Saravanan666/pypi-org huggingface dataset:
Package: camp-parser
Usageget an information about the camp and compettion currently availableaims to be used mainly on thailand event and compettion centered aroud tech and computerI don't have intention to make it a scraper for commercial purpose I just want to share the information I found in discord fasteruser of this project should only use it for personal use and not for commercial purposeSupported Sitehttps://www.camphub.in.th/Installationpipinstallcamp_parserUsageGet CampInfrofromcamp_parserimportcamphub_parser# or any thing thatREADME.rst we supportcamp=camphub_parser('https://www.camphub.in.th/computer/')#page urlprint(camp.info)#get the informationFetch many pages at oncefromcamp_parserimportcamphub_parser# or any thingcamp=camphub_parser('https://www.camphub.in.th/computer/')# source page url default is Nonedata=camp.page_fetching('https://www.camphub.in.th/computer/',pages=50)# Fetch 50 pages without MultiThreadingndata=camp.page_fetching('https://www.camphub.in.th/computer/',pages=50,workers=8)# Enable Multiprocessing#wotkers depend on your CPU coreInformation structure{'title':'title of the camp','link':'link to the camp page','small_description':'mified description of the camp','type':'event type (onsite/online/etc...)','organize_date':'start date of the camp','register_deadline':'last day yu can register to the camp','max_paticipants':'(string not number Ex. ไม่จำกัด, 200ค คน)','costs':'cost of the camp Ex. ฟรี, 1000 บาท','paticipants_requirements':'what you need to join the camp','organizer':'who organize the camp','full_description':'full description of the camp',}
Package: camproject
camprojecta python module for camera projection and reprojectionMaintainerMartin Israelm.israel@thermaldrones.deRequirementspython 2.6, 2.7, or 3.xNumPyInstallationThe easiest way is to install it from Pypi with:$ pip install camprojectTo install system-wide from source distribution:$ python setup.py installQuickstart-Usageimport numpy as np
import camproject

P = np.array([[1],[0],[10],[1]]) # this is a point in 3D (e.g. in meters)
cam = camproject.Camera()  
cam.intrinsics(640,512,1000,320,260) # inner parameters: (in pixels)
# (im_width,im_height, focal_length, centerpixel_x, centerpixel_y)
cam.attitudeMat(np.eye(4))  # outer parameters: point to z-direction
p = cam.project(P) # gives pixel coordinates on the imageto reproject it back to the 3D world we use this codeQ = cam.reprojectToPlane(p)Documentationdocumentation can be found athttps://camproject.readthedocs.io/en/latest/If you acquired this code via GitHub, then you can build the documentation using sphinx.
From the documentation directory, run:$ make html
Package: camptools
camptoolscamphor用ツールインストールpip install camptools --userコマンド一覧ジョブを投入し、ジョブIDを記録する$ nmyqsub <job file> -m <message> -d <directory>
  jobを投入し、job情報を記録する(job_status.sh, joblist.shなどに使用)
  directoryを指定した場合、指定ディレクトリに移動後にqsubを実行

$ myqsub <job file> -m <message> -d <directory>
  jobを投入し、job情報を記録する(job_status.sh, joblist.shなどに使用)
  directoryを指定した場合、指定ディレクトリに移動後にqsubを実行
  投入されるjobファイルは、パラメータファイルplasma.inpに応じてノード数を決定し置換したもの
  python環境にf90nmlが必要
  
  <job file>の形式は以下のようにすること
    1. #QSUB -A p={}:t=1:c=64:m=90G
    2. aprun -n {} -d 1 -N 64 ./mpiemses3D plasma.inp実行中のジョブ状態を確認する$ job_status
  jobの状態と標準出力の一部を出力

$ joblist
  jobの状態を出力これまでに投入したジョブ一覧を表示する$ jobhistory -n <num outputs> --correct_date
  過去のjobのリストを表示
  <job id>, <directory>, <message>, <date>

  --correct_date: 
    *.o*ファイルから日付を読み取りjobに日付情報を付加する
    (この日付情報は保存されるため毎回呼ばなくても良い)EMSESの継続ジョブを投入する$ extentsim <from-dir> <to-dir> --run
  EMSESの継続シミュレーションを行う
  from-dirに存在するmpiemses3D, job.sh, SNAPSHOT1, generate_xdmf.pyをto-dirにコピーする
  runフラグを指定するとmyqsubによるジョブの投入まで行うディレクトリセットを作成する$ mymkdir --key <key> <directory>
  keyで指定した構成のディレクトリを作成する
  ディレクトリ構成の設定は~/copylist.jsonに記載する簡易的なジョブファイルを作成する$ cmdjob [-h] {register,create} ...
$ cmdjob register -ug <usergroup> -s <system> --local
  作成するjobの設定を登録する
  localフラグを指定するとそのディレクトリ以下での設定が変更される
$ cmdjob create <command> -o <output>
  commandを実行するジョブファイルを作成する
  出力ファイル名はoutputで指定(デフォルト: tmpjob.sh)よく使うディレクトリのパスを記録する$ checkpoint {register, clear, list}
$ checkpoint register -m <message>
  カレントディレクトリをチェックポイントとして保存する
$ checkpoint clear -a -i <index>
  チェックポイントを削除する
  -aフラグを指定するとすべてのチェックポイントを削除する
  -iフラグを指定すると指定した番号をチェックポイントを削除する
$ checkpoint list
  チェックポイントのリストを表示するシステム内で同期をとる$ filesync {lock, wait, notify}
$ filesync lock <key>
  <key>名のロックを確保する (~/.camptools内にロック情報を書き込む)
$ filesync wait <key>
  <key>名のロックが確保されていたら待機する
$ filesync notify <key>
  <key>名のロックを解放する (~/.camptoolsからロック情報を削除する)
Package: campy
campy: Stanford's Graphical Libraries in PythonFeaturesAll your favoriteGObjects, likeGOvals,GRects, and more.Functional event-driving GUIs.I/O helpers.Cross-platform support.InstallationTo installcampy, simply usepip:$ pip install campyDocumentationLearn more aboutcampyathttps://campy.sredmond.io/.
Package: camrcs
CAMRCScamrcsis a Python package that manages data backups to Cambridge University Research Cold Storage (RCS).Complete documentation can be foundhere.QUICKSTARTInstallationInstallpigzfirst:sudo apt-get install pigzThen installcamrcs:pipinstallcamrcsBefore first usecamrcsrequires adata.csvfile that stores information of what to upload, etc.To create a newdata.csvrun:cdany/path/to/store/file
camrcsup--newUploading data to RCSFirst include all relevant information indata.csv, then run from the directory containingdata.csv:camrcsupThis will upload all data that has not yet been uploaded to RCS.Retrieving data from RCSFor example, to retrieve the archive withid1 fromdata.csvrun:camrcsdown-t1
Package: camset
CamsetGUI for v4l2-ctlDescriptionThis is a tool for easy GUI adjustments of the Video4Linux (V4L) settings on Linux, using v4l2-ctl as backend. All controls are dynamically generated and the application should work with any V4L device, but has only been tested with webcams. The settings applied will remain active when using the webcam in other applications, for example Skype.Theexample_ioctlfolder contains some examples of direct ioctl calls for reading and setting V4L values (equivalent tov4l2-ctl --list-formats-ext,v4l2-ctl -Landv4l2-ctl -c), using the v4l2 Python bindnings library. The library has been slightly modified for compatibility with more recent Python versions (tested with 3.8.2) and is included.NoteThe application is WIP in early development, but in a functional state. Testing, issue reporting and suggestions are welcome. Do note that there are other applications that are similar and with more functionality (at least for now). This is just a small project to practice some Python.DependenciesPython 3pipv4l2-ctlpkg-configIf running from source (not using the camset pip package) you will also need gi (PyGObject @ pip) and OpenCV (opencv-python @ pip)InstallationFor Ubuntu:sudo apt-get install python3 python3-pip v4l-utils pkg-configpip3 install camset(If encountering problems with the cairo dependency, installing these additional packages should resolve it:sudo apt-get install libcairo2-dev libgirepository1.0-devand then repeat step 2).The pip install includes a .desktop file, which should be picked up by the desktop environment. Ifcamsetis not found you may need to set up your path environment, for example (open a terminal and write):PATH=$PATH:$HOME/.local/binor persistently:echo 'export PATH="$PATH:$HOME/.local/bin"' >> ~/.bashrcNote for LinuxBrew(HomeBrew) users:Brew can sometimes take overldandpkg-configbinaries from system installed ones depending on what apps may have been installed via brew in the past.See:Install fails on pycairo dependencyTo fix this install the requried dependencies via brew (this will install a lot of things and take a very long time):brew install cairo pkg-config pygobject3 gtk+3Alternatively disable or remove brew components that provide or requirepkg-configandldand then follow theUbuntu instructionsfrom above.NotesSettings can be saved and loaded. If the "Autoload settings" button is checked, camset will look in~/.config/camsetfor a settings file with the same name as the device (as suggested when saving settings) and load it when starting the application or when switching to that device.
Package: camt
Compression CLI toolcamtis aCLItool built in python, a tool for isolating images labeled with the labelImg tool.RequirementsPythonPipHow to runStart by creating the folder that will be used as[name]in the command bellowCommand$ pip install camt
$ camt compress --dest-dir [name] --src-dir [path] --format [zip, tar, gztar, bztar, or xztar]Future plansAdd the ability to upload the zip file to a cloud providerGCPContributionYou can contribute by opening anIssueor aPull request.
Package: camtasia
A Python API for Camtasia projects.QuickstartTo work with an existing Camtasia project, first load it:importcamtasiaproj=camtasia.load_project('path/to/project.cmproj')Themedia_binattribute ofProjectgives you access to elements in the media bin:# list the Media in the MediaBinformediainproj.media_bin:print(media.source)# add a new media to the media binproj.media_bin.import_media('path/to/image/or/movie.mov')Thetimelineattribute ofProjectgives you access to elements on the timeline:# list the tracks on the timelinefortrackinproj.timeline.tracks:print(track.name)# list the timeline markersformarkerinproj.timeline.markers:print(marker.name,marker.time)# print details of media on the timelinefortrackinproj.timeline.tracks:formediaintrack.medias:print(media.start,media.duration)formarkerinmedia.markers:print(marker.name,marker.time)And if you have edits to the project that you want to write to disk, use thesave()method:proj.save()pytscThe package also installs a command-line program calledpytscwhich exposes a lot of the API. You can
get help onpytscwith the “-h” flag:$ pytsc -hYou can also get help on particular subcommands by putting “-h” after the subcommand name:$ pytsc tracks-ls -h
Package: cam-tool
Cloud Assignment Manager ToolCAM-Tool is a cloud assignment manager tool that helps you to manage your tasks across different machines. You can start several workers across different machines and upload the command to cam-tool. Cam-tool will then distribute the tasks to workers automatically.Installpip install cam-tool
conda install redis # only required for server machine.
conda install -c conda-forge nodejs # only required if you want to use a web-based guiConfigThe config file is located at~/.cam.conf. You can set the server address, port, and password for redis. You can simply runcam configto edit the yaml file.Start ServerOn the server machine, simply run the following command to start the server.cam serverStart WorkerOn a worker machine, please run the following command to start a worker. You can start many worker on the same machine.cam workerYou may also want to add more parameters as resource calculation, prefix or suffix.cam worker "ngpu()" "sbash"wherengpu()calculate how many GPU is idle and "sbash" is a prefix to all the command running on this server.Add new taskPlease run the following command to add a new taskcam add "ls -lah"StatusYou can see the status of eash task with thelscommand:> cam ls

ID  Time                 Command    Host
----  -------------------  ---------  -------
   3  2022-03-07 06:39:33  ls -lah    PendingKill tasksYou can kill task with its task id.cam kill 3Get the log of a workerYou can run the following command to get the log of a running or finished worker:cam log 3Web ConsoleCam tool provides a simple web console to add, kill, and watch tasks. Please install nodejs before you use this function. Just run the following code to start a web server. Never run this command on any public server.cam webThen open the linkhttp://localhost:8257/and you can see the following user interface:
Package: camt-to-erpnext
This CLI can convert a CAMT CSV file (provided by your bank) into a normal CSV file that can be used for importingBank Transactionsinto ERPNext.InstallationIt is easiest to installcamt-to-erpnextwithpipx, a tool to "install and run python applications in isolated environments":pipx install camt-to-erpnextUsagecamt-to-erpnext convert INPUT_PATH OUTPUT_PATHInput formatDelimiter:;Quoting: all columns quotedDate Format:28.02.99Number Format:-1234,56Encoding:cp1252Columns:AuftragskontoBuchungstagValutadatumBuchungstextVerwendungszweckGlaeubiger IDMandatsreferenzKundenreferenz (End-to-End)SammlerreferenzLastschrift UrsprungsbetragAuslagenersatz RuecklastschriftBeguenstigter/ZahlungspflichtigerKontonummer/IBANBIC (SWIFT-Code)BetragWaehrungInfoOutput format:Delimiter:,Quoting: where necessaryDate Format:1999-02-28Number Format:1234.56Encoding:utf-8Columns:Date"Buchungstag" of the input file, converted to ISO-formatDeposit"Betrag" of the input file (if > 0)WithdrawalAbsolute "Betrag" of the input file (if < 0)DescriptionThe following columns of the input file: "Beguenstigter/Zahlungspflichtiger", "Verwendungszweck", "Kontonummer/IBAN", "BIC (SWIFT-Code)", "Glaeubiger ID", "Mandatsreferenz", "Kundenreferenz (End-to-End)", "Valutadatum"Reference NumberHash of the following columns of the input file "Buchungstag", "Betrag", "Verwendungszweck", "Kontonummer/IBAN", "BIC (SWIFT-Code)".Bank Account"Auftragskonto" of the input fileCurrency"Waehrung" of the input file
Package: camu
InstallationCaMu.pycan easily be installed using pip. Just run the command:pipinstallcamupip will automatically install the required versions of the python packages that are included withinCaMu.py.Additionally, it is necessary to havesamtools>= 0.1.19 andgatk>=4.1.2 installed and added to the path.UsageAfter installingCaMu.py, the main modulecamucan be run viapython3-mcamu<additionalparameter>Giving-hor--helpas additional parameter opens the help page.Here you can see all additional parameters you need to pass in order to runcamu.For the overall script it is necessary to provide a text file giving the paths all samples VCF files in column 1 and the corresponding BAM files in column 2 with-i.Additionally, the path to the control BAM file has to be provided via-c.Finally, the path to the reference genome has to be given with-r.If you want to run any script separately, you can call the script usingpython3-m<scriptname>where <scriptname> has to be exchanged by one of the 5 modules given below (preprocessing.py,filterDupAndLinked.py, etc.).Filtering false-positive candidate mutations to accelerate DNM-counting for direct µ estimatesFor direct estimation of the spontaneous mutation rate µ, it is necessary to calculate the rate of spontaneous de-novo mutations (DNM) occuring per site per generation.
Consequently, counting DNM is essential for estimating µ.The raw approach is:Sequencing samples and control --> FASTQ filesAssembly of sequencing results --> BAM filesperform some filtering stepsVariant callingextraction of variants occurring in samples but not in control --> candidate mutationsThe resulting list of candidate mutations (CM) currently has to be manually curated using a genome browser like IGV.Unfortunately, approx. 90 % of these CM are no true DNM, they turn out to be false-positives.CaMu.pyaims to accelerate the whole procedure of DNM counting by filtering out the vast majority of false-positive CM and by preparing the remaining CM for fast manual curation with IGV.CaMu.pyconsists of 5 main Python modules:preprocessing.pyfilterDupAndLinked.pydetectFIO.pysnapshotIGV.pyIGVsessions.pypreprocessing.pystarts with an input text file containing paths to all sampels' VCF files in column 1 and paths to all corresponding BAM files in column 2.
These VCF and BAM files are fread out nd further processed in order to find variants that are possible DNM - the candidate mutations CM.The rough is approach is the following:The following scripts withinCaMu.pyfurther filter the CM for those that fully linked to other mutations, those that are only included due to reads that are most probably PCR duplicates and for those variants occurring in other samples or several times in the control sample's BAM file.Finally, for all the remaining CM IGV Sessions and IGV snapshots are created withinIGVsessions.pyandsnapshotIGV.pyto further simplify the manual curation of the remaining CM.Here is an overview:
Package: camunda-adapter
camunda-adapterПакет предоставляет адаптер к платформе Camunda для приложений на Python.Установка$pipinstallcamunda-adapterПодключение и настройкаСм. тестовое приложение src/testapp.apps.py:Инстанцируется Config с параметрами конфигурации, затем инстанцируется Adapter.При необходимости работы с внешними задачами, регистрируются обработчики задач путем подписки на соответствующие топики задач.fromdjango.appsimportAppConfigasAppConfigBasefromcamunda_adapter.adaptersimportAdapterfromcamunda_adapter.configimportConfigclassAppConfig(AppConfigBase):name=__package__defready(self):self._setup_bpmn()def_setup_bpmn(self):fromtestappimportcorefrom.importservicescore.config=Config()core.adapter=Adapter(core.config)fortopic,handlerin((services.topic1,services.topic_handler1),(services.topic2,services.topic_handler2),(services.topicN,services.topic_handlerN),):core.adapter.register_topic_handler(topic,handler)services.py:fromcamunda_adapter.domainimportmodelascamundatopic1=camunda.Topic(name='topic-1')deftopic_handler1(task:camunda.ExternalTask,adapter:Adapter):...topic2=camunda.Topic(name='topic-2')deftopic_handler2(task:camunda.ExternalTask,adapter:Adapter):...topicN=camunda.Topic(name='topic-N')deftopic_handlerN(task:camunda.ExternalTask,adapter:Adapter):...Примеры непосредственного обращения к адаптеру:Отправка сообщения:message=model.Message(messageName='DeclarationReceived',businessKey=f'attestation-declaration-{declaration.id}',processVariables={name:model.Variable(value=value)forname,valueindeclaration.dict(include={'id'}).items()})message_results:List[model.MessageResult]=list(adapter.message(message))Получение списка доступных задач:adapter.get_external_tasks(definition=definition)Выполнение доступных задач:adapter.process_external_tasks()ТестированиеДля запуска тестов используется tox с плагином tox-docker, запускающим контейнер Camunda.Установка tox:$pipinstalltoxtox-dockerЗапуск тестов из директории пакета:$tox
Package: camunda-client
camunda-clientThis package includes clients such as CamundaEngineClient and ExternalTaskClientIn addition, there is an ExternalTaskWorker for working with ExternalTaskInstallingpippipinstallcamunda-clientpdmpdmaddcamunda-clientExternalTask functional usageSource code inexamples/external_taskfromcamunda_client.workerimportExternalTaskWorkerfrom.enumsimportWorkerEnumasyncdefsubscribe(topic:str,task_worker:ExternalTaskWorker,)->None:mapping=WorkerEnum.workers()asyncwithtask_worker.subscribe(topic)astask_contexts:asyncfortask_contextintask_contexts:asyncwithtask_contextastask_dto:worker_cls=mapping[task_dto.topic_name]# Resolve the dependency on the DI container.# This initialization is provided as an example.worker=worker_cls()result=awaitworker.execute(task_dto)ifresult.is_successisFalse:print(f"Task with id{task_dto.id}was failed")awaittask_context.fail(error_message=result.message)else:print(f"Task with id{task_dto.id}was completed")awaittask_context.complete()
Package: camundatools
CamundaToolsCamundaTools is a Python REST api client for the workflow and decision automation engine Camunda.
It will provide a simple and intuitive interface for the most used features of the rest-engine api.CamundaTools also has a set of command line tools to manage definitions, instances and tasks.AuthorFelipe Rayelfelipe.rayel@gmail.comInstallpip install camundatoolsSetupCreate a file in the root path  called camundatools.cfg[config]
CAMUNDA_API_BASE_URL = http://localhost:8080/engine-rest
CAMUNDA_AUTH_USERNAME = demo
CAMUNDA_AUTH_PASSWORD = demo
REQUEST_RETRY = 3
REQUEST_TIMEOUT = 5
EXTERNAL_MAX_TASKS = 10
EXTERNAL_TASK_LOCK_DURATION_IN_MILISECONDS = 30000
EXTERNAL_TASK_RETRY_TIMEOUT = 300000
CERT_FILE = cert_file.pem
MAX_RESULTS_PER_PAGE = 100
MAX_RESULTS_NON_PAGINATED = 100Command Line ToolsDefinition Command Parameterslist                List deployed process definitions
deploy              Deploy a definition
delete              Delete a deployment
clear               Clear all instances from a definition
download            Download latest version of a XML definition from serverExample:$>definitionlist
$>definitiondeploy<filename.bpmn>
$>definitiondelete<deployment_id>
$>definitionclear<definition_key>
$>definitiondownload<definition_key>Instance Command Parametersstart               Start a process instance
list                List process instances
inspect             Inspect a process instance
find                Find a process instance by business key
delete              Delete a process instance
migrate             Migrate a process instance to a newer definition
download            Download a XML of the process instanceExample:$>instancestart<process_key><business_key>
$>instancelist
$>instanceinspect<process_instance_id>
$>instancefind<process_key><business_key>
$>instancedelete<process_instance_id><reason>
$>instancemigrateTask Command Parameterslist                List tasks
inspect             Inspect a task
complete            complete a task
delete              Delete a task
move                Move a task to other activityExample:$>tasklist
$>taskinspect<task_id>
$>taskcomplete<task_id>
$>taskdelete<task_id>
$>taskmove<process_instance_id><new_task_name>Using the APIDeploy a bpmdefinition_api = Definition('http://localhost:8080/engine-rest', 'demo', 'demo')
definition_api.deploy('file.bpmn')List Instanceinstance_api = Instance('http://localhost:8080/engine-rest', 'demo', 'demo')
instances = instance_api.list('myProcessKeyDefinition')
for instance in instances:
    print(instance)
Package: camus
CamusCamus is a raw SQL library enabling an ease integration with the newAurora Serverless Data API. Its API is based on the powerfulRecordslibrary.InstalationThe recommended installation method ispipenv:$pipenvinstallcamusBasic UsageFirst you need to have an Aurora cluster ARN and a Secret ARN. If don't have one yet, just follow theData API Getting Started Guide.With that in hands, let's drop some query in our database:importcamusdb=camus.Database(resource_arn="arn:aws:rds:us-east-1:123456789012:cluster:your-cluster-name",secret_arn="arn:aws:secretsmanager:us-east-1:123456789012:secret:your-secret-name-ByH87J",dbname="mydb",)rows=db.query("SELECT * FROM users")You can grab one row at time (like in Records library)>>>rows[0]<camus.Recordat0x109bfbd30>Or iterate over them:forrinrows:print(r.name,r.email)Like mentioned before, Camus tries to maintain the same API of the Records library, so you have the same access patterns:row.emailrow['email']row[3]Other options includerows.as_dict()androws.as_dict(ordered=True)TransactionsData API transactions are supported by Camus:withdb.transaction()astxid:db.query("INSERT INTO users (name, email) VALUES (:name, :email)",name="Rafael",email="rafael@email.com")db.query("UPDATE posts SET title = :title WHERE id = :id",title="New Title",id=999)If any exception is raised when executing any of the queries, a rollback is performed automatically.That's all folksThanks for the awesome @kennethreitz for providing his knowledge on the excelent Records library and
all the talks he has given over the years!