semvec/pypi-packages huggingface dataset:
tsemodule5 5.21
Use TseTmc.ir data for financial calculations in python
Requires Python: >=3.6

tsemodule5 description
Code by @Pythonfinance
t.me/python4finace
www.python4finace.ir

The purpose of this project is to deliver ease of use TSE data for financial calculations. 

installation:
pip install tsemodule5

### import module
import tsemodule5 as tm5


###show the list of all iran stock tickers
#### Function stockdetail have some parameters: stockdetail(tikcer,type) if you set ticker to "list" it show all tickers and names.

tm5.stockdetail("list")

####  show full information about a tickers
tm5.stockdetail("شبندر")

####  show english ticker name
tm5.stockdetail("شبندر",type="namadf")

####  show english ticker name by full farsi name
tm5.stockdetail("پالايش نفت بندرعباس",type="name")

#### show full farsi name by english ticker name
tm5.stockdetail("PNBA1",type="enamad")

#### show all ticker names
#### use it for analysis some functions on the market stocks

tm5.stocklist()


#### show the stock information for 100 last days
tm5.stock("شبندر")

#### show the stock information for custom last days
tm5.stock("شبندر",value=10)

#### Download information from the server or read from local
tm5.stock("شبندر",newfile=True)

#### Get stock information like yahoo-finance
#### use it for polt in mplfinance or other standard modules

tm5.stock("شبندر",value=10,standard=True)

#### Get stock information by english ticker name
#### use it if you know english ticker name

#### tm5.stock("PNBA1",value=10,standard=True,namadf=False)
#### Example:
#### plot the OHLC for tikcer شبندر for 50 last days
mplfinance.plot(tm5.stock("شبندر",value=50,standard=True))
DemystiPy 1.0.6
An approach towards explainable statistics and machine learning
Requires Python: >=3.6

# DemystiPy

This is a python module created for the purpose of visualizing and demystifying the statistical and machine learning concepts and algorithms used in the day to day life. For internalizing what is happening behind the scenes in these tools it is important to visualize them. The current version of the DemystiPy module offers the evaluation and visualization of the probability distribution functions which runs on top of the scipy python module to evaluate and visualize the Binomial and Gaussian probability distributions. 

## Installation

Run the following to install:

'''
pip install DemystiPy
'''

## Requirements

- Numpy
- Scipy
- Matplotlib

## Usage

from DemystiPy.pdistributions import Binomial, Gaussian

b = Binomial()
g = Gaussian()

## Calculate probability mass function and cumulative density function for a binomial distribution
### e.g. P( X = 8) where no. of trials n = 15 and probability p = 0.35
b.pmf(8,15,0.35) 

### e.g. P( X < 7) where no. of trials n = 15 and probability p = 0.35
b.cdf(6,15,0.35)

### e.g. P( X > 9) where no. of trials n = 15 and probability p = 0.35
b.cdf(9,15,0.35,upper=True)

### e.g. P( 5 < X < 10) where no. of trials n = 15 and probability p = 0.35
b.cdf2([5,9],15,0.35)

## Calculate cumulative density function and percentile point function for a gaussian/normal distribution
### e.g. P( X = 40 ) mean = 30 std_dev = 4
g.cdf(40,30,4)

### e.g. P( X > 21 ) mean = 30 std_dev = 4
g.cdf(21,30,4,upper=True)

### e.g. P( 30 < X < 35 ) mean = 30 std_dev = 4
g.cdf2([30,35],30,4)
criteo-api-marketingsolutions-stable-sdk 2021.10.1.211125
Criteo API SDK
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4
Keywords: Criteo,OpenAPI-Generator,Criteo API SDK

# Criteo API SDK for Python
[![](https://img.shields.io/pypi/pyversions/criteo-marketing.svg)](https://pypi.org/project/criteo-marketing-transition/)

IMPORTANT: This Python package links to Criteo production environment. Any test applied here will thus impact real data.

## Requirements.

Python 2.7 and 3.5+

## Installation & Usage
### pip install

```sh
pip install criteo-api-marketingsolutions-stable-sdk
```
(you may need to run `pip` with root permission: `sudo pip install criteo-api-marketingsolutions-stable-sdk`)

Then import the package:
```python
import criteo_api_marketingsolutions_v2021_10
```

Full documentation on [Github](https://github.com/criteo/criteo-api-python-sdk).

## Disclaimer

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.
haar-cascade-nms 1.1.1
Extend opencv haar-cascade detector to filter detections with Non-Maxima Suppression (NMS)
Keywords: object-recognition object-detection object-localization cascade-detectors haar-cascade opencv

# OpenCV Haar cascade with Non-Maxima Suppression
This simple project provides a `Detector` class extending the opencv `CascadeDetector`class with a new function: `detectAndFilter`.  
This function performs the detection using the cascade and filter the detections based on the score and their overlap to yield the best detections that do not overlap above the selected `overlap-threshold`.

For more details about Non-Maxima Suppression, you can refer to my article about Multi-Template Matching, which uses a similar strategy (or other online resources).  
Here the NMS shipped with OpenCV is used.

# Installation
Using pip:  
`pip install haar-cascade-nms`

Then to use in your scripts:  
```python
from haarcascade import Detector
myDetector = Detector(filepathToCascade)
myDetector.detectAndFilter(image, etc...)
```

See the tutorial notebook for a more detailed documentation.
ApexTrackerPy 1.8.3
Library for easily calling Apex tracker APIs (supports TRN and APEX LEGENDS APIs)

# ApexTrackerPy

[![PyPI version](https://badge.fury.io/py/ApexTrackerPy.svg)](https://badge.fury.io/py/ApexTrackerPy)
[![Build](https://github.com/nerrog/ApexTrackerPy/actions/workflows/Build.yml/badge.svg)](https://github.com/nerrog/ApexTrackerPy/actions/workflows/Build.yml)
[![CodeQL](https://github.com/nerrog/ApexTrackerPy/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/nerrog/ApexTrackerPy/actions/workflows/codeql-analysis.yml)

[![Downloads](https://pepy.tech/badge/apextrackerpy)](https://pepy.tech/project/apextrackerpy)
[![Downloads](https://pepy.tech/badge/apextrackerpy/month)](https://pepy.tech/project/apextrackerpy)
[![Downloads](https://pepy.tech/badge/apextrackerpy/week)](https://pepy.tech/project/apextrackerpy)

Python library that calls the ApexLegends tracker API

ApexLegendsトラッカーAPIを呼び出すpythonライブラリ

# Install
It will be installed by `pip3 install ApexTrackerPy`.

You can also use git to install

`pip3 install git+https://github.com/nerrog/ApexTrackerPy.git`


`pip3 install ApexTrackerPy`でインストールされます

gitを使用してインストールすることもできます

`pip3 install git+https://github.com/nerrog/ApexTrackerPy.git`

# usage
[See the Github Wiki](https://github.com/nerrog/ApexTrackerPy/wiki)

[Github Wiki](https://github.com/nerrog/ApexTrackerPy/wiki)を参照してください