Saravanan666/pypi-org huggingface dataset:
Package: pedRefiner
Suggested usage of PedRefiner: the pipeline() method.param  1 lst_fn:              file containing animal list to be grepped from the pedigree, one line eachparam  2 ped_fn:              input pedigree file, 3 colparam  3 opt_fn:              output pedigree file, 3 colparam  4 gen_max:     (0)     maximum recursive generation to grep for EVERYONE in lst_fnparam  5 missing_in:  (‘.’)   missing value for input file, default = ‘.’param  6 missing_out: (‘.’)   missing value for output file, default = ‘.’param  7 sep_in:      (‘,’)   separator for input file, default = ‘,’ i.e. csvparam  8 sep_out:     (‘,’)   separator for output file, default = ‘,’, i.e. csvparam  9 xref_fn:     (None)  cross-reference file to modify output pedigreeparam 10 flag_r:      (False) bool, output descendant IDs if TrueExample:Pythonfrom pedRefiner import PedRefinerpr = PedRefiner()# pr.help()pr.pipeline(‘animal_list’, ‘ped.input.csv’, ‘ped.output.csv’, gen_max=3)Bash#             1       2             3               4gen_max  5missin  6missout  7sepin 8sepout 9    10pedRefiner.py ANM_LST PED_INPUT.csv PED_OUTPUT.csv [3         0        0         ,      ,       xref True]Description:extracts the pedigree of all the ancestors of the animals in the anmList file;if ‘-r’ option is given, prints out all the descendants’ IDs from the anmList file instead of print out the refined pedigree;xref the pedigree info if specified a whitespace-delimited 3-col xref file with 1st col as command:# line starting with sharp will be ignored# cmd = A  : [A]ll ID1 (col1, 2 and 3) will be changed to ID2A ID1 ID2# cmd = S  : All ID1 in the [S]ire (2nd) column will be changed to ID2S ID1 ID2# cmd = D  : All ID1 in the [D]am  (3rd) column will be changed to ID2D ID1 ID2force accepting the latest version if multiple entries for the same animal were detected;Updates:version 2017-02-23 prevent unexpected order for complex pedigree while specifying rec_gen_maxversion 2017-02-22
- modified to be a non-recursive version to prevent segmentation fault for a very very very long pedigree
- only maintaining python versionversion 2016-03-31 added -r option: will print out all the descendants’ IDs from anmList, recursively to the endversion 2015-07-13 added output: pedOut.ID2Gender according to the ped, and ‘M’ for unknownversion 2015-07-02 added xref to correct animal IDs: replace 1st col with 2nd colversion 2015-06-29 prevent segmentation fault 11 due to pedigree loopversion 2015-06-12 added updating pre-loaded empty entriesversion 2015-04-15 added a 3rd parameter to determine the recursive generationsversion 2014-11-10 modified check() so that all the errors would be checked in one run added sort()version 2014-04-15 initial version
Package: pedroasad-attrs-patch
Python application template+A set of patches for the excellent attrs libraryForSeeDocumentationhttps://psa-exe.gitlab.io/python-attrs-patchIssue trackerhttps://gitlab.com/psa-exe/python-attrs-patch/issuesRepository contentsMANIFESTHistory of changesCHANGELOGContribution/development guideCONTRIBUTINGCopy ofMIT LicenseLICENSEInstallationpipinstallpedroasad-attrs-patchThis library contains optional support forNumpyarrays inattrs frozen classes.
It may be installed by passing the[numpy]option when installing.UsageIt acts as a drop-in replacement toattrs.
The example below shows how to use it, including all currently existing improvements.fromattrs_patchimportattr@attr.autodoc@attr.s(frozen=True)classSomeClass:a=attr.ib(metadata={"help":"An immutable numpy array."},converter=attr.frozen_numpy_array,hash=False)b=attr.ib(metadata={"help":"A positive integer."},converter=int,validator=attr.validators.positive)c=attr.ib(metadata={"help":"A non-zero integer."},converter=int,validator=attr.validators.nonzero)— Powered byGitLab CI— Created byPedro Asad <pasad@lcg.ufrj.br>usingcookiecutterand@pedroasad.com/templates/python/python-app-1.0
Package: pedroasad-marshmallow-patch
marshmallow-patch – A set of patches to the excellentMarshmallowlibraryForSeeDocumentationhttps://psa-exe.gitlab.io/python-marshmallow-patchIssue trackerhttps://gitlab.com/psa-exe//python-marshmallow-patch/issuesRepository contentsMANIFESTHistory of changesCHANGELOGContribution/development guideCONTRIBUTINGCopy ofMIT LicenseLICENSEInstallationpipinstallpedroasad-marshmallow-patchThis library contains optional support forNumpyarrays andPintphysical units and quantities.
These may be installed by passing the[numpy],[pint], or[all]options when installing.UsageIt acts as a drop-in replacement toMarshmallow:frommarshmallow_patchimportmarshmallowFurther configuration may be performed by setting up global variables inmarshmallow_patch.configprior to importing the module replacement.
OtherMarshmallowsub-modules may be imported directly from the original package.— Powered byGitLab CI— Created by Pedro Asad<pasad@lcg.ufrj.br>usingcookiecutterand@pedroasad.com/templates/python/python/app-1.0.0
Package: peds
peds: basic pedigree parsingAll this does is parsepedigreefiles and group individuals within families.
Only performs minimal validation.InstallpipinstallpedsUsagefrompedsimportopen_pedfamilies=open_ped(PATH)family=families[0]# find affected family membersaffected=[xforxinfamilyisx.is_affected()]# find parentsforpersoninfamily:father=family.get_father(person)mother=family.get_mother(person)
Package: pedsnetcdms
# PEDSnetCDMsA python app for CDM model classes and DDL autogeneration.The pedsnetcdms app creates python-importable sqlalchemy AND django style models for PEDSnet, Vocab, I2B2, and PCORnet CDMs. In addition, alembic AND django migrations can be created from those models. The alembic migrations can be used to auto-generate DDL files for all four CDMs.The pedsnetcdms app is based on the declarative definition of models in JSON format (found in themodels.jsonfiles) and the dynamic generation of python classes from those models (found in thedj_makers.pyandsa_makers.pyfiles). The generating functions are used in each CDM’smodels.pyandsa_models.pyfiles to put the model classes where the ORMs can find them. Thesettings.py,alembic.ini, andalembic/env.pyfiles are simply ORM module configuration.## InstallationApip install pedsnetcdmsshould get you the package with the generated models and migrations ready for import into your python environment.However, if you want to generate the DDLs yourself or use the migrations directly on your database, you will have to clone the repository and install some or all of the following python packages and their (in some cases non-Python) dependencies, depending on which DBMS you are using:cx-Oraclepsycopg2pymssqlMySQL-pythonThis is left as an exercise for the reader. (Although perhaps a Dockerfile is in order…PR anyone?)## DDL FilesDDL files for all four CDMs in PostgreSQL, Oracle, SQL Server, and MySQL dialects are available in thepedsnetcdms/ddloutputdirectory. They are hypothetical, so please test them and post an issue if you find a problem.## Model UsageDjango models are available atpedsnetcdms.<CDM>.modelsand sqlalchemy models are available atpedsnetcdms.<CDM>.sa_models, where<CDM>should be replaced with one of the following:pedsnetcdmitwobtwocdmvocabcdmpcornetcdmYou can also include any of these apps in your djangoINSTALLED_APPSsetting.## DDL GenerationIn order to generate the DDL, install the required package(s) for your DBMS(s) from the list above, clone the repository and install the package.Edit thepedsnetcdms/<CDM>/alembic/env.pyfile for your desired CDMs to restrict the DBMSs for which DDL will be output (hint: the iterated list of tuples inside therun_migrations_offlinefunction).From within thepedsnetcdmspackage directory (where thealembic.inifile is), runalembic -n <CDM> upgrade head –sqland watch your DDL files appear!## Direct Migration UseInstall requirements, clone, and install the package as above.Edit thealembic.iniorsettings.pyfiles to specify the database URI you wish to connect to (notice that the setting is repeated under each CDMs section inalembic.ini).Runalembic -n <CDM> upgrade headorpython manage.py migrate <CDM>.
Package: pedumper
pedumperpedumper can easily dump PE files within memory.Installationpip install pedumperHow to useC:\Users\user\Desktop>pedumper -p 24532
[!] Found a PE file in the target memory
[*] Address     : 0x133f8e80000
[*] Region      : 0x133f8e80000 - 0x133f8eb7000
[*] Protect     : 0x40 (PAGE_EXECUTE_READWRITE)
[*] Type        : 0x20000 (MEM_PRIVATE)
[*] State       : 0x1000 (MEM_COMMIT)
[!] Saved the found PE to 0x133f8e80000.exe

[!] Found a PE file in the target memory
[*] Address     : 0x133f8e9b800
[*] Region      : 0x133f8e80000 - 0x133f8eb7000
[*] Protect     : 0x40 (PAGE_EXECUTE_READWRITE)
[*] Type        : 0x20000 (MEM_PRIVATE)
[*] State       : 0x1000 (MEM_COMMIT)
[!] Saved the found PE to 0x133f8e9b800.exe
Package: peduncle
pedunclevery very very simple DOM based HTML content extraction tool, get rid of boilerplate dressing of a web page[1].easy but useablework with python 3.7+[1] the word comes fromdragnet.installpipinstallpeduncleusageimportrequestsfrompeduncle.peduncleimportextract_text# obtain the raw htmlurl="https://blog.rust-lang.org/2023/05/29/RustConf.html"html=requests.get(url).text# extractprint(extract_text(html))benchmarkdatabenchmark data comes fromdragnet_data, which contains 1381 web pages.resultsimilarity95%hit_rateavg_length_gap(char)length_gap_stda=0.010.57674567439463410.22-4673.11815343.704819895227a=0250.84516927088146620.548-2082.98814502.183923390849a=0.50.82262246987260870.47-368.6968452.075615349402a=0.990.75275915934858070.2921614.3067917.618208044891a: alpha, control how much the content extractor tens to extract larger content piecesimilarity: cosine similarity between sparse vectors of answer and extracted text95hit rate: percentage of similarity larger than 95%length gap: extracted text length - answer text lengthstd: std
Package: pee
pee(p)ython fr(ee)the Linux free command but for MacOS and built in Pythonpeedisplays stats abouttotal,used,free, andavailablememory in the system. By default (no flag), it returns the stats as bytes.timealso returnsbufferandcacheinfo which is not available from the MacOS kernal.Installationpip install peeUsagepee <-metric>wheremetricis a data metric.flags-h, --help          show this help message and exit
-g, --gigabytes     display memory info in gigabytes
-m, --megabytes     display memory info in megabytes
-k, --kilobytes     display memory info in kilobytes
-b, --bytes         display memory info in bytes (default)
-V, --version       display version infoDependenciespsutilMore InfoI am currently working throughOperating System: Three Easy Stepsand the exercises at the end of chapter 13 require the use offree. I am using MacOS so I don't have access to it.Yes, I could have just booted up Virtual Box for Ubuntu, but instead I built a tool similar tofreefor use on MacOS, because there should be one anyways.Contributingfeel free to raise an issue or pull request if you find a bug
Package: peedeeff
Py Peedeeffpythonclient for peedeeffUsagefrompeedeeffimportPeedeeffr=Peedeeff.get_pdf(apiKey='yourapikey',html='<h1>Hello World</h1>',pageConfig={'size':'A4','printBackground':True,'margin':{'top':'1cm','right':'1cm','bottom':'1cm','left':'1cm'}})r.statuspdf=r.contentCopyright Peedeeff All rights reserved
Package: peekaboo
PekabooExpose hardware info through HTTP.InstallHere are the instructions for a manual install on CentOS 7.sudogroupadddmidecodesudouseradd-M-Gdmidecodepeekaboosudotee-a/etc/sudoers.d/dmidecode<< EOF >/dev/null
%dmidecode ALL=(ALL) NOPASSWD:/usr/sbin/dmidecode
EOFsudoyuminstall-ylibselinux-utilsredhat-lsbpython-develpython-pipgccsudopipinstallpeekaboosudocp/usr/share/peekaboo/contrib/peekaboo.service/usr/lib/systemd/system/peekaboo.servicesudosystemctlenablepeekaboosudosystemctlstartpeekabooYou can also install it using:curlhttps://raw.githubusercontent.com/mickep76/peekaboo/master/setup_centos7.sh|sudobashRun using DockerYou can quickly test it by running it inside a Docker container:Build docker imagedockerbuild-tpeekaboo:latest.Run docker imagedockerrun-d-p5050:5050--name=peekaboopeekaboo:latestStop containerdockerstoppeekabooQueryQuery using YAML:curl-ihttp://<host>:5050/infocurl-ihttp://<host>:5050/info/treecurl-ihttp://<host>:5050/statuscurl-ihttp://<host>:5050/status/treeQuery using JSON:curl-i-H"Accept: application/json"http://<host>:5050/infocurl-i-H"Accept: application/json"http://<host>:5050/info/treecurl-i-H"Accept: application/json"http://<host>:5050/statuscurl-i-H"Accept: application/json"http://<host>:5050/status/tree
Package: peekextn
peeksrc+++++++Peek into the sources and list the file types contained in the sources.Also, enable display the sources in tree format.Purpose=======This tool can be used as the first step for source code reading andunderstand the basic design.* What are the different file types in the sources.* To understand file interface design.* Walk over specific extenstion on all directories.* Show directories and files with max depth level.* Generate header file path for doxygenInterface=========commandline-----------peeksrc-e [list] : Extension Lists-g group  : Extension group-l level  : File/Directory Level depth-u        : All unknown files-p        : List File extension Paths-d        : Destination Dir-t        : Directory Tree Structure-o        : Output FileAPI---source = peeksrc()source.showextn()Logging and Reporting=====================Console Logs and formattingGenerate HTML reportGenerate Doxy tool inputGenerate files to pipeDataStructures==============Data AssociationsFile Extens -> FilesFile Extens -> PathsDirectory -> File Extens -> FilesData Structuredirtree = ['dir1', "dir1", ..., ]ExtnNodes = {"group1" : {'extn1' : {'desc' : "Description",'files' :{ 'dir1' : [file1, file2, file3, ...]'dir2' : [file1, file2, file3, ...]}}'extn2' : {'descr' : "Description",'files' :{ 'dir1' : [file1, file2, file3, ...]'dir2' : [file1, file2, file3, ...]}}}
Package: peekingduck
Getting started·Tutorials·API docs·FAQ·Report a bug·CommunitiesPeekingDuckis an open-source, modular framework in Python, built for Computer Vision (CV) inference. The name "PeekingDuck" is a play on: "Peeking" in a nod to CV; and "Duck" induck typing.FeaturesBuild realtime CV pipelinesPeekingDuck enables you to build powerful CV pipelines with minimal lines of code.Leverage on SOTA modelsPeekingDuck comes with variousobject detection,pose estimation,object tracking, andcrowd countingmodels. Mix and match different nodes to construct solutions for varioususe cases.Create custom nodesYou can createcustom nodesto meet your own project's requirements. PeekingDuck can also beimported as a libraryto fit into your existing workflows.InstallationInstall fromPyPIusingpip> pip install peekingduckNote: for users with ARM-based devices such as a Raspberry Pi or Apple Silicon Mac, please refer to the documentation for more detailedinstallation instructions.PeekingDuck can also beinstalled in a virtual environment.> peekingduck verify-installYou should see a video of a person waving his hand withbounding boxes overlaid.The video will close automatically when it is run to the end, select the video window and pressqto exit earlier.UsageCreate a project folder and initialize a PeekingDuck project.> mkdir <project_dir>
> cd <project_dir>
> peekingduck initRun the demo pipeline.> peekingduck runIf you have a webcam, you should see a man waving on the output screen withskeletal frame overlaid.Terminate the program by clicking on the output screen and pressingq.Usepeekingduck --helpto display help information for PeekingDuck's command-line interface.GalleryAcknowledgementsThis project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG-RP-2019-050). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.LicensePeekingDuck is under the open sourceApache License 2.0(:Even so, your organization may require legal proof of its right to use PeekingDuck, due to circumstances such as the following:Your organization is using PeekingDuck in a jurisdiction that does not recognize this licenseYour legal department requires a license to be purchasedYour organization wants to hold a tangible legal document as evidence of the legal right to use and distribute PeekingDuckContact usif any of these circumstances apply to you.CommunitiesAI Singapore community forumDiscuss on GitHub
Package: peekpoke
PeekPokeStatus:experimentalPeekPoke is a tool for reading and writing a Propeller's hub memory from a PC. It consists of two parts: a Python module for sending commands from the PC, and a Propeller program for responding to commands.The Python module has methods to read and write hub memory as bytes, strings, integers, and lists of integers.The Propeller program can be configured to restrict reads and writes to specific ranges, or to disable writes altogether.PeekPoke has these additional features:the PAR register may be used to pass a value to the PC,the PC can set the baudrate remotely,the PC can reset the baudrate with a break condition,the Propeller can support baudrates as fast as 26 clocks per bit period,there is a four-byteidentifierconstant that can read by the PC,a four-bytetokenvariable is 0 on launch and can be set by the PC,the PC can send PASM code to be executed (disabled by default),the Propeller program is completely cog-contained after launch.InstallationPeekPoke requires Python 3. It can be installed with the commandpip install peekpoke, or the package may be downloaded fromhttps://pypi.org/project/peekpoke/.  PeekPoke also requires thehttps://pypi.org/project/crow-serial/andhttps://pypi.org/project/pyserial/packages (pip automatically handles these dependencies).To run PeekPoke on the Propeller includePeekPoke.spinin your project, and use the Spin methods to set up and launch a PeekPoke instance. The latest version ofPeekPoke.spincan be found athttps://github.com/chris-siedell/PeekPoke.DocumentationPython:https://github.com/chris-siedell/PeekPoke/wiki/PeekPoke-Python-DocumentationSpin:https://github.com/chris-siedell/PeekPoke/wiki/PeekPoke-Spin-Documentation
Package: peekStock
Peek-Stock上班偷偷看股票的利器，不會有紅紅綠綠的字體功能可設定追蹤５個股票代碼瀏覽股票現價以及漲跌幅安裝流程請先確認有安裝 python將專案 clone 到本地git clone https://github.com/Nancy-Hsu/Peek-Stock.git開發工具開發環境: python 3.11.0框架: flask
Package: peelml
peelmlpeelml away the pain of ml deploymentDevelopment1. Setup and Build1.1 build backendgit clone https://github.com/CambioML/peelml.git
conda create -n peelml python=3.10
conda activate peelml
cd peelml
pip install poetry
pip install git+https://github.com/UKPLab/sentence-transformers.git@179b659621c680371394d507683b25ba7faa0dd8
poetry install --no-root # no root to not install peelml as a package1.2 build frontendinstallnodejsnpm installnpm install vitecd into client folder andnpm run build2. Run2.1 Option 1: run the server and client directly without cliBefore you start running the command below, go to server.py and upateos.environ["OPENAI_API_KEY"] = ""After that, run the following commandpython -m peelml.server2.2 Option 2: run the cli commandThis is a interactive cli to ask you to input required environment parameters.
It will then start the server and client for you.python -m peelml.cli start3. Installinstall peelml into site-packages like a pip installpoetry install
peelml startP.S: If you build and pip install for local test, your changes will not reflect in peelml site-packages. Therefore, you have topip3 uninstall peelml, built it again, and the install.Usepip install git+https://github.com/UKPLab/sentence-transformers.git@179b659621c680371394d507683b25ba7faa0dd8
pip install peelml
Package: peepdis
Copyright (c) 2019 Austin McKayPermission 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.Description: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Package: peepingtom
PeepingTomPeepingTomis a python tool for visualising and interacting with cryo-ET and subtomogram averaging data. It leverages the fast, multi-dimensionalnapari viewerand the scientific python stack.DISCLAIMER: this package is in early development phase. Expect frequent bugs and crashes. Please, report them on the issue tracker and ask if anything is unclear!InstallationpipinstallpeepingtomBasic UsageFrom the command line:peep/path/to.star/path/to/mrc/files/PeepingTom accepts any combination of files and directory, and will try find the right files and display them in napari.peepwill also start an ipython shell with the following setup:importpeepingtomasptpeeper=Peeper([your_data])viewer=peeper.napari_viewerFor more information, check out the help page withpeep -handhead over to the docs.
Package: peepomap
PeepomapJust some extra Peepo-Powered Matplotlib colormaps.📦 Installationpipinstallpeepomap🎨 Colormapsimportpeepomappeepomap.tools.display_colormaps(pepomap.cmaps)💻 How to useSimple import and choose a colormap from the above list by it`s name.importpeepomapcmap=peepomap.cmaps["storm"]🏗️ DevelopmentCreate the virtual env usingPoetry:poetryinstall
Package: peepy
peepyA simple Observer Pattern implementation with decoratorsInstallationSimply instasll from pypi:pip3installpeepyUsageObservables can be connected to bserver callbacks siply by callling the bind_to method or by using the observe, observe_dict and observer_function decoratorsfrompeepy.ObservablesimportObservable,ObservableDict,observe,observe_dictfoo=Observable('important object')foo2=Observable('important object 2')@observe(foo,foo2)defobserver(label,old,new):print('Changing{0}\'s value from{1}to{2}'.format(label,old,new))foo.value='bar'foo.value='baz'foo2.value=foofoo_dict=ObservableDict({'foo':1,'bar':{}})@foo_dict.observer_functiondefdict_observer(label,old,new):print('Changing key{0}from{1}to{2}'.format(label,old,new))foo_dict['foo']=2foo_dict['foo2']=20foo_dict['bar']={'baz':None}
Package: peercoin-rpc
peercoin_rpcPeercoin_rpc is a simple and minimal library made for communication withpeercoindvia JSON-RPC protocol.
It has a single dependency - a Pythonrequestslibrary and it supports both mainnet and testnet peercoin network with authentication or SSL encryption.
There is a single class to be imported from the library -Client.Clientclass methods are named the same asppcoindRPC methods so learning curve is non-existant.Installpip install git+git://github.com/peercoin/peercoin_rpc.gitorpip install peercoin_rpcHow to usefrom peercoin_rpc import ClientSpawn a new Client object with desired arguments:ppcnode = Client(testnet=True, username="username", password="password", ip=, port=)Use it:ppcnode.getinfo()ppcnode.getpeerinfo()ppcnode.getbalance()