Resolution: Won't Fix
Affects Version/s: None
Fix Version/s: None
Paula's suggestions of improvements:
Some more specific improvements by Paula (regarding the top-right call-out in Animated TS):
- Speed: changes the speed of the red bar sweeping across time points. This feature is useful when the time-series visualizer is used in combination with a 3D representation of the the brain (eg, BRAIN REGION ACTIVITY IN 3D and 2D). Because in an animation the time is implicit, and you get to see one time-point per frame, then it's useful to know WHERE in the time-series you are. See an example here: http://square.github.io/cubism/
- Spacing: this control behaves both as spacing and scaling. It mainly control the spacing between the lines, but as a consequence when there is no spacing, the lines are overlapped and kind of zoomed in. Ideally, scaling and spacing should work separately.
- Page size: the time-window (in milliseconds) currently displayed. In our last discussion we agreed that this may change from a slider to a configurable field, that is the user should be able to specify the start and end time points.
- Input data 1, 2, 3: the input time-series. For the time being is set to 3, but we having more input TimeSeries is a possibility.
- For the moment we cannot use the same TimeSeries datatype more than once because the datatype ID is used to tag the lines.
- Displaying and/or overlaying time-series with different sampling frequencies is a bit of a challenge, probably a feature we need to think about.
This is a very important feature: being able to display multiple time-series.
"Overlaying" data should be supported, that is multiple lines sharing a
single set of axes with some means such as colour or line style used to
Overlaying of data will be used for comparing the results of any set of
simulations as well as potentially comparing simulation with experimental
data. Moreover, since the time-series from the basic monitors (temporal
average, spatial average, temporal subsample, raw ...) yield a n-dimensional
array, overlaying the time-series from two different state variables or modes