Saravanan666/pypi-org huggingface dataset:
Package: canvs-toolbox-canvsUser
Canvs Toolbox PackageDate Formatting"MM/DD/YY"Generalfrom canvs_toolbox import general as gengen.consolidate_data(file_path, file_type='csv')API ToolsCanvs TVfrom canvs_toolbox.api import tv as tvAPItvAPI.twitter_daily_export(api_key, data_mode, start_date, end_date)tvAPI.twitter_emotional_authors(api_key, series_id, start_date, end_date)tvAPI.airings_backfill(api_key, data_mode, start_date, end_date)tvAPI.facebook_backfill(api_key, data_mode, start_date, end_date)Canvs Watchfrom canvs_toolbox.api import watch as watchAPIwatchAPI.post_backfill(api_key, data_mode, start_date, end_date)watchAPI.series_backfill(api_key, data_mode, start_date, end_date)Canvs Socialfrom canvs_toolbox.api import social as socialAPIsocialAPI.get_facebook_posts(api_key, fb_id, org_id, start_date, end_date, query_increment=None)socialAPI.get_page_collection(api_key, org_id, start_date, end_date, fb_pages, query_increment=None)Analytics ToolsCanvs TVfrom canvs_toolbox.analytics import tv as tvAnalyticsAudience Overlap Analysisimplementation: tvAnalytics.audience_overlap_analysis(directory)input directory should contain audience csv files from calling tvAPI.twitter_emotional_authors()analysis will find the overlapping audiences across those csv filesfor best results, rename the csv files to desired series names (e.g. showA, showB)Audience Erosion Analysisimplementation: tvAnalytics.audience_erosion_analysis(filename)input file should be a single audience csv file from calling tvAPI.twitter_emotional_authors()creates an episode-over-episode drop-off analysisEmotional Fingerprinting Analysisimplementation: emotional_fingerprinting_analysis(source, filename, format)input file should be either from a direct Explore Programs export from the Canvs App
or from any of the API exports except twitter_emotional_authorscomputes an emotional similarity score for all possible combinations of passed-in contentcan choose to either return a stacked view of all pairings and their scores (format = 'stacked)
or a matrix view containing similarity scores at content intersections (format = 'matrix')
Package: cao
Python Package Template📦 一个快速上传到PyPI的 Python Package 模版。上传到 PyPI 后可以使用pip install安装。1 使用方法点击本项目右上角的绿色按钮Use this template（使用此模板），输入名称和说明，完成创建；将项目克隆到本地，这里以本项目为例，实际操作时这里需要替换你自己的项目；gitclonehttps://github.com/Ailln/python-package-template.git--depth1修改配置，文件中有提示；cdyour_package_name# 1. 替换默认项目名称 package_name 为你的项目名称# Usage: bash scripts/set_package_name.sh os_name old_name new_name# os_name 支持的有 `mac` 和 `linux`# old_name 是 package_name# new_name 是你的项目名称bashscripts/set_package_name.shmacpackage_nameyour_package_name# 2. 将 `README.md` 修改为你的项目介绍，也就是你当前在读的这个文本。编写你的 Package 代码，并进行测试。# 在本地进行充分测试bashscripts/local_test.sh上传到 PyPi（需要注册），参考如何发布自己的包到 pypi；bashscripts/upload_pypi.sh更新到 Github。gitpush2 项目结构.
├── package_name # 项目名称
│    ├── shell # 在命令行中执行的代码
│    │    ├── __init__.py
│    │    └── usage.py
│    └── src # 静态资源
│          └── temp.txt
├── scripts
│    ├── set_package_name.sh # 批量替换默认的项目名称
│    ├── local_install.sh
│    ├── local_test.sh
│    └── upload_pypi.sh
├── README.md # 项目文档
├── requirements.txt # 包依赖
├── .gitignore # 忽略文件
├── MANIFEST.in # 要包含在 sdist 命令构建的分发中的文件列表。
├── LICENSE # 这里面的内容为本项目的 License，你需要手动替换它。
└── setup.py # 安装配置3 TODO增加 test 相关代码。4 许可5 参考Packaging Python Projects如何从模板创建仓库？如何发布自己的包到 pypi ？
Package: cao-lang
Cao-LangThe node based "language" that governs the actors in the game CaoLoCore documentationProject layout|+ cao-lang/           # core library
 |+ c/                  # C interface
 |+ py/                 # Python interface
 |+ wasm/               # WASM interface
 |+ xtask/              # Additional scripts via Cargo
 |  CHANGELOG.md        # Changelog
 |  CMakeLists.txt      # Root cmake file
 |  MANIFEST.in         # Python build dependency
 |  pyproject.toml      # Python build dependency
 |  README.md
 |  setup.py            # Python build dependency
 |  tox.ini             # Python testing dependency
 |  cliff.toml          # git-cliff config
Package: caom2repo
Client caom2-repocaom2Repo provides a client (caom2-repo) to perform CRUD (Create, Read, Update, Delete) on an observation in a collection in a repository.Visitor PluginThe client also provides a visitor function which accepts a plugin. The visitor function iterates the observations of a collection and updates them according to the algorithm of the plugin function. The following is an example plugin to add a ‘PREVIEW’ Plane to an observation. More plugin examples can be found in caom2repo/tests/.from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

from caom2.observation import Observation
from caom2.plane import Plane


class ObservationUpdater(object):
    """ObservationUpdater that adds a plane to the observation."""

    def update(self, observation, **kwargs):
        """
        Processes an observation and updates it
        """
        assert isinstance(observation, Observation), (
            "observation %s is not an Observation".format(observation))
        observation.planes.add(Plane('PREVIEW'))
Package: caosadvancedtools
READMEWelcomeThis is theCaosDB Advanced User Toolsrepository and a part of the
CaosDB project.This project contains tools that are beyond the typical use of
the CaosDB python client. Especially, this includes the crawler which will
typically be used by a data curator.SetupPlease read theREADME_SETUP.mdfor instructions on how to
setup this code.Further ReadingPlease refer to theofficial
documentationfor more
information.ContributingThank you very much to all contributers—past,
present, and prospective
ones.Code of ConductBy participating, you are expected to uphold ourCode of
Conduct.How to ContributeYou found a bug, have a question, or want to request a feature? Pleasecreate
an issue.You want to contribute code? Please fork the repository and create a merge
request in GitLab and choose this repository as target. Make sure to select
"Allow commits from members who can merge the target branch" under
Contribution when creating the merge request. This allows our team to work
with you on your request.If you have a suggestion for thedocumentation, the
preferred way is also a merge request as describe above (the documentation
resides insrc/doc). However, you can also create an issue for it.You can also contact us atinfo (AT) caosdb.organd join the CaosDB
community on#caosdb:matrix.org.LicenseCopyright (C) 2018 Research Group Biomedical Physics, Max Planck Institute
for Dynamics and Self-Organization Göttingen.Copyright (C) 2020-2021 Indiscale GmbHinfo@indiscale.comAll files in this repository are licensed under aGNU Affero General Public
License(version 3 or later).
Package: caoscrawler
CaosDB-CrawlerWelcomeThis is the repository of the CaosDB-Crawler, a tool for automatic data
insertion intoCaosDB.This is a new implementation resolving  problems of the original implementation
incaosdb-advancedtoolsSetupPlease read theREADME_SETUP.mdfor instructions on how to
setup this code.Further ReadingPlease refer to theofficial documentationof the CaosDB-Crawler for more information.ContributingThank you very much to all contributers—past, present, and prospective ones.Code of ConductBy participating, you are expected to uphold ourCode of Conduct.How to ContributeYou found a bug, have a question, or want to request a feature? Pleasecreate an issue.You want to contribute code?Forking:Please fork the repository and create a merge request in GitLab and choose this repository as
target. Make sure to select "Allow commits from members who can merge the target branch" under
Contribution when creating the merge request. This allows our team to work with you on your
request.Code style:This project adhers to the PEP8 recommendations, you can test your code style
using theautopep8tool (autopep8 -i -r ./).  Please write your doc strings following theNumpyDocconventions.You can also contact us atinfo (AT) caosdb.deand join the
CaosDB community on#caosdb:matrix.org.There is the fileunittests/records.xmlthat servers as a dummy for a server state with files.
You can recreate this by uncommenting a section inintegrationtests/basic_example/test_basic.pyand rerunning the integration test.Integration Testsseeintegrationtests/README.mdContributersThe original authors of this package are:Alexander SchlemmerHenrik tom WördenFlorian SpreckelsenLicenseCopyright (C) 2021-2022 Research Group Biomedical Physics, Max Planck Institute
for Dynamics and Self-Organization Göttingen.
Copyright (C) 2021-2022 IndiScale GmbHAll files in this repository are licensed under aGNU Affero General Public
License(version 3 or later).
Package: caosdb
READMEWelcomeThis is theCaosDB Python Client Libraryrepository and a part of the
CaosDB project.SetupPlease read theREADME_SETUP.mdfor instructions on how to
setup this code.Further ReadingPlease refer to theofficial documentationfor more information.ContributingThank you very much to all contributers—past, present, and prospective ones.Code of ConductBy participating, you are expected to uphold ourCode of Conduct.How to ContributeYou found a bug, have a question, or want to request a feature? Pleasecreate an issue.You want to contribute code?Forking:Please fork the repository and create a merge request in GitLab and choose this repository as
target. Make sure to select "Allow commits from members who can merge the target branch" under
Contribution when creating the merge request. This allows our team to work with you on your
request.Code style:This project adhers to the PEP8 recommendations, you can test your code style
using theautopep8tool (autopep8 -i -r ./).  Please write your doc strings following theNumpyDocconventions.If you have a suggestion for thedocumentation,
the preferred way is also a merge request as describe above (the documentation resides insrc/doc).
However, you can also create an issue for it.You can also contact us atinfo (AT) caosdb.organd join the
CaosDB community on#linkahead:matrix.org.LicenseCopyright (C) 2018 Research Group Biomedical Physics, Max Planck Institute
for Dynamics and Self-Organization Göttingen.Copyright (C) 2020-2022 Indiscale GmbHinfo@indiscale.comAll files in this repository are licensed under aGNU Affero General Public
License(version 3 or later).
Package: caospy
CaospyCaospy is a Python package to analyze continuous dynamical systems and chaos.Its utilities are:Solve systems of ODEs.Eigenvalues, eigenvectors and roots of equations.Classification of fixed points in 1D and 2D.Poincare maps.Plots.Some well studied systems are available in the library, like Lorenz’s system, the Logistic equation, Duffing’s system and the Rosslers-Chaos systems.MotivationDynamic sisytems are one of the most researched niches of knowledge, their understanding is crucial in order to interact more effectively with our environment. In order to study any dynamical systems to an acceptable level of detail, the classical qualitative analysis fall short and different heuristic methods along with numerical approaches where born to better understand their behavior. Properties like fixed points stability or chaotic behavior, and phenomena like bifurcations are commonly obtained by means of using these so called heuristic methods. Caospy attempts to bring this different tools of analysis together in one Python package, and achieve the unification and regularization of their use in a common developing context, hoping it will provide an easier and more comprehensive analysis of the subject in question.RequirementsYou will need Python 3.9 or later to run Caospy.InstallationCaospy is available at PyPI. You can install it via the pip command:$pipinstallCaospyIf you'd like to bleeding edge of the code or you want to run the latest version,
you can clone this repo and then inside the local directory execute:$pipinstall-e.UsageLet's study thedamped harmonic oscillatorwithout driving force. It's a second order  one dimensional ODE, but we'll reduce it to a system of first-order odes.importcaospyascpimportmatplotlib.pyplotaspltvar=["x","y"]# Variablespar=["k","c"]# Parametersfun=["y","-c*y-k*x"]# Functionsname="Damped harmonic oscillator"# System's nameOde_2d=cp.TwoDim(var,fun,par,name)t0=0# Initial timetf=8# End timex0=[0,1]# Initial Conditionspar_value=[100,1]# Parameter valuesN=500# Number of time stepssol=Ode_2d.time_evolution(x0,par_value,t0,tf,N)fig,ax=plt.subplots()ax=sol.plot_trajectory("t-x")# Plot the solutionax.set_xlabel("t [s]")ax.set_ylabel("x [cm]")You'll get the next figure.For more examples, please refer to the tutorial inDocumentation.ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestLicenseDistributed under the MIT License. SeeLICENSEfor more information.AuthorsJuan Colman(E-mail:juancolmanot@gmail.com), Sebastián Nicolás Bertolo.
Package: cap
Cap: lightweight package for use network capturesThe idea is to read and write capture files like it is really a serialized data. The API is ment to be close as possible to json and pickle APIs.Installation:install the package by:pipinstallcapor from the source:pythonsetup.pyinstallUsage:read cap:importcapcaptured_packets=cap.load(open("C:\\test.cap","rb"))filter about somthingip_v4_captured_packet=[]forcaptured_packetincaptured_packets:ifcaptured_packet.data[12:14]=='\x08\x00':ip_v4_captured_packet.append(p)dump filtered packetscap.dump(ip_v4_captured_packet,open('C:\\new_test.cap',"wb"))Have a nice use and please report about problems and issues.
Thank you.
Package: cap2-assembly
CAP2 AssemblyMinimum viable module for the de novo assembly stage of metagenomics analysis.Free software: MIT licenseDocumentation:https://cap2-assembly.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2022-03-23)First release on PyPI.
Package: cap6635
CAP6635 Artificial IntelligenceA summary of the AI techniques explored in Dr. Zhu's AI classStructure of Packagecap6635.agentsdefine the different search and decision-making algorithms.See sub-directories for more details on the agentscap6635.environmentcreates and populates the world with various obstacles or other states.cap6635.utilitieshosts various helper functions for searching, environment manipulation, animation and more.Development setuppipinstall-rrequirements.txtcap6635AI ExamplesVacuumsRun thevacuums.pyexample with the optional paramters.  The output gets saved as avacuum?.gifanimation.# Type of agent defaults to random type (if not provided)# 1 --> Simple Reflex Vacuum# 2 --> Model-based Vacuum# 3 --> Goal-based Vacuum# World Height & Width defaults to random int (if not provided)python1_vacuums.py[type_of_agent][height_of_world][width_of_world]n-Queens# Hill Climbingpython2_hill_climbing.py[number_of_queens]# Simulated Annealingpython3_simulated_annealing.py[number_of_queens]# Genetic Algorithmpython4_genetic.py[number_of_queens]Tic Tac Toe# Minimax + Alpha-Beta Pruning# Algorithm {1 --> Minimax, 0 --> Alpha-Beta Pruning}# First Player {1 --> Human, 2 --> AI}python5_minimax.py[Algorithm][FirstPlayer]# e.g. Minimax (Alpha-Beta Pruning) - Player is Xpython5_minimax.py01
Package: capablerobot-usbhub
Capable Robot Programmable USB Hub DriverThis package has two functions:It provides access to internal state of the Capable Robot USB Hub, allowing you to monitor and control the Hub from an upstream computer.It creates a transparent CircuitPython Bridge, allowing unmodified CircuitPython code to run on the host computer and interact with I2C & SPI devices attached to the USB Hub.InstallingInstall and update usingpip:pip install capablerobot_usbhubThis driver requires Python 3.6 or newer.Additional Linux InstructionsOn Linux, the the udev permission system will likely prevent normal users from accessing the USB Hub's endpoint which allows for Hub Monitoring, Control, and I2C Bridging.  To resolve this, install the provided udev rule:sudo cp 50-capablerobot-usbhub.rules /etc/udev/rules.d/
sudo udevadm control --reload
sudo udevadm triggerThen unplug and replug your USB Hub.  Note, the provided udev rule allows all system users to access the Hub, but can be changed to a specific user or user group.Additional Windows InstructionsOn Windows, the low-level USB driver for the Hub's VID/PID must be re-assigned from the default one to the libusbk driver.  This can be done with theZadigGUI, or by executing the following commands:pip install capablerobot_usbregister
usbregister device usbhubFor additional details on how theusbregistercommand-line utility work, please vist theCapableRobot_USBRegisterrepository.Usage & ExamplesTheexamplesfolder has some code samples of how to use this Python API to control the Programmable USB Hub.  There is also anotherexample repositorywhich includes additional host-side code as well as examples of customizing behavior via changing the Hub's firmware.Working FunctionalityReading USB Hub registers over USB and decoding of register data.Writing USB Hub registers over USB.Reading & writing I2C data thru the Hub.Python API to control and read the two GPIO pins.CircuitPython I2C Bridge to the rear I2C1 port.CircuitPython SPI Bridge to the internal mikroBUS header.Not Working / Not Implemented YetNo known errata at this timeContributingContributions are welcome! Please read ourCode of Conductbefore contributing to help this project stay welcoming.
Package: capablerobot-usbregister
Capable Robot : USB RegisterWindows command line tool & python library to assist in registering USB drivers.This repository includes binaries built from thelibwdiproject.  Specifically:wdi-64bit.exe: 64-bit binary ofwdi-simple, version v1.3.1.wdi-32bit.exe: 32-bit binary ofwdi-simple, version v1.3.1.The python wrapper automatically runs the appropiate binary based on your computers's architecture.The wrapper also includes registration settings for the following products, enabling error-free device registration.Capable Robot Programmable USB HubPull requests to add additional devices will be gladly accepted.Installationpip install capablerobot_usbregisterUsageUsage: usbregister [OPTIONS] COMMAND [ARGS]...

    Options:
      --verbose  Increase logging level.
      --help     Show this message and exit.

    Commands:
      list      Print the known devices which can be registered to drivers.
      device    Register the specified device.
      register  Registers driver based on Vendor ID, Product ID, and interface ID.
Package: capapi
CapAPIShort for "Capitalism API for Python"This is a simple, free-to-use, API wrapper for theCapitalism Bots APISource Code:https://github.com/drapespy/CapAPISome Examples# Basic get user data examplefromcapapiimportget_useruser=get_user(763854419484999722)print(user.wallet)# Prints the amount in wallet# has_x examplesfromcapapiimportget_useruser=get_user(763854419484999722)print(user.is_admin())# Same functionality asprint(user.has_badge("admin"))print(user.has_item("kirk_juice"))
Package: capbypasswrapped
CapBypassWrappedCapBypassWrappedis a Python module that provides a wrapper for CapBypass, a captcha solving service. This module allows developers to easily integrate captcha solving functionality into their applications without having to write complex code.InstallationTo installCapBypassWrapped, simply run the following command:pip install CapBypassWrappedUsageTo useCapBypassWrappedfunctions, just import the module:importCapBypassWrappedNow initialize the CapBypassWrapped class with your according parameters.capbypass_instance=CapBypassWrapped(client_key="":str,verbal=False:bool,custom_http_client=None:any)There are lots of different functions you can use with lots of customizability.Here is every function below and how to initiate each one:capbypass_instance=CapBypassWrapped(client_key="":str,verbal=False:bool,custom_http_client=None:any)defget_balance():result=capbypass_instance.get_balance()print(result)# returns {"credits": str, "errorId": int}defget_task_result():result=CapBypassWrapped.get_task_result(task_id="":str)print(result)# returns {"solution": str, "status": str, "errorId": int}defcreate_funcaptcha_task():result=CapBypassWrapped.create_task(task_type="":str,website_url="":str,website_public_key="":str)# optional - blob: strprint(result)# returns {"taskId": str}LicenseCapBypassWrappedis licensed under the Apache License. See the LICENSE file for more information.ContactIf you have any questions or comments aboutCapBypassWrapped, please contact me on discord at @maxhithere
Package: capcalc
Capcalccapcalc is a suite of python programs used to perform coactivation
pattern analysis on time series data. It uses K-Means clustering to find
a set of “activation states” that represent the covarying patterns in
the data.HTML documentation is here:http://capcalc.readthedocs.io/en/latest/NOTEThis is an evolving code base. I’m constantly tinkering with it. That
said, now that I’m releasing this to the world, I’m being somewhat more
responsible about locking down stable release points. In between
releases, however, I’ll be messing with things.It’s very possible I
could break something while doing this, so check back for status updates
if you download the code in between releases. I’ve finally become a
little more modern and started adding automated testing, so as time goes
by hopefully the “in between” releases will be somewhat more reliable.
Check back often for exciting new features and bug fixes!Ok, I’m sold. What’s in here?roidecompose- This program uses an atlas to extract timecourses
from a 4D nifti file, producing a text file with the averaged
timecourse from each region in the atlas (each integral value in
file) in each column. This can be input to capfromtcs. There are
various options for normalizing the timecourses.capfromtcs- This does the actual CAP calculation, performing a
k-means cluster analysis on the set of timecourses to find the best
representitive set of “states” in the file. Outputs the states found
and the dominant state in each timepoint of the timecourse.maptoroi- The inverse of roidecompose. Give it a set of cluster
timecourses and a template file, and it maps the values back onto the
roisstatematch- Use this for aligning two state output files. Takes
two state timecourse files, and determines which states in the second
correspond to which states in the first. Generates a new ‘remapped’
file with the states in the second file expressed as states in the
first.