semvec/pypi-packages huggingface dataset:
sl-creatio-connector 0.1.7
Creatio (aka BPMonline) ODATA API helper
Requires Python: >=3.11,<4.0

# <p align="center">Creatio ODATA connector</p>
<p align="center">Connector to integrate <a href="https://academy.creatio.com/docs/developer/integrations_and_api/data_services/odata/overview">Creatio ODATA API</a>.</p>
<p><a href="https://documenter.getpostman.com/view/10204500/SztHX5Qb">Creatio ODATA API postman documentation</a></p>

## Getting started

This connector tested for ODATA3 and ODATA4 protocols (including .net core version)

```bash
$ pip install sl_creatio_connector
```

```python

from sl_creatio_connector.creatio import Creatio
from sl_creatio_connector.constants import ODATA_version

# get collection
def get_contact_leads():
    cr = Creatio(
        creatio_host='http://creatio.mydomen.com:5000',
        login='Supervisor',
        password='Supervisor',
        odata_version=ODATA_version.v4core
    )
    parameters = [
        "filter=Contact eq 'Marady Esther'"
    ]
    collection = cr.get_object_collection(
        object_name= 'Lead',
        parameters= parameters,
    )
    assert len(collection) == 0

def create_and_delete_contact():
    cr = Creatio(
        creatio_host='http://creatio.mydomen.com:5000',
        login='Supervisor',
        password='Supervisor',
        odata_version=ODATA_version.v4core
    )
    data = {
        'Name': "Test name"
    }
    created_contact = cr.create_object(
        object_name= 'Contact',
        data= data,
    )
    created_id = created_contact['Id']
    status_code = cr.delete_object('Contact', created_id).status_code

def get_contact_by_id():
    cr = Creatio(
        creatio_host='http://creatio.mydomen.com:5000',
        login='Supervisor',
        password='Supervisor',
        odata_version=ODATA_version.v4core
    )
    contact_dict = cr.get_contact_by_id('b2a8c568-002f-4fd1-a15a-ffda98f5f63b')
```
pyrogram-asyncpg-storage 0.1
asyncpg storage for pyrogram
Requires Python: >=3.6.0

Pyrogram asyncpg storage
========================

Usage
-----

```python
import asyncpg
from pyrogram import Client
from pyrogram_asyncpg_storage import PostgreSQLStorage


app = Client(...)


async def set_session():
	pool = await asyncpg.create_pool()
	session = PostgreSQLStorage(name=..., pool=pool)
	app.storage = session


app.run(set_session())
app.run()
```
pdf417decoder-with-opencv-python-headless 1.0.9
A PDF417 barcode decoder
Requires Python: >=3.7

# pdf417decoder-with-opencv-headless
![Image of a PDF417 barcode](https://raw.githubusercontent.com/sparkfish/pdf417decoder/dev/python/images/haiku.png?id=1)

pdf417decoder-with-opencv-headless is a python package that is forked from [pdf417decoder](https://github.com/gabrielvannier/pdf417decoder-with-opencv-python-headless)

### What difference between pdf417decoder and pdf417decoder-with-opencv-headless

pdf417decoder-with-opencv-headless has no dependancy on opencv-python but depends instead on opencv-python-headless.\
(opencv-python-headless is a lighter version of opencv for server deployement.)

## License
[CPOL](https://www.codeproject.com/info/cpol10.aspx)

This project is a derivative of code licensed under the Code Project Open License (CPOL). The Code Project Open License (CPOL) is intended to provide developers who choose to share their code with a license that protects them and provides users of their code with a clear statement regarding how the code can be used.

## Credits

 Source code is a port of a C# Library created and maintained by [Uzi Granot](https://www.codeproject.com/script/Membership/View.aspx?mid=193217). [PDF417 Barcode Decoder .NET Class Library and Two Demo Apps](https://www.codeproject.com/Articles/4042463/PDF417-Barcode-Decoder-NET-Class-Library-and-Two-D)
vertezml 0.0.20
Vertez: Statify and Infer

Vertez is a cutting-edge open-source framework company dedicated to developing powerful and versatile libraries for statistical analysis, machine learning, and deep learning. We empower data scientists, researchers, and developers with a comprehensive suite of tools to drive insights and innovations in diverse domains. At Vertez, we believe in democratizing access to advanced statistical techniques and the latest advancements in artificial intelligence. Our libraries provide an intuitive and flexible platform for statistical modeling, predictive analytics, and complex data-driven decision making. With a focus on simplicity, performance, and scalability, we enable practitioners to efficiently explore, analyze, and infer from their data.
dipl 1.1.0
Python reader and parser module for DIP language
Requires Python: >=3.7

# DIPL

This module is a quick reading/parsing tool for Dimensional Input Parameter Language (DIP) that is a part of larger [scinumtools](https://github.com/vrtulka23/scinumtools) project.
For more information about DIP, please visit its official [documentation](https://vrtulka23.github.io/scinumtools/dip/index.html).
More functionality can be reqested in [GitHub Issues](https://github.com/vrtulka23/dipl/issues).

## Reading DIP code

Loading DIP code from a string is straightforward.

``` python 
>>> import dipl
>>>
>>> text = """
>>> width float = 23.34 cm
>>> age int = 23 yr
>>>   !tags ["body"]
>>> """
>>> dipl.load(text)
{
  'width': (23.34, 'cm'),
  'age': (23, 'yr')
}
```

It is also possible to change data output format, 

``` python
>>> from dipl import Format
>>> dipl.load(text, Format.VALUE)
{
  'width': 23.34,
  'age': 23,
}
```

or to select only requested nodes using query, or tags:

``` python
>>> dipl.load(text, query="width")
{
  'width': (23.34, 'cm'),
}
>>> dipl.load(text, tags=["body"])
{
  'age': (23, 'yr'),
}
```

## Parsing DIP code

Python dictionaries can be parsed into DIP code, provided that they have a proper structure.
An example parsing is shown below.

``` python
>>> import dipl
>>> import numpy as np
>>> from scinumtools.units import Quatity
>>>
>>> data = {
>>>     'body': {
>>>         'width': Quantity(172.34, 'cm'),
>>>         'age': (23, 'yr'),
>>>     },
>>>     'married': True,
>>>     'name': "John",
>>>     'kids': ["Alice","Williams"],
>>>     'lucky_numbers': np.array([23, 34, 5]),
>>> }
>>> dipl.dump(data)
body
  width float = 172.34 cm
  age int = 23 yr
married bool = true
name str = "John"
kids str[2] = ["Alice","Williams"]
lucky_numbers int[3] = [23,34,5]
```
housing-library 0.1
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# Median housing value prediction

The housing data can be downloaded from https://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data. 

The following techniques have been used: 

 - Linear regression
 - Decision Tree
 - Random Forest

## Steps performed
 - We prepare and clean the data. We check and impute for missing values.
 - Features are generated and the variables are checked for correlation.
 - Multiple sampling techinuqies are evaluated. The data set is split into train and test.
 - All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.

## To excute the script
python nonstandardcode.py
# To create an environment 
conda env --name mle-dev biopython
conda activate mle-dev
#To export the environment to env.yml
conda env export mle-dev > env.yml
# mle-training