Saravanan666/pypi-org huggingface dataset:
Package: prometheus-freeswitch-exporter
This is an exporter that exposes information gathered from FreeSWITCH for use
by the Prometheus monitoring system.Installationpipinstallprometheus-freeswitch-exporterUsageusage: freeswitch_exporter [-h] [config] [port]

positional arguments:
  config      Path to configuration file (esl.yml)
  port        Port on which the exporter is listening (9724)
  address     Address to which the exporter will bind

optional arguments:
  -h, --help  show this help message and exitUse::for theaddressargument in order to bind to both IPv6 and IPv4
sockets on dual stacked machines.Visithttp://localhost:9724/esl?target=1.2.3.4where 1.2.3.4 is the IP of
FreeSWITCH to get metrics from. Specify themodulerequest parameter, to
choose which module to use from the config file.Thetargetrequest parameter defaults tolocalhost. Hence iffreeswitch_exporteris deployed directly on the FreeSWITCH host,targetcan be omitted.See thewikifor more examples and docs.AuthenticationExampleesl.ymldefault:port:8021# default port, can be omittedpassword:ClueConFreeSWITCH ConfigurationFor security reasons it is essential to change the default passwort in
FreeSWITCHautoload_configs/event_socket.conf.xml.Prometheus ConfigurationThe FreeSWITCH exporter can be deployed either directly on a FreeSWITCH node or
onto a separate machine.Example config for FreeSWITCH exporter running on FreeSWITCH node:scrape_configs:-job_name:'freeswitch'static_configs:-targets:-192.168.1.2:9724# FreeSWITCH node with FreeSWITCH exporter.-192.168.1.3:9724# FreeSWITCH node with FreeSWITCH exporter.metrics_path:/eslparams:module:[default]Example config for FreeSWITCH exporter running on Prometheus host:scrape_configs:-job_name:'freeswitch'static_configs:-targets:-192.168.1.2# FreeSWITCH node.-192.168.1.3# FreeSWITCH node.metrics_path:/eslparams:module:[default]relabel_configs:-source_labels:[__address__]target_label:__param_target-source_labels:[__param_target]target_label:instance-target_label:__address__replacement:127.0.0.1:9724# FreeSWITCH exporter.Grafana DashboardsNone yet.
Package: prometheus-givenergy
prometheus-givenergyprometheus exporter for GivEnergy inverter metricsprometheus-givenergy is inspired by givenergy-modbus module.
givenergy-modbus does not work with current version of pymodbus.Usage: prometheus_givenergy <host> [--port=<port>] [--prom-file=<prom-file> [--version]
    <host> ip-address or hostname
    <port> default 8899
    <prom-file> default /var/lib/prometheus/node-exporter/givenergy.promAll holding metrics are prefixed withgivenergy_holding_.
And all input metrics are prefixed withgivenergy_.
The unit type of the metric is used to add a suffix.UnitSuffixExampleScaler valueno suffixgivenergy_battery_num_cycleskilo Watt Hours_kwhgivenergy_grid_in_total_kwhWatts_wgivenergy_load_demand_wVA_vagivenergy_grid_apparent_vaVoltage_voltsgivenergy_holding_ac_high_in_voltsCurrent_ampsgivenergy_grid_port_ampsAmp Hours_ahgivenergy_holding_battery_nominal_capacity_ahTemperature C_temp_cgivenergy_battery_temp_cFrequency Hz_hzgivenergy_ac1_hzInstalling on FedoraInstall and configure Prometheus and Grafanasudo dnf copr enable barryascott/toolssudo dnf install python3-prometheus-givenergyUse systemd service and timer units to run the command periodically
(or cron if you prefer).Timer unit:/etc/systemd/system/givenergy.timer[Unit]
Description=givenergy.timer

[Timer]
OnBootSec=60 seconds
OnUnitInactiveSec=30 seconds

[Install]
WantedBy=multi-user.targetService unit:/etc/systemd/system/givenergy.service[Unit]
Description=givenergy.service

[Service]
User=prometheus

Type=oneshot
TimeoutStartSec=0

ExecStartPre=/usr/bin/id
ExecStart=/usr/bin/prometheus-givenergy hf-a21.chelsea.private

[Install]
WantedBy=multi-user.targetOnce the systemd unit files are in place:sudo systemctl daemon-reloadsudo systemctl enable --now givenergy.timer
Package: prometheus-http-sdk
A simple prometheus http sdkExampleimportfromprometheus_http_sdk.prometheus_httpimportPrometheusApiget datadata=PrometheusApi("http://prometheus").query_range(query=":node_cpu_saturation_load1:",range_time="6h",)print(data)get all datadata=PrometheusApi("http://prometheus").query_all(query=":node_cpu_saturation_load1:",step=60,end=time.time()-600)print(data)convert to dictdata.convertDict()convert to dict with tensorflow typedata.convertDict(tfType=True)convert to jsondata.convertJson()
Package: prometheus-pandas
Prometheus PandasPython library for queryingPrometheusand accessing the results asPandasdata structures.This is mostly intended for use inJupyternotebooks. SeePrometheus.ipynbfor an example.ExampleEvaluate an instant query at a single point in time:>>>fromprometheus_pandasimportquery>>>>>>p=query.Prometheus('http://localhost:9090')>>>p.query('node_cpu_seconds_total{mode="system"}','2020-05-10T00:00:00Z')node_cpu_seconds_total{cpu="0",instance="localhost:9100",job="node",mode="system"}15706.47node_cpu_seconds_total{cpu="1",instance="localhost:9100",job="node",mode="system"}15133.25node_cpu_seconds_total{cpu="2",instance="localhost:9100",job="node",mode="system"}15095.59node_cpu_seconds_total{cpu="3",instance="localhost:9100",job="node",mode="system"}14649.20dtype:float64Evaluates an expression query over a time range:>>>fromprometheus_pandasimportquery>>>>>>p=query.Prometheus('http://localhost:9090')>>>print(p.query_range('sum(rate(node_cpu_seconds_total{mode=~"system|user"}[1m])) by (mode)','2020-10-05T00:00:00Z','2020-10-05T06:00:00Z','1h'))dtype:float64---{mode="system"}{mode="user"}2020-10-0500:00:000.0226670.0382222020-10-0501:00:000.0153330.0366672020-10-0502:00:000.0280000.0406672020-10-0503:00:000.0151110.0348892020-10-0504:00:000.0155560.0380002020-10-0505:00:000.0184440.0402222020-10-0506:00:000.0182220.035111Customizing the HTTP request:>>>importrequests>>>fromprometheus_pandasimportquery>>>>>>http=requests.Session()>>>http.cert='/path/client.cert'>>>http.verify='/path/to/certfile'>>>>>>p=query.Prometheus('http://localhost:9090',http)>>>print(p.query('node_cpu_seconds_total{mode="system"}','2020-10-05T00:00:00Z'))node_cpu_seconds_total{cpu="0",instance="localhost:9100",job="node",mode="system"}3954.92dtype:float64InstallationLatest release viapip:pipinstallprometheus-pandas[--user]via Git:gitclonehttps://github.com/dcoles/prometheus-pandas.git;cdprometheus-pandas
python3setup.pyinstall[--user]LicenceLicenced under theMIT License. SeeLICENSEfor details.
Package: prometheus-paster
This is a glue betweenprometheus_clientandpastedeploy.Quick startconfiguration example[composite:main]
use = egg:Paste#urlmap
/metrics = prom
/ = papp

[pipeline:papp]
pipeline = perf app

[app:app]
use = egg:someapp

[filter:perf]
use = egg:prometheus_paster#filter
name = someapp_call

[app:prom]
use = egg:prometheus_pasterPrometheus exporter endpoint ConfigurationYou can expose the endpoint withegg:prometheus_pasterwsgi application.[app:main]
use = egg:prometheus_pasterCollector filter Configurationlatency : metric name for wsgi processing time (defaultresponse_latency_seconds)length : metric name for wsgi response length (defaultresponse_length_bytes)path_regex : if request path does not match this regex, then it will be ignored. (default.*)disable_latency: if set to true, then latency metrics will not be collected.disable_length: if set to true, then length metrics will not be collected.Multiple metrics (1)If you want to collect two different application on the same wsgi container,
then you can put different filters.[composite:main]
use = egg:Paste#urlmap
/metrics = prom
/app1 = app1
/app2 = app2

[app:app1]
use = egg:app1
filter-with = app1_perf

[filter:app1_perf]
use = egg:prometheus_paster#filter
latency = app1_latency
length = app1_length

[app:app2]
use = egg:app2
filter-with = app2_perf

[filter:app2_perf]
use = egg:prometheus_paster#filter
latency = app2_latency
length = app2_length

[app:prom]
use = egg:prometheus_pasterMultiple metrics (2)Another option is stacking filters.[composite:main]
use = egg:Paste#urlmap
/metrics = prom
/app = app1_pipe

[pipeline:app1_pipe]
pipeline = path1_perf path2_perf app1

[app:app1]
use = egg:app1

[filter:path1_perf]
use = egg:prometheus_paster#filter
latency = path1_latency
length = path1_length
path_regex = /path1/.*

[filter:path2_perf]
use = egg:prometheus_paster#filter
latency = path2_latency
length = path2_length
path_regex = /path2/.*

[app:prom]
use = egg:prometheus_paster
Package: prometheus-push-client
prometheus-push-clientPush metrics from your periodic long-running jobs to existing Prometheus/VictoriaMetrics monitoring system.Currently supports pushes directly toVictoriaMetricsvia UDP and HTTP using InfluxDB line protocol asdescribed here.Forpure Prometheussetups, several options are supported:toStatsDorstatsd-exporterin StatsD format via UDP. Prometheus and StatsD metric types are not fully compatible, so currenly all metrics become StatsD gauges, butrate,increase,histogram_quantileand other PromQL functions produce same results as if types never changed.topushgatewayorprom-aggregation-gatewayin OpenMetrics format via HTTP. Please read corresponding docs about appropriate use cases and limitations.Install it via pip:pipinstallprometheus-push-clientRead more atgithub page.
Package: prometheus-pve-sd
prometheus-pve-sdPrometheus Service Discovery for Proxmox VEThis project provides a simple custom service discovery forPrometheus. It is using theProxmox VE(PVE) API to fetch Hosts and it's meta information to generate a Prometheus compatiblefile basedservice discovery. Releases are available as Python Packages onGitHuborPyPIand as Docker Image onDocker Hub.You can find the full documentation athttps://prometheus-pve-sd.geekdocs.de.ContributorsSpecial thanks to allcontributors. If you would like to contribute,
please see theinstructions.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: prometheus-qbittorrent-exporter
Prometheus qBittorrent exporterA prometheus exporter for qBitorrent. Get metrics from a server and offers them in a prometheus format.How to use itYou can install this exporter with the following command:pip3installprometheus-qbittorrent-exporterThen you can run it withqbittorrent-exporterAnother option is run it in a docker container.docker run \
    -e QBITTORRENT_PORT=8080 \
    -e QBITTORRENT_HOST=myserver.local \
    -p 8000:8000 \
    ghcr.io/esanchezm/prometheus-qbittorrent-exporterAdd this to your prometheus.yml- job_name: "qbittorrent_exporter"
    static_configs:
        - targets: ['yourqbittorrentexporter:port']The application reads configuration using environment variables:Environment variableDefaultDescriptionQBITTORRENT_HOSTqbittorrent server hostnameQBITTORRENT_PORTqbittorrent server portQBITTORRENT_SSLFalseWhether to use SSL to connect or not. Will be forced toTruewhen using port 443QBITTORRENT_URL_BASE""qbittorrent server path or base URLQBITTORRENT_USER""qbittorrent usernameQBITTORRENT_PASS""qbittorrent passwordEXPORTER_PORT8000Exporter listening portEXPORTER_LOG_LEVELINFOLog level. One of:DEBUG,INFO,WARNING,ERROR,CRITICALMETRICS_PREFIXqbittorrentPrefix to add to all the metricsVERIFY_WEBUI_CERTIFICATETrueWhether to verify SSL certificate when connecting to the qbittorrent server. Any other value butTruewill disable the verificationMetricsThese are the metrics this program exports, assuming theMETRICS_PREFIXisqbittorrent:Metric nameTypeDescriptionqbittorrent_upgaugeWhether the qBittorrent server is answering requests from this exporter. Aversionlabel with the server version is added.qbittorrent_connectedgaugeWhether the qBittorrent server is connected to the Bittorrent network.qbittorrent_firewalledgaugeWhether the qBittorrent server is connected to the Bittorrent network but is behind a firewall.qbittorrent_dht_nodesgaugeNumber of DHT nodes connected to.qbittorrent_dl_info_datacounterData downloaded since the server started, in bytes.qbittorrent_up_info_datacounterData uploaded since the server started, in bytes.qbittorrent_alltime_dl_totalcounterTotal historical data downloaded, in bytes.qbittorrent_alltime_ul_totalcounterTotal historical data uploaded, in bytes.qbittorrent_torrents_countgaugeNumber of torrents for eachcategoryandstatus. Example:qbittorrent_torrents_count{category="movies",status="downloading"}ScreenshotMore infoLicenseThis software is released under theGPLv3 license.
Package: prometheus-quant
普罗米修斯量化交易系统 python客户端说明针对CTP期货交易，使用zmq作为通讯库，实现的c++与python语言级脱钩的简单交易系统安装pip install promethues_quant或者下载源代码后，解压后在cmd中运行：pip install ./promethues_quant***.whl
Package: prometheus-query-builder
prometheus-query-builderPackage for generation Prometheus Query Language queries.InstallationUse pip for to install from Pypi:pip install prometheus-query-builderThe package is still in alpha development, so better pin version in your requirements:prometheus-query-builder==0.1UsageSee usage examples intestsdirectory.TODOSubqueries -https://prometheus.io/docs/prometheus/latest/querying/basics/#subqueryFunctions -https://prometheus.io/docs/prometheus/latest/querying/basics/#functions
Package: prometheus_roller
# Prometheus Roller[![Build Status](https://travis-ci.org/turtlemonvh/prometheus_python_roller.png?branch=master)](https://travis-ci.org/turtlemonvh/prometheus_python_roller)[![PyPI version](https://img.shields.io/pypi/v/prometheus_roller.svg)](https://pypi.python.org/pypi/prometheus_roller)[![Downloads](http://img.shields.io/pypi/dm/prometheus_roller.svg)](https://pypi.python.org/pypi/prometheus_roller)[![Python versions](https://img.shields.io/pypi/pyversions/prometheus_roller.svg)](https://pypi.python.org/pypi/prometheus_roller)[![License](https://img.shields.io/pypi/l/prometheus_roller.svg)](https://pypi.python.org/pypi/prometheus_roller)This is a small helper utility for creating time-boxed rollups for histogram and counter metrics created using the [prometheus python client](https://github.com/prometheus/client_python).Metrics like counters and histograms increase indefinitely, but you usually care about recent activity.  This library provides a way to turn a counter or histogram into a gauge or set of gauges that give instantaneous measures of the state of the application.This isn't very useful if you're using [the prometheus server](https://github.com/prometheus/prometheus) since it provides [functions to do that for you](https://prometheus.io/docs/querying/functions/), but if you are sending metrics to multiple places (e.g. [check_mk](https://mathias-kettner.de/checkmk_localchecks.html)) and you want to send those metrics in a form that make basic alerting and reporting easier, this may help.## Usage```pythonfrom prometheus_client import Histogram, Counterfrom prometheus_roller import HistogramRoller, CounterRoller, start_update_daemon# Create a histogramh = Histogram('test_value', 'Testing roller')# Create a counterc = Counter('test_counted_value', 'Testing roller')# Create a roller for the histogram, which calculates windowed values.# By default it will create a gauge with a label for each histogram bin.# The value of each gauge will be the change in value over the last 5 minutes, updated every 5 seconds.# See the `options` parameter for more configuration options.rh = HistogramRoller(h)# Create a roller for the counter, which calculates windowed values.# The value of each gauge will be the change in value over the last 5 minutes, updated every 5 seconds.# See the `options` parameter for more configuration options.rc = CounterRoller(c)# Launch a daemon thread tracking and updating all roller objects.# See the code for more options for configuring this update process.start_update_daemon()```## Installation```bash# Install with pippip install prometheus_roller# To install as editable to work on itpip install -e git+https://github.com/turtlemonvh/prometheus_python_roller.git#egg=prometheus_python_roller# ORgit clone git@github.com:turtlemonvh/prometheus_python_roller.git prometheus-rollercd prometheus-rollerpython setup.py develop```## Running tests```bash# Plain old pythonpython -m unittest discover# If you have nose and coverage installednosetests --with-cover```## TODO* Add IQR reducer
Package: prometheus-summary
prometheus-summaryPrometheus summary with quantiles over configurable sliding time windowInstallationpip install prometheus-summary==0.1.2This package can be found onPyPI.CollectingBasic usagefromprometheus_summaryimportSummarys=Summary("request_latency_seconds","Description of summary")s.observe(4.7)With labelsfromprometheus_summaryimportSummarys=Summary("request_latency_seconds","Description of summary",["method","endpoint"])s.labels(method="GET",endpoint="/profile").observe(1.2)s.labels(method="POST",endpoint="/login").observe(3.4)With custom quantiles and precisionsBy default, metrics are observed for next quantile-precision pairs((0.50, 0.05), (0.90, 0.01), (0.99, 0.001))but you can provide your own value when creating the metric.fromprometheus_summaryimportSummarys=Summary("request_latency_seconds","Description of summary",invariants=((0.50,0.05),(0.75,0.02),(0.90,0.01),(0.95,0.005),(0.99,0.001)),)s.observe(4.7)With custom time window settingsTypically, you don't want to have a Summary representing the entire runtime of the application,
but you want to look at a reasonable time interval. Summary metrics implement a configurable sliding time window.The default is a time window of 10 minutes and 5 age buckets, i.e. the time window is 10 minutes wide, and
we slide it forward every 2 minutes, but you can configure this values for your own purposes.fromprometheus_summaryimportSummarys=Summary("request_latency_seconds","Description of summary",# time window 5 minutes wide with 10 age buckets (sliding every 30 seconds)max_age_seconds=5*60,age_buckets=10,)s.observe(4.7)QueryingSuppose we have a metric:fromprometheus_summaryimportSummarys=Summary("request_latency_seconds","Description of summary",["method","endpoint"])To show request latency bymethod,endpointandquntileuse next query:max by (method, endpoint, quantile) (request_latency_seconds)To show only 99-th quantile:max by (method, endpoint) (request_latency_seconds{quantile="0.99")
Package: prometheus-tuxedo-exporter
A Prometheus exporter of Tuxedo metrics. The code that was too long for the Chapter 6 of my book:Installpip3 install prometheus-tuxedo-exporterOr to install from the local copy:sudo -E python3 setup.py installRunTheprometheus-tuxedo-exporter.pycan be run as a stand-alone program:prometheus-tuxedo-exporter.py --port 5555It can also be run as a Tuxedo server and Tuxedo will take care of starting, stopping and restarting it as a part of the Tuxedo application. For that you will have to add entry like this toUBBCONFIG:"prometheus-tuxedo-exporter.py" SRVGRP="GROUP1" SRVID=5555
    CLOPT="-- --port 5555"
    MAX=1 MAXGEN=2  GRACE=0 RESTART=YUsageusage: prometheus-tuxedo-exporter.py [-h] --port PORT [--addr ADDR]

optional arguments:
  -h, --help   show this help message and exit
  --port PORT
  --addr ADDRThe--addrparameter can be used to specify the network interface to listen on.MonitoringExposed metrics come fromthe Management Information Base for core Oracle Tuxedo system.Example:# HELP tuxedo_numreq_total Number of tpacall or tpcall operations performed from this machine
# TYPE tuxedo_numreq_total counter
tuxedo_numreq_total 1149.0
# HELP tuxedo_numreq_created Number of tpacall or tpcall operations performed from this machine
# TYPE tuxedo_numreq_created gauge
tuxedo_numreq_created 1.616197112474299e+09
# HELP tuxedo_numpost_total Number of tppost operations performed from this machine
# TYPE tuxedo_numpost_total counter
tuxedo_numpost_total 0.0
# HELP tuxedo_numpost_created Number of tppost operations performed from this machine
# TYPE tuxedo_numpost_created gauge
tuxedo_numpost_created 1.6161971124743195e+09
# HELP tuxedo_numtran_total Number of transactions initiated from this machine
# TYPE tuxedo_numtran_total counter
tuxedo_numtran_total 0.0
# HELP tuxedo_numtran_created Number of transactions initiated from this machine
# TYPE tuxedo_numtran_created gauge
tuxedo_numtran_created 1.6161971124743354e+09
# HELP tuxedo_numtranabt_total Number of transactions aborted from this machine
# TYPE tuxedo_numtranabt_total counter
tuxedo_numtranabt_total 0.0
# HELP tuxedo_numtranabt_created Number of transactions aborted from this machine
# TYPE tuxedo_numtranabt_created gauge
tuxedo_numtranabt_created 1.6161971124743507e+09
# HELP tuxedo_numtrancmt_total Number of transactions committed from this machine
# TYPE tuxedo_numtrancmt_total counter
tuxedo_numtrancmt_total 0.0
# HELP tuxedo_numtrancmt_created Number of transactions committed from this machine
# TYPE tuxedo_numtrancmt_created gauge
tuxedo_numtrancmt_created 1.6161971124743652e+09
Package: prometheux
PrometheuxPrometheux is a Python package designed to simplify and automate the process of rule learning from structured data, leveraging the power of the PyClause library. It provides a streamlined interface for loading datasets, executing rule learning algorithms, and managing the resulting rules, all with minimal setup required from the user.FeaturesEasy integration with PyClause for rule learning.Simplified dataset loading and preprocessing.Automated generation of rules from structured data.Convenient retrieval of learned rules for analysis and application.InstallationBefore installing Prometheux, you must ensure that PyClause is properly installed in your environment. PyClause requires a C++ (14) compiler on Linux or C++ build tools on Windows.Installing PyClauseFor Linux Users: Ensure you have a C++ compiler installed.For Windows Users: Ensure you have Microsoft Visual C++ 14.0 or newer installed.To install PyClause, run the following command:pipinstallgit+https://github.com/symbolic-kg/PyClause.gitInstalling PrometheuxPrometheux requires Python 3.7 or newer. It is recommended to install Prometheux in a virtual environment to avoid conflicting with system-wide packages.Install with pipTo install the latest version of Prometheux, run the following command:pip install prometheuxPost-InstallationEnsure that you have a C++ compiler installed, as required by PyClause for Linux users, or C++ build tools for Windows users.UsageLoading DatasetsSpecify the path to your dataset specifications in aspecs.yamlfile:datasets:-path:"/path/to/your/dataset/train.txt"Running Rule LearningImport Prometheux and use it to load datasets and execute rule learning:import prometheux as pmtx

datasets_specs = "/path/to/your/specs.yaml" 
extensional = pmtx.load(datasets_specs)
intensional = pmtx.train(extensional)
print(intensional)This will print the learned rules to the console.
Package: promethor
promethorDescriptionThis is a bunch of custom prometheus collectors.Installationpython setup.py installorpip install-e.orpip install promethorHow to useRunpromethor-clvm-t30-p9000Also checkout list ofargumentsarguments-t,--timeout- Monitoring timeout-c,--collectors- List of collectors. Choose from:lvm, sentry, sql, mongo-p,--port- Port to run on--loglevel- Level of logging. Choose from:CRITICAL,ERROR,WARNING,INFO,DEBUG,NOTSET-l,--log- Redirect logging to file
Package: prometry
PROMETRYA library for calculating protein geometry parameters with criteria-based searches.To install frompypiplease runpip install prometryUse of the library is demonstrated atra-box/prometryGithub page:https://github.com/RachelAlcraft/prometryPlease raise issues:https://github.com/RachelAlcraft/prometry/issuesAn demonstration of usage of this library is hosted on streamlit:https://prometry.streamlit.app
Package: promg
DocumentationHi there 👋Over here we are developing PromG, a tool enabling you to perform multi-dimensional process analytics by exploiting a multi-layered Event Knowledge Graph.
The repository contains the source code (PromG-core) which can be installed as a Python package usingpip install promgand several example analyses.🎥 Demo Video: We created a demo videohere. In this demo, we illustrate PromG on the BPIC'17 dataset.🗃️ Documentation:https://promg-dev.github.io/promg-core/🏁 Getting started:https://promg-dev.github.io/promg-core/getting-started/🛠️ Tutorial:https://promg-dev.github.io/promg-core/tutorials📦 Python Package:https://pypi.org/project/promg/