Saravanan666/pypi-org huggingface dataset:
Package: amisrsynthdata
This module provides tools to create synthetic data files for the AMISR (Advanced Module Incoherent Scatter Radar) systems.  The files are based on both a specified ionospheric state and a radar configuration.  This can be used to generate synthetic data in the “SRI data format” both for the three existing AMISRs and for hypothetical future “AMISR-like” systems.  Primarily, it was designed to help test the functionality of various inversion algorithms that attempt to create a complete picture of ionospheric state parameters from discrete measurements by creating a way to check the output of these algorithms against known “truth” data.  Please note that this module does NOT attempt to simulate any aspect of fundamental ISR theory.Quick StartInstallationThe amisrsynthdata package is pure python and can be installed easily with pip:$ pip install amisrsynthdataAdditionalinstallation instructionsare also available.Basic UsageThis package installs the command line toolamisrsynthdata, which is used along with a YAML configuration file to generate an output hdf5 AMISR data file.  The configuration file specifies the ionosphere state and radar configuration that should be used:$ amisrsynthdata config.yamlRefer to theconfiguration file docsfor information about the contents of these configuration files and how to construct one.LimitationsThe following are NOT currently included in the amisrsynthdata module:Any kind of proper treatment or simulation of ISR theory - The module effectively assumes the radar measures plasma parameters perfectly at a particular location, although empirical errors can be added.Integration over a time period or smearing along the length of pulses, as well as pulse coding.Madrigal data format - Currently files are only generated in the SRI data format.DocumentationFull documentation for amisrsynthdata is available onReadTheDocs.ContributingContributions to this package are welcome and encouraged, particularly to expand the currently set of specified ionospheres.  Contributions can take the form ofissuesto report bugs and request new features andpull requeststo submit new code.  Please refer to thecontributing guidelinesfor more details.  Specific instructions on how to add a new state function to describe the ionosphere are available inNew State Functions.
Package: amisui
amis-python基于百度amis前端框架的python pydantic模型封装。由于原版本缺少大量amis新版本的组件或配置，因此本项目在其版本的基础上进行了扩充。相比fastapi-amis-admin的版本：涵盖amis截至3.1.0版本的所有组件使用jinja2模板支持修改主题安装pip install amisui简单使用fromamisui.componentsimportPagepage=Page(title='新页面',body='Hello World')# 输出为python字典print(page.to_dict())# 输出为jsonprint(page.to_json())# 输出为strprint(page.render())# 保存为html文件withopen('HelloWorld.html','w',encoding='utf-8')asf:f.write(page.render())详细使用详见amis官方文档感谢amis-python
Package: amit
AMITCentralise all your enumerations !Report Bug·Request FeatureTable of ContentsAbout the ProjectTools usedGetting StartedPrerequisitesInstallationUsageHelpRoadmapContributingLicenseAbout The ProjectAmit is a tool that group other tools into a unique view. Informations collected are organized and stored into a database.Tools usedAmit uses some performant tools to fetch information about the targets.NmapldapsearchrpcclientGetting StartedJust install it as a python module.pipinstallamitUsageJust run amit !amitAdd machinesHere we add 2 machines defined by their IP or domain.>add10.10.10.10example.comScan machinesThe 2 machines have id 1 and 2.>scanmachines12Show job status>showjobsShow services found>showservicesScan services>scanservices<id><id>...Show informations found for services>showservices<id>-vvvHelpYou can inspect the list of available commands using thehelpcommand.
For each command, you can view its usage using the-hflag.RoadmapSee theopen issuesfor a list of proposed features (and known issues).ContributingContributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make aregreatly appreciated.Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestLicenseDistributed under the GPL-3.0 License. SeeLICENSEfor more information.
Package: amit26april
IntroductionThis project helps the begginers to generate ai models with 1 line of code.It is also useful for experts as it will automate repetative task and experts can focus upon main modelIt works right now only for image classification but there may be updates in futureSimple Usage for BegginersHere, I have created a model to classify humuan face into 7 emotions in just 4 linesfrom amit26april.image_cnn import classification

    model = classification()
    model = model.create('TRAIN',7)
    model.predict([x_test[0]])Simple usage for ExpertsHere the preperation of data is automated and it now depends upon developer , how to move futherfrom amit26april.image_cnn import classification

    x=classification()
    df = x.create_dataframe('TRAIN')
    y_train = x.prep_y_train
    x_train = x.prep_x_train
    # Rest complex work can be done on own
Package: amitkh-wordle
Entropy based Wordle solverCLI built with Typer. Based onthisvideo from 3 Blue 1 BrownInstallationpip install amitkh-wordleQuick StartThe solver needs to generate a pattern dictionary containing all of the word and pattern combinations in order to run. To do this, runwordle gen-pattern-dict. This process will take around 10-15 minutes.By default the dictionary will be compressed using bz2 to about 200 MB. You can also specify the--no-compressoption which will save the pickle without compression and will result in quicker load times, but take about 800 MB of storage .To run the solver in interactive mode, runwordle play. This will be followed by roughly 2 minutes of setup time while the program loads in the pattern dictionary and calculates the initial guess.For maximum speed, you can also add the-sflag, which runs the solver in time saving mode. This means instead of spending 1 minute or so calculating the first guess, which is always "tares", it simply assumes this to be the first guess and allows you to enter your pattern from "tares" straight away.After this, you will be presented with some information and prompted for a guess. The program will present you the top 10 guesses (though you can guess any word you would like), along with (from left to right) the expected score should you play that guess, the expected information that guess will provide in bits, and the probability that guess is the answer.If there are under 20 words remaining, the program will also print out those remaining words along with the probability that they are a potential answer based on frequency (which is not the same as the probability that they are the answer to this game).From there, simply type in your guesses and the pattern that was returned to you according to the instructions, and repeat until solved.Other Functionswordle play-wordsThis command allows you to pass in a word or list of words to be played by the solver, like sowordle play-words snake cater abysswordle test-allThis command runs the wordle solver on all of the 2,315 answers to the additional wordle, tracking the number of guesses taken for each, and writing them as a JSON to the file name given as an argumentwordle --helpDisplay a brief help message
Package: amitool
数据库中使用Zlog表需要在创建Model的时候加上__table_args__ = {'implicit_returning':False}使用方法pipinstallamitoolfromamitools.AmiToolimportAmiToolsami=AmiTools(LogDir="./log",ConnString="",Host="127.0.0.1",PassWord="",Port=6379,db=10)# 参数不用全填logger=ami.loggerslogger.info("start session")session=ami.initSession()logger.info("start redis")rs=ami.initRedis()
Package: amitu-hstore
You needdynamic columnsin your tables. What do you do?Create lots of tables to handle it. Nice, now you’ll need more models and lots of additional sqls. Insertion and selection will be slow as hell.Use anoSQLdatabase just for this issue.Good luck.Create a serialized column. Nice, insertion will be fine, and reading data from a record too. But, what if you have a condition in your select that includes serialized data? Yeah, regular expressions.Documentation-Mailing ListProjects using this packagedjango-rest-framework-hstore:django-rest-frameworktools fordjango-hstoreNodeshot: extensible django web application for management of community-led georeferenced data - some features ofdjango-hstore, like theschema-modehave been developed for this project
Package: amivcrm
Connector to the AMIV SugarCRMSugarCRM provides a SOAP and a REST api. At the time this tool was written
the REST api was unfortunately not available. Therefore SOAP is used.The python library suds is used, more exactly the fork byjurko.Installationpip install amivcrmUsageYou will need a soap username and password. You can find them in theAMIV Wiki.
After you got the credentials, its as easy as this:from amivcrm import AMIVCRM

CRM = AMIVCRM(username, password)
# Optional: Specify `url` and/or `appname`
# CRM = AMIVCRM(username, password, url="...", appname="...")

# Get Companies
CRM.get('Accounts')

# Select only certain fields
# Filter and order with SQL statements
CRM.get('Accounts',
    # Only companies participating in job fair
    query="accounts_cstm.messeteilnahme_c = 1",
    # Order alphabetically
    order_by="accounts.name",
    # Return Name and ID only
    select_fields=['name', 'id'])

# Get a single company by id
CRM.getentry('Accounts', '505404b1-1851-1472-d63e-4d829377e30b',
             # Optional: Limit the returned fields as well
             select_fields=['name'])

# Get a company only if  modified after given date
entry_id = '505404b1-1851-1472-d63e-4d829377e30b'
date = '2016-03-20 08:05:39'
# Be careful to use quotes in query
query = ("accounts.id = '%s' and accounts.date_modified >= '%s'"
         % (entry_id, date))
CRM.get('Accounts', query=query)
Package: amix
Automatic mix of audio clips.InstallationMake sure, to haveffmpeginstalled.pipinstallamixUsageI also uploaded my first results toSoundCloud.Please check first of all the help function.amix--helpAlso make sure to always obtain the latest version.amix--versionRender audio from the definition fileamix.ymlin the current working directory to disc.amixIncrease verbosity to also output theffmpeglogging.amix-vvUse ajinja2template and supply data.amixtemplates/amix.yml.j2--data"full=8""half=4""from=7.825""tempo=0.538""pitch=1.1""original_tempo=180"Automatically create parts from clips.amix--parts_from_clipsConfigurationYou can find the JSON schemahere.A sample configuration looks like:name:DnBoriginal_tempo:180parts:-name:backbeat_partbars:16clips:-name:backbeatmix:-name:introparts:-name:backbeat_part
Package: amiyabot
Amiya-Bot简洁高效的 Python 异步渐进式 QQ 频道机器人框架！可使用内置的适配器创建 KOOK、mirai-api-http、go-cqhttp、ComWeChat Client 以及支持 OneBot v11/12 的机器人实现。官方文档：www.amiyabot.comInstallpip install amiyabotGet startedSingle modeimportasynciofromamiyabotimportAmiyaBot,Message,Chainbot=AmiyaBot(appid='******',token='******')@bot.on_message(keywords='hello')asyncdef_(data:Message):returnChain(data).text(f'hello,{data.nickname}')asyncio.run(bot.start())Multiple modeimportasynciofromamiyabotimportMultipleAccounts,AmiyaBot,Message,Chainbots=MultipleAccounts(AmiyaBot(appid='******',token='******'),AmiyaBot(appid='******',token='******'),...)@bots.on_message(keywords='hello')asyncdef_(data:Message):returnChain(data).text(f'hello,{data.nickname}')asyncio.run(bots.start())Use adapterimportasynciofromamiyabotimportAmiyaBot,Message,Chainfromamiyabot.adapters.onebot.v11importonebot11bot=AmiyaBot(appid='******',token='******',adapter=onebot11(host='127.0.0.1',http_port=8080,ws_port=8060))@bot.on_message(keywords='hello')asyncdef_(data:Message):returnChain(data).text(f'hello,{data.nickname}')asyncio.run(bot.start())Get more
Package: amiyabot-core-test
Amiya-Bot简洁高效的 Python 异步渐进式 QQ 频道机器人框架！可使用内置的适配器创建 KOOK、mirai-api-http、go-cqhttp、ComWeChat Client 以及支持 OneBot v11/12 的机器人实现。官方文档：www.amiyabot.comInstallpip install amiyabotGet startedSingle modeimportasynciofromamiyabotimportAmiyaBot,Message,Chainbot=AmiyaBot(appid='******',token='******')@bot.on_message(keywords='hello')asyncdef_(data:Message):returnChain(data).text(f'hello,{data.nickname}')asyncio.run(bot.start())Multiple modeimportasynciofromamiyabotimportMultipleAccounts,AmiyaBot,Message,Chainbots=MultipleAccounts(AmiyaBot(appid='******',token='******'),AmiyaBot(appid='******',token='******'),...)@bots.on_message(keywords='hello')asyncdef_(data:Message):returnChain(data).text(f'hello,{data.nickname}')asyncio.run(bots.start())Use adapterimportasynciofromamiyabotimportAmiyaBot,Message,Chainfromamiyabot.adapters.onebot.v11importonebot11bot=AmiyaBot(appid='******',token='******',adapter=onebot11(host='127.0.0.1',http_port=8080,ws_port=8060))@bot.on_message(keywords='hello')asyncdef_(data:Message):returnChain(data).text(f'hello,{data.nickname}')asyncio.run(bot.start())Get more
Package: amka-py
amka-pyA validator for greek social security number (AMKA)Installationpipinstallamka-pyUsagefromamka_py.amkaimportvalidate# An invalid AMKAis_valid,err=validate("09095986680")print(is_valid)# Falseprint(err)# A valid AMKAis_valid,err=validate("09095986684");print(is_valid)# True
Package: amk.kakashi
kakashi - File Doppelgangerkakashi，《火影忍者》中的天才忍者，因使用写轮眼复制了上千种忍术而被称为“拷贝忍者”，该工具借用其名，以体现「快速创建文件分身」的功能，即：在保留原文件/目录结构的同时，批量替换指定文本，以生成另一份安装pipinstallamk.kakashi使用显示帮助$kks
usage:kakashi[-h][-d][-v][-V][-fFROM_PATH][-tTO_PATH][-mMAP_PATH][-r]在保留原文件/目录结构的同时，批量替换指定文本，以快速创建文件分身

optionalarguments:-h,--helpshowthishelpmessageandexit-d,--debug启用调试模式-v,--verbose显示详细日志-V,--version查看当前版本号-fFROM_PATH,--from_pathFROM_PATH准备分身的文件或目录的路径-tTO_PATH,--to_pathTO_PATH文件或目录分身后保存的路径-mMAP_PATH,--map_pathMAP_PATH分身时需要替换的内容映射文件路径，每一行为一条映射，每条映射的格式须为"旧文本 => 新文本"-r,--remove_if_exist若保存的路径已存在文件，则直接将其删除使用方法$kks-f<from_path>-t<to_path>-m<map_path>[-[d|v]]# 示例（效果如图所示）$kks-ftest_proj-ttest_proj_2-mtest_proj_map.txt-dv更新记录
Package: amk.pytb
PythonToolbox - Python 工具箱安装pipinstallamk.pytb使用pip 相关新建 packagepytbcreate<pkg_name>发布 package发布测试包：pytb publish <-t/--test>发布正式包：pytb publish安装/更新 package安装 测试包：pytb install <pkg_name> [version] -t，例如：pytb install ampt2 0.0.1 -t安装 正式包：pytb install <pkg_name> [version]，例如：pytb install ampt2更新 测试包：pytb install <pkg_name> -ut，例如：pytb install ampt2 0.0.2 -ut更新 正式包：pytb install <pkg_name> -u，例如：pytb install ampt2 0.0.2 -u更新记录
Package: amk.rfwd
Rename Files With Date背景文件的排序方式 通常只有【创建时间、修改时间、标题】，但问题是：照片的标题通常是无效的（固定前缀+编号，甚至是无意义字符串）时间也是不准的（创建时间、修改时间  其实与照片的拍摄时间 是不一致的），所以就需要基于现有排序方式  修改相关信息，以便能够已正确的排序显示因此，我就写了这个工具：读取文件的创建时间（照片则是拍摄时间），将其重命名，以便【在以标题排序显示时  是符合预期的】为了避免命名重复，所以命名规则为「年月日时分秒+3位随机数，共15位数字」，示例如下：202204062217.699.png201601011713.247.JPG201602251612.551.JPG201712311343.878.JPG202204041615.719.HEIC202204041615.813.HEIC202204041710.478.HEIC202204041710.595.JPG202204041710.749.HEIC202204041710.750.HEIC安装可通过如下命令 快速安装：$pipinstallamk.rename_files_with_date使用查看说明$rfwd-h
usage:rfwd[-h][-d][-v][-l]path

RenamePhotosWithDate

positionalarguments:path待重命名的「文件/目录」的路径

optionalarguments:-h,--helpshowthishelpmessageandexit-d,--debug启用调试模式-v,--verbose显示详细日志-l,--list仅列表显示可能的处理，但不执行具体操作，以便检查查看将要执行的操作$rfwd./photos-l或是指定-v以查看很多信息：$rfwd./photos-lv执行重命名# 可追加 -v、-d 以启用详情、调试模式，以查看更多信息$rfwd./photos
Package: amlang
# aml
Accounting Mini Language — Small and simple expression language running on Python.amlis a very small programming language running on top of Python.amlis ideal for exposing a powerful but safe interface to users who need to define business rules but have no programming knowledge. In this respectamlis an alternative to cumbersome mouse-driven rule engine configurations.The grammar and parser utilise the [pypeg2](http://fdik.org/pyPEG/) library which must be installed for aml to work.To useamlthe programmer creates a “language instace”. Calling the compile function yields an object (essentially an AST). This object can then be evaluated directly using the evaluate function or translated to Python or SQL using the respective functions.Since the language is very simple, sufficient documentation can be given by example:>>> lang_instance = create_lang_instance()
>>> lang_instance.aml_evaluate(lang_instance.aml_compile('1 = 1'))
True
>>> li = create_lang_instance()
>>> c = li.aml_compile
>>> e = li.aml_evaluate
>>> p = li.aml_translate_python
>>> s = li.aml_translate_sql
>>> u = li.aml_suggest
>>> e(c('1 = 0'))
False
>>> e(c('"1" = "1"'))
True
>>> e(c('(1=1)'))
True
>>> e(c('1 > 1'))
False
>>> e(c('not 1 > 1'))
True
>>> e(c('1 != 1'))
False
>>> e(c('-2 = -2'))
True
>>> eval(p(c('-2 = -2')))
True
>>> eval(p(c('null = null')))
True
>>> eval(p(c('1 = null')))
False
>>> e(c('"foo" = "foo"'))
True
>>> e(c('"foo" = \\'foo\\''))
True
>>> e(c('"fo\\'o" = "fo\\'o"'))
True
>>> e(c("'foo'" + '=' + '"foo"'))
True
>>> li = create_lang_instance({'foo' : 1});
>>> c = li.aml_compile
>>> e = li.aml_evaluate
>>> e(c('foo = 1'))
True
>>> li = create_lang_instance({'foo' : 1.00})
>>> c = li.aml_compile
>>> e = li.aml_evaluate
>>> e(c('foo = 1'))
True
>>> li = create_lang_instance({'foo' : 2.24})
>>> c = li.aml_compile
>>> e = li.aml_evaluate
>>> e(c('foo = 2.24'))
True
>>> li = create_lang_instance({'foo' : 'foo'})
>>> c = li.aml_compile
>>> e = li.aml_evaluate
>>> e(c('foo = "foo"'))
True
>>> li = create_lang_instance()
>>> c = li.aml_compile
>>> p = li.aml_translate_python
>>> s = li.aml_translate_sql
>>> s(c('null = null'))
u'null is null'
>>> p(c('null = null'))
u'None == None'
>>> s(c('null != null'))
u'null is not null'
>>> p(c('null != null'))
u'None != None'
>>> s(c('5 != 3'))
u'5 <> 3'
>>> p(c('5 != 3'))
u'5 != 3'
>>> li = create_lang_instance({'foo' : 'bar', 'fo2' : 'ba2'})
>>> c = li.aml_compile
>>> p = li.aml_translate_python
>>> e = li.aml_evaluate
>>> u = li.aml_suggest
>>> u('1 = fo')
[u'fo2', u'foo']
>>> u('1 = FO')
[u'fo2', u'foo']
>>> p(c('null = null'))
u'None == None'
>>> e(c('foo = "bar"'))
True
>>> e(c('fo2 = "ba2"'))
True
Package: amlearn
# amlearn
Machine Learning Package for Amorphous Materials (WIP).To featurize the heterogeneous atom site environments in amorphous materials,
we can use amlearn to derive 1k+ candidate features that encompass short- (SRO)
and medium-range order (MRO) to describe the packing heterogeneity around each atom site.
(See the following example figure for combining site features and machine learning (ML) to predict the
deformation heterogeneity in metallic glasses).Candidate features include recognized signatures
such as coordination number (CN), Voronoi indices, characteristic motifs,
volume metrics (atomic/cluster packing efficiency), i-fold symmetry indices,
bond-orientational orders and symmetry functions (originally proposed to fit
ML interatomic potentials and recently gained success in featurizing disordered
materials). We also include our recently proposed highly interpretable and generalizable
distance/area/volume interstice distribution features in amlearn (see [A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses](https://www.nature.com/articles/s41467-019-13511-9). Qi Wang and Anubhav Jain. Nature Communications 10, 5537 (2019)).In amlearn, We integrate Fortran90
with Python (using f2py) to achieve combination of the flexibility and
fast-computation (>10x times faster than pure Python) of features.
Please refer to the SRO and MRO feature representations inamlearn.featurize.
We also include anIntersticeDistributionclass as a site featurizer in [matminer](https://github.com/hackingmaterials/matminer), a comprehensive Python library for ML in materials science.<div align=’center’><img alt=”amlearn” src=”docs_rst/_static/schematic_ML_of_deformation.png” width=”800”></div>
&nbsp;## InstallationBefore installing amlearn, please install numpy (version 1.7.0 or greater) first.We recommend to use the conda install.`sh conda install numpy `or you can find numpy installation guide from [Numpy installation instructions](https://www.scipy.org/install.html).Then, you can install amlearn. There are two ways to install amlearn:Install amlearn from PyPI (recommended):`sh pip install amlearn `Alternatively: install amlearn from the GitHub source:First, clone amlearn usinggit:`sh git clonehttps://github.com/Qi-max/amlearn`Then,cdto the amlearn folder and run thesetup.py:`sh cd amlearn sudo python setup.py install `## References
Qi Wang and Anubhav Jain. A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses. Nature Communications 10, 5537 (2019). doi:[10.1038/s41467-019-13511-9](https://www.nature.com/articles/s41467-019-13511-9)
Package: amlengine
Python AML.EngineThis provides access to the .Net implementation of theAML.Engine- an API for simple access to AutoamtionML files.Access to the functionalities of the .Net dlls is provided via thepythonnetpackage.Besides the access to the native API of the AML.Engine, the package also contains some additional helper functions implemented in Python that are available via theamlhelpermodule.Usage# ensure the package is loaded correctly and the required DLLs are registered with pythonnetimportamlengine# depending on what functionalities you want to usefromAml.Engineimport*fromAml.Engine.CAEXimport*fromAml.Engine.CAEX.Extensionsimport*fromAml.Engine.AmlObjectsimport*fromAml.Engine.AmlObjects.Extensionsimport*fromAml.Engine.Servicesimport*fromAml.Engine.Adapterimport*# access to the native .Net APIaml_file=CAEXDocument.New_CAEXDocument(CAEXDocument.CAEXSchema.CAEX2_15).Net versionBy default, the DLLs compiled for .Net Framework 4.8 are used. However, using another version provided by the AML.Engine is also possible. Therefore, set the environment variableAML_ENGINE_DOTNET_VERSIONto one of the following values before importing theamlengine:net5.0net6.0net48 (default)netcoreapp3.1
Package: amlensing
GAML: Astrometric MicroLensing prediction using Gaia's dataThis Python package searches for astrometric gravitational microlensing events
given a list of lens-source pairs, and output quality assessments and astrometric
and photometric microlensing effects of significant lensing events.This project has evolved fromKlüter's amlensing, with the following
improvements:major overhaul to standardize and generalize the codebasesubstantial refactors to adapt for general lensing objects and background sourcesE.g., allow setting their mass, mass error, and individual epochsmakes it easier to prepare the input data files, which was abcent in the original codealso a few bug fixes, which affects the result (most slightly)For more detailed changes of this fork, seeCHANGES.mdand
the commit log.What does it doGAML will perform several filters to exclude lenses and sources with low quality.By predicting the motion over a specific time span, GAML determines the time
and angular separation of lens-source closest approach.By sampling the angular Einstein ring radius and angular separation, it calculates
the astrometric and photometric observables of the gravitational microlensing event.Such as centroid shift, positive image shift, centroid shift with a luminous
lens, and magnification.Although there are quite some changes from the original codebase, but it is
still recommended to readKluter 2022for theoretical details.DocumentationFor further documentations, see thedocsfolder