Saravanan666/pypi-org huggingface dataset:
Package: imbDRL
imbDRLImbalanced Classification with Deep Reinforcement Learning.This repository contains an (Double) Deep Q-Network implementation of binary classification on unbalanced datasets usingTensorFlow 2.3+andTF Agents 0.6+. The Double DQN as published inthis paperbyvan Hasselt et al. (2015)is using a custom environment based onthis paperbyLin, Chen & Qi (2019).Example scripts on theMnist,Fashion Mnist,Credit Card FraudandTitanicdatasets can be found in the./imbDRL/examples/ddqn/folder.RequirementsPython 3.7+The required packages as listed in:requirements.txtLogs are by default saved in./logs/Trained models are by default saved in./models/Optional:./data/folder located at the root of this repository.This folder must containcreditcard.csvdownloaded fromKaggleif you would like to use theCredit Card Frauddataset.Note:creditcard.csvneeds to be split in a seperate train and test file. Please use the functionimbDRL.utils.split_csvGetting startedInstall viapip:pip install imbDRLRun any of the following scripts:python .\imbDRL\examples\ddqn\train_credit.pypython .\imbDRL\examples\ddqn\train_famnist.pypython .\imbDRL\examples\ddqn\train_mnist.pypython .\imbDRL\examples\ddqn\train_titanic.pyTensorBoardTo enableTensorBoard, runtensorboard --logdir logsTests and lintingExtra arguments are handled with the./tox.inifile.Pytest:python -m pytestFlake8:flake8Coverage can be found in the generated./htmlcovfolderAppendixThe appendix can be found in theimbDRLAppendixrepository.
Package: imbib
Imbib lets you cite papers using their URLs. It translates these citations to
BibTeX by scraping the corresponding databases.Cite with Markdown-like syntax:[@threads]: http://dl.acm.org/citation.cfm?id=1065042Imbibe:imbib < example.md > example.bibEnjoy your BibTeX:@inproceedings{threads,
    author = "Boehm, Hans-J.",
    title = "Threads Cannot Be Implemented As a Library",
    year = "2005",
    booktitle = "PLDI"
}
Package: imbibed
IMBIBED: IMpeccable BIBliographiEs with DblpA simple tool for generating and cleaning.bib(and related.tex) files for
bibliographies mainly based on DBLP.Install$pipinstallimbibedUsage$imbibed-h
usage:imbibed[-h][-s][--condense][--normal][--crossref][--no-cache][--update-filesUPDATE_FILES][--filter-filesFILTER_FILES]inputoutput

IMBIBED:IMpeccableBIBliographiEswithDblpConvertyourbibfiletouseDBLPdataandcitationkeys.
ConversionisbasedonDOIs(orDBLPkeysifexist)intheinputdatabase.

positionalarguments:inputInputbibfileoutputFilewheretheoutputbibiswritten(maybethesameasinput).

optionalarguments:-h,--helpshowthishelpmessageandexit-s,--sortSorttheoutputbibbykey.--condenseUseDBLPcondensedformat(default).--normalUseDBLPnormalformat.--crossrefUseDBLPcrossrefformat.--no-cacheNonotcacheDBLPrequestsinlocalfile.--update-filesUPDATE_FILESUpdatetex(orotherformat)filesforthenewkeys.Acceptsglobpatterns.WARNING:thisdoessimplesearch-and-replaceofthekeys,hencemightbreakyourfiles.MAKEABACKUPFIRST.--filter-filesFILTER_FILESLookforcitedkeysinthesefilesanddeltednon-citedkeys.Acceptsglobpatterns.WARNING:thisdoessimplesearchofthekeys,inthefiles.
Package: imboclient
Python client for ImboA client for Imbo written in Python for 2.7/3.4+InstallationInstall the most recently released version from pip:pip install imboclientUsageFor a quick overview of the current functionality refer to the integration tests that perform simple operations with the client.Develop/Contribute to Python client for ImboGet the code and install dependencies:git clonegit@github.com:imbo/imboclient-python.git
cd imboclient-python && make installRun the unit testsuite:make testrun the integration testsuite (NOTE: this requires a running imbo-instance as specified in imboclient/test/integration/config.py):make integration-testKeep it simple and easy to understandSolid test coveragePEP8LicenseCopyright (c) 2013, Andreas Søvik <arsovik@gmail.com>Licensed under the MIT License
Package: im-botposter
Imposter botImposter is a telegram bot which creates a post with title and message with and index to track that post, in telegram channel or group in which this bot has been added.List of Keys and Ids which is needed to work with this bot are:A group or a channelYou all know how to create a group or a channel, we would needchannel id or group id.A telegram botTo create a telegram bot you need botfather, after creation of Bot there would be anauth key for botwe would be needing that.How it works?Imposter is bascially a kind of interface given to some of the endpoints ofTelegram Bot Api, it takes data from users and put it in perticular format in telegram Channels or groups.Step 1: Print title and message in telegram channel (Done)To do thissendMessagemethod is getting used, interface takesbot auth key,channel id,a titleandmessagefrom user.Step 2: Create or Append IndexThere is a bit of processing in this situation,it has been assumed that there are no index created by any user and only that index has been pinned to that channel.if there is no pinned message it will create a new indexNow it takes thetitleandlink of the sent messageand add title as hyperlink, linked to the message.To get the pinned messagegetChatmethod is being used. \If there is no pin, to create indexsendMessageand to pin that indexpinChatMessageare being used.\if there is a pinned message, then it checks if it is index or not it it's not an index message theneditMessage.
Package: imbox-sis
Imbox - Python IMAP for HumansPython library for reading IMAP mailboxes and converting email content
to machine readable dataRequirementsPython (3.6, 3.7, 3.8, 3.9)Installationpip install imbox-sisUsagefromimbox_sisimportImbox# SSL Context docs https://docs.python.org/3/library/ssl.html#ssl.create_default_contextwithImbox('imap.gmail.com',username='username',password='password',ssl=True,ssl_context=None,starttls=False)asimbox:# Get all foldersstatus,folders_with_additional_info=imbox.folders()# Gets all messages from the inboxall_inbox_messages=imbox.messages()# Unread messagesunread_inbox_messages=imbox.messages(unread=True)# Flagged messagesinbox_flagged_messages=imbox.messages(flagged=True)# Un-flagged messagesinbox_unflagged_messages=imbox.messages(unflagged=True)# Flagged messagesflagged_messages=imbox.messages(flagged=True)# Un-flagged messagesunflagged_messages=imbox.messages(unflagged=True)# Messages sent FROMinbox_messages_from=imbox.messages(sent_from='sender@example.org')# Messages sent TOinbox_messages_to=imbox.messages(sent_to='receiver@example.org')# Messages received before specific dateinbox_messages_received_before=imbox.messages(date__lt=datetime.date(2018,7,31))# Messages received after specific dateinbox_messages_received_after=imbox.messages(date__gt=datetime.date(2018,7,30))# Messages received on a specific dateinbox_messages_received_on_date=imbox.messages(date__on=datetime.date(2018,7,30))# Messages whose subjects contain a stringinbox_messages_subject_christmas=imbox.messages(subject='Christmas')# Messages whose UID is greater than 1050inbox_messages_uids_greater_than_1050=imbox.messages(uid__range='1050:*')# Messages from a specific foldermessages_in_folder_social=imbox.messages(folder='Social')# Some of Gmail's IMAP Extensions are supported (label and raw):all_messages_with_an_attachment_from_martin=imbox.messages(folder='all',raw='from:martin@amon.cx has:attachment')all_messages_labeled_finance=imbox.messages(folder='all',label='finance')foruid,messageinall_inbox_messages:# Every message is an object with the following keysmessage.sent_frommessage.sent_tomessage.subjectmessage.headersmessage.message_idmessage.datemessage.body.plain
Package: imbutil
Additions to theimbalanced-learnpackage.fromimbutil.combineimportMinMaxRandomSampler;fromimblearnimportpipeline;# oversampling minority classes to 100 and undersampling majority classes to 800sampler=MinMaxRandomSampler(min_freq=100,max_freq=800)sampling_clf=pipeline.make_pipeline(sampler,inner_clf)Contents1Installation2Basic Use2.1combine3Contributing3.1Installing for development3.2Running the tests3.3Adding documentation4Credits1InstallationpipinstallimbutilAdditionally, theMinMaxRandomSampler, in addition toRandomUnderSamplerandRandomOverSamplerfromimbalanced-learn, can technically be used with non-numeric data. However, the current implementation ofimbalanced-learnforces a check for numeric data for all samplers. If you want to bypass this limitation, I have a fork of the project which does not force data to be numeric. You can install it with:pipinstallgit+https://github.com/shaypal5/imbalanced-learn.git@f6adc562fafdc2198931873799e725e5abdd65a12Basic Useimbutiladditions addhere to the structure of theimblearnpackage:2.1combineContaines samplers that both under-sample and over-sample:MinMaxRandomSampler- Random samples data to bring all class frequencies into a range.3ContributingPackage author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.3.1Installing for developmentClone:gitclonegit@github.com:shaypal5/imbutil.gitInstall in development mode, and with test dependencies:cdimbutilpipinstall-e".[test]"3.2Running the testsTo run the tests use:cdimbutilpytest3.3Adding documentationThe project is documented using thenumpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, followthese conventions.Additionally, if you update thisREADME.rstfile,  usepython setup.py checkdocsto validate it compiles.4CreditsCreated by Shay Palachy (shay.palachy@gmail.com).
Package: imc2zarr
IMC to Zarr converterConvert IMC scan dataset to Zarr.Installpip install imc2zarrRequirementsclicknumpypandaspython_dateutilxarrayUsageArgumentsinput_path:the root folder of the IMC scan containing a single mcd file and/or other related files: XML meta & scan data in text formator, the path of an mcd fileoutput_path: the location where to store the converted output in Zarr formatFrom Python scriptfrom imc2zarr import imc2zarr

imc2zarr(input_path, output_path)From the command lineimc2zarr input_path output_path
Package: imcascade
imcascadeis a code designed for fitting objects in astronomical images using a “cascade” of Gaussians. It uses multi-guassian expansion (MGE) to model galaxies as a mixture of Gaussians in a Bayesian Framework. It was designed to study the morphology of faint, semi-resolved galaxies. If you are planning on usingimcascadewe suggest reading our paper describing the methodhereand the documentationhere.Citationimcascadeis open source software made available under the MIT license, written by Tim Miller and Pieter van Dokkum. If you use this package in your work please citeMiller & van Dokkum (2021)Help and Issuesimcacscadeis maintained on Github. If you find a bug in the code or have a feature you would like to request, please open anissue on Github. Additionally you can reach out to me directly if you are having issues using the code or have suggestions.
Package: imcflibs3
imcflibs3- IMCF Py3 helper collection 🐍 🔩 🔧 🪛This package contains a small collection of Python 3.x functions dealing with
paths, I/O (file handles, ...), strings and so on.Initially contained in theimcflibspackage (note the missing3there!), this has been forked into a separate project for being able to support
current Python versions - theoriginalimcflibspackage is nowadays
exclusively useful within theFiji/ImageJ2ecosystem and
therefore limited to Python 2.7 (or rather Jython 2.7, see theJython 3
roadmapfor more details on this topic).As some of our other packages (e.g.micrometa) are depending on
stuff provided byimcflibs, thisimcflibs3package has been
created to provide those functions without the limitations mentioned above.
Obviously theimcflibs.imagejsubmodule doesn't make sense in aCPythonenvironment and therefore has been stripped completely from theimcflibs3package.Note about package versions and compatibilityPackage versions will go side-by-side with the corresponding releases of theimcflibspackage while skipping those releases that only affect theimcflibs.imagejsubmodule(s). In other words, there might (will) be
version jumps of theimcflibs3package in case there have been releases
of theimcflibsone in between that were not affecting the functions
contained here.Please note that this approach might change in the future if we discover the
missing versions to be a complicating factor in packaging / testing.Developed and provided by theImaging Core Facility (IMCF)of the
Biozentrum, University of Basel, Switzerland.
Package: imco
Image Comparator (imco)Compare image similarity with metrics printed automatically below every image. See example image below.InstallationpipinstallimcoTo get the latest version, install:pipinstallgit+https://github.com/pettod/imco.git@mainUsageImport following function:fromimcoimportcompareImagesHere is a sample code to use the comparison function:importcv2# Paths to imagesimage_paths=["path/to/image_1.png","path/to/image_2.png","path/to/image_3.png",]# Read imagesimages=[cv2.imread(image_path)forimage_pathinimage_paths]# Define image namesnames=["Input","Prediction 1","Ground Truth"]# Create comparison imagecomparison_image=compareImages(images,names,True,256,(10,30))cv2.imwrite("comparison_image.png",comparison_image)LicenseLicense
Package: imcode
ColabCodeInstallationInstallation is easy!$ pip install imcodeRun code server on Google Colab or Kaggle NotebooksGetting StartedImCode also has a command-line script. So you can just runimcodefrom command line.imcode -hwill give the following:usage: imcode [-h] --port PORT [--password PASSWORD] [--mount_drive]

ImCode: Run VS Code On Colab / Kaggle Notebooks

required arguments:
  --port PORT          the port you want to run code-server on

optional arguments:
  --password PASSWORD  password to protect your code-server from unauthorized access
  --mount_drive        if you use --mount_drive, your google drive will be mountedElse, you can do the following:# import imcode$fromimcodeimportImCode# run imcode with by deafult options.$ImCode()# ImCode has the following arguments:# - port: the port you want to run code-server on, default 10000# - password: password to protect your code server from being accessed by someone else. Note that there is no password by default!# - mount_drive: True or False to mount your Google Drive$ImCode(port=10000,password="nitin",mount_drive=True)How to use it?Colab starter notebook:ImCodecomes pre-installed with some VS Code extensions.Original projecthttps://github.com/abhishekkrthakur/colabcodeLicenseMIT
Package: imcp
IMCP: Imbalanced Multiclass Classification Performance CurveROC curves are a well known tool for multiple classifier performance comparison. However, it does not work with multiclass datasets (more than two labels for the target variable). Moreover, the ROC curve is sensitive to imbalance of class distribution.The package provides a tool - called Imbalanced Multiclass Classification Performance curve - that solves both weaknesses of ROC: application to multiclass and imbalanced datasets.With the IMCP curve the classification performance can be graphically shown for both multiclass and imbalanced datasets.The package provides the methods for visualizing the IMCP curve and to provide the area under the IMCP curve.InstalationIMCP can be installed fromPyPIpipinstallimcpOr you can clone the repository and run:pipinstall.Sample usagefromimcpimportplot_mcp_curvefromsklearn.linear_modelimportLogisticRegressionfromsklearn.datasetsimportload_irisX,y=load_iris(return_X_y=True)clf=LogisticRegression(solver="liblinear").fit(X,y)plot_mcp_curve(y,clf.predict_proba(X))CitationThe methodology is described in detail in:[1] J. S. Aguilar-Ruiz and M. Michalak, “Diagnostic system assessment for imbalanced multiclass data” (in review, and draft available upon request).Also, the mathematical background of the multiclass classification performance can be found in:[2] J. S. Aguilar-Ruiz and M. Michalak, "Multiclass Classification Performance Curve," in IEEE Access, vol. 10, pp. 68915-68921, 2022, doi: 10.1109/ACCESS.2022.3186444.DocumentationFull documentation is availablehere
Package: imcpipeline
Imaging mass cytometry pipelineThis is a pipeline for the processing of imaging mass cytometry (IMC) data.It is largely based onVito Zanotelli's pipeline.
It performs image preprocessing and filtering, usesilastikfor semi-supervised pixel classification,CellProfilerfor image segmentation and
quantification of single cells.The pipeline can be used in standalone mode or withimcrunnerin order to
process multiple samples in a distributed way and in parallel such as a local
computer, on the cloud, or a high performance computing cluster (HPC).
This is due to the use of the light-weight computing configuration managerdivvy.Requirements and installationRequires:Python >= 3.7One of:docker,singularity,condaorcellprofilerin a local installation.Install with:pipinstallimcpipelineMake sure to have an updated PIP version.
Development and testing is only done for Linux. If anyone is interested in
maintaining this repository in MacOS/Windows fell free to submit a PR.Quick startDemoYou can run a demo dataset using the--demoflag:imcpipeline --demoThe pipeline will try to use a localcellprofilerinstallation,dockerorsingularityin that order if any is available.
Output files are in aimcpipeline_demo_datadirectory.Running on your dataTo run the pipeline on real data, one simply needs to specify input and output
directories. A trainedilastikmodel can be provided and if not, the user will
be prompted to train it.imcpipeline \
    --container docker \
    --ilastik-model model.ilp \
    -i input_dir -o output_dirIfdockerorsingularityis not available, one could for example use acondaenvironment or avirtualenvenvironment activated only for thecellprofilercommand like this:imcpipeline \
    --cellprofiler-exec \
        "source ~/.miniconda2/bin/activate && conda activate cellprofiler && cellprofiler"
    --ilastik-model model.ilp \
    -i input_dir -o output_dirTo run one step only for a single sample, use the-s/--stepargument:imcpipeline \
    --step segmentation \
    -i input_dir -o output_dirOr provide more than one consecutive step in the same way:imcpipeline \
    --step predict,segmentation \
    -i input_dir -o output_dirTo run the pipeline for various samples in a specific computing configuration
(more details in the documentation):imcrunner \
    --divvy-configuration slurm \
    metadata.csv \
        --container docker \
        --ilastik-model model.ilp \
        -i input_dir -o output_dirDocumentationFor additional details on the pipeline,see the documentation.Related softwareVito Zanotelli's pipeline;A similar pipeline implemented inNextflow.
Package: imcpy
imcpyPython bindings forInter-Module Communication Protocol (IMC)used to communicate between modules in theLSTS toolchain.InstallationPip install (PyPi)Precompiled wheels are available from PyPi, compiled against the standard IMC message set (LSTS).
If your project does not use custom IMC messages, these wheels should be sufficient.pipinstallimcpyCheck the release notes or pypi for the precompiled architectures and python versions.Source installClone the project recursivelygitclone--recursivegit://github.com/oysstu/imcpy.gitThis includes the pybind11 submodule.Build the librarypython3setup.pyinstallIf you use the system python and only want to install for a single user, you can add --user to the install command without needing administrator rights. On Windows, the Windows SDK must be installed with Visual Studio and the CMake executable must be on the system PATH.(Optional) Use a specific IMC/Dune versionReplace the dune/imc submodules with the target versions(Optional) Only generate bindings for a subset of IMC messagesA config file named whitelist.cfg can be placed in the root folder to
only create bindings for a subset of the IMC messages. This can be necessary when compiling on
embedded systems, as the linker consumes much memory for the full message set.
If an unknown message is parsed, it will be returned as the Message baseclass rather than a specialized message.
Look at minimal_whitelist.cfg for a set of messages that should always be included.RecommendationsThe imcpy library generates stub files for the bindings, meaning that you can have autocomplete and static type checking if your IDE supports them. This can for example bePyCharmorJedi-based editors.
Package: im-critsec
# im_critsecThis library contains the decorator @critsec, for running a function as a critical section (non-reentrant task).[![Build Status](https://travis-ci.org/emlynoregan/im_critsec.svg?branch=master)](https://travis-ci.org/emlynoregan/im_critsec)## InstallUse the python package for this library. You can find the package online [here](https://pypi.org/project/im-critsec/).Change to your Python App Engine project's root folder and do the following:> pip install im_critsec --target libOr add it to your requirements.txt. You'll also need to set up vendoring, see [app engine vendoring instructions here](https://cloud.google.com/appengine/docs/python/tools/using-libraries-python-27).## UsageYou'll need to have im_task set up correctly using either im_task_flask or im_task_webapp (or both!) depending on which framework you are using.Then, you can run a function as a critical section as follows:@critsecdef MyCriticalFunc():# do some stuff that needs to not be interrupted# this function will *never* run re-entrantly, even in other processes on other computersMyCriticalFunc() # kicking off the call to it here.Critsec forms a separate critical section for each combination of arguments and outer scope references. eg:@critsecdef MyCriticalFunc(value):# do some stuff that needs to not be interrupted# this function will *never* run re-entrantly, even in other processes on other computersMyCriticalFunc(1) # first callMyCriticalFunc(1) # in same critical section as first callMyCriticalFunc(2) # in a different critical section## rerun_on_skipWhen a critical section is entered, subsequent calls to the same critical section will be ignored while that critical section is running. However, those skipped calls are remembered. Once the critical section is finished, if any calls were skipped, then the critical section runs one more time. This is called rerun_on_skip.If you would prefer not to have rerun_on_skip, you can turn it off:@critsec(rerun_on_skip = False)def ThisFunctionIsNotRerunOnSkip(value):# do a thing## @critsec is a form of @task@critsec is exactly like @task; you can pass in task arguments, eg:@critsec(queue = "myqueue")def ThisFunctionRunsOnMyQueue():# do a thing
Package: imcs
Python CLI wrapper for Maastricht's json avu.
Takes a JSON file and annotates an iRODS object accordingly.UsageOn the command line you can use the module with the following command:schema2avu [-h] -j JSON d|C|R filepositional arguments:d|C|R      the irods resource type [d=data objects (file), C=collection (directory), R=resources]file       path to data object, collection or resourceDefining input Files:-j JSON, --json  JSON  defines json schema inputAcknowledgmentsA lot of thanks to Christian Meesters who provided the initial script and who will publish an improved
version later.
Package: imcsv
imcsv - Fast Python In-Memory CSV file creator!A python library built to enhancing testingRequirementsPython:  3.*InstallationInstall using pip:pip install imcsvUsageProvides you with an in-memory csv filefrom imcsv.imcsv import generate_temp_csvfile

headers = [
        "Date",
        "Month",
        "Year",
        "Customer ID",
        "Item ID",
    ]
rows = [
    [
        "5-June-2020",
        "5",
        "2020",
        "920",
        "1380",
    ],
    [
        "4-December-2020",
        "12",
        "2020",
        "977",
        "2322",
    ],
]
temp_csvfile = generate_temp_csvfile(headers, rows)