Hello, Sorry but I am thinking of using a time-gate for my own experiment and wondered if you managed to get it working? Thanks

For future reference: Okay I just found out that the unit for the y-axis must be set in the "Properties" window of the plot.

So definitely max gain it is not useful for my purposes. I wanna construct a more symmetrical radiation pattern and maximize the gain in a certain direction. And of course, I would like to know which parameters I should set up to get such purposes.

Update: I manually summed the fields and got an answer that makes more sense (sum is always greater than amplitude modulation). I am not sure where it is going wrong when I am using the field combiner.

In general, an easy way to create a postprocessing script is to first do it in the GUI and then use the "To-Python" function (available via right-click on the algorithm in the Explorer window). Here is what the auto-generated script looks like for a simple pipeline with a Crop Filter: # Creating the analysis pipeline # Adding a new SimulationExtractor simulation = document.AllSimulations["EM"] simulation_extractor = simulation.Results() # Adding a new EmSensorExtractor em_sensor_extractor = simulation_extractor["Overall Field"] em_sensor_extractor.FrequencySettings.ExtractedFrequency = u"All" em_sensor_extractor.Normalization.Normalize = True em_sensor_extractor.SurfaceCurrent.SurfaceResolution = 0.001, units.Meters document.AllAlgorithms.Add(em_sensor_extractor) # Adding a new FieldCropFilter inputs = [em_sensor_extractor.Outputs["EM E(x,y,z,f0)"]] field_crop_filter = analysis.core.FieldCropFilter(inputs=inputs) field_crop_filter.LowerExtent = numpy.array([1, 3, 2]) field_crop_filter.UpperExtent = numpy.array([21, 21, 21]) field_crop_filter.UpdateAttributes() document.AllAlgorithms.Add(field_crop_filter) Note fyi that you don't actually need to pass a Numpy array to UpperExtent or LowerExtent, it also works if you pass any iterable, like a list or a tuple.

In Sim4Life, the complex field S(x,y,z,f0) is (one half of) the cross product of the complex fields E(x,y,z,f0) and H(x,y,z,f0), both of which are the complex representation of harmonic fields (actually S = 0.5 E cross H*, where H* is the complex conjugate of H) The time-averaged Poynting vector (if you want the power flow) is given by Re(S) See the Wikipedia article (https://en.wikipedia.org/wiki/Poynting_vector) for more detail. EDIT: this post was corrected to reflect the notations used in Sim4Life, namley that S(x,y,z,f0) is already the time-averaged quantity and includes the factor 1/2.

I typically just take screenshots (I recommend SnagIt since it 'snaps' screenshots to subwindows (for example, just the rendering window). Alternatively, for reproducibility, I sometimes use a script to take screenshots. There's a third way (unfortunately not so reliable), which is to use the built in Screen shot grabber ... Go to 3D View -> Screen Capture (you need to configure it first ... I say it's unreliable because saving a particular '3D View' (Camera Settings -> Save) doesn't work well when you reimport it (views get forgotten or overwritten) For the script I do something like this. I create a wireframe, zoom to it, and then use the following script (which you'll need to tweak via trial and error since you might get weird behavior for the color bars and ruler, axis, etc). #Manual Record Screenshot import s4l_v1 as s4l import XCoreModeling import XRendererUI import XCoreUI bb = "CameraBoundingBox" e_cam = s4l.model.AllEntities()[bb] bb_pts = XCoreModeling.GetBoundingBox([e_cam]) oglview = XCoreUI.GetUIApp().Frame.MainView[0] oglview.ZoomTo(bb_pts[0], bb_pts[1]) x_size = bb_pts[1][0]-bb_pts[0][0] z_size = bb_pts[1][2]-bb_pts[0][2] img_dim = [x_size,z_size] screen_prefix = "Test" screen_name = "Test" XRendererUI.SaveScreenCapture(width = int(1000*img_dim[0]/img_dim[1]), height = 1000, \ transparent_background = False, output_folder=r"C:\Users\montanaro\Desktop\Screen", \ output_prefix=screen_prefix+screen_name)