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5 changed files with 38 additions and 13 deletions

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@@ -61,7 +61,7 @@ setup(name='nilmtools',
long_description = "NILM Database Tools",
license = "Proprietary",
author_email = 'jim@jtan.com',
install_requires = [ 'nilmdb >= 1.5.0',
install_requires = [ 'nilmdb >= 1.6.0',
'numpy',
'scipy',
'matplotlib',

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@@ -238,12 +238,15 @@ def main(argv = None):
timestamp_to_seconds(total)))
continue
printf(" removing data before %s\n", timestamp_to_human(remove_before))
if args.yes:
client.stream_remove(path, None, remove_before)
for ap in streams[path].also_clean_paths:
printf(" also removing from %s\n", ap)
# Clean in reverse order. Since we only use the primary stream and not
# the decimated streams to figure out which data to remove, removing
# the primary stream last means that we might recover more nicely if
# we are interrupted and restarted.
clean_paths = list(reversed(streams[path].also_clean_paths)) + [ path ]
for p in clean_paths:
printf(" removing from %s\n", p)
if args.yes:
client.stream_remove(ap, None, remove_before)
client.stream_remove(p, None, remove_before)
# All done
if not args.yes:

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@@ -67,7 +67,7 @@ def get_stream_info(client, path):
class Filter(object):
def __init__(self):
def __init__(self, parser_description = None):
self._parser = None
self._client_src = None
self._client_dest = None
@@ -78,6 +78,9 @@ class Filter(object):
self.end = None
self.interhost = False
self.force_metadata = False
if parser_description is not None:
self.setup_parser(parser_description)
self.parse_args()
@property
def client_src(self):
@@ -275,6 +278,10 @@ class Filter(object):
Return value of 'function' is the number of data rows processed.
Unprocessed data will be provided again in a subsequent call
(unless 'final' is True).
If unprocessed data remains after 'final' is True, the interval
being inserted will be ended at the timestamp of the first
unprocessed data point.
"""
if args is None:
args = []
@@ -319,7 +326,13 @@ class Filter(object):
# Last call for this contiguous interval
if old_array.shape[0] != 0:
function(old_array, interval, args, insert_function, True)
processed = function(old_array, interval, args,
insert_function, True)
if processed != old_array.shape[0]:
# Truncate the interval we're inserting at the first
# unprocessed data point. This ensures that
# we'll not miss any data when we run again later.
insert_ctx.update_end(old_array[processed][0])
def main(argv = None):
# This is just a dummy function; actual filters can use the other

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@@ -3,6 +3,8 @@
# Spectral envelope preprocessor.
# Requires two streams as input: the original raw data, and sinefit data.
from nilmdb.utils.printf import *
from nilmdb.utils.time import timestamp_to_human
import nilmtools.filter
import nilmdb.client
from numpy import *
@@ -106,7 +108,6 @@ def process(data, interval, args, insert_function, final):
# 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]):
def prep_period(t_min, t_max, rot):
"""
Compute prep coefficients from time t_min to t_max, which
@@ -163,7 +164,15 @@ def process(data, interval, args, insert_function, final):
break
processed = idx_max
print "Processed", processed, "of", rows, "rows"
# If we processed no data but there's lots in here, pretend we
# processed half of it.
if processed == 0 and rows > 10000:
processed = rows / 2
printf("%s: warning: no periods found; skipping %d rows\n",
timestamp_to_human(data[0][0]), processed)
else:
printf("%s: processed %d of %d rows\n",
timestamp_to_human(data[0][0]), processed, rows)
return processed
if __name__ == "__main__":

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@@ -25,7 +25,7 @@ def main(argv = None):
help='Maximum valid frequency '
'(default: approximate frequency * 2))')
group.add_argument('-a', '--min-amp', action='store', type=float,
default=10.0,
default=20.0,
help='Minimum signal amplitude (default: %(default)s)')
# Parse arguments
@@ -157,7 +157,7 @@ def sfit4(data, fs):
(Verified to match sfit4.m)
"""
N = len(data)
t = linspace(0, (N-1) / fs, N)
t = linspace(0, (N-1) / float(fs), N)
## Estimate frequency using FFT (step b)
Fc = fft(data)
@@ -182,7 +182,7 @@ def sfit4(data, fs):
i = arccos((Z2*cos(ni2) - Z1*cos(ni1)) / (Z2-Z1)) / n
# Convert to Hz
f0 = i * fs / N
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.