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Linear time interpolation in FieldTimeSeries #3236

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merged 15 commits into from
Sep 18, 2023
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simone-silvestri
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@simone-silvestri simone-silvestri commented Aug 26, 2023

Allow linear time interpolation between different indices of the field time series triggered by a Float64 (AbstractTime not supported at the moment) index.

julia> fts2 = FieldTimeSeries("testfile.jld2", "u");

julia> fts2[1]
17×16×10 Field{Face, Center, Center} on LatitudeLongitudeGrid on CPU
├── grid: 16×16×10 LatitudeLongitudeGrid{Float64, Bounded, Bounded, Bounded} on CPU with 3×3×3 halo and with precomputed metrics
├── boundary conditions: Nothing
└── data: 23×22×16 OffsetArray(view(::Array{Float64, 4}, :, :, :, 1), -2:20, -2:19, -2:13) with eltype Float64 with indices -2:20×-2:19×-2:13
    └── max=2.0, min=2.0, mean=2.0

julia> fts2[2]
17×16×10 Field{Face, Center, Center} on LatitudeLongitudeGrid on CPU
├── grid: 16×16×10 LatitudeLongitudeGrid{Float64, Bounded, Bounded, Bounded} on CPU with 3×3×3 halo and with precomputed metrics
├── boundary conditions: Nothing
└── data: 23×22×16 OffsetArray(view(::Array{Float64, 4}, :, :, :, 2), -2:20, -2:19, -2:13) with eltype Float64 with indices -2:20×-2:19×-2:13
    └── max=4.0, min=4.0, mean=4.0

julia> fts2[1.35]
17×16×10 Field{Face, Center, Center} on LatitudeLongitudeGrid on CPU
├── grid: 16×16×10 LatitudeLongitudeGrid{Float64, Bounded, Bounded, Bounded} on CPU with 3×3×3 halo and with precomputed metrics
├── boundary conditions: FieldBoundaryConditions
│   └── west: Nothing, east: Nothing, south: ZeroFlux, north: ZeroFlux, bottom: ZeroFlux, top: ZeroFlux, immersed: ZeroFlux
├── operand: BinaryOperation at (Face, Center, Center)
├── status: time=0.0
└── data: 23×22×16 OffsetArray(::Array{Float64, 3}, -2:20, -2:19, -2:13) with eltype Float64 with indices -2:20×-2:19×-2:13
    └── max=2.7, min=2.7, mean=2.7

julia> fts2[1, 1, 1, 13]
26.0

julia> fts2[1, 1, 1, 14]
28.0

julia> fts2[1, 1, 1, 13.67]
27.34

@navidcy
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navidcy commented Aug 28, 2023

Question: If time is DateTime then only integer indices work but it fails for fractional indices. Correct?

@simone-silvestri
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simone-silvestri commented Aug 28, 2023

Well, fractional indices work, just not DateTimes indices

I am not super familiar with what date-times supports in terms of operations, if it supports summation and division it is straightforward to extend indexing with to DateTimes

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navidcy commented Aug 28, 2023

I think it can be done but it’s definitely not as straightforward as +, /.

Happy to approve now and open issue to generalize the method for TimeDates later.

@glwagner
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Are we ok with this as a user interface? it's a little implicit. We could alternatively use a function-based API to make things a little more obvious, something like

fts[1, 2, 3, 4] # get 4th time-index
at_time(4, fts, 1, 2, 3) # linearly interpolate to t=4

mainly i'd be worried about issues like

fts[1, 2, 3, 4] \ne fts[1, 2, 3, 4.0]

which is rather easy to confuse?

We also might be able to use syntax like

fts[1, 2, 3, time=4]

if that is performant. Or

fts[1, 2, 3, Time(4)]

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navidcy commented Aug 28, 2023

Out of curiosity @simone-silvestri could you check

fts2[1, 2, 3, 4] == fts2[1, 2, 3, 4.0]

in your example with the .jld2 file above?

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Out of curiosity @simone-silvestri could you check

fts2[1, 2, 3, 4] == fts2[1, 2, 3, 4.0]

in your example with the .jld2 file above?

the outcome depends on whether fts.times = [1, 2, 3, 4] or not.

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simone-silvestri commented Aug 28, 2023

Another way would be to pass Oceananigans' Clock as an index.

This might be convenient because we usually pass the clock to functions (such as forcing and bc) so we always have it available

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@navidcy I added a way to not interpolate so that in case 4 belongs to fts2.times then fts2[4.0] == fts2[4]

but this shows a bit the confusion because 4.0 here is a time and 4 is an index.

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We wouldn't have Clock available in post-processing.

@simone-silvestri
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So I would propose a clock implementation for easy internal manipulation which calls the at_time function available for postprocessing

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Clock does save the definition of a new type. However, I think it is misleading because it contains extra information like the iteration and stage. Here, the time-interpolation is independent of those.

Why not a new type Time? What is your opposition to that?

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Basically I think the "convenience" of not having to define a new type is trivial. You can define Time(clock::Clock).

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simone-silvestri commented Aug 28, 2023

I am thinking about the ease of use in bc, for example

@inline time_interpolated_bc(i, j, grid, clock, fields, p) = p.time_array[i, j, 1, clock]

vs

@inline function time_interpolated_bc(i, j, grid, clock, fields, p) 
   time = Time(clock)
   return p.time_array[i, j, 1, time]
end

I find the first implementation a bit more straightforward, but we can also have both

@glwagner
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I am thinking about the ease of use in bc, for example

@inline time_interpolated_bc(i, j, grid, clock, fields, p) = p.time_array[i, j, 1, clock]

vs

@inline function time_interpolated_bc(i, j, grid, clock, fields, p) 
   time = Time(clock)
   return p.time_array[i, j, 1, time]
end

I find the first implementation a bit more straightforward, but we can also have both

I propose we prioritize the user interface rather than the source code implementation, which basically has to be written once then left alone

@glwagner
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also worth checking out

https://github.com/rafaqz/DimensionalData.jl

the Time object is a "selector" in their terminology. IF we have something similar here, we could eventually consider sub typing AbstractDimArray (which has been proposed before), which gives us a lot of powerful functionality. Someone passionate about that stuff has to take that on for a full implementation but at least we are consistent with it, which helps

src/TimeSteppers/clock.jl Outdated Show resolved Hide resolved
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Co-authored-by: Gregory L. Wagner <[email protected]>
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@glwagner
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glwagner commented Sep 2, 2023

We will also have to figure out (perhaps in a future PR) how to insert boundary conditions to Time, eg Cyclic (so forcing / specification repeats) or Continued


# Linear time interpolation
function Base.getindex(fts::FieldTimeSeries, i::Int, j::Int, k::Int, time_index::Time)
Ntimes = length(fts.times)
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length is slow for big vectors right? Should we allow this to be passed into Time?

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then we can implement something like

how_many_times(time::Time{<:Int}, times) = time.Ntimes
how_many_times(::Time, times) = length(times)

or mahybe you have a better name

Comment on lines 204 to 211
if n₁ == n₂ # no interpolation
return getindex(fts, i, j, k, n₁)
end

# fractional index
@inbounds n = (n₂ - n₁) / (fts.times[n₂] - fts.times[n₁]) * (time - fts.times[n₁]) + n₁
return getindex(fts, i, j, k, n₂) * (n - n₁) + getindex(fts, i, j, k, n₁) * (n₂ - n)
end
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Suggested change
if n₁ == n₂ # no interpolation
return getindex(fts, i, j, k, n₁)
end
# fractional index
@inbounds n = (n₂ - n₁) / (fts.times[n₂] - fts.times[n₁]) * (time - fts.times[n₁]) + n₁
return getindex(fts, i, j, k, n₂) * (n - n₁) + getindex(fts, i, j, k, n₁) * (n₂ - n)
end
# fractional index
@inbounds n = (n₂ - n₁) / (fts.times[n₂] - fts.times[n₁]) * (time - fts.times[n₁]) + n₁
fts_interpolated = getindex(fts, i, j, k, n₂) * (n - n₁) + getindex(fts, i, j, k, n₁) * (n₂ - n)
# Don't interpolate if n = 0.
return ifelse(n₁ == n₂, getindex(fts, i, j, k, n₁), fts_interpolated)
end

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I prefer not to do the interpolation if it is not required

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Why?

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Ah, I was actually looking at the other getindex, no in the kernel it's ok

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(It's not good practice to resolve other people's comments, unless they are not responding or something --- the reviewer gets priority to decide whether their comment has been resolved or not.)

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What kernel is fast? I'm referring to the situation where this is embedded in another kernel. For example, this getindex call can be embedded in the kernels that calculate model tendencies.

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On the other hand, a simple if condition should be okay here as there should not be problems of branch divergence (all threads should look at the same time).

Got it. Is that still the case in very complicated kernels, for example kernels that calculate 3D tendencies?

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sorry, linear interpolation in GPU kernels is fast. That is if threads access the same time index. There could be a situation in which different threads access different time indexes but it doesn't immediately come to mind.

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My question is whether that principle is still useful when the linear interpolation is surrounded by lots of other expensive operations, as can happen when this is used inside a tendency kernel.

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Here's another example: FieldTimeSeries is used to store data for an advecting velocity field. This is the application you'd get if you're interested in, for example, advecting biogeochemical tracers with a velocity field derived from the ECCO state estimate. In that case the linear interpolation is inserted into an advection scheme.

@navidcy navidcy added the feature 🌟 Something new and shiny label Sep 7, 2023
@simone-silvestri simone-silvestri merged commit 2328d54 into main Sep 18, 2023
47 checks passed
@simone-silvestri simone-silvestri deleted the ss/rework-fts-1 branch September 18, 2023 12:19
navidcy pushed a commit that referenced this pull request Sep 20, 2023
* Linear time interpolation

* fixed

* little bug when n == n1 == n2

* time implementation

* Update src/TimeSteppers/clock.jl

Co-authored-by: Gregory L. Wagner <[email protected]>

* Update src/Units.jl

Co-authored-by: Gregory L. Wagner <[email protected]>

* fix

* Update src/Units.jl

Co-authored-by: Gregory L. Wagner <[email protected]>

* add triple ticks

* import

* change

---------

Co-authored-by: Gregory L. Wagner <[email protected]>
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