Saravanan666/pypi-org huggingface dataset:
Package: cos_dumps_file_list
INSTALLpip install cos_dumps_file_listUSAGEubuntu@VM-72-138-ubuntu:~/dump_bucket_file_list$ cos_dumps -h
usage: cos_dumps [-h] -b BUCKET -a APPID -i ACCESS_ID -k SECRET_KEY
                 [-o OUTPUT_FILE] [--with-directory]

optional arguments:
      -h, --help            show this help message and exit
      -b BUCKET, --bucket BUCKET
                              your bucket name(required)
      -a APPID, --appid APPID
                              your appid(required)
      -i ACCESS_ID, --accesskey ACCESS_ID
                              your accesskey(required)
      -k SECRET_KEY, --secretkey SECRET_KEY
                              your secretkey(required)
      -o OUTPUT_FILE, --output OUTPUT_FILE
                              file name that saves contents(default: stdout)
      --with-directory      dump file list with directory(default: False
Package: cosent.guruscan
ContentsIntroductionCreditsCompaniesAuthorsChangelogIntroductionGuruScanis a knowledge management application that enables your employees
to quickly locate collegues that have specific expertise.
Unlike other applications that rely on self-certification, GuruScan
crowdsources expertise profiles by asking each employee who their
“go-to person” is for a specific area of expertise.Ploneis an open source enterprise content management system
suitable for building large intranets.This package, cosent.guruscan, provides single-signon integration
of the GuruScan webservice into a Plone installation. You need
to obtain a GuruScan client id for this to work.After installation, cosent.guruscan adds a global tab ‘Knowledge Center’
that integrates the GuruScan expert locator service. Users only have to log into their
Plone account; they’re then automatically logged into GuruScan as well.For site administrators, a user export view is available at @@guruscan-exportusers
that exports the Plone user database in a format suitable for import
by GuruScan.CreditsCompaniesThis package was created byCosent.AuthorsGuido Stevens aka gyst <guido.stevens@cosent.net>Changelog1.0dev (unreleased)Initial release
Package: cosevadb
CosevaDB - Comma Separated Values DBCosevaDB is a light weight SQL database designed to store, retrieve and manage data in csv format.Targets are,Apps that require SQL DB or external DB serverApps that need lightweight replacement for under-utilised heavy-weight DBs such as oracle, MySQLThose who need a SQL processor and DB to manage existing csv data filesView detailed description onwww.github.com/ppml38/cosevadbFeatures:Written purely in pythonNo external dependenciesIts a best practice to organise folders and csv files i.e., Databases and tables in below structuredatabase_name/
      table_name.csv
      table_header.csv
      ...And to query the database like belowcosevadb.query("select name from path/to/database_name/table_name.csv, path/to/database_name/table_header.csv where role='Manager' & salary >= 5000")However it is not mandatory to follow. you can pass the path of any csv file to parse.How to installpip install cosevadbUsagefromcosevadbimportcosevadbcosevadb.query("select name,age from data/passengers.csv,data/header.csv where native='USA'")Return format[sqlcode,'<message>',[result list / empty list]]SQL return codesSQL CodeDescription1Empty result set0Successwith atleast one result-2Unexpected character on position <index>-3Unexpected character on where clause position <index>-4Unsupported operator in where clause-5Non numeric operand with arithmatic operator <operator>-6Non boolean operand(LHS) with logical operator <operator>-7Non boolean operand(RHS) with logical operator <operator>-8Operator ! must be followed by =-9Unbalanced paranthesis on LHS of operator <operator>-10Unbalanced paranthesis on RHS of operator <operator>-11No field <fieldname> found in header file-12Value for variable <variablename> is missing in atleast one row-13Where clause condition returns non-boolean result-14Incomplete condition in where clauseReserved wordsKeywordsSELECTFROMWHEREOperators supportedOperator typeOperatorsArithmatic operators+, -, *, /, %Comparison operators>, <, >=, <=, =, !=Logical operators&, |Operator precedence (in order from high to low)Operator precedence%/*+-<,<=>,>=!==&|File formatsComma seperated value(.csv) files.InstructionsString values should be within ''A file with headers(Comma separated) should be passed along with csv data fileAs of now only select operations implementedComparison operator '=' is used instead of '=='Expression evaluation will use BODMAS as you studied in school. i.e., It has individual operator precedence unlike python or java. (ex): '9-7+1' will result '1' where python eval() will return 3LicenceMIT Licence. Contributions are welcome
Package: cosg
OverviewCOSG is a cosine similarity-based method for more accurate and scalable marker gene identification.COSG is a general method for cell marker gene identification across different data modalities, e.g., scRNA-seq, scATAC-seq and spatially resolved transcriptome data.Marker genes or genomic regions identified by COSG are more indicative and with greater cell-type specificity.COSG is ultrafast for large-scale datasets, and is capable of identifying marker genes for one million cells in less than two minutes.The method and benchmarking results are described inDai et al., (2021).DocumentationThe documentation for COSG is availablehere.TutorialTheCOSG tutorialprovides a quick-start guide for using COSG and demonstrates the superior performance of COSG as compared with other methods, and theJupyter notebookis also available.QuestionFor questions about the code and tutorial, please contact Min Dai,daimin@zju.edu.cn.CitationIf COSG is useful for your research, please consider citingDai et al., (2021).
Package: cosimtlk
Cosimulation toolkitTable of ContentsInstallationUsageContributingLicenseInstallationUse the package manager pip to install foobar.pipinstallcosimtlkUsageLoad your own data and create a dataset.importcosimtlkContributingContributions and feedback are welcome! For major changes, please open an issue first to discuss
what you would like to change.If you'd like to contribute to the project, please follow these steps:Fork the repository.
Create a new branch for your feature or bug fix.
Make your changes and commit them.
Push your changes to your forked repository.
Submit a pull request describing your changes.LicenseBSD 3-Clause License
Package: cosine-warmup
Cosine Annealing Scheduler with Linear WarmupImplementation of a Cosine Annealing Scheduler with Linear Warmup and Restarts in PyTorch.It has support for multiple parameters groups and minimum target learning rates.Also works with the Lightning Modules!Installationpip install 'git+https://github.com/santurini/cosine-annealing-linear-warmup'UsageIt is important to specify the parameters groups in the optimizer instantiation as the learning rates are directly inferred from the wrapped optimizer.Example: Multiple groupsfrom cosine_warmup import CosineAnnealingLinearWarmup

optimizer = torch.optim.Adam([
    {"params": first_group_params, "lr": 1e-3},
    {"params": second_group_params, "lr": 1e-4},
    ]
)

scheduler = CosineAnnealingLinearWarmup(
    optimizer = optimizer,
    min_lrs = [ 1e-5, 1e-6 ],
    first_cycle_steps = 1000,
    warmup_steps = 500,
    gamma = 0.9
    )
    
# this is equivalent to

scheduler = CosineAnnealingLinearWarmup(
    optimizer = optimizer,
    min_lrs_pow = 2,
    first_cycle_steps = 1000,
    warmup_steps = 500,
    gamma = 0.9
    )Example: Single groupsfrom cosine_linear_warmup import CosineAnnealingLinearWarmup

optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

scheduler = CosineAnnealingLinearWarmup(
    optimizer = optimizer,
    min_lrs = [ 1e-5 ],
    first_cycle_steps = 1000,
    warmup_steps = 500,
    gamma = 0.9
    )
    
# this is equivalent to

scheduler = CosineAnnealingLinearWarmup(
    optimizer = optimizer,
    min_lrs_pow = 2,
    first_cycle_steps = 1000,
    warmup_steps = 500,
    gamma = 0.9
    )Visual Example
Package: cosinnus-core
Notedjango-bootstrap3 (https://github.com/dyve/django-bootstrap3) is the
successor of django-bootstrap-toolkit which will not receive updates for
bootstrap 3. Thus the new app has been created. However, the app is not yet
Python 3 compatible. Therefore I created a fork and pull request
(https://github.com/dyve/django-bootstrap3/pull/42). Right now the
installation of this app must be done by hand:pip install -e "git+git://github.com/Markush2010/django-bootstrap3.git@develop#egg=django-bootstrap3"Once the pull request is merged / the app has Python 3 support we can
activate it in the setup.py.Put'cosinnus'in theINSTALLED_APPSAddurl(r'^',include('cosinnus.urls',namespace='cosinnus')),to yoururls.pyAdjust theTEMPLATE_CONTEXT_PROCESSORS:``TEMPLATE_CONTEXT_PROCESSORS`` = (
    ...
    'cosinnus.utils.context_processors.settings',
    'cosinnus.utils.context_processors.cosinnus',
    ...
)
Package: cosinnus-etherpad
============Installation============* Add ``'cosinnus_etherpad'`` to ``INSTALLED_APPS``* Add the following settings:``COSINNUS_ETHERPAD_API_KEY = '<your API key>'COSINNUS_ETHERPAD_BASE_URL = '<your pad server url>'``* If you want to change the prefix of the title for archived documents / file entries ('Etherpad: '), change the setting ``COSINNUS_ETHERPAD_PREFIX_TITLE``* If you want to change the path in the file hierarchy for archived file entries ('/etherpad'), change the setting ``COSINNUS_ETHERPAD_FILE_PATH``Note that the webserver running the etherpad app has to be in the same domainas the etherpad server, e.g. http://web.yourdomain.com runs cosinnus andhttp://pad.yourdomain.com runs the etherpad server.For tests to work, you need to have a running etherpad server configured intests/settings/base.py
Package: cosinnus-event
============Installation============* Add ``'cosinnus_event', 'easy-thumbnails', 'geoposition'`` to ``INSTALLED_APPS``* Add a setting for the thumbnailer, e.g.```pythonTHUMBNAIL_PROCESSORS = ('easy_thumbnails.processors.colorspace','easy_thumbnails.processors.autocrop','easy_thumbnails.processors.scale_and_crop','easy_thumbnails.processors.filters',)```See https://github.com/SmileyChris/easy-thumbnails for more details .* You might want to specify another value than the default 480 for the height of the map widget:```GEOPOSITION_MAP_WIDGET_HEIGHT = '500'```
Package: cosinnus-marketplace
============Installation============* Add ``'cosinnus_event', 'easy-thumbnails', 'geoposition'`` to ``INSTALLED_APPS``* Add a setting for the thumbnailer, e.g.```pythonTHUMBNAIL_PROCESSORS = ('easy_thumbnails.processors.colorspace','easy_thumbnails.processors.autocrop','easy_thumbnails.processors.scale_and_crop','easy_thumbnails.processors.filters',)```See https://github.com/SmileyChris/easy-thumbnails for more details .* You might want to specify another value than the default 480 for the height of the map widget:```GEOPOSITION_MAP_WIDGET_HEIGHT = '500'```
Package: cosinnus-poll
============Installation============* Add ``'cosinnus_event', 'easy-thumbnails', 'geoposition'`` to ``INSTALLED_APPS``* Add a setting for the thumbnailer, e.g.```pythonTHUMBNAIL_PROCESSORS = ('easy_thumbnails.processors.colorspace','easy_thumbnails.processors.autocrop','easy_thumbnails.processors.scale_and_crop','easy_thumbnails.processors.filters',)```See https://github.com/SmileyChris/easy-thumbnails for more details .* You might want to specify another value than the default 480 for the height of the map widget:```GEOPOSITION_MAP_WIDGET_HEIGHT = '500'```
Package: coslab-core
Cross-service Laber Agreement ScoreImage recognition services provide different labels for the same images,
with differences broadly either being different word choices for the same
concept or not seeing same objects on both images.
Cross-service Laber Agreement Score (COSLAB) is a method to evaluate the
semantic similarity on image labels across several image recognition
systems.
It first produces each image a set of labels, one for each service.
Then it uses word embedding models to find the closest match across label
sets to see how much cross-service agreement there are for each label.Conceptual how to useOur work further highlights that image recognition services differ on what
they see on images.
Due to this, we suggest approaching them asinterprentationsinstead
of objective truths.
In response, we propose two different strategies which you can employ to
use them in your research:Inclusive strategy: to ensure that your work takes different
interprentations into account, use more than one image recognition service
to label the images.
Then merge all labels together and acknowldge that results have inheritent
differences emerging from different services.Rigorous strategy: to ensure that your work takes different
interprentations into account, use more than one image recognition service
to label the images.
Then use COSLAB to choose only labels above a pre-defined threshold level
for further analysis.Technical how to useTo be added.ReferencesBerg, A., & Nelimarkka, M. (2023). Do you see what I see? Measuring the
semantic differences in image‐recognition services' outputs.Journal of
the Association for Information Science and
Technology.Nelimarkka, M., & Berg, A. (2023). Is the World Different Depending on
Whose AI Is Looking at It? Comparing Image Recognition Services for Social
Science Research.Information Matters,
3(8).ApplicationsNone yet. Please let us know if you use it.
Package: cosmian-anonymization
anonymization-pythonCosmian Anonymization library in PythonUsageAs a libraryfromcosmian_anonymizationimportanonymize_dataframetry:# Anonymize the raw data according to a configuration.anonymized_df=anonymize_dataframe(df_raw,config)exceptValueErrorase:print("Anonymization failed:",e)From command linecosmian-anonymize<input_csv><input_config><output_csv>Build from sourceInstall dependenciespoetryinstallBuild packagepoetrybuildRun testspoetryrunpython-munittesttests/test*.py
Package: cosmian-kms
Cosmian KMS PythonThis library is part ofCloudProof Python.Building and testingYou need to havematurininstalled. To install it, run:python3-mpipinstallmaturinTo build the Python interface, run:maturinbuild--releaseNote: when a new function or class is added to the PyO3 interface, its signature needs to be added to__init__.pyi.To run tests on the Python interface, run:./python/scripts/test.sh
Package: cosmian-lib-sgx
Cosmian lib SGXOverviewPython library for code providers usingCosmian Secure Computation.ExampleA simple structure of the code should look like:$tree..├── secret_mod.py└── run.pywhererun.pyis called the entrypoint and is the only file which can't be encrypted.The goal of the entrypoint is to manage the I/O of you program whereas other module can hide the Python code you want to keep secret.Here is an example of a statistical mean code where:Code Provider hides the whole modulesecret_modwhich is decrypted in the SGX enclaveData Providers send bytes of an arbitrary long integer in big-endian as data input for the Code ProviderResult Consumers receive bytes of the statistical mean of all input datas represented as IEEE 754 binary64"""run module."""fromioimportBytesIOimportstructfromtypingimportIteratorfromcosmian_lib_sgximportEnclavedefconvert_input(datas:Iterator[BytesIO])->Iterator[int]:"""Transform input data bytes to integer."""fordataindatas:# type: BytesIOyieldint.from_bytes(data.read(),byteorder="big")defmain()->int:"""Entrypoint of your code."""withEnclave()asenclave:# import your ciphered module normallyimportsecret_mod# convert input data bytes sent by Data Providersdatas:Iterator[int]=convert_input(enclave.read())# apply function of your module on datasoutput:float=secret_mod.custom_mean(datas)# convert output resultresult:bytes=struct.pack("d",output)# write result for Result Consumersenclave.write(result)return0if__name__=="__main__":main()"""secret_mod module."""fromtypingimportIteratordefcustom_mean(input_datas:Iterator[int])->float:"""Statistical mean of input datas."""n:int=0mean:float=0.0forxininput_datas:# type: intn+=1mean+=(x-mean)/nreturnmeanifn>0elsefloat("nan")
Package: cosmian-py-ggml
Cosmian Python bindings forggmlThis library provides python bindings for theexamples modelsof ggml.Using this library, you can deploy state-of-the-art LLMs on Intel SGX hardware withCosmian MSE.Available models architecturegpt-neoxfromcosmian_py_ggmlimportGPTNeoXfromtransformersimportAutoTokenizertokenizer=AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")model=GPTNeoX("ggml_model_path")res=model(tokenizer.encode(query))print("Response:",tokenizer.decode(res))falconfromcosmian_py_ggmlimportFalconfromtransformersimportAutoTokenizertokenizer=AutoTokenizer.from_pretrained("tiiuae/falcon-40b")model=Falcon("ggml_model_path")res=model(tokenizer.encode(query))print("Response:",tokenizer.decode(res))mptfromcosmian_py_ggmlimportMPTfromtransformersimportAutoTokenizertokenizer=AutoTokenizer.from_pretrained("mosaicml/mpt-30b")model=MPT("ggml_model_path")res=model(tokenizer.encode(query))print("Response:",tokenizer.decode(res))Build from sourcesgitclone--recursivegit@github.com:Cosmian/cosmian_py_ggml.git
scripts/build.sh
Package: cosmian-secure-computation-client
Cosmian Secure Computation ClientOverviewPython client library for Cosmian Secure Computation API powered by Intel SGX.Install$pipinstallcosmian-secure-computation-clientDocumentationGet started withhttps://docs.cosmian.com/secure_computation/and generate the documentation of the Python client:$pipinstallsphinxnumpydocsphinx_rtd_themesphinx_mdincludethen$sphinx-builddocsdocs/_build-E-a-W
Package: cosmicdb
CosmicDB Django AppInstallInitial setupvirtualenv demoenv --no-site-packages
demoenv\Scripts\activate
pip install cosmicdb
django-admin startproject demoUsageAdd cosmicdb and requirements to your INSTALLED_APPS setting like this (your app must be first to override)INSTALLED_APPS = (
    'YOURAPPHERE',
    'cosmicdb',
    'crispy_forms',
    'django_tables2',
    ... (rest of django apps)
)Add cosmicdb.urls to your urls.py like this (put cosmicdb urls last)from django.conf.urls import url, include

    urlpatterns = [
        ...
        url(r'^', include('cosmicdb.urls')),
    ]Add cosmicdb settings to your settings.py like this::LANGUAGE_CODE = 'en-au'
COSMICDB_SITE_TITLE = 'Demo Site'
COSMICDB_ALLOW_SIGNUP = True
AUTH_USER_MODEL = 'cosmicdb.CosmicUser'
LOGIN_URL = '/login/'
CRISPY_TEMPLATE_PACK = 'bootstrap3'
DJANGO_TABLES2_TEMPLATE = 'django_tables2/bootstrap-responsive.html'
EMAIL_USE_TLS = True
EMAIL_HOST = 'smtp.gmail.com'
EMAIL_PORT = 587
EMAIL_HOST_USER = 'test@test.com'
EMAIL_HOST_PASSWORD = 'testpassword'
DEFAULT_FROM_EMAIL = EMAIL_HOST_USER
DEFAULT_FROM_EMAIL_NAME = COSMICDB_SITE_TITLERunpython manage.py migrate

python manage.py collectstatic

python manage.py createsuperuserDev Notesinstall required packages for packagingpip install setuptools twine wheeladjust cosmicdb/init.py for version numberpython setup.py sdist bdist_wheelreplace the following line with version numbertwine upload dist/cosmicdb-VERSION_NUMBER*twine upload dist/cosmicdb-0.0.32*
Package: cosmicdbsemantic
CosmicDBSemantic Django AppInstallvirtualenv demoenv --no-site-packages
demoenv\Scripts\activate
pip install cosmicdbsemantic --no-cache-dir
django-admin startproject demoAdd cosmicdb and requirements to your INSTALLED_APPS setting like this (your app must be first to override)INSTALLED_APPS = (
    'YOURAPPHERE',
    'cosmicdb',
    'crispy_forms',
    'sitetree',
    'django_tables2',
    ... (rest of django apps)
)Add cosmicdb.urls to your urls.py like this (put cosmicdb urls last)from django.contrib import admin
from django.urls import path, re_path, include

urlpatterns = [
    re_path(r'^', include('cosmicdb.urls')),
    path('admin/', admin.site.urls),
]Add cosmicdb settings to your settings.py like thisLANGUAGE_CODE = 'en-au'
COSMICDB_SITE_TITLE = 'Demo Site'
CRISPY_TEMPLATE_PACK = 'semanticui'
CRISPY_ALLOWED_TEMPLATE_PACKS = (CRISPY_TEMPLATE_PACK)
DJANGO_TABLES2_TEMPLATE = 'django_tables2/semantic.html'
COSMICDB_ALLOW_SIGNUP = True
AUTH_USER_MODEL = 'cosmicdb.CosmicUser'
LOGIN_URL = '/login/'
EMAIL_USE_TLS = True
EMAIL_HOST = 'mysmtp.smtp.com'
EMAIL_PORT = 465
EMAIL_HOST_USER = 'mysmtpuser'
EMAIL_HOST_PASSWORD = 'mysmtppw'
DEFAULT_FROM_EMAIL = EMAIL_HOST_USER
DEFAULT_FROM_EMAIL_NAME = COSMICDB_SITE_TITLE
SITETREE_MODEL_TREE = 'cosmicdb.CosmicDBTree'
SITETREE_MODEL_TREE_ITEM = 'cosmicdb.CosmicDBTreeItem'Runpython manage.py migrate
python manage.py collectstatic
python manage.py createsuperuserLoad sitetree from site-packages for nowpython manage.py sitetreeload SITE_PACKAGES_DIR/cosmicdb/treedump.jsonInstallation Complete!See the demo project athttps://bitbucket.org/davidbradleycole/demosemantic/src/master/OptionalCustom Semantic UI ThemesNodeJS (npm)Gulpnpm install -g gulp
npm install -g gulp-cliAdd NODE_PATH envSemantic UIcd PROJECT_DIR\cosmicdb\res\
npm install semantic-ui --savePut semanticui in semanticcd semantic/
gulp buildNow you can copy your own theme from dist to cosmicdb\static\cd PROJECT ROOT
cp -rf cosmicdb/res/semanticui/semantic/dist cosmicdb/static/cosmicdb/semanticSite TreeGenerate sitetreepython manage.py sitetreedump > treedump.jsonDev Notesadjust cosmicdb/init.py for version numberrm -rf build
python setup.py sdist bdist_wheelreplace the following line with version numbertwine upload dist/cosmicdbsemantic-0.0.1*
Package: cosmic-popsynth
COSMICCOSMIC (Compact Object Synthesis and Monte Carlo Investigation Code)COSMIC is a rapid binary population synthesis suite with a special purpose of generating realistic compact binary populations. COSMIC can also be used to evolve binaries with BSE in a python environment.COSMIC currently implements binary evolutionary processes using BSE (Hurley+2002). Several modifications have been applied to BSE to account for recent updates to binary evolution especially important to compact binary formation (e.g. metallicity-dependent stellar winds or black hole natal kick strengths). For a detailed discussion of these modifications, seeBreivik et al 2020)https://cosmic-popsynth.github.io/Release StatusInstallationThe easiest method to install cosmic is using pip directly:pip install cosmic-popsynth