![]() ![]() ![]() Returns a table that has the count and distinct count values of Ids in the lookback period, for each timeline period (by bin) and for each existing dimensions combination.Ĭalculate counts and dcounts for users in past week, for each day in the analysis period. : (optional) list of the dimensions columns that slice the activity metrics calculation. I checked the header of one problematic t1w compared to a valid one with fslinfo. If the value is a string with the format week/ month/ year, all periods will be startofweek/ startofmonth/ startofyear. this T1w file does not have exactly three dimensions. Dim2Average Average ( Measure) In (Dim2) However, the following formula does not give me the expected results. I am able to get the correct average of Dim2 in the following formula. This value can be a numeric/datetime/timestamp value. There can be multiple values of Dim2 under one value of Dim1, and multiple values of Dim3 under Dim2. Bin: Scalar constant value of the analysis step period.LookbackWindow: Scalar constant value of the lookback period (for example, for dcount users in past 7d: LookbackWindow = 7d).count: The total records count in the time window and dim (s) dcount: The distinct ID values count in the time window and dim (s) newdcount: The distinct ID values in the time window and dim (s) compared to all previous time windows. End: Scalar with value of the analysis end period. Output table schema is: TimelineColumn: The time window start time.Start: Scalar with value of the analysis start period.TimelineColumn: The name of the column representing the timeline.IdColumn: The name of the column with ID values that represent user activity.T | evaluate sliding_window_counts( IdColumn, TimelineColumn, Start, End, LookbackWindow, Bin, ) Arguments The downside is that we must calculate the indexes in the output array. dim1 91 dim2 109 dim3 91 dim4 1 datatype 2 pixdim1 2.000000 pixdim2 2.000000 pixdim3 2.000000 pixdim4 1.000000 calmax 0. This allows us to iterate multi dimensional arrays as if they were flat. it can output slightly odd files sometimes in small ways. Similar to COO, the Dictionary of Keys (DOK) format for sparse. This library also includes several other data structures. T | evaluate sliding_window_counts(id, datetime_column, startofday( ago( 30 d)), startofday( now()), 7 d, 1 d, dim1, dim2, dim3) Syntax 2 Answers Sorted by: 4 I want to present an alternative solution. FSLINFO OUTPUT from mask drawn on MRICRON: datatype FLOAT32 dim1 190 dim2 263 dim3 269 dim4 1 datatype 16 pixdim1 0.999940 pixdim2 0.999940 pixdim3 0.999940 fslstats testMRICRON -V 19416 19412. all the data was originally tortoise diffprep output if that is useful. This makes it easy to store a multidimensional sparse array, but we still need to reimplement all of the array operations like transpose, reshape, slicing, tensordot, reductions, etc., which can be challenging in general. ![]()
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