semvec/pypi-packages huggingface dataset:
easy-geoparsing 1.2.3
Easy-to-use module for streamlined parsing of countries from locations

easy-geoparsing
---

Easy-to-use module for streamlined parsing of countries from plaintext locations and top-level domains, plus manipulation of country names and ISO 2 & 3 character codes.

Implementation relies on:

- the [RESTcountries](https://restcountries.eu/) API
- the [geotext](https://pypi.org/project/geotext/) module

## installation

To install from the command line via [pip](https://pip.pypa.io/en/stable/), do:

`pip install easy-geoparsing`

To upgrade to the latest version via `pip` do:

`pip install easy-geoparsing --upgrade`

To use via [pipenv](https://docs.pipenv.org/en/latest/) put the following in your Pipfile:

```
[packages]
easy-geoparsing = ">=1.0.0"
```

## development

If you've cloned the repository, the best way to make it work is using `pipenv`

If you don't yet have `pipenv`, you can use `pip` to install it from the command line:

`pip install pipenv --upgrade`

Then, in the top level directory of this repository, `easy-geoparsing`, do:

`pipenv install --dev`

This will create the virtual environment and install the requirements (viewable in the Pipfile). The `--dev` flag will install packages needed for testing etc.

## usage

### GETTING STARTED

Do the following to get the parser utilities, noting that creating an instance of `EasyCountryParser` will automatically download the country data payload from RESTcountries and set up all the resources. Speed will therefore depend on your internet connection, but the payload is not large.

```
from easy_geoparsing import EasyCountryParser

ez_parser = EasyCountryParser()
```

or, if you don't want to use our alternative names for some of the countries (i.e. you want to exactly follow the RESTcountries standard)

`ez_parser = EasyCountryParser(altnames=False)`

The `EasyCountryParser` class provides utilities, based on the data from the RESTcountries API and the GeoText natural-language parser library, for easily extracting and handling country names and codes.

### PARSER RESOURCES

The parser is initialised with the following resources:

  - `.data`       - pandas DataFrame containing RESTcountries data
  - `.tld_to_a2c` - python dict, maps TLDs to 2-character ISO codes
  - `.tld_to_a3c` - python dict, maps TLDs to 3-character ISO codes
  - `.iso2to3`    - python dict, maps 2-character ISO codes to 3
  - `.iso3to2`    - python dict, maps 3-character ISO codes to 2
  - `.a2c_map`    - python dict, maps 2-char ISO codes to full names
  - `.a3c_map`    - python dict, maps 3-char ISO codes to full names

### PARSER METHODS

The parser has the following methods for handling locations data:

  - `.retrieve_country` - parses plaintext for extractable 2-character ISO codes for countries (which can then be manipulated using the mappers above)
pyPharGram 1.2
A telegram-bot-api library for python
Requires Python: >=3.6, <4
Keywords: telegram,bot,api,phargram,PharGram,pyPharGram

# PharGram
```python
from pyPharGram.main import Client
Client = Client("token")
Client.sendMessage({'chat_id':777000,'text':'<b>hello telegram</b>','parse_mode':'html'})
```
**coming soon ...!**
ultra-rl 0.2.1a0
Unprotected Left Turn for Robust Agents
Requires Python: >=3.7

# ULTRA

## Unprotected Left Turn for Robust Agents

ULTRA provides a gym-based environment using SMARTS to tackle intersection navigation, specifically the unprotected left turn.

Key features:
 - Customizable scenarios with different levels of difficulty.
 - Analysis tools to evaluate traffic designs including low-mid-high densities.
 - Tools to support analyzing social-vehicle behaviors.
 - Configurable train and test parameters.
 - Benchmark results to train and test custom RL algorithms against.

<p align="center">
  <img src="docs/_static/ultra_envision.gif" alt="ULTRA demo GIF">
</p>

## Index

Get started by choosing an option below.
- [Flowchart of ULTRA's interface with SMARTS](docs/_static/ULTRA-flowchart-v1.jpg)
- [Setting up ULTRA](docs/setup.md)
- [Train and Evaluate a Baseline Agent](docs/getting_started.md)
- [Create a Custom Agent](docs/custom_agent.md)
- [More about ULTRA Agents](docs/agent.md)
- [ULTRA supports RLlib](docs/rllib.md)
- [Multi-agent Experiments in ULTRA](docs/multiagent.md)

### Citing ULTRA
For a longer introduction to ULTRA, including its purpose, concepts, and benchmarks, please see [ULTRA: A reinforcement learning generalization benchmark for autonomous driving](https://ml4ad.github.io/files/papers2020/ULTRA:%20A%20reinforcement%20learning%20generalization%20benchmark%20for%20autonomous%20driving.pdf).

If you use ULTRA in your research, you can use the following citation.
```
@misc{elsayed20_ultra,
  author          = {Elsayed, Mohamed and Hassanzadeh, Kimia and Nguyen, Nhat M.
                  and Alban, Montgomery and Zhu, Xiru and Graves, Daniel and
                  Luo, Jun},
  journal         = {Machine Learning for Autonomous Driving Workshop, Neural
                  Information Processing Systems},
  title           = {ULTRA: A reinforcement learning generalization benchmark
                  for autonomous driving},
  url             = {https://ml4ad.github.io/files/papers2020/ULTRA: A
                  reinforcement learning generalization benchmark for autonomous
                  driving.pdf},
  year            = 2020,
}
```