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Improve sinefit resiliancy

tags/nilmtools-1.2.0^0
Jim Paris 8 years ago
parent
commit
cf9eb0ed48
1 changed files with 52 additions and 6 deletions
  1. +52
    -6
      src/sinefit.py

+ 52
- 6
src/sinefit.py View File

@@ -2,12 +2,18 @@

# Sine wave fitting. This runs about 5x faster than realtime on raw data.

from nilmdb.utils.printf import *
import nilmtools.filter
import nilmdb.client
from nilmdb.utils.time import (timestamp_to_human,
timestamp_to_seconds,
seconds_to_timestamp)

from numpy import *
from scipy import *
#import pylab as p
import operator
import sys

def main(argv = None):
f = nilmtools.filter.Filter()
@@ -59,12 +65,40 @@ def main(argv = None):
f.process_numpy(process, args = (args.column, args.frequency, args.min_amp,
args.min_freq, args.max_freq))

class SuppressibleWarning(object):
def __init__(self, maxcount = 10, maxsuppress = 100):
self.maxcount = maxcount
self.maxsuppress = maxsuppress
self.count = 0
self.last_msg = ""

def _write(self, sec, msg):
if sec:
now = "[" + timestamp_to_human(seconds_to_timestamp(sec)) + "] "
else:
now = ""
sys.stderr.write(now + msg)

def warn(self, msg, seconds = None):
self.count += 1
if self.count <= self.maxcount:
self._write(seconds, msg)
if (self.count - self.maxcount) >= self.maxsuppress:
self.reset(seconds)

def reset(self, seconds = None):
if self.count > self.maxcount:
self._write(seconds, sprintf("(%d warnings suppressed)\n",
self.count - self.maxcount))
self.count = 0

def process(data, interval, args, insert_function, final):
(column, f_expected, a_min, f_min, f_max) = args
rows = data.shape[0]

# Estimate sampling frequency from timestamps
fs = 1e6 * (rows-1) / (data[-1][0] - data[0][0])
fs = (rows-1) / (timestamp_to_seconds(data[-1][0]) -
timestamp_to_seconds(data[0][0]))

# Pull out about 3.5 periods of data at once;
# we'll expect to match 3 zero crossings in each window
@@ -74,26 +108,31 @@ def process(data, interval, args, insert_function, final):
if rows < N:
return 0

warn = SuppressibleWarning(3, 1000)

# Process overlapping windows
start = 0
num_zc = 0
last_inserted_timestamp = None
while start < (rows - N):
this = data[start:start+N, column]
t_min = data[start, 0]/1e6
t_max = data[start+N-1, 0]/1e6
t_min = timestamp_to_seconds(data[start, 0])
t_max = timestamp_to_seconds(data[start+N-1, 0])

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

# Check bounds. If frequency is too crazy, ignore this window
if f0 < f_min or f0 > f_max:
print "frequency", f0, "outside valid range", f_min, "-", f_max
warn.warn(sprintf("frequency %s outside valid range %s - %s\n",
str(f0), str(f_min), str(f_max)), t_min)
start += N
continue

# If amplitude is too low, results are probably just noise
if A < a_min:
print "amplitude", A, "below minimum threshold", a_min
warn.warn(sprintf("amplitude %s below minimum threshold %s\n",
str(A), str(a_min)), t_min)
start += N
continue

@@ -116,7 +155,13 @@ def process(data, interval, args, insert_function, final):
while zc_n < (N - period_n/2):
#p.plot(zc_n, C, 'ro')
t = t_min + zc_n / fs
insert_function([[t * 1e6, f0, A, C]])
if (last_inserted_timestamp is None or
t > last_inserted_timestamp):
insert_function([[seconds_to_timestamp(t), f0, A, C]])
last_inserted_timestamp = t
warn.reset(t)
else:
warn.warn("timestamp overlap\n", t)
num_zc += 1
last_zc = zc_n
zc_n += period_n
@@ -134,6 +179,7 @@ def process(data, interval, args, insert_function, final):
start = int(round(start + advance))

# Return the number of rows we've processed
warn.reset(last_inserted_timestamp)
print "Marked", num_zc, "zero-crossings in", start, "rows"
return start



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