Saravanan666/pypi-org huggingface dataset:
Package: empy
EmPy is a powerful, robust and mature
templating system for inserting Python code in template text.  EmPy
takes a source document, processes it, and produces output.  This is
accomplished via expansions, which are signals to the EmPy system
where to act and are indicated with markup.  Markup is set off by a
customizable prefix (by default the at sign,@).  EmPy can expand
arbitrary Python expressions, statements and control structures in
this way, as well as a variety of additional special forms.  The
remaining textual data is sent to the output, allowing Python to be
used in effect as a markup language.
Package: empyer
EMpyerEmpyer is an extension of thehyperspypackage.  It provides additional functionality related to analyzing 4 and 5
dimensional data sets.  Especially STEM diffraction patterns from metallic glasses. Now Empyer can be loaded just
through loadinghyperspy.  That means that once you install EMpyer it will automatically register the new methods
and signals with thehyperspypackage.Documentation can be found hostedhere.Quick Start Guide:$pipinstallempyerimporthyperspy.apiashsimportmatplotlib.pyplotaspltdif_signal=hs.load(file,signal_type='diffraction_signal')dif_signal.plot()plt.show()
Package: empymod
Open-source full 3D electromagnetic modeller for 1D VTI mediaWebsite:https://emsig.xyzDocumentation:https://empymod.emsig.xyzSource Code:https://github.com/emsig/empymodBug reports:https://github.com/emsig/empymod/issuesContributing:https://empymod.emsig.xyz/en/latest/devContact:seehttps://emsig.xyzZenodo:https://doi.org/10.5281/zenodo.593094Available throughcondaandpip; consult thedocumentationfor detailed installation instructions.
Package: empymod-plain
The branchplainis a
stripped-down version ofempymod, wherenumbais removed and the
computation is done in plain python. The only dependency is SciPy. Its
execution is slow, as the kernel is not compiled. It is mainly meant to be used
forpyodidoand similar, browser-based client-side applications, which need
plain python wheels. The name of the PyPI-package isempymod-plain, but the
module itself isempymod.The version number follows theempymodversion number, with an added.post1to distinguish it.Only use this version if you are sure why. In general it is always advisable
to use the regular empymod package.Open-source full 3D electromagnetic modeller for 1D VTI mediaWebsite:https://emsig.xyzDocumentation:https://empymod.emsig.xyzSource Code:https://github.com/emsig/empymodBug reports:https://github.com/emsig/empymod/issuesContributing:https://empymod.emsig.xyz/en/latest/devContact:seehttps://emsig.xyzZenodo:https://doi.org/10.5281/zenodo.593094Available throughpip(notconda), meant primarily formicropipfor, e.g.,jupyterlite/pyodide.
Package: empyre
EMPyRe is an open source framework for constructing and solving hyperdimensional inverse problems and visualizing the corresponding input and output data. it has
its roots in the reconstruction of three-dimensional magnetization distributions from magnetic phase images generated by electron holography but is meant to be
extendable for many other problems that are based on forward models that are expressable as a combination of linear subproblems. EMPyRe is purely Python
package, so all platforms should be supported.InstallationEMPyRe is available on thePython Package Indexand can be simply be installed viapip:$pipinstallempyrePer default, only the strictly required libraries are installed, but there are a few additional dependencies that will unlock additional capabilites of EMPYRE.iowill install thetvtk&HyperSpypackages that are used for loading and saving additional file formats.WarningDue to thisissue, a pip install of hyperspy is currently not possible. Please usecondato install HyperSpy, instead.fftwwill installPyFFTWto speed up Fourier transforms used in some forward models.colorswill install thecmocean, whosebalancecolor map is used as a default for theimshowcommmand, if available.3dwill install themayavipackage for 3D plotting capabilities.testswill install all dependencies that are needed to test the package (usually not necessary for the average user).allwill install all of the dependencies listed above.You can choose these settings by using,e.g.:$pipinstallempyre[all]StructureEMPyRe has several dedicated modules which are fully documentedhere!Thefieldsmodule provides theFieldcontainer class for multidimensional scalar or vector fields and is the fundamental data structure used in EMPyRe.Thevismodule enables the plotting ofFieldobjects, based on and similar in syntax to the commonly knownmatplotlibframework.Themodelsmodule provides tools for constructing forward models that describe processes in Electron Microscopy.Thereconstructmodule is a collection of tools for solving the inverse problems corresponding to the constructed forward models and diagnostic tools for their assessment.Theiomodule is used to load and saveFieldobjects and the models generated by themodelssubpackage.Theutilsmodule, which houses utility functionality used throughout EMPyRe.LicenseEMPyRe is licensed underGPLv3.
Package: empyrical-reloaded
Common financial return and risk metrics in Python.Installationempyrical requires Python 3.7+. You can install it usingpip:pipinstallempyrical-reloadedorconda:condainstallempyrical-reloaded-cml4t-cranaroussiempyrical requires and installs the following packages while executing the above commands:numpy>=1.9.2pandas>=1.0.0scipy>=0.15.1pandas-datareader>=0.4yfinance>=0.1.59Empyrical usesyfinanceto download price data fromYahoo! Financeandpandas-datareaderto accessFama-Frenchrisk factors.UsageSimple StatisticsEmpyrical computes basic metrics from returns and volatility to alpha and beta, Value at Risk, and Shorpe or Sortino ratios.importnumpyasnpfromempyricalimportmax_drawdown,alpha_betareturns=np.array([.01,.02,.03,-.4,-.06,-.02])benchmark_returns=np.array([.02,.02,.03,-.35,-.05,-.01])# calculate the max drawdownmax_drawdown(returns)# calculate alpha and betaalpha,beta=alpha_beta(returns,benchmark_returns)Rolling MeasuresEmpyrical also aggregates returna nd risk metrics for rolling windows:importnumpyasnpfromempyricalimportroll_max_drawdownreturns=np.array([.01,.02,.03,-.4,-.06,-.02])# calculate the rolling max drawdownroll_max_drawdown(returns,window=3)Pandas SupportEmpyrical also works with bothNumPyarrays andPandasdata structures:importpandasaspdfromempyricalimportroll_up_capture,capturereturns=pd.Series([.01,.02,.03,-.4,-.06,-.02])factor_returns=pd.Series([.02,.01,.03,-.01,-.02,.02])# calculate a capture ratiocapture(returns,factor_returns)-0.147387712263491Fama-French Risk FactorsEmpyrical downloads Fama-French risk factors from 1970 onward:importempyricalasemprisk_factors=emp.utils.get_fama_french()risk_factors.head().append(risk_factors.tail())Mkt-RFSMBHMLRFMomDate1970-01-0200:00:00+00:000.01180.01310.01000.00029-0.03411970-01-0500:00:00+00:000.00590.00660.00720.00029-0.01521970-01-0600:00:00+00:00-0.00740.00100.00200.000290.00401970-01-0700:00:00+00:00-0.00150.0039-0.00320.000290.00111970-01-0800:00:00+00:000.00040.0018-0.00150.000290.00332021-02-2200:00:00+00:00-0.0112-0.00090.03140.00000-0.03252021-02-2300:00:00+00:00-0.0015-0.01280.00900.00000-0.01852021-02-2400:00:00+00:000.01150.01200.01340.00000-0.00072021-02-2500:00:00+00:00-0.0273-0.01120.00870.00000-0.01952021-02-2600:00:00+00:00-0.00280.0072-0.01560.000000.0195Asset Prices and Benchmark ReturnsEmpyricalyfinanceto download price data fromYahoo! Finance. To obtain the S&P returns since 1950, use:importempyricalasempsymbol='^GSPC'returns=emp.utils.get_symbol_returns_from_yahoo(symbol,start='1950-01-01')importseabornassns# requires separate installationimportmatplotlib.pyplotasplt# requires separate installationfig,axes=plt.subplots(ncols=2,figsize=(14,5))withsns.axes_style('whitegrid'):returns.plot(ax=axes[0],rot=0,title='Time Series',legend=False)sns.histplot(returns,ax=axes[1],legend=False)axes[1].set_title('Histogram')sns.despine()plt.tight_layout()plt.suptitle('Daily S&P 500 Returns')DocumentationSee thedocumentationfor details on the API.SupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.Testinginstall requirements"pytest>=6.2.0",pytesttests
Package: empyrionbuildassistant
Empyrion Build AssistantWhat is this?This is a python package that I created to automate a lot of the tasks that go into building mods for Empyrion.How do I use it?assuming you have python 3.8 installed,the package is loaded using:pipinstallempyrionbuildassistantOnce installed, you an view the help using:python -m EmpyrionBuildAssistant -hwhich should show:usage: __main__.py [-h] [--copyDllsToFolder COPYDLLSTOFOLDER] [--modName MODNAME] [--bundleAndDeployMod BUNDLEANDDEPLOYMOD] [-clearLogs] [-watchLogs] [-launchServer]

scripts to help build and deploy empyrion mods

optional arguments:
  -h, --help            show this help message and exit
  --copyDllsToFolder COPYDLLSTOFOLDER
                        the folder to copied the required dlls to
  --modName MODNAME     the name of the mod being deployed
  --bundleAndDeployMod BUNDLEANDDEPLOYMOD
                        the folder containing the mod that will be deployed
  -clearLogs            clears the logs on the dedi server
  -watchLogs            watches the dedi server logs, press enter to exit
  -launchServer         launches the dedi server, press enter to kill

Note: modname must be specified when using the bundleAndDeployMod optionCan the commands be chained?Yup, the most useful command that I use for debugging ispython-mEmpyrionBuildAssistant-clearLogs-watchLogs-launchServerWhich clears the logs launches the server and creates a window that watches all of the changes to the server's log files.  When you press enter, it will terminate the server process (and its children)How does it work?It starts by scanning your windows registry (sorry at the moment this works on windows only :( ) to locate your steam installation path.  From there it traverses the steamapps manifests to locate the install location of the dedicated server (Steam AppID 530870) and it uses that path as the root for all of the commands.Is this like, "done"?HAHAHAHAHAHAHAHAAHAHAHAHAHANo, it is not, as evidence I present the complete absence of tests.  this is really just a collection of scripts that I threw together to make another problem that I'm working on easier to solve.If you'd like to help please leave issues or PRsWhat about the obvious question you haven't addressed here?If you have a question I didn't think of, feel free to leave it as an issue.  If it gets asked a lot (or it seems like a really good question) I'll add it here.
Package: empyscripts
Theempyscriptsareadd-onsfor the electromagnetic modellerempymod.
These add-ons provide some very specific, additional functionalities:tmtemod: Return up- and down-going TM/TE-mode contributions for
x-directed electric sources and receivers, which are located in the same
layer.fdesign: Design digital linear filters for the Hankel and Fourier
transforms.There is alsoempyscripts.versions(), which can be used to show date, time,
and package version information at the end of a notebook or script:versions('HTML')for Jupyter Notebooks, andversions()for IPython, QT, and Python consoles.Seehttps://empymod.github.io/#featuresfor a complete list of features of
empymod.More informationFor more information regarding installation, usage, add-ons, contributing,
roadmap, bug reports, and much more, seeWebsite:https://empymod.github.io,Documentation empymod:https://empymod.readthedocs.io,Documentation add-ons:https://empyscripts.readthedocs.io,Source Code:https://github.com/empymod,Examples:https://github.com/empymod/example-notebooks.License informationCopyright 2017-2018 Dieter WerthmüllerLicensed under the Apache License, Version 2.0. See theLICENSE-file or the
documentation for more information.
Package: empythy
# empathyMachines> A standalone NLP sentiment classifier you can import as a module## Purposes1. Offer a batteries-included NLP classifier you can use either on it's own, or to make sentiment predictions as part of a broder NLP project (for example, when classifying customer messages, whether the customer is angry or not might help you determine if this is a compensation request, or a request to adjust their address.)1. Have the entire sentiment prediction process scaffolded so you can feed in your own training corpus, and easily train an NLP sentiment classifier.## How to use1. `pip install empythy`1.```from empythy import EmpathyMachinesnlp_classifier = EmpathyMachines()nlp_classifier.train()nlp_classifier.predict(text_string)```### Corpora included#### NLTK Movie ReviewsThe classic sentiment corpus, 2000 movie reviews already gathered by NLTK.#### Assembling a custom Twitter sentiment corpus[CrowdFlower](http://www.crowdflower.com/data-for-everyone) hosts a number of Twitter corpora that have already been graded for sentiment by panels of humans.I aggregated together 6 of their corpora into a single, aggregated and cleaned corpus, with consistent scoring labels across the entire corpus. The cleaned corpus contains over 45,000 documents, with positive, negative, and neutral sentiments.### Train on your own corpusFeel free to train a classifier on your own corpus!Two ways to do this1. Read in a .csv file with header row containing "sentiment", "text", and optionally, "confidence"- Pass the name of the .csv file to train, like so:- `nlp_classifier.train(corpus='custom', corpus_path='path/to/custom/corpus.csv')`1. Pass in an array of Python dictionaries, where each dictionary has attributes for "sentiment", "text", and optionally, "confidence"- `nlp_classifier.train(corpus='custom', corpus_array=my_array_of_texts)`- Two important parts to this, both `corpus='custom'`, and `corpus_array=my_variable_holding_the_documents`.### Advanced Usage1. `nlp_classifier.train(verbose=False)` to turn off print status statements while training.1. `nlp_classifier.train(print_analytics_results=True)` to print out results of training the classifier.
Package: emqx-extension-sdk
EMQ X extension Python SDKUsing Python to handle the hooks inEMQ Xto
record connection and disconnection information, realize client authentication, ACL check and message storage.InstallEMQ X extension requires Python 3.6 or later :pip3installemqx-extension-sdkThe emqx-extension-sdk is open source, welcome tocommitto help us improve the function.DownloadYou can use the open source version for most functions, butonly EMQ X Enterprise editionsupports message storage.Download EMQ X MQTT Broker v4.1 +EMQ X Enterprise v4.1 is coming.ConfigurationUse emqx-extension-hook plugins:## Setup the supported drivers#### Value: python2 | python3 | javaexhook.drivers=python3## Search path for scripts/libraryexhook.drivers.python3.path=data/extension/hooks.py## Call timeout#### Value: Duration##exhook.drivers.python3.call_timeout = 5s## Initial module name## Your filename or module nameexhook.drivers.python3.init_module=hooksexample:## data/extension/hooks.pyfromemqx_extension.hooksimportEmqxHookSdk,hooks_handlerfromemqx_extension.typesimportEMQX_CLIENTINFO_PARSE_T,EMQX_MESSAGE_PARSE_TclassCustomHook(EmqxHookSdk):@hooks_handler()defon_client_connect(self,conninfo:EMQX_CLIENTINFO_PARSE_T=None,props:dict=None,state:list=None):print(f'[Python SDK] [on_client_connect]{conninfo.clientid}connecte')@hooks_handler()defon_client_connected(self,clientinfo:EMQX_CLIENTINFO_PARSE_T,state:list=None):print(f'[Python SDK] [on_client_connected]{clientinfo.clientid}connected')@hooks_handler()defon_client_check_acl(self,clientinfo:EMQX_CLIENTINFO_PARSE_T,pubsub:str,topic:str,result:bool,state:tuple)->bool:print(f'[Python SDK] [on_client_check_acl]{clientinfo.username}check ACL:{pubsub}{topic}')ifclientinfo.username=='':returnFalsereturnTrue@hooks_handler()defon_client_authenticate(self,clientinfo:EMQX_CLIENTINFO_PARSE_T,authresult,state)->bool:print(f'[Python SDK] [on_client_authenticate]{clientinfo.clientid}authenticate')ifclientinfo.clientid!='':returnTruereturnFalse# on_message_* only EMQ X Enterprise@hooks_handler()defon_message_publish(self,message:EMQX_MESSAGE_PARSE_T,state):print(f'[Python SDK] [on_message_publish]{message.topic}{message.payload}')emqx_hook=CustomHook(hook_module=f'{__name__}.emqx_hook')definit():returnemqx_hook.start()defdeinit():returnBenchmarkComing soon.Load plugin./bin/emqx_ctlpluginsloademqx_extension_hook
Package: emrcalc
Electromagnetic Radiation (EMR) CalculatorOverviewThis project implements a Python 3.7+ Tkinter GUI to facilitate the following EMR calculations which I find useful as a student of Astrophysics:Conversion between Energy, Frequency, and Wavelength.Editing the units or quantities of any of those 3 characteristics.Display of the kJ/mol equivalent.Display of the related EMR band as defined by NASA.Automatic update of quantities and EMR band name after editing.Some special files in addition to the emrcalc/*.py source code:HISTORY.txt- the update historymisc/SCREENSHOT.png- a sample screenshotmisc/url.txt- URLs to background informationDependenciesnumpy
scipy
tkinter - included with Python 3
tkinter.ttk - included with Python 3Installationpython3 -m pip install emrcalcInvocation from a terminal window command-lineemrcalcLicensingThis is NOT commercial software; instead, usage is covered by the GNU General Public License version 3 (2007). In a nutshell, please feel free to use the project and share it as you will but please don't sell it. Thank you!See the LICENSE file for the GNU licensing information.Feel free to create an issue record for any questions, bugs, or enhancement requests. I'll respond as soon as I can.Richard ElkinsDallas, Texas, USA, 3rd Rock, Sol, ...
Package: emr-helper
The EMR Helper library tries to help when setting up and managing an EMR cluster.AWS EMR has three distinct objects:ClusterFleetStepThis library collects some of the most common types of these elements and manages them at the python class level.DependenciesBoto3ClassesStepThere is a general classStepto wrap all subtypes of steps. Currently there are onlyCommandRunnerStepimplemented to launch a Command Runner step.You can create a step as follow:importboto3fromemrhelperimportCommandRunnerStepstep=CommandRunnerStep(name='StepName',args=process_arguments)It can be added to a cluster before it starts (see Cluster) or append to a started cluster:step.run_on_cluster(‘clusterID’)IfclusterIDis None, step is added to any available cluster.FleetThe instance fleets configuration for a cluster contains instances information for computation capacity of cluster. As steps, there is a main class ‘Fleet’ and several subclasses: OnDemandFleet and SpotFleet, depending on whether you want to launch spot or on-demand instances.You can create a fleet as follow:fromemrhelperimportSpotFleetfleet=SpotFleet(name='My Fleet',capacity=4,fleet_type='CORE')fleet.add_instance_config(instance_type='r5d.xlarge',capacity=2)fleet.add_instance_config(instance_type='r5d.2xlarge',capacity=4)ClusterYou can create a cluster, add steps and fleets, and run it.fromemrhelperimportClustercluster=Cluster(name='my-cluster',key_pair='keypair',subnets='...',sg_master='...',sg_slave='...',sg_service='...',instance_profile='...',service_role='...',log_uri='...')cluster.add_step(step)cluster.add_fleet(fleet)cluster.run_cluster()You can add as many steps and fleets as you need.Installationpipinstallemrhelper
Package: emr-launcher
# EMR LauncherLaunches EMR clusters using config files for consistent run-time behavior when setting up a cluster.## Installing```pip install emr_launcher```## UsageStarting a new cluster:```emr_launcher launch /path/to/config/<my_config>.json```Adding steps to an existing cluster```emr_launcher launch /path/to/config/<my_config>.json --job-id <job_id_of_existing_cluster>```## Creating configsthe json file maps directly to boto3's `run_job_flow` function found [here](http://boto3.readthedocs.io/en/latest/reference/services/emr.html#EMR.Client.run_job_flow), you can use the documentation as a guide to build your config or build off the [Example Config](https://github.com/tuneinc/emr_launcher/blob/master/example_config.json)## Template functionsemr_launcher uses templating within the json configuration to call useful functions, for example having an anonymous output location:```..."--conf", "spark.output=s3://mybucket/output/{{ emr_launcher.uuid() }}/...```a full set of usable template functions can be found by running:```emr_launcher list-template-functionsemr_launcher============emr_launcher.get_environReturn the environment variables dictionary,Example: {{ get_environ()['USER'] }}A parent python program can use "os.environ[key] = value" before calling the emr launcher.emr_launcher.get_relative_dateReturns a formatted datetime string,relative to the current time,as ajusted by the timedelta arguments.Example:{{ emr_launcher.get_relative_date(format='%Y-%m-01 00:00:00', timedelta_args=dict(days=-2)) }}emr_launcher.millis_to_isoconverts a given milliseconds since epoch into an iso date stringArgs:ms_epoch - intReturnstring - formatted date stringemr_launcher.uuidreturns a UUID4 hex string```## PluginsPlugins are discovered by the naming convention `emr_launcher_<plugin-name>` (ex: `emr_launcher_consul`). To install a plugin simply run:```pip install emr_launcher_<plugin-name>```Available plugins:[emr_launcher_aws](https://github.com/tuneinc/emr_launcher_aws)[emr_launcher_consul](https://github.com/tuneinc/emr_launcher_consul)[emr_launcher_artifactory](https://github.com/tuneinc/emr_launcher_artifactory)
Package: emr-launcher-aws
# EMR Launcher AWSAWS plugin providing template functions for [http://www.github.com/tuneinc/emr_launcher](emr_launcher)## Functionsaws.get_aws_access_key - retrieves the aws access keyaws.get_aws_secret_key - retrieves the aws secret keyaws.upload_to_s3 - uploads a local file to S3 for use in the EMR cluster
Package: emr-launcher-consul
# EMR Launcher ConsulConsul plugin providing template functions for [http://www.github.com/tuneinc/emr_launcher](emr_launcher)## Functionsconsul.get_value(key) - gets the value for a given Consul key## ENV variables
* CONSUL_ACCESS_TOKEN - REQUIRED, sets the consul token
* CONSUL_HOST - REQUIRED, sets the consul host to use
* CONSUL_SCHEME - http or https default: https
* CONSUL_PORT - port consul api is running on default: 443
Package: emr-serverless-sql-cli
EMR Serverless SQLAn experimental tool for running SQL on EMR Serverless.Written primarily to scratch an itch, this tool is not recommended for production use-cases.InstallingInstall and update using pip:pipinstall-Uemr-serverless-sql-cliRunningemrssassumes you have a pre-existing EMR Serverless application, IAM job role, and S3 bucket where artifacts will be stored.You can run simple commands by providing a query string.emrss\--application-id$APPLICATION_ID\--job-role-arn$JOB_ROLE_ARN\--s3-bucket$S3_BUCKET\"show tables"Or you can also provide a SQL file using the-fparameter.emrss\--application-id$APPLICATION_ID\--job-role-arn$JOB_ROLE_ARN\--s3-bucket$S3_BUCKET\-fscript.sqlAnd just for fun, you can also run a PySpark notebook through. 😮emrss\--application-id$APPLICATION_ID\--job-role-arn$JOB_ROLE_ARN\--s3-bucket$S3_BUCKET\-fnotebook.ipynbIf you don't have a default region set in your config or environment, you can use the--regionparameter:emrss\--application-id$APPLICATION_ID\--job-role-arn$JOB_ROLE_ARN\--s3-bucket$S3_BUCKET\--region$REGION\"show tables"