add dailyagg function

CHANGELOG.md

@@ -1,3 +1,9 @@
+# 0.17.0
+
+- New function `dailyagg`
+- Many bug fixes in terms of compatibility with other packages
+- Usage of Julia Package extensions for GeoMakie integration
+
 # 0.16.3
 - New functions `value_space, quantile_space`.
 - `globalattr` has been renamed to `ncglobalattr`.

Project.toml

@@ -1,7 +1,7 @@
 name = "ClimateBase"
 uuid = "35604d93-0fb8-4872-9436-495b01d137e2"
 authors = ["Datseris <[email protected]>", "Philippe Roy <[email protected]>"]
-version = "0.17.1"
+version = "0.17.2"
 
 [deps]
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"

src/exports.jl

@@ -7,7 +7,7 @@
 # Temporal
 export monthday_indices, maxyearspan, daymonth, realtime_days, realtime_milliseconds,
 temporal_sampling, timemean, timeagg, monthlyagg, yearlyagg, temporalranges, seasonalyagg,
-season, DAYS_IN_ORBIT, HOURS_IN_ORBIT, seasonality, sametimespan
+season, DAYS_IN_ORBIT, HOURS_IN_ORBIT, seasonality, sametimespan, dailyagg
 
 # Spatial
 export transform_to_coord

src/physical_dimensions/temporal.jl

@@ -20,9 +20,6 @@ no_time_datetime(::T) where {T<:TimeType} = T
 "daymonth(t) = day(t), month(t)"
 daymonth(t) = day(t), month(t)
 
-maxyearspan(A::AbstractDimArray, tsamp = temporal_sampling(A)) =
-maxyearspan(gnv(dims(A, Time)), tsamp)
-
 """
     temporal_sampling(x) → symbol
 Return the temporal sampling type of `x`, which is either an array of `Date`s or
@@ -83,7 +80,8 @@ end
 
 """
     maxyearspan(A::ClimArray) = maxyearspan(dims(A, Time))
-    maxyearspan(t::Vector{<:DateTime}) → i
+    maxyearspan(t::AbstractVector{<:DateTime}) → i
+
 Find the maximum index `i` of `t` so that `t[1:i]` covers exact(*) multiples of years.
 
 (*) For monthly spaced data `i` is a multiple of `12` while for daily data we find
@@ -126,6 +124,9 @@ function maxyearspan(times, tsamp = temporal_sampling(times))
     end
 end
 
+maxyearspan(A::AbstractDimArray, tsamp = temporal_sampling(A)) =
+maxyearspan(gnv(dims(A, Time)), tsamp)
+
 
 """
     monthday_indices(times, date = times[1])
@@ -399,6 +400,22 @@ function monthlyagg(A::ClimArray, f = mean; mday = 15)
     return timegroup(A, f, t, tranges)
 end
 
+"""
+    dailyagg(A::ClimArray, f = mean) -> B
+Create a new array where the temporal information has been aggregated into days
+using the function `f`.
+"""
+function dailyagg(A::ClimArray, f = mean)
+    t0 = gnv(dims(A, Time))
+    ts, tf = extrema(t0)
+    DT = no_time_datetime(ts)
+    startdate = DT(year(ts), month(ts), day(ts))
+    finaldate = DT(year(tf), month(tf), day(tf))
+    t = startdate:Day(1):finaldate
+    tranges = temporalranges(t0, Dates.day)
+    return timegroup(A, f, t, tranges)
+end
+
 """
     yearlyagg(A::ClimArray, f = mean) -> B
 Create a new array where the temporal information has been aggregated into years
@@ -414,6 +431,8 @@ function yearlyagg(A::ClimArray, f = mean)
     return timegroup(A, f, t, tranges)
 end
 
+# TODO: this function does not respect the order of dimensions.
+# it always puts time last
 function timegroup(A, f, t, tranges)
     other = otherdims(A, Time)
     B = ClimArray(zeros(eltype(A), length.(other)..., length(t)),

test/temporal_tests.jl

@@ -128,6 +128,17 @@ end
         Asea = seasonalyagg(A)
         tsea = dims(Asea, Ti).val
         @test Base.step(tsea) == Month(3)
+
+        @testset "dailyagg" begin
+            t = DateTime(1,1,1,0):Hour(12):DateTime(1,1,3, 12)
+            x = [float(isodd(i)) for i in 1:length(t)]
+            x = hcat([copy(x) for j in 1:4]...)
+            X = ClimArray(x, (Tim(t), Lon(1:4)))
+            D = dailyagg(X, mean)
+            @test all(isequal(0.5), D)
+            @test step(gnv(dims(D, Tim))) == Day(1)
+            @test length(dims(D, Tim)) == length(t)÷2
+        end
     end
 
     @testset "interannual variability" begin