Saravanan666/pypi-org huggingface dataset:
Package: zarinpal-payment-integration
ZarinPal Payment Python ClientA Python client for integrating ZarinPal payment gateway into your Python applications.Table of ContentsIntroductionFeaturesInstallationUsageExamplesError HandlingLicenseIntroductionThis Python package provides a simple interface for interacting with the ZarinPal payment gateway. It allows you to make payment requests, verify payments, and handle errors gracefully.FeaturesRequest payments with easeVerify payment transactionsGraceful error handling for ZarinPal-specific errors...InstallationYou can install the package using pip:pipinstallzarinpal-payment-integrationUsagefromzarinpal.apiimportZarinPalPayment# Initialize ZarinPalPayment with your merchant_id and amountpayment_instance=ZarinPalPayment("your_merchant_id",10000)# Make a payment requestresult=payment_instance.request_payment("your_callback_url","Payment for something","user_mobile","user_email")print(result)# Verify a paymentverification_result=payment_instance.verify_payment("payment_authority")print(verification_result)ExamplesFor more detailed examples, check theexamplesdirectory in this repository.Error HandlingThe package includes robust error handling for ZarinPal-specific errors. For more information on error codes and messages, refer to theZarinPal API documentation.LicenseThis project is licensed under the MIT License.
Package: zarnegar-converter
Homepage:https://github.com/behnam/python-zarnegar-converterZarnegar(Persian:زرنگار, zarnegār, meaning gold-depicting) is a
commercial, stand-alone Persian/Arabic word processor program developed for
MS-DOS and Windows.  The first version of Zarnegar (for DOS), was released in
April-May 1991, and Windows versions have been available since 2000.Zarnegar has employed two different character sets and file formats.Zarnegar1 Character SetZarnegar used anIran System-based character encoding system, namedZarnegar1, with text file formats for its early versions, up to its “Zarnegar
75” version.Zarnegar1character set is a2-form left-to-right visual
encoding, meaning the everyPerso-Arabicletter receives different
character codes based on its cursive joining form, but most letters receive
only 2 forms, because of the limited code-points available2 forms, because of
the limited code-points available.This project has a partial implementation ofZarnegar1encoding
(zarnegar_converter/zar1_encoding.py) and a full implementation of its binary
and text file formats (zarnegar_converter/zar1_file.py).Zarnegar75 Character SetWith “Zarnegar 75” version of the program, a new character encoding system was
introduced, and the file format was changed to another binary format.Zarnegar75character set is a 4-form bidirectional encoding, meaning that
everyPerso-Arabicletter receives one, two, or four character code,
depending on its cursive joining form, and these letters are stored in the
memory in the semantic order.Support forZarnegar75file format and encoding is still in progress.How to Use$./src/zarnegar-converter.pyunicode_legacy_lrosamples/zar1-sample-text-01.zar‭ﻡﺎﯾﺧﺕﺎﯾﻋﺎﺑﺭﻩﺭﺎﺑﺭﺩ|‭ﯽﻧﭘﺍﮊﺭﻌﺷﺭﺩﻭﮐﯾﺎﻫ|How to ContributePlease report any issues at
<https://github.com/behnam/python-zarnegar-converter/issues> or submit GitHub
pull requests.The encoding mappings (both Zarnegar1 and Zarnegar75) can be improved with
access to more sample files. Please write to <behnam@zwnj.org> if you like to
contribute (private or public) Zarnegar source files to improve this project.AcknowledgementsThanks toCecil H. Green Libraryof Stanford University, specially John A
Eilts and Behzad Allahyar, for sharing their collection of Zarnegar documents.Also thanks toThe Official Website of Ahmad Shamloufor sharing their
collection of documents.Legal NoticeZarnegaris a trademark ofSinaSoft Corporation. This project is NOT
affiliated with SinaSoft Corporation.Copyright (C) 2017  Behnam EsfahbodThis program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE.  See the GNU General Public License for more details.
Package: zarnevis
Zarnevis : RTL Text for your computer vision projectsInstallationThepipwayJust run this on your machine:pip install zarnevisThegitwayFirst, clone this repository using this command:git clone https://github.com/prp-e/zarnevisThen run these commands:cd zarnevis && pip install -e .NOTE: This method is only suggested for when you're going to do some development and tests on the project. If you want to do something else (such as using this tool in a face tracker drone) just use thepipway and don't make trouble for yourself :)ExampleIt is pretty straight-forward. I actually made this because I wanted to do some cool stuff and I didn't want to make it so complex. This is an example code:importcv2fromzarnevisimportZarnevisimage=cv2.imread('example.jpg')processor=Zarnevis(image=image,text="اهواز زیبا",font_file='vazir.ttf',font_size=36,text_coords=(200,20),color=(255,0,100))image=processor.draw_text()cv2.imwrite('example_zarnevis.jpg',image)Image - BeforeImage - AfterSpecial ThanksAmin Sharifi - Because he did a great job teaching this method on hiswebsiteandYouTube channel.Touhid Arastu - He pointed out inthis issuethat in new versions of Pillow, we don't really need reshaping and stuff and if we don't want to run our code on many different platforms, it can handle the thing itself.
Package: zarpaint
zarpaintPaint segmentations directly to on-disk/remote zarr arraysThisnapariplugin was generated withCookiecutterusing with@napari'scookiecutter-napari-plugintemplate.InstallationYou can installzarpaintviapip:pip install zarpaintContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"zarpaint" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: zarr
ZarrLatest ReleasePackage StatusLicenseBuild StatusPre-commit StatusCoverageDownloadsGitterCitationWhat is it?Zarr is a Python package providing an implementation of compressed, chunked, N-dimensional arrays, designed for use in parallel computing. See thedocumentationfor more information.Main FeaturesCreateN-dimensional arrays with any NumPydtype.Chunk arraysalong any dimension.Compressand/or filter chunks using any NumCodecs codec.Store arraysin memory, on disk, inside a zip file, on S3, etc...Readan arrayconcurrentlyfrom multiple threads or processes.Write to an array concurrently from multiple threads or processes.Organize arrays into hierarchies viagroups.Where to get itZarr can be installed from PyPI usingpip:pipinstallzarror viaconda:condainstall-cconda-forgezarrFor more details, including how to install from source, see theinstallation documentation.
Package: zarr-checksum
zarr_checksumAlgorithms for calculating a zarr checksum against local or cloud storageInstallpip install zarr-checksumUsageCLITo calculate the checksum for a local zarr archivezarrsum local <directory>To calculate the checksum for a remote (S3) zarr archivezarrsum remote s3://your_bucket/prefix_to_zarrPythonTo calculate the checksum for a local zarr archivefromzarr_checksumimportcompute_zarr_checksumfromzarr_checksum.generatorsimportyield_files_local,yield_files_s3# Localchecksum=compute_zarr_checksum(yield_files_local("local_path"))# Remotechecksum=compute_zarr_checksum(yield_files_s3(bucket="your_bucket",prefix="prefix_to_zarr",# Credentials can also be passed via environment variablescredentials={aws_access_key_id:"youraccesskey",aws_secret_access_key:"yoursecretkey",region_name:"us-east-1",}))Access checksum information>>>checksum.digest'c228464f432c4376f0de6ddaea32650c-37481--38757151179'>>>checksum.md5'c228464f432c4376f0de6ddaea32650c'>>>checksum.count37481>>>checksum.size38757151179
Package: zarrdataset
ZarrDatasetA class for handling large-volume datasets stored in OME-NGFF Zarr format.
This can be used primarly with PyTorch's DataLoader in machine learning training workflows.Usageimport zarrdataset as zds

# Open a set of Zarr files stored locally or in a S3 bucket. Must specify the
# group/component were the arrays are stored within the zarr file, and the 
# order of the axes of the dataset.
my_dataset = zds.ZarrDataset(
  dict(
    modality="images",
    filenames=["https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.1/9836839.zarr"],
    source_axes="TCZYX",
    data_group="0"
  ),
)IntegrationTheZarrDatasetclass is derived from PyTorch'sIterableDatasetclass, and can be used with aDataLoaderobject to generate batches of inputs for machine learning training workflows.from torch.utils.data import DataLoader
import zarrdataset as zds

my_dataset = zds.ZarrDataset(...)

# Generate batches of 16 images, uisng four threads.
# Pass the worker initialization function from zarrdataset to the DataLoader
my_dataloader = DataLoader(my_dataset,
                           batch_size=16,
                           num_workers=4,
                           worker_init_fn=zds.zarrdataset_worker_init_fn)

for x, t in my_dataloader:
    # The training loop
    ...
    output = model(x)
    loss = criterion(output, t)
    ...Multithread data loadingUse of multiple workers through multithread requires the use of thezarrdataset_worker_init_fnfunction provided in this package.
This allows to load only a fraction of the dataset on each worker instead of the full dataset.Patch samplingZarrDatasetretrieve the whole array contained indata_groupby default.
To retrieve patches from that array instead, use any of the two samplers provided within this package, or implement a custom one derived from thePatchSamplerclass.The two existing samplers arePatchSamplerandBlueNoisePatchSampler.PatchSamplerretrieves patches from an evenly distributed grid of non-overlapping squared patches of sidepatch_size.BlueNoisePatchSamplerretrieves patches of sidepatch_sizefrom random locations followingblue-noise sampling.
The patch sampler can be integated into aZarrDatasetobject as follows.import zarrdataset as zds

# Retrieve patches of size patch_size in an evenly spaced grid from the image.
my_patch_sampler = zds.PatchSampler(patch_size)

my_dataset = zds.ZarrDataset(...,
                             patch_sampler=my_patch_sampler)Examples of integration of theZarrDatasetclass with the PyTorch'sDataLoadercan be found in thedocumentation.InstallationThis package can be installed from PyPI with the following commandpip install zarrdataset
Package: zarrdump
Describezarrstores from the command line. A path to any filesystem implemented byfsspecis valid.Installation$ pip install zarrdumpUsageIf zarr store can be opened byxarray, the xarray representation will be displayed:$ zarrdump gs://bucket/dataset.zarr
<xarray.Dataset>
Dimensions:  (lat: 73, lon: 144, time: 32)
Coordinates:
* lat      (lat) float64 -90.0 -87.5 -85.0 -82.5 -80.0 ... 82.5 85.0 87.5 90.0
* lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
* time     (time) object 2016-12-01 00:00:00 ... 2017-01-01 00:00:00
Data variables:
    ps       (time, lat, lon) float32 dask.array<chunksize=(4, 73, 144), meta=np.ndarray>
    ts       (time, lat, lon) float32 dask.array<chunksize=(4, 73, 144), meta=np.ndarray>Can show information for a particular variable/array:$ zarrdump -v ts gs://bucket/dataset.zarr
<xarray.DataArray 'ts' (time: 32, lat: 73, lon: 144)>
dask.array<zarr, shape=(32, 73, 144), dtype=float32, chunksize=(4, 73, 144), chunktype=numpy.ndarray>
Coordinates:
* lat      (lat) float64 -90.0 -87.5 -85.0 -82.5 -80.0 ... 82.5 85.0 87.5 90.0
* lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
* time     (time) object 2016-12-01 00:00:00 ... 2017-01-01 00:00:00
Attributes:
    longname:  surface temperature
    units:     KDiagnostic information will also be printed for zarr arrays or zarr groups which do not represent xarray datasets:$ zarrdump group.zarr
Name        : /
Type        : zarr.hierarchy.Group
Read-only   : False
Store type  : fsspec.mapping.FSMap
No. members : 2
No. arrays  : 2
No. groups  : 0
Arrays      : bar, foo
Package: zarrita
ZarritaZarrita is an experimental implementation ofZarr v3includingsharding. This is only a technical proof of concept meant for generating sample datasets. Not recommended for production use.Setupimportzarritaimportnumpyasnpstore=zarrita.LocalStore('testoutput')# or zarrita.RemoteStore('s3://bucket/test')testdata=np.arange(0,16*16,dtype='int32').reshape((16,16))Create an arraya=zarrita.Array.create(store/'array',shape=(16,16),dtype='int32',chunk_shape=(2,8),codecs=[zarrita.codecs.bytes_codec(),zarrita.codecs.blosc_codec(typesize=4),],attributes={'question':'life','answer':42})a[:,:]=testdataOpen an arraya=zarrita.Array.open(store/'array')assertnp.array_equal(a[:,:],testdata)Create an array with shardinga=zarrita.Array.create(store/'sharding',shape=(16,16),dtype='int32',chunk_shape=(16,16),chunk_key_encoding=('v2','.'),codecs=[zarrita.codecs.sharding_codec(chunk_shape=(8,8),codecs=[zarrita.codecs.bytes_codec(),zarrita.codecs.blosc_codec(typesize=4),]),],)a[:,:]=testdataassertnp.array_equal(a[:,:],testdata)Create a groupg=zarrita.Group.create(store/'group')g2=g.create_group('group2')a=g2.create_array('array',shape=(16,16),dtype='int32',chunk_shape=(16,16),)a[:,:]=testdataOpen a groupg=zarrita.Group.open(store/'group')g2=g['group2']a=g['group2']['array']assertnp.array_equal(a[:,:],testdata)Resize arraya.resize((10,10))Update attributesa.update_attributes({'question':'life','answer':0})Zarr v2a=zarrita.ArrayV2.create(store/'array',shape=(16,16),dtype='int32',chunks=(2,8),)a[:,:]=testdataa3=a.convert_to_v3()asserta3.metadata.shape==a.shapeAsync methodsa=awaitzarrita.Array.create_async(store/'array_async',shape=(16,16),dtype='int32',chunk_shape=(2,8),)awaita.async_[:,:].set(testdata)assertnp.array_equal(awaita.async_[:,:].get(),testdata)CreditsThis is a largely-rewritten fork ofzarritaby@alimanfoo. It implements the Zarr v3 draft specification created by@alimanfoo,@jstriebel,@jbmset al.Licensed under MIT
Package: zarr-jpeg2k
zarr-jpeg2kAboutJPEG-2000 encoded and decoder for of Zarr chunks usingimagecodecs.  Works with 2D and interleaved RGB data.Usagefromzarr_jpeg2kimportjpeg2kimportnumpyasnpimportzarrcodec=jpeg2k(level=50)data=np.arange(100000000,dtype='uint8').reshape(10000,10000)# without zarrencoded=codec.encode(data)decoded=codec.decode(encoded)# with zarrz=zarr.array(data,chunks=(1000,1000),compressor=codec)ReferencesThis repo is heavily influenced byzarr-jpegwhich uses JPEG encoding to compress chunks of data.
Package: zarr-swiftstore
zarr-swiftstoreopenstack swift object storage backend for zarr. It enables direct access to
object storage to read and write zarr datasets.Installgitclonehttps://github.com/siligam/zarr-swiftstore.gitcdzarr-swiftstore
pythonsetup.pyinstallUsageOpenstack Swift Object Storage auth_v1.0 requires the following keyword arguments for authenticationFor initial authentication:auth={"authurl":"...","user":"{account}:{user}","key":"{password}",}or if pre-authenticated token is already available:auth={"preauthurl":"...","preauthtoken":"...",}using zarrimportosimportzarrfromzarrswiftimportSwiftStoreauth={"preauthurl":os.environ["OS_STORAGE_URL"],"preauthtoken":os.environ["OS_AUTH_TOKEN"],}store=SwiftStore(container='demo',prefix='zarr-demo',storage_options=auth)root=zarr.group(store=store,overwrite=True)z=root.zeros('foo/bar',shape=(10,10),chunks=(5,5),dtype='i4')z[:]=42using xarrayimportxarrayasxrimportnumpyasnpfromzarrswiftimportSwiftStoreds=xr.Dataset({"foo":(('x','y'),np.random.rand(4,5))},coords={'x':[10,20,30,40],'y':[1,2,3,4,5],},}store=SwiftStore(container='demo',prefix='xarray-demo',storage_options=auth)ds.to_zarr(store=store,mode='w',consolidated=True)# loadds=xr.open_zarr(store=store,consolidated=True)TestTest picks up authentication details from the following environment variables.If pre-authentication token is already available:exportOS_AUTH_TOKEN="..."exportOS_STORAGE_URL="..."Otherwise:exportST_AUTH="..."exportST_USER="{account}:{user}"exportST_KEY="{password}"Also set environment variable ZARR_TEST_SWIFT=1exportZARR_TEST_SWIFT=1pytest-vzarrswift
Package: zarr-tools
zarr-toolsConvert nd2 to zarrInstallationpip install zarr-toolsUsageAs command linepython -m zarr-tools file.nd2This will produce the zarr dataset with default 5 steps of binning for xy dimensions.As python moduleimportnd2importzarr_toolsdata=nd2.ND2File("input.nd2").to_dask()zarr_tools.convert.to_zarr(data,channel_axis=1,path="output.zarr",steps=4,name=['BF','TRITC'],colormap=['gray','green'],lut=((1000,30000),(440,600)),)
Package: zas
Zencore Application ServerInstallpip install zasUsageE:\zas\src\scripts>python zas.py
Usage: zas.py [OPTIONS] COMMAND [ARGS]...

Options:
-c, --config FILENAME  Config file path, use yaml format. Default to
                        config.yaml.
--help                 Show this message and exit.

Commands:
reload  Reload application server.
start   Start application server.
stop    Stop application server.Example Configapplication:
    deamon: true
    pidfile: /tmp/appname.pid
    main: app.mainCreate a new type serverfrom zascore import server
from zascore import set_default_config_path
from zascore import set_default_pidfile
from zascore import set_config_loader
from zascore import default_config_loader

def helloserver_loader(config):
    data = default_config_loader(config)
    data["server-name"] = "hello server v1.0.0"
    return data

if __name__ == "__main__":
    set_default_config_path("/etc/helloserver.yaml")
    set_default_pidfile("/var/run/helloserver.pid")
    set_config_loader(helloserver_loader)
    server()
Package: zassist
制作游戏辅助常用模块此包专为windows设计包含以下功能键盘工具 keyboard键盘监控 keyboard_monitor鼠标工具 mouse鼠标监控 mouse_monitor网络模块 network加密模块 password注册表　 regedit屏幕工具 screen系统工具 system窗口工具 window文件系统 file_system已取消以下功能脚本工具 script驱动管理 drive授权管理 jurisdiction数学库　 mathf更新日志18-12-05 0.9.1完成网络模块network, 注册表regedit, 上个版本发布的模块额外增加了一些功能18-12-03 0.9.0已经实现了大部分功能18-11-29  0.1.0本模块所有功能(包含已删除功能)已使用csharp代码实现, 正在重写为python代码, 本次发布并没有任何功能, 只是为了占名字
Package: zat
Zeek Analysis Tools (ZAT)The ZAT Python package supports the processing and analysis of Zeek data
with Pandas, scikit-learn, Kafka, and SparkInstallpip install zat
pip install zat[pyspark] (includes pyspark library)
pip install zat[all] (include pyarrow, yara-python, and tldextract)Getting StartedExamples of Using ZATAWS Data Processing and ML ModelingPlease seeSageWorksInstalling on Raspberry Pi!Raspberry Pi InstructionsRecent ImprovementsFaster/Smaller Pandas Dataframes for large log files:Large DataframesBetter Panda Dataframe to Matrix (ndarray) support:Dataframe To MatrixScalable conversion from Zeek logs to Parquet:Zeek to ParquetVastly improved Spark Dataframe Class:Zeek to SparkUpdated/improved Notebooks:Analysis NotebooksZeek JSON to DataFrame class:Zeek JSON to DataFrame ExampleVideo PresentationData Analysis and Machine Learning with ZeekWhy ZAT?Zeek already has a flexible, powerful scripting language why should I use
ZAT?Offloading:Running complex tasks like statistics, state machines,
machine learning, etc.. should be offloaded from Zeek so that Zeek can
focus on the efficient processing of high volume network traffic.Data Analysis:We have a large set of support classes that help
bridge from raw Zeek data to packages like Pandas, scikit-learn, Kafka, and
Spark. We also have example notebooks that show step-by-step how to get
from here to there.Analysis NotebooksZeek to Scikit-LearnZeek to ParquetZeek to SparkSpark ClusteringZeek to KafkaZeek to Kafka to SparkClustering: Picking K (or not)Anomaly Detection ExplorationRisky Domains Stats and DeploymentZeek to MatplotlibDocumentationhttps://supercowpowers.github.io/zat/Running the Testspip install pytest coverage pytest-cov
pytest zatAbout SuperCowPowersThe company was formed so that its developers could follow their passion for Python, streaming data pipelines and having fun with data analysis. We also think cows are cool and should be superheros or at least carry around rayguns and burner phones.Visit SuperCowPowers
Package: zatca-csr-generator
Csr and Private Key generator for Zatca Phase2Generate Private Key with EC Curve and CSR for the Zatca Phase2 for the complience CSIDInstallationTo use the Zatca Csr Generator, you need to have cryptography installed. You can install it using pip:pipinstallzatca_csr_generatorUsageFirst, import the necessary modules:Then, import and create an instance of the CsrGenerator class:from zatca_csr_generator import zatca_csr
certificate=GenerateCSR()Loading an method to obtain private key and certificate signing requestTo load an image from a file, use the load_image method:csr_type
e.g for 'sandbox' |'simulation' | 'production'.C = country
country with max 2 length.CN = common nameO = organizationOU = organization unitSN = Serial number e.g
1-name of the company|2-version|3-uuidUID = Tax Registration No (15 Digits)TITLE = Type of business e.g 1000,0100, 1100B2B =0100B2C= 1000Both= 1100CATEGORY = Type of businessADDRESS  = Registered Business Addressprivatekey,csr= certificate.generate_csr.(csr_type, C, CN, O, OU, SN, UID, TITLE, CATEGORY, ADDRESS)privatekey will return the string for the private key andExamplefrom zatca_csr_generator import zatca_csr

certificate = zatca_csr.GenerateCSR()


privatekey,csr = certificate.generate_csr("production","SA", "TST", "TST",
                                          "TST", "1-TST|2-TEST|3-uuid",
                                          "3210124145101243", "1100", "Information Technology", "RHSX46590")

print(privatekey)
print(csr)ContributingContributions are welcome! If you find any issues or have suggestions for improvements, please create an issue or submit a pull request.LicenseThis project is open source and licensed under the MIT License.ContactFor any questions or inquiries, please contact:Muhammad BilalEmail:bilaljmal@gmail.com