semvec/pypi-packages huggingface dataset:
dna-audit-new4 0.1
Data And Analytics Audit Tool
License: MIT
Requires Python: >=3.8

# **DNA AUDIT TOOL** #

DNA Audit tool helps you to check whether the application built on AWS is following the AWS Well-Architected framework by validating it against
various AWS services security requirements and the best practices. Generates report to validate the security requirements and security best practices.

# **AWS Hardening Standards & Best Practices** #

https://codaglobal.atlassian.net/l/cp/UP1BxJTr

# **AWS Tagging Standards** #

https://codaglobal.atlassian.net/l/cp/oj0Ngsdb

# **AWS Naming Standards** #

https://codaglobal.atlassian.net/l/cp/s99nYLEC

# **Audit tool setup** #

**The project basically uses boto client to connect to the aws services.**

1. Update the session credentials in the terminal.

2. Specify the region name if you need to perform the audit for services in a specific region by mentioning the region name as argument while running the program.

3. If no region is specified us-east-1 is taken as default region and audit checks are performed for the services in us-east-1.

4. If you need to check tags. Update the Config.Json file by mentioning the tags in the account tags provided.


# **Audit Tool Execution** #

Creating Virtual Environment
```
python3 -m venv venv
```

Activating the virtual environment
```
source venv/bin/activate
```

Installing the dependencies in the virtual environment
```
pip install -r requirements.txt
```
Run the following command
```
python3 src/main.py --region {region_name}
```

# **Test Cases Execution** #

```
nosetests
```

# **Final Report** #

The overall report is provided as a HTML document.

## sample report ##
![Scheme](assets/sample_report_base.png)
![Scheme](assets/sample_report.png)
vizmetrics 0.0.1
A package to plot AI/ML metrics at ease
License: MIT
Requires Python: >=3.7

# vizmetrics
## VizMetrics Library for AI Models
This is a library that provides tools to calculate various metrics for AI models, including accuracy, precision, recall, F1 score, ROC curve, and confusion matrix. It also includes functions to plot these metrics as graphs and figures.

## Features
Calculates common metrics for AI models
Plots ROC curves and confusion matrices
Provides easy-to-use functions to display metrics and plots

## Installation
To install the library, use pip:
```
pip install vizmetrics
```

## Usage
Here is an example of how to use the library to calculate and plot metrics for a binary classification model:

python
```
import vizmetrics

# Generate some example data
y_true = [0, 1, 0, 1, 0, 0, 1, 1]
y_pred = [0, 1, 0, 0, 1, 0, 1, 1]

# Example
vizmetrics.plot_confusion_matrix(y_true, y_pred)
vizmetrics.accuracy_confusion_matrix_precision_recall_multiclass(y_true, y_pred)
sens_multicalss = vizmetrics.sensitivity_multiclass(y_true, y_pred)


## License
This library is licensed under the MIT License.
dna-audit-new3 0.1
Data And Analytics Audit Tool
License: MIT
Requires Python: >=3.8

# **DNA AUDIT TOOL** #

DNA Audit tool helps you to check whether the application built on AWS is following the AWS Well-Architected framework by validating it against
various AWS services security requirements and the best practices. Generates report to validate the security requirements and security best practices.

# **AWS Hardening Standards & Best Practices** #

https://codaglobal.atlassian.net/l/cp/UP1BxJTr

# **AWS Tagging Standards** #

https://codaglobal.atlassian.net/l/cp/oj0Ngsdb

# **AWS Naming Standards** #

https://codaglobal.atlassian.net/l/cp/s99nYLEC

# **Audit tool setup** #

**The project basically uses boto client to connect to the aws services.**

1. Update the session credentials in the terminal.

2. Specify the region name if you need to perform the audit for services in a specific region by mentioning the region name as argument while running the program.

3. If no region is specified us-east-1 is taken as default region and audit checks are performed for the services in us-east-1.

4. If you need to check tags. Update the Config.Json file by mentioning the tags in the account tags provided.


# **Audit Tool Execution** #

Creating Virtual Environment
```
python3 -m venv venv
```

Activating the virtual environment
```
source venv/bin/activate
```

Installing the dependencies in the virtual environment
```
pip install -r requirements.txt
```
Run the following command
```
python3 src/main.py --region {region_name}
```

# **Test Cases Execution** #

```
nosetests
```

# **Final Report** #

The overall report is provided as a HTML document.

## sample report ##
![Scheme](assets/sample_report_base.png)
![Scheme](assets/sample_report.png)
dna-audit-new2 0.1
Data And Analytics Audit Tool
License: MIT
Requires Python: >=3.8

# **DNA AUDIT TOOL** #

DNA Audit tool helps you to check whether the application built on AWS is following the AWS Well-Architected framework by validating it against
various AWS services security requirements and the best practices. Generates report to validate the security requirements and security best practices.

# **AWS Hardening Standards & Best Practices** #

https://codaglobal.atlassian.net/l/cp/UP1BxJTr

# **AWS Tagging Standards** #

https://codaglobal.atlassian.net/l/cp/oj0Ngsdb

# **AWS Naming Standards** #

https://codaglobal.atlassian.net/l/cp/s99nYLEC

# **Audit tool setup** #

**The project basically uses boto client to connect to the aws services.**

1. Update the session credentials in the terminal.

2. Specify the region name if you need to perform the audit for services in a specific region by mentioning the region name as argument while running the program.

3. If no region is specified us-east-1 is taken as default region and audit checks are performed for the services in us-east-1.

4. If you need to check tags. Update the Config.Json file by mentioning the tags in the account tags provided.


# **Audit Tool Execution** #

Creating Virtual Environment
```
python3 -m venv venv
```

Activating the virtual environment
```
source venv/bin/activate
```

Installing the dependencies in the virtual environment
```
pip install -r requirements.txt
```
Run the following command
```
python3 src/main.py --region {region_name}
```

# **Test Cases Execution** #

```
nosetests
```

# **Final Report** #

The overall report is provided as a HTML document.

## sample report ##
![Scheme](assets/sample_report_base.png)
![Scheme](assets/sample_report.png)
dna-audit-new1 0.1
Data And Analytics Audit Tool
License: MIT
Requires Python: >=3.8

# **DNA AUDIT TOOL** #

DNA Audit tool helps you to check whether the application built on AWS is following the AWS Well-Architected framework by validating it against
various AWS services security requirements and the best practices. Generates report to validate the security requirements and security best practices.

# **AWS Hardening Standards & Best Practices** #

https://codaglobal.atlassian.net/l/cp/UP1BxJTr

# **AWS Tagging Standards** #

https://codaglobal.atlassian.net/l/cp/oj0Ngsdb

# **AWS Naming Standards** #

https://codaglobal.atlassian.net/l/cp/s99nYLEC

# **Audit tool setup** #

**The project basically uses boto client to connect to the aws services.**

1. Update the session credentials in the terminal.

2. Specify the region name if you need to perform the audit for services in a specific region by mentioning the region name as argument while running the program.

3. If no region is specified us-east-1 is taken as default region and audit checks are performed for the services in us-east-1.

4. If you need to check tags. Update the Config.Json file by mentioning the tags in the account tags provided.


# **Audit Tool Execution** #

Creating Virtual Environment
```
python3 -m venv venv
```

Activating the virtual environment
```
source venv/bin/activate
```

Installing the dependencies in the virtual environment
```
pip install -r requirements.txt
```
Run the following command
```
python3 src/main.py --region {region_name}
```

# **Test Cases Execution** #

```
nosetests
```

# **Final Report** #

The overall report is provided as a HTML document.

## sample report ##
![Scheme](assets/sample_report_base.png)
![Scheme](assets/sample_report.png)
dna-audit-new 0.1
Data And Analytics Audit Tool
License: MIT
Requires Python: >=3.8

# **DNA AUDIT TOOL** #

DNA Audit tool helps you to check whether the application built on AWS is following the AWS Well-Architected framework by validating it against
various AWS services security requirements and the best practices. Generates report to validate the security requirements and security best practices.

# **AWS Hardening Standards & Best Practices** #

https://codaglobal.atlassian.net/l/cp/UP1BxJTr

# **AWS Tagging Standards** #

https://codaglobal.atlassian.net/l/cp/oj0Ngsdb

# **AWS Naming Standards** #

https://codaglobal.atlassian.net/l/cp/s99nYLEC

# **Audit tool setup** #

**The project basically uses boto client to connect to the aws services.**

1. Update the session credentials in the terminal.

2. Specify the region name if you need to perform the audit for services in a specific region by mentioning the region name as argument while running the program.

3. If no region is specified us-east-1 is taken as default region and audit checks are performed for the services in us-east-1.

4. If you need to check tags. Update the Config.Json file by mentioning the tags in the account tags provided.


# **Audit Tool Execution** #

Creating Virtual Environment
```
python3 -m venv venv
```

Activating the virtual environment
```
source venv/bin/activate
```

Installing the dependencies in the virtual environment
```
pip install -r requirements.txt
```
Run the following command
```
python3 src/main.py --region {region_name}
```

# **Test Cases Execution** #

```
nosetests
```

# **Final Report** #

The overall report is provided as a HTML document.

## sample report ##
![Scheme](assets/sample_report_base.png)
![Scheme](assets/sample_report.png)
datacooker 0.4.1
Library for data generation based on model specs (Recipes)
License: MIT
Requires Python: >=3.10,<3.12

# Data Cooker

[![Coverage](https://sonarcloud.io/api/project_badges/measure?project=OStatsAA_data-cooker&metric=coverage)](https://sonarcloud.io/summary/new_code?id=OStatsAA_data-cooker)

[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=OStatsAA_data-cooker&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=OStatsAA_data-cooker)
OpenFG 0.1.0
Library for work with our api.
License: MIT

# Welcome to our lib OpenFG
Our small library for python

## How get an api key?

1. [Login into your account](https://kararasenok.ueuo.com/accounts/login.php) (also [registration](https://kararasenok.ueuo.com/accounts/register.php))
2. [Redirect here](https://kararasenok.ueuo.com/api/create/) and create a key
3. Get key and save his
4. Done!

## How use it?
Example code:
````python
import OpenFG
info = Chat("your API key").get_last_message_info("id") # Instead of id, you can specify this: id - message id | sender - sender's name | sender_id - sender's id | message - message | created_at - when sent

if info == "KEY_NOT_FOUND":
    print("Key not found")
else:
    print(info)
````
This code find last message use id of message

Our task list:
-[x] Create our api system
- [x] Create a lib for python
- [ ] Create find user system use his id
django-laboratory 0.0.1
A Django application for managing collections of scientific instruments
License: MIT
Requires Python: >=3.8,<4.0
Keywords: django,laboratory,science,scientific instrument,research management

# Django Laboratory 

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A Django application for managing collections of scientific instruments

Documentation
-------------

The full documentation is at https://ssjenny90.github.io/django-laboratory/

Quickstart
----------

Install Django Laboratory::

    pip install django-laboratory

Add it to your `INSTALLED_APPS`:


    INSTALLED_APPS = (
        ...
        'laboratory',
        ...
    )

Add Django Laboratory's URL patterns:

    urlpatterns = [
        ...
        path('', include("laboratory.urls")),
        ...
    ]

Features
--------

* TODO

Running Tests
-------------

Does the code actually work?

    source <YOURVIRTUALENV>/bin/activate
    (myenv) $ pip install tox
    (myenv) $ tox


Development commands
---------------------

    pip install -r requirements_dev.txt
    invoke -l


Credits
-------