Saravanan666/pypi-org huggingface dataset:
Package: tumbo
tumboDocker matrix build generatorvariables:my_var:-a-b-csome_other:-0.1.0-0.1.2context:./buildrecipe:'./Dockerfile.j2'tag:"my-tag:{{my_var}}-{{some_other}}"parallel:no# no# yes : uses all threads available# n : number of threads to use# default: yespush:yesrun:yesregistry:username:my_userpassword:my_passhost:my_host📘 Config ReferenceVariablesThe variables to build the matrix. Simply specify an array for each variable. They will be available in the template.variables:my_var:-a-b-csome_other:-0.1.0-0.1.2RecipeThe template to compile the dockerfile. Can be a template itself if you don't want to write everything in the same file.Simplerecipe:'./Dockerfile.j2'Templatevariables:my_var:-a-brecipe:'./{{my_var}}.j2'Assumingmy_varhast the valuesaandbit will render to./a.j2and./b.j2accordingly.Context (Optional)Default:directory of the config file.Specify the directory where the templates and the dockerfiles will be built.
Supports both absolute and relative paths.context:./buildTag (Optional)Default:Creates a tag that includes all variables.
In most cases it will not be necessary to specify, but can be usefull if your are pushing images. Supports templating of course.Important:The tag should be unique across the matrix, otherwise you will overwrite other tags. So always include all the variables you specified inside the image namevariables:var1:-a-bvar2:-a-bvar3:-a-btag:'my-image-name:{{var3}}-{{var1}}-{{var2}}'Parallel (Optional)Default:yesWhether the builds/push/runs should run in parallel or after each other.noParallel off.parallel:noyesUses all the threads available on the machine.parallel:yesnumberUses how many thread you specifyparallel:2run (Optional)Default:noWether to run the docker image after building. Can be usefull if running automated tests.run:yespush (Optional)Default:noWether to push the docker image after building.
Can be used to push images to the docker registry (or your own).
See below on how to login.run:yesregistry (Optional)Default:EmptyCredentials fordocker login. Used to push images and to specify a custom registry if necessary.registry:username:my_userpassword:my_passhost:my_host
Package: tumdlr
tumdlr (Tumblr Downloader)IntroductionTumdlr is a command line application for generating and maintaining
Tumblr blog archives.It works almost entirely using Tumblr’s ownAPIinstead of relying on
web scraping methods, offering vastly superior performance without the
risk of breaking with future site updates.This application is currently in pre-alpha status.FeaturesTumdlr currently supports archiving the following types of posts:Photos (including linked photos)Videos (including support for most external video embeds, such as
YouTube) - Powered viayoutube-dl[STRIKEOUT:Audio] (Coming soon)[STRIKEOUT:Text / regular posts (in both plain text and HTML)]
(Coming soon)InstallationTumdlr can be easily installed with pip:$ pip install tumdlrUsageOnline documentation is not available during alpha development.
Available commands can be found by running thetumdlr--helpcommand.LicenseThe MIT License (MIT)

Copyright (c) 2015 Makoto Fujimoto

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Package: tum-esm-lowcost-metadata
Lowcost MetadataThis repository handles the metadata around the lowcost sensor network in Munich.We selected this format over putting it in a database due to various reasons:Easy to read, modify and extend by selective group members using GitHub permissionsChanges to this are more obvious here than in database logsVersioning (easy to revert mistakes)Automatic testing of the files integritiesEasy import as a statically typed Python libraryWhat does this data look like?There is a set ofJSONfiles in the data folder holding the follwing information:SENSORS.jsonThis files contains basic information about the sensors in use in the monitoring network.{"13077":{"sensor_type":"LP8","sensor_make":"Decentlab","sensor_model":"DL-LP8","start_up_date":"2022-08-01T08:00:00+00:00","shut_down_date":null,"comment":""},}SITES.jsonThis file contains basic information about the sites/locations where sensors have been installed.{"FREV":{"site_type":"individual","site_lat":48.1615591,"site_lon":11.5860387,"elevation":514,"comment":"Lamp post ids:55.0"},}SAMPLING.jsonThis file contains basic information on which site, at which time, which sensors measured at which configuration.[{"site_id":"HANV","sensor_ids":[13171,13147],"sampling_start":"2023-05-02T12:30+02:00","sampling_end":null,"orientation":0,"elevation_ag":3,"comment":""},]How to add new measurement days?Possibly add new sensor indata/SENSORS.jsonPossibly add new site indata/SITES.jsonAdd a new sampling event todata/SAMPLING.jsonHow can I know whether my changes were correct?Whenever you make changes in the repository on GitHub, the integrity of the files will automatically be checked. You can check whether all tests have passedhere.A list of all integrity checks can be found intests/README.md.How to use it in your codebase?Install python librarypoetryaddtum_esm_lowcost_metadata# orpipinstalltum_esm_lowcost_metadataCreate a personal access token for a GitHub account that has read access to the metadata repository:https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-tokenUse the metadata anywhereimporttum_esm_lowcost_metadatalowcost_metadata_interface=tum_esm_lowcost_metadata.load_from_github(github_repository="org-name/repo-name",access_token="ghp_...")metadata=lowcost_metadata_interface.get(sensor_id="13077",date=pendulum.datetime(2023,6,6))# is of type list[tum_esm_lowcost_metadata.types.SensorDataContext]metadata=interface.get(sensor_id='13155',timestamp=pendulum.datetime(2023,6,6))interfaces.print_beautiful(metadata))... prints out:Metadata for Sensor 13155, located at MOSV.
---
Sensor type:            Decentlab DL-LP8
Site coordinates:       48.1870436 lat
                        11.5622708 lon
                        508.0 m a.s.l.
Orientation             0.0°
Elevation above ground: 3.0 m
Comment:                Lamp post ids:32.0

---For Developers: Publish the Package to PyPIpoetrybuild
poetrypublish
Package: tum-esm-signal
TUM ESM SignalReplaces theshareable-timeseries-visualizationrepository.Installation:poetryaddtum-esm-signal# orpipinstalltum-esm-signalUsage:fromtum_esm_signalimportTUM_ESM_SignalClientsignal_client=TUM_ESM_SignalClient(cms_identity=cms_identity,cms_password=cms_password,collection_name="test",table_name="test",sensor_id="test",)data_column_co2=signal_client.connect_column(column_name="co2",unit="ppm",minimum=350,maximum=4000,decimal_places=0,)data_column_ch4=signal_client.connect_column(column_name="ch4",unit="ppm",minimum=1.6,maximum=3.0,decimal_places=2,description="Methane",)whileTrue:print("Sending datapoints")data_column_co2.add_datapoint(random.randint(350,4000))data_column_ch4.add_datapoint(random.random()*1.4+1.6)time.sleep(2)
Package: tum_esm_utils
📦  TUM ESM Python UtilitiesThis library is a collection of small functions used in our research projects. Here, we can test and document the functions properly instead of every project dealing with this overhead which allows us to reduce the size of the utility directories of individual projects.Feel free to use it in any other project ✨For UsersInstall the Python library with:pdmaddtum_esm_utils# orpipinstalltum_esm_utilsUse the API reference attum-esm-utils.netlify.app.For DevelopersPublish the Package to PyPI:pdmbuild
pdmpublishOpen documentation page in dev modecddocs
npminstall
npmrundev
Package: tum-gdpr-folder-scanner
TUM-GDPR-Folder-ScannerStudents at theTechnical University of Munich (TUM)have the right to request to information on personal data after Art. 17 GDPR.
I wrote this script to check local folders for such information.Usage$tum-gdpr-folder-scanner--help
Usage:tum-gdpr-folder-scanner[OPTIONS][DIRECTORY]Scansallrelevantfiles(CSV,PDF,TXT,XLSX,XML)forthegivenname,TUMname,andmatriculationnumber.

Arguments:[DIRECTORY]Thedirectorywewanttoanalyze.[default:.]Options:-n,--name-to-searchTEXTThenamewearelookingfor.Pleasewritethenameintheform`LastnameFirstname`or`FirstnameLastname`.-m,--matriculation-noTEXTThematriculationnumberwearelookingfor.-t,--tum-idTEXTTheTUMID,e.g.,ga12acb.-S,--skip-pdfsThePDFextractiontakessometime.Youcanskipitforafirstrun.-X,--skip-xlsxTheXLSXextractiontakessometime.Youcanskipitforafirstrun.-v,--versionShowstheversionandexits.--install-completion[bash|zsh|fish|powershell|pwsh]Installcompletionforthespecifiedshell.--show-completion[bash|zsh|fish|powershell|pwsh]Showcompletionforthespecifiedshell,tocopyitorcustomizetheinstallation.--helpShowthismessageandexit.ContactIf you have any question, please contactPatrick Stöckle.
Package: tumorevo
tumorevoSimulate tumor evolution under different spatial constraints. This package aims to be as awesome asdemon.tumorevosimulates tumor growth and and produces a Muller plot, a cartoon of the 2D spatial organization of the tumor cells, and a clone tree.Installation$pipinstalltumorevoUsagetumorevocontains two command line utilities:tumorsimandtumorfig.Simulating tumor evolutiontumorsimcan be used to simulate the evolution of a tumor according to a specified spatial structure.$tumorsim--mode1--steps2000--genes20--carrying-capacity5--grid-size20--division-rate0.2--dispersal-rate0.1100%|████████████████████|1999/1999[00:07<00:00,251.69it/s]This will create a folder containing:parents.csv: file indicating each clones's parent;trace_counts.csv: file indicating the number of cells of each clone at each time step;genotypes.csv: file containing the genotypes of each clone;grid.csv: file containing the regular grid of genotypes ifmode> 0.Full overview:$ tumorsim --help
Usage: tumorsim [OPTIONS]

  Simulate tumor evolution under different spatial constraints.

Options:
  -m, --mode INTEGER              Spatial structure.
  -k, --carrying-capacity INTEGER
                                  Deme carrying capacity.
  -g, --genes INTEGER             Number of genes.
  -s, --steps INTEGER             Number of steps in simulation.
  --grid-size INTEGER             Grid size.
  --division-rate FLOAT           Divison rate.
  --mutation-rate FLOAT           Mutation rate.
  --dispersal-rate FLOAT          Dispersal rate.
  -r, --random_seed INTEGER       Random seed for the pseudo random number
                                  generator.
  --log INTEGER                   Logging level. 0 for no logging, 1 for info,
                                  2 for debug.
  -o, --output-path TEXT          Output directory
  --help                          Show this message and exit.Plotting tumor evolutiontumorfigcan be used to create a Muller plot of the tumor's evolution, the 2D spatial organization of the tumor cells, and a clone tree.$tumorfigout/trace_counts.csvout/parents.csv--plot--grid-fileout/grid.csv--normalize--removeThis will open a figure like this:Full overview:$ tumorfig --help
Usage: tumorfig [OPTIONS] GENOTYPE_COUNTS GENOTYPE_PARENTS

  Plot the evolution of a tumor.

Options:
  -c, --cells INTEGER           Number of cells in slice plot.
  -r, --average-radius INTEGER  Average radius of circles in slice plot.
  --grid-file TEXT              Path to grid file.
  --colormap TEXT               Colormap for genotypes.
  --dpi INTEGER                 DPI for figures.
  --plot                        Plot all the figures.
  --do-muller                   Make a Muller plot.
  --do-slice                    Make a slice plot.
  --do-tree                     Make a clone tree plot.
  --normalize                   Normalize the abundances in the Muller plot.
  --labels                      Annotate the clone tree plot.
  --remove                      Remove empty clones in the clone tree plot.
  -o, --output-path TEXT        Directory to write figures into.
  --help                        Show this message and exit.
Package: tun2socks
tun2socks-pythonPython bindings fortun2socks.InstallCore Building ToolsYou have to install the following tools to be able to install this package successfully.goin your PATH. go 1.20.0 and above is recommended. To check go is ready,
typego version. Also, if google service is blocked in your region(such as Mainland China), you have to configure
your GOPROXY to be able to pull go packages. For Chinese users, refer togoproxy.cnfor more
information.cmakein your PATH. To check cmake is ready, typecmake --version.A working GNU C++ compiler(i.e. GNU C++ toolchains). To check GNU C++ compiler is ready, typeg++ --version. These
tools should have been installed in Linux or macOS by default. If you don't have GNU C++ toolchains(especially for
Windows users) anyway:For Linux users: typesudo apt update && sudo apt install g++and that should work out fine.For Windows users: installMinGW-w64orCygwinand make sure you have add them to PATH.Install Packagepip install tun2socksAPI>>>importtun2socks>>>help(tun2socks)Help on package tun2socks:NAMEtun2socksPACKAGE CONTENTStun2socksFUNCTIONSstartFromArgs(...) method of builtins.PyCapsule instancestartFromArgs(device: str, networkInterface: str, logLevel: str, proxy: str, restAPI: str) -> NoneStart tun2socks with custom argumentsVERSION2.5.1Source Code ModificationThis repository, including the package that distributes to pypi,
containstun2sockssource code that's been
modified to build the binding and specific API. If without explicitly remark, the version of this package corresponds to
the version of the origin source code tag, so the binding will have full features as the original go distribution will
have. And due to its backward compatibility, there's no plan to generate bindings for older release of tun2socks.To make installation of this package easier, I didn't add the
originaltun2sockssource code as a submodule. To track what modifications have been made to the source code, you can compare it with the
same version under Python binding and corresponding go repository.Tested Platformtun2socks-python works on all major platform with all Python version(Python 3).Below are tested build ingithub actions.PlatformPython 3.7-Python 3.11ubuntu 20.04:heavy_check_mark:ubuntu 22.04:heavy_check_mark:windows-2019:heavy_check_mark:windows-2022:heavy_check_mark:macos-11:heavy_check_mark:macos-12:heavy_check_mark:macos-13:heavy_check_mark:LicenseThe license for this project follows its original go repositorytun2socksand
is underGPL v3.0.
Package: tuna
Performance analysis for Python.tuna is a modern, lightweight Python profile viewer inspired bySnakeViz. It handles runtime and import
profiles, has minimal dependencies, usesd3andbootstrap, and avoidscertainerrorspresent in SnakeViz (see
below) and is faster, too.Create a runtime profile withpython -mcProfile -o program.prof yourfile.pyor animport
profilewithpython -X importtime yourfile.py 2> import.logand show it withtuna program.profWhy tuna doesn't show the whole call treeThe whole timed call treecannotbe retrieved from profile data. Python developers
made the decision to only storeparent datain profiles because it can be computed
with little overhead. To illustrate, consider the following program.importtimedefa(t0,t1):c(t0)d(t1)defb():a(1,4)defc(t):time.sleep(t)defd(t):time.sleep(t)if__name__=="__main__":a(4,1)b()The root process (__main__) callsa()which spends 4 seconds inc()and 1 second
ind().__main__also callsb()which callsa(), this time spending 1 second inc()and 4 seconds ind(). The profile, however, will only store thatc()spent a
total of 5 seconds when called froma(), and likewised(). The information that the
program spent more time inc()when called inroot -> a() -> c()than when called inroot -> b() -> a() -> c()is not present in the profile.tuna only displays the part of the timed call tree that can be deduced from the profile.
SnakeViz, on the other hand, tries to construct the entire call tree, but ends up
providing lots ofwrongtimings.SnakeViz output.Wrong.tuna output. Only shows what can be retrieved from the profile.Installationtuna isavailable from the Python Package Index, so
simply dopip install tunato install.TestingTo run the tuna unit tests, check out this repository and typepytestIPython magicstuna includes atunaline / cell magic which can be used as a drop-in replacement for
theprunmagic. Simply run%load_ext tunato load the magic and then call it like%tuna sleep(3)or%%tunasleep(3)prunis still used to do the actual profiling and then the results are displayed in
the notebook.DevelopmentAfter forking and cloning the repository, make sure to runmake depto install
additional dependencies (bootstrap and d3) which aren't stored in the repo.LicenseThis software is published under theGPLv3 license.
Package: tunable
tunableA tiny library to support tunable parameters (think: configuration values) as they often occur in scientific analyses.
A main aspect differing from many other config approaches is, that the tunables are defined as classes where needed
throughout the source, with default value, type, optional documentation etc., which makes later access fast,
IDE-supported (code completion)
and prevents running it with "missing defaults" or misspelled keys leading to runtime errors.Theoretically, all Python types instantiable from strings are supported, it is however discouraged to use anything but{bool, int, float, str, bytes}for portability reasons.There are some more features and concepts, which will hopefully be documented correctly in the future.ExampleExample test.py:importargparsefromtunableimportTunable,TunableManager# tunable values are just defined as classes# inheriting from Tunable, their documentation# being normal inline documentation# their type is derived from their default valueclassMyValue(Tunable):"""An important tunable."""default=42.0defmain():# TunableManager provides some argparser-actions# to allow users to interact with tunablesp=argparse.ArgumentParser()TunableManager.register_argparser(p)p.parse_args()# Access to the value is straightforward:print(MyValue.value)# will be accessed once dynamically with checks,# afterwards as a simple lookupif__name__=='__main__':main()Without arguments:>pythontest.py42.0Functionality can automatically be registered with argparser.>pythontest.py--help
usage:test.py[-h][--tunables-show][-tTUNABLE][--tunables-loadTUNABLES_LOAD][--tunables-saveTUNABLES_SAVE]optionalarguments:-h,--helpshowthishelpmessageandexit--tunables-show-tTUNABLE,--tunableTUNABLE--tunables-loadTUNABLES_LOAD--tunables-saveTUNABLES_SAVEShow set tunables:>pythontest.py--tunables-show### Tunables #### An important tunable.# type: floatMyValue=42.0Changing a tunable via command line:
(See how int(21) was upcasted to float)>pythontest.py-tMyValue=2121.0Tunables can be saved/loaded from files, currently supported are key=value style config files, JSON, DER, YAML or XML.To help reproducibility, a hash of all tunables currently set can be generated:print(TunableManager.get_hash())VERSION:1:SHA256:3aHPIn6bRgA15+dNRPNhltMA1hibYMcs7dnUwqVjg0s=Cryptographic hashing is based upon the SHA-256 hash of a canonicalized DER based serialization of the tunables.StabilityWarning, this library is beta software, whose interface is subject to change without notice!No guarantees yet on binary formats, hash stability or such!LicenseMIT
Package: tunafish
TunafishAutotuner For Python FunctionsTunafish is an autotuner for plain ol' Python functions. Under the hood, it uses genetic algorithms to determine which arguments maximize a function's return value (i.e. it's fitness); however, Tunafish eliminates the need to think about genetic algorithms. Instead, you can focus more on just writing code.To use Tunafish, your objective function must satisfy the following two
conditions:Argumentsmustbe annotated as primitives types, likefloat,int,str, andbool,typing.Text, etc.The return valuemustbe expressed as a singlefloat(i.e. a fitness
value).Example: Automated TradingConsider a function whose objective is to place orders to buy and sell stocks. The input arguments areaggression, which regulates the a minimum amount of time between orders, andwindow, which determines how far back the trading algorithm should looks when deciding when take action. The return value is simply the net gain or loss generated while trading, which is used as a proxy for fitness.Define The Objective Functionfromexample_projectimportcreate_trader,load_historical_trading_datatrader=create_trader()training_data=load_historical_trading_data(start,stop,interval)deftrade(aggression:float,window:int)->float:gains=trader.trade(training_data,aggression,window)returngains# AKA fitnessTune it...fromtunafishimportFunctionTunertuner=FunctionTuner()arguments=tuner.tune(trade,options={'aggression':{'min':0.01,'max':1.0},'window':{'min':5,'max':20}})More ExamplesWorking examples can be found intunafish.examples. The "basic" and "early_stopping" examples differ only in that "early_stopping" shows you how to control a bit more of the internals of the genetic algorithm. In particular, we tell it to exit the training loop early if we reach a fitness goal before all 500 epochs have run. Running these examples should generate a plot, showing convergence of fitness versus time.Running ExamplesJust dopython -m tunafish.examples.basic!
Package: tunas2dsdl
项目简介该项目的作用是将tunas标准数据集转换为符合dsdl标准的yaml文件项目安装：pipinstalltunas2dsdl项目使用：tunas2dsdlconvert-i<dataset_infopath>-a<annotationfile>-w<workingdir>-t<task>-s或tunas2dsdlconvert-d<dataset_path>-w<workingdir>-t<task>-s注意：如果已经通过-i和-a指定了dataset_info.json和相应的 标注文件，则不可以使用-d选项；如果已经使用了-d选项，则不可以使用-i与-a选项参数详解：参数含义-d/--directorytunas标准数据集的目录路径<dataset_path>，该目录下必须包含<dataset_path>/dataset_info.json、<dataset_path>/annotations/json/<json_file>等内容-i/--dataset-infotunas标准数据集的dataset_info.json的路径-a/--annotationtunas标准数据集的标注文件的路径-w/--working-dir生成的dsdl yaml文件存储的目录路径，最终代码会在该目录中生成与segment name同名的文件夹，并将所有的内容保存到该文件夹中-t/--task当前处理的的任务，目前只支持选项detection，对应了检测任务-s/--separate-store是否将数据集中的samples单独存储到一个json文件中，当样本数量巨大的情况下可以使用该选项，默认存储的文件名为samples.json，保存在working-dir目录下-c/--config如果要从aliyun oss上读取文件，则需要通过该选项指定配置文件路径阿里云OSS配置文件为一个json文件，内容如下：{"access_key_secret":"your secret access key","endpoint":"your endpoint","access_key_id":"your access key id"}
Package: tunas2dsdl-c
执行步骤安装pipinstalltunas2dsdl-c该代码可以统一将tunas 0.3版本的数据集转换为dsdl所需的yaml文件tunas2dsdl_cconvert-s"/XXX/tunas_data_demo/CIFAR10-tunas"-o"/XXX/tunas_data_demo/CIFAR10-tunas_dsdl/"-l每个参数的意义为：参数简写参数全写参数解释-s--src_dirtunas 0.3版本的文件的路径-o--out_dir可选，生成的yaml的根路径（可以不写，不写默认为和src_dir为同一个根目录但是文件夹名字加上后缀_dsdl）-c--unique_cate可选，这个参数一般不用管，除非你的数据集的category_name是有重复的（目前只有Imagenet是这样的， 所以Imagenet此处要）设置成wordnet_id-l--local可选，是否将数据集中的samples单独存储到一个json文件中还是存储在同一个文件中，当样本数量不大的情况下可以使用该选项，默认存储的文件名为XXX_samples.json，保存在同一个目录下（如果使用-l则存在同一个yaml文件中）然后就会生成yaml文件。