|
|
|
@@ -41,41 +41,45 @@ def main(argv = None): |
|
|
|
|
|
|
|
# If source is decimated, we have to decimate a bit differently |
|
|
|
if "decimate_source" in f.client_src.stream_get_metadata(args.srcpath): |
|
|
|
n = f.src.layout_count // 3 |
|
|
|
f.process_python(function = decimate_again, rows = args.factor, |
|
|
|
args = (n,)) |
|
|
|
again = True |
|
|
|
else: |
|
|
|
n = f.src.layout_count |
|
|
|
f.process_python(function = decimate_first, rows = args.factor, |
|
|
|
args = (n,)) |
|
|
|
again = False |
|
|
|
f.process_numpy(decimate, args = (args.factor, again)) |
|
|
|
|
|
|
|
def decimate_first(data, n): |
|
|
|
"""Decimate original data -- result has 3 times as many columns""" |
|
|
|
# For this simple calculation, converting to a Numpy array |
|
|
|
# and doing the math is slower than just doing it directly. |
|
|
|
rows = iter(data) |
|
|
|
r_sum = r_min = r_max = rows.next() |
|
|
|
for row in rows: |
|
|
|
r_sum = map(operator.add, r_sum, row) |
|
|
|
r_min = map(min, r_min, row) |
|
|
|
r_max = map(max, r_max, row) |
|
|
|
r_mean = [ x / len(data) for x in r_sum ] |
|
|
|
return [ [ r_mean[0] ] + r_mean[1:] + r_min[1:] + r_max[1:] ] |
|
|
|
def decimate(data, interval, args, insert_function, final): |
|
|
|
"""Decimate data""" |
|
|
|
(factor, again) = args |
|
|
|
(n, m) = data.shape |
|
|
|
|
|
|
|
def decimate_again(data, n): |
|
|
|
"""Decimate already-decimated data -- result has the same number |
|
|
|
of columns""" |
|
|
|
rows = iter(data) |
|
|
|
r = rows.next() |
|
|
|
r_sum = r[0:(n+1)] |
|
|
|
r_min = r[(n+1):(2*n+1)] |
|
|
|
r_max = r[(2*n+1):(3*n+1)] |
|
|
|
for r in rows: |
|
|
|
r_sum = map(operator.add, r_sum, r[0:(n+1)]) |
|
|
|
r_min = map(min, r_min, r[(n+1):(2*n+1)]) |
|
|
|
r_max = map(max, r_max, r[(2*n+1):(3*n+1)]) |
|
|
|
r_mean = [ x / len(data) for x in r_sum ] |
|
|
|
return [ r_mean + r_min + r_max ] |
|
|
|
# Figure out which columns to use as the source for mean, min, and max, |
|
|
|
# depending on whether this is the first decimation or we're decimating |
|
|
|
# again. Note that we include the timestamp in the means. |
|
|
|
if again: |
|
|
|
c = (m - 1) // 3 |
|
|
|
# e.g. c = 3 |
|
|
|
# ts mean1 mean2 mean3 min1 min2 min3 max1 max2 max3 |
|
|
|
mean_col = slice(0, c + 1) |
|
|
|
min_col = slice(c + 1, 2 * c + 1) |
|
|
|
max_col = slice(2 * c + 1, 3 * c + 1) |
|
|
|
else: |
|
|
|
mean_col = slice(0, m) |
|
|
|
min_col = slice(1, m) |
|
|
|
max_col = slice(1, m) |
|
|
|
|
|
|
|
# Discard extra rows that aren't a multiple of factor |
|
|
|
n = n // factor * factor |
|
|
|
data = data[:n,:] |
|
|
|
|
|
|
|
# Reshape it into 3D so we can process 'factor' rows at a time |
|
|
|
data.shape = (n // factor, factor, m) |
|
|
|
|
|
|
|
# Fill the result |
|
|
|
out = np.c_[ np.mean(data[:,:,mean_col], axis=1), |
|
|
|
np.min(data[:,:,min_col], axis=1), |
|
|
|
np.max(data[:,:,max_col], axis=1) ] |
|
|
|
|
|
|
|
insert_function(out) |
|
|
|
return n |
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
|
main() |
