Saravanan666/pypi-org huggingface dataset:
Package: ebel-rest
e(BE:L) RESTful API Clientis a client for the easy usage of API methods provided be an e(BE:L) knowledge server.
e(BE:L) is currently in the alpha-testing phase and will be released soon!Installation$pipinstallebel_restQuick StartConnect to the database using the credentials.fromebel_restimportconnect,query# Database settingsserver="https://graphstore.scai.fraunhofer.de"db_name="covid"user="guest"password="guest"print_url=True# Connect to databaseconnect(user,password,server,db_name,print_url)Start making queriespubs=statistics.publication_by_year()pubs.dataUsageThis API interface package is designed to simplify communicating with an e(BE:L) generated knowledge graph. Examples
of how to use this package and its methods can be found inExamples notebook.DisclaimerThe COVID-19 Knowledge Graph is a resource developed in an academic capacity funded by thePHAGO projectand thus comes with no warranty or guarantee of maintenance or support.
Package: ebemse
ebemse |Entropy-Based Ensemble Members SElectionebemseis a Python library for the selection of a set of mutually exclusive, collectivelly exaustive (MECE) ensemble members.The library implements the approach presented byDarbandsari and Coulibaly (2021)as step that antecedes the further merging of a set of ensemble forecasts.InstallingThe library can be installed using the traditional pip:pip install ebemseAnd is listed on the Python Package Index (pypi) at.UsingSuppose you have a file namedexample.csvwith the following content:Date,       Memb_A, Memb_B, ...,  Memb_Z, Obsv
2020/05/15, 1.12,   1.05,   ...,  0.5,    1.01
2020/05/16, 1.15,   1.12,   ...,  0.9,    1.10
2020/05/17, 1.13,   1.32,   ...,  1.1,    1.29
...         ...     ...     ...,  ...,    ...
2020/11/30, 1.22,   0.95,   ...,  0.3,    0.87In which the columns starting with "Memb_" hold the realization of one ensemble member for the time interval and "Obsv" holds the observed values for the same time interval.If your our objective is to select a MECE set considering obaservations, it can be done using the standard parameters by:import pandas as pd
import ebemse

# read file
data_ensemble = pd.read_csv("example.csv").to_dict('list')
data_obsv = data_ensemble["Obsv"]
del data_ensemble["Obsv"], data_ensemble["Date"]

# perform selection
selected_members = ebemse.select_ensemble_members(data_ensemble, data_obsv)The variableselected_memberswill be a dictionary with the following keys and values:history: dictionary with the following additional information related with the selection process:total_correlation: list of floatsjoint_entropy: list of floatstransinformation: list of floats orNoneselected_members: list of string with the labels of the selected elementsoriginal_ensemble_joint_entropy: floatFurther informationselect_ensemble_members()Arguments:all_ensemble_members: dictobservations: Union[list, tuple, np.array, None] (default:None)n_bins: Union[int, None] (default:10)bin_by: str (default:"quantile_individual")beta_threshold: float (default:0.9)n_processes: int (default:1)minimum_n_members: int (default:2)verbose: bool (default:False)
Package: ebest
ebest PackageThis is a simple package for eBEST OpenApi.InstallationpipinstallebestUsage모든 요청은 비동기로 처리되며, 요청에 대한 응답은 await 키워드를 사용하여 받을 수 있습니다.로그인 요청은 반드시 먼저 수행되어야 하며, 로그인이 성공하면 다른 요청을 수행할 수 있습니다.importasyncioimportebestfromapp_keysimportappkey,appsecretkey# app_keys.py 파일에 appkey, appsecretkey 변수를 정의하고 사용하세요asyncdefmain():api=ebest.OpenApi()ifnotawaitapi.login(appkey,appsecretkey):returnprint(f"연결실패:{api.last_message}")print("연결성공, 접속서버: "+"모의투자"ifapi.is_simulationelse"실투자")...# 다른 작업 수행awaitapi.close()asyncio.run(main())조회/연속 요청# 주식 전종목 조회request={"t8436InBlock":{"gubun":"0",# 구분(0: 전체, 1: 코스피, 2: 코스닥)}}response=awaitapi.request("t8436",request)# 주식 종목별 시세 조회request={"t1102InBlock":{"shcode":"005930",# 삼성전자}}response=awaitapi.request("t1102",request)# 주식 차트 조회request={"t8410InBlock":{"shcode":"005930",# 삼성전자"gubun":"2",# 주기구분(2:일3:주4:월5:년)"qrycnt":100,# 요청건수(최대-압축:2000비압축:500)"sdate":"",# 시작일자"edate":"99999999",# 종료일자"cts_date":"",# 연속일자"comp_yn":"N",# 압축여부(Y:압축N:비압축)"sujung":"Y",# 수정주가여부(Y:적용N:비적용)}}response=awaitapi.request("t8410",request)# 연속조회ifresponse.tr_cont=="Y":awaitasyncio.sleep(1)# 1초 대기request["t8410InBlock"]["cts_date"]=response.body["t8410OutBlock"]["cts_date"]response=awaitapi.request("t8410",request,tr_cont=response.tr_cont,tr_cont_key=response.tr_cont_key)조건검색 리스트 조회/실시간 요청api.on_realtime=on_realtime# 실시간 이벤트 핸들러 등록# 서버저장조건 리스트조회request={"t1866InBlock":{"user_id":"abcdefgh",# 사용자ID 8자리"gb":"0",# 0 : 그룹+조건리스트 조회, 1 : 그룹리스트조회, 2 : 그룹명에 속한 조건리스트조회"group_name":"","cont":"0","cont_key":"",}}response=awaitapi.request("t1866",request)# 서버저장조건 실시간검색request={"t1860InBlock":{"sSysUserFlag":"U",# 'U' 고정"sFlag":"E",# 'E:'등록, 'D':중지"sAlertNum":"",# Flag 값 'D':중지 일떄만 입력 - 등록 요청 시 수신받은 t1860OutBlock.sAlertNum 값"query_index":"0",# t1866 TR에서 조회한 t1866OutBlock1.query_index}}response=awaitapi.request("t1860",request)sAlertNum:str=response.body["t1860OutBlock"]["sAlertNum"]ifsAlertNum=="":print("실시간검색 등록실패")else:print("실시간검색 등록성공")awaitapi.add_realtime("AFR",sAlertNum)# 실시간검색 등록awaitasyncio.sleep(3600)# 1시간동안 유효, 후에 중지awaitapi.remove_realtime("AFR",sAlertNum)# 실시간검색 중지defon_realtime(api:ebest.OpenApi,trcode,key,realtimedata):iftrcode=="AFR":print(f"실시간조건검색 received:{trcode},{key},{realtimedata}")웹소켓 실시간 요청/응답api.on_realtime=on_realtime# 실시간 이벤트 핸들러 등록# 삼성전자 주식 실시간 시세 요청awaitapi.add_realtime("S3_","005930")# 삼성전자 주식 실시간 시세 요청# 60초후 삼성전자 주식 실시간 시세 중지awaitasyncio.sleep(60)awaitapi.remove_realtime("S3_","005930")# 삼성전자 주식 실시간 시세 중지defon_realtime(api:ebest.OpenApi,trcode,key,realtimedata):iftrcode=="S3_":print(f"체결시세 received:{trcode},{key},{realtimedata}")
Package: ebfpy
EBF is a binary format for storing data. It is designed to
read and write data, easily and efficiently.Store multiple data items in one file, each having a unique tag name
+ tagnames follow the convention of unix style pathname e.g. /x or /mydata/xAutomatic type and endian conversionSupport for multiple programming languages
+ data can easily read in C, C++, Fortran, Java, IDL and Matlab
+ facilitates easy distribution of dataComprehensive numpy support
+ data is read back as numpy arrays
+ almost any numpy array can be written
+ Nested numpy structures are also supportedRead and write directly a recursive dictionary of numpy arraysInternally uses hashtable for fast retireival of data items.
+ also allows for overwrite prevention
Package: ebg
EBG: Educated Bootstrap GuesserDocumentation:https://github.com/wiegertj/EBG/wikiDescriptionEBG is a Python tool for predicting the Felsenstein Bootstrap Support of phylogenies inferred by RAxML-NG.
It was trained on empirical datasets from TreeBASE and can use both AA and DNA data.InstallationUsing condaThe latest version of EBG can easily be installed via conda:conda install ebg -c conda-forgeUsing pippip install ebgUsage ExampleA simple command line call of EBG looks like this:ebg -msa /test/example.fasta -tree /test/example.bestTree -model /test/example.bestModel -t b -o testThis command will use the MSA in fasta format, and the best tree inferred with RAxML-NG and the model.
By selecting-t b(oth) EBG will output the bootstrap predictions as well as the probabilities for exceeding different bootstrap thresholds (70, 75, 80, 85).
The results will be stored in a folder called test.Please keep in mind that EBG requires an installation of RAxML-NG. By default, it uses the commandraxml-ng.
If your RAxML-NG installation is not part of the PATH variable, you can specify the path to the RAxML-NG binary file with the parameter-raxmlng PATH_TO_RAXMLNG.ReferencesA. M. Kozlov, D. Darriba, T. Flouri, B. Morel, and A. Stamatakis (2019)RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inferenceBioinformatics, 35(21): 4453–4455.https://doi.org/10.1093/bioinformatics/btz305
Package: ebike-dl
ebike-downloaderBosch eBike Connect Activity Downloader (https://www.ebike-connect.com/dashboard)
Fetch activities by dates and download a copy of the rides.Installebike-downloader usespoetry.You can easily run it usingpipxinstallebike-dlRunebike-dl fetch --since 2022-06-15 --out-dir out --login=foo --password=barwhere login passwords are from the ebike-connect.com portal.You'll then get the downloaded files (1per ride) in the folder ./out
(you can also use env variable to provide login, password, ... see --help)To GPXYou have the ability to download GPX on Bosch's portal, but those do not contain any additional info like power, cadence or heart_rate.
by usingto-gpxyou can get a gpx with all those things (in gpx extension it might not be supported by the average gpx reader...)ebike-dl to-gpx --file out/myid.json
# output out/myid.pgxUsed extensions:gpxtpx http://www.garmin.com/xmlschemas/TrackPointExtension/v2
pwr http://www.garmin.com/xmlschemas/PowerExtension/v1To KML 2.2there is also the ability to transform those to KML (ofc some info will be lost)ebike-dl to-kml --file out/myid.json
# output out/myid.kmlAcknowledgementsCycliste UrbainAuthors@eMerzh
Package: ebi-ols-client
OLS Ontologies loaderProvide class based client for accessing OLS RestAPI from EMBL-EBIInstallwith pippipinstallebi-ols-clientSee:https://www.ebi.ac.uk/ols/docs/apifor more information on OLS APIMain files- ebi.ols.api.client.py: contains main classes to access OLS via HAL schema.
- ebi.ols.api.helpers.py: data tranfer object loaded from API callsHow to usefromebi.ols.api.clientimportOlsClientclient=OlsClient()ontology=client.ontology('fpo')terms=ontology.terms()individuals=ontology.individuals()properties=ontology.properties()# work with all 'list' item types (terms, individuals, propertiesforterminterms:# do whateverprint(term)# Direct List'like access on all list typesterm=terms[1254]individual=individuals[123]# ...ContributePlease do ! If you need to run tests locally, add to your virtual env requirements-dev.txt content.Nonepipinstall-rrequirements.txtpipinstall-rrequirements-dev.txt# to run testsnosetests--withcoverage
Package: ebird-import
ebird-import is a suite of utilities for converting records exported from
databases containing bird observations so they can easily be imported into
eBird.Tools availableebird-convert - Convert records in CSV format into eBirds Checklist Record Format.Supported DatabasesRecords have been successfully imported into eBird from the following databasesPortugalAvesBirdLife International’s database for PortugalKuşbankBirdLife International’s database for TurkeyConverting records from other Birdlife International databases hosted onhttp://www.worldbirds.orgwill probably work.Installpip install ebird-importUsageSee the descriptions for each utility in the docs directory.LicenceeBird Import is available under the terms of the [GPL](https://www.gnu.org/licenses/gpl-3.0.en.html) licence.
Package: ebird-pages
[![Build Status](https://travis-ci.org/ProjectBabbler/ebird-pages.svg?branch=master)](https://travis-ci.org/ProjectBabbler/ebird-pages)[![PyPI version](https://badge.fury.io/py/ebird-pages.svg)](https://badge.fury.io/py/ebird-pages)[![Supported Python Versions](https://img.shields.io/pypi/pyversions/ebird-pages.svg)](https://img.shields.io/pypi/pyversions/ebird-pages)# eBird PagesAlthough eBird has an API, not all the information from the database isavailable. The API, for example, does not return links to any uploadedphotos; comments on an individual observation are also missing. eBirdPages is a set of scrapers for extracting data from various pages onthe eBird web site. It complements the API, giving access to all thedata that eBird makes publicly available.## Install```shpip install ebird-pages```## UsageScraping the data from a page is as simple as a function call. For exampleto get all the data from a checklist use get_checklist() and pass in the uniqueidentifier generated when the checklist was submitted to the eBird database:```pythonfrom ebird.pages import get_checklistdata = get_checklist('S38429565')```The function returns a dict with keys for the location, date, observers, etc.### Command lineEach of the functions has a corresponding script that can be used on thecommand-line:```sh$ get-checklist --id S38429565 --out checklist.json```The script allows data for one or more checklists to be downloaded and writtento a file in JSON format.## Compatibilityebird-pages works with Python 3.3+.## DependencieseBird Pages makes use of the following packages: Requests, BeautifulSoup4, lxml and Click.See requirements.txt for the version numbers of each of the libraries.## LicenseeBird Pages is available under the terms of the [MIT](https://opensource.org/licenses/MIT) license.
Package: ebj2
Embedded Binary JSONWhat is EBJ?EBJ is a format that focuses on storing binary json use RIGHT size.Byte OrderEBJ always use little-endian. It is more friendly for morden CPU to parse.Type Codes0x00 int8
0x01 int16
0x02 int32
0x03 int64
0x04 float32
0x05 float64
0x06 str8 (uint8 length)
0x07 str32 (uint32 length)
0x08 ref8 (str only)
0x09 ref32 (str only)
0x0A true
0x0B false
0x0C null
0x0D array
0x0E end (array, object)
0x0F object
0x10 0
0x11 1
0x12 2
...
0xFF 239EBJ strXX should be encoded as UTF-8 bytes.How to use EBJ?This repo contains a base Python version EBJ implementation calledebj2. The
following lines show how to useebj2to dump and load EBJ bytes.Install:pipinstallebj2Usage:# Load moduleimportebj2# Dump EBJbyts=ebj2.dumps(['ebj2 is awesome!'])# Load EBJmsgs=ebj2.loads(byts)# Dump to fpebj2.dump([],fp)# Load from fpebj2.load(fp)Why should i use EBJ?At least EBJ will save your 33% disk space to store json. Also it is very fast.
(ebj2maybe very slow, because it use pure-python, but still usable)
Package: ebltable
Python packages to read in and interpolate tables for the photon density
of the Extragalactic Background Light (EBL) and the resulting opacity
for high energy gamma rays.PrerequisitesPython 3.5 or higher and the following packages:numpyscipyastropyInstallationYou can use pip to install the package:pip install ebltableExample scripts and notebooks are provided on the github page in the
example/ and notebooks/ folder,https://github.com/me-manu/ebltableLicenseebltable is distributed under the modified BSD License.
Package: ebm2onnx
Ebm2onnx convertsEBMmodels to
ONNX. It allows to run an EBM model on any ONNX compliant runtime.FeaturesBinary classificationRegressionContinuous, nominal, and ordinal variablesN-way interactionsMulti-class classification (support is still experimental in EBM)Expose predictions probabilitiesExpose local explanationsThe export of the models is tested againstONNX Runtime.Get StartedTrain an EBM model:# prepare datasetdf=pd.read_csv('titanic_train.csv')df=df.dropna()feature_columns=['Age','Fare','Pclass','Embarked']label_column="Survived"y=df[[label_column]]le=LabelEncoder()y_enc=le.fit_transform(y)x=df[feature_columns]x_train,x_test,y_train,y_test=train_test_split(x,y_enc)# train an EBM modelmodel=ExplainableBoostingClassifier(feature_types=['continuous','continuous','continuous','nominal'],)model.fit(x_train,y_train)Then you can convert it to ONNX in a single function call:importonnximportebm2onnxonnx_model=ebm2onnx.to_onnx(model,ebm2onnx.get_dtype_from_pandas(x_train),)onnx.save_model(onnx_model,'ebm_model.onnx')If your dataset is not a pandas dataframe, you can provide the features’ types
directly:importebm2onnxonnx_model=ebm2onnx.to_onnx(model,dtype={'Age':'double','Fare':'double','Pclass':'int','Embarked':'str',})onnx.save_model(onnx_model,'ebm_model.onnx')Try it liveYou can live test themodel conversion.You can live testlocal explanations.Supporting organizationsThe following organizations are supporting Ebm2onnx:SoftAtHome: Main supporter of Ebm2onnx development.InterpretML: Ebm2onnx is hosted under the umbrella of the InterpretML organization.
Package: ebmdatalab-python
Useful, common utilities for working with bigquery.This is currently a moving target and subject to fast, breaking
changes… in particular, our dataset and project names are hard-coded
here.Development notesTo run tests and check packaging, run tox:$ toxNote that the functional tests expectGOOGLE_APPLICATION_CREDENTIALSto be set in your environment.To push to PyPI:$ python setup.py sdist upload -r pypi
Package: ebnf
ebnf toolsA collection of tools to work with ebnf grammars, like conversion between different formats, railroad generation and random example generation.Getting StartedPrerequisitesRequires python3.9 or higher.Other requirements likelarkandrailroad-diagramsshould be installed automatically by pipInstallingpip install ebnfIf you want to use one of the extra utils with extra requirements, use theebnf[<extra>]syntax:railroaddiagram generation:pip install ebnf[railroad]ToolsThe 'base' ebnf dialect for this package the one used by thelarkpackage. All features should work for it, but might not work for others.ebnf.railroad- railroad diagram generationGenerates a html file containing svg for each rule/terminal in the input grammar. Can take a lot of different options, check the help for more infoebnf.convert- dialect translation (TODO)Attempts to convert between different dialects, with the focus being generationlarkcompatible outputebnf.example- generates an example for a grammar (TODO)VersioningWe useSemVerfor versioning. For the versions available, see thetags on this repository.AuthorsMegaIng-Initial workSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: eboekhoudennl-api
eboekhoudene-boekhouden.nl APIthis package is heavily inspired by the existing package "eboekhoudenapi", written by Roel van den Boom."get sessioncode" and "get mutaties" are, with slight adaptions, take from this package.This code can be pulled from pypi.org by installing the package "eboekhoudennl_api"pip install eboekhoudennl_apiexample code how to use the package is shown belowfrom eboekhoudennl_api import eboekimport logging# to swith of Soap logging messages: "Forcing soap:address location to HTTPS"logging.getLogger('zeep.wsdl.bindings.soap').setLevel(logging.ERROR)(where username is your "Gebruikersnaam" with e-boekhouden.nl. SecurityCode1 and SecurityCode2 are the Beveiligingscode 1 and -2 you can enter here as a string)Username = "myusername"SecurityCode1 = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"SecurityCode2 = "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXXX"generate the session codesessie1 = eboek.Eboek(Username, SecurityCode1, SecurityCode2)print(sessie1.session_id)use this session code to fetch dataget saldisaldi_fin = eboek.Saldi(0,"2022-01-01","2022-03-31","VW")saldi = sessie1.get_saldi(saldi_fin)print(saldi)get saldosaldo_fin = eboek.Saldo("1020",0,"2001-01-01", "2022-08-19")saldo = sessie1.get_saldo(saldo_fin)print(saldo)get grootboekrekeningenlijst_grootboekrekeningen = eboek.Grootboekrekeningen(0,"","VW")grootboekrekeningen = sessie1.get_grootboekrekeningen(lijst_grootboekrekeningen)print(grootboekrekeningen)get relatieslijst_relaties = eboek.Relaties("","",0)relaties = sessie1.get_relaties(lijst_relaties)print(relaties)get open postenlijst_openposten = sessie1.get_openposten("Crediteuren")print(lijst_openposten)get mutatieslijst_mutaties = eboek.Mutaties(0,0,99999,"", "2022-01-01","2022-03-31")mutaties = sessie1.get_mutaties(lijst_mutaties)print(mutaties)get kostenplaatsenlijst_kostenplaatsen = eboek.Kostenplaatsen(0,0,"")kostenpl = sessie1.get_kostenplaatsen(lijst_kostenplaatsen)print(kostenpl)get facturenlijst_facturen = eboek.Facturen("","","2022-01-01", "2022-03-31")fact = sessie1.get_facturen(lijst_facturen)print(fact)get administratiesadministratie = sessie1.get_administraties()print(administratie)adding transactions and master dataMaster dataadd relatierel = eboek.Relatie(0,"2022-03-30","Test","Testbedrijf","","","Straat","Postcode","Plaats","Land","Straat2","Postcode2","Plaats2","Land2", "Tel", "Gsm","","info@raylan.nl","","", "", "", "Dhr", "", "", "B","0", 0, 0, 0)nieuwerelatie = sessie1.add_relatie(rel)print(nieuwerelatie)add grootboekrekeninggrb = eboek.Grootboekrekening(0,"100T","Test rekening","BAL","")nieuwerekening = sessie1.add_grootboekrekening(grb)print(nieuwerekening)Transactionsadd mutatiemut_nummer = sessie1.add_mutatie(eboek.Mutatie("Memoriaal","2021-03-24","1000","","","Test","","IN", [eboek.MutatieRegel("1000", "1000", "0", "1000", "0", "0", "0140", 0 )]))
Package: eboekhouden-python
E-boekhouden-Python API clientThis is a simple API client for the E-boekhouden.nl API. It is written in Python and uses the ZEEP library. This library is partly inspired by work of Roel van den Boom.Installation and basic configurationTo install the library, simply run the following command:pipinstalleboekhouden-pythonTo use the library, you need to configure it with your API credentials. While you can add these credentials as parameters to the class constructor, it is recommended to use environment variables. The following environment variables are automatically recognized by the library:EBOEKHOUDEN_USERNAME=your_usernameEBOEKHOUDEN_CODE1=your_code_1EBOEKHOUDEN_CODE2=your_code_2UsageThe library is very simple to use. The following example shows how to retrieve a list of all mutations:fromeboekhouden_pythonimportEboekhoudenClientclient=EboekhoudenClient()mutations=client.get_mutaties()DevelopmentFeel free to extend the library, pull requests are welcome. For a development environment, you can use the following commands:condaenvcreate-fenvironment.yml
poetryinstall
pre-commitinstall
Package: ebofcis2
Biblioteca de Python para Integración Monte Carlo y Ajuste de CurvaEsta biblioteca proporciona dos clases para realizar dos tareas diferentes:Integral_montecarlo: Una clase que permite integrar funciones definidas en Sympy utilizando el método Monte Carlo. Puedes comparar los resultados con soluciones analíticas y realizar análisis de precisión.ajuste_curva: Una clase que te permite suavizar la curva de datos contenidos en un archivo CSV con columnas 'nuevos_casos' y 'fecha_actualizacion'. Esto es útil para analizar y visualizar tendencias en datos de casos.
Package: ebookconverter
# ebookconverter
code that orchestrates ebook conversion for project gutenbergEbookConverter manages the creation and update of ebook assets for Project Gutenberg. It uses a postgres database to keep track of both ebook metadata and ebook files. the postgress database is managed by the libgutenberg package.The cron-rebuild-files.sh script runs as a cron job, rebuilding 2100 books per day, so as to rebuild every book roughly once a month.ebookconverter talks to the gutenberg database to build a list of ebookmaker jobs. These jobs require some metadata about the book, and a target file to process.ebookconverter expects source files to be in numbered directories in a ‘files’ directory. The location of the files directory is given by the FILESDIR config parameter.Config parameters should be set in a file at /etc/ebookconverter.conf or ~/.ebookconverterebookconverter has been tested on Python 3.6.7. It’s not expected to run on python 2.7## Installingpipenv install ebookconverterThe following directories should exist:$PRIVATE/logs$PRIVATE/logs/json$PRIVATE/logs/json/backup$PRIVATE/logs/notifications$PRIVATE/logs/dopush$PRIVATE/logs/dopush/backup## Using the EbookConverter Scriptsyou can run these commands either by first entering apipenv shellor on a single line usingpipenv run <command> <args>Rebuild one or more booksebookconverter –range=<start>-<finish> –build=allebookconverter –range=<booknumber>  –build=allebookconverter –range=<booknumber>  –build=all –validateReload metadata from a workflow json file (use with care, it will overwrite any metadata in the DB)reload_workflow <booknumber>Regenerate the csv filemake_csvLook for any ebooks with changed files in the last X days and then check if any of the previously known files of that ebook have been deleted.autodelete
Package: ebook-converter-noimage
ebook-converterThis is a published wheel for the 3-year-unmaintainedebook-converterproject, developed by gryf.I (152334H) am merely publishing this wheel to PyPI so that I can use it in my own projects. It has a very slight modification: a hardcoded statement to ignore all images in all files. I was not responsible for 99.99% of the code present in the package.