Change nilm-cleanup config file format, tweak output

New option --nshift controls how many shifted FFT windows to perform
per period.  "nilm-prep -N 2" is similar to old prep behavior.  Note
that this is redundant information and takes up extra storage space,
though.

Makefile

@@ -8,8 +8,11 @@ else
 	@echo "Try 'make install'"
 endif
 
-test:
-	src/decimate.py
+test: test_cleanup
+
+test_cleanup:
+	src/cleanup.py -e extras/cleanup.cfg
+	src/cleanup.py -D extras/cleanup.cfg
 
 test_insert:
 	@make install >/dev/null
@@ -21,12 +24,16 @@ test_copy:
 
 test_prep:
 	@make install >/dev/null
-	src/prep.py -c 3 \
-		/lees-compressor/no-leak/raw \
-		/lees-compressor/no-leak/sinefit \
-		/lees-compressor/no-leak/prep \
-	-s '2013-02-19 18:00:00' \
-	-r 0
+	-nilmtool destroy -R /test/raw
+	-nilmtool destroy -R /test/sinefit
+	-nilmtool destroy -R /test/prep
+	nilmtool create /test/raw float32_2
+	nilmtool create /test/sinefit float32_3
+	nilmtool create /test/prep float32_8
+	nilmtool insert -s '@0' -t -r 8000 /test/raw /tmp/raw.dat
+	src/sinefit.py -c 1 /test/raw /test/sinefit
+	src/prep.py -c 2 /test/raw /test/sinefit /test/prep
+	nilmtool extract -s min -e max /test/prep | head -20
 
 test_decimate:
 	-@nilmtool destroy /lees-compressor/no-leak/raw/4 || true

README.txt

@@ -8,7 +8,7 @@ Prerequisites:
   sudo apt-get install python2.7 python2.7-dev python-setuptools
   sudo apt-get install python-numpy python-scipy python-matplotlib
 
-  nilmdb (1.3.1+)
+  nilmdb (1.5.0+)
 
 Install:
 

extras/cleanup.cfg

@@ -0,0 +1,22 @@
+[/lees-compressor/no-leak/prep]
+keep = 2d
+rate = 60
+
+[*/raw]
+keep = 2d
+
+[*/something]
+rate = 10
+
+[*/sinefit]
+keep = 1w
+decimated = False
+
+[/test/raw]
+keep = 0.01d
+
+[/test/sinefit]
+keep = 0.01d
+
+[/test/prep]
+keep = 0.01d

setup.py

@@ -61,7 +61,7 @@ setup(name='nilmtools',
       long_description = "NILM Database Tools",
       license = "Proprietary",
       author_email = 'jim@jtan.com',
-      install_requires = [ 'nilmdb >= 1.4.6',
+      install_requires = [ 'nilmdb >= 1.5.0',
                            'numpy',
                            'scipy',
                            'matplotlib',
@@ -78,6 +78,7 @@ setup(name='nilmtools',
               'nilm-prep = nilmtools.prep:main',
               'nilm-copy-wildcard = nilmtools.copy_wildcard:main',
               'nilm-sinefit = nilmtools.sinefit:main',
+              'nilm-cleanup = nilmtools.cleanup:main',
               ],
           },
       zip_safe = False,

src/cleanup.py

@@ -0,0 +1,252 @@
+#!/usr/bin/python
+
+from nilmdb.utils.printf import *
+from nilmdb.utils.time import (parse_time, timestamp_to_human,
+                               timestamp_to_seconds, seconds_to_timestamp)
+from nilmdb.utils.diskusage import human_size
+from nilmdb.utils.interval import Interval
+import nilmdb.client
+import nilmdb.client.numpyclient
+import nilmtools
+import argparse
+import ConfigParser
+import sys
+import collections
+import fnmatch
+import re
+
+def warn(msg, *args):
+    fprintf(sys.stderr, "warning: " + msg + "\n", *args)
+
+class TimePeriod(object):
+    _units = { 'h': ('hour',  60*60*24),
+               'd': ('day',   60*60*24),
+               'w': ('week',  60*60*24*7),
+               'm': ('month', 60*60*24*30),
+               'y': ('year',  60*60*24*365) }
+
+    def __init__(self, val):
+        for u in self._units:
+            if val.endswith(u):
+                self.unit = self._units[u][0]
+                self.scale = self._units[u][1]
+                self.count = float(val[:-len(u)])
+                break
+        else:
+            raise ValueError("unknown units: " + units)
+
+    def seconds(self):
+        return self.count * self.scale
+
+    def describe_seconds(self, seconds):
+        count = seconds / self.scale
+        units = self.unit if count == 1 else (self.unit + "s")
+        if count == int(count):
+            return sprintf("%d %s", count, units)
+        else:
+            return sprintf("%.2f %s", count, units)
+
+    def __str__(self):
+        return self.describe_seconds(self.seconds())
+
+class StreamCleanupConfig(object):
+    def __init__(self, info):
+        self.path = info[0]
+        self.layout = info[1]
+        if info[4] != 0 and info[5] != 0:
+            self.rate = info[4] / timestamp_to_seconds(info[5])
+        else:
+            self.rate = None
+        self.keep = None
+        self.clean_decimated = True
+        self.decimated_from = None
+        self.also_clean_paths = []
+
+def main(argv = None):
+    parser = argparse.ArgumentParser(
+        formatter_class = argparse.RawDescriptionHelpFormatter,
+        version = nilmtools.__version__,
+        description = """\
+    Clean up old data from streams using a configuration file to specify
+    which data to remove.
+
+    The format of the config file is as follows:
+
+      [/stream/path]
+      keep = 3w         # keep up to 3 weeks of data
+      rate = 8000       # optional, used for the --estimate option
+      decimated = false # whether to delete decimated data too (default true)
+
+      [*/prep]
+      keep = 3.5m       # or 2520h or 105d or 15w or 0.29y
+
+    The suffix for 'keep' is 'h' for hours, 'd' for days, 'w' for weeks,
+    'm' for months, or 'y' for years.
+
+    Streams paths may include wildcards.  If a path is matched by more than
+    one config section, data from the last config section counts.
+
+    Decimated streams (paths containing '~decim-') are treated specially:
+      - They don't match wildcards
+      - When deleting data from a parent stream, data is also deleted
+        from its decimated streams, unless decimated=false
+
+    Rate is optional and is only used for the --estimate option.
+    """)
+    parser.add_argument("-u", "--url", action="store",
+                        default="http://localhost/nilmdb/",
+                        help="NilmDB server URL (default: %(default)s)")
+    parser.add_argument("-D", "--dry-run", action="store_true",
+                        default = False,
+                        help="Don't actually remove any data")
+    parser.add_argument("-e", "--estimate", action="store_true",
+                        default = False,
+                        help="Estimate how much disk space will be used")
+    parser.add_argument("configfile", type=argparse.FileType('r'),
+                        help="Configuration file")
+    args = parser.parse_args(argv)
+
+    # Parse config file
+    config = ConfigParser.RawConfigParser()
+    config.readfp(args.configfile)
+
+    # List all streams
+    client = nilmdb.client.Client(args.url)
+    streamlist = client.stream_list(extended = True)
+
+    # Create config objects
+    streams = collections.OrderedDict()
+    for s in streamlist:
+        streams[s[0]] = StreamCleanupConfig(s)
+        m = re.search(r"^(.*)~decim-[0-9]+$", s[0])
+        if m:
+            streams[s[0]].decimated_from = m.group(1)
+
+    # Build up configuration
+    for section in config.sections():
+        matched = False
+        for path in streams.iterkeys():
+            # Decimated streams only allow exact matches
+            if streams[path].decimated_from and path != section:
+                continue
+            if not fnmatch.fnmatch(path, section):
+                continue
+            matched = True
+            options = config.options(section)
+
+            # Keep period (days, weeks, months, years)
+            if 'keep' in options:
+                streams[path].keep = TimePeriod(config.get(section, 'keep'))
+                options.remove('keep')
+
+            # Rate
+            if 'rate' in options:
+                streams[path].rate = config.getfloat(section, 'rate')
+                options.remove('rate')
+
+            # Decimated
+            if 'decimated' in options:
+                val = config.getboolean(section, 'decimated')
+                streams[path].clean_decimated = val
+                options.remove('decimated')
+
+            for leftover in options:
+                warn("option '%s' for '%s' is unknown", leftover, section)
+
+        if not matched:
+            warn("config for '%s' did not match any existing streams", section)
+
+    # List all decimated streams in the parent stream's info
+    for path in streams.keys():
+        src = streams[path].decimated_from
+        if src and src in streams:
+            if streams[src].clean_decimated:
+                streams[src].also_clean_paths.append(path)
+                del streams[path]
+
+    # Warn about streams that aren't getting cleaned up
+    for path in streams.keys():
+        if streams[path].keep is None or streams[path].keep.seconds() < 0:
+            warn("no config for existing stream '%s'", path)
+            del streams[path]
+
+    if args.estimate:
+        # Estimate disk usage
+        total = 0
+        for path in streams.keys():
+            rate = streams[path].rate
+            if not rate or rate < 0:
+                warn("unable to estimate disk usage for stream '%s' because "
+                     "the data rate is unknown", path)
+                continue
+            printf("%s:\n", path)
+            layout = streams[path].layout
+            dtype = nilmdb.client.numpyclient.layout_to_dtype(layout)
+            per_row = dtype.itemsize
+            per_sec = per_row * rate
+            printf("%17s: %s per row, %s rows per second\n",
+                   "base rate",
+                   human_size(per_row),
+                   round(rate,1))
+            printf("%17s: %s per hour, %s per day\n",
+                   "base size",
+                   human_size(per_sec * 3600),
+                   human_size(per_sec * 3600 * 24))
+
+            # If we'll be cleaning up decimated data, add an
+            # estimation for how much room decimated data takes up.
+            if streams[path].clean_decimated:
+                d_layout = "float32_" + str(3*(int(layout.split('_')[1])))
+                d_dtype = nilmdb.client.numpyclient.layout_to_dtype(d_layout)
+                # Assume the decimations will be a factor of 4
+                # sum_{k=0..inf} (rate / (n^k)) * d_dtype.itemsize
+                d_per_row = d_dtype.itemsize
+                factor = 4.0
+                d_per_sec = d_per_row * (rate / factor) * (1 / (1 - (1/factor)))
+                per_sec += d_per_sec
+                printf("%17s: %s per hour, %s per day\n",
+                       "with decimation",
+                       human_size(per_sec * 3600),
+                       human_size(per_sec * 3600 * 24))
+
+            keep = per_sec * streams[path].keep.seconds()
+            printf("%17s: %s\n\n",
+                   "keep " + str(streams[path].keep), human_size(keep))
+            total += keep
+        printf("Total estimated disk usage for these streams:\n")
+        printf("  %s\n", human_size(total))
+        raise SystemExit(0)
+
+    # Do the cleanup
+    for path in streams:
+        printf("%s: keep %s\n", path, streams[path].keep)
+
+        # Figure out the earliest timestamp we should keep.
+        intervals = [ Interval(start, end) for (start, end) in
+                      reversed(list(client.stream_intervals(path))) ]
+        total = 0
+        keep = seconds_to_timestamp(streams[path].keep.seconds())
+        for i in intervals:
+            total += i.end - i.start
+            if total < keep:
+                continue
+            remove_before = i.start + (total - keep)
+            break
+        else:
+            printf("  nothing to do (only %s of data present)\n",
+                   streams[path].keep.describe_seconds(
+                       timestamp_to_seconds(total)))
+            continue
+        printf("  removing data before %s\n", timestamp_to_human(remove_before))
+        if not args.dry_run:
+            client.stream_remove(path, None, remove_before)
+        for ap in streams[path].also_clean_paths:
+            printf("  also removing from %s\n", ap)
+            if not args.dry_run:
+                client.stream_remove(ap, None, remove_before)
+
+    # All done
+    return
+
+if __name__ == "__main__":
+    main()

src/copy_one.py

@@ -5,6 +5,7 @@
 
 import nilmtools.filter
 import nilmdb.client
+from nilmdb.client.numpyclient import NumpyClient
 import numpy as np
 import sys
 
@@ -27,14 +28,14 @@ def main(argv = None):
     meta = f.client_src.stream_get_metadata(f.src.path)
     f.check_dest_metadata(meta)
 
-    # Copy all rows of data as ASCII strings
-    extractor = nilmdb.client.Client(f.src.url).stream_extract
-    inserter = nilmdb.client.Client(f.dest.url).stream_insert_context
+    # Copy all rows of data using the faster Numpy interfaces
+    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)
         with inserter(f.dest.path, i.start, i.end) as insert_ctx:
-            for row in extractor(f.src.path, i.start, i.end):
-                insert_ctx.insert(row + "\n")
+            for data in extractor(f.src.path, i.start, i.end):
+                insert_ctx.insert(data)
 
 if __name__ == "__main__":
     main()

src/decimate.py

@@ -71,7 +71,7 @@ def decimate(data, interval, args, insert_function, final):
     data = data[:n,:]
 
     # Reshape it into 3D so we can process 'factor' rows at a time
-    data.shape = (n // factor, factor, m)
+    data = data.reshape(n // factor, factor, m)
 
     # Fill the result
     out = np.c_[ np.mean(data[:,:,mean_col], axis=1),

src/filter.py

@@ -4,6 +4,7 @@ from __future__ import absolute_import
 
 import nilmdb.client
 from nilmdb.client import Client
+from nilmdb.client.numpyclient import NumpyClient
 from nilmdb.utils.printf import *
 from nilmdb.utils.time import (parse_time, timestamp_to_human,
                                timestamp_to_seconds)
@@ -247,72 +248,7 @@ class Filter(object):
         # All good -- write the metadata in case it's not already there
         self._client_dest.stream_update_metadata(self.dest.path, data)
 
-    # Main processing helper
-    def process_python(self, function, rows, args = None, partial = False):
-        """Process data in chunks of 'rows' data at a time.
-
-        This provides data as nested Python lists and expects the same
-        back.
-
-        function: function to process the data
-        rows: maximum number of rows to pass to 'function' at once
-        args: tuple containing extra arguments to pass to 'function'
-        partial: if true, less than 'rows' may be passed to 'function'.
-                 if false, partial data at the end of an interval will
-                 be dropped.
-
-        'function' should be defined like:
-            function(data, *args)
-        It will be passed a list containing up to 'rows' rows of
-        data from the source stream, and any arguments passed in
-        'args'.  It should transform the data as desired, and return a
-        new list of rdata, which will be inserted into the destination
-        stream.
-        """
-        if args is None:
-            args = []
-        extractor = Client(self.src.url).stream_extract
-        inserter = Client(self.dest.url).stream_insert_context
-
-        # Parse input data.  We use homogenous types for now, which
-        # means the timestamp type will be either float or int.
-        if "int" in self.src.layout_type:
-            parser = lambda line: [ int(x) for x in line.split() ]
-        else:
-            parser = lambda line: [ float(x) for x in line.split() ]
-
-        # Format output data.
-        formatter = lambda row: " ".join([repr(x) for x in row]) + "\n"
-
-        for interval in self.intervals():
-            print "Processing", self.interval_string(interval)
-            with inserter(self.dest.path,
-                          interval.start, interval.end) as insert_ctx:
-                src_array = []
-                for line in extractor(self.src.path,
-                                      interval.start, interval.end):
-                    # Read in data
-                    src_array.append([ float(x) for x in line.split() ])
-
-                    if len(src_array) == rows:
-                        # Pass through filter function
-                        dest_array = function(src_array, *args)
-
-                        # Write result to destination
-                        out = [ formatter(row) for row in dest_array ]
-                        insert_ctx.insert("".join(out))
-
-                        # Clear source array
-                        src_array = []
-
-                # Take care of partial chunk
-                if len(src_array) and partial:
-                    dest_array = function(src_array, *args)
-                    out = [ formatter(row) for row in dest_array ]
-                    insert_ctx.insert("".join(out))
-
-    # Like process_python, but provides Numpy arrays and allows for
-    # partial processing.
+    # The main filter processing method.
     def process_numpy(self, function, args = None, rows = 100000):
         """For all intervals that exist in self.src but don't exist in
         self.dest, call 'function' with a Numpy array corresponding to
@@ -342,37 +278,19 @@ class Filter(object):
         """
         if args is None:
             args = []
-        extractor = Client(self.src.url).stream_extract
-        inserter = Client(self.dest.url).stream_insert_context
-
-        # Format output data.
-        formatter = lambda row: " ".join([repr(x) for x in row]) + "\n"
-
-        def batch(iterable, size):
-            c = itertools.count()
-            for k, g in itertools.groupby(iterable, lambda x: c.next() // size):
-                yield g
+        extractor = NumpyClient(self.src.url).stream_extract_numpy
+        inserter = NumpyClient(self.dest.url).stream_insert_numpy_context
 
         for interval in self.intervals():
             print "Processing", self.interval_string(interval)
             with inserter(self.dest.path,
                           interval.start, interval.end) as insert_ctx:
-                def insert_function(array):
-                    s = cStringIO.StringIO()
-                    if len(np.shape(array)) != 2:
-                        raise Exception("array must be 2-dimensional")
-                    np.savetxt(s, array)
-                    insert_ctx.insert(s.getvalue())
-
-                extract = extractor(self.src.path, interval.start, interval.end)
+                insert_function = insert_ctx.insert
                 old_array = np.array([])
-                for batched in batch(extract, rows):
-                    # Read in this batch of data.  This turns out to
-                    # be a very fast way to read and convert it (order
-                    # of magnitude faster than numpy.loadtxt)
-                    new_array = np.fromstring("\n".join(batched), sep=' ')
-                    new_array = new_array.reshape(-1, self.src.total_count)
-
+                for new_array in extractor(self.src.path,
+                                           interval.start, interval.end,
+                                           layout = self.src.layout,
+                                           maxrows = rows):
                     # If we still had old data left, combine it
                     if old_array.shape[0] != 0:
                         array = np.vstack((old_array, new_array))

src/prep.py

@@ -8,7 +8,7 @@ import nilmdb.client
 from numpy import *
 import scipy.fftpack
 import scipy.signal
-from matplotlib import pyplot as p
+#from matplotlib import pyplot as p
 import bisect
 
 def main(argv = None):
@@ -19,12 +19,14 @@ def main(argv = None):
     group.add_argument("-c", "--column", action="store", type=int,
                        help="Column number (first data column is 1)")
     group.add_argument("-n", "--nharm", action="store", type=int, default=4,
-                       help="number of odd harmonics to compute")
+                       help="number of odd harmonics to compute (default 4)")
+    group.add_argument("-N", "--nshift", action="store", type=int, default=1,
+                       help="number of shifted FFTs per period (default 1)")
     exc = group.add_mutually_exclusive_group()
     exc.add_argument("-r", "--rotate", action="store", type=float,
-                     help="rotate FFT output by this many degrees")
+                     help="rotate FFT output by this many degrees (default 0)")
     exc.add_argument("-R", "--rotate-rad", action="store", type=float,
-                     help="rotate FFT output by this many radians")
+                     help="rotate FFT output by this many radians (default 0)")
 
     group.add_argument("srcpath", action="store",
                        help="Path of raw input, e.g. /foo/raw")
@@ -51,6 +53,9 @@ def main(argv = None):
     if args.nharm < 1 or args.nharm > 32:
         parser.error("number of odd harmonics must be 1-32")
 
+    if args.nshift < 1:
+        parser.error("number of shifted FFTs must be >= 1")
+
     if args.rotate is not None:
         rotation = args.rotate * 2.0 * pi / 360.0
     else:
@@ -72,52 +77,86 @@ def main(argv = None):
 
     # Run the processing function on all data
     f.process_numpy(process, args = (client_sinefit, sinefit.path, args.column,
-                                     args.nharm, rotation))
+                                     args.nharm, rotation, args.nshift))
 
 def process(data, interval, args, insert_function, final):
-    (client, sinefit_path, column, nharm, rotation) = args
+    (client, sinefit_path, column, nharm, rotation, nshift) = args
     rows = data.shape[0]
     data_timestamps = data[:,0]
 
+    if rows < 2:
+        return 0
+
+    last_inserted = [nilmdb.utils.time.min_timestamp]
+    def insert_if_nonoverlapping(data):
+        """Call insert_function to insert data, but only if this
+        data doesn't overlap with other data that we inserted."""
+        if data[0][0] <= last_inserted[0]:
+            return
+        last_inserted[0] = data[-1][0]
+        insert_function(data)
+
     processed = 0
     out = zeros((1, nharm * 2 + 1))
     # Pull out sinefit data for the entire time range of this block
     for sinefit_line in client.stream_extract(sinefit_path,
                                               data[0, 0], data[rows-1, 0]):
-        # Extract sinefit data to get zero crossing timestamps
+
+        def prep_period(t_min, t_max, rot):
+            """
+            Compute prep coefficients from time t_min to t_max, which
+            are the timestamps of the start and end of one period.
+            Results are rotated by an additional extra_rot before
+            being inserted into the database.  Returns the maximum
+            index processed, or None if the period couldn't be
+            processed.
+            """
+            # Find the indices of data that correspond to (t_min, t_max)
+            idx_min = bisect.bisect_left(data_timestamps, t_min)
+            idx_max = bisect.bisect_left(data_timestamps, t_max)
+            if idx_min >= idx_max or idx_max >= len(data_timestamps):
+                return None
+
+            # Perform FFT over those indices
+            N = idx_max - idx_min
+            d = data[idx_min:idx_max, column]
+            F = scipy.fftpack.fft(d) * 2.0 / N
+
+            # If we wanted more harmonics than the FFT gave us, pad with zeros
+            if N < (nharm * 2):
+                F = r_[F, zeros(nharm * 2 - N)]
+
+            # Fill output data.
+            out[0, 0] = round(t_min)
+            for k in range(nharm):
+                Fk = F[2 * k + 1] * e**(rot * 1j * (k+1))
+                out[0, 2 * k + 1] = -imag(Fk) # Pk
+                out[0, 2 * k + 2] = real(Fk)  # Qk
+
+            insert_if_nonoverlapping(out)
+            return idx_max
+
+        # Extract sinefit data to get zero crossing timestamps.
+        # t_min = beginning of period
+        # t_max = end of period
         (t_min, f0, A, C) = [ float(x) for x in sinefit_line.split() ]
         t_max = t_min + 1e6 / f0
 
-        # Find the indices of data that correspond to (t_min, t_max)
-        idx_min = bisect.bisect_left(data_timestamps, t_min)
-        idx_max = bisect.bisect_left(data_timestamps, t_max)
-        if idx_min >= idx_max:
-            # something's wonky; ignore this period
-            continue
-        if idx_max >= len(data_timestamps):
-            # max is likely past the end of our chunk, so stop
-            # processing this chunk now.
-            break
+        # Compute prep over shifted windows of the period
+        # (nshift is typically 1)
+        for n in range(nshift):
+            # Compute timestamps and rotations for shifted window
+            time_shift = n * (t_max - t_min) / nshift
+            shifted_min = t_min + time_shift
+            shifted_max = t_max + time_shift
+            angle_shift = n * 2 * pi / nshift
+            shifted_rot = rotation - angle_shift
 
-        # Perform FFT over those indices
-        N = idx_max - idx_min
-        d = data[idx_min:idx_max, column]
-        F = scipy.fftpack.fft(d) / N
-
-        # If we wanted more harmonics than we have, pad with zeros
-        if N < (nharm * 2):
-            F = r_[F, zeros(nharm * 2 - N)]
-
-        # Fill output data
-        out[0, 0] = t_min
-        for k in range(nharm):
-            Fk = F[2 * k + 1] * e**(rotation * 1j * (k+1))
-            out[0, 2 * k + 1] = -imag(Fk) # Pk
-            out[0, 2 * k + 2] = real(Fk)  # Qk
-
-        # Insert it and continue
-        insert_function(out)
-        processed = idx_max
+            # Run prep computation
+            idx_max = prep_period(shifted_min, shifted_max, shifted_rot)
+            if not idx_max:
+                break
+            processed = idx_max
 
     print "Processed", processed, "of", rows, "rows"
     return processed