Split off useful math functions to math.py

Might fix the problem Mark noticed where turn-off transients
are erroneously matching the drop that follows startup transients.

Makefile

@@ -8,26 +8,33 @@ else
 	@echo "Try 'make install'"
 endif
 
-test: test_pipewatch
+test: test_trainola3
 
 test_pipewatch:
 	nilmtools/pipewatch.py -t 3 "seq 10 20" "seq 20 30"
 
 test_trainola:
-	-nilmtool -u http://bucket/nilmdb remove -s min -e max \
-		/sharon/prep-a-matches
-	nilmtools/trainola.py "$$(cat extras/trainola-test-param-2.js)"
 	-nilmtool -u http://bucket/nilmdb remove -s min -e max \
 		/sharon/prep-a-matches
 	nilmtools/trainola.py "$$(cat extras/trainola-test-param.js)"
 
+test_trainola2:
+	-nilmtool -u http://bucket/nilmdb remove -s min -e max \
+		/sharon/prep-a-matches
+	nilmtools/trainola.py "$$(cat extras/trainola-test-param-2.js)"
+
+test_trainola3:
+	-nilmtool -u "http://bucket/nilmdb" destroy -R /test/jim
+	nilmtool -u "http://bucket/nilmdb" create /test/jim uint8_3
+	nilmtools/trainola.py "$$(cat extras/trainola-test-param-3.js)"
+	nilmtool -u "http://bucket/nilmdb" extract /test/jim -s min -e max
 
 test_cleanup:
 	nilmtools/cleanup.py -e extras/cleanup.cfg
 	nilmtools/cleanup.py extras/cleanup.cfg
 
 test_insert:
-	nilmtools/insert.py --file --dry-run  /test/foo </dev/null
+	nilmtools/insert.py --skip --file --dry-run /foo/bar ~/data/20130311T2100.prep1.gz ~/data/20130311T2100.prep1.gz ~/data/20130311T2200.prep1.gz
 
 test_copy:
 	nilmtools/copy_wildcard.py -U "http://nilmdb.com/bucket/" -D /lees*
@@ -46,7 +53,8 @@ test_prep: /tmp/raw.dat
 	nilmtool create /test/sinefit float32_3
 	nilmtool create /test/prep float32_8
 	nilmtool insert -s '@0' -t -r 8000 /test/raw /tmp/raw.dat
-	nilmtools/sinefit.py -a 0.5 -c 1 /test/raw /test/sinefit
+	nilmtools/sinefit.py -a 0.5 -c 1 -s '@0' -e '@5000000' /test/raw /test/sinefit
+	nilmtools/prep.py -c 2 /test/raw /test/sinefit /test/prep
 	nilmtools/prep.py -c 2 /test/raw /test/sinefit /test/prep
 	nilmtool extract -s min -e max /test/prep | head -20
 

README.txt

@@ -6,9 +6,9 @@ Prerequisites:
 
   # Runtime and build environments
   sudo apt-get install python2.7 python2.7-dev python-setuptools
-  sudo apt-get install python-numpy python-scipy
+  sudo apt-get install python-numpy python-scipy python-daemon
 
-  nilmdb (1.8.1+)
+  nilmdb (1.8.5+)
 
 Install:
 

extras/sample-cron-scripts/capture.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+
+# Start the ethstream capture using nilm-pipewatch
+
+# Bail out on errors
+set -e
+
+nilm-pipewatch --daemon --lock "/tmp/nilmdb-capture.lock" --timeout 30 \
+    "ethstream -a 192.168.1.209 -n 9 -r 8000 -N" \
+    "nilm-insert -m 10 -r 8000 --live /sharon/raw"

extras/sample-cron-scripts/cleanup.cfg

@@ -0,0 +1,8 @@
+[/sharon/prep-*]
+keep = 1y
+
+[/sharon/raw]
+keep = 2w
+
+[/sharon/sinefit]
+keep = 1y

extras/sample-cron-scripts/crontab

@@ -0,0 +1,9 @@
+# Install this by running "crontab crontab" (will replace existing crontab)
+
+# m h dom mon dow cmd
+
+# Run NilmDB processing every 5 minutes
+*/5 * * * * chronic /home/nilm/data/process.sh
+
+# Check the capture process every minute
+*/1 * * * * chronic /home/nilm/data/capture.sh

extras/sample-cron-scripts/process.sh

@@ -0,0 +1,28 @@
+#!/bin/bash
+# Run all necessary processing on NilmDB data.
+
+# Bail out on errors
+set -e
+
+# Ensure only one copy of this code runs at a time:
+LOCKFILE="/tmp/nilmdb-process.lock"
+exec 99>"$LOCKFILE"
+if ! flock -n -x 99 ; then
+    echo "NilmDB processing already running, giving up..."
+    exit 0
+fi
+trap 'rm -f "$LOCKFILE"' 0
+
+# sinefit on phase A voltage
+nilm-sinefit -c 5 /sharon/raw /sharon/sinefit
+
+# prep on A, B, C with appropriate rotations
+nilm-prep -c 1 -r 0 /sharon/raw /sharon/sinefit /sharon/prep-a
+nilm-prep -c 2 -r 120 /sharon/raw /sharon/sinefit /sharon/prep-b
+nilm-prep -c 3 -r 240 /sharon/raw /sharon/sinefit /sharon/prep-c
+
+# decimate raw and prep data
+nilm-decimate-auto /sharon/raw /sharon/prep*
+
+# run cleanup
+nilm-cleanup --yes /home/nilm/data/cleanup.cfg

extras/trainola-test-param-3.js

@@ -0,0 +1,40 @@
+{
+    "url": "http://bucket/nilmdb",
+    "stream": "/sharon/prep-a",
+    "dest_stream": "/test/jim",
+    "start": 1364184839901599,
+    "end": 1364184942407610.2,
+
+    "columns": [ { "index": 0, "name": "P1" } ],
+
+    "exemplars": [
+        {
+            "name": "A - True DBL Freezer ON",
+            "dest_column": 0,
+            "url": "http://bucket/nilmdb",
+            "stream": "/sharon/prep-a",
+            "columns": [ { "index": 0, "name": "P1" } ],
+            "start": 1365277707649000,
+            "end": 1365277710705000
+        },
+        {
+            "name": "A - Boiler 1 Fan OFF",
+            "dest_column": 1,
+            "url": "http://bucket/nilmdb",
+            "stream": "/sharon/prep-a",
+            "columns": [ { "index": 0, "name": "P1" } ],
+            "start": 1364188370735000,
+            "end": 1364188373819000
+        },
+        {
+            "name": "A - True DBL Freezer OFF",
+            "dest_column": 2,
+            "url": "http://bucket/nilmdb",
+            "stream": "/sharon/prep-a",
+            "columns": [ { "index": 0, "name": "P1" } ],
+            "start": 1365278087982000,
+            "end": 1365278089340000
+        }
+   ]
+}
+

nilmtools/copy_one.py

@@ -32,7 +32,7 @@ def main(argv = None):
     extractor = NumpyClient(f.src.url).stream_extract_numpy
     inserter = NumpyClient(f.dest.url).stream_insert_numpy_context
     for i in f.intervals():
-        print "Processing", f.interval_string(i)
+        print "Processing", i.human_string()
         with inserter(f.dest.path, i.start, i.end) as insert_ctx:
             for data in extractor(f.src.path, i.start, i.end):
                 insert_ctx.insert(data)

nilmtools/filter.py

@@ -316,7 +316,8 @@ class Filter(object):
         self._client_dest.stream_update_metadata(self.dest.path, data)
 
     # The main filter processing method.
-    def process_numpy(self, function, args = None, rows = 100000):
+    def process_numpy(self, function, args = None, rows = 100000,
+                      intervals = None):
         """Calls process_numpy_interval for each interval that currently
         exists in self.src, but doesn't exist in self.dest.  It will
         process the data in chunks as follows:
@@ -325,6 +326,9 @@ class Filter(object):
         corresponding to the data.  The data is converted to a Numpy
         array in chunks of 'rows' rows at a time.
 
+        If 'intervals' is not None, process those intervals instead of
+        the default list.
+
         'function' should be defined as:
         # def function(data, interval, args, insert_func, final)
 
@@ -358,7 +362,7 @@ class Filter(object):
                                            maxrows = rows)
         inserter_func = functools.partial(inserter, self.dest.path)
 
-        for interval in self.intervals():
+        for interval in (intervals or self.intervals()):
             print "Processing", interval.human_string()
             process_numpy_interval(interval, extractor_func, inserter_func,
                                    rows * 3, function, args)

nilmtools/insert.py

@@ -53,7 +53,8 @@ def parse_args(argv = None):
       is stepped forward to match 'clock'.
 
     - If 'data' is running ahead, there is overlap in the data, and an
-      error is raised.
+      error is raised.  If '--ignore' is specified, the current file
+      is skipped instead of raising an error.
     """))
     parser.add_argument("-u", "--url", action="store",
                         default="http://localhost/nilmdb/",
@@ -61,6 +62,8 @@ def parse_args(argv = None):
     group = parser.add_argument_group("Misc options")
     group.add_argument("-D", "--dry-run", action="store_true",
                        help="Parse files, but don't insert any data")
+    group.add_argument("-s", "--skip", action="store_true",
+                       help="Skip files if the data would overlap")
     group.add_argument("-m", "--max-gap", action="store", default=10.0,
                        metavar="SEC", type=float,
                        help="Max discrepency between clock and data "
@@ -235,6 +238,10 @@ def main(argv = None):
                                       "is %s but clock time is only %s",
                                       timestamp_to_human(data_ts),
                                       timestamp_to_human(clock_ts))
+                        if args.skip:
+                            printf("%s\n", err)
+                            printf("Skipping the remainder of this file\n")
+                            break
                         raise ParseError(filename, err)
 
                     if (data_ts + max_gap) < clock_ts:

nilmtools/math.py

@@ -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

nilmtools/pipewatch.py

@@ -15,6 +15,7 @@ import threading
 import select
 import signal
 import Queue
+import daemon
 
 def parse_args(argv = None):
     parser = argparse.ArgumentParser(
@@ -29,6 +30,8 @@ def parse_args(argv = None):
     Intended for use with ethstream (generator) and nilm-insert
     (consumer).  Commands are executed through the shell.
     """)
+    parser.add_argument("-d", "--daemon", action="store_true",
+                        help="Run in background")
     parser.add_argument("-l", "--lock", metavar="FILENAME", action="store",
                         default=tempfile.gettempdir() +
                         "/nilm-pipewatch.lock",
@@ -74,82 +77,92 @@ def watcher_thread(queue, procs):
                 return
         time.sleep(0.25)
 
+def pipewatch(args):
+    # Run the processes, etc
+    with open(os.devnull, "r") as devnull:
+        generator = subprocess.Popen(args.generator, shell = True,
+                                     bufsize = -1, close_fds = True,
+                                     stdin = devnull,
+                                     stdout = subprocess.PIPE,
+                                     stderr = None)
+        consumer = subprocess.Popen(args.consumer, shell = True,
+                                    bufsize = -11, close_fds = True,
+                                    stdin = subprocess.PIPE,
+                                    stdout = None, stderr = None)
+
+        queue = Queue.Queue(maxsize = 32)
+        reader = threading.Thread(target = reader_thread,
+                                  args = (queue, generator.stdout.fileno()))
+        reader.start()
+        watcher = threading.Thread(target = watcher_thread,
+                                   args = (queue, [generator, consumer]))
+        watcher.start()
+        try:
+            while True:
+                try:
+                    data = queue.get(True, args.timeout)
+                    if data is None:
+                        break
+                    consumer.stdin.write(data)
+                except Queue.Empty:
+                    # Timeout: kill the generator
+                    fprintf(sys.stderr, "pipewatch: timeout\n")
+                    generator.terminate()
+                    break
+
+            generator.stdout.close()
+            consumer.stdin.close()
+        except IOError:
+            fprintf(sys.stderr, "pipewatch: I/O error\n")
+
+        def kill(proc):
+            # Wait for a process to end, or kill it
+            def poll_timeout(proc, timeout):
+                for x in range(1+int(timeout / 0.1)):
+                    if proc.poll() is not None:
+                        break
+                    time.sleep(0.1)
+                return proc.poll()
+            try:
+                if poll_timeout(proc, 0.5) is None:
+                    proc.terminate()
+                    if poll_timeout(proc, 0.5) is None:
+                        proc.kill()
+            except OSError:
+                pass
+            return poll_timeout(proc, 0.5)
+
+        # Wait for them to die, or kill them
+        gret = kill(generator)
+        cret = kill(consumer)
+
+        fprintf(sys.stderr, "pipewatch: generator returned %d, " +
+                "consumer returned %d\n", gret, cret)
+        if gret == 0 and cret == 0:
+            sys.exit(0)
+        sys.exit(1)
+
 def main(argv = None):
     args = parse_args(argv)
 
-    with open(args.lock, "w") as lockfile:
-        if not nilmdb.utils.lock.exclusive_lock(lockfile):
-            printf("pipewatch process already running (according to %s)\n",
-                   args.lock)
-            sys.exit(0)
-
-
-        with open(os.devnull, "r") as devnull:
-            generator = subprocess.Popen(args.generator, shell = True,
-                                         bufsize = -1, close_fds = True,
-                                         stdin = devnull,
-                                         stdout = subprocess.PIPE,
-                                         stderr = None)
-            consumer = subprocess.Popen(args.consumer, shell = True,
-                                        bufsize = -11, close_fds = True,
-                                        stdin = subprocess.PIPE,
-                                        stdout = None, stderr = None)
-
-            queue = Queue.Queue(maxsize = 32)
-            reader = threading.Thread(target = reader_thread,
-                                      args = (queue, generator.stdout.fileno()))
-            reader.start()
-            watcher = threading.Thread(target = watcher_thread,
-                                       args = (queue, [generator, consumer]))
-            watcher.start()
-            try:
-                while True:
-                    try:
-                        data = queue.get(True, args.timeout)
-                        if data is None:
-                            break
-                        consumer.stdin.write(data)
-                    except Queue.Empty:
-                        # Timeout: kill the generator
-                        fprintf(sys.stderr, "pipewatch: timeout\n")
-                        generator.terminate()
-                        break
-
-                generator.stdout.close()
-                consumer.stdin.close()
-            except IOError:
-                fprintf(sys.stderr, "pipewatch: I/O error\n")
-
-            def kill(proc):
-                # Wait for a process to end, or kill it
-                def poll_timeout(proc, timeout):
-                    for x in range(1+int(timeout / 0.1)):
-                        if proc.poll() is not None:
-                            break
-                        time.sleep(0.1)
-                    return proc.poll()
-                try:
-                    if poll_timeout(proc, 0.5) is None:
-                        proc.terminate()
-                        if poll_timeout(proc, 0.5) is None:
-                            proc.kill()
-                except OSError:
-                    pass
-                return poll_timeout(proc, 0.5)
-
-            # Wait for them to die, or kill them
-            gret = kill(generator)
-            cret = kill(consumer)
-
-            fprintf(sys.stderr, "pipewatch: generator returned %d, " +
-                    "consumer returned %d\n", gret, cret)
-            if gret == 0 and cret == 0:
-                sys.exit(0)
-            sys.exit(1)
+    lockfile = open(args.lock, "w")
+    if not nilmdb.utils.lock.exclusive_lock(lockfile):
+        printf("pipewatch process already running (according to %s)\n",
+               args.lock)
+        sys.exit(0)
     try:
-        os.unlink(args.lock)
-    except OSError:
-        pass
+        # Run as a daemon if requested, otherwise run directly.
+        if args.daemon:
+            with daemon.DaemonContext(files_preserve = [ lockfile ]):
+                pipewatch(args)
+        else:
+            pipewatch(args)
+    finally:
+        # Clean up lockfile
+        try:
+            os.unlink(args.lock)
+        except OSError:
+            pass
 
 if __name__ == "__main__":
     main()

nilmtools/prep.py

@@ -12,6 +12,7 @@ import scipy.fftpack
 import scipy.signal
 #from matplotlib import pyplot as p
 import bisect
+from nilmdb.utils.interval import Interval
 
 def main(argv = None):
     # Set up argument parser
@@ -82,9 +83,20 @@ def main(argv = None):
                             "prep_column": args.column,
                             "prep_rotation": repr(rotation) })
 
-    # Run the processing function on all data
+    # Find the intersection of the usual set of intervals we'd filter,
+    # and the intervals actually present in sinefit data.  This is
+    # what we will process.
+    filter_int = f.intervals()
+    sinefit_int = ( Interval(start, end) for (start, end) in
+                    client_sinefit.stream_intervals(
+                        args.sinepath, start = f.start, end = f.end) )
+    intervals = nilmdb.utils.interval.intersection(filter_int, sinefit_int)
+
+    # Run the process (using the helper in the filter module)
     f.process_numpy(process, args = (client_sinefit, sinefit.path, args.column,
-                                     args.nharm, rotation, args.nshift))
+                                     args.nharm, rotation, args.nshift),
+                    intervals = intervals)
+
 
 def process(data, interval, args, insert_function, final):
     (client, sinefit_path, column, nharm, rotation, nshift) = args

nilmtools/sinefit.py

@@ -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()

nilmtools/trainola.py

@@ -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)
@@ -104,31 +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"""
-    mins = [];
-    maxs = [];
-    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):
-                maxs.append(cur_max)
-                cur_min = (n, p)
-                lookformax = False
-        else:
-            if p > (cur_min[1] + delta):
-                mins.append(cur_min)
-                cur_max = (n, p)
-                lookformax = True
-    return (mins, maxs)
-
 def timestamp_to_short_human(timestamp):
     dt = datetime_tz.datetime_tz.fromtimestamp(timestamp_to_seconds(timestamp))
     return dt.strftime("%H:%M:%S")
@@ -164,11 +140,35 @@ 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_minmax = peak_detect(corrs[biggest], 0.1)
-        peaks = [ p[0] for p in peaks_minmax[1] ]
+        peaks = nilmtools.math.peak_detect(corrs[biggest], 0.1)
 
-        # Now look at every peak
-        for row in peaks:
+        # To try to reduce false positives, discard peaks where
+        # there's a higher-magnitude peak (either min or max) within
+        # one exemplar width nearby.
+        good_peak_locations = []
+        for (i, (n, p, is_max)) in enumerate(peaks):
+            if not is_max:
+                continue
+            ok = True
+            # check up to 'e.count' rows before this one
+            j = i-1
+            while ok and j >= 0 and peaks[j][0] > (n - e.count):
+                if abs(peaks[j][1]) > abs(p):
+                    ok = False
+                j -= 1
+
+            # check up to 'e.count' rows after this one
+            j = i+1
+            while ok and j < len(peaks) and peaks[j][0] < (n + e.count):
+                if abs(peaks[j][1]) > abs(p):
+                    ok = False
+                j += 1
+
+            if ok:
+                good_peak_locations.append(n)
+
+        # Now look at all good peaks
+        for row in good_peak_locations:
             # Correlation for each column must be close enough to 1.
             for (corr, scale) in zip(corrs, e.scale):
                 # The accepted distance from 1 is based on the relative

setup.py

@@ -61,9 +61,10 @@ setup(name='nilmtools',
       long_description = "NILM Database Tools",
       license = "Proprietary",
       author_email = 'jim@jtan.com',
-      install_requires = [ 'nilmdb >= 1.8.1',
+      install_requires = [ 'nilmdb >= 1.8.5',
                            'numpy',
                            'scipy',
+                           'python-daemon >= 1.5',
                            #'matplotlib',
                            ],
       packages = [ 'nilmtools',