Saravanan666/pypi-org huggingface dataset:
Package: imctools
imctoolsAn IMC file conversion tool that aims to convert IMC raw data files (.mcd, .txt) into an intermediary ome.tiff, containing all the relevant metadata. Further it contains tools to generate simpler TIFF files that can be directly be used as input files for e.g. CellProfiller, Ilastik, Fiji etc.Documentation is available athttps://bodenmillergroup.github.io/imctoolsRequirementsThis package requires Python 3.7 or later.Using virtual environments is strongly recommended.InstallationInstall imctools and its dependencies with:pip install imctoolsUsageSeeQuickstartAuthorsCreated and maintained by Vito Zanotellivito.zanotelli@uzh.chand Anton Rauanton.rau@uzh.chContributingContributingChangelogChangelogLicenseMIT
Package: imdabessay
Make the imdabes sun say stuffInstallationYou can install imdabessay fromPyPI:pip install imdabessayThe reader is supported on Python 2.7, as well as Python 3.4 and above.How to useimdabessay is a command line application. To use, simply call the program with an optional argument:$ imdabessay "I am the best."
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                      < I am the best. >
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Package: imdamd-tools
A sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.This is the README file for the project.The file should use UTF-8 encoding and be written usingreStructuredText. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: imdata
ImDataAuthor: JeefEmail:jeefy163@163.comInstallationUsegitto clone it for the github.com.SeeGithubOr seePyPIto installpip install imdata.Notice thatAll build files are underdistUsagethe main class is ImData.Dependeces: imageioAdvice: Use python that later than 3.5, no support for 2.7Example usage seetest.pyfor uses
Package: im-data-manager-api
Informatics Matters Data Manager API ClientA Python 3 package that provides simplified access to key parts of the
Informatics Matters Data Manager API REST interface. The functions provide
access to some of the key API methods, implemented initially to support
execution of Jobs from a Fragalysis stackbackend.The following API functions are available: -DmApi.get_access_token()DmApi.set_api_url()DmApi.get_api_url()DmApi.ping()DmApi.create_project()DmApi.delete_instance()DmApi.delete_instance_token()DmApi.delete_project()DmApi.delete_unmanaged_project_files()DmApi.get_available_jobs()DmApi.get_available_projects()DmApi.get_job()DmApi.get_job_by_name()DmApi.get_instance()DmApi.get_project()DmApi.get_project_instances()DmApi.get_task()DmApi.get_unmanaged_project_file()DmApi.get_unmanaged_project_file_with_token()DmApi.get_version()DmApi.list_project_files()DmApi.put_unmanaged_project_files()DmApi.start_job_instance()DmApi.set_admin_state()Anamedtupleis used as the return value for many of the methods: -DmApiRvIt contains a booleansuccessfield and a dictionarymsgfield. Themsgtypically contains the underlying REST API response content
(rendered as a Python dictionary), or an error message if the call failed.Installation (Python)The API utilities are published onPyPIand can be installed from
there:pip install im-data-manager-apiDocumentationProject documentation is available in thedata-manager-apiproject onRead the DocsGet in touchReport bugs, suggest features or view the source codeon GitHub.
Package: im-data-manager-job-decoder
Informatics Matters Data Manager Job DecoderA package that simplifies the decoding of encoded text strings.
Given an encoded string thedecode()method
returns the decoded value or an error.For example, given the followingjinja2encoded string'{{ foo }},bar={{bar }},baz={{baz }}'and variable map{'foo': 1, 'bar': 2, 'baz': 3}the decoder returns
the string'foo=1, bar=2, baz=3'.The following encoding/decoding formats are supported: -jinja2 (3.0)The package also providesvalidate_job_schema()andvalidate_manifest_schema()functions, which can (should) be used to
validate the Manifests and Job definitions against the
built-in schemas.Installation (Python)The Job decoder is published onPyPIand can be installed from
there:pip install im-data-manager-job-decoderOnce installed you can validate the definition (expected to be a dictionary
formed from the definition YAML file) with:>>> from decoder import decoder
>>> error: Optional[str] = decoder.validate_manifest_schema(manifest)
>>> error: Optional[str] = decoder.validate_job_schema(job_definition)And you can decode encoded fields within the job definition.
Here we’re decoding the job’s ‘command’ using a map of variables and their
values:>>> decoded, success = decoder.decode(definition['command'],
                                      variables,
                                      'command',
                                      decoder.TextEncoding.JINJA2_3_0)A method also exists to generate a Data Manger Job documentation URL
for the supported repository types. For example, to get the
auto-generated documentation URL for a job definition hosted in a GitHub
repository you can do this:>>> doc_url: str = decoder.get_job_doc_url("github",
                                           collection
                                           job_id,
                                           job_definition,
                                           manifest_url)We supportgithubandgitlabas repository types.Get in touchReport bugs, suggest features or view the source codeon GitHub.
Package: im-data-manager-job-utilities
Informatics Matters Data Manager Job UtilitiesA Python 2/3 package that simplifies the generation ofeventsandcostlines written to aSquonk2 Jobstdout stream.The following utilities are available: -DmLog.emit_event()DmLog.emit_cost()Installation (Python)The Job utilities are published onPyPIand can be installed from
there:pip install im-data-manager-job-utilitiesOnce installed you can use the available classes:>>> from dm_job_utilities.dm_log import DmLog
>>> DmLog.emit_event('Hello World!')
2022-02-03T16:39:27+00:00 # INFO -EVENT- Hello World!
>>> from decimal import Decimal
>>> DmLog.emit_cost(Decimal('5.7'))
2022-02-03T16:40:16+00:00 # INFO -COST- 5.7 1Costs aretotalby default but can be issued as an incremental value:>>> DmLog.emit_cost(Decimal('0.5'), incremental=True)
2022-02-03T16:40:16+00:00 # INFO -COST- +0.5 2The final value on each cost line is a unique sequence number. This value
is typically an integer that increments with each line. It is required and
is used by the Data Manager to avoid duplicating costs.The Data Manager uses thepython-dateutilpackage to parse
dates and times from the generated log lines. The package is extremely
tolerant of many formats but if you are in control of the
string, the preferred format would be to use a classic UTC ISO string like%Y-%m-%dT%H:%M:%S%zso that the date and time is written as:2022-03-20T10:49:41+00:00Get in touchReport bugs, suggest features or view the source codeon GitHub.
Package: im-data-manager-metadata
A metadata framework package for the Data Tier Data Manager service.
Theim-data-manager-metadatapackage is a set of utilities
employed by theInformatics MattersData-Manager service
as a metadata framework for molecular datasets.DependenciesPyYAML>=5.3Running the Unit Tests>>> python -m venv ~/.venv/data-manager-metadata
>>> source ~/.venv/data-manager-metadata/bin/activate
>>> pip install --upgrade pip
>>> pip install -r package-requirements.txt
>>> python -m unittest test.test_metadata
>>> python -m unittest test.test_apiRunning the Command Line Interfacemd-manage.pyThe data manager metadata command line interface can be used by applications to
add annotations to the Metadata by means of an annotations.json files that can be
uploaded.help functions. Note that theannotation_type(lb, fd or se) is required.>>> python md_manage.py -h
>>> python md_manage.py lb -h
>>> python md_manage.py fd -h
>>> python md_manage.py se -hCreates a label annotation in an annotations.json file placed in test/output.
Note that thelabelis required. Running the command again will create a second annotation
in the same file (i.e. a list of two).>>> python md_manage.py lb test/output/results.sdf 'foo' -lv='bar'Creates a FieldsDescriptorAnnotation in an annotations.json file placed in test/output.
If the annotations.json file exists it will add it to the list of annotations in the file.>>> python md_manage.py fd test/output/results.sdf -fo='squonk2-job'
                        -fp='minimizedAffinity,number,Binding affinity
                        predicted by smina using the vinardo scoring function,true,true'
                        -fd='Run smina docking'Creates a service execution annotation in an annotations.json file placed in test/output.>>> python md_manage.py se test/output/results.sdf -su=bob -sys='run-smina'
                       -sy='test/input/virtual-screening.yaml'
                       -sp param1=val1 param2=val2 -fo='squonk2-job'
                       -fp='minimizedAffinity,number,Binding affinity predicted,true,true'
                       -fd='Run smina docking'ContributingThe project uses: -pre-committo enforce linting of files prior to committing them to the
upstream repositoryCommitizento enforce a [Convention Commit] commit message formatBlackas a code formatterYouMUSTcomply with these choices in order to  contribute to the project.To get started review the pre-commit utility and the conventional commit style
and then set-up your local clone by following theInstallationandQuick Startsections: ->>> pip install -r package-requirements.txt
>>> pre-commit install -t commit-msg -t pre-commitNow the project’s rules will run on every commit, and you can check the
current health of your clone with: ->>> pre-commit run --all-files
Package: imdbCollector
# imdbSearches and Collects movie information with title or imdb id##Examples:```pythonfrom imdb import Moviemovie = Movie(title='The Matrix')  # ormovie = Movie(id="tt0133093")print movie.actors()>>>>[u'Keanu Reeves', u' Laurence Fishburne', u' Carrie-Anne Moss', u' Hugo Weaving']print movie.genre()>>>>[u'Action', u' Sci-Fi']print movie.rating()>>>>8.7print movie.poster()>>>>http://ia.media-imdb.com/images/M/MV5BMTkxNDYxOTA4M15BMl5BanBnXkFtZTgwNTk0NzQxMTE@._V1_SX300.jpg``````pythonfrom imdb import Searchresults = Search('eternal').get_list()>>>>[{u'imdbID': u'tt0338013', u'Year': u'2004', u'Type': u'movie', u'Title': u'Eternal Sunshine of the Spotless Mind'}, {u'imdbID': u'tt0885520', u'Year': u'2006', u'Type': u'movie', u'Title': u'Eternal Summer'}, {u'imdbID': u'tt0156524', u'Year': u'1940', u'Type': u'movie', u'Title': u'The Eternal Jew'}, {u'imdbID': u'tt0158011', u'Year': u'1998', u'Type': u'movie', u'Title': u'The Eternal'}, {u'imdbID': u'tt0382019', u'Year': u'2004', u'Type': u'movie', u'Title': u'Eternal'}, {u'imdbID': u'tt2404217', u'Year': u'2013', u'Type': u'movie', u'Title': u'The Eternal Zero'}, {u'imdbID': u'tt1488591', u'Year': u'2009', u'Type': u'movie', u'Title': u'Professor Layton and the Eternal Diva'}, {u'imdbID': u'tt0287665', u'Year': u'2002', u'Type': u'movie', u'Title': u'Eternal Blood'}, {u'imdbID': u'tt3642618', u'Year': u'2015', u'Type': u'movie', u'Title': u'Life Eternal'}, {u'imdbID': u'tt0402764', u'Year': u'2004', u'Type': u'movie', u'Title': u'Love Is Eternal While It Lasts'}]movies_with_eternal = Search('eternal').get_all() # returns a generator of Moviefor m in movies_with_eternal:print m.title()>>>> Eternal Sunshine of the Spotless Mind>>>> Eternal Summer>>>> The Eternal Jew>>>> The Eternal>>>> Eternal>>>> The Eternal Zero>>>> Professor Layton and the Eternal Diva>>>> Eternal Blood>>>> Life Eternal>>>> Love Is Eternal While It Lasts```
Package: imdbdump
IMDbDumpIMDbDump is a small library that provides the functionality to parse imdb.com for all single episode ratings for TV shows. It's easily possible to dump this information to a JSON or CSV file. You can use the command line interface or import it in your own code.Installationpip install imdbdumpUsageCLIpython imdb_fetcher.py --json --csv tt0944947Importingfromimdbdump.imdb_fetcherimportImdbFetcherimdb=ImdbFetcher('tt0944947')imdb.save_csv()imdb.save_json()
Package: imdb-lib
IMDB_LIB PackageThe main purpose of this package is to give acess to imdb data by scraping the website.FunctionalitiesImport statemanthttpIMDBis the main class that you'll be using for now (version 0.1). It is responsible for all available data that can be retrieved.from imdb_lib.session import httpIMDB

imdb = httpIMDB()httpIMDB.getPopularTVShows()andhttpIMDB.getPopularMovies()Scrape the top 100 most popular TV Shows/Movies according to the IMDB rankingReturns a pandas dataframe with 100 rows and the following columns:Id,title, poster, ratinghttpIMDB.getTopRatedTVShows()andhttpIMDB.getTopRatedMovies()Scrape the top 100 most popular TV Shows/Movies according to the IMDB rankingReturns a pandas dataframe with 250 rows and the following columns:Id,title, poster, ratinghttpIMDB.getTitleDetails(id)Scrape the given title id for detailsReturns a pandas dataframe with one row and the following columns:title, poster, sinapse, rating, popularity, genres, types, top_cast, runtime, durationhttpIMDB.searchTitle(title)As the name suggests, it is used to search for a title by its nameRequires 1 parameter, the title to be searchedReturns a pandas dataframe with the following columns:Id and Title
Package: imdb-metadata-extractor
IMDB metadata extractorExtracts a movie metadata from the IMDB using the JSON+LD annotations provided in the
IMDB HTML pages.Installing$ pip install imdb-metadata-extractorUsage$ imdb-metadata-extractor tt0058700
{
    "name": "The Last Man on Earth",
    "poster": "https://m.media-amazon.com/images/M/MV5BODA0MzRhYWMtMGUxYi00NmQyLWEyOWYtMGI4NjM3ZDE5NTEyXkEyXkFqcGdeQXVyMTQxNzMzNDI@._V1_.jpg",
    "description": "When a disease turns all of humanity into the living dead, the last man on earth becomes a reluctant vampire hunter.",
    "rating": 6.7,
    "release": "1964-05-06",
    "genre": "Drama; Horror; Sci-Fi",
    "actors": "Vincent Price; Franca Bettoia; Emma Danieli"
}By default the results are displayed in the STDOUT, but It's also possible to set a output file and
format (JSON, CSV of XLSX).$ imdb-metadata-extractor tt0058700 --output tt0058700.csv --format csv
Package: imdbparser
Search IMDb and get movie information.
Incredibly basic, limited feature-set, somewhat fast.UsageGet from ID>>>fromimdbparserimportIMDb>>>imdb=IMDb()>>>movie=imdb.get_movie(1954470)>>>movie.fetchedFalse>>>movie.fetch()>>>movie.fetchedTrue>>>movie.__dict__...Search>>>fromimdbparserimportIMDb>>>imdb=IMDb()>>>search_result=imdb.search_movie('Matrix')>>>search_result.fetchedFalse>>>search_result.fetch()>>>search_result.fetchedTrue>>>search_result.results[<Moviefetched=Falseimdb_id='0133093'title='The Matrix'year=1999>,<Moviefetched=Falseimdb_id='0234215'title='The Matrix Reloaded'year=2003>,...]>>>fromimdbparserimportIMDb>>>imdb=IMDb()>>>search_result=imdb.search_tv_show('it crowd')>>>search_result.fetchedFalse>>>search_result.fetch()>>>search_result.fetchedTrue>>>search_result.results[<Moviefetched=Falseimdb_id='0487831'title='The IT Crowd'year=None>,<Moviefetched=Falseimdb_id='0944954'title='The IT Crowd'year=None>,...]LicenseMIT, see LICENSE
Package: imdb-rotten-tomatoes-metacritic
imdb-rotten_tomatoes-metacriticWith the help of this module, you can get the imdb,rotten tomatoes and metacritic rating.Also you can get how many peoples have voted on imdb,rotten tomatoes and metacritic so thay there ratings are calculated--InstallationInstall the package--Usagefromimdb_rtomatoes_metacriticimportRating#In the quotes give the movie name or webseries name for which you want to get the ratings or votesmovie=Rating("Avengers Endgame")#To get the imdb scoreimdb_score=movie.imdb_score()orprint(movie.imdb_score())#To get the number of people who have voted on imdbimdb_votes=movie.imdb_votes()orprint(movie.imdb_votes())#To get the Rotten Tomatoes rating that is tomatometertomatometer=movie.tomatometer()orprint(movie.tomatometer())#To get the number of reviews on rotten tomatoesreviews=movie.tomatometer_reviews()orprint(movie.tomatometer_reviews())#To get the metacritic rating that is metascoremetascore=movie.metacritic_score()orprint(movie.metacritic_score())#To get the critic reviews on metacriticmetacritic_votes=movie.metacritic_votes()orprint(movie.metacritic_votes())
Package: imdb-search
IMDB SearchA small CLI to search for movies than an actor/actress has starred in.Installationpip3 install imdb-searchUsageusage: imdb-search [-h] [-r] [-o filename] name

positional arguments:
  name                  name of the actor/actress to search for

optional arguments:
  -h, --help            show this help message and exit
  -r, --reverse         return movies in reverse order (most recent first)
  -o filename, --output filename
                        save results to file in JSON format
Package: imdby
imdbyimdbyis a Python package useful to retrieve and manage the data of theIMDbmovie database about movies, people, characters and companies.SourceLinkPyPI:https://pypi.org/project/imdby/Repository:https://github.com/santhoshse7en/imdby/Documentation:https://santhoshse7en.github.io/imdby_doc/Main featuresimdbyis a Python package useful to retrieve and manage the data of the IMDb movie database about movies, people, characters and companies. Sentiment Analysis for IMDb user reviews is included.imdbypackage can be very easily used by programmers and developers to provide access to the IMDb's data to their programs.Platform-independent and written in Python 3 it can retrieve data from both the IMDb's web server and a local copy of the whole database.Some simple example scripts - useful for the end users - are included in this package;imdby powers many other software and has been used in various research papers.Curious about that?Dependenciesbeautifulsoup4seleniumchromedriver-binaryvaderSentimenttextblobpandasInstallationWhenever possible, please use the latest version from the repository::pipinstallgit+https://github.com/santhoshse7en/imdbBut if you want, you can also install the latest release from PyPI::pipinstallimdbyA GlanceDownload it by clicking the green download button here on Github. Here's an example that demonstrates how to use imdby:# create an instance of the IMDb classfromimdby.imdbimportimdb# get a moviedetails=imdb('tt4154796')# print the names of the directors of the movieprint('Directors:')foriinrange(len(details.directors)):print(details.directors[i])# print the genres of the movieprint('Genres:')foriinrange(len(details.genre)):print(details.genre[i])Directory of IMDb classContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: imd-cookie-cutter
IMD Cookie CutterA tool for removing atoms in IMD config files, just like a cookie cutter in higher dimensions.FeaturesMultiprocessed (not implemented yet)Without external dependenciesOptimized for IMD *.chkpt filesWorks in 2D and 3D (theoretically unlimited dimensions supported)Flexible slicing setup for your atom configuration by simply overriding a classPossibility to modify the atom data (coordinates, velocity, type, etc.) individuallyInstallationIMD Cookie Cutter is uploaded to thePython Package Index (PyPI). This makes it easy installing it via pip:python3 -m pip install [--user] imd-cookie-cutter
Package: imddaily
imddailyimddaily is a python package to download and convert Daily Real Time data from
India Meteorological Department.The Daily Real Time Data available,rain = Daily Rainfall dataraingpm = Daily Merged Satellite Guage Rainfall (GPM) datatmax = Daily Maximum Temperature datatmin = Daily Minimum Temperature Datatmaxone = Daily Maximum Temperature data (1.0 degree)tminone = Daily Minimum Temperature Data (1.0 degree)Installationimddaily can be installed through pip,pip install imddailydependencies include rasterio, numpy, affine, tqdm, requests.Usageimddaily package can download and convert the IMD datafrom imddaily import imddaily
data = imddaily.get_data('rain', '2020-01-01', '2020-12-31', '/Users/home/rain/grd/')
data.to_geotiff('/Users/home/rain/tif/')Licenseimddaily is available under theMITLicense.ReferencesGitHub:https://github.com/balakumaran247/imddailyIMD:https://www.imdpune.gov.inData:https://www.imdpune.gov.in/Seasons/Temperature/temp.html