Saravanan666/pypi-org huggingface dataset:
Package: embedpvp
EmbedPVPPrioritizing genomic variants (SNP/InDls) through neuro-symbolic, knowledge-enhanced learningAnnotation data sources (integrated in the candidate SNP prediction workflow)We integrated the annotations from different sources:Gene ontology (GO)Mammalian Phenotype ontology (MP)Human Phenotype Ontology (HPO)Uber-anatomy ontology (UBERON)Gene expression in human Celltypes Ontology (CL)DependenciesThe code was developed and tested using python 3.9.We used (mOWL) library to process the input dataset as well as generated the embedding representation using different
embedding-based methods.Installationpip install embedpvpRunning EmbedPVP using pretrained models:Download the distribution file and set up environmentgit clone https://github.com/bio-ontology-research-group/EmbedPVP.git
cd EmbedPVP
conda env create -f environment.yml
conda activate embedpvpDownload all the files fromdataand place the uncompressed the file in the folder named/data.Download the required database usingCADD.Run the commandembedpvp --helpto display help and parameters:EmbedPVP: Prioritizing Causative Variants by Integrating Functional Embedding and Biological Annotations for Genes.


Options:
  -d, --data-root TEXT      Data root folder  [required]
  -i, --in_file TEXT        Annotated Input VCF file  [required]
  -p, --pathogenicity TEXT  Path to the pathogenicity prediction file (CADD) [required]
  -hpo, --hpo TEXT          List of phenotype codes separated by commas [required]
  -m, --model_type TEXT     Ontology model, one of the following (go , mp , hp, uberon, union)
  -e, --embedding TEXT      Preferred embedding model (e.g. TransD, TransE, TranR, ConvE ,DistMult, DL2vec, OWL2vc, EL, ELBox)
  -dir, --outdir TEXT       Path to the output directory
  -o, --outfile TEXT        Path to the results output file
  --help                    Show this message and exit.Run the example:mbedpvp -d data/ -i example_annotation.vcf.hg38_multianno.txt  -p example_cadd.tsv.gz -hpo HP:0004791,HP:0002020,HP:0100580,HP:0001428,HP:0011459 -m hp -e dl2vec -dir output/ -o example_output.tsv

 Annotate VCF file (example.vcf) with the phenotypes (HP:0003701,HP:0001324,HP:0010628,HP:0003388,HP:0000774,HP:0002093,HP:0000508,HP:0000218,HP:0000007)...
 |========                        | 25% Annotated files generated successfully.
 |================                | 50% Phenotype prediction...
 |========================        | 75% Variants prediction...
 |================================| 100%
The analysis is Done. You can find the priortize list in the output file: example_output.txtOutput:The script will output a ranking a score for the candidate caustive list of variants.NoteFor any questions or comments please contactazza.althagafi@kaust.edu.sa
Package: embed-templator
Embed TemplatorA Python Template manager for your discord bot to keep your embeds simple & consistentInstallationUse The following command to install Embed-Templator into your python environment:pipinstallEmbed-Templatordidn't works?Depending on your python installation, you might need to use one of the following.pip isn't in the path but python ispython-mpipinstallembed-templatorpython isn't in the pathpath/to/python.exe-mpipinstallembed-templatorUsing multiple python versionspy-mpipinstallembed-templatorUsageSimplestThe simplest way to use the Embed templator in your project is the following:fromembed_templatorimportEmbed# Loading your client instance withing the embed templator.Embed.load(client)@client.command()asyncdefping(ctx):# Use it like a regular Embed.awaitctx.send(embed=Embed(description='pong!'))Context EmbedsHowever, the embed templator can have more advanced usage, and use context embeds.fromembed_templatorimportEmbedclient=...# Note that auto_author requires the ctx to be passed at embeds init.Embed.load(client,auto_author=True)@client.command()asyncdefping(ctx):# Use it like a regular Embed.awaitctx.send(embed=Embed(ctx)(description='pong!'))CogsIf you are using a cog based system, don't forget to init the embed in your cogs with the following:classMyBeautifulCog(commands.Cog):def__init__(self,client):self.client=clientEmbed.load(self.client)Then you'll be able to use it like the previous examples:@commands.command()asyncdefmy(self,ctx):awaitctx.send(embed=Embed(ctx)(description="Cabbage"))Advanced UsageIf you want an advanced embed configuration, you can create a custom embed class
that will inherit the embed templator.from__future__importannotationsimportembed_templatorclassEmbed(embed_templator.Embed):defsetup(self)->Embed:returnself.set_footer(text=f"{self.ctx.command}| more info @{self.ctx.prefix}help")You can also use the `update` method for last changes in the embed, better it will be sent.:exclamation:This example uses a custom ctx that have time tracking !from__future__importannotationsimportembed_templatorclassEmbed(embed_templator.Embed):defsetup(self)->Embed:returnself.set_author(name=f"Requested by{self.ctx.author}🚀",icon_url=self.ctx.author.avatar_url)defupdate(self)->Embed:self.set_footer(icon_url=self.client.user.avatar_url,text='   '.join((f"⚙️{self.ctx.time.elapsed()}",f"⏳{self.client.latency}",f"🔑{self.ctx.prefix}help",)))returnselfThanks for using Embed-Templator!License© 2020 copyright EdhyjoxThis repository is licensed under the MIT License. See LICENSE for details.
Package: embedX
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](http://kmonsoor.mit-license.org/)[![PyPI version](https://badge.fury.io/py/embedX.svg)](https://badge.fury.io/py/embedX)[![Code Issues](https://www.quantifiedcode.com/api/v1/project/69f2bf7e3b404e6c90015053e48a1cbf/badge.svg)](https://www.quantifiedcode.com/app/project/69f2bf7e3b404e6c90015053e48a1cbf)# embedXGenerate embeddable HTML or JavaScript code for a online content from its URL in a single step.The content can be anything like* Youtube or Vimeo video,* Twitter status,* Github Gist,etc.Embeddable code-generation be simple like this:>>> from embed_x import OnlineContent>>> oc = OnlineContent('http://www.youtube.com/embed/_lOT2p_FCvA')>>> oc.get_embed_code()"<div class='embedx-yt'><iframe src='http://www.youtube.com/embed/_lOT2p_FCvA' 'frameborder='0' allowfullscreen></iframe></div>"### Currently, supports* Youtube video,* Vimeo video,* Twitter: single status,* Github gist,* Flickr image### Examples of URLs####  Valid*  http://youtu.be/_lOT2p_FCvA*  www.youtube.com/watch?v=_lOT2p_FCvA&feature=feedu*  http://www.youtube.com/embed/_lOT2p_FCvA*  https://www.youtube.com/watch?v=rTHlyTphWP0&index=6&list=PLjeDyYvG6-40qawYNR4juzvSOg-ezZ2a6*  youtube.com/watch?v=_lOT2p_FCvA*  https://vimeo.com/groups/animation/videos/150618894/*  https://vimeo.com/150519302*  https://twitter.com/codinghorror/status/686254714938200064*####  Invalid:* youtu.be/watch?v=_lOT2p_FCvA  [ because Youtube don't give any url like this ]* https://twitter.com/gvanrossum/with_replies*## Install### from PyPIpip install embedx### Directly from sourcepip install git+https://github.com/kmonsoor/embedX.git## RequirementsThis library don't have any external dependencies other than standard Python installation.## Usage>>> from embedx import OnlineContent>>> oc = OnlineContent('http://www.youtube.com/embed/_lOT2p_FCvA')>>> oc.extract_id()'_lOT2p_FCvA'>>> oc.get_embed_code()"<div class='embed-container'><iframe src='http://www.youtube.com/embed/_lOT2p_FCvA' 'frameborder='0'allowfullscreen></iframe></div>">>> oc = OnlineContent('https://vimeo.com/groups/animation/videos/150618894/')>>> oc.get_embed_code()"<div class='embed-container'> <iframe src='http://player.vimeo.com/video/150618894' frameborder='0' webkitAllowFullScreen mozallowfullscreen allowFullScreen></iframe></div>"## To-do* Create test cases for automated testing* Provision Travis-CI for automatic building & testing* Add support for [conda](http://conda.pydata.org/docs/index.html)-based installation* Add more rich-content sites- [x] Github gists- [ ] Facebook status- [ ] Facebook notes- [ ] Scribd docs- [ ] Imgur images## Contributors* Seed idea :  [A StackOverflow answer](http://stackoverflow.com/a/7936523) by [Mikhail Kashkin](http://stackoverflow.com/users/85739/mikhail-kashkin)* Author : [Khaled Monsoor](http://github.com/kmonsoor)*Please try to contribute by submitting more content-sites with their different link formats and embed-codes. You can submit through pull-requests or by creating issue.
Package: embellish-folder
Embellish FolderInstallation$ pip install embellish_folderExamplesEmbellish a folder icon with a badge, symbol, or other image.>>> from embellish_folder import *
>>> embellish_folder("/Users/janet/MyStuff", stuff_image)CI/CD$ bumpversion patch
$ git push --follow-tagsMIT LicenseCopyright (c) 2020 Sixty North ASPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: ember-compressor-compiler
Ember Compressor Compiler=========================[![Build Status](https://travis-ci.org/Juvenal1228/ember-compressor-compiler.png?branch=master)](https://travis-ci.org/Juvenal1228/ember-compressor-compiler)Purpose-------This tool is meant to be used as an extension to [django-compressor](https://github.com/jezdez/django_compressor)It precompiles [handlebars.js](https://github.com/wycats/handlebars.js) templates specifically for [ember.js](https://github.com/emberjs/ember.js)Features--------- platform independent- no need to install node.js packages- flexible template naming conventions- inline handlebars in django templates- 100% test coverage- [PEP 8](http://www.python.org/dev/peps/pep-0008/) compliance- [semver](http://semver.org/) complianceInstalling----------Install with pip/easy_install from the pypi`pip install ember-compressor-compiler`or clone the latest sourcegit clone https://github.com/Juvenal1228/ember-compressor-compiler.gitcd ember-compressor-compilerpython setup.py installYou must also install [node.js](http://nodejs.org/) or [PyV8](https://code.google.com/p/pyv8/)The latest versions of node.js can be found [here](http://nodejs.org/download/)Using-----Using this tool is as simple as installing it and adding it to the `COMPRESS_PRECOMPILERS` django setting```pythonCOMPRESS_PRECOMPILERS = (('text/x-handlebars', 'embercompressorcompiler.filter.EmberHandlebarsCompiler'),)```Then, in your django templates you can embed handlebars templates like so```html+django{% load staticfiles %}{% load compress %}{% compress js %}<script type="text/x-handlebars" src="{% static 'app/templates/application.hbs' %}" ></script><script type="text/x-handlebars" data-template-name="index">{{outlet}}</script>{% endcompress %}```Template Names--------------Template names are determined in one of two ways1. the `data-template-name` attribute on the `script` tag in your django template2. the `src` attribute on the `script` tag in your django templateWhen specified, the `data-template-name` value is used verbatimIf not, the `src` value is manipulated to give proper template names.- the file extensions `.handlebars` and `.hbs` are removed- everything before the parent directory `templates` is removed```html+django<!-- results in template named 'application' --><script type="text/x-handlebars" src="{% static 'app/templates/application.hbs' %}" ></script><!-- results in template named 'example/index' --><script type="text/x-handlebars" src="{% static 'app/templates/example/index.handlebars' %}" ></script>```Advanced Usage--------------If you need to change the template naming behavior, you can subclass `embercompressorcompiler.filter.EmberHandlebarsCompiler````pythonfrom embercompressorcompiler.filter import EmberHandlebarsCompilerclass MyCompiler(EmberHandlebarsCompiler):# override default parent directoryparent_dir = 'tpls'# override default extensionsextensions = ['.tpl']```Then you register your own compiler subclass in `COMPRESS_PRECOMPILERS````pythonCOMPRESS_PRECOMPILERS = (('text/x-handlebars', 'myapp.MyCompiler'),)```
Package: embermaker
EmberMakerPurposeEmberMaker is a scientific graphic library aimed at (re)producing "burning ember" diagrams
of the style used in IPCC (Intergovernmental Panel on Climate Change) reports.
All graphics elements are in vector form, including color gradients (this implies specific drawing code).EmberMaker was formerly a part of the EmberFactory project:https://pypi.org/project/EmberFactory/.
If you need to produce ember diagrams from your data,first consider using the onlineEmberFactory.
This package is intended to meet more specific needs, by drawing embers from your own code.In addition to 'burning embers', EmberMaker can also draw common x-y plots which share the
same vertical axis as a set of embers.While EmberMaker is a Python package, it is quite easy to use withinR,
as shown in the last example below.Installationpip install embermakerOnly Python >= 3.10 is supported.Basic usageCreating embers only needs calling a few functions, as in this basic example:from embermaker.embergraph import EmberGraph
from embermaker.ember import Ember

# Create ember
be = Ember(name='What this ember is about', haz_valid=[0, 5])

# Add two transitions to this ember
be.trans_create(name='undetectable to moderate', min=0.6, median=1.1, max=1.4, confidence='very high')
be.trans_create(name='moderate to high', min=1.7, median=2.2, max=2.5, confidence='high')

# Create an ember graph (available native image formats are PDF and SVG)
egr = EmberGraph("output_file_name", grformat="PDF")

# Change a parameter (list of parameters: https://climrisk.org/emberfactory/doc/parameters )
egr.gp['haz_axis_top'] = 5.0  # max value on axis 

# Add ember to graph (a list would work as well; more advanced uses define ember groups)
egr.add(be)

# Actually produce the diagram
outfile = egr.draw()Usage examplesExamples are provided onFramaGit(a GitLab instance)Access within python (/examples/python):'create_and_draw_embers.py' is the shorter and simpler. It illustrates a few functions constructing embers from data.'draw_embers_from_file.py' reads 'traditional' ember Excel sheets and shows how an x-y plot can be added.'test_error_reporting.py' illustrates a few cases which produce warning or error messages and how to access these.Access from R (/examples/R):'create_and_draw_embers.R' illustrates functions constructing embers, within an R script and using data produced in R.As this is the first "standalone" version, the API functions may be improved in the future, as well as more documented.
We are interested in learning how you use this and any difficult you might face, to steer future development - thanks!Development historyThe EmberFactory software was created by philippe.marbaix -at- uclouvain.be at the end of 2019.
The first objective was to produce figure 3 of Zommers et al. 2020 (doi.org/10/gg985p).
Package: embers
Experimental Measurement of BEam Responses with SatellitesEMBERSis a pure-Python astronomy software package designed to enable polarised measurements of radio telescope antenna beam-patterns using satellites.EMBERSis designed to install cleanly with a single invocation of the standard Python package tool:$ pip install embersHere are the essential project links:Home page and documentationInstalling EmbersEmbers by ExampleEmbers packageon the Python Package IndexContributing to EMBERSIssue trackeron GitHub
Package: embestore
IntroductionImplement the sentence embedding retriever with local cache from the embedding store.FeaturesEmbedding store abstraction classSupportJinaclient implementation embedding storeSupport LFU, LRU cache eviction policy for limited cache size, if the eviction policy is not specified then won't
apply any eviction policySave the cache to parquet fileLoad the cache from existed parquet fileQuick StartOption 1.Using Jina flow serve the embedding modelInstallationpipinstallembestore"[jina]"To start up the Jina flow service with default sentence transformer modelsentence-transformers/paraphrase-multilingual-MiniLM-L12-v2embestoreservestart-jinaUse other sentence transformer model fromhugging face# Take sentence-transformers/all-MiniLM-L6-v2 for exampleexportSENTENCE_TRANSFORMER=sentence-transformers/all-MiniLM-L6-v2
embestoreservestart-jinaRetrieve the embeddingfromembestore.store.jinaimportJinaEmbeddingStoreJINA_EMBEDDING_STORE_GRPC="grpc://0.0.0.0:54321"query_sentences=["I want to listen the music.","Music don't want to listen me."]jina_embedding_store=JinaEmbeddingStore(embedding_grpc=JINA_EMBEDDING_STORE_GRPC)embeddings=jina_embedding_store.retrieve_embeddings(sentences=query_sentences)>>>embeddingsarray([[2.26917475e-01,8.17841291e-02,2.35427842e-02,-3.02357599e-02,1.15757119e-02,-8.42996314e-02,4.42815214e-01,1.80795133e-01,1.04702041e-01,...]])Stop the docker containerembestoreservestop-jinaOption 2.Using local sentence embedding modelInstallationpipinstallembestore"[sentence-transformers]"Serve the sentence embedding modelsentence-transformers/paraphrase-multilingual-MiniLM-L12-v2by in-memoryfromembestore.store.torchimportTorchEmbeddingStorequery_sentences=["I want to listen the music.","Music don't want to listen me."]torch_embedding_store=TorchEmbeddingStore()embeddings=torch_embedding_store.retrieve_embeddings(sentences=query_sentences)>>>embeddingsarray([[2.26917475e-01,8.17841291e-02,2.35427842e-02,-3.02357599e-02,1.15757119e-02,-8.42996314e-02,4.42815214e-01,1.80795133e-01,1.04702041e-01,...]])Option 3.Inherit from the abstraction classInstallationpipinstallembestorefromtypingimportList,Textimportnumpyasnpfromsentence_transformersimportSentenceTransformerfromembestore.store.baseimportEmbeddingStoremodel=SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2").eval()classTorchEmbeddingStore(EmbeddingStore):def_retrieve_embeddings_from_model(self,sentences:List[Text])->np.ndarray:returnmodel.encode(sentences)Save the cachetorch_embedding_store.save("cache.parquet")Load from the cachetorch_embedding_store=TorchEmbeddingStore("cache.parquet")Apply eviction policyLRUtorch_embedding_store=TorchEmbeddingStore(max_size=100,eviction_policy="lru")LFUtorch_embedding_store=TorchEmbeddingStore(max_size=100,eviction_policy="lfu")Road Map[TODO] Badges
Package: embeval
EmbEval is a framework that aims to provide a way to evaluate
an arbitrary amount of word embeddings in an arbitrary amount
of tasks, in parallel.To aid with the interpretability of the results, embeval
resorts to graphs to visualize the performance of the different
type of embeddings across each task.Getting StartedInstallationInstall embeval with pip:pip3installembevalUsage (Command Line)embeval--helpUsage:embeval[OPTIONS]COMMAND[ARGS]...Options:--helpShowthismessageandexit.Commands:semantic-similarityembevalsemantic-similarity--helpUsage:embevalsemantic-similarity[OPTIONS]EMBEDDING_DIRTESTSET_DIROptions:--workersINTEGERNumberofworkerprocessestouse.--output_pathTEXTPathtowriteoutputfilesto.--output_format[text|graph|both]--helpShowthismessageandexit.embevalsemantic-similarity--output_pathoutput/embeddings/testsets/Using/Extending EmbEvalTo extend the code to include tasks not provided in the current implementation (contributions would be most welcome), n concepts must be implemented:Command (SeeSemantic Similarity Command) – This is what will make your task available under the CLI and also willcommandthe flow of execution when called upon.Clickis used as the CLI package. The entrypoint for an extended application must import the main cli object and register all the available commands (Seemain).Processing Pipeline (SeegenericsandSemantic Similarity Pipeline– This is where the producer, processor and consumer are implemented to execute tasks. The implementation makes use of the library and methodology ofpseq.Store (SeeSemantic Similarity Store) – Simple object to keep track of evaluation results obtained during the processing pipeline.Task (SeeSemantic Similarity Task) – A task object which encapsulates needed information to be shared in the pipeline, such as paths to files.Visualization (Seetext visualization) – Defines a method of visualization.Plans☐ Finish Semantic Similarity visualization.☐ Integrate GLUE tasks viajiant framework.LicenseDistributed under GPL-3.0 License. See theLICENSEfile for details.
Package: embexpr
safe embedded python expression parser (for mainly easy DSL or config file).Getting StartedpipinstallembexprHow to UsefromembexprimportExpr,ParseErrorassertExpr('3 * 5')()==15assertExpr('"foo" + "bar"')()=="foobar"assertExpr('len("example")')()==7assertExpr('s.startswith("prefix_")')(s="prefix_suffix")==Truetry:Expr('eval("1")')()exceptParseErrorase:print(e)Referencehttps://github.com/ansible/ansible/blob/devel/lib/ansible/template/safe_eval.pyhttp://stackoverflow.com/questions/12523516/using-ast-and-whitelists-to-make-pythons-eval-safeAuthorNao Yonashiro(@orisano)LicenseMIT
Package: embfile
A package for working with files containing word embeddings (aka word vectors).
Written for:providing a common interface for different file formats;providing a flexible function for building “embedding matrices” that you can use
for initializing theEmbeddinglayer of your deep learning model;taking as less RAM as possible: no need to load 3M vectors like withgensim.load_word2vec_formatwhen you only need 20K;satisfying my (inexplicable) urge of writing a Python package.FeaturesSupports textual and Google’s binary format plus a custom convenient format (.vvm)
supporting constant-time access of word vectors (by word).Allows to easily implement, test and integrate new file formats.Supports virtually any text encoding and vector data type (though you should
probably use only UTF-8 as encoding).Well-documented and type-annotated (meaning great IDE support).Extensively tested.Progress bars (by default) for every time-consuming operation.Installationpip install embfileQuick startimportembfilewithembfile.open("path/to/file.bin")asf:# infer file format from file extensionprint(f.vocab_size,f.vector_size)# Load some word vectors in a dictionary (raise KeyError if any word is missing)word2vec=f.load(['ciao','hello'])# Like f.load() but allows missing words (and returns them in a Set)word2vec,missing_words=f.find(['ciao','hello','someMissingWord'])# Build a matrix for initializing an Embedding layer either from# a list of words or from a dictionary {word: index}. Handles the# initialization of eventual missing word vectors (see "oov_initializer")matrix,word2index,missing_words=embfile.build_matrix(f,words)ExamplesThe examples shows how to use embfile to initialize theEmbeddinglayer of
a deep learning model. They are just illustrative, don’t skip the documentation.Keras using TokenizerKeras using TextVectorization(tensorflow >= 2.1)DocumentationRead the full documentation athttps://embfile.readthedocs.io/.Changelogv0.1.1 (2021-02-15)No changes in the code.Add support to python 3.9.Migrate from TravisCI+AppVeyor to GitHub Actions.Add examples for Keras.Minor doc changes.v0.1.0 (2020-01-24)First release on PyPI.