Scan Framework

In general terms, we call scan to a macro that moves one or more motors and acquires data along the path of the motor(s). See the introduction to the concept of scan in Sardana.

While a scan macro could be written from scratch, Sardana provides a higher- level API (the scan framework) that greatly simplifies the development of scan macros by taking care of the details about synchronization of motors and of acquisitions.

The scan framework is implemented in the scan module, which provides the GScan base class and its specialized derived classes SScan and CScan for step and continuous scans, respectively.

Creating a scan macro consists in writing a generic macro (see the generic macro writing instructions) in which an instance of GScan is created (typically in the prepare() method) which is then invoked in the run() method.

Central to the scan framework is the generator function, which must be passed to the GScan constructor. This generator is a function that allows to construct the path of the scan (see GScan for detailed information on the generator).

A basic example on writing a step scan

Step scans are built using an instance of the SScan class, which requires a step generator that defines the path for the motion. Since in a step scan the data is acquired at each step, the generator controls both the motion and the acquisition.

Note that in general, the generator does not need to generate a determinate (or even finite) number of steps. Also note that it is possible to write generators that vary their current step based on the acquired values (e.g., changing step sizes as a function of some counter reading).

The ascan_demo macro illustrates the most basic features of a step scan:

class ascan_demo(Macro):
    """
    This is a basic reimplementation of the ascan` macro for demonstration
    purposes of the Generic Scan framework. The "real" implementation of
    :class:`sardana.macroserver.macros.ascan` derives from
    :class:`sardana.macroserver.macros.aNscan` and provides some extra features.
    """

    hints = { 'scan' : 'ascan_demo'} #this is used to indicate other codes that the macro is a scan
    env = ('ActiveMntGrp',) #this hints that the macro requires the ActiveMntGrp environment variable to be set

    param_def = [
       ['motor',      Type.Moveable, None, 'Motor to move'],
       ['start_pos',  Type.Float,    None, 'Scan start position'],
       ['final_pos',  Type.Float,    None, 'Scan final position'],
       ['nb_interv',  Type.Integer,  None, 'Number of scan intervals'],
       ['integ_time', Type.Float,    None, 'Integration time']
    ]

    def prepare(self, motor, start_pos, final_pos, nb_interv, integ_time, **opts):
        #parse the user parameters
        self.start = numpy.array([start_pos], dtype='d')
        self.final = numpy.array([final_pos], dtype='d')
        self.integ_time = integ_time

        self.nb_points = nb_interv+1
        self.interv_size = ( self.final - self.start) / nb_interv
        self.name='ascan_demo'
        env = opts.get('env',{}) #the "env" dictionary may be passed as an option

        #create an instance of GScan (in this case, of its child, SScan
        self._gScan=SScan(self, generator=self._generator, moveables=[motor], env=env)


    def _generator(self):
        step = {}
        step["integ_time"] =  self.integ_time #integ_time is the same for all steps
        for point_no in xrange(self.nb_points):
            step["positions"] = self.start + point_no * self.interv_size #note that this is a numpy array
            step["point_id"] = point_no
            yield step

    def run(self,*args):
        for step in self._gScan.step_scan(): #just go through the steps
            yield step

    @property
    def data(self):
        return self._gScan.data #the GScan provides scan data

The ascan_demo shows only basic features of the scan framework, but it already shows that writing a step scan macro is mostly just a matter of writing a generator function.

It also shows that the GScan’s data property can be used to provide the needed return value of the Macro’s data.

A basic example on writing a continuous scan

Continuous scans are built using an instance of the CScan class. Since in the continuous scans the acquisition and motion are decoupled, CScan requires two independent generators:

  • a waypoint generator: which defines the path for the motion in a very similar way as the step generator does for a step scan. The steps generated by this generator are also called “waypoints”.

  • a period generator which controls the data acquisition steps.

Essentially, CScan implements the continuous scan as an acquisition loop (controlled by the period generator) nested within a motion loop (controlled by the waypoint generator). Note that each loop is run on an independent thread, and only limited communication occurs between the two (basically the acquisition starts at the beginning of each movement and ends when a waypoint is reached).

The ascanc_demo macro illustrates the most basic features of a continuous scan::

class ascanc_demo(Macro):
    """
    This is a basic reimplementation of the ascanc` macro for demonstration
    purposes of the Generic Scan framework. The "real" implementation of
    :class:`sardana.macroserver.macros.ascanc` derives from
    :class:`sardana.macroserver.macros.aNscan` and provides some extra features.
    """

    hints = { 'scan' : 'ascanc_demo'} #this is used to indicate other codes that the macro is a scan
    env = ('ActiveMntGrp',) #this hints that the macro requires the ActiveMntGrp environment variable to be set

    param_def = [
       ['motor',      Type.Moveable, None, 'Motor to move'],
       ['start_pos',  Type.Float,    None, 'Scan start position'],
       ['final_pos',  Type.Float,    None, 'Scan final position'],
       ['integ_time', Type.Float,    None, 'Integration time']
    ]

    def prepare(self, motor, start_pos, final_pos, integ_time, **opts):
        self.name='ascanc_demo'
        #parse the user parameters
        self.start = numpy.array([start_pos], dtype='d')
        self.final = numpy.array([final_pos], dtype='d')
        self.integ_time = integ_time
        env = opts.get('env',{}) #the "env" dictionary may be passed as an option

        #create an instance of GScan (in this case, of its child, CScan
        self._gScan = CScan(self,
                            waypointGenerator=self._waypoint_generator,
                            periodGenerator=self._period_generator,
                            moveables=[motor],
                            env=env)

    def _waypoint_generator(self):
        #a very simple waypoint generator! only start and stop points!
        yield {"positions":self.start, "waypoint_id": 0}
        yield {"positions":self.final, "waypoint_id": 1}


    def _period_generator(self):
        step = {}
        step["integ_time"] =  self.integ_time
        point_no = 0
        while(True): #infinite generator. The acquisition loop is started/stopped at begin and end of each waypoint
            point_no += 1
            step["point_id"] = point_no
            yield step

    def run(self,*args):
        for step in self._gScan.step_scan():
            yield step

See also

for another example of a continuous scan implementation (with more elaborated waypoint generator), see the code of meshc

Deterministic scans

By deterministic scans we call these scans which know a priori the number of points and the integration time. In some cases the experimental channel controllers may take profit of this information and prepare for the whole scan measurement up-front instead of preparing before each scan point. When writing this type of scan macros it is enough to set two attributes: nb_points and integ_time in your macro. When these attributes are present in your macro, the generic scan framework will take care of preparing the experimental channels for the whole scan measurement upfront.

Warning

Since nb_points and integ_time attributes identify deterministic scan macros as deterministic and you must not use these attribute names for any other needs.

See also

More information about deterministic scans and measurement preparation can be found in SEP18.

Hooks support in scans

In general, the Hooks API provided by the Hookable base class allows a macro to run other code (the hook callable) at certain points of its execution. When registering, hooks are identified by hook places so the macro knows how/when they should be executed. The hook places are just arbitrary strings and are not fixed by the API, being up to each macro to identify, use and/or ignore them.

See the code of the hooked_scan macro in Macro hooks examples that demonstrates how to use the Hooks API in scans. Other examples of the same chapter can be illustrative.

Also, note that the Sardana-Taurus widget Sequencer widget allows the user to dynamically add hooks to existing macros before execution.

In the case of the scan macros, the hooks can be registered not only via the Hooks API but also passed as key:value pairs, where key is a concatenation of a hook place and the -hooks string e.g. pre-scan-hooks and value is a list of callable hooks, of the “step” dictionary returned by the scan generator (see GScan for more details and aNscan as a use case).

The following is a list of the supported keys:

  • ‘pre-scan-hooks’ : before starting the scan.

  • ‘pre-move-hooks’ : for steps: before starting to move.

  • ‘post-move-hooks’: for steps: after finishing the move.

  • ‘pre-acq-hooks’ : for steps: before starting to acquire.

  • ‘post-acq-hooks’ : for steps: after finishing acquisition but before recording the step.

  • ‘post-step-hooks’ : for steps: after finishing recording the step.

  • ‘post-scan-hooks’ : after finishing the scan.

More examples

Other macros in Macro scans examples illustrate more features of the scan framework.

See also the code of the standard scan macros in the scan module.

Finally, the documentation and code of GScan, SScan and CScan may be helpful.