archives/pebble
2
0
Fork 0
mirror of https://github.com/google/pebble.git synced 2025-03-15 08:41:21 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions
pebble/tools/activity/fft.py
Matthieu Jeanson 3b92768480 Import of the watch repository from Pebble
2025-01-27 11:38:16 -08:00

438 lines
12 KiB
Python
Raw Permalink Blame History

# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#################################################################################################
# Test FFT algorithm
#
# This is used to experiment with various step-tracking algorithms. It is a python implementation
# of an FFT as described in:
# "Real-valued Fast Fourier Transform Algorithm", from IEEE Transactions on Acoustics,
# Speech, and Signal Processing, Vol. ASSP-35, No. 6, June 1987
#
# The firmware uses this same algorithm, but implemented in C in the prv_fft_2radix_real()
# method of kraepelin_algorithm.c
##################################################################################################
import argparse
import os
import sys
import logging
import math
###########################################################################################
g_walk_10_steps = [
[-362, -861, 69],
[-309, -899, 45],
[-266, -904, 21],
[-242, -848, -134],
[-272, -839, 34],
[-207, -919, 14],
[-244, -879, 93],
[-238, -856, 91],
[-185, -883, 37],
[-217, -855, -156],
[-200, -883, 25],
[-154, -927, 42],
[-179, -935, 71],
[-184, -956, 32],
[-129, -999, 99],
[-195, -950, -112],
[-222, -969, -164],
[-351, -996, -190],
[-277, -1218, -259],
[-212, -1018, -250],
[-209, -812, -142],
[-182, -680, -200],
[-257, -642, -169],
[-269, -797, -289],
[-142, -1107, -330],
[-185, -909, -300],
[-229, -706, -155],
[-171, -750, -161],
[-181, -811, -218],
[-173, -845, -149],
[-118, -887, -126],
[-150, -871, -100],
[-164, -908, -146],
[-175, -958, -161],
[-231, -952, -113],
[-273, -1006, -205],
[-321, -1047, -351],
[-321, -1064, -300],
[-262, -945, -210],
[-298, -770, -124],
[-338, -772, 95],
[-325, -818, -179],
[-329, -780, -153],
[-280, -796, -151],
[-230, -755, -100],
[-234, -759, 44],
[-248, -807, 90],
[-217, -872, 79],
[-204, -887, 74],
[-189, -939, 78],
[-220, -1014, -129],
[-147, -1107, -129],
[-274, -1013, -158],
[-301, -1007, -258],
[-351, -1131, -346],
[-118, -1086, -355],
[-290, -716, -213],
[-288, -720, -290],
[-235, -825, -344],
[-179, -819, -243],
[-228, -670, -185],
[-125, -790, -145],
[-145, -795, -207],
[-152, -809, 76],
[-98, -871, -115],
[-89, -855, -111],
[-116, -879, 84],
[-161, -945, -172],
[-147, -1017, -173],
[-278, -1012, -146],
[-268, -1049, -247],
[-279, -1026, -260],
[-286, -958, -187],
[-288, -890, -167],
[-359, -873, -168],
[-324, -904, -147],
[-263, -804, -134],
[-214, -712, 37],
[-189, -698, 29],
[-183, -755, 74],
[-182, -841, 98],
[-115, -894, 73],
[-149, -857, 57],
[-93, -927, -68],
[-145, -988, -120],
[-112, -1095, -112],
[-201, -1059, -146],
[-278, -1104, -206],
[-284, -1204, -213],
[-214, -966, -254],
[-272, -730, -140],
[-233, -785, -252],
[-259, -813, -272],
[-156, -840, -205],
[-163, -765, -110],
[-165, -741, 97],
[-164, -791, 86],
[-99, -849, -69],
[-99, -820, -81],
[-94, -842, -37],
[-142, -881, -109],
[-153, -978, -155],
[-212, -934, 71],
[-341, -947, 99],
[-406, -1039, -283],
[-265, -1146, -206],
[-296, -979, -163],
[-345, -864, 98],
[-216, -907, 38],
[-242, -809, 47],
[-154, -736, 52],
[-137, -700, -101],
[-184, -743, -136],
[-191, -850, 86],
[-206, -883, 85],
[-194, -875, 48],
[-148, -937, 46],
[-193, -983, 31],
[-176, -1062, 43],
[-251, -1006, -114],
[-284, -1036, -192],
[-374, -1181, -248],
[-167, -1177, -271],
[-253, -794, -128],
[-285, -651, -129],
[-228, -757, -227],
[-260, -843, -201],
[-189, -899, -253],
[-212, -800, -136],
[-218, -728, -136],
[-177, -761, -129],
[-165, -806, -137],
[-157, -839, -122],
[-116, -899, -104],
[-191, -874, 77],
[-174, -911, 95],
[-193, -971, -147],
[-255, -961, -127],
[-222, -1052, -124],
[-333, -1021, -223],
[-245, -1018, -215],
[-269, -850, 91],
[-318, -754, -120],
[-335, -878, -199],
[-322, -986, -224],
[-192, -902, -179],
[-177, -712, 86],
[-196, -673, 88],
[-178, -751, -101],
[-182, -847, 70],
[-147, -909, -131],
[-170, -939, 43],
[-224, -994, 60],
[-189, -1051, 42],
[-242, -968, -183],
[-312, -978, -213],
[-317, -1298, -334],
[-184, -1131, -330],
[-287, -754, -141],
[-249, -773, -287],
[-166, -842, -297],
[-196, -742, -214],
[-163, -729, -198],
[-177, -757, -197],
[-174, -830, -155],
[-159, -860, -149],
[-145, -856, 72],
[-132, -849, 47],
[-145, -839, 62],
[-179, -843, 76],
[-163, -941, -114],
[-230, -963, -110],
]
##################################################################################################
def real_value_fft(x):
""" Real value FFT as described in Appendix of:
"Real-valued Fast Fourier Transform Algorithm", from IEEE Transactions on Acoustics, Speech,
and Signal Processing, Vol. ASSP-35, No. 6, June 1987
"""
# Make sure we have a power of 2 length input
n = len(x)
m = int(math.log(n, 2))
if (math.pow(2, m) != n):
raise RuntimeError("Length must be a power of 2")
# The rest of the code assumes 1-based indexing (it comes from fortran)
x = [0] + x
# ---------------------------------------------------------------------------------
# Digit reverse counter
j = 1
n1 = n - 1
for i in range(1, n1 + 1):
if (i < j):
xt = x[j]
x[j] = x[i]
x[i] = xt
k = n/2
while (k < j):
j = j - k
k = k / 2
j = j + k
# ---------------------------------------------------------------------------------
# Length 2 butterflies
for i in range(1, n + 1, 2):
xt = x[i]
x[i] = xt + x[i+1]
x[i+1] = xt - x[i+1]
# ---------------------------------------------------------------------------------
# Other butterflies
n2 = 1
for k in range(2, m + 1):
n4 = n2
n2 = 2 * n4
n1 = 2 * n2
e = 2 * math.pi / n1
for i in range(1, n+1, n1):
xt = x[i]
x[i] = xt + x[i + n2]
x[i + n2] = xt - x[i + n2]
x[i + n4 + n2] = -x[i + n4 + n2]
a = e
for j in range(1, n4 - 1):
i1 = i + j
i2 = i - j + n2
i3 = i + j + n2
i4 = i - j + n1
cc = math.cos(a)
ss = math.sin(a)
a = a + e
t1 = x[i3] * cc + x[i4] * ss
t2 = x[i3] * ss - x[i4] * cc
x[i4] = x[i2] - t2
x[i3] = -x[i2] - t2
x[i2] = x[i1] - t1
x[i1] = x[i1] + t1
return x[1:]
###################################################################################################
def compute_magnitude(x):
""" The real_value_fft() produces an array containing outputs in this order:
[Re(0), Re(1), ..., Re(N/2), Im(N/2-1), ..., Im(1)]
This method returns the magnitudes. The magnitude of term i is sqrt(Re(i)**2 + Im(i)**2)
"""
result = []
n = len(x)
real_idx = 0
im_idx = n - 1
result.append(x[real_idx])
real_idx += 1
while real_idx <= n/2 - 1:
mag = (x[real_idx]**2 + x[im_idx]**2) ** 0.5
result.append(mag)
real_idx += 1
im_idx -= 1
result.append(x[real_idx])
return result
###################################################################################################
def apply_gausian(x, width=0.1):
""" Multiply x by the gaussian function. Width is a fraction, like 0.1
"""
result = []
n = len(x)
mid = float(n/2)
denominator = n**2 * width
for i in range(len(x)):
print i-mid, (i-mid)**2, -1 * (i - mid)**2/denominator, \
math.exp(-1 * (i - mid)**2/denominator)
g = math.exp(-1 * (i - mid)**2/denominator)
result.append(g * x[i])
return result
###################################################################################################
def print_graph(x):
min_value = min(x)
max_value = max(x)
extent = max(abs(min_value), abs(max_value))
scale = 2 * extent
min_value = -extent
for i in range(len(x)):
print "%4d: %10.3f: " % (i, x[i]),
position = int((x[i] - min_value) * 80 / scale)
if position < 40:
print ' ' * position,
print '*' * (40 - position)
else:
print ' ' * 40,
print '*' * (position - 40)
###################################################################################################
if __name__ == '__main__':
# Collect our command line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--debug', action='store_true', help="Turn on debug logging")
args = parser.parse_args()
level = logging.INFO
if args.debug:
level = logging.DEBUG
logging.basicConfig(level=level)
# -------------------------------------------------------------------------------------
# Constant signal
if 0:
input_len = 128
input = [1 for x in range(input_len)]
print "\n############ INPUT ######################"
print_graph(input)
result = real_value_fft(input)
print "\n############ RESULT ######################"
print_graph(result)
# -------------------------------------------------------------------------------------
# N sine waves
if 0:
input_len = 128
freq = 7
input = [math.cos(float(x)/input_len * freq * 2 * math.pi) for x in range(input_len)]
print "\n############ INPUT ######################"
print_graph(input)
print "\n############ GAUSIAN OF INPUT ############"
# input = apply_gausian(input, 0.1)
print_graph(input)
result = real_value_fft(input)
print "\n############ REAL, IMAG ######################"
print_graph(result)
print "\n############ MAGNITUDE ######################"
mag = compute_magnitude(result)
print_graph(mag)
# -------------------------------------------------------------------------------------
# Step data
if 1:
input_len = 128
raw_input = g_walk_10_steps[0:input_len]
x_data = [x for x, y, z in raw_input]
x_mean = sum(x_data) / len(x_data)
x_data = [x - x_mean for x in x_data]
y_data = [y for x, y, z in raw_input]
y_mean = sum(y_data) / len(y_data)
y_data = [y - y_mean for y in y_data]
z_data = [z for x, y, z in raw_input]
z_mean = sum(z_data) / len(z_data)
z_data = [z - z_mean for z in z_data]
print "\n############ X ######################"
print_graph(x_data)
print "\n############ Y ######################"
print_graph(y_data)
print "\n############ Z ######################"
print_graph(z_data)
input = []
for (x, y, z) in raw_input:
mag = x**2 + y**2 + z**2
mag = mag ** 0.5
input.append(mag)
mean_mag = sum(input) / len(input)
input = [x - mean_mag for x in input]
print "\n############ INPUT ######################"
# input = apply_gausian(input)
print_graph(input)
result = real_value_fft(input)
print "\n############ REAL, IMAG ######################"
print_graph(result)
print "\n############ MAGNITUDE ######################"
mag = compute_magnitude(result)
print_graph(mag)
Reference in a new issue View git blame Copy permalink