semvec/pypi-packages huggingface dataset:
skyworker-dev-petroleum-price-random 0.3.0
Skyworker AI Processor
Requires Python: >=3.6

# Skyworker Processor: {processor_name}

Template repository for the Skyworker AI Processor

## Requirements

- `Python >= 3.6`
- `pip`
- `venv (recommended)`

## Usage

- Clone this repository
- Update todos in [setup.py](setup.py) file
- Update todos in [skyworker_processor/\_\_init\_\_.py](skyworker_processor/__init__.py) file
- Define your logic in [skyworker_processor/core/processor.py](skyworker_processor/core/processor.py) module's `process` function

The return value of `skyworker_processor.process()` function should be the `pandas.DataFrame` object with following columns:

- `measurement` - Measurement Name
- `value` - Calculated Value

and the index of each row should be the unique timestamp.

### Example

```python
import pandas as pd
from datetime import datetime

time_series = [
    {
        "timestamp": 1628150400,
        "measurement": "Electricity Price",
        "value": 0.23,
    },
    {
        "timestamp": 1628236800,
        "measurement": "Electricity Price",
        "value": 0.25,
    }
]

data_frame = pd.DataFrame(time_series)
# Set "timestamp" column as index.
data_frame.index = data_frame["timestamp"]
# Compose "time" column from the "timestamp"
data_frame["time"] = data_frame["timestamp"].map(datetime.fromtimestamp)
del data_frame["timestamp"]
```

## Build & Publish

Build and Publish is a part of the CI/CD pipeline, but you can publish your package manually:

```shell
# Check the package.
$ python setup.py check

# Build the package.
$ python setup.py sdist

# Publish the package.
$ twine upload dist/*
```

## Changelog

Please see [CHANGELOG](CHANGELOG.md) for details.

## Authors

- [Temuri Takalandze](https://abgeo.dev) - *Author and Maintainer*

## License

Copyright © 2021 [Omedia](https://omedia.dev).  
Released under the [Other/Proprietary License](LICENSE).
skyworker-dev-electricity-price-random-other 0.3.0
Skyworker AI Processor
Requires Python: >=3.6

# Skyworker Processor: {processor_name}

Template repository for the Skyworker AI Processor

## Requirements

- `Python >= 3.6`
- `pip`
- `venv (recommended)`

## Usage

- Clone this repository
- Update todos in [setup.py](setup.py) file
- Update todos in [skyworker_processor/\_\_init\_\_.py](skyworker_processor/__init__.py) file
- Define your logic in [skyworker_processor/core/processor.py](skyworker_processor/core/processor.py) module's `process` function

The return value of `skyworker_processor.process()` function should be the `pandas.DataFrame` object with following columns:

- `measurement` - Measurement Name
- `value` - Calculated Value

and the index of each row should be the unique timestamp.

### Example

```python
import pandas as pd
from datetime import datetime

time_series = [
    {
        "timestamp": 1628150400,
        "measurement": "Electricity Price",
        "value": 0.23,
    },
    {
        "timestamp": 1628236800,
        "measurement": "Electricity Price",
        "value": 0.25,
    }
]

data_frame = pd.DataFrame(time_series)
# Set "timestamp" column as index.
data_frame.index = data_frame["timestamp"]
# Compose "time" column from the "timestamp"
data_frame["time"] = data_frame["timestamp"].map(datetime.fromtimestamp)
del data_frame["timestamp"]
```

## Build & Publish

Build and Publish is a part of the CI/CD pipeline, but you can publish your package manually:

```shell
# Check the package.
$ python setup.py check

# Build the package.
$ python setup.py sdist

# Publish the package.
$ twine upload dist/*
```

## Changelog

Please see [CHANGELOG](CHANGELOG.md) for details.

## Authors

- [Temuri Takalandze](https://abgeo.dev) - *Author and Maintainer*

## License

Copyright © 2021 [Omedia](https://omedia.dev).  
Released under the [Other/Proprietary License](LICENSE).
skyworker-dev-electricity-price-random 0.3.0
Skyworker AI Processor
Requires Python: >=3.6

# Skyworker Processor: {processor_name}

Template repository for the Skyworker AI Processor

## Requirements

- `Python >= 3.6`
- `pip`
- `venv (recommended)`

## Usage

- Clone this repository
- Update todos in [setup.py](setup.py) file
- Update todos in [skyworker_processor/\_\_init\_\_.py](skyworker_processor/__init__.py) file
- Define your logic in [skyworker_processor/core/processor.py](skyworker_processor/core/processor.py) module's `process` function

The return value of `skyworker_processor.process()` function should be the `pandas.DataFrame` object with following columns:

- `measurement` - Measurement Name
- `value` - Calculated Value

and the index of each row should be the unique timestamp.

### Example

```python
import pandas as pd
from datetime import datetime

time_series = [
    {
        "timestamp": 1628150400,
        "measurement": "Electricity Price",
        "value": 0.23,
    },
    {
        "timestamp": 1628236800,
        "measurement": "Electricity Price",
        "value": 0.25,
    }
]

data_frame = pd.DataFrame(time_series)
# Set "timestamp" column as index.
data_frame.index = data_frame["timestamp"]
# Compose "time" column from the "timestamp"
data_frame["time"] = data_frame["timestamp"].map(datetime.fromtimestamp)
del data_frame["timestamp"]
```

## Build & Publish

Build and Publish is a part of the CI/CD pipeline, but you can publish your package manually:

```shell
# Check the package.
$ python setup.py check

# Build the package.
$ python setup.py sdist

# Publish the package.
$ twine upload dist/*
```

## Changelog

Please see [CHANGELOG](CHANGELOG.md) for details.

## Authors

- [Temuri Takalandze](https://abgeo.dev) - *Author and Maintainer*

## License

Copyright © 2021 [Omedia](https://omedia.dev).  
Released under the [Other/Proprietary License](LICENSE).
txtimg 0.0.9
A library to easily create text-based images (e.g. images that primarily contain text).
Requires Python: >=3.6

# TxtImg

A library to easily create text-based images (e.g. images that primarily contain text).

This is just a thin layer on top of Pillow.

## Installation

This is compatible with Python 3.6 and above.

Run the following command to install:

```
$ pip install txtimg
```

## Example

```python
from txtimg import TxtImg

text = """
What did Sushi A say to Sushi B?

What's up B? (WASABI)
"""

t = TxtImg()
img = t.generate_from_text(text)
img.save("wasabi.png")
```

## Configuration

You can specify parameters to the `TxtImg` constructor:


* font - Font used for your text. This is a PIL ImageFont object
* base_img - You can specify a base image to use, rather than the default white background. This is a PIL Image object.
* horizontal_margin - Horizontal margin in pixels
* vertical_margin - Vertical margin in pixels
* chars_per_row - Number of characters per row
* txt_color - A tuple representing the RGB values, e.g. (255, 0, 0) for red.
pyExecutioner 0.0.15
A library to execute code
Requires Python: >=3.6

# EXecutioner

[![Codacy Badge](https://app.codacy.com/project/badge/Grade/1fab847da8434968bb3c7bdaeae8fcb1)](https://www.codacy.com/gh/iCodeDevs/EXecutioner?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=iCodeDevs/EXecutioner&amp;utm_campaign=Badge_Grade)
![Build](https://github.com/iCodeDevs/EXecutioner/actions/workflows/test.yml/badge.svg)
![Build Docs](https://github.com/iCodeDevs/EXecutioner/actions/workflows/doc.yml/badge.svg)
[![codecov](https://codecov.io/gh/iCodeDevs/EXecutioner/branch/master/graph/badge.svg?token=OTT5WHUS9K)](https://codecov.io/gh/iCodeDevs/EXecutioner)

A python library to execute untrusted code and evaluate the output.

## Requirements

- Python Libraries denoted by requirements.txt

- [FireJail](https://firejail.wordpress.com/) (for FireJail Sandbox)

  ```bash
  sudo apt install firejail
  ```

## Installation

- Install PyPI package

  ```bash
  pip install pyExecutioner
  ```

## Installation for development

- Clone the repository.

- make a virtual environment named 'venv' inside the repository. (Learn about [venv](https://docs.python.org/3/tutorial/venv.html))

  ```bash
  python -m venv venv
  ```

- access the virtualenv

  ```bash
  source venv/bin/activate
  ```

- install python dev dependencies

  ```bash
  pip install -r requirements.txt
  ```

- Run Test after installing the default supported language compilers/interpreters.

  ```bash
  pytest executioner/
  ```

## Default supported languages

- Python3

  ```bash
  sudo apt install python3
  ```

- C - uses GCC compiler

  ```bash
  sudo apt install gcc
  ```

## Documentation

Documentation can be found at <https://iCodeDevs.github.io/EXecutioner/>

## NOTE

- under heavy development, alot of changes might occur over time till we hit v1
oneNeuron-pypi-Gopichandyarra 0.0.2
Its an implimentation of Perceptron
Requires Python: >=3.7

# oneNeuron_pypi
oneNeuron_pypi

# Reference -
[official python docs](https://packaging.python.org/tutorials/packaging-projects/)

[github docs for github actions](https://docs.github.com/en/actions/guides/building-and-testing-python#publishing-to-package-registries)
keithferns98-OneNeuron 0.0.1
Perceptron package
Requires Python: >=3.6

# oneneuron_pypi
oneneuron_pypi

# Reference -
[official python docs]('https://packaging.python.org/tutorials/packaging-projects/)

[Github actions docs]('https://docs.github.com/en/actions/guides/building-and-testing-python#publishing-to-package-registries)
typegenie-test-version 0.1.5
Client Library for TypeGenie API. Check out https://api.typegenie.net for more info.

# TypeGenieAPIClient
Python API client to access TypeGenie API. 

# Documentation
Check out detailed API documentation at [https://api.typegenie.net](https://api.typegenie.net)

## Installation
Run the following to install:

`pip install typegenie`


## Using `typegenie-cli`
Once a model has been deployed, you can use `typegenie-cli` to test the model quality. 

```bash
typegenie-cli -dsi <DATASET-ID> -dpi <DEPLOYMENT-ID> -u <ACCOUNT-USER-ID> -p <PASSWORD> -ui <USER-ID> --multiline
```

where `<USER-ID>` is the id of the user that exists within the deployment `<DEPLOYMENT-ID>` The tool
 randomly samples dialogues from the given dataset, and shows messages with different colors.
 
Use `ctr+c` to start a new dialogue and `ctr+c` twice to exit.
hassleFreeAlgorithms 0.0.1
This is a package that is still under developement and it is highly recommended to only use this for some very long and advanced programs and you dont want to code all the algorithms from scratch.
Keywords: python,math,algorithms,prime-numbers,sequences

This package contains only 2 functions at the moment. They are "prime_number_detector" and "arithmetic_sequence_n". To use "prime_number_detector" call it like this 

prime_number_detector("m", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) 

In here, the m stands for "manual". This means you will have to give your prime numbers manually. And the list is the prime numbers list 


 Another way to call it is like this 

prime_number_detector("s", [0, 200]) 
 In here, "s" stands for series. If you give this, you just have to put 2 numbers in the second argument and it will find all the prime numbers between the first number you put and the second number you put (including the second number)


 The second function is "arithmetic_sequence_n". This program finds the nth term of the arithmetic sequence you provide. This also takes in 2 arguments. Here is an example: 

 arithmetic_sequence_n([1, 5, 9], 65) 

 The first argument is a list of numbers which make up an arithmetic sequence. Only 3 numbers are needed in the list. the second argument is the number in the sequence you want to find.


I hope this package helps you in coding not cheating in your math test. :)
EasyFileHandling 1.5.2
A simple and useful package for File Handling
Requires Python: >=3.6

### Package for File Handling

**[DOCUMENTATION]**(https://filehandling.netlify.app/docs.html)


**Getting started**
```py
from EasyFileHandling.main import FileHandler

x = FileHandler('test.py', 'py')

print(x.get_file_content()) # This will print file content.

```
**Whats so special about package**


*It lets to handle files easliy, whether is JSON, a image, text file and etc.*


**BETA**

*Howdy all, we have decided to add `AsyncFileHandler` for asynchronous python codes. Now you can do all those `append`, `write` and  `read` asynchronouly.* ***REMEMBER THIS IS A BETA***


*How do I use it?*


*Simply import this and get started.*


```py
from EasyFileHandling.beta.main import AsyncFileHandler
```


**Whats New?**

*Howdy, new version `1.4.0` just dropped. You all be thinking whats new `byte` and `json` support added. We have also added `type` hints. Let me show you a example how json handler works*


**ImageHandler**


```py
from EasyFileHandling.imagehandler import ImageHandler
x = ImageHandler('nice.jpg')
x.filter_image('blur') # it will make a imagehandlerimages folder with this file in it.
x.draw_text(100, 50, "Hello World",50, rgb=(0, 0, 0), font="arial.ttf") # rgb and font are default params.
```


*If you are having any issues let us know [Here](https://github.com/ProjectsWithPython/FileHandling/issues).*
jose5 0.4.0
JSON Composer
Requires Python: >=3.6

# jose5

JSON Composer

The fifth José in Aplazame

## Example

The following file, `dev.json` ...

```json
{
    "__extends": "./base.json",
    "__variables": {
        "ENVIRONMENT": "dev",
        "tfstate": {
            "with": {
                "stuff": 42
            }
        }
    },
    "environment": [
        {
            "name": "LOG_LEVEL",
            "value": "DEBUG"
        },
        {
            "name": "EXTRA_DEVELOPMENT_PARAM",
            "value": 20
        },
        {
            "name": "TFSTATE_STUFF",
            "value": "${tfstate.with.stuff}"
        }
    ],
    "secrets": [
        {
            "name": "SECRET_KEY",
            "valueFrom": "${PARAM_BASE_PATH}/SECRET_KEY"
        }
    ],
    "mergemap": {
        "b": "dev",
        "c": "dev"
    },
    "list": "${LIST_TEST}",
    "map": "${LIST_MAP}"
}
```

... with `base.json`

```json
{
    "__variables": {
        "PARAM_BASE_PATH": "/app/${ENVIRONMENT}",
        "LIST_TEST": [
            1,
            2,
            3
        ],
        "LIST_MAP": {
            "a": 44
        }
    },
    "environment": [
        {
            "name": "ENVIRONMENT",
            "value": "${ENVIRONMENT}"
        },
        {
            "name": "LOG_LEVEL",
            "value": "INFO"
        }
    ],
    "mergemap": {
        "a": "base",
        "b": "base"
    }
}
```

... produces this result

```json
{
  "environment": [
    {
      "name": "ENVIRONMENT",
      "value": "dev"
    },
    {
      "name": "LOG_LEVEL",
      "value": "DEBUG"
    },
    {
      "name": "EXTRA_DEVELOPMENT_PARAM",
      "value": 20
    },
    {
      "name": "TFSTATE_STUFF",
      "value": 42
    }
  ],
  "mergemap": {
    "a": "base",
    "b": "dev",
    "c": "dev"
  },
  "secrets": [
    {
      "name": "SECRET_KEY",
      "valueFrom": "/app/dev/SECRET_KEY"
    }
  ],
  "list": [
    1,
    2,
    3
  ],
  "map": {
    "a": 44
  }
}
```
aifrruflowmeter 1.0.1
Python Wrapper Implementation of AifrruFlowMeter Written in Java
Requires Python: >=2.7

## What is AifrruFlowMeter

The AifrruFlowMeter is an open source tool that generates Biflows from pcap files, and extracts features from these flows.  
  
AifrruFlowMeter is a network traffic flow generator available from here . It can be used to generate bidirectional flows, where the first packet determines the forward (source to destination) and backward (destination to source) directions, hence the statistical time-related features can be calculated separately in the forward and backward directions. Additional functionalities include, selecting features from the list of existing features, adding new features, and controlling the duration of flow timeout.  
  
NOTE: TCP flows are usually terminated upon connection teardown (by FIN packet) while UDP flows are terminated by a flow timeout.  
  
The flow timeout value can be assigned arbitrarily by the individual scheme e.g., 600 seconds for both TCP and UDP.
rqalpha-mod-log 0.1.3
RQAlpha Mod for logbook
Requires Python: >=3.6

# log Mod

RQAlpha log Mod，supporting logbook output

## Enable/Disable log Mod

```bash
# Disable log Mod
$ rqalpha mod disable log

# Enable log Mod
$ rqalpha mod enable log
```

## Mod Config

Configs of log Mod are as followed.

```python
{
    # Specify the log output file
    "log_file": "./log.txt",
    # Python open() method to read/write file
    "log_mode": "a",
}
```

## Extend command

With this mod enabled, you can use follow function:


- `rqalpha run` command can use `--log-file file_path` option to export the log to `file_path` location
- `rqalpha run` command can use `--log-mode log_mode` option，which is the 'mode' param in  python built-in `open()` method，to determine how to read/write the output log file