Data Interpolation using interp1
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Barbara Schläpfer
am 8 Dez. 2020
Kommentiert: Walter Roberson
am 11 Dez. 2020
Hi
I am trying to interpolate different data sets, so that they all have the same size for my further analysis.
maxLength = max([length(unique_expperimental_dis), length(unique_experimental_force), length(abaqus_force)]);
xFit = 1:maxLength;
experimental_dis_interp = interp1(1:length(unique_expperimental_dis), unique_expperimental_dis, xFit);
size('experimental_dis_interp')
experimental_force_interp = interp1(1:length(unique_experimental_force), unique_experimental_force, xFit);
size('experimental_force_interp')
abaqus_force_interp = interp1(1:length(abaqus_force), abaqus_force, xFit);
size('abaqus_force_interp')
But wenn I read out the sizes of the different data sets, they do not match eachother. Any suggestions what I am doing wrong?
Barbara
Akzeptierte Antwort
Walter Roberson
am 8 Dez. 2020
Bearbeitet: Walter Roberson
am 8 Dez. 2020
maxLength = max([length(unique_expperimental_dis), length(unique_experimental_force), length(abaqus_force)]);
xFit = 1:maxLength;
experimental_dis_interp = interp1(1:length(unique_expperimental_dis), unique_expperimental_dis, xFit);
size(experimental_dis_interp)
experimental_force_interp = interp1(1:length(unique_experimental_force), unique_experimental_force, xFit);
size(experimental_force_interp)
abaqus_force_interp = interp1(1:length(abaqus_force), abaqus_force, xFit);
size(abaqus_force_interp)
You were calculating the size() of the character vectors, not the variables.
Caution: you have not turned on extrapolation, so the above code is equivalent to padding the short vectors with NaN.
2 Kommentare
Walter Roberson
am 11 Dez. 2020
If padding with NaN was not your intention, then you need to be more clear about your desired outcome.
If you have two variables in which there is a linear relationship between the two, so (say) 2/3 of the way through the range of one should correspond to 2/3 of the way through the range of the other, then you can scale between them:
fraction_through_first_range = (value_in_first_range - minimum_of_first_range) / (maximum_of_first_range - minimum_of_first_range);
projected_into_second_range = minimum_of_second_range + (maximum_of_second_range - mininum_of_second_range) * fraction_through_first_range
Is it really the number of different entries in a group that matters to you, or are you wanting to project by portion of the way through the respective range?
linspace(unique_expperimental_dis(1), unique_expperimental_dis(end), maxLength)
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