semvec/pypi-packages huggingface dataset:
waf-run 0.0.1
A dumb package
Requires Python: >=2.3

# wafrun

First go to src folder, and install

```
$ pip install wafrun
```

Once the package is installed, open python console by typing `python`

```
$ python
>>> from wafrun import make
>>> make(1)
```

The make function accepts values from 1 - 5.

The output will be in the same folder with file name as program.cc

> *NOTE* - Please uninstall once you use in the system `pip uninstall revacnlab`
gtfs2nx 0.2.1
Convert GTFS feeds to realistic, routable NetworkX graph.
Requires Python: >=3.10,<4.0

# gtfs2nx

Create routable [NetworkX](https://github.com/networkx/networkx) graph with realistic transfer times from [GTFS](https://developers.google.com/transit/gtfs/) feeds. 🚌 🚆 🚡 


## How
* Cleaning & preprocessing
    * Remove trips with unrealistic travel times and speeds
    * Fix trips contradictory stop sequences and departure times
* Enable routability
    * Ensure each node belongs only to single route
    * Calculate average segment travel times
    * Calculate average stop headway and service frequency
    * Add edges for walking transfer between routes with realistic transfer time (walking time + headway / 2)


## Install
```bash
pip install gtfs2nx
```

## Usage
```Python
import gtfs2nx as gx

G = gx.transit_graph('path/to/GTFS-feed.zip')
```

Customize transit network:
```Python
G = gx.transit_graph(
    gtfs_paths='path/to/GTFS-feed.zip',
    time_window=('06:00', '08:00'),
    route_types=[700], # only buses
    walk_transfer_max_distance=400, # allow transfers with long walking distance
)
```

See the [API docs](./docs/api.md) for more details and the [getting-started notebook](./docs/getting_started.ipynb) for a small demo.


## Exemplary NetworkX analysis
```Python
import networkx as nx

# access precomputed stop characteristics
frequency = nx.get_node_attributes(G, 'frequency')

# routing
route = nx.shortest_path(G, from_stop, to_stop, weight='weight')

# travel time to other stops
travel_times = nx.single_source_dijkstra_path_length(G, source=from_stop, weight='weight')

# centrality analysis
centrality = nx.closeness_centrality(G, distance='weight')
```

## Further use cases

* [Using graph neural networks to classify missing GTFS route types](https://gist.github.com/FlorianNachtigall/9df1c9f7417aa512220756a35c36b45f)
* [Validating NetworkX transit graph: Comparing routes and travel times to Google Maps](https://gist.github.com/FlorianNachtigall/3e0d2f5e4fa8b2e893a29445a99dfb4f)


## Development
Build from source using [poetry](https://python-poetry.org/):
```
poetry build
pip install dist/gtfs2nx-*.whl
```

Configure post-checkout hook for branch specific .gitignore files:
```
git config --local core.hooksPath .githooks/
```
wafrun 0.0.1
A dumb package
Requires Python: >=2.3

# revacnlab

First go to src folder, and install

```
$ pip install wafrun
```

Once the package is installed, open python console by typing `python`

```
$ python
>>> from wafrun import make
>>> make(1)
```

The make function accepts values from 1 - 5.

The output will be in the same folder with file name as program.cc

> *NOTE* - Please uninstall once you use in the system `pip uninstall revacnlab`
wafrun-undopatika 0.0.1
A dumb package
Requires Python: >=2.3

# revacnlab

First go to src folder, and install

```
$ pip install revacnlab
```

Once the package is installed, open python console by typing `python`

```
$ python
>>> from revacnlab import make
>>> make(1)
```

The make function accepts values from 1 - 5.

The output will be in the same folder with file name as program.cc

> NOTE \to Please uninstall once you use in the system `pip uninstall revacnlab`
bayes-jones 0.0.1
Bayesian inference of Jones matrices.
Requires Python: >=3.8

[![Python](https://img.shields.io/pypi/pyversions/bayes_jones.svg)](https://badge.fury.io/py/bayes_jones)
[![PyPI](https://badge.fury.io/py/bayes_jones.svg)](https://badge.fury.io/py/bayes_jones)
[![Documentation Status](https://readthedocs.org/projects/bayes_jones/badge/?version=latest)](https://bayes_jones.readthedocs.io/en/latest/?badge=latest)

Main
Status: ![Workflow name](https://github.com/JoshuaAlbert/bayes_jones/actions/workflows/unittests.yml/badge.svg?branch=main)

Develop
Status: ![Workflow name](https://github.com/JoshuaAlbert/bayes_jones/actions/workflows/unittests.yml/badge.svg?branch=develop)

## Mission: _To make ionospheric calibration **faster, easier, and more powerful**_

# What is it?

Bayes is:

1) a set of tools for Bayesian inference of Jones matrices using JAXNS as the engine;
2) coded in JAX in a manner that allows lowering the entire inference algorithm to XLA primitives, which are
   JIT-compiled for high performance

# Documentation

You can read the documentation [here](https://bayes_jones.readthedocs.io/en/latest/#).

# Install

**Notes:**

1. BayesJones requires >= Python 3.8.
2. It is always highly recommended to use a unique virtual environment for each project.
   To use `miniconda`, have it installed, and run

```bash
# To create a new env, if necessary
conda create -n bayes_jones_py python=3.11
conda activate bayes_jones_py
```

## For end users

Install directly from PyPi,

```bash
pip install bayes_jones
```

## For development

Clone repo `git clone https://www.github.com/JoshuaAlbert/bayes_jones.git`, and install:

```bash
cd bayes_jones
pip install -r requirements.txt
pip install -r requirements-tests.txt
pip install -r requirements-examples.txt
pip install .
```


# Quick start

Checkout the examples [here](https://bayes_jones.readthedocs.io/en/latest/#).

# Change Log

14 Dec, 2023 -- BayesJones 0.0.1 released for SaLF 9 conference.

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