nilmtools/nilmtools/decimate.py

#!/usr/bin/python

import nilmtools.filter
import nilmdb.client
import numpy as np

def main():
    f = nilmtools.filter.Filter()
    parser = f.setup_parser("Decimate a stream")
    group = parser.add_argument_group("Decimate options")
    group.add_argument('-f', '--factor', action='store', default=4, type=int,
                       help='Decimation factor (default: %(default)s)')
    # Parse arguments
    try:
        args = f.parse_args()
    except nilmtools.filter.MissingDestination as e:
        # If no destination, suggest how to create it by figuring out
        # a recommended layout.
        print "Source is %s (%s)" % (e.src, e.layout)
        print "Destination %s doesn't exist" % (e.dest)
        if "decimate_source" in f.client.stream_get_metadata(e.src):
            rec = e.layout
        elif 'int32' in e.layout_type or 'float64' in e.layout_type:
            rec = 'float64_' + str(e.layout_count * 3)
        else:
            rec = 'float32_' + str(e.layout_count * 3)
        print "You could make it with a command like:"
        print "  nilmtool create", e.dest, rec
        raise SystemExit(1)

    f.check_dest_metadata({ "decimate_source": args.srcpath,
                            "decimate_factor": args.factor })

    # If source is decimated, we have to decimate a bit differently
    if "decimate_source" in f.client.stream_get_metadata(args.srcpath):
        f.process(function = decimate_again, rows = args.factor)
    else:
        f.process(function = decimate_first, rows = args.factor)

def decimate_first(data):
    """Decimate original data -- result has 3 times as many columns"""
    data = np.array(data)
    rows, cols = data.shape
    n = cols - 1
    out = np.zeros(1 + 3 * n)

    out[0] = np.mean(data[:, 0], 0)
    out[    1 : n+1  ] = np.mean(data[:, 1 : n+1], 0)
    out[  n+1 : 2*n+1] = np.min( data[:, 1 : n+1], 0)
    out[2*n+1 : 3*n+1] = np.max( data[:, 1 : n+1], 0)

    return [out]

def decimate_again(data):
    """Decimate already-decimated data -- result has the same number
    of columns"""
    data = np.array(data)
    rows, cols = data.shape
    n = (cols - 1) // 3
    out = np.zeros(1 + 3 * n)

    out[0] = np.mean(data[:, 0], 0)
    out[    1 : n+1  ] = np.mean(data[:,     1 :   n+1], 0)
    out[  n+1 : 2*n+1] = np.min( data[:,   n+1 : 2*n+1], 0)
    out[2*n+1 : 3*n+1] = np.max( data[:, 2*n+1 : 3*n+1], 0)

    return [out]

if __name__ == "__main__":
    main()