Saravanan666/pypi-org huggingface dataset:
Package: seqwho
SeqWho - An accruate and rapid FASTQ(A) file origin classifierThis is the official SeqWho Repository.SeqWho is a reliable and extremely rapid program designed to determine a FASTQ(A) sequencing file identity, both source protocol and species of origin. This is accomplished using an alignment-free algorithm that leverages a Random Forest classifier that learns from biases in k-mer frequencies and repeat sequence identity. SeqWho is capable of achieving greater than 96% accuracy in its ability to classify files.You can find the Documentation for SeqWho at:https://daehwankimlab.github.io/seqwho/First time setupSeqWho is written in Python 3 and we recommend using a conda environment built from the environment.yml included with SeqWho for optimal performance.Please readhttps://daehwankimlab.github.io/seqwho/manual/for more details.Download pre-trained SeqWho indexSpeciesLibrariesIndexHuman, MouseAmplicon, ChIP-Seq, WGS, WES, miRNA-Seq, RNA-Seq, Bisulfite-Seq, DNase-Seq, ATAC-SeqIndex(md5sum)Training File ListTesting File ListHuman, Mouse, Rattus norvegicusChIP-Seq, WGS, RNA-SeqIndex(md5sum)Training File ListTesting File ListHuman, Mouse, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiaeChIP-Seq, WGS, RNA-SeqIndex(md5sum)Training File ListTesting File ListCurrent releasev1.0.3 - Added option to select number of reads drawn from files during model buildingv1.0.2 - Removed extra commas in some fields to facilitate CSV conversionv1.0.1 - Addition of test files and scriptsv1.0.0 - Initial public release
Package: ser
SER modelThis minimal model of spreading excitations has a rich history in many disciplines, ranging from the propagation of forest-fires, the spread of epidemics, to neuronal dynamics.
SER stands for susceptible, excited and refractory.InstallationpipinstallserExampleimportmatplotlib.pyplotaspltimportnumpyasnpimportseabornassnsfromserimportSERsns.set(style="white",font_scale=1.5)# Build a random adjacency matrix (weighted and directed)n_nodes=50adj_mat=np.random.uniform(low=0,high=1,size=(n_nodes,n_nodes))adj_mat[np.random.random(adj_mat.shape)<.9]=0# make sparser# Instantiate SER model once, use as many times as we want (even on different graphs)ser_model=SER(n_steps=500,prop_e=.1,prop_s=.4,threshold=.4,prob_recovery=.2,prob_spont_act=.001)# Run activity. The output is a matrix (node vs time)activity=ser_model.run(adj_mat=adj_mat)#Plot the activity matrix and the global activity levelactivity_only_active=activity.copy()activity_only_active[activity==-1]=0n_active_nodes=activity_only_active.sum(axis=0)fig,(ax1,ax2)=plt.subplots(nrows=2,figsize=(15,8),sharex=True)ax1.plot(n_active_nodes,linewidth=4,color="#6D996A",alpha=.8)ax1.set_ylabel("Number\nactive nodes",fontsize=25)ax2.imshow(activity,cmap="binary_r")ax2.set_xlabel("Time",fontsize=25)ax2.set_ylabel("Nodes",fontsize=25)ax2.set_aspect("auto")ax2.grid(False)sns.despine()fig.tight_layout()Requirementsnumpy>=1.20.3numba==0.54.1scipy>=1.7.0Other versions might work, but these are the latest one I tested.Tested in Ubuntu 20.04.3 LTS with Python 3.9.ImplementationThe graph (or network) is represented as an adjacency matrix (numpy array).
Dynamics is implemented in numpy and accelerated with numba, so it is fast - quick benchmarks show between 2-3 times faster simulations than pure vectorized numpy versions!Numba tips and tricksDon't use adj_mat with type other than np.float32, np.float64.Pro-tip: use np.float32 for adj_mat – it will run faster.LimitationsThe graph is represented as a numpy array, which is less memory efficient than a list or a dictionary representation.
That limits the size of the network you can use (of course, depending on your RAM).ReferencesJ. M. Greenberg and S. P. Hastings, SIAM J. Appl. Math. 34, 515 (1978).A. Haimovici et al. Phys. Rev. Lett. 110, 178101 (2013).Messé et al. PLoS computational biology (2018)TODOTestsExamplesImplement multi runsOptional turn off numbanetworkx and igraph conversions
Package: serac
Incremental permanent data archiver with encryption.Designed for permanently backing up data which does not change frequently,
suitable for write-only storage such as Amazon Glacier.InstallationThis requires Python 3.7.Install serac with:pip install seracIf you don’t have Python 3.7 installed, you can install it withpyenv:curl https://pyenv.run | bash
# restart your shell
pyenv update
pyenv install 3.7.3
pip install virtualenv
virtualenv python=python3.7 venv
. venv/bin/activate
pip install seracUsageSerac must always be run with the config file path as the first argument, then
the command to perform as the second argument.To run serac:/path/to/venv/bin/serac CONFIG COMMAND [OPTIONS]It is safe to run Serac from a cron job; it will not allow multiple processes to work
with the same config file at the same time.CommandsAfter that it accepts one of the following commands:testTest the configuration fileinitInitialise an index for a new config by creating the databasearchiveArchive any changes since the last archive was performedls[--at=DATE][--pattern=PATTERN]Show the state of the archive.This follows the roughly the same layout asls-l, with the following
columns:File permissionsOwner (as it will be restored to on this system)Group (as it will be restored to on this system)Size (in kibi/mebib/gibibytes, or in bytes if not specified)Last modified date (this year if not specified)Last modified timestamp (for ease of use in calls tolsandrestore)Path (as it was on the originating system)restore DESTINATION[--at=DATE][--pattern=PATTERN]Restore some or all of an archiveIf an archived object is in glacier it will be marked for retrieval.ArgumentsDATEThis should be a date in one of the following formats:epoch timestamp, eg1582165202YYYY-MM-DD, eg2020-02-20YYYY-MM-DDHH:MM:SS, eg2020-03-2002:20:02YYYY-MM-DDTHH:MM:SS, eg2020-03-20T02:20:02PATTERNThis can either be an exact path to a file, or a partial path to a
directory.Globs are not yet supported.ConfigurationConfigure serac using a config file:[source]
# Define the source for the backups

# List of paths to include and exclude (glob patterns)
include =
    /path/to/source
    /path/somewhere/else
exclude =
    /path/to/source/unprocessed
    /path/somewhere/else/*.jpg

[archive]
# Define where the backups are saved

# Backup to a local path
#storage = local
#path = /path/to/backup

# Backup to S3
storage = s3
key = 4p1_k3y
secret = 53cr3t
bucket = arn:aws:s3:::my_bucket_name
path = path/within/bucket

# Encrypt backups with this password
password = l0ng_s3cr3t

[index]
# Define how indexed files are treated

# Location for index database
# This should then be backed up by another service, eg duplicity
path = /path/to/index.sqliteContributingTo work on serac, install it in a virtual environment:mkdir serac
cd serac
git clone <path-to-repo> repo
virtualenv --python=python3.7 venv
. venv/bin/activate
pip install pip-tools
cd repo
pip-syncTo run during development:python -m serac CONFIG COMMAND [OPTIONS]To run tests:cd serac/repo
. ../venv/bin/activate
pytestChangelog0.0.3, 2019-10-12Bugfix:Correct entrypoint0.0.2, 2019-09-25Feature:Add process locking0.0.1, 2019-09-23Feature:Initial release
Package: serafin
#######serafin#######**serafin** is a python library that allows to selectively serialize differentkinds of python object into something that can be dumped to JSON or YAML... note::The project follows the semantic versioning scheme: Until 1.0 the minor* The *patch* versions only include bugfixes and changes that do notmodify the existing interface. You can safely update a patch versionwithout worrying it will break your code.* The *minor* versions will contain changes to the interface. With asingle version update your code will most likely work or might requiresmall adjustments. The more minor versions you update at once thebigger the chance that something will brake.* The *major* versions are reserved for significant refactorings andarchitecture changes. This should not happen very often so the majorversion should not change much... note::The CircleCI builds can be found`here <https://circleci.com/gh/novopl/serafin>`_.. readme_inclusion_markerInstallation============.. code-block:: shell$ pip install serafinContributing============Cloning and setting up the development repo-------------------------------------------.. code-block:: shell$ git clone git@github.com:novopl/serafin.git$ cd serafin$ virtualenv env$ source ./env/bin/activate$ pip install .$ pip install -r ops/devrequirements.txt$ peltak git add-hoooksRunning tests.............**Config**: The types of tests are defined in ``pelconf.py`` and thepytest configuration is defined in ``ops/tools/pytest.ini``... code-block:: shell$ peltak testLinting.......**Config**: The list of locations to lint is defined in ``pelconf.py`` and thelinters configuration is defined in ``ops/tools/{pylint,pep8}.ini``... code-block:: shell$ peltak lintGenerating docs...............**Config**: The list of documented files and general configuration is in``pelconf.py`` and the Sphinx configuration is defined in ``docs/conf.py``... code-block:: shell$ peltak docs
Package: serafin-appengine
#################serafin-appengine#################**serafin-appengine** is a small python library that provides Google Datastoremodels integration for *serafin*... note::The project follows the semantic versioning scheme: Until 1.0 the minor* The *patch* versions only include bugfixes and changes that do notmodify the existing interface. You can safely update a patch versionwithout worrying it will break your code.* The *minor* versions will contain changes to the interface. With asingle version update your code will most likely work or might requiresmall adjustments. The more minor versions you update at once thebigger the chance that something will brake.* The *major* versions are reserved for significant refactorings andarchitecture changes. This should not happen very often so the majorversion should not change much... note::The CircleCI builds can be found`here <https://circleci.com/gh/novopl/serafin-appengine>`_.. readme_inclusion_markerInstallation============.. code-block:: shell$ pip install serafin-appengineContributing============Setting up development repo---------------------------.. code-block:: shell$ git clone git@github.com:novopl/serafin-appengine.git$ cd serafin$ virtualenv env$ source ./env/bin/activate$ pip install -r ops/devrequirements.txt$ python setup.py develop$ peltak git add-hoooksRunning tests.............**Config**: The types of tests are defined in ``pelconf.py`` and thepytest configuration is defined in ``ops/tools/pytest.ini``... code-block:: shell$ peltak testLinting.......**Config**: The list of locations to lint is defined in ``pelconf.py`` and thelinters configuration is defined in ``ops/tools/{pylint,pep8}.ini``... code-block:: shell$ peltak lintGenerating docs...............**Config**: The list of documented files and general configuration is in``pelconf.py`` and the Sphinx configuration is defined in ``docs/conf.py``... code-block:: shell$ peltak docs
Package: serafin-sqlalchemy
serafin-appengineis a small python library that provides SQLAlchemy
models integration forserafin.NoteThe CircleCI builds can be foundhereInstallation$pipinstallserafin-sqlalchemyContributingSetting up development repo$gitclonegit@github.com:novopl/serafin-sqlalchemy.git$cdserafin-sqlalchemy$virtualenvenv$source./env/bin/activate$pipinstall-rops/devrequirements.txt$peltakgitadd-hoooksRunning testsConfig: The types of tests are defined inpelconf.pyand the
pytest configuration is defined inops/tools/pytest.ini.$peltaktestLintingConfig: The list of locations to lint is defined inpelconf.pyand the
linters configuration is defined inops/tools/{pylint,pep8}.ini.$peltaklintGenerating docsConfig: The list of documented files and general configuration is inpelconf.pyand the Sphinx configuration is defined indocs/conf.py.$peltakdocs
Package: serapeum-utils
Current build statusAll platforms:Current release infoNameDownloadsVersionPlatformsserapeum_utils - utility library for Pythonserapeum_utilsis an open-source Python utility packageserapeum_utilsMain FeaturesInstalling serapeum_utilsInstallingserapeum_utilsfrom theconda-forgechannel can be achieved by:conda install -c conda-forge serapeum_utilsIt is possible to list all of the versions ofserapeum_utilsavailable on your platform with:conda search serapeum_utils --channel conda-forgeInstall from Githubto install the last development to time you can install the library from githubpip install git+https://github.com/Serapieum-of-alex/serapeum_utilspipto install the last release you can easly use pippip install serapeum_utils==0.2.0Quick start>>> import serapeum_utils
Package: serapis
Current build statusAll platforms:Current release infoNameDownloadsVersionPlatformsserapis - Hydrological library for Pythonserapisis an open-source Python Framework for building raster-based conceptual distributed hydrological models using HBV96 lumped
model & Muskingum routing method at a catchment scale (Farrag & Corzo, 2021), serapis gives a high degree of flexibility to all components of the model
(spatial discretization - cell size, temporal resolution, parameterization approaches and calibration (Farrag et al., 2021)).serapisInstalling serapisInstallingserapisfrom theconda-forgechannel can be achieved by:conda install -c conda-forge serapisIt is possible to list all of the versions ofserapisavailable on your platform with:conda search serapis --channel conda-forgeInstall from Githubto install the last development to time you can install the library from githubpip install git+https://github.com/MAfarrag/serapispipto install the last release you can easly use pippip install serapis==0.1.0Quick start>>> import serapisother code samplesNaming ConventionPEP8module names: lower case word, preferably one word if not, separate words with underscores (module.py, my_module.py).class names: PascalCase (Model, MyClass).class method/function: CamelCase(getFile, readConfig).should have a verb one them, because they perform some action=======
History0.1.0 (2022-12-26)separate the hydrodynamic model from Hapi package.
Package: serasa
Integração com os webservices do SERASA===============================version number: 0.0.1author: Luis Felipe MileoOverview--------Interação com SERASA:  aná Analise e informacoes para decisoes de credito e apoio a negociosInstallation / Usage--------------------To install use pip:$ pip install serasaOr clone the repo:$ git clone https://github.com/mileo/serasa.git$ python setup.py installContributing------------TBDExample-------TBD
Package: serbia-zip-codes
Serbia zip codesEasy access to Serbia zip codes. You can search zip code by city, or city by zip code.Installationpip install serbia-zip-codesUsageimportserbia_zip_codesSearch city by city:>>>result=serbia_zip_codes.find_by_city("Loznica")>>>print(result)[{'city':'Loznica','zip_code':'15300'}]>>>print(result[0]['zip_code'])15300Search city by zip code:>>>result=serbia_zip_codes.find_by_zip("15300")>>>print(result)[{'city':'Loznica','zip_code':'15300'}]>>>print(result[0]['city'])LoznicaMultiple results example:>>>frompprintimportpprint>>>importserbia_zip_codes>>>result=serbia_zip_codes.find_by_city("beograd")>>>pprint(result)[{'city':'Beograd','zip_code':'11000'},{'city':'Beograd Voždovac','zip_code':'11010'},{'city':'Beograd Čukarica','zip_code':'11030'},{'city':'Beograd Zvezdara','zip_code':'11050'},{'city':'Beograd Palilula','zip_code':'11060'},{'city':'Beograd Zemun','zip_code':'11080'},{'city':'Beograd Rakovica','zip_code':'11090'}]Get all:>>>result=serbia_zip_codes.get_all()>>>pprint(result)[{'city':'Beograd','zip_code':'11000'},{'city':'Beograd Voždovac','zip_code':'11010'},{'city':'Beograd Čukarica','zip_code':'11030'},{'city':'Beograd Zvezdara','zip_code':'11050'},{'city':'Beograd Palilula','zip_code':'11060'},{'city':'Novi Beograd','zip_code':'11070'},{'city':'Beograd Zemun','zip_code':'11080'},{'city':'Beograd Rakovica','zip_code':'11090'},{'city':'Kaluđerica','zip_code':'11130'},{'city':'Rušanj','zip_code':'11194'},{'city':'Borča','zip_code':'11211'},{'city':'Ovča','zip_code':'11212'},{'city':'Padinska Skela','zip_code':'11213'},{'city':'Beli Potok','zip_code':'11223'},{'city':'Vrčin','zip_code':'11224'},{'city':'Zuce','zip_code':'11225'},{'city':'Pinosava','zip_code':'11226'},{'city':'Ripanj','zip_code':'11232'},{'city':'Ralja','zip_code':'11233'},{'city':'Mali Požarevac','zip_code':'11235'},{'city':'Ostružnica','zip_code':'11251'},{'city':'Sremčica','zip_code':'11253'},{'city':'Umka','zip_code':'11260'},{'city':'Mala Moštanica','zip_code':'11261'},{'city':'Velika Mostanica','zip_code':'11262'},{'city':'Surčin','zip_code':'11271'},{'city':'Dobanovci','zip_code':'11272'},...ContributingFork itCreate your feature branch (git checkout -b my-new-feature)Commit your changes (git commit -am 'Add some feature')Push to the branch (git push origin my-new-feature)Create a new Pull Request
Package: serbus
# SerbusCopyright (c) 2015 - Gray Cat Labs -https://graycat.ioSerbus provides basic C APIs for the I2C and SPI serial bus protocols on
GNU/Linux based systems, as well as a Python package built on top of them.It’s really just a wrapper for the ioctl commands provided by the standard Linux
I2C and SPI drivers, so it should be pretty universal. That said, I’ve currently
only tested it extensively on the BeagleBone Black, so use it at your own risk!
(And let me know if it’s working for you on another system)Source code:https://github.com/graycatlabs/serbus/C API documentation:https://graycat.io/docs/serbus/Python API documentation:https://graycat.io/docs/serbus/python/## ContributingHave something to contribute? Great! This project follows the Contributor
Covenant Code of Conduct, so be sure to readcode_of_conduct.md.## LicenseReleased under the MIT license.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: serdespy
serdespyA python library for system-level SerDes modelling and simulationTable of ContentsGeneral InfoFeaturesSetupDescriptionProject StatusRoom for ImprovementContactGeneral InformationRichard Barrie's undergraduate thesis project. University of Toronto, Engineering Science. ESC499 2021/2022.
Supervisors: Tony Chan Causone, Ming Yang
Authors: Richard Barrie, Katherine LiangFeaturesIncludes functions and classes for time-domain model of serdes systemChannel ModellingTX FIRTX JitterContinuous-Time Linear EqualizerFeed-Forward EqualizerDecision Feedback EqualizerPRBS/PRQS generationEye Diagram PlotterBit Error Rate CheckerForward Error Correction with Reed-Solomon CodesSetupPython 3.7+ requiredpip install serdespyDescriptionpython module containing functions and classes for SerDes ModellingContactrichard.barrie@mail.utoronto.ca
Package: serdesripy
serdesripyis a lightweight wrapper aroundserde_esriwhich is a
Rust library that can serialize and deserialize Esri JSON objects.This python library is a proof of concept package illustrating how we
can callserde_esrito process JSON and return a PyArrow object.Install fromPyPIimportrequestsimportserdesripy# make a simple request for US Counties boundariescounties=requests.get("https://services.arcgis.com/P3ePLMYs2RVChkJx/ArcGIS/rest/services/USA_Counties_Generalized_Boundaries/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&resultRecordCount=10&f=json")# pass in raw json and process the resultfeatureset=serdesripy.process_featureset(counties.text)print(featureset)# convert to pandasfeatureset.to_pandas()
Package: serdio
SerdioAutomatic serialization of function inputs and outputs using MessagePack.Why Serdio?Remotely executing Python code is complicated. A common pattern is to wrap the code into a function that maps serialized data to serialized data, however this leads to heavy amounts of boilerplate related to serialization of native and custom Python types.Serdio makes this easy by handling this serialization boilerplate for you as much as possible. For any custom types, it only requires the user to provide a encoder/decoder helpers that break the types down into Python-native components. An example can be foundbelow.InstallationpipinstallserdioUsageBasicHere, we use Serdio to lift a function that performs some simple math on native Python numbers.@serdio.lift_iodefmy_cool_function(x:int,y:float,b:float=1.0)->float:z=x*yz+=breturnzbytes_handler:Callable[bytes,bytes]=my_cool_function.as_bytes_handler()z=my_cool_function(2,3.0)assertz==7.0Now we can use thebytes_handlerfunction on Serdio-encoded bytes:xyb_bytes=serdio.serialize(2,3.0,b=2.0)zbytes=bytes_handler(xyb_bytes)z=serdio.deserialize(zbytes)assertz==8.0Using Serdio with Custom TypesIn this example, we reproduce the above example with custom typesMyCoolClassandMyCoolResult, instead of native Python numbers.
To give Serdio a little guidance, we provide helper functions that can convert our custom types  into Python native values and back (my_cool_encoderandmy_cool_decoder).The resulting function can operate on our custom types, while Serdio automatically applies the encoder/decoder helpers to function inputs and outputs.@dataclasses.dataclassclassMyCoolResult:cool_result:floatdefshift(self,other:float)->MyCoolResult:returnMyCoolResult(self.cool_result+other)@dataclasses.dataclassclassMyCoolClass:cool_int:floatcool_float:intdefmul(self)->MyCoolResult:returnMyCoolResult(self.cool_int*self.cool_float)defmy_cool_encoder(x):ifdataclasses.is_dataclass(x):return{"__type__":x.__class__.__name__,"fields":dataclasses.asdict(x)}returnxdefmy_cool_decoder(obj):if"__type__"inobj:obj_type=obj["__type__"]ifobj_type=="MyCoolClass":returnMyCoolClass(**obj["fields"])elifobj_type=="MyCoolResult":returnMyCoolResult(**obj["fields"])returnobj@serdio.lift_io(encoder_hook=my_cool_encoder,decoder_hook=my_cool_decoder)defmy_cool_function(a:MyCoolClass,b:float=1.0)->MyCoolResult:x:MyCoolResult=a.mul()returnx.shift(b)my_handler=my_cool_function.as_bytes_handler()a=MyCoolClass(2,3.0)ab_bytes=serdio.serialize(a,b=2.0,encoder=my_cool_function.encoder)c_bytes=my_handler(ab_bytes)c=serdio.deserialize(c_bytes,my_cool_function.decoder)assertc==my_cool_function(a,b=2.0)print(c.cool_result)# 8.0
Package: sereia
DOXPaper:DOX - Document Store Exploration through KeywordsDOX (Document stOre eXploration) is a framework for keyword exploration over Document Stores.Source codeAll code can be found at thesrcfolder.Folders outside are used for bootstrap and runtime configurations.DemoAn online demo of the framework is ready to run inGoogle Colaboratory. This demo uses an instance of a MongoDB database hosted atMongoDB Atlas.DatasetsAll datasets used throughout our experiments can be foundhere.