Saravanan666/pypi-org huggingface dataset:
Package: holodex-cli
holodex-cliAtextualbased interface forHolodexDirectionsCreate an API key for your Holodex account.Create a copy of example-config.toml called config.toml and paste your key into this file as API_KEY:API_KEY="YOUR_API_KEY_HERE"Run:justinstallORpoetryinstall
poetrybuild
pipxinstall./dist/`ls-tdist|head-n2|grepwhl`mkdir-p~/.local/holodex-cli
cp--updateconfig.toml~/.local/holodex-cli/config.tomlUsageUsage:holodex[-h][-gGROUP][-rRESOLUTION][-v]Options:-h,--helpshow this help message and exit-g,--groupGROUPthe VTuber group you want to watch (default: Hololive)-r,--resolutionRESOLUTIONthe video resolution to play (default: 720)-v,--versionshow program's version number and exitNOTE:Requiresffplayto use
Package: holographic
HolographicIt's annoying and unsustainable to author resusable code in a python notebook. This problem is exacerbated when the notebook system is not running in a local development environment or otherwise does not have access to the same filesystem as the IDEs which the author would prefer to use. So, wouldn't it be nice if you could develop your code wherever you wanted, and as long as that place had an internet connection (and as long as your notebook has an internet connection) you could create a read-only copy of your code in the notebook environment, holographic code if you will, and have the best of both worlds: a fully-featured authoring experience with static-analysis tools, multi-select, etc. and the rich interactive runtime experience of a notebook environment.
Package: hologres-vector
hologres-vectorUseHologresto store large amount of vector data and perform high speed k-nearest-neighbour search!Table of ContentsInstallationUsageLicenseInstallationpip install hologres-vectorUsage输入Hologres实例连接信息fromhologres_vectorimportHologresVectorimportoshost=os.environ["HOLO_HOST"]port=os.environ["HOLO_PORT"]dbname=os.environ["HOLO_DBNAME"]user=os.environ["HOLO_USER"]password=os.environ["HOLO_PASSWORD"]connection_string=HologresVector.connection_string_from_db_params(host,port,dbname,user,password)与数据库建立连接并建表建表时，需要指定向量的维数，以及表中的除向量数据、主键、json元数据以外的其他强schema列。table_name="test_table"holo=HologresVector(connection_string,# 连接信息5,# 向量维度table_name=table_name,# 表名table_schema={"t":"text","date":"timestamptz","i":"int"},distance_method="SquaredEuclidean",# 距离函数，推荐用默认值，也可以选择"Euclidean"或"InnerProduct"pre_delete_table=False,# 若表已存在则先删除)插入向量数据与对应的其他列信息支持强schema列schema_datas与一个json列metadatas。该接口为批量导入，内部会将输入数据切分为512行的批进行插入。vectors=[[0,0,0,0,0],[1,1,1,1,1],[2,2,2,2,2]]ids=['0','1','2']# primary keyschema_datas=[{'t':'text 0','date':'2023-08-02 18:30:00','i':0},{'t':'text 1','date':'2023-08-02 19:30:00','i':1},{'t':'text 2','date':'2023-08-02 20:30:00','i':2},]metadatas=[{'a':"hello"},{'b':123},{},]holo.upsert_vectors(vectors,ids,schema_datas=schema_datas,metadatas=metadatas)查询普通查询：从数据库中任取一条数据（可加filter）holo.query(limit=1)[{'id': '2', 'vector': [2.0, 2.0, 2.0, 2.0, 2.0], 'metadata': {}}]近邻查询：根据向量从数据库中取最近邻holo.search([0.1,0.1,0.1,0.1,0.1],k=2,select_columns=['t'])[{'id': '0', 'metadata': {'a': 'hello'}, 'distance': 0.05, 't': 'text 0'},
{'id': '1', 'metadata': {'b': 123}, 'distance': 4.05, 't': 'text 1'}]融合查询：根据向量从数据库中取最近邻，并用其他列查询条件约束holo.search([0.1,0.1,0.1,0.1,0.1],k=2,schema_data_filters={'t':'text 1'})[{'id': '1', 'metadata': {'b': 123}, 'distance': 4.05}]替换（upsert）本SDK目前默认使用根据主键id的一种插入替换策略：当插入的数据和已有数据主键相同时，用新插入的整行替换已有的行。# 先插入一行id为3的数据holo.upsert_vectors([[3,3,3,3,3]],[3],schema_datas=[{'t':'old data'}])# 再插入一行id为3的数据，下面这行会将上面的整行替换掉holo.upsert_vectors([[-3,-3,-3,-3,-3]],[3],schema_datas=[{'t':'new data'}])holo.query(schema_data_filters={'id':'3'})[{'id': '3', 'vector': [-3.0, -3.0, -3.0, -3.0, -3.0], 'metadata': {}}]删除可使用与查询格式相同的filter条件来对数据进行部分删除。holo.delete_vectors(schema_data_filters={'id':'2'})holo.query(limit=10)[{'id': '0', 'vector': [0.0, 0.0, 0.0, 0.0, 0.0], 'metadata': {'a': 'hello'}},
 {'id': '1', 'vector': [1.0, 1.0, 1.0, 1.0, 1.0], 'metadata': {'b': 123}},
 {'id': '3', 'vector': [-3.0, -3.0, -3.0, -3.0, -3.0], 'metadata': {}}]holo.delete_vectors()# 删除全部数据holo.query(limit=10)Licensehologres-vectoris distributed under the terms of theMITlicense.
Package: hologridgen
# hologridgenInteractive tool for the generation of orthonormal grids using
[pygridgen](https://github.com/pygridgen/pygridgen) and the
[HoloViz](holoviz.org) tool suite for use within [Jupyter
notebooks](https://jupyter.org/) or deployable with
[Panel](panel.pyviz.org).## InstallationUntil hologridgen is made available on conda-forge you can install it as
follows in a Python 3.7 conda environment:` conda install-cjlstevens-cconda-forgehologridgen `If you need a new, blank Python 3.7 conda environment, you can create it
and activate it using:` conda create-nhologridgen python=3.7 conda activate hologridgen `## Core featuresAdd, move and delete nodes. First node indicated with a special marker (triangle by default)<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/add-mode-delete.gif”></img>Toggle node polarity (beta) with the Tap Tool. Generate Mesh button indicates when mesh generation is possible. Hide button to hide current grid/mesh.<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/polarity-toggle.gif”></img>Easy insertion of new nodes into edges selected with the Tap Tool (which can then be easily moved):<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/edge_insertion.gif”></img>Pythonic access of boundary as it is drawn and the grid once it is generated:<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/pythonic-access.gif”></img>Easy adjustment of node size and edge width via the GUI. Easy regeneration with different x- and y-resolutions:<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/node-edge-size.gif”></img>Easy selection between a predefined set of tile sources in the background:<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/tilesources.gif”></img>Easy use of a custom element (e.g a different tile source) as the background:<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/custom_background.gif”></img>Download boundary as GeoJSON:<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/geojson_download.gif”></img>Capture serializable editor state and restore from it (can also be restored from a geopandas boundary DataFrame):<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/save_restore_state.gif”></img>Set a focus function and update mesh accordingly:<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/focus_edit.gif”></img>Highly customizable styling of boundary, nodes and start marker:<img width=400 src=”https://github.com/pygridgen/hologridgen/blob/master/images/customizable_style.gif”></img>Can be served as a Panel dashboard using theserveable()method.
Package: hololive
hololive-python-apiGets stream info from holodex(no longer limited to only hololive)InstallationpipinstallhololiveUsagefromhololiveimporthololivestreams=awaithololive.get_live(limit=5)forstreaminstreams:print(stream.title)# -----------------# 【APEX】カスタム3日目【#あの伝WIN】# 【APEX】 casual & arena rank?# 【APEX】V最協決定戦カスタム4 #KGSWIN 【ぶいすぽ / 花芽すみれ】# 【APEX】最協カスタム4日目→ソロランク修行【ふぇありす/弦月藤士郎/パカエル】# 【Portal 2】 😈마왕적 포탈! 👾 魔王的ポータル😈 【NIJISANJI KR】
Package: holonote
holonoteTheholonotelibrary offers tools to create, edit and persist
annotated regions forHoloViews.An annotated region marks a region-of-interest that is overlaid on top
of the visual representation of a HoloViews element. Such a region has a
unique identifier as well as additional domain-specific information
associated with it. Whatholonoteoffers is a flexible way to
interactively work with these visual regions as well as tools to add,
update and delete whatever domain-specific information you want to
associate with them.There are two primary components currently offered byholonote:Annotators: These classes offer the ability to interactively define
regions, add associated domain-specific information and then persist
this information to a database. You can interactively add, delete and
update these annotation regions as well as their associated
domain-specific data.Editors: These tools are designed to let you interactively
manipulate and edit the visual regions themselves. These regions are in
fact HoloViews elements which means you can use these editors as
generic edit tools for the corresponding HoloViews elements.By default, annotators automatically persist any annotations you create
to a local SQLite database file. This is a very convenient way to get
started with your annotation task, allowing you to immediate begin the
process of collecting useful information.For production usage where you are investing significant resources
authoring annotations, it is advisable that you define a custom
connector to a more appropriate database system. You can also use
connectors to load annotations from existing databases of annotation
data.Do not use the SQLite connector when deploying to production. It is
intended for use only during local development. It is not designed to
be particularly efficient, stable, or secure.TerminologyThere are two particularly important pieces of terminology that are used
throughout this library to be aware of:Regions: These denote the visual regions-of-interest with a final
visual representation as a HoloViews element. These may be one or two
dimensional, may have different visual representations for the same data
(e.g. a point marker or a crosshair to mark a point in 2D space) and
have different types:Points: A single value of interest in an N-dimensional space. For
instance, on a time axis, this corresponds to a moment in time
which may be represented visually as a vertical line. On an image,
this can ve visually represented with a crosshair.Ranges: A range is an axis-aligned span between a start and end
point. For instance, along a time axis, this corresponds to a time interval and
on an image, this corresponds to a 2D box.Fields: These are the domain-specific component of the annotation
data, containing information that is not to be visualized byholonote. These are defined by the user or application and can have
arbitrary types and contents. A complete set of fields together with one
or more regions constitutes an annotation.
Package: holoocean
HoloOceanHoloOcean is a high-fidelity simulator for underwater robotics built on top of Unreal Engine 4, and forked from Holodeck by BYU's PCCL Lab.Features3+ rich worlds with various infrastructure for generating data or testing underwater algorithmsComplete with common underwater sensors including DVL, IMU, optical camera, imaging sonar, depth sensor, and moreHighly and easily configurable sensors and missionsMulti-agent missions, including optical and acoustic communicationsNovel imaging sonar implementation, produces accurate, realistic sonar data at 2 FPS.Easy installation and simple, OpenAI Gym-like Python interfaceHigh performance - simulation speeds of up to 2x real time are possible. Performance penalty only for what you needRun headless or watch your agents learnLinux and Windows supportInstallationpip install holoocean(requires >= Python 3.6)SeeInstallationfor complete instructions (including Docker).DocumentationQuickstartChangelogExamplesAgentsSensorsAvailable Packages and WorldsDocsUsage OverviewHoloOcean's interface is similar toOpenAI's Gym.We try and provide a batteries included approach to let you jump right into using HoloOcean, with minimal
fiddling required.To demonstrate, here is a quick example using theDefaultWorldspackage:importholoocean# Load the environment. This environment contains a hovering AUV in a pierenv=holoocean.make("PierHarbor-Hovering")# You must call `.reset()` on a newly created environment before ticking/stepping itenv.reset()# The AUV takes commands for each thrustercommand=[0,0,0,0,10,10,10,10]foriinrange(30):state=env.step(command)state: dict of sensor name to the sensor's value (nparray).If you want to access the data of a specific sensor, import sensors and
retrieving the correct value from the state dictionary:print(state["DVLSensor"])Multi Agent-EnvironmentsHoloOcean supports multi-agent environments.Calls tosteponly provide an action for the main agent, and then tick the simulation.actprovides a persistent action for a specific agent, and does not tick the simulation. After an
action has been provided,tickwill advance the simulation forward. The action is persisted until another call toactprovides a different action.importholooceanimportnumpyasnpenv=holoocean.make("Dam-Hovering")env.reset()# Provide an action for each agentenv.act('auv0',np.array([0,0,0,0,10,10,10,10]))# Advance the simulationforiinrange(300):# The action provided above is repeatedstates=env.tick()You can access the sensor states as before:dvl=states["auv0"]["DVLSensor"]location=states["auv0"]["DepthSensor"](auv0comes from thescenario configuration file)Running HoloOcean HeadlessHoloOcean can run headless with GPU accelerated rendering. SeeUsing HoloOcean Headless
Package: holophonor
holophonorLive looping, unlimited!TheHolophonorproject is an plugin based architecture, designed to allow complex control of loops with a variety of hardware components and network controllers to allow complete control of any number of loops running simultaneously, even over multiple machines with large network latency!installClone this repo:git clone https://github.com/quaternionmedia/holophonor.gitInstall locally:pip install -e holophonor/configurechange holophonor/main.py to match your configurationrunFrom a terminal, run:holophonor
Package: holoscan
Holoscan SDKTheHoloscan SDK Python Wheelis part ofNVIDIA Holoscan, the AI sensor processing platform that combines hardware systems for low-latency sensor and network connectivity, optimized libraries for data processing and AI, and core microservices to run streaming, imaging, and other applications, from embedded to edge to cloud. It can be used to build streaming AI pipelines for a variety of domains, including Medical Devices, High Performance Computing at the Edge, Industrial Inspection and more.Getting StartedVisit the Holoscan User Guide toget startedwith the Holoscan SDK.PrerequisitesPrerequisites for each supported platform are documented inthe user guide.Python: 3.8 to 3.11TroubleshootingversionGLIBC_2.32not found [...] versionGLIBCXX_3.4.29not foundThe current version of the wheels were built and tested on Ubuntu 22.04 with libc 2.35 instead ofmanylinux. They were named as such to make them available early on PyPI. You'll need to use a system with a more recent version of libc to use the Holoscan SDK python wheels.ImportError: libcudart.so.12.0: cannot open shared object file: No such file or directoryCuda runtime is missing from your system (required even for CPU only pipelines). Follow links in the section above to install CUDA, or install it through the python wheels:python3-mpipinstallnvidia-cuda-runtime-cu12--indexhttps://pypi.ngc.nvidia.comError: libnvonnxparser.so.8: cannot open shared object file: No such file or directoryTensorRT is missing from your system. It is only needed by theholoscan.operators.InferenceOpoperator.If you do not need it, only import the holoscan submodules or operators required for your use case (ex:from holoscan import core).If you do need it, follow links in the section above to install TensorRT, or install it through the python wheels:python3-mpipinstallnvidia-tensorrt~=8.6--indexhttps://pypi.ngc.nvidia.com
Package: holospy
HoloSpyis a Python package extending the functionality for multi-dimensional
data analysis provided by theHyperSpylibrary. It is
aimed at helping with the analysis of (off-axis) electron holography data.Go to the documentation for instructions on how to install HoloSpy and start an
analysis:Read the docs.Everyone is welcome to contribute. Please read ourcontributing guidelinesand get started!Development of HoloSpy is documented in thechangelog.
Package: holotools
HolotoolsHolotools is the code base used by the Holobiome teamInstallationUse the package managerpipto install Holotools.pipinstallholotools==0.0.22Usagefromholotools.trimimport*# trimming algorithmslongest_contig('test16.fna')ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: holoviews
HoloViewsStop plotting your data - annotate your data and let it visualize
itself.DownloadsBuild StatusCoverageLatest dev releaseLatest releasePythonDocsBinderSupportHoloViews is anopen-sourcePython library designed to make data analysis and visualization seamless
and simple. With HoloViews, you can usually express what you want to do
in very few lines of code, letting you focus on what you are trying to
explore and convey, not on the process of plotting.Check out theHoloViews web sitefor extensive examples and documentation.InstallationHoloViews works withPython 3on Linux, Windows, or Mac, and works seamlessly withJupyter Notebook and JupyterLab.The recommended way to install HoloViews is using thecondacommand provided byAnacondaorMiniconda:conda install -c pyviz holoviews bokehThis command will install the typical packages most useful with
HoloViews, though HoloViews itself depends only onNumpyPandasandParam.
Additional installation and configuration options are described in theuser guide.You can also clone holoviews directly from GitHub and install it with:git clone git://github.com/holoviz/holoviews.git
cd holoviews
pip install -e .UsageOnce you've installed HoloViews, you can get a copy of all the examples shown on this website:holoviews --install-examples
cd holoviews-examplesNow you can launch Jupyter Notebook or JupyterLab to explore them:jupyter notebook

jupyter labIf you are working with a JupyterLab version <2.0 you will also need the PyViz JupyterLab
extension:jupyter labextension install @pyviz/jupyterlab_pyvizFor more details on setup and configuration seeour website.For general discussion, we have agitter channel.
If you find any bugs or have any feature suggestions please file a GitHubissueor submit apull request.
Package: holoviews-samap
HoloViewsStop plotting your data - annotate your data and let it visualize
itself.HoloViews is anopen-sourcePython library designed to make data analysis and visualization seamless
and simple. With HoloViews, you can usually express what you want to do
in very few lines of code, letting you focus on what you are trying to
explore and convey, not on the process of plotting.Check out theHoloViews web sitefor extensive examples and documentation.InstallationHoloViews works withPython 2.7 and Python 3on Linux, Windows, or Mac, and works seamlessly withJupyter Notebook and JupyterLab.The recommended way to install HoloViews is using thecondacommand provided byAnacondaorMiniconda:conda install -c pyviz holoviews bokehThis command will install the typical packages most useful with
HoloViews, though HoloViews itself depends only onNumpyPandasandParam.
Additional installation and configuration options are described in theuser guide.You can also clone holoviews directly from GitHub and install it with:git clone git://github.com/holoviz/holoviews.git
cd holoviews
pip install -e .UsageOnce you've installed HoloViews, you can get a copy of all the examples shown on this website:holoviews --install-examples
cd holoviews-examplesNow you can launch Jupyter Notebook or JupyterLab to explore them:jupyter notebook

jupyter labIf you are working with a JupyterLab version <2.0 you will also need the PyViz JupyterLab
extension:jupyter labextension install @pyviz/jupyterlab_pyvizFor more details on setup and configuration seeour website.For general discussion, we have agitter channel.
If you find any bugs or have any feature suggestions please file a GitHubissueor submit apull request.
Package: holoviz
HoloViz: High-level tools to simplify visualization in PythonBuild StatusLatest dev releaseLatest releaseDocsDependenciesWhat is it?This repository provides examples, demos, and training materials
documenting how to solve visualization problems using Python
tools that work with web browsers, includingBokeh,HoloViews,GeoViews,Matplotlib,Datashader,hvPlot,Panel, andParam.It includes a shared conda environment that ensures all these tools work together,
facilities for downloading sample data for use with these tools, and provides
starting points for solving your own visualization problems.InstallationSeeholoviz.org(or doc/installation.rst for a github clone).About holoviz.orgHoloViz is a project supported in part byAnaconda.
All projects used here are freely available for commercial or
non-commercial use according to a permissive open-source license as
described in each project's website.
Package: holtair
Project descriptionHoltAir is a tool for python that reads and analyzes data from Holter blood pressure test results in PDF format.UsageQuickstart:fromholtairimportexams# you have to specify the tesseract executable in your PATH:tesseract_path=r'<full_path_to_your_tesseract_executable>'#also you have to specify the pdf2image poppler executable in your PATH:poppler_path=r'<full_path_to_your_poppler_executable>'#create Exam object with test result dataexam=exams.get_exam(r'test_file.pdf',poppler_path,tesseract_path)#get pandas dataframe with raw dataprint(exam.results_df)#create ExamStatistics object calculated statisticsstatistics=exam.exam_statistics#get basic statistics such sa mean,std,min and max for different blood pressure indicatorsprint(statistics.basic_overall)# get basic statistics for detected night:print(statistics.basic_night)# create Patient with information about patientpatient=exam.patient# get patient idprint(patient.patient_id)Necessary files:WindowsPoppler:https://github.com/oschwartz10612/poppler-windows/releases/Tesseracthttps://github.com/UB-Mannheim/tesseract/wikiLinuxPoppler (using conda):conda install -c conda-forge popplerTesseract :sudo apt install tesseract-ocr,sudo apt install libtesseract-devFunctions and classes descriptionget_exam() functionThis function is used to return the ExamFromImage or ExamFromText object that contain all the analysis and test results data.Parameters:get_exam(fielpath,popplerpath,tesseractpath)filepathPath to the PDF file containing the exam result.popplerpathPath to the poppler executable used in the pdf2image package.tesseractpathPath to the Tesseract executable used in pytesseract package.Exam classExamFromImage and ExamFromText inherit from Exam class. Their objects store all the analysis and information data regarding
test result. ExamFromText object contains exam information that is read directly from the pdf file and ExamFromImage object contains the information that is read from not readable pdf using Tesseract OCR.Attributes:results_dfDataframe containing measure values from the exam.exam_statisticsExamStatistics object storing dataframes with calculated statistics.patientPatient object which stores data about patient.scan_end_timeString value with time of the end of the exam.scan_start_timeString value with time of the start of the exam.ExamStatistics classExemStatistics objects stores dataframes with calculated statistics from data from exam results for day, night, both
and dictionary with pb_load values.Attributes:basic_overallDataframe with statistics for day and night.basic_dayDataframe with  basic statistics for a  day.basic_nightDataframe with  basic statistics for a night.param bp_loadDictionary with pb_load values for day, night and joined.PatientPatient object stores the basic information about the patient.Attributes:first_namePatient first name.last_namePatient second name.patient_idPatient ID.birrthdatePatient birthdate.