Saravanan666/pypi-org huggingface dataset:
Package: sdds
SDDSThis package provides reading and writing functionality forself describing data sets (sdds)files.
On the python side, the data is stored in a class structure with attributes corresponding to the sdds-format itself (seesdds-format).See theAPI documentationfor details.InstallingInstallation is easily done viapip:python-mpipinstallsddsOne can also install in acondaenvironment via theconda-forgechannel with:condainstall-cconda-forgesddsExample Usageimportsddssdds_data=sdds.read("path_to_input.sdds")sdds.write(sdds_data,"path_to_output.sdds")Read files with different endiannessBy default the endianness (byte order) of the file is determined either by
a comment!# little-endianor!# big-endianin the header of the file.
If this comment is not found, the endianness of the running machine is assumed.One can force a certain kind of endianness to the reader by supplying it to
the read function:importsddssdds_data=sdds.read("path_to_input_with_big_endian.sdds",endianness="big")sdds_data=sdds.read("path_to_input_with_little_endian.sdds",endianness="little")Be aware thatsdds.writewill always write the file in big-endian order and
will also leave a comment in the file, so that the reader can determine the
endianness and there is no need to supply it when reading
a file written by this package.Known IssuesCan't read binary columnsNo support for&includetagLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: sdd-segmentation
Slope Difference Distribution SegmentationThe python implementation of algorithm proposed by Z. Wang, "A New Approach for Segmentation and Quantification of Cells or Nanoparticles," in IEEE Transactions on Industrial Informatics, vol. 12, no. 3, pp. 962-971, June 2016, DOI: 10.1109/TII.2016.DescriptionBadgesTODO: Add bagdeshttps://shields.io/InstallationYou can installsdd-segmentationdirectly from PyPi viapip:pipinstallsdd-segmentationUsageimportcv2fromsdd_segmentation.sddimportsdd_threshold_selectionimg_np=cv2.imread("image.png")T=sdd_threshold_selection(img_np.astype(float),15)print(T)th_image_np=img_np>T[-1]LicenseThis software is licensed under the BSD 3-Clause License.If you use this software in your scientific research, please cite our paper:AND originalwork:@ARTICLE{Wang2016,author={Wang, ZhenZhou},journal={IEEE Transactions on Industrial Informatics},title={A New Approach for Segmentation and Quantification of Cells or Nanoparticles},year={2016},volume={12},number={3},pages={962-971},doi={10.1109/TII.2016.2542043}}
Package: sde
sdesdeis notsed. It's a structured data editor for CLI.Why?Many people asked for a simple JSON in-place editing andjqwas the solution:Modify a key-value in a json using jq in-placeHow to modify a key's value in a JSON file from command linejq'.address = "abcde"'test.json|spongetest.jsonDoes this seem readable or elegant to you?How about this instead:sdeaddressabcdetest.jsonsdeis not a substitute forjqorsed.It allowssimplein-place JSON/YAML value changes, forstructureddata.Sample JSON{"name":"John","age":31,"city":"New York","extra":{"gender":null}}Sample YAMLdatabase:user:examplepassword:secretModify datasdenameJackdata.json
sdeextra.gendermaledata.json
sdedatabase.userjohndata.ymlIt is possible to modify data in arrays using a dotted notation. Let's take another sample:{"users":[{"username":"foo","enabled":true},{"username":"bar","enabled":true}],}We can set the first user'senabledproperty tofalse:sdeusers.0.enabledfalsedata.jsonInstallation for CentOS/RHEL 7, 8 or Amazon Linux 2, or Fedora Linuxsudoyum-yinstallhttps://extras.getpagespeed.com/release-latest.rpm
sudoyum-yinstallsdeInstallation for other systemsInstalling withpipis easiest:pipinstallsdeNotesQuoting in JSONQuoting is avoided fornull,true,false, and numeric values.
To ensure that a given value is quoted, use-s(or--string) option:sde-skeynullfile.jsonForce-fail on missing keysIf you musteditthe file, by ensuring to update only the existing key, use-e(--must-exist)
option. The program will exit without adding the key which doesn't exist.sde-ekeyvalfile.jsonForce-fail on unchanged fileIf the data is unchanged after runningsde(values already match), you can force
a failure exit code2by passing the-moption:sde-mkeysamevalfile.json# > exit code 0sde-mkeysamevalfile.json# > exit code 2TODOWork with stdinecho$json|sdenameJackQuery simple datasdgnamedata.json
Package: sde4onnx
sde4onnxSimpleDoc_stringEraser forONNX.https://github.com/PINTO0309/simple-onnx-processing-toolsKey conceptdoc_string eraser for ONNX. e.g. Hagging Face Stable Diffusion ONNX.1. Setup1-1. HostPC### option$echoexportPATH="~/.local/bin:$PATH">>~/.bashrc\&&source~/.bashrc### run$pipinstall-Uonnx\&&python3-mpipinstall-Uonnx_graphsurgeon--index-urlhttps://pypi.ngc.nvidia.com\&&pipinstall-Usde4onnx1-2. Dockerhttps://github.com/PINTO0309/simple-onnx-processing-tools#docker2. CLI Usage$ sde4onnx -h

usage:
  sde4onnx [-h]
  -if INPUT_ONNX_FILE_PATH
  [-of OUTPUT_ONNX_FILE_PATH]
  [-n]

optional arguments:
  -h, --help
      show this help message and exit.

  -if INPUT_ONNX_FILE_PATH, --input_onnx_file_path INPUT_ONNX_FILE_PATH
      Input onnx file path.

  -of OUTPUT_ONNX_FILE_PATH, --output_onnx_file_path OUTPUT_ONNX_FILE_PATH
      Output onnx file path.

  -n, --non_verbose
      Do not show all information logs. Only error logs are displayed.3. In-script Usage>>>fromsde4onnximporterase>>>help(erase)Helponfunctioneraseinmodulesde4onnx.onnx_opname_generator:erase(input_onnx_file_path:Union[str,NoneType]='',onnx_graph:Union[onnx.onnx_ml_pb2.ModelProto,NoneType]=None,output_onnx_file_path:Union[str,NoneType]='',non_verbose:Union[bool,NoneType]=False)->onnx.onnx_ml_pb2.ModelProtoParameters----------input_onnx_file_path:Optional[str]Inputonnxfilepath.Eitherinput_onnx_file_pathoronnx_graphmustbespecified.Default:''onnx_graph:Optional[onnx.ModelProto]onnx.ModelProto.Eitherinput_onnx_file_pathoronnx_graphmustbespecified.onnx_graphIfspecified,ignoreinput_onnx_file_pathandprocessonnx_graph.output_onnx_file_path:Optional[str]Outputonnxfilepath.Ifnotspecified,noONNXfileisoutput.Default:''non_verbose:Optional[bool]Donotshowallinformationlogs.Onlyerrorlogsaredisplayed.Default:FalseReturns-------renamed_graph:onnx.ModelProtoRenamedonnxModelProto.4. CLI Execution$sde4onnx\--input_onnx_file_pathvae_encoder.onnx\--output_onnx_file_pathvae_encoder_erased.onnx5. In-script Executionfromsde4onnximporteraseonnx_graph=erase(input_onnx_file_path="vae_encoder.onnx",output_onnx_file_path="vae_encoder_erased.onnx",)6. Samplehttps://huggingface.co/bes-dev/stable-diffusion-v1-4-onnx/resolve/main/vae_encoder.onnxBeforeAfter7. Referencehttps://github.com/onnx/onnx/blob/main/docs/Operators.mdhttps://docs.nvidia.com/deeplearning/tensorrt/onnx-graphsurgeon/docs/index.htmlhttps://github.com/NVIDIA/TensorRT/tree/main/tools/onnx-graphsurgeonhttps://github.com/PINTO0309/simple-onnx-processing-toolshttps://github.com/PINTO0309/PINTO_model_zoo8. Issueshttps://github.com/PINTO0309/simple-onnx-processing-tools/issues
Package: sdeconv
SDeconvSDeconvis a python framework to develop scientific image deconvolution algorithms. This
library has been developed for microscopy 2D and 3D images, but can be use to any image
deconvolution application.System RequirementsSoftware RequirementsOS RequirementsTheSDeconvdevelopment version is tested onWindows 10,MacOSandLinuxoperating systems.
The developmental version of the package has been tested on the following systems:Linux: 20.04.4Mac OSX: Mac OS Catalina 10.15.7Windows: 10installLibrary installation from PyPIInstall anAnacondadistribution of Python -- ChoosePython 3.9and your operating system. Note you might need to use an anaconda prompt if you did not add anaconda to the path.Open an anaconda prompt / command prompt withcondaforpython 3in the pathCreate a new environment withconda create --name sdeconv python=3.9.To activate this new environment, runconda activate sdeconvTo install theSTrackinglibrary, runpython -m pip install sdeconv.if you need to update to a new release, use:python-mpipinstallsdeconv--upgradeLibrary installation from sourceThis install is for developers or people who want the last features in themainbranch.Install anAnacondadistribution of Python -- ChoosePython 3.9and your operating system. Note you might need to use an anaconda prompt if you did not add anaconda to the path.Open an anaconda prompt / command prompt withcondaforpython 3in the pathCreate a new environment withconda create --name sdeconv python=3.9.To activate this new environment, runconda activate sdeconvPull the source code from git with `git pullhttps://github.com/sylvainprigent/stracking.gitThen install theSTrackinglibrary from you local dir with:python -m pip install -e ./sdeconv.Use STracking with napariThe SDeconv library is embedded in a napari plugin that allows usingSDeconvwith a graphical interface.
Please refer to theSDeconvnapari plugindocumentation to install and use it.STracking documentationThe full documentation with tutorial and docstring is availablehere
Package: sdeIU
Stochastic Differential EquationsPackageDocumentationCode CoveragesdeSo you say you are interested in stochastic differential equations? And you also subscribe to the idea of learning by example? Well, you are in luck!sdeprovides basic tools for simulating brownian motions which is the basic ingredients to lots of SDE models, performing different types of stochastic integrations, Eular-Maruyama methods and so much more (to come...).We have also built a detailedonline documentationwhere we guide you step-by-step on how to use our package.The origin of this project is this wonderful introductorypaperby Desmond J. Higham.Dependenciesnumpy==1.22.4tqdm==4.64.1matplotlib==3.7.1pandas==2.0.0seaborn==0.12.2Enviroment SetupWe highly recommend creating a virtual environment before proceeding to installing the package. For how to manage virtual
environment via conda, check outtheir tutorial.pipinstall-rrequirements.txtInstallationpipinstallsde
Package: sdelib
sdelib is a Python package (written in Python 3.5) for performing various
actions on Ito stochastic differential equations (SDE). These actions include:
1) Input SDEs as SDEprob objects.
2) Solve SDEs with an SDEint class that performs numerical Ito integration on
SDEprob objects.
3) Estimate strong and weak orders of convergence through a SDEanalysis class
4) Perform these actions either through creating a Python script which uses the
above classes, or running gui.py which allows you to perform these actions
quickly through a GUI.sdelib was written as part of a Master’s thesis and is intended to be able to
do simple actions on SDEs without requiring a lot of coding to do so.This version requires Python 3 or later;
Package: sdelmo
sdelmoSimple Soundcloud music downloader.FeaturesDownload any public soundcloud audio file.audio files can be downloaded with cover if you haveeyeD3.Installationsdelmorequires Python 3.Usepipto install the package from PyPI:pipinstallsdelmoOr, install the latest version of the package from GitHub:pipinstallgit+https://github.com/elmoiv/sdelmo.gitUsageimport sdelmo

sdelmo.scdl(client_id, track_url)How to getclient_id(in case of expiry)You can find client_id by searching through XHR requests:ContributingPlease contribute! If you want to fix a bug, suggest improvements, or add new features to the project, justopen an issueor send me a pull request.
Package: sdeper
SDePERSDePER (SpatialDeconvolution method withPlatformEffectRemoval) is a two-stephybridmachine learning and regression method considering platform effect, spatial information and sparsity indeconvolutionof spatial transcriptomic data using reference single-cell RNA sequencing data from same tissue type. It's also able toimputecell type compositions and gene expression atenhanced resolution.Quick StartSDePER can be installed viapipcondacreate-nsdeper-envpython=3.9.12
condaactivatesdeper-env
pipinstallsdeperSDePER requires4 input filesfor cell type deconvolution:raw nUMI counts ofspatial transcriptomic data(spots × genes):spatial.csvraw nUMI counts ofreference scRNA-seq data(cells × genes):scrna_ref.csvcell type annotationsfor all cells in scRNA-seq data (cells × 1):scrna_anno.csvadjacency matrixof spots in spatial transcriptomic data (spots × spots):adjacency.csvTo start cell type deconvolution by runningrunDeconvolution-qspatial.csv-rscrna_ref.csv-cscrna_anno.csv-aadjacency.csvFull Documentationfor SDePER is available onRead the Docs.Example Analysiswith SDePER are available in our GitHub repositorySDePER_Analysis.
Package: sdepy
The SdePy package provides tools to state and numerically
integrate Ito Stochastic Differential Equations (SDEs), including equations
with time-dependent parameters, time-dependent correlations, and
stochastic jumps, and to compute with, and extract statistics from,
their realized paths.Several preset processes are provided, including lognormal,
Ornstein-Uhlenbeck, Hull-White n-factor, Heston, and jump-diffusion processes.Computations are fully vectorized across paths, via NumPy and SciPy,
making live sessions with 100000 paths reasonably fluent
on single cpu hardware.This package came out of practical need, so expect a flexible tool
that gets real-life things done. On the other hand, not every part of it
is clean and polished, so expect rough edges, and the occasional
bug (please report!).Developers are committed to the stability of the public API,
here again out of practical need to safeguard dependencies.Start hereInstallation:pip install sdepyQuick Guide(asnotebook)Documentation(aspdf)SourceLicenseBug Reports
Package: sdeul
sdeulStructural Data Extractor using LLMsInstallation$pipinstall-UsdeulUsagePrepare a Llama 2 GGUF file.Example:$curl-SLOhttps://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q4_K_M.ggufCreate a JSON Schema file for the outputExtract structural data from given text usingsdeul extract.Example:sdeulextract--pretty-json\llama-2-13b-chat.Q4_K_M.gguf\test/data/medication_history.schema.json\test/data/patient_medication_record.txtExpected output:{"MedicationHistory":[{"MedicationName":"Lisinopril","Dosage":"10mg daily","Frequency":"daily","Purpose":"hypertension"},{"MedicationName":"Metformin","Dosage":"500mg twice daily","Frequency":"twice daily","Purpose":"type 2 diabetes"},{"MedicationName":"Atorvastatin","Dosage":"20mg at bedtime","Frequency":"at bedtime","Purpose":"high cholesterol"}]}Runsdeul --helpfor more details.
Package: sdeux
By: chiesa
Date: March 31, 2020
Copyright Alpes Lasers SA, Neuchatel, SwitzerlandDriver for the Alpes Lasers S-2 pulser. Visithttps://www.alpeslasers.ch/for more informations.UsageSee also the examples folder in the sdeux package.import os

from sdeux.auto_detect import init_driver
from sdeux.communication import RETRY_NO, RETRY_SOME
from sdeux.serial_handler import S2SerialHandler

th = None

port = os.path.expanduser('~/tty.AL-S2')

retry_policy = RETRY_SOME  # on communication errors, retries up to 3 times until a timeout of 3 seconds

# retry_policy = RETRY_NO   # on communications errors, raise an exception directly


def print_status(s2_instance):
    print_status = 'Measured current (A): {:.5f}\n'.format(s2_instance.measured_current) + \
                   'Measured voltage (V): {:.5f}\n'.format(s2_instance.measured_voltage) + \
                   'Target voltage (V): {:.5f}\n'.format(s2_instance.applied_voltage) + \
                   'Laser temperature (degC): {:.5f}\n'.format(s2_instance._info.laser_temperature) + \
                   'Overcurrent Limit (A): {:.5f}\n'.format(s2_instance.current_limit) + \
                   'MCU temperature (degC): {:.5f}\n'.format(s2_instance._info.MCU_temperature) + \
                   'Pulse Mode: {}\n'.format(s2_instance.pulsing_mode) + \
                   'Board Status: {}\n'.format(s2_instance.status_label)
    print(print_status)


try:
    th = S2SerialHandler(port)
    th.open()
    s2 = init_driver(th)
    s2.set_up()
    s2.retry_policy = retry_policy
    print_status(s2)

finally:
    if th:
        th.close()
Package: sdeval
sdevalEvaluation for stable diffusion model trainingInstallationYou can simply install it withpipcommand line from the official PyPI site.pipinstallsdevalIf your operating environment includes a available GPU, you can use the following installation command to achieve higher
performance:pipinstallsdeval[gpu]For more information about installation, you can refer
toInstallation.Quick StartCCIP Metricsfromsdeval.fidelityimportCCIPMetricsccip=CCIPMetrics(images='/path/of/character/dataset')# ccip score of one imageprint(ccip.score('/path/of/one/image'))# ccip score of a directory of imagesprint(ccip.score('/directory/of/images'))Bikini Plus Metricsfromsdeval.controllabilityimportBikiniPlusMetrics# build bikini plus score metricsbp=BikiniPlusMetrics(tag_blacklist=['bangs','long_hair','blue_eyes','animal_ears','sleeveless','breasts','grey_hair','medium_breasts'])# bp score of one image# the image should contain a1111 webui's metadata of promptsprint(bp.score('/path/of/one/image'))# bp score of a directory of images# the images should contain a1111 webui's metadata of promptsprint(bp.score('/directory/of/images'))AI Corrupt Metricsfromsdeval.corruptimportAICorruptMetrics# build metricsmetrics=AICorruptMetrics()# get ai corrupt score for one image fileprint(metrics.score('/path/of/one/image'))# get ai corrupt score of a directory of image filesprint(metrics.score('/directory/of/images'))# get ai corrupt score of list of imagesprint(metrics.score(['image1.png','image2.png']))
Package: sdevpy
SDev.PythonPython repository for various tools and projects in Machine Learning for Quantitative Finance. In the current release,
we focus on stochastic volatility surfaces and their calibration through Machine Learning methods. See other work on our main
websiteSDev-Finance.Stochastic volatility calibrationIn this project we use Neural Networks to improve the calibration speed for stochastic volatility models. For now
we consider only the direct map, i.e. the calculation from model parameters to implied volatilities.We first generate datasets of parameters (inputs) and vanilla option prices (outputs) and then train the network to replicate the prices.
In this manner, the learning model is used as a pricing function to replace costly closed-forms or PDE/MC price calculations.Our models can be saved to files for later usage, and can be re-trained from a saved state. We cover (Hagan) SABR, No-Arbitrage SABR
(i.e. the actual SABR dynamic), Free-Boundary SABR, ZABR and Heston models.Jupyter notebooks are available for demo under ./notebooks.Trained models are saved under ./models/stovol. Sample training data is provided under ./datasets/stovol. However, these are only small sets for demo
(50k samples). The larger sets we used for training (500k-2m) can be downloaded from ourKaggle account.The notebook ./notebooks/StoVol Dataset Generation.ipynb can be used to generate samples (beware of setting up output paths to your local drive).
The notebook ./notebooks/StoVol Training.ipynb can be used to train models (pre-trained or not) on the samples. The .py scripts corresponding to these
notebooks are under sdevpy/projects/stovol.AAD Monte-CarloIn script ./projects/aad/aad_mc.py, we show how to calculate 1st and 2nd order Greeks on a Monte-Carlo simulation of Black-Scholes model (1 asset) using AAD. We compare the results with standard Monte-Carlo Greeks by Finite Differences and the closed-form. We make use of payoff smoothers for both AAD and Standard MC.In ./projects/aad/aad_mc_nd.py, we compare AAD to standard MC and Closed-Form on a product with generic dimension. We can then benchmark the performance of AAD compared to MC bump-based sensitivities varying dimension and number of simulations.Other ToolsThe package contains various other tools including Black-Scholes/Bachelier formulas, Monte-Carlo simulation of vanilla prices and
other utilities. It also features a wrapper class above Keras for easier management of trained models with their scalers,
as well as custom callbacks and learning schedules.Jupyter notebooks of previous work are also available (PINNs, AAD Monte-Carlo) but are not yet integrated in the framework.
Package: sdf4sim
sdf4simA package which demostrates the use of SDF as the model of computation for a non-iterative co-simulation.Tested with Python 3.7 64 bitpip install sdf4sim
python -c "import sdf4sim; sdf4sim.example.control.main()"Free software: MIT licenseDocumentation:https://sdf4sim.readthedocs.io.History0.1.0 (2019-08-16)First release on PyPI.
Package: sdfascii
sdfasciisdfasciiis a Python (3.8+) module for reading the HP/Agilent
Standard Data Format (SDF) binary files and the ASCII files saved by
HP/Agilent Dynamic Signal Analyzers (DSA).The HP/Agilent 35670A Dynamic Signal Analyzer has the ability to save
files as either SDF or ASCII format.HP/Agilent SDF Binary FormatThe"Standard Data Format Utilities User's Guide"version
B.02.01, P/N 5963-1715 was used to determine the SDF file format while
developingsdfascii.HP/Agilent DSA ASCII FormatFour files are created when saving to the HP/Agilent DSA ASCII format:.HDRcontains SDF header information.TXTcontains the y-axis information, preserving the same units as
displayed on the analyzer screen when the trace was saved.Xcontains the x-axis information.Zcontains the z-axis information, if the trace contains waterfall
dataSource: Agilent discussion forum questionWhen I save the trace in the
ASCII format, there are 4 files created with extensions of .txt, .hdr,
.x, and .z. What are the contents of these files?ContributingContributions are welcome! To contribute please:Fork the repositoryCreate a feature branchAdd code and testsPass lint and testsSubmit apull requestDevelopment SetupDevelopment Setup Using pyenvUse the following commands to create a Python 3.9.9 virtualenv usingpyenvandpyenv-virtualenv, install the requirements in the virtualenv namedsdfascii, and list the availableInvoketasks.$pyenvvirtualenv3.11sdfascii
$pyenvactivatesdfascii
$pipinstall--upgradepip
$pipinstall-rrequirements.txt
$inv-lLicensesdfasciiis released under the MIT license. Please see theLICENSE.txtfile for more information.
Package: sdf-heroku-deploy
Project DescriptionThis script generates a Dockerfile with the basic settings to deploy your flask application to heroku. And as a bonus asteps.txtwith the step by step for you to publish the project.Note: Run the script inside your project folder.Installation from Pypipip install sdf-heroku-deployModule Usagefromsdf_heroku_deployimportmainmain()CLI UsageGet the list of available commandssdfh_cli --helpOutputOptions:
  -img, --image_name TEXT       Type the name of the image you want to give   
                                the image to be created.
  -hap, --heroku_app_name TEXT  Type the name of the heroku app you want to   
                                give the app to be created.
  -r, --run TEXT                Type the name of the file that runs your flask
                                application.
  --help                        Show this message and exit.Commands usage examplesdfh_cli -img my_docker_image -hap my_heroku_app_name -r flask_app_runnersdfh_cli --image_name my_docker_image --heroku_app_name my_heroku_app_name --run flask_app_runnerOutput./Dockerfile
./steps.txt
Package: sdflex-ironic-driver
sdflex-ironic-driveris a package to provide a hardware typesdflex-redfishfor OpenStack Ironic that manages HPE Superdome Flex
Servers in cloud environment. This package enables the additional features
for Superdome Flex Servers over the features of Ironicredfishhardware type.InstallationInstall the module on Ironic conductor node fromPyPI:pip install sdflex-ironic-driveror:#. git clone https://github.com/HewlettPackard/sdflex-ironic-driver
#. cd sdflex-ironic-driver
#. pip install .ConfigurationFor configuration instructions and usage details, seesdflex-ironic-driver
wiki page.Reporting BugsTo report bugs, usegithub issues.