Saravanan666/pypi-org huggingface dataset:
Package: autobean-format
autobean-formatYet another formatter for beancount.FeaturesDeep cleanup.Normalizesallspacing.Full beancount v2 syntax support (except out-of-line tags / links intransaction).Currency / cost aligning.Works foropen,balance,priceand posting.Entry sorting.Honorspushtag/pushmeta.Honorstimemeta (HH:MM/HH:MM:SS/ epoch seconds / epoch ms / epoch us).Preserves comments.Preserves existing structure and ordering whenever possible.Thousands separator normalization.Adds / removes / keeps thousands separators.Recursive formatting.Recursively formats included files.Honors glob includes.Flexible output.Prints results / prints diff / writes back to file.Typed and extensible.For example, the code for thousands separators is as simple asthis.Usagepipinstallautobean-format# format and print resultsautobean-formatfoo.bean# format and write back to fileautobean-formatfoo.bean--output-mode=inplace# format and print the diffautobean-formatfoo.bean--output-mode=diff# sort entries by date and timeautobean-formatfoo.bean--sort# recursively format all included filesautobean-formatfoo.bean--recursive# add thousands separators to all numbersautobean-formatfoo.bean--thousands-separator=add# see more optionsautobean-format-hExamplesautobean-formatfoo.bean\--indent='    '\--currency-column=30\--cost-column=35\--thousands-separator=add\--spaces-in-braces\--sort\--recursive\--output-mode=inplaceBeforeAfterfoo.bean:2000-01-01 open Assets:Foo USD,XXX
2000-01-01  *  "foo"   ^aa  #bb
  meta:123
     Assets:Foo  -1.23*4000USD    ;X
    Assets:Foo 1000 XXX {4.92  USD}
   meta:456


include "price.*"
2000-01-02 balance Assets:Foo -4920USD
2000-01-02 balance Assets:Foo 1000XXXprice.bean:2021-01-01 price EUR 1.21 USD
 ;inner comment
2021-01-04 price GBP  1.36 USD  ; Y
;leading comment
2021-01-01 price JPY  0.009 USD

2020-01-01 price EUR  1.12 USD
2020-01-02 price GBP  1.32USD
2020-01-01 price JPY  0.009 USDfoo.bean:2000-01-01 open Assets:Foo    USD, XXX

2000-01-01 * "foo" ^aa #bb
    meta: 123
    Assets:Foo  -1.23 * 4,000 USD ; X
    Assets:Foo          1,000 XXX  { 4.92 USD }
        meta: 456

include "price.*"

2000-01-02 balance Assets:Foo -4,920 USD
2000-01-02 balance Assets:Foo 1,000 XXXprice.bean:2020-01-01 price EUR     1.12 USD
2020-01-01 price JPY    0.009 USD
2020-01-02 price GBP     1.32 USD

2021-01-01 price EUR     1.21 USD
    ; inner comment
; leading comment
2021-01-01 price JPY    0.009 USD
2021-01-04 price GBP     1.36 USD ; Y
Package: auto-behave
A sphinx extension for behave to auto document.SetupTo add auto behave to your sphinx modifyconf.pyby adding auto behave to extensions like so:extensions=['..','..','auto_behave']To enable auto behave’s rst generation modifyconf.pyby adding the following to the bottom:fromauto_behave.gen_step_rstimportgenerate_step_rst_filesgenerate_step_rst_files('abs/path/to/project','rel/path/features/dir','rel/path/doc/features')File LayoutAuto behave’s rst generation supports both the standard behave & more custom folder structure layouts.Standard Behavefeatures
├── environment.py
├── order.feature
│
└── steps
    ├── add.py
    └── remove.pyCustom Folder Structurefeatures
├── environment.py
├── order
|   ├── create_order.feature
│   └── steps
|       ├── add.py
|       └── remove.py
|
└── steps
    ├── __init__.py
    ├── navigation.py
    └── common.pyBy default behave doesn’t allow you to have step files in different directories outside a single step folder. Being locked to single folder becomes a problem with a large scale project. To get around behave’s limitation within behave’s step directory i.e.features/stepscreate__init__.pyand import all other steps files that are not in this directory i.e.# features/steps/__init__.pyimportfeatures.order.steps.addimportfeatures.order.steps.removeUsageYou can of course create your own rst for more manual approach by using autobehave directive, like so:OrderSteps===========..autobehave::features.order.steps.addfeatures.order.steps.remove
Package: autobench2
HTTP benchmarking suite forwrk2.InstallationAutobench2 requirewrk2to be installed.$ pip install autobench2or upgrade to newest version$ pip install --upgrade autobench2Usage$ autobench --verbose --connection 8 --thread 4 --duration 1m \
            --script wrk.lua --warmup_duration 1m --low_rate 10 \
            --high_rate 20 --rate_step 10 http://example.com/display help message$ autobench --helpdisplay autobench2 version$ autobench --version
Package: autobib
autobibautobibautomatically fetches BibTeX entries from online databases (currently Inspire and ADS) based on references that were added to the LaTeX document but not yet to the.bibfile. The automatically fetched entries are appended to the first.bibfile defined in your LaTeX source (first entry of\bibliography{...}). For this to work, the cite key has to be a key used by the online database.In other words, you still need to look up a citation online and copy the cite key to your LaTeX file, but you don't have to also copy the whole BibTeX entry to your local.bibfile (essentially adding it twice), sinceautobibdoes the latter for you.autobibonly appends new entries to your.bibfile and otherwise leaves it as is. Using it is therefore safe.Installation and usagepip install autobibThis installs a new script calledbibtex-autobib, which is a drop-in replacement of the originalbibtexcommand. The easiest way to make it work automatically with tooling likelatexmkis to create a symlinkln -s /path/to/bibtex-autobib /some/path/bibtexwhere/some/path/bibtexcomes before the path in which the originalbibtexcommand is located in the PATH environment variable. Be careful that you do not override the originalbibtexcommand.After doing that, you can check whether the commandbibtexis callingautobibby callingbibtex --versionon the command-line. You should see something like thisautobib 0.6.0
BibTeX 0.99d (TeX Live 2020)
[...]If this is not the case, then you may have to change the order of search paths in thePATHenvironment variable.ADS tokenNo extra steps are needed to download from Inspire, but to download from ADS you need tofollow these instructionsto get an API token. Export this token in your shell as ADS_TOKEN, e.g.export ADS_TOKEN=<insert token here>.
Package: autobiber
Autobiber: A tool to find bibs via dblpFind and reformat bib viahttps://dblp.org/search/publ/api?h=1000&q="Installationpip install autobiberOrgit clone https://github.com/Yeba/autobiber.git
cd autobiber/
pip install -e .Usageautobiber-iinput.bib-ooutput.bib
autobiber-ilist.txt-ooutput.bib
autobiber-t"this is a title"Input Filetxt:attention is all you need
node2vec: node2vec: Scalable Feature Learning for NetworksWe accept two formats in txt:titlenickname:titleNote that, if ":" in title, it will be regraded as nickname, so add nickname manually.bib:@{attention,title={attention is all you need}}More information is welcome, but only title is used while matching right now.OutputIf more than one bib is found, "xxx find many, please choose manually" will be printed.
All bib matched wil be saved.@inproceedings{attentionisallyouneed,
 author    = {Ashish Vaswani and
              Noam Shazeer and
              Niki Parmar and
              Jakob Uszkoreit and
              Llion Jones and
              Aidan N. Gomez and
              Lukasz Kaiser and
              Illia Polosukhin},
 editor    = {Isabelle Guyon and
              Ulrike von Luxburg and
              Samy Bengio and
              Hanna M. Wallach and
              Rob Fergus and
              S. V. N. Vishwanathan and
              Roman Garnett},
 title     = {Attention is All you Need},
 booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference
              on Neural Information Processing Systems 2017, December 4-9, 2017,
              Long Beach, CA, {USA}},
 pages     = {5998--6008},
 year      = {2017},
 url       = {https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html},
 timestamp = {Thu, 21 Jan 2021 15:15:21 +0100},
 biburl    = {https://dblp.org/rec/conf/nips/VaswaniSPUJGKP17.bib},
 bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{attentionisallyouneed,
 author    = {Ashish Vaswani and
              Noam Shazeer and
              Niki Parmar and
              Jakob Uszkoreit and
              Llion Jones and
              Aidan N. Gomez and
              Lukasz Kaiser and
              Illia Polosukhin},
 title     = {Attention Is All You Need},
 journal   = {CoRR},
 volume    = {abs/1706.03762},
 year      = {2017},
 url       = {http://arxiv.org/abs/1706.03762},
 eprinttype = {arXiv},
 eprint    = {1706.03762},
 timestamp = {Sat, 23 Jan 2021 01:20:40 +0100},
 biburl    = {https://dblp.org/rec/journals/corr/VaswaniSPUJGKP17.bib},
 bibsource = {dblp computer science bibliography, https://dblp.org}
}Supported Conferences and ArticlesEvery conference and article since you can find in vdlp.One more thingWelcome PR.Please emailyjliu045@stu.suda.edu.cnor create Github issues here if you have any questions or suggestions.
Package: auto-blob-saver
auto-blob-saverSave your blobs automatically.Table of ContentsInstallUsageMaintainersContributingLicenseInstallNOTE: You need to install tsschecker first.1. Get the latest release by running pip install auto-blob-saver
3. Run 'auto_blob_saver' command
4. Once it's off, edit ~/.auto_blob_saver/devices.json for your phone
5. Run 'auto_blob_saver' command again and enjoy!

Automatically updates tsschecker's cached data since 'v0.1.2'.Usageusage: auto_blob_saver [-h] [--devices DEVICES_JSON_PATH] [--shsh SHSH_DIRECTORY_PATH] [--time MILLISECONDS]

optional arguments:
  -h, --help            show this help message and exit
  --devices DEVICES_JSON_PATH
                        Sets the devices.json path
  --shsh SHSH_DIRECTORY_PATH
                        Sets the shsh directory path
  --time MILLISECONDS, -t MILLISECONDS
                        Sets shsh download time intervalMaintainers@HelloyunhoContributingPRs accepted.Small note: If editing the README, please conform to thestandard-readmespecification.LicenseMIT © 2020 Helloyunho
Package: autoblockchainify
autoblockchainify— Turn a directory into a git-based Blockchaingitis probably the oldest and most widely used Blockchain with the largest
user base and toolset, even though most people think ofgitas a source code
control system. To learn more, seeGitBlockchainTimestamping.md.How doesautoblockchainifywork?Frequently (default: every 10 minutes), the directory tree is checked for
changes. If there are changes, they are commited togitand timestamped
using Zeitgitter.If no changes have been made in a larger period (default: 1 hour), a commit
is forced and Zeitgitter-timestamped, as an immediate evidence of no changes.If at the time of commit the last timestamp using the mail-basedPGP Digital
Timestamping Serviceis older than this larger period (again: 1 hour by
default) and the mail interface has been configured, a timestamp will be
requested there.All timestamps requested from the Zeitgitter network will be regularily
cross-timestamped within the Zeitgitter network and with thePGP Digital
Timestamping Serviceas well as other (Blockchain-based) timestamping
services.What do I need to configure?If you are happy with the default configuration, nothing. This default
configuration includes:A commit and Zeitgitter timestamp every 10 minutes, if there have been
changes.A commit and Zeitgitter timestamp every hour, even if there have been no
changes.If you would like to change the above intervals, or if you would like the
following additional features, do changeautoblockchainify.confin the
working directory or set theAUTOBLOCKCHAINIFY_*environment variables:Additional, direct timestamping against thePGP Digital Timestamping
Serviceby mail; orPushing to a remote repository for backup and/or publication purposes on
every change.
After changes to the configuration, you need to restartautoblockchainify(or
the Docker container) to have changes picked up.If you would like to exclude files from inclusion in thegitrepository (and
therefore the Blockchain, the timestamps, and the remote repositories):Modify.gitignorein the working directory.How do I run it?The preferred way is to run a Docker image usingdocker-composeand point the/blockchaindirectory to the directory you want be automatically archived to
your Blockchain. See the filesdocker-compose.ymlandsample.env.
Package: autoblocks
django-cms-autoblocksDjango-cms-autoblocks is an app that allows you to place arbitrary content blocks into your templates, so that you can take advantage of django-cms placeholders in the templates for your custom apps, without having to include django-cms as a requirement for your custom app (because the django-cms dependency is introduced at the template level by the template author).InstallationFrom PyPi:pip install autoblocksFrom GitHub:pip installgit+git@github.com:Strathcom/django-cms-autoblocks.gitSetupAddautoblocksto settings.INSTALLED_APPS.Runmanage.py syncdb.If you want to do frontend editing, add the dependencies for the django-cms toolbar to your templates.UsageIn your template:{% load autoblocks_tags %}
{% autoblock 'this is my auto block' %}
{% autoblock 'this is' 'also' 'an autoblock' %}
{% autoblock 'blurb-' model_instance.id %}
{% autoblock 'blurb-' model_instance.id on site %}
{% autoblock 'blurb-' model_instance.id on site as foo %}The templatetag will generate an Autoblock with a placeholder field for you to work with tied to either the current site (Site.objects.get_current()), or the site instance you pass in when you use the{% autoblock ... on <site> %}variation of the tag.ExtrasIf you adddjango.core.context_processors.requesttosettings.TEMPLATE_CONTEXT_PROCESSORS, autoblocks will only be created when the page is accessed by an authenticated user with theautoblocks.add_autoblockpermission.Autoblocks - Roll Out!
Package: autoblocksai
📚Documentation•
   
  🖥️Application•
   
  🏠HomeInstallationpoetryaddautoblocksaipipinstallautoblocksaiExamplesSee ourPythonexamples.Quickstartimportosimportuuidimporttracebackimporttimeimportopenaifromautoblocks.tracerimportAutoblocksTraceropenai.api_key=os.environ["OPENAI_API_KEY"]messages=[{"role":"system","content":"You are a helpful assistant. You answer questions about a software product named Acme.",},{"role":"user","content":"How do I sign up?",},]request_params=dict(model="gpt-3.5-turbo",messages=messages,temperature=0.7,top_p=1,frequency_penalty=0,presence_penalty=0,n=1,)tracer=AutoblocksTracer(os.environ["AUTOBLOCKS_INGESTION_KEY"],# All events sent below will have this trace IDtrace_id=str(uuid.uuid4()),# All events sent below will include this property# alongside any other properties set in the send_event callproperties=dict(provider="openai",),)tracer.send_event("ai.request",properties=request_params,)try:start_time=time.time()response=openai.ChatCompletion.create(**request_params)tracer.send_event("ai.response",properties=dict(response=response,latency_ms=(time.time()-start_time)*1000,),)exceptExceptionaserror:tracer.send_event("ai.error",properties=dict(error_message=str(error),stacktrace=traceback.format_exc(),),)# Simulate user feedbacktracer.send_event("user.feedback",properties=dict(feedback="good",),)DocumentationSeethe full documentation.Issues / QuestionsPleaseopen an issueif you encounter any bugs, have any questions, or have any feature requests.ContributingSeeCONTRIBUTING.md.
Package: autobmt
##自动构建评分卡## 思想碰撞微信 |  微信公众号 |:—: | :—-: |<img src=”https://github.com/ZhengRyan/autotreemodel/blob/master/images/%E5%B9%B2%E9%A5%AD%E4%BA%BA.png” alt=”RyanZheng.png” width=”50%” border=0/> | <img src=”https://github.com/ZhengRyan/autotreemodel/blob/master/images/%E9%AD%94%E9%83%BD%E6%95%B0%E6%8D%AE%E5%B9%B2%E9%A5%AD%E4%BA%BA.png” alt=”魔都数据干饭人.png” width=”50%” border=0/> |干饭人  | 魔都数据干饭人 |> 仓库地址：https://github.com/ZhengRyan/autobmt>
> 微信公众号文章：https://mp.weixin.qq.com/s/u8Nsp5M93WIGL2M0tU4U_g>
> pipy包：https://pypi.org/project/autobmt/>
> 实验数据：链接:https://pan.baidu.com/s/1BRIHH9Wcwy2EZaO5xSgH9w?pwd=tdq5提取码: tdq5## 一、环境准备
可以不用单独创建虚拟环境，都是日常常用的python依赖包。需要创建虚拟环境，请参考”五、依赖包安装”###autobmt安装
pip install（pip安装）`bash pip install autobmt # to install pip install-Uautobmt # to upgrade `Source code install（源码安装）`bash python setup.py install `## 二、使用教程
1、1行代码自动构建评分卡：请查看autobmt/examples/autobmt_lr_tutorial_code.py。里面有例子2、1步1步拆解自动构建评分卡的步骤：请查看autobmt/examples/tutorial_code.ipynb。里面有详细步骤拆解例子## 三、训练、自动选变量、自动单调最优分箱、自动构建模型、自动构建评分卡
1、Step 1: EDA，整体数据探索性数据分析2、Step 2: 特征粗筛选3、Step 3: 对粗筛选后的变量调用最优分箱4、Step 4: 对最优分箱后的变量进行woe转换5、Step 5: 对woe转换后的变量进行stepwise6、Step 6: 用逻辑回归构建模型7、Step 7: 构建评分卡8、Step 8: 持久化模型，分箱点，woe值，评分卡结构9、Step 9: 持久化建模中间结果到excel，方便复盘## 四、保存的建模结果相关文件说明
1、all_data_eda.xlsx：整体数据的EDA情况2、build_model_log_var_jpg文件夹，最终入模变量的分箱画图，在”build_model_log.xlsx”最后1个sheet也有记录3、build_model_log.xlsx：构建整个模型的过程日志，记录有利复盘4、fb.pkl、woetf.pkl、lrmodel.pkl、in_model_var.pkl：fb.pkl分箱文件，woetf.pkl转woe文件，lrmodel.pkl模型文件，入模变量文件5、scorecard.pkl、scorecard.csv、scorecard.json：评分卡的pkl、csv、json格式。在”build_model_log.xlsx”的”scorecard_structure”sheet也有记录6、var_bin_woe_format.csv、var_bin_woe_format.json、var_bin_woe.csv、var_bin_woe.json、var_split_point_format.csv、var_split_point_format.json、var_split_point.csv、var_split_point.json：分箱文件和转woe文件的csv、json格式7、lr_auc_ks_psi.csv：模型的auc、ks、psi8、lr_pred_to_report_data.csv：构建建模报告的数据9、lr_test_input.csv：用于模型上线后，将次数据喂入模型，对比和lr_pred_to_report_data.csv结果是否一致。验证模型上线的正确性## 五、依赖包安装（建议先创建虚拟环境，不创建虚拟环境也行，创建虚拟环境是为了不和其它项目有依赖包的冲突，不创建虚拟环境的话在基础python环境执行pip install即可）
####创建虚拟环境
conda create -y –force -n autobmt python=3.7.2
####激活虚拟环境
conda activate autobmt### 依赖包安装方式一，执行如下命令安装依赖的包
pip install -r requirements.txt -ihttps://mirrors.aliyun.com/pypi/simple/
Package: autoBoostcamp
A moudule for self-authenticationQuickstart$ pip install autoBoostcamp
import autoBoostcamp as abc
abc.quick_start()1. get google sms app installed on your phone. set the app as default sms app.https://play.google.com/store/apps/details?id=com.google.android.apps.messaging2. check if isready functions below work properly.sign into the slackabc.backend.isready_slack_driver()sign into the naverabc.backend.isready_naver_driver()make a pairing between google sms app and google sms web.abc.backend.isready_auth_driver()the function isready_table() makes auth_key.txt file at the current working directory. you should modify it to have proper credential informationtable_path = abc.backend.isready_table()
print(table_path)3. the package has three frontend function.abc.doHi() # say Hi to slack and edwith
abc.doBye() # say Bye to edwith
abc.do() # say either Hi or Bye, upon time condition.be careful with the chromedriver_profile security !Especially remember the fact that you paired your sms messages with the desktop.
Package: autobots
# autobotsTable of Contents[Installation](#installation)[License](#license)## Installationautobots is distributed on [PyPI](https://pypi.org) as a universal
wheel and is available on Linux/macOS and Windows and supports
Python 3.7+.`bash $ pip install autobots `## Licenseautobots is distributed under the terms of the
[MIT License](https://choosealicense.com/licenses/mit).
Package: auto-bots
auto-botsauto-botsis an easy-to-use time series forecasting package that does all the model selection work for you.InstallationYou can installauto-botswith a simplepip install auto-botsThen, to use the AutoTS model in your code, import it like so:fromauto_bots.AutoTSimportAutoTSQuickstartAutoTS follows sci-kit learn'smodel.fit()/model.predict()paradigm. The only requirement of your
data is that it must be a pandas dataframe with a datetime index. Given such a dataframe, here is how
to train your model and make predictions:model=AutoTS()model.fit(data,series_column_name='passengers')model.predict(start=pd.to_datetime('1960-1-1'),end=pd.to_datetime('1960-12-1'))Tips/Tricks/Things to knowSince you provide the name of the time series column during fit, the dataframe provided
during fit can contain as many extra columns as you like and the model will ignore them. No need to do
a bunch of filtering before training!You can have the model predict in-sample by setting thestart_dateequal to a date inside the data given during fit.For a more thorough introduction, check outthis example
Package: autobot-tg
AUTOBOTFramework for making bot from config files (we all loveYAML, does't we?)FeaturesUsageFeaturesAutomatic back button configuration- not need to plainly think about branchingBeautiful inline buttons experience- previous message gets updated on button pressed (much cleaner message history)RegEx message filtersInstallationUsageautobotexamples/configs/simple.yamlConfig examplesSimple oneInline buttons
Package: autobound
AutoBound: Automatically Bounding FunctionsAutoBound is a generalization of automatic differentiation.  In addition to
computing a Taylor polynomial approximation of a function, it computes upper
and lower bounds that are guaranteed to hold over a user-specifiedtrust region.As an example, here are the quadratic upper and lower bounds AutoBound computes
for the functionf(x) = 1.5*exp(3*x) - 25*(x**2), centered at0.5, and
valid over the trust region[0, 1].The code to compute the bounds shown in this plot looks like this (seequickstart):importautobound.jaxasabimportjax.numpyasjnpf=lambdax:1.5*jnp.exp(3*x)-25*x**2x0=.5trust_region=(0,1)# Compute quadratic upper and lower bounds on f.bounds=ab.taylor_bounds(f,2)(x0,trust_region)# bounds.upper(1) == 5.1283045 == f(1)# bounds.lower(0) == 1.5 == f(0)# bounds.coefficients == (0.47253323, -4.8324013, (-5.5549355, 28.287888))These bounds can be used for:Computing learning rates that are guaranteed to reduce a loss functionUpper and lower bounding integralsProving optimality guarantees in global optimizationand more!Under the hood, AutoBound computes these bounds using an interval arithmetic
variant of Taylor-mode automatic differentiation.  Accordingly, the memory
requirements are linear in the input dimension, and the method is only
practical for functions with low-dimensional inputs.  A reverse-mode algorithm
that efficiently handles high-dimensional inputs is under development.A detailed description of the AutoBound algorithm can be found inthis paper.InstallationAssuming you haveinstalled pip, you can install this package directly from GitHub withpipinstallgit+https://github.com/google/autobound.gitor from PyPI withpipinstallautoboundYou may need toupgrade pipbefore running these commands.LimitationsThe current code has a few limitations:Only JAX-traceable functions can be automatically bounded.Many JAX library functions are not yet supported.  Whatissupported is bounding the squared error loss of a multi-layer perceptron or convolutional neural network that uses thejax.nn.sigmoid,jax.nn.softplus, orjax.nn.swishactivation functions.To compute accurate bounds for deeper neural networks, you may need to usefloat64rather thanfloat32.Citing AutoBoundTo cite this repository:@article{autobound2022,
  title={Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications},
  author={Streeter, Matthew and Dillon, Joshua V},
  journal={arXiv preprint arXiv:2212.11429},
  url = {http://github.com/google/autobound},
  year={2022}
}This is not an officially supported Google product.
Package: autobricks
What the heck is this?It's a high level wrapper around the Databricks API so that we can write simple short expressive python scripts in CI/CD pipelines to deploy databricks bits and peices. Also use adhocly if you wish.The Docs are hereMany operations mimick the API but there's also some handy high level handling for common operations.https://autobricks.readthedocs.io/en/latest/About the authorMade by me, Shaun Ryan