ioHub Configuration

The ioHub Event Framework has been designed to be highly configurable, ensuring that is can be tailored to the needs of a specific use case or experiment paradigm.

This section outlines how ioHub configuration works, what mechanisms are in place allowing a user to update configuration settings, and how configuration settings are validated. There are two ways configuration settings can be specified, using configuration files for using configuration dictionaries within the experiment script.

ioHub tries to seperate experiment logic ( the python code that defines how your experimental paradigm is implemented, how stimuli are read and presented, etc. ) from experiment configuration ( defining the experiment and experiment session metadata, experiment session variable input, and the configuration of the device hardware being used by the experiment ).

By doing so, the experiment scripts will often become much reusable when switching between device hardware for a given device type. The definition and collection of experiment and experiment session information becomes more structured, easier to and understand visually, and also can help to ensure different memebers of a lab are following the same or similar process when providing and collecting data about participants and the experiment devices used. This can help in experiment reproducablity and result validaion if ever needed.

The current implementation state of ioHub is a work in process towards fully meeting the above goals and objectives; much more can be done to both improve and refine the existing functionailty provide as well as working towards a more functional implementation.

This section outlines:

Areas of Configuration

The ioHub Event Framework supports the configuration of several different areas of the functionality. How the ioHub Framework is used defines which of these areas can be taken advantage of by the experiment.

Configuration Options with ioHubExperimentRuntime Class

  • Experiment metadata.
  • Experiment Session metadata definition, including custom session level variables.
  • Device selection and configuration.
  • Experiment Resource (condition variable files, images, audio files, vidio files, etc.) location specifiecation.
  • Experiment Output File(s) (custom files, ioHub DataStore files, Native Device Data Files, ect) locations.
  • Some Experiment and ioHub Process OS configuration

Other than the ioHub device spcification, and any configuration settings needed for the devices being used, all other items in the list are optional. By providing all the above information (at a level of detail you can choose), your experiment logic script will be able to take advantage of the full fucntionality set provided by the ioHub Framework.

When using the ioHubExperimentRuntime Class, aff configuration details provided are done so using two configuration files that reside in the same directory as the experiment python script. These are descibed in detail later on this page.

Configuration Options with the launchHubProcess Function

  • Experiment Code.
  • Session Code.
  • PsychoPy Monitor Configuration File Name.
  • Defice configuration.

When using the launchHubProcess function to interface to the ioHub framework any configuration information that is provided is done using kwargs to the fucntion, or a python dictionary for each device that needs configuration settings updated.

A default set of devices are enabled when the launchHubProcess function is used:

  • Display
  • Experiment
  • Keyboard
  • Mouse

Therefore the launchHubProcess can be very useful for quick initial setup of access to the ioHub Framework is is completely useable in many cases. However the trade-offs (you deside if they are possitive or negative) are:

  • Access to all the extended functionality witin the ioHub Framework is significantly reduced
  • When devices are used that require a moderate degree of configuration, directly using python dictionaries to do so starts to become combersome and error prone.
  • This approach to using the ioHub Framework effectively voids any of the possible beneifits outlined at the start of this section regarding the speration of experiment logic and configuration.

Default and Custom Provided Configuration Settings

Regardless of which of the two approaches just described are used, all the possible configuration options are set when the ioHUb Process starts. What can differ is which are sets using default values and which are set using confiruation options defined by the experiment designer. the following process is used when setting configutation options:

  1. Default settings are read from default configuration files which exist in the ioHub package directory structure.

  2. Any custom settings or values specified by the PsychoPy Process are read.

  3. These two configuration sets are merged, where any settings not provided by the PsychoPy Process are given the default value specified by the default configuration file in question.

    1. If the configuration group is for an ioHub Device, the combined confiuartion set for the device is validated against a spcification of what thhat device accepts for configuration options, whether it is manditory or not, and what the valid value set or range is for each option.
    2. If the configuration validator finds problems, an error is generated when the experiment starts and the device is not loaded.
    3. If the configuration of the device passes, the device is created and the full set of configuration otpns and values used can be read as a Python dictionary using the device.getConfiguration() method.
  4. The configuation settings are used when initializing the ioHub Process and creating the ioHub runtime objects used within the PsychoPy script.

Default Configuration Settings

All default confiuration settings are specified in configuration files, as mentioned above.

The default settings for the ioHub Process and DataStore are located in the ‘default_config.yaml’ file located in the root iohub module directory.

The default settings for each ioHub device are located in a file called default_<device_name>.yaml found in the device submodule directory within the iohub package; where <device_name> is the unqualified class name of the defive in all lowercase form.

For example, the default Mouse device settings are located in:


For the Keyboard:


For the different Eye Tracker implementations:







The documentation page for each device includes the ioHub default settings for each device based on the latest default file settings for that device. This can be used to quickly review the configuartion options available, get a description of each, and see what the default value is.

It is important to understand that if the default setting for a device configuration option is satisfactory for the experiment being written, there is no need to provide it at the experiment confiuration setting level. However doing so does not hurt and provides a direct statement of what setting values are being used for the experiment.

Custom Configuration Settings

Any configuration setting that need to use a non-default value are specified in one of two ways, depending on whether the launchHubProcess function is used to create the access point to the ioHub Process, or if the ioHubExperimentRuntime class is being used to embed the experiment logic within the ioHub Framework.

When using the launchHubProcess function:

When the launchHubProcess function is used, device configuratiions can be specified by creating a python dictionary for the device settings being specified. One dictionary is created for each device that is needed.


Remember that, as a convience, the launchHubProcess function will create four base device insatnces without the need to specify them as launchHubProcess function kwargs. These defaults use the default settings for the device. If a configuration dictionary is provided for a device of the same Classs, then the default device that would have been created is created using the provided parameter dictionary instead

Please see the launchHubProcess function documentation for more details.

When using the ioHubExperimentRuntime class:

When using the ioHubExperimentRuntime class, all experiment, session, process, and device configuation settings are specified in two configuratiion files that are created in the same directory as the PsychoPy Python script file.

ioHub Configuration Files are defines using a simple subset of the YAML synax , which is parsed using the PyYAML package.

The two configuartion files are:

  1. experiment_config.yaml
    • Specifies the experiment and session metadata for the experiment.
    • Defines any custom session variables, which allow custom input fields to be provided in the Session Information Input Dialog at the start of any experiment
    • Can include experiment resource path information and result data file save locations.
    • Specifies the PsychoPy and ioHub Process OS settings (Windows and Linux only).
  2. iohub_config.yaml
    • Specifies the ioHub Process UDP port number to use.
    • Defines the maximum number of events to store in the Global Event Buffer.
    • Specifies the list of ioHub devices to use within the experiment, allong with any configuration settings needed for each device specified.
    • Defines ioHub DataStore parameter settings.

Several of the ioHub examples use the ioHubExperimentRuntime class and two configuration files, and they provide a good way to gain better insite into how the configuration options can be used.


The documentation page for each device includes the ioHub default settings for each device based on the latest default file settings for that device. This can be used to quickly review the configuartion options available, get a description of each, and see what the default value is.

Example Configurations

In YAML file Format

An example of a experiment_config.yaml file:

# This text is being written in a YAML comment block. Looks familar right?
# Here are a few pointers to keep in mind about the confiuration file format
# that will help ensure they are created with proper YAML syntax:
# * Think of the whole file as representing a python dictionary. Infact when the
#   the file is read by PyYAML, it results in the data from the file being returned as a Python dictionary.
# * If you have ever created a Python dictionary using the form
#   mydict={ 
#           'key1': 'value1',
#           'key2': 2
#           }
#   Then it should be quite easy to see that the YAML format for a file is very
#   similar, other than these differences:
#     + The file does not begin with a '{' or end with a '}'.
#     + key: value pairs are seperation by lines, not by ','s.  
# * The indendation level of the line in the file indicates the scope of the key:value pair, 
#     ( scope meaning the dictionary or list level that the key: value pair is associated with) 
#    Again, should be a familar idea. ;)
# * A key can have a dictionary as it;s value, by specifying the key name and
#   then providing the key's dictionary value starting on the nexxt line, indented by a soft tab.
# * Keys should only contain a-z,A-Z,and underscores. (This is an ioHub spec. more than a YAML one)
# * Keys never need to have quotes around them, and never should.
# * String values also do not need quotes around them.
# * Other system data types used in values can usually just be types as if you were entering the value
#   in a python script. For example:
#       dict_of_mixed_type_values:     # So a dict value is created by having each key: value pair for the key indented one soft tab.
#          str_type_param: This is the value for my str_type_param.
#          int_type_param: 10          # Converted into a Python int with value 10
#          float_type_param: 10.11     # Converted into a Python float with value 10.11
#          bool_type_param: True       # Converted into a Python bool == True
#          none_type_param:            # Converted into value of None
#          another_str_type: '10'      # By placing quotes around a type that would notmally not be a string, it is made one.
#          list_type_param: [1,2,3,Four,Five,Six]  # A Python list is created  [1,2,3,'Four','Five','Six']
#          list2_type_param:
#              - 1                      # This is another way to define a list value
#              - 2                      # each element is on a seperate line
#              - 3                      # indented by one from the key that the list is associated with.
#              - Four                   # will also equal [1,2,3,'Four','Five','Six'] in python
#              - Five
#              - Six

# This is an example experiment_config.yaml. Values that are also the default
# value for the setting are indicated as such. 

# tile: A short but non criptic name of the experiment. 
#       Similar to what you might title a paper about the experiment.   
title: Effect of Exogenous and Endogenous Cues on Saccadic Reaction Time

# code: A vert short, usually criptic, code for the experiment.
#       An experiment code is 'required' when using the ioHub DataStore.
#       While not technically inforced, it is a good practive to use a unique
#       code for each experiment you create.
code: sac_ee_cue

# version: The version of the experiment being run, in string format.
#       Each version on an experiment should have it's own experiment folder
#       that has the experiment source and configuaration.    
version: '1.1'

# description: Can be used to give longer, more informative text about what the experiment is for.
#       Can also be used to indicate anything important to remember about running the experiment.
description: This study looks at how central cues in the form of arrow graphics, and peripheral cues if the form of a flash of light, around the location of an upcoming target's interact to influence saccadic onset time.

# display_experiment_dialog: If True, a read-only dialog will be displayed 
#       each time the experiment starts stating the above four parameter values.
#       This can be useful so the person running the experiment can check that
#        they started the right one!
display_experiment_dialog: True    # Default if False

# session_defaults: This parameter is defined as a sub dictionary containing
# the experiment session metadata and user defined custom parameters.
    # name: Allows the entry of a short namefor the session. This can be the same across
    #       multiple sessions within the experiment.
    name: Session Name

    # code: A short code for the experiment session. Each run of the experiment must have 
    #       a unique session code. It the code enteried already exists in the experiments DataStore
    #       An error is returned and a different code can be entered.
    code: Sxxxxxx

    # comments: Can be used to give any information the experiment operator
    #       Thinks may be important to note about the session about to be run.
    comments: Ensure the particpant's right eye is tracked and that the data collection room's light are turned off before the experiment begins.

    # user_variables: Allow for custom session data entry fields to be displayed in the Session Input Dialog.
    #   If no extra session variables are needed, this section can be removed. The default is no
    #   extra user defined variable.
    #   To create user defines variables, add one line for each variable wanted to the user_variables
    #   parameter section. The key of each line will be shown ad the label for the input.
    #   The value of each line specifies the default value for string field, 
    #   and the possible values to be shown for a list field, which is displayed as a dropdown list in the dialog.
    #   For list fileds, the first element of the list is the default. 
    #   Fields that have a boolean default are displayed as a checkbox.
        participant_age: Unknown
        participant_gender: [ Select, Male, Female ]
        glasses: False
        contacts: False
        eye_color: Unknown
    # session_variable_order: This setting accepts a list value, each element of which
    #   is a session variable key (either built-in or custom). The order the keys
    #   are provided in the list will be the order that each appears in the Session Input Dialog.
    session_variable_order: [ name, code, comments, participant_age, participant_gender, glasses, contacts, eye_color ]

# To document....
        blocks: True
        trials: True
# display_session_dialog: If True, an input dialog is shown
#       each time the experiment starts allowing the operator to enter data for
#       The session_default parameters and any user_variables defined.
display_session_dialog: True        # Default

# process_affinity: Specifies the processing units / cpu's that the PsychoPy
#       Process should be allowed to run on. Not supported on OSX.
#       An empty list indicates that the process should be able
#       to run on any processing unit of the computer.
process_affinity: []                # Default

# remaining_processes_affinity: Lists the processing units / cpu's that
#       all other processes running on the computer (other than the ioHub Process)
#       should be allowed to run on.
#       An empty list indicates that the process should be able
#       to run on any processing unit of the computer.
#       Not supported on OSX.
remaining_processes_affinity: []    # Default

# event_buffer_length: The maximum number of events that can be in the
#       PsychoPy Process ioHub event cache. This is used when iohub.wait()
#       is called and new events are received from the ioHub process.        
event_buffer_length: 1024           # Default

# Settings for the ioHub Process itself.
    # Do you want the ioHub process to run ?  True == Yes
    # False == why are you creating an ioHub confiuration file then? ;)
    enable: True                    # Default

    # process_affinity: Specifies the processing units / cpu's that the
    #       ioHub Process should be allowed to run on. 
    #       An empty list indicates that the process should be able
    #       to run on any processing unit of the computer.
    #       Not supported on OSX.
    process_affinity: []            # Default

    # config: The name of the ioHub config file, specifying device 
    #       settings, etc
    config: ioHub_config.yaml       # Default

####### End of experiment_config.yaml example ########

An example of an iohub_config.yaml file:

    # The iohub_config.yaml includes settings for the iohub Process itself,
    # the ioHub DataStore, and the list of ioHub devices that will be monitored
    # for events with the ioHub Process runs.
    # This is just an example of a properly defined iohub_config.yaml file,
    # with lots of comments. The settings included, other than for monitored devices, 
    # defines all possible parameters. If the default value, as shown here,
    # does not need to be changed for your experiment, then you do not need to
    # include that parameter in your experiment's iohub_config.yaml file.
    # global_event_buffer: Specifies the maximum number of events that can be stored in
    #       the ioHub Process's Global Event Buffer, which holds events as they are
    #       received from 'all' devices being monitored.
    global_event_buffer: 2048
    # udp_port: This sets the port number the ioHub UDP Server uses to accept incoming
    #       message requests from.
   udp_port: 9034

    # data_store: A dictionary for prefernces related to the ioHub DataStore.
        # enable: Should the ioHub DataStore to used while the ioHub process is running.
        #   True = the DataStore will be active and used by devices that have been configured to save events.
        #   False = Total disabling of the ioHub DataStore, regardless of any related device level preferences.
        enable: False

        # filename: The name of the file that will be created by the DataStore.
        #           The full file name is the name given here + the '.hdf5' extension.
        filename: events

        # multiple_experiments: The ioHub DataStore was deigned so that data 
        #   from multiple experiments could be saved in the same hdf5 file.
        #   However in practive this has never really been used, so keeping the default
        #   value of False (each experiment creates a different DataStore file) is
        #   likely a good idea.
        multiple_experiments: False

        # flush_interval: As events are given to the DataStore to be saved peristantly,
        #   events are buffered to memory and then written to the hdf5 file every
        #   flush_interval number of events. A smaller flush_interval means it will ccur more often
        #   but take less time; a larger value means event flushing will occur less
        #   frequently, but take longer to perform when it is done.
        flush_interval: 32
    # monitor_devices: specifies the list of devices that will be monitored for evenst while the ioHub
    #   Process is running. All available settings for each device is listed in the device's manual page.
    #   We only give any example of a possible device list here.
        # A Display device will be created by the ioHub Process
        - Display:
            # The unique name to assign to the evice instance created.
            # The device is accessed from within the PsychoPy script 
            # using the name's value; therefore it must be a valid Python
            # variable name as well.
            name: display
            # The coordinate , or unit, type that the Display's surface area should
            # be represented in. Valid values are pix, deg, norm, or cm.
            reporting_unit_type: pix
            # The Display index to assign to the device. On a single Display
            # computer this must always be 0. On a computer with multiple displays, 
            # the value can be between 0 and display_count - 1.     
            device_number: 0
            # This section of parameters defines the actual size of the Display's 
            # 2D stimulus surface. Both width and height values are the total length of each dimention.
            # The unit_type field must currently be in mm, and therefore so must 
            # the specified width and height.
                width: 500
                height: 281
                unit_type: mm
            # Enter the expected, average, distance that the participants eye(s) will
            # be from the display's stimulus surface. Currently the only supported 
            # distance reference type is surface_center, and the distance must be specified in 
            # the unit_type of mm.
                surface_center: 550
                unit_type: mm
            # If the Display device should open a PsychoPy Monitor Configuration
            # file, provide the name of it here.
            psychopy_monitor_name: default
            # If a valid PsychoPy Monitor Configuration file 
            # has been provided, specify if the physical parameters
            # stored in it should override any duplicate parameter types
            # defined in this Display device configuartion.
            # True == Use the PsychoPy settings and update the Display config with them.
            # False == Use the measurements provided in this file and update the
            # the PsychoPy Monitor Configuration with the values specified
            # in the ioHub Device configuration.
            override_using_psycho_settings: False
            # name: The name you want to assign the keyboard device for the experiment
            #   This name is what will be used to access the device within the experiment
            #   script via the devices.[device_name] property of the ioHubConnection or
            #   ioHubExperimentRuntime classes.
            name: keyboard
            # monitor_event_types: *If* the ioHubDataStore is enabled for the experiment, then
            #   indicate which KeyboardEvent types should be monitored
            #   for and therefore saved to the DataStore or sent to the Experiment Process.
            monitor_event_types: [KeyboardPressEvent, KeyboardReleaseEvent, KeyboardCharEvent]

            # report_auto_repeat_press_events: Should the keyboard report key press events
            #   that are generated by the OS when a key is held down for an extended 
            #   period of time.
            report_auto_repeat_press_events: False

            # event_buffer_length: Specify the maximum number of events (for each
            #   event type the device produces) that can be stored by the ioHub Server
            #   before each new event results in the oldest event of the same type being
            #   discarded from the ioHub device event buffer.
            event_buffer_length: 256

            # name: The name you want to assign the xinput.Gamepad device for the experiment
            #   This name is what will be used to access the device within the experiment
            #   script via the devices.[device_name] property of the ioHubConnection or
            #   ioHubExperimentRuntime classes.
            name: gamepad
            # device_number: Up to 4 XInput 'users' can be connected to the computer at one time. The
            #   gamepad's user ID is based on how many other gamepads are already connected to the
            #   computer when the new gamepad turns on. If a gamepad with a lower user id
            #   the disconnects from the computer, other gamepad user ids remain the same.
            #   Therefore XInput user id is not equal to the index of the gamepad in a set 
            #   of gamepads. If the experiment should connect to the first active gamepad found, enter -1.
            #   Otherwise enter the user_id (1-4) of he gamepad you wish to connect to.
            device_number: -1

            # monitor_event_types: *If* the ioHubDataStore is enabled for the experiment, then
            #   indicate if events for this device should be saved to the
            #   monitor_event_types: Specified which xinput.Gamepad Event types should be monitored
            #   for and therefore saved to the DataStore or sent to the Experiment Process.
            monitor_event_types:  [GamepadStateChangeEvent, GamepadDisconnectEvent]

            # save_events: Save xinput.Gamepad events to the data_collection/xinput.Gamepad event 
            #   group in the hdf5 event file.
            #   True = Save events for this device to the ioDataStore.
            #   False = Do not save events for this device in the ioDataStore.
            save_events: True

            # streamEvents: Indicate if events from this device should be made available
            #   during experiment runtime to the Experiment / PsychoPy Process.
            #   True = Send events for this device to  the Experiment Process in real-time.
            #   False = Do *not* send events for this device to the Experiment Process in real-time.
            stream_events: True

            # event_buffer_length: Specify the maximum number of events (for each
            #   event type the device produces) that can be stored by the ioHub Server
            #   before each new event results in the oldest event of the same type being
            #   discarded from the ioHub device event buffer.
            event_buffer_length: 256

            # device_timer: Devices that require polling to read any new native events
            #   that have become available must specifiy a device_timer property, with an
            #   interval sub proerty that specifies how frequently the device would be polled
            #   by the ioHub Server. The time is specified in sec.msec format and is a 'requested'
            #   polling interval. The actual polling interval will vary from this somewhat, the 
            #   magnitude of which depends on the computer hardware and OS being used and the
            #   number of other polled devices being monitored. The 'configdence_interval'
            #   attribute of events that have a parent device that is polled often can be used to
            #   determine the actual polling rate being achieved by the ioHub Process.
                interval: 0.005

In Python Dictionary Format

The following example python code illustrates how the launchHubProcess function could be used to to explitied specify the settings for the same device set listed in the above iohub_config.yaml, but as Python dictionaries directly:

# create the Display configuration
                    reporting_unit_type= 'pix',
                    physical_dimensions = dict(
                    psychopy_monitor_name= 'default',

# create the Keyboard configuration
                    monitor_event_types= ['KeyboardPressEvent', 'KeyboardReleaseEvent', 'KeyboardCharEvent'],
                    report_auto_repeat_press_events= False,
                    event_buffer_length= 256

# create the XInput Gamnepad configuration
                    monitor_event_types= ['GamepadStateChangeEvent', 'GamepadDisconnectEvent'],
                    save_events= True,
                    stream_events= True,
                    event_buffer_length= 256,
                    device_timer= {interval:0.005}


# resr of your script .....


As previously mentioned, the example set provide for the ioHub is an excellent resource for further examples of confuration in ioHub.