Saravanan666/pypi-org huggingface dataset:
Package: emanate
emanateSymlink files from one directory into another directory.Inspired byeffuseandstow.Installation$ pip3 install emanateOr find thelatest releaseand grab theemanate-<version>.pyzfile. This should work as a
standalone executable. If it doesn't, trypython3 emanate-<version>.pyz.Emanate version numbers follow thesemantic versioningconvention.
APEP 440version specification forcompatible releases, like~= 7.0,
is the recommended way to select appropriate versions.Usage~$ cat ~/.bashrc
cat: /home/pup/.bashrc: No such file or directory
~$ pip3 install emanate
~$ cd ~/dotfiles
~/dotfiles$ ls -al
drwxr-xr-x.  9 pup pup 4096 Jun  3 12:06 ./
drwx------. 74 pup pup 4096 Jun  3 12:06 ../
drwxr-xr-x.  2 pup pup 4096 Jun  3 11:59 bin/
drwxr-xr-x.  6 pup pup 4096 Jun  3 11:59 .config/
drwxr-xr-x.  8 pup pup 4096 Jun  3 12:10 .git/
-rw-r--r--.  1 pup pup  486 Jun  3 11:59 .bash_aliases
-rw-r--r--.  1 pup pup   78 Jun  3 11:59 .bash_env
-rw-r--r--.  1 pup pup 1987 Jun  3 11:59 .bashrc
-rw-r--r--.  1 pup pup  163 Jun  3 12:06 README.md
~/dotfiles$ cat .bashrc
# TODO: Write .bashrc.
~/dotfiles$ echo '{"ignore": ["README.md"]}' > emanate.json
~/dotfiles$ emanate
~/dotfiles$ cat ~/.bashrc
# TODO: Write .bashrc.ContributingBug reports and pull requests are welcome on GitHub athttps://github.com/duckinator/emanate.
Please report security problems to the normal issue tracker.When adding new functionality, please add tests. Mention @duckinator if you
need assistance with adding tests.LicenseThe project is available as open source under the terms of theMIT License.Code of ConductEveryone interacting in the emanate project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow thecode of conduct.
Package: emanjson2imodxf
emanjson2imodxfConvert tilt-series alignments from EMAN2 json files to IMOD format xf files.pipinstallemanjson2imodxfUsage:emanjson2imodxf[OPTIONS]ConvertanEMAN2jsonfileintoanIMODxffile.

Options:-i,--inputPATHEMAN2jsonfile[required]-o,--output-basenamePATHbasenameforIMOD.xfand.tltfiles[required]--helpShowthismessageandexit.Changelogv0.0.1 produce .xf filev0.0.2 produce .xf and corresponding .tlt file, API change
Package: emantis
e-MANTIS: Emulator for Multiple observable ANalysis in extended cosmological TheorIeSTable of ContentsDescriptionInstallationDocumentation and usageLicenceDescriptione-MANTIS is an emulator for the study of the non-linear large-scale structure formation in the context
of alternative dark energy and gravity theories.
It uses Gaussian Processes to perform a fast and accurate interpolation between the outputs of
high resolution cosmological N-body simulations.
Currently, e-MANTIS is able to give predictions for the following quantities:Matter power spectrum boost in\(f(R)\)gravity, described inThe e-MANTIS emulator: fast predictions of the non-linear matter power spectrum in f(R)CDM cosmology.Please cite the corresponding papers if you use e-MANTIS in your work.More observables and cosmological models will be added in the future. Stay tuned!InstallationYou can install the emulator fromPyPIvia pip:pip install emantisOr you can directly clone the emulator from our publicrepositoryand install it from source:git clone https://gitlab.obspm.fr/e-mantis/e-mantis.git
cd e-mantis
pip install [-e] .The emulator needs the following python packages to be installed in order to work:NumPyscikit-learnh5pyDocumentation and usageThe up-to-date documentation for this project (with code examples and a detailed API) is availablehere.LicenceCopyright (C) 2023 Iñigo Sáez-Casares - Université Paris Citéinigo.saez-casares@obspm.frThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, seehttps://www.gnu.org/licenses/.
Package: emanual
EManual-CLI-----------[![PyPI version](https://badge.fury.io/py/emanual.svg)](http://badge.fury.io/py/emanual)[![Downloads](https://pypip.in/download/emanual/badge.png)](https://pypi.python.org/pypi/emanual/)Command Line Interface for EManualinstall-------```shell$ pip install emanual```Usage-----## 语言资料模块0. 初始化模块```shell$ emanual init {module name}```1. 创建`info.json` & 把中文文件名变为拼音```shell$ cd path/to/md-xxx$ emanual create {lang}// `./dist/{lang}`就是生成的内容````2. 生成lang.zip```shell$ emanual dist {lang} //lang为指定的语言，小写```## NewsFeeds模块1. 更新```shell$ emanual newsfeeds update```## 文件名处理```shellcd path/to/md-xxx/markdown //通常修改这目录emanual filename check [path=.] //检查路径目录(默认是当前)下的文件名是否存在中文字符的标点emanual filename fix [path=.]   //修复存在中文标点的```Development-----------#### 1. 使用virtualenv,未安装则`pip install virtualenv````shell//创建虚拟的python开发环境$ virtualenv env//开启$ source env/bin/activate//退出$ deactivate```#### 2. 安装依赖```$ pip install -r requirements-dev.txt```#### 3. 动态加载当前库```shell$ cd path/to/EManual-CLI$ pip install --edit .//or$ pip install -e .```#### 4. 安装测试```shell$ python setup.py install$ emanual --version```dependency--- 命令行创建工具[click](https://github.com/mitsuhiko/click)- 路径解释[path.py](https://github.com/jaraco/path.py)- 中文转拼音[pypinyin](https://github.com/smallqiao/pypinyin)- markdown渲染[mistune](https://github.com/lepture/mistune)- DOM操作[beautifulsoup4](http://www.crummy.com/software/BeautifulSoup/)License-------MIT
Package: emaps
Emaps is a collection of python base modules for electron diffraction simulations and crystallographic caculations. It is free for personal and non-commercial use with pyemaps >= 1.0.8. Check out the fullpyemaps documentationfor its latest list of features exposed by pyemaps.An extended version of emaps with 4D-STEM features, such as experimental diffraction pattern indexing and annular dark fields, for commercial use is also availavle. Contactsupport@emlabsoftware.comfor pricing.
Package: emark
Django eMark↓Markdown template based HTML and text emails for Django.simple email templates with markdownsupport for HTML and text emailsi18n supportbuilt-in UTM trackingautomatic CSS inliner viapremailerSetuppython3 -m pip install emark# settings.pyINSTALLED_APPS=['emark',# ...]python3 manage.py migrateUsage<!-- myapp/my_message.md --># Hello WorldHi {{ user.short_name }}!# myapp/emails.pyfromemark.messageimportMarkdownEmailclassMyMessage(MarkdownEmail):subject="Hello World"template_name="myapp/my_message.md"# myapp/views.pyfrom.importemailsdefmy_view(request):message=emails.MyMessage.to_user(request.user)message.send()TemplatesYou can use Django's template engine, just like you usually would.
You can use translations, template tags, filters, blocks, etc.You may also have a base template, that you inherit form in your individual
emails to provide a consistent salutation and farewell.<!-- base.md -->
{% load static i18n %}
{% block salutation %}Hi {{ user.short_name }}!{% endblock %}

{% block content %}{% endblock %}

{% block farewell %}
{% blocktrans trimmed %}
Best regards,
{{ site_admin }}
{% endblocktrans %}
{% endblock %}

{% block footer %}
Legal footer.
{% endblock %}<!-- myapp/email.md -->
{% extends "base.md" %}

{% block content %}
This is the content of the email.
{% endblock %}ContextThe context is passed to the template as a dictionary. Furthermore, you may
override theget_context_datamethod to add additional context variables.# myapp/emails.pyfromemark.messageimportMarkdownEmailclassMyMessage(MarkdownEmail):subject="Hello World"template_name="myapp/email.md"defget_context_data(self):context=super().get_context_data()context["my_variable"]="Hello World"returncontextTrackingEveryMarkdownEmailsubclass comes with automatic UTM tracking.
UTM parameters are added to all links in the email. Existing UTM params on link
that have been explicitly set, are not overridden. The default parameters are:utm_source:websiteutm_medium:emailutm_campaign:{{ EMAIL_CLASS_NAME }}The global UTM parameters can be overridden via theEMARK_UTM_PARAMSsetting,
which is a dictionary of parameters:# settings.pyEMARK_UTM_PARAMS={"utm_source":"website",# default"utm_medium":"email",# default}You may also change the UTM parameters by overriding theget_utm_paramsor passing autm_paramsdictionary to class constructor.# myapp/emails.pyfromemark.messageimportMarkdownEmailclassMyMessage(MarkdownEmail):subject="Hello World"template_name="myapp/email.md"# override the parameters for this email classdefget_utm_params(self):return{"utm_source":"myapp","utm_medium":"email","utm_campaign":"my-campaign",}# or alternatively during instantiationMyMessage(utm_params={"utm_campaign":"my-other-campaign"}).send()CreditsDjango eMark uses modified version ofResponsive HTML Email Templateas a base templateFor CSS inlining, Django eMark usespremailer
Package: emarket-data-explorer
OverviewE-Market Data Explorer is a Python crawler and exploratory data
analysis(EDA) tool for marketing specialties who would like to conduct
the STP methods for working out their marketing strategy for product
development and sale.PreviewE-MarketDataExplorer SCRAPscrap-asyncE-MarketDataExplorer EDAapp testE-MarketDataExplorer HELP$ python -m emarket_data_explorer --help

    Usage: emarket_data_explorer [OPTIONS] COMMAND [ARGS]...

    E-Market Data Explorer is a Python-based crawler and exploratory data
    analysis(EDA) tool for marketing specialties who would like to conduct the
    STP methods for working out their marketing strategy for sale and promotion.

    Updated for E-Market Data Explorer 1.5, July 2022

    Author:

    Currently written and maintained by Paul Yang <paulyang0125@gmail> and Kana
    Kunikata <vinaknkt@gmail.com>.

    Options:
    -v, --version                   Show the application's version and exit.
    --install-completion [bash|zsh|fish|powershell|pwsh]
                                    Install completion for the specified shell.
    --show-completion [bash|zsh|fish|powershell|pwsh]
                                    Show completion for the specified shell, to
                                    copy it or customize the installation.
    --help                          Show this message and exit.

    Commands:
    eda          Create EDA process and charts from csv files
    init         Initialize the shopee explorer data folder.
    scrap-async  Scrap commercial data from the data source specified by userQuick-startPlease refer to theE-Market Data Explorer online
documentationfor Get-Started, EDA explanation, Testing and Troubleshootingdoc_preview_githubLicenseIt is distributed under the Apache License. See LICENSE.txt for more
information.ContactPaul Yang;Kana
KunikataRoadmapWe’ve developedour roadmap of this
utility.
Check it out if you’re interested in joining us.
Package: emarsysauthenticator
emarsysauthenticatorPython helper to easily create X-WSSE header for Authentication with Emarsys RESTful APIUsageimportemarsysauthenticatorasea# For authentication with Emarsys RESTful API the user name and secret is neededuser_name='account_name001'user_secret='abc123def456'# Create X_WSSE headerxwsse_header=ea.create_xwsse_header(user_name,user_secret)# Make requestresponse=requests.request('GET','https://api.emarsys.net/api/v2/email',headers=xwsse_header)ContributionI am thankful for any feedback and improvements.LicenseThis project is licensed under MIT License - see theLICENSE.mdfile for details
Package: emass-client-api
## eMASS API v3.12 SpecificationThe emass_client_api is a Python client that implements theEnterprise Mission Assurance Support Service (eMASS)Representational State Transfer (REST) Application Programming Interface (API) specifications.This Python package was generated from the eMASS API specification:API version: v3.12Package version: 3.12.0Build date: 2023-10-10T14:36:02.975730Z[Etc/UTC]Requirements.Python 3.7+Installation & Usagepip installIf the python package is hosted on a repository, you can install directly using:pipinstallgit+https://github.com/GIT_USER_ID/GIT_REPO_ID.git(you may need to runpipwith root permission:sudo pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git)Then import the package:importemass_clientSetuptoolsInstall viaSetuptools.pythonsetup.pyinstall--user(orsudo python setup.py installto install the package for all users)Then import the package:importemass_clientTestsExecutepytestto run the tests.
Package: emat
TMIP-EMAT: Exploratory Modeling and Analysis ToolTMIP-EMAT is amethodological approachto exploratory modeling and analysis.
It provides a window to rigorous analytical methods for handling uncertainty
and making well informed decisions using travel forecasting models of all
types. It is designed to integrate with an existing transportation model or
tool to perform exploratory analysis of a range of possible scenarios.The methodologies demonstrated in this toolset build on and extend the
capabilities of theEMA Workbench.Documentation for this tool is published athttp://tmip-emat.github.io.
Package: ema-workbench
Exploratory Modeling workbenchExploratory Modeling and Analysis (EMA) is a research methodology that uses computational experiments to analyze complex and uncertain systems (Bankes, 1993). That is, exploratory modeling aims at offering computational decision support for decision making underdeep uncertaintyandrobust decision making.The EMA workbench aims at providing support for performing exploratory modeling with models developed in various modelling packages and environments. Currently, the workbench offers connectors toVensim,Netlogo,Simio,Vadereand Excel.The EMA workbench offers support for designing experiments, performing the experiments - including support for parallel processing on both a single machine as well as on clusters-, and analysing the results. To get started, take a look at the high level overview, the tutorial, or dive straight into the details of the API.The EMA workbench currently under development at Delft University of Technology. If you would like to collaborate, open an issue/discussion or contactJan Kwakkel.DocumentationDocumentation for the workbench is availabe atRead the Docs, including an introduction on Exploratory Modeling, tutorials and documentation on all the modules and functions.There are also a lot of example models available atema_workbench/examples, both for pure Python models and some using the different connectors. A release notes for each new version are available atCHANGELOG.md.InstallationThe workbench is available fromPyPI, and currently requires Python 3.9 or newer. It can be installed with:pip install -U ema_workbenchTo also install some recommended packages for plotting, testing and Jupyter support, use therecommendedextra:pip install -U ema_workbench[recommended]There are way more options installing the workbench, including installing connector packages, edible installs for development, installs of custom forks and branches and more. SeeInstalling the workbenchin the docs for all options.ContributingWe greatly appreciate contributions to the EMA workbench! ReportingIssuessuch as bugs or unclairties in the documentation, opening aPull requestswith code or documentation improvements or opening aDiscussionwith a question, suggestions or comment helps us a lot.Please checkCONTRIBUTING.mdfor more information.LicenseThis repository is licensed under BSD 3-Clause License. SeeLICENSE.md.
Package: embarrassing
EmbarrassingA package to embarrassingly parallelize your code.InstallationYou can install embarrassing using pip:pipinstallembarrassingUsagefromembarrassingimportembarrassing# Define your callback functiondefmy_callback(item):# Perform some operations on the itemreturnresult# List of contents to processcontents=[...]# Process the contents using the embarrassing functionresults=embarrassing(contents,my_callback)# Print the resultsforresultinresults:print(result)To Deploypython setup.py sdist bdist_wheeltwine upload dist/*LICENSEThis project is licensed under the MIT License.Feel free to modify and expand this template to provide more information about your package, its features, installation instructions, and how to use it. Replace "my_package" with the actual name of your package throughout the README.md file.Ensure that you have a LICENSE file in the root directory of your project, and update the[![PyPI version](https://badge.fury.io/py/my_package.svg)](https://badge.fury.io/py/my_package)and[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)badges with the appropriate links and package name.Including a README.md file with relevant information helps users understand and effectively utilize your package.
Package: embarrassingly
embarrassinglyEmbarrassingly obvious (in retrospect) ways to hack objective functions before you send them to optimization routines.
Seeblog articlefor motivation and explanationInstallpip install embarrassinglyExample 1 : Parallel objective computationSeeoptuna_parallel.pyfrom embarrassingly.parallel import Parallel
import optuna

def pre_objective(worker, trial):
    print('Hi this is worker ' + str(worker))
    x = [trial.suggest_float('x' + str(i), 0, 1) for i in range(3)]
    return x[0] + x[1] * x[2]

def test_optuna():
    objective = Parallel(pre_objective, num_workers=7)
    study = optuna.create_study()
    study.optimize(objective, n_trials=15, n_jobs=7)Example 2 : Plateau findingSeeunderpromoted_shgo.pyfrom scipy.optimize import shgo
from embarrassingly.underpromoted import plateaudinous, Underpromoted2d

bounds = [(-1 ,1) ,(-1 ,1)]
f = plateaudinous
res1 = shgo(func=f, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})
print('Minimum at '+str(res1.x))

f_tilde = Underpromoted2d(f, bounds=bounds, radius=0.05)
res1 = shgo(func=f_tilde, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})
print('Landed at '+str(res1.x))Example 3 : Expensive functionsSeeshy_shgo.pydef slow_and_pointless(x):
""" Example of a function with varying computation time """
    r = np.linalg.norm(x)
    quad = (0.5*0.5-r*r)/(0.5*0.5)
    compute_time = max(0,0.5*quad+x[0])
    time.sleep(compute_time)
    return schwefel([1000*x[0],980*x[1]])[0]

# Save time by making it a "shy" objective function
bounds = [(-0.5, 0.5), (-0.5, 0.5)]
SAP = Shy(slow_and_pointless, bounds=bounds, t_unit=0.01, d_unit=0.3)
from scipy.optimize import minimize
res = scipy.optimize.shgo(func=SAP, bounds=bounds, n=8, iters=4, options={'minimize_every_iter': True, 'ftol': 0.1})
Package: embarrassment
Convenience functions for pandas dataframes containing triples. Fun fact: a group of pandas (e.g. three) is commonly referred to as anembarrassment.This library's main focus is to easily make commonly used functions available, when exploringtriplesstored in pandas dataframes. It is not meant to be an efficient graph analysis library.UsageYou can use a variety of convenience functions, let's create some simple example triples:>>>importpandasaspd>>>rel=pd.DataFrame([("e1","rel1","e2"),("e3","rel2","e1")],columns=["head","relation","tail"])>>>attr=pd.DataFrame([("e1","attr1","lorem ipsum"),("e2","attr2","dolor")],columns=["head","relation","tail"])Search in attribute triples:>>>fromembarrassmentimportsearch>>>search(attr,"lorem ipsum")headrelationtail0e1attr1loremipsum>>>search(attr,"lorem",method="substring")headrelationtail0e1attr1loremipsumSelect triples with a specific relation:>>>fromembarrassmentimportselect_rel>>>select_rel(rel,"rel1")headrelationtail0e1rel1e2Perform operations on the immediate neighbor(s) of an entity, e.g. get the attribute triples:>>>fromembarrassmentimportneighbor_attr_triples>>>neighbor_attr_triples(rel,attr,"e1")headrelationtail1e2attr2dolorOr just get the triples:>>>fromembarrassmentimportneighbor_rel_triples>>>neighbor_rel_triples(rel,"e1")headrelationtail1e3rel2e10e1rel1e2By default you get in- and out-links, but you can specify a direction:>>>neighbor_rel_triples(rel,"e1",in_out_both="in")headrelationtail1e3rel2e1>>>neighbor_rel_triples(rel,"e1",in_out_both="out")headrelationtail0e1rel1e2Using pandas'pipeoperator you can chain operations.
Let's see a more elaborate example by loading a dataset fromsylloge:>>>fromsyllogeimportMovieGraphBenchmark>>>fromembarrassmentimportclean,neighbor_attr_triples,search,select_rel>>>ds=MovieGraphBenchmark()>>># clean attribute triples>>>cleaned_attr=clean(ds.attr_triples_left)>>># find uri of James Tolkan>>>jt=search(cleaned_attr,query="James Tolkan")["head"].iloc[0]>>># get neighbor triples>>># and select triples with title and show values>>>title_rel="https://www.scads.de/movieBenchmark/ontology/title">>>neighbor_attr_triples(ds.rel_triples_left,cleaned_attr,jt).pipe(select_rel,rel=title_rel)["tail"])12234ANeroWolfeMystery12282DoortoDeath12440DieLikeaDog12461TheNextWitnessName:tail,dtype:objectInstallationYou can installembarrassmentvia pip:pip install embarrassment
Package: embasp-python
EmbASP - Python VersionEmbASP is a framework for the integration of Logic Programming in external systems for generic applications, conceived in order to help developers at designing and implementing complex reasoning tasks by means of logic-based solvers on different platforms.We currently provide 3 implementations (inJava, inPythonand inC#) and ready-made libraries for the embedding of theASP (Answer Set Programming)solversDLV,clingoandDLV2and thePDDL (Planning Domain Definition Language)cloud solverSolver.Planning.Domains(SPD) on the Desktop platform and of DLV, DLV2 and SPD on the Mobile (Android™) one (available only for the Java version). The Java version requires the antlr4 library.The framework has been designed to be easily extensible and adaptable to different solvers and platforms. Solvers can be invoked in different modes; for instance, SPD is invoked via a remote connection, while for the other, binaries are effectively embedded and natively executed.ContactsFor further information, visithttps://www.mat.unical.it/calimeri/projects/embasp
Package: embassy
Alignment and Stability of Embeddingsembassy:embeddingalignment andstabilityThis Python package provides toolsto measure alignment and stability errors of given embeddings,to align given embeddings.The package will soon be updated with new functionality for extracting rotation angles of a rotation matrix, creating rotation matrices with a desired magnitude, adding random walk-based noise with a desired magnitude, and other operations.DependeciesnumpyscipyTested for numpy==1.20.1 and scipy==1.6.1 but should work with most versions.InstallationUse the package managerpipto installembassy.pipinstallembassyExample Usagefromembassyimportalign_and_measureimportnumpyasnpX=np.array([[1.8,2.0],[2.3,2.5],[1.8,4.2],[4.1,3.1]])Y=np.array([[0.0,1.2],[0.3,1.6],[-0.4,3.5],[1.6,2.5]])output=align_and_measure(X,Y)print("\nTranslation Error :",output['translation_error'],"\nRotation Error    :",output['rotation_error'],"\nScale Error       :",output['scale_error'],"\nStability Error   :",output['stability_error'],"\n","\nX_aligned:\n",output['emb1'],"\n","\nY_aligned:\n",output['emb2'])SeeExamples/Demonstration.ipynbfor visual and real-world examples.CitationIf you find this software useful in your work, please cite:Furkan Gursoy, Mounir Haddad, and Cecile Bothorel. (2021)."Alignment and stability of embeddings: measurement and inference improvement."(2020).ContributingPlease feel free to open an issue for bug reports, change requests, or other contributions.LicenseMITPackaged with:Flit