semvec/pypi-packages huggingface dataset:
mdcompiler 2021.4.1
a free & OpenSource Markdown Compiler
Requires Python: >=3.6

# Markdown Compiler (MDC)<br>
[![PyPI Release Create](https://github.com/TechGeeks-Dev/MDC/actions/workflows/Release-Create.yaml/badge.svg)](https://github.com/TechGeeks-Dev/MDC/actions/workflows/Release-Create.yaml)<br>

## How to use ?
You need to create a `convert.json` file with a key of `name` in the following format:
```json
{
    "name": "file.md"
}
```
And then in Terminal:
```shell
mdc compile
```
**Note: It is based on the convert.json File**

## How to install ?

```shell
pip install mdcompiler
```
dk-test-py2 0.0.14
A small example package
Requires Python: >=3.6

``` python
    import collpay
    
    collpay_obj = collpay.Collpay("0810545410c0dd7822cdd7c7caa336b2", collpay.ENV_SANDBOX, collpay.V1)
    
    response = collpay_obj.get_exchange_rate("USD", "BTC")
    print(response)
    
    response = collpay_obj.create_transaction({
        'payment_currency': 'BTC',
        'order_currency': 'USD',
        'order_amount': '10',
        'payer_name': 'Xxx',
        'payer_email': 'xxx@gmail.com',
        'payer_phone': 'xxxxxxxx',
        'payer_address': 'Bangladesh 1216',
        'ipn_url': 'https://webhook.site/3b3ed4bf-8adb-4aec-b360-1d0c1c394766',
        'ipn_secret': 'Cf9mx4nAvRuy5vwBY2FCtaKr',   #Any random secret string of your's, It can be empty.
        'success_url': 'https://www.success.io/success.html',
        'cancel_url': 'https://www.failure.io/cancel.html',
        'cart': '{"item_name":"t-shirt","item_number":"23","invoice":"SDF-453672-PMT"}',   #Send any data format like json
        'webhook_data': '{"order_id":"ABC12345-12"}'    #Send any data format like json
    })
    print(response)
    
    response = collpay_obj.get_transaction("xxxxxxxxxxx")
    print(response)

```
datavita-python-sdk 0.1.0
DataVita Python SDK

# 华创证券VitaX Python 工具套件


欢迎使用华创证券开发者工具套件（SDK）。

## 环境准备

要使用VitaX Python SDK ，您需要联系华创证券金融科技部工作人员申请获取 `Access Key ID`和`Access Key Secret`。 

## SDK 获取和安装

### 使用 pip 安装(推荐)

```bash
pip install datavita-python-sdk # 安装阿里云 SDK 核心库

```

## 开始调用

以下这个代码示例向您展示了调用阿里云 Python SDK 的3个主要步骤：

1. 创建 Client 实例
2. 获取所需数据
3. 发起请求并处理异常


```python
# -*- coding: utf8 -*-
from datavita.andes.andes_data import AndesData
from datavita.core.auth import Auth

# 创建 AcsClient 实例
access_key_id = "1"
access_secret = "1"
auth = Auth(access_key_id, access_secret)
ad = AndesData(auth=auth, timeout=1, max_retries=1)
df = ad.get_dataset_by_tagcode(tag_code='xxxx')
print(df)
```

在创建 Auth 实例时，您需要填写 2 个参数：Access Key ID 和 Access Secret。Access Key ID 和 Access Secret 申请获取；
NRInsightsApi 0.0.2
REST API for New Relic
Requires Python: >=3.6

# Wrapper for New Relic Insights API

## Avaliable Methods

- get_data(account,query_api_key,query)
    - returns result in JSON format
- insert_data(account,insert_api_key,data_df)
    - return response status as a list

### Sample Code

__Get Request__
```
from NRInsightsApi import get_data
r =  get_data('2929995','JSJW_WKJSJ_192920_192820','SELECT average(duration) FROM PageView')
```

```
{'results': [{'average': None}], 'performanceStats': {'inspectedCount': 0, 'omittedCount': 0, 'matchCount': 0, 'wallClockTime': 1}, 
'metadata': {'eventTypes': ['PageView'], 'eventType': 'PageView', 'openEnded': True, 'beginTime': '2021-03-31T02:48:58Z', 'endTime': '2021-03-31T03:48:58Z', 'beginTimeMillis': 1617158938335, 'endTimeMillis': 1617162538335, 'rawSince': '60 MINUTES AGO', 'rawUntil': 'NOW', 'rawCompareWith': '', 'guid': '4ade46ba-5782-6d40-7627-1411ca37131f', 'routerGuid': '74da0adc-aa35-7d58-a977-667954ca56aa', 'messages': ['No events found -- do you have the correct event type and time range?'], 'info': ['No events found -- do you have the correct event type and time range?'], 'contents': [{'function': 'average', 'attribute': 'duration', 'simple': True}]}}
```
seg-crf 1.0.0
Conditional Random Field Implementation for segmentation models as used in Deeplab-v2
Keywords: python,conditional random fields,segmentation,crf,semantic segmentation,fully connected crfdense crf,deeplabv2

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ![PyPI](https://img.shields.io/pypi/v/a)
# Fully Connected CRF

This repo implements CRF as described in Deeplab paper it takes about 0.2 seconds per image. Following image is taken form **DeepLab** paper

![alt text](https://github.com/Mr-TalhaIlyas/Conditional-Random-Fields-CRF/raw/master/screens/img1.png)

## Requirements

```
Python <= 3.6
pydensecrf
cv2
matplotlib
gray2color
```

It takes following arguments.
For details visit project [page](https://github.com/Mr-TalhaIlyas/Conditional-Random-Fields-CRF).


```
        ⚠ Zero pixels are consdered background
        img_path : path to an image, 
                        Format [H, W, 3]; values ranging from [0, 255]
        model_op_path : path model output of the same input image.
                        Format [H, W]; values ranging from [0, num_of_classes]
        num_of_classes : number of classes in a dataset e.g. in cityscape has 30 classes
        clr_op : color the output or not a bool
        pallet2use : see https://pypi.org/project/gray2color/ for details
        img_w : for resizing image and mask to same size default is 1024
        img_h : for resizing image and mask to same size default is 512
        apperance_kernel : The PairwiseBilateral term in CRF a list of values in order [sxy, srgb, compat]  
                            default values are [8, 164, 100]
        spatial_kernel : The PairwiseGaussian term in CRF a list of values in order [sxy, compat]  
                            default values are [3, 10]
```

```python

from seg_crf import Seg_CRF

img_path='D:/Anaconda/Image_analysis/cat.png'
model_op_path='D:/Anaconda/Image_analysis/mask.png'

crf = Seg_CRF(img_path, model_op_path, 2, img_w=1024, img_h=512, clr_op=True, pallet2use ='cityscape')

gray, rgb = crf.start()
plt.imshow(rgb)

```
## Appearance and Spatial Kernel

```python
# Default Values are
apperance_kernel = [8, 164, 100] # PairwiseBilateral [sxy, srgb, compat]  
spatial_kernel = [3, 10]         # PairwiseGaussian  [sxy, compat] 

# or if you want to to specify seprately for each XY direction and RGB color channel then

apperance_kernel = [(1.5, 1.5), (64, 64, 64), 100] # PairwiseBilateral [sxy, srgb, compat]  
spatial_kernel = [(0.5, 0.5), 10]                  # PairwiseGaussian  [sxy, compat] 
# Use like
crf = Seg_CRF(img_path, model_op_path, 2, img_w=1024, img_h=512,
                 apperance_kernel=apperance_kernel, spatial_kernel=spatial_kernel,
                 clr_op=True, pallet2use ='cityscape')

gray, rgb = crf.start()
```
dollarN 1.2.4
Implementation of the $N 2D gesture recognizer
Requires Python: >=3.6

# dollarN
**_Python implementation of $N, the 2D multistrokes recognizer_**

http://depts.washington.edu/acelab/proj/dollar/ndollar.html

> The $N Multistroke Recognizer is a 2-D multistroke recognizer designed
> for rapid prototyping of gesture-based user interfaces. $N is built upon
> the $1 Unistroke Recognizer. $N automatically generalizes examples of
> multistrokes to encompass all possible stroke orders and directions,
> meaning you can make and define multistrokes using any stroke order and
> direction you wish, provided you begin at either endpoint of each
> component stroke, and $N will generalize so as to recognize other ways
> to articulate that same multistroke. A version of $N utilizing
> Protractor, optional here, improves $N's speed.

## Features
- [Python 3](https://www.python.org/)
- [Numpy](https://numpy.org/)

## Example of use:
```
import dollarN as dN

r = dN.recognizer()
#By default, a recognizer gives a positive result when gestures have
#the same number of strokes only. This can be turned off:
#r.set_same_nb_strokes(False)

#Rotation invariance can also be turned off:
#r.set_rotation_invariance(False)

#Adding gestures: multistrokes with names
r.add_gesture('U', [   [[0.,5.], [0.,0.], [5.,0.], [5.,5.]]    ]) # one stroke
r.add_gesture('X', [   [[0.,0.], [5.,5.]], [[0.,5.], [5.,0.]]  ]) # two strokes
r.add_gesture('T', [   [[0.,5.], [5.,5.]], [[2.5,0.], [2.5,5.]]]) # two strokes

#Launching a recognition
test = [[[0, 5.2], [5.,5.]], [[2.5, 0.], [2.5,5.]]]
print( r.recognize(test) )
```
```
{'name': 'T', 'value': 0.9484976300936439, 'time': 0.006083965301513672}
```
## Demo
A demo is available with tkDollarN.py [here](https://github.com/mikefromlig/dollarN)
Hierarch 0.1.1
Hierarchical hypothesis testing library
Requires Python: >=3.7

# Hierarch

## A Hierarchical Resampling Package for Python

Version 0.1

Hierarch is a package for hierarchical resampling (bootstrapping, permutation, jackknifing) datasets in Python. Because for loops are ultimately intrinsinc to cluster-aware resampling, Hierarch uses Numba to accelerate many of its key functions.

Hierarch has several functions for performing resampling-based hypothesis tests on hierarchical data. Additionally, Hierarch can be used to construct power analyses for hierarchical experimental designs. 

### Dependencies
* numpy
* numba
* scipy (for power analysis)
* sympy (for jackknifing)
mumoco 0.1.0
This is tool helps to work with multiple conan modules simultaneously.
Requires Python: >=3.6

# mumoco

mumoco or multi module conan is a tool to help for working with multiple conan package simultaneously. mumoco is using pure conan commands and should only simplify the workflow.

mumoco has several features, but the essential feature is that mumoco can find all conanfile.py in a given folder and in their subfolders to create conan packages. Another feature that mumoco has is to execute the conan source command in all found conan packages, this is extremely helpful if you work with conan packages that has external sources. 
mumoco also can upload all the conan packages which where created and to login to the remote mumoco has another feature to add and to login to conan remotes.


## Parameters

|===
|Argument |Description | Required | Default

|--root
|Root folder of mumoco. This is the top folder where mumoco starts to search all conanfile.py files. All conanfile.py in the subfolders will be considered by mumoco, except conanfile.py in test_package folders" 
|False
|Current Working Directory (CWD)

|--config
|Path to config-build.json file. The config-build.json is the configuration of mumoco. More details can be found here link:doc/configuration.adoc[configuration].
|False
|(Current Working Directory)/config-build.json

|--sources
|If this argument is given. Mumoco executes the conan source command with the argument PACKAGE-PATH/tmp, meaning the sources are copied to the PACKAGE-PATH/tmp folder.
|False
|NONE

|--remove
|If this argument is given. Mumoco removes all folders PACKAGE-PATH/tmp. This is helpful to cleanup all sources which mumoco copied with the sources command. 
|False
|NONE

|--create
|If this argument is given. Mumoco exports all conan packages into cache and creates all conan packages according to the config-build.json definition.
|False
|None

|--remotes
|Mumoco updates all the conan remotes which are defined in config-build.json file. If the remotes.login parameter in the config-build.json file is set on a remote then the --user and --password parameter must be set.
|False
|None


|--user
|If the remotes.login parameter in the config-build.json file is set this is the user which is needed to login.
|False
|None

|--password
|If the remotes.login parameter in the config-build.json file is set this is the password which is needed to login.
|False
|None
|===
nepaldatascrapper 0.0.5.2
Package that scraps data related to Nepal for further analysis through panda or spreadsheet.
Requires Python: >=3.6

# NepalDataScrapper

A python project that scraps the data from website to collect the data about Nepal for the use in further analysis through pandas or libreoffice sheet.

## Implemented scrapper
- nepse.py :: scraps data to get of nepse
starify 0.0.1b2
Starify: convert string to asterisks for confidentiality display
Requires Python: >3.6

# starify

Convert part of strings to asterisks to display confidential information
# Installation
```bash
pip install starify
```
# Usage:
```python
import starify

credential = "ABCDEFGHI_TH1SCR3D3ENTIAL"
starify.star(credential) 
# "***************TH1SCR3D3ENTIAL"
```
rockanalysis 1.0.0
ROCK Data Analysis Package
Requires Python: >=3.6

#rockanalysis
Code for Analyzing ISO-10328 Testing Data Obtained from MTS 

## Use
Analyze Class Holds All Data, takes filename as argument

### Methods
decode(), unpacks dat file into arrays \n
plotforcevtime() \n
plotdisplacementvtime() \n
plotforcevdisplacement()
paste_it 0.1.2
a cli script and tiny library to upload files to pastebin
Requires Python: >=3.8

# Paste it
Paste it is a python script that I made to upload files to pastebin using requests. it aims to be fast and light and hence doesn't support a large array of options. Currently paste_it reads from json.config in the directory its launched from, in the future it may support reading from appdata dirs using the `appdirs` python library. I don't intened for this to be used as a library but it can be used as one if you wish to, all functions do have docstrings incase you do decied to use this a library.
## Usage

```
usage: paste_it [-h] [--format FORMAT] [--private PRIVATE] [--title [TITLE]] path

positional arguments:
  path               file who's contents you want to paste on pastebin

optional arguments:
  -h, --help         show this help message and exit
  --format FORMAT    format of the file contents. you can find the avalible formats at https://pastebin.com/doc_api
  --private PRIVATE  if your file should be privte, 0 public, 1 unlisted, 2 Private (only allowed in combination with api_user_key, as you have to be logged into your account to
                     access the paste)
  --title [TITLE]    the title of your paste
  ```