Saravanan666/pypi-org huggingface dataset:
Package: metarace-tagreg
metarace-tagregRead transponder ids from a connected
decoder.RequirementsPython >= 3.9Gtk >= 3.0metarace >= 2.1.1InstallationDebian 11+Install system requirements for tagreg and metarace with apt:$ sudo apt install python3-venv python3-pip python3-cairo python3-gi python3-gi-cairo
$ sudo apt install gir1.2-gtk-3.0 gir1.2-rsvg-2.0 gir1.2-pango-1.0
$ sudo apt install python3-serial python3-paho-mqtt python3-dateutil python3-xlwtIf not already created, add a virtualenv for metarace packages:$ python3 -m venv --system-site-packages ~/Documents/metarace/venvActivate the virtualenv and install tagreg with pip:$ source ~/Documents/metarace/venv/bin/activate
(venv) $ pip3 install tagreg
Package: metarace-ttstart
metarace-ttstartTime trial start console application. Includes time of day,
rider number, rider name, a 30 second count-down and audible
start beeps.For battery operated terminals, the screen can be set to dim
in between starters.ConfigurationConfiguration is via metarace sysconf section 'ttstart' with the
following keys:key(type) description [default]topic(string) telegraph topic for start list updates [startlist]fullscreen(boolean) run application fullscreen after initialisationbacklightdev(string) sysfs path to backlight device [null] (1) eg: /sys/class/backlight/acpi_video0backlightlow(float) dimmed backlight level between starters [0.25]backlighthigh(float) backlight level during countdown [1.0]startlist(string) filename for a csv startlist file [startlist.csv]syncthresh(float) maximum allowed audio de-sync in seconds [0.12] (2)Notes:The backlight brightness file must be writable in order
for dimming to workThe acoustic start signal is terminated if it is not playing
in sync with the displayed countdown.Remote Controlttstart connects to telegraph and subscribes to the topic nominated.
To re-configure the start list, publish a JSON encoded array of arrays
with the following columns:start time (string), requiredrider number (string), optionalrider series (string), ignoredrider name (string), optionalFor example:[["9h15:00","","","[Event Start]"], ["9h16:00","1","","First RIDER"]]RequirementsPython >= 3.9Gtk >= 3.0metarace >= 2.1.1tex-gyre fontsgstreamer alsa pluginsNote: Some 32 bit systems (notably Intel Atom Toughbooks) will not
play audio with the default Debian desktop installation.
The workaround is to remove pulseaudio and use alsa directly:$ sudo apt remove 'pulseaudio*'InstallationDebian 11+Install system requirements for ttstart and metarace with apt:$ sudo apt install python3-venv python3-pip python3-cairo python3-gi python3-gi-cairo
$ sudo apt install gir1.2-gtk-3.0 gir1.2-rsvg-2.0 gir1.2-pango-1.0 gir1.2-gstreamer-1.0 gstreamer1.0-alsa tex-gyre
$ sudo apt install python3-serial python3-paho-mqtt python3-dateutil python3-xlwtIf not already created, add a virtualenv for metarace packages:$ mkdir -p ~/Documents/metarace
$ python3 -m venv --system-site-packages ~/Documents/metarace/venvActivate the virtualenv and install ttstart with pip:$ source ~/Documents/metarace/venv/bin/activate
(venv) $ pip3 install metarace-ttstart
Package: metarchive
Metarchivewas created during the need to analyse some archived sites fromhttp://archive.orgto create some metrics. In this endeavor certain procedures repeated
itself over and over again, so I decided to put them to a package. In the spirit of Open Source
I decided to make the project public, maybe someone out there can cut some corners using it.
If so, great thing, let me know if you like to.Please be reasonable when accessing pages archived by archive.org. It's a great project and there
is no need to create excessive stress on their servers.Below is a sample invocation of the library, for more information please have a look at the
documentation.frommetarchiveimportcreate_url_analyser,AnalyseURL,ArchiveLinkanalyse:AnalyseURL=create_url_analyser()links:list[ArchiveLink]=list(analyse('http://web.archive.org/web/20210101000012/http://example.com/'))print(links)# Will yield something like[ArchiveLink(timestamp=datetime.datetime(2020,11,30,23,42,54),original_url=AnyUrl('https://example.com/',scheme='https',host='example.com',tld='com',host_type='domain',path='/'),category=None),ArchiveLink(timestamp=datetime.datetime(2021,2,1,0,5,5),original_url=AnyUrl('https://example.com/',scheme='https',host='example.com',tld='com',host_type='domain',path='/'),category=None),ArchiveLink(timestamp=datetime.datetime(2020,12,30,23,54,41),original_url=AnyUrl('http://example.com/',scheme='http',host='example.com',tld='com',host_type='domain',path='/'),category=None),ArchiveLink(timestamp=datetime.datetime(2021,1,2,0,7,38),original_url=AnyUrl('http://example.com/',scheme='http',host='example.com',tld='com',host_type='domain',path='/'),category=None),ArchiveLink(timestamp=datetime.datetime(2019,12,31,23,45,1),original_url=AnyUrl('http://example.com/',scheme='http',host='example.com',tld='com',host_type='domain',path='/'),category=None),ArchiveLink(timestamp=datetime.datetime(2022,1,1,0,19,39),original_url=AnyUrl('https://example.com/',scheme='https',host='example.com',tld='com',host_type='domain',path='/'),category=None),ArchiveLink(timestamp=datetime.datetime(2021,1,1,0,0,12),original_url=AnyUrl('http://web.archive.org/screenshot/http://example.com/',scheme='http',host='web.archive.org',tld='org',host_type='domain',path='/screenshot/http://example.com/'),category=None),ArchiveLink(timestamp=datetime.datetime(2021,1,1,0,0,12),original_url=AnyUrl('http://example.com/',scheme='http',host='example.com',tld='com',host_type='domain',path='/'),category=None),ArchiveLink(timestamp=datetime.datetime(2021,1,1,0,0,12),original_url=AnyUrl('http://example.com/',scheme='http',host='example.com',tld='com',host_type='domain',path='/'),category=None),ArchiveLink(timestamp=datetime.datetime(2021,1,1,0,0,12),original_url=AnyUrl('https://www.iana.org/domains/example',scheme='https',host='www.iana.org',tld='org',host_type='domain',path='/domains/example'),category=None)]
Package: metarclone
metarclonemetarcloneis a command-line utility to sync files with file attributes to rclone remotes. It aggregates files smaller than a certain threshold into larger tarballs, thus useful for remotes where creating lots of small files are not feasible, e.g. Google Drive, S3 Glacier.*Note: Still under development; partial download sync is not implemented currentlyFeaturesSmall-file aggregation while preserving directory structure whenever possibleFile attributes preservation (ctime, mtime, sparse files; permission, owner, ACLs on Linux) using tarPartial sync based on whole-file content and/or timestampsConfigurable compression methods
Package: metarepo
Metarepo: An alternative to git submodulesMetarepo is used to manage dependencies on other git repositories when git submodules is not sufficient.
It was inspired by therepotool by Google but instead of requiring the
manifest to be stored in its own repository, it is stored in the same repository.Installation# Install from PyPIpip3install--upgrademetarepo# Install from git using PIPpip3install--upgradegit+https://github.com/blejdfist/git-metarepoUsageYou can runmetarepoin two ways, standalone or using git. Both methods work the same and it is only a matter of personal taste.gitmeta
metarepoCreate an initialmanifest.ymlconfiguration using the init commandgitmetainitSynchronize the repositoriesgitmetasyncManifest structurerepos:
  - url: https://github.com/blejdfist/pycodegen
    path: tools/pycodegen
    track: masterFieldExplanationRequiredurlGit URL to cloneYespathWhere to clone the repoYestrackWhat branch/tag to trackNo (default: master)
Package: meta-rknn
metarknnmetarknn部署通用框架1、安装最新版 meta-cvpip install meta-cv2、安装最新版 meta-rknnpip install meta-rknn3、图像分类示例（参考classification_demo.py代码）import cv2, platform
import metarknn as m

Classification = m.Classification

y = Classification(model_path='models/mixnet_xl_bs24_2.rknn',
                   input_width=224,
                   input_height=224,
                   use_preprocess=True,
                   class_names=classnames,
                   device_id=0)

batch_size = 24
img = cv2.imread('models/bottle.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)

# 显示
y.show(img, _dets, _scores, _labels)
cv2.imwrite("models/bottle.png", img)4、目标检测示例（参考detection_demo.py代码）import cv2, platform
import metarknn as m

Detection = m.Detection

y = Detection(model_path='models/yolov8m.rknn',
              input_width=640,
              input_height=480,
              use_preprocess=True,
              pad=True,
              confidence_thresh=0.5,
              nms_thresh=0.3,
              class_names=classnames,
              device_id=0)

batch_size = 1
img = cv2.imread('models/bus.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)

# 显示
y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/bus.png", img)5、实例分割示例（参考segment_demo.py代码）import platform, cv2
import metarknn as m

Segment = m.Segment

y = Segment(model_path='models/yolov8m-seg.rknn',
            input_width=640,
            input_height=480,
            use_preprocess=True,
            pad=True,
            confidence_thresh=0.5,
            nms_thresh=0.3,
            class_names=classnames,
            device_id=0)

batch_size = 1
img = cv2.imread('models/bus.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)

# 显示
y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/bus.png", img)6、模型转换与量化（本地运行）import metarknn as m

Quantization = m.Quantization

q = Quantization(model_path，    # onnx模型路径
                 dataset,   # dataset文件路径
                 mean=[[0, 0, 0]],
                 std=[[255, 255, 255]],
                 batch_size=1)   # 定义模型输出层

if is_hybrid:   # 是否混合量化
    self.model = q.hybrid_convert()
else:   # 非混合量化（是否int8量化）
    self.model = q.convert(quantize)
Package: meta-rt
metartmetart部署通用框架1、安装最新版 meta-cvpip install meta-cv2、安装最新版 meta-rtpip install meta-rt3、目标检测示例（参考detection_demo.py代码）import platform, cv2
import metart as m

Detection = m.Detection

y = Detection(model_path='models/yolov8m.rt',
              input_width=640,
              input_height=480,
              use_preprocess=True,
              pad=True,
              normal=True,
              swap=(2, 0, 1),
              confidence_thresh=0.5,
              nms_thresh=0.3,
              class_names=classnames,
              device_id=0)

batch_size = 1
img = cv2.imread('models/bus.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)

# 显示
y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/bus.png", img)4、实例分割示例（参考segment_demo.py代码）import platform, cv2
import metart as m

Segment = m.Segment

y = Segment(model_path='models/yolov8m-seg.rt',
            input_width=640,
            input_height=480,
            use_preprocess=True,
            pad=True,
            normal=True,
            swap=(2, 0, 1),
            confidence_thresh=0.5,
            nms_thresh=0.3,
            class_names=classnames,
            device_id=0)

batch_size = 1
img = cv2.imread('models/bus.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)

# 显示
y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/bus.png", img)
Package: metascapes.web2cache
ContentsChange history0.2 (xxxx-xx-xx)Detailed DocumentationContributorsDownloadCode repository:http://svn.somewhere.com/…Questions and comments to somemailing_listReport bugs athttp://bug.somewhere.com/..Change historyChangelog0.2 (xxxx-xx-xx)Created recipe with ZopeSkel
[Bernhard Snizek]Detailed DocumentationIntroductionThis is a full-blown functional test. The emphasis here is on testing what
the user may input and see, and the system is largely tested as a black box.
We use PloneTestCase to set up this test as well, so we have a full Plone site
to play with. Wecaninspect the state of the portal, e.g. using
self.portal and self.folder, but it is often frowned upon since you are not
treating the system as a black box. Also, if you, for example, log in or set
roles using calls like self.setRoles(), these are not reflected in the test
browser, which runs as a separate session.Being a doctest, we can tell a story here.First, we must perform some setup. We use the testbrowser that is shipped
with Five, as this provides proper Zope 2 integration. Most of the
documentation, though, is in the underlying zope.testbrower package.>>> from Products.Five.testbrowser import Browser
>>> browser = Browser()
>>> portal_url = self.portal.absolute_url()The following is useful when writing and debugging testbrowser tests. It lets
us see all error messages in the error_log.>>> self.portal.error_log._ignored_exceptions = ()With that in place, we can go to the portal front page and log in. We will
do this using the default user from PloneTestCase:>>> from Products.PloneTestCase.setup import portal_owner, default_passwordBecause add-on themes or products may remove or hide the login portlet, this test will use the login form that comes with plone.>>> browser.open(portal_url + '/login_form')
>>> browser.getControl(name='__ac_name').value = portal_owner
>>> browser.getControl(name='__ac_password').value = default_password
>>> browser.getControl(name='submit').click()Here, we set the value of the fields on the login form and then simulate a
submit click.  We then ensure that we get the friendly logged-in message:>>> "You are now logged in" in browser.contents
TrueFinally, let’s return to the front page of our site before continuing>>> browser.open(portal_url)-- extra stuff goes here --ContributorsBernhard Snizek, AuthorDownload
Package: metaschema
Meta SchemaThis library contains the Pydantic models for the metadata schema standard outlined in thisSchema Guide.The pydantic models for each data type will allow for an easy conversion across JSON, dict, and Python-objects (Pydantic model). Additionally, the metadata is also automatically validated using the expected field types to guarantee that the metadata is consistent.InstallThe package is available on PyPi:pip install metaschemaNotesGenerating the models from the JSON schema. This is done using thedatamodel-codegen.First, download the JSON schema from NADA API. For example, for documents (https://ihsn.github.io/nada-api-redoc/catalog-admin/#tag/Documents). Store the JSON schema indata/examples/doc.json.Then, run the following command to generate the models. Note that the--input-file-typeisjsonand notjsonschemapoetry run datamodel-codegen  --input data/examples/doc.json --input-file-type json --output src/metaschema/doc.pyWe then change theModelto a more explicit name:DocModel.
Package: meta-scraper
Python SDK that allows scraping Facebook (Meta) pages, reviews and more.Generate API TokenInstallationPython 3+pipinstallmeta-scraperLink to the python package
pageInitializationfrommeta_scraperimportMetaClientclient=MetaClient(api_key='API_KEY_FROM_OUTSCRAPER.COM')Scrape Facebook pages data# Get Facebook pages dataresults=client.get_page_data(['outscraper'])## Scrape Facebook pages reviews# Get Facebook pages reviewsresults=client.get_page_reviews(['outscraper'],limit=50)Facebook pages data demo{"id":"your-request-id","status":"Success","data":[{"id":"your-request-id","status":"Success","data":[{"query":"outscraper","about":"At Outscrpaer, we believe that public data is for everybody, and we apply cutting-edge technologies to prove it.","email":"service@outscraper.com","followers":45,"likes":43,"name":"Outscraper","phone":"+1 281-236-8208","rating":5,"reviews":6,"site":"https://outscraper.com/","types":["App page","Website","Business Service"]}]}]}
Package: metasdk
# MetaSDK Python[Документация](http://metasdk.readthedocs.io/)[Полный список примеров](https://github.com/devision-io/metasdk/tree/master/metasdk/examples/)#### Как обновить metasdk в pypi.orgЕсли кто-то из новых людей будет пробовать обновлять - надо дописать гайд как зарегаться на pypi.org и получить возможность обновлять метасдкОбновляем наш репозиторий в соответствии с общими правилами, не забываем апнуть версию (version) в info.py, как обычно в пайтон-либах (на момент написания это - обновляем мастер, создаём ветку с номером задачи из мастера, заливаем коммиты в ветку, создаём пулл-реквест в стеше, при необходимости мержимся с актуальным мастером, проходим ревью, заливаем в мастер)Если не устанавливали, устанавливаем pip3 install wheel && pip3 install twine (если ещё не делали (это как раз тот этап, когда у кого-то может деплоиться, а у кого-то нет)4. Делаем make publish. С 1 января 2024 года pypi больше не использует логин/пароль юзера для обновления проектов. Теперь сначала нужно включить двухфакторную аутентификацию.
Затем можно выпустить API токен для своего аккаунта. При make publish передать в username: “__token__”, в password: <полученый токен>. Подробности:https://pypi.org/help/#apitokenОбновить версию в тех репозиториях, для которых, собственно, делаются изменения (например, metafeedconnectors)Связанные задачи:
- META-2941
- META-2571
Package: metasearch
Meta SearchWelcome to Meta Search. Work like mat1 but you need to use requests ( Actually no building webapplication I am fucking lazy)Result up to 10000000 results! (Modify it in main.py or minified.py)How to use?Runpythonmain.pyCurl TestYou may use curl something like thiscurl-ihttp://127.0.0.1:80it should return{"Result":[null]}and add header with thiscurl-i-H"search: your search query here"http://127.0.0.1:80it may return lots of shit thingsPython TestYou may do something like thisimportrequestsrequests.get("http://127.0.0.1:80",headers={"search":"metaname you want to search"})Node.JS TestYou can try something like thisconstrequest=require('request');request('http://127.0.0.1',function(error,response,body){console.error('error:',error);console.log('statusCode:',response&&response.statusCode);console.log('body:',body);});Test on this serverThe link ishttps://Meta-Search-But-Only-Request.mooping.repl.coYou can do same as above but replacehttp://127.0.0.1with my linkPython Module Documentationimportmetasearchmetasearch.metaserver()That is how you launch serverMeme (not funni)Other's code be likeMy code be likepog
Package: metaseg
MetaSeg: Packaged version of the Segment Anything repositoryThis repo is a packaged version of thesegment-anythingmodel.InstallationpipinstallmetasegUsagefrommetasegimportSegAutoMaskPredictor,SegManualMaskPredictor# If gpu memory is not enough, reduce the points_per_side and points_per_batch.# For imageresults=SegAutoMaskPredictor().image_predict(source="image.jpg",model_type="vit_l",# vit_l, vit_h, vit_bpoints_per_side=16,points_per_batch=64,min_area=0,output_path="output.jpg",show=True,save=False,)# For videoresults=SegAutoMaskPredictor().video_predict(source="video.mp4",model_type="vit_l",# vit_l, vit_h, vit_bpoints_per_side=16,points_per_batch=64,min_area=1000,output_path="output.mp4",)# For manuel box and point selection# For imageresults=SegManualMaskPredictor().image_predict(source="image.jpg",model_type="vit_l",# vit_l, vit_h, vit_binput_point=[[100,100],[200,200]],input_label=[0,1],input_box=[100,100,200,200],# or [[100, 100, 200, 200], [100, 100, 200, 200]]multimask_output=False,random_color=False,show=True,save=False,)# For videoresults=SegManualMaskPredictor().video_predict(source="video.mp4",model_type="vit_l",# vit_l, vit_h, vit_binput_point=[0,0,100,100],input_label=[0,1],input_box=None,multimask_output=False,random_color=False,output_path="output.mp4",)SAHI+ Segment Anythingpipinstallsahimetasegfrommetaseg.sahi_predictimportSahiAutoSegmentation,sahi_sliced_predictimage_path="image.jpg"boxes=sahi_sliced_predict(image_path=image_path,detection_model_type="yolov5",# yolov8, detectron2, mmdetection, torchvisiondetection_model_path="yolov5l6.pt",conf_th=0.25,image_size=1280,slice_height=256,slice_width=256,overlap_height_ratio=0.2,overlap_width_ratio=0.2,)SahiAutoSegmentation().image_predict(source=image_path,model_type="vit_b",input_box=boxes,multimask_output=False,random_color=False,show=True,save=False,)FalAI(Cloud GPU)+ Segment Anythingpipinstallmetasegfal_serverless
fal-serverlessauthlogin# For Auto Maskfrommetasegimportfalai_automask_imageimage=falai_automask_image(image_path="image.jpg",model_type="vit_b",points_per_side=16,points_per_batch=32,min_area=0,)image.show()# Show imageimage.save("output.jpg")# Save image# For Manual Maskfrommetasegimportfalai_manuelmask_imageimage=falai_manualmask_image(image_path="image.jpg",model_type="vit_b",input_point=[[100,100],[200,200]],input_label=[0,1],input_box=[100,100,200,200],# or [[100, 100, 200, 200], [100, 100, 200, 200]],multimask_output=False,random_color=False,)image.show()# Show imageimage.save("output.jpg")# Save imageExtra FeaturesSupport for Yolov5/8, Detectron2, Mmdetection, Torchvision modelsSupport for video and web application(Huggingface Spaces)Support for manual single multi box and point selectionSupport for pip installationSupport for SAHI librarySupport for FalAI
Package: metaseq
Briefly, the goal ofmetaseqis to tie together lots of existing software into
a framework for exploring genomic data.  It focuses on flexibility and
interactive exploration and plotting of disparate genomic data sets.The main documentation formetaseqcan be found athttps://daler.github.io/metaseq.If you usemetaseqin your work, please cite the following publication:Dale, R. K., Matzat, L. H. & Lei, E. P. metaseq: a Python package for
integrative genome-wide analysis reveals relationships between chromatin
insulators and associated nuclear mRNA. Nucleic Acids Res. 42, 9158–9170
(2014).http://www.ncbi.nlm.nih.gov/pubmed/25063299Example 1: Average ChIP-seq signal over promotersExample 1walks you
through the creation of the following heatmap and line-plot figure:Top: Heatmap of ATF3 ChIP-seq signal over transcription start sites (TSS) on
chr17 in human K562 cells.  Middle: average ChIP enrichment over all TSSs
+/- 1kb, with 95% CI band.  Bottom: Integration with ATF3 knockdown RNA-seq
results, showing differential enrichment over transcripts that went up,
down, or were unchanged upon ATF3 knockdown.Example 2: Differential expression scatterplotsExample 2walks
you through the creation of the following scatterplot and marginal histogram
figure:Control vs knockdown expression (log2(FPKM + 1)) for an ATF3 knockdown
experiment.  Each point represents one transcript on chromosome 17.
Marginal distributions are shown on top and side.  1:1 line shown as
a dotted line.  Up- and downregulated genes determined by a simple 2-fold
cutoff.Other featuresIn addition,metaseqoffers:A format-agnostic API for accessing “genomic signal” that allows you to work
with BAM, BED, VCF, GTF, GFF, bigBed, and bigWig using the same API.Parallel data access from the file formats mentioned above“Mini-browsers”, zoomable and pannable Python-only  figures that show genomic
signal and gene models and are spawned by clicking on features of interestA wrapper around pandas.DataFrames to simplify the manipulation and plotting
of tabular results data that contain gene information (like DESeq results
tables)Integrates data keyed by genomic interval (think BAM or BED files) with data
keyed by gene ID (e.g., Cufflinks or DESeq results tables)Check out thefull documentationfor
more.