reshapetimeseries documentation

The rehspaetimeseries function reshapes a vector of timeseries data onto a time-of-year x year grid, accounting for messiness associated with leap days or unevenly-spaced data.

Back to Climate Data Tools Contents

Contents

Syntax

[xg, yr] = reshapetimeseries(t, x)
[xg, yr] = reshapetimeseries(t, x, 'bin', bin)
[xg, yr] = reshapetimeseries(t, x, 'func', func)
[xg, yr] = reshapetimeseries(t, x, 'yrlim', yrlim)
[xg, yr] = reshapetimeseries(t, x, 'pivotdate, [mon day])

Description

Example

When working with timeseries data, one often wants to analyze or plot things by time of year. For example, let's look at the sea ice extent example:

load seaice_extent;

plot(t, extent_N);

We can use doy to put this data on a day-of-year axis rather than datetimes:

cla;
plot(doy(t), extent_N);

But ugh, that wrapping from one year to the next prevents any quick and easy plotting. And we can calculate statistics across years by using splitapply or accumarray:

[g, tdoy] = findgroups(floor(doy(t)));
iceavg = splitapply(@nanmean, extent_N, g);

hold on;
plot(tdoy, iceavg, 'linewidth', 2);

But that syntax gets awfully clunky when you want to do more in-depth analysis. Both of these tasks would be a lot simpler if we could just reshape the data into matrix. The problem, of course, is those pesky leap days, always leading to uneven numbers of datapoints in different years and throwing off attempts to reshape. The reshapetimeseries function cleans up that messiness:

[ice, yr, tmid] = reshapetimeseries(t, extent_N);

cla;
plot(tmid, ice, 'b')
hold on

By default, it uses date bins, which makes sure to keep calendar dates aligned from one year to the next, and averages Feb 28-29 data in leap years. Monthly binning can also be achieved by setting the 'bin' input appropriately. (Note that the first year in this dataset started mid-October, which leads to the outlier-like point in the monthly averages).

[icem, yr, tmidm] = reshapetimeseries(t, extent_N, 'bin', 'month');

plot(tmidm, icem, 'r')

While Jan 1 is usually a good place to place the year-to-year wrap, sometimes your dataset may call for a different placement. For example, we can place the wrap near the low point of this timeries, in mid September. In this example, we choose 52 evenly-spaced bins per year, which is approximately a weekly average:

[ice, yr, tmid] = reshapetimeseries(t, extent_N, ...
    'pivotdate', [9 15], 'bin', 52);

cla
plot(tmid, ice, 'b')
hold on

One thing to be aware of with this function is that it will add a NaN placeholder for any bins where no data existed in the original timeseries. Most of the time this will correspond to larger gaps in your timeseries, e.g. at the beginning or end if the timeseries doesn't start and end on the same calendar day. However, this can also occur if you choose a binning interval on a finer scale than your original data. For example, in this dataset, the early data is sampled every 2 days. If we try binning this on a daily basis, we end up with sparse data, staggered every other year:

isearly = t < datetime(1988,1,1);
[ice, yr, tmid] = reshapetimeseries(t(isearly), extent_N(isearly));

tlbl = doy(tmid, 'decimalyear')- year(tmid(1));

figure;
imagesc(yr, tlbl, ice);
shading flat;
cmocean('ice');
xlabel('Year');
ylabel('Year fraction');

In this case, we'd want to make sure we chose a larger interval when doing any real analysis:

[ice, yr, tmid] = reshapetimeseries(t(isearly), extent_N(isearly), ...
    'bin', floor(365/2));

figure;
imagesc(yr, tlbl, ice);
shading flat;
cmocean('ice');
xlabel('Year');
ylabel('Year fraction');

Published with MATLAB® R2018a