docs

Author: yha

src/RunningQuantiles.jl

@@ -28,14 +28,31 @@ function make_window(winlen::Int)
     -winlen÷2:winlen÷2
 end
 
+
+"""
+    running_median(v, w, nan_mode=SkipNaNs())
+
+Computes the running median of the vector `v` with window `w`, where `w` is an odd window length, or a range of offsets.
+See [`running_quantile`](@ref) for details.
+"""
 function running_median(v, w, nan_mode=SkipNaNs(); buffer = default_buffer(v,0.5,w))
     running_quantile(v, 0.5, w, nan_mode; buffer)
 end
 
+"""
+    result = running_quantile(v, p, w, nan_mode=SkipNaNs())
+
+Computes the running `p`-th quantile of the vector `v` with window `w`, where `w` is an odd window length, or a range of offsets.
+Specifically, 
+ - if `w` is a `AbstractUnitRange`, `result[i]` is the `p`-th quantile of `v[(i .+ w) ∩ eachindex(v)]`, where `NaN`s are handled according to `nan_mode`:
+   - `nan_mode==SkipNaN()`: `NaN` values are ignored; quantile is computed over non-`NaN`s
+   - `nan_mode==PropagateNaNs()`: the result is `NaN` whenever the input window contains `NaN`
+   - `nan_mode==ErrOnNaN()`: an error is raise if at least one input window contains `NaN`
+ - if `w` is an odd integer, a centered window of length `w` is used, namely `-w÷2:w÷2`
+ """
 function running_quantile(v, p, w, nan_mode=SkipNaNs(); buffer = default_buffer(v,p,w))
     _running_quantile(v, p, make_window(w), nan_mode; buffer)
 end
-
 function _running_quantile(v, p, r::AbstractUnitRange, nan_mode; buffer)
     result = similar(v, float(eltype(v)))
     # wrapping this Int in a `Ref` helps the compiler not create a `Box`