86 lines
2.9 KiB
Python
Executable File
86 lines
2.9 KiB
Python
Executable File
#!/usr/bin/python
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import nilmtools.filter
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import nilmdb.client
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import numpy as np
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import operator
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def main(argv = None):
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f = nilmtools.filter.Filter()
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parser = f.setup_parser("Decimate a stream")
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group = parser.add_argument_group("Decimate options")
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group.add_argument('-f', '--factor', action='store', default=4, type=int,
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help='Decimation factor (default: %(default)s)')
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# Parse arguments
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try:
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args = f.parse_args(argv)
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except nilmtools.filter.MissingDestination as e:
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# If no destination, suggest how to create it by figuring out
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# a recommended layout.
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src = e.src
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dest = e.dest
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print "Source is %s (%s)" % (src.path, src.layout)
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print "Destination %s doesn't exist" % (dest.path)
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if "decimate_source" in f.client_src.stream_get_metadata(src.path):
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rec = src.layout
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elif 'int32' in src.layout_type or 'float64' in src.layout_type:
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rec = 'float64_' + str(src.layout_count * 3)
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else:
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rec = 'float32_' + str(src.layout_count * 3)
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print "You could make it with a command like:"
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print " nilmtool -u %s create %s %s" % (e.dest.url,
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e.dest.path, rec)
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raise SystemExit(1)
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if not (args.factor >= 2):
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raise Exception("factor needs to be 2 or more")
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f.check_dest_metadata({ "decimate_source": f.src.path,
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"decimate_factor": args.factor })
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# If source is decimated, we have to decimate a bit differently
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if "decimate_source" in f.client_src.stream_get_metadata(args.srcpath):
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again = True
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else:
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again = False
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f.process_numpy(decimate, args = (args.factor, again))
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def decimate(data, interval, args, insert_function, final):
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"""Decimate data"""
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(factor, again) = args
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(n, m) = data.shape
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# Figure out which columns to use as the source for mean, min, and max,
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# depending on whether this is the first decimation or we're decimating
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# again. Note that we include the timestamp in the means.
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if again:
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c = (m - 1) // 3
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# e.g. c = 3
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# ts mean1 mean2 mean3 min1 min2 min3 max1 max2 max3
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mean_col = slice(0, c + 1)
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min_col = slice(c + 1, 2 * c + 1)
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max_col = slice(2 * c + 1, 3 * c + 1)
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else:
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mean_col = slice(0, m)
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min_col = slice(1, m)
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max_col = slice(1, m)
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# Discard extra rows that aren't a multiple of factor
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n = n // factor * factor
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data = data[:n,:]
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# Reshape it into 3D so we can process 'factor' rows at a time
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data = data.reshape(n // factor, factor, m)
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# Fill the result
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out = np.c_[ np.mean(data[:,:,mean_col], axis=1),
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np.min(data[:,:,min_col], axis=1),
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np.max(data[:,:,max_col], axis=1) ]
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insert_function(out)
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return n
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if __name__ == "__main__":
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main()
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