fourier-transform | Minimalistic and efficient FFT implementation | Android library
kandi X-RAY | fourier-transform Summary
kandi X-RAY | fourier-transform Summary
Minimalistic and efficient FFT implementation
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Top functions reviewed by kandi - BETA
- Formats the current heap .
- Creates an instance of the FFT module .
- Multipline module
- AMF module
- Reversed sort .
- Calculate FFT
- Returns the magnitude of the vector
- Set n zeros
fourier-transform Key Features
fourier-transform Examples and Code Snippets
Lines of Code : 118License : Non-SPDX (Apache License 2.0)
Copydef inverse_stft(stfts,
frame_length,
frame_step,
fft_length=None,
window_fn=window_ops.hann_window,
name=None):
"""Computes the inverse [Short-time Fourier Transf def stft(signals, frame_length, frame_step, fft_length=None,
window_fn=window_ops.hann_window,
pad_end=False, name=None):
"""Computes the [Short-time Fourier Transform][stft] of `signals`.
Implemented with TPU/GPU-compatible op def _solve(self, rhs, adjoint=False, adjoint_arg=False):
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
spectrum = _to_complex(self.spectrum)
if adjoint:
spectrum = math_ops.conj(spectrum)
rhs, spectrum = self._broadcast_bat Community Discussions
Trending Discussions on fourier-transform
QUESTION
Below you will find my python code for a class assignment I was given a couple weeks ago which I have been unable to successfully debug. The problem is about finding the value at risk (i.e., the p% quantile) for an aggregate loss random variable, using FFT. We are given a clear mathematical procedure by which we can gain an estimation of the discretized CDF of the aggregate loss random variable. My results are, however, seriously off and I am making some kind of mistake which I have been unable to find even after hours of debugging my code.
The aggregate loss random variable S is given such that S=sum(X_i for i in range(N)), where N is negative binomially distributed with r=5, beta=.2, and X_i is exponentially distributed with theta=1. The probability generating function for this parametrization is P(z)=[1-\beta(z-1)]^{-r}.
We were asked to approximate the distribution of S by
- choosing a grid width
hand an integernsuch thatr=2^nis the number of elements to discretizeXon, - discretizing
Xand calculating the probabilities of being in equally spaced intervals of widthh, - applying the FFT to the discretized
X, - applying the PGF of
Nto the elements of the Fourier-transformedX, - applying the inverse FFT to this vector.
The resulting vector should be an approximation for the probability masses of each such interval for S. I know from previous methods that the 95% VaR ought to be ~4 and the 99.9% VaR ought to be ~10. But my code returns nonsensical results. Generally speaking, my index where the ECDF reaches levels >0.95 is way too late, and even after hours of debugging I have not managed to find where I am going wrong.
I have also asked this question on the math stackexchange, since this question is very much on the intersection of programming and math and I have no idea at this moment whether the issue is on the implementation side of things or whether I am applying the mathematical ideas wrong.
...ANSWER
Answered 2022-Apr-03 at 14:31Not sure about math, but in snippet variable r gets overrided, and when computing f_tilde_vec_fft function PGF uses not 5 as expected for r, but 1024. Fix -- change name r to r_nb in definition of hyperparameters:
r_nb, beta, theta = 5, .2, 1
and also in function PGF:
return (1 - beta * (z - 1)) ** (-r_nb)
After run with other parameters remain same (such as h, n etc.) for VaRs I get [4.05, 9.06]
QUESTION
I tried to convert a character array to string array. Means single string to multiple sentences.
...ANSWER
Answered 2021-Dec-15 at 09:48char **arr = (char **) malloc ((6) * sizeof (char));
QUESTION
I am trying to plot a fourier transform of a sign wave based on the scipy documentation
...ANSWER
Answered 2021-Jan-11 at 04:24import numpy as np
import matplotlib.pyplot as plt
import scipy.fft
def sinWav(amp, freq, time, phase=0):
return amp * np.sin(2 * np.pi * (freq * time - phase))
def plotFFT(f, speriod, time):
"""Plots a fast fourier transform
Args:
f (np.arr): A signal wave
speriod (int): Number of samples per second
time ([type]): total seconds in wave
"""
N = speriod * time
# sample spacing
T = 1.0 / 800.0
x = np.linspace(0.0, N*T, N, endpoint=False)
yf = scipy.fft.fft(f)
xf = scipy.fft.fftfreq(N, T)[:N//2]
amplitudes = 1/speriod* np.abs(yf[:N//2])
plt.plot(xf, amplitudes)
plt.grid()
plt.xlim([1,3])
plt.show()
speriod = 800
time = {
0: np.arange(0, 4, 1/speriod),
1: np.arange(4, 8, 1/speriod),
2: np.arange(8, 12, 1/speriod)
}
signal = np.concatenate([
sinWav(amp=0.25, freq=2, time=time[0]),
sinWav(amp=1, freq=2, time=time[1]),
sinWav(amp=0.5, freq=2, time=time[2])
]) # generate signal
plotFFT(signal, speriod, 12)
QUESTION
I was following the tutorial on this website to create a low-pass filter using NumPy and OpenCV in python. Instead of displaying the transformed image using pyplot as shown in the tutorial, I tried using OpenCV's imshow function but I am not getting the desired output.
...ANSWER
Answered 2020-Dec-19 at 12:20If you want to display data in type np.float32 you have to normalize it to 1.0
QUESTION
I am looking to implement KL Divergence in C++ efficiently. (CPU Only for now).
Much like AES or FTT (Fast Fourier transform) whereby use of a common function has lead to hardware level optimizations (Intel AES and Intel FTT). Is there anything similar for natural log, or slightly higher level efficiencies (ASM/C) that prevent bottlenecks of executing many Natural log functions in success (If they exist)?
Same use case examples:
...ANSWER
Answered 2020-Oct-10 at 21:28You can use SSE instructions to calculate the logarithm of many values in parallel. But whether you can actually make use of those instructions depends heavily on how the rest of the calculations you are going to do depend on the logarithms you calculate, so it is not possible to give a more specific answer.
QUESTION
So I am making an AI project that classifies speech into either "up", "down", "left", right or background noise, and from this, a character in a videogame is moved.
I have made an FFT algorithm deriving it from the mathematical explanation, which I believe is correct as I have tested its output against that from this site (https://engineering.icalculator.info/discrete-fourier-transform-calculator.html)
I then have tried to generate a spectrogram and have used code based on the code from the main function of the App class from this site (Creating spectrogram from .wav using FFT in java)
I tested my code on a .wav file of me saying hello and the spectrogram generated is not what I was expecting out, see below the difference between my java made spectrogram and my python made spectrogram (ignore the colour difference).
New Java Spectrogram with SleuthEyes help
Here is the original code I have used/written:
...ANSWER
Answered 2020-Jul-24 at 10:47Reading the .wav file
The .wav file decoding included in that other question you linked is hardly a full blown decoder. It accounts for the OP's specific stereo 2bytes-per-sample use-case.
It looks like you stumbled upon other decoding issues while trying to adapt it to a different use case. As a general piece of advice, I'd suggest to use a more complete .wav decoder which would take into account the number of channels, the number of bytes-per-sample, etc.
If on the other hand you want to craft your own decoder (for example as a learning exercise), then a slightly more robust implementation may look like the following:
QUESTION
When I am using Intel IPP's ippsFFTFwd_RToCCS_64f and then ippsMagnitude_64fc I get a massive peak at zero index in magnitudes array.
My sine wave is long and main component I am interested is somewhere between 0.15 Hz and 0.25 Hz. I take the sample with 500Hz sampling frequency. If I reduce mean from the signal before FFT I get really small zero component not that peak anymore. Below is a pic of magnitudes array head:
Also the magnitude scaling seems to be 10 times the magnitude I see in the time series of the signal e.g. if amplitude is 29 in magnitudes it is 290.
I Am not sure why this is so and my question is 1. Do I really need to address the zero index peak with mean reduction and 2. Where does this scale of 10 come?
...ANSWER
Answered 2020-Jul-23 at 02:17Do I really need to address the zero index peak with mean reduction?
For low frequency signal analysis a small bias can really interfere (especially due to spectral leakage). For sake of illustration, consider the following low-frequency signal tone and another one with a constant bias tone_with_bias:
QUESTION
Source code
...ANSWER
Answered 2020-Jul-07 at 16:09You need to use reshape:
QUESTION
I am actually want to convert this blur detection into C++. As a beginner in OpenCV, I am actually following this for conversion, But maybe I am getting it wrong. Here is my approach. I have to use DFT instead of FFT in C++.
...ANSWER
Answered 2020-Jun-29 at 12:43I think this comes close to the original Python code
QUESTION
I am receiving data from a vibration sensor in the form of two numpy arrays. The first array represents the actual values for the vibration measurement and the second array is the corresponding time information (timestamp). For example:
...ANSWER
Answered 2020-Jun-19 at 16:45This is really a signal processing question, not a Python one. You have several options here:
- if your data is uniformly sampled - you can ignore the timestamps altogether. All the information you need is in the data, and the (constant) sampling frequency: f_s = 1.0 / (timestamps[1] - timestamps[0])
- if not, you can either:
- use Non-uniform DFT (here is one implementation, haven't tried)
- interpolate the data between non-uniform timestamps so it becomes uniform. Note that effectively, this applies a low-pass filter to your data, which may be not what you want (more on the effects of interpolation here).
In all cases, when you perform FFT, time information is not required anymore, as you are in the frequency domain.
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