New answers tagged time-series
0
Generally resampling data that is non-uniformly sampled and/or contains non-valid samples is an interesting task.
If the downsampling ratio is large compared to the maximum (or average) sampling interval, you can possibly use a simple method. Take some average of a neighbourhood.
For a filtering approach, designing a continuous domain filter with some cutoff ...
0
Why does my amplitude change upon inverse Fourier Transform...?
Because sines and cosines don't work the way you seem to think. Here's a really simple case -- $\sin \omega t \pm \frac{\sin 3\omega t}{3}$. One plot is the sum, one plot is the difference. Note that the two plots are of markedly different shape -- just from changing the phase of one of the ...
2
OP seeks to randomize phase while keeping spectrum magnitude unchanged - and has achieved it. All that remains is to plot the spectrum.
Just add this code:
def plot(x0, x1, title):
plt.plot(x0)
plt.plot(x1)
plt.title(title, weight='bold', fontsize=16, loc='left')
plt.show()
plot(amp, np.abs(nft), title="RFFT, original vs new | ...
0
Let's have a quick look at the difference between sample times.
It's quite obvious that these differences come in very discrete steps that are integer multiples of each other. That indicates a regular sample grid but with time gaps of an integer number of samples. That means samples are being "dropped".
To find the base period, you can simply ...
0
I think you looking for linear regression.
Your data looks like a linear function in the form of
$$
y = kx + b
$$
where $x$ is the time index, $k$ is the slope (or regression coefficient) which is exactly what you want and $b$ is the $y$-intercept.
Now you have a set of linear equations
$$
y_1=kx_1+b\\
y_2=kx_2+b\\
\vdots\\
y_N=kx_N+b
$$
These can be written ...
Top 50 recent answers are included
