Saravanan666/pypi-org huggingface dataset:
Package: hipcat
Pipe things to HipChat from the command-line!Inspired bySlackcat!InstallationYou will need a working installation ofPython
3(you probably already have this if you’re
on Linux or OSX) andPIP.pip3installhipcatGo tohttps://www.hipchat.com/account/apiand create a new token. It
only needs the ‘Send Message’ scope, but this may change in future.Create a file at~/.hipcat.inithat looks a bit like this:[hipchat]access_token=<put-your-token here>Using hipcatYou can get help by runninghipcat--help, but the basics look like
this:# Pipe output from one command to HipChat:echo'This is my message'|hipcat'Room Name'If you don’t want to pass the message via a pipe, you can instead
provide it as a parameter:# Run a long running command and then notify on HipChatmy-long-task&&hipcat'Notifications'-m"The long-running task is done"If the text you are sending to HipChat is formatted, use the-qflag
to prefix with/quote, and if it should be syntax-highlighted code,
use the-cflag to prefix with/code.You can send a direct message by using the ‘mention name’ of a user
instead of a room name:```bash echo “You are great” | hipcat‘@FriendlyDave’NotificationsThe message you are sending may also be treated as aroom
notificationvia the--notificationflag. The following additional options and
flags are then recognized by hipcat:--colorto specify background color for the message. Valid
values: yellow, green, red, purple, gray, random. Defaults toyellow.--senderto specify a label to be shown in addition to the
sender’s name. Defaults tohipcatif--notify(see below) is
specified as well.--notifyto ensure this message triggers a user notification.# Send a message "Message" with a purple background, from "BOT" and with a
# user notification:hipcat'Notifications'--notification--notify-m"Message"--color=purple--sender="BOT"Contributing to hipcatYou want to contribute? Love you!There’s some stuff you should know:Please don’tbackport it to Python 2. Yes, I know it’s easy. We
should all be writing Python 3 now.Please treat other contributors and commenters with respect.Please ensure that any changes follow PEP-8.When I add tests (there aren’t any yet!) I plan to use py.test. If
you want to help by addind tests, let’s talk about it first.To DoAdd a flag to send the output to HipChat line-by-line rather than at
the end.Allow message posting to individuals as well as rooms.Allow uploading content as a file rather than inline text.Err… post a GitHub issue if you want to see a new feature, and
we’ll talk!
Package: hipchat_notif
# hipchat_notifhipchat_notif is [HipChat](https://www.hipchat.com) client library for sending notification using API v1.## VER. 0.2.3Added the ability to change the message format.(Thanks to https://bitbucket.org/cou_z)message_format: Determines how the message is treated by our server and rendered inside HipChat applications.html -  Message is rendered as HTML and receives no special treatment. Must be valid HTML and entitiesmust be escaped (e.g.: &amp; instead of &). May contain basic tags: a, b, i, strong, em, br, img,pre, code, lists, tables. Special HipChat features such as @mentions, emoticons, and image previewsare NOT supported when using this format.text -  Message is treated just like a message sent by a user. Can include @mentions, emoticons, pastes,and auto-detected URLs (Twitter, YouTube, images, etc)## Python version2.73.4## Dependencies- requests>=2.3## Sample Code```pythonfrom hipchat_notif import *TOKEN = "AUTH_TOKEN" # Get token from https://hipchat.com/admin/apiROOM_ID = 10000NOTIFICATION_NAME = "NOTIFICATOR"# Create a new instance.notif =  HipchatNotificator(TOKEN)notif.notification_name = NOTIFICATION_NAMEnotif.room = ROOM_ID# Send Alert notificationnotif.alert("Alert", message_format = "text")# Send Ok messagenotif.ok("Not Alert", message_format = "html")# Send messagenotif.message("Message")# Send Notificationnotif.notif("Notification")```### Correspondence table|Method |  Notify | Color:----: |  :----: | :----:alert() |  True | redok() |  False | greenmessage() |  False | yellownotif() | True | gray
Package: hipchat-notifications
HipChat NotificationsThis is a very simple library for sending user and room notifications to HipChat via their v2 API.It contains a couple of generic functions for sending messages
to rooms and users within HipChat.>>>hipchat.notify_room('Customer Service','This is a message')>>>hipchat.notify_user('hugo','Hello, Hugo')It includes some colour-specific helpers for room-only notifications:>>>hipchat.green('Customer service','This is a green message')The functions support themessage_formatvalues:html- Message is rendered as HTML and receives no special treatment. Must be valid HTML and entities must be escaped (e.g.: ‘&amp;’ instead of ‘&’). May contain basic tags: a, b, i, strong, em, br, img, pre, code, lists, tables.text- Message is treated just like a message sent by a user. Can include @mentions, emoticons, pastes, and auto-detected URLs (Twitter, YouTube, images, etc).>>> hipchat.green('Customer service', 'Hello @fred', message_format='text')Thecolor,notifyandfromparameters are also supported (fromis passed as a kwarg calledlabel):>>>notify_room(...'customer service',...'Hey there @Tony',...color='red',...label='baz',...notify=True,...message_format='text'...)SettingsThe following settings are read from the environment usingos.getenvHIPCHAT_API_TOKENA valid API access token. See theAPI docsfor details on how to
generate a token. (Hint: the easiest way is to use a “Personal access token”
generated through the site.)If there is no token set in the environment the notifications will be logged
using the ‘hipchat’ logger, with a DEBUG level.InstallationThe library is available on PyPI as ‘hipchat_notifications’.TestsThe tests can be run usingtox:$toxContributorsHugo Rodger-Brown - @hugorodgerbrownRoberto Salgado - @droberin
Package: hipchatpy
hipchatpy isHipChatclient library for specific logging.InstallpipinstallhipchatpyDependenciesrequestsSample CodeimporthipchatpyAUTH_TOKEN='hogehoge'ROOM_ID=10000# Create a new instance.logging=hipchatpy.HipChatLogger(AUTH_TOKEN,ROOM_ID)# LogLevel: INFOlogging.info(message='INFO Message')# LogLevel: WARNlogging.warn(message='WARN Message')# LogLevel: ERRORlogging.error(message='ERROR Message')Correspondence tableMethodLogLevelNotifyColorinfo()INFO (1)Falsegreenwarn()WARNING (2)Trueyellowerror()ERROR (3)TrueredCommand lineexportHIPCHAT_TOKEN=your_api_token# LogLevel: INFOhipchatpy-r10000-m'INFO Message'-l1# LogLevel: WARNhipchatpy-r10000-m'WARN Message'-l2# LogLevel: ERRORhipchatpy-r10000-m'ERROR Message'-l3
Package: hipchav
A minimal Python HipChat client with command-line interface.SetupInstallhipchavfrom source, or usingpip:pip install hipchavThen you’ll need to configure it with your HipChat API key:export HIPCHAT_V1_TOKEN=thisismyhextokentheygavemeor for the v2 API:export HIPCHAT_V2_TOKEN=thisismyhextokentheygavemeUsageHipChav currenly only supports two functions, listing rooms and sending messages.Listing rooms$ hipchav.py rooms
Developers
Whole company
Bots onlySending messagesMessages are quite straight-forward to send:$ hipchav.py message 'Whole company' 'Freshly baked cookies in the kitchen'You can also change their color and send notifications to people in the room:$ hipchav.py message --color=red --notify 'Developers' 'Um, the build is broken'With the v1 API, you can also choose who the message appears from:$ hipchav.py message --from=Glados 'Whole company' 'Dispensing deadly neurotoxin'Check its usage information for full options:$ hipchav.py --helpChangelogv0.1.3Install as both a script and a modulev0.1.2Support HTML messages using the--formatflagv0.1.1Also support v1 API, autodetect based on environment variables presentFor v1 API, support the from field with the--fromoption.You’ll need to updateHIPCHAT_AUTH_TOKENto eitherHIPCHAT_V1_TOKENorHIPCHAT_V2_TOKENv0.1.0Initial release, supports listing rooms and messaging with color and notifications
Package: hip-data-ml-utils
data-ml-utilsA utility python package that covers the common libraries we use.InstallationThis is an open source library hosted on pypi. Run the following command to install the library.pip install hip-data-ml-utils --upgradeDocumentationHead over tohttps://hip-data-ml-utils.readthedocs.io/en/latest/index.html#to read our library documentationFeaturePyathena client initialisationAlmost one linerimportosfromhip_data_ml_utils.pyathena_client.clientimportPyAthenaClientos.environ["AWS_ACCESS_KEY_ID"]="xxx"os.environ["AWS_SECRET_ACCESS_KEY"]="xxx"# pragma: allowlist secretos.environ["S3_BUCKET"]="xxx"pyathena_client=PyAthenaClient()Pyathena queryAlmost one linerquery="""SELECT*FROMdev.example_pyathena_client_tableLIMIT 10"""df_raw=pyathena_client.query_as_pandas(final_query=query)MLflow utilsVisitlinkMore to ComeYou suggest, raise a feature request issue and we will review!TutorialsPyathenaThere is a jupyter notebook to show how to use the package utility package forpyathena:notebookMLflow utilsThere is a jupyter notebook to show how to use the package utility package formlflow_databricks:notebook
Package: hipdf
hipdf - pdf highlighterHighlight the first word of English sentences in PDF file.Installationpip install hipdfUsageBasic usage:hipdf <path_to_file>Hipdf will save the highlighted file in the same folder of the original file, with prefix like "[highlighted.{count}][{time}]"Specify an out name:hipdf <path_to_file> -o <out_path>Theout_pathis relative to the current working directory. Examples:../my docs/highlighted.pdfor./highlighted/doc.pdfSpecify a prefix:hipdf <path_to_file> -p <prefix>or with-o:hipdf <path_to_file> -o <out_path> -p <prefix>prefix will only affect filename but not file path.
Package: hipe4ml
Minimal heavy ion physics environment for Machine LearningTo install:pipinstallhipe4mlMac OS X users:the latest XGBoost version will require OpenMP installed. One easy way of getting it is using brew:brewinstalllibompDocumentationhttps://hipe4ml.github.io/CiteIf you use this package for your analysis please consider citing it! You can use the following reference.https://doi.org/10.5281/zenodo.5070131ContributeInstall in-development packageTo install the in-development package, from the repository base directory:pipinstall-e.[dev]Run testsIf you havePylintandFlake8on your development machine, you can run tests locally by using:tests/run_tests.shin the repository base directory.TutorialsIf you want to get familiar with hipe4ml, the following tutorials are available:| Type | Link || -------------- | ------------- || Binary classifier ||| Multiclass classifier ||
Package: hipe4ml-converter
hipe4ml_converterPackage for conversion of machine learning models trained with hipe4ml into ONNX and tensor formatsIntroductionhipe4ml-converter enables you to convert models trained withhipe4mlintoONNXformat and tensor formats (PyTorch,TorchScript,TVMorONNX) via thehummingbirdlibrary.Documentationhttps://hipe4ml.github.io/hipe4ml_converter/index.htmlRequirementshipe4mlonnxonnxruntimeonnxmltoolshummingbird
Package: hipersim
HipersimIntroductionHipersim is a collection of tools for computationally-efficient simulation of Wind Energy problems using statistical and machine learning methods. Currently, the package contains the following submodules:turbgen:Generation of 3D frozen turbulence fields ("turbulence boxes") with the Mann turbulence spectrumConstrained turbulence field generation and application of constraints on pre-generated turbulence fieldssurrogates:A feedforward neural network toolbox, training and evaluation of feedforward neural networks.A set of tools for surrogate-based modeling and analysis of wind farmsInstallationHipersim is registered as installable Python package in PyPi.org. Installation happens automatically with the following command:pip install hipersimReferencesLicenseHipersim uses the MIT License.
Package: hiperwalk
HiperwalkSource code of Hiperwalk projecthttp://qubit.lncc.br/qwalk/Currently, it is necessary to haveneblina-coreandpyneblinainstalled.
Paulo Motta described on his blog theinstallation steps for both neblina-core and pyneblina.Style Guide for Python CodeThe code must be written accordinglyPEP 8.
Some code may need to be updated to match PEP 8's requirements.Python Libraries DependenciesMatplotlib;NetworkX >= 2.7;PIL;NumPy;Scipy >= 1.8;Sphinx;numpydoc;Optional Libraries For AnimationsPyGObject: backend for showing animations;ImageMagick: for saving animated gifs;ffmpeg: for saving movies.TODO ListUpdate installation instructionsRefactor Coined_QW_Adjacency_Matrix-v2.ipynbCreate separated file for plottingGeneral plotting based on networkxSpecific functions for widely used walks?Support for different coinsHadamard Coin general dimensionsGenerating shift operator: checking whether argument type is sparse or notOptimization: use lil_matrix instead of csr_matrix while generating S operatorConstruct shift operators based on sympy expressions?Implement and refactor testsComplete GraphLine walkMesh walkTests for plotting graphs?Clean examplesAdd commentaries to qwalk/coinedCreate documentationpdf (with LaTeX)webpagepyneblina_interface.pyRename functionsRemove definition from examplesMove PyneblinaInterface.py functions to Pyneblina project?Give support for more backends to show animationsMatplotlib animation (show and save may be slow)
Package: hiphive
hiPhiveis a tool for efficiently extracting high-order force constants from atomistic simulations, most commonly density functional theory calculations.
A detailed description of the functionality provided as well as an extensive tutorial can be found in theuser guide.
Complete examples of usinghiphivefor force constants extraction can be found in thehiphive-examples repository.hiPhiveis written in Python, which allows easy integration with countless first-principles codes and analysis tools accessible in Python, and allows for a simple and intuitive user interface.
For example using the following snippet one can train a force constant potential:cs=ClusterSpace(primitive_cell,cutoffs)sc=StructureContainer(cs,list_of_training_structure)opt=Optimizer(sc.get_fit_data())opt.train()fcp=ForceConstantPotential(cs,opt.parameters)after which it can be used in various ways, e.g., for generating phonon dispersions, computing phonon lifetimes, or running molecular dynamics simulations.For questions and help please use thehiphive discussion forum on matsci.org.hiPhiveand its development are hosted ongitlab.InstallationhiPhivecan be installed viapip:pip3 install hiphiveor viaconda:conda install -c conda-forge hiphivehiPhiverequires Python3 and invokes functionality from several external libraries including theatomic simulation environment,scikit-learn,spglib,SymPy, andtrainstation.
Please consult theinstallation section of the user guidefor details.CreditshiPhivehas been developed at theDepartment of PhysicsofChalmers University of Technology(Gothenburg, Sweden) in theCondensed Matter and Materials Theory division.When usinghiPhivein your research please cite the following paper:Fredrik Eriksson, Erik Fransson, and Paul ErhartThe Hiphive Package for the Extraction of High‐Order Force Constants by Machine LearningAdv. Theory. Sim., 1800184 (2019)doi: 10.1002/adts.201800184Please consult theCreditspage of the documentation for additional references.
Package: hiphopscrap
MSVDD + Bloc(Microsoft + DataKind AI in Cities Virtual Accelerator - DataDive) + (Bloc)setupClone this repositoryto a directory on your local machine:$ cd /path/to/your/preferred/directory
$ git clone git@github.com:datakind/msvdd_Bloc.git
$ cd msvdd_BlocCreate a virtual environmentto isolate our project's dependencies from your other projects'. Use whichever tool you prefer (e.g.virtualenv,pyenv,pipenv). Here's an example usingpyenv:msvdd_Bloc(master)$ pyenv virtualenv 3.7.4 bloc-env
msvdd_Bloc(master)$ pyenv shell bloc-envInstall the packagein one of two ways.To use themsvdd_bloccode as-is without further development, installation is simple:(bloc-env) msvdd_Bloc(master)$ pip install .To further develop the code, install the package in locally-editable (aka develop) mode, plus a few additional dependencies:(bloc-env) msvdd_Bloc(master)$ pip install -e .
(bloc-env) msvdd_Bloc(master)$ pip install -r requirements-dev.txtCreate a branchwith a descriptive name for you to hack on, as needed:(bloc-env) msvdd_Bloc(master)$ git pull
(bloc-env) msvdd_Bloc(master)$ git checkout -b my-example-branch-namedocumentationStand-alone doc files live under the top-leveldocs/directory and are written inreStructured Text format. They are built usingsphinx:$ cd docs
$ make htmlAs needed, commit the latest version of the built HTML docs to the project'smasterbranch:$ git commit -am "Update built HTML docs"
$ git push origin masterThese files are automatically published through GitHub Pages, and are accessible via web browser athttps://datakind.github.io/msvdd_Bloc.In-code docstrings followGoogle style. These docstrings are automatically incorporated into the main docs viasphinx.ext.sphinx-autodoc. Refer to thesphinx sitefor details.testsTest modules live under the top-leveltests/directory. They are run usingpytest:$ cd tests
$ pytest -vv .A coverage report may additionally be generated usingpytest-cov:$ pytest -vv --cov=msvdd_bloc --cov-report=term-missing .Refer to thepytest sitefor details.
Package: hipims
Python APIs for hipimsThis package provides python APIs for running the open source hydraulic modelhipims-cuda. It also includes thehipims-iopackage for pre-processing and result visualisation. The full documentation is athttps://pypims.readthedocs.io/en/latest/.InstallationBefore installing pypims, you need to installCmake, NVIDIA CUDA Toolkit and the C++ compiler. Cmake can be simply installed bypipinstallcmakeThe CUDA Toolkit can be downloaded from theNVIDIA website. If you use Linux system, the C++ compiler 'GCC' is usually on the system as default. If you use windows, you should download and installVisual Studio. If there exists several different versions of CUDA Toolkits on your system, cmake may struggle to decide which one to use and return an error. In such a case, you need to define the CUDAToolkit_ROOT environmental varialble. On Linux, this can be done byexportCUDAToolkit_ROOT=<pathtoyourcudadirectory>For exampleexportCUDAToolkit_ROOT=/usr/local/cuda-10.1Once the aforementioned dependencies have been properly installed. Installing pypims is straightforward, simple type in the following in your terminalpipinstallpypimsBasic usageThe flood simulation engine can be imported byfrompypimsimportfloodThe inputs can be prepared byhipims-io. This package has already been included in pypims, you can import it byfrompypimsimportIOA quick demonstration to prepare input files with attached sample data contaning the following files:DEM.gz/.asc/.tif (essential file, in projected crs, map unit:m)rain_mask.gz/.asc/.tif (optional file for setting rainfall, having the same crs with DEM)rain_source.csv (optional file for setting rainfall rate in timeseries, unit:m/s]landcover.gz/.asc/.tif (optional file for setting landcover-based parameters, having the same crs with DEM)importosfromIO.demo_functionsimportget_sample_datadata_path=get_sample_data(return_path=True)# get the path of sample datacase_folder=os.path.join(os.getcwd(),'model_case')# define a case folder in the current directoryobj_in=IO.InputHipims(case_folder=case_folder,num_of_sections=1,data_path=data_path)# create input objectobj_in.domain_show()# show domain mapprint(obj_in)# show case informationobj_in.write_input_files()# create all input filesOnce the inputs have all been prepared, the simulations  can be started byflood.run('path to your inputs')orflood.run_mgpus('path to your inputs')for multiple GPUs.
Package: hipims-io
hipims_ioPython code to process input and output files ofHiPIMS flood model. This code followsGoogle Python Style Guide.Python version: >=3.6. The main content of this package is also included in aPython API of HiPIMS.To install hipims_io from command window/terminal:pip install hipims_ioTo install using github repo:git clone https://github.com/mingxiaodong/hipims_io_python.git
cd hipims_io_python
pip install .A quick demonstration to setup a HiPIMS input object with a sample DEM:import hipims_io as hp
obj_in = hp.demo_input() # create an input object and show domain mapA quick demonstration to setup a HiPIMS input object with a data path contaning the following files:DEM.gz/.asc/.tif (essential file, in projected crs)rain_mask.gz/.asc/.tif (optional file for setting rainfall, having the same crs with DEM)rain_source.csv (optional file for setting rainfall rate in timeseries]landcover.gz/.asc/.tif (optional file for setting landcover-based parameters, having the same crs with DEM)import os
import hipims_io as hp
from hipims_io.demo_functions import get_sample_data
dem_path, _ = get_sample_data(return_path=True) # get the path of sample data
data_path = os.path.dirname(dem_path)
case_folder = os.path.join(os.getcwd(), 'hipims_case') # define a case folder in the current directory
obj_in = hp.InputHipims(case_folder=case_folder, num_of_sections=1, 
                          data_path=data_path) # create input object
obj_in.domain_show() # show domain map
print(obj_in) # show case informationA step-by-step tutorial to setup a HiPIMS input object with sample data:import os
import numpy as np
import hipims_io as hp

dem_file, model_data, _ = hp.get_sample_data() # get sample data
case_folder = os.path.join(os.getcwd(), 'hipims_case') # define a case folder in the current directory
# create a single-gpu input object
obj_in = hp.InputHipims(dem_data=dem_file, num_of_sections=1, case_folder=case_folder)

# set a initial water depth of 0.5 m
obj_in.set_initial_condition('h0', 0.5)

# set boundary condition
bound_list = model_data['boundary_condition'] # with boundary information
obj_in.set_boundary_condition(bound_list, outline_boundary='fall')

# set rainfall mask and source
rain_source = model_data['rain_source']
obj_in.set_rainfall(rain_mask=0, rain_source=rain_source)

# set manning parameter
manning_array = np.zeros(obj_in.DEM.shape)+0.03 # create an array with the same shape of the DEM array
obj_in.set_grid_parameter(manning=manning_array)

# set monitor positions
gauges_pos = model_data['gauges_pos']
obj_in.set_gauges_position(gauges_pos) 

# display model information
obj_in.domain_show() # show domain map
print(obj_in) # print model summary

# write all input files for HiPIMS to the case folder
obj_in.write_input_files()The domain map will be shown like this:
Package: hipisejm
# hipisejmProcess textual data from Polish Sejm.Does the same thing as is available onhttps://kdp.nlp.ipipan.waw.pl/query_corpus/but maybe someone will find those scripts useful.ConfigurationCreate virtualenv:source enter.shThen every time you start working you can simply type it again.To add virtualenv fromenter.shto jupyter (one time):python -m ipykernel install –user –name=hipisejmTestingTo run all tests type (use -s for debug show of prints ;) ):python -m pytest testsTo run specific test put path to the test file:python -m pytest tests/t_stenparser/test_sejm_parser.pyFormatWe use XML-alike format to store parsed data.
Each day is stored in the separate file (so in general one PDF transcription will create one XML file).You will find XSD schema for the corpus output file in resources/hipisejm-transcript-schema.xsdYou can find example of the parsed file in resources/hipisejm-transcript-example.xmlHint, to validate your XML file:xmllint –schema resources/hipisejm-transcript-schema.xsd resources/hipisejm-transcript-example.xml –noout