Saravanan666/pypi-org huggingface dataset:
Package: aemm
Autoencoder Market Models (AEMM)OverviewThis package implements autoencoder-based models in Q- and P-measure.
The initial set of models is for interest rates. More asset classes
may be added at a later date.The package takes specialized autoencoders and classical methods for performing dimension
reduction in quant models of financial markets fromaencpackage (https://pypi.org/project/aenc/).Quick Start GuideInstall using:pipinstallaemmNamespacesNamespaceaemm.coreimplements autoencoder-based market models (AEMM)
and related classical models.The implementation uses PyTorch and can be easily ported to TensorFlow 2
and other machine learning frameworks that support dynamic computational
graphs.Namespaceaemm.dummyincludes dummy objects and generators for dummy market
data for testing purposes. To perform testing or training on real
historical or market-implied data, provide your own data files in the same
format as the dummy data files, or use pretrained components.Namespaceaemm.pretrainedincludes pretrained components to avoid lengthy
test execution time. Use flags to ignore pretrained parameters
and perform training from scratch (calculation time will increase).LicensingThe code in this project is licensed under Apache 2.0 license.
SeeLICENSEfor more information.CopyrightEach individual contributor holds copyright over their contributions to the
project. The project versioning is the sole means of recording all such
contributions and copyright details. Specifying corporate affiliation or
work email along with the commit shall have no bearing on copyright ownership
and does not constitute copyright assignment to the employer. Submitting a
contribution to this project constitutes your acceptance of these terms.Because individual contributions are often changes to the existing code,
copyright notices in project files must specify The Project Contributors and
never an individual copyright holder.Publications and LinksAlexander Sokol, Autoencoder Market Models for Interest Rates, SSRN Working Paperhttps://ssrn.com/abstract=4300756This project on GitHub:https://github.com/compatibl/aemmAutoencoders for financial markets on GitHub:https://github.com/compatibl/aenc
Package: aenc
Autoencoders for Financial Markets (AENC)OverviewThis package implements specialized autoencoders and related classical methods
for performing dimension reduction in quant models of financial markets. Potential
uses include investment strategy research, portfolio valuation, and risk management.Quick Start GuideInstall using:pipinstallaencNamespacesNamespaceaenc.coreimplements autoencoders and related
classical methods, including generic (such as PCA) and specialized
(such as Nelson-Siegel).The implementation uses PyTorch and can be easily ported to TensorFlow 2
and other machine learning frameworks that support dynamic computational
graphs.Namespaceaenc.dummyincludes dummy objects and generators for dummy market
data for testing purposes. To perform testing or training on real
market data, provide your own historical market data files in the same
format as the dummy data files, or use pretrained components.Namespaceaenc.pretrainedincludes pretrained components to avoid lengthy
test execution time. Use flags to ignore pretrained parameters
and perform training from scratch (calculation time will increase).LicensingThe code in this project is licensed under Apache 2.0 license.
SeeLICENSEfor more information.CopyrightEach individual contributor holds copyright over their contributions to the
project. The project versioning is the sole means of recording all such
contributions and copyright details. Specifying corporate affiliation or
work email along with the commit shall have no bearing on copyright ownership
and does not constitute copyright assignment to the employer. Submitting a
contribution to this project constitutes your acceptance of these terms.Because individual contributions are often changes to the existing code,
copyright notices in project files must specify The Project Contributors and
never an individual copyright holder.Publications and LinksAlexander Sokol, Autoencoder Market Models for Interest Rates, SSRN Working Paperhttps://ssrn.com/abstract=4300756GitHub repository:https://github.com/compatibl/aenc
Package: ae-notify
notify 0.3.3ae namespace module portion notify: send notifications via email, telegram or whatsapp.installationexecute the following command to install the
ae.notify module
in the currently active virtual environment:pipinstallae-notifyif you want to contribute to this portion then first forkthe ae_notify repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_notify):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs.
Package: aenoxiccord
An easy-to-use extension forDiscord.pyandPycordwith some utility functions.Features✏️ Reduce boilerplate codeEasy cog managementEmbed templatesDatetime and file utilitiesWrapper foraiosqlite✨ Error handlingAutomatic error handling for slash commandsError webhook reportsCustom logging⚙️ ExtensionsHelp command- Automatically generate a help command for your botStatus changer- Change the bot's status in an intervalBlacklist- Block users from using your botInstallingPython 3.9 or higher is required.pip install aenoxiccordYou can also install the latest version from GitHub. Note that this version may be unstable
and requiresgitto be installed.pip install git+https://github.com/aenoxic/aenoxiccordIf you need the latest version in yourrequirements.txtfile, you can add this line:aenoxiccord @ git+https://github.com/aenoxic/aenoxiccordUseful LinksDocumentation|Getting startedPycord|Discord.pyPyPiExamplesFor more examples, see theexample repositoryor thesample code.Note:It's recommended toload the tokenfrom a.envfile instead of hardcoding it.
aenoxiccord can automatically load the token if aTOKENvariable is present in the.envfile.Pycordimportaenoxiccordimportdiscordbot=aenoxiccord.Bot(intents=discord.Intents.default())if__name__=="__main__":bot.load_cogs("cogs")# Load all cogs in the "cogs" folderbot.run("TOKEN")Discord.pyimportasyncioimportdiscordimportaenoxiccordclassBot(aenoxiccord.Bot):def__init__(self):super().__init__(intents=discord.Intents.default())asyncdefsetup_hook(self):awaitsuper().setup_hook()awaitself.tree.sync()asyncdefmain():asyncwithBot()asbot:bot.add_help_command()bot.load_cogs("cogs")# Load all cogs in the "cogs" folderawaitbot.start("TOKEN")if__name__=="__main__":asyncio.run(main())ContributingI am always happy to receive contributions. Here is how to do it:Fork this repositoryMake changesCreate a pull requestYou can alsocreate an issueif you find any bugs.
Package: aenum
Advanced Enumerations (compatible with Python’s stdlib Enum), NamedTuples, and NamedConstantsWARNING: Version 3.1 has breaking changes in custom Enum settings
WARNING:
WARNING: AutoNumber has been removed
WARNING: AutoValue has been removedaenum includes a Python stdlib Enum-compatible data type, as well as a metaclass-based NamedTuple implementation and a NamedConstant class.An Enum is a set of symbolic names (members) bound to unique, constant values. Within an enumeration, the members can be compared by identity, and the enumeration itself can be iterated over.  Support exists for unique values, multiple values, auto-numbering, and suspension of aliasing (members with the same value are not identical), plus the ability to have values automatically bound to attributes.A NamedTuple is a class-based, fixed-length tuple with a name for each possible position accessible using attribute-access notation as well as the standard index notation.A NamedConstant is a class whose members cannot be rebound; it lacks all other Enum capabilities, however.Enum classes:Enum: Base class for creating enumerated constants.IntEnum: Base class for creating enumerated constants that are alsosubclasses of int.Flag: Base class for creating enumerated constants that can be combinedusing the bitwise operations without losing their Flag membership.IntFlag: Base class for creating enumerated constants that can be combinedusing the bitwise operators without losing their IntFlag membership.
IntFlag members are also subclasses of int.AutoNumberEnum: Derived class that automatically assigns an int value to eachmember.OrderedEnum: Derived class that adds <, <=, >=, and > methods to an Enum.UniqueEnum: Derived class that ensures only one name is bound to any onevalue.Utility functions include:convert: helper to convert target global variables into an Enumconstant: helper class for creating constant membersenum: helper class for creating members with keywordsenum_property: property to enable enum members to have same named attributes(e.g.nameandvalue)export: helper to insert Enum members into a namespace (usually globals())extend_enum: add new members to enumerations after creationmodule: inserts NamedConstant and Enum classes into sys.moduleswhere it will appear to be a module whose top-level names
cannot be reboundskip: class that prevents attributes from being converted to aconstant or enum memberunique: decorator that ensures no duplicate members
Package: aeolis
AeoLiS is a process-based model for simulating aeolian sediment
transport in situations where supply-limiting factors are important,
like in coastal environments. Supply-limitations currently supported
are soil moisture contents, sediment sorting and armouring, bed slope
effects, air humidity and roughness elements.The maintenance and development is done by the AEOLIS developer team:
Current members are:Bart van Westenat Deltares,Nick Cohnat U.S. Army Engineer Research and Development Center (ERDC),Sierd de Vries(founder) at Delft University of Technology,Christa van IJzendoornat Delft University of Technology,Caroline Hallinat Delft University of Technology,Glenn Strypsteenat Katholieke Universiteit Leuven andJanelle Skadenat U.S. Army Engineer Research and Development Center (ERDC).Previous members are:Bas Hoonhout(founder), Tom Pak, Pieter Rauwoens and Lisa Meijer
Package: aeon
⌛ Welcome to aeonaeonis an open-source toolkit for learning from time series. It is compatible withscikit-learnand provides access to the very latest
algorithms for time series machine learning, in addition to a range of classical
techniques for learning tasks such as forecasting and classification.We strive to provide a broad library of time series algorithms including the
latest advances, offer efficient implementations using numba, and interfaces with other
time series packages to provide a single framework for algorithm comparison.The latestaeonrelease isv0.7.0. You can view the full changeloghere.Our webpage and documentation is available athttps://aeon-toolkit.org.OverviewCI/CDCodeCommunity⚙️ Installationaeonrequires a Python version of 3.8 or greater. Our full installation guide is
available in ourdocumentation.The easiest way to installaeonis via pip:pipinstallaeonSome estimators require additional packages to be installed. If you want to install
the full package with all optional dependencies, you can use:pipinstallaeon[all_extras]Instructions for installation from theGitHub sourcecan be foundhere.⏲️ Getting startedThe best place to get started for allaeonpackages is ourgetting started guide.Below we provide a quick example of how to useaeonfor forecasting and
classification.Forecastingimportpandasaspdfromaeon.forecasting.trendimportTrendForecastery=pd.Series([20.0,40.0,60.0,80.0,100.0])>>>020.0>>>140.0>>>260.0>>>380.0>>>4100.0>>>dtype:float64forecaster=TrendForecaster()forecaster.fit(y)# fit the forecaster>>>TrendForecaster()pred=forecaster.predict(fh=[1,2,3])# forecast the next 3 values>>>5120.0>>>6140.0>>>7160.0>>>dtype:float64Classificationimportnumpyasnpfromaeon.classification.distance_basedimportKNeighborsTimeSeriesClassifierX=[[[1,2,3,4,5,5]],# 3D array example (univariate)[[1,2,3,4,4,2]],# Three samples, one channel, six series length,[[8,7,6,5,4,4]]]y=['low','low','high']# class labels for each sampleX=np.array(X)y=np.array(y)clf=KNeighborsTimeSeriesClassifier(distance="dtw")clf.fit(X,y)# fit the classifier on train data>>>KNeighborsTimeSeriesClassifier()X_test=np.array([[[2,2,2,2,2,2]],[[5,5,5,5,5,5]],[[6,6,6,6,6,6]]])y_pred=clf.predict(X_test)# make class predictions on new data>>>['low''high''high']💬 Where to ask questionsTypePlatforms🐛Bug ReportsGitHub Issue Tracker✨Feature Requests & IdeasGitHub Issue Tracker&Slack💻Usage QuestionsGitHub Discussions&Slack💬General DiscussionGitHub Discussions&Slack🏭Contribution & DevelopmentSlack
Package: aeoncloud
# Aeon Cloud Client## Install Aeon CloudThe Aeon Cloud client is hosted on Artifactory and can be installed via Pip.```bashpip install aeoncloud -i <artifactory_url_here>```## Run a Blocking Session ClientNote,  settings can be any Aeon property used by Aeon.```pythonfrom aeoncloud import get_session_factoryaeon = get_session_factory()session = aeon.get_session(settings={'settings': {'aeon.platform.http.url': 'http://localhost:8081/api/v1/','aeon.browser': 'Chrome','aeon.environment': 'launch-web.apps.mia.ulti.io','aeon.protocol': 'https','aeon.timeout': 10,'aeon.wait_for_ajax_responses': True,}})session.execute_command('GoToUrlCommand', ['https://google.com'])session.quit_session()```## Run an Async Session ClientA reactor loop of some type is required to run async Python code, in addtion to Python 3.5 and above.In the example below retrieving an existing session is also demoed.```pythonimport asynciofrom aeoncloud import get_session_factoryasync def do():# Setup Sessionaeon = get_session_factory()session = await aeon.get_async_session(settings={'settings': {'aeon.platform.http.url': 'http://localhost:8081/api/v1/','aeon.browser': 'Chrome','aeon.environment': 'launch-web.apps.mia.ulti.io','aeon.protocol': 'https','aeon.timeout': 10,'aeon.wait_for_ajax_responses': True,}})# Execute command on sessionawait session.execute_command('GoToUrlCommand', ['https://google.com'])# Setup the session againsession = await aeon.get_async_session(settings={'settings': {'aeon.platform.http.url': 'http://localhost:8081/api/v1/','aeon.browser': 'Chrome','aeon.environment': 'launch-web.apps.mia.ulti.io','aeon.protocol': 'https','aeon.timeout': 10,'aeon.wait_for_ajax_responses': True,'aeon.platform.session_id': session.session_id},})# Execute command on restored sessionawait session.execute_command('GoToUrlCommand', ['https://microsoft.com'])# Kill sessionawait session.quit_session()loop = asyncio.get_event_loop()loop.run_until_complete(do())```
Package: ae-parse-date
parse_date 0.3.5ae namespace module portion parse_date: parse date strings more flexible and less strict.installationexecute the following command to install the
ae.parse_date module
in the currently active virtual environment:pipinstallae-parse-dateif you want to contribute to this portion then first forkthe ae_parse_date repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_parse_date):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs.
Package: ae-paths
paths 0.3.26ae namespace module portion paths: generic file path helpers.installationexecute the following command to install the
ae.paths module
in the currently active virtual environment:pipinstallae-pathsif you want to contribute to this portion then first forkthe ae_paths repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_paths):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs.
Package: aepp
Adobe Experience Platform API made for humansThis repository will document the AEP wrapper on python.
It is focusing on helping accessing the different endpoints of Adobe Experience Platform API.
Documentation on the different endpoints can be found here :AEP API docThe wrapper is currently namedaepp, it stands for Adobe Experience Platform Python.InstallationYou can install the module directly from a pypi command:pipinstallaeppThe version of the wrapper can be seen by the following command (once loaded):importaeppaepp.__version__Consider upgrading regularypipinstallaepp--upgradeNOTE TO PYTHON 3.10.XAt the moment, not all packages are supported on python 3.10.X, therefore, please use this module with a python 3.9.X version.You can use pyenv to generate a local environment if required.Getting StartedIn order to get started, I have compile a guide to help you initialize this module and what is required.
You can find this documentationhereAEPP docsAt the moment the current wrapper is containing the following sub modules:schemaSchemaManagerFieldGroupManagerDataTypeManagerqueryservice(see note below)identitysandboxesdataaccesscatalogcustomerprofilesegmentationdataprepflowservicepolicydatasetsingestiondestination Authoringdestination Instanceobservabilityaccesscontrolprivacyservice(see 2nd note below)Last but not least, the core methods are described here:mainqueryservice moduleThe queryservice Module contains 2 classes:QueryService classThe QueryService class is the wrapper around the AEP Query Service API.It provides access to the different endpoints available from the API.Use-Case example : At the moment the capability to scheduled query is only accessible from the API.Detail documentationInteractiveQuery  and InteractiveQuery2 classesThese classes are based on the pyGreSQL and psycopg2 module for python.It provides you the capability to realize query directly from your local Jupyter notebook and returns a dataframe.
In order to use these classes, you would need to install these module and a PSQL server.
On top of that, you would need to the psql server accessible in the environment path.PrivacyService moduleThe privacy service module is part of the AEP python wrapper but requires a different JWT connection in console.adobe.io.
Be careful that your JWT connection has the correct setup to access this API endpoints.ReleasesRelease notes are accessiblehere.
Package: aep-parser
aep_parserAn After Effects file parser in Python!Report Bug·Request FeatureTable of ContentsAbout The ProjectInstallationUsageRoadmapContributingLicenseContactAcknowledgmentsAbout The ProjectThis as a .aep (After Effects Project) parser in Python. After Effects files (.aep) are mostly binary files, encoded in RIFX format. This parser usesKaitai Structto parse .aep files and return a Project object containing items, layers, effects and properties.(back to top)Installationpipinstallaep-parser(back to top)Usagefromaep_parser.parsers.projectimportparse_projectaep_file_path="01_empty.aep"project=parse_project(aep_file_path)(back to top)RoadmapSee theopen issuesfor a full list of proposed features (and known issues).(back to top)ContributingAny contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a merge request. You can also simply open an issue with the tag "enhancement".Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request(back to top)LicenseDistributed under the MIT License.(back to top)ContactBenoit Delaunay -benoit.delaunay@forticheprod.comProject Link:https://github.com/forticheprod/aep_parser(back to top)Acknowledgmentsaftereffects-aep-parserKaitai StructLottieAfter Effects Scripting Guide(back to top)
Package: aeppl-nightly
aepplprovides tools for a[e]PPL written inAesara.FeaturesConvert graphs containing AesaraRandomVariables into joint
log-probability graphsTransforms forRandomVariables that map constrained support spaces to
unconstrained spaces (e.g. the extended real numbers), and a rewrite that
automatically applies these transformations throughout a graphTools for traversing and transforming graphs containingRandomVariablesRandomVariable-aware pretty printing and LaTeX outputExamplesUsingaeppl, one can create a joint log-probability graph from a graph
containing AesaraRandomVariables:importaesarafromaesaraimporttensorasatfromaepplimportjoint_logprob,pprint# A simple scale mixture modelS_rv=at.random.invgamma(0.5,0.5)Y_rv=at.random.normal(0.0,at.sqrt(S_rv))# Compute the joint log-probabilitylogprob,(y,s)=joint_logprob(Y_rv,S_rv)Log-probability graphs are standard Aesara graphs, so we can compute
values with them:logprob_fn=aesara.function([y,s],logprob)logprob_fn(-0.5,1.0)# array(-2.46287705)Graphs can also be pretty printed:fromaepplimportpprint,latex_pprint# Print the original graphprint(pprint(Y_rv))# b ~ invgamma(0.5, 0.5) in R, a ~ N(0.0, sqrt(b)**2) in R# aprint(latex_pprint(Y_rv))# \begin{equation}#   \begin{gathered}#     b \sim \operatorname{invgamma}\left(0.5, 0.5\right)\,  \in \mathbb{R}#     \\#     a \sim \operatorname{N}\left(0.0, {\sqrt{b}}^{2}\right)\,  \in \mathbb{R}#   \end{gathered}#   \\#   a# \end{equation}# Simplify the graph so that it's easier to readfromaesara.graph.rewriting.utilsimportrewrite_graphfromaesara.tensor.rewriting.basicimporttopo_constant_foldinglogprob=rewrite_graph(logprob,custom_rewrite=topo_constant_folding)print(pprint(logprob))# s in R, y in R# (switch(s >= 0.0,#         ((-0.9189385175704956 +#           switch(s == 0, -inf, (-1.5 * log(s)))) - (0.5 / s)),#         -inf) +#  ((-0.9189385332046727 + (-0.5 * ((y / sqrt(s)) ** 2))) - log(sqrt(s))))Joint log-probabilities can be computed for some terms that arederivedfromRandomVariables, as well:# Create a switching model from a Bernoulli distributed indexZ_rv=at.random.normal([-100,100],1.0,name="Z")I_rv=at.random.bernoulli(0.5,name="I")M_rv=Z_rv[I_rv]M_rv.name="M"# Compute the joint log-probability for the mixturelogprob,(m,z,i)=joint_logprob(M_rv,Z_rv,I_rv)logprob=rewrite_graph(logprob,custom_rewrite=topo_constant_folding)print(pprint(logprob))# i in Z, m in R, a in Z# (switch((0 <= i and i <= 1), -0.6931472, -inf) +#  ((-0.9189385332046727 + (-0.5 * (((m - [-100  100][a]) / [1. 1.][a]) ** 2))) -#   log([1. 1.][a])))InstallationThe latest release ofaepplcan be installed from PyPI usingpip:pip install aepplThe current development branch ofaepplcan be installed from GitHub, also usingpip:pip install git+https://github.com/aesara-devs/aeppl
Package: aepp-sdk
aepp-sdk-pythonWelcome to theAeternitySDK for PythonFor support visit theforum, for bug reports or feature requests visit open anissue.DocumentationStablefor themasterbranch (current release).Latestfor thedevelopbranchRelevant repositoriesAeternity nodeProtocol documentationUseful linksMainnet API GatewayMainnet frontend and middleware(Aeternal)Testnet API GatewayTestnet frontend and middleware(Aeternal)Testnet faucet