MATLAB: So I need to sort a time data out. First I have a column of data e.g 24401 means 244th days and 01 means the first 30 minutes. So in one day I have 48 data and I have a month full of time. How to convert the time into hours To show on graph easier

timetime series data analysis

24401 means 244th days and the first 30 minutes of the day.

24448 means 244th days and the 24th hours

So it goes up by 30 Minutes interval. How can I convert the 5 digit code into hours to plot it on the graph. I don’t want the x axis to be 244401 I want it to be 0.5 hour instead.

Best Answer

I would think that you would just do this:

>> .5*(x - 100*(round(x/100)))
ans =
          0.5           24

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f = fopen('name_of_your_txt_file.txt');
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MATLAB: How to plot hourly data in the same figure

Use the features of Matlab to your advantage...granted, it takes some "time in grade!" to learn one's way around! :)

As a general rule, when one finds that are building specific indices into the data, that's a real sign there should be a better way...in this case, converting to datetime in order to do the comparisons is the trick. And, it turns out your Q? immediately follows essentially the same problem in financial analysis and is quite common also with such data as yours...the same 'trick" as used there (link) works very well here as well--

load winter.txt
w=table(datetime(winter(:,1),1,winter(:,2),winter(:,3),0,0),winter(:,6) ...
              'VariableNames',{'Date','Data'});                           % create table from array
w.WYear=fix((w.Date-w.Date(1))./duration(24*365+isleapyr(w.Date),0,0));   % compute the winter year
yr=unique(year(w.Date));                                                  % retrieve the years covered
yrlabel=cellstr(num2str([yr mod(round(100*(yr/100-fix(yr/100)))+1,100)],'%d-%02d')); % build a label array
labels={'Winter','Mean','Median','Minimum','Maximum','StdDev','LowLimit','UpLimit'};  % output statistics
g=findgroups(w.WYear);                                                    % grouping variable by winter
[av,md,mn,mx,sd,lo,hi]=splitapply(@multipleStatistics,w.Data,g);          % compute statistics 
summ=table(categorical(yrlabel),av,md,mn,mx,sd,lo,hi,'VariableNames',labels);  % build a summary table
>> summ =
summ =
  20×8 table
    Winter      Mean     Median    Minimum    Maximum    StdDev    LowLimit    UpLimit
    _______    ______    ______    _______    _______    ______    ________    _______
    1998-99    17.909     14.9     4.1          51       10.114    4.3556      31.462 
    1999-00    21.358       18     3.4        63.2        13.36    3.4553       39.26 
    2000-01    22.821    19.75     5.3        60.7       13.627    4.5608      41.082 
    2001-02    38.467    34.45     5.3        92.5       20.885    10.481      66.453 
    2002-03    20.464     16.1     3.8        60.6       11.386    5.2074      35.721 
    2003-04    14.101     11.3     3.9        45.2       7.4069    4.1757      24.026 
    2004-05    11.731     10.1     0.2        31.2       5.4401    4.4414      19.021 
    2005-06    10.189      9.4     3.3        25.6       3.3511    5.6982      14.679 
    2006-07     9.063      8.1     0.7        19.8       3.1426    4.8519      13.274 
    2007-08    8.5001        8     3.2        22.2       2.6795    4.9095      12.091 
    2008-09    8.7789      8.6     2.8        17.9       2.3483    5.6322      11.926 
    2009-10    9.8379      9.5     2.8          25       3.2059     5.542      14.134 
    2010-11    10.533    10.05     3.5        35.4       3.7726    5.4773      15.588 
    2011-12    15.377    13.55     3.3        41.7       6.7471    6.3357      24.418 
    2012-13    13.316     11.5     3.2        41.1       5.8716    5.4482      21.184 
    2013-14    18.659     16.3       4        60.8       9.9698    5.2995      32.019 
    2014-15    20.451     17.1     5.1        50.9        10.52    6.3538      34.548 
    2015-16    12.464     10.7     3.3        34.8       5.6957    4.8319      20.096 
    2016-17    9.2583      8.5     3.6        23.9        3.185    4.9904      13.526 
    2017-18    8.8444      8.5     4.1          17       2.4073    5.6186       12.07 
    >>

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