Saravanan666/pypi-org huggingface dataset:
Package: autolamella
OverviewAutoLamella is a python package for automated cryo-lamella preparation with focused ion beam milling. It is based onopenFIBSEM, and currently supports theTESCAN Automation SDKandThermoFisher AutoScript. Support for other FIBSEM systems is planned.DocumentationDocumentation SiteInstallRecommended Installation GuideCreate a new virtual environment from the Anaconda Prompt terminal:condacreate-nfibsempython=3.9pip
condaactivatefibsem
pipinstallautolamellaRunning AutolammelaOpen the Anaconda Prompt terminal and run the following commands.condaactivatefibsem
autolamella_uiTo launch liftout related methods:condaactivatefibsem
autoliftout_uiCitationIf you find this useful, please cite our work.Genevieve Buckley, Gediminas Gervinskas, Cyntia Taveneau, Hariprasad Venugopal, James C. Whisstock, Alex de Marco,Automated cryo-lamella preparation for high-throughput in-situ structural biology,Journal of Structural Biology,
Volume 210, Issue 2,
2020https://doi.org/10.1016/j.jsb.2020.107488.SeeCITATIONfor more details.
Package: autolatex
AutoLaTeXGenerate LaTeX code by Python for experiment report.用Python生成LaTeX代码写实验报告。如果有帮助就点个 star 🌟呗欢迎 Pull Request, Fork, 提 issue.Features 特性读取excel等表格文件转为latex表格(假的)word讲义转tex报告线性拟合等数学处理Install 安装pip安装：pipinstall-Uautolatex源码安装gitclonegit@github.com:Benature/AutoLaTeX.gitcdAutoLaTeX
pythonsetup.pysdistbdist_wheel
pipinstall-Udist/autolatex-0.x.x-py3-none-any.whl# 其中 0.x.x 处是 pypi 版本号Usage 使用xlsx文件 excel 表格数据转 tex 代码在命令行输入（两种写法等效）：autolatex<文件路径>
alt<文件路径>更多参数说明请用alt -h查询。.py文件内# import autolatex as altfromautolateximporttabledf=table.read_excel('data.xlsx')print(table.convert(df))word2tex详见word2tex下的 README
Package: autoleastsq
Install# only for python3
# need: sqlite3

pip install autoleastsqUsage# -*- coding: utf-8 -*-  

import autoleastsq

### 从文件获取数据到 list   其他数据源只要符合数据类型和对齐就行了

filename="./test.xlsx"
lists=autoleastsq.excel_col2list(filename,["a","b","result"])


################# 多元多项式回归样例

# 挑选所需要的因子  注意append 因子次序决定了拟合的公式，和后续使用时的入口次序, append 几个因子多项式就有几元，可灵活使用

factor=[]  # factor自由多个因子的二维list
factor.append(lists[0])
factor.append(lists[1])
y = lists[2]

##################### 拟合得到多项式模型

# 返回：多项式的参数列表、 幂次，并可根据新输入的因子list 进行预测

"""
支持的参数：
pows_start=1	# pows_start 起始拟合的幂次(默认为1)
loss_min=0.01		# loss 起始误差率要求（默认0.01, 达到即拟合结束）, 过程中会自动判断最可能的误差率
leastsq_mult(factor,y,pows_start,loss_min)
"""

model=autoleastsq.leastsq_mult(factor,y)

print(model.args,model.pows)  ## 最终的多项式参数 按高次向低次逐个因子排列 ; 最终的多项式参数最高幂次
print(model.func)  ##  模型公式

################# 根据拟合后结果多项式，按顺序输入得到结果

factor=[29,101]

result=model.leastsq_result(factor)

print(result)
Package: autolib-volatile-int
autolib -- stm32 driver library automatic generatorstm32是一款功能强大的单片机，但是由于配置使用复杂，在科创比赛中的应用被大大限制。autolib是一个精简的python脚本，可以根据用户需求自动生成所需的库函数。优点autolib产生的库函数完全基于寄存器，无论是使用hal库还是标准库的用户都可以轻松的把autolib融入在工程中autolib是一个精简的python脚本，可以在任何安装有python的电脑上运行，而无关操作系统环境，无需编译。autolib通过pypi发布，可以通过pip安装，只要有一台连接互联网的电脑就可以很方便的下载，在陌生的环境中也可以很容易的使用。有stm32cubeMX，为什么需要autolib？stm32CubeMX需要java环境，经常因为jre版本问题出错。stm32CubeMX操作更加复杂精细，因此不容易上手。例如时钟树的配置这些大学生科创很少用到的功能不会出现在autolib中，操作简单不易出错。stm32CubeMX需要安装，对硬盘空间和网络带宽都有要求，而autolib可以在任何具有python环境的电脑上运行，安装只需要pip，代码量少。autolib源码简单完全开源，使用者可以很容易进行修改。
Package: autolike
Automatically like any given Facebook URL if the user is logged in.
The facebook(url, run_time) method takes a Facebook URL and run time in seconds.
It will return a dictionary with status, message, url, like and time attribute.
If the status has “success” value then the program is successfully completed.DO NOT MISUSE IT FOR DISTURBING INDIVIDUALS.The current version is tested on Windows 11, Windows 10, and Windows 8.RequirementLogin to Facebook using your default browser and turn on the keyboard shortcuts in Facebook:Click your profile picture in the top right of Facebook.ClickDisplay and accessibility, thenKeyboard.ClickOnbelow Use single-character keyboard shortcuts.Installationautolikeonly supports Python 3. Install it using pip.$pipinstallautolikeExampleTo use (with caution), simply do:importautolikeurl="https://www.facebook.com/"# any Facebook URLrun_time=30# time in secondslike_result_dict=autolike.facebook_autolike(url,run_time)print(like_result_dict)Demonstration video:https://youtu.be/OLQNz0mbJg4DevelopmentYou can build the code by yourself in any Windows 8/8.1/10/11 machine that has Python 3 installed.
For development purpose we recommend to use a virtual environment.$python-mvenvvenv--clear$venv\Scripts\activate$pipinstall-rrequirements.txt$pythonautolike\__init__.pyContributeCreate Github Pull Requesthttps://github.com/arsho/autolike/pullsIf you have suggestion use GitHub issue system or send a message in Facebookhttps://www.facebook.com/ars.shovon.
Package: autolink
Convert URL-like and email-like strings into links.Installationpip install autolinkUsagefromautolinkimportlinkifylinkify('some text google.com, ...')# -> 'some text <a href="http://google.com">google.com</a>, ...'linkify('https://github.com',{'rel':'nofollow'})# -> '<a href="https://github.com" rel="nofollow">https://github.com</a>'linkify('me@ya.ru')# -> '<a href="mailto:me@ya.ru">me@ya.ru</a>'
Package: autolink_py
The Repo extracts url/email substring from given string and converts it into
links(or any customized format).Currenty, we apply HTML and MARKDOWN replacement
type, and also apply easy way to customize the replacement.InstallationInstallation usingpip:pipinstallautolink_pyUsagereplace URL with html formatfromautolink_py.coreimportAutoLinkertext='This website is google.com'al=AutoLinker()new_text=al.linkify(text,replaced_type='HTML')# new_text -> 'This website is <a href="http://google.com">google.com</a>'replace URL with markdown formatfromautolink_py.coreimportAutoLinkertext='This website is google.com'al=AutoLinker()new_text=al.linkify(text,replaced_type='MARKDOWN')# new_text -> 'This website is [google.com](http://google.com)'replace URL with customized format# example: 'google.com' -> '<google.com><http://google.com>'fromautolink_py.coreimportAutoLinkerclassNewAutoLinker(AutoLinker):defreplace_url(self,text,url):'''
        implement replace_url to customize the format you need.

        Params:
            text: url text that shown originally
            url: newly generated link including complete protocal based on text.
        '''returnu'<{0}><{1}>'.format(text,url)text='The website is google.com'nal=NewAutoLinker()new_text=nal.linkify(text)# new_text -> 'The website is <google.com><http://google.com>'CreditsThe core algorithm is referred by the open source Repo:autolink
Package: autolink-references-mkdocs-plugin
Autolink References (MkDocs Plugin)Thismkdocs pluginlook in each MkDocs article for the presence of a reference to tickets from issues
trackers like Jira, Linear, etc and convert them to links that point to respective
platforms:Getting startedTo install it, usingpip:pip install autolink-references-mkdocs-pluginEdit yourmkdocs.ymlfile and add these few lines of code:plugins:-autolink_references:autolinks:-reference_prefix:AF-target_url:https://linear.com/AF-<num>-reference_prefix:PROJ-target_url:https://jiracloud.com/PROJ-<num>reference_prefix: This prefix appended by a number will generate a link any time it is found in a page.target_url: The URL must contain<num>for the reference number.An exampleFor example, you could edit thedocs/index.mdfile and insert the ticket references like this:Changelog:-AF-100: add new feature.This will generate pre-processed to:Changelog:

- [AF-100](https://linear.com/AF-100): add new feature.Changelog0.2.2 (2023-12-28)Allow extended set for  and ignore ref style links, already linked items, and attr_list cases with '#' before the ref0.2.0Ignore already linked references.Converts text[AF-100]to a linked version and removes the bracketsAF-100LicenseMITBuilt with ❤️ bySaurabh Kumar
Package: autolinux
AutoLinuxThe purpose of these classes are to help people with limited knowledge of linux.With the help of these classes, we can create a list of actions for specific purposes.from linux import User, Server

u = User(username="ilker", keyfile="/home/ilker/.ssh/id_rsa")
s = Server("blog.manap.se",u)
out = s.user.run_command("df -h")
print(out["stdout"])
Package: autolist
AutolistPython Basic Library for Auto-Correct purposes using lists.Download Autolist:pip install autolistIf the above didn't work, check if Python3 is working properly.Examples# Import the Moduleimportautolistasal# Create a list which has all the correct words OR Take input from usercorrect_words_list=["autolist","advanced","listing"]string="autosift"# Take input from user or Give a String# Autolist checks the list and corrects it using the followingprint(al.autocorrect_list(string,correct_words_list))Application & FeaturesDiscord Bots:
Where the words are auto-corrected using the given correct words list. This will help make the bot more easier and better.Python Programs:
Where you take input from user for a definite options but you fear that user will mistype. Autolist autochecks and corrects the mistyped word.Functions Availableautocorrect_list : Autocorrects mistyped words using the given correct words listautocorrect : Autocorrects words easily and has accuracy rate over 98%accuracy_correction : Corrects words with above 90% word accuracyWe are ready to accept your suggestions.Message me on Discord (AbelR007#5096)Create an Issue inAutolist GithubCreated with ❤️ by AbelR007
Package: autoliter
autoLiteratureautoLiterature是一个基于Python的自动文献管理命令行工具。Inspired byMu Li.识别规则：自动识别- {xxx}。当笔记文件中包含- {paper_id}时候，会下载该文献的信息，不下载PDF。当笔记文件中包含- {{paper_id}}时候，会下载该文献的信息，以及PDF。注意：paper_id支持已发表文章的doi,预发布文章的arvix_id,biorvix_id,medrvix_id。安装pip 安装pipinstallautoliter
或者
pip3installautoliter源码安装gitclonehttps://github.com/WilmerWang/autoLiterature.gitcdautoLiterature
pythonsetup.pyinstall软件参数autolter

optionalarguments:-h,--helpshowthishelpmessageandexit-iINPUT,--inputINPUTThepathtothenotefileornotefilefolder.-oOUTPUT,--outputOUTPUTFolderpathtosavepaperpdfsandiamges.NOTE:MUSTBEFOLDER-pPROXY,--proxyPROXYTheproxy.e.g.127.0.0.1:1080-d,--deleteDeleteunreferencedattachmentsinnotes.Usewithcaution,whenused,-imustbeafolderpathincludingallnotes-mMIGRATION,--migrationMIGRATIONthepdffolderpathyouwanttoreconnectto使用基本使用假设input为文献笔记(md文件)的文件夹路径，output为要保存PDF的文件夹路径。# 更新input文件夹下所有md文件autoliter-iinput-ooutput# 仅更新input/example.md文件autoliter-iinput/example.md-ooutput# -d 是个可选项，当 -i 是文件夹路径时候，使用 -d 会删除PDF文件夹下和文献笔记内容无关的pdf文件autoliter-iinput-ooutput-d迁移笔记和PDF文件当要移动文献笔记或者PDF文件夹的时候，文献笔记中的PDF链接可能会变的无法使用。可以使用-m来重新关联PDF文件和文献笔记。# 更新input文件夹下所有md文件autoliter-iinput-mmovedPDFs/# 仅更新input/example.md文件autoliter-iinput/example.md-mmovedPDFs/更多可以本地查看jupyter note，或者在线查看githubdoc文件夹。LicenseMIT
Package: autolive
# Autolive: AWS medialive automationThe core idea of this proyect is to offload video transcoding from an NGNIX-RTMP server to AWS Medialive, replacing FFMPEG for transcoding and reducing server load from NGINX server.NGINX RTMP:https://github.com/arut/nginx-rtmp-module## How to make it work:Install Nginx with rtmp module.Git clone Autolive into preferred dir.Set AWS details in conf.py.Simlink autolive to /usr/local/bin/autolive.Change nginx.conf to trigger autolive. (using nginx-rtmp exec options)## AWS resources needed:DynamoDB table that defines channel details. (schema comming soon)s3 bucket for AWS medalive outputs.CDN distribution. (optional)## CLI options:‘-s’, ‘–streamkey’ –> (string) | Define streamkey. Streamkey should reference dynamodb table.‘-a’, ‘–action’ –> (string) | Define what action to perform: ‘create’, ‘delete’.‘-A’, ‘–application’ –> (string) | manually define Nginx rtmp application, default ‘live’.## Notes:ATM only pull channels are supported.
Lots of improvment needed but core functionality is working.
Package: autoloads
autoloadspython web and databases’s tools.简介:autoloads是tornado web 简易开发工具包，支持数据库models自动生成加载，
handler采用装饰器方式配置，并自动扫描handler并加载，使用方式见demo。Changelog0.2.2.3 - 30-Jun-2015修改bug.0.2.2.1 - 19-Jun-2015重构, 并为可读性而增加注释, 增加Demo, 规范风格PEP8, 兼容之前项目.0.1 - 14-Jan-2015青哥之前的版本.
Package: auto-logger
Auto-loggerAutomatically add function call logs and method call logs for Python code.Installpipinstall-Uauto-loggerUseimportloggingfromdataclassesimportdataclassfromauto_loggerimportConfig,MethodLoggerMeta,logFuncCall,formatJsonlogging.basicConfig(level=logging.INFO)@logFuncCalldefadd(a:int,b:int):returna+badd(1,2)# INFO:auto_logger:CALL FUCNTION <add> WITH ARGS (1, 2) KWARGS {} RETURNS  3add(a=1,b=2)# INFO:auto_logger:CALL FUCNTION <add> WITH ARGS () KWARGS {'a': 1, 'b': 2} RETURNS  3@dataclassclassA(metaclass=MethodLoggerMeta):a:int=0defadd(self,n:int):self.a+=1defabs(self):returnabs(self.a)A().add(1)# INFO:auto_logger:CALL METHOD <add> OF A(a=0) WITH ARGS (1,) KWARGS {} RETURNS  NoneConfig.format=formatJsonA().add(1)# INFO:auto_logger:{"args": [1], "kwargs": {}, "ret": null, "method": "add", "object": "A(a=0)"}Config.ignoreMethods[A]={'abs'}A().abs()# Log nothing
Package: autology
# AutologyAutology is a text file based life log.  Using the contents of markdown files (to include frontmatter) it will provide acollection of reports for later viewing.  It currently uses git as a backend for syncing between multiple clients.## ContributingPlease report all software issues to [Bug tracker](https://github.com/MeerkatLabs/autology/issues).  Any issuesassociated with the templates that are provided by default should be reported to the[Autology Template Bug Tracker](https://github.com/MeerkatLabs/autology_templates/issues).## InstallationIt is recommended that you use this tool in a virtual environment associated with the directory that you'll be using tostore all of the content.```bashmkdir log; cd logpython3 -m venv envsource env/bin/activatepip install autology```To create the log directory structure, execute:```bashautology init```This will retrieve the [template files](https://github.com/MeerkatLabs/autology_templates) from GitHub, and create a new`config.yaml` file with the default values of all of the parameters that can be used.## Configuration### Git StorageIn order to use this log with git as the backend you will need to modify the `config.yaml` file and enable git support.The first time it is run, it will initialize a git environment (if necessary).  Configuring the git environment with aremote and refspec will allow the make_note sub-command to push the changes to the backend of your choosing.### Configuring EditorBy default, the editor used to modify the text files is `xdg-open`.  Depending on the editor that is currently set as thedefault, this may not work correctly.  For example, executing `gvim` will automatically return when the editor isopened instead of waiting for the user to save and close the file.  To get around this, you will need to set the valueof the editor to something more sane.  To continue the example of gvim, the setting is:```yamlmake_note:editor: gvim -f {file} +2```You will need to provide the full command for the editor, including the `{file}` parameter.  The `-f` flag isresponsible for executing the tool in the foreground.  `+2` moves the editor to the second line of the file (skippingthe opening frontmatter tag).## ExecutionTo generate a new note file, use the following command while the virtual environment is activated.```bashautology make_note```Once you have some notes, you can then use the `generate` sub-command to translate the log files into a HTML output.```bashautology generate```To view the content that has been generated, you can use the `serve` sub-command to create a simple http server forviewing.  Note this server does not auto-generate the content on changes, and is incredibly slow.  As such, you shouldnot use it in production.
Package: autolr
Auto Logistic RegressionOverviewThis project implements an Auto Logistic Regression framework for easy model training, evaluation, and prediction. The framework includes two main classes:AutoLogisticRegressionandAutoPreprocessor.AutoLogisticRegressionClassTheAutoLogisticRegressionclass is designed to automate the process of logistic regression model training and evaluation. Key functionalities include:Initialization: Accepts the path to the training dataset (data_path), the target column name (target_column), the output path for the model and predictions (output_path), and an optional parameter for the number of folds in cross-validation (num_folds).Training: Uses logistic regression with cross-validation to train a model on the provided dataset. The best model is saved as a pickle file for future use.Prediction: Given a new dataset, the trained model can be used to make predictions. The predictions are saved to a CSV file at the specified output path.AutoPreprocessorClassTheAutoPreprocessorclass handles the preprocessing steps required before training or making predictions with the logistic regression model. Key functionalities include:Initialization: Accepts optional parameters for specifying categorical columns (categorical_columns), numeric columns (numeric_columns), and whether to perform oversampling (oversample).Fit and Transform: Fits an imputer, encoder (for categorical columns), and scaler (for numeric columns) on the provided data. The transformations are then applied to the data.Oversampling: Optionally applies oversampling to balance the class distribution in the training data.Split Data: Splits the data into training and testing sets.UsageImporting:fromautolrimportAutoLogisticRegressionInitialization: Create an instance of theAutoLogisticRegressionclass by providing the path to the training dataset, the target column name, and the output path for the model and predictions. Optionally, you can specify the number of folds for cross-validation.auto_lr=AutoLogisticRegression(data_path='path/to/training_data.csv',target_column='target',output_path='output',num_folds=5)Training Model: After training, the model is automatically evaluated using metrics such as accuracy, classification report, and confusion matrix. The feature importance is also displayed.# Training the model and evaluating itauto_lr.train()Prediction: After training, you can use the trained model to make predictions on new data by providing the path to the new dataset.predictions=auto_lr.predict(data_path='path/to/test_data.csv')Dependenciespandasscikit-learnimbalanced-learnpickleosNotesEnsure that the required dependencies are installed before running the code.TheAutoPreprocessorclass is used internally for data preprocessing and is not intended for standalone use.Customize and extend this framework based on your specific needs.If you encounter any issues or have suggestions for improvements, please let us know.
Package: autolux
inspired bylumen <https://github.com/anishathalye/lumen>but written for
*nix machines. autolux takes a screenshot every few seconds, figures out the
‘average value’ of the resulting image (theluminence) and changes the
screen brightness based on the screen’s contents.installationpip install autoluxdependenciespython2 (not python3)python-daemon (optional, for running as a service)imagemagick (for screenshots + determining average brightness)xdotool (finding focused window)xbacklight (for setting display brightness)usage# runs autolux
autoluxor# running autolux as a service
autolux --daemonxrandrif your display does not support hardware adjustment through xbacklight,
its possible to use xrandr to adjust the brightness via software.# find out the display of your name using xrandr
xrandr --listmonitors

autolux --xrandr eDP1 # my monitor is named eDP1
autolux --xrandr eDP1 --reset # reset brightness to normal
autolux --xrandr eDP1 --adjust -1 # lower brightness by 1
autolux --xrandr eDP1 --adjust +1 # increase brightness by 1modelinitially, autolux uses linear interpolation to figure out the brightness to
map to different luminence inputs. as the brightness is manually adjusted away
from its predicted value, autolux records the time of day and luminence values
that prompted you to change the brightness level and integrates the new
information into its model.autolux assumes that time of day and screen content are the sole predictors of
one’s preferred brightness, but the model could further be improved by adding
in several factors, such asambient lightoruser active time(as a
stand-in for eye strain). because of these and other unknown confounding
variables, autolux has an emphasis on quick learning of new preferences and not
burning in old ones.visualsautolux --vis --plot-bright
# show brightness prediction model
# requires matplotlib# color is the input luminence. (5K - 65K, lower value is darker screen content)
# x axis is hour (0 - 24, with UTC offset applied)
# y axis is predicted brightness level from the model (0 - 100%)


autolux --vis --plot-luma
# --plot-luma will swap the luma and brightness on the Y axis and colorbar
Package: autolycus
UsageUsinghg-fast-exportis quite simple for a Mercurial repository :mkdir repo-git # or whatever
cd repo-git
git init
hg-fast-export.sh -r <repo>Please note thathg-fast-exportdoes not automatically check out the newly imported repository. You probably want to follow up the import with agit checkout-command.Incremental imports to track hg repos is supported, too.Usinghg-resetit is quite simple within a Git repository that ishg-fast-export‘ed from Mercurial:hg-reset.sh -R <revision>will give hints on which branches need adjustment for starting over again.When a Mercurial repository does not use UTF-8 for encoding author strings and commit messages the-e<encoding>command line option can be used to force fast-export to convert incoming meta data from to UTF-8. This encoding option is also applied to file names.In some locales Mercurial uses different encodings for commit messages and file names. In that case, you can use--fe<encoding>command line option which overrides the-eoption for file names.As Mercurial appears to be much less picky about the syntax of the author information than Git, an author mapping file can be given to hg-fast-export to fix up malformed author strings. The file is specified using the-Aoption. The file should contain lines of the formFromAuthor=ToAuthor. The exampleauthors.mapbelow will translateUser <garbage<user@example.com>toUser <user@example.com>.-- Start of authors.map --
User <garbage<user@example.com>=User <user@example.com>
-- End of authors.map --Tag and Branch NamingAs Git and Mercurial have differ in what is a valid branch and tag name the-Band-Toptions allow a mapping file to be specified to rename branches and tags (respectively). The syntax of the mapping file is the same as for the author mapping.Notes/Limitationshg-fast-exportsupports multiple branches but only named branches with exactly one head each. Otherwise commits to the tip of these heads within the branch will get flattened into merge commits.As eachgit-fast-importrun creates a new pack file, it may be required to repack the repository quite often for incremental imports (especially when importing a small number of changesets per incremental import).The way the Hg API and remote access protocol is designed it is not possible to usehg-fast-exporton remote repositories (HTTP/SSH). First clone the repository, then convert it.Mercurialdoesn’t support Python 3and probably won’t for a while.Designhg-fast-export.pywas designed in a way that doesn’t require a 2-pass mechanism or any prior repository analysis: if just feeds what it finds intogit-fast-import. This also implies that it heavily relies on strictly linear ordering of changesets from Hg, i.e. its append-only storage model so that changesetshg-fast-exportalready saw never get modified.