Saravanan666/pypi-org huggingface dataset:
Package: discco
DISCCo: Differentiable Image Simulation of the Cell CortexQuantification of membrane and cytoplasmic concentrations based on differentiable simulation of cell cortex images.
Designed for use on images of PAR proteins in C. elegans zygotes.This extends on the segmentation and straightening algorithm describedhere, and uses straightened cortices obtained by that method as input.Installationpip install disccoMethodsOur method is adapted from previous methods that model intensity profiles perpendicular to the membrane as the sum of distinct cytoplasmic and membrane signal components (Gross et al., 2018; Reich et al., 2019). Typically these two components are modelled as an error-function and Gaussian function respectively, representing the expected shape of a step and a point convolved by a Gaussian point spread function (PSF) in one dimension. Using this model, one can generate simulated images of straightened cortices as the sum of two tensor products which represent distinct membrane and cytoplasmic signal contributions (Figure 1):sim = ccyt⊗ scyt+ cmem⊗ smemwhere ccytand cmemare cytoplasmic and membrane concentration profiles and scytand smemare, by default, error-function and Gaussian profiles. We impose the constraint that the cytoplasmic concentration ccytis uniform throughout each image.Figure 1: Schematic of differentiable model for image quantificationUsing a differentiable programming paradigm, the input parameters to the model can be iteratively adjusted by backpropagation to minimize the mean squared error between simulated images and ground truth images.
As well as allowing the image-specific concentration parameters (ccytand cmem) to be learnt, this procedure also allows the global signal profiles scytand smemto be optimised and take any arbitrary form, allowing the model to generalise beyond a simple Gaussian PSF model and account for complex sample-specific light-scattering behaviors. In practice we find that this additional flexibility is necessary to minimise model bias and prevent underfitting:Figure 2: Example of ground truth and simulated images. Naive model refers to a mechanistic optical model with a Guassian PSF. Gaussian noise has been added to simulated images to allow for closer visual comparison to the ground truth image.An additional step, described in the paper, puts the cytoplasmic and membrane concentrations outputted by the model into biologically meaningful units, which has great utility for mathematical models.For full details of the model and training procedures, see the paper:Optimized dimerization of the PAR-2 RING domain drives cooperative and selective membrane recruitment for robust feedback-driven cell polarization(preprint)And the accompanyingGitHub repository.Limitations:Relies on a few assumptions about the system (uniform cytoplasmic concentration, rotational symmetry)Requires several calibrations with a few different samplesLicenseThis work is licensed under aCreative Commons Attribution 4.0 International License.
Package: discer
discerGeneric linear-time sorting and partitioningBased onthis paperby Fritz Henglein.Warning before you start using this libraryThe standard librariessortedfunction has linear-time sorting in the average case (Timsort) and is way more optimized than this library and will almost certainly be faster than any sorting done with this library.discer.relationscontains aRelationtype that represents theOrderandEquivtypes from the paper along with a few of standard relations from the paper.discer.groupinganddiscer.sortingcontain the respectiveEquivandOrderbased from functions from the paper.Future workNot in any particular order:Better README and docsAdd some testsOptimizeLazy evaluation over the key/pair list to reduces passes on itDefer tosortedwhen it will likely (or definitely) run in linear timeThe paper includes some optimizations not includedGeneric Multiset Programmingmay also contain further optimizationsSetupSumLandProductLto useTypeVarTupleFunctions to extend Relationslist :: Relation a -> Relation [a]tuple :: Relation a -> Relation Tuple[a, ...]dict :: Relation a -> Relation Dict[k, a]Refactorsdiscanddisc, the functions are basically exactly the same except how they deal withNaturalAdd compatibility for at least Python 3.8+ReferencesHenglein, Fritz. "Generic top-down discrimination for sorting and partitioning in linear time."Journal of Functional Programming22.3 (2012): 300-374.
Package: discere
Protein Feature Extraction for Machine LearningPython code to extract features from Protein sequences for Machine Learning/Deep LearningProtein feature extraction is carried out using Biopython package**Features inculde:AA-countaromaticitysecondary_structure_fractionisoelectric_pointmolecular_weightinstability_indexPackages required (other than built-in) for the execution of code...
-Pandas
-pickle
-Biopython
-subprocessInstallationFor windowspip install discereFor linuxpip3 install discereUsageimportdiscere.discereasdidi.extract_feature('./Documents/positive_training.fasta','./Documents/negative_training.fasta','./Documents')output
Package: discern-reconstruction
DISCERNDISCERN is a deep learning approach to reconstruction expression information
of single-cell RNAseq data sets using a high quality reference.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
An interactive tutorial can be found inTutorial.ipynb.PrerequisitesWe usepoetryfor dependency management. You can get poetry bypipinstallpoetryor (the officially recommended way)curl-sSLhttps://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py|pythonInstallingTo get discern you can clone the repository bygitclonehttps://github.com/imsb-uke/discern.gitpoetry can be used to install all further dependencies in an virtual environment.cddiscernpoetryinstall--no-devTo finally run discern you can also directly use poetry withpoetryruncommandsor spawn a new shell in the virtual environmentpoetryshellFor further examples the first approach is presented.Using discernYou can use the main function of discern for most use cases. Usually you have to preprocess your data by:poetryrundiscernprocess<parameters.json>An example parameters.json is provided together with an hyperparameter_search.json for hyperparameter optimization using ray[tune].
The training can be done withpoetryrundiscerntrain<parameters.json>Hyperparameter optimization needs a ray server with can be started withpoetryrunraystart--head--port57780--redis-password='password'and can started withpoetryrundiscernoptimize<parameters.json>For projection 2 different modes are available:
Eval mode, which is a more general approach and can save a lot of files:poetryrundiscernproject--all_batches<parameters.json>Or projection mode which offers a more fine grained controll to which is projected.poetryrundiscernproject--metadata="metadatacolumn:value"--metadata="metadatacolumn:"<parameters.json>which creates to files, one is projected to the average batch calculated by ametadatacolumnand a containedvalue.
The second file is projected to the the average for each value in “metadatacolumn”; individually.DISCERN also supports online training. You can add new batches to your dataset after the usualtrainwith:poetryrundiscernonlinetraining--freeze--filename=<new_not_preprocessed_batch[es].h5ad><parameters.json>The data gets automatically preprocessed and added to the dataset. You can runprojectafterwards as usual (without the--filenameflag).--freezeis important to freeze non-conditional layers in training.TestingFor critical parts of the model several tests has been implemented. They can be run with:poetryrunpytest--cov=discern--cov-report=term(Requires the development version of discern).Some tests are slow and don’t run by default, but you can run them using:poetryrunpytest--runslow--cov=discern--cov-report=termCoding styleTo enforce code style guidlinespylintandmypyare use. Example commands are shown below:poetryrunpylintdiscernray_hyperpara.pypoetryrunmypydiscernray_hyperpara.pyFor automatic code formattingyapfwas used:yapf-i<filename.py>These tools are included in the dev-dependencies.AuthorsCan ErgenPierre MachartFabian Hausmann
Package: discern-xai
DisCERN-XAIDisCERN: Discovering Counterfactual Explanations using Relevance Features from NeighbourhoodsInstalling DisCERNDisCERN supports Python 3+. The stable version of DisCERN is available onPyPI:pip install discern-xaiTo install the dev version of DisCERN and its dependencies, clone this repo and runpip installfrom the top-most folder of the repo:pip install -e .DisCERN requires the following packages:numpypandaslimeshapscikit-learnCompatible LibrariesAttribution Explainerscikit-learnTensorFlow/KerasPyTorchLIME✓✓N/ASHAP✓ shap.TreeExplainer✓  shap.DeepExplainerN/AIntegrated Gradients✗✓N/AGetting Started with DisCERNBinary Classification example on the Adult Income dataset using RandomForest and Keras Deep Neural Net classifiers arehereMulti-class Classification example on the Cancer risk dataset using RandomForest and Keras Deep Neural Net classifiers arehereCitingPlease cite it follows:Wiratunga, N., Wijekoon, A., Nkisi-Orji, I., Martin, K., Palihawadana, C., & Corsar, D. (2021, November). Discern: discovering counterfactual explanations using relevance features from neighbourhoods. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1466-1473). IEEE.Wijekoon, A., Wiratunga, N., Nkisi-Orji, I., Palihawadana, C., Corsar, D., & Martin, K. (2022, August). How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations. In Case-Based Reasoning Research and Development: 30th International Conference, ICCBR 2022, Nancy, France, September 12–15, 2022, Proceedings (pp. 33-47). Cham: Springer International Publishing.Bibtex:@misc{wiratunga2021discerndiscovering,
  title={DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods}, 
  author={Nirmalie Wiratunga and Anjana Wijekoon and Ikechukwu Nkisi-Orji and Kyle Martin and Chamath Palihawadana and David Corsar},
  year={2021},
  eprint={2109.05800},
  archivePrefix={arXiv},
  primaryClass={cs.LG}}@inproceedings{wijekoon2022close,
    title={How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations},
    author={Wijekoon, Anjana and Wiratunga, Nirmalie and Nkisi-Orji, Ikechukwu and Palihawadana, Chamath and Corsar, David and Martin, Kyle},
    booktitle={Case-Based Reasoning Research and Development: 30th International Conference, ICCBR 2022, Nancy, France, September 12--15, 2022, Proceedings},
    pages={33--47},
    year={2022},
    organization={Springer}
}This research is funded by theiSee projectwhich received funding from EPSRC under the grant number EP/V061755/1. iSee is part of theCHIST-ERA pathfinder programmefor European coordinated research on future and emerging information and communication technologies.
Package: discfit
Discfit reads disc permeameter data in the field and calculates hydraulic parameters of the soil; saturated hydraulic conductivity and Gardner’s alpha constant.Disc permeameteris a tool for measuring hydraulic properties in the field. Steady-state periods in multiple constant head period are selected automatically from the data, and infiltration rate for each steady-state period is calculated. After that, infiltration rate at saturated condition is estimated by Gardner’s exponential model,K(h) = Ks exp(αh)where K is unsaturated hydraulic conductivity (cm/s), h is matric head (cm), Ks is saturated hydraulic conductivity (cm/s), α is a constant (/cm). Note that h is negative and αh is negative. Based on this equation, the linear relationship ln(q) = ln(qs) + αh is used for estimating qs (cm/s), the infiltration rate at the saturated condition, and α constant, as we have multiple point of (h, q) data from the disc permeameter. The hydraulic conductivity is calculated from the infiltration rate based on steady-state analysis of Wooding (1968);q = K(1 + 4 / παr)where α is the Gardner’s α and r is the diameter of the disc (cm). See equations (10) and (13) in Minastry and George (1999).InstallPython 2 or 3 is required. Install Python athttps://www.python.org/. After that, install discfit by runningpip install discfitUsagediscfit Filename dd dpwhereFilename: File name of data filedd: diameter of the disc (cm)dp: inner diameter of the pipe (cm)Format of data fileIt is a csv file with time, water level, and suction head (absolute value of the matric head) as follows.# Comment line starts with '#'
# Time (sec), Water level (cm), Suction head (cm)
0, 58.32, 10.53
10, 58.30, 10.57
20, 58.33, 10.48ReferenceMinasny, B., and B. H. George. 1999.The measurement of soil hydraulic properties in the field.in Cattle S.R. & George B.H. (Eds) Describing, Analysing and Managing Our Soil.Wooding, R.A., 1968. Steady infiltration from a shallow circular pond. Water Resources Research 4, 1259-1273.
Package: discid
Python-discid implements Python bindings for MusicBrainz Libdiscid. This
module works with Python 2 >= 2.6, or Python 3 >= 3.1.
Libdiscid >= 0.2.2 is needed.Libdiscid’s main purpose is the calculation of an identifier of audio
discs (disc ID) to use for theMusicBrainz database.That identifier is calculated from the TOC of the disc, similar to the
freeDB CDDB identifier. Libdiscid can calculate MusicBrainz disc IDs and
freeDB disc IDs.
Additionally the MCN of the disc and ISRCs from the tracks can be extracted.This module is a close binding that offloads all relevant data
storage and calculation to Libdiscid. On the other hand it gives a
pythonic API and uses objects and exceptions.The official API documentation can be found atreadthedocs.orgFor more information on Libdiscid seelibdiscid.For more information about the calculation of these disc ids seeDisc
ID Calculation.Usage# this will load Libdiscidimportdisciddisc=discid.read()# use default deviceprint"id:%s"%disc.id# Python 2print("id:%s"%disc.id)# Python 3See also the examples.py.LicenseThis module is released under the GNU Lesser General Public License
Version 3. See COPYING.LESSER for details.BugsYou can submit tickets atGitHub.
Package: discirc
DiscIRCis a bot that syncs messages betweenDiscordandIRC. It’s written in Python using thediscord.pyandbottomlibraries.InstallationYou can install it simply usingpip:$ pip install discircUseLaunchYou first need to configureDiscIRCthanks to a config file. By defaultDiscIRCtries to load a~/.discircfile, if not present you need to give it to the app with the– configoption.To create a valid config file please see theexample.Then simply runDiscIRCthanks to the available command:$ discircPrivate messagesDiscIRChandles private messages. You need to prefix your message with a@Userwhen sent fromDiscordand withUser:from IRC.
Package: discli
discli(still in progress)🎲 Discord Bot CLI for creating / deploying your discord bots!About|Installation|Usage|Author|Contributors|Help|LicenseAboutdiscliis a discord cli (bot) for bot developers out there who want to develop bots, easily and fast!
Discli offers you a development and deployment system (heroku) (comming soon). It's easy to use and reliable.Usagediscli createLicenseMIT
Package: dis-cli
dis-clidis-cliis a command line tool for displaying Python source and bytecode.Installationdis-clican be installed fromPyPI.
To install viapip, run$pipinstalldis-cliUsagedis-cliprovides a command line program,dis,
which takes a "import-like" path to a function to display information about.
For example, if you have a packagea, with a submoduleb, containing a functionc,
you could rundison it like this:$disa.b.cJust like you could importcin a script:importa.b.cdistakes a few other options.
Try runningdis --helpto see what's available!
Package: disclist
Guides:Here are guides of how to use this Python Wrapper.Initiating your Client:fromdisclistimportClientclient=Client(token="...",sync=True)# sync is a kwarg to set if functions need to be async or not. Defaults to True.Posting Stats:# If you set sync to True, it will not need to be awaited# Else, it needs to.# You can check it using `client.sync` or see the kwarg you set# if you are unsure.# For synchronous:client.post_stats(server_count=1)# Set your server count to 1.# For asynchronous:awaitclient.post_stats(server_count=1)# Set your server count to 1.For checking if user has voted or not:# For synchronous:has_voted=client.has_voted(user_id=1234567890)# For asynchronous:has_voted=awaitclient.has_voted(user_id=1234567890)print(has_voted)# True/FalseFor searching a bot using it's ID:# For synchronous:result=client.search(bot_id=1234567890)# For asynchronous:result=awaitclient.search(bot_id=1234567890)print(result)# {'...': ...}, Returns a dict.Where should I ask help?Join ourdiscordto ask for help!License:MIT
Package: disclog
disclogdisclogis a Python package for logging messages to a Discord channel using a webhook. It provides a simple and easy-to-use interface for sending messages of different levels (info, warning, and error) to a Discord channel, with customizable formatting and colors.InstallationYou can installdisclogusing pip:pip install disclogUsageTo use disclog in your Python project, first import theDisclogclass:fromdisclogimportDisclogThen create a new instance of Disclog with your Discord webhook URL:logger = Disclog('https://discord.com/api/webhooks/1234567890/abcdefghijklmnopqrstuvwxyz')You can then use the info(), warning(), and error() methods to send messages of different levels to your Discord channel:logger.info('Server started successfully on port 8080')
logger.warning('Authentication failed for user: jane.doe@example.com')
logger.error('Oops! Something went wrong')Licensedisclogis released under the MIT License. See LICENSE for more information.MIT License

Copyright (c) [year] [fullname]

Permission 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.
Package: disclog-burslf
disclogdisclogis a Python package for logging messages to a Discord channel using a webhook. It provides a simple and easy-to-use interface for sending messages of different levels (info, warning, and error) to a Discord channel, with customizable formatting and colors.InstallationYou can installdisclogusing pip:pip install disclogUsageTo use disclog in your Python project, first import theDisclogclass:fromdisclogimportDisclogThen create a new instance of Disclog with your Discord webhook URL:logger = Disclog('https://discord.com/api/webhooks/1234567890/abcdefghijklmnopqrstuvwxyz')You can then use the info(), warning(), and error() methods to send messages of different levels to your Discord channel:logger.info('Server started successfully on port 8080')
logger.warning('Authentication failed for user: jane.doe@example.com')
logger.error('Oops! Something went wrong')Licensedisclogis released under the MIT License. See LICENSE for more information.MIT License

Copyright (c) [year] [fullname]

Permission 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.
Package: disclosurecheck
Disclosure CheckA tool for detecting disclosure mechanisms for open source projectsUsageDownload the latest .tar.gz file from the releases page.$ pip install disclosurecheck-VERSION.tar.gz
$ disclosurecheck --help

usage: OpenSSF Vulnerability Disclosure Mechanism Detector [-h] [--verbose] [--json] package_url

positional arguments:
  package_url  Package URL for the project/package you want to analyze.

options:
  -h, --help   show this help message and exit
  --verbose    Show extra logging.
  --json       Output as JSON.
Package: disco
Dis.co Python SDKThis package lets you run jobs onDis.coplatform from a Python program.Using the SDK, you can create computing jobs, run them onDis.co, and get their results to your local machine.
It is possible to offload any kind of Python program to be run onDis.co.PrerequisitesAnDis.coaccount.Python version 3.6 and up.Dis.co Python SDK installed:$pipinstalldiscoExampleimportdiscoimportpathlibdisco.set_credentials('john@example.com','my_password',save_to_config=True)my_file_id=disco.upload_file('my_file.py',pathlib.Path('/home/bob/my_file.py'))job=disco.Job.create(my_file_id)job.start()(task_result,)=job.get_results(block=True)task_result.write_files('/home/bob/my_result')The code above uploads the file/home/bob/my_file.py, runs it onDis.cocloud, waits for the job to finish,
and writes the results to/home/bob/my_result.When the job has been created, you can see it listed inlist_jobs():>>>importdisco>>>disco.Job.list_jobs())[{'id':'5d650d25179854000d72b8cc','archived':False,'name':'d08e3e029bfc412ebc316e21f1a7dae0','status':'Done','taskStates':['Success']}]This functions returns an instance ofJob, which gives access to many useful operations on a job
(see the API documentation).Environment variablesThediscopackage supports these environment variables:DISCO_BASE_URL- base URL of the Dis.co server. Default:https://app.dis.coDISCO_EMAIL- user's email to for authenticating with the Dis.co server.DISCO_PASSWORD- password for authenticating with the Dis.co server.DISCO_LIVE_TESTS- set true to run our live tests.
Package: discoauth
DiscoAuthautomatically follows through with the requests for Discord OAuthFeaturesMakes a Authorization Url automatically, using the client ID, redirect_uri, and the scopes you wantCan get:The access tokenThe userThe user's guildsA specific guild, using it's IDIt also makes bot auth url'sWhat I'm working on for version 2.0.0Get GuildsReleased in version 1.1Errors, will be fixed in version 1.1.4Found error in the scope part of the url creationModelsUser ObjectErrors, fixed in version 1.1.3Guild ObjectReleased in version 1.2Bot OAuthReleased in version 1.3Functioning CLIUseful Links:LicenseContributing & GuidelinesRoadmapContributorsArcader717💻📖💡✅LicenseThis project is under theMIT License