semvec/pypi-packages huggingface dataset:
pydublinbus 0.0.4
Python library to get the real-time transport information (RTPI) for Dublin Bus
License: MIT
Requires Python: >=3.6
Keywords: dublin bus RTPI

# PyDublinBus

Python Interface for the Dublin Bus RTPI API.

## Example usage

#### Time table:

```
>>> from pydublinbus import DublinBusRTPI
>>> mybus=DublinBusRTPI(stopid='312')
>>> mybus.bus_timetable()
[{'due_in': '6', 'route': '26'},
 {'due_in': '8', 'route': '25B'},
 {'due_in': '15', 'route': '67'},
 {'due_in': '22', 'route': '66B'},
 {'due_in': '23', 'route': '25A'},
 {'due_in': '31', 'route': '66A'},
 {'due_in': '38', 'route': '25B'},
 {'due_in': '39', 'route': '66'},
 {'due_in': '41', 'route': '26'},
 {'due_in': '41', 'route': '25'},
 {'due_in': '45', 'route': '67'},
 {'due_in': '46', 'route': '25A'}]
```

#### Time table filtered by route

```
>>> from pydublinbus import DublinBusRTPI
>>> mybus=DublinBusRTPI(stopid='312', route='25A')
>>> from pprint import pprint
>>> mybus.bus_timetable()
[{'due_in': '20', 'route': '25A'},
 {'due_in': '43', 'route': '25A'}]

```

#### Raw json data:

```
>>> from pydublinbus import DublinBusRTPI
>>> mybus=DublinBusRTPI(stopid='312')
>>> mybus.get_rtpi_data()
```
docoseco 1.0.0
Automatize management of docker confgs and secrets 
License: MIT
Requires Python: >=3.8,<4.0

# Docoseco

**Do**cker-**co**mpose **se**crets and **co**nfigs.

Automatize management of docker confgs and secrets.

## Usage

```
docoseco [CONFIG_ROOT_DIR] < docker-compose.template.yaml > docker-compose.yaml

  CONFIG_ROOT_DIR  Root directory for file search (default: .)
```

It reads docker-compose yaml from stdin, updates all config and secret names with corresponding file content hashsums and writes result to stdout.

## Rationale

Docker-compose configs and secrets are immutable by design. So, when config or secret is created from file via
`docker stack deploy`, it's impossible to update the file and deploy in the same way again. For example:

```yaml
# docker-compose.yaml
version: "3.8"
services:
  redis:
    image: redis:latest
    configs:
      - source: my_config
        target: /redis_config
configs:
  my_config:
    file: ./my_config.txt
```

If, after the initial deployment, `my_config.txt` is changed, the next deployment attempt will fail.

The common workaround is creating a new config, when a source file changes.
This is done by changing config name:

```yaml
# docker-compose.yaml
...
configs:
  my_config:
    name: my_config-2     # Changing name creates new docker config
    file: ./my_config.txt # This file was changed
```

To avoid manual management of config names, numerical suffix might be replaced by a file content hashsum, which can be automatically calculated.

```yaml
# docker-compose.yaml
...
configs:
  my_config:
    name: my_config-bee414b86ee02806b17104813d44eea4 # Auto-generated config name
    file: ./my_config.txt # This file was changed
```
cdxGenerator 0.0.1
cdxjGenerator
License: MIT
Keywords: web archives replay cdxj memento

# CDXJ Generator

A rudimentary Python 3 script to generate CDXJ TimeMaps for testing elsewhere. 

## Usage

To run, call the script:

    python cdxjGenerator.py [number of lines] [URI-R]
    
For example:

    python cdxjGenerator.py 12
    
...will generate CDXJ output (to stdout by default) consisting of entries for 12 random URIs. Alternatively:

    python cdxjGenerator.py 25000 memento.us
    
...will generate 25,000 entries for the URI-R `memento.us`. This output can be written to a file like:

    python cdxjGenerator.py 25000 memento.us > sample.cdxj

The resulting file will likely need to be sorted before used elsewhere. Do this via:

    LC_ALL=C sort sample.cdxj > sample_sorted.cdxj

This can also be performed in a single command, instead of writing to the temporary, unsorted `sample.cdxj` like:

    python cdxjGenerator.py 25000 memento.us | LC_ALL=C sort > sample_sorted.cdxj

## Background
TimeMaps are lists that enumerate URIs of resources that encapsulate prior states of the given resource. ([RFC7089 - Memento](https://tools.ietf.org/html/rfc7089)). TimeMaps are often expressed in an extension of the Web Linking ([RFC5988](https://tools.ietf.org/html/rfc5988)) format. Additional, less common formats, like JSON and CDXJ TimeMaps can also express the same information in a less rigid format. [CDXJ](https://github.com/oduwsdl/ORS/wiki/CDXJ) is the most flexible of the three and is used by [InterPlanetary Wayback (ipwb)](https://github.com/oduwsdl/ipwb), which sparked the initial need for this software existing.

## A Note
This tool uses the Faker module to generate valid dates. Some Python environments don't like this. Generate a fresh one using `python3 -m venv test_venv` rather than the typical syntax to remedy the issue.
hfhd 0.1.4
hfhd is an accelerated Python library for estimating (integrated) covariance matrices with high frequency data in high dimensions.
License: MIT
Requires Python: >=3.6.0

![logo](https://github.com/jpwoeltjen/hfhd/blob/master/docs/img/logo_full.png)
[![Documentation Status](https://readthedocs.org/projects/hfhd/badge/?version=latest)](https://hfhd.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/hfhd.svg)](https://badge.fury.io/py/hfhd)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)


hfhd is an accelerated Python library for estimating (integrated) covariance matrices with high frequency data in high dimensions. Its main focus lies on the estimation of covariance matrices of financial returns. This is a challenging task since high frequency data are observed irregularly (and non-synchronously across assets) and are contaminated with microstructure noise. The hfhd.hf module provides a collection of tools for synchronization and noise reduction.

When many assets are considered relative to the sample size, we say that the covariance matrix has high dimension. Then, sample eigenvalues are overdispersed relative to the population eigenvalues due to the curse of dimensionality. This overdispersion leads to an ill-conditioned covariance matrix estimate. The hfhd.hd module provides tools to improve the condition of the matrix. 

[Read the documentation.](https://hfhd.readthedocs.io/en/latest/)

# Install hfhd
```bash
$ pip install hfhd
```

# Developing hfhd
To install hfhd, along with the tools to develop and run tests, run the following command in your virtualenv after cloning the repo:

```bash
$ pip install -e .[dev]
```
http-constants 0.5.0
The set of standard http constants for headers and statuses
License: MIT
Requires Python: >=3.6.0

# Http Constants

A set of constants for HTTP statuses and headers.

## Installation

You can install Http Constants from [PyPI](https://pypi.org/project/http-constants/):

    pip install http-constants

The library is supported on Python 3.7+.

## Usage

### Headers

```py
from http_constants.headers import HttpHeaders

>>> HttpHeaders.ACCEPT
'Accept'

>>> HttpHeaders.CONTENT_TYPE_VALUES.json
'application/json'
```

### Statuses

```py
from http_constants.status import HttpStatus

>>> HttpStatus.SERVICE_UNAVAILABLE
<HttpStatus.SERVICE_UNAVAILABLE: (503, 'Service Unavailable')>

>>> HttpStatus.SERVICE_UNAVAILABLE.value
503

>>> int(HttpStatus.SERVICE_UNAVAILABLE)
503

>>> HttpStatus.SERVICE_UNAVAILABLE.reason_phrase
'Service Unavailable'

>>> str(HttpStatus.SERVICE_UNAVAILABLE)
'503 SERVICE_UNAVAILABLE'
```