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Split off useful math functions to math.py

tags/nilmtools-1.4.5^0
Jim Paris 8 years ago
parent
commit
492445a469
3 changed files with 111 additions and 104 deletions
  1. +107
    -0
      nilmtools/math.py
  2. +2
    -73
      nilmtools/sinefit.py
  3. +2
    -31
      nilmtools/trainola.py

+ 107
- 0
nilmtools/math.py View File

@@ -0,0 +1,107 @@
#!/usr/bin/python

# Miscellaenous useful mathematical functions
from nilmdb.utils.printf import *
from numpy import *
from scipy import *

def sfit4(data, fs):
"""(A, f0, phi, C) = sfit4(data, fs)

Compute 4-parameter (unknown-frequency) least-squares fit to
sine-wave data, according to IEEE Std 1241-2010 Annex B

Input:
data vector of input samples
fs sampling rate (Hz)

Output:
Parameters [A, f0, phi, C] to fit the equation
x[n] = A * sin(f0/fs * 2 * pi * n + phi) + C
where n is sample number. Or, as a function of time:
x(t) = A * sin(f0 * 2 * pi * t + phi) + C

by Jim Paris
(Verified to match sfit4.m)
"""
N = len(data)
t = linspace(0, (N-1) / float(fs), N)

## Estimate frequency using FFT (step b)
Fc = fft(data)
F = abs(Fc)
F[0] = 0 # eliminate DC

# Find pair of spectral lines with largest amplitude:
# resulting values are in F(i) and F(i+1)
i = argmax(F[0:int(N/2)] + F[1:int(N/2+1)])

# Interpolate FFT to get a better result (from Markus [B37])
U1 = real(Fc[i])
U2 = real(Fc[i+1])
V1 = imag(Fc[i])
V2 = imag(Fc[i+1])
n = 2 * pi / N
ni1 = n * i
ni2 = n * (i+1)
K = ((V2-V1)*sin(ni1) + (U2-U1)*cos(ni1)) / (U2-U1)
Z1 = V1 * (K - cos(ni1)) / sin(ni1) + U1
Z2 = V2 * (K - cos(ni2)) / sin(ni2) + U2
i = arccos((Z2*cos(ni2) - Z1*cos(ni1)) / (Z2-Z1)) / n

# Convert to Hz
f0 = i * float(fs) / N

# Fit it. We'll catch exceptions here and just returns zeros
# if something fails with the least squares fit, etc.
try:
# first guess for A0, B0 using 3-parameter fit (step c)
s = zeros(3)
w = 2*pi*f0

# Now iterate 7 times (step b, plus 6 iterations of step i)
for idx in range(7):
D = c_[cos(w*t), sin(w*t), ones(N),
-s[0] * t * sin(w*t) + s[1] * t * cos(w*t) ] # eqn B.16
s = linalg.lstsq(D, data)[0] # eqn B.18
w = w + s[3] # update frequency estimate

## Extract results
A = sqrt(s[0]*s[0] + s[1]*s[1]) # eqn B.21
f0 = w / (2*pi)
phi = arctan2(s[0], s[1]) # eqn B.22 (flipped for sin instead of cos)
C = s[2]
return (A, f0, phi, C)
except Exception as e:
# something broke down; just return zeros
return (0, 0, 0, 0)

def peak_detect(data, delta = 0.1):
"""Simple min/max peak detection algorithm, taken from my code
in the disagg.m from the 10-8-5 paper.

Returns an array of peaks: each peak is a tuple
(n, p, is_max)
where n is the row number in 'data', and p is 'data[n]',
and is_max is True if this is a maximum, False if it's a minimum,
"""
peaks = [];
cur_min = (None, inf)
cur_max = (None, -inf)
lookformax = False
for (n, p) in enumerate(data):
if p > cur_max[1]:
cur_max = (n, p)
if p < cur_min[1]:
cur_min = (n, p)
if lookformax:
if p < (cur_max[1] - delta):
peaks.append((cur_max[0], cur_max[1], True))
cur_min = (n, p)
lookformax = False
else:
if p > (cur_min[1] + delta):
peaks.append((cur_min[0], cur_min[1], False))
cur_max = (n, p)
lookformax = True
return peaks

+ 2
- 73
nilmtools/sinefit.py View File

@@ -3,6 +3,7 @@
# Sine wave fitting.
from nilmdb.utils.printf import *
import nilmtools.filter
import nilmtools.math
import nilmdb.client
from nilmdb.utils.time import (timestamp_to_human,
timestamp_to_seconds,
@@ -11,7 +12,6 @@ from nilmdb.utils.time import (timestamp_to_human,
from numpy import *
from scipy import *
#import pylab as p
import operator
import sys

def main(argv = None):
@@ -119,7 +119,7 @@ def process(data, interval, args, insert_function, final):
t_max = timestamp_to_seconds(data[start+N-1, 0])

# Do 4-parameter sine wave fit
(A, f0, phi, C) = sfit4(this, fs)
(A, f0, phi, C) = nilmtools.math.sfit4(this, fs)

# Check bounds. If frequency is too crazy, ignore this window
if f0 < f_min or f0 > f_max:
@@ -187,76 +187,5 @@ def process(data, interval, args, insert_function, final):
printf("%sMarked %d zero-crossings in %d rows\n", now, num_zc, start)
return start

def sfit4(data, fs):
"""(A, f0, phi, C) = sfit4(data, fs)

Compute 4-parameter (unknown-frequency) least-squares fit to
sine-wave data, according to IEEE Std 1241-2010 Annex B

Input:
data vector of input samples
fs sampling rate (Hz)

Output:
Parameters [A, f0, phi, C] to fit the equation
x[n] = A * sin(f0/fs * 2 * pi * n + phi) + C
where n is sample number. Or, as a function of time:
x(t) = A * sin(f0 * 2 * pi * t + phi) + C

by Jim Paris
(Verified to match sfit4.m)
"""
N = len(data)
t = linspace(0, (N-1) / float(fs), N)

## Estimate frequency using FFT (step b)
Fc = fft(data)
F = abs(Fc)
F[0] = 0 # eliminate DC

# Find pair of spectral lines with largest amplitude:
# resulting values are in F(i) and F(i+1)
i = argmax(F[0:int(N/2)] + F[1:int(N/2+1)])

# Interpolate FFT to get a better result (from Markus [B37])
U1 = real(Fc[i])
U2 = real(Fc[i+1])
V1 = imag(Fc[i])
V2 = imag(Fc[i+1])
n = 2 * pi / N
ni1 = n * i
ni2 = n * (i+1)
K = ((V2-V1)*sin(ni1) + (U2-U1)*cos(ni1)) / (U2-U1)
Z1 = V1 * (K - cos(ni1)) / sin(ni1) + U1
Z2 = V2 * (K - cos(ni2)) / sin(ni2) + U2
i = arccos((Z2*cos(ni2) - Z1*cos(ni1)) / (Z2-Z1)) / n

# Convert to Hz
f0 = i * float(fs) / N

# Fit it. We'll catch exceptions here and just returns zeros
# if something fails with the least squares fit, etc.
try:
# first guess for A0, B0 using 3-parameter fit (step c)
s = zeros(3)
w = 2*pi*f0

# Now iterate 7 times (step b, plus 6 iterations of step i)
for idx in range(7):
D = c_[cos(w*t), sin(w*t), ones(N),
-s[0] * t * sin(w*t) + s[1] * t * cos(w*t) ] # eqn B.16
s = linalg.lstsq(D, data)[0] # eqn B.18
w = w + s[3] # update frequency estimate

## Extract results
A = sqrt(s[0]*s[0] + s[1]*s[1]) # eqn B.21
f0 = w / (2*pi)
phi = arctan2(s[0], s[1]) # eqn B.22 (flipped for sin instead of cos)
C = s[2]
return (A, f0, phi, C)
except Exception as e:
# something broke down, just return zeros
return (0, 0, 0, 0)

if __name__ == "__main__":
main()

+ 2
- 31
nilmtools/trainola.py View File

@@ -3,6 +3,7 @@
from nilmdb.utils.printf import *
import nilmdb.client
import nilmtools.filter
import nilmtools.math
from nilmdb.utils.time import (timestamp_to_human,
timestamp_to_seconds,
seconds_to_timestamp)
@@ -104,36 +105,6 @@ class Exemplar(object):
self.name, self.stream, ",".join(self.columns.keys()),
self.count)

def peak_detect(data, delta):
"""Simple min/max peak detection algorithm, taken from my code
in the disagg.m from the 10-8-5 paper.

Returns an array of peaks: each peak is a tuple
(n, p, is_max)
where n is the row number in 'data', and p is 'data[n]',
and is_max is True if this is a maximum, False if it's a minimum,
"""
peaks = [];
cur_min = (None, np.inf)
cur_max = (None, -np.inf)
lookformax = False
for (n, p) in enumerate(data):
if p > cur_max[1]:
cur_max = (n, p)
if p < cur_min[1]:
cur_min = (n, p)
if lookformax:
if p < (cur_max[1] - delta):
peaks.append((cur_max[0], cur_max[1], True))
cur_min = (n, p)
lookformax = False
else:
if p > (cur_min[1] + delta):
peaks.append((cur_min[0], cur_min[1], False))
cur_max = (n, p)
lookformax = True
return peaks

def timestamp_to_short_human(timestamp):
dt = datetime_tz.datetime_tz.fromtimestamp(timestamp_to_seconds(timestamp))
return dt.strftime("%H:%M:%S")
@@ -169,7 +140,7 @@ def trainola_matcher(data, interval, args, insert_func, final_chunk):

# Find the peaks using the column with the largest amplitude
biggest = e.scale.index(max(e.scale))
peaks = peak_detect(corrs[biggest], 0.1)
peaks = nilmtools.math.peak_detect(corrs[biggest], 0.1)

# To try to reduce false positives, discard peaks where
# there's a higher-magnitude peak (either min or max) within


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