster
[![Flood Lobster Graph](https://img.youtube.com/vi/WnKLoU-TY3o/maxresdefault.jpg)](https://youtu.be/WnKLoU-TY3o)

# References

1. https://www.wolframphysics.org/technical-introduction/limiting-behavior-and-emergent-geometry/the-notion-of-dimension/

# Data Sources
The national road data comes from [this FTP site](https://ftp.maps.canada.ca/pub/nrcan_rncan/vector/geobase_nrn_rrn/).