Saravanan666/pypi-org huggingface dataset:
Package: hdstats
hdstatsA library of multivariate, high-dimensional statistics, and time series algorithms for spatial-temporal stacks.Geometric median PCMGeneration of geometric median pixel composite mosaics from a stack of data; seeexample.If you are using this algorithm in your research or products, please cite:Roberts, D., Mueller, N., & McIntyre, A. (2017). High-dimensional pixel composites from earth observation time series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264.Geometric Median Absolute Deviation (MAD) PCMAccelerated generation of geometric median absolute deviation pixel composite mosaics from a stack of data; seeexample.If you are using this algorithm in your research or products, please cite:Roberts, D., Dunn, B., & Mueller, N. (2018). Open data cube products using high-dimensional statistics of time series. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 8647-8650).Feature generation for spatial-temporal time series stacks.seeexample.AssumptionsWe assume that the data stack dimensions are ordered so that the spatial dimensions are first (y,x), followed by the spectral dimension of sizep, finishing with the temporal dimension. Algorithms reduce in the last dimension (typically, the temporal dimension).Research and Development / Advanced ImplementationsAll advanced implementations and cutting-edge research codes are now found ingithub.com/daleroberts/hdstats-private. These are only available to research collaborators.
Package: hdt
Read and query HDT document with ease in PythonOnline DocumentationRequirementsPythonversion 3.6.4 or higherpipgcc/clangwithc++11 supportPython Development headers> You should have thePython.hheader available on your system.
> For example, for Python 3.6, install thepython3.6-devpackage
on Debian/Ubuntu systems.Then, install thepybind11
librarypip install pybind11InstallationInstallation in avirtualenvisstrongly advised!Pip install (recommended)pip install hdtManual installationgit clone https://github.com/Callidon/pyHDT
cd pyHDT/
./install.shGetting startedfromhdtimportHDTDocument# Load an HDT file.# Missing indexes are generated automatically, add False as the second argument to disable themdocument=HDTDocument("test.hdt")# Display some metadata about the HDT document itselfprint("nb triples:%i"%document.total_triples)print("nb subjects:%i"%document.nb_subjects)print("nb predicates:%i"%document.nb_predicates)print("nb objects:%i"%document.nb_objects)print("nb shared subject-object:%i"%document.nb_shared)# Fetch all triples that matches { ?s ?p ?o }# Use empty strings ("") to indicates variablestriples,cardinality=document.search_triples("","","")print("cardinality of { ?s ?p ?o }:%i"%cardinality)fortripleintriples:print(triple)# Search also support limit and offsettriples,cardinality=document.search_triples("","","",limit=10,offset=100)# etc ...Handling non UTF-8 strings in pythonIf the HDT document has been encoded with a non UTF-8 encoding the
previous code won’t work correctly and will result in aUnicodeDecodeError. More details on how to convert string to str
from c++ to pythonhereTo handle this we doubled the API of the HDT document by adding:search_triples_bytes(...)return an iterator of triples as(py::bytes,py::bytes,py::bytes)search_join_bytes(...)return an iterator of sets of solutions mapping aspy::set(py::bytes,py::bytes)convert_tripleid_bytes(...)return a triple as:(py::bytes,py::bytes,py::bytes)convert_id_bytes(...)return apy::bytesParameters and documentation are the same as the standard versionfromhdtimportHDTDocument# Load an HDT file.# Missing indexes are generated automatically, add False as the second argument to disable themdocument=HDTDocument("test.hdt")it=document.search_triple_bytes("","","")fors,p,oinit:print(s,p,o)# print b'...', b'...', b'...'# now decode it, or handle any errortry:s,p,o=s.decode('UTF-8'),p.decode('UTF-8'),o.decode('UTF-8')exceptUnicodeDecodeErroraserr:# try another other codecspass
Package: hdtgraph
HDT poly-logarithmic fully-dynamic connectivity algorithmImplementation of the dynamic graph data structure described by Holm,
de Lichtenberg and Thorup in 2001 (HDT algorithm).The graph is represented as an ensemble of spanning forests where each spanning
tree is stored as a balanced binary Euler tour tree. This enables connectivity
queries in O(log n) as well as insertions and deletions in poly-logarithmic
time.References:Jacob Holm, Kristian de Lichtenberg, and Mikkel Thorup.
Poly-logarithmic deterministic fully-dynamic algorithms for
connectivity, minimum spanning tree, 2-edge, and biconnectivity.
J. ACM, 48(4):723–760, July 2001.Monika Rauch Henzinger, Valerie King. Randomized fully dynamic graph
algorithms with polylogarithmic time per operation. J. ACM 46(4)
July 1999. 502–536.
Package: hdtools
hdtoolsHD Tools for cyrptocurrencies like BTC, and BTCt
base onmcdallas/cryptotools.RequirementsCreate an virtualenv:virtualenv-ppython3.envInstall requirements:pipinstall-rrequirementsUse library:fromhdtools.extended_keysimport*private_key=XPrv.from_mnemonic('mnemonic phrase')private_key.encode()How to installpipinstallhdtoolsExamplesCreate HD Wallets>>>fromhdtools.extended_keysimport*>>>M=XPrv.from_mnemonic('lemon child success once board usual cigar buffalo video cheese kitten onion build axis dose')>>>M.encode()b'xprv9s21ZrQH143K38p5ouMV2qFYest2F3uRQC51JPLqsdi8Lh1rkXUJRUy1m7rd5TvooJn6gerthNmntuJag6e73mrf8GmG96Ua8rpayQtUEsL'Address Generation>>>(M/44./0./0./0/0).address('P2PKH')# BIP44b'1DgEh5Y6NioqaxHBBc2puDYq6SvG5NDsG9'>>>(M/49./0./0./0/0).address('P2WPKH-P2SH')# BIP49b'39Qn8kHG6h7zv1Fh1iwjjyeRibx7gHTq1Z'>>>(M/84./0./0./0/0).address('P2WPKH')# BIP84'bc1qrxxtlul9j3p95wrt33zg7vdf74skujnhnghaey'Run testspython3-muninttestRunsetup.pypythonsetup.pysdistbdist_wheel
Package: hdtopology
hdtopologyTopological data analysis library for NDDAV (n-dimensional data analysis and visualization) system (https://github.com/LLNL/NDDAV.git)Requirements:python3, swig3, numpy, cmake (3.12 or up), ANN (Approximate Nearest Neighbor, original repo:https://www.cs.umd.edu/~mount/ANN/, for compiling with CMake:https://github.com/dials/annlib)The hdtopology library also incorporated the core functionality of the NGL library (developed by Carlos Correa) for empty region graph computation, the full NGL code base can be access at:http://www.ngraph.org/InstallInstall (e.g.,apt-get install libann-devon linux, orport install annon mac) or compile ANN library from source.Compile the hdtopology library and python wrapper:mkdir buildcd buildcmake .. -DENABLE_PYTHON=ON -DANN_INCLUDE_DIR=path/to/ann/include -DANN_LIBRARY=path/to/ann/libANN.amake installReleased under LLNL-CODE-772013
Package: hdu
This software computes an estimate of the file disk usage for files and
directories, similarly to the UNIX program ‘du’.
The result is then displayed to the console in a human friendly format.It includes the following features:a progress bar of each item for quick visual inspectionpercentage information (precision can be set)show the size of each item in the specified unitssupport for SI, IEC and UNIX (‘du’) unitsmaximum depth (of the displayed items) can be specifiedoptionally follow symlinks, mount points, special files and hidden filescan filter results to display only directoriescan output results ending with ‘\0’ instead of newlines (useful for parsing)can sort results by name or by sizeInstallationThe recommended way of installing the software is throughPyPI:$pipinstallhduAlternatively, you can the clone the source repository fromGitHub:$mkdirhdu$cdhdu$gitclonegit@github.com:norok2/hdu.git$pythonsetup.pyinstall(last step may require additional permissions depending on your configuration)The shoftware does not have additional dependencies beyond Python and its
standard library.It was tested with Python 2.7 and 3.5.
Other version were not tested.NoteAlthough the software is ready, the packaging is still experimental.
If you experience any issues, please consider reporting it.
Suggestions and improvements are welcome!
Package: hdu-api
A simple SDK for HDU.hdu-api 是一个集结 HDU 所有教务管理服务的 SDK，提供了一卡通服务、考试、课表、选课和一些公共信息如空闲教室、上课时间等信息的 API。 hdu-api 主要基于 Requests 库和 Beautiful Soup 库写成。特性支持一卡通服务的信息查询支持教务管理系统的考试、课程等信息查询支持学生管理系统的信息查询支持 ihdu PC 版和手机版的信息查询易用，友好的 API基于 requests 库，支持每个网站的 session 使用和管理，重用性高自定义，对返回数据进行自定义化安装使用包管理器安装，如 pip:pip install hdu-api快速开始>>> import hdu_api
>>> hdu = hdu_api.HDU('学号', '密码')
>>> client = hdu.create()
>>> client.exam.schedule_current()
[{'classroom': '第12教研楼201',
  'course_name': '操作系统（甲）',
  'exam_time': '2019年1月17日(09:00-11:00)',
  'exam_type': '',
  'seat': '10',
  'select_code': '(2018-2019-1)-A0507050-06018-1',
  'staff_name': 'xxx'},

 ...

 {'classroom': '第6教研楼北308',
  'course_name': '软件工程（甲）',
  'exam_time': '2019年1月9日(13:45-15:45)',
  'exam_type': '',
  'seat': '24',
  'select_code': '(2018-2019-1)-A0507190-06061-2',
  'staff_name': 'xxx'}]

>>> client.card.balance()
[{'account_id': 'xxxxxx',
  'balance': '69.97',
  'card_id': 'xxxxxx',
  'staff_id': 'xxxxxx',
  'staff_name': 'xxx'}]文档https://liuxingran.gitbook.io/hdu-api/
Package: hdx-data-freshness
PyPI Package DEPRECATED - code fromhttps://github.com/OCHA-DAP/hdx-data-freshnessnow directly copied to Docker
image rather than installing from PyPI packageHDX data freshness is a process that runs against theHumanitarian Data Exchangeportal every day. It attempts to determine the freshness of datasets on the platform (ie. how up to date they
are) and updates the last modified field of resources held externally to HDX that have changed.For more information, please read thedocumentation.This library is part of theHumanitarian Data Exchange(HDX) project. If you have
humanitarian related data, please upload your datasets to HDX.
Package: hdx-data-freshness-dbclean
Utility to clean Freshness DatabaseDEPRECATED - code moved tohttps://github.com/OCHA-DAP/hdx-data-freshnessThis script cleans the freshness database.Usagepython -m hdx.freshness.dbactions [-db/--db_uri=] [-dp/--db_params=] [action]Either db_uri or db_params must be provided or the environment variable DB_URI
must be set. db_uri or DB_URI are of form:postgresql+psycopg://user:password@host:port/databasedb_params is of form:database=XXX,host=X.X.X.X,username=XXX,password=XXX,port=1234, ssh_host=X.X.X.X,ssh_port=1234,ssh_username=XXX, ssh_private_key=/home/XXX/.ssh/keyfileaction:"clone" which creates a shallow clone of the database which only
has all the runs and one dataset and its resources per run for testing
purposes."clean" (the default) cleans the database by removing runs according to these
rules:Keep a handful of runs around the end of each quarter all the way back to
the first run in 2017Keep daily runs going back 2 yearsKeep weekly runs from 2 to 4 years backKeep monthly runs for 4 years back and earlier
Package: hdx-data-freshness-emailer
DEPRECATED - code moved tohttps://github.com/OCHA-DAP/hdx-data-freshnessThe HDX freshness emailer reads the HDX data freshness database and finds datasets whose status has changed.
It sends emails to system administrators if the change is from overdue to delinquent or to maintainers if
from due to overdue. It also alerts when there are new candidates for the data grid and reports datasets that
are broken or which have invalid maintainers. An email is also sent for organisations with invalid
administrators.For more information, please read thedocumentation.This library is part of theHumanitarian Data Exchange(HDX) project. If you have
humanitarian related data, please upload your datasets to HDX.
Package: hdxms-datasets
HDXMS DatasetsFree software: MIT licenseInstallation$pipinstallhdxms-datasetsExample codefromhdxms_datasetsimportDataVaultvault=DataVault()# Download a specific HDX datasetvault.fetch_dataset("20221007_1530_SecA_Krishnamurthy")# Load the datasetds=vault.load_dataset("20221007_1530_SecA_Krishnamurthy")# Load the FD control of the first 'state' in the dataset.fd_control=ds.load_peptides(0,"FD_control")# Load the corresponding experimental peptides.peptides=ds.load_peptides(0,"experiment")
Package: hdxpop
hdxpopSimple tool to download High Resolution Population Density Maps from Humanitarian Data ExchangeFor usage go to the medium articleSamapriya Roy. (2020, March 31). samapriya/hdxpop: hdxpop: Simple tool to download High Resolution Population Density Maps from
Humanitarian Data Exchange (Version 0.0.3). Zenodo. http://doi.org/10.5281/zenodo.3735435Table of contentsPrerequisitesInstalling hdxpopUsing without installPrerequisitesThis assumes that you have native python & pip installed in your system, you can test this by going to the terminal (or windows command prompt) and trying. I recommend installation within virtual environment if you are worries about messing up your current environment.pythonand thenpip listIf you get no errors and you have python 3.2 or higher you should be good to go.Installing hdxpopOnce you have determined you have python, you can simply install hdxpop using two methodspip install hdxpopFor linux I found it helps to specify the pip type and use --user. Here pip refers to your default python and pip installations, assumption here is you are using python3 and up.pip install hdxpop --user

or

pip3 install porder --useror you can also trygit clone https://github.com/samapriya/hdxpop.git
cd hdxpop
python setup.py installUsing without installYou can do this too,by simply going downloading and unzipping the repo onto your machine and migrating to the innner hdxpop folder with the hdxpop.py file.you can then do apython3 hdxpop.py -hor simply if you have Git enabledgit clone https://github.com/samapriya/hdxpop.git
cd hdxpop/hdxpop/
python hdxpop.py -hChangelogv0.0.4Changes to search URL and fuzzy search to look for overall population datasets.v0.0.3Changes to search URL to include geotif, zipped only, goetiff and geotiff keyword filters.v0.0.2Added installation instructions and setup
Package: hdx-python-api
The HDX Python API Library is designed to enable you to easily develop code that
interacts with theHumanitarian Data Exchange(HDX)
platform. The major goal of the library is to make pushing and pulling data from HDX as
simple as possible for the end user.For more information, please read thedocumentation.This library is part of theHumanitarian Data Exchange(HDX) project. If you have humanitarian related data, please upload your datasets to
HDX.
Package: hdx-python-country
The HDX Python Country Library provides utilities to map between country and region
codes and names and to match administrative level names from different sources.
It also provides utilities for foreign exchange enabling obtaining current and historic
FX rates for different currencies.It provides country mappings including ISO 2 and ISO 3 letter codes (ISO 3166) and regions
using live official data from theUN OCHAfeed with
fallbacks to an internal static file if there is any problem with retrieving data from
the url. (Also it is possible to force the use of the internal static files.)It can exact match English, French, Spanish, Russian, Chinese and Arabic. There is a
fuzzy matching for English look up that can handle abbreviations in country names like
Dem. for Democratic and Rep. for Republic.Mapping administration level names from a source to a given base set is also handled
including phonetic fuzzy name matching.It also provides foreign exchange rates and conversion from amounts in local currency to
USD and vice-versa. The conversion relies on Yahoo Finance, falling back oncurrency-apifor current rates,
and Yahoo Finance falling back on IMF data via IATI for historic rates.For more information, please read thedocumentation.This library is part of theHumanitarian Data Exchange(HDX) project. If you have humanitarian related data, please upload your datasets to
HDX.
Package: hdx-python-scraper
The HDX Python Scraper Library is designed to enable you to easily develop code that
assembles data from one or more tabular sources that can be csv, xls, xlsx or JSON. It
uses a YAML file that specifies for each source what needs to be read and allows some
transformations to be performed on the data. The output is written to JSON, Google sheets
and/or Excel and includes the addition ofHumanitarian Exchange Language (HXL)hashtags specified in
the YAML file. Custom Python scrapers can also be written that conform to a defined
specification and the framework handles the execution of both configurable and custom
scrapers.For more information, please read thedocumentation.This library is part of theHumanitarian Data Exchange(HDX) project. If you have
humanitarian related data, please upload your datasets to HDX.
Package: hdx-python-utilities
The HDX Python Utilities Library provides a range of helpful utilities for Python developers.
Note that these are not specific to HDX.Easy downloading of files with support for authentication, streaming and hashingRetrieval of data from url with saving to file or from data previously savedDate parsing utilitiesLoading and saving JSON and YAML (inc. with OrderedDict)Dictionary and list utilitiesHTML utilities (inc. BeautifulSoup helper)Compare files (eg. for testing)Simple emailingEasy logging setup and error loggingPath utilitiesText processingEncoding utilitiesCheck valid UUIDEasy building and packagingFor more information, please read thedocumentation.This library is part of theHumanitarian Data Exchange(HDX) project. If you have
humanitarian related data, please upload your datasets to HDX.
Package: hdx-scraper-geonode
The HDX Scraper Geonode Library enables easy building of scrapers for extracting data
fromGeoNodeservers. GeoNode is a web-based application and
platform for developing geospatial information systems (GIS) and for deploying spatial
data infrastructures (SDI).For more information, please read thedocumentation.This library is part of theHumanitarian Data Exchange(HDX) project. If you have humanitarian related data, please upload your datasets to
HDX.
Package: hdytto
hdytto: How do you turn this onA library adding new syntax into your Python.
i.e. It is NOT Pythonic :)CompatibilityGetting StartedREPLhdytto ReferenceIncrement / Decrementdo...whileCommentconstCompatibilityPython >=3.8Getting StartedInstall fromPyPI:$pipinstallhdyttoThis library cannot be used byimport hdyttoas in other libraries, but the usage is simple. At first, makesitecustomize.py:fromhdyttoimportregister_hdyttoregister_hdytto()Set the environmental variablePYTHONPATHthe path to the directory containing thesitecustomize.py(e.g.export PYTHONPATH=.).
Then, runmain.py:# coding: hdytto

a = 5
print(a++)
print(a)The output will be5
6REPLAdditionally, make.pythonrc.py:importsysimportsitecustomizesys.stdin.reconfigure(encoding='hdytto')Set the environmental variablePYTHONSTARTUPthe path to this.pythonrc.py(e.g.export PYTHONSTARTUP=.pythonrc.py).
Then, typepython:$ python
>>> a = 5
>>> a++
5
>>> do:
...     b = ++a
...     while b < 10
...
>>> b
10hdytto ReferenceIncrement / DecrementSyntaxx++
++x
x--
--xExample# coding: hdytto
a = 5
print(a++)
print(++a)
b = 10 - --a
print(b--)Output:5
7
4do...whileSyntaxdo:
    statement
    while conditionExample# coding: hdytto
do:
    a = 5
    while a < 3
do:
    b = a * 2
    while a++ < 10:
        pass
    while b <= 20
print(b)Output:22CommentSyntax/* */Example# coding: hdytto
a = /* foo */ 10
print(a)
if a == 10:
    b = 5
    /*b = 10
print(a - 20)*/
print(b)Output:10
5constSyntaxconst name = valueExample# coding: hdytto

import time

def foo(x, const y):
    y = 20
    print(x + y)

print('long time process here.')
const a = 30
time.sleep(a) # a process requiring long time

foo(1, 2)Output:File "main.py", line 6
    y = 20
         ^
SyntaxError: invalid assignment to const y

SyntaxError: encoding problem: hdyttoThis isSyntaxError, so does not require running the process requiring long time.Cautionhdytto'sconstonly prohibits=,+=,-=,*=,/=,%=,&=,|=,^=,<<=,>>=,**=,//=,@=,:=, otherwise is allowed (e.g.const l = []andl.append(5)is allowed).
Package: hea-aiohttp-remotes
The library is a set of useful tools foraiohttp.webserver. It is a
hopefully temporary fork of the aiohttp-remotes project containing a fix for
parsing the X-Forwarded-Host header.The full list of tools is:AllowedHosts– restrict a set of incoming connections to
allowed hosts only.BasicAuth– protect web application bybasic authauthorization.Cloudflare– make sure that web application is protected
by CloudFlare.ForwardedRelaxedandForwardedStrict– processForwardedHTTP header and modify correspondingscheme,host,remoteattributes in strong secured and
relaxed modes.Secure– ensure that web application is handled by HTTPS
(SSL/TLS) only, redirect plain HTTP to HTTPS automatically.XForwardedRelaxedandXForwardedStrict– the same
asForwardedRelaxedandForwardedStrictbut process old-fashionX-Forwarded-*headers instead of new standardForwarded.Readhttps://aiohttp-remotes.readthedocs.iofor more information.The library was donated by Ocean S.A.https://ocean.io/Thanks to the company for contribution.
Package: headbot
Headbot.io Python ClientPython client for theheadbot.io API.Installingpip install headbotSupported versionsPython 3.6+Usageimportasynciofrompprintimportpprintfromheadbot.clientimportHeadbotAsyncClientasyncdefmain():asyncwithHeadbotAsyncClient(email="{email}",password="{password}")asclient:my_crawlers=awaitclient.crawlers()pprint(my_crawlers)if__name__=='__main__':loop=asyncio.get_event_loop()loop=loop.run_until_complete(main())
Package: head-context
head-contextEasily manage your assets in meta tags (scripts, css, preload etc.) from anywhere
in the template code (and outside).WhyImagine a form widget, which requires a heavy image processing library that we want to include ONLY IF the widget itself was rendered. Thanks tohead-contextyou can specify what resources you need locally (in template fragments, widgets and so on) yet load them in theheadsection of your page with ease.What does it do?<!doctype html><html><head><title>My Title!</title><!-- this is where we want all our js/css rendered to be rendered -->{{head_placeholder()}}</head><body>{%include"my-cool-component.html"%}</body></html>Andmy-cool-component.html:<!-- we can call these from anywhere and they will be automatically rendered in the right place! -->{%dopush_js('/static/cool-component.js',mode="async")%}{%dopush_css('/static/cool-component.css')%}{%dopush_preload('/static/some-image-we-need.png','image')%}<divclass="my-cool-component"><!-- ... ---></div>And that's pretty much it. You canpush_js/push_css/push_preloadfrom anywhere in the template (and even outside of templates) and it will be automatically attached to the page being rendered.FeaturesSupports scripts, styles and preload directivesWorks with Jinja2Can be used from Python code toosimply usehead_context.push_js/push_css/push_preloadfrom Python codeit needs to run during template rendering though (otherwise it wouldn't make sense)useful if you have form widget rendering written in Python for exampleor basically any kind of rendering written in PythonInstallation and setupSimply installhead-contextpackage:pipinstallhead-context# or with poetrypoetryaddhead-contextAdd extension to the Jinja2 environment:fromjinja2importEnvironmentenv=Environment()env.add_extension("head_context.jinja_ext.HeadContextExtension")and that's it! From now on you can usepush_css()/push_js()/push_preload()andhead_placeholder().Usage with FlaskTo use this extension withFlasksimply add it when configuring the app:defcreate_app():app=Flask("app",__name__)app.jinja_env.add_extension("head_context.jinja_ext.HeadContextExtension")app.jinja_env.add_extension("jinja2.ext.do")returnappFAQDoes this work withasyncio?head-contextusescontextvarsunder the hood, which are compatible withasynciobut it integrates withJinjain a way that won't work with templates which useasynciorendering. If you have any good ideas how to make it work a PR would be welcome.