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nilmtools-
...
nilmtools-
Author | SHA1 | Date | |
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8ab31eafc2 | |||
979ab13bff | |||
f4fda837ae | |||
5547d266d0 | |||
372e977e4a | |||
640a680704 | |||
2e74e6cd63 |
2
setup.py
2
setup.py
@@ -61,7 +61,7 @@ setup(name='nilmtools',
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long_description = "NILM Database Tools",
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license = "Proprietary",
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author_email = 'jim@jtan.com',
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install_requires = [ 'nilmdb >= 1.5.0',
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install_requires = [ 'nilmdb >= 1.6.0',
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'numpy',
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'scipy',
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'matplotlib',
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@@ -238,12 +238,15 @@ def main(argv = None):
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timestamp_to_seconds(total)))
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continue
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printf(" removing data before %s\n", timestamp_to_human(remove_before))
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if args.yes:
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client.stream_remove(path, None, remove_before)
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for ap in streams[path].also_clean_paths:
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printf(" also removing from %s\n", ap)
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# Clean in reverse order. Since we only use the primary stream and not
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# the decimated streams to figure out which data to remove, removing
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# the primary stream last means that we might recover more nicely if
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# we are interrupted and restarted.
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clean_paths = list(reversed(streams[path].also_clean_paths)) + [ path ]
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for p in clean_paths:
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printf(" removing from %s\n", p)
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if args.yes:
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client.stream_remove(ap, None, remove_before)
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client.stream_remove(p, None, remove_before)
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# All done
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if not args.yes:
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@@ -67,7 +67,7 @@ def get_stream_info(client, path):
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class Filter(object):
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def __init__(self):
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def __init__(self, parser_description = None):
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self._parser = None
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self._client_src = None
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self._client_dest = None
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@@ -78,6 +78,9 @@ class Filter(object):
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self.end = None
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self.interhost = False
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self.force_metadata = False
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if parser_description is not None:
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self.setup_parser(parser_description)
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self.parse_args()
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@property
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def client_src(self):
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@@ -275,6 +278,10 @@ class Filter(object):
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Return value of 'function' is the number of data rows processed.
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Unprocessed data will be provided again in a subsequent call
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(unless 'final' is True).
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If unprocessed data remains after 'final' is True, the interval
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being inserted will be ended at the timestamp of the first
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unprocessed data point.
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"""
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if args is None:
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args = []
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@@ -319,7 +326,13 @@ class Filter(object):
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# Last call for this contiguous interval
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if old_array.shape[0] != 0:
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function(old_array, interval, args, insert_function, True)
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processed = function(old_array, interval, args,
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insert_function, True)
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if processed != old_array.shape[0]:
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# Truncate the interval we're inserting at the first
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# unprocessed data point. This ensures that
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# we'll not miss any data when we run again later.
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insert_ctx.update_end(old_array[processed][0])
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def main(argv = None):
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# This is just a dummy function; actual filters can use the other
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13
src/prep.py
13
src/prep.py
@@ -3,6 +3,8 @@
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# Spectral envelope preprocessor.
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# Requires two streams as input: the original raw data, and sinefit data.
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from nilmdb.utils.printf import *
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from nilmdb.utils.time import timestamp_to_human
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import nilmtools.filter
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import nilmdb.client
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from numpy import *
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@@ -106,7 +108,6 @@ def process(data, interval, args, insert_function, final):
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# Pull out sinefit data for the entire time range of this block
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for sinefit_line in client.stream_extract(sinefit_path,
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data[0, 0], data[rows-1, 0]):
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def prep_period(t_min, t_max, rot):
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"""
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Compute prep coefficients from time t_min to t_max, which
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@@ -163,7 +164,15 @@ def process(data, interval, args, insert_function, final):
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break
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processed = idx_max
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print "Processed", processed, "of", rows, "rows"
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# If we processed no data but there's lots in here, pretend we
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# processed half of it.
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if processed == 0 and rows > 10000:
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processed = rows / 2
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printf("%s: warning: no periods found; skipping %d rows\n",
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timestamp_to_human(data[0][0]), processed)
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else:
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printf("%s: processed %d of %d rows\n",
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timestamp_to_human(data[0][0]), processed, rows)
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return processed
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if __name__ == "__main__":
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@@ -25,7 +25,7 @@ def main(argv = None):
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help='Maximum valid frequency '
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'(default: approximate frequency * 2))')
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group.add_argument('-a', '--min-amp', action='store', type=float,
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default=10.0,
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default=20.0,
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help='Minimum signal amplitude (default: %(default)s)')
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# Parse arguments
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@@ -157,7 +157,7 @@ def sfit4(data, fs):
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(Verified to match sfit4.m)
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"""
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N = len(data)
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t = linspace(0, (N-1) / fs, N)
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t = linspace(0, (N-1) / float(fs), N)
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## Estimate frequency using FFT (step b)
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Fc = fft(data)
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@@ -182,7 +182,7 @@ def sfit4(data, fs):
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i = arccos((Z2*cos(ni2) - Z1*cos(ni1)) / (Z2-Z1)) / n
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# Convert to Hz
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f0 = i * fs / N
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f0 = i * float(fs) / N
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# Fit it. We'll catch exceptions here and just returns zeros
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# if something fails with the least squares fit, etc.
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