FFT Not Getting Expected Results And Too Much Noise - matlab

Question

Here's my code:
close all;clc;clear all;
f=fopen ('sum_021223.txt');
Adata=cell2mat(textscan(f, '%f %f %f %f %f'));
time=Adata(:,5);
data = Adata;
data(:,5:end) = 0;
final=size(data,1);
data(1,1)=data(2,1);
testsum=sum(data,2);
Fs = .5; % Sampling frequency
T = 1/Fs; % Sampling period
L = final; % Length of signal
t = (0:L-1)*T;
t=t';
Y=fft(testsum)
P2 = abs(Y/L);
P1 = P2(1:L/2+1);
P1(2:end-1) = 2*P1(2:end-1);
f = Fs*(0:(L/2))/L;
% P1(1,1)=P1(2,1);
plot(f,P1)
title("Single-Sided Amplitude Spectrum of X(t)")
xlabel("f (Hz)")
ylabel("|P1(f)|")
I believe I setup my code correct. I am sampling data once per 2 seconds. I have 30530 rows of data (About 17 hours worth)
However my FFT just looks like a bunch of noise, and I would expect some spikes around the smaller frequencies that would represent changes in air temperature and such over the day I took the data.
The input data on Dropbox.
I tried to set P1(1,1) to be equal to P1(2,1). This somewhat helped, but not really.

Answer 1

Your FFT looks corect, you have a very noisy data. You can maybe plot a power spectrum of that signal. You can distinguish different parts in the signal, here is function:
close all;clc;clear all;
f=fopen ('sum_021223.txt');
Adata=cell2mat(textscan(f, '%f %f %f %f %f'));
time=Adata(:,5);
data = Adata;
data(:,5:end) = 0;
final=size(data,1);
data(1,1)=data(2,1);
testsum=sum(data,2);
Fs = .5; % Sampling frequency
T = 1/Fs; % Sampling period
L = numel(testsum); % Length of signal
t = (0:L-1)*T;
t=t';
[f,y]=MyPower(testsum,Fs);
plot(f,y)
title("Single-Sided Amplitude Spectrum of X(t)")
xlabel("f (Hz)")
ylabel("|P1(f)|")
ylim([0,6e-3]);
function [f,y]=MyPower(y,freq)
tuReal = "seconds";
samples=numel(y);
period=1/freq;
time=linspace(0,samples*period,samples)';
signal=y;
Fs=freq;
% Compute effective sampling rate.
tNumeric = time2num(time,tuReal);
[Fs,irregular] = effectivefs(tNumeric);
Ts = 1/Fs;
% Resample non-uniform signals.
x = signal;
if irregular
x = resample(x,tNumeric,Fs,'linear');
end
% Set Welch spectrum parameters.
L = fix(length(x)/4.5);
noverlap = fix(L*50/100);
win = window(#hamming,L);
% Compute the power spectrum.
[ps,f] = pwelch(x,win,noverlap,[],Fs);
w = 2*pi*f;
% Convert frequency unit.
factor = funitconv('rad/TimeUnit', 'Hz', 'seconds');
w = factor*w;
Fs = 2*pi*factor*Fs;
% Remove frequencies above Nyquist frequency.
I = w<=(Fs/2+1e4*eps);
w = w(I);
ps = ps(I);
% Configure the computed spectrum.
ps = table(w, ps, 'VariableNames', ["Frequency", "SpectrumData"]);
ps.Properties.VariableUnits = ["Hz", ""];
ps = addprop(ps, {'SampleFrequency'}, {'table'});
ps.Properties.CustomProperties.SampleFrequency = Fs;
f=ps.Frequency;
y=ps.SpectrumData;
end