Saravanan666/pypi-org huggingface dataset:
Package: tascCODA
tascCODATree-aggregated compositional analysis for high-throughput sequencing datatascCODA extends thescCODA model(Büttner, Ostner et al., 2021)
with a method to perform sparse, tree-aggregated modeling of high-throughput sequencing data.
Hereby, tascCODA can infer credible changes on the features (i.e. cell types/ASVs/taxa) of a high-throughput sequencing dataset,
as well as effects on subgroups of the set of features, which are defined by a tree structure,
for example a cell lineage hierarchy or a taxonomic tree.The statistical methodology and benchmarking performance are described here:Ostner, J., Carcy, S., and Müller, C. L. (2021). tascCODA: Bayesian Tree-Aggregated Analysis of Compositional Amplicon and Single-Cell Data. Front. Genet. 12, 766405.Code for reproducing the analysis from the paper is availablehere.InstallationRunning the package requires a working Python environment (>=3.8).This package uses thetensorflow(>=2.8) andtensorflow-probability(>=0.16) packages.
The GPU computation features of these packages have not been tested with tascCODA and are thus not recommended.To install tascCODA via pip, call:pip install tasccodaTo install tascCODA from source:Navigate to the directory that you want to install tascCODA inClone the repository fromGithub:git clone https://github.com/bio-datascience/tascCODANavigate to the root directory of tascCODA:cd tascCODAInstall dependencies::pip install -r requirements.txtInstall the package:python setup.py installUsageImport tascCODA in a Python session via:import tasccodaYou can then import a dataset in the same way as scCODA (seeherefor an instruction on scCODA's data structure)
Once you imported your dataset, add atoytreetree object, for example generated from a Newick string, asdata.uns["phylo_tree"].Then, initialize your analysis object, together with your formula and reference feature (see the scCODA documentation for explanations).
To set the aggregation bias, pass"phi"as a key in thepen_argsparameter`model = tasccoda.tree_ana.CompositionalAnalysisTree(
data,
reference_cell_type="9",
formula="x_0",
pen_args={"phi":phi},
)
`Then, run HMC sampling with dual-averaging step size adaptation by calling:`result = model.sample_hmc_da()`Finally, you can look atresult.node_dfto find credible effects of covariates on tree nodes
or plot a tree with indicators for credible effects withresult.draw_tree_effects()
Package: taschenmesser
Taschenmesser is a toolbelt containing builders for SCons. It helps you getting stuff done.It contains builders for:SVG optimization (Scour-based)SVG2PNG conversion (Inkscape-based)Amazon Web Service (S3 Delta Uploads etc)Google Closure (JavaScript optimization)File utils (GZip, MD5, SHA1, SHA256, etc)License: Apache 2.0Fork me on GitHub:https://github.com/oberstet/taschenmesser
Package: tasep
example:This is just a proof of concept package.
Please do not use unless instucted by author.importtasep# import this awesome modulefrommatplotlibimportpyplotasplt# for ploting.N=1000# size of the tasept=tasep.Tasep(N)# create the tasepr=tasep.RandState()# This provides random numbers for tasep.# normal tasep#rho = t.evolve(alpha=0.8, beta=0.8, mc_step=10000000, rand=r)# lk_tasep# simulaterho=t.lk_evolve(alpha=0.2,beta=0.2,Omega_a=0.3,Omega_d=0.3,mc_step=100000,rand=r)# plot the density.plt.plot(rho)plt.xlim(1,N)plt.ylim(0,1)plt.show()
Package: taser
TaserTASER(Testing and Security Resource)is an abstraction library used to simplify the process of creating offensive security tooling. The various protocols and classes help streamline development of custom tooling during engagements.Theexamplesdirectory contains a number of scripts demonstrating usage and helpful tools for penetration testers, red teamers, and bug bounty hunters!⚠ Warning: Taser is a working library and breaking changes may be made.InstallOption 1: Dev VersionGet the latest code (virtual environment recommended):gitclonehttps://github.com/m8sec/tasercdtaser
python3setup.pyinstallOption 2: Stable ReleaseInstall the last stable release directly from PyPi:pip3installtaserTroubleshootingDepending on your setup & install method, you may receive an error messages when runningsetup.py. Below are a few solutions:Install taser from PyPipip3 install taserInstall from git repo usingpip3 install -r requirements.txtDisclaimerAll information is provided for educational purposes ONLY. Never test against systems you don't own or have explicit permission. Use at your own risk - not responsible for impact on future systems. With great power comes great responsibility.
Package: tasho
TashoDBA fast and portable python NoSQL database. Sucessor to KoDB.Using the DBTo initalize or open a database, it's as straightforward as callingtasho.Database.newortasho.Database.open.>>>importtasho>>>database=tasho.Database.new("AnimeDatabase")# Creates a new database.>>>database=tasho.Database.open("AnimeDatabase")# Opens a database.Tables can be called throughtasho.Database.get_table(table_name)or throughtasho.Database.table.table_name>>>tbl_anime=database.table.Anime# These all return>>>tbl_anime=database.get_table("Anime")# the same Table>>>tbl_anime=database.table['Anime']# object.>>>tbl_anime<TashoDBTable>:Anime|Chunks:1Note:  Tables are set to auto commit by default. When doing bulk inserts, make sure to setTable.auto_committoFalseand runningTable.commit()manually afterwards.Data Storage>>>tbl_anime.insert('001',{'title':'Nichijou','episodes':24,'rating':99})'Shows-d545998bc3485346'This stores the data with001as the Document ID. Document IDs can either be String or Int. SinceTable.auto_commithas been set to true, runningTable.commit()is no longer needed.RetrievalThere are multiple ways of accessing data.# The document can be accessed through a regular get method>>>show=tbl_shows.get('001')>>>show<TashoDBDocument:001>Origin:Shows>>>show.dict{'title':'Nichijou','episodes':24,'rating':99,'_id':'001'}>>># Table.query allows you to pass a callable to filter the data.>>>tbl_shows.query(lambdaid,data:data['rating']>50)[<TashoDBDocument:001>Origin:Shows]>>># Table.query_one works the same as Table.query but stops at the first match.>>>tbl_shows.query_one(lambdaid,data:data['rating']>50)<TashoDBDocument:001>Origin:ShowsTable.get(id)returns a Document object that contains the data.Manipulating DataManipulating data is as easy as changing the values in the Document object.>>>show<TashoDBDocument:001>Origin:Shows>>>show.dict{'title':'Nichijou','episodes':24,'rating':99,'_id':'001'}>>>show.rating=98>>>show['title']='Nichibros'>>>show.save()'Shows-d545998bc3485346'>>>show.dict{'title':'Nichibros','episodes':24,'rating':98,'_id':'001'}Document deletion can also be done withDocument.delete().>>>list(tbl_shows.items())[('001',{'title':'Nichijou','episodes':24,'rating':98})]>>>show.delete()True>>>list(tbl_shows.items())[]Note: Document objects behaves almost the same way as dictionaries.Document.pop,Document.updateandDocument.getworks the same way.See: test.py for more use cases.
Package: task
taskTaskwarrior-like CLI toolUsage: task [OPTIONS] COMMAND [ARGS]...

Options:
  -v, --version  print version
  --help         Show this message and exit.

Commands:
  add       Add task
  describe  Describe task.
  done      Finished task.
  ls        List tasks.
  rm        Remove a task.InstallationMake sure to havepoetryGithub repository$gitclonehttps://github.com/matteyeux/task
$cdtask
$poetryinstallPyPIInstallation :pip3 install taskUpdate :pip3 install --upgrade taskSetupMake sure to have~/.local/binin your$PATH(export PATH=$PATH:~/.local/bin)The first time you runtask addcommand it will setup the SQLite3 database.ExamplesAddAdd task:task add "Start TIC-BLK4" -p BLK4Add task in project:task add "Start TIC-BLK4" -p BLK4ListList tasks :λ ~ » task ls
  ID  Project    Task                          Urgency  Due    Age
   1  etna_cli   fix my bug                          0         4 minutes
   2  etna_cli   add tabulate to list stuff          0         4 minutes
   3  Master2    Get that diploma                    0         3 minutes
   4             Buy a new iPhone                    0         2 minutes
   5             Patch the covid-19 bug              0         41 seconds
   6  BLK4       Start TIC-BLK4                      0         9 secondsDoneSet task to done :λ ~ » task done 6
Done 6RemoveDelete row from database :λ ~ » task rm 5
DoneCreditsTask Warrior :  because it's a pretty cool projectme : cuz I wrote it
Package: task5
task_5RequirementsPython 3.7This code makes use of thef"..."orf-string
syntax. This syntax was
introduced in Python 3.6.Sample Execution & OutputIf run without command line arguments, using./precisionEstimatethe following usage message will be displayed.Usage: ./estimatePrecision.py num_execs [arbitrary precision]If run using./precisionEstimate 1000000outputsimliartofloat|  3.7247|2.220446049250313e-16
 float-type-hint|  3.6731|2.220446049250313e-16
      Decimal-28| 31.8602|0.999999999999999999999999999will be generated. Note that thefloatandfloat-type-hintlines may vary.An optional precision command line argument can be supplied to change the
arbitrary precision used by the Pythondecimalmodule. For example:./precisionEstimate 1000000 16will generate output similar tofloat| 0.3979|2.220446049250313e-16
 float-type-hint| 0.4053|2.220446049250313e-16
      Decimal-16| 3.1643|0.999999999999999
Package: task-5-python-package-by-AG
Task 5 Python Package by Anton GalkinDescriptionThe package contains a program that counts the number of authentic characters in a string.To download data, you can specify the following parameters
when starting the program in the terminal:--string "your string"--file path_your_fileProject structuretask_5_python_package│├── application│ ├──__init.py__│ └── app.py│└──__main__.pyInstallationTo install the package, input the following in your terminal:python3pipinstalltask_5_python_package_by_AGUsageRun package in CLI with String Argument:task_5_python_package_by_AG --string "your string"Example:task_5_python_package_by_AG --string "aabcdeeff"Run package in CLI with File Argument:task_5_python_package_by_AG --file path_your_fileExample:task_5_python_package_by_AG --file /home/anton/Documents/test_textImport in python consoleExample:from task_5_python_package_by_AG import count_authentic_signspython -c 'from task_5_python_package_by_AG import cli(); cli()' --string "aabcdeefff"python -c 'from task_5_python_package_by_AG import cli(); cli()' --file home/anton/Documents/test-textAuthors and acknowledgmentAnton GalkinLicenseMITProject statusStudy
Package: taskagents
Mask-PredictDownload modelDescriptionDatasetModelMASK-PREDICT[WMT14 English-German]download (.tar.bz2)MASK-PREDICT[WMT14 German-English]download (.tar.bz2)MASK-PREDICT[WMT16 English-Romanian]download (.tar.bz2)MASK-PREDICT[WMT16 Romanian-English]download (.tar.bz2)MASK-PREDICT[WMT17 English-Chinese]download (.tar.bz2)MASK-PREDICT[WMT17 Chinese-English]download (.tar.bz2)Preprocesstext=PATH_YOUR_DATAoutput_dir=PATH_YOUR_OUTPUTsrc=source_languagetgt=target_languagemodel_path=PATH_TO_MASKPREDICT_MODEL_DIRpython preprocess.py --source-lang ${src} --target-lang ${tgt} --trainpref $text/train --validpref $text/valid --testpref $text/test  --destdir ${output_dir}/data-bin  --workers 60  --srcdict ${model_path}/maskPredict_${src}${tgt}/dict.${src}.txt --tgtdict ${model_path}/maskPredict${src}_${tgt}/dict.${tgt}.txtTrainmodel_dir=PLACE_TO_SAVE_YOUR_MODELpython train.py ${output_dir}/data-bin --arch bert_transformer_seq2seq --share-all-embeddings --criterion label_smoothed_length_cross_entropy --label-smoothing 0.1 --lr 5e-4 --warmup-init-lr 1e-7 --min-lr 1e-9 --lr-scheduler inverse_sqrt --warmup-updates 10000 --optimizer adam --adam-betas '(0.9, 0.999)' --adam-eps 1e-6 --task translation_self --max-tokens 8192 --weight-decay 0.01 --dropout 0.3 --encoder-layers 6 --encoder-embed-dim 512 --decoder-layers 6 --decoder-embed-dim 512  --fp16 --max-source-positions 10000 --max-target-positions 10000 --max-update 300000 --seed 0 --save-dir ${model_dir}Evaluationpython generate_cmlm.py ${output_dir}/data-bin  --path ${model_dir}/checkpoint_best_average.pt  --task translation_self --remove-bpe --max-sentences 20 --decoding-iterations 10  --decoding-strategy mask_predictLicenseMASK-PREDICT is CC-BY-NC 4.0.
The license applies to the pre-trained models as well.CitationPlease cite as:@inproceedings{ghazvininejad2019MaskPredict,title={Mask-Predict: Parallel Decoding of Conditional Masked Language Models},author={Marjan Ghazvininejad, Omer Levy, Yinhan Liu, Luke Zettlemoyer},booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},year={2019},}
Package: taskanalytics-data-wrapper
Task Analytics Data WrapperThis is a wrapper for Task Analytics APIs. You can use it to download survey responses and metadata for each survey.Supported APIsTask Analytics Data WrapperSupported APIsLog in to Task AnalyticsDownload Survey responsesDownload Survey metadataDownload Discovery survey responsesLog in to Task AnalyticsYou can log in with email and passwordstatus=log_in_taskanalytics(username=email,password=password)status.status_codeDownload Survey responsesYou can download survey responses for a Top Task survey using the survey ID, email, password and setting a path for where to store the file.importtaskanalytics_data_wrapper.taskanalytics_apiastaskget_survey=task.download_survey(username=email,password=password,survey_id="03324",filename="data/survey.csv")get_survey.status_codeDownload Survey metadataYou can download the survey metadata which includes the questions and response options for each survey using the survey ID, email and password.survey_metadata=task.get_survey_metadata(username=email,password=password,survey_id="03324")survey_metadata.status_codeThe object can be easily inspected transformed into a dictionary for analysissurvey_metadata.text# survey metadataour_dict=survey_metadata.json()# convert string to dict and store as a variableDownload Discovery survey responsesYou can download responses from open ended task discovery surveys as wellget_openended_survey=task.download_discovery_survey(username=email,password=password,organization_id=organization,survey_id="03230")See how to turn this into csv in the code example below by expandingExpandable exampledata=get_openended_survey.json()#create a new dict from our subset of datadefflatten_openended_dict(data):""" """respondent=[]completion=[]category=[]discovery=[]comment=[]foriindata:respondent.append(i["id"])completion.append(i["completion"])category.append(i["category"])discovery.append(i["answers"]["discovery"])try:comment.append(i["answers"]["comment"])except:comment.append("")newlist=[{"id":respondent,"completion":completion,"category":category,"discovery":discovery,"comment":comment,}forrespondent,completion,category,discovery,commentinzip(respondent,completion,category,discovery,comment)]returnnewlistnewlist=flatten_openended_dict(data["responses"])# write open ended survey to csv with your preferred encoding and delimiterkeys=newlist[0].keys()withopen("data/open_survey.csv","w",encoding="utf-8-sig",newline="")asoutput_file:writer=csv.DictWriter(output_file,fieldnames=keys,delimiter=";")writer.writeheader()writer.writerows(newlist)
Package: task-automation
This module contains functions to perform the small tasks such as
downloading email attachment, FTP upload and FTP download,
importing or appending the csv or txt file to Microsoft SQL server database etc.Functionsattachment_verification(file_path, files)
Compares size of downloaded file with the file attached to the email.
download_attachment(email_sender, email_subject, files, download_location, received_duration=1,email_folder="Inbox")
Downloads attachments from an email.
csv_to_txt(csvfile,txtfile,delimiter='\t',replace_if_exist=True)
Converts csv file to txt file using delimiter.
ftp_upload_verify(hostname, username, password, ftp_dir, files, filepath,TLS)
Verifies the uploaded file size with original file size.
ftp_upload(hostname, username, password, ftp_dir, files, filepath,TLS=False)
Uploads files to the FTP server and compares the uploaded file size with
original file size.
ftp_download_verify(hostname, username, password, ftp_dir, files, filepath,TLS)
Veryfies the file size of the downloaded file with the original file.
ftp_download(hostname, username, password, ftp_dir, files, filepath,TLS=False)
Downloads files from FTP server and compares the file size of the
downloaded file with the original file.
execute_msql(sql_file, con)
Executes query from '.sql' file.
append_csv_msql(filepath,con,insert_query,has_header=False)
Appends the .CSV file to the existing table
in Microsoft SQL Server database.
append_txt_msql(filepath, con, insert_query,has_header=False,delimiter='|')
Appends the .txt file to the existing table
in Microsoft SQL Server database.
import_csv_msql(filepath,con,create_query,insert_query,has_header)
Imports the .CSV file to the Microsoft SQL Server database.
NOTE: This function will drop the table if already exist and will
create a new one.
import_text_msql(filepath, con, create_query, insert_query, has_header)
Imports .TXT file to the microsoft SQL server database.
NOTE: This function will drop the table if already exist and will
create a new one.
msql_results_to_file(sql_file, output_file, con, save_as='CSV', header=False)
Executes query in .sql file and saves resulting table as 'CSV' or
'TXT'('|' delimited) file.
query_results_to_file(query, output_file, con, save_as="CSV", header=False)
Executes the query and saves the result as 'CSV' or 'TXT'('|' delimited) file.
run_msql(con, query)
Runs SQL query.
send_email(profilename, recipients, CC, subject, email_body, attachments)
Sends email to the recipients, can also send attachments with email.
send_reply(email_sender, email_subject, reply_body, received_duration=1,email_folder="Inbox")
Sends reply(Reply all) to the email.
sftp_file_verify(hostname,port,  username, password,ftp_dir,filepath,file)
After upload or download transaction, compares size of the file on
the sftp server with size of the file on the device/network.sftp_upload(hostname,port,  username, password,ftp_dir,filepath,file)
Uploads file to SFTP server.
sftp_download(hostname,port,  username, password,ftp_dir,filepath,file)
Downloads file from SFTP server.
Package: task-automation-test
This module contains functions to perform the small tasks such as
downloading email attachment, FTP upload and FTP download,
importing or appending the csv or txt file to Microsoft SQL server database etc.Functionsattachment_verification(file_path, files)
Compares size of downloaded file with the file attached to the email.
download_attachment(email_sender, email_subject, files, download_location, received_duration=1,email_folder="Inbox")
Downloads attachments from an email.
csv_to_txt(csvfile,txtfile,delimiter='\t',replace_if_exist=True)
Converts csv file to txt file using delimiter.
ftp_upload_verify(hostname, username, password, ftp_dir, files, filepath,TLS)
Verifies the uploaded file size with original file size.
ftp_upload(hostname, username, password, ftp_dir, files, filepath,TLS=False)
Uploads files to the FTP server and compares the uploaded file size with
original file size.
ftp_download_verify(hostname, username, password, ftp_dir, files, filepath,TLS)
Veryfies the file size of the downloaded file with the original file.
ftp_download(hostname, username, password, ftp_dir, files, filepath,TLS=False)
Downloads files from FTP server and compares the file size of the
downloaded file with the original file.
execute_msql(sql_file, con)
Executes query from '.sql' file.
append_csv_msql(filepath,con,insert_query,has_header=False)
Appends the .CSV file to the existing table
in Microsoft SQL Server database.
append_txt_msql(filepath, con, insert_query,has_header=False,delimiter='|')
Appends the .txt file to the existing table
in Microsoft SQL Server database.
import_csv_msql(filepath,con,create_query,insert_query,has_header)
Imports the .CSV file to the Microsoft SQL Server database.
NOTE: This function will drop the table if already exist and will
create a new one.
import_text_msql(filepath, con, create_query, insert_query, has_header)
Imports .TXT file to the microsoft SQL server database.
NOTE: This function will drop the table if already exist and will
create a new one.
msql_results_to_file(sql_file, output_file, con, save_as='CSV', header=False)
Executes query in .sql file and saves resulting table as 'CSV' or
'TXT'('|' delimited) file.
query_results_to_file(query, output_file, con, save_as="CSV", header=False)
Executes the query and saves the result as 'CSV' or 'TXT'('|' delimited) file.
run_msql(con, query)
Runs SQL query.
send_email(profilename, recipients, CC, subject, email_body, attachments)
Sends email to the recipients, can also send attachments with email.
send_reply(email_sender, email_subject, reply_body, received_duration=1,email_folder="Inbox")
Sends reply(Reply all) to the email.
sftp_file_verify(hostname,port,  username, password,ftp_dir,filepath,file)
After upload or download transaction, compares size of the file on
the sftp server with size of the file on the device/network.sftp_upload(hostname,port,  username, password,ftp_dir,filepath,file)
Uploads file to SFTP server.
sftp_download(hostname,port,  username, password,ftp_dir,filepath,file)
Downloads file from SFTP server.
Package: taskbadger
Task Badger Python ClientThis is the official Python SDK forTask Badger.For full documentation go tohttps://docs.taskbadger.net/python/.Getting StartedInstallpipinstall--upgradetaskbadgerClient UsageConfigurationimporttaskbadgertaskbadger.init(organization_slug="my-org",project_slug="my-project",token="***")Usage with CeleryimporttaskbadgerfromceleryimportCeleryapp=Celery("tasks")@app.task(bind=True,base=taskbadger.Task)defmy_task(self):task=self.taskbadger_taskforiinrange(1000):do_something(i)ifi%100==0:task.update(value=i,value_max=1000)task.success(value=1000)API ExamplefromtaskbadgerimportTask,Action,EmailIntegration,WebhookIntegration# create a new task with custom data and an action definitiontask=Task.create("task name",data={"custom":"data"},actions=[Action("*/10%,success,error",integration=EmailIntegration(to="me@example.com")),Action("cancelled",integration=WebhookIntegration(id="webhook:demo")),])# update the task status to 'processing' and set the value to 0task.started()try:foriinrange(100):do_something(i)ifi!=0andi%10==0:# update the progress of the tasktask.update_progress(i)exceptExceptionase:# record task errorstask.error(data={"error":str(e)})raise# record task successtask.success()CLI USageConfiguration$taskbadgerconfigure

Organizationslug:my-org
Projectslug:project-x
APIKey:XYZ.ABC

Configwrittento~/.config/taskbadger/configUsage ExamplesThe CLIruncommand executes your command whilst creating and updating a Task Badger task.$taskbadgerrun"demo task"--actionerroremailto:me@test.com--path/to/script.sh

Taskcreated:https://taskbadger.net/public/tasks/xyz/