Saravanan666/pypi-org huggingface dataset:
Package: hisparc-sapphire
IntroductionSAPPHiRE is a Simulation and Analysis Program Package forHiSPARCResearch and Education.  It was created in the
process of completing the PhD research of David Fokkema.  The history of this
repository contains the complete simulation, analysis and plot generation code
that formed the basis for David’sthesis.
Arne de Laat took over development of SAPPHiRE while working on his own PhD
research.This repository is created with a sole purpose in mind: to enable HiSPARC
students, teachers and researchers to easily gain access to the data and
perform common simulation and analysis tasks.  Historically, starting work
on the data, or extending an existing analysis code, has involved
elaborate installation instructions, heavy customizations to the software,
countless hours going over opaque parts of code and a general feeling of
anguish and despair.  SAPPHiRE’s ultimate goal: no more of that.InstallationRequired: Python. pip will take care of dependencies, but installing
numpy, scipy and pytables from a python distribution is preferred. We use
miniconda, which includes the conda package manager.First,install condaand optionally create a virtualenv:$ conda create --name hisparc python numpy scipy pytables
$ source activate hisparcor alternatively just install the dependencies:$ conda install numpy scipy pytables sphinxThen, using pip:$ pip install hisparc-sapphireThis should install sapphire with all requirements. More extensive
installation instructions are available in the documentation in thedoc/directory.  You can compile them using Sphinx, or you can
follow this link:https://docs.hisparc.nl/sapphire/.To check if it worked start Python and load the package:importsapphireYou’re done!DevelopmentInstall python (preferably using conda) as described above but clone
the sapphire repo instead of installing using pip:$ git clone https://github.com/HiSPARC/sapphire.git
$ cd sapphire
$ python setup.py developVersion releaseImportant: First check if the last commit passes the tests on GitHub Actions!To release a new version modify the version number insetup.py. Then
create a commit for the new release with a title like ‘Bump version to vX.Y.Z’
and a message that contains a summary of the most important changes since the
last release. Then tag the commit and push it to GitHub:$ git tag vX.Y.Z
$ git push --tagsThen upload the new version to PyPI (this requires thewheelandtwinepackages):$ python setup.py sdist bdist_wheel
$ twine upload dist/hisparc-sapphire-X.Y.Z.tar.gz
$ twine upload dist/hisparc_sapphire-X.Y.Z-py2.py3-none-any.whlThe latest version is then available from PyPI.
Package: hi-spike
GPU 自动选择、排队gpu_queue(config: Union[DictConfig, dict] = {})：设置处理器支持自动选择和手动选择、排队和不排队

Args:
    config (dict, optional): Defaults to {}.
        queuing: 是否排队，依赖 Redis，默认为 True
        visible_devices: 可见的 GPU 序号，用逗号分隔，默认为 -1, 表示全部可见
        memo: 备注
Returns:
    Union[DictConfig, dict]: config
Package: hisql
简单修改pugsql使其支持直接执行sql。使用fromhisqlimport*fromdatetimeimportdatetimeq=hisql()q.connect('mysql+pymysql://bbq:bbq@localhost/bbq')# 选择9.1号前10个接近10块股票res=q.execute('select a.code as code, b.name as name, a.close as close ''from stock_daily a left join stock_info b on a.code=b.code ''where a.trade_date=:trade_date and a.close < 10 order by close desc limit 10',Many(),trade_date=datetime(year=2021,month=9,day=1))data=[dfordinres]data结果[{'code':'sz002820','name':'桂发祥','close':Decimal('9.9900')},{'code':'sz002556','name':'辉隆股份','close':Decimal('9.9700')},{'code':'sz002322','name':'理工环科','close':Decimal('9.9700')},{'code':'sz000920','name':'南方汇通','close':Decimal('9.9600')},{'code':'sz002545','name':'东方铁塔','close':Decimal('9.9600')},{'code':'sz002926','name':'华西证券','close':Decimal('9.9600')},{'code':'sh603056','name':'德邦股份','close':Decimal('9.9600')},{'code':'sz300434','name':'金石亚药','close':Decimal('9.9500')},{'code':'sz300437','name':'清水源','close':Decimal('9.9400')},{'code':'sz300030','name':'阳普医疗','close':Decimal('9.9300')}]
Package: hissbytenotation
Hiss Byte NotationLibrary to make it easy to use python literal syntax as a data formatHave you seen people try to print a dict and then use the JSON library to parse the output? This library is some
helper function for that scenario. It is a small wrapper around ast.literal_eval and will have a API similar to other
serializer/deserializers such as json, pickle, pyyaml, etc.Safetyast.literal_evalis safer thanevalbut the python docs still imply that there are malicious payloads. I'm not
sure if they are the same problems that could affect json or other formats.Usageimporthissbytenotationashbndata={"mammal":"cat","reptile":["snake","lizard"],"version":1}data_as_string=hbn.dumps(data)rehydrated=hbn.loads(data_as_string)print(rehydrated)# {'mammal': 'cat', 'reptile': ['snake', 'lizard'], 'version': 1}How it worksSerialization is done by calling repr, checking if ast.literal_eval can read it. Repr can be called on more data
structures than ast.literal_eval can handle.Because ast.literal_eval is so slow, there are other options for deserialization:default: ast.literal_eval with validation enabled. Very slow, very safe.eval. Slow, only for trusted data.exec. Slow, only for trusted data.import. Two times faster than exec, only for trusted data.Serialization and deserialization times for 10,000 dumps/loadsPickle:  0.89 seconds
JSON: 2.00 seconds
HBN (no validation): 20.80 seconds
HBN (validation): 40.57 seconds
HBN (unsafe): 15.95 seconds
HBN (exec): 18.08 seconds
HBN (by import): 12.26 secondsPrior artliteral-python-to-pickleA faster replacement for
ast.literal_eval and
correspondingquestion on stackoverflow.You could just callreprandast.literal_evaldirectly.Possiblyastorwhich serializes to a string representation of the AST, which looks
nothing like the source code, nor json.
Package: hiss-cli
What is the hiss?hissis simple cli tool to control the database schema. If you care
a stable enterprise service, You need to know hiss cli.If you designed the schema of development environment database, and you
want to move it to a production server.It would be difficult and unstable. hiss can help to migrate for
deployment and also help to ETL for another database engine.Installationhiss can be installedpypi.$pipinstallhiss-cliFeatures (Plan)Supporting common databases (MySQL, MSSQL, PostgreSQL, MariaDB,
Oracle)Controlling revision history of changes the schema.Commit, Rollback, Push, Reset, Tag, List, Show (The simple command
designs based onGit.ETL to another database and analyze (To another common databases or
distribute engines: Athena, Presto, Impala, Druid, Hadoop system, or
JSON, XML, CSV)Design (Plan)$hissinit$hissremoteaddorigindatabase.endpoint.com:3306>connecting...done.>database`MySQL`,v5.7.11>Typeusername:{username}>Typepassword:{password}$hissshowdatabases>coutingdatabses:32.>databse_1_schema>databse_2_schema>databse_3_schema>more...$hisscheckoutdatabase_1_schema>switchedtodatabase`database_1_schema`.$hissshowtables>countingtables:160.>information_schema>table_1_sample>table_2_sample>table_3_sample$hisssettablestable_1_sample>nowyourcurrenttableis`table_1_sample`.$hissaddcolumnnew_columnint(64)pkauto_incrementdefault0notnull-m'column comment'>addcolumn`new_column`,done.$hissstatus>countingchanges:1,done.>addcolumn'new_column'int(64)primaryauto_incrementdefault0notnull>:columncomment>end$hisscommit-m'add new column for testing'.>1changesarecommitted,1add(+),0remove(-),0modified(=).$hissresetHEAD^--hard>reset...done.>:firstcommitmessage.$hisstag-av1.0-m'marking for first commition.'>done.NoticeThis repository is still progress under development.If you want to use the demo version, Please send a message via gitter
(Check the above badge).Thank you.TODO[] Continous Integration & Continous Deployment[] Add UnitTest[] Add commands: init, show, checkout[] Integration with MySQL[] Branding: LOGO
Package: hissdb
HissDB is a SQLite3 frontend with a focus on ease-of-use and tight integration with Python syntax.Installationpython3-mpipinstallhissdbUsageDatabase SetupfromhissdbimportDatabase# make a new db or load one from an existing pathdb=Database('test_location.db')# add some tables to the databaseusers=db.create_table(id='INTEGER PRIMARY KEY',first_name='TEXT',last_name='TEXT',age='INTEGER',)posts=db.create_table(user_id='INTEGER NOT NULL',text='TEXT',date='INTEGER',foreign_keys={'user_id':users.id},)Writing Data# inserting a row returns the new rowidjane_id=users.insert(first_name='Jane',last_name='Doe')john_id=users.insert(first_name='John',last_name='Doe')posts.insert(user_id=john_id,date=20210817,text="I'm John Doe and this is my first post!")# you can also insert many rows at once using a list or generatorposts.insertmany(cols=['user_id','date','text'],rows=[(jane_id,20210814,"First!"),(jane_id,20210816,"The weather is nice today."),(jane_id,20210817,"Do you ever post on the internet just so there's content?"),],)# you can update data based on matching criteria.# for instance, let's add a signature to each of Jane's postsposts.update(text=posts.text+' - '+users.first_name,where=users.id==jane_id,)# finally, we must write the changes to the filedb.commit()Reading Data# get all usersnames=users.fetchall(cols=['first_name','last_name'])assertnames==[('Jane','Doe'),('John','Doe')]# get a single columnfirst_names=users.first_name.fetchall()assertfirst_names==['Jane','John']# easily write WHERE queriesdoes=users.id.fetchall(where=users.last_name=='Doe')# an even simpler equivalent:does=users.id.fetchall(last_name='Doe')assertdoes=[1,2]# a few methods like startswith() have been translated to SQL expressionsusers.insert(first_name='Dave',last_name='Guy')non_j_name=users.first_name.fetchone(~users.first_name.startswith('J'))assertnon_j_name=='Dave'# you can construct all kinds of queriesfull_names=users.fetchall(cols=(users.first_name+' '+users.last_name))assertfull_names==[('Jane Doe',),('John Doe',),('Dave Guy',)]For more sample code using HissDB, see thetests.If you're looking for more detailed documentation, check out thelibrary reference.
Package: hissw
:snake: :sunny: hiss(w)Easily integrate SSWIDL into your Python workflows.
See thedocsfor more info.InstallTo install hissw,$pipinstallhisswwhich will automatically install the package and its dependencies.
hissw depends on theJinja2andscipylibraries.
You can also install these manually withcondaor from PyPI, i.e.pip install <package-name>.
Additionally, you'll need a local install of IDL and theSolarsoft library.Alternatively, you can install hissw from GitHub,$gitclonehttps://github.com/wtbarnes/hissw.git
$cdhissw&&python-mpipinstall-e[all,test]and then run the tests to make sure everything is working alright. Note that you must specify paths to SSW and IDL appropriately.$pytest--ssw-home=<pathtotopofSSWtree>--idl-home=<pathtotopleveldirofIDLinstall>Note: hissw relies on executing several shell commands. This has not been tested on Windows.CitinghisswIf you usehisswin your work, please use the following citation,@software{will_barnes_2022_6640421,author={Will Barnes andBin Chen},title={wtbarnes/hissw: v2.0},month=jun,year=2022,publisher={Zenodo},version={v2.0},doi={10.5281/zenodo.6640421},url={https://doi.org/10.5281/zenodo.6640421}}Reporting Issues and ContributingOpen anissue on GitHubto report a problem.Pull requestswelcome.
Package: histastro
HistAstroA Python package for historical-astronomy calculations of Sun, Moon and planets.InstallationThis package can be installed usingpip install histastro.  This should automatically install the
dependency packagesnumpyandfortranformatif they haven't been installed already.  If you are installing
by hand, you have to ensure these packages are installed as well.Data filesYou will need to manually download the eight data filesVSOP87D.xxx(click on FTP), one for each of the planets,
and save them in a directory of your choice (e.g. the subdirectorydata/of your working directory).  To
compute the Moon position, the filemoonposMeeus.csvis needed, which can be downloaded from this page:http://astro.ru.nl/~sluys/HistAstro/HistAstro pagesRadboud University: HistAstro homepage: data files and code documentationPypi: HistAstro Python packageGitHub: HistAstro source codeAuthor and licenceAuthor: Marc van der SluysContact:http://astro.ru.nl/~sluys/Licence:GPLv3+ReferencesMeeus,Astronomical algorithms, 2nd Ed.VSOP87 planet theory:Bretagnon & Francou, 1988A+A...202..309BVSOP87 data filesThis Python code is adapted from the Fortran implementation inlibTheSkyCelestial mechanics in a nutshell (CMiaNS)
Package: histbook
histbook computes histograms from Numpy arrays. It differs from most other histogramming tools in that its histograms are primarily tables of numbers, rather than display graphics. Histograms can be filled and refilled iteratively through a large dataset, or in parallel and later combined with addition*. Histograms have arbitrarily many dimensions with convenient methods for selecting, rebinning, and projecting into lower-dimensional spaces.Axis dimensions are managed by algebraic expressions, rather than string labels or index positions, so they are computable: an axis namedx + yrequires two Numpy arrays,xandy, which will be added before filling the histogram. Expressions in different axes or different histograms in the same “book” (a collection of named histograms) are computed in an optimized way, reusing subexpressions wherever possible for quicker filling without giving up clarity.Histogram data may be exported to a variety of formats, such asPandas,ROOT, andHEPData. It can also be plotted withVega-Lite, which makes short work of projecting many dimensions of data as overlays and trellises.(*In this respect, histbook is like histogramming packages developed for particle physics, fromCERN HBOOKin the 1970’s (name similarity intended) to modern-dayROOT.)InstallationInstall histbook like any other Python package:pipinstallhistbook--useror similar (usesudo,--user,virtualenv, or pip-in-conda if you wish).Strict dependencies:Python(2.7+, 3.4+)Numpy(1.8.0+)Recommended dependencies:Pandasfor more convenient programmatic access to bin contentsJupyter Notebookfor interlaced histogramming and plottingJupyterLabfor a complete IDE-like environmentipyvegato view plots in a Jupyter Notebook (not needed for JupyterLab)Altairto mix histograms with Altair graphics (usable in both Jupyter Notebook and JupyterLab)VegaScopeto view plots in a web browserwithoutJupyterROOTto analyze histograms in a complete statistical toolkituprootto access ROOT files without the full ROOT frameworkTutorialSee theproject homepagefor atutorial.Interactive tutorialRunthis tutorialon Binder.Reference documentationHistogramsBooks of histogramsAxis descriptors
Package: histcite-python
HistCite工具的Python实现由于原引文分析工具HistCite已停止维护，目前国内使用较多的为中科大某同学 (知乎昵称Tsing) 在源程序基础上修复的版本HistCite Pro，仅适用于Windows平台，存在较大限制。借助pandas 2.0和可视化工具Graphviz，本工具实现了HistCite的核心功能，可以跨平台使用，同时拓展了对其他数据源的支持。核心功能：生成引文网络图；生成统计数据，包括文献、作者、机构、文献来源、作者关键词等分析对象；发现不在本地文献集中、但被本地文献集引用较多的文献，即本次文献获取过程忽略的重要文献；工具对比：对比项histcite-pythonhistcite pro是否开源是否是否跨平台是否，仅限 Windows是否支持其他数据源是否，仅限 Web of Science是否提供前端界面否是引文网络图矢量图，比较清晰位图，比较模糊快速开始pip install histcite-python数据准备数据来源下载说明Web of Science核心合集，格式选择Tab delimited file/制表符分隔文件，导出内容选择Full Record and Cited References/全记录与引用的参考文献或者是Custom selection/自定义选择项，全选字段。CSSCI从CSSCI数据库正常导出即可。Scopus格式选择CSV文件，导出字段需要额外勾选Author keywords和Include references，或者直接全选字段。[!WARNING]文件下载之后不要重命名(会根据文件名识别有效的题录数据文件)，下载完成后放在一个单独的文件夹内。使用方法使用命令行工具$histcite-husage: histcite [-h] (--top TOP | --threshold THRESHOLD | --node NODE) [--disable_timeline] folder_path {wos,cssci,scopus}A Python interface for histcite.positional arguments:folder_path           Folder path of downloaded data.{wos,cssci,scopus}    Data source.options:-h, --help            show this help message and exit--top                 Top N nodes with the highest LCS.--threshold           Nodes with LCS greater than threshold.--disable_timeline    Whether to disable timeline.$histcite/Users/.../Downloads/datasetwos--top100--disable_timeline[!NOTE]生成的结果保存在folder_path下的result文件夹内，包含描述统计表 descriptive_statistics.xlsx引文网络图节点信息表 graph_node_info.xlsx引文网络图的数据文件 graph.dot借助Graphviz在线编辑器或下载到本地的Graphviz工具生成引文网络图。引文网络图示例：对应的节点信息如下(以CSSCI数据源为例，不同文献数据库的节点信息字段存在差异)：AUTIPYSOLCS55张坤; 查先进我国智慧图书馆的发展沿革及构建策略研究2021国家图书馆学刊660石婷婷; 徐建华; 张雨浓数字孪生技术驱动下的智慧图书馆应用场景与体系架构设计2021情报理论与实践763卢小宾; 宋姬芳; 蒋玲; 洪先锋; 刘静; 张薷智慧图书馆建设标准探析2021中国图书馆学报981程焕文; 钟远薪智慧图书馆的三维解析2021图书馆论坛1086段美珍; 初景利; 张冬荣; 解贺嘉智慧图书馆的内涵特点及其认知模型研究2021图书情报工作7...使用Jupyter，比命令行更加灵活，可以自定义更多参数，查看demo.ipynb字段说明Field NameDescriptionGCSGlobal Citation Score, 表示一篇文献在文献数据库中的总被引次数LCSLocal Citation Score, 表示一篇文献在本地文献集中的被引次数GCRGlobal Cited References, 表示一篇文献的参考文献数量LCRLocal Cited References, 表示一篇文献的参考文献在本地文献集中的数量T*Total score, e.g. TLCS = Total Local Citation Scores.RecsCount of RecordsFAUFirst AuthorAUAuthors or InventorsTIArticle TitleSOSource TitleDTDocument TypeFUFunding OrgsCRCited ReferencesDEAuthor KeywordsC3Author AffiliationsNRCited Reference CountTCTimes Cited CountJ9Journal AbbreviationPYPublication YearVLVolumeISIssueBPStart PageEPEnd PageDIDOI...Please refer to Web of Science fields.FAQ为什么生成的引文网络图时间线会错乱？Graphviz 会自动调整节点位置，节点数量过少时容易出现这一问题。可以通过设置参数来关闭时间线。为什么有些学科领域的参考文献解析的错误率较高？不同学科领域的主要引用来源不同，期刊引用格式要求不同，导致不同数据库、不同学科领域的引文格式与解析方式差异较大。您可以查看解析后的参考文献表 (refs_df)，如果错误率较高，或者发现具有某种特征的参考文献均出现了解析错误，欢迎提交Feature Request，我们会尽快修复。想要分析其他数据源的文献元数据？该数据源能够导出文献的参考文献或引文数据。如果满足这一条件的话，欢迎提交Feature Request，我们会尽快支持。是否存在其他类似的工具？Connected PapersLitmpasCitNetExplorer
Package: histdata-downloader
Histdata DownloaderEducational purpose project !I’m a student and this is a dummy project to learn.Simple script to download forex historical data from ‘www.histdata.com’Free software: MIT licenseDocumentation:https://histdata-downloader.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-10-13)First release on PyPI.
Package: histeqkit
HistEQKitHistEQKit is a Python package that provides a collection of histogram equalization algorithms and image accuracy measures for image enhancement and analysis tasks.InstallationYou can install HistEQKit using pip:pipinstallhisteqkit==0.0.5HistEQKit requires Python 3.6 or higher.UsageTo use HistEQKit, import the desired histogram equalization algorithms from the package,
along with OpenCV for image processing. Here's an example:fromhisteqkitimportalgorithms
importcv2# Load an imageinput_image=cv2.imread("/path/to/input_image.png",cv2.IMREAD_GRAYSCALE)# Apply histogram equalization using an algorithmie=algorithms.IE(input_image)output_image=ie.INVERT()# Save the output imagecv2.imwrite('output.jpg',output_image)Make sure to replace "/path/to/input_image.png" with the path to your actual input image.AlgorithmsHistEQKit currently provides the following histogram equalization algorithms:algorithms.IE: Inverts the intensity values of the input image.
Note: This is just an example algorithm. HistEQKit can include other algorithms as well.ContributingContributions to HistEQKit are welcome! If you have any bug fixes, enhancements, or new algorithms/measures to add, please open an issue or submit a pull request on GitHub.LicenseThis project is licensed under the MIT License. See the LICENSE file for more details.Feel free to customize the content further to provide additional details about the algorithms, accuracy measures, or any other relevant information about your package.
Package: histexport
History ExporterTable of ContentsIntroductionPrerequisitesInstallationUsageCommand Line InterfaceData ExtractionOutput FormatsLoggingLicenseIntroductionHistoryExtractor is a Python utility aimed at exporting Chromium-based browser history and downloads data to various formats such as CSV, XLSX, and TXT. Designed with a focus on user flexibility, this tool provides customizable extraction options to suit different needs.PrerequisitesPython 3.7+InstallationMethod 1: Using pipYou can installhistexportdirectly using pip:pip install histexportMethod 2: From GitHub RepositoryClone the GitHub repository.git clone https://github.com/darkarp/histexport.gitNavigate to the project directory.cd histexportInstall the required Python packages.pip install -e .Either of these methods will install the required Python packages and makehistexportavailable for use.UsageCommand Line InterfaceBasic extraction of URLs and Downloads intxt:histexport -i path/to/history/history_file -o output_fileSpecify output directory and formats:histexport -i path/to/history/history_file -o output_file -d path/to/output -f csv xlsxEnable logging (-l):histexport -i path/to/history/history_file -o output_file -lExtract from a folder of SQLite files:histexport -i path/to/history_folder -t folder -o output_file -d path/to/output -f csv xlsx -e urls downloadsArguments-i,--input: Path to the SQLite history file. (required)-t,--type: Type of the input: file or folder. Default is file-o,--output: Base name for output files. (required)-d,--dir: Output directory. (optional, default is./)-f,--formats: Output formats (csv, xlsx, txt). (optional, default istxt)-e,--extract: Data to extract (urls, downloads). (optional, default is both)-l,--log: Enable logging. (optional, default is disabled.-l 1: CRITICAL-l 2: ERROR-l 3: WARNING-l 4: INFO-l 5: DEBUG-v,--version: Show version.Data ExtractionThe tool allows extraction of:URLs: FetchesURL,Title,Visit_Count, andLast_Visit_Time.Downloads: ExtractsTarget_Path,Start_Time,End_Time,Total_Bytes,Received_Bytes, andURL.Output FormatsYou can export the data into:CSVXLSX (Excel)TXT (Pretty printed text file)LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details.
Package: histgrinder
histgrinderGeneric system to perform streaming transformations on histogramsFeatures:core does not depend on any histogram library or input/output format; plugins can be written (and these need not live in this package). Probably most useful if histograms live in a hierarchical namespace but this is not necessary. Example implementation to read/write ROOT files provided.intended for streaming, e.g. for online environments where histograms are updated asynchronously.pattern matching makes it easy to apply the same transformation to multiple histogramsno code needed to configureThis is still very much early-release software, you can test it as follows (e.g. should work on lxplus, if you have a CERN account):set up ROOT and Python (>=3.7) in a way that you like. For ATLAS people you can set up a master nightly. (The code may run on Python 3.6 but we no longer test it there.)install (not needed if you are on ATLAS and using master,2020-10-20T2101 or later):python3 -m pip install -U --user histgrinder==0.1.6prepare a sample ROOT file:python3 -m histgrinder.make_sample_filedownload an example YAML configuration fromhttps://raw.githubusercontent.com/ponyisi/histogram_postprocessing/master/resources/example.yamlrun. The following will postprocessexample.root, created above, according to theexample.yamlconfiguration, ignoring the top-level path "prefix", then add the outputs toexample.root:python3 -m histgrinder.engine example.root example.root -c example.yaml --prefix prefixthe transformation above will perform a number of operations on the histograms of the input file. For example, 20 different histogram divisions are configured with the first config block.Command line arguments:ArgumentDescription-c,--configfile CONFIGFILE [CONFIGFILE ...]one or more YAML configuration file(s)--inmodulePython class which implements an input module (default:histgrinder.io.root.ROOTInputModule)--outmodulePython class which implements an output module (default:histgrinder.io.root.ROOTOutputModule)--prefixPath prefix to ignore in histogram locations in input (will also be prepended to output locations)--loglevelSet the logging level (choices:DEBUG,INFO,WARNING,ERROR,CRITICAL; default:INFO)--deferIf specified, defer processing of histograms until all input histograms are read. Major speedups possible if some transformations take a lot of histograms as input. Not for streaming-type jobs.--delaywriteIf specified, write histograms at once at end of job. Can speed up tasks if I/O is a bottleneck. Not for streaming-type jobs.This work was supported by the US Department of Energy, Office of Science, Office of High Energy Physics, under Award Number DE-SC0007890.