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Adds method for splitting data into periods #528

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11 changes: 11 additions & 0 deletions docs/src/split.md
Original file line number Diff line number Diff line change
Expand Up @@ -124,3 +124,14 @@ using MarketData
tail(cl)
tail(cl, 3)
```

## Splitting by period

Splitting data by a given function, e.g. `Dates.day` into periods.

```@repl
using TimeSeries
using MarketData

split(cl, Dates.day)
```
12 changes: 6 additions & 6 deletions src/TimeSeries.jl
Original file line number Diff line number Diff line change
Expand Up @@ -12,12 +12,12 @@ using Tables
using PrettyTables: pretty_table

export TimeArray, AbstractTimeSeries,
when, from, to, findwhen, timestamp, values, colnames, meta, head, tail,
lag, lead, diff, percentchange, moving, upto,
uniformspaced, uniformspace, dropnan,
basecall,
merge, collapse,
readtimearray, writetimearray
when, from, to, findwhen, timestamp, values, colnames, meta, head, tail, split,
lag, lead, diff, percentchange, moving, upto,
uniformspaced, uniformspace, dropnan,
basecall,
merge, collapse,
readtimearray, writetimearray

# modify.jl
export rename, rename!
Expand Down
52 changes: 43 additions & 9 deletions src/split.jl
Original file line number Diff line number Diff line change
Expand Up @@ -7,17 +7,17 @@ when(ta::TimeArray, period::Function, t::String) =

# from, to ######################

from(ta::TimeArray{T, N, D}, d::D) where {T, N, D} =
from(ta::TimeArray{T,N,D}, d::D) where {T,N,D} =
length(ta) == 0 ? ta :
d < timestamp(ta)[1] ? ta :
d > timestamp(ta)[end] ? ta[1:0] :
ta[searchsortedfirst(timestamp(ta), d):end]
d < timestamp(ta)[1] ? ta :
d > timestamp(ta)[end] ? ta[1:0] :
ta[searchsortedfirst(timestamp(ta), d):end]

to(ta::TimeArray{T, N, D}, d::D) where {T, N, D} =
to(ta::TimeArray{T,N,D}, d::D) where {T,N,D} =
length(ta) == 0 ? ta :
d < timestamp(ta)[1] ? ta[1:0] :
d > timestamp(ta)[end] ? ta :
ta[1:searchsortedlast(timestamp(ta), d)]
d < timestamp(ta)[1] ? ta[1:0] :
d > timestamp(ta)[end] ? ta :
ta[1:searchsortedlast(timestamp(ta), d)]

###### findall ##################

Expand All @@ -43,7 +43,7 @@ findwhen(ta::TimeArray{Bool,1}) = timestamp(ta)[findall(values(ta))]
end
end

@generated function tail(ta::TimeArray{T,N}, n::Int=6) where {T,N}
@generated function tail(ta::TimeArray{T,N}, n::Int=6) where {T,N}
new_values = (N == 1) ? :(values(ta)[start:end]) : :(values(ta)[start:end, :])

quote
Expand All @@ -58,3 +58,37 @@ end
Base.first(ta::TimeArray) = head(ta, 1)

Base.last(ta::TimeArray) = tail(ta, 1)


"""
split(data::TimeSeries.TimeArray, period::Function)

Split `data` by `period` function, returns a vector of `TimeSeries.TimeArray`.

## Arguments

- `data::TimeSeries.TimeArray`: Data to split
- `period::Function`: Function, e.g. `Dates.day` that is used to split the `data`.
"""
split(data::TimeSeries.TimeArray, period::Function) = map(i -> data[i], _split(data, period))
function _split(data::TimeSeries.TimeArray, period::Function)
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well, I'm think about : does return an iterator better than just an Array?

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I now return an Iterators.map

isempty(data) && return data

m = length(data)
ts = TimeSeries.timestamp(data)
idx = UnitRange{Int}[]
sizehint!(idx, m)

t0 = period(ts[1])
j = 1
for i in 1:(m-1)
t1 = period(ts[i+1])
t0 == t1 && continue
push!(idx, j:i)
j = i + 1
t0 = t1
end
push!(idx, j:m)

return idx
end
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