Saravanan666/pypi-org huggingface dataset:
Package: ethiack-job-manager
Ethiack Job ManagerPython package for managing jobs using Ethiack's Public APIIntroductionThis is Python package and command-line interface (CLI) designed as a wrapper aroundEthiack's Public API(API docs). It simplifies the management of jobs by providing convenient access to the primary API endpoints related to job operations.FeaturesManage jobs through the Ethiack API with ease.Command-line interface for quick interaction.Compatible with Python 3.8 and higher.Easy installation via PyPI.InstallationYou can installethiack-job-managerusingpip:pipinstallethiack-job-managerCredentials SetupUsing Ethiack's API - and, therefore, this package - requires authentication using anAPI KeyandAPI Secret, which can be retrieved inEthiack's Portal settings page. These credentials must be available as environment variablesETHIACK_API_KEYandETHIACK_API_SECRET, repectively, whenever the package is used.To set up these credentials, you can either set the environment variables directly:exportETHIACK_API_KEY=your_api_keyexportETHIACK_API_SECRET=your_api_secretor create a.envfile:ETHIACK_API_KEY=your_api_key
ETHIACK_API_SECRET=your_api_secretUsageCommand-line InterfaceRun the CLI commands to manage jobs through the Ethiack API.❯ ethiack-job-manager --help

 Usage: ethiack-job-manager [OPTIONS] COMMAND [ARGS]...

 Ethiack Job Manager CLI.
 CLI for managing jobs using Ethiack's Public API.

╭─ Options ─────────────────────────────────────────────────────────────╮
│ --version    Show the version and exit.                               │
│ --help       Show this message and exit.                              │
╰───────────────────────────────────────────────────────────────────────╯
╭─ Commands ────────────────────────────────────────────────────────────╮
│ await          Wait for a job to finish.                              │
│ cancel         Cancel a queued or running job.                        │
│ check          Check if a URL is valid and a job can be submitted.    │
│ info           Get information about a job.                           │
│ launch         Launch a job.                                          │
│ list           List all jobs.                                         │
│ status         Show the status of a job.                              │
│ success        Show the success of a job.                             │
╰───────────────────────────────────────────────────────────────────────╯Python PackageImport the package and use the available functions to manage jobs through the Ethiack API. For more information:importethiack_job_managerasmanagerhelp(manager)LicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: eth-kms-signer
eth_kms_signerAWS KMS Signer for ETH transactions(EIP 155/EIP 1559/ EIP 2930)>> Go to documentationDependenciesPython 3.5+QuickStartThis library is available on PyPI. Install via pip as:pipinstalleth-kms-signerUsageInstantiate eth kms signer clientfrometh_kms_signerimportEthKmsClientclient=EthKmsClient(region_name="{aws-kms-region}")Initailize web3fromweb3importWeb3w3=Web3(Web3.HTTPProvider(endpoint_uri="{rpc_url}"))Sign aEIP 155transactiontx={"nonce":nonce,"to":"0x7EeD368F105a6eaC9Ac645bC3440fEa9A6C3D531","value":w3.toWei(12,"wei"),"gas":21000,"gasPrice":w3.toWei(1,"gwei"),"chainId":"0x4",}signed_tx=client.sign_transaction(tx,key_id)tx_hash=w3.eth.sendRawTransaction(signed_tx)Sign aEIP 1559Dynamic Fee transactiontx={"nonce":nonce,"to":"0x7EeD368F105a6eaC9Ac645bC3440fEa9A6C3D531","value":w3.toWei(12,"wei"),"gas":21000,"maxFeePerGas":w3.toWei(1,"gwei"),"maxPriorityFeePerGas":w3.toWei(1,"gwei"),"type":"0x2","chainId":"0x4",}signed_tx=client.sign_transaction(tx,key_id)tx_hash=w3.eth.sendRawTransaction(signed_tx)ExamplesChoose one of the following for a fully detailed example:EIP 155 legacy transfer transactionEIP 155 legacy contract invocationEIP 1559 transfer transactionEIP 1559 contract invocationTerraform to provision AWS KMS KeyThe following example makes use of theCloudPosse Terraform Module:Detailed Example
Package: eth-logo
eth-logoPython lib to print Ethereum logo of any size and style on your terminal or standard output.Quickstartpipinstalleth-logoUsage (in terminal)# Optionals: print-eth SIZE CHAR [defaults: size=20 char='#']print-eth
print-eth30%Usage (in python)from eth_logo import print_eth# Optionals: Size, Character, Background, Paddingprint_eth(size=20, char='#', back=' ', pad=' ')ContributeYou can set up your dev environment with:gitclonegit@github.com:0xMarto/eth-logo.gitcdeth-logo
virtualenv-ppython3venv
.venv/bin/activate
Package: ethmeet
ethmeetVideo-chat API. Compatible with most famous platforms.InstallationPip:pip3installethmeetInstall Geckodriver:gitclonegit@github.com:elleaech/ethmeet.git
python3ethmeet/scripts/gecko_install.pyDocsAPI ReferenceBasic Usagefromethmeetimport(Driver,AttendGoogle,CreateGoogle,LoginGoogle)# SET COMMON DRIVERdriver=Driver()# INSTANCE OBJECTSsadm_login=LoginGoogle(driver=driver())create=CreateGoogle(driver=driver())login=LoginGoogle(driver=driver())attend=AttendGoogle(driver=driver())# SET LOGIN DATAadm_login.login_data={"some.email@service.com","SomePa$$word123"}# IF LOGIN IS SUCCESSFUL, CREATE NEW MEET CODEifadm_login.doLogin():create.new_meet()# GET LOGIN INPUTlogin.login_data={}# IF LOGIN IS SUCCESSFUL AND MEET CODE WAS CREATED, GO TO MEETINGiflogin.doLogin()andcreate.code!=None:attend.set_meeting_url(create.code)attend.goto_meet()# CLOSE DRIVERdriver.close()Contributing & Issue TrackerBranch &Pull RequestIssuesGet sourcegitclonegit@github.com:elleaech/ethmeet.git&&cdethmeet

python3-mvirtualenv.&&pip3install-rrequirements.txtLicenseApache License 2.0
Package: ethminer_supervisor
Supervise ethminer daemon and restart process or machine when appropriate.This assumes ethminer was set up using ansible-ethminer, where systemd manages
the ethminer.service process. This is required, since this is based on parsing
logs from sytemd.FeaturesRestart process when process gets stuckRestart machine when graphics driver fails (WIP, currently I restart nightly)UsageInstall ethminer supervisor with:pip install ethminer-supervisorThen hook up either supervisor or systemdCLIThe command line interface may be used to run manually:ethminer_supervisorYou can restart the service if only old times were found by passing–restart:ethminer_supervisor --restart–delta(default 180) can be used to override the default ‘old’ time in
seconds before action is taken to recover the service:ethminer_supervisor --restart --delta 60LicenseSee LICENSE file for further licensing information
Package: eth-mpt
Modified Merkle Paticia TrieMPT is the data structure used inEthereumas a cryptographically authenticated key-value data storage.This library is a Python implementation of Modified Merkle Patrica Trie with a very simple interface.Examplestorage={}trie=MerklePatriciaTrie(storage)trie.update(b'do',b'verb')trie.update(b'dog',b'puppy')trie.update(b'doge',b'coin')trie.update(b'horse',b'stallion')old_root=trie.root()old_root_hash=trie.root_hash()print("Root hash is{}".format(old_root_hash.hex()))trie.delete(b'doge')print("New root hash is{}".format(trie.root_hash().hex()))trie_from_old_hash=MerklePatriciaTrie(storage,root=old_root)print(trie_from_old_hash.get(b'doge'))try:print(trie.get(b'doge'))exceptKeyError:print('Not accessible in a new trie.')InstallingInstall and update usingpip:pip install -U eth_mptDocumentationDocumentation can be found onreadthedocs.ContributingAll the contributors are welcome. If you can make this library better, don't hesitate to :)Testingpython-munittest
Package: ethnicolr2
ethnicolr2: Predict Race and Ethnicity From NameA pytorch implementation ofethnicolrwith new models that make different assumptions (for instance, this package has models trained on unique names) than ethnicolr. The package uses the US census data and the Florida voting registration data to build models to predict the race and ethnicity (Non-Hispanic Whites, Non-Hispanic Blacks, Asians, Hispanics, and Other) based on first and last name or just the last name. For notebooks underlying the package, seehere.Caveats and NotesIf you picked a random person with the last name ‘Smith’ in the US in 2010 and asked us to guess this person’s race (as measured by the census), the best guess is the modal race of the person named Smith (which you can get from the census popular last name data file). It is the Bayes Optimal Solution. So what good are predictive models? A few things—if you want to impute race and ethnicity for last names that are not in the census file, which can be because of errors, infer the race and ethnicity in different years than when the census was conducted (if some assumptions hold), infer the race of people in different countries (if some assumptions hold), etc. The biggest benefit comes in cases where both the first and last name are known.InstallWe strongly recommend installingethnicolor2inside a Python virtual environment
(seevenv documentation)pip install ethnicolr2ExampleTo predict race/ethnicity using the Florida Last Name Model to afile with first and last namesimport pandas as pd
df = pd.read_csv("ethnicolr2/data/input-with-header.csv")
pred_fl_last_name(df, lname_col = "last_name")


names = [
 {"last": "sawyer", "first": "john", "true_race": "nh_white"},
 {"last": "torres", "first": "raul", "true_race": "hispanic"},
]
df = pd.DataFrame(names)
df = pred_fl_full_name(df, lname_col = "last", fname_col = "first")

      last  first true_race   preds
0  sawyer   john  nh_white nh_white
1  torres   raul  hispanic hispanicAuthorsRajashekar Chintalapati, Suriyan Laohaprapanon, and Gaurav SoodContributor Code of ConductThe project welcomes contributions from everyone! In fact, it depends on
it. To maintain this welcoming atmosphere, and to collaborate in a fun
and productive way, we expect contributors to the project to abide by
theContributor Code of
Conduct.LicenseThe package is released under theMIT
License.
Package: ethnicseer
ethnicseer - a name-ethnicity classifierethnicseer('ethnic-seer') is a name-ethnicity classifier, written in python. It can determine the ethnicity of a given name, using linguistic features such as sequences of characters found in the name and its phonetic pronounciation.ethnicseercomes with a pre-trained model, which can handle the following 12 ethnicities: Middle-Eastern, Chinese, English, French, Vietnam, Spanish, Italian, German, Japanese, Russian, Indian, and Korean. The included pre-trained model can achieve around 84% accuracy on the test data set.ethnicseeris based on the name-ethnicity classifier, orginally proposed here:Treeratpituk, Pucktada, and C. Lee Giles. "Name-ethnicity classification and ethnicity-sensitive name matching." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 26. No. 1. 2012.Paper URL :https://ojs.aaai.org/index.php/AAAI/article/download/8324/8183Requirementsabydosscikit-learnnltkpython >= 3.7.6Installationethnicseercan be installed usingpippip install ethnicseerUsagesOnce installed, you can useethnicseerwithin your python code to classify whether a Thai name is a person name or a corporate name.>>> from ethnicseer import EthnicClassifier

>>> ec = EthnicClassifier.load_pretrained_model()
>>> ec.classify_names(['Yūta Nakayama','Marcel Halstenberg','Raphaël Varane'])
['jap', 'ger', 'frn']CitationTreeratpituk, Pucktada, and C. Lee Giles. "Name-ethnicity classification and ethnicity-sensitive name matching." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 26. No. 1. 2012.AuthorPucktada Treeratpituk, Bank of Thailand (pucktadt@bot.or.th)LicenseThis project is licensed under the Apache Software License 2.0 - see theLICENSEfile for details
Package: ethomap
EthomapLightweight library with basic modules for generating behavioral embeddings from dynamic time warping of postural time
series. Also includes modules to decompose behavioral sequences into transition modes.Based onMearns et al. (2020).Installationpip install ethomapExamplesDynamic time warpingfromethomapimportDynamicTimeWarping# from dtw moduleimportnumpyasnp# Create some 3D time seriesx=np.random.rand(200,3)y=np.random.rand(200,3)# Align time series y to xdtw=DynamicTimeWarping(x)dist=dtw.align(y)print("DTW distance between x and y:",dist)Behavioral embeddingfromethomapimportpdist_dtw,affinity_propagation,IsomapPrecomputedimportnumpyasnpimporttimefromscipy.spatial.distanceimportsquareform# Create some 3D time series to align pairwisexs=np.random.rand(1000,50,3)# Compute pairwise distances between time series (pdist_dtw from ethomap.distance)t0=time.time()distances=pdist_dtw(xs,parallel_processing=True,n_processors=4,bw=0.1,fs=100)distance_matrix=squareform(distances)# make squaret1=time.time()print("Time taken:",t1-t0,"seconds")# ~30 seconds for data of this sizeprint(distance_matrix.shape)# (1000, 1000)# Create behavioral space from all data (IsomapPrecomputed from ethomap.embed)isomap=IsomapPrecomputed(n_neighbors=5,n_components=2)embedding=isomap.fit_transform(distance_matrix)print(embedding.shape)# (1000, 2)# Find exemplars by clustering distance matrix (affinity_propagation from ethomap.cluster)ap=affinity_propagation(distance_matrix,preference="median")cluster_labels=ap.labels_exemplar_indices=ap.cluster_centers_indices_# Select exemplars from original dataexemplars=xs[exemplar_indices]exemplar_distances=distance_matrix[exemplar_indices,:][:,exemplar_indices]print(exemplars.shape)# (n_exemplars, 50, 3)print(exemplar_distances.shape)# (n_exemplars, n_exemplars)# Create behavioral space from exemplars onlyembedding_exemplars=isomap.fit_transform(exemplar_distances)print(embedding_exemplars.shape)# (n_exemplars, 2)
Package: ethome
ethomeA library for human behavioural data.├── README.md
├── conf               <- YAML files should be placed here for running multiple instances
├── utilities          <- A series of utility functions for running Ethome, including an MVNX parser, BVH2Biomech ports, SMI libraries etc.
├── tests              <- Tests for the library
├── notebooks          <- Jupyter notebooks
├── devices            <- Device classes which inherit from the base Ethome class. Each piece of equipment should be a subclass and in this folder.This library is still very much a work in progress, andwe very much encourage pull requests. We are aiming to keep some reasonably consistent rules to allow for interoperability and to prevent code rot.To run the MVNX parser, instantiate a ParseObject, and then parse just the relevant attributes you want in the following manner:frommvnx.mvnximportMVNXmvnx=MVNX('path/to/file.mvnx')mvnx.orientationmvnx.jointAngleor you can access them invididually, with:from mvnx.mvnx import MVNX
mvnx = MVNX('path/to/file.mvnx')
mvnx.parse_modality('orientation')
mvnx.parse_modality('jointAngle')
mvnx.parse_segments()
mvnx.parse_time()Alternatively, you can do this from the command line:python3 mvnx.py path/to/file.mvnx --modality jointAngleThis package is also published on Docker Hub so can either be pulled directly throughdocker pullor built from scratch using the included Dockerfile usingdocker build.Keep in mind, whilst this is in theory a port of the existing MATLAB Ethome codebase, in practice it is a ground-up rewrite, centered around open standards and using object-oriented Pythonic principles.Until v1.0 is released, this is a highly experimental codebase and is liable to change at any pointFor contributing to the ethome library, please consider the following rules:Your code should follow PEP8 standards where possible, save for the special cases below:We have a soft 80 character limit (should be adhered to as much as possible, though if, say, your function arguments would fit on one line and fill 81 characters, that'll be fine. If they take up more like 90 or 100, split your arguments across multiple lines.)We use CapitalCase for class names, and snake_case for function namesAs much as possible, every function name should be a verb (i.e.
'get_file' or 'parse_data'. )
*As much as possible, all functions should have a docstring, in the style of existing docstrings, documenting what the argument types should beNew devices (say EEG, or Robotic Arms etc) should have their own separate class which subclasses Device. Device is an abstract class which all other subclasses will inherit from.This library is built around HDF5 as a standard, so where possible will use HDF5's official methods to maintain interoperability.
Package: ethon
ethon• Telegram_
• Github_install.. code:: pythonpip install ethonUsageethon.pyutils.. code:: pythonfrom ethon.pyutils import rename, file_extension

#rename files
rename(file, new_path) 

#get file extension using path of file
extension = file_extension(file)ethon.pyfunc.. code:: pythonfrom ethon.pyfunc import bash, total_frames, video_metadata

#handy subprocess
o, e = bash(cmd)

#get total number of frames in a video
tf = total_frames(file)

#get basic metadata of video
data = video_metadata(file)

height = data["height"]
width = data["width"]
duration = data["duration"]ethon.uploader.. code:: pythonfrom ethon.uploader import download_from_youtube, ytdl, weburl

#Download videos from youtube
filename = await download_from_youtube(url)

#Download videos from YtDlp supported sites
filename = await ytdl(url)

#Download files from the web
filename = weburl(url).. _Telegram:https://t.me/MaheshChauhan.. _Github :https://Github.com/Vasusen-code
Package: ethoscopy
ethoscopyHead to:https://bookstack.lab.gilest.ro/books/ethoscopyfor an in-depth tutorial on how to use ethoscopyA data-analysis toolbox utilising the python language for use with data collected from 'Ethoscopes', a Drosophila video monitoring system.For more information on the ethoscope system:https://www.notion.so/The-ethoscope-60952be38787404095aa99be37c42a27Ethoscopy is made to work alongside this system, working as a post experiment analysis toolkit.Ethoscopy provides the tools to download experimental data from a remote ftp servers as setup in ethoscope tutorial above. Downloaded data can be curated during the pipeline in a range of ways, all formatted using the pandas data structure.Further the ethoscopy package provides behavpy a subclassed version of pandas that combines metadata with the data for easy manipulation. Behavpy can be used independently of the Ethoscope system data if following the same structure. Within behavpy there are a range of methods to curate your data and then to generate plots using the plotly plotting package. Additionally, there are methods to analyse bout length, contiguous sleep, and many circadian analysis methods including periodograms.Within Behavpy is a wrapped version of the python package hmmlearn, a tool for creating hidden markov models. With the update the user can easy train new HMMs from their data and use bult in methods to create graphs analysing the decoded dataset.-- Update to 1.3.0 --The latest update now allows the user to choose the colour palette from plotly to be applied to all plotting methods, see the updated tutorial on how to do this.
Additionally, the .pivot() method has been changed to .analyse_column() to avoid overwriting the original pandas method. Also, puff_mago and find_motifs analysing functions have been renamed to stimulus_response and stimulus_prior respectively.
Package: ethosdistro-py
ethosdistro_pyA Python wrapper for the EthOS REST APIInstallationInstall with pip:pipinstallethosdistro_pyUsagefromethos_pyimportEthosAPIsession=aiohttp.ClientSession()ethosapi=EthosAPI(session=session)ConfigurationEnvironment FileETHOS_PANEL_ID=<api_key>ReferencesContributeIssue TrackerSource CodeSupportOpen an issueLicenseThe project is licensed under the Apache 2 license
Package: eth-permissions
Eth permissions audit libraryThis project defines a simple library for obtaining smart contract permissions and building a graph.It's aimed at contracts usingOpenzeppelin's AccessControl module.InstallationSimply install withpipor your preferred package manager:pip install eth-permissionsUsage as a libraryWe useeth-prototype's wrappers for accessing the blockchain information. The simplest way to use it is to export the following environment variables:exportDEFAULT_PROVIDER=w3# You can use any json-rpc node supported by web3py.exportWEB3_PROVIDER_URI=https://polygon-mainnet.g.alchemy.com/v2/<YOURKEY>Use thechaindatamodule to get the full permissions detail:frometh_permissions.chaindataimportEventStreamstream=EventStream("IAccessControl","0x47E2aFB074487682Db5Db6c7e41B43f913026544")stream.snapshot# [#  {'role': Role('DEFAULT_ADMIN_ROLE'),#   'members': ['0xCfcd29CD20B6c64A4C0EB56e29E5ce3CD69336D2']},#  {'role': Role('UNKNOWN ROLE: 0x2582...a559'),#   'members': ['0x9dA2192C820C5cC37d26A3F97d7BcF1Bc04232A3']},#  ...#  {'role': Role('UNKNOWN ROLE: 0xf17c...fd8a'),#   'members': ['0x76B349e14a5B5FAF8090313Aa393e1b37aC5E126']},# ]You can register your roles to get the actual names in the result. Seemain.pyfor an example of how to do that.Usage as a command line toolFirst set up some env vars:# Env vars for eth-prototype
export DEFAULT_PROVIDER=w3
export WEB3_PROVIDER_URI=https://polygon-mainnet.g.alchemy.com/v2/<YOUR KEY>

# Values for ensuro v2 on mainnet as of dec 2023, change accordingly for other contracts
export KNOWN_ROLES=GUARDIAN_ROLE,LEVEL1_ROLE,LEVEL2_ROLE,LEVEL3_ROLE,RESOLVER_ROLE,POLICY_CREATOR_ROLE,PRICER_ROLE,...
export KNOWN_COMPONENTS=0xa65c9dE776d1f30c095EFF9C775E001a1d366df8,0x37fE456EFF897CB5dDF040A5e95f399EaBc162ca
export KNOWN_COMPONENT_NAMES="KoalaV2,Koala Partner B"Then runeth-permissions:python -m eth_permissions --view --output test.png 0x47E2aFB074487682Db5Db6c7e41B43f913026544This will create the filetest.pngand open it with the default viewer. It will look like this:Runpython -m eth_permissions --helpto see all available flags and options.AppCheckapp/Readmefor a simple app that exposes this API over http for use on a frontend app.TODOAdd support forOwnablecontractsAddress bookAdd multisig intelligence (detect when a role member is a multisig and obtain its members)Timelock detection