GitHub

Retime

The retime function allows you to retime, i.e. change the timestamps of a TimeArray, similar to what Matlab's retime does.

using Plots, Dates, TimeSeries
gr()
timestamps = range(DateTime(2020, 1, 1), length = 7*24, step = Hour(1))
ta = TimeArray(timestamps, cumsum(randn(7*24)), [:a])
168×1 TimeArray{Float64, 1, DateTime, Vector{Float64}} 2020-01-01T00:00:00 to 2020-01-07T23:00:00
┌─────────────────────┬──────────┐
│                     │ a        │
├─────────────────────┼──────────┤
│ 2020-01-01T00:00:00 │ 0.996166 │
│ 2020-01-01T01:00:00 │  1.76978 │
│ 2020-01-01T02:00:00 │  2.82177 │
│ 2020-01-01T03:00:00 │  3.63933 │
│ 2020-01-01T04:00:00 │  4.65231 │
│ 2020-01-01T05:00:00 │   5.1083 │
│ 2020-01-01T06:00:00 │  5.26788 │
│ 2020-01-01T07:00:00 │  5.57466 │
│          ⋮          │    ⋮     │
│ 2020-01-07T17:00:00 │ -8.22973 │
│ 2020-01-07T18:00:00 │ -9.80054 │
│ 2020-01-07T19:00:00 │ -10.5312 │
│ 2020-01-07T20:00:00 │ -11.0073 │
│ 2020-01-07T21:00:00 │ -11.2241 │
│ 2020-01-07T22:00:00 │ -12.2125 │
│ 2020-01-07T23:00:00 │ -13.1386 │
└─────────────────────┴──────────┘
                  153 rows omitted

Using a new time step

retime(ta, Minute(15))
669×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-07T23:00:00
┌─────────────────────┬──────────┐
│                     │ a        │
├─────────────────────┼──────────┤
│ 2020-01-01T00:00:00 │ 0.996166 │
│ 2020-01-01T00:15:00 │ 0.996166 │
│ 2020-01-01T00:30:00 │ 0.996166 │
│ 2020-01-01T00:45:00 │ 0.996166 │
│ 2020-01-01T01:00:00 │  1.76978 │
│ 2020-01-01T01:15:00 │  1.76978 │
│ 2020-01-01T01:30:00 │  1.76978 │
│ 2020-01-01T01:45:00 │  1.76978 │
│          ⋮          │    ⋮     │
│ 2020-01-07T21:30:00 │ -11.2241 │
│ 2020-01-07T21:45:00 │ -11.2241 │
│ 2020-01-07T22:00:00 │ -12.2125 │
│ 2020-01-07T22:15:00 │ -12.2125 │
│ 2020-01-07T22:30:00 │ -12.2125 │
│ 2020-01-07T22:45:00 │ -12.2125 │
│ 2020-01-07T23:00:00 │ -13.1386 │
└─────────────────────┴──────────┘
                  654 rows omitted

Using new timestep vector

new_timestamps = range(DateTime(2020, 1, 1), DateTime(2020, 1, 2), step = Minute(15))
retime(ta, new_timestamps)
97×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-02T00:00:00
┌─────────────────────┬──────────┐
│                     │ a        │
├─────────────────────┼──────────┤
│ 2020-01-01T00:00:00 │ 0.996166 │
│ 2020-01-01T00:15:00 │ 0.996166 │
│ 2020-01-01T00:30:00 │ 0.996166 │
│ 2020-01-01T00:45:00 │ 0.996166 │
│ 2020-01-01T01:00:00 │  1.76978 │
│ 2020-01-01T01:15:00 │  1.76978 │
│ 2020-01-01T01:30:00 │  1.76978 │
│ 2020-01-01T01:45:00 │  1.76978 │
│          ⋮          │    ⋮     │
│ 2020-01-01T22:30:00 │   2.4099 │
│ 2020-01-01T22:45:00 │   2.4099 │
│ 2020-01-01T23:00:00 │  2.41997 │
│ 2020-01-01T23:15:00 │  2.41997 │
│ 2020-01-01T23:30:00 │  2.41997 │
│ 2020-01-01T23:45:00 │  2.41997 │
│ 2020-01-02T00:00:00 │  2.68938 │
└─────────────────────┴──────────┘
                   82 rows omitted

Irregular timestamps

You can perform retime on irregularly spaced timestamps, both using a TimeArray with irregular timestamps or using a vector of irregular timestamps. Depending on the timestamps upsampling or downsampling is used. 

new_timestamps = vcat(
    range(DateTime(2020, 1, 1), DateTime(2020, 1, 2)-Minute(15), step = Minute(15)),
    range(DateTime(2020, 1, 2), DateTime(2020, 1, 3), step = Hour(1)),
)
retime(ta, new_timestamps)
121×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-03T00:00:00
┌─────────────────────┬──────────┐
│                     │ a        │
├─────────────────────┼──────────┤
│ 2020-01-01T00:00:00 │ 0.996166 │
│ 2020-01-01T00:15:00 │ 0.996166 │
│ 2020-01-01T00:30:00 │ 0.996166 │
│ 2020-01-01T00:45:00 │ 0.996166 │
│ 2020-01-01T01:00:00 │  1.76978 │
│ 2020-01-01T01:15:00 │  1.76978 │
│ 2020-01-01T01:30:00 │  1.76978 │
│ 2020-01-01T01:45:00 │  1.76978 │
│          ⋮          │    ⋮     │
│ 2020-01-02T18:00:00 │ -8.00855 │
│ 2020-01-02T19:00:00 │  -8.4651 │
│ 2020-01-02T20:00:00 │ -8.66854 │
│ 2020-01-02T21:00:00 │ -8.31003 │
│ 2020-01-02T22:00:00 │ -8.39098 │
│ 2020-01-02T23:00:00 │ -7.62926 │
│ 2020-01-03T00:00:00 │ -8.93226 │
└─────────────────────┴──────────┘
                  106 rows omitted

Upsampling

Interpolation is done using the upsample argument. If no data is directly hit, the specified upsample method is used. Available upsample methods are:

  • Linear() or :linear
  • Nearest() or :nearest
  • Previous() or :previous
  • Next() or :next
ta_ = retime(ta, Minute(15), upsample=Linear())
669×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-07T23:00:00
┌─────────────────────┬──────────┐
│                     │ a        │
├─────────────────────┼──────────┤
│ 2020-01-01T00:00:00 │ 0.996166 │
│ 2020-01-01T00:15:00 │  1.18957 │
│ 2020-01-01T00:30:00 │  1.38297 │
│ 2020-01-01T00:45:00 │  1.57638 │
│ 2020-01-01T01:00:00 │  1.76978 │
│ 2020-01-01T01:15:00 │  2.03278 │
│ 2020-01-01T01:30:00 │  2.29577 │
│ 2020-01-01T01:45:00 │  2.55877 │
│          ⋮          │    ⋮     │
│ 2020-01-07T21:30:00 │ -11.7183 │
│ 2020-01-07T21:45:00 │ -11.9654 │
│ 2020-01-07T22:00:00 │ -12.2125 │
│ 2020-01-07T22:15:00 │ -12.4441 │
│ 2020-01-07T22:30:00 │ -12.6756 │
│ 2020-01-07T22:45:00 │ -12.9071 │
│ 2020-01-07T23:00:00 │ -13.1386 │
└─────────────────────┴──────────┘
                  654 rows omitted
plot(ta)
plot!(ta_)

Downsampling

Downsampling or aggregation is done using the downsample argument. This applies a function to each interval not including the right-edge of the interval. If no data is present in the interval the specified upsample method is used. Available downsample methods are:

  • Mean() or :mean
  • Min() or :min
  • Max() or :max
  • Count() or :count
  • Sum() or :sum
  • Median() or :median
  • First() or :first
  • Last() or :last
ta_ = retime(ta, Hour(6), downsample=Mean())
28×1 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-01T00:00:00 to 2020-01-07T18:00:00
┌─────────────────────┬───────────┐
│                     │ a         │
├─────────────────────┼───────────┤
│ 2020-01-01T00:00:00 │   3.16461 │
│ 2020-01-01T06:00:00 │   5.58825 │
│ 2020-01-01T12:00:00 │   5.32029 │
│ 2020-01-01T18:00:00 │   2.98802 │
│ 2020-01-02T00:00:00 │ -0.346681 │
│ 2020-01-02T06:00:00 │   -5.3348 │
│ 2020-01-02T12:00:00 │  -7.13805 │
│ 2020-01-02T18:00:00 │  -8.24541 │
│          ⋮          │     ⋮     │
│ 2020-01-06T06:00:00 │  -4.93375 │
│ 2020-01-06T12:00:00 │  -2.40611 │
│ 2020-01-06T18:00:00 │  -1.81383 │
│ 2020-01-07T00:00:00 │   -3.9201 │
│ 2020-01-07T06:00:00 │  -5.56728 │
│ 2020-01-07T12:00:00 │   -7.9966 │
│ 2020-01-07T18:00:00 │  -11.3191 │
└─────────────────────┴───────────┘
                    13 rows omitted
plot(ta)
plot!(ta_)

Extrapolation

Extrapolation at the beginning and end of the time series is done using the extrapolate argument. Available extrapolate methods are:

  • FillConstant(value) or :fillconstant
  • NearestExtrapolate() or :nearest
  • MissingExtrapolate() or :missing
  • NaNExtrapolate() or :nan
new_timestamps = range(DateTime(2019, 12, 31), DateTime(2020, 1, 2), step = Minute(15))
ta_ = retime(ta, new_timestamps, extrapolate=MissingExtrapolate())
193×1 TimeArray{Union{Missing, Float64}, 2, DateTime, Matrix{Union{Missing, Float64}}} 2019-12-31T00:00:00 to 2020-01-02T00:00:00
┌─────────────────────┬─────────┐
│                     │ a       │
├─────────────────────┼─────────┤
│ 2019-12-31T00:00:00 │ missing │
│ 2019-12-31T00:15:00 │ missing │
│ 2019-12-31T00:30:00 │ missing │
│ 2019-12-31T00:45:00 │ missing │
│ 2019-12-31T01:00:00 │ missing │
│ 2019-12-31T01:15:00 │ missing │
│ 2019-12-31T01:30:00 │ missing │
│ 2019-12-31T01:45:00 │ missing │
│          ⋮          │    ⋮    │
│ 2020-01-01T22:30:00 │  2.4099 │
│ 2020-01-01T22:45:00 │  2.4099 │
│ 2020-01-01T23:00:00 │ 2.41997 │
│ 2020-01-01T23:15:00 │ 2.41997 │
│ 2020-01-01T23:30:00 │ 2.41997 │
│ 2020-01-01T23:45:00 │ 2.41997 │
│ 2020-01-02T00:00:00 │ 2.68938 │
└─────────────────────┴─────────┘
                 178 rows omitted