Saravanan666/pypi-org huggingface dataset:
Package: enhterm
enhtermenhterm is anopen source,
MIT licensed,Cmd-basedframework for writing command interpreters.enhterm provides a class that extendscmd.Cmdand which is also intended to be inherited by a user class,
with the end purpose being to create a shell.Functionality provided by this package is split among mixins, allowing you
to construct your own base class if EnhTerm is not suitable.As withcmd.Cmd, the class
constructed as described above can be used like so:from enhterm import EnhTerm

from enhterm.provider.parser.argparser import ArgParser
from enhterm.provider.parser.argparser.commands import register_commands
from enhterm.provider.stream_provider import StreamProvider

# The stream provider by default reads the input from console.
provider = StreamProvider()

# The text entered by the user then needs to be interpreted.
# Here we use an interpreter based on argparse.
provider.parser = ArgParser(provider=provider)
subparsers = provider.parser.add_subparsers()

# Add built-in commands.
register_commands(subparsers)

# Define you own handler.
def do_add(command, integers):
    return sum(integers)

# Add this command to the argparse library.
parser_add = subparsers.add_parser('add')
parser_add.add_argument(
     'integers', metavar='int', nargs='+', type=int,
     help='integers to be summed (space separated list)')
parser_add.set_defaults(func=do_add)


# Construct the terminal.
class MyShell(EnhTerm):
    """
    Simple terminal.

    Has a command that can add integers and another one
    that quits the interpreter.
    """
    def __init__(self):
        super().__init__(providers=provider)


if __name__ == '__main__':
    print("Type 'add 1 2 3 4 5 6 7 8 9' and you should get back 45")
    print("Type '-h' to get back the usage")
    MyShell().cmd_loop()Installpip install enhtermYou can also download/clone the source, in which case you have to:git clone https://github.com/pyl1b/enhterm.git
python setup.py installTo contribute a patch clone the repo, create a new branch, install in
develop mode:python setup.py developWhat is includedEach of the elements below are implemented in a distinct "mixin" class,
which mean that you can create your own combination using EnhTerm class as
a template.CommandAllows python strings to be executed as if the user typed the input at the
prompt. This is the base for executing commands in a file.ExitProvides theexitcommand that terminates command loop.HelpProvides thehelpcommand which prints information about
the use of the command while accounting for custom commands
and shortcuts.Log LevelAllows changing logging verbosity by issuing commands likeset loglevel debug.MacroCan record, remove, list and execute previously recorded commands.MessagesDoes not expose any commands but provides the class with a standardized
way of issuing messages distinct from the logging mechanism.RunAllows executing multiple commands from a string or from a file.Sub-commandsCommands are usually identified by using the first word the user types.
This mixin allows for a more natural way of issuing commands likenew macroinstead ofmacro new. Other mixins then add subcommands
in their__init__method.
Package: enhydris
Enhydris is a system for the storage and management of hydrological
and meteorological time series. You can see it in action athttp://openmeteo.org/.The database is accessible through a web interface, which includes
several data representation features such as tables, graphs and
mapping capabilities. Data access is configurable to allow or to
restrict user groups and/or privileged users to contribute or to
download data. With these capabilities, Enhydris can be used either as
a public repository of free data or as a private
system for data storage. Time series can be downloaded in plain text
format that can be directly loaded toHydrognomon, a free
tool for analysis and processing of meteorological time series.Enhydris is written in Python/Django, and can be installed on every
operating system on which Python runs, including GNU/Linux and Windows.
It is free software, available under the GNU General Public License
version 3 or any later version.  It is being used byopenmeteo.org,Hydrological Observatory of Athens, theAthens Water Supply
Company, andWQ DREAMS.For more information about Enhydris, read its documentation in thedocdirectory orlive at readthedocs.Installing a development instanceAfter creating avirtualenvand installing prerequisites, execute
this in the top-level directory:python enhydris/bin/enhydris-admin newinstance devinstanceThen, edit filedevinstance/settings.py, and execute this:python devinstance/manage.py runserverFinally, point your browser athttp://localhost:8000/.
Package: enhydris-cache
enhydris-cacheUtilities for spatial integration of time series.Free software: GNU General Public License v3Documentation:https://enhydris-cache.readthedocs.io.History1.0.2 (2023-07-31)Allow running with enhydris-api-client v.31.0.1 (2022-03-25)Fixed error when attempting to connect to Enhydris unauthenticated.1.0.0 (2021-08-31)The configuration file has changed; it now needs the timeseries_group_id
parameter.Needs Python 3.7 or later.Updated dependencies.Updated pypi metadata.0.1.1 (2019-06-13)Initial release
Package: enhydris-synoptic
Enhydris-synoptic is an app that adds a page to Enhydris that shows
current conditions in several stations.Note that it does not check permissions; any synoptic view created
will be public, regardless of whether the timeseries from which it is
derived are marked top secret.Enhydris-synoptic is free software, available under the GNU Affero
General Public License.Installing and configuringpip installenhydris-synopticIn the Enhydrissettings.pyfile, addenhydris_synoptictoINSTALLED_APPS, and setENHYDRIS_SYNOPTIC_ROOTandENHYDRIS_SYNOPTIC_URL.Enhydris-synoptic works by creating static files which are then served
by the web server.ENHYDRIS_SYNOPTIC_ROOTindicates where this
files shall be stored.ENHYDRIS_SYNOPTIC_URLis currently not used
anywhere, but it’s better to set it anyway; later versions might start
to use it without warning.In the Enhydris configuration directory, executepython manage.py migrate.Runceleryandcelerybeat, and configurecelerybeatto
execute theenhydris_synoptic.tasks.create_static_filestask once
in a while.Configure your web server to serveENHYDRIS_SYNOPTIC_ROOTatENHYDRIS_SYNOPTIC_URL.Go to the admin and setup a view.After celery executes, the report will be available atENHYDRIS_SYNOPTIC_URL + slug + '/', whereslugis the URL identifier
given to the synoptic view.
Package: eniam
Cross-platform ENIAM with useful bindings :rocket:This project provides only a wrapper for ENIAM (see onlinesource code)
Author of original code is Wojciech Jaworski.Using eniam libraryFirst, you have to install eniam by callingpip install eniamThen you can try parsing some example phrases (seeexamples/example.ipynb):# !pip install opencv-python# !pip install camelot-py# !pip install wandfromeniamimport*dom_result=Eniam(['KOT','MYSZ'],{rule(lemma='kot',pos=subst,case=nom):gram('KOT'),rule(lemma='gonić',pos=fin,person=ter):(ip<gram('KOT'))>gram('MYSZ'),rule(lemma='mysz',pos=subst,case=acc):gram('MYSZ'),root_rule():s%ip,},['KOT','MYSZ','ZDARZENIE'],{valence_rule('kot','noun'):'KOT',valence_rule('mysz','noun'):'MYSZ',valence_rule('gonić','verb'):'ZDARZENIE',}).dom("Kot goni mysz.")dom_result.show()Eniam syntaxThe eniam library uses custom primitive AST the most of the operations looks exactly the same as in normal ENIAM convensions except for those modifications:/is replaced with>\is replaced with<ip{ |x1, |x2, |x3... |xn}is replaced withip[ x1 | x2 | ... | xn]?xis replaced with functionoptional(x)a\?(x)is replaced witha % xthe grammar can contain inline literals created usinggram()function for examplerule(lemma='kot',pos=subst): gram('np/np\\np'),would be a valid rule.Useful methodsShowing the result in Jupyter notebookEniam(...).dom("Kot goni mysz.").show()Getting the html codeEniam(...).dom("Kot goni mysz.").html()Saving the html codeEniam(...).dom("Kot goni mysz.").save_html('output_file')Multiple input sentencesShowing the result in Jupyter notebookEniam(...).dom(["Kot goni mysz.", "Mysz goni kota."]).show()Using raw ENIAM interfaceRunning inside dockerTo run ENIAM docker please use the following commands:# Run subsyntax tool$dockerrun-itstyczynski/eniam:1.0/root/subsyntax--help# Run lexicon printer$dockerrun-itstyczynski/eniam:1.0/root/print_lexicon--help# Run DOM parser$dockerrun-itstyczynski/eniam:1.0/root/domparser--helpRunning with Python wrapperYou can install Python wrapper to get a nice wrapper around the docker container (this requires Python +3 and Docker installed):$pipinstalleniam$eniam-clisub--help$eniam-clilex--help$eniam-clidom--helpNote:Before installing you may wish to add python /bin/ directory to the PATH variable.
On MacOS for Python 3.7 that would be:$exportPATH="$PATH:/usr/local/Cellar/python/3.7.5/Frameworks/Python.framework/Versions/3.7/bin"Seesetting up Python PATH
Package: eniat
ENIATEniat is a Python template for various ML packages including PyTorch, Scikit-learn.Currently supports only PyTorch :moyai:It provides several convenient featuresautomated training: you don't have to re-write same epoch-wise loop everytime!automated distributed learning for PyTorch.Easy configurations for your experiments.Various package support (in the future)Quick Start1. InstallationInstall eniat with the below command.pipinstalleniateniat installation doesn't convey ML packages such astorchorsklearn, so install one you need before using eniat. :moyai:2. CLI executionYou can initiate any tasks with only console commands. :moyai:With below command, you can conduct ML experiemnts on console provided that you have proper resources for models and data. These configurations except basic arguments are based onomegaconf, so refer to the documentation and provide valid arguments.eniat-pPACKAGE-tTASK--OTHER_CONFIGS_YOU_NEEDOr you can simply write every configurations you need into one yaml and just type the path of it like below. I strongly recommend this, because ML experiments usually require quite large amount of parameters. Make a base configuration yaml file, and modify slightly by typing additional configurations into console command, and it will be much more convenient.eniat-c=PATH_TO_CONFIG_FILE3. Custom code executioneniat is designed for various environments from mere.pycodes to jupyter notebooks.The most fundamental component of eniat isTrainer. It is in charge of training your models. In the other hand,Graderonly evaluates your model. Below is an example code of torch training wih eniat.fromeniat.utils.stateloggerimportDummyLoggerfromeniat.torchimportTorchTrainer,TorchGraderlogger=DummyLogger()# Default logger for Eniatgrader=TorchGrader(grader_cfg,logger=logger)# If none, train without evaluationtrainer=TorchTrainer(trainer_cfg,learner_cfg,data_cfg,logger,grader)trainer.fit(device=0)# training on dev 0
Package: enig
enigma_machineA python package that allows a user to encrypt a message, simulating an enigma machine.Examples-Simplest formimportenigfromenigimportemmac=em.Enigma_Machine()print(mac.result("Hello, World!"))>>>rsnyf,gidsm!Different ways to set the rotorsimportenigfromenigimportemmac=em.Enigma_Machine()#default rotor values are 1,1,1print(mac.rotor1,mac.rotor2,mac.rotor3)>>>111mac.result("Hello, World!")>>>rsnyf,gidsm!#random rotorsmac.random_rotors()>>>Yourrotornumberis202215print(mac.rotors())#Using a methord to view rotors instead of the variables directly>>>(20,22,15)mac.result("Hello, World!")>>>wgqgt,sivcz!#set valuesmac.set_rotors_as(23,4,1)print(mac.rotors())>>>(23,4,1)mac.result("Hello, World!")>>>dzkzp,eywix!Different ways to set up a plugboard#Output without plugboardmac.set_rotors_as(23,4,1)print(mac.rotors())>>>(23,4,1)mac.result("Hello, World!")>>>dzkzp,eywix!#Setting up a plug board the long waymac.plug_board_init({'1f':'a','1s':'b','2f':'c','2s':'d','3f':'e','3s':'f','4f':'g','4s':'h','5f':'i','5s':'j','6f':'k','6s':'l','7f':'m','7s':'n','8f':'o','8s':'p','9f':'q','9s':'r','10f':'s','10s':'t'})print(mac.plug_list)>>>{'a':'b','c':'d','e':'f','g':'h','i':'j','k':'l','m':'n','o':'p','q':'r','s':'t'}#Setting up the same plug board the short wayprint(mac.quick_plug_board("abcdefghijklmnopqrst"))#this method also returns the dict>>>{'1f':'a','1s':'b','2f':'c','2s':'d','3f':'e','3s':'f','4f':'g','4s':'h','5f':'i','5s':'j','6f':'k','6s':'l','7f':'m','7s':'n','8f':'o','8s':'p','9f':'q','9s':'r','10f':'s','10s':'t'}print(mac.plug_list)>>>{'a':'b','c':'d','e':'f','g':'h','i':'j','k':'l','m':'n','o':'p','q':'r','s':'t'}#output with plugboardmac.set_rotors_as(23,4,1)mac.result("Hello, World!")>>>dnlzp,fnwjx!
Package: enigma-12312
🐋 equitiesOverview:equities allows for easy access to the SEC's XBRL Financial Statement Dataset
Parsed data is stored locally and served to the user in pandas dataframesThe Dataset:https://www.sec.gov/dera/data/financial-statement-data-sets.htmlInstall:pip3 install equitiesDonate:Consider donating bitcoin to fund the future development of this project.bitcoin wallet address: 3LU5MEaAXRJoCo6vx67g1Jj7qDFRKhMs5tTUTORIAL:The library consists of two central objects, Universe and Company.Universe:Building the UniverseWe begin by initializing our universe and downloading our sec data packages.from equities import Universe
u = Universe()Essential MethodsTo get the number of companies in the universe call:
len(u)To get a dataframe of XBRL metadata from of all companies in the universe call:u.properties()"CIK" numbers are the sec's official unique identifier for public companies. To get a full list of the cik numbers call:u.ciks()Accessing CompaniesUniverse objects are indexable by "CIK" integers. As an example, to access the first company in the universe call:first_cik = universe.ciks()[0]
u[first_cik] # This returns an Company object.Company:A Company object should be thought of as an abstract representation of a real company. Every
company must have an associated Universe of origin.from equities import CompanyAccessing the Financial StatementsConsider the first Company in our universe, universe[u.ciks()[0]]. It is a Company object.c = u[u.ciks()[0]]Dataframes of the company's financial statements over the universe in question is given by:c.income()      # income statement dataframe

c.balance()     # Balancesheet dataframe

c.cash()        # Cash Flow Statement dataframe

c.equity()      # Consolidated Equity dataframeAdditional Company DetailsTo get the XBRL metadata for a given company as a pandas series call:c.properties()ExampleI really want to demonstrate the beauty of this dataset since this is often difficult when looking
at thousands of numeric datatables. Let's take a very naive peek by plotting various statements
as a kind of stacked timeseries.The following is a start to finish example of how one might plot the financial statements
of the first three companies in the universe.To perform this experiment, run the following:from equities import test
test()Here is the code that this function executes:import pandas as pd
from equities import Universe, Company
import matplotlib.pyplot as plt

u = Universe()
u.build()

k,f,s = 'bar',(20,10),True
for cik in u.ciks()[:3]:

    u[cik].income().T.plot(
        kind=k,
        figsize=f,
        stacked=s)

    u[cik].cash().T.plot(
        kind=k,
        figsize=f,
        stacked=s)

    u[cik].balance().T.plot(
        kind=k,
        figsize=f,
        stacked=s)

plt.show()
Package: enigma-ai
enigma_aiTools for simple and efficient training of LLMs for code generation.Installationpipinstallenigma_aiUsageDetailed usage instructions and API documentation will be available soon.For more detailed documentation about the project, please refer to the SingularityNET projecthereScraping GitHub Repositoriesfromenigma_ai.dataimportscrape# Set up your GitHub API tokengithub_token='your_github_api_token'# Define your search query and parameterssearch_term='pentest'max_results=100filename='fetched_repos.csv'# Fetch repositories matching the queryrepos_df=scrape.fetch_repos(github_token,max_results,filename,search_term,min_stars=100)# The 'repos_df' dataframe now contains information about the fetched repositoriesExtracting Code from Repositoriesfromenigma_ai.dataimportprocessimportpandasaspd# Load the previously fetched repository datafilename='fetched_repos.csv'repos_df=pd.read_csv(filename)#Limit the number of repositories to processrepos_df=repos_df.head(1)# Extract code files from the repositoriesrepos_with_code=process.extract_code_from_repos(repos_df,filename,github_token)#Print the first 1000 characters of the README.md file of the first repositoryprint(repos_with_code['code'].values[0]['Markdown']['README.md'][:1000])Estimating Compute Cost and Performanceimportenigma_ai.cost.resourcesasresimportenigma_ai.cost.performanceasperfgpus=[res.GPUTensorCoreSpec(name="A100",clock_rate_ghz=1.41,num_tensor_cores=6912),res.GPUTensorCoreSpec(name="V100",clock_rate_ghz=1.53,num_tensor_cores=5120),]gpu_specs=res.GPUSpec(name="NVIDIA",architecture="Ampere",gpus=gpus)# Define hardware specificationshardware=res.HardwareSpec(gpus=[gpu_specs])# Define experiment specificationsspec=res.ExperimentSpec(model_params=1e9,dataset=1e12,hardware=hardware,precision="fp32",hours_trained=1.0)# Calculate compute costcompute=res.calculate_compute_cost(spec)print(compute)# Performancescaling_factor="Model Size"model_size=1e9# 1 billion parametersdataset_size=1e12# 1 trillion samplesscaling_params=perf.estimate_finetuning_performance(scaling_factor,model_size=model_size,dataset_size=dataset_size,)print(f'Expected Perplexity:{scaling_params["L"]}')Finetuning the LLM for Code GenerationTo finetune the LLM for your own code using CodeGen2, follow these steps:Navigate to the directory of the LLM intelligence project.Install the Enigma AI package by following the installation steps mentioned above.Run the following script, replacing the placeholders with your specific paths and parameters:pythoncli.py--main_path/PATH/Customizable-Code-Assistant/LLM-for-code-intelligence-Project/LLM-for-code-intelligence--experiment_namemy_experiment--training_data_pathJS_files.csvLicenseThis project is licensed under theMIT License.
Package: enigmacifra
EnigmaCipherA Python package that simulates the encryption and decryption mechanisms of an Enigma machine with PIN protection.CapabilitiesUtilize the shuffle command to introduce new rotorsSecurely encrypt and decrypt messages with a PIN-based systemInstallationTo install EnigmaCipher, simply run:pipinstallenigmacifraVerify the installation by checking the version:$enigma--versionUsageThe EnigmaCipher package can be used via the command line.Command LineTo shuffle rotors:$enigmashuffleTo encrypt a message:$enigmaencryptTo decrypt a message:$enigmadecryptDevelopmentTo contribute to EnigmaCipher, clone the repository and make sure to install the development dependencies.gitclonehttps://github.com/1darshanpatil/enigmaciphercdenigmacipher
pipinstall-e.SupportIf you encounter any problems or have suggestions, please file an issue on theGitHub issue tracker.LicenseThe project is licensed under the MIT license.
Package: enigma-cipher
Enigma CipherTheEnigma Cipheris a package that allows ciphering texts as an enigma machine would.It is referred to 'cipher' the text instead of encoding/decoding, as any text can be encoded or decoded bz ciphering it with a machine with the same configuration.
For example, if the textVLGBR OYWVV!is ciphered through a machine with the correct configuration, the outcome will beHELLO WORLD!📖 DocumentationYou can find the documentation:Within the/docsfolderAt themkdocs generated page(official documentation)🐍 Python SetupRequirementsPython 3.8 or higherInstallationThe package is installable via pip:pip install enigma-cipherThe 'develop' branch can also be installed to work with unreleased features:pip install git+https://github.com/Jtachan/enigma_cipher.git@develop
Package: enigmacrypt
EnigmaCryptEnigmaCrypt is a Python package that provides a simple and secure way to encrypt and decrypt strings. It uses a unique encryption algorithm and generates a key that can be used to decode the encrypted string.InstallationTo install EnigmaCrypt, you can use pip:pip install enigmacryptUsageHere's a simple example of how to use EnigmaCrypt to encrypt and decrypt a string:fromenigmacryptimportEncryption,Decryption# Create an instance of Encryptionencryption=Encryption()# Encrypt a stringencrypted_string,key=encryption.encrypt("Hello, World!")# Create an instance of Decryptiondecryption=Decryption()# Decrypt the string using the generated keydecrypted_string=decryption.decrypt(encrypted_string,key)print(decrypted_string)# Output: Hello, World!ContributingContributions are welcome! If you have any bug reports, feature requests, or suggestions, please open an issue on the GitHub repository. If you'd like to contribute code, you can fork the repository and create a pull request with your changes.LicenseEnigmaCrypt is licensed under the MIT License.ContactIf you have any questions or need support, you can reach out to the project maintainer atwookscode.kontakt@gmail.com.AcknowledgmentsEnigmaCrypt works by shuffling the alphabet and digits in random order and then generating quite complex key to show crucial information for decryption processDisclaimerPlease note that EnigmaCrypt is provided as-is and is not responsible for any security breaches or issues that may arise from its usage. It is always recommended to use additional security measures and best practices when handling sensitive information.
Package: enigma-emulator
Enigma Machine EmulatorThis is a simulator for general Enigma Machines, it includes rotor definitions for most versions of the machine (see list below), and is flexible enough that it can be used to simulate any other ones.ExamplefromenigmaimportMachinefromenigma.builtin_rotorsimportRotorsmachine=Machine([Rotors["I"](),Rotors["II"](),Rotors["III"]()],Rotors["A"]())assert(machine.encode("ENIGMA MACHINE")=="BYEJNJ RSRWHTF")Enigma VersionsThis simulator includes a comprehensive list of rotors that were used in the different enigma versions that existed over time.These rotors are available inenigma/builtin_rotors.py, and tests are available inenigma/tests/custom_extensions_test.pyfor most of them.The following variants have rotors available:Enigma M3 & M4German Railway (Rocket)M3 & M4 NavalSwiss K (custom entry wheel)M4 R1Enigma DEnigma KDEnigma T - Tirpitz (custom entry wheel)Norway (Norenigma)Railway EnigmaMachine SettingsArbitrary Number of RotorsThis simulator supports both arbitrary rotos and setting which is the last rotor that can turn. This is because in most versions of the machine, only the first 3 rotors would turn with each keystroke.Multiple NotchesRotors can have from 0 to any number of notches.Entry Wheel (i.e. Enigma T - Tirpitz)Enigma machines could have an entry wheel that determined the wiring from the keys to the plugboard, and from the plugboard to the lamps.Extended AlphabetThe rotors allow defining an input alphabet. This can be used to extend from the standard uppercase ASCII used in most machines, to use any unicode character.Examples of characters that can be encoded withenigma_py:AlphanumericMixed-case messagesNon-roman alphabets (Russian, Japanese... )Special charactersenigma_py/enigma/tests/custom_extensions_test.pyfeatures a test (test_machine_encodes_custom_alphabets) with mixed-case inputs and numbers.It is trivial to use any unicode character - evenemoji(as they are just a unicode extension)!Just for fun I made a few custom "emoji-rotors", so that we can encode:Input:THIS ENIGMA MACHINE SUPPORTS EMOJIS! 😜😍🥰Encoded:😆QK😗 😋🤣FUR😉 SX😚😇😜T😜 😃😂😃😄U😌A😚 😝😛😘😇W🙃! TO😍To see the Machine and rotors used for the strings above look for thetest_emoji_machine.DocumentationAutogenerated documentation for all modules is available indocs/index.htmland live inhttps://aurbano.github.io/enigma.py/To update the docs:pip install sphinx
cd docs-config
make githubTestpython -m unittest discover -s ./enigma/tests -p '\*\_test.py'Coverate reportcoverage run -m unittest discover -s ./enigma/tests -p '*_test.py'
coverage html --omit="*/test*" && open ./htmlcov/index.htmlLintflake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics