#
# FFT_Python_1.4.0.py
#
# Simple FFT for Python Versions 1.4.0 to 3.7.0 on _ALL_ platforms.
#
# Requirements:
# 1) Standard A1200 with HDD, AMIGA OS3.0.x.
# 2) Python 1.4.0, full install on AMINET.
# 3) This DEMO file, renamed to "FFT_Python_1.4.0.py".
#
# Checked on:
# A stock AMIGA, OS3.0.x using Python 1.4.0 and 2.0.1.
# WinUAE and FS-UAE using AMIGA OS3.1x, using Python 1.4.0 and 2.0.1.
# A Macbook Pro, OSX 10.13.6, Python, 2.7.x and 3.5.2.
# A Windows 10 machine with the current, as of the upload date, Python 3.7.0.
# A Linux Mint 19 machine with Python 2.7.x.
#
# After much searching, different approaches were found and every one didn't
# work on Python 1.4.0, and was virtually unmodifyable to suit.
# There was ONE however that was, with a little work, modifyable to work from
# Python 1.4.0 to 3.7.0 on almost ANY platform.
# These variations of code blocks have pointed out to me the limitations of
# Python 1.4.0 compared to even Python 2.0.1 for the AMIGA.
# Strangely enough it was Public Domain so FULLY CC0 it will remain, and
# almost totally impossible to find buried in Google's search facility.
#
# I TAKE NO CREDIT FOR THE ORIGINAL CODE BLOCK.
# I do however return this code back to the Public Domain, CC0 Licence, with
# the modifications to run on the lowly Python 1.4.0 that runs on a STOCK
# AMIGA 1200(HD), yet still have the ability to work on Python 3.7.0.
#
# TAKE NOTE: This is the SIMPLEST possible method using zero, [0.0], padding
# to powers of 2 and does cause minor problems with the results that are created.
# I'll let you do the research as to why... ;o)
import cmath
def fft(DATA):
# DATA points from +1.0 to -1.0 using 0.0 as the _DC_ level padders.
N=len(DATA)
# Getout if DATA size is less than or equal to 1.
if N<=1: return DATA
RESULT=[]
# For each RESULT list element...
for K in range(N):
S=complex(0)
# For each DATA element...
for T in range(N):
ANGLE=2j*cmath.pi*T*K/int(N)
S=S+(DATA[T]*cmath.exp(-ANGLE))
RESULT.append(S)
return RESULT
# Padded with trailing 0.0(s) to make up a power of 2 DATA size, (16 samples).
FFT_LIST=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, 0.0, 0.0, 0.0]
# A perfect frequency to power of 2 sample size, (16 samples).
#FFT_LIST=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0]
# Example code to display a horizontal spectrum display.
FFT=fft(FFT_LIST)
print(FFT)
print("")
print("Quick text mode spectrum display...")
# Quick text mode spectrum display.
for FFT_LISTING in range(0,len(FFT),1):
CHAR='*'
HORIZ=(4*(int(abs(FFT[FFT_LISTING]))))
for DISPLAY in range(1,HORIZ,1):
CHAR=CHAR+'*'
print(CHAR)
# What to expect using this VERY basic FFT program using the enabled "FFT_LIST"...
#
# [(1+0j), (7.275642373458594-3.8614288110854966j), (-3.4142135623730967-0.9999999999999999j),
# (0.10285042102434117-0.4829360166025667j), (3.0000000000000027-1.9999999999999987j),
# (-0.34549110814362877+0.9312775457705329j), (-0.5857864376269075+0.9999999999999972j),
# (0.9669983136606939-2.4472152487124035j), (1+5.022289837777324e-15j),
# (0.9669983136606818+2.447215248712401j), (-0.5857864376268997-1.000000000000009j),
# (-0.3454911081436235-0.9312775457705185j), (2.999999999999994+2.000000000000008j),
# (0.10285042102433833+0.48293601660255336j), (-3.414213562373087+1.000000000000001j),
# (7.275642373458576+3.8614288110855126j)]
#
# Quick text mode spectrum display...
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