semvec/pypi-packages huggingface dataset:
IPweather 0.1.8
A package to get the current weather using a IP address or other data and to get the Ip address of the user.

<div align=center>
  
  # IPweather v0.1.6
  
  **A python package to get the Ip of the user and current weather with an IP or other information**
</div>

## +example:
![image](https://user-images.githubusercontent.com/81589649/178151640-464aa9a9-4993-4ca5-9805-3a2cfc7dd3a0.png)

## +Info and usage:
go to the wiki on the Github Page.

## +Install:
**manually**
- Download project
- cd in the project dir with the console
- run: `pip install . --upgrade`
- your done!

**install with pip**
- run: `pip install IPweather`
- your done!


### author:
@cookie0o on github
gotify-message 0.2.1
Python module to push messages to gotify server
Requires Python: >=3.6

# gotify_message

## Description

Python module to push messages to gotify server

Gotify is messaging service that can be installed in LAN network and used without Internet access.

[https://gotify.net/](https://gotify.net/)

## Example use

```python
>>> from gotify_message import GotifyNotification
>>> message=GotifyNotification("http://10.0.0.7:8090", "AiOLxxDxYOCc7bY", "test_title", "test_message")
>>> print(message.json)
{
    "url": "http://10.0.0.7:8090/message",
    "headers": {
        "X-Gotify-Key": "AiOLxxDxYOCc7bY",
        "Content-type": "application/json"
    },
    "payload": {
        "title": "test_title",
        "priority": 5,
        "message": "test_message",
        "extras": {
            "client::display": {
                "contentType": "text/plain"
            }
        }
    }
}
>>> message.send()
<Response [200]>
>>> message.send("test message")
<Response [200]>
>>> message.send("test message", "test_title")
<Response [200]>
>>> message.delivered
False
>>> message.send('test message')
<Response [200]>
>>> message.delivered
True
```
svhnl 0.0.5
SVHN dataset preprocessing and annotation file reading and converting python library
Requires Python: >=3.5, <4
Keywords: sample,setuptools,development

<h1 aling='center'>
    svhnL
</h1>
<p align='center'>
    SVHN dataset preprocessing and annotation file reading and converting python library
</p>

## Installation
> From PyPI : 
```
$ pip install svhnl
```

## Documentation
> From ReadtheDocs : [link](https://svhnl.readthedocs.io/en/latest/index.html)

## Functionalities

### _Dataset Download & extract_
To download the original SVHN dataset [train, test or extra] from their website and extract the downloaded .tar.gz file, use. \
Code Example : 
```
>>>> import svhnl
>>>> train_dt_filename = svhnl.download(extract=False)
'./data/train.tar.gz'
>>>> test_dt_folder_path = svhnl.download(dataset_type='test', save_path='../dataset/svhn', extract=True, force=False, del_zip=False)
'../dataset/svhn/test'
```

### _Convert Annotation file into JSON_
To read the .mat annotation file provided with the original svhn dataset and generate more flexible and light-weight .json annotation file, use. \
Code Example : 
```
import svhnl
svhnl.ann_to_json(file_path='./train/digitStruct.mat', save_path='./svhn_ann.json', bbox_type='normalize')
```

### _Convert Annotation file into csv_
To read the .mat annotation file provided with the original svhn dataset and generate more operatable and light-weight .csv annotation file, use. \
Code Example :
```
import svhnl
svhnl.ann_to_csv(file_path='./train/digitStruct.mat', save_path='./svhn_ann.csv', bbox_type='normalize')
```

### _Generate MDR dataset_
To easily use the SVHN dataset in any MDR task [defined number of digit recognition or without restrictions on object detection] with digit cropping, RGB to Gray-scale conversion, digit count limiting, etc.
Code Example :
```
import svhnl
image_np, ann_dict = svhnl.gen_dataset(image_path='../data/svhn/train', mat_path='../data/svhn/train/digitStruct.mat')
```
compflow 0.5.0
Fast compressible flow tables for aerodynamic calculations in Python.
Requires Python: >=3.7
Keywords: compressible,flow,aerodynamics,fluid,engineering

# compflow

The compflow library contains functions to convert back and forth between Mach
number and other non-dimensional groups in compressible flows. By using a
NumPy--Fortran interface, the code is vectorised and lightning-fast, yielding a
speed-up of up to two orders of magnitude.

[Full documentation](https://jamesbrind.uk/compflow-docs/) is available online. 

![Compressible flow quantities](sample/sample.png)

## Features

* Evaluation of ten non-dimensional flow quantities as explicit functions of
  Mach number;
* Iteration with Newton's method to invert explicit relations and solve for
  Mach number;
* Creation and caching of lookup tables to speed up inversions;
* Fortran-accelerated, fully-vectorised in both directions.

## Basic usage
 
compflow is available on the Python Package Index, so installation is as simple as,

```python

   python3 -m pip install compflow

   ```
**Note:** as the library uses Fortran subroutines behind the scenes, you will
need a working Fortran compiler for the installation to complete successfully. 

We can now start doing some calculations. First, an explicit evaluation of
stagnation pressure ratio given a Mach number,

```python

   >>> import compflow
   >>> ga = 1.4
   >>> compflow.Po_P_from_Ma(0.3, ga)
   1.0644302861529382

```

Second, an inversion of flow function where
iterative solution for Mach number is required,

```python

   >>> compflow.Ma_from_mcpTo_APo(0.8, ga)
   0.39659360325173604

```

The names and symbols of non-dimensional quantities are fairly
self-explanatory, but a full list is given in the
[Nomenclature](https://jamesbrind.uk/compflow-docs/api.html#nomenclature).
All functions and the equations used for the calculations are documented in the
[API](https://jamesbrind.uk/compflow-docs/api.html).

Numpy arrays are also accepted as inputs,

```python

   >>> import numpy
   >>> Ma1 = numpy.array([0., 0.5, 1., 2.])
   >>> compflow.To_T_from_Ma(Ma1, ga)
   array([1.  , 1.05, 1.2 , 1.8 ])
   >>> Ma2 = numpy.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])
   >>> compflow.To_T_from_Ma(Ma2, ga)
   array([[1.002, 1.008],
          [1.018, 1.032],
          [1.05 , 1.072]])
```

When solving for Mach number at a given normalised mass flow, it is assumed
that we are on the subsonic branch of the curve unless a flag is specified.
Where no solution is possible, i.e. if the flow would choke, `NaN` is
returned,

```python

   >>> capacity = [0.6, 2.]
   >>> compflow.Ma_from_mcpTo_APo(capacity, ga)
   array([0.28442265,        nan])
   >>> compflow.Ma_from_mcpTo_APo(capacity, ga, sup=True)
   array([2.27028708,        nan])
```

## TODO

* Sort out packaging so that NumPy gets installed automatically (distutils due to be deprecated?).

James Brind
Mar 2022
os-env-bob-ross 0.4
a small dot env package
Requires Python: >=3.2
Keywords: os env .env os-env os_env dotenv



## Project
Easy access to the all the global envions like .env and os env

## Installing

```bash
    pip install os-env
```

Usage
=====

```py

    >>> import os-env
    >>> os-envs()
    '0.24.2'
```
spvideoutils 0.0.3
Scripts for saving and splitting video files for other projects

# Overview
Utilities to handle video from camera. Contains various utilites that crop and split the video files.

# Installation
In your virtual env do
```
$ pip install spvideoutils
```

# Scripts
The following scripts are provided by the package.
* video_capture.py
  * Captures a video from a camera to mp4 file on disk.
* video_split.py
  * Splits a mp4 file into frames stored as png file on output dir.
* video_merge.py
  * Merges the frames in png file from dir to a video file.
* video_crop.py
  * Crops a video file from frame number i to j.

# Developing VideoUtils
This involves 
* Creating a python virtual env.
* Downloading the source code from [GitHub](https://github.com/sanjeevs/spvideoutils)
* Installing all the dependencies
* Installing the package in dev mode.

For example these are steps I typically do on my windows machine.
```commandline
python -m venv venv_dev_spvideoutils  
env_spvideoutils\Scripts\activate
git clone https://github.com/sanjeevs/spvideoutils.git
cd spvideoutils
pip install -r requirements.txt
pip install -e . 
```
I can check that the env is correctly setup by invoking any of the scripts above from the same window.
```commandline
>video_capture
usage: video_capture [-h] [--index INDEX] [--time TIME] outfile
video_capture: error: the following arguments are required: outfile

```

# Creating a distribution package
Check the resource [packaging.python.org](http://packaging.python.org) on  how to buiild the
distribution.
* Bump the version of the software in setup.py
* Run the script to create the distribution in a new folder called dist.
```commandline
python setup.py bist_wheel
```
This is done by publishing the changes to pypi. It involves the following steps.
* This is a pure python project and so a wheel distribution is required. Source distribtution
  (sdist) is for packages with C extensions.
```commandline

```
* Use twine to upload the source distribution.
```commandline
twine upload dist/*
```