Saravanan666/pypi-org huggingface dataset:
Package: heartandsole
heartandsole: Python library for analysis of running data filesTable of ContentsIntroductionDependenciesInstallationExampleLicenseDocumentationContactIntroductionheartandsole is designed to work with running activity files.
It reads data from.fit,.tcx,.gpx, and.csvfiles, converts it to
pandas data structures, then performs calculations and summarizes the data,
for example:elevation gainelapsed timetimer timedistance from GPS coordinatesDependenciesPandasandNumPyare required.A number of optional dependencies enable various features, such as
reading data from specific activity file formats and performing geospatial
calculations:fitparseallows data
to be read in from.fitfiles.activereaderallows data
to be read in from.tcxand.gpxfiles.pandas-xyzallows geospatial
calculations, like converting GPS coordinates to distance and determining
elevation gain along a route.Installationpip install heartandsoleto install.Exampleheartandsole provides theActivityclass.Activities can be constructed manually with a required records DataFrame
and optional summary Series and laps DataFrame, or they can be constructed
directly from various activity file formats usingActivity.from_*class
methods.fromheartandsoleimportActivity# Reading from a fit file requires the fitparse package to be# installed.activity=heartandsole.Activity.from_fit('my_activity.fit')# Various field accessors provide methods related to specific data fields# commonly found in activity files.print(activity.time.elapsed(source='records'))print(activity.time.timer(source='summary'))# Geospatial calculations require pandas-xyz to be installed.print(activity.elevation.gain(source='records'))# scalarprint(activity.distance.records_from_position())# SeriesLicenseThis project is licensed under the MIT License. SeeLICENSEfile for details.DocumentationThe official documentation is hosted on readthedocs.io:https://heartandsole.readthedocs.io/en/stable.BackgroundMy impetus for this project was to implement a version of Philip Friere Skiba's
GOVSS algorithm (with tweaks to better align with the underlying research).
The end result will be a free, open-source version of proprietary calculations
found in platforms like Strava and Training Peaks (eventually - bear with me).
My hope is that other runners will benefit as I have from taking these secret
algorithms out of their black box, by understanding the science behind these
calculations, and training smarter.This package was originally forked from Michael Traver'sfitanalysis package, but the two
projects diverged significantly enough for me to move my fork to a separate
repository. I am indebted to Michael for writing such a clean, useful,
easy-to-understand package that served as heartandsole's starting point.ContactYou can get in touch with me at the following places:Website:trailzealot.comLinkedIn:linkedin.com/in/aarondschroederTwitter:@trailzealotInstagram:@trailzealotGitHub:github.com/aaron-schroeder
Package: heartbeatpy
heartbeatpyHeartbeat survey analysis in python, inspired by the R packages"heaRtbeat"and"heartbeat". For more information on heartbeat analysis, check out the2021 Wiley Award for Excellence in Survey ResearchExample usageheartbeatpy has one main function:calculate_votes()which takes in a dataframe of survey data, a list of the columns to perform heartbeat transformation on, and a standard deviation threshold.import pandas as pd
import numpy as np
from heartbeatpy import create_sample_data, calculate_votes

df, _ = create_sample_data(ncols=2, nrows=10)
votes, stats = calculate_votes(data=df, columns=["Q1", "Q2"], threshold=.5)The functioncalculate_votes()returns two objects, the first,votes, contains the data provided to the function with each specified column's likert data replaced with upvotes and downvotes:votes.head()

	ID	Q1	Q2
0	1	1	-1
1	2	1	-1
2	3	-1	1
3	4	-1	1
4	5	1	-1The second object returned bycalcuate_votes()isstats, which contains the standard deviation for each indiviual (row) in the data.stats.head()

individual_sd	individual_mean
0	1.414214	3.0
1	2.121320	2.5
2	1.414214	2.0
3	0.707107	1.5
4	0.707107	1.5After finding votes, the functioncalculate_summary()can be used to generate the percent of downvotes, neutral votes, and upvotes for each question.calculate_summary()also calculates controversy score for each question.from heartbeatpy import calculate_summary

summary = calculate_summary(votes=votes, columns=cols)

summary.head()
	Question	Upvote	Downvote	Neutral	Controversy_Score
0	Q1			0.3		0.3			0.4		1.5	
1	Q2			0.4		0.4			0.2		4.0Lastly, you can use the built-invisualize_heartbeat()function to generate an altair stacked bar chart visualization:import altair as alt
from heartbeatpy import visualize_heartbeat

chart = visualize_heartbeat(summary)
display(chart)
Package: heartbeat-sh
Heartbeat.sh Python ClientThis is a Python client library forheartbeat.sh.Quick startInstall withpip install heartbeat-sh requestsfromdatetimeimporttimedeltafromheartbeat_shimportHeartbeatClientHeartbeatClient("example").send_beat("example:python",timedelta(days=1,hours=2),timedelta(days=2))Use a custom requests library (Dependency Injection)By default, this module uses therequestslibrary to make HTTP
requests. This is not a requirement. You may inject any request library:fromdatetimeimporttimedeltafromheartbeat_shimportHeartbeatClientdefrequest(url:str,method:str):return{"fake":"json result"}HeartbeatClient(subdomain="example",request=request,).send_beat("example:python",timedelta(days=1,hours=2),timedelta(days=2))LinksPyPI:https://pypi.org/project/heartbeat-sh/
Package: heartcovid
heart covid"heartcovid" is Various numerical output and graphing software related to heart disease and Covid-19."heartcovid" scours the Internet for the latest data from the following sites.https://data.cdc.gov/Heart-Disease-Stroke-Prevention/Center-for-Medicare-Medicaid-Services-CMS-Medicare/iw6q-r3jaHow to install heartcovid$ pip install heartcovidHow to run heartcovid$ heartcovidEnter the number of the menu displayed.Diseases of the Heart (Heart Disease)The vertical axis is the percentage of deaths due to the selected cause of death.
Covid-19 impact is about 1.2.
This graph is used as a basis for comparison with other diseases.Acute Myocardial Infarction (Heart Attack)Covid-19 impact is about 1.33.
This is about 0.13 higher than "Diseases of the Heart (Heart Disease).Major Cardiovascular DiseaseCovid-19 impact is about 1.34.
This is about 0.14 higher than "Diseases of the Heart (Heart Disease).Finally summaryAll of the above graphs show that the number of deaths from each disease has risen since 2020, the year of the Covid-19 epidemic.
Package: heartcv
HeartCVHeartCV is an easy to use library for automated, noninvasive quantification of cardiac traits in transparent animals. We developed this library to primarily reduce the barrier to entry for researchers seeking to assess the cardiac physiology of their animals through bioimaging.Documentation:https://heartcv.readthedocs.io/en/stable/Example scripts:https://github.com/EmbryoPhenomics/heartcv/examplesInstallation:https://heartcv.readthedocs.io/en/stable/installing.htmlSource code:https://github.com/EmbryoPhenomics/heartcv/heartcv
Package: hearthgen
HearthGen - create your own hearthstone cards inside pythonAboutHearthgen is a python library using selenium webdriver to accesshttps://hearthcards.net/and make hearthstone cards inside python.InstallingYou can install hearthgen from Github:pip install https://github.com/h4x4d/hearthgen/blob/main/archive/hearthgen-0.0.1.tar.gzOr from PyPi:pip install hearthgenUsing hearthgenYou can create card with 2 different methods:Using function create_card()Using class CardCreator()What is the difference between them?CardCreator initializes webdriver on the and can be used many times, but
create_card() initializes webdriver on start of function and stop it at
the end. So if you make one card, it's pretty good to use function, but
if you make more cards in one run it's more suitable to use class.So, what about using?And don't forget about chromedriver in script path to run it!fromhearthgenimportcreate_cardcreate_card(card_type='minion',save_name='card.png',card_name='h4x4d',card_class='priest',rarity='leg',card_text='[b]Battlecry:[/b] create this test!',card_image=r'C:\Documents\favorite_image.png',# You need to pass full path to imagemana_cost=3,attack=4,health=3,hd=True,addon='karazhan')Not all of them a necessary and if you do spells, for example with class, you don't need to pass atack and health:fromhearthgenimportCardCreatorcard_maker=CardCreator()card_maker.create_card(card_type='spell',save_name='card.png',card_name='h4x4d`s spell',card_class='priest',rarity='leg',card_text='Creates class and one more test, but with spell!',card_image=r'C:\Documents\favorite_image_2.png',# You need to pass full path to imagemana_cost=3,hd=True,addon='karazhan')Also, if you need som changes in chromedriver, it's possible to pass it to CardCreator():fromhearthgenimportCardCreatorfromseleniumimportwebdriverdriver=webdriver.Chrome(r'C:\very_special_path\chromedriver.exe')card_maker=CardCreator(driver=driver)Full documentation is placed here:soon it will be here...
Package: hearthstone
python-hearthstoneA Hearthstone Python library containing:A CardDefs.xml parser (hearthstone.cardxml)A DbfXml parser (hearthstone.dbf)Hearthstone enums as IntEnum (hearthstone.enums)The log parser previously inhearthstone.hsloghas moved to thepython-hslog project.The CardDefs.xml data for the latest build is available in thepython-hearthstone-data repositoryor on PyPI withpip install hearthstone_data.RequirementsPython 3.6+lxmlInstallationTo install from PyPI:pip install hearthstoneLicenseThis project is licensed under the MIT license. The full license text is
available in the LICENSE file.CommunityThis is aHearthSimproject.
Join the HearthSim Developer communityon Discord.
Package: hearthstonearenalogwatcher
To use (with caution), simply do:>>> from hearthstonearenalogwatcher import HearthstoneArenaLogWatcher
>>> halw = HearthstoneArenaLogLocation(log_folder="log folder")
>>> for event in halw.event_generator(speed=1):  # get events at most once per second (rate = 1 / speed)
>>> if event.type == HearthstoneArenaLogWatcher.Event.ENTERED_ARENA:
>>>     # event.date is an ArenaDraft object, containing hero, drafted cards, and whether the draft is over.
>>>     arena_state = event.data
>>>     # do something interesting
>>> elif event.type == HearthstoneArenaLogWatcher.Event.EXITED_ARENA:
>>>     passAnd so on for the following events:ENTERED_ARENAEXITED_ARENAHERO_SELECTEDCARD_DRAFTEDDRAFT_ENDEDEvent ObjectsEvent objects contain the following attributes:type: one of the enumerated types listed just abovedata: an ArenaDraft object, containing the following attributes.drafted: list of cards currently in player’s deck, in order of selection.hero: None or String representation of currently selected hero. Hero names are“priest”“mage”“warlock”,“druid”,“hunter”,“paladin”,“rogue”,“shaman”,“warrior”.draft_over: boolean, whether draft contains thirty (30) cards.It’s that simple!ErrorsFileNotFoundErrorRaised when the passed parameterlog_locationis invalid and a log file is attempted to be accessed.Tests and ExamplesCheck out thetestsfolder for examples of different logs and how they are parsed by the module. The tests are
divided into two categories:get_state_of_current_log()andget_state_of_current_screen().LicenseThe MIT License (MIT)Copyright (c) 2016 Tristan KernanPermission 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: hearthstonecarddetector
To use (with caution), simply do:>>> from hearthstonecarddetector import image_to_card_id
>>> from PIL import Image
>>> with Image.open("test_image_here.ext") as img:
>>>     # if image is not cropped, perform the crop now
>>>     img = img.crop(bbox=(x1, y1, x2, y2))
>>>     card_id = image_to_card_id(img)
>>>     print(card_id)It’s that simple!dbThe most interesting part of this module is the hashing of all the cards.
The hashes are taken of only a certain crop of the card image, a good portion
of the artwork. This is explained in “crop_explained.png” and you can see
the examples in the tests folder for more help.Each card was hashed in the four ways provided by the imagehash library. These are
average_hash, dhash, phash, and whash. phash is the default algorithm used in this
module.Tests and ExamplesPlease take a look at the tests directory. You will find several images
that are cropped appropriately, in order to match. The TestDetector.py file
also contains a working code example.Please examine the file “crop_explained.png” for information about
specific areas to crop. For even more information, you can find the
entire collection of cropped images that were used to create these hashes
online athttps://s3.amazonaws.com/draftwithme/hashed_images/+ CARD_ID .png.
For example, the first card is linked at
“https://s3.amazonaws.com/draftwithme/hashed_images/AT_001.png”.Future WorkIdeally, this library should support working with full card images. The problem
was that the bottom half of cards are all too similar, so hashing the full image
ended up with lots of errors. Focusing on the card artwork has provided a lot of
success.One idea for improvement is to organize the cards by mana (and subsequently by attack / health).
Then, parse each of these individually and compare some region to the region previously hashed and indexed.LicenseThe MIT License (MIT)Copyright (c) 2016 Tristan KernanPermission 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: heartland
Python modules for working with Heartland Payment Systems (HPS) via
their SOAP interface.http://www.heartlandpaymentsystems.com/PosGateway - for talking to the HPS Exchange PosGateway and sending E3encrypted track data.E3Reader - for getting data from the Heartland End-to-End Encryption(E3) credit card reader in USB HID Keyboard mode aka
“Heartland Payment Systems MSR215E Card Reader” USB ID 6352:215a
Package: heartsupersound
SupersoundReconstruction提供超声建模的一些方法参数image.pyimage_to_millimeterpoints_to_millimeterget_point_thresholdread_and_clear_imageread_imageplot_contoursmatrix.pyquaternion_to_matrixquaternion_to_eulereuler_to_matrixmatrix_to_eulercloud_rotate_around_pointcloud_translatereadfile.pyread_record()read_json()get_format_file_list()point.pyfarthest_point_sample
Package: heartwave
IntroductionFace detection and tracking are used to observe the tiny variations
in skin tone that result from the increased blood flow after a heart beat.
By averaging, filtering and Fourier-transforming these variations the heart
rate is estimated and graphed.Multiple persons in the scene are analysed simultaneously.Installationpip3 install heartwaveUsageheartwave [optional filename, URL or camera ID]Linkswebcam-pulse-detectorThe enigma of Mayer wavesRemote Detection of Photoplethysmographic Systolic and Diastolic Peaks Using a Digital CameraMeasuring heart rate with a smartphone camera
Package: hearvalidator
HEAR Benchmark Submission ValidatorThis package provides a command-line tool to verify that a python module follows the
HEARcommon API.For full details on the HEAR benchmark please visithttps://hearbenchmark.comInstallationTested with Python 3.7 and 3.8. Python 3.9 is not officially supported
because pip3 installs are very finicky, but it might work.pipinstallhearvalidatorThis will install a command-line tool:hear-validatorUsageLet's validate theHEAR naive baseline model:pip install hearbaseline
wget https://github.com/hearbenchmark/hear-baseline/raw/main/saved_models/naive_baseline.pt
hear-validator hearbaseline --model ./naive_baseline.ptOptional arguments forhear-validator:--model: Load the model weights from this path.--device: Device to run validation on. If this isn't specified then this
will run on a GPU if one is present. If no GPU is available then this will
default to the CPU.
Package: heaserver-activity
HEA Server Activity MicroserviceResearch Informatics Shared Resource,Huntsman Cancer Institute,
Salt Lake City, UTThe HEA Server Activity Microservice is A service for tracking activity in hea.Version 1.0.3Improved performance.Version 1.0.2Actually send received desktop objects over any web socket connections.Paginate desktop object actions correctly.Version 1.0.1Fixed a wrong method call generating strings to send to the message broker.Version 1Initial release.Runtime requirementsPython 3.10 or 3.11Development environmentBuild requirementsAny development environment is fine.On Windows, you also will need:Build Tools for Visual Studio 2019, found athttps://visualstudio.microsoft.com/downloads/. Select the C++ tools.git, found athttps://git-scm.com/download/win.On Mac, Xcode or the command line developer tools is required, found in the Apple Store app.Python 3.10 or 3.11: Download and install Python 3.10 fromhttps://www.python.org, and select the options to install
for all users and add Python to your environment variables. The install for all users option will help keep you from
accidentally installing packages into your Python installation's site-packages directory instead of to your virtualenv
environment, described below.Create a virtualenv environment using thepython -m venv <venv_directory>command, substituting<venv_directory>with the directory name of your virtual environment. Runsource <venv_directory>/bin/activate(or<venv_directory>/Scripts/activateon Windows) to activate the virtual
environment. You will need to activate the virtualenv every time before starting work, or your IDE may be able to do
this for you automatically.Note that PyCharm will do this for you, but you have to create a new Terminal panel
after you newly configure a project with your virtualenv.From the project's root directory, and using the activated virtualenv, runpip install wheelfollowed bypip install -r requirements_dev.txt.Do NOT runpython setup.py develop. It will break your environment.Running testsRun tests with thepytestcommand from the project root directory. To improve performance, run tests in multiple
processes withpytest -n auto.Running integration testsInstall DockerOn Windows, install pywin32 version >= 223 fromhttps://github.com/mhammond/pywin32/releases. In your venv, make sure thatinclude-system-site-packagesis set totrue.Trying out the APIsThis microservice has Swagger3/OpenAPI support so that you can quickly test the APIs in a web browser. Do the following:Install Docker, if it is not installed already.Run therun-swaggerui.pyfile in your terminal. This file contains some test objects that are loaded into a MongoDB
Docker container.Go tohttp://127.0.0.1:8080/docsin your web browser.Oncerun-swaggerui.pyis running, you can also access the APIs viacurlor other tool. For example, in Windows
PowerShell, execute:Invoke-RestMethod -Uri http://localhost:8080/activity/ -Method GET -Headers @{'accept' = 'application/json'}`In MacOS or Linux, the equivalent command is:curl -X GET http://localhost:8080/activity/ -H 'accept: application/json'Packaging and releasing this projectSee theRELEASING.mdfile for details.
Package: heaserver-buckets
HEA Server Buckets MicroserviceResearch Informatics Shared Resource,Huntsman Cancer Institute,
Salt Lake City, UTThe HEA Server Buckets Microservice is a service for managing buckets and their data within the cloud.Version 1.0.2Improved performance.Version 1.0.1Improved performance.Version 1Initial release.Runtime requirementsPython 3.10 or 3.11.Development environmentBuild requirementsAny development environment is fine.On Windows, you also will need:Build Tools for Visual Studio 2019, found athttps://visualstudio.microsoft.com/downloads/. Select the C++ tools.git, found athttps://git-scm.com/download/win.On Mac, Xcode or the command line developer tools is required, found in the Apple Store app.Python 3.10 or 3.11: Download and install Python 3.10 fromhttps://www.python.org, and select the options to install
for all users and add Python to your environment variables. The install for all users option will help keep you from
accidentally installing packages into your Python installation's site-packages directory instead of to your virtualenv
environment, described below.Create a virtualenv environment using thepython -m venv <venv_directory>command, substituting<venv_directory>with the directory name of your virtual environment. Runsource <venv_directory>/bin/activate(or<venv_directory>/Scripts/activateon Windows) to activate the virtual
environment. You will need to activate the virtualenv every time before starting work, or your IDE may be able to do
this for you automatically.Note that PyCharm will do this for you, but you have to create a new Terminal panel
after you newly configure a project with your virtualenv.From the project's root directory, and using the activated virtualenv, runpip install wheelfollowed bypip install -r requirements_dev.txt.Do NOT runpython setup.py develop. It will break your environment.Running testsRun tests with thepytestcommand from the project root directory. To improve performance, run tests in multiple
processes withpytest -n auto.Running integration testsInstall DockerOn Windows, install pywin32 version >= 223 fromhttps://github.com/mhammond/pywin32/releases. In your venv, make sure thatinclude-system-site-packagesis set totrue.Trying out the APIsThis microservice has Swagger3/OpenAPI support so that you can quickly test the APIs in a web browser. Do the following:Install Docker, if it is not installed already.Run therun-swaggerui.pyfile in your terminal. This file contains some test objects that are loaded into a MongoDB
Docker container.Go tohttp://127.0.0.1:8080/docsin your web browser.Oncerun-swaggerui.pyis running, you can also access the APIs viacurlor other tool. For example, in Windows
PowerShell, execute:Invoke-RestMethod -Uri http://localhost:8080/buckets/ -Method GET -Headers @{'accept' = 'application/json'}`In MacOS or Linux, the equivalent command is:curl -X GET http://localhost:8080/buckets/ -H 'accept: application/json'Packaging and releasing this projectSee theRELEASING.mdfile for details.