Saravanan666/pypi-org huggingface dataset:
Package: edafa
EdafaEdafa is a simple wrapper that implements Test Time Augmentations (TTA) on images for computer vision problems like: segmentation, classification, super-resolution, Pansharpening, etc. TTAs guarantees better results in most of the tasks.Test Time Augmentation (TTA)Applying different transformations to test images and then average for more robust results.InstallationpipinstalledafaGetting startedThe easiest way to get up and running is to followexample notebooksfor segmentation and classification showing TTA effect on performance.How to use EdafaThe whole process can be done in 4 steps:Import Predictor class based on your task category: Segmentation (SegPredictor) or Classification (ClassPredictor)fromedafaimportSegPredictorInherit Predictor class and implement the main functionpredict_patches(self,patches): where your model takes image patches (numpy.ndarray) and return prediction (numpy.ndarray)classmyPredictor(SegPredictor):def__init__(self,model,*args,**kwargs):super().__init__(*args,**kwargs)self.model=modeldefpredict_patches(self,patches):returnself.model.predict(patches)Create an instance of you classp=myPredictor(model,patch_size,model_output_channels,conf_file_path)Callpredict_images()to run the prediction processp.predict_images(images,overlap=0)Configuration fileConfiguration file is a json file containing two pieces of informationAugmentations to apply (augs). Supported augmentations:NO: No augmentationROT90: Rotate 90 degreesROT180: Rotate 180 degreesROT270: Rotate 270 degreesFLIP_UD: Flip upside-downFLIP_LR: Flip left-rightBRIGHT: Change image brightness randomlyCONTRAST: Change image contrast randomlyGAUSSIAN: Add random gaussian noiseGAMMA: Perform gamma correction with random gammaCombination of the results (mean). Supported mean types:ARITH: Arithmetic meanGEO: Geometric meanNumber of bits image (default is 8-bits) (bits).Example of a conf file injsonformat{"augs":["NO","FLIP_UD","FLIP_LR"],"mean":"ARITH","bits":8}Example of a conf file inyamlformataugs:[NO,FLIP_UD,FLIP_LR]mean:ARITHbits:8You can either pass file path (json or yaml) or the actual json text toconfparameter.ContributionAll contributions are welcomed. Please make sure that all tests passed before pull request. To run testsnosetests
Package: eda-fe-model
EDA FEATURE_EXTRACTOR MODELA python package to do EDA, feature selection and display the best hyperparameters for a pre-built classification model.Useful for datasets with no NaNs or null values present.
Can be used for normal classification tasks, next update will work for Regression type problems and incorporate a sorted arrangement of variables.before utilizing the package, ensure no Null or NaN values remain.pip install eda-fe-model

pip install eda-fe-model==0.3.2Using the libraryfrom eda_fe_model import package

package.EDA()
package.feature_extraction()Use to_categorical from keras.utils, to One Hot Encode the labelspackage.build_best_model()
package.model_create()EDApackage.EDA accepts the following:dataset = pandas dataframe
        columns_drop = columns to drop as a list. Accepts None
        one_hot_encode = True/False
        label_encode = True/False
        normalize = True/False
        standardize = True/False
        target_varaible = single target, y, as array
        test_size = percentage of the dataset to be used for testing purposes
        random_state

If the dataset only consists of categorical variables, set normalize or standardize to True.

returns the splitted dataset: x_train, x_test, y_train, y_test (respectively)FEATURE EXTRACTIONpackage.feature_extraction accepts the following:train_X = train dataset consisting of predictors
        train_Y = train labels
        test_X = test dataset consisting of predictors
        test_Y = test labels
        rfe = True/False; Do you want to use Random Feature Extractor
        dim_out = Used only if rfe=True; output dimension; number of features to be selected 
        distribution = Distibution of the dataset you want to use for GLM

If rfe is False, set dim_out and distribution to be None, to return the input x and y for train and test datasets.
Try changing the distribution if error due to convergence appear.

returns x_train and x_test datasets with the user entered dimension/predictorsBUILD BEST MODELpackage.build_best_model accepts the follwoing:x = train dataset consisting of predictors
        y = One HOt Encoded training labels

returns a RandomizedSearchCV object.

Best Score: results.best_score_
Best Parameters: results.best_params_CREATING THE MODELpackage.model_create accpets the best parameters from the build_best_model() and runs the model for a user specified epochs.x = the new train dataset consisting of just the predictors.
        y = One Hot encoded training labels
Package: edagames-grpc
edagames-grpgRPC interface for edagames platform-game communication## CompilingTo compile protobuf definition into Python files install grpcio-tools and executepython -m grpc_tools.protoc -I./src --python_out=./src --grpc_python_out=./src ./src/edagames_grpc/eda_games.proto## ExamplesExamples for simple client and server are located in theexamples folderTo run them each example use:Client:python -m examples.clientServer:python -m examples.serverPackageCommand to install the package:pip install edagames-grpcSteps for running a new version package with changes:Change the version in the setup.pyrun:python setup.py bdist_wheel sdistto update the package locallyrun:twine upload dist/*to upload the new package version to pypi
Package: edakit
EDAKIT: Toolkit for data exploratory analysisA tool for analysing sentiment in a given text-based query.⚙️ Installationpipinstalleda📖 UsagefromedaimportSentimentAnalyzersentiment_analyzer=SentimentAnalyzer()# Example textstexts=["This movie sucks","This one is great","The world is about to end",]pred_results=sentiment_analyzer.get_list_sentiment(texts=texts)# This should returns ["negative", "positive", "negative"]⚖️ LicenseThis repository is using the Apache 2.0 license that is listed in the repo. Please take a look atLICENSEas you wish.✍️ BibTeXIf you wish to cite the framework feel free to use this (but only if you loved it 😊):@misc{phat2020urbans,author={Truong-Phat Nguyen},title={EDAKIT: Toolkit for data exploratory analysis},year={2022},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/TagHubAI/edakit}}Contributors:Patrick Phat Nguyen
Package: ed-amino
edaminoAn unofficial python wrapper for the async Amino API, based off aiohttp.Installingpip install ed-aminoExamplefromedaminoimportBot,Context,loggerfromedamino.objectsimportUserProfilebot=Bot(email='email',password='password',prefix="/")@bot.event()asyncdefon_ready(profile:UserProfile):logger.info(f'{profile.nickname}ready')@bot.command('ping')asyncdefecho(ctx:Context):awaitctx.reply('Pong!')if__name__=='__main__':bot.start()ExamplesYou can see all the examples at this link.ContactIf you encounter a bug, please open anissue.Discord ServerTranslationsThis README is available in other languages:SpanishOnly this README file is guaranteed to be up-to-date.
Package: edam-ontology
edam-ontology.pyEDAM is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) and
can be found athttp://edamontology.org/.EDAM is described in the following article. If you use, or refer to EDAM or its part, please cite:Ison, J., Kalaš, M., Jonassen, I., Bolser, D., Uludag, M., McWilliam, H., Malone, J., Lopez, R., Pettifer, S. and Rice, P. (2013). EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics, 29(10): 1325-1332. DOI: 10.1093/bioinformatics/btt113 PMID: 23479348 The article is freely available without restrictions (Open access)This project is just a Python packaged version of that data - initially developed to avoid non-Python dependencies
within Galaxy packages that need to leverage the ontology.History1.25.2 (2021-08-03)Fix project name on PyPI.1.25.1 (2021-08-03)Attempt to fix automated publishing to PyPI.1.25.0 (2021-08-02)Initial project creation including the tabular version of EDAM 1.25 (https://zenodo.org/record/3899895#.YP7TqBNKh6o).
Package: edam-ontology.py
edam-ontology.pyEDAM is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) and
can be found athttp://edamontology.org/.EDAM is described in the following article. If you use, or refer to EDAM or its part, please cite:Ison, J., Kalaš, M., Jonassen, I., Bolser, D., Uludag, M., McWilliam, H., Malone, J., Lopez, R., Pettifer, S. and Rice, P. (2013). EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics, 29(10): 1325-1332. DOI: 10.1093/bioinformatics/btt113 PMID: 23479348 The article is freely available without restrictions (Open access)This project is just a Python packaged version of that data - initially developed to avoid non-Python dependencies
within Galaxy packages that need to leverage the ontology.History1.25.1 (2021-08-03)Attempt to fix automated publishing to PyPI.1.25.0 (2021-08-02)Initial project creation including the tabular version of EDAM 1.25 (https://zenodo.org/record/3899895#.YP7TqBNKh6o).
Package: edanif
EDA-NIf (EDA NIfTI)Tool forExploratory Data Analysis of Neuroimaging Informatics Technology Initiative(NIfTI) formatEDA-NIfcreates a dataframe containing meta information of NIfTi file format and provides several useful functions.Installationpip install edanifBeta versions with official documentation are provided starting from major version 1.TutorialWe provide tutorial notebooks for all the features we offer. We plan to provide additional docstrings or documentation from the official distribution version (major version 1 or higher).Main-tutorials:https://github.com/DSDanielPark/EDA-NIf/blob/main/tutorials/edanif_tutorial.ipynbSub-tutorial-folder:https://github.com/DSDanielPark/EDA-NIf/tree/main/tutorialsMain FeatureSee sample data folder tree...The sample data folder is designed with an unnecessary and complex structure to show that all nifti files under the top-level folder path are collected recursively. If you are using theBIDS format, you only need to insert keywords properly.Example folder treeedanif.eda_nif.meta_dfIf you enter only the top-level folder containing nifti files, you can get a data frame for all nifti files.In case of raw nifti filesimportedanifraw_nifti_folder='../data/raw_nifti'df_raw_nii=edanif.meta_df(raw_nifti_folder,'nii.gz','df_raw_nii_meta.csv',False)result dataframe:https://github.com/DSDanielPark/EDA-NIf/blob/main/tutorials/result/df_raw_nii_meta.csvIn case of mask nifti files (binary filesonly)importedanifmask_nifti_folder='../data/mask_nifti'df_mask_nii=edanif.meta_df(mask_nifti_folder,'mask.nii.gz','df_mask_nii_meta.csv',True)result dataframe:https://github.com/DSDanielPark/EDA-NIf/blob/main/tutorials/result/df_mask_nii_meta.csvFeaturesedanif.eda_nif1.1count_center_voxel_labels1.2meta_df: enter only the top-level folder containing nifti files, you can get a data frame for all nifti files.edanif.vis_nif2.1save_nifti_imagesedanif.utils.util3.1find_all_files3.2save_print_instanceedanif.process.preprocess4.1count_center_voxel_labels4.2if_minus_return_04.3get_nonzero3d4.4get_nonzero3d_shape4.5get_hardcrop4.6threshold_at_two4.7monai_cropforeground4.8get_nonzero_xyz_of_niiedanif.process.registration5.1RegistrationMetricedanif.process.resampling6.1make_isotropic6.2resample_fixedsize_fixedspacing6.3resamplingedanif.process.trans_morph7.1voxel_erosion7.2voxel_dilation7.3get_boundary_diff_indexFeature development and unit testing are ongoing. We will update it whenever time permits.References[1] NiBabelhttps://nipy.org/nibabel/[2] SimpleITKhttps://simpleitk.org/[3] MONAIhttps://monai.io/[4] AntsPyhttps://github.com/ANTsX/ANTsPyContactsMaintainer:Daniel Park, South Koreae-mailparkminwoo1991@gmail.com
Package: edapi
edapiThis package is an (unofficial) integration of the Ed API with Python. Since as of now there is no detailed documentation on the HTTP endpoints for the Ed API, I've reverse-engineered the endpoints by snooping through Chrome devtools.Further, since the Ed API is in beta, the API endpoints can change at any time, and this package may break.This package is still a work in progress, and currently contains the following features:Authenticating through an Ed API token (accessible throughhttps://edstem.org/us/settings/api-tokens)Creating threadsEditing existing threads (both through global ids and through course-specific ids)Uploading files to Ed (through direct file upload)Get user informationList existing threadsThis list may expand as the package is developed further.DocumentationMost documentation can be found inedapi/docs/api_docs.md; it contains documentation for the API, and also several notes on the HTTP endpoints as I've worked through this package.UsageThe bare minimum to utilize the API integration is to create a.envfile in your project storing your API key, or store the API key in an environment variable in an equivalent manner:ED_API_TOKEN=your-token-hereYour API key can be created throughhttps://edstem.org/us/settings/api-tokens. The API key should be kept secret, and not committed through any version control system.The following snippet is an example of using the API:fromedapiimportEdAPI# initialize Ed APIed=EdAPI()# authenticate user through the ED_API_TOKEN environment variableed.login()# retrieve user information; authentication is persisted to next API callsuser_info=ed.get_user_info()user=user_info['user']print(f"Hello{user['name']}!")Types for all methods are also documented and type hints are used for every method. You can peruse the types inedapi/edapi/types/.Working with thread contentEd uses a special XML format to format thread bodies. The various tags are also documented inedapi/docs/api_docs.mdfor your reference.There are utility methods included to help with the process of creating thread documents throughBeautifulSoup:new_document(): creates a new blank document containing the bare XML tags necessary to create a new thread.Returns a newBeautifulSoupinstance for the new document, along with the root document tag (use the document tag to serialize for the API).parse_document(content: str): parses the content string, which holds the XML content of a thread.Similar tonew_document, returns a newBeautifulSoupinstance for the parsed document, along with the root document tag.Building the packageTo build the package, just runpython3 -m buildin the root directory. This will create adist/folder containing the package wheel, which can be installed viapip3 install dist/edapi-x.x.x-py3-none.whl.
Package: eda-plugin
Event-driven acquisition (EDA)'event-driven-acquisition' is a Python library for advanced microscope control routines to enable
acquisitions to react to specific biological events of interest.More information on the project it was first used on can be found in thebioRxiv article.See theDocumentationfor more details.Components of EDAAnalyzersAnalyzers receive an image or images from the microscope. Their task is to reduce the information in
these images to a single or a set of parameters. These parameters are passed on to interpreters.InterpretersAn interpreter receives parameters from an analyzer and uses these to take a decision for how to
proceed with acquisition. This decision is passed on to an actuator. The interpretation can for
example take into account the temporal context of results from the analyzer.ActuatorsThe responsibility of an actuator is both to handle start/stop of acquisitions and to apply the
decisions from an interpreter to the ongoing acquisition.Installing Event-driven acquisitionInstall the latest version ofmicro-manager2.0pip install eda_pluginInstall the Micro-Manager plugins:>>> import eda_plugin>>> eda_plugin.install_mm_plugins()Choose the main Micro-Manager folder in the file dialog (e.g. C:\Program Files\Micro-Manager-2.0)Run micro-manager with the zmq server (pycromanager installation)Run the PythonEventServer from Plugins -> Developer Tools -> Python Event ServerNow you can run one of the examplesimporteda_plugineda_plugin.examples.basic()# or if you have CUDA and tensorflow installededa_plugin.examples.pyro()Or construct your own EDA loop e.g.importsysfromPyQt5importQtWidgetsfromeda_plugin.utility.event_busimportEventBusimporteda_plugin.utility.settingsfromeda_plugin.eda_guiimportEDAMainGUIfromeda_plugin.interpreters.frame_rateimportBinaryFrameRateInterpreterfromeda_plugin.analysers.imageimportImageAnalyserfromeda_plugin.actuators.micro_managerimportMMActuator,TimerMMAcquisitioneda_plugin.utility.settings.setup_logging()app=QtWidgets.QApplication(sys.argv)event_bus=EventBus()gui=EDAMainGUI(event_bus,viewer=True)actuator=MMActuator(event_bus,TimerMMAcquisition)analyser=ImageAnalyser(event_bus)interpreter=BinaryFrameRateInterpreter(event_bus)gui.show()actuator.gui.show()interpreter.gui.show()sys.exit(app.exec_())
Package: eda-report
eda-report- Automated Exploratory Data AnalysisA Python program to help automate the exploratory data analysis and reporting process.Input data is analyzed usingpandasandSciPy. Graphs are plotted usingmatplotlib. The results are then nicely packaged as aWord (.docx)document usingpython-docx.InstallationYou can install the package fromPyPIusing:pipinstalleda-reportBasic Usage1. Graphical User InterfaceTheeda-reportcommand launches a graphical window to help select acsv/excelfile to analyze:eda-reportYou'll be prompted to set areport title,group-by/target variable (optional),graph colorandoutput filename; after which the contents of the input file are analyzed, and the results saved in aWord (.docx)document.NOTE:For help withTk- related issues, consider visitingTkDocs.2. Command Line Interface$eda-report-iiris.csv-oiris-report.docx
Analyzevariables:100%|███████████████████████████████████|5/5
Plotvariables:100%|███████████████████████████████████|5/5
Bivariateanalysis:100%|███████████████████████████████████|6/6pairs.[INFO02:12:22.146]Done.Resultssavedas'iris-report.docx'$eda-report-h
usage:eda-report[-h][-iINFILE][-oOUTFILE][-tTITLE][-cCOLOR][-gGROUPBY]Automaticallyanalyzedataandgeneratereports.Agraphicaluserinterface
willbelaunchedifnoneoftheoptionalargumentsisspecified.

optionalarguments:-h,--helpshowthishelpmessageandexit-iINFILE,--infileINFILEA.csvor.xlsxfiletoanalyze.-oOUTFILE,--outfileOUTFILETheoutputnameforanalysisresults(default:eda-report.docx)-tTITLE,--titleTITLEThetoplevelheadingforthereport(default:ExploratoryDataAnalysisReport)-cCOLOR,--colorCOLORThecolortoapplytographs(default:cyan)-gGROUPBY,-TGROUPBY,--groupbyGROUPBY,--targetGROUPBYThevariabletouseforgroupingplottedvalues.Anintegervalueistreatedasacolumnindex,whereasastringistreatedasacolumnlabel.3. Interpreter Session>>>eda_report.summarize(iris_data)SummaryStatisticsforNumericfeatures(4)-------------------------------------------countavgstddevmin25%50%75%maxskewnesskurtosissepal_length1505.84330.82814.35.15.806.47.90.3149-0.5521sepal_width1503.05730.43592.02.83.003.34.40.31900.2282petal_length1503.75801.76531.01.64.355.16.9-0.2749-1.4021petal_width1501.19930.76220.10.31.301.82.5-0.1030-1.3406SummaryStatisticsforCategoricalfeatures(1)-----------------------------------------------countuniquetopfreqrelativefreqspecies1503setosa5033.33%Pearson's Correlation (Top 20)------------------------------petal_length&petal_width->verystrongpositivecorrelation(0.96)sepal_length&petal_length->verystrongpositivecorrelation(0.87)sepal_length&petal_width->verystrongpositivecorrelation(0.82)sepal_width&petal_length->moderatenegativecorrelation(-0.43)sepal_width&petal_width->weaknegativecorrelation(-0.37)sepal_length&sepal_width->veryweaknegativecorrelation(-0.12)Check out thedocumentationfor more features and details.
Package: edaSQL
SQL Bridge Tool to Exploratory Data AnalysisedaSQLis a library to link SQL toExploratory Data Analysisand further more in the Data Engineering. This will solve many limitations in the SQL studios available in the market. Use the SQL Query language to get your Table Results.InstallationInstall dependency Packages before installing edaSQLpipinstallpyodbc
pipinstallipythonOptional dependency for better visualization -Jupyter NotebookpipinstallnotebookNow Install using pip.Offical Python Package Here!!pipinstalledaSQL(OR)Clone this Repository. Run this from the root directory to installpythonsetup.pyinstallDocumentationRead the detailed documentation in readthedocs.ioedaSQL Jupyter NoteBook TutorialImport PackagesimportedaSQLimportpandasaspd1. Connect to the DataBaseedasql=edaSQL.SQL()edasql.connectToDataBase(server='your server name',database='your database',user='username',password='password',sqlDriver='ODBC Driver 17 for SQL Server')2. Query DatasampleQuery="select  * from INX"data=pd.read_sql(sampleQuery,edasql.dbConnection)3. Data Overviewinsights=edaSQL.EDA(dataFrame=data,HTMLDisplay=True)dataInsights=insights.dataInsights()deepInsights=insights.deepInsights()4. Correlationeda=edaSQL.EDA(dataFrame=data)eda.pearsonCorrelation()eda.spearmanCorrelation()eda.kendallCorrelation()5. Missing Valueseda.missingValuesPlot(plot='matrix')eda.missingValuesPlot(plot='bar')eda.missingValuesPlot(plot='heatmap')eda.missingValuesPlot(plot='dendrogram')6. Outlierseda.outliersVisualization(plot='box')eda.outliersVisualization(plot='scatter')outliers=eda.getOutliers()
Package: eda-SQL
SQL Bridge to Exploratory Data Analysis TooledaSQLis a library to link SQL toExploratory Data Analysisand further more in the Data Engineering. This will solve many limitations in the SQL studios available in the market.InstallationClone this Repository. Run this from the root directory to installpythonsetup.pyinstallDocumentationImport PackagesimportedaSQLimportpandasaspd1. Connect to the DataBaseedasql=edaSQL.SQL()edasql.connectToDataBase(server='your server name',database='your database',user='username',password='password')2. Query DatasampleQuery="select  * from INX"data=pd.read_sql(sampleQuery,edasql.dbConnection)3. Data Overviewinsights=edaSQL.EDA(dataFrame=data,HTMLDisplay=True)dataInsights=insights.dataInsights()deepInsights=insights.deepInsights()4.Correlationeda=edaSQL.EDA(dataFrame=data)eda.pearsonCorrelation()eda.spearmanCorrelation()eda.kendallCorrelation()5. Missing Valueseda.missingValuesPlot(plot='matrix')eda.missingValuesPlot(plot='bar')eda.missingValuesPlot(plot='heatmap')eda.missingValuesPlot(plot='dendrogram')6. Outlierseda.outliersVisualization(plot='box')eda.outliersVisualization(plot='scatter')outliers=eda.getOutliers()Jupyter NoteBook Tutorial
Package: edata
edataedata means 'easy data', is a wrapper of csv, xlrd, xlwt, pylightxl.
edata can be used to read csv, xls, xlsx format file as dict list data and write dict list data to those format file.installpip install edataexampleimportedatadata_list=[{'id':1,'name':'Lena'},{'id':2,'name':'Java'},{'id':3,'name':'Gaga'}]# write data to fileedata.write('test.csv',data_list[0:100])edata.write('test.xls',data_list[0:100])edata.write('test.xlsx',data_list[0:100])# read data from fileprint(edata.read('test.csv'))print(edata.read('test.xls'))print(edata.read('test.xlsx'))# get sheet namesprint(edata.get_sheet_names('test.xls'))print(edata.get_sheet_names('test.xlsx'))3 file will be created and console will print:[{'id': '1', 'name': 'Lena'}, {'id': '2', 'name': 'Java'}, {'id': '3', 'name': 'Gaga'}]
[{'id': 1, 'name': 'Lena'}, {'id': 2, 'name': 'Java'}, {'id': 3, 'name': 'Gaga'}]
[{'id': 1, 'name': 'Lena'}, {'id': 2, 'name': 'Java'}, {'id': 3, 'name': 'Gaga'}]
['Sheet1']
['Sheet1']
Package: edatest
EdatestThis is a simple package used to run Edabit tests.How to useImport the package to your documentfromedatestimportEdatestTest=Edatest()Write your codedefhas_friday_13(month,year):return"answer here"Insert tests available in the tests tab in the Edabit challengeTest.assert_equals(has_friday_13(3,2020),True)Test.assert_equals(has_friday_13(10,2017),True)Test.assert_equals(has_friday_13(1,1985),False)Test.assert_equals(has_friday_13(5,1619),False)Test.assert_equals(has_friday_13(6,1614),True)Run your python script to test your code$pythonedabit.py❌FAILED:"answer here"shouldequal"True"❌FAILED:"answer here"shouldequal"True"❌FAILED:"answer here"shouldequal"False"❌FAILED:"answer here"shouldequal"False"❌FAILED:"answer here"shouldequal"True"