Saravanan666/pypi-org huggingface dataset:
Package: cross-border-mover
AboutA Cross-Border E-Commerce Mover 跨境电商搬运工GoalMaking cross-border business easy worldwidecross-border-moverA Cross-border E-commerce MoverdesginFeaturesscrapetranslateextra-batch-images with sdwebuiapiuse pandas auto fill amazon listing excelPlanned Features:title generate by AIInstallpython -m pip install cross-border-moverUsageopen .\XXBand.xlsm with Excel firstform cross-border-mover import Mover

# move from taobao to amazon
df_amazon = Mover.fromTaobao(url).toAmazon()

# move from pinduoduo to amazon
df_amazon = Mover.fromPDD(url).toAmazon()

# move from 1688 to amazon
df_amazon = Mover.from1688(url).toAmazon()

# move from taobao to Wish
df_wish = Mover.fromTaobao(url).toWish()

# move from pinduoduo to Wish
df_wish = Mover.fromPDD(url).toAmazon()

# move from 1688 to Wish
df_wish = Mover.from1688(url).toWish()
Package: cross-cal-resourcesat
cross_cal_resourcesatIntroductionThis package applies a cross calibrates LISS III and AWiFS with the help of a reference image like Landsat 8 and Sentinel 2.Takes two inputs:inpf_liss= path to folder LISS III or AWiFS bandsinpf_ref= path to folder containing reference image bands. Could be Landsat 8 or Sentinel 2.Final output folder:inpf_liss\Reflectance\CalibrationFour temporary images are also generated which are automatically deleted after the execution. These are two composite images,
one resampled image, and one clipped imagePackage informationA python package to cross calibrate ResourceSat 2 sensors like LISS III, AWiFS or LISS IV.Github repo:https://github.com/akhi9661/cross_cal_resourcesatPypI:https://pypi.org/project/cross-cal-resourcesat/Free software: MIT licenseDocumentation:https://akhi9661.github.io/cross_cal_resourcesatNote: At the moment, ifreference_sensor  = 'Landsat 8', path to reflectance images will have to provided ininpf_ref.How to useClone the repository in the desired folder.git clone https://github.com/akhi9661/cross_cal_resourcesat.gitIn case this fails, download the zip file and extract it in a folder.Install the required dependencies.pip install -r requirements.txtIf it throws up an error, create a new virtual environment and install the dependencies. Installation ofGDALandrasteriois complex and generally fails in the base environment or an already existing environment. Creating a new virtual environment generally resolves this.CreditsThis package was created withCookiecutterand thegiswqs/pypackageproject template.
Package: crossclip
CrossclipA cross platform clipboard API for python.Current State of the PackageBackends are written but untested. Package needs a better test
suite.OverviewAboutCrossclip provides a standard python API for interacting with the system
clipboard. As of now, Crossclip supports MacOS, Windows, and GNU/Linux desktop
environments using Gtk and Qt environments. More environments can be added due
to the modular design.DependenciesAll systemsPillow (5.4.0)LinuxGtk users: PyGObjectQt users: PySide2Windowspywin32MacpyobjcInstallationThis package is hosted on PyPI. Install via:$ pip install crossclipUsageHere is an example program:# Import the clipboard frontend class and Pillow Image package
from crossclip.clipboard import Clipboard
from PIL import Image

# Create a new clipboard instance
cb = Clipboard()

# Get text from the clipboard
mytext = cb.get_text()

# Get an image from the clipboard
myimg = cb.get_image() # myimg is a PIL.Image class

# Put text onto the clipboard
my_message = 'Hello World'
cb.set_text(my_message)

# Put an image onto the clipboard
cb.set_image(my_pil_image_instance)

# Access the backend instance
backend_text = cb.backend.get_text()It's as easy as that. The frontend wraps all of the backend specifics and
provides a simple, uniform interface.Implementation DetailsThis library uses a collection of backends to provide clipboard functionality
for a specific system or clipboard. For example, there is a clipboard backend
for the Windows system clipboard and the Gtk+ clipboard. Each backend inherits
from an abstract base class that provides a set of common abstract methods that
each backend must implement.Each of these backends are used in the frontend class,clipboard.Clipboard.
TheClipboardclass determines the backend to use based on the system platform
determined by thesys.platformvalue. The Clipboard class carries an instance
of the backend and uses its functions to provide clipboard functionality.With a design like this, the library is extensible. New backends can be added
and removed.ContributingSee CONTRIBUTING.mdLicenseThis package is licensed under the GPLv3.
Package: crosscompute
CrossCompute Analytics Automation FrameworkAutomate your Jupyter notebooks and scripts as web-based reports, tools, widgets, dashboards, forms. Use this framework to create your own automations, then serve locally or deploy on your own instance of theCrossCompute Analytics Automation System.Reports are documents that update when the data changes.Forms are step-by-step questions that generate a specific web-based report.Tools are forms that transform input variables into output variables.Widgets are interactive visualizations that update when the data changes.Dashboards are widgets in a layout.Here are some available extensions:jupyterlab-crosscomputecrosscompute-views-mapcrosscompute-views-barcodecrosscompute-printers-pdfHere are some available views:linkstringnumberpasswordemailtextmarkdownimageradiocheckboxtableframejsonpdffilemap-mapbox (crosscompute-views-map)map-mapbox-location (crosscompute-views-map)map-deck-screengrid (crosscompute-views-map)barcode (crosscompute-views-barcode)Here are the currently supported configuration options.Usage# Upgrade packagepipinstallcrosscompute>=0.9.4--upgrade# Initialize configurationcrosscompute# Serve automationcrosscomputeautomate.ymlHere are some examples and tutorials:Gallery[source]Documentation[source]ForumDevelopment# Clone repositorygitclonehttps://github.com/crosscompute/crosscompute# Install with dependencies for testscdcrosscompute
pipinstall-e.[test]# Run testspytest--cov=crosscompute--cov-reportterm-missing:skip-coveredtests# Build package for PyPIpipinstallbuild
python-mbuild--sdist--wheel# Publish package on PyPIpipinstalltwine--upgrade
python-mtwineuploaddist/*TroubleshootingSyntaxError: Invalid SyntaxIf you get the following error, you are running on an older version of Python:$ crosscompute

    while chunk := f.read(CHUNK_SIZE_IN_BYTES):
                 ^
SyntaxError: invalid syntaxTo solve this issue, create a virtual environment using python >= 3.10.sudo dnf -y install python3.10
# sudo apt -y install python3.10

python3.10 -m venv ~/.virtualenvs/crosscompute
source ~/.virtualenvs/crosscompute/bin/activate

pip install crosscompute>=0.9.4AcknowledgmentsKashfi FahimOlga RyabtsevaSalah AhmedRodrigo GuarachiPolina ChernomazMiguel Ángel GordiánNoé Domínguez PorrasMarta MorenoNing WeiElaine ChanAida ShoydokovaJennifer Ruda
Package: crosscompute-analytics
CrossCompute Analytics Software Development KitMake apps from scripts quickly.The CrossCompute Analytics Software Development Kit drastically reduces the time and effort needed to update a software as a service (SaaS) solution directly from your command-line scripts and Jupyter notebooks.This is a convenience package that installs the following dependencies:crosscomputecrosscompute-printers-pdfcrosscompute-views-barcodecrosscompute-views-mapipythonjupyterlabjupyterlab-crosscomputepudbHere are examples and tutorials:ExamplesDocumentationForumGallery
Package: crosscompute-geotable
0.7Increase MAXIMUM_DISPLAY_COUNT to 2560.6Use mapbox.js 3.0.10.5Accept MAPBOX_TOKEN environment variableExtract tile layer name from table nameFix _get_summary_domainNormalize NaN to 0 for fill color indexParse geometry from WKT or (Latitude, Longitude)Remove count summary typeRender local table when user clicks on geometryRender normalized fill color from columnRender normalized pixel or meter radius from columnSupport ColorBrewer color schemesCrossCompute Geotable
Package: crosscompute-notebook-extensions
Thanks toSalah Ahmedfor prototyping these extensions.Installationsudo dnf -y install procps
pip install crosscompute-notebook-extensions
jupyter nbextension install \
    --py --sys-prefix crosscompute_notebook_extensions
jupyter nbextension enable \
    --py --sys-prefix crosscompute_notebook_extensions
jupyter serverextension enable \
    --py --sys-prefix crosscompute_notebook_extensionsUsage via command-linegit clone https://github.com/crosscompute/crosscompute-notebook-examples
cd crosscompute-notebook-examples/Examples
crosscompute serve "Suggest Concepts.ipynb"Usage via Jupyter Notebookcd tests/notebooks
jupyter notebookClick the blue paper plane icon (or press SHIFT-C SHIFT-P) to preview the notebook as a tool.Click the red paper plane icon (or press SHIFT-C SHIFT-D) to deploy the notebook as a tool.0.5Add ToolDeployJson extensionAdd ConfigurationUpdateJson extensionShow errors if deploy fails0.4Preview only if first code cell has the word CrossComputeShow errors if preview failsIgnore IPython magicImprove unicode supportSave tool template if first cell is markdownSave result template if last cell is markdown0.3Support custom base_url0.2Add example notebooksAdd ToolPreview redirectFix Python 3 supportReplace pgrep with psutil to support non-POSIX systems0.1Add ToolPreviewJson extensionExtract notebook support from crosscompute package
Package: crosscompute-printers-pdf
PDF Printers for CrossComputeInstallation# Install packagessudodnf-yinstallchromiumnpm# Install latest version of nodesudonpmcacheclean-f
sudonpminstall-gn
sudonlatest# Install packagepipinstallcrosscompute-printers-pdf# Install dependenciescd$(python-c"import crosscompute_printers_pdf; print(crosscompute_printers_pdf.__path__[0] + '/scripts')")npminstallUsageAdd prints to your configuration file.print:variables:-id:reportview:pdfpath:report.pdfconfiguration:header-footer:font-family:sans-seriffont-size:8ptcolor:'#808080'padding:0.1in 0.25inskip-first:truepage-number:location:footeralignment:rightname:'m{mean}-v{variance}-{timestamp}.pdf'Run batch print.crosscompute--print
Package: crosscompute-types
Use these data types to build computational apps using the CrossCompute framework.integertexttableselectimageaudiovideogeotableInstallRun the following command inside your virtual environment for basic functionality.pip install -U crosscompute-typesInstalling additional packages will enable additional features.matplotlibgdalshapelygeotableFor convenience, you can install the full set usingthese Ansible scripts.AcknowledgementsThanks toSalah Ahmedfor his work on the following data types:https://github.com/salah93/crosscompute-selecthttps://github.com/salah93/crosscompute-audiohttps://github.com/salah93/crosscompute-video
Package: crosscompute-views-barcode
Barcode Views for CrossComputeScan barcodes in your CrossCompute automations. Here are the views included in this package:barcodePlease seehttps://github.com/crosscompute/crosscomputefor more information about the CrossCompute Analytics Automation Framework.Usage# Upgrade packagepipinstallcrosscompute-views-barcode# Update configurationvimautomate.ymloutput:variables:-id:yview:barcodepath:variables.dictionaryconfiguration:frames-per-second:10scanner-width-in-pixels:256scanner-height-in-pixels:256
Package: crosscompute-views-map
Map Views for CrossComputeRender maps in your CrossCompute automations. Here are the views included in this package:map-mapboxmap-mapbox-locationmap-deck-screengridPlease seehttps://github.com/crosscompute/crosscomputefor more information about the CrossCompute Analytics Automation Framework.Usage# Upgrade packagepipinstallcrosscompute-views-map# Update configurationvimautomate.ymlinput:variables:-id:incidentview:map-mapbox-locationoutput:variables:-id:buildingsview:map-mapbox-id:incidentsview:map-deck-screengridTroubleshootingNo Matching Distribution Found$ pip install crosscompute-views-map
ERROR: Could not find a version that satisfies the requirement crosscompute-views-map (from versions: none)
ERROR: No matching distribution found for crosscompute-views-mapTo solve this issue, create a virtual environment using python >= 3.10.sudo dnf -y install python3.10
# sudo apt -y install python3.10
python3.10 -m venv ~/.virtualenvs/crosscompute
source ~/.virtualenvs/crosscompute/bin/activate
pip install crosscompute-views-map
Package: crossdeploy
crossdeploy is a declarative approach to manage MLOps resources.Simply define the desired state of the resources and the necessary steps required to get to that state are executed automatically.Subsequent modifications to the resources are automatically created, updated or deleted based on dependencies inferred between them.Under the hood, crossdeploy is a thin wrapper around provisioning engines such as Terraform or Pulumi (coming soon) to manage resources.Refer tohttps://github.com/crossdeploy-io/crossdeployfor more details.
Package: cross-doc
CrossDoc is a comment management system that expands the documentation
capabilities of teams and individuals. By separatingcommentsfromcode, CrossDoc is able to optimize how and when you can edit your
project’s comments.InstallationCrossDoc can be installed throughpipby using the following command:pip installcross-docDeveloper NotesBefore committing, ensure that your code conforms to the standards
addressed in thecontributing write-up. For a collection of
frequently used commands and development processes, check thedeveloper
notesdocument.
Package: crossedwires
CrossedWiresWelcome to the CrossedWires dataset. This is a Python package used in order to interact with our data, hosted on Google Cloud storage.All documentation on how to use the dataset and how to contribute can be found at:https://crossedwires.readthedocs.io.If you wish to download the entire dataset as a zip file, you are welcome to do so using this link:https://storage.googleapis.com/crossed-wires-dataset/full_cifar10_results.zipThe dataset is 324 GB compressed and 340 GB expanded.The dataset itself is licensed under CC-BY-4.0.
Package: crossense
CrossenseCrossense is a collection of utilities designed to facilitate working with scikit-learn models, including training, Inferencing and benchmarking.Features(to be written)InstallationYou can install Crossense using pip:pipinstallcrossenseUsagefromcrossense.ensembleimportCrossBaggingClassifierfromsklearn.linear_modelimportLogisticRegressionfromsklearn.datasetsimportload_irisfromsklearn.model_selectionimportStratifiedKFoldds=load_iris()clf=CrossBaggingClassifier(LogisticRegression(),cv=StratifiedKFold(5))clf.fit(ds.data,ds.target)For more detailed usage and examples, please refer to thedocumentation.ContributingIf you'd like to contribute to Crossense, please follow the contribution guidelines (comming soon).LicenseThis project is licensed under the MIT License - see the LICENSE file for details.
Package: cross-entropy-method
The Cross Entropy MethodTheCross Entropy Method(CE or CEM) is an approach for optimization or rare-event sampling in a given class of distributions{D_p}and a score functionR(x).In its sampling version, it is given a referencep0and aims to sample from the tail of the distributionx ~ (D_p0 | R(x)<q), whereqis defined as either a numeric valueqor a quantilealpha(i.e.q=q_alpha(R)).In its optimization version, it aims to findargmin_x{R(x)}.Why to use?The sampling version is particularly useful for over-sampling of certain properties. For example, you have a parametric pipeline that generates examples for learning, and you wish to learn more from examples that satisfy X, but you're not sure how to generate such ones. The CEM will learn how to tune the parameters of your pipeline to achieve that, while you can easily control the extremety level.How to use?Installation:pip install cross-entropy-method.The exact implementation of the CEM depends on the distributions family{D_p}as defined in the problem.
This repo provides a general implementation as an abstract class, where a concrete use requires writing a simple, small inherited class.
The attachedtutorial.ipynbprovides a more detailed background on the CEM and on this package, along with usage examples.CEM for sampling(left): the mean of the sample distribution (green) shifts from the mean of the original distribution (blue) towards its 10%-tail (orange).CEM for optimization(right): the mean of the sample distribution aims to be minimized.   (images fromtutorial.ipynb)Supporting non-stationary score functionsOn top of the standard CEM, we also support a non-stationary score functionR.
This affects the reference distribution of scores and thus the quantile thresholdq(if specified as a quantile).
Thus, we have to repeatedly re-estimateq, using importance-sampling correction to compensate for the CEM distributional shift.Application to risk-averse reinforcement learningIn our separate work (available incodeand as aNeurIPS paper, with Yinlam Chow, Mohammad Ghavamzadeh and Shie Mannor), we demonstrate the use of the CEM for the more realistic problem of sampling high-risk environment-conditions in risk-averse reinforcement learning. There,D_pdetermines the distribution of the environment-conditions,p0corresponds to the original distribution (or test distribution), andR(x; agent)is the return function of the agent given the conditionsx.
Note that since the agent evolves with the training, the score function is indeed non-stationary.Cite usThis repo: non-stationary cross entropy method@misc{cross_entropy_method,
  title={Cross Entropy Method with Non-stationary Score Function},
  author={Ido Greenberg},
  howpublished={\url{https://pypi.org/project/cross-entropy-method/}},
  year={2022}
}Application to risk-averse reinforcement learning@inproceedings{cesor,
  title={Efficient Risk-Averse Reinforcement Learning},
  author={Ido Greenberg and Yinlam Chow and Mohammad Ghavamzadeh and Shie Mannor},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022}
}
Package: crosservice
This tool is aimed to provide simple and customizable interface for
communication between different services over sockets.No encryption yet.Simple example:Client:from crosservice.signals import BaseSignal

class PingSignal(BaseSignal):
    _host = '127.0.0.1'
    _port = 5555
    signal = 'ping'
    expect_data = ['time']

if __name__ == '__main__':
    ping = PingSignal({'foo': 'bar'})
    if ping.result:
        print ping.result.foo, ':', ping.result.time
    else:
        print "Error: ", ping.result.errorServer:from crosservice.server import start_server
from crosservice.handlers import BaseHandler
import time

class PingHandler(BaseHandler):
    signal = 'ping'
    required_data = ['foo']

    def run(self, foo):
        if foo == 'bar':
            foo = 'received at'
            self.result.foo = foo
            self.result.time = time.time()
        else:
            self.result.error = 'Bad foo!'

if __name__ == '__main__':
    start_server('127.0.0.1', 5555, 1000)
Package: crosseval
crossevalTODOs: Configuring this templateCreate a Netlify site for your repository, then turn off automatic builds in Netlify settings.Add these CI secrets:PYPI_API_TOKEN,NETLIFY_AUTH_TOKEN(Netlify user settings - personal access tokens),DEV_NETLIFY_SITE_ID,PROD_NETLIFY_SITE_ID(API ID from Netlify site settings)Set up Codecov at TODOOverviewInstallationpipinstallcrossevalUsageDevelopmentSubmit PRs againstdevelopbranch, then make a release pull request tomaster.# Optional: set up a pyenv virtualenvpyenvvirtualenv3.9crosseval-3.9echo"crosseval-3.9">.python-version
pyenvversion# Install requirementspipinstall--upgradepipwheel
pipinstall-rrequirements_dev.txt# Install local packagepipinstall-e.# Install pre-commitpre-commitinstall# Run testsmaketest# Run lintmakelint# bump version before submitting a PR against master (all master commits are deployed)bump2versionpatch# possible: major / minor / patch# also ensure CHANGELOG.md updatedChangelog0.0.1First release on PyPI.
Package: crossfiledialog
CrossFileDialogA Python wrapper for opening files and folders with the native file dialog.Makes it easy to prompt the user with a native filepicker on all supported platforms.Currently supports:Zenity (GTK)KDialog (KDE)Windows 2000 and newer (via PyWin32)Basic API usage:importcrossfiledialogfilename=crossfiledialog.open_file()multiple_filenames=crossfiledialog.open_multiple()save_filename=crossfiledialog.save_file()foldername=crossfiledialog.choose_folder()Documentationcrossfiledialog.open_file(title,start_dir,filter)->strOpen a file selection dialog for selecting a file.Parameters:title (str, optional) — The title of the file selection dialog. Default is 'Choose a file'start_dir (str, optional) — The starting directory for the dialog.filter (str, list, dict, optional) — The filter for file types to display. It can be either:a single wildcard (e.g.:"*.py", all files are displayed ending .py)a list of wildcards (e.g.:["*.py" "*.md"], all files are displayed ending either .py or .md)a list of list optional one or more wildcards (e.g.:[["*.py", "*.md"], ["*.txt"]],
user can switch between (.py, .md) and (.txt))a dictionary mapping descriptions to wildcards (e.g.:{"PDF-Files": "*.pdf", "Python Project": ["\*.py", "*.md"]})Returns:str: The selected file's path.crossfiledialog.open_multiple(title,start_dir,filter)->list[str]Open a file selection dialog for selecting multiple files.Parameters:title (str, optional) — The title of the file selection dialog. Default is 'Choose one or more files'start_dir (str, optional) — The starting directory for the dialog.filter (str, list, dict, optional) — The filter for file types to display. It can be either:a single wildcard (e.g.:"*.py", all files are displayed ending .py)a list of wildcards (e.g.:["*.py" "*.md"], all files are displayed ending either .py or .md)a list of list optional one or more wildcards (e.g.:[["*.py", "*.md"], ["*.txt"]],
user can switch between (.py, .md) and (.txt))a dictionary mapping descriptions to wildcards (e.g.:{"PDF-Files": "*.pdf", "Python Project": ["\*.py", "*.md"]})Returns:list[str]: A list of selected file paths.crossfiledialog.save_file(title,start_dir)->strOpen a save file dialog.Parameters:title (str, optional) — The title of the file selection dialog. Default is 'Enter the name of the file to save to'start_dir (str, optional) — The starting directory for the dialog.Returns:str: The selected file's path for saving.crossfiledialog.save_file(title,start_dir)->strOpen a folder selection dialog.Parameters:title (str, optional) — The title of the file selection dialog. Default is 'Choose a folder'start_dir (str, optional) — The starting directory for the dialog.Returns:str: The selected folder's path.LicenceLicensed under the GNU GPL 3.0
Package: crossfit
crossfitMetric calculation libraryInstallationInstall this library usingpip:pip install crossfitUsageUsage instructions go here.DevelopmentTo contribute to this library, first checkout the code. Then create a new virtual environment:cd crossfit
python -m venv venv
source venv/bin/activateNow install the dependencies and test dependencies:pip install -e '.[test]'To run the tests:pytest
Package: crossflow
# CrossflowA Python-based workflow system.Crossflow allows you to build and execute complex workflows that chain together
command-line driven tools.For details, see the [Wiki](https://bitbucket.org/claughton/crossflow/wiki/Home).### Authors:Charlie Laughton [charles.laughton@nottingham.ac.uk](mailto:charles.laughton@nottingham.ac.uk)Christian Suess [christian.suess@nottingham.ac.uk](mailto:christian.suess@nottingham.ac.uk)