Saravanan666/pypi-org huggingface dataset:
Package: hardcoded
This package wants to replace your hardcoded.password, your hardcoded.api_key, actually  hardcoded("Everything you really shouldn't be hardcoding").To move a hardcoded value from your code to an external file, use hardcoded.foo as if it was holding your value already. On running the code, this will happen:if an environment variable exist with exactly the same name ("foo" in our example), the value of that environment variable is returnedotherwise, hardcoded will find a file holding your dataif you initialised hardcoded as hardcoded.File(path='secrets.yml'), secrets.yml is usedif you are running code inside a git repository, .hardcoded.yml is used, at the root of the repository (next to .git/)otherwise, ~/.config/hardcoded.yml is usedif the file is encrypted:with GPG, then hardcoded interfaces with your GPG installation (possibly using the gpg-agent)if symmetric encryption was used:if a password was cached, it is used to decrypt the dataif Apple Keychain is available, a password is requested to decrypt the dataif running interactively, you will be asked for a decryption passwordthe (decrypted if needed) YAML data is loadedif "foo" is a key in the YAML, the value of that key is returnedif "foo" does not exist in the YAML yet:if not running interactively (stdin is not an open TTY), a KeyError is raisedif running interactively, you are asked to input the valueif encryption is requested with hardcoded.File(secret=True), the YAML is encrypted:if GPG is available, the YAML is GPG encrypted with a key of your choosingif Apple Keychain is available, a random password is generated and stored in the keychainotherwise, you are asked for a new passwordthe value of foo is returned to your code
Package: hardest
HardestThe HARDness ESTimation package: A library for ranking images from a dataset by hardness with respect to a specific detector.
Currently, we provide examples in the library for torchvision datasets and detectors, but other datasets and detectors
can be used by converting the data to the torchvision format.Getting startedTo install:pipinstallhardestHere we provide instructions for computing the hardness of an entire dataset.
Firstly obtain a pytorch dataset:fromtorchvision.datasets.cocoimportCocoDetectionimportitertoolsdataset=CocoDetection(...)# Run on a subset of the datasetn_examples=50images,targets=zip(*itertools.islice(dataset,n_examples))Obtain detections:importtorch,torchvisionmodel=torchvision.models.detection.retinanet_resnet50_fpn(pretrained=True)model.eval()withtorch.no_grad():detections=[model([image])[0]forimageinimages]Choose a definition of hardness:fromhardest.pycocotoolsimportTotalFalsehardness_definition=TotalFalse()Estimate hardness using annotations:fromhardest.hardness_strategiesimportSupervisedHardnessCalculationrank=SupervisedHardnessCalculation(hardness_definition).eval_dataset(detections,targets,images,)If annotations are not available, you can estimate hardness without annotations:fromhardest.hardness_strategiesimportScoreSamplingHardnessCalculationrank=ScoreSamplingHardnessCalculation(hardness_definition,n_samples=10).eval_dataset(detections,images)Reproducing published resultsTo repeat the experiments from our paper (details here), first install theCOCOas well as thenuImagesdatasets and convert it to a COCO compatible formatusing these instructions.
Then export the detections for your desired detector to torchvision json format.Finally, run the script:pythonscripts/paper_experiments.py--coco-rootdatasets/coco--nuimages-rootdatasets/nuimages-coco--save-dir./results--detection-path-cocodetections/coco--detection-path-nuimagesdetections/nuimagesThis will require that you have stored detections in json files in the appropriate paths and downloaded the datasets to
the specified paths.To reproduce the nuimages results you will need to convert the nuimages dataset to the coco schema:https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages/README.mdIf you use the package in your research please consider citing our paper:details hereContributorsAuthorsJonathan SadeghiEdward AyresInternal ReviewAnuj SharmaBlaine RogersRomain MuellerZygmunt Lenyk
Package: hardfight-devtools
Hardfight DevtoolsBuild, test and deploy Hardfight projects easly💡 What is itDevtools is a Python tool to makebuilding,testinganddeployinginternal hardfight projects easy.It offers all thescripting powerof the python language with a lightweight API to registersimple tasksin one script.🛠️ How to installHardfight devtools is available as a PyPi package, you can install it using:pip install hardfight_devtoolsNote that dev_tools isassumed installedto build any Hardfight project.❓ How to useThis repository itself uses dev_tools to perform build and deploy routines.On a new project, create adevtools_script.pyfile on the root.To declare a new task, add the@dev_tools_taskdecorator to the function of your task.fromdev_tools.dev_tools_apiimportdev_tools_main,dev_tools_task@dev_tools_task(name='hello-world',desc='Prints "Hello world!" as an example task')defprint_hw():print('Hello world!')Devtools provides a bunch of usefull scripts and functions for build, test and deploy automations (seedev_tools.utilsmodule).Don't forget to call thedev_tools_mainmethod at the end of your script to make it functional.# Script entry pointif__name__=='__main__':dev_tools_main()You can then execute your tasks using:python3 dev_tools.py <tasks_1> [task_2] ...General conventions on hardfight projectsIf you need additional python scripts for your tasks, add them in themisc/scriptsdirectory in your repositoryAll files related to the meta-project or build (README images, requierments...) have to be in themisc/filesdirectory in your repository
Package: hardhat-gas-report-diff
HardHat Gas Report DiffA Python package that processes reports generated byhardhat-gas-reporterand generates a Markdown table comparing the two.UsageHavehardhat-gas-reporterinstalled in your environmentRunhardhat testand save the report in a file calledbefore.txtMake any changes to the source code you're testingRunhardhat testagain and save the new report in a file calledafter.txtRunpython -m hhgrdiff before.txt after.txtIt should output a Markdown table comparing the two reportsHelpRunpython -m hhgrdiff --help
Package: har-dl
HAR-DL (.HAR Downloader)Utility for downloading content from .HAR files.InstallationPython >=3.6 is required.$ pip3 install har-dlUsage (CLI)$ har-dl <har_path> <output_path> [-h] [-p] [-q]Options-p,--proxy: Enable proxy-q,--quiet: Disable console outputUsage (API)fromhar_dlimportDownloaderdl=Downloader("path/to/file.har")dl.download(proxy=True)LicenseMIT
Package: hardle
HardleA simple & lightweight utility for (synchronously) downloading content from .HAR files. Useful when you want to download static sites for offline use.Installation$ pip3 install hardleHardle uses type annotations, so Python 3.5 (minimum) is required.UsageFrom the Command Line$ hardle path/to/file.har out/From Pythonfromhardleimportdownloaddownload("path/to/file.har","out/")LicenseMIT
Package: hardlink
OverviewThis package provides ahardlinkmodule and command line script that copies
files and merges directories using hard links.Why? Because OS X doesn’t supportcp-lR.InstallationLatest code direct from the repository:$ pip install hardlinkUsageVia the command line script:$ hardlink -h
usage: hardlink [-h] [-d] [-f] [-q | -v] src dst

Copy files and merge directories using hard links.

positional arguments:
  src            Source file or directory. Follow symbolic links.
  dst            Destination file or directory. Merge existing directories.

optional arguments:
  -h, --help     show this help message and exit
  -d, --dry-run  Do not link files or create directories. Only log operations.
  -f, --force    Replace existing files and symbolic links.
  -q, --quiet    Silence standard output.
  -v, --verbose  Increase verbosity for each occurrence.Via the module:import hardlink
link = hardlink.Command(dry_run=False, force=True)
link(src, dst)
Package: hardshare
Introductionhardshareis a system for sharing your robot hardware through thererobotsinfrastructure.  It is in the early stages of
development. Until version 1.0.0, the protocol can change without warning.This Python package provides the CLI and WebSocket-based daemon for the client
side. The official repository is athttps://github.com/rerobots/hardshareIt is listed on PyPI athttps://pypi.org/project/hardshare/Recent releases of the full documentation are available athttps://hardshare.readthedocs.io/LicenseThis is free software, released under the Apache License, Version 2.0.
You may obtain a copy of the License athttps://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: hardshell
hardshellBranches:Main:Release:Config File Locations:windows admin: C:\Program Files\hardshell\hardshell.tomlwindows user: C:\Users\user\AppData\Local\hardshell\hardshell.tomllinux admin: /etc/hardshell/hardshell.tomllinux user: /home/$USER/.hardshell/hardshell.tomlLog File Locations:windows admin: C:\Program Files\hardshell\logswindows user: C:\Users\user\AppData\Local\hardshell\logslinux admin: /var/logs/hardshell/linux user: /home/$USER/.hardshell/logs
Package: hardware
Hardware detection and classification utilitiesHomepage:https://github.com/redhat-cip/hardwareFeaturesdetect hardware features of a Linux systems:RAIDhard drivesIPMInetwork cardsDMI infosmemory settingsprocessor featuresfilter hardware according to hardware profilesInstallInstalling from pypi:pip install -U hardwareUsageRun the hardware-detect program:hardware-detect --humanRuntime dependenciesThe hardware detection is divided in modules that detects a specific hardware type. Each module have its own dependencies.Therefore, we cannot enforce installing all the dependencies as some are not relevant regarding a particular hardware type.
To avoid a situation where we cannot use/install hardware because of one of those deps, we do prefer let users installing the one they need.The hardware detection code will ignore all the missing deps and continue, so not installing a deps is not fatal.Please find bellow the list of dependencies per module:Arecacli64 fromhttp://www.areca.com.tw/support/s_linux/linux.htmLogical diskshdparmsmartmontoolsNetworkingethtoollldp fromhttp://open-lldp.org/ibstat if you have infiniband devices fromhttps://www.openfabrics.org/Systemipmitool fromhttps://sourceforge.net/projects/ipmitool/pciutilslshw fromhttp://www.ezix.org/project/wiki/HardwareLiSterRaid controllersfor HP controllers: hpacucli fromhttp://h20564.www2.hpe.com/hpsc/swd/public/detail?swItemId=MTX_d6ebba0f5cd642edace4648b9afor Dell controllers: megacli fromhttp://www.avagotech.com/docs-and-downloads/raid-controllers/raid-controllers-common-files/8-07-14_MegaCLI.zip
Package: hardware-control
What is this repository for?The goal is to provide reusable code for hardware control using python and Qt.Documentation can be found atRead the Docs.How do I get set up?The easiest way is to pip install the software:pip install hardware-controlExamplesIf you want to have a quick look at some basic functionality, you can
run the demos in the example directory. If you have the same hardware
that is used in the example, you will have to update the connection
address, e.g. ip address. Otherwise, you can still run the examples by
providing the--dummycommand line argument that will simulate
instrument output. The logging level can also be redirected to the
terminal using--consoleand the log-level changed by providing--infoor--debugoptions on the command line.TestsWe usepytestto provide some simple tests for some of the
functionallity of the package. However, to check if an instrument is
really working, one needs to have the instrument connected, which
makes testing harder. When the instruments are not available some
functionallity, such as the Qt layout, can still be tested using
"--dummy" mode. In this mode instrument parameters can return a
predefined value or the result of a function.To run the tests use:python -m pytestContribution guidelinesFeel free to contribute new drivers for hardware or other changes.We use black to format the code, so please format your code
accordingly. The easiest way to achieve this is to install pre-commit
and use the config file we provide:pip install pre-commit
# cd into repo
pre-commit installHow to cite this packagePlease use the followingcitation.@article{Giesbrecht2022,
  doi = {10.21105/joss.02688},
  url = {https://doi.org/10.21105/joss.02688},
  year = {2022},
  publisher = {The Open Journal},
  volume = {7},
  number = {72},
  pages = {2688},
  author = {Grant Giesbrecht and Ariel Amsellem and Timo Bauer and Brian Mak and Brian Wynne and Zhihao Qin and Arun Persaud},
  title = {Hardware-Control: Instrument control and automation package},
  journal = {Journal of Open Source Software}
}Who do I talk to?If you have questions, please contact Arun atapersaud@lbl.gov.Copyright and LicenseSee the files COPYRIGHT and LICENSE in the top level directory
Package: hardware-tools
hardware-toolsA library for automating hardware development and testingEnvironmentList of dependencies for package to run.Requiredpython modules, installed viapip install hardware_toolsnumpypyvisacythoncoloramamatplotlibscipyskleanPillowscikit-imageOptionalTest extensions, installed viapip install hardware_tools[test]time-machinehardware-toolsVirtual EnvironmentIf using hardware-tools in a virtual environment, do not place it within this repo directory. Set it outside due to an issue compiling the extensions with cython since it tries to compile the code inside the virtual environment as well.mkdirworkspace-hardware-toolscdworkspace-hardware-tools
python-mvenv.source./Scripts/activate# or .\Scripts\activate.batgitclonehttps://github.com/WattsUp/hardware-toolscdhardware-tools
python-mpipinstall.Installation / Build / Deployment# To install latest stable version on PyPi, execute:python-mpipinstallhardware_tools# To install from source, execute:gitclonehttps://github.com/WattsUp/hardware-toolscdhardware-tools
python-mpipinstall.# For development, install as a link to repository such that code changes are used. And include testing packagesgitclonehttps://github.com/WattsUp/hardware-toolscdhardware-tools
python-mpipinstall-e.[test]UsageExplain how to use your project.# TODORunning TestsMake sure to install package withtesting extension# To run the automated tests, execute:python-mtestsdiscover-stests-t.--locals# To save the results to file, execute:python-mtestsdiscover-stests-t.--locals&>testing.log## The following is a synopsis of unittest main arguments ### To run a singular test file, execute:python-mtests$path_to_test_filepython-mteststests.measurement.test_mask# To run a singular test class, execute:python-mtests$path_to_test_file.$classpython-mteststests.measurement.test_mask.TestMaskDecagon# To run a singular test method, execute:python-mtests$path_to_test_file.$class.$methodpython-mteststests.measurement.test_mask.TestMaskDecagon.test_init# Multiple can be strung togetherpython-mteststests.measurement.test_masktests.test_math# To run coverage and print the report with missing lines, execute:python-mcoveragerun&&python-mcoveragereport-m# To run profiler, execute:python-mcProfile-stottime-mtestsdiscover-stests-t.>profile.log# To run linting, execute:python-mpylinthardware_toolsteststoolssetup.pyDevelopmentCode development of this project adheres toGoogle Python GuideStylingUseyapfto format files, based on Google's guide with the exception of indents being 2 spaces.# To format all files, execute:yapf-ir.VersioningVersioning of this projects adheres toSemantic Versioningand is implemented using git tags.
Package: hardxor
Hardxorhardxoris a tool to xor a file byte by byte.bash>hexdumpinputfile.hex
$0000000696d706f727420706174686c69620a660000010726f6d207365747570746f6f6c732069bash>python3-mhardxorinputfile.hex-k0xe4-ooutputfile.hex

bash>hexdumpoutputfile.hex
$00000008d89948b9690c49485908c888d86ee820000010968b89c49781909194908b8b8897c48d
Package: hardyweinbergcalculator
Hardy Weinberg EquilibriumHardy-Weinberg Equilibrium Calculator. Calculates the expected
genotype frequencies based on the allele frequencies of a
population in Hardy-Weinberg equilibrium.InstallationpipinstallhardyweinbergcalculatorUsageusage: hwc [-h] [--version] [--verbose] [--debug] [--samples SAMPLES] [--p P] [--q Q] [--tpop TPOP] [--ppop PPOP] [--qpop QPOP] [--pq2pop PQ2POP] [--genes GENES [GENES ...]] [--json JSON [JSON ...]]

Hardy-Weinberg Equilibrium Calculator. Calculates the expected genotype frequencies based on the allele frequencies of a population in Hardy-Weinberg equilibrium. See: https://en.wikipedia.org/wiki/Hardy%E2%80%93Weinberg_principle


optional arguments:
  -h, --help         show this help message and exit
  --version          show program's version number and exit
  --verbose          Enable verbose logging. (default: False)
  --debug            Enable debug logging. (default: False)
  --samples SAMPLES  Number of samples to generate, if using random data generator. (default: None)
  --p P              Frequency of dominant allele. (default: None)
  --q Q              Frequency of recessive allele. (default: None)
  --tpop TPOP        Total population. (default: None)
  --ppop PPOP        Original population of dominant allele. (default: None)
  --qpop QPOP        Original population of recessive allele. (default: None)
  --pq2pop PQ2POP    Original population of heterozygous allele. (default: None)

Example: python3 -m hwc --ppop 10 --qpop 10 --pq2pop 200 --verboseGenerate random datapython3-mhwc--samples1000--verboseCalculate from known datapython3-mhwc--ppop10--qpop10--pq2pop200--verboseIn your codeTest for Hard-Weinberg Equilibrium from generated datafromhwcimportgenerate_population,HardyWeinberg# Generate random datapopulation=generate_population(n=1000)res=HardyWeinberg(genes=population)print(res)Test for Hardy-Weinberg Equilibrium from known datafromhwcimportHardyWeinberg# Known datares=HardyWeinberg(homozygous_dominant_population=20,homozygous_recessive_population=44,heterozygous_population=95)print(res)Results Data ObjectThe results returned from the Hardyweinberg test will ultimately look like this, in json format:{"2*pq":0.4986501057404244,"chi_square_test":20.439848879167698,"expected_heterozygous_population":1132.434390136504,"expected_homozygous_dominant_population":510.28280493174816,"expected_homozygous_recessive_population":628.2828049317482,"genes":[],"heterozygous_population":1025.0,"homozygous_dominant_population":564.0,"homozygous_recessive_population":682.0,"p":0.47402025539409953,"p + q":1.0,"p**2":0.22469520252388733,"p**2 + 2*pq + q**2":1.0,"q":0.5259797446059005,"q**2":0.2766546917356883,"total_population":2271.0}
Package: hared
To use (with caution), simply run:haredThe program reads a configuration file at/usr/local/etc/hared.inior one specified as$HARED_INI. This INI file looks like this with
the following defaults:[defaults]
verbose = False
listenhost = localhost
listenport = 8035
mqtthost = 127.0.0.1
mqttport = 1883
topic = logging/hareFreeBSD:pkg install py36-pipYou are encouraged to use our Go version ofhared
Package: har-extractor
OverviewExtractor forHAR,
HTTP Archive format.RequirementsPython 3OptionalYAJL 2CFFIInstallationpipinstallhar-extractorUsageusage: har-extractor [-h] [-V] [-l] [-o DIRECTORY] [-v] [-nv] [-i] [-ni]
                     [-s] [-ns] [-d] [-nd]
                     FILE

positional arguments:
  FILE                  HAR file

optional arguments:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -l, --list            list the contents of input file
  -o DIRECTORY, --output DIRECTORY
                        set output directory (default: ./<filename>.d)
  -v, --verbose         turn on verbose output (default)
  -nv, --no-verbose     turn off verbose output
  -i, --iterative       use iterative json parser
  -ni, --no-iterative   do not use iterative json parser (default)
  -s, --strict          exit and delete extracted data after first error
  -ns, --no-strict      ignore errors (default)
  -d, --directories     create url directories (default)
  -nd, --no-directories
                        do not create url directoriesDevelopmentInstallationgitclonehttps://github.com/dead-beef/har-extractorcdhar-extractorpipinstall-e.[dev]Building./build.shTesting./testLicenseshar-extractor
Package: harf
A package of utilities for parsing, analyzing, and displaying data inharfiles.
Particularly in relation to helping developlocustperformance test scripts.HarFis heavily biased towards REST(ish) json based apisCurrently the project only provides a CLI and library to help track data through a har file.
The CLI (correlations) displays what data is used where in a har file, some basic filters, and two ways to interact with the data.
For more information usecorrelations --helpto see everything supported by the CLI.Basic OutputFor smallharfiles this basic output is probably sufficient.
For example, if you use thetests/example1.harfile with modified filters you will see all the values used in a request.correlations tests/example1.har -x 100Value ('products') used in:  
   "entry_0.request.url[0]"  
  
Value (1) used in:  
   "entry_0.response.body[0].id",  
   "entry_1.request.url[1]",  
   "entry_1.response.body[0].id",  
   "entry_2.request.body.productId"  
  
Value ('test') used in:  
   "entry_0.response.body[0].name",  
   "entry_1.response.body[0].name"  
  
Value ('product') used in:  
   "entry_1.request.url[0]"  
  
Value (1.1) used in:  
   "entry_1.response.body[0].price"  
  
Value ('cart') used in:  
   "entry_2.request.url[0]"Interactive OutputOnce you start getting into larger files with hundreds of requests and dozens of values that need to be tracked the basic output is not all that helpful.
HarF provides two ways to interact with the data dynamically.With-iyou will be dropped into a python shell with the har data, correlation info, and some utility functions to inspect and manipulate the data as needed.And because I am anobsidiannerd-o <vault_path>will output a bunch of markdown files to thevault_pathwhere every request, response, and used value gets their own note and are back-linked through usage.Colors are made from thepagerefinfo where entries on page earlier in the trace are closer to the red end of the rainbow and later requests are closer to the purple/pink end.
Additionally if there areComment Requests(requests that begin withhttp://COMMENT) every entry is "re-paged" based on the Comment Request before it and the Comment Requests are removed.
E.G.GET http://COMMENT/homepage; GET http://www.example.com pageref:page_1;becomesGET http://www.example.com pageref:homepage;