Saravanan666/pypi-org huggingface dataset:
Package: autotorrent2
Autotorrent2Match torrents and data, remove torrents based on data, cleanup your disk for unseeded files.
Autotorrent2 does everything you currently miss in your flow.SupportedTorrent clients: rtorrent, Deluge, Transmission and qBittorrentOS: Any, but only tested on linuxPython: 3.7+ are the only tested versions, might work with lower 3.x.Quickstart guideInstall Autotorrent2python3-mvenv~/.autotorrent# Create virtual environment where we install autotorrent2~/.autotorrent/bin/pipinstallautotorrent2# Actually install autotorrent2# Optional, add at2 to your commandlineecho"alias at2=~/.autotorrent/bin/at2">>~/.bashrcsource~/.bashrcThe first time Autotorrent2 is run without a known config file, it will create a base config file.at2check-configThe default location is ~/.config/autotorrent/config.toml - edit it to match your setup.
See the example configuration file for setting descriptionfound here.Test the connections and see if it can connect to all your configured clients.at2test-connectionNow you are ready to actually use it, check out theUsage page for fun things to doandCLI page for featurelistNote about running autotorrent2 in a scriptIt can be fun to run scripts automatically and see cross-seeding just happen.
Autotorrent2 is not really designed for multiple instances at once so it is recommenced to use a lock to prevent this.Instead of justat2then use e.g.flock ~/.autotorrent.lock -c 'at2'which prevents multiple instances of Autototrrent2 at once.Note about DockerIf you use Autotorrent2 in a docker container or with a torrent client running in docker then the recommendation is to align the paths.
If your torrent data is located in /mnt/data outside docker then you should map it similarly inside the docker container and you will save yourself from a lot of headaches.Personally I recommend mounting your data source as read-only because bittorrent clients are write-happy that might corrupt your data.Docker usageThere is a docker image published automatically now.Basic usage:dockerrun-ti--rm-v${PWD}/autotorrent.db:autotorrent.db-v${PWD}/config.toml:config.tomlghcr.io/johndoee/autotorrent2:mastercheck-configCron usage, check config every 5 minute.dockerrun-ti--rm-v${PWD}/autotorrent.db:autotorrent.db-v${PWD}/config.toml:config.tomlghcr.io/johndoee/autotorrent2:mastercron'*/5 * * * *'check-configTodoAssortment of stuff that is planned.When Autotorrent2 is working on a task, e.g. copying a file to cache, then it might look like as it is stalled. An indicator should be added.Client normalization indexing, e.g. index based on how transmission and qbittorrent handles problematic filenamesTorrent discovery for a torrent site.Known bugsAssortment of stuff I am not sure I can do much about.Transmission (3.x) does not parse all emojis correctly and will return the wrong filename.This is fixed in Transmission 4.0.2.LicenseMIT
Package: auto-tqdm
Wrapper over tqdm for automatically handle switch between jupyter notebook and cli and tunes bar to my liking (automatic resizing, stronger smoothing and tuning for erratic iterations).How do I install this package?As usual, just download it using pip:pipinstallauto_tqdmTests CoverageSince some software handling coverages sometime get slightly different results, here’s three of them:Usage ExampleSo basically, this is a tqdm with only a twist: it is tuned with both my preferred settings and allows for total remaining time prediction.fromauto_tqdmimporttqdm,remaining_timefor_intqdm(range(10)):for_intqdm(range(10)):for_intqdm(range(10)):print(remaining_time())
Package: autotracer
Auto TracerAutomatic tracing of Python code.PurposeWe developed this module for educational purposes. The output of this module is
used to verify code tracings performed by students who are learning to code.UsageTo use this program, run:python auto_tracer.py FILENAME.pyWhereFILENAME.pyis the name of the Python file that you wish be checked.DisclaimerThis module is in VERY early stage and I don't know how it behaves with larger
pieces of software.
Package: autotraders
AutotradersA spacetraders API focused on automation and ease of useUsageFirst you need a client, which can be generatedfromautotradersimportsessions=session.get_session("YOUR_TOKEN_HERE")And now you're all set to use they actual API.Shipsfromautotraders.shipimportShipfromautotraders.sessionimportget_session# create a session heresession=get_session("YOUR TOKEN HERE")ship=Ship("SYMBOL-Here",session)# This makes an API requestships=Ship.all(session)# This also only makes one API requestship.dock()ship.refuel()ship.orbit()# All these functions make API calls (one each), but the line below doesn't make anyprint(ship.fuel.current+"/"+ship.fuel.total)Contractfromautotraders.faction.contractimportContractfromautotraders.sessionimportget_session# create a session heresession=get_session("YOUR TOKEN HERE")contract=Contract("id-here",session)contracts=Contract.all(session)contract.accept()print(contract.accepted)# Truecontract.deliver("SHIP_SYMBOL","ALUMINUM_ORE",30)contract.fulfill()print(contract.fulfilled)# True
Package: autotrain-advanced
🤗 AutoTrain AdvancedAutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning modelsDocumentationDocumentation is availablehere.InstallationYou can Install AutoTrain-Advanced python package via PIP. Please note you will need python >= 3.8 for AutoTrain Advanced to work properly.pip install autotrain-advancedPlease make sure that you have git lfs installed. Check out the instructions here:https://github.com/git-lfs/git-lfs/wiki/InstallationColabsTaskColab LinkLLM Fine TuningDreamBooth TrainingTutorialsTitleLinkHow to Finetune LLMshttps://youtu.be/3fsn19OI_C8How to Train SDXL Dreamboothhttps://youtu.be/gF078Lhnr94DocumentationDocumentation is available athttps://hf.co/docs/autotrain/
Package: autotraino
Autotraino :truck::warning: Warning, alpha version, everything brakes. :warning:autotrainois a small wrapper library for AutoML on tabular datasets.fromautotraino.gluonimportAutogluonTrainerfromdatasetsimportload_datasettrain=load_dataset("mstz/adult","income")["train"].to_pandas()# train the modeltrainer=AutogluonTrainer()trainer=trainer.fit(train,target_feature="over_threshold",time_limit=100)When fitting we can control basic parameters such as where to store the resulting models
(parametersave_pathof the trainer constructor) or the time budget assigned to the trainer (parametertime_limit, expressed in seconds).Once trained, we can access the single models# trained modelsprint(trainer.names)print(trainer["LightGBM"])and predict directly from theTraineritself:train_x=train.copy().drop("over_threshold",axis="columns")predictions=trainer.predict(train_x,with_models=["LightGBM","RandomForest"])QuickstartYou can installautotrainovia test-pypi:# optionalmkvirtualenv-ppython3.10autotraino

pipinstall--extra-index-urlhttps://test.pypi.org/simple/autotrainoDatasetsautotrainois based offpandas.DataFrames.
You can find a large collection on a Huggingface repository I'm curating athuggingface.co/mstz.
Datasets are sourced from UCI, Kaggle, and OpenML.
Most are still to be updated (especially dataset cards).What model families to train?Currently based onAutogluon,autotrainocurrently trains the following models:Boosting modelsLightGBMCatBoostBagging modelsRandom ForestExtraTree ClassifierNeural NetworkFastAINNTorchClassical AI modelsk-NNLogistic RegressionPreprocessingautotrainoautomatically detects feature types and performs the necessary feature preprocessing per model.
To ease the process, consider setting the appropriatedtypesin the inputpandas.DataFrame.In the worksFuture developments include:Fitting arbitrary functions (ray tune)Fitting multi-output models.
Package: autotrainz
TrainingopzThis library is a pre-alpha right nowThe intent of this package is to work as a back-end build and deploy engine to take - given a configuration and an infrastructure,
on-demand scientific computing workflows.Application Configurationversionon:1docker_configuration:docker_registry:REPO_URLworkflow_validation:type:YAMLValidatorconf:project_path:/pipeline_composition:type:YAMLPipelineComposerconf:project_path:/package:type:DockerPackagerconf:project_path:/build_deployment_artifact:type:BuildAirflowArtifactconf:nulldeploy:type:AirflowDeployerconf:null
Package: autotranscribe
AutotranscribeAutotranscribe is a simple library to convert video/audio to text. The library currently supports transcription of youtube files as well as implementing multiprocessing for faster transcription. It is built around the OpenAI whisper model.InstallationUse the package managerpipto install autotranscribe.pipinstallautotranscribeUsageimportautotranscribe# Transcribe a youtube file with basic functionality.autotranscribe.transcribe_from_youtube(url,'transcripts/transcript.txt',multiproc=True)ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: auto-translation
auto-translationauto-translation is a simple python library to translate any other
language or transform it to speech with google traduction , all that
using the user's Chrome browser with selenium in hidden mode , mean time
to return the translation or speech 15 secondsInstallation :| $ pip install auto-translation |Simple usage :| >>> from auto_browser.selenium_translation import Chrome || >>> test=Chrome()  || >>> print(
test.translate_text(text='Bonjour',from_lang='fr',to_lang='fr') )|| >>> Good morning |Methods :MethodsReturntranslation_url(from_lang=str,to_lang=str)url of google translation (str)translation_text_url(text=str,from_lang=str,to_lang=str)url of the translation needed (str)translate_text(text=str,from_lang=str,to_lang=str)translation of text (str)text_2_speech(text=str, language=str)None , text speechtranslation_2_speech(text=str,from_lang=str,to_lang=str)None , translation speechNote :Use language codes >>>ISO-639 Language Codesexample :Arabic:arFrench:fr
Package: auto-translator
AutoTranslator--------------For Installation, please read INSTALL file.USAGE:(Inside the directory uncompressed)1. For Tkinter (Visual) Interface:* From Filemanager: Double Click in autotranslate.py* From CommandLine: ./autotranslate.py2. For Command Line Interface:* ./autotranslate.py (FileName) (Origin Language) (Destination Language)(it translates, also, web2py language files, just point the path of them)
Package: autotreemodel
#自动构建树模型
##自动构建xgboost或lightgbm模型### 一、训练、自动选变量、自动调参数
1、训练模型2、shap 或 feature importance自动筛选变量3、相关性筛选变量4、PSI筛选变量5、自动调参6、逐步剔除变量7、构建最终模型### 二、建模相关结果保存
1、将模型文件持久化2、将变量重要性持久化3、将模型效果持久化4、500条x数据用于验证后续部署是否一致5、模型在建模数据集上的预测结果持久化## 三、使用教程
请查看ryan_tutorial_code.py。里面有两个例子，一个列子使用的数据集随机生成的数据，一个是虚构现实数据## 四、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 auto_build_tree_model python=3.7.2
####激活虚拟环境
conda activate auto_build_tree_model### 依赖包安装方式一####安装依赖包
pip install pandas==1.2.4pip install joblib==0.14.1pip install xgboost==1.2.0pip install bayesian-optimization==1.1.0pip install lightgbm==3.2.1pip install shap==0.36.0### 依赖包安装方式二，执行如下命令安装依赖的包
pip install -r requirements.txtHistory0.1.5 (2023-11-14)First release on PyPI.
Package: auto-ts-ensemble
DependenciesMake sure you have install the fbprophet and NeuralProphetfbprophet version == 0.7.1Steps to install fbprophetconda env -n test_env python==3.8.5conda install -c anaconda ephemconda install -c conda-forge pystanconda install -c conda-forge fbprophetif error degrade to pystan==2.19.1.1###Steps to install NeuralProphetpip install neuralprophet ==0.3.0#importfrom auto_ts_ensemble_rupakbob import auto_ts_ensemble#call the package function 1results = auto_ts_ensemble.neural_analysis(data,freq="H")###access the modelneural_prophet = results[0]###access the metricsmetrics = results[1]###access the predictionspredictions = results[2]####plot the componentsneural_prophet.plot_components(predictions)#call the package function 2results2 = auto_ts_ensemble.ts_analysis(data,n_future=7)#access the modelts_model = results2[0]#access the predictionspredictions2 = results2[1]#plot the componentsts_model.plot_components(predictions2)#call the package function 3 ensembleensemble_predictions = auto_ts_ensemble.ensemble_analysis(predictions,predictions2)
Package: autotsk
DescriptionAutomatically creates mmls, fsstat and fls results for image filesInstallation# Sleuthkit needs to be installed# and available in PATHsudoaptinstallsleuthkitpipinstallautotskUsageFrom command line:python -m autotsk --path PATH [--result RESULT]OptionShortTypeDefaultDescription--path-pString-Path to img/dd file--result-rString./autotsk-resultPath to result dirExamplepython -m autotsk -p example.dd################################################################################

autotsk by 5f0
Automatically creates mmls, fsstat and fls results for image files

Current working directory: /path/to/autotsk

 Datetime: 01/01/1970 10:11:12

################################################################################

mmls Output
----------

DOS Partition Table
Offset Sector: 0
Units are in 512-byte sectors

      Slot      Start        End          Length       Description
000:  Meta      0000000000   0000000000   0000000001   Primary Table (#0)
001:  -------   0000000000   0000008191   0000008192   Unallocated
002:  000:000   0000008192   0000110591   0000102400   Win95 FAT32 (0x0b)
003:  -------   0000110592   0000212991   0000102400   Unallocated
004:  000:001   0000212992   0000458751   0000245760   NTFS / exFAT (0x07)
005:  -------   0000458752   0000466943   0000008192   Unallocated
006:  000:002   0000466944   0000569343   0000102400   Linux (0x83)
007:  -------   0000569344   0000577535   0000008192   Unallocated
008:  000:003   0000577536   0000782335   0000204800   Linux (0x83)
009:  -------   0000782336   0003911679   0003129344   Unallocated



Extracting Offsets
----------

Offset found: 8192
Offset found: 212992
Offset found: 466944
Offset found: 577536



Applying TSK Commands
----------

fsstat and fls for offset: 8192
fsstat and fls for offset: 212992
fsstat and fls for offset: 466944
fsstat and fls for offset: 577536



Result Location
----------

Path: ./autotsk-result

################################################################################

Execution Time: 0.075163 secCreated files in result directory:autotsk-result/
â”œâ”€â”€ 0-8192-fls.txt
â”œâ”€â”€ 0-8192-fsstat.txt
â”œâ”€â”€ 1-212992-fls.txt
â”œâ”€â”€ 1-212992-fsstat.txt
â”œâ”€â”€ 2-466944-fls.txt
â”œâ”€â”€ 2-466944-fsstat.txt
â”œâ”€â”€ 3-577536-fls.txt
â”œâ”€â”€ 3-577536-fsstat.txt
â””â”€â”€ mmls.txtLicenseMIT
Package: autottlcache
autottlcacheA time-to-live (TTL) cache dictionary with auto-expiry.InstallationTo install from PyPI:$ pip install autottlcacheUsageThe TTL cache behaves very much like a dictionary. In fact, it is aMutableMappingso supports the
dictionary interface. It has one important difference though, which is that the items within it will
expire and automatically be removed.Let's create anAutoTTLCachewith a a maximum size of twelve entires, and a TTL of 30 seconds:>>> from autottlcache import AutoTTLCache
>>> cache = AutoTTLCache(maxsize=12, ttl=30.0)We can add entries to the cache:>>> cache[1] = 56
>>> cache[2] = 89
>>> cache[3] = 99
>>> cache[4] = 10
>>> cache[5] = 20And watch them expire, by repeatedly asking the cache to list its keys:>>> list(cache.keys())
[2, 3, 4, 5]We took long enough adding the keys that the first one had already expired:>>> list(cache.keys())
[3, 4, 5]Now the second one has gone too:>>> list(cache.keys())
[3, 4, 5]This time we're quick enough to get in before the third one expires:>>> list(cache.keys())
[3, 4, 5]
>>> list(cache.keys())
[3, 4, 5]
>>> list(cache.keys())
[3, 4, 5]
>>> list(cache.keys())
[4, 5]
>>> list(cache.keys())
[4, 5]
>>> list(cache.keys())
[4, 5]
>>> list(cache.keys())
[5]
>>> list(cache.keys())
[5]
>>> list(cache.keys())
[5]
>>> list(cache.keys())
[5]
>>> list(cache.keys())
[5]
>>> list(cache.keys())
[]All gone.Stale items are removed from the cache either when an operation on the cache object is invoked, such
as when an item is retrieved, or by a continuously running background thread that periodically
removes expired items. The cache will be checked with a frequency of ten times the TTL period, so
an item in a cache with a TTL of 30 seconds should live no longer than 33 seconds.Programming styleOwing to the fact that items will auto-expire, to avoid race conditions you should strongly prefer
anEasier to Ask for Forgiveness than Permission(EAFP) programming style, rather than aLook
Before You Leap(LBYL) style.Risky race-condition:from autottlcache import AutoTTLCache
cache = AutoTTLCache(maxsize=100, ttl=30)
cache["daffodils"] = Image("daffodils-3280x2040.png")

# ...

if "daffodils" in cache:
    # What happens if "daffodils" expires here?
    image = cache["daffodils"]  # This could raise a key error which is unhandled
else:
    print("No flowers for you!")Better to use a single operation to retrieve an object, and handle the failure:from autottlcache import AutoTTLCache
cache = AutoTTLCache(maxsize=100, ttl=30)
cache["daffodils"] = Image("daffodils-3280x2040.png")

# ...

try:
    image = cache["daffodils"]
except KeyError:
    print("No flowers for you!")
else:
    display(image)Resource useA single additional thread will be running during any periods for which one or moreAutoTTLCacheobjects are extant. The thread will start when the firstAutoTTLCacheis created, and will
terminate shortly after the lastAutoTTLCacheis finalized.DevelopmentTo release, there is a short manual process:$ bumpversion patch
$ python setup.py sdist bdist_wheel
$ twine upload dist/* --config-file=path/to/sixty-north.pypirc
Package: autotune
# autotuneHyperparameter tuning on GPUs[![Build Status](https://travis-ci.org/vzhong/autotune.svg?branch=master)](https://travis-ci.org/vzhong/autotune)## Installation```bashpip install git+git://github.com/vzhong/autotune.git# Or get it straight from PyPIpip install autotune```## UsageYou can use the binary:```bashautotune -h```Or use it programmatically:```pythonfrom autotune.tuner import RandomSearchfrom autotune.spec import Specconfig = Spec.load('myconf.json')tuner = RandomSearch('myprog.bin', config)tuner.tune(2, out='output')```where `myconf.json` looks something like:```json{"foo": [-1, 1],"bar": [2.0, 3.0]}```This will run 2 commands `myprog.bin --foo $FOO --bar $BAR` where `$FOO` is an integer sampled between `-1` and `1` and `$BAR` is a float sampled between `2.0` and `3.0`.You can pass in an optional parameter `name='nickname'`, which will add to the command `--nickname $HASH`, where `$HASH` is a hash of the specific parameters used for this command.You can also pass in an optional parameter `gpu=True`, which will queue jobs onto aavailable GPUs.The command then becomes `CUDA_VISIBLE_DEVICES=$GPU myprog.bin --foo $FOO --bar $BAR --gpu 0`, where `$GPU` is a free GPU (e.g. no memory usage).
Package: auto-tune
Estimate hyper-parameter search using evolutionary algorithmsFree software: MIT licenseInstallationpip install auto-tuneDocumentationhttps://auto-tune.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.1 (2017-09-01)First release on PyPI.
Package: autotweet
Learn your response pattern on twitter.Installation$pipinstallautotweet#Orwithtelegrambot$pipinstall'autotweet[telegram]'UsageLearning user’s tweetAutotweet can learn your tweet by collector.$autotweetcollectorAutomatic answering by clone accountYour clone account can automatic answer by answer command.$autotweetanswerAutomaticaly tweet after deathAutotweet can be auto tweeting using your main account.
Use it when you are leaving from twitter for any reason.$autotweetafter_deathAdd manuallyYou can add question/answer set manually.$autotweetadd'Question''Answer'Telegram integrationYou can run a telegram bot with learned database.$autotweettelegram-hConfigureYou can configure Autotweet by editing~/.autotweetrcfile.[auth]token=<OAuth token (automaticaly generated)>answerer_token=<OAuth token (automaticaly generated)>[database]db_url=<database url>[answer]threshold=<Auto answering to tweet that is not mention to answerer. (float 0.0 ~ 1.0)>[tweet after death]message0=This is message sent by auto tweet.message1=If you are reading this, I'm already dead.LinksPackage Index (PyPI)https://pypi.python.org/pypi/autotweet/Docs (ReadTheDocs)https://autotweet.readthedocs.org/LicenseAutotweet is following MIT license.
Package: autotypedict
autotypedictDictionary-like class that automatically typecasts string-like values to standard types.InstallationUsing pippython-mpipinstall-UautotypedictUsageImporting the classfromautotypedictimportAutoTypeDictExamples>>>d={"foo":{"baz":"42"},"bar":{"baz":"n/a","qux":"true"}}>>>AutoTypeDict(d)# typecast every value{'foo':{'baz':42},'bar':{'baz':None,'qux':True}}>>>AutoTypeDict(d,ignored_keys=("baz",))# ignore "baz"{'foo':{'baz':'42'},'bar':{'baz':'n/a','qux':True}}>>>AutoTypeDict(d,ignored_keys=("bar.baz",))# ignore d["bar"]["baz"]{'foo':{'baz':42},'bar':{'baz':'n/a','qux':True}}>>>td=AutoTypeDict()>>>td.data=d# updating data attribute to ignore typecasting>>>td{'foo':{'baz':'42'},'bar':{'baz':'n/a','qux':'true'}}CaveatsValues are only typecasted when theAutoTypeDictinstance is updated directly, so avoid using type-specific operations on mutable objects within the dictionary (e.g.list.append,dict.update).If using these methods is unavoidable, call the.refresh()instance method to re-process the dictionary.Alternatives to common type operations:OperationNon-typecastedTypecastedAppend to listtd["list"].append(v)td["list"] += [v]Update dicttd["dict"].update({k: v})td["dict"][k] = vMerge dicttd["dict"].update(other_dict)td["dict"] |= merge_dict[1][1]: Python 3.9 or later
Package: autotyper
autotyperA simple command line tool to write the contents of the clipboard or a file to the cursor after a delay of few seconds. It uses
thepyautoguipackage to simulate the keystrokes to write out the file where the cursor is placed.InstallationMake sure you have Python3 installed on your system.pip install autotyperYou will have theautotypercommand available to go on your system.UsageWrite file/clipboard content on cursor

optional arguments:
  -h, --help   show this help message and exit

clipboard:
  --clipboard  copy from clipboard flag

file:
  -f FILE      input file path, not required for clipboard mode
  -d DELAY     delay in sec before writing to the cursor$ autotyper -f bubblesort.c -d 10
Writing file content to cursor in 10 sec...In this time, position your cursor to where you want to type out the text, when the delay ends,
the content of the file will be written to the cursor. Can be used in online exams
to write the content of a file, where copy paste is not allowed in the text box.$ autotyper --clipboard -d 10
Writing clipboard content to cursor in 10 sec...The content of your clipboard will be pasted on the cursor after the specified delayLicenseCopyright (c)Junaid H. Rahim. All rights reserved. Licensed under the MIT License
Package: autoui
This Python library implements intend to implement automatic generation of
User-Interfaces from function/class signatures.The pseudo-widget semantic follows HTML5 conventions.There are plans to includes support for:Graphical User-Interfaces (through Tk)Command-Line Interfaces (with coloring support viablessings/blessed)Text User-Interfaces (throughasciimatics)Web Forms (with Flask)InstallationThe recommended way of installing the software is throughPyPI:$pipinstallautouiAlternatively, you can clone the source repository fromBitbucket:$mkdirautoui$cdautoui$gitclonegit@bitbucket.org:norok2/autoui.git$pythonsetup.pyinstall(some steps may require additional permissions depending on your configuration)The software does not have additional dependencies beyond Python and its
standard library.It was tested with Python 2.7 and 3.5.
Other version were not tested.