Saravanan666/pypi-org huggingface dataset:
Package: sensorpush-bleak
pysensorpush-bleakPython library which uses gatttool to read temperature data from a Sensorpush HT.w temperature sensor.Find the mac address of your sensor:pi@raspberrypi:~ $ sudo hcitool lescan
LE Scan ...
A4:34:F1:7F:CD:D8 SensorPush HT.w CDD8Example usage:pi@raspberrypi:~/p/pysensorpush-bleak/src/pysensorpush-bleak $ python
Python 3.9.2 (default, Mar 12 2021, 04:06:34)
[GCC 10.2.1 20210110] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sensorpush as sp
>>> import asyncio
>>> from bleak import BleakClient
>>> async def main():
...     client = BleakClient("A4:34:F1:7F:CD:D8")
...     await client.connect()
...     temp_c = await sp.read_temperature(client)
...     print("temperature = {}".format(temp_c))
...     client.disconnect()
...
>>> asyncio.run(main())
temperature = 13.92
>>>
Package: sensors-control-gpio-xzf8971
sensors-control-gpioThis is a repository desgined for the arduino 37 sensors bundle for raspberry pi 4. This package implemented in raspberry pi 4B GPIO with RPi.GPIO.Get startedpipinstallsensors-control-gpio-xzf8971AHT20How toFor help infromationpython-mAHT20_sensor_control--help# Open I2C bus1. Save data into example.log. Measure data every 60s.python-mAHT20_sensor_control-n1-oexample.log-t60# Enable CRC8 check. Only save data when crc check is OKpython-mAHT20_sensor_control-n1-oexample.log-t60-cDHT11How topython-mDHT11_sensor_control-ppin-badafruit
Package: sensors.py
sensors.pypython bindings using ctypes for libsensors3 of thelm-sensors project. The code was written against libsensors 3.3.4.For documentation of the low level API seesensors.h. For an example of the high level API seeexample.py.For a GUI application that displays the sensor readings and is based on this library, take a look atsensors-unity.FeaturesFull access to low level libsensors3 APIHigh level iterator APIunicode handlingPython2 and Python3 compatibleLicensingLGPLv2 (same as libsensors3)Usage NotesAs Python does not support call by reference for primitive types some of the libsensors API had to be adapted:# nr is changed by refrence in the C APIchip_name,nr=sensors.get_detected_chips(None,nr)# returns the value. throws on errorval=sensors.get_value(chip,subfeature_nr)Missing Features (pull requests are welcome):sensors_subfeature_typeenumsensors_get_subfeature
Package: sensor-state-data
Sensor State DataModels for storing and converting Sensor Data stateInstallationInstall this via pip (or your favourite package manager):pip install sensor-state-dataContributors ✨Thanks goes to these wonderful people (emoji key):This project follows theall-contributorsspecification. Contributions of any kind welcome!CreditsThis package was created withCookiecutterand thebrowniebroke/cookiecutter-pypackageproject template.
Package: sensortransformer
Sensor Transformer (SeT)Adaptation ofVision Transformer (ViT)for Time-Series and Sensor Data in Tensorflow.Usageimportargparseimporttensorflowastfimportsensortransformerimportset_networkparser=argparse.ArgumentParser()parser.add_argument("--signal-length",type=int)parser.add_argument("--segment-size",type=int)parser.add_argument("--num_channels",type=int)parser.add_argument("--num_classes",type=int)args=parser.parse_args()"""TF-Data objects. Instances must be of shapex = (batch, signal_length, num_channels), y = (batch, num_classes)"""ds_train,ds_test=...model=set_network.SensorTransformer(signal_length=args.signal_length,segment_size=args.segment_size,channels=args.num_channels,num_classes=args.num_classes,num_layers=4,d_model=64,num_heads=4,mlp_dim=64,dropout=0.1,)model.compile(loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),optimizer=tf.keras.optimizers.Adam(),metrics=[tf.keras.metrics.CategoricalAccuracy()],)model.fit(ds_train,epochs=50,verbose=1)model.evaluate(ds_test)
Package: sensory-cloud
Python SDKSensory Cloud is built using python 3.8, but it is compatible with python 3.6 and higherGeneral InformationBefore getting started, you must spin up a Sensory Cloud inference server or have Sensory spin one up for you. You must also have the following pieces of information:Your inference server URL (fully_qualified_domain_name)Your Sensory Tenant ID (UUID)Your configured secret key (device_credential) used to register OAuth clients and devicesCode ExamplesTheexamplessection of this repository shows the user how to do the following:Set client credentials and register devicesCreate enrollments and enrollment groups for the audio and video servicesAuthenticate against enrollments and enrollment groupsDetect audio events such as coughing or door knocksTranscribe speech to textDetect video livenessMonitor server health
Package: sensospot_parser
Sensospot Data ParserParsing the numerical output fromSensoSpotmicroarray analysis.TheSensoSpotmicroarray analyzer is an automated fluorescence microscope with an image analysis software for detecting and measuring microarrays. The original name of the product was "FLAIR" by the company Sensovation, that was later acquired by Miltenyi.There is no affiliation on my side regarding Sensovation or Miltenyi, I just use the product and needed a way to make the data available for further analysis.Example:importsensospot_parser# read the raw data of a folderraw_data=sensospot_parser.parse_folder(<pathtoresultsdirectory>)sorted(raw_data.columns)==['Analysis.Datetime','Analysis.Image','Analysis.Name','Bkg.Area','Bkg.Mean','Bkg.Median','Bkg.StdDev','Bkg.Sum','Exposure.Id','Parameters.Channel','Parameters.Time','Pos.Id','Pos.Nom.X','Pos.Nom.Y','Pos.X','Pos.Y','Spot.Area','Spot.Diameter','Spot.Found','Spot.Mean','Spot.Median','Spot.Saturation','Spot.StdDev','Spot.Sum','Well.Column','Well.Name','Well.Row']ConstantsThere is acolumnsmodule available, providing constans that define the column names.importsensospot_parsersensospot_parser.columns.ANALYSIS_NAME=="Analysis.Name"Avaliable public functions:All public functions return apandas DataFrameobject.Be aware that some columns might contain no values. This is depending on the parsing
method (xml or csv) and if a parameters file could be found or not.parse_folder(path_to_folder)Tries theparse_xml_folder()function first and if an error occurs,
it falls back to theparse_csv_folder()parse_xml_folder(path_to_folder)Searches the folder for a parsable Sensospot XML result file and parses it into
a pandas data frame. It will add additional meta data from parameters folder,
if it is present.parse_csv_folder(path_to_folder)Searches the folder for parsable Sensospot .csv files, parses them into one
big pandas data frame and will add additional meta data from parameters folder,
if it is present.CLIFor the (propably) most important function, there is a cli commandUsage:sensospot_parse[OPTIONS]SOURCES

Arguments:SOURCES:OneormorefolderswithSensospotmeasurements

Options:-o,--outputFILEOutputfilepath,defaultsto'collected_data.csv'-q,--quietIgnoresanitycheckforcsvfileparsing--helpShowthismessageandexit.DevelopmentTo install the development version of Sensovation Data Parser:git clone https://git.cpi.imtek.uni-freiburg.de/holgi/sensospot_data.git

# create a virtual environment and install all required dev dependencies
cd sensospot_data
make devenvTo run the tests, usemake tests(failing on first error) ormake coveragefor a complete report.To generate the documentation pages usemake docsormake serve-docsfor
starting a webserver with the generated documentation
Package: sensospot_tools
Sensospot ToolsSome small tools for working with parsed Sensospot data.Selecting and spliting a pandas data frameselect(data: DataFrame, column: str, value: Any) -> DataFrameSelects rows of a dataframe based on a value in a columnExample:fromsensospot_toolsimportselectprint(data)categoryvalue0dog11cat22horse33cat4print(select(data,"category","cat"))categoryvalue1cat23cat4split(data: DataFrame, *on: Any) -> Iterator[tuple[Any, ..., DataFrame]]Splits a data frame on unique values in multiple columnsReturns a generator of tuples with at least two elements.
Thelastelement is the resulting partial data frame,
the element(s) before are the values used to split up the original data.Example:fromsensospot_toolsimportsplitprint(data)categoryvalue0dog11cat22horse33cat4result=dict(split(data,column="category"))print(result["dog"])categoryvalue0dog1print(result["cat"])categoryvalue1cat23cat4print(result["horse"])categoryvalue2horse3Working with data with multiple exposure timesselect_hdr_data(data: DataFrame, spot_id_columns: list[str], time_column: str, overflow_column: str) -> DataFrame:Selects the data for increased dynamic measurement range.To increase the dynamic range of a measurement, multiple exposures of one
microarray might be taken.This function selects the data of only one exposure time per spot, based
on the information if the spot is in overflow. It starts with the weakest
signals (longest exposure time) first and chooses the next lower exposure
time, if the result in theoverflow_columnisTrue.This is done for each spot, and therfore a spot needs a way to be
identified across multiple exposure times. Examples for this are:
- for a single array:
the spot id (e.g. "Pos.Id")
- for multiple arrays:
the array position and the spot id (e.g. "Well.Name" and "Pos.Id")
- for multiple runs:
the name of the run, array position and the spot id
(e.g. "File.Name", "Well.Name" and "Pos.Id")The function will raise a KeyError if any of the provided column names
is not present in the data framenormalize(data: DataFrame, normalized_time: Union[int, float], time_column: str, value_columns: list[str], template: str) -> DataFrame:normalizes values to a normalized exposure timeWill raise a KeyError, if any column is not in the data frame;
raises ValueError if no template string was provided.DevelopmentTo install the development version of Sensospot Tools:git clone https://git.cpi.imtek.uni-freiburg.de/holgi/sensospot_tools.git

# create a virtual environment and install all required dev dependencies
cd sensospot_tools
make devenvTo run the tests, usemake testsormake coveragefor a complete report.To generate the documentation pages usemake docsormake serve-docsfor
starting a webserver with the generated documentation
Package: sensplit
SenSplitThis python package helps to splits a sensor dataset (in Pandas DataFrame format) into train and test sets.Homepage:https://github.com/mmalekzadeh/sensplitInstallationInstall SenSplit from PyPI (recommended):pipinstallsensplitAlternatively: install SenSplit from the GitHub source:First, clone SenSplit usinggit:gitclonehttps://github.com/mmalekzadeh/sensplitThen,cdto the SenSplit folder and run the install command:cdsensplit
pythonsetup.pyinstall
Package: sensu-cli
sensu-cli=========version number: 0.1.0author: Duncan HuttyOverview--------A convenient CLI for some of the [Sensu REST API].[Sensu REST API]: https://sensuapp.org/docs/0.28/api/Installation------------To install use pip:$ pip install sensu-cliOr clone the repo:$ git clone https://github.com/dhutty/sensu-cli.git$ python setup.py installUsage-----See the built-in help:$ sensu --helpUsage: sensu [OPTIONS] COMMAND [ARGS]...Options:-l, --log-level [ERROR|INFO|DEBUG]Specify log verbosity.-u, --username TEXT             Sensu username.-p, --password TEXT             Sensu password.--scheme [http|https]           Specify protocol  [default: http]--server TEXT                   Specify the Sensu server  [default:localhost]--port INTEGER                  Specify the Sensu port  [default: 4567]--help                          Show this message and exit.Commands:info       Show info about the Sensu APIsilence    Create Silencesilenced   List Silencessubs       Show info on subscriptionsunsilence  Clear silencing
Package: sensu-handler-rocketchat
sensu-handler-rocketchatThis is a notification handler for Sensu written in Python. It should
also work with Slack and any other Chat service which uses Slack
compatible WebHooks.Why this and not sensu-plugins-slack?Because it was easier to rewrite this in Python than to figure out how
to quote and escape JSON in ERB in a JSON config file ;-). The orignal
sensu plugin identifies monitored hosts by their IP address, which is
not helpful, when using cloud setups, where IPs may repeat.UsageCreate a handler config for sensu:{"handlers":{"rockethandler":{"type":"pipe","filters":["state_change_only"],"command":"sensu-handler-rocketchat"}}}Refer to thesensu
documentationfor more info on how to write this.Then create the config for this specific handler. The default config
section is “rockethandler”, but this can be overridden on the
commandline, to enable handling multiple channels or Rocketchat servers.{"rockethandler":{"hook_url":"https://rocketchat.example.com/hooks/iPw6s7Ykseuhf88kkhf8s4fn0392cnfh83mcfnbsWCzxHTSK","nickname":"sensu","channel":"openstack-events","dashboard_url":"http://10.0.1.4:3000","pretext":""}}Parametershook_url: (required) Full URL to the WebHook (incoming
integration) you want to use.nickname: (optional) Nickname to use by the handler. Defaults to
“RocketChat defined”.channel: (optional) Channel name to deliver notification to.
Defaults to “RocketChat defined”.dashboard_url: (optional) URL to uchiwa or sensu dashboard (root
URL). This is used to create a link in the title of every
notificationpretext: (optional) Text to prepend to each notification. Can be
used e.g. for highlights.Changing config namespaceBy default the handler will use the top-level namespace “rockethandler”
in the sensu config. If you need to notify multiple channels depending
on different events you can define multiple config under different
namespaces.These configs can then be referenced by using the-cnamespacecommandline argumentExample:rockethandler config{"rockethandler":{"hook_url":"https://rocketchat.example.com/hooks/iPw6s7Ykseuhf88kkhf8s4fn0392cnfh83mcfnbsWCzxHTSK","nickname":"sensu","channel":"all-events","dashboard_url":"http://10.0.1.4:3000","pretext":""},"rockethandler_1":{"hook_url":"https://rocketchat.example.com/hooks/iPw6s7Ykseuhf88kkhf8s4fn0392cnfh83mcfnbsWCzxHTSK","nickname":"sensu-emerg","channel":"emergency-events","dashboard_url":"http://10.0.1.4:3000","pretext":"@all"},"rockethandler_2":{"hook_url":"https://rocketchat.example.com/hooks/iPw6s7Ykseuhf88kkhf8s4fn0392cnfh83mcfnbsWCzxHTSK","nickname":"sensu-resolve","channel":"all-events","dashboard_url":"http://10.0.1.4:3000","pretext":"**PROBLEM RESOLVED**"}}sensu handler config{"handlers":{"rocket_handler":{"type":"pipe","filters":["state_change_only"],"command":"sensu-handler-rocketchat"},"rocket_handler":{"type":"pipe","filters":["state_change_only","emergency"],"command":"sensu-handler-rocketchat -c rockethandler_1"},"rocket_handler":{"type":"pipe","filters":["state_change_only","resolution"],"command":"sensu-handler-rocketchat -c rockethandler_2"}}}
Package: sensu_plugin26
![sensu](https://raw.github.com/sensu/sensu/master/sensu-logo.png)# Python Sensu PluginThis is a framework for writing your own [Sensu](https://github.com/sensu/sensu) plugins in Python.It's not required to write a plugin (most Nagios plugins will workwithout modification); it just makes it easier.[![Build Status](https://travis-ci.org/sensu/sensu-plugin-python.png?branch=master)](https://travis-ci.org/sensu/sensu-plugin-python)## ChecksTo implement your own check, subclass SensuPluginCheck, likethis:from sensu_plugin import SensuPluginCheckclass MyCheck(SensuPluginCheck):def setup(self):# Setup is called with self.parser set and is responsible for setting up# self.options before the run method is calledself.parser.add_argument('-w','--warning',required=True,type=int,help='Integer warning level to output')self.parser.add_argument('-m','--message',default=None,help='Message to print')def run(self):# this method is called to perform the actual checkself.check_name('my_awesome_check') # defaults to class nameif self.options.warning == 0:self.ok(self.options.message)elif self.options.warning == 1:self.warning(self.options.message)elif self.options.warning == 2:self.critical(self.options.message)else:self.unknown(self.options.message)if __name__ == "__main__":f = MyCheck()## Metrics### JSONfrom sensu_plugin import SensuPluginMetricJSONclass FooBarBazMetricJSON(SensuPluginMetricJSON):def run(self):self.ok({'foo': 1, 'bar': { 'baz': 'stuff' } })if __name__ == "__main__":f = FooBarBazMetricJSON()### Graphitefrom sensu_plugin import SensuPluginMetricGraphiteclass FooBarBazMetricGraphite(SensuPluginMetricGraphite):def run(self):self.output('a', 1)self.output('b', 2)self.ok()if __name__ == "__main__":f = FooBarBazMetricGraphite()### StatsDfrom sensu_plugin import SensuPluginMetricStatsdclass FooBarBazMetricStatsd(SensuPluginMetricStatsd):def run(self):self.output('a', 1, 'ms')self.output('b.c.d', 15)self.ok()if __name__ == "__main__":f = FooBarBazMetricStatsd()## License* Based heavily on [sensu-plugin](https://github.com/sensu/sensu-plugin) Copyright 2011 Decklin Foster* Python port Copyright 2014 S. Zachariah SprackettReleased under the same terms as Sensu (the MIT license); see LICENSEfor details
Package: sensus
templateThis is a template generated by farzad in order to be used as a standard in Adler software development.
this readme file is written in MarkDown in a github friendly way.Installation and setupjust use the following code for generating a wheel file.pythonsetup.pybdist_wheeland for installing and using the created wheel file locally run the following.pipinstall-e.
pipinstall-e.[dev]for installing it on another environment use this:pipinstall/path/wheel.whluse pytest to make sure package is working properly. it is a powerfull tool to investigate the modules and everything automatically.pytestalsotwinehas a good testing mechanism.Any doubts about installation and setuptools refere tosetuptoolsor watch thisvideo.important to know: in the next version the setup.py must send the wheel file to the database!Python Packaging User GuideSource distributionFor sending the source code to another distination use:pythonsetup.pysdistthen check for all the needed files. if they were not their use maifest using:pipinstallcheck-manifest
check-manifest--create
gitaddMANIFEST.inthen compress it and publish it (internally of course!).tartzfdist/template-0.0.1.tar.gzImportingimport the package name and then call for the functions and objects.fromtemplateimporttemplatetemplate.say_hello()TestEverything must have a unit test and api test files.Please check everything before you request a code review or merge.The correctness of your code is your responsibility.Check the speed of your code and if possible report the code complexity indexes.use pytest for general syntax check.use tox for testing on different versions of python.# tox.ini[tox]envlist=py36,py37[testenv]deps=pytestcommands=pytestpipinstalltox
toxTest and DeployOnly those packages that are indipendent are going to be deployed others will be imported and recieve an authorization from the guardian sentinel.
Deployment will be done using DpckerFile.
Our system is linux ubuntu focal forsa, so all the codes must be compatible with this disto of operating system.Other stuffyou can use pip freeze to extract all the installed packages and their versions.pipfreeze>requirements.txtWarningsDo not push into master, main, or dev!You only and only push into your own branches!Farzad Ziaie Nezhad | ZLION
Package: sensys
Sen(try)Sys(log)Raven Python Syslog Client (sentry messaging through syslog).System requirementspython--version==3.4.*,3.5.*,3.6.*InstallationParameterSEPARATOR- how plugin must separatesyslogtagfrommsgbody.${PWD}/scripts/install.shSEPARATOR# new map will be copied with --backup=numbered optionsudovim/etc/sensys/map.json# configure mappingsudosystemctlrestartrsyslog.serviceTodoUpdate deployment script;Make plugin as binary.
Package: sent
sentA Fast Telegram API for Lighweight botsInstallationTo install this package, use pip:pip install sentUsageHere is an example of how to use the package:fromsent.telegramimportTelegramtoken='BOT_TOJJEN'# bot token from bot fatherchatid='CHAT_ID'# chat id of user | group | channelbot=Telegram(token)bot.send_message(chatid,"Wow !!!")@bot.message_handler(commands=['start','help'])defsend_welcome(message):bot.reply_to(message,"Howdy, how are you doing?",protect_content=True)bot.polling()DocumentationFor more detailed information about how to use this package, please refer to the full documentation atContributingWe welcome contributions to this package. To contribute, please fork the repository, make your changes, and submit a pull request.LicenseThis package is open-sourced under the MIT licenseIssuesIf you encounter any issues or bugs, please open an issue in the issue tracker.
Package: sentaku
Sentakuis a python library that allows objects to dynamically choose their implementation.Sentakuis the Japanese word for ‘choice’.WithSentakuit is possible to describe the elements of the applications you interact with
and the actions/attributes they have.Sentakutakes care of choosing the implementation of the actions/attributes.Installingpip install sentakuUse casesA common use for such a system is testing various layers of an application with the same code,
as well as using different layers of an application for setup/teardown and for concise acceptance-tests.A typical use case is testing your modern html5 application.
Such an application usually consists of different layers.The internal back-end APIA rich front-endA REST APIAPIs that directly interact with other services the application usesWhen doing test setup/teardown it is
desirable to run against the fast back-end API or REST API,
while when running the actual acceptance/system tests
it is more desirable to run against the rich user interface or the REST API.
Package: sentence-classifier
sentence_classifierframework to fine-tune various algorithms on sentence classification tasksFree software: MIT licenseDocumentation:https://sentence-classifier.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-11-21)First release on PyPI.
Package: sentencecloud
LinuxmacOSWindowsPyPIword_cloudA little sentence cloud generator in Python. This is a small extension to wordcloud generator by amueller (github.com/amueller/word_cloud). Read more about it on theblog
postor thewebsite.The code is tested against Python 2.7, 3.4, 3.5, 3.6 and 3.7.InstallationIf you are using pip:pip install wordcloudIf you are using conda, you can install from theconda-forgechannel:conda install -c conda-forge wordcloudInstallation noteswordcloud depends onnumpyandpillow.To save the wordcloud into a file,matplotlibcan also be installed. Seeexamplesbelow.If there are no wheels available for your version of python, installing the
package requires having a C compiler set up. Before installing a compiler, report
an issue describing the version of python and operating system being used.ExamplesCheck outexamples/simple.pyfor a short intro. A sample output is:Or runexamples/masked.pyto see more options. A sample output is:Getting fancy with some colors:Generating wordclouds for Arabic:Command-line usageThewordcloud_clitool can be used to generate word clouds directly from the command-line:$ wordcloud_cli --text mytext.txt --imagefile wordcloud.pngIf you're dealing with PDF files, thenpdftotext, included by default with many Linux distribution, comes in handy:$ pdftotext mydocument.pdf - | wordcloud_cli --imagefile wordcloud.pngIn the previous example, the-argument orderspdftotextto write the resulting text to stdout, which is then piped to the stdin ofwordcloud_cli.py.Usewordcloud_cli --helpso see all available options.LicensingThe wordcloud library is MIT licenced, but contains DroidSansMono.ttf, a true type font by Google, that is apache licensed.
The font is by no means integral, and any other font can be used by setting thefont_pathvariable when creating aWordCloudobject.