Saravanan666/pypi-org huggingface dataset:
Package: chip
This package is only approved for use with the OpenKIM Virtual Machine at this time.  Chip is a simple package manager with great flexibility that handles the packages needed to run OpenKIM tests and models.  Chip implements lightweight version switching so that multiple versions of a package can be used in different tests and models.  Different versions of a package can even be used in separate processes at the same time.  Chip also supports dependency resolution, consistency checking, and self updating.
Package: chipcard
This project provides an async (anyio) interface
to communicate with thepcscdprocess.It’s a 100% python implementation, i.e. no C code or C dependencies are involved.Requirementspcscd (apt install pcscd)Some async event loop, e.g. trio (venv/bin/pip install trio)A smart card that is reachable through pcscdExampleCode to generate a signature on OpenPGP card:fromanyioimportrunfromcontextlibimportsuppressfromchipcardimportconnect_chipcardfromchipcard.card.openpgpimportOpenPgpCardUSER_PW=b"123456"asyncdefmain():asyncwithconnect_chipcard(OpenPgpCard)ascard,card.card.transaction():awaitcard.select_openpgp()awaitcard.unlock_pw1_sign(USER_PW)sig=awaitcard.compute_digital_signature(b"Hello, World!")print(sig.hex())withsuppress(KeyboardInterrupt):run(main,backend="trio")Development statusIt’s currently more of a PoC. API needs to be cleaned up a lot and will
probably change in incompatible ways.Vital features like Secure Messaging are missing entirely.
And eventually high level APIs for existing smart cards should be implemented.
Package: chipgr8
Chip-Gr8 – Emulation for AIChip-Gr8 is a sandbox for creating AI Agents for retro video games like Pong, Breakout, and Space Invaders. Chip-Gr8 lets you interactively program AI agents, easily record their behaviour, collect data, pause and play their actions, and even play against them! If you have never programmed an AI before, Chip-Gr8 is a great place to start!InstallingChip-Gr8 is a Python package. Use pip to install it!pip install chipgr8Getting StartedIf you want to play a game using Chip-Gr8, all you have to do is start it on the command line! For example, to play breakout you would runpython -m chipgr8 -r breakoutYou will be greated by the Chip-Gr8 display and can start playing!Creating an AI Agent is just as straightforward, just dropping the following code into your favorite text editor and you are ready to go!importchipgr8fromchipgr8.gamesimportBreakoutvm=chipgr8.init(display=True,ROM=Breakout.ROM)whilenotvm.done():vm.act(Breakout.actions.left)To find out more about Chip-Gr8, its API, included games, and more examples, download theReference Manual, or head over to thedocs!ContactFeel free to contact us with questions, bug reports, or feature requests atchipgr8.contact@gmail.com.
Package: chipi
CHIPI - Comprehensive History and Interactive Python InterfaceCHIPI is a flexible and easy-to-use Python package for managing and manipulating data in buffers. It provides a comprehensive history tracking system and an interactive Python interface for handling data operations in buffers.FeaturesCreate and manage multiple buffersFlexible buffer size with optional maximum lengthBuilt-in methods for data manipulation and analysisExtendable with custom methodsImport and export data from JSON and CSV filesInstallationTo install CHIPI, simply run:pipinstallchipiQuick StartfromchipiimportBuffer,BufferManager# Create a buffer and add some databuf=Buffer("my_data")buf.add(1)buf.add(2)buf.add(3)# Retrieve dataprint(buf.current_value)# Output: 3print(buf.previous_value)# Output: 2print(buf.data)# Output: [1, 2, 3]# Create a BufferManager for managing multiple bufferslabels=["data1","data2","data3"]buff_mgr=BufferManager(labels)# Add data to the managed buffersbuff_mgr.d["data1"].add(10)buff_mgr.d["data2"].add(20)buff_mgr.d["data3"].add(30)# Get data from a managed bufferprint(buff_mgr.get_data("data1"))# Output: [10]
Package: chipmunk
A very small and simple usage mechanism for Python threadlocals.This is an abstraction on type ofthreading.localthat attempts to
simply usage a bit and combat the common problem of accidentally
overwriting values that may have been added by other portions of code.
It also implements theinoperator to test for inclusion and the
bool() method to test whether the Chipmunk is holding anything.Example UsageInstantiatingTheChipmunkobject is a sort of singleton that is instantiated upon
import.from chipmunk import Chipmunk #  Ready for useStoring DataAsking theChipmunkto hold something can be done in one of three
ways:Setting Attributes DirectlyChipmunk.acorn = "Acorn"
Chipmunk.acorn_count = 5With thestore_datamethodChipmunk.store_data("acorn","Acorn")Chipmunk.store_data("acorn_count",5)And with a context manager for short-term storage that removes the need
for checking whether it is already holding something with the given
name.Chipmunk.nut = "acorn"

with Chipmunk.hold_this("nut", "walnut"):
    do_something_with_Chipmunk()

return Chipmunk.nut #  Returns "acorn"If theChipmunkis already holding something and you ask it to hold
something else with the same name it will raise an AttributeError. If
you want to replace an object permanently you must call either thedelete_dataorclearmethods or usedel Chipmunk.attr.Chipmunk.nut = "acorn"
Chipmunk.nut = "walnut" #  AttributeError thrown

Chipmunk.nut = "acorn"
del Chipmunk.nut
Chipmunk.nut = "walnut" #  OK

Chipmunk.nut = "acorn"
Chipmunk.delete_data("nut")
Chipmunk.nut = "walnut" #  OKRetrieving DataWhen retrieving data from theChipmunkabool()check will tell
you if it’s holding anything at all.from chipmunk import Chipmunk
if Chipmunk: #  Conditional fails
    return "Not Empty"

Chipmunk.nut = "acorn"
if Chipmunk: #  Conditional succeeds
    return "Not Empty"Testing whether theChipmunkis holding something with a given name
is as simple as anincheck.Chipmunk.nut = "acorn"
"nut" in Chipmunk #  TrueData can be accessed by doing an attribute lookup directly, using theget_datamethod, orgetattr.Chipmunk.nut = "acorn"

# These all return "acorn"
Chipmunk.nut
Chipmunk.get_data("nut")
getattr(Chipmunk, "nut")
Package: chipmunkdb-python-client
chipmunkdb :chipmunk: python-clientModify and save dataframes with timeseries or without to an ultra-fast ipc chipmunk database with olap features. (Powered by duckdb)Installationpip install chipmunkdb-python-clientUsageimportsyssys.path.insert(0,'..')importpandasaspdimportnumpyasnpfromchipmunkdb.ChipmunkDbimportChipmunkDbdb=ChipmunkDb("localhost")#receive all collectionscollections=db.collections()# create a dataframedf=pd.DataFrame(np.array([[1,2,3],[4,5,6],[7,8,9]]),columns=['a','b','c'])print(df)## save your pandasdb.save_as_pandas(df,"mydf")## get infos abotu your dataframecollection=db.collection_info("mydf")# read your dataframe againdf2=db.collection_as_pandas("mydf")print(df2)# call any query on your datad=db.query("SELECT * FROM mydf WHERE a=1 LIMIT 100")print(d)Info
Package: chipmunks
chipmunksChipmunks（以下简称munks）是用python开发的基于nginx的轻量级微服务网关。munks可以为你监控集群中所有docker节点中服务，并生成nginx配置文件，将到指定服务的访问请求转发到对应的docker节点指定端口上。Whymunks是休闲时候为了不让自己手生的练习之作。在使用python开发时，推行微服务架构其实是不太容易的。python没有java这样完整的微服务生态，非常感谢当时有nameko，解决了基本的服务注册发现和RPC机制（nameko的实现也相当精彩，感兴趣的可以看看）。nameko也有自己的问题，比如服务终端了无法及时发现，导致RPC一直等待。想为python写一个微服务网关的想法由来已久，我是一个会让工作填满所有时间的人，所以也没有太多的空闲时间来做，趁着2020国庆长假，将这个想法落地了。Whatmunks的整个结构非常简单。如下图所示：munks由绿色的agent和黄色的monitor组成。agent用于和集群（无论是k8s or swarm或其他形式的集群）中的docker节点交互，监控docker中container的生命周期，并将信息保存到指定的etcd集群中。monitor利用了etcd3支持watch prefix的特性，监听了/chipmunks这个key prefix，并解析具体的key内容生成了nginx所需要的location的配置文件。你只需要在nginx的配置文件中include对应的文件即可。How0. RequirementsDocker (in K8S or Swarm)etcd3Python3.2+1. Install munks agent当前这个阶段Munks并不打算上到pipy仓库中，主要基于为大家负责的出发点。该项目并没有在生产环境有应用，我尽量让我的考虑更加周全，但是未经验证就上到仓库的确是太草率了。所以注定了这个教程不会给出非常具体的指令，在使用这个工具的时候你需要明确知道你在做什么。如果你有自己的私服，相信也应该知道如何加入进去，如果你还没有，但是比较感兴趣，可以试试nexus。首先你需要clone本项目到docker节点上，然后通过pip进行安装即可。Install Munks Service如果你使用systemd，那么下面的service文件可以帮你快速的建立起启动脚本Agent Service[Unit]
Description=chipmunks service
After=docker.target

[Service]
Type=simple

## replace with munks install location
## chip-agents configure file。
ExecStart=/usr/local/bin/munks agent -c /etc/chips-agent.conf
Restart=on-abort
TimeoutSec=600

[Install]
WantedBy=multi-user.target使用systemd相关命令enable并且start。Munks agent 的配置文件实例最简化配置munks可以只配置etc3即可。如果需要更详细的个性化配置，可以参考cli.py[etcd3]
port = 2379
host = 192.168.77.1002. Install Munks monitor在你的nginx前端机上，同样安装munks。方法也就不在详细给出。同样如果你使用systemd，那么下面的service文件可以帮出你建立启动脚本Monitor Service[Unit]
Description=chipmunks service
After=docker.target

[Service]
Type=simple

## replace with munks install location
## chip-agents configure file。
ExecStart=/usr/local/bin/munks chips -c /etc/chips.conf
Restart=on-abort
TimeoutSec=600

[Install]
WantedBy=multi-user.target配置monitor最简化配置monitor你配置etcd和nginx相关配置，如下[etcd3]
host=192.168.77.100

[nginx]
config_path=devops/chipmunks/vagrant-env.d
pid_path=/usr/local/var/run/nginx.pid其中config_path为nginx配置文件的路径，该了路径需要配置到nginx中，同时需要nginx pid文件的路径，我们需要读取pid文件并发消息给nginx process让nginxreload配置文件。3. 配置nginxmunks将在config_path中生成两类文件：location.conf和upstream.conf。
需要在nginx中配置include相应的文件即可。在http段include *.upstream.conf。在server段include *.location.conf。如果你不清楚上面的在说什么，说明你对nginx也不太熟悉，可以先了解一下nginx的配置先。vagrant如果你仅仅想试试munks，并且比较了解vagrant，我也提供了vagrant的脚本，帮你快速的建立起docker swarm模式的集群并且安装好munks。相关的文件可以参考vagrant目录。About LogoLogo是一只松鼠尾巴，特别强调一下是因为Logo是我自己画的，担心大家认不出来。至于为什么是松鼠尾巴，那是在国庆旅途中，延川到壶口段，本来有点迷糊长时间车程，突然跃出到公路上的一只小松鼠，点亮了后续旅程，小孩子也兴奋起来，大人也开始活跃起来。回家后仍然记得那只松鼠，干脆在作为munks的Logo吧。
Package: chipnumpy
ChipnumpyChipnumpy is a module for creating simple chiptune style audio waveforms using numpy.Chipnumpy was inspired bychippy. It has a similar API and generates the same types of waveforms. Unlike chippy, chipnumpy uses numpy, which makes it significantly faster.To install:pip3installchipnumpyFor example implementation, seeexamples/c_scale.py(requires pygame to play the audio).Synthesizer APIThe constructorStart to generate audio by creating a Synthesizer:fromchipnumpy.synthesizerimportSynthesizers=Synthesizer()You can optionally set the random seed:s = Synthesizer(seed=0). This is used when generating noise waveforms; it can be useful if you want to recreate noise waveforms with the same seed.Generate a sine waveformfromchipnumpy.synthesizerimportSynthesizers=Synthesizer()data=s.sine(note=293.66,amplitude=0.5,length=1.1)noteis either a frequency in Hz, or a string representing a note + octave:fromchipnumpy.synthesizerimportSynthesizers=Synthesizer()data=s.sine(note="C5",amplitude=0.5,length=1.1)amplitudecontrols the volume and is clamped to be between 0 and 1.lengthis the duration of the waveform in seconds.The returneddatais an int16 byte array.Generate other waveformsTo generate atriangle waveform:data = s.triangle("C5", 0.5, 1.1)To generate asawtooth waveform:data = s.sawtooth("C5", 0.5, 1.1)To generate apulse waveform:data = s.pulse("C5", 0.5, 1.1)You can optionally set the duty cycle parameter, which controls the length of the pulse (must be between 1 and 100):s.pulse("C5", 0.5, 1.1, duty_cycle=50).To generate asawtooth waveform:data = s.sawtooth("C5", 0.5, 1.1)To generate anoise waveformwith the same syntax:data = s.noise("C5", 0.5, 1.1)This uses random values; see above for how to seed the random number generator.Generate and write wav dataTo convert data to wav data (i.e. to add a wav header):wav = s.to_wav(data)To convert data to wav data and write to disk:s.write(data, path)wheredatais an int16 byte array andpathis either a string or aPath.
Package: chippai
💳  Chipp.ai for Python⚡ Building the pay-per-generation monetization platform for AI applications ⚡Be sure to sign up for an account and complete Stripe onboarding athttps://app.chipp.aiInstallationpip install chippaiDependenciesThis package requires requests for making API calls. However, it will be automatically installed when you install the chippai package using pip.UsagefromchippaiimportChippchipp=Chipp(api_key="your-api-key-from-Chipp-dashboard")# userId needs to come from your database or auth sessionuser=chipp.get_user(user_id="a-unique-identifier-for-user")# If the user is None, the user does not exist in the Chipp system and needs to be createdifuserisNone:user=chipp.create_user(user_id="a-unique-identifier-for-user")# Get the number of credits remaining for this usernum_chipps=user.get_credits()# Deduct credits from this useruser.deduct_credits(1)# Generate a payment URL where this user can choose their preferred packages of credits and buy more.# Pass in a redirect URL to bring your user back to your app after the user payment succeedsurl=user.get_packages_url(return_to_url="https://your-app.com/wherever-user-was")Further DocumentationFor a more detailed guide and advanced usage patterns, refer to theofficial documentation.
Package: chippy
Chippy is a module for creating simple “chiptune” style audio waveformsChippy is a pure Python module for creating various types of basic waveforms,
such as Sine, Triangle, Saw, Square/Pulse, etc. It also does simple FM synthesis.
Under the hood are Python generators for each of these waveforms, which can give
you an endless stream of signed integers representing the waves. There are also
methods that will return a specific length of PCM data, with or without a standard
RIFF header. Use it directly in your application, or save the waveforms to disk.Chippy is not, however, an audio player. It simply creates the waveforms.Inspired by Zach Denton’sWavebenderhttp://github.com/zacharydenton/wavebenderWhat’s New0.1.0- First release! Everything should work fine, but bug reports or improvementsuggestions are most welcome. There isn’t any documentation yet, but the
code should be pretty easy to understand.1) CompatibilityChippy is developed for Python 3. It will also work on Pypy3 and, being written in pure
Python, should work on any compliant interpreter. Please let me know if you encounter any
issues.2) InstallationNo installation is necessary. Chippy is a tiny library with no dependencies. Simply copy
thechippydirectory into the top level of your project folder, andimport chippy.If you prefer, Chippy is also available on PyPI for easy installation via pip.3) UsageSeveral types of waveforms are supported: sine, saw, triange, square, and FM. If you just
want to make a byte string of raw PCM data, you can use the<waveform>_pcm(length=1)methods.
There is another set of methods for producing standard RIFF format wave data, ready to play:<waveform>_riff(length=1). You can save this data to disk with the following methods:save_wave(pcm_data, filename)andsave_raw_pcm(pcm_data, filename). Here is a quick
example of usage:import chippysynth = chippy.Synthesizer(framerate=44100)# Create a raw PCM Sine wave:
sine_wave = synth.Sine(length=2, frequency=220)
# Save it to disk with a RIFF wave header:
synth.save_wave(sine_wave, “wavefile.wav”)# Make an FM waveform with RIFF header:
fm_wave = synth.FM(carrier=440, modulator=220)MORE DOCUMENTATION TO COME!
Package: chipscopy
🐍 ChipScoPy READMEChipScoPy is an open-source project from Xilinx® that enables high-level control of Versal debug IP running in hardware.
Using a simple Python API, developers can control and communicate with ChipScope® debug IP such as the Integrated Logic
Analyzer (ILA), Virtual IO (VIO), device memory access, and more.ChipScoPy communicates with Versal devices. It does not work with older devices such as Ultrascale+ and 7-Series devices.ChipScoPy OverviewSystem RequirementsChipScoPy InstallationChipScoPy ExamplesFAQAPI Documentation
Package: chipsec
CHIPSEC is a framework for analyzing the security of PC platforms including hardware, system firmware (BIOS/UEFI), and platform components. It includes a security test suite, tools for accessing various low level interfaces, and forensic capabilities. It can be run on Windows, Linux, Mac OS X and UEFI shell. Instructions for installing and using CHIPSEC can be found in the [manual](chipsec-manual.pdf).NOTE: This software is for security testing purposes. Use at your own risk. Read WARNING.txt before using.First version of CHIPSEC was released in March 2014:
Announcement at CanSecWest 2014:http://www.c7zero.info/stuff/Platform%20Firmware%20Security%20Assessment%20wCHIPSEC-csw14-final.pdfRecent presentation on how to use CHIPSEC to find vulnerabilities in firmware, hypervisors and hardware configuration, explore low level system assets and even detect firmware implants:
Exploring Your System Deeper:https://www.slideshare.net/CanSecWest/csw2017-bazhaniuk-exploringyoursystemdeeperupdatedProjects That Include CHIPSECLinux UEFI Validation (LUV):https://01.org/linux-uefi-validationBlackArch Linux:https://www.blackarch.org/index.htmlContact UsFor any questions or suggestions please contact us at:chipsec@intel.comMailing lists:CHIPSEC users:https://groups.google.com/forum/#!forum/chipsec-usersCHIPSEC discussion list on 01.org:https://lists.01.org/mailman/listinfo/chipsecTwitter:For CHIPSEC release alerts: Follow CHIPSEC Release athttps://twitter.com/ChipsecRFor general CHIPSEC info: Follow CHIPSEC athttps://twitter.com/ChipsecFor AMD related questions or suggestions please contact Gabriel Kerneis at:Gabriel.Kerneis@ssi.gouv.fr
Package: chipseqpeaks
chipseqpeaksA wrapper for MACS2 that abstracts out some things and makes it easier to use.
Thanks due to Joshua Chiou for inspiration and contributions.Warning:Before using chipseqpeaks, please make sure that one of the
environment variables TMPDIR, TEMP, or TMP is set to an appropriate path.
This is the only way to ensure MACS2 writes temporary files to the correct
location, and failing to do so may cause errors on some systems. (ง •̀_•́)งInstallationpipinstallchipseqpeaksorpipinstall--userchipseqpeaksExample API usagefromchipseqpeaksimportChIPSeqPeakswithChIPSeqPeaks(<bytesobjectorpathtoBAMfile>)ascp:cp.cleans_up=Falsecp.remove_blacklisted_peaks(<path/to/blacklist.bed>)cp.write(<outputprefix>)Example command line usageFor help text, see:chipseqpeaks-call-hFor ChIP-seq:chipseqpeaks-call--controlinput.bamchip.bamFor ATAC-seq:chipseqpeaks-call--atac-seqatac.bam
Package: chipshot
Set up game-winning headers!Chipshot helps standardize header information in software development files.Its target goal is to ensure that copyright dates are standardized
and that licensing information is present.It supports a wide range of file formats,
including source code and documentation formats.
It purposely does not support configuration file formats by default
(such as TOML, INI, or YAML)
but can be configured to support those, too.Sample configurationCreate a file named.chipshot.tomlwith the following content:[chipshot]template="""
Copyright 2021-{{ year }} Developer or Company
Released under the terms of the MIT license.
SPDX-License-Identifier: MIT
"""You can then runchipshot path1 path2to see what files will be modified.
If you’re satisfied, runchipshot--updatepath1 path2to update the files.Pre-commit hooksChipshot offers two pre-commit hooks to help you manage your projects:check-headersupdate-headersHere’s a sample configuration for ensuring your files have correct headers:# .pre-commit-config.yamlrepos:-repo:'https://github.com/kurtmckee/chipshot'rev:'v0.4.0'hooks:-id:'update-headers'
Package: chipshover
ChipShover®ChipShover is an XYZ table & driver, with handy Python interface. It's designed for close analysis of ICs using tools like Electromagnetic Fault Injection (EMFI), EM Probes for side-channel, and more. In both specifications and cost it falls somewhere between between 3D printers & microscope stages.Python InterfaceA Python interface simplifies use from existing Jupyter notebooks and similar.Here is an example usage to sweep an IC surface from (10.0, 10.0) to (12.5, 12.5) in 0.05mm steps. Also plunge the Z-axis down to touch a probe to the surface at each location (useful when probe cannot be dragged across surface safely).from chipshover import ChipShover

shv = ChipShover('com3')

shv.home()

for x,y in shv.sweep_x_y(10, 12.5, 10, 12.5, step=0.05, z_plunge=1.5):
    print("At %f, %f"%(x,y))Installing ChipShover PythonInstallchipshoverlike any other Python package. It should be available onpypi.orgso you can do:pip install chipshoverFull Python DocumentationSeechipshover.readthedocs.iofor the full API documentaiton (auto-built from Python).
Package: chipster-job-manager
Chipster JobManager provides capabilities to move the Chipster job management
responsibility away from clients to remote Chipster JobManager service. When
JobManager is in use, the Chipster clients can start a job execution, save the
session state locally and disconnect. Job is then executed on Chipster analysis
servers and managed by the JobManager, which saves the results upon completion
and reschedules the job in case the execution fails.Documentation:http://csc-it-center-for-science.github.io/chipster-job-manager/To learn more about Chipster refer to:http://chipster.csc.fi/
Package: chipstream
ChipStreamis a graphical user interface for postprocessing
deformability cytometry (DC) data. This includes background computation,
event segmentation, and feature extraction.DocumentationThe documentation, is available atchipstream.readthedocs.io.InstallationInstallers for Windows and macOS are available at therelease page.If you have Python installed, you can install ChipStream from PyPI# graphical user interface
pip install chipstream[gui]
# command-line interface
pip install chipstream[cli]
# both
pip install chipstream[cli, gui]ExecutionIf you have installed ChipStream from PyPI, you can start it with# graphical user interface
chipstream-gui
# command-line interface
chipstream-cliCiting ChipStreamPlease cite ChipStream either in-line(...) using the postprocessing software ChipStream version X.X.X
(available at https://github.com/DC-analysis/ChipStream).or in a bibliographyPaul Müller and others (2023), ChipStream version X.X.X: Postprocessing
software for deformability cytometry [Software]. Available at
https://github.com/DC-analysis/ChipStream.and replaceX.X.Xwith the version of ChipStream that you used.Testingpip install -e .
pip install -r tests/requirements.txt
pytest tests
Package: chiral-client
chiral-client-python: Python client for Chiral Computing Cloud APIVersions0.3.0: for chiral service V20.2.1: ReCGen for virtual molecular library; Large language model LLama2 from Meta;0.1.0: gromacsRequirements for Running Examplespython >= 3.12numpy, matplotlib, grpcio, jupyterInstallationconda install -c conda-forge grpcio protobufnot valid any more, usepython -m pip install grpciopip install notebook
Package: chiral-db-grpc-client
ChiralDB gRPC Client in PythonInstallationpip install chiral_db_grpc_clientCreate a Client instanceimport chiral_db_grpc_client

host = ''
port = ''
client = chiral_db_grpc_client.Client(host, port)Similarity Querysmiles = ''
cutoff = 0.0
client.query_similarity(smiles, cutoff)