Saravanan666/pypi-org huggingface dataset:
Package: billionfong
IntroductionThis is a package created by Billy Fong.The package aims to create 24 Billy (but only 4 are created), and some mini games for time killing.billionfongThis is the master class billionfong, with the followings:Subclass:Child, IT Dog, Musician, NarcissistMethods:create(personality = PERSONALITY), play(game = GAME), shout(), love()GamesThere are 2 games created at this moment.Bingo:Guess number between 1 to 100Mastermind:Guess 4 numbers between 1-6 with exact order.Exampleimport billionfong

billy = billionfong.create(personality = "narcissist")
billy.love()
billy.shout()
billy.play(game = "mastermind")
Package: billionprompt
BillionPrompt Python SDKBillionPrompt Python SDK can use python to call the functions ofBillionPromptproducts, including prompt writing, prompt retrieval and image creation.Installing By PIP$pipinstallbillionpromptAPI secretkeyTo generate an API secretkey, you need to go to our website:BillionPromptExample usageImport the SDK and instantiate a new client with your authentication secrets:frombillionpromptimportImageClient,RetrivalClient,PromptClientsecretkey={"Authorization":"PUT YOUR KEY HERE"}promptclient=PromptClient(headers=secretkey)Description continued:text="一个女孩穿着白色的衣服"response=""foroutputinpromptclient.fcDict(text=text):response+=outputprint(response)Convert Description to Prompt:text="一个女孩穿着白色的衣服"response=promptclient.descToPrompt(prompt=text)print(",".join(response))Prompt continued:text="1girl,long hair,white clothes"response=promptclient.promptContinu(text=text)print(",".join(response))More examples can be found inexamples.py.
Package: billogram_api
Billogram v2 API client libraryThis library provides convenient wrappers for connecting to the public API
of the Billogram invoicing service.Full documentation for the API: <https://billogram.com/api/documentation>The file examples.py contains several code examples for calling the API
using the library. Note that this file is not installed when using the
distutils installation.Copyright 2013 Billogram AB.
Made available under MIT license, see LICENSE file.
Package: billomat
Deprecation noticeThis library is unmaintained!
My employer stopped using billomat services. Therefore no fix or update will be done on this project anymore.
If you're interested in taking over maintenance, let me know.Python client for thebillomat.comAPIThe syntax of the client is inspired by the greatDjango ORM.Example usagefrombillomatimportmodelsfrombillomat.baseimportClientClient.api_name='apiname'Client.api_key='apikey'# set optional app-id and -secretClient.app_id='app_id'Client.app_secret='app_secret'articles=models.Article.objects.all()forarticleinarticles:printunicode(article.title)articles=articles.filter(title='Awesome')forarticleinarticles:printunicode(article.title)article=models.Article.objects.get(article_number=2)article.title=u'This is an awesome article'article.save()article=models.Article.objects.create(title=u'This is an awesome article',)article.delete()Django supportAutomatic client configuration by django config is supportedBILLOMAT_API_NAME='aaa'BILLOMAT_API_KEY='bbb'BILLOMAT_APP_ID='ccc'BILLOMAT_APP_SECRET='ddd'The client sends 3 django signals:billomatclient_request, send before the requestbillomatclient_response, send after the request containing the responsebillomatclient_error, send after a request error containing the exceptionEach signal sends a request_id which is a unique identifier (uuid4) for each request to track the status along the signal flow.LicenseMIT
Package: billy
billy is a suite of tools developed as a part ofOpen Statesthat provide a framework for scraping, storing, and sharing legislative information.Features:Scraper architecture for scraping bills, votes, legislators, committees, and events.Utility scripts for data cleanup and analysis.Bulk data export.Server that includes a data browser and API.Installationbilly is available on PyPI and can be installed viapip install billyPyPI package:https://pypi.python.org/pypi/billySource:https://github.com/openstates/billy/Documentation:http://billy.readthedocs.org/en/latest/
Package: billzio-api
billzio-apiAsynchronous Python wrapper forBillz.ioPublic API (v2)Installationpip install billzio-apiSupported Billz API methodsAuth loginGet Products listGet Categories listGet Shops listGet Currencies listGet Payment types listGet Brands listGet all ClientsCreate a new ClientUpdate a ClientCreate a new orderCreate a draft OrderAdd an Item (product) to the draft OrderAdd a Consumer to the draft OrderCreate an Order from the draft Order (make payment)Usage:frombillzio_apiimportBillzHandler,ShopsListFiltershandler=BillzHandler("<secret_key>")filters=ShopsListFilters(limit=1)shops=handler.get_shops(filters)print(shops.count)print(shops.shops)Asynchronousimportasyncio# for running synchronouslyfrombillzio_apiimportAsyncBillzHandler,ShopsListFilters...handler=AsyncBillzHandler("<secret_key>")filters=ShopsListFilters(limit=1)shops=asyncio.run(handler.get_shops(filters))print(shops.count)print(shops.shops)TODOUpload to PyPi as a python packageSynchronous handlerWrite unit testsCaching auth data with its expiration
Package: bilma
BilmaBert In Latin aMericABilma is a BERT implementation in tensorflow and trained on the Masked Language Model task under thehttps://sadit.github.io/regional-spanish-models-talk-2022/datasets.The regional models can be downloaded fromhttp://geo.ingeotec.mx/~lgruiz/regional-models-bilma/. You will also need to download the vocabulary file which is common to all the model and regions.The accuracy of the models trained on the MLM task for different regions are:We also fine tuned the models for emoticon prediction, the resulting accuracy is as follows:Pre-requisitesYou will need TensorFlow 2.4 or newer.Quick guideYou can see the demo notebooks for a quick guide on how to use the models.Clone this repository and then runbash download-emoji15-bilma.shto download the MX model. Then to load the model you can use the code:from bilma import bilma_model
vocab_file = "vocab_file_All.txt"
model_file = "bilma_small_MX_epoch-1_classification_epochs-13.h5"
model = bilma_model.load(model_file)
tokenizer = bilma_model.tokenizer(vocab_file=vocab_file,
max_length=280)Now you will need some text:texts = ["Tenemos tres dias sin internet ni senal de celular en el pueblo.",
         "Incomunicados en el siglo XXI tampoco hay servicio de telefonia fija",
         "Vamos a comer unos tacos",
         "Los del banco no dejan de llamarme"]
toks = tokenizer.tokenize(texts)With this, you are ready to use the modelp = model.predict(toks)
tokenizer.decode_emo(p[1])which produces the output:each emoji correspond to each entry intexts.
Package: biloba
As in Ginkgo (of which this module was inspired). Provides gevent primitives to
orchestrate different orthogonal servers and services together.Basic usage:from gevent import wsgi

import biloba


def hello_world(environ, start_response):
    start_response('200 OK', [('Content-Type', 'text/html')])

    return ['<b>Hello world!</b>\n']


class MyService(biloba.Service):
    def make_web_server(self):
        return wsgi.WSGIServer(
            ('localhost', 5000),
            hello_world
        )

    def do_start(self):
        self.web_server = self.make_web_server()

        self.spawn(self.web_server.serve_forever)

        # you can add more servers/services here


if __name__ == '__main__':
    my_service = MyService()

    # start is called by join

    try:
        my_service.join()
    except KeyboardInterrupt:
        pass

    # as is stopExtensionsBiloba supports extensions. Define your module asbiloba_foobarand importbiloba.ext.foobarwill work.
Package: bi-lstm-crf
A PyTorch implementation of the BI-LSTM-CRF model.Features:Compared withPyTorch BI-LSTM-CRF tutorial, following improvements are performed:Full support for mini-batch computationFull vectorized implementation. Specially, removing all loops in "score sentence" algorithm, which dramatically improve training performanceCUDA supportedVery simple APIs forCRF moduleSTART/STOP tags are automatically added in CRFA inner Linear Layer is included which transform from feature space to tag spaceSpecialized for NLP sequence tagging tasksEasy to train your own sequence tagging modelsMIT LicenseInstallationdependenciesPython 3PyTorchinstall$pipinstallbi-lstm-crfTrainingcorpusprepare your corpus in the specifiedstructure and formatthere is also a sample corpus inbi_lstm_crf/app/sample_corpustraining$python-mbi_lstm_crfcorpus_dir--model_dir"model_xxx"moreoptionsdetail of model_dirtraining curveimportpandasaspdimportmatplotlib.pyplotasplt# the training losses are saved in the model_dirdf=pd.read_csv(".../model_dir/loss.csv")df[["train_loss","val_loss"]].ffill().plot(grid=True)plt.show()Predictionfrombi_lstm_crf.appimportWordsTaggermodel=WordsTagger(model_dir="xxx")tags,sequences=model(["市领导到成都..."])# CHAR-based modelprint(tags)# [["B", "B", "I", "B", "B-LOC", "I-LOC", "I-LOC", "I-LOC", "I-LOC", "B", "I", "B", "I"]]print(sequences)# [['市', '领导', '到', ('成都', 'LOC'), ...]]# model([["市", "领导", "到", "成都", ...]])  # WORD-based modelCRF ModuleThe CRF module can be easily embeded into other models:frombi_lstm_crfimportCRF# a BERT-CRF model for sequence taggingclassBertCrf(nn.Module):def__init__(self,...):...self.bert=BERT(...)self.crf=CRF(in_features,num_tags)defloss(self,xs,tags):features,=self.bert(xs)masks=xs.gt(0)loss=self.crf.loss(features,tags,masks)returnlossdefforward(self,xs):features,=self.bert(xs)masks=xs.gt(0)scores,tag_seq=self.crf(features,masks)returnscores,tag_seqReferencesZhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv:1508.01991.PyTorch tutorialADVANCED: MAKING DYNAMIC DECISIONS AND THE BI-LSTM CRF
Package: bimage
bimagebimage is a simple Python CLI that lets you find and download images in bulk based on your query.InstallationInstall the repo:pipinstallbimageUsageTo get help with the commandline arguments use:bimage--helpDevelopmentClone the repository and install packages from the requirements.txt filepipinstall-rrequirements.txtMetaxobekme@gmail.comDistributed under the MIT license. SeeLICENSEfor more information.ContributingFork from the repository (https://github.com/xobek/bimage/fork)
Package: bimato
Bio Matrix Topology (BiMaTo) is a library containing all the biopolymer matrix topology analyses published by the Biological Physics Group, (BIP), Peter Debye Institute, University Leipzig, Germany.Documentation can be foundhere.How to installbimatouses Python3.8 and up. Installation is trivial:pip install bimatoExemplary analysis workflowThis is an exemplary workflow to analyze pore sizes of two different collagen scaffolds. The matrices have been fluorescently stained and 3D images were recorded using an LSM.Usually, we have for example different collagen scaffolds and want to compare structural parameters. For this, we would load several images, calculate their structural parameters and plot them. Below is an exemplary workflow for this:load each image in the LIF fileanalyze itextract meta-data such as collagen concentration from image nameconcatenate this data to global DataFrameplot comparison boxplotPore-SizeWe load a lif file with multiple samples per collagen concentration and analyze these in a loop:importpandasaspdfromreadlif.readerimportLifFileimportseabornassnsimportbimatolif_file=LifFile("/path/to/sample.lif")df_poresize=list()forlif_imageinlif_file.get_iter_image():data=bimato.utils.read_lif_image(lif_image)bw=bimato.get_binary(data)sampling={'x':1/lif_image.info["scale"][0],'y':1/lif_image.info["scale"][1],'z':1/lif_image.info["scale"][2]}df_tmp=bimato.get_pore_sizes(binary=bw,sampling=sampling)df_tmp['Concentration [g/l]']=lif_image.namedf_poresize.append(df_tmp)df_poresize=pd.concat(df_poresize)g=sns.catplot(data=df_poresize,kind='box',x='Concentration [g/l]',y='Diameter [µm],)g.set_ylabels("Pore-size [µm]")Resulting in the following plot:InhomogeneityWe load a lif file with multiple samples per collagen concentration and analyze these in a loop:importpandasaspdfromreadlif.readerimportLifFileimportseabornassnsimportbimatolif_file=LifFile("/path/to/sample.lif")df_inhomogeneity=list()forlif_imageinlif_file.get_iter_image():data=bimato.utils.read_lif_image(lif_image)bw=bimato.get_binary(data)sampling={'x':1/lif_image.info["scale"][0],'y':1/lif_image.info["scale"][1],'z':1/lif_image.info["scale"][2]}df_tmp=bimato.poresize.get_fragmented_poresizes(binary=bw,sampling=sampling,part_size_micron=30)df_tmp['Inhomogeneity']=bimato.poresize.calc_inhomogeneity(df_tmp)df_tmp['Concentration [g/l]']=lif_image.namedf_inhomogeneity.append(df_tmp)df_inhomogeneity=pd.concat(df_inhomogeneity)g=sns.catplot(data=df_poresize,kind='box',x='Concentration [g/l]',y='Inhomogeneity,)Resulting in the following plot:How to citeFischer T, Hayn A, Mierke CT (2019) Fast and reliable advanced two-step pore-size analysis of biomimetic 3D extracellular matrix scaffolds. Scientific Reports 9:8352.https://doi.org/10.1038/s41598-019-44764-5Hayn A, Fischer T, Mierke CT (2020) Inhomogeneities in 3D Collagen Matrices Impact Matrix Mechanics and Cancer Cell Migration. Front Cell Dev Biol 8:593879.https://doi.org/10.3389/fcell.2020.593879
Package: bimcvcovid19i
BIMCV COVID 19 interfaceThis repository provides a python interface for working withBIMCV COVID 19 datasetBefore use, please read thelicense agreementfor the use of the BIMCV-COVID19 Dataset.Installationpipinstallbimcvcovid19iSystem requirementsDatasetOriginalPreperedTotalBIMCV COVID19 positive cIter 12390G408G797GBIMCV COVID19 negative cIter 12469G480G948GBIMCV COVID19 positive cIter 1232.8T?T?T
Package: bimmm
bimmmThebimmmis a package to analyse MMM models and visualize themInstallationUse the package managerpipto install bimmm.pipinstallbimmm1. Usage Decomposition for logistic regressions over time# import modulesimportpandasaspdimportnumpyasnpfrombimmm.analyse_mmm_modelsimportmmm,s_curve,decayimportsslimportstatsmodels.formula.apiassmfimportplotly.ioaspiopio.renderers.default="browser"importwarningswarnings.filterwarnings("ignore")# Get some example data (you might need to run ssl._create_default_https_context = ssl._create_unverified_context:ssl._create_default_https_context=ssl._create_unverified_contexturl='https://raw.githubusercontent.com/SimonTeg/nlodatascience/master/sales_vs_media.csv'df_example=pd.read_csv(url)# make a dataset for the historical data with the sales, and one to forecast the salesdf_train=df_example.iloc[:26]df_forecast=df_example.iloc[26:]# Make your modelformule='sales ~ s_curve(decay(tv, 0.3), 3) + s_curve(decay(radio, 0.2), 7) + jackpot + jan + apr + dec + '\'sunday_near_drawing + event + competitor + consumer_trust'model=smf.ols(formula=formule,data=df_train)# mmm object makenanalyse_model=mmm(model=model,var_date=df_train.maand_jaar,df=df_train)# Actual vs fitanalyse_model.actual_vs_fit_graph().show()# Decompositieanalyse_model.decomposition_graph().show()analyse_model.decompositie_sum()# Kosten van de kanalen waarvan je de ROI wilt wetenmedia_dict={'tv':10,'radio':5}analyse_model.roi(media_dict,'sales')# Model resultsanalyse_model.model_characteristics()analyse_model.VIF()# Analyse for adding variablesanalyse_model.select_n_largest_outliers(5)analyse_model.check_variables_to_add()LicenseCopyright (c) 2023 Rumiko
Permission 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.
Package: bin2coe
bin2coebin2coe is, as its name suggests, a tool to convert binary files toCOEfiles
for initializing Xilinx FPGA block RAM.UsageBasic usage looks like this:bin2coe-iINPUT-wWIDTH-oOUTPUTAdditionally, you can specify depth manually (rather than having it be
inferred), specify a value to fill in empty words, and specify the radix. Seebin2coe --helpfor more information.Installationbin2coe can be installed fromPyPI:pipinstallbin2coeAfter running this command, thebin2coebinary should be available on your$PATH.LicenseCopyright (c) Anish Athalye. Released under the MIT License. SeeLICENSE.mdfor details.
Package: bin2fasta
Disclaimer: this is only a proof-of-concept (and a joke), please don't actually use this.bin2fastaStore any file as a fasta file!Installation$pipinstallbin2fastaUsage$bin2fasta--help
Usage:bin2fasta[OPTIONS]FILENAMEStoreanyfileasafastafile

Options:-D,--decodeEnableconversionfromFASTAtobinary.-o,--outputFILENAMEFiletowriteto.--helpShowthismessageandexit.Basic example:$filefoo.png
foo.png:PNGimagedata,618x257,8-bit/colorRGBA,non-interlaced
$bin2fasta-obar.fastafoo.png
319400it[00:00,683649.99it/s]$head-c50bar.fasta
>Sequence_master
AGTTGAGGCGCCTTACTGCCGAATTAGTTAAGA
$bin2fasta--decode-obaz.pngbar.fasta
159700it[00:00,455825.67it/s]$filebaz.png
baz.png:PNGimagedata,618x257,8-bit/colorRGBA,non-interlaced
$difffoo.pngbaz.png
$Note that you can easily chain multiple commands by piping their respective outputs and using-:$catfoo.png|xz|gpg-c|bin2fasta->bar.fasta
$catbar.fasta|bin2fasta-D-|gpg-d|xz--decompress>baz.png
$difffoo.pngbaz.png
$Poetry workflowOnly relevant fordevelopers:Run executable:$poetryrunbin2fastaPublish to PyPi:$poetry--buildpublish
Package: bin2hpm
bin2hpmThis is a tool that creates HPM.1 firmware update files to be used by a HPM.1 updater such asipmitool hpm upgrade.It can be used withMMC-STAMPbased AMC boards, such as theDAMC-FMC2ZUPandDAMC-FMC1Z7IO, to facilitate in-system-upgrade of various components (MMC, CPLDs, FPGAs) over IPMI.FeaturesConverts from binary file to firmware update file according to HPM.1 specCreates a sequence of two HPM.1 actions; firstPrepare component, thenUpload imageEmbeds metadata according to HPM.1 spec:IANA manufacturer / product IDTarget device / componentVersion informationAuxillary metadataParses Xilinx bitstream file (optional)Performs RLE compression (optional / DESY MMC proprietary)File modeBy default, Xilinx bitfile mode is determined from the input file name. If it ends on.bit, the bitfile mode is selected. Bitfile mode can also be forced (-b) or suppressed (-n) independent of file name.Usage$ bin2hpm --help
usage: bin2hpm [-h] [--version] [-o OUTFILE] [-v FILE_VERSION] [-a AUXILLARY] [-c COMPONENT] [-d DEVICE] [-m MANUFACTURER] [-p PRODUCT] [-r] [-s DESCRIPTION] [-b | -n] infile

HPM.1 update file converter

positional arguments:
  infile                Input file

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -o OUTFILE, --outfile OUTFILE
                        output file (derived from input file if not set)
  -v FILE_VERSION, --file-version FILE_VERSION
                        file version information (format major.minor, e.g. 1.2)
  -a AUXILLARY, --auxillary AUXILLARY
                        additional metadata, hex format, 4 bytes
  -c COMPONENT, --component COMPONENT
                        HPM component ID (default 1)
  -d DEVICE, --device DEVICE
                        HPM device ID (hex, default 0)
  -m MANUFACTURER, --manufacturer MANUFACTURER
                        IANA manufacturer ID (hex, 6 bytes max)
  -p PRODUCT, --product PRODUCT
                        IANA product ID (hex, 4 bytes max)
  -r, --compress        Enable RLE compression (requires DESY MMC)
  -s DESCRIPTION, --description DESCRIPTION
                        Additional description string (max. 21 chars)
  -b, --bitfile         Force bitfile mode
  -n, --binfile         Force binfile mode
Package: binah
Binah is a system for training multi-modal embeddings jointly. We develop
an image embedding and a text embedding where objects of similar abstract
meaning are near each other in a shared vector space. With this we are
able create image and video search using arbitrary language.ScreenshotInstallationpip install binahUsageBinah comes bundled with the etz command line tool.To completely set up the project, simply run:etz upThis command sets up the whole Binah system de novo. It takes days to
run since it downloads datasets and trains complicated deep learning
models.After “etz up” is finished you can start the image search web demo.
Simply run:etz run search
Package: binalyzer
BinalyzerBinalyzer's goal is to ease binary data handling. It uses templates to describe
binary data making it possible to access and modify individual data regions.
There's no need to write binary data parsers or manually seek through binary
streams anymore.Binalyzer supports the following use cases:Binary data handling for analysis or modificationBinary data generationBinary data transformationInstallationBinalyzer is published onPyPIand can be installed from there:~$pip3install--upgradebinalyzerIf you wish to installBinalyzerfor development purposes, refer tothe
developer's guide.DocumentationDocumentation is available frombinalyzer.readthedocs.io.Get in touchReport bugs, suggest features or view the source codeon GitHub.For contributions refer tothe contributor's guide.LicenseLicensed under the MIT license (LICENSE-MITorhttp://opensource.org/licenses/MIT).
Package: binalyzer-cli
Binalyzer Command Line Interface (CLI)This repository implements Binalyzer's CLI. It is part of the overallBinalyzerproject and comes with its installation.InstallationBinalyzer is published onPyPIand can be installed from there:~$pip3install--upgradebinalyzerIf you wish to installBinalyzerfor development purposes, refer tothe
developer's guide.DocumentationDocumentation is available frombinalyzer.readthedocs.io.Get in touchReport bugs, suggest features or view the source codeon GitHub.For contributions refer tothe contributor's guide.LicenseLicensed under the MIT license (LICENSE-MITorhttp://opensource.org/licenses/MIT).
Package: binalyzer-core
Binalyzer CoreThis repository implements Binalyzer's core features. Functionality that is not
central to Binalyzer's concept is added through its extension mechanism.Core Features:Template EngineTemplate ProvidersTemplate TransformationsData Binding SystemData ProvidersValue ProvidersExtension MechanismThe core features are part of the overallBinalyzerproject and come with its
installation.InstallationBinalyzer is published onPyPIand can be installed from there:~$pip3install--upgradebinalyzerIf you wish to installBinalyzerfor development purposes, refer tothe
developer's guide.DocumentationDocumentation is available frombinalyzer.readthedocs.io.Get in touchReport bugs, suggest features or view the source codeon GitHub.For contributions refer tothe contributor's guide.LicenseLicensed under the MIT license (LICENSE-MITorhttp://opensource.org/licenses/MIT).
Package: binalyzer-data-provider
Binalyzer Data ProviderThis repository implements data providers. It is part of the overallBinalyzerproject and comes with its installation.InstallationBinalyzer is published onPyPIand can be installed from there:~$pip3install--upgradebinalyzerIf you wish to installBinalyzerfor development purposes, refer tothe
developer's guide.DocumentationDocumentation is available frombinalyzer.readthedocs.io.Get in touchReport bugs, suggest features or view the source codeon GitHub.For contributions refer tothe contributor's guide.LicenseLicensed under the MIT license (LICENSE-MITorhttp://opensource.org/licenses/MIT).
Package: binalyzer-patch
Binalyzer Patch ExtensionThis repository provides the patch extension for [Binalyzer]. It implements
a mechanism for patching binary files.InstallationThe Binalyzer patch extension is published onPyPIand can
be installed from there:~$ pip3 install binalyzer-patchGet in touchReport bugs, suggest features or view the source codeon GitHub.For contributions refer tothe contributor's guide.LicenseLicensed under the MIT license (LICENSE-MITorhttp://opensource.org/licenses/MIT).