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zombie-translator

Zombie Translator is the easiest way to understand and be understood by your zombie friends and neighbors. It’s simple, easy, and makes speaking zombie fun!Installing`bash pip installzombie-translator`Using`python >>> from zombie_translator import to_zombish, from_zombish >>> z =to_zombish("Hello,world!")>>> print z Bgbbnhnhhr, MrhrrrnhRA! >>> print from_zombish(z) "Hello, world!" `There’s also an optional profanity filter that replaces a set of arguably ‘bad’ words with zombie-speak. For example,`python >>> printfrom_zombish(to_zombish("Don'tbe a jackass,man."))Don't be a GRAANH, man. `You can also turn off the profanity filter:`python >>> printfrom_zombish(to_zombish("Don'tbe a jackass,man.",with_profanity_filter=False)) Don't be a jackass, man. `Updates and RoadmapRoadmapThis version is pretty much all we can see adding to this package. But if other people have ideas, send ‘em as pull requests!Recent updates (full log in CHANGES)0.1Initial release

zomboid-rcon

Zomboid-rcon: Python RCON for Project Zomboid ServersVersion: 1.0.2Zomboid-rcon enables you to easily communicate with your Project Zomboid servers via RCON. With zomboid-rcon, you can send commands to your server, manage players, and more, all from within your Python script.HomepageGitHub RepoPypi PackageInstallationTo get started, simply install zomboid-rcon using pip:pipinstallzomboid-rconUsageUsing zomboid-rcon is easy. Here's a basic example:fromzomboid_rconimportZomboidRCONif__name__=="__main__":pz=ZomboidRCON(ip='localhost',port=12345,password='myPassword')command=pz.serverMsg("You dead yet?")print(command.response)This example connects to a server running on your local machine and sends the message "You dead yet?".Zomboid-rcon provides several built-in methods for common server management tasks, such as getting a list of connected players:fromzomboid_rconimportZomboidRCONif__name__=="__main__":pz=ZomboidRCON(ip='localhost',port=12345,password='myPassword')print(pz.players().response)This example prints a list of all players currently connected to the server.Available CommandsZomboid-rcon provides built-in methods for the available RCON commands within Project Zomboid.General Commandsadditem("user", "item"): Items can be found on the PZ wiki:https://pzwiki.net/wiki/Itemsaddvehicle("user"): Spawns a vehicle.addxp("user", "perk", xp): Gives XP to a player.alarm(): Sounds a building alarm at the admin's position. Must be in a room.changeoption("option", "newOption"): Changes a server option.chopper(): Places a helicopter event on a random player.changepwd("pwd", "newPwd"): Changes your password.createhorde("number"): Spawns a horde near a player.godmode("user"): Makes a player invincible.gunshot(): Makes a gunshot noise near the player.help(): Brings up the help menu. (Lists native RCON commands. For all zomboid_rcon commands, refer to this list)invisible("user"): Makes a player invisible to zombies.noclip("user"): Allows a player to pass through solid objects.quit(): Saves and quits the server.releasesafehouse(): Releases a safehouse you own.reloadlua("filename"): Reload a lua script on the server.reloadoptions(): Reloads server options.replay("user", [-record | -play | -stop], "filename"): Records and plays a replay for a moving player.save(): Saves the current world.sendpulse(): Toggles sending server performance info to the client.showoptions(): Shows a list of current server options and values.startrain(): Starts rain on the server.stoprain(): Stops rain on the server.teleport("user", "toUser"): Teleports to a player.teleportto(x, y, z): Teleports to certain coordinates.Moderation Commandsaddalltowhitelist(): Adds all current users connected with a password to the whitelist.adduser("user", "pwd"): Adds a new user to the whitelist.addusertowhitelist("user"): Adds a single user connected with a password to the whitelist.removeuserfromwhitelist("user"): Removes a single user connected with a password to the whitelist.banid("SteamID"): Bans a Steam ID.unbanid("SteamID"): Unbans a Steam ID.banuser("user"): Bans a user.unbanuser("user"): Unbans a user.checkModsNeedUpdate(): Indicates whether a mod has been updated. Writes answer to log file.grantadmin("user"): Gives admin rights to a user.removeadmin("user"): Removes admin rights to a user.kickuser("user"): Kicks a user from the server.players(): Lists all connected players.servermsg("message"): Broadcast a message to all players. (Spaces are replaced with underscores for compatibility)setaccesslevel("user", [admin | moderator | overseer | gm | observer]): Set the access/permission level of a player.voiceban("user", [-true | -false]): Ban a user from using the voice feature.Command not listed?You can execute any custom command using the command method:pz.command("command","arg1","arg2","etc")DemonstrationKnown IssuesPlease note that zomboid-rcon uses thetimeout_decoratorpackage, which is currently only compatible with Unix/Linux systems. As a result,timeouts may cause errors on Windows machines. We are actively working on finding an alternative solution for Windows users.ContributingWe welcome contributions from anyone! If you would like to contribute to the project, please open an issue or submit a pull request onGithub.LicenseZomboid-rcon is licensed under the GPL-3.0 license.

zompt

Python Zomptzomptis library for generating user input prompts for Python clis.Zompt featuresText input promptMultiple choice prompt (using arrow keys)Installationpip install zomptGetting StartedInput PromptThe most simple example of the Input Prompt would look like this:if __name__ == "__main__": print("Example prompt (type some text): ") input_prompt = InputPrompt() result = input_prompt.run() print() print("You typed: " + result)Multiple Choice PromptThe most simple example of a mupltiple choice prompt would look like this:if __name__ == "__main__": if(len(sys.argv) < 2): print("No arguments given, include at least two commandline arguments.") sys.exit(1) options = [] for index in range(1, len(sys.argv)): arg = sys.argv[index] options.append(arg) print("Select an option (use arrow keys to change selection, press enter when done): ") arrow_selection_prompt = ArrowSelectionPrompt(options) result = arrow_selection_prompt.run() print() print() print("You selected: " + result)PhilosophySometimes for CLIs less is more. Providing users with a simplified prompting mechanism will help guide them through complex scenarios but with an easy to use interface.

zon

zon - Zod-like validator library for PythonWant to have the validation power ofzodbut with the ease-of-use of Python?Enterzon.zonis a Python library that aims to provide a simple, easy-to-use API for validating data, similiar tozod's own API'. In fact, the whole library and its name were inspired byzod: Zod + Python = Zon !!!.WhyWhile doing a project for college, we were using both Python and JS/TS in the code-base. We were also using a JSON-Schema document to define a data schema for the data that we would be consuming.There exists tooling that allows for one schema to generate TS types and from that Zod validator code, but there was nothing of the sort for Python. What's more, none of the validation libraries I found for Python had an API that was as easy to use as Zod's. So, I set out to build it.

zonal-variograms

mussels

zonar-ds-env-arg-parser

zonar_ds_env_arg_parser======Overview------This is a simple helper that is meant to enforce and validate that environment variables are set correctlyUsage------```python3from zonar_ds_env_arg_parser.env_arg_parser import env_arg_parser as parserparser.add_argument(env_var="SOME_EVN_VAR",required=True,type=int,help="Message to display about the variable")parser.add_argument(env_var="SOMETHING_ELSE",required=False,default="TEST",validation=lambda x: x.lower() == "test" orx.lower() == "something" orx.lower() == "something_else",help="Some message about this variable")parser.add_argument(env_var="TRUE",required=False,default="True",type=bool, # Will convert ("yes", "true", "t", "1", "y", "yeah") to Truehelp="Another description about converting to True")parser.initialize()options = parser.get_options()# This var doesn't have a default so 'SOME_EVN_VAR' needs to be set or it'll throw an exceptionprint("This is the type of SOME_EVN_VAR " + type(options.SOME_EVN_VAR)) # Should be int becasue we specified typeprint("This is the value of SOMETHING_ELSE " + options.ENVIRONMENT) # Should be 'TEST' since that's the defaultprint("This is the value of TRUE " + options.TRUE) # Should be 'TEST' since that's the default```Arguments------Parameters that can be passed into env_arg_parser.add_argument are as follows:| param | required | Description ||-------|----------|-------------|| env_var | Yes | The environment variable to look for. This will also be the name of the attribute when retrieving it. || required | Yes | Can be True or False, if required=False a default must be provided. || help | Yes | This message is displayed if an argument doesnt exist or fails validation. It should explain to the user what the argument is for to assist them in defining it. || default | Conditional | The default value. Must be set if required=False, cannot be set if required=True. || validation | No | A function should be provided that takes a single value and should return True, False or raise an Exception. False or an exception will prevent the program from launching. || type | No | If the value needs to be something other than a string, specify what it should be converted to.

zonar-ds-logger

zonar_ds_logger======Overview------A logger class that initializes a logger and logs in json when specified.It also redirects flask logs so it doesn’t always show up as ERRORUsage------```pythonfrom zonar_ds_logger.logger import loggerlogger.initialize("evan-test", "info", False)log = logger.get_logger()log.debug("debug")log.info("info")log.warning("warning")log.error("error")```Arguments------Parameters that can be passed into logger.initialize are as follows:- name (str): Name of the logger- log_level (str): Lowest log level to log. Default is "debug". Options are "debug", "info", "warning", "error"- json_logging (bool): Whether to log in json or not. Options that evaluate to true are ("true", "t", "1", "y", "yes")Default converts value in JSON_LOGGING env var to bool if set else True

zonarPy

zonarPyZonar

zonda-exchange

Zonda Exchange

zondocs-theme

UsageInstall the package$ pip install zondocs_themeThen sethtml_theme = 'zondocs_theme'in your Sphinxconf.py.FeaturesAutomatically uses the ZON logo.Adds an “edit this page” link to the sidebar. To customize how this link is created, you can set the following:html_theme_options = { 'editme_link': ( 'https://github.com/zeitonline/{project}/edit/master/{page}') }(This is the default value, it supports two variables,projectis takendirectly fromconf.py, andpageevaluates topath/to/current/page.suffix)Local testingRunbin/buildto build whl and usepip installzondocs_theme...whlto use it in your project.Release processpipenvis needed to run the release process.Update the version inpyproject.toml.For atestrelease run$ bin/release testFor a offical release run$ bin/release prod

zone2gandi

zone2gandi==========This is a tool for pushing simple zone files (skip the SOA and NSrecords) up to Gandi.Install by running: pip install zone2gandiGetting API Credentials~~~~~~~~~~~~~~~~~~~~~~~http://doc.rpc.gandi.net/Config~~~~~~The config file needs to be in the current directory and look like this:.. code:: yamlapiendpoint: 'https://rpc.ote.gandi.net/xmlrpc/'apikey: 'm4ljWR8zQdSQUMhE03GJCv5p'

zone3k-csv-converter

No description available on PyPI.

zone4

Zone4Zone4 is a Python library for interacting with the Apart Zone4 pre-amplifier over serial.InstallationUse the package managerpipto install zone4.pipinstallzone4Usageimportasynciofromzone4importZone4Managerasyncdefset_volume(manager,zone,volume):awaitmanager.zone(zone).set_volume(volume)asyncdefupdate(manager,loop):awaitmanager.update()loop.create_task(update(manager,loop))if__name__=='__main__':loop=asyncio.new_event_loop()asyncio.set_event_loop(loop)manager=Zone4Manager('/dev/ttyS3')loop.run_until_complete(manager.setup())loop.create_task(set_volume(manager,'a',55))loop.create_task(update(manager,loop))loop.run_forever()loop.close()ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseGNU GPLv3

zone53

zone53is a convenient Python API to manage Amazon’s DNS web serviceroute53. Essentially, it is a thin layer on top ofboto.route53providing Zone and Record classes.

zone-api

Zone API - an alternative approach to writing rulesIn OpenHab, items are defined in a flat manner in the.itemsfiles under the/etc/openhab/items folder. They are typically linked to a channel exposed by the underlying hardware. This flat structure has an impact on how rules (whether in Xtend or Jython) are organized. As there is no higher level abstraction, rules tend to listen to changes from the specific devices. When the rules need to interact with multiple devices of the same type, they can utilize thegroup concept. An example of good usage of group is to turn off all lights. By linking all smart lights to a group switch, turning off all the lights can be done by changing the state of the group switch to OFF.What is more tricky is when rules need to interact with different devices within the same area. The typical solution is to group unrelated items that belong to the same zone either by using a naming pattern, or by dedicated groups. For example, the light switch and motion sensor in the Foyer area can be named like this: "FF_Foyer_Light", and "FF_Foyer_MotionSensor". When a sensor is triggered, the zone can be derived from the name of the triggering item, and other devices/sensors can be retrieved using that naming convention. This works but as there is not sufficient abstraction, the rules are highly coupled to the naming pattern.TheZone APIprovides another approach. It is a layer above the devices / sensors. EachZoneManager(i.e. a house) contains multiplezones(i.e. rooms), and each zone contains multipledevices. Each zone is associated with a set ofactionsthat are triggered by certainevents. The usual OpenHab events are routed in this manner:OpenHab events --> ZoneManager --> Zones --> ActionsThe actions operate on the abstract devices and do not concern about the naming of the items or the underlying hardware. They replace the traditional OpenHab rules. Actions can be unit-tested with various levels of mocking.Most importantly, it enables reusing of action logics.There is no need to reinvent the wheels for common rules such as turning on/off the lights. All ones need to do is to populate the zones and devices / sensors, and the applicable actions will be added and processed automatically.ZoneApi comes with a set of built-inactions. There is no need to determine what action to add to a system. Instead, they are added automatically based on the zones structure and based on the type of devices available in each zone.Here is a sample info log that illustrate the structure of the managed objects.Zone: Kitchen, floor: FIRST_FLOOR, internal, displayIcon: kitchen, displayOrder: 3, 7 devices AstroSensor: VT_Time_Of_Day HumiditySensor: FF_Kitchen_Humidity IlluminanceSensor: FF_Kitchen_LightSwitch_Illuminance Light: FF_Kitchen_LightSwitch, duration: 5 mins, illuminance: 8 MotionSensor: FF_Kitchen_SecurityMotionSensor, battery powered MotionSensor: FF_Kitchen_LightSwitch_PantryMotionSensor, battery powered TemperatureSensor: FF_Kitchen_Temperature Action: HUMIDITY_CHANGED -> AlertOnHumidityOutOfRange Action: MOTION -> TurnOnSwitch Action: MOTION -> AnnounceMorningWeatherAndPlayMusic Action: MOTION -> PlayMusicAtDinnerTime Action: SWITCH_TURNED_ON -> TurnOffAdjacentZones Action: TEMPERATURE_CHANGED -> AlertOnTemperatureOutOfRange Action: TIMER -> TellKidsToGoToBed Neighbor: FF_Foyer, OPEN_SPACE Neighbor: FF_GreatRoom, OPEN_SPACE_MASTER Zone: Foyer, floor: FIRST_FLOOR, internal, displayIcon: groundfloor, displayOrder: 4, 6 devices AlarmPartition: FF_Foyer_AlarmPartition, armMode: ARM_STAY AstroSensor: VT_Time_Of_Day Door: FF_Foyer_Door Light: FF_Foyer_LightSwitch, duration: 5 mins, illuminance: 8, no premature turn-off time range: 0-23:59 MotionSensor: FF_Foyer_LightSwitch_ClosetMotionSensor, battery powered MotionSensor: FF_Foyer_LightSwitch_MotionSensor, battery powered Action: MOTION -> TurnOnSwitch Action: MOTION -> DisarmOnInternalMotion Action: MOTION -> ManagePlugs Action: PARTITION_ARMED_AWAY -> ChangeThermostatBasedOnSecurityArmMode Action: PARTITION_ARMED_AWAY -> ManagePlugs Action: PARTITION_ARMED_AWAY -> TurnOffDevicesOnAlarmModeChange Action: PARTITION_DISARMED_FROM_AWAY -> ChangeThermostatBasedOnSecurityArmMode Action: PARTITION_DISARMED_FROM_AWAY -> ManagePlugs Action: PARTITION_DISARMED_FROM_AWAY -> TurnOffDevicesOnAlarmModeChange Action: SWITCH_TURNED_ON -> TurnOffAdjacentZones Action: TIMER -> ArmStayIfNoMovement Action: TIMER -> ArmStayInTheNight Action: TIMER -> ManagePlugs Neighbor: SF_Lobby, OPEN_SPACE Neighbor: FF_Office, OPEN_SPACE_MASTERRunning on top of HABApp but with minimal dependency:The original Zone API moduleswere written in Jython. It was recently migrated over to theHABAppframework with minimal changes needed to the core code. Seeherefor the comparison between HABApp and JSR223 Jython.There are 3 peripheral modules that are tightly coupled to the HABApp API. The rest of the modules is framework neutral. It is possible to migrate Zone API to another framework running on top of GravVM when it is available. Zone API is now written in Python 3 and thus is not compatible with Jython (equivalent to Python 2.8).Set up your home automation system with zone_api1. Name the OpenHab items using the default naming conventionZone_apicomes with a default parser that builds the zone manager using a pre-defined naming convention. See the ZoneParser section at the end of this page for details.Here are a few sample .items files. Note that the file organization doesn't matter; all items can be defined in a single file if desired.zones.items: defines two zones and their relationship.String Zone_Office { level="FF", displayIcon="office", displayOrder="2", openSpaceSlaveNeighbors="FF_Foyer" } String Zone_Foyer { level="FF", displayIcon="groundfloor", displayOrder="4", openSpaceMasterNeighbors="FF_Office", openSpaceNeighbors="SF_Lobby" }foyer.items: defines the items in the Foyer zone.Switch FF_Foyer_LightSwitch "Foyer Light" (gWallSwitch, gLightSwitch, gFirstFloorLightSwitch) { channel="zwave:device:9e4ce05e:node2:switch_binary", disableMotionTriggeringIfOtherLightIsOn="FF_Office_LightSwitch", durationInMinutes="5"} Switch FF_Foyer_LightSwitch_ClosetMotionSensor "Foyer Closet Motion Sensor" (gWallSwitchMotionSensor) { channel="mqtt:topic:myBroker:xiaomiMotionSensors:FoyerMotionSensor"}office.items: defines the items in the Office zone.Switch FF_Office_LightSwitch "Office Light" (gWallSwitch, gLightSwitch, gFirstFloorLightSwitch) [shared-motion-sensor] { channel="zwave:device:9e4ce05e:node8:switch_binary", durationInMinutes="15" } Switch FF_Office_LightSwitch_MotionSensor "Office Motion Sensor" (gWallSwitchMotionSensor, gFirstFloorMotionSensors) { channel="mqtt:topic:myBroker:xiaomiMotionSensors:OfficeMotionSensor"}That's it. Once the system is fully set up, ZoneApi's default actions will be registered automatically depending on the available devices.In the example above, the two zones have light switches and motion sensor. Thus the light rule is applicable and will automatically turn on the light when a motion sensor is triggered, and turn it off if there is no activity for the pre-defined duration. It will also turn off lights in the dependent zones.2. Clone this repositorygit clone [email protected]:yfaway/zone-apis.git3. Install, configure, and run HABappRefer to the instructions on theofficial HABApp website. The instruction below is specifically for the zone_api.sudoapt-getinstallpython3-venv# to install python3-venv librarycdzone_api# the cloned project in the section abovepython3-mvenv.sourcebin/activate# to get into our virtual environmentpython3-mpipinstall--upgradepip# to upgrade the pip library.python3-mpipinstallhabapprequestschedule# request and schedule are required by zone_apiTo manually run HABApp, execute this command within thezone_apifolder:./bin/habapp--config./habapp/config.ymlThe./habapp/rulesfolder contains the bootstrapruleto initialize thezone_apiframework. The rule is pretty simple with its entire content below.importHABAppfromzone_apiimportzone_parseraszpfromzone_api.core.devices.activity_timesimportActivityType,ActivityTimesclassConfigureZoneManagerRule(HABApp.Rule):def__init__(self):super().__init__()self.run_soon(self.configure_zone_manager)# noinspection PyMethodMayBeStaticdefconfigure_zone_manager(self):time_map={ActivityType.WAKE_UP:'6 - 9',ActivityType.LUNCH:'12:00 - 13:30',ActivityType.QUIET:'14:00 - 16:00, 20:00 - 22:59',ActivityType.DINNER:'17:50 - 20:00',ActivityType.SLEEP:'23:00 - 7:00',ActivityType.AUTO_ARM_STAY:'20:00 - 2:00',ActivityType.TURN_OFF_PLUGS:'23:00 - 2:00',}zone_manager=zp.parse(ActivityTimes(time_map))ConfigureZoneManagerRule()The code above defines an ActivityTimes object with various activity time periods and pass it over to thezone_parsermodule. The zone_parser parses the OpenHab items following a specific naming pattern, and construct the zones and the devices / sensors. It then registers the handlers for the events associated with the devices / sensors. Finally, it loads all the actions and add them to the zones based on the pre-declared execution rules associated with each action (more on this later). That's it; from this point forward, events generated by the devices / sensors will trigger the associated actions.It is important to note that the zone_parser is just a default mechanism to build the ZoneManager. A custom module can be used to parse from a different naming pattern for the OpenHab items, or the ZoneManager can be constructed manually. The role of the parser is no longer needed once the ZoneManager has been built.ZoneManagerContains a set of zones and is responsible for dispatching the events to the zones.ZoneContains a set of devices, actions, and is responsible for dispatching the events to the actions.A zone is aware of its neighbors. Certain rules such as theturning on/off of the lightsis highly dependent on the layout of the zones. The followingneighbortypes are available.CLOSED_SPACEOPEN_SPACEOPEN_SPACE_MASTEROPEN_SPACE_SLAVEDevicesThedevicescontains one or more underlying OpenHab items. Rather than operating on a SwitchItem or on a NumberItem, the device represents meaningful concrete things such as aMotionSensor, or aLight. Devices contain both attributes (e.g. 'is the door open') and behaviors (e.g. 'arm the security system').EventsSimilar to the abstraction for the devices, the events are also more concrete. Zone API maps the OpenHab items events to the event enums inZoneEventsuch asZoneEvent.HUMIDITY_CHANGEDorZoneEvent.PARTITION_ARMED_AWAY. There is also the special eventZoneEvent.TIMERthat represents triggering from a scheduler.The event is dispatched to the appropriate zones which then invokes the actions registered for that event. SeeEventInfofor more info.ActionsAll theactionsimplement theActioninterface. The action's life cycle is represented by the three functions:on_startup()- invoked after the ZoneManager has been fully populated, via the eventZoneEvent.STARTUP.on_action()- invoked where the device generates an event or when a timer event is triggered (viaZoneEvent.TIMER).on_destroy()- currently not invoked.The@actiondecorator provides execution rules for the action as well as basic validation. If the condition (based on the execution rules) does not match, the action won't be executed. Below are the currently supported decorator parameters:devices- the list of devices the zone must have in order to invoke the action.events- the list of events for which the action will response to.internal- if set, this action is only applicable for internal zoneexternal- if set, this action is only applicable for external zonelevels- the zone levels that this action is applicable to. the empty list default value indicates that the action is applicable to all zone levels.unique_instance- if set, do not share the same action instance across zones. This is the case when the action is stateful.zone_name_pattern- if set, the zone name regular expression that is applicable to this action.external_events- the list of events from other zones that this action processes. These events won't be filtered using the same mechanism as the internal events as they come from other zones.priority- the action priority with respect to other actions within the same zone. Actions with lower priority values are executed first.These parameters are also available to the action and can be used as a filtering mechanism to make sure that the action is only added to the applicable zones.Here is a simple action to disarm the security system when a motion sensor is triggered:fromzone_apiimportsecurity_managerassmfromzone_api.core.devices.activity_timesimportActivityTimesfromzone_api.core.devices.motion_sensorimportMotionSensorfromzone_api.core.zone_eventimportZoneEventfromzone_api.core.actionimportactionfromzone_api.core.devices.alarm_partitionimportAlarmPartition@action(events=[ZoneEvent.MOTION],devices=[AlarmPartition,MotionSensor])classDisarmOnInternalMotion:"""Automatically disarm the security system when the motion sensor in the zone containing thesecurity panel is triggered and the current time is not in the auto-arm-stay or sleeptime periods."""defon_action(self,event_info):events=event_info.get_event_dispatcher()zone_manager=event_info.get_zone_manager()ifnotsm.is_armed_stay(zone_manager):returnFalseactivity=zone_manager.get_first_device_by_type(ActivityTimes)ifactivityisNone:self.log_warning("Missing activities time; can't determine wake-up time.")returnFalseifactivity.is_auto_arm_stay_time()or(activity.is_sleep_time()andnotactivity.is_wakeup_time()):returnFalsesm.disarm(zone_manager,events)returnTrueThe decorator for the action above indicates that it is triggered by the motion event, and should only be added to a zone that contains both the AlarmPartition and the Motion devices.ZoneParserThe default parser uses this naming pattern for the OpenHab items.The zones are defined as a String item with this pattern Zone_{name}:String Zone_GreatRoom { level="FF", displayIcon="player", displayOrder="1", openSpaceSlaveNeighbors="FF_Kitchen" }The levels are the reversed mapping of the enums in Zone::Level.Here are the list of supported attributes: level, external, openSpaceNeighbors, openSpaceMasterNeighbors, openSpaceSlaveNeighbors, displayIcon, displayOrder.The individual OpenHab items are named after this convention:{zone_id}_{device_type}_{device_name}.Here's an example:Switch FF_Office_LightSwitch "Office Light" (gWallSwitch, gLightSwitch, gFirstFloorLightSwitch) [shared-motion-sensor] { channel="zwave:device:9e4ce05e:node8:switch_binary", durationInMinutes="15" }See here for asample .itemsfile that is parsable by ZoneParser.

zonebot

# ZoneBot - A ZoneMinder Slack BotThis is a [Slack Bot](https://api.slack.com/bot-users) that monitors one or more [Slack](https://slack.com) channels for commands and interacts with a [ZoneMinder](https://www.zoneminder.com/) system to report events and obtain information.There are two parts to the project1. A script that is invoked by ZoneMinder whenever an event/alarm is detected (`zonebot-alert`). This will post a message with the most significant frame of the event to a specified Slack channel.2. A bot that listens in a Slack channel for commands from approved users and passes them along to the ZoneMinder server.[](https://rjclark.github.io/zoneminder-slack-bot/docs/images/ZoneBot-Screen-Cast.webm)The primary use for this bot is to allow access to some parts of a ZoneMinder system that is behind a firewall, without having to expose the actual system to the Internet. Making a ZoneMinder system available to the Internet has several requirements (static IP, secure system) that may not be feasible for all users.By providing a bot that can interact with both ZoneMinder and Slack, remote access to and notification from ZoneMinder is possible, without needing a static IP. The security and authentication provided by the Slack environment is used to control access to the script, and the bot also has a permissions section in it's configuration that controls which users are allowed which actions.## Installation### Easiest : Using pipThe easiest method of installation is via `pip` as the package is available from the [Python Package Index](https://pypi.python.org/pypi)```sh> pip install zonebot```This will create a script called `zonebot` in your path ("`which zonebot`" will tell you exactly where) that you can run.### Download source and buildYou can download the source from GitHub and build it yourself if you would like.1. Download the release you want from https://github.com/rjclark/zoneminder-slack-bot/releases1. Extract it1. Run `python setup.py build install`### Clone the source and buildYou can also clone the source from GitHub if you would like to build the very latest version. **This is not guaranteed to work**. The unreleased source code from GitHub could be in the middle of development and running it directly is not recommended.1. Clone this repository https://github.com/rjclark/zoneminder-slack-bot1. Run `python setup.py build install`## Configuration### Bot ConfigurationAlso installed is a sample configuration file called `zonebot-example-config.cfg`. You can copy this to your preferred location for config files and edit it to put in your [Slack API token](https://api.slack.com/tokens) and the [ID of your bot user](https://api.slack.com/bot-users)The example configuration file is installed into the Python package directory on your system, which can be somewhat difficult to find. The latest version of the file is always available from [the GitHub repository](https://github.com/rjclark/zoneminder-slack-bot/blob/master/docs/zonebot-example-config.cfg) if needed.To configure the bot, you will need several pieces of information1. Your Slack API token. This can be found by1. Going to the [Slack Bot user page](https://api.slack.com/bot-users) and creating a new bot user. You will have a chance to get the API token here2. Going to the page for your [existing bot user](https://my.slack.com/apps/manage/custom-integrations).2. The User ID of your bot user. This can be found by:1. Running the script `zonebot-getid` distributed with this package and providing the name of the Slack bot user and you Slack API token as command line options. For example:```sh> zonebot-getid -a "your-slack-token" -b zoneminder> User ID for bot 'zoneminder' is AA22BB44C```Once you have those, make a copy of the config file and add the Slack API token and user ID of the bot, You will also want to edit the `Permissions` section.**NOTE**: The default config file allows only read permission to the ZoneMinder system.### ZoneMinder ConfigurationIf you want ZoneMinder events posted to Slack as they occur, you must define a ZoneMinder filter that invokes the `zonebot-alert` script. The script gets all required configuration information from the same config file as the bot.### Config File LocationsThe default config file can be placed in any of these locations (checked in this order)* Any file specified by the `-c/--config` command line option* `$XDG_CONFIG_HOME/zonebot/zonebot.conf` if the `XDG_CONFIG_HOME` environment variable is defined* `${DIR}/zonebot/zonebot.conf` for any directory in the `XDG_CONFIG_DIRS` environment variable* `~/.config/zonebot/zonebot.conf`* `/etc/zonebot/zonebot.conf`* `/etc/default/zonebot`## Reporting Problems1. The best way to report problems is to log a report on the GitHub Issues page [https://github.com/rjclark/zoneminder-slack-bot/issues](https://github.com/rjclark/zoneminder-slack-bot/issues) for this project.2. If you do not have a GItHub account, you can also contact me via email: [[email protected]](mailto:[email protected])## Building and ContributingIf you wish to contribute, pull requests against the [GitHub repository](https://github.com/rjclark/zoneminder-slack-bot), `master` branch, are welcomed.[](https://travis-ci.org/rjclark/zoneminder-slack-bot)[](https://coveralls.io/github/rjclark/zoneminder-slack-bot?branch=master)[](https://pypi.python.org/pypi/zonebot)[](https://www.versioneye.com/user/projects/57def689037c2000458f770d)[](https://landscape.io/github/rjclark/zoneminder-slack-bot/master)### Requirements and ToolsIf you are new to the concept of building either a Python application or a Slack bot, I encourage you to review the excellent posting over at [Full Stack Python](https://www.fullstackpython.com) called[How to Build Your First Slack Bot with Python](https://www.fullstackpython.com/blog/build-first-slack-bot-python.html). This document will provide a summary of the requirements and steps necessary, but it assumes a basica familiarity with the tools and environment that the linked article covers in some depth.This list of tools from the [First Slack Bot](https://www.fullstackpython.com/blog/build-first-slack-bot-python.html) blog is all that is needed to build this bot.> * Either [Python 2 or 3](https://wiki.python.org/moin/Python2orPython3)> * [pip](https://pip.pypa.io/en/stable/) and [virtualenv](https://virtualenv.pypa.io/> en/stable/) to handle Python application dependencies> * A [Slack account](https://slack.com/) with a team on which you have API access.> * Official Python [slackclient](https://github.com/slackhq/python-slackclient) code library built by the Slack team> * [Slack API testing token](https://api.slack.com/tokens)>> It is also useful to have the [Slack API docs](https://api.slack.com/) handy while you're building this tutorial.### Setup1. Use `virtualenv` and `pip` to create a development```sh> virtualenv venv> source venv/bin/activate(or . venv/bin/activate.fish of you use the fish shell)> venv/bin/pip install install -r requirements.txt```2. Obtain a Slack API token (and optionally create a dedicated [bot user](https://api.slack.com/bot-users) for the API token) from Slack3. Since the API token needs to remain secret, you should set it as an environmentvariable rather than putting it into any source file.```sh> export SLACK_BOT_TOKEN='your slack token pasted here'```4. Run `utils/get_bot_id.py` to get the number ID of the bot (as opposed to the name you gave the bot user. This is also our first real test of the API token5. Put the bot ID into an environment variable as well.```sh> export BOT_ID='bot id returned by script'```Later on the BOT_ID and SLACK_API_TOKEN (along with a lot of the other config options) will be loaded from a config file. This is to make running the script as a daemon less of a hassle.

zonebuilder

zonebuilderThe goal of zonebuilder is to break up large geographic regions such as cities into manageable zones. Zoning systems are important in many fields, including demographics, economy, health, and transport. The zones have standard configuration, which enabled comparability across cities. See its website atzonebuilders.github.io. Also checkouthttps://zonebuilders.github.io/zonebuilder-rust/for visualization. This package provides the python bindings forzonebuilder-rust.UsageInstallationpipinstallzonebuilderuse withPython 3.6and above.Creating zonesThe core function of this library isclockboard.importzonebuilderzones=zonebuilder.clockboard(center,kwargs)#eg. zonebuilder.clockboard([0,0])## OPTIONS:## center, REQUIRED: a list with an x and y coordinate specifying the center of the region of interest## kwargs, OPTIONAL keyword arguments## distances = <distances>...## The distances between concentric rings. `zonebuilder.triangular_sequence` is useful to## generate these distances [default: 1.0,3.0,6.0,10.0,15.0] given by triangular_sequence(5)## num-segments = <num-segments>## The number of radial segments. Defaults to 12, like the hours on a clock [default: 12]## num-vertices-arc = <num-vertices-arc>## The number of vertices per arc. Higher values approximate a circle more accurately [default: 10]## precision = <precision>## The number of decimal places in the resulting output GeoJSON files. Set to 6 by default.## Larger numbers mean more precision, but larger file sizes [default: 6]## projected = <projected>## Boolean, is the data projected? [default: False]which will return a stringified geojson object. You can load it into other python geo libraries as follows:#geojsonimportgeojsonfeaturecollection=geojson.loads(zones)#geopandasimportgeopandasgeodataframe=geopandas.GeoDataFrame.from_features(featurecollection["features"])ExampleExecutingclockboard([0,0])will yield the 49 zones shown in the following figure centered on [0,0]:Dev-notesInstallationSteps to test the python bindings forzonebuilder-pyCreate a local virtual environment for python and activate it.python -m venv env source env/bin/activateInstall necessary libraries into the environmentpip install -r dev-requirements.txtDevelop the rust code into a python package and install it into the local environmentmaturin developalternately you can alsopip install .Test it (somehow directly typingpytestthrows error), use belowpython -m pytestCurrent functionstriangular_sequenceclockboard

zonecheck

ZonecheckHomepage:https://github.com/icann-dns/zonecheckValidate DNS zones against DNS master servers and create custom facts.To be used in tandem withpuppet-dns moduleRequirementsdnspython:https://pypi.org/project/dnspython/PyYAML:https://pypi.org/project/PyYAML/Usageusage: zonecheck [-h] [--serial-lag SERIAL_LAG] [--log LOG] [--puppet-facts][--puppet-facts-dir PUPPET_FACTS_DIR] [--config CONFIG] [-v]optional arguments:-h, --help show this help message and exit--serial-lag SERIAL_LAGalert if the serial is behind by this value or more--log LOG location of error file--puppet-facts--puppet-facts-dir PUPPET_FACTS_DIR--config CONFIG comma seperated list of zones-v, --verbose

zone-common

No description available on PyPI.

zonecreate

zonecreate is a very simple Python project that contains a couple of different scripts/templates that make it easier to manage various different zone files that are in the standard zone file format (BIND/NSD).Scriptszone_createIs used to create a new zone file, contains the standard stuff, SOA record and some NS records. Nothing else.zone_update_soaThis is used to update the SOA record of a zone file. Given a zone file it will grab the current SOA record, and update the serial number to the current date + a counter. This way the zone file can be updated twice in the same day and the serial will correctly have increased.HackingIf you would like to hack on this project, you can do it as follows:$ virtualenv –distribute .venv$ . .venv/bin/activate$ python setup.py developThis will set up a new virtualenv and start development, it will also install all the scripts under their bin names.Pull requestsIf you want to contribute please do so by creating a topic branch on a fork of this project, and issuing a pull request. Please make sure to add yourself to the CONTRIBUTORS file for credit.LicenseThis is under an OpenBSD license. See LICENSE.2013-02-15Version 0.1 releasedCreated the new git repositoryImported the project

zonefile

UNKNOWN

zone-file

UNKNOWN

zonefilegen

zonefilegenA simple tool to generate synchronized forward and reverse DNS zone files based on an input text file.The intended use case is where a local authoritative DNS server is used to serve lookups for hosts confined within a single forward DNS zone. Thus, only a single forward zone is supported in order to simplify the input file.Reverse zones will be generated for specified subnets, and they will be automatically populated with PTR records corresponding to the firstAandAAAArecords found in the input file for a certain host.Zone serial numbers will automatically be incremented when the input file has changed.InstallationInstall via pip:pip install zonefilegenUsageThe package installs a command line toolzonefilegenwhich generates zone files in a specified directory based on an input file:zonefilegen input.toml output_folderThis will parseinput.tomland generate one forward zone file and zero and more reverse zone files inoutput_dir.Input file formatThe input file is written in theTOMLformat which is easy to read by both humans and machines.For an example file, seedocs/sample.toml.Required entriesThe following entries are required in the input file:origin: The FQDN of the forward DNS zone.$ORIGINin the zone file.default_ttl: Default TTL for resource records if they have none set.$TTLin the zone file.[soa]: Contains the entries to put in theSOArecord, except for the serial number:mnamernamerefreshretryexpirenegative_caching_ttlOne or more[[rrset]]with entries for the forward records. Each[[rrset]]entry contains one or more records with the same name, type and ttl value:name:@, unqualified name or FQDN.type: The record type likeAorMX.ttl: Optional TTL value.data: A string or a list of strings with the record data. A separate record will be created for every string in the list.Optionally, one can also supply anetworksentry, which should contain a list of networks in CIDR notation (ipv4 or ipv6) for which reverse zones should be created. The networks must end on whole-octet (ipv4) or whole-nibble edges (ipv6). So only/16,/24etc in the ipv4 case and/48,/52,/56etc for ipv6.

zonefile-migrate

Namezonefile-migrate - Migrate DNS managed zonesSynopsiszonefile-migrate to-cloudformation [OPTIONS] [SRC]... DST zonefile-migrate to-terraform [OPTIONS] [SRC]... DSTOptionsto-cloudformation --sceptre-group DIRECTORY to write sceptre stack group configuration --maximum-ttl INTEGER maximum TTL of domain name records to-terraform --maximum-ttl INTEGER maximum TTL of domain name records --provider PROVIDER to generate forDescriptionConverts one or moreSRCzonefiles into AWS CloudFormation or Terraform templates inDST.The zonefiles must contain a $ORIGIN and $TTL statement. If the SRC points to a directory all files which contain one of these statements will be converted. If a $ORIGIN is missing, the name of the file will be used as the domain name.Optionally generates the Sceptre stack config for each of the templates in the--sceptre-groupdirectory.Each generated CloudFormation template contains a single Route53 HostedZone and all associated ResourceRecordSet. The SOA and NS records for the origin domain are not copied into the template.Installationto install the utility, type:pipinstallzonefile-migrateExample - to-cloudformationIn the source code we have an example, to try it out, type:$gitclonehttps://gitlab.com/binxio/zonefile-migrate.git $cdzonefile-migrate/example $zonefile-migrateto-cloudformation--sceptre-groupconfig/dns./zones./templates/dns INFO:readingzonefilezones/asample.org INFO:readingzonefilezones/land-5.comTo deploy all the managed zones to AWS, type:$sceptre--varaws_profile=$AWS_PROFILElaunch-ydns[2022-05-1414:58:23]-dns/zone-land-5-com-LaunchingStack[2022-05-1414:58:23]-dns/zone-example-org-LaunchingStack[2022-05-1414:58:23]-dns/zone-land-5-com-StackisinthePENDINGstate[2022-05-1414:58:23]-dns/zone-land-5-com-CreatingStack[2022-05-1414:58:23]-dns/zone-asample-org-StackisinthePENDINGstate[2022-05-1414:58:23]-dns/zone-asample-org-CreatingStack[2022-05-1414:58:24]-dns/zone-asample-orgbinxio-dns-zone-asample-orgAWS::CloudFormation::StackCREATE_IN_PROGRESSUserInitiated[2022-05-1414:58:24]-dns/zone-land-5-combinxio-dns-zone-land-5-comAWS::CloudFormation::StackCREATE_IN_PROGRESSUserInitiated ...Example - to-terraform$gitclonehttps://gitlab.com/binxio/zonefile-migrate.git $cdzonefile-migrate/example $zonefile-migrateto-terraform--providergoogle./zones./terraform INFO:readingzonefilezones/asample.org INFO:readingzonefilezones/land-5.comTo deploy all the managed zones to Google Cloud Platform, type:$cdterraform$terraforminit $exportGOOGLE_PROJECT=$(gcloudconfigget-valuecore/project)$terraformapply-auto-approve ... Terraformwillperformthefollowingactions:# module.asample_org.google_dns_managed_zone.managed_zone will be created+resource"google_dns_managed_zone""managed_zone"{+description="Managed by Terraform"+dns_name="asample.org."+force_destroy=false+id=(knownafterapply)+name="asample-org"+name_servers=(knownafterapply)+project=(knownafterapply)+visibility="public"}... Plan:49toadd,0tochange,0todestroy. module.land-5_com.google_dns_managed_zone.managed_zone:Creating... module.asample_org.google_dns_managed_zone.managed_zone:Creating... ...

zone-file-parser

No description available on PyPI.

zonefile-parser

No description available on PyPI.

zoneplanningteam

This is a package to be used by zone planning team

zoner

Zoner is a web application to make management of DNS zone files simple and easy. The authoritative copy of each domain remains in the original zone file, which Zoner reads & writes as needed, as opposed to storing domain details in a database. This means that zone files can still be edited manually and Zoner will pick up the changes as necessary.Zoner features:Domain details remain in original zone files, not in a database.Zoner reads & writes actual zone files, which can also be safely modified outside of Zoner.Zone serial numbers are incremented automatically when changes are made.Zoner can signal bind to reload a zone, via rndc.An audit of all zone changes is maintained for each domain. Any previous version of a zone file can be inspected and zones can be rolled back to any previous version.Requirements:Zoner is a Python application built with the TurboGears framework. Both Python and TurboGears (version 1.x) are required.Zoner requires the easyzone and dnspython Python packages for DNS/zone management.Zoner also requires SQLAlchemy, TGBooleanFormWidget and TGExpandingFormWidget Python packages.(All dependencies should be installed automatically if using setuptools, which will usually be the case for a properly installed TurboGears environment.)InstallationThe easiest way to install Zoner is by using setuptools:$ easy_install zonerAlternatively, install TurboGears then download the Zoner package and install with:$ python setup.py installThen create a config file. A template sample-prod.cfg file is included with the package (or installed alongside the package). Example:$ cp /usr/lib/python2.4/site-packages/zoner-1.3.1-py2.4.egg/config/sample-prod.cfg zoner.cfgCustomise the config file, then initialise the database:$ tg-admin sql createNext, create a user to login to the Zoner application with:$ zoner_users -c zoner.cfg addFinally, start the Zoner application:$ zoner zoner.cfgPoint your browser athttp://localhost:8080/(or the appropriate host/port as per your configuration) and you should be able to login.

zones

No description available on PyPI.

zonesmart-utils

No description available on PyPI.

zonesmart-utils-fork

No description available on PyPI.

zonevu

ZoneVu Web API PackageThis is the ZoneVu Web API interface package by Ubiterra. AccessZonevu Knowledge Basefor documentation about using this package.

zonic

TODO

zonis

ZonisA coro based callback system for many to one IPC setups.pip install zonis======= See theexamplesfor simple use cases.Build the docs locallyIf you want to build and run the docs locally using sphinx runsphinx-autobuild -a docs docs/_build/html --watch zonisthis will build the docs and start a local server; additionally it will listed for changes to the source directoryzonisand to the docs source directorydocs/. You can find the builded files atdocs/_build.

zonisss

ZonisA coro based callback system for many to one IPC setups.pip install zonisSee theexamplesfor simple use cases.Build the docs locallyIf you want to build and run the docs locally using sphinx runsphinx-autobuild -a docs docs/_build/html --watch zonisthis will build the docs and start a local server; additionally it will listed for changes to the source directoryzonisand to the docs source directorydocs/. You can find the builded files atdocs/_build.

zono

Failed to fetch description. HTTP Status Code: 404

zonotify

ZonotifyZonotify is a versatile Python package designed to provide convenient notification services for developers and users working on extensive tasks. Born out of the need for staying updated on the status of long-running tasks without the hassle of constantly checking the code, Zonotify simplifies the process by sending notifications directly through Discord or email. Whether you're running a complex data processing task, a lengthy machine learning model training, or any other time-consuming operation, Zonotify keeps you informed about the task's completion or progress updates, seamlessly integrating with just a few lines of code.InstallationInstall Zonotify easily using pip:pipinstallzonotifyUsageTo use Zonotify, first import the package and set up the notifier for either Discord or email notifications or both.fromzonotifyimportZonotify# Initialize with your Discord webhook URL and gmail credentialsnotifier=Zonotify(discord_webhook_url='your_discord_webhook_url',gmail_credentials={'email':'[email protected]','smtp_server':'smtp.gmail.com','smtp_port':587,'username':'your_username','password':'your_password'})Send a notification to Discordnotifier.notify_discord('Task Completed','Your long-running task has finished.')Send a notification via Emailnotifier.notify_email('[email protected]','Task Status','Your task')ContributingWe welcome contributions from the community! If you have suggestions, improvements, or want to report an issue, please visit our GitHub repository. Your input is valuable to us, and we look forward to seeing your ideas and contributions!ConclusionZonotify aims to make the life of developers and users easier by automating the notification process for various tasks. It's a simple yet powerful tool that can be integrated into numerous workflows and systems. We hope Zonotify enhances your productivity and helps you stay updated on your tasks with minimal disruption. Happy coding!

zoo

UNKNOWN

zoo-animal-classification

# DATS6450-final-project This package contains different machine learning models for prediction of zoo animals based on a given set of features.Machine Learning Models Used: * Decision Tree * Random Forest * Support Vector Machine## Installation You can installanimal_classificationfollowing the below steps:# git clone https://github.com/yijiaceline/CSF-project.git# cd CSF-project# python3 setup.py install## Usage Refer to example.py## Example OutputFitting and predicting using Decision Tree Model:Accuracy:0.9523809523809523Fitting and predicting using Random Forest Model:Accuracy: 1.0Fitting and predicting using Support Vector Machine:Accuracy: 1.0Fitting and predicting using KNN:Accuracy: 0.9047619047619048## License The MIT License. Refer to LICENSE.

zooboss

UNKNOWN

zoobot

ZoobotZoobot classifies galaxy morphology with deep learning.Zoobot is trained using millions of answers by Galaxy Zoo volunteers. This code will let youretrainZoobot to accurately solve your own prediction task.InstallQuickstartWorked ExamplesPretrained WeightsDatasetsDocumentation(for understanding/reference)InstallationYou can retrain Zoobot in the cloud with a free GPU using thisGoogle Colab notebook. To install locally, keep reading.Download the code using git:git clone [email protected]:mwalmsley/zoobot.gitAnd then pick one of the three commands below to install Zoobot and either PyTorch (recommended) or TensorFlow:# Zoobot with PyTorch and a GPU. Requires CUDA 11.3. pip install -e "zoobot[pytorch_cu113]" --extra-index-url https://download.pytorch.org/whl/cu113 # OR Zoobot with PyTorch and no GPU pip install -e "zoobot[pytorch_cpu]" --extra-index-url https://download.pytorch.org/whl/cpu # OR Zoobot with PyTorch on Mac with M1 chip pip install -e "zoobot[pytorch_m1]" # OR Zoobot with TensorFlow. Works with and without a GPU, but if you have a GPU, you need CUDA 11.2. pip install -e "zoobot[tensorflow]This installs the downloaded Zoobot code using pipeditable modeso you can easily change the code locally. Zoobot is also available directly from pip (pip install zoobot[option]). Only use this if you are sure you won't be making changes to Zoobot itself. For Google Colab, usepip install zoobot[pytorch_colab]To use a GPU, you mustalreadyhave CUDA installed and matching the versions above. I share my install stepshere. GPUs are optional - Zoobot will run retrain fine on CPU, just slower.QuickstartTheColab notebookis the quickest way to get started. Alternatively, the minimal example below illustrates how Zoobot works.Let's say you want to find ringed galaxies and you have a small labelled dataset of 500 ringed or not-ringed galaxies. You can retrain Zoobot to find rings like so:importpandasaspdfromgalaxy_datasets.pytorch.galaxy_datamoduleimportGalaxyDataModulefromzoobot.pytorch.trainingimportfinetune# csv with 'ring' column (0 or 1) and 'file_loc' column (path to image)labelled_df=pd.read_csv('/your/path/some_labelled_galaxies.csv')datamodule=GalaxyDataModule(label_cols=['ring'],catalog=labelled_df,batch_size=32)# load trained Zoobot modelmodel=finetune.FinetuneableZoobotClassifier(checkpoint_loc,num_classes=2)# retrain to find ringstrainer=finetune.get_trainer(save_dir)trainer.fit(model,datamodule)Then you can make predict if new galaxies have rings:fromzoobot.pytorch.predictionsimportpredict_on_catalog# csv with 'file_loc' column (path to image). Zoobot will predict the labels.unlabelled_df=pd.read_csv('/your/path/some_unlabelled_galaxies.csv')predict_on_catalog.predict(unlabelled_df,model,label_cols=['ring'],# only used forsave_loc='/your/path/finetuned_predictions.csv')Zoobot includes many guides and working examples - see theGetting Startedsection below.Getting StartedI suggest starting with theColab notebookor the worked examples below, which you can copy and adapt.For context and explanation, see thedocumentation.For pretrained model weights, precalculated representations, catalogues, and so forth, see thedata notesin particular.Worked ExamplesPyTorch (recommended):pytorch/examples/finetuning/finetune_binary_classification.pypytorch/examples/finetuning/finetune_counts_full_tree.pypytorch/examples/representations/get_representations.pypytorch/examples/train_model_on_catalog.py(only necessary to train from scratch)TensorFlow:tensorflow/examples/train_model_on_catalog.py(only necessary to train from scratch)tensorflow/examples/make_predictions.pytensorflow/examples/finetune_minimal.pytensorflow/examples/finetune_advanced.pyThere is more explanation and an API reference on thedocs.I alsoincludethe scripts used to create and benchmark our pretrained models. Many pretrained models are availablealready, but if you need one trained on e.g. different input image sizes or with a specific architecture, I can probably make it for you.When trained with a decision tree head (ZoobotTree, FinetuneableZoobotTree), Zoobot can learn from volunteer labels of varying confidence and predict posteriors for what the typical volunteer might say. Specifically, this Zoobot mode predicts the parameters for distributions, not simple class labels! For a demonstration of how to interpret these predictions, see thegz_decals_data_release_analysis_demo.ipynb.(Optional) Install PyTorch or TensorFlow, with CUDAIf you're not using a GPU, skip this step. Use the pytorch_cpu or tensorflow_cpu options in the section below.Install PyTorch 1.12.1 or Tensorflow 2.10.0 and compatible CUDA drivers. I highly recommend usingcondato do this. Conda will handle both creating a new virtual environment (conda create) and installing CUDA (cudatoolkit,cudnn)CUDA 11.3 for PyTorch:conda create --name zoobot38_torch python==3.8 conda activate zoobot38_torch conda install -c conda-forge cudatoolkit=11.3CUDA 11.2 and CUDNN 8.1 for TensorFlow 2.10.0:conda create --name zoobot38_tf python==3.8 conda activate zoobot38_tf conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/ # add this environment variableLatest minor features (v1.0.4)Now supports multi-class finetuning. Seepytorch/examples/finetuning/finetune_multiclass_classification.pyRemovedsimplejpegdependency due to M1 install issue.Pinnedtimmversion to ensure MaX-ViT models load correctly. Models supporting the latesttimmwill follow.(internal until published) GZ Evo v2 now includes Cosmic Dawn (HSC). Significant performance improvement on HSC finetuning.Latest major features (v1.0.0)v1.0.0 recognises that most of the complexity in this repo is training Zoobot from scratch, but most non-GZ users will probably simply want to load the pretrained Zoobot and finetune it on their data.Adds new finetuning interface (finetune.run_finetuning()), examples.Refocuses docs on finetuning rather than training from scratch.Rework installation process to separate CUDA from Zoobot (simpler, easier)Better wandb logging throughout, to monitor trainingRemove need to make TFRecords. Now TF directly uses images.Refactor out augmentations and datasets togalaxy-datasetsrepo. TF and Torch now use identical augmentations (via albumentations).Many small quality-of-life improvementsContributions are very welcome and will be credited in any future work. Please get in touch! SeeCONTRIBUTING.mdfor more.Benchmarks and Replication - Training from ScratchThebenchmarksfolder contains slurm and Python scripts to train Zoobot from scratch. We use these scripts to make sure new code versions work well, and that TensorFlow and PyTorch achieve similar performance.Training Zoobot using the GZ DECaLS dataset option will create models very similar to those used for the GZ DECaLS catalogue and shared with the early versions of this repo. The GZ DESI Zoobot model is trained on additional data (GZD-1, GZD-2), as the GZ Evo Zoobot model (GZD-1/2/5, Hubble, Candels, GZ2).CitingIf you use this software, or otherwise wish to cite Zoobot as a software package, please use theJOSS paper:@article{Walmsley2023, doi = {10.21105/joss.05312}, url = {https://doi.org/10.21105/joss.05312}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {85}, pages = {5312}, author = {Mike Walmsley and Campbell Allen and Ben Aussel and Micah Bowles and Kasia Gregorowicz and Inigo Val Slijepcevic and Chris J. Lintott and Anna M. m. Scaife and Maja Jabłońska and Kosio Karchev and Denise Lanzieri and Devina Mohan and David O’Ryan and Bharath Saiguhan and Crisel Suárez and Nicolás Guerra-Varas and Renuka Velu}, title = {Zoobot: Adaptable Deep Learning Models for Galaxy Morphology}, journal = {Journal of Open Source Software} }You might be interested in reading papers using Zoobot:Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314,000 galaxies(2022)A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification(2022)Practical Galaxy Morphology Tools from Deep Supervised Representation Learning(2022)Towards Foundation Models for Galaxy Morphology(2022)Harnessing the Hubble Space Telescope Archives: A Catalogue of 21,926 Interacting Galaxies(2023)Astronomaly at Scale: Searching for Anomalies Amongst 4 Million Galaxies(2023)Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys(2023)Galaxy mergers in Subaru HSC-SSP: A deep representation learning approach for identification, and the role of environment on merger incidence(2023)Many other works use Zoobot indirectly via theGalaxy Zoo DECaLScatalog (and now via the newGalaxy Zoo DESIcatalog).

zoobus

Vincent Wen’s Personal Common Packagespagination, web pagination classlockrun, running command in cronjob avoid calling mutiple times by cronsystemsnapshoot, system snapshoot for CPU, Memory, load average

zoochory

a python module as pypi deployment demonstration

zoocli

UNKNOWN

zoo-cmd

I hope I can operate zookeeper path like localfile systemINSTALLinstall from pypi:pip install zoo_cmdinstall from source:git clone [email protected]:liujinliu/zoo_cmd.git cd zoo_cmd make install make uninstall ---UNDEPLOY METHODUSEAGE[liujinliu@liujinliu zoo_cmd]$ zk_cmd zoo#> conn 127.0.0.1:2181 ---连接zookeeper [email protected]:2181#> addauth digest zkljl 123456 ----acl策略设置(如果目标zk设置了acl的话) [email protected]:2181#> ls ----查看当前节点下的子节点 + zookeeper 2016-02-03 16:25:12 + test 2016-02-03 16:25:12 [email protected]:2181#> wc ----查看当前节点下的子节点个数 2 [email protected]:2181#> cd test ----进入子节点路径(支持跟绝对路径参数，类似"/test/docker"这种) /test [email protected]:2181#> ls ----查看当前节点下的子节点 + docker 2016-02-03 16:25:12 [email protected]:2181#> cd docker ----if only there is only one child, you can also use cdcd /test/docker [email protected]:2181#> ls + acb896d8 2016-02-03 16:25:12 [email protected]:2181#> touch tmp_ljl ----创建新节点 /test/docker/tmp_ljl [email protected]:2181#> set tmp_ljl csdn0 ----向节点写入内容(会覆盖原有内容) ZnodeStat(czxid=313532612647, ...... pzxid=313532612647) [email protected]:2181#> cat tmp_ljl ----查看节点内容 csdn0 [email protected]:2181#> pwd ----查看当前所处的绝对路径 /test/docker [email protected]:2181#> ls - tmp_ljl 2016-02-03 16:25:12 acb896d8 [email protected]:2181#> rm tmp_ljl ----删除节点 None [email protected]:2181#> cd .. ----回退(同时支持类似于"../.."这样的回退多层路径) /test [email protected]:2181#> ls + gary 2016-11-06 10:40:04 + zookeeperuse vi to edit the node:[email protected]:2181#> vi tmp_ljl

zoocut

No description available on PyPI.

zood

zoodzood 网页文档生成的 python 库, 可以将本地 Markdown 文件转为 Web 网页zood 的页面风格更倾向于纯文档内容而非博客, 您可利用 Github Pages 为每一个仓库部署单独的网页文档主题预览点击图片查看安装与使用pipinstallzood参见用户使用文档参考UItholman github-cornerslokeshdhakar lightbox2

zoodle

command-line Moodle query tool

zoodumper

zoodumperSometimes you need to make a fast dump of zookeeper tree and store it on other host, for example when moving from one virtual machine to another. Hope these small utility could help.Tested on python 3.6, kazoo 2.2.1Installation:A tool can be easily installed using pip:pip install zoodumperDump data from zookeeperzoodumper dump [OPTIONS]Dumps zookeeper nodes and their values to json file.Options: -s, --server TEXT zookeeper host [required] -p, --port INTEGER zookeeper port, by default it is 2181 -b, --branch TEXT root branch for reading, default behaviour is to read everything from root -e, --exclude TEXT comma-separated list of branches to exclude, default behaviour is not to exclude branchesExample of usage:zoodumper dump -s testsrv -p 2181 -b /startbranch -e /branch/subbranch,/branch/not/export/me/pleaseLoad data from dump to zookeeperzoodumper load [OPTIONS]Loads zookeeper nodes and their values from dump file. If node exists - it's value will be updated, if doesn't exist - will be created.Usage: Options:-s, --server TEXT zookeeper host [required]-p, --port INTEGER zookeeper port, by default it is 2181-f, --dump_file TEXT dump file, created when executed dump command [required]Example of usage:zoodumper load -f testsrv.zk.json -s testsrv -p 2181

zoo-framework

READMEProject descriptionZoo Framework is a simple and quick multi-threaded framework.InstallationYou may install this software from your distribution packages, or through pip:pip install zooConfigurationCreate a simple objectzfc --create simple_objectOther CommandsCreate a demo threadzfc --thread demo

zoofs

🐾 zoofs ( Zoo Feature Selection )zoofsis a Python library for performing feature selection using a variety of nature inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics based to Evolutionary. It's an easy to use, flexible and powerful tool to reduce your feature size.🌟 Like this Project? Give us a star !📘 Documentationhttps://jaswinder9051998.github.io/zoofs/🔗 Whats new in V0.1.24pass kwargs through objective functionimproved logger for resultsadded harris hawk algorithmnow you can passtimeoutas a parameter to stop operation after the given number of second(s). An amazing alternative to passing number of iterationsFeature score hashing of visited feature sets to increase the overall performance🛠 InstallationUsing pipUse the package manager to install zoofs.pipinstallzoofs📜 Available AlgorithmsAlgorithm NameClass NameDescriptionReferences doiParticle Swarm AlgorithmParticleSwarmOptimizationUtilizes swarm behaviourhttps://doi.org/10.1007/978-3-319-13563-2_51Grey Wolf AlgorithmGreyWolfOptimizationUtilizes wolf hunting behaviourhttps://doi.org/10.1016/j.neucom.2015.06.083Dragon Fly AlgorithmDragonFlyOptimizationUtilizes dragonfly swarm behaviourhttps://doi.org/10.1016/j.knosys.2020.106131Harris Hawk AlgorithmHarrisHawkOptimizationUtilizes hawk hunting behaviourhttps://link.springer.com/chapter/10.1007/978-981-32-9990-0_12Genetic Algorithm AlgorithmGeneticOptimizationUtilizes genetic mutation behaviourhttps://doi.org/10.1109/ICDAR.2001.953980Gravitational AlgorithmGravitationalOptimizationUtilizes newtons gravitational behaviourhttps://doi.org/10.1109/ICASSP.2011.5946916More algos soon, stay tuned ![Try It Now?]⚡️ UsageDefine your own objective function for optimization !Classification Examplefromsklearn.metricsimportlog_loss# define your own objective function, make sure the function receives four parameters,# fit your model and return the objective value !defobjective_function_topass(model,X_train,y_train,X_valid,y_valid):model.fit(X_train,y_train)P=log_loss(y_valid,model.predict_proba(X_valid))returnP# import an algorithm !fromzoofsimportParticleSwarmOptimization# create object of algorithmalgo_object=ParticleSwarmOptimization(objective_function_topass,n_iteration=20,population_size=20,minimize=True)importlightgbmaslgblgb_model=lgb.LGBMClassifier()# fit the algorithmalgo_object.fit(lgb_model,X_train,y_train,X_valid,y_valid,verbose=True)#plot your resultsalgo_object.plot_history()Regression Examplefromsklearn.metricsimportmean_squared_error# define your own objective function, make sure the function receives four parameters,# fit your model and return the objective value !defobjective_function_topass(model,X_train,y_train,X_valid,y_valid):model.fit(X_train,y_train)P=mean_squared_error(y_valid,model.predict(X_valid))returnP# import an algorithm !fromzoofsimportParticleSwarmOptimization# create object of algorithmalgo_object=ParticleSwarmOptimization(objective_function_topass,n_iteration=20,population_size=20,minimize=True)importlightgbmaslgblgb_model=lgb.LGBMRegressor()# fit the algorithmalgo_object.fit(lgb_model,X_train,y_train,X_valid,y_valid,verbose=True)#plot your resultsalgo_object.plot_history()Suggestions for UsageAs available algorithms are wrapper algos, it is better to use ml models that build quicker, e.g lightgbm, catboost.Take sufficient amount for 'population_size' , as this will determine the extent of exploration and exploitation of the algo.Ensure that your ml model has its hyperparamters optimized before passing it to zoofs algos.objective score plotAlgorithmsParticle Swarm AlgorithmIn computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.class zoofs.ParticleSwarmOptimization(objective_function,n_iteration=50,population_size=50,minimize=True,c1=2,c2=2,w=0.9)Parametersobjective_function: user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.The function must return a value, that needs to be minimized/maximized.n_iteration: int, default=1000Number of time the algorithm will runtimeout: int = NoneStop operation after the given number of second(s). If this argument is set to None, the operation is executed without time limitation and n_iteration is followedpopulation_size: int, default=50Total size of the populationminimize: bool, default=TrueDefines if the objective value is to be maximized or minimizedc1: float, default=2.0first acceleration coefficient of particle swarmc2: float, default=2.0second acceleration coefficient of particle swarmw: float, default=0.9weight parameterAttributesbest_feature_list: array-likeFinal best set of featuresMethodsMethodsClass NamefitRun the algorithmplot_historyPlot results achieved across iterationfit(model,X_train, y_train, X_test, y_test,verbose=True)Parametersmodel:machine learning model's objectX_train: pandas.core.frame.DataFrame of shape (n_samples, n_features)Training input samples to be used for machine learning modely_train: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The target values (class labels in classification, real numbers in regression).X_valid: pandas.core.frame.DataFrame of shape (n_samples, n_features)Validation input samplesy_valid: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The Validation target values .verbose: bool,default=TruePrint results for iterationsReturnsbest_feature_list: array-likeFinal best set of featuresplot_history()Plot results across iterationsExamplefromsklearn.metricsimportlog_loss# define your own objective function, make sure the function receives four parameters,# fit your model and return the objective value !defobjective_function_topass(model,X_train,y_train,X_valid,y_valid):model.fit(X_train,y_train)P=log_loss(y_valid,model.predict_proba(X_valid))returnP# import an algorithm !fromzoofsimportParticleSwarmOptimization# create object of algorithmalgo_object=ParticleSwarmOptimization(objective_function_topass,n_iteration=20,population_size=20,minimize=True,c1=2,c2=2,w=0.9)importlightgbmaslgblgb_model=lgb.LGBMClassifier()# fit the algorithmalgo_object.fit(lgb_model,X_train,y_train,X_valid,y_valid,verbose=True)#plot your resultsalgo_object.plot_history()Grey Wolf AlgorithmThe Grey Wolf Optimizer (GWO) mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented to perform optimization.class zoofs.GreyWolfOptimization(objective_function,n_iteration=50,population_size=50,minimize=True)Parametersobjective_function: user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.The function must return a value, that needs to be minimized/maximized.n_iteration: int, default=50Number of time the algorithm will runtimeout: int = NoneStop operation after the given number of second(s). If this argument is set to None, the operation is executed without time limitation and n_iteration is followedpopulation_size: int, default=50Total size of the populationmethod: {1, 2}, default=1Choose the between the two methods of grey wolf optimizationminimize: bool, default=TrueDefines if the objective value is to be maximized or minimizedAttributesbest_feature_list: array-likeFinal best set of featuresMethodsMethodsClass NamefitRun the algorithmplot_historyPlot results achieved across iterationfit(model,X_train,y_train,X_valid,y_valid,method=1,verbose=True)Parametersmodel:machine learning model's objectX_train: pandas.core.frame.DataFrame of shape (n_samples, n_features)Training input samples to be used for machine learning modely_train: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The target values (class labels in classification, real numbers in regression).X_valid: pandas.core.frame.DataFrame of shape (n_samples, n_features)Validation input samplesy_valid: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The Validation target values .verbose: bool,default=TruePrint results for iterationsReturnsbest_feature_list: array-likeFinal best set of featuresplot_history()Plot results across iterationsExamplefromsklearn.metricsimportlog_loss# define your own objective function, make sure the function receives four parameters,# fit your model and return the objective value !defobjective_function_topass(model,X_train,y_train,X_valid,y_valid):model.fit(X_train,y_train)P=log_loss(y_valid,model.predict_proba(X_valid))returnP# import an algorithm !fromzoofsimportGreyWolfOptimization# create object of algorithmalgo_object=GreyWolfOptimization(objective_function_topass,n_iteration=20,method=1,population_size=20,minimize=True)importlightgbmaslgblgb_model=lgb.LGBMClassifier()# fit the algorithmalgo_object.fit(lgb_model,X_train,y_train,X_valid,y_valid,verbose=True)#plot your resultsalgo_object.plot_history()Dragon Fly AlgorithmThe main inspiration of the Dragonfly Algorithm (DA) algorithm originates from static and dynamic swarming behaviours. These two swarming behaviours are very similar to the two main phases of optimization using meta-heuristics: exploration and exploitation. Dragonflies create sub swarms and fly over different areas in a static swarm, which is the main objective of the exploration phase. In the static swarm, however, dragonflies fly in bigger swarms and along one direction, which is favourable in the exploitation phase.class zoofs.DragonFlyOptimization(objective_function,n_iteration=50,population_size=50,minimize=True)Parametersobjective_function: user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.The function must return a value, that needs to be minimized/maximized.n_iteration: int, default=50Number of time the algorithm will runtimeout: int = NoneStop operation after the given number of second(s). If this argument is set to None, the operation is executed without time limitation and n_iteration is followedpopulation_size: int, default=50Total size of the populationmethod: {'linear','random','quadraic','sinusoidal'}, default='sinusoidal'Choose the between the three methods of Dragon Fly optimizationminimize: bool, default=TrueDefines if the objective value is to be maximized or minimizedAttributesbest_feature_list: array-likeFinal best set of featuresMethodsMethodsClass NamefitRun the algorithmplot_historyPlot results achieved across iterationfit(model,X_train,y_train,X_valid,y_valid,method='sinusoidal',verbose=True)Parametersmodel:machine learning model's objectX_train: pandas.core.frame.DataFrame of shape (n_samples, n_features)Training input samples to be used for machine learning modely_train: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The target values (class labels in classification, real numbers in regression).X_valid: pandas.core.frame.DataFrame of shape (n_samples, n_features)Validation input samplesy_valid: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The Validation target values .verbose: bool,default=TruePrint results for iterationsReturnsbest_feature_list: array-likeFinal best set of featuresplot_history()Plot results across iterationsExamplefromsklearn.metricsimportlog_loss# define your own objective function, make sure the function receives four parameters,# fit your model and return the objective value !defobjective_function_topass(model,X_train,y_train,X_valid,y_valid):model.fit(X_train,y_train)P=log_loss(y_valid,model.predict_proba(X_valid))returnP# import an algorithm !fromzoofsimportDragonFlyOptimization# create object of algorithmalgo_object=DragonFlyOptimization(objective_function_topass,n_iteration=20,method='sinusoidal',population_size=20,minimize=True)importlightgbmaslgblgb_model=lgb.LGBMClassifier()# fit the algorithmalgo_object.fit(lgb_model,X_train,y_train,X_valid,y_valid,verbose=True)#plot your resultsalgo_object.plot_history()Harris Hawk OptimizationHHO is a popular swarm-based, gradient-free optimization algorithm with several active and time-varying phases of exploration and exploitation. This algorithm initially published by the prestigious Journal of Future Generation Computer Systems (FGCS) in 2019, and from the first day, it has gained increasing attention among researchers due to its flexible structure, high performance, and high-quality results. The main logic of the HHO method is designed based on the cooperative behaviour and chasing styles of Harris' hawks in nature called "surprise pounce". Currently, there are many suggestions about how to enhance the functionality of HHO, and there are also several enhanced variants of the HHO in the leading Elsevier and IEEE transaction journals.class zoofs.HarrisHawkOptimization(objective_function,n_iteration=50,population_size=50,minimize=True,beta=0.5)Parametersobjective_function: user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.The function must return a value, that needs to be minimized/maximized.n_iteration: int, default=1000Number of time the algorithm will runtimeout: int = NoneStop operation after the given number of second(s). If this argument is set to None, the operation is executed without time limitation and n_iteration is followedpopulation_size: int, default=50Total size of the populationminimize: bool, default=TrueDefines if the objective value is to be maximized or minimizedbeta: float, default=0.5value for levy random walkAttributesbest_feature_list: array-likeFinal best set of featuresMethodsMethodsClass NamefitRun the algorithmplot_historyPlot results achieved across iterationfit(model,X_train, y_train, X_test, y_test,verbose=True)Parametersmodel:machine learning model's objectX_train: pandas.core.frame.DataFrame of shape (n_samples, n_features)Training input samples to be used for machine learning modely_train: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The target values (class labels in classification, real numbers in regression).X_valid: pandas.core.frame.DataFrame of shape (n_samples, n_features)Validation input samplesy_valid: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The Validation target values .verbose: bool,default=TruePrint results for iterationsReturnsbest_feature_list: array-likeFinal best set of featuresplot_history()Plot results across iterationsExamplefromsklearn.metricsimportlog_loss# define your own objective function, make sure the function receives four parameters,# fit your model and return the objective value !defobjective_function_topass(model,X_train,y_train,X_valid,y_valid):model.fit(X_train,y_train)P=log_loss(y_valid,model.predict_proba(X_valid))returnP# import an algorithm !fromzoofsimportHarrisHawkOptimization# create object of algorithmalgo_object=HarrisHawkOptimization(objective_function_topass,n_iteration=20,population_size=20,minimize=True)importlightgbmaslgblgb_model=lgb.LGBMClassifier()# fit the algorithmalgo_object.fit(lgb_model,X_train,y_train,X_valid,y_valid,verbose=True)#plot your resultsalgo_object.plot_history()Genetic AlgorithmIn computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, automatically solve sudoku puzzles, hyperparameter optimization, etc.class zoofs.GeneticOptimization(objective_function,n_iteration=20,population_size=20,selective_pressure=2,elitism=2,mutation_rate=0.05,minimize=True)Parametersobjective_function: user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.The function must return a value, that needs to be minimized/maximized.n_iteration: int, default=50Number of time the algorithm will runtimeout: int = NoneStop operation after the given number of second(s). If this argument is set to None, the operation is executed without time limitation and n_iteration is followedpopulation_size: int, default=50Total size of the populationselective_pressure: int, default=2measure of reproductive opportunities for each organism in the populationelitism: int, default=2number of top individuals to be considered as elitesmutation_rate: float, default=0.05rate of mutation in the population's geneminimize: bool, default=TrueDefines if the objective value is to be maximized or minimizedAttributesbest_feature_list: array-likeFinal best set of featuresMethodsMethodsClass NamefitRun the algorithmplot_historyPlot results achieved across iterationfit(model,X_train,y_train,X_valid,y_valid,verbose=True)Parametersmodel:machine learning model's objectX_train: pandas.core.frame.DataFrame of shape (n_samples, n_features)Training input samples to be used for machine learning modely_train: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The target values (class labels in classification, real numbers in regression).X_valid: pandas.core.frame.DataFrame of shape (n_samples, n_features)Validation input samplesy_valid: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The Validation target values .verbose: bool,default=TruePrint results for iterationsReturnsbest_feature_list: array-likeFinal best set of featuresplot_history()Plot results across iterationsExamplefromsklearn.metricsimportlog_loss# define your own objective function, make sure the function receives four parameters,# fit your model and return the objective value !defobjective_function_topass(model,X_train,y_train,X_valid,y_valid):model.fit(X_train,y_train)P=log_loss(y_valid,model.predict_proba(X_valid))returnP# import an algorithm !fromzoofsimportGeneticOptimization# create object of algorithmalgo_object=GeneticOptimization(objective_function_topass,n_iteration=20,population_size=20,selective_pressure=2,elitism=2,mutation_rate=0.05,minimize=True)importlightgbmaslgblgb_model=lgb.LGBMClassifier()# fit the algorithmalgo_object.fit(lgb_model,X_train,y_train,X_valid,y_valid,verbose=True)#plot your resultsalgo_object.plot_history()Gravitational AlgorithmGravitational Algorithm is based on the law of gravity and mass interactions is introduced. In the algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion.class zoofs.GravitationalOptimization(self,objective_function,n_iteration=50,population_size=50,g0=100,eps=0.5,minimize=True)Parametersobjective_function: user made function of the signature 'func(model,X_train,y_train,X_test,y_test)'.The function must return a value, that needs to be minimized/maximized.n_iteration: int, default=50Number of time the algorithm will runtimeout: int = NoneStop operation after the given number of second(s). If this argument is set to None, the operation is executed without time limitation and n_iteration is followedpopulation_size: int, default=50Total size of the populationg0: float, default=100gravitational strength constanteps: float, default=0.5distance constantminimize: bool, default=TrueDefines if the objective value is to be maximized or minimizedAttributesbest_feature_list: array-likeFinal best set of featuresMethodsMethodsClass NamefitRun the algorithmplot_historyPlot results achieved across iterationfit(model,X_train,y_train,X_valid,y_valid,verbose=True)Parametersmodel:machine learning model's objectX_train: pandas.core.frame.DataFrame of shape (n_samples, n_features)Training input samples to be used for machine learning modely_train: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The target values (class labels in classification, real numbers in regression).X_valid: pandas.core.frame.DataFrame of shape (n_samples, n_features)Validation input samplesy_valid: pandas.core.frame.DataFrame or pandas.core.series.Series of shape (n_samples)The Validation target values .verbose: bool,default=TruePrint results for iterationsReturnsbest_feature_list: array-likeFinal best set of featuresplot_history()Plot results across iterationsExamplefromsklearn.metricsimportlog_loss# define your own objective function, make sure the function receives four parameters,# fit your model and return the objective value !defobjective_function_topass(model,X_train,y_train,X_valid,y_valid):model.fit(X_train,y_train)P=log_loss(y_valid,model.predict_proba(X_valid))returnP# import an algorithm !fromzoofsimportGravitationalOptimization# create object of algorithmalgo_object=GravitationalOptimization(objective_function_topass,n_iteration=50,population_size=50,g0=100,eps=0.5,minimize=True)importlightgbmaslgblgb_model=lgb.LGBMClassifier()# fit the algorithmalgo_object.fit(lgb_model,X_train,y_train,X_valid,y_valid,verbose=True)#plot your resultsalgo_object.plot_history()SupportzoofsThe development ofzoofsrelies completely on contributions.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.First roll out18,08,2021Licenseapache-2.0

zookeeper

ZookeeperA small library for configuring modular applications.Installationpip install zookeeperComponentsThe fundamental building blocks of Zookeeper are components. The@componentdecorator is used to turn classes into components. These component classes can have configurable fields, which are declared with theFieldconstructor and class-level type annotations. Fields can be created with or without default values. Components can also be nested, withComponentFields, such that child componenents can access the field values defined on their parents.For example:fromzookeeperimportcomponent@componentclassChildComponent:a:int=Field()# An `int` field with no default setb:str=Field("foo")# A `str` field with default value `"foo"`@componentclassParentComponent:a:int=Field()# The same `int` field as the childchild:ChildComponent=ComponentField()# A nested component field, of type `ChildComponent`After instantiation, components can be 'configured' with a configuration dictionary, containing values for a tree of nested fields. This process automatically injects the correct values into each field.If a child sub-component declares a field which already exists in some containing ancestor component, then it will pick up the value that's set on the parent, unless a 'scoped' value is set on the child.For example:from zookeeper import configure p = ParentComponent() configure( p, { "a": 5, "child.a": 4, } ) >>> 'ChildComponent' is the only concrete component class that satisfies the type >>> of the annotated parameter 'ParentComponent.child'. Using an instance of this >>> class by default. print(p) >>> ParentComponent( >>> a = 5, >>> child = ChildComponent( >>> a = 4, >>> b = "foo" >>> ) >>> )Tasks and the CLIThe@taskdecorator is used to define Zookeeper tasks and can be applied to any class that implements an argument-lessrunmethod. Such tasks can be run through the Zookeeper CLI, with parameter values passed in through CLI arguments (configureis implicitly called).For example:fromzookeeperimportcli,task@taskclassUseChildA:parent:ParentComponent=ComponentField()defrun(self):print(self.parent.child.a)@taskclassUseParentA(UseChildA):defrun(self):print(self.parent.a)if__name__=="__main__":cli()Running the above file then gives a nice CLI interface:python test.py use_child_a >>> ValueError: No configuration value found for annotated parameter 'UseChildA.parent.a' of type 'int'. python test.py use_child_a a=5 >>> 5 python test.py use_child_a a=5 child.a=3 >>> 3 python test.py use_parent_a a=5 child.a=3 >>> 5Using Zookeeper to define Larq or Keras experimentsSeeexamples/larq_experiment.pyfor an example of how to use Zookeeper to define all the necessary components (dataset, preprocessing, and model) of a Larq experiment: training a BinaryNet on MNIST. This example can be easily adapted to other Larq or Keras models and other datasets.

zookeeper-healthcheck

zookeeper-healthcheckA simple healthcheck wrapper to monitor ZooKeeper.ZooKeeper Healthcheck is a simple server that provides a singular API endpoint to determine the health of a ZooKeeper instance. This can be used to alert or take action on unhealthy ZooKeeper instances.The service checks the health by sendingnetcatcommands as well as checking that ZooKeeper is in a desired mode. It utilizes the commandsecho ruok | nc zookeeper-host zookeeper-portandecho stat | nc zookeeper-host zookeeper-port | grep Modeto do so.By default, the root endpoint/will return200 OKhealthy if ZooKeeper respondsimokand is in modeleaderorfollower. It will return503 Service Unavailableif ZooKeeper does not respond withimokor if its in an undesired mode (by defaultstandalone).UsageZooKeeper Healthcheck can be installed viapip. Bothpythonandpipare required, as well asecho,ncandgrep.Command-LineInstallzookeeper-healthcheckviapip:pipinstallzookeeper-healthcheckTo start the healthcheck server, run:zookeeper-healthcheckThe server will now be running onlocalhost:12181.ConfigurationZooKeeper Healthcheck can be configured via command-line arguments or by environment variables.PortThe port for thezookeeper-healthcheckAPI.UsageValueEnvironment VariableHEALTHCHECK_PORTCommand-Line Argument--portDefault Value12181ZooKeeper HostThe host of the ZooKeeper instance to run the health check against. This is used withnc.UsageValueEnvironment VariableHEALTHCHECK_ZOOKEEPER_HOSTCommand-Line Argument--zookeeper-hostDefault ValuelocalhostZooKeeper PortThe port of the ZooKeeper instance to run the health check against. This is used withnc.UsageValueEnvironment VariableHEALTHCHECK_ZOOKEEPER_PORTCommand-Line Argument--zookeeper-portDefault Value2181Healthy ModesA comma-separated list of ZooKeeper modes to be marked as healthy. Any modes not in this list will mark ZooKeeper as unhealthy.UsageValueEnvironment VariableHEALTHCHECK_HEALTHY_MODESCommand-Line Argument--healthy-modesDefault Valueleader,followerValid Valuesleader,follower,standaloneLog LevelThe level of logs to be shown by the application.UsageValueEnvironment VariableHEALTHCHECK_LOG_LEVELCommand-Line Argument--log-levelDefault ValueINFOValid ValuesDEBUG,INFO,WARNING,ERRORAll healthy responses are logged atINFO. Unhealthy responses are logged atWARNING. Any unexpected errors are logged atERROR.LicenseCopyright (c) 2019 Shawn Seymour.Licensed under theApache 2.0 license.

zookeeper_monitor

Module lets you call ZooKeeper commands - four letters commands over TCP -https://zookeeper.apache.org/doc/r3.1.2/zookeeperAdmin.html#sc_zkCommands. It also has built-in web monitor. Based on Tornado, compatibile with Python 2.7.x, 3.x and above. It doesn’t require zookeeper, nor zookeeper’s headers (since it doesn’t utilize zkpython).InstallationIt can be installed from pypi or directly from git repository.pipinstallzookeeper_monitor#orgitclonehttps://github.com/kwarunek/zookeeper_monitor.gitcdzookeeper_monitor/pythonsetup.pyinstallUsageExample:[email protected]_coroutine()host=zk.Host('zookeeper.addr.ip',2181)# you can run each command as a coroutine example:# get srvr datasrvr_data=yieldhost.srvr()# get stat datastat_data=yieldhost.stat()# get server stateruok=yieldhost.ruok()# stop zookeeperyieldhost.kill()You can wrap it to sync code if you are not using tornadofromtornado.ioloopimportIOLoopIOLoop.instance().run_sync(some_coroutine)Web monitorTo run web monitor you need to provide configuration, if you don’t, it will usedlocalhost:2181by default.python-mzookeeper_monitor.web# with configuration filepython-mzookeeper_monitor.web-c/somepath/cluster.json# to see available optionspython-mzookeeper_monitor.web--helpNext you navigate tohttp://127.0.0.1:8080/(or whatever you specified).ConfigurationDefining clustercluster.json(json or yaml){"name":"brand-new-zookeeper-cluster","hosts":[{"addr":"10.1.15.1","port":2181,"dc":"eu-west"},{"addr":"10.2.31.2","port":2181,"dc":"us-east"},{"addr":"10.1.12.3","port":2181,"dc":"eu-west"}]}name (string) - cluster name.hosts (list) - List of hosts running ZooKeeper connected in cluster:addr (string): IP or domain, mandatoryport (int): ZooKeeper port, optional, default 2181dc (string): datacenter/location name, optionalScreenshotsCluster viewNode stat viewLicenseMITTODOmore testsmore stats in webmonitorparse zookeeper versionnew commands in zookeeper 3.3 and 3.4parse output of dump, reqsChangelog0.3.0 - implementedmntr, credits toRobb Wagoner0.2.5 - implementedwith_timeout, handlers get_template, py3.3 gen.Task in Host, css colors0.2.4 - separate getter template/static dir0.2.3 - fix import in py3 web0.2.2 - clean ups: pylint, README, classifiers0.2.1 - fix package, fix tests0.2.0 - implement more commands, updated docs0.1.2 -release- pypi0.1.1 - clean up0.1.0 - public standalone0.0.3 - 0.0.9 - refactor, tests0.0.2 - working draft0.0.1 - initial concept

zool

zoolA cli for reviewing PRs and zuul's progresspip install .orpython -m zool$ zool --help usage: __main__.py [-h] -c COLLECTION [-o ORGANIZATION] [-u USERNAME] [-t TOKEN] [-l {debug,info,warning,error,critical}] [-f LOGFILE] [-zh ZUUL_HOST] [-zt ZUUL_TENANT] [-gh GH_HOST] [-gha GH_API_HOST] [-p PBAR_WIDTH] optional arguments: -h, --help show this help message and exit -c COLLECTION, --collection COLLECTION The collection name, used as the githubh repo (ie ansible.netcommon) (default: None) -o ORGANIZATION, --organization ORGANIZATION The Github organization (default: ansible-collections) -u USERNAME, --username USERNAME The Github username for the token (default: cidrblock) -t TOKEN, --token TOKEN Github personal access token (env var GH_TOKEN) (default: None) -l {debug,info,warning,error,critical}, --log-level {debug,info,warning,error,critical} Set the logging level (default: info) -f LOGFILE, --log-file LOGFILE Log file location (default: /tmp/zool.log) -zh ZUUL_HOST, --zuul-host ZUUL_HOST The zuul hostname (default: dashboard.zuul.ansible.com) -zt ZUUL_TENANT, --zuul-tenant ZUUL_TENANT The zuul tenant (default: ansible) -gh GH_HOST, --github_host GH_HOST The github host (browser) (default: github.com) -gha GH_API_HOST, --github_api_host GH_API_HOST The github host (api) (default: api.github.com) -p PBAR_WIDTH, --pbar-width PBAR_WIDTH The width of the progress bars (default: 10)

zoolander

UNKNOWN

zoology-lin

Failed to fetch description. HTTP Status Code: 404

zoom

UNKNOWN

zoomai

this is my first projectrm -r build dist *.egg-info آگر نام رو تغییر بدی دوباره باید بیلد رو بسازی

zoomaker

Zoomaker - Friendly house keeping for your AI model zoo and related resources.Zoomaker is a command-line tool that helps install AI models, git repositories and run scripts.single source of truth: all resources are neatly defined in thezoo.yamlfilefreeze versions: know exactly which revision of a resources is installed at any timeonly download once: optimize bandwidth and cache your models locallyoptimize disk usage: downloaded models are symlinked to the installation folder (small files <5MB are duplicate)😻 TL;DRInstall Zoomakerpip install zoomakerDefine your resources in thezoo.yamlfileRunzoomaker installto install them (on Windows:zoomaker install --no-symlinks, seehintsbelow)📦 Installationpipinstallzoomaker🦁 zoo.yaml ExamplesExample of thezoo.yamlof a Stable Diffusion project with theAutomatic1111image generator:name:my-automatic1111-model-zooversion:1.0description:Lorem ipsumauthor:your nameresources:image_generator:-name:automatic1111src:https://github.com/AUTOMATIC1111/stable-diffusion-webui.gittype:gitrevision:22bcc7be428c94e9408f589966c2040187245d81install_to:./models:-name:v2-1_768-ema-prunedsrc:stabilityai/stable-diffusion-2-1/v2-1_768-ema-pruned.safetensorstype:huggingfaceinstall_to:./stable-diffusion-webui/models/Stable-diffusion/`zoo.yaml` example longname:my-automatic1111-model-zooversion:1.0description:Lorem ipsumauthor:your namealiases:image_generator:&image_generator./models:&models./stable-diffusion-webui/models/Stable-diffusion/controlnet:&controlnet./stable-diffusion-webui/models/ControlNet/embeddings:&embeddings./stable-diffusion-webui/embeddings/extensions:&extensions./stable-diffusion-webui/extensions/resources:image_generator:-name:automatic1111src:https://github.com/AUTOMATIC1111/stable-diffusion-webui.gittype:gitrevision:22bcc7be428c94e9408f589966c2040187245d81install_to:*image_generatormodels:-name:v1-5-pruned-emaonlysrc:runwayml/stable-diffusion-v1-5/v1-5-pruned-emaonly.safetensorstype:huggingfaceinstall_to:*modelscontrolnet:-name:control_sd15_cannysrc:lllyasviel/ControlNet/models/control_sd15_canny.pthtype:huggingfaceinstall_to:*controlnetembeddings:-name:midjourney-stylesrc:sd-concepts-library/midjourney-style/learned_embeds.bintype:huggingfaceinstall_to:*embeddingsrename_to:midjourney-style.bin-name:moebiussrc:sd-concepts-library/moebius/learned_embeds.bintype:huggingfaceinstall_to:*embeddingsrename_to:moebius.binextensions:-name:sd-webui-tunnelssrc:https://github.com/Bing-su/sd-webui-tunnels.gittype:gitinstall_to:*extensions`zoo.yaml` with script snippetsHere are a few examples of how to run scripts snippets from thezoo.yamlfile. For example for starting the Automatic1111's webui, you could setup snippets like these and then run them withzoomaker run start_webui. All scripts are run from the root of the project, please adjust the paths accordingly.scripts:start_webui:|cd .\stable-diffusion-webui && call webui.batscripts:start_webui:|conda activate automatic1111cd /home/$(whoami)/stable-diffusion-webui/./webui.sh --theme dark --xformers --no-half`zoo.yaml` with web downloadresources:models:-name:analog-diffusion-v1src:https://civitai.com/api/download/models/1344type:downloadinstall_to:./stable-diffusion-webui/models/Stable-diffusion/rename_to:analog-diffusion-v1.safetensorsPlease note: The resourcetype: downloadcan be seen as the last resort. Currently there is no caching or symlinking of web downloads. Recommended to avoid it :)🧮 zoo.yaml StructureTop level:name(mandatory)version,description,author,aliases(optional)resources(mandatory) :<group-name>:[](array of resources)scripts(optional) :<script-name>Resource:name,src,type,install_to(mandatory)rename_to(optional)revision(optional), if none is defined the latest version from the main branch is downloadedtypecan either begit,huggingfaceordownload🧞 Zoomaker CommandsAll commands are run from the root of the project, where also yourzoo.yamlfile is located.CommandActionzoomaker installInstalls resources as defined inzoo.yamlzoomaker run <script_name>Run CLI scripts as defined inzoo.yamlzoomaker --helpGet help using the Zoomaker CLIzoomaker --versionShow current Zoomaker versionzoomaker --no-symlinksDo not use symlinks for installing resources⚠️ Limitations on WindowsSymlinks are not widely supported on Windows, which limits the caching mechanism used by Zoomaker. To work around this limitation, you can disable symlinks by using the--no-symlinksflag with the install command:zoomakerinstall--no-symlinksThis will still use the cache directory for checking if files are already cached, but if not, they will be downloaded and duplicated directly to the installation directory, saving bandwidth but increasing disk usage. Alternatively, you can use theWindows Subsystem for Linux "WSL"(don't forget toenable developer mode) or run Zoomaker as an administrator to enable symlink support on Windows.🤗 Hugging Face Access TokenYou might be asked for aHugging Face Access Tokenduringzoomaker install. Some resources on Hugging Face require accepting the terms of use of the model. You can set your access token by running this command in a terminal. The commandhuggingface-cliis automatically shipped alongside zoomaker.huggingface-clilogin🙏 AcknowledgementsMost of the internal heavy lifting is done be thehuggingface_hub libraryby Hugging Face. Thanks!"Zoomaker Safari Hacker Cat" cover image by Alia Tasler, based on thisOpenMoji. Thanks!

zoom-api-helper

Zoom API HelperUtilities to interact with theZoom API v2Free software: MIT licenseDocumentation:https://zoom-api-helper.readthedocs.io.InstallationThe Zoom API Helper library is availableon PyPI, and can be installed withpip:$pipinstallzoom-api-helperYou’ll also need to create a Server-to-Server OAuth app as outlinedin the docs.FeaturesZoom API v2: List users, create meetings, and bulk create meetings.Support for aServer-to-Server OAuthflow.Local caching of access token retrieved from OAuth process.QuickstartStart by creating a helper client (ZoomAPI) to interact with the Zoom API:>>>fromzoom_api_helperimportZoomAPI>>>zoom=ZoomAPI('<CLIENT ID>','<CLIENT SECRET>',...# can also be specified via `ZOOM_ACCOUNT_ID` env variable...account_id='<ACCOUNT ID>')Retrieve a list of users viaZoomAPI.list_users():>>>zoom.list_users(){'page_count':3,'page_number':1,'page_size':300,'total_records':700,'users':[{'id':'-abc123','first_name':'Jon','last_name':'Doe','email':'[email protected]','timezone':'America/New_York',...},...]}Or, a mapping of each Zoom user’sEmailtoUser ID:>>>zoom.user_email_to_id(use_cache=True){'[email protected]':'-abc123','[email protected]':'-xyz321',...}Create an individual meeting viaZoomAPI.create_meeting():>>>zoom.create_meeting(topic='My Awesome Meeting'){'uuid':'T9SwnVWzQB2dD1zFQ7PxFA==','id':91894643201,'host_id':'...','host_email':'[email protected]','topic':'My Awesome Meeting','type':2,...}Tobulk createa list of meetings in a concurrent fashion, please see the section onBulk Create Meetingsbelow.Local StorageThis library uses a local storage for cache purposes, located under the user home directory at~/.zoom/cacheby default – though this location can be customized, via theCACHE_DIRenvironment variable.The format of the filenames containing cached data will look something similar to this:{{ Purpose }}_{{ Zoom Account ID }}_{{ Zoom Client ID }}.jsonCurrently, the helper library utilizes a local file cache for two purposes:Storing the access token retrieved fromthe OAuth step, so that the token only needs to be refreshed after~1 hour.Storing a cached mapping of Zoom User emails to User IDs, as generally the Zoom APIs only require the User ID’s.As otherwise, retrieving this mapping from the API can sometimes be expensive, especially for Zoom accounts that have a lot of Users (1000+).Bulk Create MeetingsIn order tobulk create meetings– for example, if you need to create 100+ meetings in a short span of time – use theZoomAPI’sbulk_create_meetings()method.This allows you to pass in an Excel (.xlsx) file containing the meetings to create, or else pass in therowswith the meeting info directly.ExampleSuppose you have an Excel file (meeting-info.xlsx) with the following data:Group NameZoom UsernameTopicMeeting DateMeeting TimeDuration HrDuration MinMeeting URLMeeting IDPasscodeA-BC:TEST:Sample Group [email protected] Meeting #1: Just an example10/26/253:30 PM130A-BC:TEST:Sample Group [email protected] Meeting #2: Here’s another one11/27/257:00 PM10A-BC:TEST:Sample Group [email protected] Meeting #3: This is the last for now9/29/259:00 PM115Then, here is a sample code that would allow you tobulk createthe specified meetings in the Zoom Account.Note: replace the credentials such as<CLIENT ID>below as needed.fromdatetimeimportdatetimefromzoom_api_helperimportZoomAPIfromzoom_api_helper.modelsimport*defmain():zoom=ZoomAPI('<CLIENT ID>','<CLIENT SECRET>','<ACCOUNT ID>')# (optional) column header to keyword argumentcol_name_to_kwarg={'Group Name':'agenda','Zoom Username':'host_email'}# (optional) predicate function to initially process the row datadefprocess_row(row:'RowType',dt_format='%Y-%m-%d%I:%M %p'):start_time=f"{row['Meeting Date'][:10]}{row['Meeting Time']}"row.update(start_time=datetime.strptime(start_time,dt_format),# Zoom expects the `duration` value in minutes.duration=int(row['Duration Hr'])*60+int(row['Duration Min']),)returnTrue# (optional) function to update row(s) with the API responsedefupdate_row(row:'RowType',resp:dict):row['Meeting URL']=resp['join_url']row['Meeting ID']=resp['id']row['Passcode']=resp['password']# create meetings with dry run enabled.zoom.bulk_create_meetings(col_name_to_kwarg,excel_file='./meeting-info.xlsx',default_timezone='America/New_York',process_row=process_row,update_row=update_row,# comment out below line to actually create the meetings.dry_run=True,)if__name__=='__main__':main()CreditsThis package was created withCookiecutterand thernag/cookiecutter-pypackageproject template.History0.1.1 (2022-09-07)Update docs.0.1.0 (2022-09-06)First release on PyPI.

zoomascii

No description available on PyPI.

zoom-audio-transcribe

zoom_audio_transcribeTranscribe zoom meetings audio locally.Transcribe zoom meetings audio locally, using.InstallSupported python version >=3.6. Supported OS, Linux. Support for Windows and OSX will come in second version.pipinstallzoom_audio_transcribeUsage$zoom_audio_transcribeOnce you are at the menu, select the appropiate meeting and let it run.Once the transcription is done the, file will be saved in the same folder as the zoom meetings recording. For linux users its `/home/$USER/Documents/Zoom/{meeting_which_was_selected}

zoom-auto-creator

No description available on PyPI.

zoom-background-changer

Zoom Background ChangerThis is a python script that will use OpenAI's GPT-3 API to generate a new background image for your Zoom meetings.It can be run as a cron task, manually, or as part of a workflow when you open Zoom!The script will generate a new background image based on the current date and weather, and overwrite you existing Zoom background.RequirementsOnly works on macOS (for now!)Python 3.9+OpenAI API KeyYou can get a free API key from OpenAIhere.You will need to create an account and generate an API key.You will need to add your API key to theOPENAI_API_KEYenvironment variable.Installationpipinstallzoom-background-changerUsageFirst you will need to set a custom background image in Zoom.You can do this by going toPreferences > Video > Virtual Background > Choose Virtual Background...and selecting an image.Then from the command line, run:zoom-background-changerPrompt TemplateCan be adjusted by creating a file called.zoom-background-changerin your$HOMEdirectory.This file should contain the following:{"prompt":"Today is {date} and the weather is {weather} in {city}.","city":"Boston"}Available Variables{date}: The current date{city}: The current city, set from thecitykey in the.zoom-background-changerfile. Defaults toBoston, MA. If set, the following extra variables will be available:{weather}: The current weather, fromhttps://wttr.in/{temperature}: The current temperature, fromhttps://wttr.in/Any other key/value pairs in the.zoom-background-changerfile will be available as variables in the prompt template, so get creative!If you would like to request a new functional variable similar tocity, please open an Issue or Pull Request!

zoombot

zoombotPython wrapper for Zoom Chatbot APIUsagefromzoombot.coreimportBot,MessageclassMyBot(Bot):asyncdefon_message(self,message:Message):awaitmessage.reply("Hello",f"You sent{message.content}")if__name__=="__main__":bot=MyBot(client_id="CLIENT_ID",client_secret="CLIENT_SECRET",bot_jid="BOT_JID",verification_token="VERIFICATION_TOKEN",)bot.run()

zoom-chat-anonymizer

Zoom Chat Anonymizer$zoom-chat-anonymizer--helpUsage:zoom-chat-anonymizer[OPTIONS]COMMAND[ARGS]...HelpfulscripttoprocessZoomchats. Options:--versionVersion--helpShowthismessageandexit. Commands:anonymize-zoom-chatsAnonymizeZoomchats.create-html-from-markdownCreateHTMLfilesfromthemarkdownfiles.Anonymize Zoom Chats$zoom-chat-anonymizeranonymize-zoom-chats--help Usage:zoom-chat-anonymizeranonymize-zoom-chats[OPTIONS][INPUT_FOLDER]AnonymizeZoomchats. Arguments:[INPUT_FOLDER]Thefolderwiththechatfiles.[default:.]Options:-o,--output-folderDIRECTORYThescriptwillwritetheanonymizedfilesinthisfolder.[default:out]-t,--tutorTEXTThetutors'names.Thescriptwillpreservethesenamesinthechatprotocol.-p,--pause-fileFILEAJSONfilewiththepausesmadeduringthelecture/tutorial.-s,--starting-timeTEXTThestartingtimeofthelecture/tutorial.[default:14:15]--helpShowthismessageandexit.Create HTML from Markdown$zoom-chat-anonymizercreate-html-from-markdown--help Usage:zoom-chat-anonymizercreate-html-from-markdown[OPTIONS]CreateHTMLfilesfromthemarkdownfiles. Options:--bib_fileFILE-i,--input_folderDIRECTORY--helpShowthismessageandexit.

zoom-client

Zoom ClientA Python client for interfacing with the Zoom API to perform various tasks.Requirements and DocumentationPython >= 3.6:https://www.python.org/downloads/Enable Zoom API key:https://zoom.us/developer/api/credentialZoom API documentation can be found at the following URL:https://marketplace.zoom.us/docs/api-reference/zoom-apiInstallation# from pypipipinstallzoom_client# from githubpipinstallgit+https://github.com/CUBoulder-OIT/zoom_client@main#egg=zoom_clientUsageEnsure requirements outlined above are completed.Provide necessary <bracketed> areas in examples/sample_config.json specific to your accountExample Usagefromzoom_client.controllerimportcontroller#open config file with api key/secret informationconfig_file=open(run_path+"/config/config.json")config_data=json.load(config_file)#create Zoom python clientzoom=controller.controller(config_data)zoom.users.get_current_users()zoom_user_counts=zoom.users.get_current_user_type_counts()Linting and TestingThis repo makes use ofBlackandBanditfor linting andPyTestfor testing. See below for an example of how to peform these checks manually.# assumes pwd as repo dir# black lintingblack.--check# bandit lintingbandit-r.-x./tests# pytest testingpytestDistribution Packagingpythonsetup.pysdistbdist_wheelNoticeAll trademarks, service marks and company names are the property of their respective owners.Reference in this site to any specific commercial product, process, or service, or the use of any trade, firm or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the University of Colorado.LicenseMIT - See LICENSE

zoomconnect-sdk

ZoomConnect Python SDKThis Python package provides a wrapper for theZoomConnect API.Installationpip install zoomconnect-sdkAuthenticationPlease visit thesignuppage to create an account and generate an API key.Example usagefromzoomconnect_sdk.clientimportClientc=Client(api_token='xxx-xxx-xxx-xxx',account_email='[email protected]')try:res=c.send_sms("0000000000","Welcome to ZoomConnect")exceptExceptionase:print(e)else:print(res)RequirementsPython 3.6+LicenseMIT

zoomdl

Failed to fetch description. HTTP Status Code: 404

zoomdl-2

ZoomDLSupportLike this project? Consider supporting me, for more awesome updatesBuy me a coffeeANNOUNCEMENT LOOKING FOR TESTERSMore and more, I face the challenge of testing. I code on my own Debian machine, and use a Windows 10 VM to compile the executable. But testing has become more and more a challenge, especially for new features.If you are anyhow interested in helping (there are various ways to!), go to the dedicated issue (#32), and comment there. The concept is still very young for me, sorry that.ZoomDLSupportANNOUNCEMENT LOOKING FOR TESTERSGoalAvailabilityInstallationLinux/OSXWindowsUsageCookies / SSO / Captcha / LoginAbout syntaxAbout quotes [IMPORTANT]Validity of urlsBuilding from sourcesLinuxWindowsRequirementsAcknowledgementsGoalConferences, meetings and presentations held on Zoom are often recorded and stored in the cloud, for a finite amount of time. The host can chose to make them available to download, but it is not mandatory.Nonetheless, I believe if you can view it, you can download it. This script makes it easy to download any video stored on the Zoom Cloud. You just need to provide a valid zoom record URL, and optionally a filename, and it will download the file.AvailabilityThe script was developed and tested under GNU/Linux (precisely, Debian 10). Thus, it should work for about any GNU/Linux distro out there, with common settings. You basically only need Python3 in your path.New from 2020.06.09There now exists an executable filezoomdl.exefor Windows. It was kinda tested under Windows 10. Because I never coded under Windows, I have very few tests, mostly empirical ones. Expect bugs! If you encounter a Windows-specific error, don't expect much support. If the error is related to the general logic of the program, report it and I'll do my best to fix it.InstallationLinux/OSXYou need to have a valid Python3 installation. Except from that, just download the scriptzoomdl(notzoomdl.exe) and run it like a normal binary. If you wish to make it available system-wide, you can copy it to/usr/local/bin/(or anywhere else available in your PATH). Then you can simply use it wherever you want.The following two commands make that easy. In a terminal, run:sudowgethttps://github.com/Battleman/zoomdl/releases/latest/download/zoomdl-O/usr/bin/zoomdl sudochmod+x/usr/bin/zoomdlYou will be prompted to enter your password (your computer password, not zoom). It's likely that you won't see anything as you type it, don't worry, itis normal(even for OSX)Once you have done that, you can use your terminal and type the commands normally.WindowsThis is still in betaGrab the dedicated binaryzoomdl.exe, and launch it using your command line. If you don't know how, visitthis wikihow. You may encounter warning from your anti-virus, you can ignore them (I'm not evil, pinky-promise). You probably don't need a Python3 installation, itshouldbe contained within the executable.If there is a domain in your url, make sure to include it, it's crucial.Usagezoomdl [-h] -u/--url 'url' [-f/--fname 'filename'] [-p/--password 'password'] [-c/--count-clips count] [-d/--filename-add-date] [--user-agent 'custom_user_agent]-u/--urlis mandatory, it represents the URL of the video-f/--fnameis optional, it is the name of the resulting filewithout extension. If nothing is provided, the default name given by Zoom will be used. Extension (.mp4,.mkv,... is automatic)-p/--passwordis too optional. Set it when your video has a password.-c/--count-clips: Sometimes, one URL can contain multiple clips. This tunes the number of clips that will be downloaded. Recordings with multiple clips seem to be quite rare, but do exist. The parametercountworks as follow:0 means: download all of them (starting from the current clip)1 means: download only the first/given clip> 1 means: download until you reach this number of clip (or the end)-d/--filename-add-datewill append the date of the recording to the filename.without effect if-fis specified--user-agent(no shorthand notation): lets you specify a custom User-Agent (only do that if you know what you're doing and why)--cookies(no shorthand notation): specify the path to a cookie jar file.Cookies / SSO / Captcha / LoginSome videos are protected with more than a password. You require an SSO, or to solve a captcha. Thecookiesoption allows you to perform all the steps in a browser, and then use the cookies to access the video. This functionality is similar to Youtube-dl's same option.Howto:(Only once, the first time) In your favourite browser (works for Firefox-based or Chrome-based), install a cookies-export extension. Cookies must some in theNetscape format. There are multiple extensions out there, chose your favourite. For exampleFirefox,Other Firefox,Chrome,Other ChromeWith the same browser, visit the video you want to download; pass all required verifications (SSO, captcha, login,...), until you are able to view the videoUsing the aformentioned extension, export your cookies. You need the cookies for the domain (.zoom.us), so export at least "cookies for this site", or "cookies for this domain", or whatever it's called.Save generated file somewhere (for example,Downloads/cookies.txt)When calling ZoomDL, use the option--cookies path/to/the/cookies.txtIf you want to download several videos who use the same login (like SSO), you only need to export the cookies onceAbout syntaxI see a lot of people who don't understand what the above means. Here is a short explanation:This is acommand, with multiple possibleparameters.Parameters usually have a short version (with one dash, like-u) and an equivalent long version (with two dashes, like--url); the short and long version are shown separated by a/; you must only use one of them.The parameters between square brackets are optional (like-f, that allows you to input a custom filename). The others (for the moment, only-u) are mandatory.The order of parameters don't matterthe-hparameter only displays a short help and commands explanationFor example, those are all valid commands (ofc by replacing the URLs):zoomdl -u 'https://my_url' -f "recording_from_monday" zoomdl --url 'https://my_url' zoomdl -p '$28fn2f8' --filename-add-date --filename "recording_from_tuesday" -u 'https://my_url' --user-agent "Windows 10 wonderful User-Agent" -v 3About quotes [IMPORTANT]The quotes are not mandatory, but if your filename/url/password/... contains reserved characters (\,&,$,%...), quotes are the way to go.Under Linux/OSX, it is strongly advised to usesingle quotes, because"4$g3.2"will replace$g3by nothing, while'4$g3.2'will leave the string as-is.Under Windows, Ithinkyou must use double quotes everywhere. Don't quote me on that.Validity of urlsThere are 3 type of valid urls.Those starting withhttps://zoom.us/rec/play/...Or, with a domain,https://X.zoom.us/rec/play/...whereXis a domain, something likeus02web,epfl,... or similar.Finally, governemantal urls:https://X.zoomgov.com/rec/play/...(same as above; X may be empty)Building from sourcesIf you wish to build from sources, here is a quick howto. First, you need to clone the repository and enter it with a terminal. Then:LinuxRun the command./devscript.sh compile. It basically installs all the dependencies with pip in a temporary directory, then zips it.WindowsInstallpyinstaller(usuallypip install -U pyinstaller)Run the commandwincompile.bat. It calls just callspyinstallerand cleans the generated folders and files, leaving only the exe file.RequirementsAll dependencies are bundled within the executable. This allows to make a standalone execution without need for external libraries.If you wish to build it yourself, seerequirements.txt. The most important requirement isrequests. Please seeacknowledgementsfor a note on that.AcknowledgementsThe folder executable containsrequests(and its dependencies), an awesome wrapper for HTTP(s) calls. Please check them out!

zoomdotpy

ZoomDotPyDesigned for use with a server-to-server oauth zoom developer appInstallpython3 -m pip install zoomdotpyBleeding Edge:pip install git+https://github.com/Sceptyre/python-zoom.git@develUsagefromzoomdotpyimportZoomClientzoom=ZoomClient('<API_KEY>','<API_SECRET>')# List phone deviceszoom.phones.devices.list_devices()# list phone siteszoom.phones.sites.list_sites()# List roomszoom.rooms.list_rooms()# Get specific roomzoom.rooms.get_room('abc123')

zoom-downloader

ZoomDLSupportLike this project? Consider supporting me, for more awesome updatesBuy me a coffeeANNOUNCEMENT LOOKING FOR TESTERSMore and more, I face the challenge of testing. I code on my own Debian machine, and use a Windows 10 VM to compile the executable. But testing has become more and more a challenge, especially for new features.If you are anyhow interested in helping (there are various ways to!), go to the dedicated issue (#32), and comment there. The concept is still very young for me, sorry that.ZoomDLSupportANNOUNCEMENT LOOKING FOR TESTERSGoalAvailabilityInstallationLinux/OSXWindowsUsageCookies / SSO / Captcha / LoginAbout syntaxAbout quotes [IMPORTANT]Validity of urlsBuilding from sourcesLinuxWindowsRequirementsAcknowledgementsGoalConferences, meetings and presentations held on Zoom are often recorded and stored in the cloud, for a finite amount of time. The host can chose to make them available to download, but it is not mandatory.Nonetheless, I believe if you can view it, you can download it. This script makes it easy to download any video stored on the Zoom Cloud. You just need to provide a valid zoom record URL, and optionally a filename, and it will download the file.AvailabilityThe script was developed and tested under GNU/Linux (precisely, Debian 10). Thus, it should work for about any GNU/Linux distro out there, with common settings. You basically only need Python3 in your path.New from 2020.06.09There now exists an executable filezoomdl.exefor Windows. It was kinda tested under Windows 10. Because I never coded under Windows, I have very few tests, mostly empirical ones. Expect bugs! If you encounter a Windows-specific error, don't expect much support. If the error is related to the general logic of the program, report it and I'll do my best to fix it.InstallationFirst, install python > 3.7. Then, runpip install zoom-downloader. This should work on all operating systems that support python.Usagezoomdl [-h] -u/--url 'url' [-f/--fname 'filename'] [-p/--password 'password'] [-c/--count-clips count] [-d/--filename-add-date] [--user-agent 'custom_user_agent'] [--save-chat (txt|srt)] [--chat-subtitle-dur number] [--save-transcript (txt|srt)] [--dump-pagemeta]-u/--urlis mandatory, it represents the URL of the video-f/--fnameis optional, it is the name of the resulting filewithout extension. If nothing is provided, the default name given by Zoom will be used. Extension (.mp4,.mkv,... is automatic)-p/--passwordis too optional. Set it when your video has a password.-c/--count-clips: Sometimes, one URL can contain multiple clips. This tunes the number of clips that will be downloaded. Recordings with multiple clips seem to be quite rare, but do exist. The parametercountworks as follow:0 means: download all of them (starting from the current clip)1 means: download only the first/given clip> 1 means: download until you reach this number of clip (or the end)-d/--filename-add-datewill append the date of the recording to the filename.without effect if-fis specified--user-agent(no shorthand notation): lets you specify a custom User-Agent (only do that if you know what you're doing and why)--cookies(no shorthand notation): specify the path to a cookie jar file.--save-chat(no shorthand notation): save chat messages in the meeting to either a plain-text file or.srtsubtitle file. Known issue for this function: #70--chat-subtitle-dur(no shorthand notation): set the duration in seconds that a chat message subtitle appears on the screen. The default value is 3 (seconds). Only works when you specify--save-chat srt.--save-transcript(no shorthand notation): save audio transcripts in the meeting to either a plain-text file or.srtsubtitle file.--dump-pagemeta(no shorthand notation): dump the page's meta data to a json file for further usages. Usually you do not need this.Cookies / SSO / Captcha / LoginSome videos are protected with more than a password. You require an SSO, or to solve a captcha. Thecookiesoption allows you to perform all the steps in a browser, and then use the cookies to access the video. This functionality is similar to Youtube-dl's same option.Howto:(Only once, the first time) In your favourite browser (works for Firefox-based or Chrome-based), install a cookies-export extension. Cookies must some in theNetscape format. There are multiple extensions out there, chose your favourite. For exampleFirefox,Other Firefox,Chrome,Other ChromeWith the same browser, visit the video you want to download; pass all required verifications (SSO, captcha, login,...), until you are able to view the videoUsing the aformentioned extension, export your cookies. You need the cookies for the domain (.zoom.us), so export at least "cookies for this site", or "cookies for this domain", or whatever it's called.Save generated file somewhere (for example,Downloads/cookies.txt)When calling ZoomDL, use the option--cookies path/to/the/cookies.txtIf you want to download several videos who use the same login (like SSO), you only need to export the cookies onceAbout syntaxI see a lot of people who don't understand what the above means. Here is a short explanation:This is acommand, with multiple possibleparameters.Parameters usually have a short version (with one dash, like-u) and an equivalent long version (with two dashes, like--url); the short and long version are shown separated by a/; you must only use one of them.The parameters between square brackets are optional (like-f, that allows you to input a custom filename). The others (for the moment, only-u) are mandatory.The order of parameters don't matterthe-hparameter only displays a short help and commands explanationFor example, those are all valid commands (ofc by replacing the URLs):zoomdl -u 'https://my_url' -f "recording_from_monday" zoomdl --url 'https://my_url' zoomdl -p '$28fn2f8' --filename-add-date --filename "recording_from_tuesday" -u 'https://my_url' --user-agent "Windows 10 wonderful User-Agent" -v 3About quotes [IMPORTANT]The quotes are not mandatory, but if your filename/url/password/... contains reserved characters (\,&,$,%...), quotes are the way to go.Under Linux/OSX, it is strongly advised to usesingle quotes, because"4$g3.2"will replace$g3by nothing, while'4$g3.2'will leave the string as-is.Under Windows, Ithinkyou must use double quotes everywhere. Don't quote me on that.Validity of urlsThere are 3 type of valid urls.Those starting withhttps://zoom.us/rec/play/...Or, with a domain,https://X.zoom.us/rec/play/...whereXis a domain, something likeus02web,epfl,... or similar.Finally, governemantal urls:https://X.zoomgov.com/rec/play/...(same as above; X may be empty)Building from sourcesIf you wish to build from sources, here is a quick howto. First, you need to clone the repository and enter it with a terminal. Then:LinuxRun the command./devscript.sh compile. It basically installs all the dependencies with pip in a temporary directory, then zips it.WindowsInstallpyinstaller(usuallypip install -U pyinstaller)Run the commandwincompile.bat. It calls just callspyinstallerand cleans the generated folders and files, leaving only the exe file.RequirementsAll dependencies are bundled within the executable. This allows to make a standalone execution without need for external libraries.If you wish to build it yourself, seerequirements.txt. The most important requirement isrequests. Please seeacknowledgementsfor a note on that.AcknowledgementsThe folder executable containsrequests(and its dependencies), an awesome wrapper for HTTP(s) calls. Please check them out!

zoomds

No description available on PyPI.

zoome

zoomeGetting Started$ pip install zoomecreate ZoomClient objectfromzoome.apiimportZoomClientzc=ZoomClient(api_key='<api_key>',secret_api_key='<secret_api_key>')orfromzoome.apiimportZoomClientzc=ZoomClient(jwt_token='<jwt_token>')get meetings listmeetings=zc.get_meetings_list()download filezc.download_file(full_path='<full_path>',url='<url>')Utilsget download urls from list of meetingsfromzoome.apiimportZoomClientfromzoome.utilsimportget_meetings_download_urlszc=ZoomClient(jwt_token='<jwt_token>')meetings=zc.get_meetings_list()links=get_meetings_download_urls(meetings)links:[[{"download_url":"<download_url>","recording_type":"<recording_type>","file_type":"<file_type>"},...],...]get download links from one meetingfromzoome.apiimportZoomClientfromzoome.utilsimportget_download_urls_from_meetingzc=ZoomClient(jwt_token='<jwt_token>')meetings=zc.get_meetings_list()links=get_download_urls_from_meeting(meetings[0])links:[{"download_url":"<download_url>","recording_type":"<recording_type>","file_type":"<file_type>"},...]

zoomeye

English |中文文档ZoomEyeis a cyberspace search engine, users can search for network devices using a browserhttps://www.zoomeye.org.ZoomEye-pythonis a Python library developed based on theZoomEye API. It provides theZoomEye command linemode and can also be integrated into other tools as anSDK. The library allows technicians tosearch,filter, andexportZoomEyedata more conveniently.0x01 installationIt can be installed directly frompypi:pip3 install zoomeyeor installed fromgithub:pip3 install git+https://github.com/knownsec/ZoomEye-python.git0x02 how to use cliAfter successfully installingZoomEye-python, you can use thezoomeyecommand directly, as follows:$ zoomeye -h usage: zoomeye [-h] [-v] {info,search,init,ip,history,clear} ... positional arguments: {info,search,init,ip,history,clear} info Show ZoomEye account info search Search the ZoomEye database init Initialize the token for ZoomEye-python ip Query IP information history Query device history clear Manually clear the cache and user information optional arguments: -h, --help show this help message and exit -v, --version show program's version number and exit1.initialize tokenBefore using theZoomEye-pythoncli, the usertokenneeds to be initialized. The credential is used to verify the user’s identity to query data fromZoomEye; only support API-KEY authentication methods.You can view the help throughzoomeye init-h, and useAPIKEYto demonstrate below:$ zoomeye init -apikey "01234567-acbd-00000-1111-22222222222" successfully initialized Role: developer Quota: 10000Users can login toZoomEyeand obtainAPIKEYin personal information (https://www.zoomeye.org/profile);APIKEYwill not expire, users can reset in personal information according to their needs.2.query quotaUsers can query personal information and data quota through theinfocommand, as follows:$ zoomeye info user_info: { "email": "", "name": "", "nick_name": "", "api_key": "", "role": "", # service level "phone", "", "expired_at": "" } quota: { "remain_free_quota": "", # This month remaining free amount "remain_pay_quota": "", # Amount of remaining payment this month "remain_total_quota": "" # Total amount remaining by the service date }3.searchSearch is the core function ofZoomEye-python, which is used through thesearchcommand. thesearchcommand needs to specify the search keyword (dork), let’s perform a simple search below:$ zoomeye search "telnet" -num 1 ip:port service country app banner 222.*.*.*:23 telnet Japan Pocket CMD telnetd \xff\xfb\x01\xff\xfb\x03\xff\x... total: 1Using thesearchcommand is as simple as using a browser to search inZoomEye. by default, we display five more important fields. users can use these data to understand the target information:1.ip:port ip address and port 2.service the service that the port is open 3.country country of this ip address 4.app application type 5.banner characteristic response of the portIn the above example, the number to be displayed is specified using the-numparameter. in addition,searchalso supports the following parameters (zoomeye search-h) so that users can handle the data. we will explain and demonstrate below.-num set the number of displays/searches, support 'all' -count query the total amount of this dork in the ZoomEye database -facet query the distribution of the full data of the dork -stat the distribution of statistical data result sets -filter query the list of a certain area in the data result set, or filter according to the content -save the result set can be exported according to the filter conditions -force ignore the local cache and force the data to be obtained from the API -type select web or host search4.number of dataThrough the-numparameter, we can specify the number of search and display, and the specified number is the number of consumed quantities. you can query the volume of thedorkin the ZoomEye database through the-countparameter, as follows:$ zoomeye search "telnet" -count 56903258One thing to note, the consumption of the-numparameter is an integer multiple of 20, because the minimum number of a single query of theZoomEye APIis 20.5.statisticsWe can use-facetand-statto perform data statistics, use-facetto query the statistics of the dork’s full data (obtained throughAPIafter statistics byZoomEye), and-statYou can perform statistics on the query result set. The fields supported by the two commands include:# host searhc app statistics by application type device statistics by device type service statistics by service type os statistics by operating system type port statistics by port country statistics by country city statistics by city # web search webapp statistics by Web application component statistics by Web container framework statistics by Web framework server statistics by Web server waf statistics by Web firewall(WAF) os statistics by operating system country statistics by countryuse-facetto count the application types of alltelnetdevices:$ zoomeye search "telnet" -facet app app count [unknown] 28317914 BusyBox telnetd 10176313 Linux telnetd 3054856 Cisco IOS telnetd 1505802 Huawei Home Gateway telnetd 1229112 MikroTik router config httpd 1066947 Huawei telnetd 965378 Busybox telnetd 962470 Netgear broadband router... 593346 NASLite-SMB/Sveasoft Alc... 491957use-statto count and query the application types of 20telnetdevices:$ zoomeye search "telnet" -stat app app count Cisco IOS telnetd 7 [unknown] 5 BusyBox telnetd 4 Linux telnetd 3 Pocket CMD telnetd 16.data filterUse the-filterparameter to query the list of partial segments in the data result set, or filter based on content. The segments supported by this command include:# host/search app show application type details version show version information details device show device type details port show port information details city show city details country show country details asn show as number details banner show details of characteristic response timestamp show record data time * when this symbol is included, show all field details # web/search app show application type details headers HTTP header keywords meta keyword title HTTP Title information site site search city show city details country show country details webapp Web application component Web container framework Web framework server Web server waf Web firewall(WAF) os operating system timestamp updated timestamp * when this symbol is included, show all field detailsCompared to the omitted display by default, the complete data can be viewed through-filter, as follows:$ zoomeye search "telnet" -num 1 -filter banner ip banner 222.*.*.* \xff\xfb\x01\xff\xfb\x03\xff\xfd\x03TELNET session now in ESTABLISHED state\r\n\r\n total: 1When using-filterto filter, the syntax is:key1,key2,key3=value, wherekey3=valueis the filter condition, and the displayed content iskey1,key2Example:$ zoomeye search telnet -num 1 -filter port,app,banner=Telnet ip port app 240e:*:*:*::3 23 LANDesk remote managementIn the above example:banner=Telnetis the filter condition, andport,appis the displayed content. If you need to displaybanner, the filter statement is like this$ zoomeye search telnet -num 1 -filter port,app,banner,banner=Telnet7.data exportThe-saveparameter can export data. the syntax of this parameter is the same as that of-filter, and the result is saved to a file in the format of line json, as follows:$ zoomeye search "telnet" -save banner=telnet save file to telnet_1_1610446755.json successful! $ cat telnet_1_1610446755.json {'ip': '218.223.21.91', 'banner': '\\xff\\xfb\\x01\\xff\\xfb\\x03\\xff\\xfd\\x03TELNET session now in ESTABLISHED state\\r\\n\\r\\n'}if you use-savewithout any parameters, the query result will be saved as a file according to the json format ofZoomEye API. this method is generally used to integrate data while retaining metadata; the file can be as input, it is parsed and processed again throughcli, such aszoomeye search "xxxxx.json".8.graphical dataThe-figureparameter is a data visualization parameter. This parameter provides two display methods:pie (pie chart)andhist (histogram). The data will still be displayed without specifying it. When-figureis specified , Only graphics will be displayed. The pie chart is as follows:The histogram is as follows:9. IP historyZoomEye-pythonprovides the function of querying IP historical device data. Use the commandhistory [ip]to query the historical data of IP devices. The usage is as follows:$zoomeye history "207.xx.xx.13" -num 1 207.xx.xx.13 Hostnames: [unknown] Country: United States City: Lake Charles Organization: fulair.com Lastupdated: 2021-02-18T03:44:06 Number of open ports: 1 Number of historical probes: 1 timestamp port/service app raw_data 2021-02-18 03:44:06 80/http Apache httpd HTTP/1.0 301 Moved Permanently...By default, five fields are shown to users:1. time recorded time 2. service Open service 3. port port 4. app web application 5. raw fingerprint informationUsezoomeye history-hto view the parameters provided byhistory.$zoomeye history -h usage: zoomeye history [-h] [-filter filed=regexp] [-force] ip positional arguments: ip search historical device IP optional arguments: -h, --help show this help message and exit -filter filed=regexp filter data and print raw data detail. field: [time,port,service,app,raw] -force ignore the local cache and force the data to be obtained from the APIThe following is a demonstration of-filter:$zoomeye history "207.xx.xx.13" -filter "time=^2019-08,port,service" 207.xx.xx.13 Hostnames: [unknown] Country: United States City: Lake Charles Organization: fulair.com Lastupdated: 2019-08-16T10:53:46 Number of open ports: 3 Number of historical probes: 3 time port service 2019-08-16 10:53:46 389 ldap 2019-08-08 23:32:30 22 ssh 2019-08-03 01:55:59 80 httpThe-filterparameter supports the filtering of the following five fields:1.time scan time 2.port port information 3.service open service 4.app web application 5.banner original fingerprint information * when this symbol is included, show all field detailsA display of theidfield is added during the display.idis the serial number. For the convenience of viewing, it cannot be used as a filtered field.Note: At present, only the above five fields are allowed to filter.The user quota will also be consumed when using thehistorycommand. The user quota will be deducted for the number of pieces of data returned in thehistorycommand. For example: IP “8.8.8.8” has a total of944historical records, and the user quota of944is deducted for one query.10. search IP informationYou can query the information of the specified IP through thezoomeye ipcommand, for example:$ zoomeye ip 185.*.*.57 185.*.*.57 Hostnames: [unknown] Isp: [unknown] Country: Saudi Arabia City: [unknown] Organization: [unknown] Lastupdated: 2021-03-02T11:14:33 Number of open ports: 4{2002, 9002, 123, 25} port service app banner 9002 telnet \xff\xfb\x01\xff\xfb\x0... 123 ntp ntpd \x16\x82\x00\x01\x05\x0... 2002 telnet Pocket CMD telnetd \xff\xfb\x01\xff\xfb\x0... 25 smtp Cisco IOS NetWor... 220 10.1.10.2 Cisco Net...Thezoomeye ipcommand also supports the filter parameter-filter, and the syntax is the same as that ofzoomeye search. E.g:$ zoomeye ip "185.*.*.57" -filter "app,app=ntpd" Hostnames: [unknown] Isp: [unknown] Country: Saudi Arabia City: [unknown] Organization: [unknown] Lastupdated: 2021-02-17T02:15:06 Number of open ports: 0 Number of historical probes: 1 app ntpdThe fields supported by thefilterparameter are:1.port port information 2.service open service 3.app web application 4.banner original fingerprint informationNote: This function limits the number of queries per user per day based on different user levels.Registered users and developers can query 10 times a dayAdvanced users can query 20 times a dayVIP users can query 30 times a dayAfter the number of times per day is used up, it will be refreshed after 24 hours, that is, counting from the time of the first IP check, and the number of refreshes after 24 hours.11.cleanup functionUsers search for a large amount of data every day, which causes the storage space occupied by the cache folder to gradually increase; if users useZoomEye-pythonon a public server, it may cause their ownAPI KEYandACCESS TOKENto leak . For this reason,ZoomEye-pythonprovides the clear commandzoomeye clear, which can clear the cached data and user configuration. The usage is as follows:$zoomeye clear -h usage: zoomeye clear [-h] [-setting] [-cache] optional arguments: -h, --help show this help message and exit -setting clear user api key and access token -cache clear local cache file11.data cacheZoomEye-pythonprovides a caching inclimode, which is located under~/.config/zoomeye/cacheto save user quota as much as possible; the data set that the user has queried will be cached locally for 5 days. when users query the same data set, quotas are not consumed.13.domain name queryZoomEye-pythonprovides the domain name query function (including associated domain name query and subdomain name query). To query a domain name, run the domain [domain name] [query type] command as follows:$ python cli.py domain baidu.com 0 name timestamp ip zszelle.baidu30a72.bf.3dtops.com 2021-06-27 204.11.56.48 zpvpcxa.baidu.3dtops.com 2021-06-27 204.11.56.48 zsrob.baidu.3dtops.com 2021-06-27 204.11.56.48 zw8uch.7928.iwo7y0.baidu82.com 2021-06-27 59.188.232.88 zydsrdxd.baidu.3dtops.com 2021-06-27 204.11.56.48 zycoccz.baidu.3dtops.com 2021-06-27 204.11.56.48 ... total: 30/79882By default, the user is presented with three more important fields:1. name 域名全称 2. timestamp 建立时间戳 3. ip ip地址Usezoomeye domain-hto view parameters provided by thedomain.$ python cli.py domain -h usage: zoomeye domain [-h] [-page PAGE] [-dot] q {0,1} positional arguments: q search key word(eg:baidu.com) {0,1} 0: search associated domain;1: search sub domain optional arguments: -h, --help show this help message and exit -page PAGE view the page of the query result -dot generate a network map of the domain nameThe following is a demonstration of-page:(default query for the first page when not specified)$ python cli.py domain baidu.com 0 -page 3 name timestamp ip zvptcfua.baidu6c7be.mm.3dtops.com 2021-06-27 204.11.56.48 zmukxtd.baidu65c78.iw.3dtops.com 2021-06-27 204.11.56.48 zhengwanghuangguanxianjinkaihu.baidu.fschangshi.com 2021-06-27 23.224.194.175 zibo-baidu.com 2021-06-27 194.56.78.148 zuwxb4.jingyan.baidu.66players.com 2021-06-27 208.91.197.46 zhannei.baidu.com.hypestat.com 2021-06-27 67.212.187.108 zrr.sjz-baidu.com 2021-06-27 204.11.56.48 zp5hd1.baidu.com.ojsdi.cn 2021-06-27 104.149.242.155 ... zhidao.baidu.com.39883.wxeve.cn 2021-06-27 39.98.202.39 zhizhao.baidu.com 2021-06-27 182.61.45.108 zfamnje.baidu.3dtops.com 2021-06-27 204.11.56.48 zjnfza.baidu.3dtops.com 2021-06-27 204.11.56.48 total: 90/79882The-dotparameter can generate a network map of domain name and IP,Before using this function, you need to installgrapvhiz. Please refer tograpvhizfor the installation tutorial. It is supported on Windows/Linux/Mac. The-dotparameter will generate a picture inpngformat and save the original dot language script at the same time.0x03 videoZoomEye-python is demonstrated under Windows, Mac, Linux, FreeBSD0x04 use SDK1.initialize tokenSimilarly, the SDK also supports API-KEY authentication methods,APIKEY, as follows:APIKEYfromzoomeye.sdkimportZoomEyezm=ZoomEye(api_key="01234567-acbd-00000-1111-22222222222")2.SDK APIThe following are the interfaces and instructions provided by the SDK:1.dork_search(dork, page=0, resource="host", facets=None) search the data of the specified page according to dork 2.multi_page_search(dork, page=1, resource="host", facets=None) search multiple pages of data according to dork 3.resources_info() get current user information 4.show_count() get the number of all matching results under the current dork 5.dork_filter(keys) extract the data of the specified field from the search results 6.get_facet() get statistical results of all data from search results 7.history_ip(ip) query historical data information of an ip 8.show_site_ip(data) traverse the web-search result set, and output the domain name and ip address 9.show_ip_port(data) traverse the host-search result set and output the ip address and port 10.generate_dot(self, q, source=0, page=1) Generate graphviz files and pictures written in the domain center3.SDK example$python3>>>importzoomeye.sdkaszoomeye>>>dir(zoomeye)['ZoomEye','ZoomEyeDict','__builtins__','__cached__','__doc__','__file__','__loader__','__name__','__package__','__spec__','fields_tables_host','fields_tables_web','getpass','requests','show_ip_port','show_site_ip','zoomeye_api_test']>>># Use API-KEY search>>>zm=zoomeye.ZoomEye(api_key="01234567-acbd-00000-1111-22222222222")>>>data=zm.dork_search('apache country:cn')>>>zoomeye.show_site_ip(data)213.***.***.46.rev.vo***one.pt['46.***.***.213']me*****on.o****e.net.pg['203.***.***.114']soft********63221110.b***c.net['126.***.***.110']soft********26216022.b***c.net['126.***.***.22']soft********5084068.b***c.net['126.***.***.68']soft********11180040.b***c.net['126.***.***.40']...4.searchAs in the above example, we usedork_search()to search, and we can also set thefacetsparameter to obtain the aggregated statistical results of the full data of the dork. for the fields supported byfacets, please refer to2.use cli - 5.statistics. as follows:>>>data=zm.dork_search('telnet',facets='app')>>>zm.get_facet(){'product':[{'name':'','count':28323128},{'name':'BusyBox telnetd','count':10180912},{'name':'Linux telnetd',......multi_page_search()can also search. use this function when you need to obtain a large amount of data, where thepagefield indicates how many pages of data are obtained; anddork_search()only obtains the data of a specified page.5.data filterthedork_filter()function is provided in the SDK, we can filter the data more conveniently and extract the specified data fields as follows:>>>data=zm.dork_search("telnet")>>>zm.dork_filter("ip,port")[['180.*.*.166',5357],['180.*.*.6',5357],......since the fields returned byweb-searchandhost-searchinterfaces are different, you need to fill in the correct fields when filtering. the fields included inweb-search: app / headers / keywords / title / ip / site / city / country the fields included inhost-search: app / version / device / ip / port / hostname / city / country / asn / banner0x05 contributionsr0oike@knownsec 4040x7F@knownsec 404fenix@knownsec 404dawu@knownsec 4040x06 issue1.The minimum number of requests for SDK and command line tools is 20Due to API limitations, the minimum unit of our query is 20 pieces of data at a time. for a new dork, whether it is to view the total number or specify to search for only 1 piece of data, there will be an overhead of 20 pieces; of course, in the cli, we provide a cache, the data that has been searched is cached locally (~/.config/zoomeye/cache), and the validity period is 5 days, which can greatly save quota.2.How to enter dork with quotes?When using cli to search, you will encounter dork with quotes, for example:"<bodystyle=\"margin:0;padding:0\"><palign=\"center\"><iframe src=\"index.xhtml\"", when dork contains quotation marks or multiple quotation marks, the outermost layer of dork must be wrapped in quotation marks to indicate a parameter as a whole, otherwise command line parameter parsing will cause problems. Then the correct search method for the following dork should be:'"<bodystyle=\"margin:0;padding:0\"><palign=\"center\"><iframesrc=\"index.xhtml\""'.3.Why is there inconsistent data in facet?The following figure shows the full data statistics results oftelnet. the result of the first query is that 20 data query requests (including the statistical results) were initiated by cli one day ago by default, and cached in a local folder; the second time We set the number of queries to 21, cli will read 20 cached data and initiate a new query request (actually the smallest unit is 20, which also contains statistical results), the first query and the second query a certain period of time is in between. during this period of time,ZoomEyeperiodically scans and updates the data, resulting in the above data inconsistency, so cli will use the newer statistical results.4.Why may the total amount of data in ZoomEye-python and the browser search the same dork be different?ZoomEyeprovides two search interfaces:/host/searchand/web/search. InZoomEye-python, only/host/searchis used by default, and/web/searchis not used. Users can choose the search method according to their needs by specifying thetypeparameter.5.The quota information obtained by the info command may be inconsistent with the browser side?The browser side displays the free quota and recharge quota (https://www.zoomeye.org/profile/record), but only the free quota information is displayed inZoomEye-python, we will fix it in the subsequent version This question.0x07 404StarLink ProjectZoomEye-pythonis a part of 404TeamStarlink Project. If you have any questions aboutZoomEye-pythonor want to talk to a small partner, you can refer to The way to join the group of Starlink Project.https://github.com/knownsec/404StarLink-Project#communityReferences:https://www.zoomeye.org/docknownsec 404Time: 2021.01.12

zoomeye-dev

Failed to fetch description. HTTP Status Code: 404

zoomeye-python

No description available on PyPI.

zoomeye-sdk

ZoomEye is a search engine for cyberspace that lets the user find specific network components(ip, services, etc.).ZoomEye API is a web service that provides convenient access to ZoomEye features, data, information over HTTPS. The platform API empowers developers to automate, extend and connected with ZoomEye. You can use the ZoomEye platform API to programmatically create apps, provision some add-ons and perform some automate tasks. Just imagine that what you could do amazing stuff with ZoomEye.How to install ZoomEye SDK$ sudo easy_install zoomeye-SDKor$ sudo pip install git+https://github.com/knownsec/ZoomEye.gitHow to use ZoomEye SDKlocate zoomeye.py, and try to execute it as follow:# use API-KEY $ python zoomeye.py ZoomEye API-KEY(If you don't use API-KEY , Press Enter): 3******f-b**9-a***c-3**5-28******fd8 ZoomEye Username: ZoomEye Password: {'plan': 'developer', 'resources': {'search': 9360, 'stats': 100, 'interval': 'month'}} ec2-1*7-**-***-116.compute-1.amazonaws.com ['1*7.**.***.116'] myh****life.com ['**.35.*.5'] ... 113.**.**.161 1611 113.**.***.63 1611or# use username and password to login $ python zoomeye.py ZoomEye API-KEY(If you don't use API-KEY , Press Enter): ZoomEye Username: [email protected] ZoomEye Password: {'plan': 'developer', 'resources': {'search': 9280, 'stats': 100, 'interval': 'month'}} ec2-1*7-**-***-116.compute-1.amazonaws.com ['1*7.**.***.116'] myh****life.com ['**.35.*.5'] ... 113.***.*.35 1611 113.***.**.162 1611zoomeye.py can be also a library. You can choose to log in with your account Username and Password or use API-KEY to search. API-KEY can be foundhttps://www.zoomeye.org/profile. ex:>>> print(zoomeye.ZoomEye(username=username, password=password).login()) or >>> zm = zoomeye.ZoomEye(api_key="3******f-b**9-a***c-3**5-28******fd8")$ python3 Python 3.8.5 (default, Aug 19 2020, 14:11:20) [Clang 11.0.3 (clang-1103.0.32.62)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import zoomeye >>> dir(zoomeye) ['ZoomEye', '__author__', '__builtins__', '__cached__', '__classes__', '__description__', '__doc__', '__file__', '__funcs__', '__license__', '__loader__', '__name__', '__package__', '__spec__', '__version__', 'getpass', 'raw_input', 'requests', 'show_ip_port', 'show_site_ip', 'sys', 'zoomeye_api_test'] >>> # Use username and password to login >>> zm = zoomeye.ZoomEye() >>> zm.username = '[email protected]' >>> zm.password = 'password' >>> print(zm.login()) ....JIUzI1NiIsInR5cCI6IkpXVCJ9..... >>> data = zm.dork_search('apache country:cn') >>> zoomeye.show_site_ip(data) 213.***.***.46.rev.vo***one.pt ['46.***.***.213'] me*****on.o****e.net.pg ['203.***.***.114'] soft********63221110.b***c.net ['126.***.***.110'] soft********26216022.b***c.net ['126.***.***.22'] soft********5084068.b***c.net ['126.***.***.68'] soft********11180040.b***c.net ['126.***.***.40'] >>> # Use API-KEY >>> zm = zoomeye.ZoomEye(api_key="3******f-b**9-a***c-3**5-28******fd8") >>> data = zm.dork_search('apache country:cn') >>> zoomeye.show_site_ip(data) 213.***.***.46.rev.vo***one.pt ['46.***.***.213'] me*****on.o****e.net.pg ['203.***.***.114'] soft********63221110.b***c.net ['126.***.***.110'] soft********26216022.b***c.net ['126.***.***.22'] soft********5084068.b***c.net ['126.***.***.68'] soft********11180040.b***c.net ['126.***.***.40']How to use ZoomEye API1) Authenticate If a valid ZoomEye credential (username and password), please use the credential for authentication.curl -XPOST https://api.zoomeye.org/user/login -d '{ "username": "[email protected]", "password": "foobar" }'2) ZoomEye Dorks When everything goes ok, you can try to search ZoomEye Dorks with ZoomEye API Token.curl -X GET https://api.zoomeye.org/host/search?query="port:21"&page=1&facet=app,os \ -H "Authorization: JWT eyJhbGciOiJIUzI1NiIsInR5..."If you want more, please access ZoomEye API References.Change Logv1.0.6(10 Nov 2020):Add API-KEY usage;Change default search resource type to “host”Linkshttps://www.zoomeye.org/https://www.zoomeye.org/doc

zoomg

No description available on PyPI.

zoomies

zoomiesStars get the zoomies too. This is a kinematic age prediction package that uses only Gaia parallax, proper motion, and radial velocity to produce a stellar age prediction based on its galactic orbit.To install:git clone https://github.com/ssagear/zoomies cd zoomies pip install .Under active development proceed with caution

zoom-ips

No description available on PyPI.

zoom-kurokesu

Zoom KurokesuPython library to pilote the dual zoom board controller from Kurokesu. VisitKurokesu's websiteto get more information about its products.Install:pipinstallzoom_kurokesuUse the libraryYou can get/set either the zoom or focus of both cameras.There is also three pre defined positions.'in': for close objects'out': for further objects'inter': in between the 'in' and 'out' positionsBecause each predined position is relative to the starting position, youmust perform a homing sequencebefore sending other commands.The motors speed can also be changed with this library.Check theDemo.ipynbnotebook for use case example and more info.Visitpollen-robotics.comto learn more or visitour forumif you have any questions.

zoomlet

No description available on PyPI.

zoomlog

ZOOM LOGSimple python loggerQuick start:fromzoomlogimportLogger,DEBUG,INFOlogger=Logger("test.log",name="test",level=DEBUG)logger.debug("My logger is work!")name="Sergey"logger.addLevel("NEW USER",INFO)logger.log("NEW USER","Created new user -%s"%name)00:00:00 01.01.22 DEBUG:test >> My logger is work! 00:00:01 01.01.22 NEW USER:test >> Created new user - Sergeytest.log00:00:00 01.01.22 00:00:00 01.01.22 DEBUG:test >> My logger is work! 00:00:01 01.01.22 NEW USER:test >> Created new user - Sergey

zoomlog-ZOOM-DEVELOPER

ZOOM LOGSimple python loggerQuick start:fromzoomlogimportLogger,DEBUG,INFOlogger=Logger("test.log",name="test",level=DEBUG)logger.debug("My logger is work!")name="Sergey"logger.addLevel("NEW USER",INFO)logger.log("NEW USER","Created new user -%s"%name)00:00:00 01.01.22 DEBUG:test >> My logger is work! 00:00:01 01.01.22 NEW USER:test >> Created new user - Sergeytest.log00:00:00 01.01.22 00:00:00 01.01.22 DEBUG:test >> My logger is work! 00:00:01 01.01.22 NEW USER:test >> Created new user - Sergey

zoom-meet-attendance-visualizer

Zoom Meet Attendance VisualizerAboutThis module is to help to visualize your meeting attendance helper data created usingzoom_meet_attendance. You don't have to repeat anything except selecting data file. It will remember everything in config.json.Installationpip install zoom-meet-attendance-visualizerHow to useWrite this code, and you're ready to go.from zoom_meet_attendance_helper.main import run_attendance_helperrun_attendance_helper()When you run this code it will ask you to select the text file created byzoom_meet_attendance.Then it will ask **class**. Class will be used to avoid repetition. In class all attendee name will be saved to show who is absent. Also, if rename any name, it will remember it and if you reuse the same class it will automatically suggest renaming.Then it will ask if you want to rename any name. If you want to rename then type a new name, otherwise pre Enter without typing anything.Then it will show all the names and ask you if you want to add more name. If you want to add then type else type Exit to show you the graph.If you made any mistake you can manually edit config.json file automatically created on the same directory.

zoom-narrator

No description available on PyPI.

zoompy

Zoompy is a Python wrapper for calling the Ergast API, to get data related to Formula 1. It can be used to get the races, results, driver standings, constructors standings and qualifying results from any year or from a custom durationInstallationUse the package managerpipto install zoompy.pipinstallzoompy==1.0UsageTo obtain any of the data it is neccesary to first obtain the races in that particular time frame and then get the rounds in those season(s).Obtaining Lists of Raceszoompy.races(start_year,end_year)# To obtain races from 2020zoompy.races(2020,2021)Obtaining Roundsraces=zoompy.races(2020,2021)rounds=zoompy.get_rounds(races)Obtaining Resultsraces=zoompy.races(2020,2021)rounds=zoompy.get_rounds(races)results=zoompy.results(rounds)Obtaining Driver Standingraces=zoompy.races(2020,2021)rounds=zoompy.get_rounds(races)d_standings=zoompy.driver_standings(rounds)Obtaining Constructor Standingraces=zoompy.races(2020,2021)rounds=zoompy.get_rounds(races)c_standings=zoompy.constructor_standings(rounds)Obtaining Qualifying datazoompy.qualifying(start_year,end_year)#obtaining qualifying results from 2020qualifying=zoompy.qualifying(2020,2021)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT

zoom-python

zoom-pythonzoom-pythonis an API wrapper for Zoom, written in Python.This library uses Oauth2 for authentication.Installingpip install zoom-pythonUsagefromzoom.clientimportClientclient=Client(client_id,client_secret,redirect_uri=None)To obtain and set an access token, follow this instructions:Get authorization URLurl=client.authorization_url(code_challenge,redirect_uri=None,state=None)# redirect_uri required if not given in Client.Get access token using coderesponse=client.get_access_token(code,code_verifier)# code_verifier is the same code_challengeSet access tokenclient.set_token(access_token)If your access token expired, you can get a new one using refresh token:response=client.refresh_access_token(refresh_token)And then set access token again... Read more about Zoom Oauth:https://developers.zoom.us/docs/integrations/oauth/- Get current useruser=client.get_current_user()- List Usersusers=client.list_users()Meetings- List meetingsmeetings=client.list_meetings()- Get a meetingmeeting=client.get_meeting(meeting_id)- Create Meetingmeeting=client.create_meeting(self,topic:str,duration:int,start_time:str,type:int=2,agenda:str=None,default_password:bool=False,password:str=None,pre_schedule:bool=False,schedule_for:str=None,timezone:str=None,recurrence:dict=None,settings:dict=None,)More info:https://developers.zoom.us/docs/api/rest/reference/zoom-api/methods/#operation/meetingCreate- Add meeting registrant (this feature requires premium auth)meeting=client.add_meeting_registrant(self,meeting_id,email:str,first_name:str,last_name:str=None,address:str=None,city:str=None,state:str=None,zip:str=None,country:str=None,phone:str=None,comments:str=None,industry:str=None,job_title:str=None,org:str=None,no_of_employees:str=None,purchasing_time_frame:str=None,role_in_purchase_process:str=None,language:str=None,auto_approve:bool=None,)More info:https://developers.zoom.us/docs/api/rest/reference/zoom-api/methods/#operation/meetingRegistrantCreate

zoom-python-client

Zoom Python clientZoom API Python client with support forServer to Server Oauth tokensInstallThis package isavailable on Pypipipinstallzoom-python-clientRequirementsPython >= 3.9UsageDefining your env variablesDefine the following variables in yourenvor your.envfile:ZOOM_ACCOUNT_IDZOOM_CLIENT_IDZOOM_CLIENT_SECRETInitialize the ZoomApiClient from environment variablesfromzoom_python_client.zoom_api_clientimportZoomApiClientzoom_client=ZoomApiClient.init_from_env()Initialize the ZoomApiClient from .envfromzoom_python_client.zoom_api_clientimportZoomApiClientzoom_client=ZoomApiClient.init_from_dotenv()Initialize the ZoomApiClient manuallyfromzoom_python_client.zoom_api_clientimportZoomApiClientzoom_client=ZoomApiClient(account_id="<YOUR ACCOUNT ID>",client_id="<YOUR CLIENT ID>",client_secret="<YOUR CLIENT SECRET>")Use the file system to store the access token instead of environmentThere are some cases where you might want to store the access token in the file system in order to share its value with other elements of the application (Ex. different pods on a Kubernetes/Openshift application).You can define the path where the token will be stored, passing theuse_pathvariable to the constructor:fromzoom_python_client.zoom_api_clientimportZoomApiClientzoom_client=ZoomApiClient(account_id="<YOUR ACCOUNT ID>",client_id="<YOUR CLIENT ID>",client_secret="<YOUR CLIENT SECRET>",use_path="/path/to/token/folder")How to make API callsMEETING_ID="12345"USER_ID="abcdfgh"result=zoom_client.users.get_user(USER_ID)print(result)result=zoom_client.meetings.get_meeting(MEETING_ID)print(result)Optional: How to configure the loggingfromzoom_python_client.utils.loggerimportsetup_logssetup_logs(log_level=logging.DEBUG)Available endpointsusers:get user detailsget user meetingsmeetings:get meeting detailsget meeting tokenmeeting live streams:get meeting live streamupdate live streamupdate livestream statuswebinars:get webinar detailswebinar live streams:get webinar live streamupdate webinar live streamupdate webinar livestream statuszoom rooms:get all zoom roomsget zoom room detailsget zoom room sensor datacalendars:get calendar services listget calendar resources listget calendar resources by service id listget calendar resource details by id

zoom-registrant

Failed to fetch description. HTTP Status Code: 404

zoomrlib

zoomrlibzoomrlib is a library that let you read and write a Zoom R16 project file and export it into a JSON file. It provide also a little cli to show content of a Zoom R16 project as text.InstallationPython >= 3.8 is needed (older python version may work, but it's not tested).pipinstallzoomrlibUsageMost important information from a project file can be read and write:For the wholeproject:nameheaderbitlengthprotectedinsert_effect_ontracksmasterFor atrack:filestatusstereo_oninvert_onpanfaderchorus_onchorus_gainreverb_onreverb_gaineqhigh_oneqhigh_freqeqhigh_gaineqmid_oneqmid_freqeqmid_qfactoreqmid_gaineqlow_oneqlow_freqeqlow_gainFor themaster track:filefaderFrom an Effects file, you can learn:send_reverb_onsend_reverb_patch_numsend_reverb_patch_namesend_chorus_onsend_chorus_patch_numsend_chorus_patch_nameIn a python program, use it like this:importzoomrlibwithzoomrlib.open("PRJDATA.ZDT","r")asfile:prjdata=zoomrlib.project.load(file)print(prjdata.name)fortrackinprjdata.tracks:print(track.file)print(prjdata.master.file)withzoomrlib.open("EFXDATA.ZDT","r")asfile:efxdata=zoomrlib.effect.load(file)print(efxdata.send_reverb_patch_name)print(efxdata.send_chorus_patch_num)The package brings a small binary that let you export ZDT file to json:zoomrlibPRJDATA.ZDT>prjdata.jsonOr directly from the library:python-mzoomrlibPRJDATA.ZDT>prjdata.jsonHow to contributeRead the document:DEVELOP.mdContributorsRémy Taymans (@remytms)Glenn Boysko (@gboysko)Other projects using this libraryZoomProjectReaderThanksThis library can't exist without the huge work and help of Leonhard Schneider (http://www.audiolooper.de/zoom/home_english.shtml). Thanks for his help. If you are looking to a GUI to manage your Zoom R16 take a look at his project.

zoom-select-image-component

zoom-select-image-componentStreamlit component that allows you to select parts of an image by panning and zooming.Installation instructionspipinstallzoom-select-image-componentUsage instructionsimportstreamlitasstfromPILimportImagefromzoom_select_image_componentimportzoom_select_image_component@st.cache_resourcedefload_image_as_pil(image_path:str):returnImage.open(image_path)image=load_image_as_pil('image.png')rectangles=zoom_select_image_component(image,'A')The component takes the following arguments:image: PIL.Image The image to display in the component. This should be referentially stable, otherwise there will be an noticeable delay each time your app runs. One way to achieve this is by loading it through a function decorated with @st.cache_resource. key: str A key that identifies this instance of the component. Can be set to any value, as long as it's unique throughout the app. Changing this value on an existing instance will rerender the whole component and reset state. rectangle_width: number The width of the zoom rectangle in image pixels. rectangle_height: number The height of the zoom rectangle in image pixels.It returns an array of selected rectangles. Each rectangle is a dictionary with the following keys:left: int number of horizontal pixels from top left corner of image to top left corner of rectangle top: int number of vertical pixels from top left corner of image to top left corner of rectangle width: int width of rectangle in pixels height: int height of rectangle in pixels is_enabled: bool whether the checkbox in the rectangle is checked or not cropped_image: PIL.Image the part of the image covered by this rectangleLook at the fileszoom_select_image_component/example.pyandzoom_select_image_component/example_sa.pyfor complete usage examples. The latter file runs the segment anything model on the selected parts of the image.Development instructionsSet_RELEASE = Falsein__init__.py. Enterzoom_select_image_component/frontendand runnpm updatefollowed bynpm run start. In another terminal window, create a virtual environment by runningpython -m venv venv. Run. venv/bin/activateand runpip install -e .to install this component as a local package. Runstreamlit run zoom_select_image_component/example.py.Deployment instructionsSet_RELEASE = Truein__init__.py. Runnpm run buildinzoom_select_image_component/frontend. Create a Python wheel by runningpip wheel --no-deps -w dist .in the root directory. Upload to pypi usingpython -m twine upload --repository pypi dist/*.

zoom_shortener

UNKNOWN

zoomto

No description available on PyPI.

zoom-toolkit

Zoom ToolkitThis is a simple yet useful toolkit implemented for working on zoom records. There exists 3 functionalities that automates the video processing procedure designed specifically for Zoom videos.FeaturesSilence CutDetecting and eliminating high amount and long duration silent parts in videos.Face RemovalAutomatically detects and blurs the portrait of the speaker that shares screen.Scene DetectDetects important frames.InstallationYou can simple use pip,pip install zoom-toolkitDownload the source code fromhereand then just type the command below inside the project folder.python setup.py installQuick Start GuideSilence Cutfromzoom_toolkit.silencecutimportSilenceCutSilenceCut.operate(FRAME_RATE=30,SAMPLE_RATE=44100,SILENT_THRESHOLD=0.03,FRAME_SPREADAGE=1,NEW_SPEED=[5.00,1.00],FRAME_QUALITY=3,"path to the file","path to output file")Face Removalfromzoom_toolkit.face_removeimportFaceRemoverFaceRemover().face_remove("path to the file",False,1000,1050)Module also allows users to state manual time zones for face removal with blurring or darkening option.Scene Detectfromzoom_toolkit.scene_detectorimportSceneDetectSceneDetect.Detect(input_vid_name="video.mp4")Further documentations will be announced soon.This project has been developed under the supervision of Berrin Yanıkoğlu for ENS-492 (Graduation Project).

zoomtools

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zoom-tormysql

TorMySQLThe highest performance asynchronous MySQL driver.PyPI page:https://pypi.python.org/pypi/tormysqlAboutPresents a Future-based API and greenlet for non-blocking access to MySQL.Support bothtornadoandasyncio.Installationpip install TorMySQLUsed Tornadoexample poolfrom tornado.ioloop import IOLoop from tornado import gen import tormysql pool = tormysql.ConnectionPool( max_connections = 20, #max open connections idle_seconds = 7200, #conntion idle timeout time, 0 is not timeout wait_connection_timeout = 3, #wait connection timeout host = "127.0.0.1", user = "root", passwd = "TEST", db = "test", charset = "utf8" ) @gen.coroutine def test(): with (yield pool.Connection()) as conn: try: with conn.cursor() as cursor: yield cursor.execute("INSERT INTO test(id) VALUES(1)") except: yield conn.rollback() else: yield conn.commit() with conn.cursor() as cursor: yield cursor.execute("SELECT * FROM test") datas = cursor.fetchall() print datas yield pool.close() ioloop = IOLoop.instance() ioloop.run_sync(test)example helpersfrom tornado.ioloop import IOLoop from tornado import gen import tormysql pool = tormysql.helpers.ConnectionPool( max_connections = 20, #max open connections idle_seconds = 7200, #conntion idle timeout time, 0 is not timeout wait_connection_timeout = 3, #wait connection timeout host = "127.0.0.1", user = "root", passwd = "TEST", db = "test", charset = "utf8" ) @gen.coroutine def test(): tx = yield pool.begin() try: yield tx.execute("INSERT INTO test(id) VALUES(1)") except: yield tx.rollback() else: yield tx.commit() cursor = yield pool.execute("SELECT * FROM test") datas = cursor.fetchall() print datas yield pool.close() ioloop = IOLoop.instance() ioloop.run_sync(test)Used asyncio aloneexample poolfrom asyncio import events import tormysql pool = tormysql.ConnectionPool( max_connections = 20, #max open connections idle_seconds = 7200, #conntion idle timeout time, 0 is not timeout wait_connection_timeout = 3, #wait connection timeout host = "127.0.0.1", user = "root", passwd = "TEST", db = "test", charset = "utf8" ) async def test(): async with await pool.Connection() as conn: try: async with conn.cursor() as cursor: await cursor.execute("INSERT INTO test(id) VALUES(1)") except: await conn.rollback() else: await conn.commit() async with conn.cursor() as cursor: await cursor.execute("SELECT * FROM test") datas = cursor.fetchall() print(datas) await pool.close() ioloop = events.get_event_loop() ioloop.run_until_complete(test)example helpersfrom asyncio import events import tormysql pool = tormysql.helpers.ConnectionPool( max_connections = 20, #max open connections idle_seconds = 7200, #conntion idle timeout time, 0 is not timeout wait_connection_timeout = 3, #wait connection timeout host = "127.0.0.1", user = "root", passwd = "TEST", db = "test", charset = "utf8" ) async def test(): async with await pool.begin() as tx: await tx.execute("INSERT INTO test(id) VALUES(1)") cursor = await pool.execute("SELECT * FROM test") datas = cursor.fetchall() print(datas) await pool.close() ioloop = events.get_event_loop() ioloop.run_until_complete(test)ResourcesYou can readPyMySQL Documentationonline for more information.LicenseTorMySQL uses the MIT license, see LICENSE file for the details.

zoomus

Python client library for Zoom.us REST API v1 and v2