deleting a part of a column - date to date??
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daten1=floor(gas_calcorr(1,1));
% daten1=datenum(2018,08,20);
% daten2=floor(gas_calcorr(end,1));
daten2=datenum(2018,08,31);
RemoveData=(gas_calcorr(daten1:daten2,7));
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Benjamin Großmann
2020년 3월 9일
Okay, I think i got the problem and prepare a example script. Is the column 2 a criterion for the malfunction so that if column2 is true than CO should be NaN?
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Benjamin Großmann
2020년 3월 9일
clearvars
close all
clc
% lets create the date column (I only use 1 hour with increment of 1 minute), but this should works for any length
dt_datetime = [datetime(2018,02,01,14,00,00):minutes(1):datetime(2018, 02, 01, 14, 59, 59)];
dt = datenum(dt_datetime); % this should look like your first column transposed
% Generate the rest of your data as random values and attach it to the time
% vector
data_orig = [dt' rand(size(dt,2), 6)];
% now, the variable "data_orig" should have the dimensions of your gas_calcorr variable
% We now can try to manipulate the data
%% Example 1) Give specific start and end date and set the CO values (seventh column) within these dates to NaN
data1 = data_orig; % do not override the original data since we need it for another example
start_date = datenum(2018, 02, 01, 14, 20, 00);
end_date = datenum(2018, 02, 01, 14, 25, 00);
% generate a mask where the date fullfills the criterion
mask1 = (data1(:, 1) >= start_date) & (data1(:, 1) <= end_date); % creates a logical vector with 1s and 0s
% use logical indexing as row index to apply the mask:
% Set the values in the 7th column and each row where the mask is 1 to NaN
data1(mask1, 7) = NaN;
%% Example 2) Search for a criterion in column 2 and apply the mask
% do not override the original data since we may need it for another example
data2 = data_orig;
mask2 = data2(:,2) >= 0.5;
% use logical indexing as row index to apply the mask for the corresponding mask
data2(mask2, 7) = NaN;
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Benjamin Großmann
2020년 3월 10일
Hey Micky,
your code seems fine. It could be improved at one point or another, but it gets the job done. I think, that you only looked at data points where the CO value is NaN. Remember, as I said in the earlier comment, the data that you uploaded to google drive already contains close to 200.000 NaNs. If you dont see any CO data in the plot, then the whole day contains NaNs.
Please set your daten1 variable to something like
daten1=datenum(2018, 11, 15);
to get some data for the CO plot.
If you set it to
daten1=datenum(2018, 12, 04);
you can see a gap in the data in all subplots.
Please let me know if you need further help. Do you know where these NaNs in your original data come from? We can also try to investigate the NaNs in your original data, maybe graphically.
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