Update decimate to use process_numpy

Makefile

@@ -9,6 +9,9 @@ else
 endif
 
 test:
+	src/decimate.py
+
+test_insert:
 	@make install >/dev/null
 	src/insert.py --file --dry-run  /test/foo </dev/null
 

src/decimate.py

@@ -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
+        again = True
-        f.process_python(function = decimate_again, rows = args.factor,
-                         args = (n,))
     else:
-        n = f.src.layout_count
+        again = False
-        f.process_python(function = decimate_first, rows = args.factor,
+    f.process_numpy(decimate, args = (args.factor, again))
-                         args = (n,))
 
-def decimate_first(data, n):
+def decimate(data, interval, args, insert_function, final):
-    """Decimate original data -- result has 3 times as many columns"""
+    """Decimate data"""
-    # For this simple calculation, converting to a Numpy array
+    (factor, again) = args
-    # and doing the math is slower than just doing it directly.
+    (n, m) = data.shape
-    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_again(data, n):
+    # Figure out which columns to use as the source for mean, min, and max,
-    """Decimate already-decimated data -- result has the same number
+    # depending on whether this is the first decimation or we're decimating
-    of columns"""
+    # again.  Note that we include the timestamp in the means.
-    rows = iter(data)
+    if again:
-    r = rows.next()
+        c = (m - 1) // 3
-    r_sum = r[0:(n+1)]
+        # e.g. c = 3
-    r_min = r[(n+1):(2*n+1)]
+        # ts mean1 mean2 mean3 min1 min2 min3 max1 max2 max3
-    r_max = r[(2*n+1):(3*n+1)]
+        mean_col = slice(0, c + 1)
-    for r in rows:
+        min_col = slice(c + 1, 2 * c + 1)
-        r_sum = map(operator.add, r_sum, r[0:(n+1)])
+        max_col = slice(2 * c + 1, 3 * c + 1)
-        r_min = map(min, r_min, r[(n+1):(2*n+1)])
+    else:
-        r_max = map(max, r_max, r[(2*n+1):(3*n+1)])
+        mean_col = slice(0, m)
-    r_mean = [ x / len(data) for x in r_sum ]
+        min_col = slice(1, m)
-    return [ r_mean + r_min + r_max ]
+        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()

src/filter.py

@@ -367,8 +367,11 @@ class Filter(object):
                 extract = extractor(self.src.path, interval.start, interval.end)
                 old_array = np.array([])
                 for batched in batch(extract, rows):
-                    # Read in this batch of data
+                    # Read in this batch of data.  This turns out to
-                    new_array = np.loadtxt(batched)
+                    # be a very fast way to read and convert it (order
+                    # of magnitude faster than numpy.loadtxt)
+                    new_array = np.fromstring("\n".join(batched), sep=' ')
+                    new_array = new_array.reshape(-1, self.src.total_count)
 
                     # If we still had old data left, combine it
                     if old_array.shape[0] != 0: