Saravanan666/pypi-org huggingface dataset:
Package: ereuse-workbench
# eReuse.org WorkbenchCreate a hardware report (JSON) of your computer including components,serial numbers, testing (SMART and stress test), benchmarking (sysbench),erasing (using certified tools and well-known standards),and installing an OS.You parametrize Workbench to execute the actions you want and thenruns without user interaction, generating a human and machinefriendly report in JSON. This report can be uploaded to the IT AssetManagement System [Devicehub](https://github.com/ereuse/devicehub-teal).Check example reports [here](https://github.com/eReuse/devicehub-teal/blob/master/ereuse_devicehub/dummy/files/asus-eee-1000h.snapshot.11.yaml),[here](https://github.com/eReuse/devicehub-teal/blob/master/ereuse_devicehub/dummy/files/dell-optiplexgx520.snapshot.11.yaml),and [here](https://github.com/eReuse/devicehub-teal/blob/master/ereuse_devicehub/dummy/files/lenovo-3493BAG.snapshot.11.yaml).Workbench can be used in an [USB or CD](https://github.com/ereuse/workbench-live),or even [over the network through PXE](https://github.com/ereuse/workbench-server),specially useful when erasing and installing OSes or workingwith many computers.This repository is the stand-alone core version of Workbench.Workbench uses several well-known Linux packages to perform eachaction, avoiding re-inventing the wheel. It is usedprofessionally by refurbishers and regular companies to register,prepare, and track their devices, and has great emphasis in datacorrectness. Workbench is free software from [eReuse.org](https://ereuse.org).## InstallationWorkbench is developed and tested in Debian 9, and it shouldwork in any Debian based OS, even in any Linux as long as the OShas the debian packages listed below.1. Install the [debian packages](debian-requirements.txt), likethe following way `cat debian-requirements.txt | sudo xargs apt install -y`.2. `sudo pip3 install ereuse-workbench --pre -U`Note that you need to install this as sudo, as the software can onlybe run with root due to the sensitive tools it uses.## UsageExecute Workbench through the CLI or directly in Python.To use the CLI check the help for more info: `sudo erwb --help`From a python file you can:```pythonfrom ereuse_workbench.workbench import Workbencherwb = Workbench() # Check the docs of this class for more infoerwb.run()```## Testing1. Clone this repository and go to the repository main folder.2. Install Workbench as `pip3 install -e .[test] -r requirements.txt`.3. Run the tests with `python3 setup.py test`.Note that you do not need to be root to execute tests, and thatthey can be executed in Mac and Windows, as they do not use anyof the system tools, but stubs.## Known limitationsWe want to overcome them in the future :-)- Unsupported USB network adaptors.- It cannot not install Windows OS.
Package: erexplain
ExplainERERExplain is a package to explain any Entity Resolution model, using a Black-Box approachKey FeaturesThe explanations can be provided into two different ways:Attribute-Level: the system shows to the user a histogram reporting the importance of the attributes with respect to the prediction. The importance is a number in [0,1]Value-level: the system shows to the user, for each chosen attribute set/single attribute A, the most influential values for A, with respect to the predictionTechDillinger uses some well-know libraries:Pandas: To create perturbationNumpyMLXtend: To create explanations at value levelInstallationTo install the package simply run$pipinstallerexplain
Package: erf
erf下の方に日本語の説明がありますOverviewTools to easily write exception handling.Usageimporterf# case 1@erf({"KeyError":"no_data"})defgetter(dic,key):returndic[key]dic={"fuga":23}print(getter(dic,"hoge"))# -> "no_data"# case 2@erf(KeyError="no_data")defgetter2(dic,key):returndic[key]dic={"fuga":23}print(getter2(dic,"hoge"))# -> "no_data"Advanced Usageimporterfimportmath# case 3@erf({"division by zero":math.inf},error_str_func="str")definf_div(a,b):returna/bprint(inf_div(3,0))# -> inf# case 4-1print(erf({"division by zero":math.inf})(lambdaa,b:a/b)(3,0))# case 4-2func=lambdaa,b:a/bcatcher_dic={"division by zero":math.inf}print(erf(catcher_dic)(func)(3,0))# case 4-3func=lambdaa,b:a/bprint(erf(div=math.inf)(func)(3,0))# case 4-4func=lambdaa,b:a/bsafe_div=erf(div=math.inf)(func)print(safe_div(3,0))# case 5# Example of recursion: recursion must be wrappeddefrec_func(arg):iftype(arg)==type(23):returnstr(arg)eliftype(arg)==type([]):return[rec_func(e)foreinarg]else:raiseException("invalid type.")@erf(recur=...)defwrapper(arg):returnrec_func(arg)print(wrapper([1,[2,3]]))obj=[1,2]obj[1]=objprint(wrapper(obj))概略例外処理を簡単に書けるツール利用例importerf# 「KeyError」が含まれる例外時に「"no_data"」を返却するようにする@erf({"KeyError":"no_data"})defgetter(dic,key):returndic[key]dic={"fuga":23}print(getter(dic,"hoge"))# -> "no_data"# 同様のコードの略記法@erf(KeyError="no_data")defgetter2(dic,key):returndic[key]dic={"fuga":23}print(getter2(dic,"hoge"))# -> "no_data"発展的な利用例importerfimportmath# 例外オブジェクトを文字列化する方法を指定@erf({"division by zero":math.inf},error_str_func="str")definf_div(a,b):returna/bprint(inf_div(3,0))# -> inf# 無名関数の例外処理print(erf({"division by zero":math.inf})(lambdaa,b:a/b)(3,0))# 無名関数の例外処理 その2func=lambdaa,b:a/bcatcher_dic={"division by zero":math.inf}print(erf(catcher_dic)(func)(3,0))# 無名関数の例外処理 その3func=lambdaa,b:a/bprint(erf(div=math.inf)(func)(3,0))# 無名関数の例外処理 その4func=lambdaa,b:a/bsafe_div=erf(div=math.inf)(func)print(safe_div(3,0))# 再帰の例: 再帰の場合は1重ラップする必要があるdefrec_func(arg):iftype(arg)==type(23):returnstr(arg)eliftype(arg)==type([]):return[rec_func(e)foreinarg]else:raiseException("invalid type.")@erf(recur=...)defwrapper(arg):returnrec_func(arg)print(wrapper([1,[2,3]]))obj=[1,2]obj[1]=objprint(wrapper(obj))
Package: ergo
ERGOSimple Microservice Development FrameworkErgo is the substrate for the rapid development of coordinated microservices.The primary emphasis with Ergo is to eliminate - as much as possible - the boilerplate infrastructure that is common to most software stacks to facilitate a larger developer emphasis on business-oriented development.  This is achieved with inversion of control and procedural injection.For example; Consider the following python function in a filemath.py:# math.py

def product(x, y):
    return float(x) * float(y)Ergo provides the tooling to bootstrap this simple function into any one of a variety of environments (eg. console application, http service, MQ worker, etc.)To start an http service for the above function:$ ergo http math.py:productthen to make a request against this service$ curl -i "http://localhost?x=4&y=5"
20.0
Package: ergonames
Ergo Names Python SDKExample for resolving an ergonamename="~balb";address=ergonames.resolve_ergoname(name);Example for address lookupaddress="3WwKzFjZGrtKAV7qSCoJsZK9iJhLLrUa3uwd4yw52bVtDVv6j5TL";token_list=ergonames.reverse_search(address);
Package: ergonomics-metrics
ErgonomicsErgonomics metrics: REBA scoreInstallationpip install ergonomics-metricsExamplefromergonomics.rebaimportRebaScoreimportrequestsimportnumpyasnpimportjsonurl='https://www.dropbox.com/s/e72lwn9n9gkoygj/ergonomics.json?dl=1'r=requests.get(url)sample_pose=np.array(r.json()['sample_pose'])rebaScore=RebaScore()body_params=rebaScore.get_body_angles_from_pose_right(sample_pose)arms_params=rebaScore.get_arms_angles_from_pose_right(sample_pose)rebaScore.set_body(body_params)score_a,partial_a=rebaScore.compute_score_a()rebaScore.set_arms(arms_params)score_b,partial_b=rebaScore.compute_score_b()score_c,caption=rebaScore.compute_score_c(score_a,score_b)print("Score A: ",score_a,"Partial: ",partial_a)print("Score A: ",score_b,"Partial: ",partial_b)print("Score C: ",score_c,caption)TestPose Left

Neck Score: 2
Trunk Score: 2
Legs Score: 2
Upper Arm Score: 1
Lower Arm Score: 2
Wrist Score: 3

A Score: 4
B Score: 3
C score: 4
REBA score: 4

Pose Right

Neck Score: 2
Trunk Score: 2
Legs Score: 2
Upper Arm Score: 1
Lower Arm Score: 2
Wrist Score: 2

A Score: 4
B Score: 2
C score: 4
REBA score: 4

Rank
[0]'Negligible Risk',
[2-3]'Low Risk. Change may be needed',
[4-7]'Medium Risk. Further Investigate. Change Soon',
[8-10]'High Risk. Investigate and Implement Change',
[11+]'Very High Risk. Implement Change'
Package: ergo-python-appkit
ergo-python-appkitThis is a wrapper around Ergo's appkit, which is based on scala/java. Jpype is used to map to the JVM and the jar running within. That means that Java needs to be installed for this package to work.InstallationInstallation can be done through pip. Currently the package is not available yet on PyPi so you need to refer directly to the github.pip install git+https://github.com/ergo-pad/ergo-python-appkit.gitUsageThe ergo appkit can be accessed by importing java classes AFTER importing the appkit, like this:importergo_python_appkit.appkitfromorg.ergoplatform.appkitimportAddressmyErgoAddress=Address.create("9gF2wmTWcEX2EZu5QY3dJwURep1HWsXjUZdVavVWESun4Sp8BYj")#will print the ergotree belonging to the addressprint(myErgoAddress.toErgoContract().getErgoTree())There are also convenience methods created to allow for easier use from Python for appkit functionality that is used often.An example of a simple intra wallet transaction send from the nodes wallet:fromergo_python_appkit.appkitimportErgoAppKitfromorg.ergoplatform.appkitimportAddressappKit=ErgoAppKit("http://myergonode:9053","mainnet","http://api.ergoplatform.org","nodeapikey")myAddress="9gF2wmTWcEX2EZu5QY3dJwURep1HWsXjUZdVavVWESun4Sp8BYj"#collect boxes to spend 1 erginputs=appKit.boxesToSpend(address=myAddress,nergToSpend=int(1e9))#Define output boxoutputBox=appKit.buildOutBox(value=int(1e9),tokens=None,registers=None,contract=appKit.contractFromAddress(myAddress))#Build the unsigned transactionunsignedTx=appKit.buildUnsignedTransaction(inputs=inputs,outputs=[outputBox],fee=int(1e6),sendChangeTo=Address.create(myAddress).getErgoAddress())#Sign the transaction with the nodesignedTx=appKit.signTransactionWithNode(unsignedTx)#Send the transactionappKit.sendTransaction(signedTx)
Package: ergpy
ergpyA python-jvm wrapper for interacting with the Ergo blockchain.UsageRequires Java 8 or higher to be installed on systempip install ergpyThere are helper methods created to do simple transactionsPlease checkout theexamples.py(on github) to see how to use them.Simple Sendis availableAllows sending ERG to one address or multiple addresses in ONE transactionsGet wallet addressesis availableThis gives derivations of wallet addresses given the mnemonicIf specified2, a list of two addresses will be printed in the consoleGet Box Infois availableGiven the index and address, the selected box will be printedSend Tokenis availableAllows sending token(s) to one or multiple addressesThe token id paramter must be given as a 2D arrayRemember that tokens/nfts are seen as the same when transacting with themCreate NFTis availableAllows creating NFTsOne mint is one transactionThere are helper methods as well to get the sha-256 hash required while mintingSHA256 casterPut the hash as a string and this will be converted into bytesString HasherInput a string and this will be converted into a hash (only for testing purposes)File HasherInput the file name and the hash will be generateedCreate Tokenis availableAllows a token to be mintedCustomA bit more customization is available using methods fromappkit.pyTo truly have custom transactionsappkit.pymust be editedGithub usagegit clone https://github.com/mgpai22/ergpy && cd ergpypip3 install -r requirements.txtpython -m stubgenj --convert-strings --classpath "./ergpy/ergo.jar" org.ergoplatform javaIf using an ide such as pycharm , make sure the root folder is ergpyThis allows code completion via stubgenj to work properlyThis method is recommended for developer as it allows for direction customizations inappkit.pyRoadmapThis is the begining of python support for Ergo! Contributions are essential for this to continue.Ergoscript support.Better documentation and packaging (first time creating a package).Would love some help with this!CreditsMrStahlfelgeShowcased jpype usage with appkitErgopadBorrowed/Modified some of their code
Package: eri
# ERIPYThis package contains commonly useful functions and modules for data science purposes at Elder Research, Inc.## SubmodulesWhat is provided here is simply a high-level discussion of what is avilable ineri. For detailed instructions, please refer to the sub-module-levelREADMEfiles, and, of course, the function documentation. If any of the above does not exist, please seek out the author and shame them mercilessly###eri.cleanUseful functions for cleaning your data (usually assumespandasdata frames).###eri.configShared configuration details.###eri.htmlHTML parsing and munging utilities.###eri.validateUseful functions for validating the data we have (often just logging useful facts about a particular dataframe).## Testingto test, installpytestandcoverageand then run the following:` python # run all tests coverage run-mpytest `` python # print coverage statistics coverage report-m`## Deploying toPyPiI’m usingtwine. Instructions are [here](https://github.com/pypa/twine). to summarize, though:` bash python setup.py sdist bdist_wheel twine upload--repository-urlhttps://test.pypi.org/legacy/dist/* twine upload--skip-existingdist/* twine upload--skip-existing-reri dist/* `
Package: eric6-diff-merge
eric6_diff_mergepython diff merge module copy from eric6.install:pip install eric6_diff_mergeuseage:fromeric6_diff_mergeimportEric6Diffdefmain():diff=Eric6Diff("./speedtext1.txt","./speedtext2.txt",output="./result.txt")diff.on_diff()if__name__=="__main__":main()input :speedtext1.txt==Capital-Saratoga==aaabbbcccdddspeedtext2.txt==Capital-Saratoga==abcbbbcccoutput : result.txt--- ./speedtext1.txt	Thu Dec 12 03:08:59 2019
+++ ./speedtext2.txt	Thu Dec 12 03:08:59 2019
@@ -1,7 +1,5 @@
 == Capital-Saratoga ==
-aaa
+abc
 bbb
 ccc
-ddd

-the same module:If you found the same module, please inform me in issue, thanks!
Package: erica
ericaflask-like zero denpendency web server.SamplefromericaimportErica,RequestHandlerapp=Erica()@app.get("/")defhello(request:RequestHandler)->None:request.response.json({"message":"Hello, World!"})@app.post("/")defq(request:RequestHandler)->None:request.response.text(f"you posted{request.text}")app.run()
Package: eric-chen-forward
eric_chen_forwardTo train the model:fromeric_chen_forward.modelimportClassifier# alpha parameter used in data augmentation, 0.2 by defaultmodel=Classifier(alpha=0.2)# option 1# text files of labels and paragraphs respectively, separated by newlines# model_type is 'SVM' by default, you can use 'LR' for logistic regressionmodel.train(labels_file="labels_file_path",paragraphs_file="paragraphs_file_path",model_type='SVM')# option 2# csv file must have a 'label' column and 'paragraph' column only, and the column names are hardcodedmodel.train(csv_file="csv_file_path",model_type='LR')To use the saved model in code:withopen('model_name.pkl','rb')asf:model=pickle.load(f)To run the classifier demo:fromeric_chen_forwardimporturl_classifier_demoAPI_KEY=[pasteapikeyhere]SEARCH_ENGINE_ID=[pastesearchengineIDhere]url_classifier_demo.Demo('file path of model.pkl',API_KEY,SEARCH_ENGINE_ID,max_summary_length=100)max_summary_length is set to 100 words by default.Register an API Key and set up the Programmable Search Engine to be able to use the Google Custom Search API:https://developers.google.com/custom-search/v1/overviewAfter setting up, the Search engine ID can also be found in the control panel:https://programmablesearchengine.google.com/controlpanel/all
Package: ericdemo
Demo of creating and uploading a packageFree software: MIT licenseInstallationpip install ericdemoYou can also install the in-development version with:pip install https://github.com/ericchagnon15/ericdemo/archive/main.zipDocumentationhttps://ericdemo.readthedocs.io/DevelopmentTo run all the 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 (2022-04-12)First release on PyPI.
Package: erich
Erichenrich local errors without repeated try except using decoratorsExamplemain.pyfromtypingimportCallableimporterich# Print a formatted message where format field names must# be present in the function signature.@erich.fmt("Tried starting task{name}({desc})")defstart_task(name:str,prio:int,fn:Callable,desc:str=None):schedule(prio,fn)# Print that this function has been called but only include the prio# in the output@erich.signature("prio")defschedule(prio:int,fn:Callable):can_schedule(prio)fn()# add some nicer output to the final result@erich.fmt("Cannot schedule task due to")defcan_schedule(prio:int):can_schedule_internal(prio)# here the exception is actually raised with a limited# amount of information which will be enriched by# parent/calling functions.defcan_schedule_internal(prio:int):raiseException(f"Cannot schedule something with prio{prio}. It's invalid")start_task("test",-1,lambda:None,desc="very important")>pythonmain.py

..
stacktrace
..

Triedstartingtasktest(veryimportant)↳duringcallofschedule(prio=-1)↳Cannotscheduletaskdueto↳Cannotschedulesomethingwithprio-1.It'sinvalidReleaseupdate version in setup.cfg, pyproject.toml and root __init__.pybuildpython-mbuilduploadpython3-mtwineupload--repositorypypidist/*
Package: eric-tools
ToolsRedisProject management of Python daily toolspipinstalleric_toolshttps://pypi.org/project/eric_tools/encryption_classmethod.py  Python HMAC+MD5加密签名算法

exception_class.py  异常类

resize_image.py 图片压缩

ip.py           ip地址定位API

logger.py       日志模块类和高级日志装饰器

remove.py       删除文件

send_email.py   发送邮件

sftp.py         ssh远程下载文件

pgsql.py        对postgresql 增删改查操作

readconfig      针对读取配置文件

jwt_encrypt     生成jwt Access Token 加密及解密

convert_json    支持json和object之间转换

Abstract.py     抽象类模型

decorator.py    惰性属性装饰器

BingDwenDwen.py  冰墩墩
Package: erie-connect
Unofficial Python client to interact with Erie Connect cloud to retrieve information from IQsoft 26 water softener.For Home Assistant integration check this repository:https://github.com/tgebarowski/erie-watertreatment-homeassistantInstallation[sudo]pipinstallerie_connectUsageYou can import the module aserie_connect.ConstructorErieConnect(username,password,auth,device)usernameandpasswordare mandatory in order to be able to authenticate and get access token.If authorization info is already stored somewhere you may pass it directly to constructor, so that no extra step will be performed before querying for the data.
Similarly, Device info can be passed directly to the constructor to avoid selecting default device when invoking queries.If auth and device are not set, both Auth and Device object will be stored upon first successful data fetching.RequirementsPython 3.7DisclaimerThis is an experimental library based on network traffic sniffing, made mainly for private purposes to integrate IQsoft 26 water softener with Home Assistant.Authorserie_connectPython client was written byTomasz Gebarowski.
Package: erika
Erika is a GTK+ podcast manager for Linux.FeaturesSuscribe, download and listen to audio podcastsKeep your podcast library organised, and synchronize it withgpodder.netInstallationInstall Python 3,PyGObject, and webkitgtk (not webkit2gtk) and run the commandpip install erikaCreditsErika is released under the MIT License, with the exception of the fileerika/frontend/widgets/scrolled_window.pywhich is released under the
GNU/GPLv3 License.
Package: erin
ErinBatteries IncludedWhat is Erin?Erin is an initiative to build a bot development framework like how flask is a framework for web applications. It is built on top of thediscord.pylibrary and supports all of it's extensions by default.Can I get started with Erin today?Yes, Erin works out of the box with alldiscord.pyextensions. You can read how to set it up in thedocumentation. However, keep in mind that this project has not even released a pre-alpha yet. This is because we are still deliberating core architecture and we want to get it right the first time. As a result expect plenty of changes to the API without any notice at all.
Package: eriparse
EriparseHTML parsing logic forhttps://www.erieri.com/cost-of-living-calculator. Was written to help my wife and I better understand the differences between potential cities for her fellowship.NotesBe aware that website frontend can changewhenver, so, rely on the build badge above for is_still_valid to know whether the website HTML has changed.UsageIntended to be called like:wget -q https://www.erieri.com/cost-of-living/united-states/illinois/chicago -O - | pipenv run python eriparse/main.py >> chicago.jsonOr, if you'd like to integrate it into your Python code,>>> pip install eriparsefrom eriparse import parse
Package: eris
python-erisThis is a Python implementation of theEncoding for Robust Immutable Storage (ERIS).Usageimport eris
import asyncio

async def main():

    # create a store
    store = eris.DictStore()

    # encode content
    encoder = eris.Encoder(eris.null_convergence_secret(), store, block_size = 1024)
    await encoder.write(b'Hello world!')
    read_capability = await encoder.close()

    # decode content
    decoder = eris.Decoder(store, read_capability)
    decoded = await decoder.readall()

    print(decoded)

asyncio.run(main())See also theonline documentationand theexamples.DevelopmentRunning Testspython -m unittest tests/*.pyBuilding Documentationmake -C docs htmlPublishing to PyPipython -m build
python3 -m twine upload --repository pypi dist/*LicenseAGPL-3.0-or-later
Package: eristropy
EristroPy: End-to-End Entropy Analysis of Time Series SignalsOverview and IntroductionWelcome to EristroPy, a powerful Python package designed for end-to-end entropy analysis of time series signals via entropy. EristroPy provides an all-in-one solution for researchers and practitioners looking to perform entropy/variability analysis of time series data.For more detailed information, check out thedocumentation.Features & BenefitsEristroPy offers a multitude of features aimed at simplifying the time series analysis process:Automatic Signal Stationarity: Ensure the validity of your entropy and variability analyses by automatically rendering signals stationary.Scalable Entropy Calculations: Utilizes Numba's just-in-time compilation for efficient sample and permutation entropy calculations.Optimal Parameter Selection: Provides intelligent suggestions for entropy measure parameters through rigorous statistical approaches.InstallationThe easiest way to install EristroPy is using pip by calling:pipinstalleristropyUsageUsing EristroPy, you can go from having the base time series signals, to a coherent & optimized entropy estimates in just a few lines of code. For instance, consider the following problem of estimating the sample entropy of some synthetic time series signals:importnumpyasnpimportpandasaspdfromeristropy.stationarityimportStationarySignalsfromeristropy.sample_entropyimportSampleEntropysignal_ids=np.repeat(["signal_1","signal_2"],100)timestamps=np.tile(np.arange(100),2)rng=np.random.default_rng(17)signal_1_values=rng.uniform(-5,5,size=(100,))signal_2_values=rng.uniform(-5,5,size=(100,))values=np.concatenate((signal_1_values,signal_2_values))df=pd.DataFrame({"signal_id":signal_ids,"timestamp":timestamps,"value":values})signals=StationarySignals(df,method="difference")stationary_df=signals.make_stationary_signals()sampen=SampleEntropy(stationary_df)result_df=sampen.compute_all_sampen(optimize=True,estimate_uncertainty=True)In just a few lines of code, you have access to state-of-the-art results that follows & implements best practices. It's that easy!LicenseEristroPy is released under the MIT License. See theLICENSEfile for more details.
Package: erix-python-utils
erix-python-utilsThis is a project that has utils that I like using, and want to make a packageInstallationpipinstallerix-python-utilsUsagehomebrew.pyfrom helloworld import helloworld
helloworld()Developing erix-python-packagesTo install, along with the tools you need and run tests, run the following in you virtualenvpipinstall-e.[dev]