Saravanan666/pypi-org huggingface dataset:
Package: xrpatcher
XRPatcher - A ML-Oriented Generic Patcher forxarrayData StructuresInstallation|ExamplesJ. Emmanuel JohnsonQuentin FebvreAboutThis is a lightweight package to create patchitemsfrom xarray data structures.
This makes it more compatible with machine learning datasets and dataloaders like PyTorch or TensorFlow.
The user simply needs to define the patch dimensions and the stride dimensions and you are good to go!
It also reconstructs (or unpatchifies) from arbitrary patches which allows for more robust inference procedures, e.g. to account for border effects from CNN models.⏩ ExamplesQuick ExampleimportxarrayasxrfromxrpatcherimportXRDAPatcher# load demo datasetdata=xr.tutorial.load_dataset("eraint_uvz")# extract demo dataarraydata=data.u[...,:240,:360]# Instantiate the patching logic for trainingpatches=dict(longitude=30,latitude=30)train_patcher=XRDAPatcher(da=data,patches=patches,strides=patches,# No Overlapcheck_full_scan=True# check no extra dimensions)# Instantiate the patching logic for testingpatches=dict(longitude=30,latitude=30)strides=dict(longitude=5,latitude=5)test_patcher=XRDAPatcher(da=data,patches=patches,strides=strides,# Overlapcheck_full_scan=True# check no extra dimensions)Extended ExampleExample 1: Patching Crash CourseWe have an extended example where we demonstrate some of the ways to do the reconstruction!Example 2: PyTorch IntegrationWe have an extended example where we demonstrate some nifty PyTorch Integration.🛠️ InstallationpipWe can directly install it via pip from thepipinstall"git+https://github.com/jejjohnson/xrpatcher.git"CloningWe can also clone the git repositorygitclonehttps://github.com/jejjohnson/xrpatcher.gitcdxrpatcherConda Environment (RECOMMENDED)We use conda/mamba as our package manager. To install from the provided environment files
run the following command.mambaenvcreate-nenvironment.yamlpoetryThe easiest way to get started is to simply use the poetry package which installs all necessary dev packages as wellpoetryinstallpipWe can also install viapipas wellpipinstall.InspirationThere are a few other packages that gave us inspiration for this.xbatcherPatchExtractor (scikit-learn)
Package: xrpl-sidechain-cli
xrpl-sidechain-cliInstallpipinstallxrpl-sidechain-cliNOTE: if you're looking at the repo before it's published, this won't work. Instead, you'll do this:gitclonehttps://github.com/xpring-eng/sidechain-cli.gitcdsidechain-cli# install poetrycurl-sSLhttps://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py|python-
poetryinstall
poetryshellInstall rippled and the xbridge witness.rippled:https://xrpl.org/install-rippled.htmlwitness:https://github.com/seelabs/xbridge_witnessGet startedexportXCHAIN_CONFIG_DIR={filepathwhereyouwantyourconfigfilesstored}exportRIPPLED_EXE={rippledexefilepath}exportWITNESSD_EXE={witnessdexefilepath}./scripts/tutorial.shTo stop the servers:sidechain-cliserverstop--allUse Commandssidechain-cli--helpEach subcommand also has a--helpflag, to tell you what fields you'll need.
Package: xrpl-websocket
XRPL WebsocketWebsocket client for rippled with reconnecting feature, support both python 2 and 3InstallationVia pip:pipinstallxrpl_websocketExamplesShort-lived connectionSimple example to send a payload and wait for responseimportjsonfromxrpl_websocketimportClientif__name__=="__main__":# create instanceclient=Client()# connect to the websocketclient.connect(nowait=False)# send server info commandresp=client.send(command='server_info')print("Server Info:")print(json.dumps(resp,indent=4))# close the connectionclient.disconnect()More advanced: Custom classYou can also write your own class for the connection, if you want to handle the nitty-gritty details yourself.classExample(Client):def__init__(self):super(self.__class__,self).__init__(log_level=logging.ERROR,server="wss://xrpl.ws")# connect to the websocketself.connect()defon_transaction(self,data):print(json.dumps(data,indent=4))defon_ledger(self,data):print('on_ledger')defon_open(self,connection):print("Connection is open")print("Subscribe to ledger transactions")self.subscribe_transactions()defon_close(self):print("on_close")defsubscribe_transactions(self):self.send({'command':'subscribe','streams':['transactions']})
Package: xrprimer
IntroductionEnglish |简体中文XRPrimer is a fundational library for XR-related algorithms.
The XRPrimer provides reusable data structures, efficient operaters and extensible interfaces both in C++ and Python.Major FeaturesVarious camera models and conversion tools (Pinhole, Fisheye, Omni etc.)Basic 3D operations (Triangulation, Projection etc.)Multi-camera extrinsic calibration toolsRendering and visualization toolsOperation SystemsIt currently supports the following systems.LinuxiOSInstallationPythonIf using xrprimer in a Python project, it can be installed by:pipinstallxrprimerIf you want to use the latest updates of xrprimer, please refer toPython Installationfor detailed installation.C++If using xrprimer in a C++ project, please refer toC++ Installationfor compilation and test.Getting startedUse XRPrimer in Python projectsThe below code is supposed to run successfully upon you finish the installation.python-c"import xrprimer; print(xrprimer.__version__)"Use XRPrimer in C++ projectsAn example is given below to show how to link xrprimer in C++ projects. More details can be foundhere.cmake_minimum_required(VERSION3.16)project(sample)#setpathforfindXRPrimerpackage(configmode)set(XRPrimer_DIR"<package_path>/lib/cmake")find_package(XRPrimerREQUIRED)add_executable(samplesample.cpp)target_link_libraries(sampleXRPrimer::xrprimer)FAQIf you face some installation issues, you may first refer to thisFrequently Asked Questions.LicenseThe license of our codebase is Apache-2.0. Note that this license only applies to code in our library, the dependencies of which are separate and individually licensed. We would like to pay tribute to open-source implementations to which we rely on. Please be aware that using the content of dependencies may affect the license of our codebase. Refer toLICENSEto view the full license.CitationIf you find this project useful in your research, please consider cite:@misc{xrprimer,title={OpenXRLab Foundational Library for XR-related Algorithms},author={XRPrimer Contributors},howpublished={\url{https://github.com/openxrlab/xrprimer}},year={2022}}ContributingWe appreciate all contributions to improve XRPrimer. Please refer toCONTRIBUTING.mdfor the contributing guideline.AcknowledgementXRPrimer is an open source project that is contributed by researchers and engineers from both the academia and the industry.
We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new models.Projects in OpenXRLabXRPrimer: OpenXRLab foundational library for XR-related algorithms.XRSLAM: OpenXRLab Visual-inertial SLAM Toolbox and Benchmark.XRSfM: OpenXRLab Structure-from-Motion Toolbox and Benchmark.XRLocalization: OpenXRLab Visual Localization Toolbox and Server.XRMoCap: OpenXRLab Multi-view Motion Capture Toolbox and Benchmark.XRMoGen: OpenXRLab Human Motion Generation Toolbox and Benchmark.XRNeRF: OpenXRLab Neural Radiance Field (NeRF) Toolbox and Benchmark.
Package: xrqwpgfaey
Xrqwpgfaey=================This API SDK was automatically generated by [APIMATIC Code Generator](https://apimatic.io/).This SDK uses the Requests library and will work for Python ```2 >=2.7.9``` and Python ```3 >=3.4```.How to configure:=================The generated code might need to be configured with your API credentials.To do that, open the file "Configuration.py" and edit its contents.How to resolve dependencies:===========================The generated code uses Python packages named requests, jsonpickle and dateutil.You can resolve these dependencies using pip ( https://pip.pypa.io/en/stable/ ).1. From terminal/cmd navigate to the root directory of the SDK.2. Invoke ```pip install -r requirements.txt```Note: You will need internet access for this step.How  to test:=============You can test the generated SDK and the server with automatically generated testcases. unittest is used as the testing framework and nose is used as the testrunner. You can run the tests as follows:1. From terminal/cmd navigate to the root directory of the SDK.2. Invoke ```pip install -r test-requirements.txt```3. Invoke ```nosetests```How to use:===========After having resolved the dependencies, you can easily use the SDK following these steps.1. Create a "xrqwpgfaey_test.py" file in the root directory.2. Use any controller as follows:```pythonfrom __future__ import print_functionfrom xrqwpgfaey.xrqwpgfaey_client import XrqwpgfaeyClientapi_client = XrqwpgfaeyClient()controller = api_client.clientresponse = controller.get_basic_auth_test(<required parameters if any>)print(response)```
Package: xrscipy
scipy for xarrayxr-scipy is a thin wrapper of scipy for thexarrayeco-system. You can read the documentationhere.Many scipy functions, such asscipy.integrate.trapezoidrequires coordinate array as an argument.
xr-scipy wraps these functions to use native coordinate objects of xarray and returns an xarray object with the computed data.
This enables more xarray-oriented data analysis with scipy.Other usage/options are kept almost the same as the original scipy function.Exampleimportxarrayasxrimportnumpyasnpimportxrscipy.integrateIn[1]:da=xr.DataArray([0,3,2,4,6],coords={'x':np.linspace(0,1,5)})In[2]:daOut[2]:<xarray.DataArray(x:5)>array([0,3,2,4,6])Coordinates:*x(x)float640.00.250.50.751.0In[3]:xrscipy.integrate.cumulative_trapezoid(da,coord='x')Out[3]:<xarray.DataArray(x:5)>array([0.,0.375,1.,1.75,3.])Coordinates:*x(x)float640.00.250.50.751.0Installationpip install xrscipy
Package: xrsmtp
XRSmtpXploitsR | XRSmtp is a module for fetching the details of any SMTP provider for your development or personal use.Usage# import the xrsmtp module
import xrsmtp

#XRSmtp returns an a list which can be accessed with only 0 and 1 index
xr = xrsmtp.XRSmtp()

# For SSL
smtp_server = xr.smtp_portSSL("your-smtp-providers-name")[0] #example: Gmail
smtp_port = xr.smtp_portSSL("your-smtp-providers-name")[1] #example: Gmail


# For TLS
smtp_server = xr.smtp_portTLS("your-smtp-providers-name")[0] #example: Gmail
smtp_port = xr.smtp_portTLS("your-smtp-providers-name")[1] #example: Gmail


# [0] is used to retrieve the smtp server from the returned list
# [1] is used to retrieve the smtp server's port from the returned list


# You can list all possible smtp provider names XRsmtp module has using
smtp_listAll()

example: 
    smtps = xr.smtp_listAll()
    print("SMTP provider names ", smtps)
Package: xrtpy
# XRTpy[![License](https://img.shields.io/badge/License-BSD%202–Clause-blue.svg)](./LICENSE)
[![GitHub Actions — CI](https://github.com/HinodeXRT/xrtpy/workflows/CI/badge.svg)](https://github.com/HinodeXRT/xrtpy/actions?query=workflow%3ACI+branch%3Amain)
[![Read the Docs Status](https://readthedocs.org/projects/xrtpy/badge/?version=latest&logo=twitter)](http://xrtpy.readthedocs.io/en/latest/?badge=latest)
[![astropy](http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat&logo=astropy)](http://www.astropy.org/)XRTpy is a Python package being developed for the analysis of observations
made by the X-Ray Telescope (XRT) on theHinodespacecraft.## AcknowledgementsThe development of XRTpy is supported by NASA contract NNM07AB07C to the
Smithsonian Astrophysical Observatory.
Package: xrtr
A generic string router based on a Radix Tree structure, (partially) Cython optimized for speed.Documentationhttps://xrtr.readthedocs.io/en/latest/Inspirationxrtris highly inspired inRouter, byshiyanhui.Licensexrtris a free software distributed under theMITlicense, the same license asRouter’s license.To DoThere is a LOT of room for improvement (specially when migrating the code to C and Cythonandthe fact this is my first project with Cython);Fix test coverage (and why is it not covering method declarations, as an example);There is a lot of fixes to be done regarding Cython, distribution, naming conventions and so on;Add Windows buildsusing AppVeyor;Changelogv0.2.1 on 2018-10-09Fixed bug where theno_conflictflag where not being propagated to theadd_methodif a “non-conflicting method” was the first node being created in that tree.v0.2.0 on 2018-10-03Addmethod_forfunction.v0.1.4 on 2018-10-03Addsentinelobject.v0.1.3 on 2018-09-21Add testing for repeated variables.v0.1.2 on 2018-09-14Minor tweaks and improvements (search optimizations).v0.1.1 on 2018-09-11Minor tweaks and improvements.v0.1.0 on 2018-08-22First release on PyPI.
Package: xrview
xrviewVisualizing xarray data with bokeh.Documentation:https://xrview.readthedocs.ioFeaturesPlot xarray Datasets and DataArrays in a few lines of codeVisualize large timeseries data setsInstallationxrview can be installed viapip:$pipinstallxrvieworconda:$condainstall-cphausamannxrviewCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.1 (Oct 19, 2020)Bug fixesFixed tooltip rendering for datetime axis.0.2.0 (Sep 8, 2020)Breaking changesDropped compatibility with Python 2.7 and 3.5 and bokeh versions < 2.20.1.0 (Sep 8, 2020)First official releaseLast release compatible with Python 2.7/3.5
Package: xrviz
# XrViz[![Anaconda-Server Badge](https://anaconda.org/conda-forge/xrviz/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge)
[![PyPI version](https://badge.fury.io/py/xrviz.svg)](https://badge.fury.io/py/xrviz)
[![Build Status](https://travis-ci.org/intake/xrviz.svg?branch=master)](https://travis-ci.org/intake/xrviz)
[![Documentation Status](https://readthedocs.org/projects/xrviz/badge/?version=latest)](https://xrviz.readthedocs.io/en/latest/?badge=latest)
[![Gitter](https://badges.gitter.im/ESIP_GUI/community.svg)](https://gitter.im/ESIP_GUI/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)XrViz is an interactive graphical user interface(GUI) for visually browsing Xarrays.
You can view data arrays along various dimensions, examine data values, change
color maps, extract series, display geographic data on maps and much more.
It is built on [Xarray](http://xarray.pydata.org),
[HvPlot](https://hvplot.pyviz.org) and [Panel](https://panel.pyviz.org/).
It can be used with [Intake](http://intake.readthedocs.io/)
to ease the process of investigating and loading datasets.Documentation is available at [Read the Docs](https://xrviz.readthedocs.io).
Try it out on binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intake/xrviz/master?filepath=examples%2Fgreat_lakes.ipynb)<img src=”https://raw.githubusercontent.com/intake/xrviz/master/docs/source/_static/images/dashboard.png” data-canonical-src=”https://raw.githubusercontent.com/intake/xrviz/master/docs/source/_static/images/dashboard.png” width=”800”/><br>### InstallationRecommended method using conda:` conda install-cconda-forgexrviz `You can also install using pip:` pip install xrviz `### Usage
You can view the example dashboard by running following in command line (this will open a tab in your browser):` python-c"import xrviz;xrviz.example()"`
Package: xrypt-xethhung12
Installationpipinstall-Uxrypt-xethhung12Usagefromxrypt_xethhung12.rsa_encryptionimportgen_key_pair,read_public_key_from_pem_str,read_private_key_from_pem_str
fromxrypt_xethhung12.rsa_formatimportpub_key_to_pem,pri_key_to_pem
fromxrypt_xethhung12.xrypt.xryptimportDeEnCryptor,Signer,Verifierif__name__=='__main__':# Generate key pairs_pub,s_pri=gen_key_pair()r_pub,r_pri=gen_key_pair()# encrypt and decryptdata_encrypted=DeEnCryptor(s_pub,s_pri).encryptToContainer(r_pub,"helloworld")data=DeEnCryptor(r_pub,r_pri).decryptContainer(s_pub,data_encrypted)print(data)# sign and verifysigned_data=Signer(s_pri).sign("helloworld")print(Verifier(s_pub).verify(signed_data))# To pem format stringprint(pub_key_to_pem(s_pub))print(pri_key_to_pem(s_pri))reloaded_s_pub_key=read_public_key_from_pem_str(pub_key_to_pem(s_pub))reloaded_s_pri_key=read_private_key_from_pem_str(pri_key_to_pem(s_pri))reloaded_r_pub_key=read_public_key_from_pem_str(pub_key_to_pem(r_pub))reloaded_r_pri_key=read_private_key_from_pem_str(pri_key_to_pem(r_pri))# encrypt and decrypt - againdata_encrypted=DeEnCryptor(reloaded_s_pub_key,reloaded_s_pri_key).encryptToContainer(reloaded_r_pub_key,"helloworld")data=DeEnCryptor(reloaded_r_pub_key,reloaded_r_pri_key).decryptContainer(reloaded_s_pub_key,data_encrypted)print(data)# sign and verify - againsigned_data=Signer(reloaded_s_pri_key).sign("helloworld")print(Verifier(reloaded_s_pub_key).verify(signed_data))ScriptsGenerate rsa key pairpri={pri}pub={pub}opensslgenrsa-out$pri4096opensslrsa-in$pri-outformPEM-pubout-out$pubBuildrm-frdist
python3-mbuildBuild and Deployrm-frdist
python3-mbuild
twineuploaddist/*-u__token__-p$pwdUpdate version devpython3-mxh_py_project_versioning--patchUpdate versionpython3-mxh_py_project_versioning--patch-d
Package: xrzz
XRZZHTTP RequestDescriptionJust a simple module for HTTP Request 

 supports : {

 	 1: GET,
 	 2: POST 

 }TodoAccept Cookies
Persistent Connection to server
Auto Encode url 
proxy support
Auto Redirect facility
PUT; PATCH; TRACE; CONNECT
optimization neededSupportsGET, POSTInstallationtill now it doesn't need any external modules

pip install xrzzUsageimport xrzz

a = xrzz.http("get", 
		url="https://httpbin.org/get", 
		headers={"user-agent": "Xrzz/v1"},
		tls=True)
print(a.body())


# output (Bytes)

		b'{\n"args": {},\n"headers": {\n"Host": "httpbin.org",\n "User-Agent": "Xrzz/v1", \n
		"X-Amzn-Trace-Id": "Root=1-606b416e-79346cf934ed2d6f4aa2cbf7"\n  },\n  "origin": "45.103.157.9", \n 
		"url": "https://httpbin.org/get"\n}\n'

		Usually it returns in bytes 

# output (decoded)

		{
		  "args": {},
		  "headers": {
		    "Host": "httpbin.org",
		    "User-Agent": "Xrzz/v1",
		    "X-Amzn-Trace-Id": "Root=1-606b416e-79346cf934ed2d6f4aa2cbf7"
		  },
		  "origin": "45.103.157.9",
		  "url": "https://httpbin.org/get"
		}


print(a.head() 

# output (dict)

		{
			'Date': 'Mon, 05 Apr 2021 17:06:13 GMT', 
			'Content-Type': 'application/json', 
			'Content-Length': '242', 
			'Connection': 'close', 
			'Server': 'gunicorn/19.9.0', 
			'Access-Control-Allow-Origin': '*', 
			'Access-Control-Allow-Credentials': 'true'
		}Whats newPOST METHODVersionv0.1.3
Package: xs3d
xs3d: Compute cross sectional area for 3D image objectsimportxs3d# let binary image be a boolean numpy array# in fortran order that is 500 x 500 x 500 voxels# containing a shape, which may have multiple# connected components, representing e.g. a neuronbinary_image=np.load(...)# a point inside the shape (must be integer)vertex=np.array([200,121,78])# normal vector defining sectioning plane# it doesn't have to be a unit vector# vector can be of arbitrary orientation# This vector is given in voxel space,# not physical space (i.e. divide by anisotropy)!normal=np.array([0.01,0.033,0.9])# voxel dimensions in e.g. nanometersresolution=np.array([32,32,40])# cross sectional area returned as a floatarea=xs3d.cross_sectional_area(binary_image,vertex,normal,resolution)# optionally return a bitfield that tells you if the section# plane touched the image border, indicating a possible# underestimate of the area if the image is a cutout of# a larger scene.# if the bitfield is > 0, then some edge contact is made# the bitfield order is -x+x-y+y-z+z00# where - means left edge (0), and + means right edge (size-1)# and 0 means unusedarea,contact_warning=xs3d.cross_sectional_area(binary_image,vertex,normal,resolution,return_contact=True)# Returns the cross section as a float32 3d image# where each voxel represents its contribution# to the cross sectional areaimage=xs3d.cross_section(binary_image,vertex,normal,resolution,)# Get a slice of a 3d image in any orientation.# Note: result may be reflected or transposed# compared with what you might expect.image2d=xs3d.slice(labels,vertex,normal,anisotropy)Installationpip install xs3dCross Section CalculationWhen using skeletons (one dimensional stick figure representations) to create electrophysiological compartment simulations of neurons, some additional information is required for accuracy. The caliber of the neurite changes over the length of the cell.Previously, the radius from the current skeleton vertex to the nearest background voxel was used, but this was often an underestimate as it is sensitive to noise and divots in a shape.A superior measure would be the cross sectional area using a section plane that is orthogonal to the direction of travel along the neurite. This library provides that missing capability.How Does it Work?The algorithm roughly works as follows.Label voxels that are intercepted by the sectioning plane.Label the connected components of those voxels.Filter out all components except the region of interest.Compute the polygon formed by the intersection of the plane with the 8 corners and 12 edges of each voxel.Add up the area contributed by each polygon so formed in the component of interest.
Package: xsampleproject
A sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.Most of the configuration for a Python project is done in thesetup.pyfile, an example of which is included in this project. You should edit this
file accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and be written usingreStructuredText. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: xsamtools
xsamtoolsxsamtools makes thesamtoolstooling fromhtslibandbcftoolsavailable through pypi packaging. These tools have been lightly
modified to allow merges on VCF streams without an index.Installationpip install xsamtoolsInstallation requires a C toolchain. Typically Ubuntu/Debian systems should have the following packages installed:libbz2-devliblzma-devlibcurl4-openssl-devlibncurses5-devlibcurl4-openssl-devmay be omitted at the cost of some cloud support features in htslib.UsageAfter successful installation, the following executables are available:samtools:htsfilebgziptabixbcftoolsxsamtools:merge_vcfs.pyxsamtools also provides Python tooling to create named (FIFO) pipes to Google Storage objects:from xsamtools import pipes

reader = pipes.BlobReaderProcess("bucket-name", "read-key")
print("reader path", reader.filepath)  # local FIFO filepath

writer_key = pipes.BlobWriterProcess("bucket-name", "writ-key")
print("writer path", writer.filepath)  # local FIFO filepathThese streams appear as either readable or writable files on the filesystem. Such objects are not seekable.A utility method is also provided to merge VCFs from GS objects:from xsamtools import vcf

vcf.combine("src-bucket-name", ["first-src-vcf-key", "second-src-vcf-key"], "dst-bucket-name", "dst-vcf-key")There is no formal limit on the number of VCF keys. Care should be taken that the VCF objects provided are aligned by
chromosome or the merge will fail.DockerA Docker image with xsamtools installed is published athttps://hub.docker.com/r/xbrianh/xsamtools