How can I plot this X and Y data?

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Ashfaq Ahmed
Ashfaq Ahmed 2023-1-27
评论： Star Strider 2023-1-27
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

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采纳的回答

Star Strider
Star Strider 2023-1-27
Ran in:
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','_','\_'))
.
  2 个评论
Ashfaq Ahmed
Ashfaq Ahmed 2023-1-27
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
Star Strider 2023-1-27
Ran in:
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
.

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更多回答（1 个）

the cyclist
the cyclist 2023-1-27
Ran in:
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_,'.')
  1 个评论
Ashfaq Ahmed
Ashfaq Ahmed 2023-1-27
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.

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