Saravanan666/pypi-org huggingface dataset:
Package: gninatorch
gnina-torchPyTorch implementation ofGNINAscoring function.ReferencesIf you are usinggnina-torch, please consider citing the following references:Protein-Ligand Scoring with Convolutional Neural Networks,
M. Ragoza, J. Hochuli, E. Idrobo, J. Sunseri, and D. R. Koes,J. Chem. Inf. Model.2017, 57 (4), 942-957.
DOI:10.1021/acs.jcim.6b00740libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications
J. Sunseri and D. R. Koes,J. Chem. Inf. Model.2020, 60 (3), 1079-1084.
DOI:10.1021/acs.jcim.9b01145If you are using the pre-traineddefault2018anddensemodels fromGNINA, please consider citing the following reference as well:Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design,
P. G. Francoeur, T. Masuda, J. Sunseri, A. Jia, R. B. Iovanisci, I. Snyder, and D. R. Koes,J. Chem. Inf. Model.2020, 60 (9), 4200-4215.
DOI:10.1021/acs.jcim.0c00411If you are using the pre-traineddefaultmodel ensemble fromGNINA, please consider citing the following reference as well:GNINA 1.0: molecular docking with deep learning,
A. T. McNutt, P. Francoeur, R. Aggarwal, T. Masuda, R. Meli, M. Ragoza, J. Sunseri, D. R. Koes,J. Cheminform.2021, 13 (43).
DOI:10.1186/s13321-021-00522-2InstallationThegninatorchPython package has several dependencies, including:PyTorchPyTorch-IgnitelibmolgridA full developement environment can be installed using thecondapackage manager and the providedcondaenvironment file (devtools/conda-envs/gninatorch.yaml):condaenvcreate-fdevtools/conda-envs/gninatorch.yaml
condaactivategninatorchOnce thecondaenvironment is created and activated, thegninatorchpackage can be installed usingpipas follows:python-mpipinstall.TestsIn order to check the installation, unit tests are provided and can be run withpytest:pytest--cov=gninatorchUsageTraining and inference modules try to follow the originalCaffeimplementation ofgnina/scripts, however not all features are implemented.The folderexamplesincludes some complete examples for training and inference.The foldergninatorch/weightscontains pre-trained models fromGNINA, converted from Caffe to PyTorch.Pre-trained GNINA modelsPre-trained GNINA models can be loaded as follows:fromgninatorch.gninaimportsetup_gnina_modelmodel=setup_gnina_model(MODEL)whereMODELcorresponds to the--cnnargument inGNINA.A single model will returnlog_CNNscoreandCNNaffinity, while an ensemble of models will returnlog_CNNscore,CNNaffinity, andCNNvariance.Inference with pre-trainedGNINAmodels (--cnnargument inGNINA) is implemented in thegninamodule:python-mgninatorch.gnina--helpTrainingTraining is implemented in thetrainingmodule:python-mgninatorch.training--helpInferenceInference is implemented in theinferencemodule:python-mgninatorch.inference--helpAcknowledgmentsProject based on theComputational Molecular Science Python Cookiecutterversion 1.6.The pre-trained weights ofGNINAconverted to PyTorch were kindly provided by Andrew McNutt (@drewnutt).Copyright (c) 2021-2022, Rocco Meli
Package: gnista-cli
gnista-cliA cli for accessing gnista.ioInstallingInstall gnista-cli for all Users, remove-aif you want to install for your current user onlypipinstallgnista-cli
python-mgnista_clii-aInstalling Visplore Python LibraryIn order to open Visplore directly from gnista.io you need to install Visplore's Python library asdocumentedhere underInstallation 2. Alternative: using pip to install it in your terminalOpening Custom URL ProtocolThe commandline is able to handlehgnistaURLs and redirect them to a specific client.In this example it opens the datapoint45623780-6a9a-45e6-a522-650d4484a019inVisplorepython-mgnista_cliohgnista://45623780-6a9a-45e6-a522-650d4484a019?client=visploreLinksWebsitePyPiGIT Repository
Package: gnista-library
gnista-libraryA client library for accessing gnista.ioTutorialCreate new Poetry ProjectNavigate to a folder where you want to create your project and typepoetrynewmy-gnista-clientcdmy-gnista-clientAdd reference to your ProjectNavigate to the newly created project and add the PyPI packagepoetryaddgnista-libraryYour first DataPointCreate a new file you want to use to receive data this demo.pyfromgnista_libraryimportKeyringGnistaConnection,GnistaDataPoint,GnistaDataPointsconnection=KeyringGnistaConnection()data_point_id="56c5c6ff-3f7d-4532-8fbf-a3795f7b48b8"data_point=GnistaDataPoint(connection=connection,data_point_id=data_point_id)data_point_data=data_point.get_data_point_data()print(data_point_data)You need to replace theDataPointIdwith an ID from your gnista.io workspace.For example the DataPointId of this DataPointhttps://aws.gnista.io/secured/dashboard/datapoint/4684d681-8728-4f59-aeb0-ac3f3c573303is4684d681-8728-4f59-aeb0-ac3f3c573303Run and LoginRun your file in poetry's virtual environment$poetryrunpythondemo.py2021-09-02 14:51.58 [info     ] Authentication has been started. Please follow the link to authenticate with your user: [gnista_library.gnista_connetion] url=https://aws.gnista.io/authentication/connect/authorize?client_id=python&redirect_uri=http%3A%2F%2Flocalhost%3A4200%2Fhome&response_type=code&scope=data-api%20openid%20profile%20offline_access&state=myStateIn order to login copy theurlinto your Browser and Login to gnista.io or, if allowed a browser window will open by itself.KeystoreOnce you loggedin, the library will try to store your access token in your private keystore. Next time you run your programm, it might request a password to access your keystore again to gain access to gnista.io
Please take a look atKeyringfor details.Advanced ExampleShow received Data in a plotpoetrynewmy-gnista-clientcdmy-gnista-client
poetryaddgnista-library
poetryaddstructlib
poetryaddmatplotlibimportmatplotlib.pyplotaspltfromstructlogimportget_loggerfromgnista_libraryimportKeyringGnistaConnection,GnistaDataPoint,GnistaDataPointslog=get_logger()connection=KeyringGnistaConnection()data_point_id="56c5c6ff-3f7d-4532-8fbf-a3795f7b48b8"data_point=GnistaDataPoint(connection=connection,data_point_id=data_point_id)data_point_data=data_point.get_data_point_data()log.info("Data has been received. Plotting")data_point_data.plot()plt.show()Filter by DataPoint Namespoetrynewmy-gnista-clientcdmy-gnista-client
poetryaddgnista-library
poetryaddstructlib
poetryaddmatplotlibimportmatplotlib.pyplotaspltfromstructlogimportget_loggerfromgnista_libraryimportKeyringGnistaConnection,GnistaDataPoint,GnistaDataPointslog=get_logger()connection=KeyringGnistaConnection()dataPoints=GnistaDataPoints(connection=connection)data_point_list=list(dataPoints.get_data_point_list())fordata_pointindata_point_list:log.info(data_point)# Find Specific Data Pointsfiltered_data_points=filter(lambdadata_point:data_point.name.startswith("371880214002"),data_point_list)fordata_pointinfiltered_data_points:# get the datadata_point_data=data_point.get_data_point_data()log.info(data_point_data)data_point_data.plot()plt.show()LinksWebsitePyPiGIT Repository
Package: gnm
Rock and Rolling awesome Python package for affine-invariant MCMC samplinggnm is a well tested Python implementation of the affine-invariant sampler for Markov chain Monte Carlo (MCMC) that uses the Gauss-Newton-Metropolis (GNM) Algorithm. The GNM algorithm is specialized in sampling highly non-linear posterior probability distribution functions of the form exp(-||f(x)||^2/2).Pageshome,githubAttributionGoodmanCopyrightCopyright 2017Mehmet Ugurbiland contributors.Licensegnm is a free software made available under the MIT LICENSE.
Package: gnmi-proto
gNMI ProtobufThis project aims to be a base building block forgNMIprojects written in Python.The process of building pythonic libraries and applications usinggRPCandProtocol Buffershave been fragmented. This often means that a developer needs to copy overprotofiles, generate Python source from
these usingprotocand use them in-tree for their project. There already exists several projects built in this fashion.
While functional, these can be hard to reuse or maintain, often times resulting stale code and no versioning.gnmi-protobuilds on top of the improvement already done bybetterprotoand
in turn by thegrpcliblibrary. Here, we make available, as versioned packages,
code generated fromgNMI protocol buffers.The default implementation makes use of thebetterprotoprotocplugin to
generate clean modern code. In addition, this also provides agnmi.proto.legacymodule exposing code generated byprotocusing the the in-built Python generator.Example UsageClientThe following code expects a server at127.0.0.1:9339with thetest configuration. Refer togNMI Target Serversection inCONTRIBUTING.mdfor information on how to set it up.Using betterproto and grpclibimportgnmi.protoimportgrpclib.clientasyncdefmain():channel=grpclib.client.Channel(host="127.0.0.1",port=9339,ssl=None)service=gnmi.proto.gNMIStub(channel,metadata={"username":"admin","password":"secret"})response=awaitservice.capabilities()print(response.to_json(indent=2))response=awaitservice.get(path=[gnmi.proto.Path(elem=[gnmi.proto.PathElem(name="interfaces")])],)print(response.to_json(indent=2))if__name__=="__main__":importasyncioasyncio.run(main())Using vanilla grpcimportgnmi.proto.legacyimportgrpcdefmain():channel=grpc.insecure_channel("127.0.0.1:9339")metadata=[("username","admin"),("password","secret")]service=gnmi.proto.legacy.gNMIStub(channel)response=service.Capabilities(gnmi.proto.legacy.CapabilityRequest())print(response)response=service.Get(gnmi.proto.legacy.GetRequest(path=[gnmi.proto.legacy.Path(elem=[gnmi.proto.legacy.PathElem(name="interfaces")])],),metadata=metadata,)print(response)if__name__=="__main__":main()
Package: gnn
This repo contains a Tensorflow implementation of the Graph Neural Network model.Website (including documentation):https://mtiezzi.github.io/gnn_site/Authors:Matteo Tiezzi,Alberto RossiInstallRequirementsThe GNN framework requires the packagestensorflow,numpy,scipy.To install the requirements you can use the following commandpip install -U -r requirements.txtInstall the latest version of GNN:pip install gnnFor additional details, please seeInstall.Simple usage exampleimport gnn.GNN as GNN
import gnn.gnn_utils
import Net as n

# Provide your own functions to generate input data
inp, arcnode, nodegraph, labels = set_load()

# Create the state transition function, output function, loss function and  metrics
net = n.Net(input_dim, state_dim, output_dim)

# Create the graph neural network model
g = GNN.GNN(net, input_dim, output_dim, state_dim)

#Training

for j in range(0, num_epoch):
    g.Train(inp, arcnode, labels, count, nodegraph)

    # Validate
    print(g.Validate(inp_val, arcnode_val, labels_val, count, nodegraph_val))CitingTo cite the GNN implementation please use the following publication:Rossi, A., Tiezzi, M., Dimitri, G.M., Bianchini, M., Maggini, M., & Scarselli, F. (2018).
"Inductiveâ€“Transductive Learning with Graph Neural Networks",
In Artificial Neural Networks in Pattern Recognition (pp.201-212).
Berlin : Springer-Verlag.Bibtex:@inproceedings{rossi2018inductive,
  title={Inductive--Transductive Learning with Graph Neural Networks},
  author={Rossi, Alberto and Tiezzi, Matteo and Dimitri, Giovanna Maria and Bianchini, Monica and Maggini, Marco and Scarselli, Franco},
  booktitle={IAPR Workshop on Artificial Neural Networks in Pattern Recognition},
  pages={201--212},
  year={2018},
  organization={Springer}
}To cite GNN please use the following publication:F. Scarselli, M. Gori,  A. C. Tsoi, M. Hagenbuchner, G. Monfardini,
"The Graph Neural Network Model", IEEE Transactions on Neural Networks,
vol. 20(1); p. 61-80, 2009.Bibtex:@article{Scarselli2009TheGN,
  title={The Graph Neural Network Model},
  author={Franco Scarselli and Marco Gori and Ah Chung Tsoi and Markus Hagenbuchner and Gabriele Monfardini},
  journal={IEEE Transactions on Neural Networks},
  year={2009},
  volume={20},
  pages={61-80}
}LicenseReleased under the 3-Clause BSD license (seeLICENSE.txt):Copyright (C) 2004-2019 Matteo Tiezzi
Matteo Tiezzi <mtiezzi@diism.unisi.it>
Alberto Rossi <alrossi@unifi.it>
Package: gnnex
Introductiongnnex is to explain the complex GCN model,
it shows by drawing the most important edges in whole networks.Absolutely, there are many different  basic GCN models, so you can define your own
model.By rewriting graph.py to define your own GCN Model and rewriting train.py to train your
own GCN modeland generate the WEIGHT matrix.At last, draw the important edges according to WEIGHT matrix above.Installedkeras==2.3.1numpy==1.17.2pandas==0.25.3matplotlib==3.1.1scipy==1.3.2networkx==2.2seaborn==0.9.0Usagepip install gnnexBasic ModelYou should define your own model according to your task to be resolved. By rewriting graph.py to define model and rewriting train.py to train your own model.
Package: gnnnas
Library to write symbolic programs to generate expressive message passing neural networkskgforge DocumentationWhat is it?•Features•Installation•Usage•ContributingWhat is it?kgforgeis a library which automates the generation of knowledge graphs from scholarly text.Features:TODO: TODO: Description.Installation:PoetrypoetryaddkgforgePippipinstallkgforgeSetup your local environment:Any necessary environment variables description:exportSAMPLE_ENV_VARIABLE=${VALUE}UsageNow thatkgforgeis installed, you're ready to start using it!It's time to point you to the officialDocumentation Websitefor more information on how to usekgforgeContributingIf you'd like to contribute, be sure to check out ourcontributing guide! If you'd like to work on any outstanding items, check out theroadmapsection in the docs and get started :smiley:Thanks goes to these incredible people.
Package: gnnrl
GNN-RL-Model-CompressionGNN-RL Compression: Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement LearningDependenciesCurrent code base is tested under following environment:Python   3.8PyTorch  1.8.0 (cuda 11.1)torchvision 0.7.0torch-geometric1.6.1Results on ImageNetModelsFLOPs ratioTop1 Acc. (%)Acc.DatasetMobileNet-v140% FLOPs69.50-1.40ImageNetMobileNet-v170% FLOPs70.70-0.20ImageNetMobileNet-v258% FLOPs70.04-1.83ImageNetVGG-1620% FLOPs70.992+0.49ImageNetResNet-5047% FLOPs74.28-1.82ImageNetResNet-1850% FLOPs68.66-1.10ImageNet
Package: gnnuers
GNNUERS generates explanations in the form of user-item interactions that make
a GNN-based recommender system favor a demographic group over another. GNNUERS learns a perturbation
vector that modifies the adjacency matrix representing the training network. The edges modified by
the perturbation vector are the explanations genereated by the framework. GNNUERS then needs to work on a slight
extended version of a recommender system in order to include the perturbation vector and optimize it.
Package: gnocchi
Gnocchi is an open-source time series database.The problem that Gnocchi solves is the storage and indexing of time series
data and resources at a large scale. This is useful in modern cloud platforms
which are not only huge but also are dynamic and potentially multi-tenant.
Gnocchi takes all of that into account.Gnocchi has been designed to handle large amounts of aggregates being stored
while being performant, scalable and fault-tolerant. While doing this, the goal
was to be sure to not build any hard dependency on any complex storage system.Gnocchi takes a unique approach to time series storage: rather than storing
raw data points, it aggregates them before storing them. This built-in feature
is different from most other time series databases, which usually support
this mechanism as an option and compute aggregation (average, minimum, etc.) at
query time.Because Gnocchi computes all the aggregations at ingestion, getting the data
back is extremely fast, as it just needs to read back the pre-computed results.You can read the full documentation online athttp://gnocchi.osci.io.
Package: gnocchi-nagios
Gnocchi-nagiosGnocchi-nagios allows to push Nagios perfdata to GnocchiFree software: Apache licenseDocumentation:http://gnocchi-nagios.readthedocs.org/Source:https://github.com/sileht/gnocchi-nagiosBugs:https://github.com/sileht/gnocchi-nagios/issuesContribution via Github pull requests:https://github.com/sileht/gnocchi-nagios/pullsInstallationAt the command line:$ pip install gnocchi-nagiosOr, if you have virtualenvwrapper installed:$ mkvirtualenv gnocchi-nagios
$ pip install gnocchi-nagiosNagios/Icinga configurationThis is example of perfdata configuration for Gnocchi-nagiosPreparing working directories:$ mkdir -p /var/spool/gnocchi-nagios/{nagios, ready}In /etc/nagios3/nagios.conf:host_perfdata_file_mode=ahost_perfdata_file_processing_interval=5host_perfdata_file_template=DATATYPE::HOSTPERFDATA\tTIMET::$TIMET$\tHOSTNAME::$HOSTNAME$\tHOSTPERFDATA::$HOSTPERFDATA$\t$\tHOSTSTATE::$HOSTSTATE$\tHOSTSTATETYPE::$HOSTSTATETYPE$service_perfdata_file_mode=aservice_perfdata_file_processing_interval=5service_perfdata_file_template=DATATYPE::SERVICEPERFDATA\tTIMET::$TIMET$\tHOSTNAME::$HOSTNAME$\tSERVICEDESC::$SERVICEDESC$\tSERVICEPERFDATA::$SERVICEPERFDATA\tHOSTSTATE::$HOSTSTATE$\tHOSTSTATETYPE::$HOSTSTATETYPE$\tSERVICESTATE::$SERVICESTATE$\tSERVICESTATETYPE::$SERVICESTATETYPE$host_perfdata_file=/var/spool/gnocchi-nagios/nagios/host-perfdatahost_perfdata_file_processing_command=gnocchi-nagios-hostservice_perfdata_file=/var/spool/gnocchi-nagios/nagios/service-perfdataservice_perfdata_file_processing_command=gnocchi-nagios-serviceIn /etc/nagios3/conf.d/gnocchi-nagios.cfg:define command {command_name    gnocchi-nagios-servicecommand_line    /bin/mv /var/spool/gnocchi-nagios/nagios/service-perfdata /var/spool/gnocchi-nagios/ready/service-perfdata.$TIMET$}define command {command_name    gnocchi-nagios-hostcommand_line    /bin/mv /var/spool/gnocchi-nagios/nagios/host-perfdata /var/spool/gnocchi-nagios/ready/host-perfdata.$TIMET$}Gnocchi SetupInstallation and configuration of Gnocchi can be foundhereDon’t forget to create archive policies and archive policies rules that match your needs.Gnocchi-nagiosOnce it’s setup, you can configure gnocchi-nagios by creating a gnocchi-nagios.conf:[DEFAULT]spool_directory=/var/spool/gnocchi-nagios/ready[gnocchi]auth_type=gnocchi-noauthroles=adminuser_id=nagiosproject_id=nagiosendpoint=http://localhost:8041Note: Gnocchi-data assumes nagios TIMET epoch are in UTCAnd then run it with:$gnocchi-nagios--config-file=gnocchi-nagios.confTo get all configuration option you can run$tox-egenconfig$lessetc/gnocchi-nagios/gnocchi-nagios.conf
Package: gnodeclient
The Python G-Node Client is a library, that provides access to theG-Node REST-APIfor the Python programming language.
The client provides a high-level interface to this service which includes useful features such as
lazy loading, caching and the prevention of so called lost updates.
Further more this client library can handle native neo and odml objects.DependenciesUsing the G-Node Client requires some other python packages to be installed:neoA package for representing electrophysiology data in PythonquantitiesArithmetic and conversions of physical quantitiesodmlOpen metadata markup languageappdirsGet appropriate platform-specific user dirsrequest-futuresAsynchronous Python HTTP Requesth5pyHDF5 for PythonIn order to build the documentation and install the package using setup.py the following
packages are recommended.sphinxsetuptoolsAll dependencies with the exception of the python odml package, can be installed
using easy_install.
On debian bases linux distributions one install packages for quantities (python-quantities),
neo (python-neo), sphinx (python-sphinx), setuptools (python-setuptools) and h5py (python-h5py)
using the package manager (apt-get, aptitude).To install the odml package use the following commands, with appropriate permissions
(e.g. with sudo for the last command).git clone https://github.com/G-Node/python-odml.git
cd python-odml
python setup.py installInstallTo install the client invoke the following commands using appropriate permissions.git clone https://github.com/G-Node/python-gnode-client.git
cd python-gnode-client
python setup.py installHowto use the clientMore information about how to use the client can be found in the onlinedocumentation.If this packagessphinxandsetuptoolsare installed on your system, the documentation can
also be build from the cloned repository by executing the following command:python setup.py build_sphinx
Package: gnomad
Hail utilities for gnomADThis repo contains a number ofHailutility functions and scripts for thegnomAD projectand theTranslational Genomics Group. As we continue to expand the size of our datasets, we are constantly seeking to find ways to reduce the complexity of our workflows and to make these functions more generic. As a result, the interface for many of these functions will change over time as we generalize their implementation for more flexible use within our scripts. We are also continously adapting our code to regular changes in the Hail interface. These repos thus represent only a snapshot of the gnomAD code base and are shared without guarantees or warranties.We therefore encourage users to browse through theAPI referenceto identify modules and functions that will be useful in their own pipelines, and to edit and reconfigure relevant code to suit their particular analysis and QC needs.
Package: gnome-background-generator
This package propose two ways to dynamically change your desktop automatically.
One is by generating a XML file, that can be used by the gnome system, and the
other one is a little script you can run to randomly select a picture from a
path.Using XML filesGnome background can use XML files to make an animated background.
This little script allows you to generate automaticaly your own XML background
changer file, easily, using the command line.To generate the XML file, usegnome-background-generator:usage: gnome-background-generator [-h] [-p PATH] [-o OUTPUT]
                              [-t TRANSITION_TIME] [-d DISPLAY_TIME] [-s]
                              [-b]

A simple command line tool to generate an XML file to use for gnome
wallpapers, to have dynamic walls

optional arguments:
  -h, --help            show this help message and exit
  -p PATH, --path PATH  Path to look for the pictures. If no output is
                        specified, will be used too for outputing the dynamic-
                        wallpaper.xml file. Default value is the current
                        directory (.)
  -o OUTPUT, --output OUTPUT
                        Output filename. If no filename is specified, a
                        dynamic-wallpaper.xml file will be generated in the
                        path containing the pictures. You can also use "-" to
                        display the xml in the stdout.
  -t TRANSITION_TIME, --transition-time TRANSITION_TIME
                        Time (in seconds) transitions must last (default value
                        is 2 seconds)
  -d DISPLAY_TIME, --display-time DISPLAY_TIME
                        Time (in seconds) a picture must be displayed. Default
                        value is 900 (15mn)
  -s, --set-background  '''try to set the background using gnome-appearance-
                        properties
  -b, --debugChanging your desktop randomlyThere is also another command you can use to change your background to a random
file from a known directory:$ gnome-wallpaper-select /path/to/wallsThis second one is useful if combined with a cronjob; Unfortunately, it’s
sometimes a hard process because crontabs use dbus, and this conflicts with the
use ofgconftool-2.Here is a simple tutorial on how to make it work:$ wget http://github.com/ametaireau/gnome-background-generator/raw/master/xdbus -O ~/.Xdbus
$ crontab -eAnd then add*/4 * * * * . ~/.Xdbus; /usr/local/bin/gnome-wallpaper-select ~/Images/walls