Welcome to my Fourier Transform Journey!
PURPOSES of program:
-Given 2^m data points, where m is a positive integer, return the DFFT. Store the real, imaginary, and frequency components in respectve files for graphing
-Given 2^m data points, where m is a positive integer, return the Inverse DFFT. Store the inversed data and the associated time-points in respective files
-Given two data sets where sizeof(dataA) + sizeof(dataB) - 1 is 2^m data points, and m is a positive integer, return the convolution using the DFFT trick. Store the convolution into its respective file
NOTE(S):
-Please change the value for LIMITER to be the amount of data points (2^m) -Please change FREQRES to the frequency resolution of the DFFT'd data -Ensure that SIZEF + SIZEG - 1 = 2^m where m is any positive integer -If doing a convolution: CONVOLUTION 1 else, CONVOLUTION 0
GOALS and TIMELINE of the project:
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Implement an Algorithm for the Discrete Fourier Transform -Use this algorithm to correctly obtain the frequencies of a given wave function (completed on December 10th, 2022).
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Implement the Discrete Fourier Transform but using the Fast Fourier Algorithm. Also, make sure to actually understand what is happening mathematically. -To my understanding, the most siginifcnt imporvemmnt in the Fast Fourier Algorithm comes from the fact that the Discrete Fast Fourier Transform requires N^2 operations, however, the Fast Fourier Algorithm requires only N*log_2(N) operations. The reason is because we utilize the conjugaet properties of complex numbers to make use of reqpeat calculations. Essentially, instead of doing (large num)^2 calculations, You just do (large num / 2)^2 + (small num) calculations instead. -COMPARISON: 4096 samples and 4096 samples/1 radian, the DFT averaged 67 ms, and the DFFT averaged 1.8 ms. (using O() notation, the DFFT is 341 times faster).
-COMPARISON: With 65536 samples and 65536 samples/1 radian, the DFT averaged 23 SECONDS, and the DFFT averaged 0.038 SECONDS. (using O() notation, the DFFT is 4096 times faster)
-COMPARISON: With 131072 samples and 131072 samples/1 radian, the DFT just got core dumped... The DFFT averaged 86.25ms. (using O() notation, the DFFT is 7710 times faster)
-COMPARISON: With 262144 samples and 262144 samples/1 radian, the DFT probably got core dumped because the DFFT got core dumped this time.
-COMPLETED on December 24th 2022 (kind of late because of exams and understanding took a while)
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Implement an algorithm to perform an inverse DFFT -This requires inversing the DFT matrix and multiplying by the recipricol of the number of samples -COMPLETED on December 26th, 2022
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Implement an algorithm to perform a convolution of two arrays of numbers -COMPLETED on December 27th, 2022