Saravanan666/pypi-org huggingface dataset:
Package: psym-nttdata-test
Psym is a python package that allows for querying and modifying the Symphony data using graphql queries.
Graphql is a query language developed by Facebook (https://graphql.org/)PrerequisitesPython >= 3.7InstallationInstall it with:pip install psymUsageFirst connect to symphony with your credentialsfrompsymimportPsymClient# since symphony is multi tenant system you will need to insert which partner you connect asclient=PsymClient(email,password,tenant_name)The tenant is the company nameStart creating location types, equipment types:locationType=client.add_location_type("City",[])equipment_type=client.add_equipment_type("Antenna HW","Category Name",[("altitude","int",None,True)],{},[])Start creating locations, equipment and links:location=client.add_location(-1.22,2.66,('City','New York'))equipment=client.add_equipment('HW1569','Antenna HW',location,{'altitude':53.5})Licensepsym isBSD Licenselicensed, as found in theLICENSEfile.
Package: psym-TEST
Psym is a python package that allows for querying and modifying the Symphony data using graphql queries.
Graphql is a query language developed by Facebook (https://graphql.org/)PrerequisitesPython >= 3.7InstallationInstall it with:pip install psymUsageFirst connect to symphony with your credentialsfrompsymimportPsymClient# since symphony is multi tenant system you will need to insert which partner you connect asclient=PsymClient(email,password,tenant_name)The tenant is the company nameStart creating location types, equipment types:locationType=client.add_location_type("City",[])equipment_type=client.add_equipment_type("Antenna HW","Category Name",[("altitude","int",None,True)],{},[])Start creating locations, equipment and links:location=client.add_location(-1.22,2.66,('City','New York'))equipment=client.add_equipment('HW1569','Antenna HW',location,{'altitude':53.5})Licensepsym isBSD Licenselicensed, as found in theLICENSEfile.
Package: psync
psync is a mirroring utility. its main goal is to be able to update a
distribution mirror but keep it as consistent as possible. one of the main
problems updating a mirror is that if the mirroring process is stopped for any
reason, the mirror stays inconsistent: either some of the packages in the old
databases were removed or not all the new packages were downloaded. psync aims
to fix this. it also aims to support as many distributions as possible.
Package: psynet
PsyNet is a powerful new Python package for designing and running the next generation of online behavioural experiments.
It builds on the virtual lab frameworkDallingerto streamline the development of highly complex experiment paradigms, ranging from simulated cultural evolution to
perceptual prior estimation to adaptive psychophysical experiments. Once an experiment is implemented, it can be
deployed with a single terminal command, which looks after server provisioning, participant recruitment, data-quality
monitoring, and participant payment. Researchers using PsyNet can enjoy a paradigm shift in productivity, running many
high-powered variants of the same experiment in the time it would ordinarily take to run an experiment once.To try some real-world PsyNet experiments for yourself, visit the following repositories:Consonance profiles for carillon bellsEmotional connotations of musical scalesVocal pitch matching in musical chordsFor more information about PsyNet, visit thedocumentation website.
Package: psyplot-gui
docstestspackageimplementationsWelcome! This package enhances the interactive visualization frameworkpsyplotwith a graphical user interface usingPyQt. See the homepage of the
main project on examples on the possibilities withpsyplot.You can see the full documentation underpsyplot.github.io/psyplot-gui/CopyrightCopyright © 2021 Helmholtz-Zentrum Hereon, 2020-2021 Helmholtz-Zentrum
Geesthacht, 2016-2021 University of Lausannepsyplot-gui is released under the GNU LGPL-3.O license.
See COPYING and COPYING.LESSER in the root of the repository for full
licensing details.This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License version 3.0 as
published by the Free Software Foundation.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU LGPL-3.0 license for more details.You should have received a copy of the GNU LGPL-3.0 license
along with this program.  If not, seehttps://www.gnu.org/licenses/.
Package: psypy
SummaryPython library to calculate psychrometric states of moist air.InstallationPlace either psySI.py or psyIP.py file in your working directory. If you
are working with the international system of units (i.e. SI) then use psySI.py.
If you are are working with imperial units (i.e. IP) then use psyIP.py.Descriptionpsypy is a python library that calculates psychrometric states of moist air
using ASHRAE 2009 Fundamentals formulations. Atmospheric  pressure, and two
independent properties must be given to calculate all other state properties.
The state properties include: dry bulb temperature (DBT), specific enthalpy
(H), relative humidity (RH), specific volume (V), humidity ratio (W), and wet
bulb temperature (WBT). Examples can be found in the examples.py file.
Package: psy-reg
docstestspackageimplementationsWelcome to the psyplot plugin for visualizating and calculating regression
plots. This package uses thescipyandstatsmodelspackages to evaluate your
data, fit a regression to it and visualize it through thepsy-simpleplugin.It’s plot methods are thelinreganddensityregplot methods.See the full documentation onpsyplot.github.io/psy-reg/for allplot methods, and checkout theexamples.CopyrightCopyright © 2021 Helmholtz-Zentrum Hereon, 2020-2021 Helmholtz-Zentrum
Geesthacht, 2016-2021 University of Lausannepsy-reg is released under the GNU LGPL-3.O license.
See COPYING and COPYING.LESSER in the root of the repository for full
licensing details.This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License version 3.0 as
published by the Free Software Foundation.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU LGPL-3.0 license for more details.You should have received a copy of the GNU LGPL-3.0 license
along with this program.  If not, seehttps://www.gnu.org/licenses/.
Package: psyrun
PsyrunPsyrun is aPythontool to define parameter spaces
and execute an evaluation function for each parameter assignment. In addition
Psyrun makes it easy to use serial farming, i.e. evaluating multiple parameter
assignments in parallel, on a multicore computers and high-performance clusters.DocumentationThe documentation can be found here.OverviewDefine parameter spaces and evaluate them:frompsyrunimportmap_pspace,Paramdefobjective(a,b,c):returna*b+cpspace=(Param(a=np.arange(1,5))*Param(b=np.linspace(0,1,10))*Param(c=[1.,1.5,10.,10.5]))results=map_pspace(objective,pspace)Or do it in parallel:frompsyrunimportmap_pspace_parallelresults=map_pspace_parallel(objective,pspace)Define tasks by placingtask_<name>.pyfiles in thepsy-tasks`directory:frompsyrunimportParampspace=(Param(a=np.arange(1,5))*Param(b=np.linspace(0,1,10))*Param(c=[1.,1.5,10.,10.5]))defexecute(a,b,c):return{'result':a*b+c}and run them by typingpsy runwith support for serial farming on high
performance clusters.Installationpip install psyrunTo be able to use the NPZ store:pip install numpy
pip install 'psyrun[npz]'To be able to use the HDF5 store:pip install numpy
pip install 'psyrun[h5]'RequirementsPython>=2.7, >=3.4sixOptional requirementsTo havefaulthandleractivated for
jobs submitted withpsy runin Python 2.7:faulthandlerPython 3.4+ already includes the faulthandler module.To usemap_pspace_parallel:joblibTo use NPZ files as store:NumPyTo use HDF5 files as store:NumPypytablesTo run the unit tests:joblibNumPypytablespytest>= 2.8To build the documentation:numpydocChanges0.8.0 (June 26, 2018)New featuresAdded the possibility to specify asetupfunction in task files that
will be executed once at the start of each worker process and can be used
to acquire resources and pass them to the processing function for individual
parameter sets.0.7.1 (April 19, 2018)Documentation improvementsAdded documentation for thepool_sizetask attribute and included it in
the task template.Bug fixesAllow to continue tasks if results are missing even if the result file is
newer than the task file.0.7.0 (February 18, 2018)New featuresAdded support for the Slurm Workload Manager.0.6.0New featuresAddpsynew-taskandpsy killcommands.AddedAutodetectStorethat determines the appropriate store from the
filename extension.Added possibility to letpsy mergecustom stores if provided aspsyrun.storesentry point.Added capability to set scheduler arguments based on the job name.0.5.4Bug fixesFix thepsy runcontinue functionality.0.5.3Bug fixesFixpsy statusandpsyrun.backend.distribute.DistributeBackend.get_missingtrying to read
incompatible data files in the output directory.Fixpsy statusandpsyrun.backend.distribute.DistributeBackend.get_missingeasily hitting
Python’s recursion depth limit.Fix merging of npz files with missing integer values by converting them to
float wherenp.nancan be used.0.5.2Bug fixesFix incorrectpsy status.Fixpsy run <task1> <task2> ...not running all tasks and run them in
order.0.5.1Bug fixesFixpsy mergealways assumingPickleStore.Documentation improvementsAdd recipe for converting data to Pandas data frame to documentation.0.5Initial release
Package: psysh-kernel
[![Build Status](https://travis-ci.org/Jaesin/psysh_kernel.svg?branch=master)](https://travis-ci.org/Jaesin/psysh_kernel)### A Jupyter kernel for PsySHThis requires **Jupyter Notebook** <http://jupyter.readthedocs.org/en/latest/install.html>and [PsySH](http://psysh.org/) installed.To install:```bashpython -m pip install psysh_kernelpython -m psysh_kernel install```To use it, run one of:```bashjupyter notebook# In the notebook interface, select Psysh from the 'New' menujupyter qtconsole --kernel psyshjupyter console --kernel psysh```This is based on MetaKernel <http://pypi.python.org/pypi/metakernel>which means it features a standard set of magics.A sample notebook is available [online](https://github.com/Jaesin/psysh_kernel/blob/master/psysh_kernel.ipynb).You can specify the path to your Psysh executable by creating an`PSYSH_EXECUTABLE` environmental variable.### ThanksThanks to the metakernel project (<https://github.com/Calysto/metakernel>)and it's [contributers](https://github.com/Calysto/metakernel/graphs/contributors).
Package: psyshort
Making psycopg2 a little simpler to use.Usage:frompsyshortimportPsyshortpsy=Psyshort(hostname="db.example.com",dbname="example_database",username="postgres_user",password="pa$$w0rd")select=psy.select(table="my_table",# Table's name (mandatory).fields=["id","first_name","last_name","phone",],# Fields to include in the result (optional).where="last_name = 'Smith'",# Select only rows where last_name = 'Smith' applies (optional).limit=1000,# Limit result to 1000 records (optional).order_by="first_name"# Order the result by 'first_name' column (optional).)result=select["result"]# The selected records.duration=select["duration"]# The duration of the operation.psy.insert(table="my_table",# Table's name (mandatory).columns=["first_name","last_name","phone",],# Columns by which to insert values (mandatory).row={"first_name":"John","last_name":"Smith","phone":"KL5-2390"}# Row to insert to the table (mandatory).)psy.disconnect()# Occurs on psy.__del__
Package: psy-simple
docstestspackageimplementationsWelcome to the psyplot plugin for simple visualization. This package targets
simple visualization like line plots, 2D plots, bar plots, density plots, etc.
It provides the basics for all the more advanced and specialized plugins like
thepsy-mapsorpsy-regplugin.See the full documentation onpsyplot.github.io/psy-simple/for allplot methods, and checkout theexamples.CopyrightCopyright © 2021 Helmholtz-Zentrum Hereon, 2020-2021 Helmholtz-Zentrum
Geesthacht, 2016-2021 University of LausanneThis file is part of psy-simple and is released under the GNU LGPL-3.O license.
See COPYING and COPYING.LESSER in the root of the repository for full
licensing details.This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License version 3.0 as
published by the Free Software Foundation.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU LGPL-3.0 license for more details.You should have received a copy of the GNU LGPL-3.0 license
along with this program.  If not, seehttps://www.gnu.org/licenses/.
Package: psy-strat
docstestspackageWelcome to psy-strat! The interactive python package for the production of
stratigraphic plots (e.g. pollen diagrams).To install this package, we recommend to usecondaand install the package
via:$ conda install -c chilipp psy-strator use pip:$ pip install psy-stratThe full documentation is hosted athttps://psy-strat.readthedocs.io
Package: psython
PsythonThis package include SPSS related calculations done using python.Installationpip install psythonImportingimport psython as psyCronbach's alpha - with "if deleted"This package is for calculating Cronbach's alpha of an entire dataset with a "if deleted" table for finding items that should be removed.The package is using the pingouin package for the actuall calculation of Cronbach's alpha.UsageHere is an example of the SAQ DataFrame (q3r = q3 reversed):q1q2q4q5q6q7q8q9q10q11...q15q16q17q18q19q20q21q22q23q3r02122231121...231232225211132222522...432234442222324122223...232314322433143342242...332424443542122332422...22233424435 rows × 23 columnspsy.cronbach_alpha_scale_if_deleted(df)Where df is the items dataframe (each item as a column) and the function will return two objects - the Cronbach's alpha of the entire DataFrame at position 0 and the table of the "if delete" items in position 1.ItemScale Mean if Item DeletedScale Variance if Item DeletedCorrected Item-Total CorrelationCronbach's Alpha if Item Deleted0q159.89226090.1210720.5209280.7918871q260.642940101.063899-0.1631580.8199782q459.48035887.9679990.5692410.7881773q559.54414689.3034010.4805790.7924194q660.03928487.6050710.4824160.7913975q759.34266885.6556850.5942450.7850326q860.02956089.9002930.5037040.7921417q959.420070100.881838-0.1371910.8286138q1059.98560992.2328670.3557840.7986939q1160.01128088.7901450.5683180.78911210q1259.10696288.4519790.5629420.78888911q1359.81719287.8397200.5769020.78779812q1459.39012187.4917160.5624760.78793113q1559.50019488.7660510.4842960.79191614q1659.38700988.3291540.5707720.78852015q1759.79968988.4419610.5878490.78816516q1859.69778385.9930650.6089250.78477117q1959.974329104.442142-0.2957950.83224318q2058.64216391.6991400.3137820.80071119q2159.09568387.6787790.5611280.78815720q2259.378452101.109248-0.1527040.82379821q2358.83197298.820783-0.0440390.81868022q3r58.85180989.0212210.4347620.794258Reliability: Split-Halfresult = psy.split_half_reliability(df)Will return an array of values:(pearson,spearman_brown,a_cronbach's 1, a_cronbach's 2, printable table)print(result[4])Will print the output in the familiar SPSS format.
Package: psytrack
PsyTrackPsyTrack is a package for fitting a dynamic psychophysical model to behavioral data as described in our 2021Neuronpaper, 'Extracting the dynamics of behavior in sensory decision-making experiments.'DocumentationDocumentation and examples can be found inExampleNotebook.ipynbHow to installJust runpip install psytrackCode authorsNick Roy,Ji Hyun Bak, andJonathan PillowPlease cite as:Roy NA, Bak JH, Akrami A, The IBL, Brody CD, & Pillow JW,"Extracting the dynamics of behavior in sensory decision-making experiments."Neuron(2021).
Package: psy-view
docstestspackageimplementationsget in touchThis package defines a viewer application for netCDF files, that is highly
motivated by thencviewpackage but entirely built upon the psyplot framework.
It supports strucutured and unstructured grids and provides an intuitive
graphical user interface to quickly dive into the data inside a netCDF file.This package is currently under development and we highly appreciate your
feedback! Please try it out yourself and, if you would like to see more features,
find bugs or want to say anything else, please leave your comments and
experiences at https://github.com/psyplot/psy-view/issues or send a mail to
psyplot@hzg.de.FeaturesSome of the most important features offered by psy-view are:intuitive GUI to select variables, dimensions, slices, etc. and change the
plotautomatically decodes CF-conventions and supports unstructured grid, such asICONorUGRIDanimation interfacedifferent projectionsimplemented inpsyplot-guifor full flexibility (if desired)Test it without installationYou can try the functionalities with some selected example files headless in
your browser by clicking on. Note that it might take a bit to load
and that the speed depends on your WiFi-connection.InstallationTo install the current work-in-progress, pleasedownloadMinicondaopen the terminal (orAnaconda Prompton Windows) and type:conda create -n psyplot -c conda-forge psy-viewOn Linux and OS X, you may instead want to type:$ conda create -n psyplot -c conda-forge --override-channels psy-viewin order to not mix the anaconda defaults and and conda-forge channel, because
mixing them can sometimes cause incompatibilities.The commands above will installed psy-view and all it’s necessary
dependencies into a separate environment. You can activate it via:conda activate psyplotNow launch the GUI via typing:psy-viewor:psy-view <path-to-your-netcdf-file.nc>

See ``psy-view --help`` for more optionsAs an alternativ to a local installation, you can also run it
headless in you browser by clickingFor alternative installation instructions, update information or deinstallation
instructions, please have a look into theinstallation docs.Get in touchAny quesions? Do not hessitate to get in touch with the psyplot developers.Create an issue at thebug trackerChat with the developers in outchannel on gitterSubscribe to themailing listand ask for supportSent a mail topsyplot@hzg.deSee also thecode of conduct, and ourcontribution guidefor more
information and a guide about good bug reports.Copyright disclaimerCopyright (C) 2020 Philipp S. SommerThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, seehttps://www.gnu.org/licenses/.
Package: ps.zope.i18nfield
ps.zope.i18nfieldThis package provides azope.schemabased field which allows multiple translations within the field.
It uses a custom dictionary class for the storage.
There is also a widget available for thez3c.formlibrary.ContributorsThomas Massmann,thomas@propertyshelf.comZach Cashero,zach@propertyshelf.comChangelog0.8 (2018-06-19)Don’t use formControls div to prevent modals bug.Moved sorting method to utility to make it easier to customize sort order.0.7 (2018-01-23)Add custom display widget for Plone 5.0.6 (2018-01-19)Add widget option for custom default language.Add option to remove existing languages for Plone 5.0.5 (2018-01-13)Don’t depend on z3c.indexer which might only be used in pure Zope 3 applications.0.4 (2018-01-13)Fix unicode error in z3c.form data converter.Add custom widget template for Plone 5.0.3 (2017-11-17)Add __hash__ method to I18NDict.0.2 (2016-10-26)Add missing method definition for ILanguageAvailability interface.Add plone.registry persistent adapters.Add required Plone utilities.0.1.4 (2015-02-13)Allow for indexing of normal dict and other values as well as I18NDict.Better to iterate over the existing indices in case the available language configuration changes.0.1.3.1 (2014-11-14)Added en translation.0.1.3 (2014-11-14)Added translations (de, es).Provide the ‘Add translation’ value already translated (e.g. for JS).0.1.2 (2014-11-01)Return empty dict when value is None.Test for correct value types before we set any data.Correctly evaluate the bool value of the I18NDict when the field is required.When the fallback is the first value found, sort the values so that the result is consistent with each repeated call.0.1.1 (2014-09-25)Always return the super call instead of None when no sub-indices have been created yet.Add tests to cover the sort method with and without values.0.1 (2014-09-23)Initial release.
Package: pt2keras
pt2kerasA simple PyTorch To Keras Model Converter.MotivationI needed to export existing models that were developed in PyTorch to edgetpu so that
we cn utilize the Google TensorBoard and Coral Edge TPUS. Although models already developed
in TensorFlow were easy to export, I was having difficulty exporting models developed in PyTorch.This project was designed to export PyTorch models to TensorFlow while maintaining the ability to
upload the model to the EdgeTPU without running into errors.Supported NetworksThe following networks have been tested and are supportedEfficientNetMobileNetV2ResNetAlexNetInception_v3 (warning: converted model shows relatively larger distance (Network output value does not fall within atol=1e-4))VggGoogleNetInstallationThe package can be installed via the following command.# -U for upgrading existing packagespipinstall-Upt2kerasAfterwards, try importing the library using the following command:frompt2kerasimportPt2KerasIf it works without any errors, then the package has been successfullyinstalled.
Afterwards, check out thedemo/demo.pysource code for examples on how to use pt2keras.How to useFirst, import the modulefrompt2kerasimportPt2KerasAfterwards, we proceed with the following steps:Define model to convert in PyTorchConvert the model into KerasPerform inferenceHave a coffee and compare raw outputs. Yee!For more information, check out the examples insidedemo. To run the demo, type in the following:Resnet18 democddemopythondemo.pyCustom PyTorch model democddemopythoncustom_pytorch_demo.py# For available arguments, type in the following# python custom_pytorch_demo.py -hFAQQuestion: What should I do if I get the following error?Traceback(mostrecentcalllast):File"---",line90,in<module>keras_model=converter.convert()File"---",line78,inconvertreturnself.graph._convert()File"---",line233,in_convertraiseValueError('Failed to convert model. The following operations are currently unsupported: 'ValueError:Failedtoconvertmodel.Thefollowingoperationsarecurrentlyunsupported:AveragePoolAnswer: This means that theAveragePooloperator is currently not supported.
The framework can be extended without modifying the source code by adding the converter using the following decorator.frompt2kerasimportconverter# Update the Relu onnx operator converter@converter('Relu',override=True)defadd(onnx_node,input_tensor,*inputs):print('overriding ReLU')fromtensorflowimportkerasreturnkeras.activations.relu(input_tensor)The example below is how to override the existingReLUoperation.
If the override flag is not provided and theoperatoris already implemented, we will get the following error:Traceback(mostrecentcalllast):File"---",line50,in<module>@converter('Relu')File"----",line270,inconverterraiseDuplicateOperatorConverterError(f'Converter for "{onnx_op}" already exists ...')pt2keras.core.onnx.convert.common.DuplicateOperatorError:Converterfor"Relu"alreadyexists...Updatespt2kerasnow supports onnx inputs.
However, the onnx model must perform operations PyTorch style.
E.g. Model input must be in the form (Batch, Channel, Height, Width).LicenseThis software is covered by the MIT license.