add test for curvefit

.gitignore

@@ -15,6 +15,9 @@ temp/
 examples/
 *.R
 *.jld
-*.jld2
+# *.jld2
 
 Manifest.toml
+
+## Figures
+*.pdf

Project.toml

@@ -13,10 +13,15 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 nlminb_jll = "c04a2911-6870-53d6-aa47-4d7d5aff1028"
 
 [compat]
+Parameters = "0.12"
+StaticArrays = "1.9"
+nlminb_jll = "0.1.1"
 julia = "1.9"
 
 [extras]
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test"]
+test = ["Test", "JLD2", "UnPack"]

data/data-CA_NS6.jl

@@ -0,0 +1,17 @@
+using JLD2
+using RData
+using UnPack
+
+l = load("data/phenofit-CA_NS6.rda")
+@unpack INPUT, brks2 = l
+
+dt = brks2["dt"]
+nptperyear = INPUT["nptperyear"] |> Int
+y = INPUT["y"]
+t = INPUT["t"]
+w = INPUT["w"]
+
+ylu = INPUT["ylu"]
+ylu = [ylu[1], ylu[2]]
+
+jldsave("data/phenofit-CA_NS6.jld2", true; y, t, w, ylu, nptperyear, dt)

src/CurveFit/Constant.jl

@@ -0,0 +1,19 @@
+DATE_ORIGIN = Date("2000-01-01")
+
+FUNCS_FITDL = Dict(
+  "AG"     => FitDL_AG,
+  "Zhang"  => FitDL_Zhang,
+  "Beck"   => FitDL_Beck,
+  "Elmore" => FitDL_Elmore,
+  "Gu"     => FitDL_Gu,
+  "Klos"   => FitDL_Klos
+)
+
+FUNCS_doubleLog = Dict(
+  "AG"     => doubleLog_AG,
+  "Zhang"  => doubleLog_Zhang,
+  "Beck"   => doubleLog_Beck,
+  "Elmore" => doubleLog_Elmore,
+  "Gu"     => doubleLog_Gu,
+  "Klos"   => doubleLog_Klos
+)

src/CurveFit/CurveFit.jl

@@ -1,33 +1,18 @@
+include("_get_ylu.jl")
 include("init_param.jl")
 include("doubleLogistics.jl")
 
-include("get_ylu.jl")
 include("get_extent.jl")
 include("FitDL.jl")
+include("Constant.jl")
 
 
-DATE_ORIGIN = Date("2000-01-01")
+"""
+# Arguments
+- `y::AbstractVector{T}`: input time series
 
-FUNCS_FITDL = Dict(
-  "AG" => FitDL_AG,
-  "Zhang" => FitDL_Zhang,
-  "Beck" => FitDL_Beck,
-  "Elmore" => FitDL_Elmore,
-  "Gu" => FitDL_Gu,
-  "Klos" => FitDL_Klos
-)
-
-FUNCS_doubleLog = Dict(
-  "AG" => doubleLog_AG,
-  "Zhang" => doubleLog_Zhang,
-  "Beck" => doubleLog_Beck,
-  "Elmore" => doubleLog_Elmore,
-  "Gu" => doubleLog_Gu,
-  "Klos" => doubleLog_Klos
-)
-
-
-function curvefits(y::AbstractArray{T,1}, t, w::AbstractArray{T,1},
+"""
+function curvefits(y::AbstractVector{T}, t, w::AbstractVector{T},
   ylu::Vector{Float64}, nptperyear::Integer,
   dt;
   methods=["AG", "Zhang", "Beck", "Elmore"]) where {T<:Real}
@@ -45,7 +30,7 @@ function curvefits(y::AbstractArray{T,1}, t, w::AbstractArray{T,1},
 
   begs = getDateId(dt[:, :beg], t)
   # peaks = getDateId(dt[:, :peak], t)
-  ends = getDateId(dt[:, :end], t, "after")
+  ends = getDateId(dt[:, :_end], t, "after")
   ns = length(begs) # number of seasons
 
   opts = []
@@ -54,7 +39,7 @@ function curvefits(y::AbstractArray{T,1}, t, w::AbstractArray{T,1},
     I_end = ends[i]
     I = I_beg:I_end
     I_extend = get_extentI(w0, I_beg, I_end, nptperyear)
-    ylu_period = get_ylu(input.y, years, w0, width_ylu, I; wmin=0.2) # ylu_period 
+    ylu_period = _get_ylu(input.y, years, w0, width_ylu, I; wmin=0.2) # ylu_period 
     merge_ylu!(ylu, ylu_period)
     # println(ylu_period)
 
@@ -69,6 +54,7 @@ function curvefits(y::AbstractArray{T,1}, t, w::AbstractArray{T,1},
     ## solve double logistics
     inputI = input_struct(yi, ti, wi)
     opt = Dict{String,Any}()
+
     for meth = methods
       temp = FUNCS_FITDL[meth](inputI) # par, obj, method
       # opt["ypred"] = getfield(curvefit, Symbol("doubleLog_$meth"))(opt["par"], tout)

src/CurveFit/FitDL.jl

@@ -113,8 +113,8 @@ end
 function FitDL_template(input::input_struct, method=nothing; options...)
   par0, lims = init_param(input)
 
-  func_init = getfield(curvefit, Symbol("init_$method"))
-  func! = getfield(curvefit, Symbol("doubleLog_$(method)!"))
+  func_init = getfield(VegCurveFit, Symbol("init_$method"))
+  func! = getfield(VegCurveFit, Symbol("doubleLog_$(method)!"))
 
   prior, lower, upper = func_init(par0, lims)
   # opt (Dict): par, obj

src/CurveFit/get_ylu.jl → src/CurveFit/_get_ylu.jl

@@ -1,8 +1,4 @@
-get_range(y::AbstractArray{T,1}) where {T<:Real} = [minimum(y), maximum(y)]
-
-get_ylu(y) = get_range(y)
-
-function get_ylu(y, years, w0, width::Integer, I; wmin=0.2)
+function _get_ylu(y, years, w0, width::Integer, I; wmin=0.2)
   n = length(w0)
   I_beg = first(I)
   I_end = last(I)
@@ -43,4 +39,4 @@ function merge_ylu!(ylu, ylu_new)
 end
 
 
-export get_range, get_ylu
+# export get_range, get_ylu

src/DataTypes.jl

@@ -31,21 +31,23 @@ end
 
 
 # input of check_input object
-mutable struct input_struct
-  y::AbstractArray{<:Real,1}
+# TODO: 尚有提升空间
+mutable struct input_struct{T<:Real}
+  y::AbstractVector{T}
   # t::AbstractArray{<:Dates.Date, 1}
   t::AbstractArray{<:Union{Dates.Date,<:Real},1}
-  w::AbstractArray{<:Real,1}
+  w::AbstractVector{T}
 end
 
-function input_struct(y::AbstractArray{T,1}, t::AbstractArray{T,1}) where {T<:Real}
-  input_struct(y, t, ones(y))
+function input_struct(y::AbstractVector{T}, t::AbstractVector{T}) where {T<:Real}
+  input_struct{T}(y, t, ones(T, y))
 end
 
-mutable struct inputI_struct
-  y::AbstractArray{<:Real,1}
-  t::AbstractArray{<:Real,1}
-  w::AbstractArray{<:Real,1}
+
+mutable struct inputI_struct{T}
+  y::AbstractVector{T}
+  t::AbstractVector{<:Real}
+  w::AbstractVector{T}
   ind::UnitRange{Int64}
   # tout::AbstractArray{<:Real, 1}
 end

src/VegCurveFit.jl

@@ -8,11 +8,11 @@ using Statistics
 
 include("DataTypes.jl")
 include("tools.jl")
+include("get_ylu.jl")
 
 include("Optim/Optim.jl")
 include("weights/wFUN.jl")
 
-include("get_ylu.jl")
 
 include("CurveFit/CurveFit.jl")
 

src/get_ylu.jl

@@ -1,17 +1,20 @@
 import Statistics: quantile
 
 
+get_range(y::AbstractVector{T}) where {T<:Real} = [minimum(y), maximum(y)]
+
+get_ylu(y) = get_range(y)
+
+
 """
     get_ylu(y::AbstractVector{<:Real}, w::AbstractVector{<:Real};
         wmin=0.2, wmid=0.5, wmax=1.0, 
         ratio = 0.4, 
         alpha=0.02, alpha_high=nothing)
-
-
 """
 function get_ylu(y::AbstractVector{<:Real}, w::AbstractVector{<:Real};
-  wmin=0.2, wmid=0.5, wmax=1.0, 
-  ratio = 0.4, 
+  wmin=0.2, wmid=0.5, wmax=1.0,
+  ratio=0.4,
   alpha=0.02, alpha_high=nothing)
 
   alpha_high = null_default(alpha_high, alpha)
@@ -27,7 +30,7 @@ function get_ylu(y::AbstractVector{<:Real}, w::AbstractVector{<:Real};
     w_critical = wmid
   end
   y_good = @view y[w.>=w_critical]
-  
+
   # alpha/2, alpha_high set to 0.05 for remote sensing (20200322)
   ymin = max(quantile(y_good, alpha / 2), 0) # TODO: fix CN-Din
   ymax = quantile(y_good, 1 - alpha_high / 2)

src/smooth_SG/SG_weighted.jl

@@ -115,7 +115,7 @@ wSG(y::AbstractVector{FT}; kw...) where {FT<:Real} = wSG(y, ones(FT, size(y)); k
 
 
 # weighted Savitzky Golay filter
-function wSG_low(y::AbstractArray{T,1}, w::AbstractArray{T2,1}; halfwin=1, d=2) where {
+function wSG_low(y::AbstractVector{T}, w::AbstractArray{T2,1}; halfwin=1, d=2) where {
   T<:Real,T2<:Real}
   # constrain the w_min, unless it will lead to matrix division erorr
   # w = deepcopy(w)

src/tools.jl

@@ -3,12 +3,12 @@
 #     println("optimized improved:", mean(b_old.times)/mean(b_new.times), " times")
 # end
 
-first(x::AbstractArray{T,1}) where {T<:Real} = x[1]
-last(x::AbstractArray{T,1}) where {T<:Real} = x[end]
+# first(x::AbstractVector) = x[1]
+# last(x::AbstractVector) = x[end]
 
 day2num(x::AbstractArray{Dates.Day,1}) = map(x -> x.value, x)
 
-function aggregate(x::AbstractArray{T,1}, by::AbstractArray{T2,1}, FUN::Function=mean) where {
+function aggregate(x::AbstractVector{T}, by::AbstractArray{T2,1}, FUN::Function=mean) where {
   T<:Real,T2<:Real}
 
   grps = unique(by)
@@ -30,5 +30,4 @@ function pmap(vec::Vector{Dict}, prop)
   map(x -> x[prop], vec)
 end
 
-export first, last, day2num, aggregate,
-  pmap2
+export day2num, aggregate, pmap2

src/weights/wBisquare.jl

@@ -1,7 +1,7 @@
 using Statistics
 
 # Genius function
-function wBisquare(y::AbstractArray{T,1}, yfit::AbstractArray{T,1}, w::AbstractArray{T2,1},
+function wBisquare(y::AbstractVector{T}, yfit::AbstractVector{T}, w::AbstractArray{T2,1},
   nptperyear::Int;
   options...) where {T<:AbstractFloat,T2<:AbstractFloat}
 
@@ -32,7 +32,7 @@ end
 
 # Examples
 """
-function wBisquare(y::AbstractArray{T,1}, yfit::AbstractArray{T,1}, w::AbstractArray{T2,1};
+function wBisquare(y::AbstractVector{T}, yfit::AbstractVector{T}, w::AbstractArray{T2,1};
   # QC_flag;
   # iter::Integer = 2, 
   # to_upper = true, 

src/weights/wTSM.jl

@@ -6,7 +6,7 @@ Weight updating method in TIMESAT
 # Author
 Translated from TIMESAT to Julia by Dongdong Kong (20200808)
 """
-function wTSM!(y::AbstractArray{T,1}, yfit::AbstractArray{T,1}, w::AbstractArray{T,1};
+function wTSM!(y::AbstractVector{T}, yfit::AbstractVector{T}, w::AbstractVector{T};
   iter::Integer=2, nptperyear::Integer=46, wfact::Float64=0.5) where {T<:AbstractFloat}
 
   n = length(y)
@@ -48,7 +48,7 @@ function wTSM!(y::AbstractArray{T,1}, yfit::AbstractArray{T,1}, w::AbstractArray
   end
 end
 
-# function wTSM(y::AbstractArray{T,1}, yfit::AbstractArray{T,1}, w::AbstractArray{T,1}; options...) where {T <: AbstractFloat}
+# function wTSM(y::AbstractVector{T}, yfit::AbstractVector{T}, w::AbstractVector{T}; options...) where {T <: AbstractFloat}
 #     w = copy(w)
 #     wTSM!(y, yfit, w; options...)
 # end

test/runtests.jl

@@ -1,6 +1,7 @@
 using Test
 using VegCurveFit
 
+include("test-curvefit.jl")
 include("test-lambda_init.jl")
 include("test-smooth_SG.jl")
 include("test-smooth_whit.jl")

test/test-curvefit.jl

@@ -0,0 +1,9 @@
+using Test
+using JLD2, UnPack
+@unpack y, w, t, ylu, dt, nptperyear = load("data/phenofit-CA_NS6.jld2")
+
+@testset "curvefits" begin
+  @test_nowarn begin
+    @time r = curvefits(y, t, w, ylu, nptperyear, dt)
+  end
+end