Extracting data from timetable

I have a timetable with multiple variables which vary spatially and temporally and spatially across a transect.

The variables are recorded at millimeter and minutely intervals. These are shown below as 'V_1, V_2, V_3, V_4, V_5... V_300'. The number here refers to the millimeter at which the measurements were taken, at time steps of t1, t2, t3... tn.

One variable ('Event') occurs at discrete time points and at discrete locations along the transect. Event data is either not present (NaN), or present (recorded as a number). For the 'present' events, the number refers to the spatial dimension: the millimeter along the transect at which tte event occurred.

The picture below shows a simplified view of the data, with annotations showing what I'd like to do.

I would like to:

1. Output the value of the environmental variable at the time of each 'Event'.
2. Calculate and output the change in each environmental variable for the preceding n timesteps that lead up to each 'Event'.

Is this possible, and how would I got about it?

Thank you very much.

Answer 1

[Star Strider]

I am not certain what you want.

This should get you started —

tv = minutes(0:15).';                                               % Create Data, Table & Timetable
V = sort(rand(numel(tv), 4)*100);
Event = NaN(size(tv));
Event(6:5:end) = 1:3;
T1 = table(tv,V(:,1),V(:,2),V(:,3),V(:,4),Event, 'VariableNames',{'Time(min)','V1','V2','V3','V4','Event'});
TT1 = table2timetable(T1)

TT1 = 16×5 timetable
    Time(min)      V1        V2        V3        V4      Event
    _________    ______    ______    ______    ______    _____

    0 min        7.8016    4.3482    2.6771     1.868     NaN 
    1 min         11.65    6.2408    2.9078    11.692     NaN 
    2 min        27.461    8.4738    8.1575    17.296     NaN 
    3 min        29.376    11.939    27.023    17.797     NaN 
    4 min        32.054    21.408    29.515     22.26     NaN 
    5 min        33.782    40.783    35.796     27.53       1 
    6 min        34.252    45.865    40.116    32.314     NaN 
    7 min        42.098     47.42    42.998    35.885     NaN 
    8 min         64.39    52.906    65.534    42.117     NaN 
    9 min        66.281    55.008    68.684    48.175     NaN 
    10 min       69.237    59.847    87.426    50.402       2 
    11 min       70.956    60.483    90.498    52.169     NaN 
    12 min       73.418     66.35    91.692    84.902     NaN 
    13 min       75.665    66.668     95.33    87.244     NaN 
    14 min       79.617    81.282    95.451    90.413     NaN 
    15 min        89.86    90.803    98.999    93.516       3 

EvIdx = find(~ismissing(TT1.Event));                           % Event Index Vector

TTs = TT1(EvIdx(1):EvIdx(2)-1,1:end-1)                          % First Section

TTs = 5×4 timetable
    Time(min)      V1        V2        V3        V4  
    _________    ______    ______    ______    ______

    5 min        33.782    40.783    35.796     27.53
    6 min        34.252    45.865    40.116    32.314
    7 min        42.098     47.42    42.998    35.885
    8 min         64.39    52.906    65.534    42.117
    9 min        66.281    55.008    68.684    48.175

td(1) = TTs.('Time(min)')(end) - TTs.('Time(min)')(1);
Vd(1,:) = TTs{end,:} - TTs{1,:};

for k = 1:numel(EvIdx)-1
    TTs = TT1(EvIdx(k):EvIdx(k+1)-1,1:end-1)                        % Intermediate Sections
    td(k+1) = TTs.('Time(min)')(end) - TTs.('Time(min)')(1);
    Vd(k+1,:) = TTs{end,:} - TTs{1,:};
end

TTs = 5×4 timetable
    Time(min)      V1        V2        V3        V4  
    _________    ______    ______    ______    ______

    5 min        33.782    40.783    35.796     27.53
    6 min        34.252    45.865    40.116    32.314
    7 min        42.098     47.42    42.998    35.885
    8 min         64.39    52.906    65.534    42.117
    9 min        66.281    55.008    68.684    48.175

TTs = 5×4 timetable
    Time(min)      V1        V2        V3        V4  
    _________    ______    ______    ______    ______

    10 min       69.237    59.847    87.426    50.402
    11 min       70.956    60.483    90.498    52.169
    12 min       73.418     66.35    91.692    84.902
    13 min       75.665    66.668     95.33    87.244
    14 min       79.617    81.282    95.451    90.413

Out = array2table([minutes(td(:)), Vd], 'VariableNames',{'TimeSpan(min)',TT1.Properties.VariableNames{1:4}})

Out = 3×5 table
    TimeSpan(min)      V1        V2        V3        V4  
    _____________    ______    ______    ______    ______

          4          32.498    14.225    32.888    20.645
          4          32.498    14.225    32.888    20.645
          4           10.38    21.434    8.0243    40.011

It takes the differences between the beginning and end values of each secton and saves them to ‘td’ and ‘Vd’ then puts those results in a table.

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