3.2. Instrument Plugins

Any new hardware has to be included in PyMoDAQ within a plugin. A PyMoDAQ’s plugin is a python package containing several added functionalities such as instruments objects. A instrument object is a class inheriting from either a DAQ_Move_Base or a DAQ_Viewer_Base class`and implementing mandatory methods for easy and quick inclusion of the instrument within the PyMoDAQ control modules.

Plugins are articulated given their type: Moves or Viewers and for the latter their main dimensionality: 0D, 1D or 2D. It is recommended to start from the template repository that includes templates for all kind of instruments and also the generic structure to build and publish a given plugin.

You will find below some information on the how to but comparison with existing plugins packages will be beneficial.


Some more detailed documentation is under work

3.2.1. Installation

The main and official list of plugins is located in the pymodaq_plugin_manager repository on github. This constitutes a list of (contributed) python package that can be installed using the Plugin Manager (or directly using pip). Other unofficial plugins may also be installed if they follow PyMoDAQ’s plugin specifications but you are invited to let know other users of the plugins you develop in order to contribute to PyMoDAQ’s development.

PyMoDAQ is looking at startup for all installed packages that it can consider as its plugins. This includes by default the pymodaq_plugins_mock package of mock instruments installed on the site_packages location in your python distribution.

3.2.2. Contributions:

Users are welcomed to contribute to PyMoDAQ by writing their own plugins. Two approaches are possible:

  • Fork one of the official plugin package repositories and add within your own plugin scripts

  • Copy the plugin template package on you disk and work on the templates within then ask to create an official plugin package

3.2.3. Naming convention:

For an instrument plugin to be properly recognised by PyMoDAQ, the location and name of the underlying script must follow some rules and syntax. The plugin template package could be copied locally as a starting point:

  • An actuator plugin (name: xxxx) will be a script whose name is daq_move_Xxxx (notice first X letter is capital)

  • The main instrument class within the script will be named DAQ_Move_Xxxx (notice the capital letters here as well and sorry if it is troublesome)

  • A detector plugin of dimensionality N (N=0, 1, 2 or N) (name: xxxx) will be a script whose name is daq_NDviewer_Xxxx (notice first X letter is capital, and replace N by 0, 1, 2 or leave it for higher dimensionality)

  • The main instrument class within the script will be named DAQ_NDViewer_Xxxx (notice the capital letters here as well)

3.2.4. Hardware Settings

An important feature similar for all modules is the layout as a tree structure of all the hardware parameters. These settings will appear on the UI as a tree of parameters with a title and different types, see Fig. 3.1. On the module side, they will be instantiated as a list of dictionaries and later exist in the object self.settings. This object inherits from the Parameter object defined in pyqtgraph.

Settings example

Fig. 3.1 Typical hardware settings represented as a tree structure (here from the daq_2Dviewer_AndorCCD plugin)

Here is an example of such a list of dictionaries corresponding to Fig. 3.1:

[{'title': 'Dll library:', 'name': 'andor_lib', 'type': 'browsepath', 'value': libpath},
 {'title': 'Camera Settings:', 'name': 'camera_settings', 'type': 'group', 'expanded': True, 'children': [
     {'title': 'Camera SN:', 'name': 'camera_serialnumber', 'type': 'int', 'value': 0, 'readonly': True},
     {'title': 'Camera Model:', 'name': 'camera_model', 'type': 'str', 'value': '', 'readonly': True},
     {'title': 'Readout Modes:', 'name': 'readout', 'type': 'list', 'values': ['FullVertBinning','Imaging'], 'value': 'FullVertBinning'},
     {'title': 'Readout Settings:', 'name': 'readout_settings', 'type': 'group', 'children':[
         {'title': 'single Track Settings:', 'name': 'st_settings', 'type': 'group', 'visible': False, 'children':[
             {'title': 'Center pixel:', 'name': 'st_center', 'type': 'int', 'value': 1 , 'default':1, 'min':1},
             {'title': 'Height:', 'name': 'st_height', 'type': 'int', 'value': 1 , 'default':1, 'min':1},

The list of available types of parameters (defined in pymodaq.utils.parameter.pymodaq_ptypes.py) is:

  • group : “camera settings” on Fig. 3.1 is of type group

  • int : settable integer (SpinBox_Custom object)

  • float : settable float (SpinBox_Custom object)

  • str : a QLineEdit object (see Qt5 documentation)

  • list : “Readout Modes” Fig. 3.1 is a combo box

  • bool : checkable boolean

  • bool_push : a checkable boolean in the form of a QPushButton

  • led : non checkable boolean in the form of a green (True) of red (False) led

  • led_push : checkable boolean in the form of a green (True) of red (False) led

  • date_time : a QDateTime object (see Qt5 documentation)

  • date : a QDate object (see Qt5 documentation)

  • time : a QTime object (see Qt5 documentation)

  • slide : a combination of a slide and spinbox for floating point values (linear of log scale)

  • itemselect : an object to easily select one or more items among a few

  • browsepath: a text area and a pushbutton to select a given path or file

  • text : a text area (for comments for instance)

Important: the name key in the dictionnaries must not contain any space, please use underscore if necessary!


For a live example of these Parameters and their widget, type in parameter_example in your shell or check the example folder

Once the module is initialized, any modification on the UI hardware settings will be send to the plugin through the commit_settings method of the plugin class and illustrated below (still from the daq_2Dviewer_AndorCCD plugin). The param method argument is of the type Parameter (from pyqtgraph):

def commit_settings(self,param):
        | Activate parameters changes on the hardware from parameter's name.
        if param.name()=='set_point':

        elif param.name() == 'readout' or param.name() in custom_parameter_tree.iter_children(self.settings.child('camera_settings', 'readout_settings')):

        elif param.name()=='exposure':
            self.controller.SetExposureTime(self.settings.child('camera_settings','exposure').value()/1000) #temp should be in s
            (err, timings) = self.controller.GetAcquisitionTimings()
        elif param.name() == 'grating':
            index_grating = self.grating_list.index(param.value())
            self.emit_status(ThreadCommand('show_splash', ["Setting wavelength"]))
            err = self.controller.SetWavelengthSR(0, self.settings.child('spectro_settings','spectro_wl').value())

3.2.5. Emission of data

When data are ready (see Data ready? to know about that), the plugin has to notify the viewer module in order to display data and eventually save them. For this PyMoDAQ use two types of signals (see pyqtsignal documentation for details):

  • data_grabed_signal_temp

  • data_grabed_signal

They both emit the same type of signal but will trigger different behaviour from the viewer module. The first is to be used to send temporary data to update the plotting but without triggering anything else (so that the DAQ_Scan still awaits for data completion before moving on). It is also used in the initialisation of the plugin in order to preset the type and number of data viewers displayed by the viewer module. The second signal is to be used once data are fully ready to be send back to the user interface and further processed by DAQ_Scan or DAQ_Viewer instances. The code below is an example of emission of data:

from pymodaq.utils.daq_utils import Axis
from pymodaq.utils.daq_utils import DataFromPlugins
x_axis = Axis(label='Wavelength', units= "nm", data = vector_X)
y_axis = Axis(data=vector_Y)
self.data_grabed_signal.emit([DataFromPlugins(name='Camera',data=[data2D_0, data2D_1,...],
                                    dim='Data2D', x_axis=x_axis,y_axis=y_axis),
                              DataFromPlugins(name='Spectrum',data=[data1D_0, data1D_1,...],
                                    dim='Data1D', x_axis=x_axis, labels=['label0', 'label1', ...]),
                              DataFromPlugins(name='Current',data=[data0D_0, data0D_1,...],
                              DataFromPlugins(name='Datacube',data=[dataND_0, dataND_1,...],
                                    dim='DataND', nav_axes=[0,2]),
                                    nav_x_axis=NavAxis(data=.., label='Xaxis', units= "µm", nav_index=0])

Such an emitted signal would trigger the initialization of 4 data viewers in the viewer module. One for each DataFromPlugins in the emitted list. The type of data viewer will be determined by the dim key value while its name will be set to the name key value. The data key value is also a list of numpy arrays, their shape should be adequate with the dim key of the dictionary. (in fact the dim key could be omitted as the DataFromPlugins class check its values or assess it from the data numpy array shape. Each array will generate one channel within the corresponding viewer. Here is the detailed list of the possible keys:

  • name: will display the corresponding value on the viewer dock

  • dim: (either ‘Data0D’, ‘Data1D’, ‘Data2D’ or ‘DataND’) will set the viewer type (0D, 1D, 2D or multi-dimensional ND). The ND viewer will be able to deal with data dimensionality up to 4)

  • data: list of numpy array. Each array shape should correspond to the type

  • labels: list of string, one for each numpy array within the data field. Will be displayed on 0DViewer and 1DViewer

  • x_axis: Axis instance containing various fields to set the axis label, units and data on the viewer (see code above and the Axis object in the daq_utils module)

  • y_axis: Axis instance containing various fields to set the axis label, units and data on the viewer (see code above and the Axis object in the daq_utils module)

  • nav_axes: in case of a ND data viewer, will be the index of the navigation axis, see ND Viewer

  • nav_x_axis: NavAxis instance containing various fields to set the axis label, units and data on the NDViewer, concerning the navigation viewer (see code above, the NavAxis object in the daq_utils module and the paragraph below)

  • nav_y_axis: NavAxis instance containing various fields to set the axis label, units and data on the NDViewer, concerning the navigation viewer (see code above, the NavAxis object in the daq_utils module and the paragraph below)

To export properly ND datas, the DataFromPlugins object must have 2 other arguments set (compared to 0D, 1D or 2D datas):

  • nav_axes: it is a tuple of integers telling which dimensions of the data numpy array is to be considered as navigation axis. The first integer of the tuple will be used as the xaxis in the viewers (1D or 2D) while the second will be used as the yaxis in the viewers.

  • the navigation axis objects (optional): these are arguments starting by nav (the _x_axis or _y_axis or whatever part is just there to clarify the meaning for the reader of the code) and are instances of NavAxis. A NavAxis is similar to the Axis object but have an important supplementary argument that is nav_index. This one will be used to sort all navigation axes. An index of 0 means this particular NavAxis will be used to display properties of the xaxis on the viewers

Fig. 3.2 highlights how these arguments will change the behaviour of the NDviewer.

NavAxis stuff

Fig. 3.2 An example of 3D datas with 2 navigation axes and how these will be displayed by the NDviewer. Data ready?

One difficulty with these viewer plugins is to determine when data is ready to be read from the controller and then to be send to the user interface for plotting and saving. There are a few solutions:

  • synchronous: The simplest one. When the grab command has been send to the controller (let’s say to its grab_sync method), the grab_sync method will hold and freeze the plugin until the data are ready. The Mock plugin work like this.

  • asynchronous: There are 2 ways of doing asynchronous waiting. The first is to poll the controller state to check if data are ready within a loop. This polling could be done with a while loop but if nothing more is done then the plugin will still be freezed, except if one process periodically the Qt queue event using QtWidgets.QApplication.processEvents() method. The polling can also be done with a timer event, firing periodically a check of the data state (ready or not). Finally, the nicest/hardest solution is to use callbacks (if the controller provides one) and link it to a emit_data method. Synchronous example:

The code below illustrates the poll method using a loop:

def poll_data(self):
    Poll the current data state
    data_ready = False
    while not self.controller.is_ready():


        if ind*sleep_ms>=self.settings.child(('timeout')).value():


    self.emit_data() Asynchronous example:

The code below is derived from daq_Andor_SDK2 (in andor hardware folder) and shows how to create a thread waiting for data ready and triggering the emission of data

class DAQ_AndorSDK2(DAQ_Viewer_base):

    callback_signal = QtCore.Signal() #used to talk with the callback object

    def ini_camera(self):
        callback = AndorCallback(self.controller.WaitForAcquisition) # the callback is linked to the controller WaitForAcquisition method
        self.callback_thread = QtCore.QThread() #creation of a Qt5 thread
        callback.moveToThread(self.callback_thread) #callback object will live within this thread
        callback.data_sig.connect(self.emit_data)  # when the wait for acquisition returns (with data taken), emit_data will be fired

        self.callback_signal.connect(callback.wait_for_acquisition) #
        self.callback_thread.callback = callback

    def grab(self,Naverage=1,**kwargs):
        self.callback_signal.emit()  #trigger the wait_for_acquisition method

def emit_data(self):
        Function used to emit data obtained by callback.
    self.data_grabed_signal.emit([OrderedDict(name='Camera',data=[np.squeeze(self.data.reshape((sizey, sizex)).astype(np.float))], type=self.data_shape)])

class AndorCallback(QtCore.QObject):

    def __init__(self,wait_fn):
        super(AndorCallback, self).__init__()
        self.wait_fn = wait_fn

    def wait_for_acquisition(self):
        err = self.wait_fn()

        if err != 'DRV_NO_NEW_DATA': #will be returned if the main thread called CancelWait

Documentation from Andor SDK concerning the WaitForAcquisition method of the dll:

unsigned int WINAPI WaitForAcquisition(void)

WaitForAcquisition can be called after an acquisition is started using StartAcquisition to put the calling thread to sleep until an Acquisition Event occurs.
It will use less processor resources than continuously polling with the GetStatus function. If you wish to restart the calling thread without waiting for an Acquisition event, call the function CancelWait. Hardware averaging

By default, if averaging of data is needed the Viewer module will take care of it software wise. However, if the hardware controller provides an efficient method to do it (that will save time) then you should set the class field hardware_averaging to True.

class DAQ_NDViewer_Template(DAQ_Viewer_base):
 Template to be used in order to write your own viewer modules
    hardware_averaging = True #will use the accumulate acquisition mode if averaging
    #is True else averaging is done software wise Live Mode

By default, the live Grab mode is done software wise in the core code of the DAQ_Viewer. However, if one want to send data as fast as possible, the live mode is possible within a plugin.

For this, the plugin class attribute, live_mode_available, should be set to True.

class DAQ_2DViewer_MockCamera(DAQ_Viewer_base):

    live_mode_available = True

The method grab_data will then receive a named boolean parameter (in kwargs) called live that tells if one should grab or snap data. The MockCamera plugin illustrates this feature:

def grab_data(self, Naverage=1, **kwargs):
    """Start a grab from the detector

    Naverage: int
        Number of hardware averaging (if hardware averaging is possible, self.hardware_averaging should be set to
        True in class preamble and you should code this implementation)
    kwargs: dict
        others optionals arguments
    if 'live' in kwargs:
        if kwargs['live']:
            self.live = True
            # self.live = False  # don't want to use that for the moment

    if self.live:
        while self.live:
            data = self.average_data(Naverage)
            QThread.msleep(kwargs.get('wait_time', 100))

3.2.6. Hardware needed files

If you are using/referring to custom python wrappers/dlls… within your plugin and need a place where to copy them in PyMoDAQ, then use the \hardware folder of your plugin package. For instance, the daq_2Dviewer_AndorCCD plugin need various files stored in the andor folder (on github repository). I would therefore copy it as \pymodaq_plugins_andor\hardware\andor and call whatever module I need within (meaning there is a __init__.py file in the andor folder) as:

#import controller wrapper
from pymodaq_plugins.hardware.andor import daq_AndorSDK2 #this import the module DAQ_AndorSDK2 containing classes, methods...
#and then use it as you see fit in your module

3.2.7. How to contribute?

If you wish to develop a plugin specific to a new hardware not present on the github repo (and I strongly encourage you to do so!!), you will have to follow the rules as stated above.

Two cases are possible: either you want to add a new hardware from a manufacturer for which a repository already exists 1) (thorlabs, PI, Andor…) or not 2)

  1. You have to fork the existing repo

  2. you will use the pymodaq_plugins_template on github to create a new repo.

Once you’ve done that, you can clone the package locally and install it in developer using pip install -e . from the command line where you cd within the cloned package. This command will install the package but any change you apply on the local folder will be applied on the package. Then just add a new python file in the correct location

In the case of a new repo, you will have to rename a few files (plugin_info.toml, README.rst …) then add the python file of your instrument at the right location.

Once you’re ready with a working plugin, you can then:

  1. Publish your repo on pypi (just by doing a release on github will trigger the creation of a pypi repository, you’ll just have to create an account on pypi and enter your credentials in the SECRETS on github)

  2. do a pull request on the initial repository to merge your new implementations.

All the packages published on pypi using the template and the naming convention will be available in the plugin manager.

Some more detailed instruction would be published and you can in the mean time look at this video