Saravanan666/pypi-org huggingface dataset:
Package: hdfpath
HDFPathA python package to enable quick searches of Hierarchical Data Format (HDF)-Files based on JSONPath.
These files include the HDF5-Format provided byh5pyas well as more recent libraries likezarr. Likejsonpath-ngany data structure
consisting of dict-like objects conforming to thecollections.abc.Mapping-interface and lists is also supported.This package is derived from thejsonpath-nglibrary.
For the query syntax and capabilities please refer to the original documentation athttps://github.com/h2non/jsonpath-ngInstallationUsing pippip install hdfpathFrom sourcegit clone https://github.com/mortacious/hdfpath.git
cd hdfpath
python setup.py installUsageAs HDF-Files are organized as groups containing datasets with both containing optional metadata attributes, this package
adds support to use these attributes directly inside queries. With the optionalmetadata_attributeparameter
to the parse function, the attribute to retrieve the metadata can be chosen.fromhdfpathimportparseimporth5pyash5withh5.File("<HDF5-File>")asf:# query for all groups/datasets of type "scan" with the num_points attribute being larger than 40_000_000expr=parse('$..*[?(@._type == "scan" & @._num_points > 40000000)]',metadata_attribute='attrs')val=[match.valueformatchinexpr.find(f)]print(val)The metadata attributes are accessible inside the query using the_prefix.
Additionally, the use of regular expressions to match the fields is available through the`regex()`function.
For example`regex(\\\d+)`will only match groups/datasets that can be parsed into an integer number.TODOscode examplesCopyright and LicenseCopyright 2021 - Felix IgelbrinkLicensed under the Apache License, Version 2.0 (the "License"); you may
not use this file except in compliance with the License. You may obtain
a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: hdfqs
HDFQS=====HDFQS is a file format for Quantified Self data storage, which is:- Flexible- Fast- Easy to back up- Based on an open and standard formatMotivation----------Many health monitors (e.g. FitBit) currently support exporting data in CSV or XML format. While these are open standard formats, they are generally much slower, and take up more space, when compared with binary file formats. They are also harder to search for data by time. Using Excel or OpenOffice allows for more ease of plotting data, however, they also suffer from problems with speed when the volume of data grows large.Data Structure--------------Each type of data (e.g. Heart Rate, Accelerometer) is stored in a separate table. All tables have a time column, a timezone column, and any number of additional columns for the data values. Many data types will likely have just one additional column (e.g. a column for heart rate, weight, etc). Data types marking events (e.g. step times) will have no additional columns. Other data types (e.g. 3-axis accelerometer) will have multiple additional columns (e.g. x, y, z).Time----Time is stored as ns since the epoch, in UTC.The timezone column specifies the timezone in which the data is collected. This allows calculation of the local time the data was collected, if required. Timezone is specified as the number of 15 minute blocks from UTC. This enables handling of certain timezones which are 15 minutes from a standard timezone, and minimizes the space required to store the value.Hierarchical Layout-------------------An HDFQS file can contain multiple tables. They are located in a hierarchical tree, similar to a filesystem.The first level of hierarchy above the root is the location. All data from wearable devices are in the location "self". Other data from fixed locations, such as temperature loggers in a particular room or building, would be in its respective location (e.g.  "livingroom" or "office").The second level of hierarchy is the category. While any categories may be created, the basic categories which should cover most data sources are:- Activity - anything related to movement, such as accelerometer, gyroscope.- Environment - measurements of the environment such as temperature, humidity.- Health - health-related data such as heart rate, ECG.- Location - location data such as GPS, Wi-Fi networks in range.- Social - data related to interactions with other people, such as dialed/received calls, bluetooth networks in range.The data tables are located within the categories. They are named for what they measure (e.g. "temperature" or "ecg"), with common abbreviations used (e.g. "hr" for heart rate). As multiple devices can measure the same variable, the device name can optionally be appended with an underscore (e.g. "calories_gwf" or "calories_fitbit").Backups-------Data in HDFQS can be spread over multiple HDF5 files. New data can either be appended to the most recent file, or a new file can be created. This allows for incremental backups, instead of dealing with a single huge file. Any backup tool may be used, but git annex is recommended, as it maintains checksums to protect the integrity of the individual files.
Package: hdfs
HdfsCLIAPI and command line interface for HDFS.$ hdfscli --alias=dev

Welcome to the interactive HDFS python shell.
The HDFS client is available as `CLIENT`.

In [1]: CLIENT.list('models/')
Out[1]: ['1.json', '2.json']

In [2]: CLIENT.status('models/2.json')
Out[2]: {
  'accessTime': 1439743128690,
  'blockSize': 134217728,
  'childrenNum': 0,
  'fileId': 16389,
  'group': 'supergroup',
  'length': 48,
  'modificationTime': 1439743129392,
  'owner': 'drwho',
  'pathSuffix': '',
  'permission': '755',
  'replication': 1,
  'storagePolicy': 0,
  'type': 'FILE'
}

In [3]: with CLIENT.read('models/2.json', encoding='utf-8') as reader:
  ...:     from json import load
  ...:     model = load(reader)
  ...:FeaturesPython 3 bindings for theWebHDFS(andHttpFS) API,
supporting both secure and insecure clusters.Command line interface to transfer files and start an interactive client
shell, with aliases for convenient namenode URL caching.Additional functionality through optional extensions:avro, toread and write Avro files directly from HDFS.dataframe, toload and save Pandas dataframes.kerberos, tosupport Kerberos authenticated clusters.See thedocumentationto learn more.Getting started$pipinstallhdfsThen hop on over to thequickstartguide. AConda
feedstockis also
available.TestingHdfsCLI is tested against bothWebHDFSandHttpFS. There are two ways
of running tests (seescripts/for helpers to set up a test HDFS cluster):$HDFSCLI_TEST_URL=http://localhost:50070pytest# Using a namenode's URL.$HDFSCLI_TEST_ALIAS=devpytest# Using an alias.ContributingWe'd love to hear what you think on theissuespage. Pull requests are also
most welcome!
Package: hdfs3
This project is not undergoing developmentPyarrow’s JNIhdfs interfaceis mature and stable. It also has fewer problems
with configuration and various security settings, and does not require the complex
build process of libhdfs3. Therefore, all users who have trouble with hdfs3 are
recommended to try pyarrow.Old READMEhdfs3 is a lightweight Python wrapper forlibhdfs3, a native C/C++ library to interact with the Hadoop File System (HDFS).View thedocumentationfor hdfs3.
Package: hdfsbrowser
HdfsBrowserHadoop JupyterLab ExtensionThis extension is composed of a Python package namedhdfsbrowser, which installs the server+nbextension and a NPM package named@swan-cern/hdfsbrowserfor the JupyterLab extension.RequirementsJupyterLab >= 2.1InstallNote: You will need NodeJS to install the extension.pipinstallhdfsbrowser
jupyternbextensioninstallhdfsbrowser--py
jupyternbextensionenablehdfsbrowser--py
jupyterlabbuildConfigure extension to work with Hadoop cluster through hdfs-site.xmlConfigure notebookjupyter_notebook_config.py:c.HDFSBrowserConfig.hdfs_site_path = "/cvmfs/sft.cern.ch/lcg/etc/hadoop-confext/conf/etc/analytix/hadoop.analytix/hdfs-site.xml"
c.HDFSBrowserConfig.hdfs_site_namenodes_property = "dfs.ha.namenodes.analytix"
c.HDFSBrowserConfig.hdfs_site_namenodes_port = "50070"
c.HDFSBrowserConfig.webhdfs_token = "dummy"TroubleshootIf you are not seeing the frontend, check if it's installed:jupyterlabextensionlistIf it is installed, try:jupyterlabclean
jupyterlabbuildContributingInstallThejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Move to hdfsbrowser directory# Install server extension# This will also build the js codepipinstall-e.# Install and enable the nbextensionjupyternbextensioninstallhdfsbrowser--py--sys-prefix
jupyternbextensionenablehdfsbrowser--py--sys-prefix# Link your development version of the extension with JupyterLabjupyterlabextensionlink.# Rebuild JupyterLab after making any changesjupyterlabbuild# Rebuild Typescript source after making changesjlpmbuild# Rebuild JupyterLab after making any changesjupyterlabbuildYou can watch the source directory and run JupyterLab in watch mode to watch for changes in the extension's source and automatically rebuild the extension and application.# Watch the source directory in another terminal tabjlpmwatch# Run jupyterlab in watch mode in one terminal tabjupyterlab--watchUninstallpipuninstallhdfsbrowser
jupyterlabextensionuninstall@swan-cern/hdfsbrowser
Package: hdfs-kerberos-no-verify
HDFS Kerberos but no SSL verifyA custom offrom hdfs.ext.kerberos import KerberosClientfromhdfspackage that allow you to use with self-signed https url.UsageUse as you use KerberosClientfromhdfs_kerberos_no_verifyimportNoVerifyKerberosClientasKerberosClient# use as you would use KerberosClientdefcreate_keberos_hdfs_client():"""require kinitplease kinit from terminal to get ticket first"""importosos.environ["KRB5_CONFIG"]="/where/did/you/put-krb5/etc/krb5.conf"hostport="https://master1975.me.meowingful.com:8999"# your self-signed httpsclient=KerberosClient(hostport,mutual_auth='DISABLED')returnclientclient=create_keberos_hdfs_client()client.list("/home/myfile")To build from sourcepip install build
python -m build
Package: hdfs-lmdc
hdfs-pythonEsta biblioteca tem como objetivo generalizar funções da integração entre HDFS e Python utilizando HDFS3Como usar localmentePara utilizar esta biblioteca localmente, é necessário cloná-la para o diretório onde se encontra o arquivo no qual você deseja importar a mesma. Isto fará com que a biblioteca esteja somente disponível para tal diretório.Como usar globalmenteUtilizar com PyPip.
Package: hdfs-native
Native HDFS Python bindings based on hdfs-native Rust package.Installationpipinstallhdfs-nativeExamplefromhdfs_nativeimportClientclient=Client("hdfs://localhost:9000")status=client.get_file_info("/file.txt")Running testsThe same requirements apply as the Rust tests, requiring Java, Maven, Hadoop, and Kerberos tools to be on your path. Then you can:python3-mvenv.venvsource.venv/bin/activate
pip3installmaturin
pip3install-rrequirements-dev.txt
maturindevelop
pytest
Package: hdfs-pycompss
Integration: PyCOMPSs and HDFSThe abstraction that is provided by this version is exactly the same as
that provided by the version in java. Please read the Java version
before continuing.How to install the HDFSPyCOMPSs moduleThis module is available at PyPi,$pip3installhdfs-pycompssAfter install it, you need set up some environment variables:HADOOP_HOME: the root of your installed Hadoop distribution. Often has lib/native/libhdfs.so.JAVA_HOME: the location of your Java SDK installation.CLASSPATH: must contain the Hadoop jarsexportCLASSPATH=$CLASSPATH:`$HADOOP_HOME/bin/hdfsclasspath--glob`Because COMPSs don't copy all environment variables to all workers, it's important to set these variables at /etc/environment.Example of how to use the API (without StorageAPI)defwordcount(blk,word):fromhdfspycompss.blockimportBlockdata=Block(blk).read_block()...returnresultdefmain():importhdfspycompss.hdfsimportHDFSdfs=HDFS(host='localhost',port=9000)HDFS_BLOCKS=dfs.find_blocks('/input.data')nFrag=len(HDFS_BLOCKS)result=[{}forfinrange(nFrag)]forf,blkinenumerate(HDFS_BLOCKS):result[f]=wordcount(blk,'word')...
Package: hdhog
HDHogAboutFeaturesUsageScreenshotsNotesBrowse a folder, list files and subfolderssorted by size accross the directory treeso that you see the biggest first.AboutFree disk space. This tool makes it easy to conveniently clean big disk space consumers by listing big files and subfolders in a given folder,
out of which you can then select and delete.Only tested under Linux (Ubuntu) so far, but since all file operations are written generically, theoretically it should work for all OSes that that Python supports and that have Tk, though.FeaturesBrowse viewsFile listFolder listView as treeUsageInstall from PyPi:pip3installhdhogand runhdhogin a terminal.Or clone repository an install:gitclonehttps://github.com/mafleischer/HDHogcdHDHog
pip3install./and runhdhogin a terminal.Choose a folder to list. In either view you can select and delete items.ScreenshotsView filesView foldersView as treeNotesSymlinks are ignored and are not displayed for now
Package: hdhomerun-recorder
==================HDHOMERUN recorder==================Description:============A python program to record TV programs using hdhomerun tuner device.This should run on any system capable of running python andhdhomerun_config binary program.The hdhomerun tuner doesn't need much processing power for recordinga program. So this is mainly intended for embedded systems. I used iton my WDTV Live media player. Now I use it on my pogoplug runningarch Linux.INSTALL:========1. Install python if you don't have it already2. Install apscheduler from here. The old version of apscheduler hassome issues, so be sure to install the latest V2 version.(https://bitbucket.org/agronholm/apscheduler)3. Install this package.If you have "pip", do "pip install hdhomerun-recorder"ORIf you have "easy_install". do "easy_install install hdhomerun-recorder"ORYou can manually download the source from PyPI(https://pypi.python.org/pypi/hdhomerun-recorder), extract andthen install it using: "python setup.cfg install"4. Create config-filea) Run /usr/bin/hdhomerun_recorder_setup /etc/hdhomerun_recorder/config-fileThis will ask few question and write your tuner and channelmappings into /etc/hdhomerun_recorder/config-file file. Thiswill take some time as it scans your local channelsSee /etc/hdhomerun_recorder/config-file.example to see how anexample config file looks like. You may need to create one byhand if the above script fails to create one for any reason!5. Edit schedule fileSee /etc/hdhomerun_recorder/schedule-file.example for details.You have given schedule-file location in the config-file. Editthat file with what and when you want to record etc.6. Run /usr/bin/hdhomerun_recorder /etc/hdhomerun_recorder/config-file7. Optional: See the logfile for information or errors.8. Optional. I have included /etc/hdhomerun_recorder/hdhomerun_recorder.servicefile that can be used to automatically start the recording daemonon reboot.  You can stop, start and reload the daemon. The"reload" option is useful when you update schedule file and youwant the daemon to use the changes. Of course, this only works ifyou have systemd based start up (latest Ubuntu, Fedora, Archdistros will have)!a) Install servicecp /etc/hdhomerun_recorder/hdhomerun_recorder.service /etc/systemd/system/b) Enable the servicesystemctl enable hdhomerun_recorder.servicec) Start the service nowsystemctl start hdhomerun_recorder.serviceRun "systemctl reload hdhomerun_recorder.service" whenever you change your schedule-file!
Package: hdhrhttpapi
HDHRHTTPAPIThis is a small library to retrieve information from HD HomeRun devices through the HTTP API and Python 3.BackgroundTheHDHomeRun productsfrom SiliconDust are basically network TV tuners that broadcast the TV from
your cable or antenna to your network. These devices has one or more built-in TV tunners so it allows to
stream the TV channels to theSiliconDustand third-party applications.There are several libraries for different lenguages to communicate to HDHomeRun devices but I develop this
library to use with homeassistant, a home automation software, to allow it to know the state of the tunners.As homeassistant is developed in Python and has some restrictions to access directly to the I/O hardware, so
this library has only some functions needed for my custom component for home assistant.The goals of this project were as follows:Be able to discover the device(s) on the local network.Be able to poll for status.For a tuner to be able to get the current channel.All goals have been completed.RequirementsThere is not other additional requirements than Python 3.8. Other versions not tested.Development ResourcesNat Burnscustom component for home assistantSiliconDust HD HomeRun development guideHDHomeRun HTTP Development Guide
Package: hdidx
HDIdx: Indexing High-Dimensional DataWhat isHDIdx?HDIdxis a python package for approximate nearest neighbor (ANN)
search. Nearest neighbor (NN) search is very challenging in
high-dimensional space because of the*Curse of
Dimensionality*problem. The basic idea ofHDIdxis to compress the original feature
vectors into compact binary codes, and perform approximate NN search
instead of extract NN search. This can largely reduce the storage
requirements and can significantly speed up the search.ArchitectureHDIdxhas three main modules: 1)Encoderwhich can compress the
original feature vectors into compact binary hash codes, 2)Indexerwhich can index the database items and search approximate nearest
neighbor for a given query item, and 3)Storagemodule which
encapsulates the underlying data storage, which can be memory or NoSQL
database like LMDB, for theIndexer.The current version implements following feature compressing algorithms:Product Quantization[1].Spectral Hashing[2].To use HDIdx, first you should learn aEncoderfrom some learning
vectors. Then you can map the base vectors into hash codes using the
learnedEncoderand building indexes over these hash codes by anIndexer, which will write the indexes to the specified storage
medium. When a query vector comes, it will be mapped to hash codes by
the sameEncoderand theIndexerwill find the similar items to
this query vector.InstallationHDIdxcan be installed bypip:[sudo]pipinstallcython[sudo]pipinstallhdidxBy default,HDIdxuse kmeans algorithm provided by*SciPy*. To be more efficient, you can
install python extensions of*OpenCV*, which
can be installed viaapt-geton Ubuntu. For other Linux
distributions, e.g. CentOS, you need to compile it from source.[sudo]apt-getinstallpython-opencvHDIdxwill use*OpenCV*automatically if it
is available.Windows GuideGeneral dependencies:AnacondaMicrosoft Visual C++ Compiler for
PythonAfter install the above mentioned software, download`stdint.h<http://msinttypes.googlecode.com/svn/trunk/stdint.h>`__
and put it under theincludefolder of Visual C++, e.g.C:\Users\xxx\AppData\Local\Programs\Common\Microsoft\VisualC++ for Python\9.0\VC\include.
Then hdidx can be installed bypipfrom theAnaconda Command
Prompt.ExampleHere is a simple example. See thisnotebookfor more examples.# import necessary packagesimporthdidximportnumpyasnp# generating sample datandim=16# dimension of featuresndb=10000# number of dababase itemsnqry=10# number of queriesX_db=np.random.random((ndb,ndim))X_qry=np.random.random((nqry,ndim))# create Product Quantization Indexeridx=hdidx.indexer.IVFPQIndexer()# build indexeridx.build({'vals':X_db,'nsubq':8})# add database items to the indexeridx.add(X_db)# searching in the database, and return top-10 items for each queryids,dis=idx.search(X_qry,10)printidsprintdisReference[1] Jegou, Herve, Matthijs Douze, and Cordelia Schmid.
    "Product quantization for nearest neighbor search."
    Pattern Analysis and Machine Intelligence, IEEE Transactions on 33.1 (2011): 117-128.
[2] Weiss, Yair, Antonio Torralba, and Rob Fergus.
    "Spectral hashing."
    In Advances in neural information processing systems, pp. 1753-1760. 2009.
Package: hdiiofficial
This project is archived for the time being.HDevpyAn add-on extension toPyrogramfor absolute beginners, to create telegram bots more easily.DocumentationRead the Documentation :https://hdiiofficial.sytes.net/docsWhat's the main purpose?There are some things that are common in most of the telegram bots. This library simplifies the development process by generating boilerplate code and easing the overall creation.FeaturesAddons- Any project usinghdiiofficialcan enable in-built addons that come with pystark. For example, default addons automatically add some commands like/start,/help,/aboutand/id.Of course that can be turned off usingproject settingsDatabases- There are in-built functions that help in using databases. The databases includepostgres,tinydbandtelegramdbTo know more about using databases in hdiiofficial -Click HereBoilerplate Code- You can generate a boilerplate with all the files you will need using our command line utility. Optionally, you can generate it with added Heroku Support (app.json, Procfile, etc.).To know more, about generating a boilerplate -Click HereEasy-To-Use- There are multiple things that make this library easy-to-use.Start the bot using only 2-lines of code.fromhdiiofficialimportHDevHDev().activate()Easy-to-use methods and decorators.Ability to customize everything.And much more.CreditsDanfor hisPyrogramLibrary on top of whichpystarkworks.Thanks ToPyStark Telegram Updates -pystarkTelegram Channel -StarkBotsTelegram Chat -StarkBotsChatCopyright and LicenseCopyright (C) 2021-2022Stark Bots<https://github.com/StarkBotsIndustries>@hdiiofficialLicensed under the terms ofGNU General Public License v3 or later (GPLv3+)
Package: hdi-utils
hdi-utilsGeneral importing and utility functions for high-dimensional image data. hdi-utils is a general utility toolbox built as part of theMIAAIMimage analysis workflowUsageInstall hdi-utils with pip and github with the following command:pipinstall-egit+https://github.com/JoshuaHess12/hdi-utils.git@main#egg=hdi-utilsAll modules are located within thehdiutilsfolder, and they can be imported withimport hdiutilswithin Python. The structure of each module is located within module folders.Current ModulesCurrent modules and a brief description within hdi-utils can be found below:ModuleDescriptionPython UsageHDIimportimage data importingfrom hdiutils import HDIimport
Package: hdk
ZHDKOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimporthdkhdk.hello_world()
Package: hdlcomposer
# HDL Composer <img src=”/docs/img/icon.png” alt=”HDL Composer” width=”48px”>
### HDL simulation and autoverification made agile<br>#### :information_source: Most relevant features:star:Automate simulation runs with parameter randomizationstar:Generate stimuli HDL packagesstar:Automate verification of simulation resultsstar:Parse HDL and build object-oriented representations of your designstar:Automatic sources location and compilationstar:Compile and include vendor librariesstar:Handle multiple testbenches with several user-defined wave views in a single runstar:Display and save waveforms with GtkWavestar:Diff simulation result waveformsstar:Replay ILA / ChipScope captures in simulation
Package: hdlcontroller
HDLCcontroller written in Python and based on thepython4yahdlcPython module to encode and
decode the HDLC frames.InstallationFrom PyPI (recommended)pip3 install --upgrade hdlcontrollerFrom sourcesgit clone https://github.com/SkypLabs/python-hdlc-controller.git
cd python-hdlc-controller
pip3 install --upgrade .DocumentationThe full documentation is availablehere.LicenseMIT