semvec/pypi-packages huggingface dataset:
agps3 0.36.1
Python client for gpsd
Requires Python: >=3.9

#### README #####
This package is based on a fork of gps3 (https://github.com/wadda/gps3) which hasn't been updated in a while.  
It includes some bug fixes for GST class handling.
Only agps versions exist in this package.

To use:
>     from agps3threaded import AGPS3mechanism

Then engage the thread triumvirate,

>     agps_thread = AGPS3mechanism()  # This instantiate the mechanism, as I believe it's called.
>     agps_thread.stream_data()    #  Stream the data from host, port, devicepath.
>     agps_thread.run_thread()  #  Iterate stream as a thread with throttle control for empty look ups.


Four lines of code that lets you connect, communicate and control most of what you expect a gpsd to do.

>     while True:  # All data is available via instantiated thread data_stream attributes. Confur
                 # lines #140-ff of the client /usr/local/lib/python3.5/dist-packages/gps3/agps.py
          print('----------------')
          print(                   agps_thread.data_stream.time)
          print('Lat:{}   '.format(agps_thread.data_stream.lat))
          print('Lon:{}   '.format(agps_thread.data_stream.lon))
          print('Speed:{} '.format(agps_thread.data_stream.speed))
          print('Course:{}'.format(agps_thread.data_stream.track))
          print('----------------')
          sleep(60)  # Sleep, or do other things for as long as you like.

Without arguments between the parentheses, the threaded client defaults to `host='127.0.01'`, `port=2947`, `gpsd_protocol='json'`, and `usnap=0.2`, for a respectable default of 2/10th of a second micro nap after each empty socket lookup.

The rest of the project is in `DESCRIPTION.rst`, or documented in the files themselves.
project-ml-AB3NzaC1yc-hatch 1.0.1
Machine learning applications for dadi
Requires Python: >=3.7

# Machine Learning Applications for Diffusion Approximation of Demographic Inference

## Introduction
Diffusion Approximation of Demographic Inference ([dadi](https://dadi.readthedocs.io/en/latest/)) is a powerful software tool for simulating the joint frequency spectrum (FS) of genetic variation among multiple populations and employing the FS for population-genetic inference. Here we introduce machine learning-based tools for easier application of dadi's underlying demographic models. These machine learning models were trained on dadi-simulated data and can be used to make quick predictions on dadi demographic model parameters given FS input data from user and specified demographic model. The pipeline we used to train the machine learning models are also available here for users interested in using the same framework to train a new predictor for their customized demographic models.

## Installation
### Get the dadi-ml repo
Clone this repo to your local directory and `cd` into the `dadi-ml` dir
```console
$ git clone https://github.com/lntran26/dadi-ml.git
$ cd dadi-ml/
```

### Set up your python environment to run the dadi-ml pipeline
We recommend you start by creating a new `conda` environment. This can be done using the command below, which will create a new `conda` env called `dadi-ml` and installed the required packages to this env. The env can then be activated for each subsequent use.

```console
$ conda env create -f environment.yml
$ conda activate dadi-ml
```

## Requirements
1. Python 3.9+
2. [dadi](https://dadi.readthedocs.io/en/latest/)
3. [scikit-learn](https://scikit-learn.org/1.0/) 1.0.2
4. [MAPIE](https://mapie.readthedocs.io/en/latest/) 0.3.1


## References
1. [Gutenkunst et al., *PLoS Genet*, 2009.](https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000695)