semvec/pypi-packages huggingface dataset:
apio-django 1.0.13
Application error & performance monitoring
License: MIT

#Project Description 

[*Currently only Django Framework supported*]

Apio helps in monitoring of Applications. You can easily integrate(see setup below) it with your application and instantly start tracking errors and performance metrics.


##Setup

Installation instructions: https://www.youtube.com/watch?v=tpx4mTuv0-0

1. Create an account on [Apio signup](https://apio.in/signup)

2. Register an application on [Apio onboarding](https://apio.in/onboarding)

3. Install Apio-django using pip

`pip install apio-django`

4. Add `apio-django` to your installed Apps in your `settings.py`.

5. Add `apio_django.middleware.ApioMiddleware` to list of Middleware

6. Add `application_key` received after you onboarded your application on step 2 in your `settings.py`

`APIO_D = { "application_key": generated_application_key }`

7. Thats it :) Now on any error you will receive an email on the registered email id and also you can see all the details of your application on the dashboard of [Apio](https://apio.in) 


##Disclaimer
The Apio service is currently in beta testing phase. We don't take any responsbility of continued service or issues that may arise. 

##Contact (any queries)

Email: apio.monitor@gmail.com
bhoonidhi 0.1.0
Useful tools to work with Bhoonidhi (ISRO's open data access portal) in Python
License: MIT
Keywords: bhoonidhi,SmartSearch,NLP,AI

# bhoonidhi
A Python library for Bhoonidhi, ISRO's Open data access portal (https:/bhoonidhi.nrsc.gov.in). The current version contains AI helper called "smart search" using NLP. The functionality can enable the power of searching in bhoonidhi using common search sentences and fetch the bhoonidhi results.

## Installation (https://pypi.org/project/bhoonidhi/0.1.0/)
```
pip install bhoonidhi
```

## Importing and using the module
```
from bhoonidhi.SmartSearch import bhoonidhiSmartSearch

user_query = "Get me Landsat 8 data from the region of hyderabad and Guntur from the last 1 month"
bhoonidhiResponse = bhoonidhiSmartSearch(user_query)
```

## Smart search for Event Based
```
from bhoonidhi.SmartSearch import bhoonidhiSmartSearch

user_query = "Get me available data from siachen avalanche and kerala floods 2020"
bhoonidhiResponse = bhoonidhiSmartSearch(user_query)
```

## Multiple query parameters
```
from bhoonidhi.SmartSearch import bhoonidhiSmartSearch

user_query = "I want Landsat8 and Sentinel 2A data from the region of Guntur over the radius of 20 km and cloud threshold of 35%"
bhoonidhiResponse = bhoonidhiSmartSearch(user_query)
```
Downloading a product requires a valid login from bhoonidhi or uops. For free registration visit https://uops.nrsc.gov.in/ImgeosUops/FinalImgeosUops/OdapUserRegister.html 
## Download a single product from the searched products
```
from bhoonidhi.SmartSearch import bhoonidhiSmartSearch
from bhoonidhi.SmartSearch import bhoonidhiDownload

user_query = "I want Landsat8 and Sentinel 2A data from the region of Guntur over the radius of 20 km and cloud threshold of 35%"
bhoonidhiProducts = bhoonidhiSmartSearch(user_query)
# Want to download the 12th product from the response, pass the 12th response product object
bhoonidhiDownload(bhoonidhiProducts[12],user_id='bhoonidhi_user', password='password', output_path='D:\\bhoonidhiDownloads')
```

## Bulk Download searched products (Batch downloading with custom number of concurrent/parallel downloads [maximum limit of 5 by https:/bhoonidhi.nrsc.gov.in])
```
from bhoonidhi.SmartSearch import bhoonidhiSmartSearch
from bhoonidhi.SmartSearch import bhoonidhiBatchDownload

user_query = "I want Landsat8 and Sentinel 2A data from the region of Guntur over the radius of 20 km and cloud threshold of 35%"
bhoonidhiProducts = bhoonidhiSmartSearch(user_query)
# Want to download all (maximum downloads allowed by website is 5)
bhoonidhiBatchDownload(bhoonidhiProducts,user_id='bhoonidhi_user', password='password', parallelDownloads=5, output_path='D:\\bhoonidhiDownloads')
```

The module fetches the results seperately and concatenates the results in a list format. 

One can download products easily from this library.
This module shall be enabled for bhoondihiAPI which is currently under construction. Thematic based search, on-demand-LULC classification module libraries shall be released soon.
lawa 1.2.6
Chinese Words Segmentation Utilities
License: MIT
Keywords: NLP


lawa
=====

“法阿”中文分词：做最好的 Python 法律中文分词组件

"lawa" (Law-a) Chinese text segmentation: built to
be the best Python Chinese word segmentation module.

完整文档见 ``README.md``

GitHub: https://github.com/ShenDezhou/lawa

特点
====

-  支持三种分词模式：

   -  精确模式，试图将句子最精确地切开，适合文本分析；
   -  全模式，把句子中所有的可以成词的词语都扫描出来,
      速度非常快，但是不能解决歧义；
   -  搜索引擎模式，在精确模式的基础上，对长词再次切分，提高召回率，适合用于搜索引擎分词。

-  支持繁体分词
-  支持自定义词典
-  MIT 授权协议

在线演示： ShenDezhou

安装说明
========

代码对 Python 2/3 均兼容

-  全自动安装： ``easy_install lawa`` 或者 ``pip install lawa`` / ``pip3 install lawa``
-  半自动安装：先下载 https://pypi.python.org/pypi/lawa/ ，解压后运行
   python setup.py install
-  手动安装：将 lawa 目录放置于当前目录或者 site-packages 目录
-  通过 ``import lawa`` 来引用

词典介绍
=========

* law_doc.dic和law_term.dic是使用了法规、期刊以及案例标题统计的文档频和词频字典。
* case_doc.dic和case_term.dic是案例全文统计的文档频和词频字典。
* lawa_doc.dic和lawa_term.dic是融合和法规、案例和期刊全部特征统计的文档频和词频字典，默认使用`lawa_doc.dic`作为词典加载。

中文全角字符
============

* 对于全角字符同样看作ASCII字符（英文字符）进行处理：
    * 例：“---６０００元” 分成  -, -, -, ６０００, 元

新增网络词典
=============

* 从中文维基和百度百科收集了一些互联网词语对法律语料进行了扩充，默认新词典改为`wiki_baike_law_doc.dic`。词典共7627641个词，其中法律词231316个，其余为网络词。

持久化
=============

* 用joblib替换marshal做持久化
seu-insitu-tools 0.0.4b0.post3
Find and compare In Situ expression in Drosophila embryos
License: MIT
Requires Python: >=3.8

# insitu-tools

A set of command line tools that find and compare *In situ* expression patterns between mRNA expression images, specifically, the *Drosophila* embryos.

The algorithm used is described in this [paper](https://dl.acm.org/doi/10.1145/974614.974636).

![Flow chart](https://github.com/zzhmark/insitu-tools/blob/main/insituTools.png)

Some steps are taken to enhance performance, including:

* Coversion of Color images to grayscale, differentiating stains and embryo texture.

* Linear Transformation of intensity to normalize signals across images.

* 4 orientations' comparison to determine the best score.

* Normalize local GMM scores using the self comparison score.

* Global GMM scores (mutual information scores) are calculated on the union area of the 2 embryos.

## Installation

```
pip install seu-insitu-tools
```

## Usages

Detailed descriptions of commands and their arugments are located in our [wiki](https://github.com/zzhmark/insitu-tools/wiki).

## Tutorial
Check out the [example](https://github.com/zzhmark/insitu-tools/wiki/Example:-Comparing-Fly-Embryo-Staining-Patterns) in our wiki for quick learning of the tools.
sf-rfc-validate 0.0.8
Validate HTTP Structured Fields in XML2RFC documents
License: MIT
Requires Python: >=3.7

# sf-rfc-validate

This is a simple script to validate [Structured Fields](https://httpwg.org/http-extensions/draft-ietf-httpbis-header-structure.html) in [xml2rfcv3](https://tools.ietf.org/html/rfc7991) documents.

It examines all `sourcecode` and `artwork` elements; when one has a `type` of `http-sf-item`, `http-sf-list` or `http-sf-dict`, it validates the contents.

When validating, it assumes that the content is a HTTP field section; that is, one or more lines, each in the format `field_name: field_value`. Line folding is supported, so that long lines can be formatted appropriately. Multiple lines with the same name will be combined into one value. Then, each value is parsed as the Structured Field type indicated by the `type` attribute.

For example,

~~~ xml
<sourcecode type="http-sf-list">
Foo: bar; baz
Foo: one,
     two
</sourcecode>
~~~

... will be validated as a Structured Field List with the value `bar; baz, one, two`.

Note that in your XML, there **must not be any whitespace** at the start of lines, unless they're continuation of previous lines (folding, as seen above).


## Installation

The script requires Python 3, and can be installed with pip:

> pip3 install sf-rfc-validate


## Use

> sf-rfc-validate.py rfcNNNN.xml

If there are errors, they will be listed, and the program will return 1.