semvec/pypi-packages huggingface dataset:
image-processing-sc 0.0.1
Image Processing Program
Requires Python: >=3.5

# package_name

Description. 
The package package_name is used to:
	- 
	-

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing_sc
```



## Author
SauloCarvalho

## License
[MIT](https://choosealicense.com/licenses/mit/)
gdbundle-pycortexmdebug 0.0.7
gdbundle wrapper for PyCortexMDebug
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*

[![GitHub](https://img.shields.io/badge/GitHub-memfault/gdbundle--pycortexmdebug-8da0cb?style=for-the-badge&logo=github)](https://github.com/memfault/gdbundle-PyCortexMDebug)
[![PyPI
version](https://img.shields.io/pypi/v/gdbundle-pycortexmdebug.svg?style=for-the-badge)](https://pypi.org/project/gdbundle-pycortexmdebug/)
[![PyPI
pyversions](https://img.shields.io/pypi/pyversions/gdbundle-pycortexmdebug.svg?style=for-the-badge)](https://pypi.python.org/pypi/gdbundle-pycortexmdebug/)

# gdbundle-PyCortexMDebug

This is a [gdbundle](https://github.com/memfault/gdbundle) plugin for
[bnahill/PyCortexMDebug](https://github.com/bnahill/PyCortexMDebug)

The original `PyCortexMDebug` plugin is embedded as a git submodule, rather than
specifying as a typical dependency, because it is not as of yet deployed to
PyPi, and so this package cannot depend on it via `git+` dependency spec.

## Compatibility

- GDB

## Installation

After setting up [gdbundle](https://github.com/memfault/gdbundle), install the
package from PyPi.

```bash
$ pip install gdbundle-PyCortexMDebug
```

If you've decided to manually manage your packages using the
`gdbundle(include=[])` argument, add it to the list of plugins.

```bash
# .gdbinit

[...]
import gdbundle
plugins = ["PyCortexMDebug"]
gdbundle.init(include=plugins)
```

## Building

Be sure to `git submodule update --init` to get the `cmdebug` dependency.

```bash
$ poetry build
$ poetry publish
```
dev-test-mult4 1.0.0
A test Python package
Requires Python: >=3.6

# `dev_test_mult4`

This is a simple package to test pypi upload.


### Usage:
```
from multiply4 import mult_by_four

mult_by_four(17)
```

MIT License

Copyright (c) 2023 Devharsh Trivedi

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
commonfate-provider 0.9.2
Python Provider Development kit
Requires Python: >=3.9,<4.0

# Provider Development Kit

Common Fate Provider Development Kit for Python.

## What is a provider?

A Provider is a Python service which provides a consistent API for managing fine-grain permissions.

Managing permissions in cloud providers, SaaS applications, and CI/CD platforms usually requires access to highly sensitive secrets, like administrative API tokens. The Provider framework allows for access to be granted and revoked to these platforms without requiring direct access to these tokens:

![diagram of Provider framework](./docs/provider.drawio.svg)

## What does the Provider Development Kit do?

The Provider Development Kit (PDK) makes it easy to develop and deploy Providers.

```python
class Provider(provider.Provider):
    api_url = provider.String()

@access.target()
class Target:
    ...

@access.grant()
def grant(p: Provider, subject: str, target: Target):
    # perform API calls here to grant access
    ...

@access.revoke()
def revoke(p: Provider, subject: str, target: Target):
    # perform API calls here to revoke access
    ...
```

The PDK handles configuration and packaging into a cloud-native function which can be executed by an application.

## Supported runtimes

Currently the supported runtimes for Providers are as follows:

- AWS Lambda

## Provider Schemas

Each Provider has a strongly-typed schema. An example schema is shown below:

```json
{
  "audit": {
    "resourceLoaders": {},
    "resources": {}
  },
  "config": {},
  "target": {
    "MyTarget": {
      "schema": {
        "first": {
          "description": "first var",
          "id": "first",
          "resourceName": null,
          "title": "First",
          "type": "string"
        }
      }
    }
  }
}
```

The schema is based on [JSON Schema](https://json-schema.org/) and allows applications using Providers to interpret the available resources and display the appropriate UI.
BiasBuster 0.0.4
A Python package to check for algorithmic bias.
Requires Python: >=3.6

This package provides functions to test one's ML models bias.

# BiasBuster package
The metrics are calculated by a slicing analysis. Four fairness metrics are supported: ABROCA, Equal Opportunity, Predictive Equality and Predictive Parity.

ABROCA: met, if both groups have equal Absolute Between ROC Area
Equal Opportunity: met, if both groups have equal FNR
Predictive Equality: met, if both groups have equal FPR
Predictive Parity: met, if both groups have the same PPV

# Installation
This package can be installed using pip.
An installation with pip automatically downloads the latest version from PyPI:
```
pip install BiasBuster
```

# Example & Usage

Import the metric you'd like to use.

```python
from BiasBuster import abroca
from BiasBuster import predictive_equality
from BiasBuster import predictive_parity
from BiasBuster import equal_opportunity

df = load_your_dataset

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
clf = DecisionTreeClassifier(criterion="entropy", splitter="best")
clf = clf.fit(X_train, y_train)

X_test_advantaged = X_test[X_test['group'] == 1]
y_test_advantaged = y_test[y_test['group'] == 1]

X_test_disadv = X_test[X_test['group'] == 0]
y_test_disadv =y_test[y_test['group'] == 0]


abroca = abroca.calculate_abroca(clf,X_test_advantaged,y_test_advantaged, X_test_disadv, y_test_disadv)
pe = predictive_equality.predictive_equality(clf,X_test_advantaged,y_test_advantaged, X_test_disadv, y_test_disadv)
pp = predictive_parity.calculate_predictive_parity(clf,X_test_advantaged,y_test_advantaged, X_test_disadv, y_test_disadv)
eo =equal_opportunity.calculate_equal_opportunity(clf,X_test_advantaged,y_test_advantaged, X_test_disadv, y_test_disadv)

print(abroca)

-0.005211848509902417

```

# Interpretation

The threshold to delineate biased from unbiased depends on the underlying data.
However, typical thresholds lie between 0.01 and 0.05.
typernexrad-cli 0.1.0
CLI which helps to access several functionalities of NEXRAD

# Typer NEXRAD


A Python CLI Application to access the funcionalities of NEXRAD

Created five user functionalities in Typer 

1. createuser - Creates user with their username, password, and their tiers and uploads them to database<br>
  `typer_t7 createuser <username> <passwod>`

2. download - Download files from users public s3 bucket by specifying the file name <br>
   `typer_t7 download <username> <password> <bucketname> <filename>`

3. fetch - Lists all files in the public S3 bucket <br>
   `typer_t7 fetch <username> <password> <bucketname>`

4. fetchnexrad - Generates NOAA public S3 link and Users S3 link based on the parameters year, day, hour, station, and filename <br>
   `typer_app fetchnexrad <username> <password>`

5. fetchnexrad_filename - Generates link based on NOAA Nexrad S3 bucket based on the filename given by the user <br>
   `typer_app  fetchnexrad_filename <username> <password>`
typernexrad-t7 0.1.0
CLI which helps to access several functionalities of NEXRAD

# Typer NEXRAD


A Python CLI Application to access the funcionalities of NEXRAD

Created five user functionalities in Typer 

1. createuser - Creates user with their username, password, and their tiers and uploads them to database<br>
  `typer_t7 createuser <username> <passwod>`

2. download - Download files from users public s3 bucket by specifying the file name <br>
   `typer_t7 download <username> <password> <bucketname> <filename>`

3. fetch - Lists all files in the public S3 bucket <br>
   `typer_t7 fetch <username> <password> <bucketname>`

4. fetchnexrad - Generates NOAA public S3 link and Users S3 link based on the parameters year, day, hour, station, and filename <br>
   `typer_app fetchnexrad <username> <password>`

5. fetchnexrad_filename - Generates link based on NOAA Nexrad S3 bucket based on the filename given by the user <br>
   `typer_app  fetchnexrad_filename <username> <password>`
enumy 1.0.2
Module to set predefined allowed values for a variable
Requires Python: >=3.7

# Enumy
The problem with Python's "Enum" module is that it is not a reasonable way to restrict variables to a predefined set of values.

Latest version: `v1.0.2`

## Installation
```shell
pip install --user enumy
```

## Special methods
### `check_type`
Check if a variable matches a certain data type.

```python
var.check_type(str)
```

### `check_value`
Check if a value is present in the allowed values.

```python
text = "ByteSentinel"
var.check_value(text)
```

## Example
```python
from enumy import Enumy

test = Enumy(("Value 1", "Value 2"), str)
test = "Value 2"        # Working
test = 123              # Exception
```