Saravanan666/pypi-org huggingface dataset:
Package: sim-modem
sim-modemEasy library for interfacing with mobile modems. Tested with Simcom SIM7600G-H on Raspberry PI Zero W. The commands could be different for other modems.Installationpipinstallsim-modemUsagefromsim_modemimportModemmodem=Modem('/dev/ttyUSB2')signal_quality=modem.get_signal_quality()print(signal_quality)modem.send_sms('+393383928434','Hello World!')APIModem (Class)Main class for interfacing with the modem. Each method raise an exception if the modem returns an error. If the command is successful, the function returns the response from the modem.Modem(address,# Address of the device tty (e.g. "/dev/ttyUSB2")baudrate=460800,# Baudrate of the device. Default: 460800timeout=5,# Timeout for the serial connection. Default: 5at_cmd_delay=0.1,# Delay between AT commands. Default: 0.1debug=False# Log commands and responses from modem, test command support before executing them. Default: False)MethodDescriptionreconnect() -> strReconnect to serialclose() -> strClose the serial connectionHardware related methodsget_model_identification() -> strGet the model identificationget_manufacturer_identification() -> strGet the manufacturer identificationget_serial_number() -> strGet the serial numberget_firmware_version() -> strGet the firmware versionget_volume() -> strGet the volume. The volume range is between 0 and 5set_volume(index: int) # 0-5Set the volume. The volume must be between 0 and 5improve_tdd() -> strDecrease TDD Noise effectenable_echo_suppression() -> strEnable echo suppressiondisable_echo_suppression() -> strDisable echo suppressionNetwork related methodsget_network_registration_status() -> strGet the network registration statusget_network_mode() -> NetworkModeGet the network modeget_network_name() -> strGet the network nameget_network_operator() -> strGet the network operatorget_signal_quality() -> strGet the signal qualityget_signal_quality_db() -> intGet the signal quality in dBget_signal_quality_range() -> SignalQualityGet the signal quality as a range (seeSignalQuality)get_phone_number() -> strGet the phone numberget_sim_status() -> strGet the SIM statusset_network_mode(mode: NetworkMode) -> strSet the network modeCalls related methodscall(number: str) -> strCall a numberanswer() -> strAnswer a callhangup() -> strHangup a callSMS related methodsget_sms_list() -> listGet the list of SMSempty_sms() -> strEmpty the SMS storagesend_sms(number: str, message: str) -> strSend an SMSget_sms(index: int) -> dictGet an SMS by IDdelete_sms(index: int) -> strDelete an SMS by IDGPS related methodsget_gps_status() -> strGet the GPS statusstart_gps() -> strStart the GPSstop_gps() -> strStop the GPSget_gps_coordinates() -> dictGet the GPS coordinatesSignalQuality (enum)Signal quality expressed as rangesKeyDescriptionSignalQuality.LOWSignal is under 7SignalQuality.FAIRSignal is between 7 and 15SignalQuality.GOODSignal is between 15 and 20SignalQuality.EXCELLENTSignal is over 20NetworkMode (enum)Network mode of the modem (get/set)KeyDescriptionNetworkMode.AUTOMATICAutomaticNetworkMode.GSM_ONLYGSM onlyNetworkMode.LTE_ONLYLTE onlyNetworkMode.ANY_BUT_LTEAny but LTELicenseMIT
Package: sim-modem-cli
sim-modem-cliA command line interface for SIM modems. It uses thesim-modemlibrary to communicate with the modem through AT commands. Tested with Simcom SIM7600G-H on Raspberry PI Zero W. The commands could be different for other modems.InstallationFrom pippython3-mpipinstallsim-modem-cliUsagesim-modem-cli<command>address[<args>...][--options]OptionsOptionDescription--helpShow help message--versionShow the version--baudrateThe baudrate to use.--timeoutThe timeout to use.--debugEnable debug mode.CommandsCommandArgumentsDescriptionHardware related commandsget_model_identificationGet the model identificationget_manufacturer_identificationGet the manufacturer identificationget_serial_numberGet the serial numberget_firmware_versionGet the firmware versionget_volumeGet the volume. The volume range is between 0 and 5set_volumeint volume (1-5)Set the volume. The volume must be between 0 and 5improve_tddDecrease TDD Noise effectenable_echo_suppressionEnable echo suppressiondisable_echo_suppressionDisable echo suppressionNetwork related commandsget_network_registration_statusGet the network registration statusget_network_modeGet the network modeget_network_nameGet the network nameget_network_operatorGet the network operatorget_signal_qualityGet the signal qualityget_signal_quality_dbGet the signal quality in dBget_signal_quality_rangeGet the signal quality as a rangeget_phone_numberGet the phone numberget_sim_statusGet the SIM statusset_network_modeauto | gsm_only | lte_only | no_lteSet the networkCalls related commandscallNumber to call (with international prefix)Call a numberanswerAnswer a callhangupHangup a callSMS related commandsget_sms_listGet the list of SMSempty_smsEmpty the SMS storagesend_sms str, message: strNumber, messageSend an SMSget_smsSMS indexGet an SMS by IDdelete_smsSMS indexDelete an SMS by IDGPS related commandsget_gps_statusGet the GPS statusstart_gpsStart the GPSstop_gpsStop the GPSget_gps_coordinatesGet the GPS coordinatesLicenseMIT
Package: simms
Creates empty measurement sets using the the CASA simulate tool.RequiresNRAO CASA 6 (modular)numpyInstallGithub$ git clone https://github.com/radio-astro/simms
$ cd simms
$ pip install .Pippip install simmsExamplesOn the command lineTo get farmiliar with options run:simms –help;)The antenna positions can be specified as a CASA Table or an ASCII file. Bellow is an example of you can an empty MS
using both formats:NOTE: Some antenna tables are provided in Simms/observatories. Run the examples below in thesimmsdirectory or
change the paths of the antenna files accordingly in the lines bellow.CASA TableKAT7_ANTENNASsimms -T kat-7 -t casa -l test_casa -dec -30d0m0s -ra 0h0m0s -st 1 -dt 60 -f0 1.4GHz -nc 4 -df 10MHz Simms/observatories/KAT7_ANTENNASASCIIvlac.enu.itrfsimms -T vla -t ascii -cs itrf -l test_ascii -dec 30d0m0s -ra 0h0m0s -st 1 -dt 60 -f0 1.4GHz -nc 4 -df 10MHz Simms/observatories/vlac.itrf.txtIn both cases, we create an empty MS (VLA-A and KAT-7) at 1400MHz with 4 10MHz channels, the observtion is 1hr and has a
60s integrations time.In Pythonimport simms

simms.create_empty_ms(msname="Name_of_ms.MS", tel="kat-7", synthesis=1, pos_type='casa', pos="kat-7_antenna_table")
Package: sim-name
TEST SIM in sorted orderIt fetches CNAME from hosted zone in Route53 and sorts them according to CNAME.
Generates a html page and update it onto the confluence page.Pre-requisitePlease get the below details and replace in the script:hosted-zone-id:  You can get this from AWS accountCONFURL: This is the rest url of your confluence pageCONFPAGEID:  This is the confluence pageidCONFUSERNAME, CONFPASSWORD: Confluence username and passwordExecution & Outputarjun$ python aws_cname.pyINFO:aws_cname.py:FetchingCNAME records from AWS account. This may take few minutes.INFO:aws_cname.py:Fetchingdata succesful.INFO:aws_cname.py:Proceedingdata scrubbing.INFO:aws_cname.py:Updatingconfluence page:https://CONFURL/AWS+Route+53+CNAMEINFO:aws_cname.py:Finishupdating records
Package: simnetpy
simnetpyPython Package for the creation and analysis of similarity networks.InstallationInstall latest release from PyPi with pippip install simnetpyInstall from sourceThe source code for this project can be installed usinggitandpip.
Clone repositorygit clone https://github.com/amarnane/simnetpy.git
cd simnetpy
pip install .To remove the package simply usepip uninstall simnetpyDeveloper ModeTo install in developer mode (have changes in source code update without reinstallation) add-eflagpip install -e .Note: removing the package is slightly more complicated and a different command is needed to uninstallpython setup.py develop -uGraph ToolThere is one dependency that cannot be installed through pip -Graph-Tool. This is a result of it's underlyingc++dependencies.
The simplest method for python users is to make use of a conda environment, install this package using the commands above and installgraph-toolusingconda-forgeconda install -c conda-forge graph-toolNote: this will not work on Windows. Alternative (conda independent) solutions can be found on theGraph Tool WebsiteUsingsimnetpyimportsimnetassnimportnumpyasnp# create mixed guassian data with 100 nodes, 2 dimensions and 3 equally sized clusters.N=100sizes=np.array([34,33,33])d=2dataset=sn.datasets.mixed_multi_guassian(len(sizes),d,N,sizes=sizes)# calculate pairwise similarityS=sn.pairwise_sim(dataset.X,metric='euclidean',norm=True)# Create igraph Igraph from matrixgg=sn.network_from_sim_mat(S,method='knn',K=10)# print graph statsprint(gg.graph_stats())# true cluster qualitycqual_ytrue=sn.clustering.cluster_quality(gg,dataset.y)print(cqual_ytrue)# clusterylabels=sn.clustering.spectral_clustering(gg,laplacian='lrw')# cluster accuracycacc=sn.clustering.cluster_accuracy(dataset.y,ylabels)print(cacc)# predicted cluster qualitycqual=sn.clustering.cluster_quality(gg,ylabels)print(cqual)
Package: simobility
simobilitysimobilityis a light-weight mobility simulation framework. Best for quick prototypingsimobilityis a human-friendly Python framework that helps scientists and engineers to prototype and compare fleet optimization algorithms (autonomous and human-driven vehicles). It provides a set of building blocks that can be used to design different simulation scenarious, run simulations and calculate metrics. It is easy to plug in custom demand models, customer behavior models, fleet types, spatio-temporal models (for example, useOSRMfor routing vehicles and machine learning models trained on historical data to predictETA).MotivationCreate an environment for experiments with machine learning algorithms for decision-making problems in mobility services and compare them to classical solutions.Some examples:Deep Reinforcement Learning with Applications in TransportationT. Oda and C. Joe-Wong, "Movi: A model-free approach to dynamic fleet management". 2018A. Alabbasi, A. Ghosh, and V. Aggarwal, "DeepPool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning", IEEETrans. Intelligent Transportation Systems (to appear). 2019C Wang,Y Hou, M Barth, "Data-Driven Multi-step Demand Prediction for Ride-hailing Services Using Convolutional Neural Network". 2019J. Ke, F. Xiao, H. Yang, and J. Ye. Optimizing online matching for ride-sourcing services with multi-agent deep reinforcement learning. 2019Installationpip install simobilityContributions and thanksThanks to all who contributed to the concept/code:Steffen HäußlerStephen PriviteraSultan ImanhodjaevYábir BenchakhtirSimulation exampleSimple simulation exampleLog exampleMetrics example{"avg_paid_utilization":63.98,"avg_utilization":96.87,"avg_waiting_time":292.92,"created":3998,"dropoffs":589,"empty_distance":640.37,"empty_distance_pcnt":33.67,"fleet_paid_utilization":63.98,"fleet_utilization":96.87,"num_vehicles":50,"pickup_rate":15.48,"pickups":619,"total_distance":1902.04,}Simulation logsRead logs with pandasimportpandasaspddata=pd.read_csv("simulation_output.csv",sep=";",converters={"details":lambdav:eval(v)},)details=data.details.apply(pd.Series)Run OSRMwgethttp://download.geofabrik.de/north-america/us/new-york-latest.osm.pbf
dockerrun-t-v"${PWD}:/data"osrm/osrm-backendosrm-extract-p/opt/car.lua/data/new-york-latest.osm.pbf
dockerrun-t-v"${PWD}:/data"osrm/osrm-backendosrm-partition/data/new-york-latest.osrm
dockerrun-t-v"${PWD}:/data"osrm/osrm-backendosrm-customize/data/new-york-latest.osrm
dockerrun-d-t-i-p5010:5000-v"${PWD}:/data"osrm/osrm-backendosrm-routed--algorithmmld/data/new-york-latest.osrm
Package: simobject
ReadmeGeneral framework for a simulation:Simulationobject withQuantitiesthat can be updated in a specified orderQuantitiesobjects that behave likenumpy.ndarraysbut can be set to be constant, contain information, and have functions to update them.Updaterobject that can define how a quantity is updated.SimulationandQuantitiesinherit from theHeartbeatObjectwhich provide aSystoler,Updater, andDiastoler(those are also allUpdaterobjects) and functions to call or set themTheSimulationobject has its own update function which in this order
- calls its ownsystoleupdate
- calls thesystoleupdates of its quantities
- calls theupdateupdates of its quantities
- calls thediastoleupdates of its quantities
- calls its owndiastoleupdate
Package: simoc-abm
SIMOC-ABMSIMOC-ABM is a Agent-Based Model (ABM) built in Python and based on a
currency-exchange paradigm. It was developed for high-fidelity ecosystem
simulations and includes a library of pre-built agents and configurations,
but can also be used to simulate arbitrary agent-based models.SIMOC-ABM is the 'backend' package of the Scalable Interactive Model of an
Off-world Community (SIMOC), and is designed to work seamlessly with SIMOC-Web,
a graphical user interface, and SIMOC-Live, a package for taking live readings
from physical sensors.SIMOC has been developed for specific use-cases: first to model a Mars habitat
using data from NASA, and second to model the Biosphere 2 Missions using data
from published literature and direct input from Biosphere 2 scientists.FeaturesBuilt on acurrency-exchange paradigm, with built-in functions for
specifying in-flows and out-flows, storage and capacities, thresholds and
environmental responses, and more.Includes anagent librarybuilt on scientific literature including
humans, plants, environmental control and life support systems, and more, as
well as severalpreset configurationsfor functional Mars Habitats and
the historic Biosphere 2 missions.Functions as a stand-alone ABM or within theSIMOC ecosystem, which
includes a graphical user interface (SIMOC-Web) and pacakge for taking live
readings from physical sensors (SIMOC-Live).
Package: simod
Simod: Automated discovery of business process simulation modelsSimod combines process mining and machine learning techniques to automate the discovery and tuning of Business Process
Simulation models from event logs extracted from enterprise information systems (ERPs, CRM, case management systems,
etc.).
Simod takes as input an event log in CSV format, a configuration file, and (optionally) a BPMN process model, and
returns a business process simulation scenario that can be simulated using
theProsimossimulator, which is embedded in Simod.DependenciesRequiredDependencyVersionNotesPython3.9For Windows,Python 3.9.13is the last distribution with Windows installers.Java1.8For example, useAmazon Corretto 8.PoetrylatestIf using Docker or compiling from source, usePoetryfor building, installing, and managing Python dependencies.OptionalDepending on your CPU architecture, some dependencies might not be pre-compiled for your platform. In that case, you
will
most likely also need the following dependencies:DependencyVersionNotesCargo and RustlatestInstall it withrustup.rs.Getting StartedPyPI❗️Make surejava -versionreturns1.8andpipis installed.Then, install Simod and run it with the following commands:pipinstallsimod
simod--configurationresources/config/configuration_example.ymlUse your own configuration file instead ofresources/config/configuration_example.ymland specify the path to the
event log in the configuration file itself. Paths are relative to the configuration file, or absolute.PyPI project is available athttps://pypi.org/project/simod/.Dockerdockerpullnokal/simodTo start a container:dockerrun-it-v/path/to/resources/:/usr/src/Simod/resources-v/path/to/output:/usr/src/Simod/outputsnokal/simodbashUse theresourcesdirectory to store event logs and configuration files. Theoutputsdirectory will contain the
results of Simod.From inside the container, you can run Simod with:poetryrunsimod--configuration<path-to-config>Docker images for different Simod versions are available athttps://hub.docker.com/r/nokal/simod/tags.Configuration fileA set of example configurations can be found in theresourcesfolder along with a description
of each element:Basic configuration to discover the full BPS
model (here).Basic configuration to discover the full BPS model using fuzzy (probabilistic) resource
calendars (here).Basic configuration to discover the full BPS model, and evaluate it with a specified event
log (here).Basic configuration to discover a BPS model with a provided BPMN process model as starting
point (here).Basic configuration to discover a BPS model with no optimization process (one-shot) (here).Complete configuration example with all the possible
parameters (here).For developersTestingUsepytestto run tests on the package:poetryrunpytestTo run unit tests, execute:poetryrunpytest-m"not integration"Coverage:poetryrunpytest-m"not integration"--cov=simod
Package: simond
simond -SimpleMonitorDaemonThe daemon forsimon, collects system information every second and logs them
to STDOUT (redirect or pipe it to your liking) at a certain interval.
If the interval is not 1 sec, the sample mean will be calculated and logged.Installationsimond requires Python 3.6+, the easiest way to install it is withpipor any other package manager supporting PyPI:pipinstallsimondUsageusage: simond [-h] [-V] [-i LOGINTERVAL]

simond - Simple Monitor Daemon, fetches and logs system information

optional arguments:
  -h, --help            show this help message and exit
  -V, --version         Print simond version number
  -i LOGINTERVAL, --loginterval LOGINTERVAL
                        The log interval in seconds, if it's anything but 1,
                        averages (sample mean) will be loggedLog structurehost: The system's hostnamets: Timestamp of when the data were gathered as UTC Unix timeloadavg_1min: 1minLoad averageloadavg_5min: 5min Load averageloadavg_15min: 15min Load averagemem: Total memory usage in percent as number (0.01 = 1%)cpu: Total CPU usage in percent as numberdisk: Total disk usage in percent as numberdiskio_rps: Disk reads in bytes per seconddiskio_wps: Disk writes in bytes per secondnetio_rps: Network traffic received in bytes per secondnetio_sps: Network traffic sent in bytes per secondCurrentlysimonlogs each measure as a JSON object separated by newline, the
file will therefor not be valid JSON, with the benefit that each line is valid JSON and can be read per line (streaming FTW!):{"host":"simon","ts":1592943008,"loadavg_1min":1.598,"loadavg_5min":2.104,"loadavg_15min":2.504,"mem":0.03,"cpu":0.014,"disk":0.545,"diskio_rps":0,"diskio_wps":0,"netio_rps":0,"netio_sps":0}{"host":"simon","ts":1592943009,"loadavg_1min":1.598,"loadavg_5min":2.104,"loadavg_15min":2.504,"mem":0.03,"cpu":0.036,"disk":0.545,"diskio_rps":0,"diskio_wps":0,"netio_rps":1024,"netio_sps":0}This might change in the future if it is not sufficent.
Package: simone
Simone :: Django WebmailDocumentationThe full documentation is athttps://simone.readthedocs.org.QuickstartInstall simone:pip install simoneThen use it in a django project:INSTALLED_APPS = [
    ...
    'simone',
    ...
]There is also a working minimal example that can be used via docker:make dockerThen point your browser athttp://localhost:8000FeaturesReads email… not much elseRunning TestsDoes the code actually work?source <YOURVIRTUALENV>/bin/activate
(myenv) $ pip install -r requirements_test.txt
(myenv) $ python runtests.pyCreditsTools used in this package:Cookiecuttercookiecutter-djangopackageImapClientjQueryjQueryUIJsonTreeHistory0.1.0 (2016-08-27)First release on PyPI.
Package: simono-especificaciones
# Especificaciones para Sistema de Monitoreo de NodosEn este proyecto se declaran especificaciones para los Sensores del Sistema de Monitoreo de Nodos### Prerequisites
Python 3.6.3### InstallingPara obtener el paquete instalarlo con pip de la siguiente manera:` pip installsimono-especificaciones`Una vez instalado solo se debera hacer la importacion` import simono_especificaciones `En caso de necesitar actualizar la version de la libreria:` python-mpip install--upgradesimono-especificaciones`
Package: simon-search
#!/SimonHello! Welcome to Simon. Simon is a Python library that powers your entire semantic search stack: OCR, ingest, semantic search, extractive question answering, textual recommendation, and AI chat.Check out 🌐this online demo of the tooland browse 📖the full documentation!🏃 Quick StartGather Your ToolsPostgresQL 15 with the Vector PluginA cloud service likeneon,supabase,digital oceanis probably easiestOR, you can alsoself host the database following these instructionsOpenAI API keyPython 3.9 or above. We recommend Python 3.11.Optional:Javaif you want to use Simon's built in OCR toolingInstall the PackageYou can get the package from PyPi.pipinstallsimon-search-UConnect to Databaseimportsimon# connect to your databasecontext=simon.create_context("PROJECT_NAME",# an arbitrary string id to silo your data.# (store and search are per-project.)"sk-YOUR_OPENAI_API_KEY",# must support GPT-4# postgres options. get these from your postgres provider.{"host":"your_db_host.com","port":5432,"user":"your_username","password":"password",# or None"database":"your_database_name"})# if needed, provision the databasesimon.setup(context)# do this *only once once per new database*!!Theproject_nameis an arbitrary string you supply as the "folder"/"index" in the database where your data get stored. That is, the data ingested for one project cannot be searched in another.You optionally can store the OpenAI key and Database info in an.envfile or as Bash shell variablesfollowing these instructions.Storing Some Filesds=simon.Datastore(context)# storing a remote webpage (or, if Java is installed, a PDF/PNG)ds.store_remote("https://en.wikipedia.org/wiki/Chicken",title="Chickens")# storing a local file (or, if Java is installed, a PDF/PNG)ds.store_file("/Users/test/file.txt",title="Test File")# storing some textds.store_text("Hello, this is the text I'm storing.","Title of the Text","{metadata: can go here}")To learn more about ingestion,head on over to the ingest overview page!Search Those FilesWe all know why you came here: search!s=simon.Search(context)# Semantic Searchresults=s.search("chicken habits")# Recommendation (check out the demo: https://wikisearch.shabang.io/)results=s.brainstorm("chickens are a species that")# LLM Answer and Extractive Question-Answering ("Quoting")results=s.query("what are chickens?")To learn more about search, including how to perform a boring keyword search or to stream your LLM output,head on over to the search overview page!That's it! Simple as that.📖 Full DocumentationCheck out the full documentationavailable hereavailable here: from customizing your LLM, a REST API, and streaming your search results—we've got you covered.🙌 Friends!We are always looking for more friends to build together. If you are interested in learning more, getting enterprise support, or just want to chat,visit this page.If you have a question about the package, please feel free topost a discussion.(C) 2023 Shabang Systems, LLC. Built with ❤️ and 🥗 in the SF Bay Area
Package: simoorg
Simoorg is linkedin’s very own failure inducer framework, It was
designed to be easy to extend and most of the important components are
pluggable.InstallationThe recommended way of installing simoorg is via pip, through the
commandsudo pip install simoorgFor details on how to install from source please check our User GuideMore DocumentationUser GuideDesign DocumentConfiguration filesGuide to writing new pluginsGuide to using posix failuresCreditsMayuresh GharatSarath SreedharanTofig Suleymanov
Package: simopt
# simopt, the SIMple OPTion parserSimopt is a lightweight module for parsing command line options in python programs.
The options can be written in a separate file or at the top of a script. They are
(currently) written in a list of tuples, with each entry corresponding to an option
consisting ofint: the option level (allowing differentiating help levels)str: the option name (command line)str: the option target (option class attribute)class: the option unit type (parser)int: the number of parameters for the optionobject: the default value (None if no default)int: flags (MULTI, MANDATORY, …)str: description (help text)String items in the option list are part of the help and allow sectioning
options.