Saravanan666/pypi-org huggingface dataset:
Package: hdcorepy
# Hdcorepy
A simple python wrapper around [HostDime](http://www.hostdime.in/)’sclient [API](https://api.hostdime.com/).## Installing as a Python ModuleThe recommended method of installation is via [pip](https://pypi.org/project/pip/)# Install pip
yum install python-pip (CentOS)apt-get install python-pip (Ubuntu)# Install hdcorepy
pip install hdcorepy## Python Usage~~
from hdcorepy import Client
hdinClient = Client(privateKey, publicKey)
response = hdinClient.call(“server.list”)
print response
~~## ReferenceFor a comprehensive overview of the HostDime API, please see our [API reference](https://api.hostdime.com/docs/).## License - MITPermission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
“Software”), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: hdcproto
The HDC protocolThe purpose of theHost Device Communicationprotocol is to simplify the
communication between the firmware of a device with severely limited computing resources and the software
on the computer to which it's connected via a serial communication link, like UART / USB-CDC / Virtual COM Port.Python implementation of the HDC protocolThehdcprotopackage contains the Python implementation of the HDC protocol, and it's mainly meant for the
implementation of HDC-host software.Implementing HDC-devices in Python offers some interesting possibilities:Mocking an HDC-device:For demonstration purposes of the HDC-host software, whenever a physical device is not available.For testing purposes, to create test-scenarios for the HDC-host software, which would otherwise
be difficult to recreate on a physical HDC-device. e.g.: A Continuous Integration build server.Implementing an actual HDC-device on sufficiently powerful hardware:Because you can. ;-)In most cases, though, you would be better off using more conventional technologies
likegRPCorRESTful APIWARNING: The HDC-device implementation in thehdcprotopackage is still work in progress.InstallationThehdcprotopackage is currently in pre-alpha stage, thus might be not listed by default in your graphical
package management tool.You can install it into your project's virtual environment via the usual command:pipinstallhdcprotoAttention testers!Installing from the alternative Test-PyPi repository isa bit trickier:pipinstall--index-urlhttps://test.pypi.org/simple/--extra-index-urlhttps://pypi.org/simple/hdcprotoAlternatively, if you take care of installing anymissing dependencyyourself:pipinstall--index-urlhttps://test.pypi.org/simple/--no-depshdcprotoUsageThehdcproto.host.proxymodule implements generic proxy base-classes that can be used as building blocks to
define proxy-classes that are specific for a given device and provide a convenient API for the HDC-host
software to interact with the HDC-device.Have a look at thehdcproto.demo.minimalsub-package, where you'll see an example of howproxy-classes are definedandhow they are usedto communicate with an HDC-device running theDemo_Minimal firmwareexample.
Package: hddm-wfpt
This little convenience package extracts the cython basedNavarro and Fusslikelihoods from theHDDMpackage for fitting hierarchical versions of theDrift Diffusion Model.The code of this repo is essentially taken from theHDDMpackage.The Copyright information for the original package is:Author:Thomas V. Wiecki, Imri Sofer, Mads L. Pedersen, Alexander Fengler, Lakshmi Govindarajan, Krishn Bera, Michael J. FrankContact:thomas.wiecki@gmail.com,imri_sofer@brown.edu,madslupe@gmail.com,alexander_fengler@brown.edu,krishn_bera@brown.edu,michael_frank@brown.eduWeb site:https://hddm.readthedocs.ioGithub:http://github.com/hddm-devs/hddmMailing list:https://groups.google.com/group/hddm-users/Copyright:This document has been placed in the public domain.License:HDDM is released under the BSD 2 license.Version:0.9.7
Package: hdecompose
HdecomposeDecomposition of Hydrogen content of simulation particles into neutral/ionized and atomic/molecular. Implementations of Blitz & Rosolowsky (2006) and Rahmati et al (2013).Installation:From pypi:'pip install hdecompose'From github:Download via web UI, or 'git clonehttps://github.com/kyleaoman/Hdecompose.git'Install dependencies if necessary (see 'setup.py'), some may be found in other repositories by kyleaoman.Global install (Linux):cd to directory with 'setup.py'run 'sudo pip install -e .' (-e installs via symlink, so pulling repository will do a 'live' update of the installation)User install (Linux):cd to directory with 'setup.py'ensure '~/lib/python3.7/site-packages' or similar is on your PYTHONPATH (e.g. 'echo $PYTHONPATH'), if not, add it (perhaps in .bash_profile or similar)run 'pip install --prefix ~ -e .' (-e installs via symlink, so pulling repository will do a 'live' update of the installation)cd to a directory outside the module and launch python; you should be able to do 'from Hdecompose import *'
Package: hdeps
hdepsSimple dependency not-a-solver lets you debug where backtracking would happen,
or figure out what would change on a platform that's not the same as you're
running right now.This code was originally part ofhonestybut is easier to iterate on with pypi-simple as its source.$hdepsrequests
...
$hdeps--install-orderrequests
...
$hdeps--haveurllib3==1.999requests
...Why isn't it a solver?Think of this as a debugging solver.  It doesn't come up with one single
solution, but does the bulk of the legwork to let you, the human, figure out
what the problematic part of your dep tree is (even if the machine you're
running on isn't the same as you're trying to figure out).If you want a real solver, I highly recommend look atresolvelibfor low-level operations, orpoetrywhich includes a higher-level solver
that keeps track of operations (like "upgrade" separately from "install").Version CompatThis project should work on 3.10-3.12, including mypy compatibility as checked
by tests.  Linting on older versions will not catch all issues (e.g. whitespace
in f-strings), so 3.12 is recommended.  Some transitive dependencies
(pydantic-core and libcst) rely on binary wheels that are not available yet on
3.13 and do not easily build from source.LicenseMIT, seeLICENSEfor details.
Package: hd-es-tools
For complete documentation, check out theMongo Connector Wiki.System Overviewmongo-connectorcreates a pipeline from a MongoDB cluster to one or more
target systems, such as Solr, Elasticsearch, or another MongoDB cluster.  It
synchronizes data in MongoDB to the target then tails the MongoDB oplog, keeping
up with operations in MongoDB in real-time. Detailed documentation is
available on thewiki.Getting Startedmongo-connector supports Python 2.6, 2.7, and 3.3+ and MongoDB versions
2.6, 3.0, 3.2, and 3.4.InstallationTo install mongo-connector with the MongoDB doc manager suitable for
replicating data to MongoDB, usepip:pip install mongo-connectorThe install command can be customized to include theDoc Managersand any extra dependencies for the target system.Target SystemInstall CommandMongoDBpip installmongo-connectorElasticsearch 1.xpip install'mongo-connector[elastic]'Amazon Elasticsearch 1.x Servicepip install'mongo-connector[elastic-aws]'Elasticsearch 2.xpip install'mongo-connector[elastic2]'Amazon Elasticsearch 2.x Servicepip install'mongo-connector[elastic2-aws]'Elasticsearch 5.xpip install'mongo-connector[elastic5]'Solrpip install'mongo-connector[solr]'You may have to runpipwithsudo, depending
on where you’re installing mongo-connector and what privileges you have.DevelopementYou can also install the development version of mongo-connector
manually:git clone https://github.com/mongodb-labs/mongo-connector.git
pip install ./mongo-connectorUsing mongo-connectormongo-connector replicates operations from the MongoDB oplog, so areplica
setmust be running before startup. For development purposes, you may find
it convenient to run a one-node replica set (note that this isnotrecommended for production):mongod --replSet myDevReplSetTo initialize your server as a replica set, run the following command in
the mongo shell:rs.initiate()Once the replica set is running, you may start mongo-connector. The
simplest invocation resembles the following:mongo-connector -m <mongodb server hostname>:<replica set port> \
                -t <replication endpoint URL, e.g. http://localhost:8983/solr> \
                -d <name of doc manager, e.g., solr_doc_manager>mongo-connector has many other options besides those demonstrated above.
To get a full listing with descriptions, trymongo-connector--help.
You can also use mongo-connector with aconfiguration file.If you want to jump-start into using mongo-connector with a another particular system, check out:Usage with SolrUsage with ElasticsearchUsage with MongoDBDoc ManagersElasticsearch 1.x:https://github.com/mongodb-labs/elastic-doc-managerElasticsearch 2.x and 5.x:https://github.com/mongodb-labs/elastic2-doc-managerSolr:https://github.com/mongodb-labs/solr-doc-managerThe MongoDB doc manager comes packaged with the mongo-connector project.Troubleshooting/QuestionsHaving trouble with installation? Have a question about Mongo Connector?
Your question or problem may be answered in theFAQor in thewiki.
If you can’t find the answer to your question or problem there, feel free toopen an issueon Mongo Connector’s Github page.
Package: h-devops
h_devops v0.3.2Python support package from 3.6 and upHow to installPlease run this command on your terminal window 🎉️$pipinstallh-devopsor$pip3installh-devopsHow to useWelcome to my world 😄Now you can use the package with aliash-devops$h-devops--helpOh !! You will most likely encounter problems during use. I'm so sorry about this. :'(If you don't mind, you canfeedbackthe error to me by email:levuthanhtung11@gmail.comI am so happy about that ❤️Author: Võ Hoàng
Package: hdex
Installation'''pip install hdex'''How to use it?It will generate words using keywords
Patterns 1987keyword keyword07 113@keyword keyword@67233 1987Keyword KEYWORD@67233
hdex.crunch(lowerlimit, upperlimit, ["keyword", "another keyword"], "special char", "file name")
to generate file
use any number at the place of "file name" for returning string'''hdex.crunch(4, 5, ["xyz", "abc", "ijkl"], "@", "dictionary.txt")'''
It will generate a file distionary'''str = hdex.crunch(4, 5, ["xyz", "abc"], "@", 1)'''
It will return values without generating any filelower limit must be greater than 0
upper limit required
string must have at least one value ["name"]
only one special character requiredLicenseÂ© Harsh Pratap Bhardwaj
This repository is licensed under the MIT license.
See LICENSE for details.ContactMail:harshpratap@outlook.in
Package: hdf5extractor
hdf5-extractorExtract mini hdf5 files from an epc and an H5 file.
The mini-h5 are created by finding Datasets referenced from the epc. Each representation will have its own mini-h5 file.installation :With poetry :poetry add hdf5extractorWith pip :pip install hdf5extractorRun :Extract a small h5 from a bigger one, to only have dataset of a specific resqml file :extracth5 -i myResqmlFile.xml --h5 myH5File.h5 -o outputFolderExtract every h5 parts from a bigger one, to only have in each, the dataset of a specific resqml file inside an epc :extracth5 -i myEPCFile.epc --h5 myH5File.h5 -o outputFolder
Package: hdf5matfile
Load data from v7.3 *.mat files. Only reading is supported, no writing.UsageProvides a classHdf5Matfileand a convience functionload_hdf5mat.To load all the variables from the file, useHdf5Matfile.load_file:withHdf5Matfile(filename)asfile:data=file.load_file()To fully load a specific variable from disk, useHdf5Matfile.load_variable:withHdf5Matfile(filename)asfile:results=file.load_variable('results')For partial loading, a mapping/dict-like interface is also supported:withHdf5Matfile(filename)asfile:results=file['results']time=results[0,:]disp=results[1,:]...If you’re not using a context manager, make sure to close the file after
you’re done:file=Hdf5Matfile(filename)data=file.load_file()...file.close()By default, arrays are not squeezed; since MATLAB represents even scalars
as 2-D arrays, this means that something you expect to be a scalar will in
fact be a 1-by-1 np.ndarray. You can change this by passingsqueeze=Trueto the constructor:withHdf5Matfile(filename,squeeze=True)asfile:data=file.load_file()Supported data typesData type support is pretty limited; this isn’t a terribly fancy class.
Supported MATLAB data types, and the Python objects or NumPy dtypes they map
to:MATLAB typePython objectNumPy dtypecellnp.ndarrayobjectcharstrn/adoublenp.ndarraynp.doubleint8np.ndarraynp.int8int16np.ndarraynp.int16int32np.ndarraynp.int32int64np.ndarraynp.int64logicalnp.ndarraynp.bool8singlenp.ndarraynp.singlestruct (scalar)dictn/astruct (array)np.ndarrayobject (dict)uint8np.ndarraynp.uint8uint16np.ndarraynp.uint16uint32np.ndarraynp.uint32uint64np.ndarraynp.uint64
Package: hdf5objects
FeaturesTODORequirementsTODOInstallationYou can installhdf5objectsviapipfromPyPI:$pipinstallhdf5objectsUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,hdf5objectsis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: hdf5pickle
Create easily interfaceable representations of Python objects in HDF5
files. The aim of this module is to provide bothconvenient Python object persistencecompatibility with non-Python applicationsPoint 2 is useful, for example, if results from numerical
calculations should be easily transferable for example to a non-Python
visualization program. For example, if program state is serialized to
a HDF5 file, it can easily be examined with for exampleOctave.
Package: hdf5plugin
hdf5pluginprovidesHDF5 compression filters(namely: Blosc, Blosc2, BitShuffle, BZip2, FciDecomp, LZ4, SZ, SZ3, Zfp, ZStd) and makes them usable fromh5py.Seedocumentation.InstallationTo install, run:pip install hdf5plugin [--user]or, with conda (https://anaconda.org/conda-forge/hdf5plugin):conda install -c conda-forge hdf5pluginTo install from source and recompile the HDF5 plugins, run:pip install hdf5plugin --no-binary hdf5plugin [--user]Installing from source can achieve better performances by enabling AVX2 and OpenMP if available.For more details, see theinstallation documentation.How-to useTo use it, just useimport hdf5pluginand supported compression filters are available fromh5py.For details, seeUsage documentation.LicenseThe source code ofhdf5pluginitself is licensed under theMIT license.Embedded HDF5 compression filters are licensed under different open-source licenses:
see thelicense documentation.
Package: hdf5-reader-service
Microservice for reading HDF5 data and serving it via REST, aimed at performance and concurrencyPyPIpip installhdf5-reader-serviceSource codehttps://github.com/DiamondLightSource/hdf5-reader-serviceDocumentationhttps://DiamondLightSource.github.io/hdf5-reader-serviceReleaseshttps://github.com/DiamondLightSource/hdf5-reader-service/releasesTo run a basic server:hdf5-reader-service --host <host> --port <port>Seehttps://DiamondLightSource.github.io/hdf5-reader-servicefor more detailed documentation.
Package: hdf5tools
This git repository contains a python package with an H5 class to load and combine one or more HDF5 data files (or xarray datasets) with optional filters. The class will then export the combined data to an HDF5 file, file object, or xr.Dataset. This class is designed to be fast and safe on memory. This means that files of any size can be combined and saved even on a PC with low memory (unlike xarray).TODO:Create a sel method for the File class. This should mimic xarray, but create a copy of the File with optional saving to a local file just like creating a new file using the File class. At least initially to make it easier, just do straightforward indexing like is done with the LocationIndexer which gets copied over to a newly created File/dataset object.The to_pandas method in datasets should be similar to xarray, which creates pandas indexes. The to_pandas method for the File class should concat all of the datasets to_pandas output together.The to_xarray method for both Dataset and File should use the existing hdf5 local file if it exists and close the file via h5py before linking to it via the xarray open_dataset.
Package: hdf5_vds_check
hdf5-vds-checkCheck access to the source files of virtual datasetsWhen you read a virtual dataset, HDF5 will skip over source files it can't open,
giving you the virtual dataset's fill value instead.
It's not obvious whether you have a permissions problem, a missing file, or
a genuinely empty part of the dataset.This tool checks all virtual datasets in a file to alerts you to any
problems opening the source files.Installpip install hdf5_vds_checkUsagehdf5-vds-check file_with_virtual_datasets.h5If any sources for virtual datasets can't be accessed, it will list details of
these, and then exit with status 1.
Package: hdf5widget
This is hdf5widget, a widget for viewing the contents of aHDF5-file inJupyterNotebooks usingipywidgets.InstallInstalling with pippip install hdf5widgetInstalling with condaconda install -c mrossi hdf5widgetUsageAssume you have a HDF-filefile.hdf5.fromhdf5widgetimportHDF5WidgetHDF5Widget('file.hdf5').widget
Package: hdface
hdfacehttps://github.com/hanson-young/hdfacepython3 -m pip install --user --upgrade setuptools wheelpython3 setup.py sdist bdist_wheel
pip install hdface-0.1.2-py3-none-any.whltwine upload dist/*
python3 -m twine upload --repository-urlhttps://test.pypi.org/legacy/dist/*pip install hdface==0.1.2
pip uninstall hdface
Package: hdf-compass
Welcome to the project!  HDF Compass is an experimental viewer program for
HDF5 and related formats, designed to complement other more complex
applications like HDFView.  Strong emphasis is placed on clean minimal design,
and maximum extensibility through a plugin system for new formats.HDF Compass is written in Python, but ships as a native application on
Windows, OS X, and Linux, by using PyInstaller and Py2App to package the app.Binary executables are available for Windows (Windows 7 or later) and Mac OS X (Yosemite or later) at
the Project Page listed below.Bug reports and pull requests are welcome!  For non-trivial PRs please
open an issue first, so the core developers can give feedback on your idea.Development EnvironmentYou will need:Python 2.7(support for Python 3.4+ in progress)NumPyMatplotlibwxPython Phoenix 3.0.2(later releases have not been tested)Cartopyh5py[HDF plugin]hydroffice.bag[BAG plugin]Pydap[OPeNDAP plugin]Requests[HDF Rest API plugin]For packaging the app:PyInstaller(Linux & Windows, experimental for OS X)(>= 3.0)Py2App(OS X)Running the Program$ python HDFCompass.pyNote: If you are using the Anaconda distribution on the Mac, you will see the
message: “This program needs access to the screen.  Please run with a Framework
build of python…”.  In this case use the pythonw command:$ pythonw HDFCompass.pyNote: on Mac, HDF Compass doesn’t create an initial window, use the system Application
menu to open a file or remote resource.Packaging on OS X$ python setup.py py2appPackaging on WindowsSingle-file:$ pyinstaller--clean-yHDFCompass.1file.specSingle-folder (useful for debugging thepyinstallersettings):$ pyinstaller--clean-yHDFCompass.1folder.specOther infoGithub:http://github.com/HDFGroup/hdf-compassProject page:https://www.hdfgroup.org/projects/compass/License: BSD-like HDF Group license (SeeCOPYING)
Package: hdfdict
hdfdict helps h5py to dump and load python dictionariesIf you have a hierarchical data structure of numpy arrays in a dictionary for example, you can use this tool to save this dictionary into a h5pyFile()orGroup()and load it again.
This tool just maps the hdfGroupsto dictkeysand theDatsetto dictvalues.
Only types supported by h5py can be used.
The dicitonary-keys need to be strings until now.A lazy loading option is activated per default. So big h5 files are not loaded at once. Instead a dataset gets only loaded if it is accessed from the LazyHdfDict instance.Exampleimporthdfdictimportnumpyasnpd={'a':np.random.randn(10),'b':[1,2,3],'c':'Hallo','d':np.array(['a','b']).astype('S'),'e':True,'f':(True,False),}fname='test_hdfdict.h5'hdfdict.dump(d,fname)res=hdfdict.load(fname)print(res)Output:{'a': <HDF5 dataset "a": shape (10,), type "<f8">, 'b': <HDF5 dataset "b": shape (3,), type "<i8">, 'c': <HDF5 dataset "c": shape (), type "|O">, 'd': <HDF5 dataset "d": shape (2,), type "|S1">, 'e': <HDF5 dataset "e": shape (), type "|b1">, 'f': <HDF5 dataset "f": shape (2,), type "|b1">}This are all lazy loding fields in the resultres.
Just callres.unlazy()ordict(res)to get all fields loaded.
If you only want to load specific fields, just use item access e.g.res['a']so only field 'a' will be loaded from the file.print(dict(res))`Output:{'a': array([-0.47666824,  0.11787749,  0.51405835, -1.49557787, -0.33617182, -0.22381693,  0.25966526,  0.58160661,  0.17019176,  1.3167669 ]), 'b': array([1, 2, 3]), 'c': 'Hallo', 'd': array([b'a', b'b'], dtype='|S1'), 'e': True, 'f': array([ True, False])}Installationpip install hdfdictpoetry install hdfdictgit clone https://github.com/SiggiGue/hdfdict.gitandpython hdfdict/setup.py install
Package: hdfe
This package contains functionality useful for econometric work with panel data.
Its name, originally standing for "high-dimensional fixed effects," is now misleading.Useful features areGroupby: A class allowing for fast operations similar to Pandas groupby-apply and groupby-transform
functionality, but performing significantly faster with user-written functions. See
documentationhere.find_collinear_cols and remove_collinear_cols: Functions
for dealing with multicollinearity which operate quickly on CSC matrices.make_lags: Makes within-group lags (frequently useful with panel data)You can install hdfe through pip: "pip install hdfe"
Package: hdfio
hdfioAll the useful python I/O utility functions for HDF5 filesRationaleWriting this software package aggregator is motivated by the multitude of read/write functions currently existing in the python open source community. The package combines existing mature hdf(5) I/O source code and a few similar isolated functions into a single simple and user-friendly platform to save the labor of writing newer functions of similar capabilities. It's also the basis for building more sophisticate I/O operations.ResourcesThe major resources gathered here include the following packages that covers generic I/O and interconversions between HDF and other popular key-value-pair file formats, such asdictionary(Python),json,mat(MATLAB).h5pysilxdeepdishh5ioInstallationInstall and upgrade to the latest updatespip install --upgrade git+https://github.com/RealPolitiX/hdfio.gitInstall the latest build from PyPIpip install hdfio