Example M8: Compute trajectory average of a global attribute — OVITO Python Reference 3.16.0 documentation

# Example M8: Compute trajectory average of a global attribute

This Python modifier lets the user specify the name of a global attribute for which to compute the time average over all frames of the current simulation trajectory. It provides a similar functionality as the built-in `TimeAveragingModifier` of OVITO Pro.

The modifier is based on the advanced programming interface, i.e., it is implemented as a Python class inheriting from `ModifierInterface`.

See also

- Caching (time-independent) computation results

- Performing long-running computations

- User parameters

    from ovito.pipeline import ModifierInterface
    from ovito.data import DataCollection
    from traits.api import Str

    class TimeAverageOfAttribute(ModifierInterface):

        # Specifies the name of the global attribute for which to compute the trajectory time average.
        input_attribute = Str(default_value='', label='Input attribute')

        def modify(self, data: DataCollection, *, input_slots: dict[str, ModifierInterface.InputSlot], data_cache: DataCollection, **kwargs):

            # Display an info string in the status bar of OVITO Pro to indicate what we are doing.
            yield f'Computing time average of attribute {self.input_attribute}'

            if self.input_attribute == '':
                print("Please specify the name of the global attribute to be averaged.")
                return
            elif self.input_attribute not in data.attributes:
                print(f"Global attribute '{self.input_attribute}' does not exist.")
                return

            # Determine the name of the new output attribute, which will store the computed time average.
            output_attribute = self.input_attribute + ' (Average)'

            # Check whether the computed time average is already stored in the modifier's cache. If not, compute it now:
            if output_attribute not in data_cache.attributes:
                slot = input_slots['upstream']

                # Iterate over all frames of the input trajectory to sample instantaneous values of input attribute:
                sum = 0.0
                for frame in range(slot.num_frames):
                    frame_data = slot.compute(frame)
                    sum += frame_data.attributes[self.input_attribute]

                    # Progress reporting: Percentage value will be displayed in the status bar of OVITO Pro.
                    yield frame / slot.num_frames

                # Compute mean value and store it in the cache.
                data_cache.attributes[output_attribute] = sum / slot.num_frames

            # Copy value over from the cache to the actual output data collection:
            data.attributes[output_attribute] = data_cache.attributes[output_attribute]