fourier / wavelet analysis
DESCRIPTION
Fourier / Wavelet Analysis. ASTR 3010 Lecture 19 Textbook : N/A. Fourier Transform. in signal processing, (time and frequency). Add bunch of zeros in your data!. Number of input data points number of frequency sampling in FT!. Example of FFT in astronomy : defringing a spectrum. - PowerPoint PPT PresentationTRANSCRIPT
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Fourier / Wavelet Analysis
ASTR 3010
Lecture 19
Textbook : N/A
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Fourier Transform
in signal processing, (time and frequency)
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Add bunch of zeros in your data!
Number of input data points number of frequency sampling in FT!
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Example of FFT in astronomy : defringing a spectrum
heavily fringed raw spectrum
power spectrum of the input
defringed spectrum
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Limits on Fourier Transformit can only “see” one variable (period or time) at a time at sufficient precision!
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Short-Time Fourier Transform• Using a window function in time
• Limited by the Uncertainty Principle : t*ω = constant
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STFT resolution problem
• Four different Gaussian windows
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Wavelet Transform • Wavelet transform can get two different information (i.e., time and
frequency) simultaneously!
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Wavelet Transform
where basis function is
s : scale parameterτ : translation parameter
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Practical use of wavelet transformation• Decomposition and recomposition of a signal
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PyWaveletshttp://www.pybytes.com/pywavelets
['bior1.1', 'bior1.3', 'bior1.5', 'bior2.2', 'bior2.4',… 'coif1', 'coif2',… 'db1', 'db2', 'db3',… 'sym15', 'sym16', 'sym17', 'sym18', 'sym19', 'sym20']
• pywto pywt.wavelisto pywt.waveleto pywt.wavedeco pywt.waverec
import pywtpywt.wavelist()
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PyWaveletshttp://www.pybytes.com/pywavelets
• pywto pywt.wavelisto pywt.waveleto pywt.wavedeco pywt.waverec
import pywtmyw=pywt.wavelet(‘db4’)phi,psi,wx = myw.wavefun()plot(wx,phi,’r’)plot(wx,psi,’b’)
Daubechies Wavelet : order 4
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PyWaveletshttp://www.pybytes.com/pywavelets
• pywto pywt.wavelisto pywt.waveleto pywt.wavedeco pywt.waverec
import pywtmyw=pywt.wavelet(‘sym20’)phi,psi,wx = myw.wavefun()plot(wx,phi,’r’)plot(wx,psi,’b’)
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Wavelets Decomposition Tree
• decomposition of a signal into several resolution levels.
• First, the original signal is decomposed by two complementary half-band filters (high-pass and low-pass filters) that divide a spectrum into high-frequency (detail coefficients; D1) and low-frequency (approximation coefficients; A1) components (bands). For example, the low-pass filter will remove all half-band highest frequencies. Information from only the low frequency band (A1), with a half number of points, will be filtered in the second decomposition level. The A2
outcome will be filtered again for further decomposition.
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PyWaveletsdecompositionreconstruction
• pywto pywt.wavelisto pywt.waveleto pywt.wavedeco pywt.waverec
import pywtmyw=pywt.wavelet(‘db4’)dec =
myw.wavedec(data,’db4’,’zpd’,5)
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PyWaveletsdecompositionreconstruction
• pywto pywt.wavelisto pywt.waveleto pywt.wavedeco pywt.waverec
import pywtmyw=pywt.wavelet(‘sym20’)dec =
myw.wavedec(data,’sym20’,’zpd’,5)
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pywt : Denoising
import pywt… set high order “difference” coeffs to zero.… among “diff” coeffs, clip small coeffs < 0.2*sigma… then, reconstructdec = myw.wavedec(data,’db4’,’zpd’,5)
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Wavelet: Denoisinghttp://www.toolsmiths.com/docs/CT199809.pdf
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Wavelet: Denoise in 2D
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Wavelet: Denoise in 2D
http://www.pixinsight.com/doc/legacy/LE/21_noise_reduction/example_1/04.html