Saravanan666/pypi-org huggingface dataset:
Package: xreload
xreloadThis is Pypi-packaged version of Guido van Rossum originalxreload.pyscript:http://svn.python.org/projects/sandbox/trunk/xreload/It also re-use some changes to this code made on theplone.reloadpackage:https://github.com/plone/plone.reload/blob/master/plone/reload/xreload.pyLicenseNeither Guido's original code norplone.reloadcode has any specific license,
so none is provided with this package.Usagefrom xreload import xreload
xreload(sys.modules[__name__], new_annotations={'RELOADING': True})DevelopmentReleasing a new versionWith a valid~/.pypirc:edit version insetup.pypython setup.py sdisttwine upload dist/*git tag $version && git push && git push --tags
Package: xremovebg
Install Librarypypi$ pip3 install xremovebggithub$pipinstallgit+https://github.com/krypton-byte/removebgLibraryfromremovebgimportRemoveBgrb=RemoveBg()res=rb.upload('images.png')res.save('saved.png')Run server$python-mremovebg-p8000Command Linewithout session$python-mremovebg--file=3.jpg--json2>/dev/null{"url":"https://o.remove.bg/downloads/47b11d7f-3157-4dea-88ba-a47632c348a2/3-removebg-preview.png","filename":"3-removebg-preview.png","width":500,"height":500,"foreground_type":"product","rated":false}save session$python-mremovebg--file=3.jpg--save-session=session--json2>/dev/null{"url":"https://o.remove.bg/downloads/47b11d7f-3157-4dea-88ba-a47632c348a2/3-removebg-preview.png","filename":"3-removebg-preview.png","width":500,"height":500,"foreground_type":"product","rated":false}load session$python-mremovebg--file=3.jpg--load-session=session--json2>/dev/null{"url":"https://o.remove.bg/downloads/47b11d7f-3157-4dea-88ba-a47632c348a2/3-removebg-preview.png","filename":"3-removebg-preview.png","width":500,"height":500,"foreground_type":"product","rated":false}get histories removebg$python-mremovebg--get-histories--json2>/dev/null[{"url":"https://o.remove.bg/downloads/47b11d7f-3157-4dea-88ba-a47632c348a2/3-removebg-preview.png","filename":"3-removebg-preview.png","width":500,"height":500,"foreground_type":"product","rated":false}]
Package: xrennerjsonnlp
Xrenner to JSON-NLP(C) 2019 byDamir Cavar,Oren Baldinger, Maanvitha Gongalla, Anurag Kumar, Murali Kammili, Boli FangBrought to you by theNLP-Lab.org!IntroductionXrennerwrapper forJSON-NLP.Xrennerspecializes in coreference and anaphora resolution, in a more highly annotated manner
than just a coreference chain.Required Dependency ParseXrenner requires aDependency ParseinCoNLL-Uformat.
This can come fromCoreNLP, or another parser that provides universal dependencies in [CoNNL-U] format.
There are two ways to accomplish this:CoreNLP ServerTheXrennerPipelineclass will take care of the details, however it requires an availableCoreNLPserver.
The easiest way to create one is withDocker:docker pull nlpbox/corenlp
docker run -p 9000:9000 -ti nlpbox/corenlpTo test this, open a new tab,wget -q --post-data "Although they didn't like it, they accepted the offer."   'localhost:9000/?properties={"annotators":"depparse","outputFormat":"conll"}' -O /dev/stdoutYou then need to create a.envfile in the root of the project, follow the example insample_env.
The default entry that corresponds to theDockercommand above is:CORENLP_SERVER=http://localhost:9000Provide your own CoNLL-UUse theXrennerPipeline.process_conllfunction, with your conll data passed as a string via
theconllargument.You may find thepyjsonnlp.conversion.to_conllufunction helpful for convertingJSON-NLP,
maybe fromspaCy, toCoNLL-U.MicroserviceTheJSON-NLPrepository provides a Microservice class, with a pre-built implementation ofFlask. To run it, execute:python xrennerjsonnlp/server.pySinceserver.pyextends theFlaskapp, a WSGI file would contain:from xrennerjsonnlp.server import app as applicationText is provided to the microservice with thetextparameter, via eitherGETorPOST. If you passurlas a parameter, the microservice will scrape that url and process the text of the website.Here is an exampleGETcall:http://localhost:5000?text=John went to the store. He bought some milk.Theprocess_conllendpoint mentioned above is available at the/process_conllURI. Instead of passingtext, passconll. A POST operation will be easier than GET
in this situation.
Package: xRevChatGPT
ChatGPTReverse Engineered ChatGPT by OpenAI. Extensible for chatbots etc.Connect with me onLinkedinto support this project. I'm graduating high school soon and knowing some people might help my chances at finding employment (in the far future).You can also follow me onTwitterto stay up to date.Related worksAPI that scales:https://github.com/ChatGPT-Hackers/ChatGPT-API-server(By me as well)Lightweight version:https://github.com/acheong08/ChatGPT-lite(In collaboration withPawan)These are separate works and not part of this libraryUsageInstallationpip3 install --upgrade xRevChatGPTConfigurationRefer to the setupguidefor more information.Usagepython3 -m xRevChatGPTfromxRevChatGPT.ChatGPTimportChatbotchatbot=Chatbot({"session_token":"<YOUR_TOKEN>"},conversation_id=None,parent_id=None)# You can start a custom conversationresponse=chatbot.ask("Prompt",conversation_id=None,parent_id=None)# You can specify custom conversation and parent ids. Otherwise it uses the saved conversation (yes. conversations are automatically saved)print(response)# {#   "message": message,#   "conversation_id": self.conversation_id,#   "parent_id": self.parent_id,# }Awesome ChatGPTMy listIf you have a cool project you want added to the list, open an issue.DisclaimersThis is not an official OpenAI product. This is a personal project and is not affiliated with OpenAI in any way. Don't sue meCreditsrawandahmad698- Reverse engineering Auth0FlorianREGAZ- TLS clientPyRo1121- LintingHarry-Jing- Async supportUkenn2112- Documentationaliferouss19- LogoAll other contributors
Package: xrft
xrftis an open-source Python package for
taking the discrete Fourier transform (DFT) onxarrayanddaskarrays.It is:Powerful: It keeps the metadata and coordinates of the original xarray dataset and provides a clean work flow of DFT.Easy-to-use: It uses the native arguments ofnumpy FFTand provides a simple, high-level API.Fast: It uses thedask API of FFTandmap_blocksto allow parallelization of DFT.Please cite thedoiif you find this
package useful in order to support its continuous development.Get in touchReport bugs, suggest features or view the source codeon GitHub.
Package: xrf-tomo
XRF Tomography ReconstructionInstallationThe software is expected to work with Python version 3.7-3.9. Create conda environment
with the preferable python version:$ conda create -n xrf-tomo-env python=3.8 -c conda-forge
$ conda activate xrf-tomo-envtomopyandxraylib(dependency of PyXRF) are not available from PyPI and need
to be installed fromconda-forge:$ conda install tomopy pyxrf -c conda-forgesvmbiris an optional dependency. Installsvmbirseparately if needed. Instructions
are slightly different depending on OS. Linux:$ pip install svmbirOSX:$ ln -sf /usr/local/bin/gcc-10 /usr/local/bin/gcc
$ CC=gcc pip install --no-binary svmbir svmbirWindows:$ CC=gcc pip install svmbirFinally install this package. From PyPI:$ pip install xrf-tomoFrom source (develop install). Clone the repository in the appropriate directory and
then install withpip:$ git clone https://github.com/NSLS-II-SRX/xrf-tomo
$ cd xrf-tomo
$ pip install -e .Using the packageActivate the environment:$ conda activate xrf-tomo-envIn IPython environment or a script import necessary or all functions from the package, e.g.from xrf_tomo import *
Package: xripl
XRIPLXRIPL (C22023): X-Ray Radiographic Image Processing LibraryPawel M. Kozlowski 2018-10-19XRIPL (read as "zripple") is a library of tools for processing x-ray
radiographs and extracting contours of interest within the image using
computer vision techniques. Features include spatial calibration, denoising,
background and attenuation correction through pseudo-flatfielding, watershed
segmentation, contour processing, and visualization. The details of the XRIPL
analysis pipeline are published in the Proceedings of the 23rd Topical
Conference on High-Temperature Plasma Diagnostics Proceedings [1].[1] P. M. Kozlowski, Y. Kim, B. M. Haines, H. F. Robey, T. J. Murphy,
H. M. Johns, and T. S. Perry. Use of Computer Vision for analysis of image
data sets from high temperature plasma experiments. Review of Scientific
Instruments 92, 033532 (2021)https://doi.org/10.1063/5.0040285InstallationOfficial releases of XRIPLare published to pypi.org and can simply be pip installed like so:pip install xriplMore detailed installation instructions can be foundhere.LicenseXRIPL is released under a3-clause BSD license.Citing XRIPLIf you use XRIPL in your work, please follow the best practices for citing
XRIPL which can be foundhere.AcknowledgementsDevelopment of Fiducia was supported by the U.S. Department of Energy, and
the NNSA.
Package: xrit
xRIT Parser Toolkit======================A toolkit to parse / dump HRIT / LRIT files.### xritparseParses xRIT file Header and print in a human readable format.```Usage:xritparse [-hi] filen.lrit [file2.lrit]-h    Print Structured Header Record-i    Print Image Data Record```##### Example:```# xritparse DCSdat363042229684.lritParsing file DCSdat363042229684.lritPrimary Header:File Type Code: DCSHeader Length: 89Data Field Length: 1617088NOAA Specific HeaderSignature: NOAAProduct ID: DCSProduct SubId: NoneParameter: 11500Compression: Not CompressedAnnotation RecordFilename: DCSdat363042229684.lritTimestamp RecordDateTime: 2016-12-28 04:22:00DCS Filename:Filename: pL-16363042229-A.dcs```### xritdumpDumps the data section of a HRIT/LRIT file.PS: This does not convert or process anything. It just saves the data section.```Usage:xritdump filename.lrit output.bin```### xritcatReads the data section of a HRIT/LRIT file and prints to stdout.```Usage:xritcat filename.lrit```## Python LibraryThis also can be used as a python library by importing `xrit`. The documentation is still WIP. Please us the module executables as a reference.## InstallingThe package is available at `pip`. Just run:```sudo pip install xrit```
Package: xrocket
xRocketxRocket Python SDK.DescriptionPay and Trading xRocket API.Getting StartedDependencieshttpxInstallingpip install xrocketUsing PayAPIimportasynciofromxrocketimportPayAPIasyncdefmain():api=PayAPI(api_key='your api key here')cheque=awaitapi.cheque_create(currency='BOLT',amount=1,enable_captcha=False)awaitapi.cheque_delete(cheque['data']['id'])invoice=awaitapi.invoice_create(currency='BOLT',amount=1)awaitapi.invoice_delete(invoice_id=invoice['data']['id'])awaitapi.transfer(user_id=6037851294,currency='TONCOIN',amount=0.1)awaitapi.withdrawal(network='TON',currency='TONCOIN',amount=0.1,address='EQAsl59qOy9C2XL5452lGbHU9bI3l4lhRaopeNZ82NRK8nlA')asyncio.run(main())Using TradeAPIimportasynciofromxrocketimportTradeAPIasyncdefmain():api=TradeAPI(api_key='your api key here')balance=awaitapi.balance()print(balance)awaitapi.order_execute(pair='BOLT-TONCOIN',order_type='BUY',execute_type='LIMIT',rate=0.02,amount=5,currency='BOLT')price=awaitapi.rates_crypto_in_fiat(crypto='BOLT',fiat='USD')print(f"1 BOLT ={price['data']['rate']}USD")asyncio.run(main())Authors@shibdev@VladPavlyVersion History0.2.1Added testnet support0.2.0Additions0.1.0Initial versionLicenseThis project is licensed under the MIT License - see the LICENSE.md file for detailsDonateIf you like the library, I will be glad to accept donations.TON: EQCgphx8rTI0PukwmgpVqiPgqguTujhQscg2h7jgc4U0t347Acknowledgmentsxrocket-pay-docsxrocket-trade-docs
Package: xroms
xromsxromscontains functions for commonly used scripts for working with ROMS output in xarray.There are functions to...help read in model output with automatically-calculated z coordinatescalculate many derived variables with correct grid metrics in one line including:horizontal speedkinetic energyeddy kinetic energyvertical shearvertical vorticityhorizontal divergencenormalized surface divergenceErtel potential vorticitydensity as calculated in ROMSpotential densitybuoyancy$N^2$ (buoyancy frequency/vertical buoyancy gradient)$M^2$ (horizontal buoyancy gradient)useful functions including:derivatives in all dimensions, accounting for curvilinear grids and sigma layersgrid metrics (i.e., grid lengths, areas, and volumes)subset horizontal grid such that the staggered grids are consistenteasily change horizontal and vertical grids usingxgcmgrid objectseasily reorder to dimensional conventionslice along a fixed valuewrapper for interpolation in longitude/latitude and for fixed depthsmixed-layer depthDemonstrations:selecting data in many different waysinterpolationchanging time samplingcalculating climatologiesvarious calculationsprovide/track attributes and coordinates through functionswrapscf-xarrayto generalize coordinate and dimension calling.ability to automatically choose colormaps for plotting withxarraywrapsxcmoceanfor thisInstallationYou need to havecondainstalled for these installation instructions. You'll have best results if you use the channelconda-forge, which you can prioritize withconda config --add channels conda-forge --force.Install, the easy wayPyPI:pip install xromsconda-forge:mamba install -c conda-forge xromsCreate environment if neededAs a first step, you can create an environment for this package with conda if you want. If you do this, you'll need to git clone the package first as below. Note thatmambaandcondacan be used interchangeably, butmambais faster for installation.mamba env create -f environment.ymlYou can choose to install with conda the optional dependencies for full functionality:conda install --file requirements-opt.txtand to install optional dependencyxcmocean:pip install git+git://github.com/pangeo-data/xcmoceanThen choose one of the following to installxromsfrom GitHub:Clonexromsinto a particular directory then install so that it is editable (-e)git clone git@github.com:xoceanmodel/xroms.git
cd xroms
pip install -e .Directly installxromsfrom githubpip install git+git://github.com/xoceanmodel/xroms
Package: xron
Xron (ˈkairɑn) is a methylation basecaller that could identify m6A methylation modification from ONT direct RNA sequencing.Using a deep learning CNN+RNN+CTC structure to establish end-to-end basecalling for the nanopore sequencer.The name is inherited fromChironBuilt withPyTorchand python 3.8+m6A-aware RNA basecall one-liner:xron call -i <input_fast5_folder> -o <output_folder> -m models/ENEYFT --boostnanoTable of contentsTable of contentsInstallInstall from SourceInstall from PypiBasecallSegmentation using NHMMPrepare chunk datasetRealign the signal using NHMM.TrainingInstallFor either installation method, recommend to create a vritual environment first using conda or venv, take conda for examplecondacreate--nameYOUR_VIRTUAL_ENVIRONMENTpython=3.8
condaactivateYOUR_VIRTUAL_ENVIRONMENTThen you can install from our pypi repository or install the newest version from github repository.InstallpipinstallxronXron requires at least PyTorch 1.11.0 to be installed. If you have not yet installed PyTorch, install it via guide fromofficial repository.BasecallBefore running basecall using Xron, you need to download the models from our AWS s3 bucket by runningxron initxroninitThis will automatically download the models and put them into themodelsfolder.
We provided sample code in xron-samples folder to achieve m6A-aware basecall and identify m6A site.
To run xron on raw fast5 files:xron call -i ${INPUT_FAST5} -o ${OUTPUT} -m models/ENEYFT --fast5 --beam 50 --chunk_len 2000Segmentation using NHMMPrepare chunk datasetXron also include a non-homegeneous HMM (NHMM) for signal re-sqquigle. To use it:
Firstly we need to extract the chunk and basecalled sequence usingpreparemodulexronprepare-i${FAST5_FOLDER}-o${CHUNK_FOLDER}--extract_seq--basecallerguppy--reference${REFERENCE}--moderna_meth--extract_kmer-k5--chunk_len4000--write_correctionReplace the FAST5_FOLDER, CHUNK_FOLDER and REFERENCE with your basecalled fast5 file folder, your output folder and the path to the reference genome fasta file.Realign the signal using NHMM.Then run the NHMM to realign ("resquiggle") the signal.xronrelabel-i${CHUNK_FOLDER}-m${MODEL}--device$DEVICEThis will generate a paths.py file under CHUNK_FOLDER which gives the kmer segmentation of the chunks.TrainingTo train a new Xron model using your own dataset, you need to prepare your own training dataset, the dataset should includes a signal file (chunks.npy), labelled sequences (seqs.npy) and sequence length for each read (seq_lens.npy), and then run the xron supervised training modulexrontrain-ichunks.npy--seqseqs.npy--seq_lenseq_lens.npy--model_folderOUTPUT_MODEL_FOLDERTraining Xron model from scratch is hard, I would recommend to fine-tune our model by specify --load flag, for example we can finetune the provided ENEYFT model (model trained using cross-linked ENE dataset and finetuned on Yeast dataset):xrontrain-ichunks.npy--seqseqs.npy--seq_lenseq_lens.npy--model_foldermodels/ENEYFT--load
Package: xrootdpyfs
XRootDPyFS is a PyFilesystem interface to XRootD.XRootD protocol aims at giving high performance, scalable fault tolerant access
to data repositories of many kinds. The XRootDPyFS adds a high-level interface
on top of the existing Python interface (pyxrootd) and makes it easy to e.g.
copy a directory in parallel or recursively remove a directory.Further documentation is available onhttps://xrootdpyfs.readthedocs.io/.Getting startedIf you just want to try out the library, the easiest is to use Docker.Build the image:$dockerbuild--platformlinux/amd64-txrootd.Run the container and launchxrootd:$dockerrun--platformlinux/amd64-hxrootdpyfs-itxrootdbashYou will see the logs in the stdout. Next, in another shell, connect the container
and fire up an ipython shell:$dockerps# find the container id$dockerexec-it<container-id>bash[xrootdpyfs@xrootdpyfs code]$ipythonQuick examplesHere is a quick example of a file listing with the xrootd PyFilesystem
integration:>>> from xrootdpyfs import XRootDPyFS
>>> fs = XRootDPyFS("root://localhost//tmp/")
>>> fs.listdir("xrootdpyfs")
['test.txt']Or, alternatively using the PyFilesystem opener (note the firstimport xrootdpyfsis required to ensure the XRootDPyFS opener is registered):>>> import xrootdpyfs
>>> from fs.opener import open_fs
>>> fs = open_fs("root://localhost//tmp/")
>>> fs.listdir("xrootdpyfs")
['test.txt']Reading files:>>> f = fs.open("xrootdpyfs/test.txt")
>>> f.read()
b'Hello XRootD!\n'
>>> f.close()Reading files using thereadtext()method:>>> fs.readtext("xrootdpyfs/test.txt")
b'Hello XRootD!\n'Writing files:>>> f = fs.open("xrootdpyfs/hello.txt", "w+")
>>> f.write("World")
>>> f.close()Writing files using thewritetext()method:>>> fs.writetext("xrootdpyfs/test.txt", "World")DevelopmentThe easiest way to develop is to build the Docker image and mount
the source code as a volume to test any code modification with a
running XRootD server:$dockerbuild--platformlinux/amd64-txrootd--progress=plain.$dockerrun--platformlinux/amd64-hxrootdpyfs-it-v<absolutepathtothisproject>:/codexrootdbash[xrootdpyfs@xrootdpyfs code]$xrootdIn another shell:$dockerps# find the container id$dockerexec-it<container-id>bash[xrootdpyfs@xrootdpyfs code]$python-mpytest-vvvtestsIf you want to test a specific version of xrootd, run:$dockerbuild--platformlinux/amd64--build-argxrootd_version=4.12.7-txrootd--progress=plain.DocumentationDocumentation is available at <http://xrootdpyfs.readthedocs.io/> or can be
build using Sphinx:pip install Sphinx
python setup.py build_sphinxTestingRunning the tests are most easily done using docker:$dockerbuild--platformlinux/amd64-txrootd.&&dockerrun--platformlinux/amd64-hxrootdpyfs-itxrootd
Package: xrosfs
Mount a Running Docker Container File Sytem via FUSE.No requirement to install additional agents to container side. it’s
only required to have permitted todocker execcommand.Docker containerxros-over-sshfsthat mount other containers file system automatically by XrosFS with
autofs and sshfs is released.RequirementsDocker Host SidePython 3.5 or laterFUSE 2.6 (or later)Permitted to execute$ docker execDocker Container SideShell (ashorbash) and some commands(test,stat,ddbase64etc.) (Usually, they are already installed plain
image of alpine, debian etc.)InstallationpipinstallxrosfsUsageMount/ofcontainer1to~/mnt.$xrosfscontainer1:/~/mntIn above step, xrosfs connect tocontainer1asrootuser. Passuser@container1:/to xrosfs, if you want to connect as other users.Known IssuesCan’t access to file that had\nincluded filename.Bad response time in operates.Some operations methods are not full implemented
yet(flush(fsync)utimensetc.).LicenseCopyright (c) 2018 hankei6kmLicensed under the MIT License. See LICENSE.txt in the project root.
Package: xroute-env
xroute_envRL environment for detailed routing.QuickstartInstallationTo interact with the xroute environment, you need to download the simulator first:Operating SystemDownload LinkUbuntu 22.04DownloadThen, put the simulator in thethird_party/openroadfolder.Launch Algorithm BackendDQNPPOLaunch SimulatorRun the following command to get launch script:cdexamples
python3init.py[start_port][worker_num]start_port: the listen port number of the first worker instance.worker_num: the number of worker instances.
Package: xrpa
xrpaWhat is this?by: axinerxrpaThis is a xrpa.InstallationThis package can be installed using pip (Python3):pip install xrpaCopyrightCopyright (c) 2022 axinerPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.