semvec/pypi-packages huggingface dataset:
TIXhjq 0.0.1
code
Requires Python: >=3.6

# CTR_Function

![image](https://img.shields.io/badge/author-TIXhjq-orange)
![image](https://img.shields.io/badge/License-tensorflow2.1-darkgreen)
![image](https://img.shields.io/badge/python-v3.7-darkgreen)
![image](https://img.shields.io/badge/pandas-v0.25.0-darkgreen)
![image](https://img.shields.io/badge/License-Apache_2.0-blue)
Email:hjq1922451756@gmail.com


>1.[code folder](https://github.com/TIXhjq/CTR_Function/tree/master/code)=[nn,feature_eng]
> 
>>1)新鲜,可食用  
>>>[CTR](https://github.com/TIXhjq/CTR_Function/tree/master/code/model/ctr_model)  
>
>>2)需要重构部分(历史代码)  
>>>[Graph(重构)](https://github.com/TIXhjq/CTR_Function/tree/master/code/model/embedding)  
>>>[Feature Enginner(重构)](https://github.com/TIXhjq/CTR_Function/tree/master/code/model/feature_eng)  
>
>2.[paper](https://github.com/TIXhjq/CTR_Function/tree/master/paper)=[nn(目前只有CTR部分,剩下的下次一定^_^)]  
>3.[data](https://github.com/TIXhjq/CTR_Function/tree/master/data)=[nn test sample data]  
  
  p.s 每层目录有对应的详细介绍...
  
  [部分nn模型小结,不过比较懒,就几个就是了](https://zhuanlan.zhihu.com/c_1145034612807028736)
      
  不过由于feature_eng基本都是以前收录的,有很多需要重构,还有GNN部分也是,目前可食用只有CTR部分(不过其实由于并没有开了上帝视角写东西,还有有一些需要重构等等),
  以后会把他们组合打包,最后希望是一个快速实现end2end的工具库.
pyttsx3eaidk 2.91
Text to Speech (TTS) library for Python 3. Works without internet connection or delay. Supports multiple TTS engines, including Sapi5, nsss, and espeak.
Keywords: pyttsx,ivona,pyttsx for python3,TTS for python3,pyttsx3,text to speech for python,tts,text to speech,speech,speech synthesis,offline text to speech,offline tts,gtts

*****************************************************
pyttsx3 (offline TTS for Python 3)
*****************************************************

``pyttsx3`` is a text-to-speech conversion library in Python. Unlike alternative libraries, it works offline, and is compatible with both Python 2 and 3.

Installation
************
::

	pip install pyttsx3


> If you get installation errors , make sure you first upgrade your wheel version using :  
`pip install --upgrade wheel`

**Linux installation requirements :**
#####################################

+ If you are on a linux system and if the voice output is not working , then  : 

Install espeak , ffmpeg and libespeak1 as shown below: 

::

	sudo apt update && sudo apt install espeak ffmpeg libespeak1


Usage :
************
::

	import pyttsx3
	engine = pyttsx3.init()
	engine.say("I will speak this text")
	engine.runAndWait()
	
	
**Changing Voice , Rate and Volume :**

::

	import pyttsx3
	engine = pyttsx3.init() # object creation

	""" RATE"""
	rate = engine.getProperty('rate')   # getting details of current speaking rate
	print (rate)                        #printing current voice rate
	engine.setProperty('rate', 125)     # setting up new voice rate


	"""VOLUME"""
	volume = engine.getProperty('volume')   #getting to know current volume level (min=0 and max=1)
	print (volume)                          #printing current volume level
	engine.setProperty('volume',1.0)    # setting up volume level  between 0 and 1

	"""VOICE"""
	voices = engine.getProperty('voices')       #getting details of current voice
	#engine.setProperty('voice', voices[0].id)  #changing index, changes voices. o for male
	engine.setProperty('voice', voices[1].id)   #changing index, changes voices. 1 for female

	engine.say("Hello World!")
	engine.say('My current speaking rate is ' + str(rate))
	engine.runAndWait()
	engine.stop()

	"""Saving Voice to a file"""
	# On linux make sure that 'espeak' and 'ffmpeg' are installed
	engine.save_to_file('Hello World', 'test.mp3')
	engine.runAndWait()


**Full documentation of the Library**
#####################################

https://pyttsx3.readthedocs.io/en/latest/


Included TTS engines:
*********************
* sapi5
* nsss
* espeak

Feel free to wrap another text-to-speech engine for use with ``pyttsx3``.

Project Links:
**************

* PyPI (https://pypi.python.org)
* GitHub (https://github.com/nateshmbhat/pyttsx3)
* Full Documentation (https://pyttsx3.readthedocs.org)
pyttsx3-eaidk 2.91
Text to Speech (TTS) library for Python 3. Works without internet connection or delay. Supports multiple TTS engines, including Sapi5, nsss, and espeak.
Keywords: pyttsx,ivona,pyttsx for python3,TTS for python3,pyttsx3,text to speech for python,tts,text to speech,speech,speech synthesis,offline text to speech,offline tts,gtts

*****************************************************
pyttsx3 (offline TTS for Python 3)
*****************************************************

``pyttsx3`` is a text-to-speech conversion library in Python. Unlike alternative libraries, it works offline, and is compatible with both Python 2 and 3.

Installation
************
::

	pip install pyttsx3


> If you get installation errors , make sure you first upgrade your wheel version using :  
`pip install --upgrade wheel`

**Linux installation requirements :**
#####################################

+ If you are on a linux system and if the voice output is not working , then  : 

Install espeak , ffmpeg and libespeak1 as shown below: 

::

	sudo apt update && sudo apt install espeak ffmpeg libespeak1


Usage :
************
::

	import pyttsx3
	engine = pyttsx3.init()
	engine.say("I will speak this text")
	engine.runAndWait()
	
	
**Changing Voice , Rate and Volume :**

::

	import pyttsx3
	engine = pyttsx3.init() # object creation

	""" RATE"""
	rate = engine.getProperty('rate')   # getting details of current speaking rate
	print (rate)                        #printing current voice rate
	engine.setProperty('rate', 125)     # setting up new voice rate


	"""VOLUME"""
	volume = engine.getProperty('volume')   #getting to know current volume level (min=0 and max=1)
	print (volume)                          #printing current volume level
	engine.setProperty('volume',1.0)    # setting up volume level  between 0 and 1

	"""VOICE"""
	voices = engine.getProperty('voices')       #getting details of current voice
	#engine.setProperty('voice', voices[0].id)  #changing index, changes voices. o for male
	engine.setProperty('voice', voices[1].id)   #changing index, changes voices. 1 for female

	engine.say("Hello World!")
	engine.say('My current speaking rate is ' + str(rate))
	engine.runAndWait()
	engine.stop()

	"""Saving Voice to a file"""
	# On linux make sure that 'espeak' and 'ffmpeg' are installed
	engine.save_to_file('Hello World', 'test.mp3')
	engine.runAndWait()


**Full documentation of the Library**
#####################################

https://pyttsx3.readthedocs.io/en/latest/


Included TTS engines:
*********************
* sapi5
* nsss
* espeak

Feel free to wrap another text-to-speech engine for use with ``pyttsx3``.

Project Links:
**************

* PyPI (https://pypi.python.org)
* GitHub (https://github.com/nateshmbhat/pyttsx3)
* Full Documentation (https://pyttsx3.readthedocs.org)
cvtools 0.1.16
OpenCV tools for CV applications
Keywords: opencv,image,http

# cvtools

OpenCV tools for cv applications.

## cvtools.cv_load_image 

Use opencv to load images, either in local file system or remote http servers.

```
from cvtools import cv_load_image
cv_load_image('https://ss1.baidu.com/6ONXsjip0QIZ8tyhnq/it/u=4234657924,3898859385&fm=173&app=25&f=JPEG?w=640&h=756&s=DA202EC74623C8EE582E73620300D07F')
```

## cvtools.get_image_info

To get image meta info really fast.

```
from cvtools import get_image_info, verify_jpeg
with open("test.png", 'rb') as f:
    print(get_image_info(f))
    print(verify_jpeg(f))

# 'image/png', 1506, 1186
# False
```
NameGenderPredictor 0.0.1
Prediction of gender probabilities for English names.
Requires Python: >=3.7

Prediction of genders of english names based on US Social Security data. The gender probability of each name is computed based on the number of male and female babies that were given this name between 1880 and 2017. 

The full data can be found at https://www.ssa.gov/oact/babynames/limits.html

# Description

The main function returns the probability for a given english name to be a male name. There are optional arguments start_year (default 1880) and end_year (default 2018) to use a specific period of time for calculation. For instance, for gender prediction of babyboomers, the period can be set to 1946-1965. If the name is absent from the database, `None` is returned.

# Installation
Requires package sqlite3. The code was only tested with Python 3.7.
```
$ pip install NameGenderPredictor
```

# Basic Usage
Function `predict_gender` is case insensitive for names (see below). 
```python
>>> from NameGenderPredictor import predict_gender
>>> print(predict_gender('john'))
0.9957852975260119
>>> print(predict_gender('Avi'))
0.8783253379851723
>>> print(predict_gender('chris'))
0.8632186721917374
>>> print(predict_gender('chris'),start_year=2000,end_year=2010)
0.9833235466823254
>>> print(predict_gender('chris'),start_year=1950,end_year=1965)
0.8111958317448215
```
marlowe_ui 0.5.2
UI program for Marlowe input data file

==============================================
Marlowe User Interface (marlowe_ui)
==============================================

Takaaki AOKI (aoki.takaaki@nagoya-u.jp)

About Malouwe User Interface
=============================

Marlowe User Interface is support program for marlowe (a binary collision simulator, http://www.oecd-nea.org/tools/abstract/detail/psr-0137/)
to desgin input data.

Programs and scripts
--------------------

- mui (mui.py, mui.exe):
    GUI program to construct marlowe input file

- ml_post_ui (ml_post_ui.exe or ml_post_ui.py):
    GUI program to parse output data.

- ml_post (ml_post.exe or ml_post.py):
    Command line program to parse output data.

Project URLs
================

https://bitbucket.org/marlowe_ui/marlowe_ui/

Documents
  https://bitbucket.org/marlowe_ui/marlowe_ui/wiki/

Windows Binary
  https://bitbucket.org/marlowe_ui/marlowe_ui/downloads/

  (download *marlowe_ui-(version)-win-amd64.exe*)

PyPI
   https://pypi.python.org/pypi/marlowe_ui
zipbiaschecker 0.0.1
A quick check for racial bias using zipcode-level Census data

# zipbiaschecker

One challenge of assessing algorithmic racial bias is sometimes that the data are missing (not collected as part of sign-up forms, for example) or unavailable for privacy reasons. In these cases, zipcode-level bias is an indirect measure. We can go one step further by analyzing Census data that contain racial demographic data by zip code. This package helps run this indirect check by looking at the correlation between the algorithmic output and the percentage of Black, Hispanic, and Indigenous people in that zip code.

## Installation

This package can be installed using the command below:
```
pip install zipbiaschecker
```

## Example

In this example, the data is taken from the [Illinois Department of Public Health COVID statistics](https://www.dph.illinois.gov/covid19/covid19-statistics) as of 7/15/20. We will examine the correlation between the positive rate of testing by zip code vs. the demographics of the zip code to check the disparate impact of COVID on racial minorities.


```python
import pandas as pd
from zipbiaschecker import zipbiaschecker as zbc

df = pd.read_csv('zipbiaschecker/data/example/2020_07_15_illinois_covid_data.csv')
df['positive_rate'] = df['Positive Cases'] / df['Tested']
print(df.shape)
df.head()
```

    (646, 4)





<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Zip</th>
      <th>Tested</th>
      <th>Positive Cases</th>
      <th>positive_rate</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>60002</td>
      <td>1925</td>
      <td>130</td>
      <td>0.067532</td>
    </tr>
    <tr>
      <th>1</th>
      <td>60004</td>
      <td>9441</td>
      <td>406</td>
      <td>0.043004</td>
    </tr>
    <tr>
      <th>2</th>
      <td>60005</td>
      <td>4771</td>
      <td>255</td>
      <td>0.053448</td>
    </tr>
    <tr>
      <th>3</th>
      <td>60007</td>
      <td>4191</td>
      <td>383</td>
      <td>0.091386</td>
    </tr>
    <tr>
      <th>4</th>
      <td>60008</td>
      <td>4672</td>
      <td>380</td>
      <td>0.081336</td>
    </tr>
  </tbody>
</table>
</div>



To interpret the cell below, we see that the rate of positive cases has a positive correlation of about .278 with the proportion of Black people in the zip code, .585 with the proportion of Hispanic people in the zip code, and .108 with the proportion of Indigenous people in the zip code.


```python
zip_bias_checker = zbc.ZipBiasChecker()
zip_bias_checker.check_bias(df, zip_col_name='Zip', target_col_name='positive_rate')
```

    1 row(s) could not be matched out of 646

    percent_black         0.277773
    percent_hispanic      0.585238
    percent_indigenous    0.107945
    Name: positive_rate, dtype: float64



## Documentation notebook for process to generate reference data

In the `notebooks` folder, the process to map zip codes to demographic data is documented in a Jupyter notebook. To run the notebook, clone this repository to obtain the data used.