How can I plot this X and Y data?

Hi coders!

I have a very simple data (Data is attached as a .mat file) set but I want to know the most efficient way of plotting it.

I have a few tide height (m) data and I want to plot this in an hour-based manner. In X axis, I want to keep hours (00:00, 01:00, .... 23:00, 24:00) and in Y-axis, I want to keep the heights. The data is in a table format and given below -

'00:00'	0.820000000000000
'01:00'	1.11600000000000
'01:48'	NaN
'02:00'	1.23800000000000
'03:00'	1.11400000000000
'04:00'	0.799000000000000
'05:00'	0.428000000000000
'06:00'	0.106000000000000
'07:00'	-0.0750000000000000
'07:18'	NaN
'08:00'	-0.0330000000000000
'09:00'	0.178000000000000
'10:00'	0.413000000000000
'11:00'	0.646000000000000
'12:00'	0.936000000000000
'13:00'	1.23000000000000
'14:00'	1.39400000000000
'14:30'	NaN
'15:00'	1.33300000000000
'16:00'	1.06400000000000
'17:00'	0.704000000000000
'18:00'	0.363000000000000
'19:00'	0.101000000000000
'19:54'	NaN
'20:00'	-0.00200000000000000
'21:00'	0.0770000000000000
'22:00'	0.222000000000000
'23:00'	0.369000000000000

'00:00'	0.589000000000000
'01:00'	0.898000000000000
'02:00'	1.16400000000000
'03:00'	1.23200000000000
'04:00'	1.04800000000000
'05:00'	0.698000000000000
'06:00'	0.330000000000000
'07:00'	0.0420000000000000
'07:48'	NaN
'08:00'	-0.0820000000000000
'09:00'	0.0220000000000000
'10:00'	0.262000000000000
'11:00'	0.491000000000000
'12:00'	0.711000000000000
'13:00'	0.990000000000000
'14:00'	1.26400000000000
'15:00'	1.38800000000000
'15:12'	NaN
'16:00'	1.27300000000000
'17:00'	0.959000000000000
'18:00'	0.586000000000000
'19:00'	0.259000000000000
'20:00'	0.0410000000000000
'20:30'	NaN
'21:00'	0.00500000000000000
'22:00'	0.136000000000000
'23:00'	0.292000000000000

Any feedback from you will be greatly appreciated!! <3

Answer 1

[Star Strider]

Perhaps something like this —

LD = load(websave('Tide_Data','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1276250/Tide_Data.mat'));
Tide_info = LD.Tide_info

Tide_info = 10048×3 table
         Date         Time_GMT_    Predicted_m_
    ______________    _________    ____________

    {'2021/01/01'}    {'00:00'}         0.82   
    {'2021/01/01'}    {'01:00'}        1.116   
    {'2021/01/01'}    {'01:48'}          NaN   
    {'2021/01/01'}    {'02:00'}        1.238   
    {'2021/01/01'}    {'03:00'}        1.114   
    {'2021/01/01'}    {'04:00'}        0.799   
    {'2021/01/01'}    {'05:00'}        0.428   
    {'2021/01/01'}    {'06:00'}        0.106   
    {'2021/01/01'}    {'07:00'}       -0.075   
    {'2021/01/01'}    {'07:18'}          NaN   
    {'2021/01/01'}    {'08:00'}       -0.033   
    {'2021/01/01'}    {'09:00'}        0.178   
    {'2021/01/01'}    {'10:00'}        0.413   
    {'2021/01/01'}    {'11:00'}        0.646   
    {'2021/01/01'}    {'12:00'}        0.936   
    {'2021/01/01'}    {'13:00'}         1.23   

Tide_info.Date = datetime(Tide_info.Date, 'InputFormat','yyyy/MM/dd');
Tide_info.Time_GMT_ = datetime(Tide_info.Time_GMT_, 'InputFormat','HH:mm');
DateTime = Tide_info.Date + timeofday(Tide_info.Time_GMT_);                     % Combine Date & Time Variables
Tide_info = addvars(Tide_info, DateTime, 'After','Time_GMT_')                   % Add As A New Variable

Tide_info = 10048×4 table
       Date             Time_GMT_                DateTime          Predicted_m_
    ___________    ____________________    ____________________    ____________

    01-Jan-2021    27-Jan-2023 00:00:00    01-Jan-2021 00:00:00         0.82   
    01-Jan-2021    27-Jan-2023 01:00:00    01-Jan-2021 01:00:00        1.116   
    01-Jan-2021    27-Jan-2023 01:48:00    01-Jan-2021 01:48:00          NaN   
    01-Jan-2021    27-Jan-2023 02:00:00    01-Jan-2021 02:00:00        1.238   
    01-Jan-2021    27-Jan-2023 03:00:00    01-Jan-2021 03:00:00        1.114   
    01-Jan-2021    27-Jan-2023 04:00:00    01-Jan-2021 04:00:00        0.799   
    01-Jan-2021    27-Jan-2023 05:00:00    01-Jan-2021 05:00:00        0.428   
    01-Jan-2021    27-Jan-2023 06:00:00    01-Jan-2021 06:00:00        0.106   
    01-Jan-2021    27-Jan-2023 07:00:00    01-Jan-2021 07:00:00       -0.075   
    01-Jan-2021    27-Jan-2023 07:18:00    01-Jan-2021 07:18:00          NaN   
    01-Jan-2021    27-Jan-2023 08:00:00    01-Jan-2021 08:00:00       -0.033   
    01-Jan-2021    27-Jan-2023 09:00:00    01-Jan-2021 09:00:00        0.178   
    01-Jan-2021    27-Jan-2023 10:00:00    01-Jan-2021 10:00:00        0.413   
    01-Jan-2021    27-Jan-2023 11:00:00    01-Jan-2021 11:00:00        0.646   
    01-Jan-2021    27-Jan-2023 12:00:00    01-Jan-2021 12:00:00        0.936   
    01-Jan-2021    27-Jan-2023 13:00:00    01-Jan-2021 13:00:00         1.23   

VN = Tide_info.Properties.VariableNames;

figure
plot(Tide_info.DateTime, Tide_info.Predicted_m_)
grid
xlabel(VN{3})
ylabel(strrep(VN{4},'_','\_'))
title(strrep('Tide_info','_','\_'))

.

[Ashfaq Ahmed]

Hi! This is really great! But I wanted to keep the hours (00:00 to 24:00) in the x-axis and all the co-responding height for those hours in the Y axis. Can you please give me an idea on what part of your code do I need to change to get the plot?

[Star Strider]

Thank you!

That is certainly possible, however even using retime on a timetable version of your data to take the hourly mean creates an extremely large (8760 rows) result.

LD = load(websave('Tide_Data','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1276250/Tide_Data.mat'));
Tide_info = LD.Tide_info;

Tide_info.Date = datetime(Tide_info.Date, 'InputFormat','yyyy/MM/dd');
Tide_info.Time_GMT_ = datetime(Tide_info.Time_GMT_, 'InputFormat','HH:mm');
DateTime = Tide_info.Date + timeofday(Tide_info.Time_GMT_);                     % Combine Date & Time Variables
Tide_info = addvars(Tide_info, DateTime, 'After','Time_GMT_');                  % Add As A New Variable

VN = Tide_info.Properties.VariableNames;
TTide_info = removevars(Tide_info, [1 2]);
TTide_info = table2timetable(TTide_info);
TTide_info = retime(TTide_info, 'hourly', @(x)mean(x,'omitnan'))

TTide_info = 8760×1 timetable
          DateTime          Predicted_m_
    ____________________    ____________

    01-Jan-2021 00:00:00         0.82   
    01-Jan-2021 01:00:00        1.116   
    01-Jan-2021 02:00:00        1.238   
    01-Jan-2021 03:00:00        1.114   
    01-Jan-2021 04:00:00        0.799   
    01-Jan-2021 05:00:00        0.428   
    01-Jan-2021 06:00:00        0.106   
    01-Jan-2021 07:00:00       -0.075   
    01-Jan-2021 08:00:00       -0.033   
    01-Jan-2021 09:00:00        0.178   
    01-Jan-2021 10:00:00        0.413   
    01-Jan-2021 11:00:00        0.646   
    01-Jan-2021 12:00:00        0.936   
    01-Jan-2021 13:00:00         1.23   
    01-Jan-2021 14:00:00        1.394   
    01-Jan-2021 15:00:00        1.333   

figure
plot(TTide_info.DateTime, TTide_info.Predicted_m_)
grid
xlabel(VN{3})
ylabel(strrep(VN{4},'_','\_'))
title(strrep('TTide_info','_','\_'))

figure
plot(TTide_info.DateTime, TTide_info.Predicted_m_)
grid
xlabel(VN{3})
ylabel(strrep(VN{4},'_','\_'))
title(strrep('TTide_info','_','\_'))
Ax = gca;
Ax.XTick = TTide_info.DateTime(1:25,1);
xlim([TTide_info.DateTime(1,1), TTide_info.DateTime(25,1)])

It would not be possible to plot all of them in the first plot (as I plotted in the expanded second plot) and have them all visible and readable. The information is available in the ‘TTide_info’ timetable and can be plotted with either an extremely long plot, or as individual plots, such as I did here.

You can change the first plot to be the same as the second plot, however the result is likely not what you want. (Actually, I cannot even force that here, so I posted the appropriate code, however commented-out. Please experiment with it offline, since I cannot display it here.)

% figure
% plot(TTide_info.DateTime, TTide_info.Predicted_m_)
% grid
% xlabel(VN{3})
% ylabel(strrep(VN{4},'_','\_'))
% title(strrep('TTide_info','_','\_'))
% Fg = gcf;
% pos = Fg.Position;
% Fg.Position = pos + [-500 0 900 0];
% HourInterval = 6;
% Ax.XTick = TTide_info.DateTime(1:HourInterval:end,1);               % Change 'HourInterval' To Plot The Desired REsult

.

Answer 2

[the cyclist]

It's unclear to me exactly what you want to plot. That file has 10,048 data points.

Here is one possibility, which puts a dot at each time point, with the time (but not the date) on the x-axis. Or, did you want to only plot a segment of the data, but not have data from different dates in the same column?

load("Tide_Data.mat")
plot(datetime(Tide_info.Time_GMT_,"InputFormat","HH:mm"),Tide_info.Predicted_m_,'.')

[Ashfaq Ahmed]

Hi! Excellent! That's exactly what I wanted to plot.

Actually, the tide height data were taken in every one hour for the whole year 2021. That's why there are so many (10,048) data point. I wnated to plot the height with respect to hours of the day.