semvec/pypi-packages huggingface dataset:
devanagari 0.1
Package for applying NLP to devanagari datasets
Requires Python: >=3.6

devanagari
==============================

NLP library for nepali textual dataset

Project Organization
------------

    ├── LICENSE
    ├── Makefile           <- Makefile with commands like `make data` or `make train`
    ├── README.md          <- The top-level README for developers using this project.
    ├── data
    │   ├── external       <- Data from third party sources.
    │   ├── interim        <- Intermediate data that has been transformed.
    │   ├── processed      <- The final, canonical data sets for modeling.
    │   └── raw            <- The original, immutable data dump.
    │
    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
    │
    ├── models             <- Trained and serialized models, model predictions, or model summaries
    │
    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
    │                         the creator's initials, and a short `-` delimited description, e.g.
    │                         `1.0-jqp-initial-data-exploration`.
    │
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
    │
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    │
    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
    │                         generated with `pip freeze > requirements.txt`
    │
    ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
    ├── src                <- Source code for use in this project.
    │   ├── __init__.py    <- Makes src a Python module
    │   │
    │   ├── data           <- Scripts to download or generate data
    │   │   └── make_dataset.py
    │   │
    │   ├── features       <- Scripts to turn raw data into features for modeling
    │   │   └── build_features.py
    │   │
    │   ├── models         <- Scripts to train models and then use trained models to make
    │   │   │                 predictions
    │   │   ├── predict_model.py
    │   │   └── train_model.py
    │   │
    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
    │       └── visualize.py
    │
    └── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io


--------

<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
hy-language-server 0.0.6
hy language server using Jedhy
Requires Python: >=3.6

hy-language-server
===

[![PyPI version](https://badge.fury.io/py/hy-language-server.svg)](https://pypi.org/project/hy-language-server)

[Hy](https://github.com/hylang/hy) Language Server built using [pygls](https://github.com/openlawlibrary/pygls) and [Jedhy](https://github.com/ekaschalk/jedhy).

## Supported Features

Note: Currently, these features are available only for Hy's built-in core functions.

- `textDocument/completion`
- `textDocument/hover`

## Installation

```sh
$ pip install hy-language-server
```

`hyls` will be installed under your PATH.

## license

MIT
devanagari-nlp 0.0.6
Package for applying NLP to devanagari datasets
Requires Python: >=3.6

devanagari_nlp
==============================

NLP library for nepali textual dataset

Project Organization
------------

    ├── LICENSE
    ├── Makefile           <- Makefile with commands like `make data` or `make train`
    ├── README.md          <- The top-level README for developers using this project.
    ├── data
    │   ├── external       <- Data from third party sources.
    │   ├── interim        <- Intermediate data that has been transformed.
    │   ├── processed      <- The final, canonical data sets for modeling.
    │   └── raw            <- The original, immutable data dump.
    │
    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
    │
    ├── models             <- Trained and serialized models, model predictions, or model summaries
    │
    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
    │                         the creator's initials, and a short `-` delimited description, e.g.
    │                         `1.0-jqp-initial-data-exploration`.
    │
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
    │
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    │
    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
    │                         generated with `pip freeze > requirements.txt`
    │
    ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
    ├── src                <- Source code for use in this project.
    │   ├── __init__.py    <- Makes src a Python module
    │   │
    │   ├── data           <- Scripts to download or generate data
    │   │   └── make_dataset.py
    │   │
    │   ├── features       <- Scripts to turn raw data into features for modeling
    │   │   └── build_features.py
    │   │
    │   ├── models         <- Scripts to train models and then use trained models to make
    │   │   │                 predictions
    │   │   ├── predict_model.py
    │   │   └── train_model.py
    │   │
    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
    │       └── visualize.py
    │
    └── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io


--------

<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
exceltoamx 0.0.5
xlsx to amx dict list
Requires Python: >=3.6

# exceltoamx
import excel spreadsheet to list of amx dicts.

This is NOT a generic Excel importer. It's specific to campus AMX systems.

Each amx system is represented by a dictionary populated by the excel columns.
These dictionaries are then put into a single list and returned.

Required columns:
	building_name,
	room_number,
	master_ip

Columns handled here but not required:
	system_number,
	space_use_code,
	tp_generation

Any additional columns are allowed. They will be converted to dict k,v pairs

Requires tablib. Dictionary keys are populated from the top row of data in
the .xlsx file.
Each additional row is turned into a dictionary with the values from that row.
Then all of the rows/dictionaries are put into a list and returned.
We could also return the xlsx dataset, but there's no current use for it so
I left that commented out.
:param path: path to .xlsx file
:return: list of dictionaries
amxfirmware 0.0.5
amx telnet info, firmware checks, logs, etc.
Requires Python: >=3.6

# amxfirmware
use amxtelnet results to check master and device firmware against versions provided. asyncio: n/a

## MasterFirmware(firmware_dir):
#### firmware_dir: Directory to store firmware lists that will be created.

### set_systems(systems):
#### List of dicts, where each dict is an AMX system (or at least an AMX master controller).
#### Required keys in each dict:
##### full_name: string. Name that will end up being imported into your Netlinx address book.
##### ip_address: string. Formatted like '192.168.1.1'
##### master_model: string. Formatted like 'NI-700'
##### master_serial: string. AMX serial number.

### set_versions(ni_700_current,ni_x100_current,nx_current):
#### ni_700_current: string in this format: '1.23.456'. Length of segments does not matter. No letters.
#### ni_x100_current: string in this format: '1.23.456'. Length of segments does not matter. No letters.
#### nx_current: string in this format: '1.23.456'. Length of segments does not matter. No letters.

### run():
#### Begins parsing information from the files located in input_path, then creates .csv files of each version that has updates. Masters that are already up to date will be put in a list named 'master fw up to date.csv'. Masters that didn't have all of the required keys will be put into 'master fw skipped.csv'. New k:v pairs named 'master_update_list' and 'master_update_info' will also be added to each system which is then bundled with the rest and returned in a list.


## DeviceFirmware(firmware_dir):
#### firmware_dir: Directory to store firmware lists that will be created.

### set_systems(systems):
#### List of dicts, where each dict is an AMX system (or at least an AMX master controller).
#### Required keys in each dict:
##### full_name: string. Name that will end up being imported into your Netlinx address book.
##### ip_address: string. Formatted like '192.168.1.1'
##### master_model: string. Formatted like 'NI-700'
##### master_serial: string. AMX serial number.

### set_versions(ni_700_current,nx_current):
#### ni_current: string in this format: '1.23.456'. Length of segments does not matter. No letters.
#### nx_current: string in this format: '1.23.456'. Length of segments does not matter. No letters.

### run():
#### Begins parsing information from the files located in input_path, then creates .csv files of each version that has updates. Controllers that are already up to date will be put in a list named 'device fw up to date.csv'. Masters that didn't have all of the required keys will be put into 'device fw skipped.csv'. New k:v pairs named 'device_update_list' and 'device_update_info' will also be added to each system which is then bundled with the rest and returned in a list.
everest-nlp 0.0.5
Package for applying NLP to devanagari datasets
Requires Python: >=3.6

everest-nlp
==============================

NLP library for nepali textual dataset

Project Organization
------------

    ├── LICENSE
    ├── Makefile           <- Makefile with commands like `make data` or `make train`
    ├── README.md          <- The top-level README for developers using this project.
    ├── data
    │   ├── external       <- Data from third party sources.
    │   ├── interim        <- Intermediate data that has been transformed.
    │   ├── processed      <- The final, canonical data sets for modeling.
    │   └── raw            <- The original, immutable data dump.
    │
    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
    │
    ├── models             <- Trained and serialized models, model predictions, or model summaries
    │
    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
    │                         the creator's initials, and a short `-` delimited description, e.g.
    │                         `1.0-jqp-initial-data-exploration`.
    │
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
    │
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    │
    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
    │                         generated with `pip freeze > requirements.txt`
    │
    ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
    ├── src                <- Source code for use in this project.
    │   ├── __init__.py    <- Makes src a Python module
    │   │
    │   ├── data           <- Scripts to download or generate data
    │   │   └── make_dataset.py
    │   │
    │   ├── features       <- Scripts to turn raw data into features for modeling
    │   │   └── build_features.py
    │   │
    │   ├── models         <- Scripts to train models and then use trained models to make
    │   │   │                 predictions
    │   │   ├── predict_model.py
    │   │   └── train_model.py
    │   │
    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
    │       └── visualize.py
    │
    └── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io


--------

<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
apachecn-python-docs-zh 2021.5.6.0
ApacheCN Python 中文文档汇总
Requires Python: >=3.6

# ApacheCN Python 中文文档汇总

> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
> 
> 欢迎任何人参与和完善：一个人可以走的很快，但是一群人却可以走的更远。

* [在线阅读](https://pydoc.apachecn.org)
* [在线阅读（Gitee）](https://apachecn.gitee.io/apachecn-python-docs-zh/)
* [ApacheCN 学习资源](http://docs.apachecn.org/)

## 目录

+   [Cython 中文文档](doc/cython-doc-zh/SUMMARY.md)
+   [Django 1.8 中文文档](doc/django-doc-18-zh/SUMMARY.md)
+   [Numba 中文文档](doc/numba-doc-zh/SUMMARY.md)
+   [PyQt4 中文文档](doc/pyqt4-doc-zh/SUMMARY.md)
+   [RPy2 中文文档](doc/rpy2-doc-zh/SUMMARY.md)
+   [Scapy 中文文档](doc/scapy-doc-zh/SUMMARY.md)
+   [Scrapy 1.6 中文文档](doc/scrapy-doc-zh/SUMMARY.md)

## 贡献指南

本项目需要校对，欢迎大家提交 Pull Request。

> 请您勇敢地去翻译和改进翻译。虽然我们追求卓越，但我们并不要求您做到十全十美，因此请不要担心因为翻译上犯错——在大部分情况下，我们的服务器已经记录所有的翻译，因此您不必担心会因为您的失误遭到无法挽回的破坏。（改编自维基百科）

## 联系方式

### 负责人

* [飞龙](https://github.com/wizardforcel): 562826179

### 其他

*   在我们的 [apachecn/apachecn-python-docs-zh](https://github.com/apachecn/apachecn-python-docs-zh) github 上提 issue.
*   发邮件到 Email: `apachecn@163.com`.
*   在我们的 [组织学习交流群](http://www.apachecn.org/organization/348.html) 中联系群主/管理员即可.

## 下载

### Docker

```
docker pull apachecn0/apachecn-python-docs-zh
docker run -tid -p <port>:80 apachecn0/apachecn-python-docs-zh
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install apachecn-python-docs-zh
apachecn-python-docs-zh <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g apachecn-python-docs-zh
apachecn-python-docs-zh <port>
# 访问 http://localhost:{port} 查看文档
```

## 赞助我们

![](http://data.apachecn.org/img/about/donate.jpg)
tabiyan 0.0.1
tabiyan let's you do subdomain enumeration
Keywords: python,tabiyan,subdomain enumeration,sources

Siker - Siker is an subdomain enumeration tool which collect subdomain from different sources.

Sources include :

1 - Alien Vault

2 - Anubis

3 - Binary edge

4 - Certspotter

5 - Chaos

6 - Crtsh

7 - ReconDev

8 - Security Trails

9 - Shodan

10 - Sonar

11 - Spyse

12 - Sublist3r

13 - Threatcrowd

14 - Threatminer

15 - VirusTotal