Saravanan666/pypi-org huggingface dataset:
Package: cp-tool
cp-toolcp-toolis an auto generator for solved problems at different online judges. It gathers all the problems you have solved at an online judge and generates a git repository for you.GeneratedSample Git Repbycp-toolSupported PlatformsCodeforcesRequirmentspython >=3.6InstructionInstallationLinux :pip install cp-toolWindows :python -m pip install cp-toolNote : Please use updated versionUpgrade :Linux :pip install --upgrade cp-toolWindows :python -m pip install --upgrade cp-toolUsageUsing commandcp-toolit will show the usagesInit with git repo :Create a git repository first (Github)Then initcp-tool -c init -j codeforcesYou will be asked forhandleexample :shifat57repository name (A folder will be created based in repo name)example :My Cp Trackrepository urlexample :https://github.com/jspw/cp-tool-sampleUpdate submissions :Go to the repository folder in your local fileThencp-tool -c update -j codeforcesLicenseMIT
Package: cptpy
cptpyJoseph P. VantasselAboutcptpycptpyis a Python package for processing cone penetration test (CPT) data. The
package is still in thealpha stagemeaning you as a usershouldexpect
bugs and API changes, of course feedback is greatly appreciated. With that said
the packages includes two primary class definitionsCPTandCPTufor
standard CPT and CPT plus pore water pressure readings, respectively.
Package: cptree
cptreersync interface adding total transfer progress and redundant cryptographic hash verificationFree software: MIT licenseUsage: cptree [OPTIONS] SRC DST

  rsync transfer with progress indicator and checksum verification

Options:
  --version                       Show the version and exit.
  -d, --debug                     debug mode
  --shell-completion TEXT         configure shell completion
  -c, --create [ask|force|never]  create DST if nonexistent
  -D, --delete [ask|force|never|force-no-countdown]
                                  delete DST contents before transfer
  -p, --progress [enable|ascii|none]
                                  animated transfer progress
  -o, --output DIRECTORY          checksum output dir
  -h, --hash [sha3_512|blake2s|sha3_256|md5|sha256|sha512|sha3_384|sha384|sha3_224|blake2b|sha224|sha1|none]
                                  select checksum hash
  -r, --rsync / -R, --no-rsync    enable/disable rsync transfer
  -r, --rsync / -R, --no-rsync    enable/disable rsync transfer
  -a, --rsync-args TEXT           rsync pass-through arguments
  --help                          Show this message and exit.Usage: cptree [OPTIONS] SRC DST

  rsync transfer with progress indicator and checksum verification

Options:
  --version                       Show the version and exit.
  -d, --debug                     debug mode
  --shell-completion TEXT         configure shell completion
  -c, --create [ask|force|never]  create destination directory if nonexistent
  -D, --delete [ask|force|never|force-no-countdown]
                                  delete destination directory before transfer
  -p, --progress [enable|ascii|none]
                                  total transfer progress
  -o, --output DIRECTORY          checksum output dir
  -h, --hash [md5|sha1|sha256|sha512|none]
                                  select checksum hash
  -r, --rsync / -R, --no-rsync    enable/disable rsync transfer
  -a, --rsync-args TEXT           rsync pass-through arguments
  --help                          Show this message and exit.Usage: cptree [OPTIONS] SRC DST

  rsync transfer with progress indicator and checksum verification

Options:
  --version                       Show the version and exit.
  -d, --debug                     debug mode
  --shell-completion TEXT         configure shell completion
  -c, --create [ask|force|never]  create destination directory if nonexistent
  -D, --delete [ask|force|never|force-no-countdown]
                                  delete destination directory before transfer
  -p, --progress [enable|ascii|none]
                                  total transfer progress
  -o, --output_dir DIRECTORY      output directory
  -h, --hash [md5|sha1|sha256|sha512|none]
                                  select checksum hash
  -r, --rsync / -R, --no-rsync    enable/disable rsync transfer
  -a, --rsync-args TEXT           rsync pass-through arguments
  -f, --file-list                 scan only, output file list
  --help                          Show this message and exit.
Package: cpucoolerchart
CPU Cooler Chart (CCC) is CPU cooler performance and price database.
It merges data from CPU cooler performance measurements and price information
from Coolenjoy and Danawa.CPU Cooler Chart is comprised of two parts, the API server part (this project)
and the web client part. You can find the web part atgithub.com/clee704/cpucoolerchart-web.InstallCPU Cooler Chart depends onlxml, which in turn depends on liblxml2
and libxslt. You can install these with following commands.Debian/Ubuntu:sudoapt-getinstalllibxml2-devlibxslt1-devMac OS X (withHomebrew):brew install libxml2 libxsltFor more information, seeInstalling lxml.If you are ready to install lxml, you can install CPU Cooler Chart. There are
many ways to do that but using pip is recommended:$pipinstall--precpucoolerchartCurrently--preargument is needed but it will be unnecessary once a
non-developmental release is out.RunningBefore running the web server, you must initialize a database:$cpucoolerchartcreatedbIt will make a SQLite database atinstance/development.dbunder the current
directory. Although not tested, there is no restrictions on the choice of
the database to use as long asSQLAlchemysupports it. SeeConfigurationfor
how to change database options.Now the database is ready and empty. Run the following command to fill it with
data:$cpucoolerchartupdateIt will fetch measurement data fromCoolenjoy. It might spit out some
warnings due to inconsistencies in the data or because you haven’t providedDanawaAPI keys. Nothing is a serious problem for now.To see the data, first you need to run a web server:$cpucoolerchartrunserverIt will run a development server at port 5000. Open your browser and go tohttp://localhost:5000/makers. It should show some heatsink makers in JSON
format. Go tohttp://localhost:5000/allto download a CSV file that
contains all data. For the complete list of HTTP APIs, seethe docs.
Meanwhile, you can readviews.pyfile for what’s there.For production, there aremany optionsto run a web server (you should not
use the development server in production). CPU Cooler Chart is built withFlask, which means it’s WSGI-compatible. The endpoint iscpucoolerchart.wsgi:app. Or you can make a custom Python file and create an
app there:fromcpucoolerchart.appimportcreate_appapp=create_app({'SQLALCHEMY_DATABASE_URI':'postgres://user:pass@somewhere:5432/ccc'})LinksGitHub repositoryDocumentationCPU Cooler Chart(deployed site)LicenseCPU Cooler Chart is licensed under the MIT License. SeeLICENSEfor more.
Package: cpu_cores
What is it ?cpu_coresis small python library and utility to get the number of
“physical” cpu cores (without hyperthreading logical cores) of a
linux/osx box.On Linux, this is not an easy task because of hyperthreaded logical
cores included in/proc/cpuinfo. Please, read carefullythis
excellent postto understand why.Special featuressupport Linux (well tested) and OSX (needs some extra tests files),
easy to extend to support other OSunit tests (> 90% coverage)python2 and python3 supportno dependenciesUsageSee theREADMEfor more details.
Package: cpu-count
cpu_countModified version of python'scpu_countthat takes into account system
constraints to calculate the number of available CPUsMotivationThe Python standard library offers an implementation of cpu_count that returns
the real number of CPUs even when they are not actually available to be used by
the python process (due to constraints such as CPU affinity or CPU scheduler
configurations). This is the preferred behaviour for most applications. However,
when the interest is the amount of CPUs available for data processing, that
approach could be misleading. Especially when it is used behind the scenes such
as by theconcurrent.futures.Executorwhen defining its defaults.The purpose of this module is to provide this functionality in an API equal to
the standard implementation. By taking into account the described constraints,
this implementation attempts to return the amount of usable CPUs that are
available. If no constraint is identified the result will be the same as the
standard implementation.How to installpip install cpu_countHow to useAs an external moduleThis is the standard way. Just import and callcpu_countfromcpu_countimportcpu_countprint(cpu_count())# $> 8Monkey-patch standard libThis an alternative way that replaces python's standardcpu_countwith the
one from this module (Affected internal modules areposix,osandmultiprocessing). The advantage of this approach is not needing to port any
code, just import and callsetup_monkey_patchate the begin of your
application and everything will just work™.Note: This will also have implications in the behaviour of standard libraries
that use this functionimportosfromcpu_count.monkey_patchimportsetup_monkey_patchprint(os.cpu_count())# $> 12setup_monkey_patch()print(os.cpu_count())# $> 8LimitationThis approach has one limitation: it can't replace previous code that
imported the standard implementation usingfrom os import cpu_count.fromosimportcpu_countfromcpu_count.monkey_patchimportsetup_monkey_patchprint(cpu_count())# $> 12setup_monkey_patch()print(cpu_count())# $> 12System wide monkey-patchThis approach also replaces python's standardcpu_count. However instead of
calling this module'ssetup_monkey_patchin your application code, it will be
called at python startup. For this to work you need to create a file calledcpu_count_monkey_patch.pthat your python's global or local site-package's
folder with the following content:importcpu_count;cpu_count.monkey_patch.setup_monkey_patch()NOTE: This approach is specially useful when creating container images of
python applications. An example of using this on a Dockerfile can be foundhereTODOAdd logic for Realtime Scheduler constraintCreate unit testsContributionsAll contributions are very welcome.Code styles is defined in theEditorconfigfile. Besides that
I use black and isort for auto-format, their configurations are defined in theEditorconfigand theblack.tomlfiles
respectively.LicenseBSD 3-Clause “New” or “Revised” LicenseSeeLICENSE
Package: cpufreq
Python module to control the frequency on Linux systems.FeaturesGet current Frenquency and governor by CPU.Set frequency by CPU.Enable and Disable CPUs.PrerequisitesCustom CPU power managment enabled on BIOSPower Management Driver installed (acpi for example)Python3.5 or newerSitehttps://github.com/VitorRamos/cpufreqInstallation$ pip3 install cpufreqUsageUse the example file script: cpufreq_example.py
Package: cpuid-py
|travis| |appveyor|cpuid-py=======cpuid Pythonic APIGetting started---------------Stable version:.. code-block:: bash$ pip install --upgrade cpuid-pyDevelopment version:.. code-block:: bash$ pip install --upgrade --index-url https://test.pypi.org/pypi/ cpuid-py.. |travis| image:: https://travis-ci.org/fpelliccioni/cpuid-py.svg?branch=master:target: https://travis-ci.org/fpelliccioni/cpuid-py.. |appveyor| image:: https://ci.appveyor.com/api/projects/status/github/fpelliccioni/cpuid-py?branch=master&svg=true:target: https://ci.appveyor.com/project/fpelliccioni/cpuid-py?branch=master
Package: cpu-load-generator
CPU Load GeneratorThis package allows to generate a fixed CPU load for a finite time period.
The script takes in input the desired CPU load, the duration of the experiment and
the CPU core or all cores on which the load has to be generated.Python versionsThis master branch refers to Python versions 3.xDependenciesInstallment of psutil is required (it is already set as dependency in the setup.py)Install psutil:pip install psutilOther dependencies for unit tests and linters:pip install mock pytest flake8 toxUsageTo generate 20% of load on core 0 for 20 seconds run:python -m cpu_load_generator -l 0.2 -d 20 -c 0To generate 50% of load on all logical cores for 20 seconds run:python -m cpu_load_generator -l 0.5 -d 20 -c -1There is an option to run CPU load based on profile file. An exemplary profile is under
load_profiles/default_profile.json. In order to run generator based on a profile issue
the following command:python -m cpu_load_generator -p <path_to_profile_json>To use the package features from python code:Install the package from PyPi by issuing the following command:python -m pip instal cpu-load-generatorTo use its features from your code:fromcpu_load_generatorimportload_single_core,load_all_cores,from_profileload_single_core(core_num=0,duration_s=20,target_load=0.4)# generate load on single core (0)load_all_cores(duration_s=30,target_load=0.2)# generates load on all coresfrom_profile(path_to_profile_json=r"c:\profiles\profile1.json")
Package: cpumodel
cpumodelA Python library to parse and return various attributes from the CPU stringIt has only been tested on relatively modern AMD and Intel CPUs that are likely to still be in use,
i.e. AMD Ryzen, AMD EPYC, Intel i-series, Intel Xeon E3/E5 and newer. Other CPUs may or may not produce
useful output.If you want to request support for an unsupported CPU or if you see a bug in the output, see the
Contributing section.Installationpip install cpumodelUsageAs a libraryfromcpumodel.cpumodelimportget_cpu_modelinfo=get_cpu_model()As a script$ cpumodel
{'cpuVendor': 'AMD', 'cpuString': 'AMD Ryzen 7 5700G', 'cpuModel': 'Ryzen 7 5700G', 'cpuFamily': 'Ryzen 7', 'cpuGeneration': '5', 'cpuLetter': 'G'}You can optionally supply a CPU string with-tor--testwhich will cause cpumodel to parse this CPU
string rather than get the local system's CPU string. This is useful for testing the logic.$ cpumodel -t "Intel(R) Xeon(R) Gold 6234 CPU @ 3.30GHz"
{'cpuVendor': 'Intel', 'cpuString': 'Intel Xeon Gold 6234', 'cpuModel': 'Xeon Gold 6234', 'cpuFamily': 'Xeon Gold', 'cpuGeneration': '6'}OutputThis library returns up to 6 values (it is possible for values to beNone)cpuVendor, the CPU vendor, e.g.IntelcpuString, the full model string of the CPU, e.g.Intel Core i7-6700ScpuModel, the shorter model name of the CPU, e.g.Core i7-6700ScpuFamily, the concise family name of the CPU, e.g.Core i7cpuGeneration, the numeric generation of the CPU, e.g.6cpuLetter, any trailing letter codes for this CPU, e.g.SExamplesThe best explanation of the data returned is probably by giving various examples.cpuVendorcpuStringcpuModelcpuFamilycpuGenerationcpuLetterIntelIntel Xeon Platinum 8358Xeon Platinum 8358Xeon Platinum8IntelIntel Xeon Gold 6226RXeon Gold 6226RXeon Gold6RIntelIntel Xeon E3-1220 v6Xeon E3-1220 v6Xeon E36IntelIntel Celeron G1610Celeron G1610Celeron1GIntelIntel Core i5-6500TCore i5-6500TCore i56TIntelIntel Core i9-9900KCore i9-9900KCore i99KIntelIntel Core i9-10900Core i9-10900Core i910Intel12th Gen Intel Core i7-1265UCore i7-1265UCore i712UIntel12th Gen Intel Core i9-12900Core i9-12900Core i912AMDAMD Ryzen 7 5700GRyzen 7 5700GRyzen 75GAMDAMD Ryzen 7 PRO 5850URyzen 7 PRO 5850URyzen 7 PRO5UAMDAMD Ryzen 9 7950XRyzen 9 7950XRyzen 97XAMDAMD Ryzen Threadripper 3990XRyzen Threadripper 3990XRyzen Threadripper3XAMDAMD Athlon 5350 APUAthlon 5350 APUAthlon5AMDAMD Opteron 6366 HEOpteron 6366 HOpteron6366HAMDAMD EPYC 7551PEPYC 7551PEPYC7PContributionIf this library doesn't work properly on your CPU, it's probably because I've never tested on a CPU of that type.Pleaseopen an issue, and describe what you would expect the
output of this module to be, and include the output of this command:grep'model name'/proc/cpuinfo|sort-u
Package: cpunks-10k
CPUNKS-10K are subsets of the 10,000 labeled images in the CryptoPunks collection by Larva Labs. They have been collected, organized & modified for use in Machine Learning research by tnn1t1s.eth. The source images files and meta-data were designed and created by John Watkinson & Matt Hall.### The CPUNKS-10K dataset
The CPUNKS-10k dataset consists of 10000 24x24 colour images labeled with 92 classes, with a mixed number of images per class. There are 8000 training images and 2000 test images.The dataset is divided into four training batches and one test batch, each with 2000 images. The test batch contains randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another.Five classes are mutually exclusive: alien, ape, zombie, male and female. The remaining classes are not mutually exclusive. For example, there is overlap between Choker and Tiara.### Download & Install
cpunks-10k is available for installation usingpip. In a virtual environment or conda environment, use the following command to install cpunks-10k.`pip installcpunks-10k`To get started, use thecpunkscommand line utility to create a scratch space for notebook development using cpunks.`cpunks tutorial install`### Using CPUNKS10
The repository contains a tutorial section that will help you get setup using CPUNKS-10K with Tensorflow and Keras. The [Simple Neural Net Classifier](https://github.com/tnn1t1s/cpunks-10k/blob/main/tutorial/Simple%20Neural%20Net%20Classifier.ipynb) goes through all the steps needed to load the dataset, build nd train a model using it, evaluate the model and visualize the predictions.### Dataset layout
The archive is distributed as a Python pickle file which is intended to be loaded by the CPUNKS-10K Here is a python routine which will open the files and return training and test data sets for use in Tensorflow with Python 3.`(x_train, y_train), (x_test, y_test) =cpunks10k.load_data()`Loaded using the defaults, each of the batch files are grouped into a training set and paired with corresponding test data containing with the following elements:x_train – an 8000x24x24x4 numpy array of uint8s. Each row of the array stores a 24x24x4 colour image with four channels: red, green, blue & transparency.x_test – a 2000x24x24x4 numpy array of uint8s. Each row of the array stores a 24x24x4 colour image with four channels: red, green, blue & transparency.y_train – an 8000 row numpy array with integers in the range 0 … 92. The number at index i indicates the label of the ith image in the array data.y_test – a 2000 row numpy array with integers in the range 0 … 92. The number at index i indicates the label of the ith image in the array data.The dataset contains another file, labels.meta ,which contains a Python dictionary object with the following entries:label_names – a 93-element list which gives meaningful names to the numeric labels in the labels array described above. For example, label_names[0] == “alien”, label_names[1] == “ape”, etc.
Package: cpu-power.py
cpu-power.pyView and edit some CPU power saving settings:Minimum frequencyMaximum frequencyActive coresMultithreading / SMTTurbo BoostInstallationThe script needs root permissions to change the CPU settings.
Thus it is recommended to install the package as root:sudo pip install cpu-power.pyUsageYou can view your CPU settings by calling the script without parameters:cpu-powerYou can also set CPU settings like this (power saving mode):sudo cpu-power set -u 2.2 -d 2.2
Package: cpuprofile
CPU ProfileThis is a simple package to measure CPU speed by calculating the time it takes to
compute Fibonacci numbers. It is useful in contexts where CPU speed is unknown or can
fluctuate, such as cloud function environments, and can serve as a rough measure of how
long subsequent compute-intensive code will take to run.InstallationpipinstallcpuprofileUsagefromcpuprofileimportprofile_cpu# Calculate the CPU speed by computing Fibonacci numbers.elapsed_time=profile_cpu()# elapsed_time is ~0.18 (seconds) on a 2.5 GHz Intel Core i7 2015 Macbook Pro.# Or, a number can be specified to calculate the nth Fibonacci (default: 28).elapsed_time=profile_cpu(15)# 15th Fibonacci; takes around 0.0006 secondselapsed_time=profile_cpu(35)# 35th Fibonacci; takes about 5 secondsCreditThe minimalist approach of recursively calculating Fibonacci numbers as a way to profile
the CPU is inspired by:Booth, J. (2015).Not so incognito: Exploiting resource-based side channels in javascript engines(Undergraduate thesis, Harvard University).
Package: cp-upsolve-helper
cp-upsolve-helperhttps://pypi.org/project/cp-upsolve-helper/0.0.1/Usageif on localpipinstall.from pypipipinstallcp-upsolve-helper==0.0.1importuthelper.helpersashelperimportunittest@helper.runclassTestClass(unittest.TestCase):deftest_example(self):self.assertEqual(True,True)classTestClassDoesntRun(unittest.TestCase):deftest_example_doesnt_run(self):self.assertEqual(True,False)defining custom solution function and testingimportcphelper.helpersascphelperinp_str="""82 31 23 111 55 104 63 9250000000 500000000"""expected_output="""6433252012271000000000"""r=cphelper.Runner(inp_str,[cphelper.MUL])defsolution(a,b):ifa==2andb==3:return6return0r.test_solution(solution,expected_output)output: note, passes the first case, fails on the othersF
======================================================================
FAIL: test_function (cphelper.helpers.Runner.test_solution.<locals>._.test_function)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/cphelper/helpers.py", line 67, in test_function
    sself.assertEqual(result, expected)
AssertionError: '0' != '4'
- 0
+ 4


----------------------------------------------------------------------
Ran 1 test in 0.001s

FAILED (failures=1)
Package: cpus
cpusDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-ncpus--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallcpusnbdev# activate conda environment$condaactivatecpus# make sure the cpus package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the cpus package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/cpus.gitor fromconda$condainstall-cdsm-72cpusor frompypi$pipinstallcpusDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: cpuspeedy
Cpuspeedy allows you to change the clock speed and voltage of CPUs using Linux’s
CPUFreq driver. It is a user space program, so it will work on every processor
supported by the kernel’s CPUFreq driver.
All you need is a supported CPU and a kernel which has the CPUFreq driver
compiled in. Cpuspeedy is a user space program, so it will work on every
processor supported by the kernel’s CPUFreq driver.
Use it and you should save battery consumption and reduce the temperature of
your laptop.
Package: cputemp
Show Raspberry Pi's or Orange Pi's CPU temperatureIt reads CPU temperature from/sys/class/thermal/thermal_zone0/temp.Install# pip install cputempUsage$cputemp[option]Options-h, --help display this help and exit-f, --format telegraf|n setting output format-c, --continuous refresh every 10 secondsDonationsIf you like this program, please donate me!PayPal:https://paypal.me/gyengusBitcoin: 1QJzLBK9uQP4RthmKJRQwy3v5sd4XS4S7PBitcoin Cash: qp04tazu4fe7lv6zr99suu40swqqp747nsm0kcfckvEthereum: 0x2bD68120A56acBf6Dbd11da2060228b8912C1e3CTested on Raspberry Pi Zero W and Orange Pi ZeroProject page:https://github.com/gyengus/cputemp