scripts/random-muscle-activation.py: Revert to fixed smoothing method

1 files changed, 27 insertions, 21 deletions

diff --git a/scripts/random-muscle-activation.py b/scripts/random-muscle-activation.py
index 2af82b5..870fee3 100755
--- a/scripts/random-muscle-activation.py
+++ b/scripts/random-muscle-activation.py
@@ -26,6 +26,7 @@ DEFAULT_YDMAX = 0.5
 
 WINDOWS = [ 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' ]
 WINDOW_LEN = 11
+DEFAULT_WINDOW = 'blackman'
 
 def usage():
     print("""usage: {} [OPTION...] FILE...
@@ -33,21 +34,19 @@ def usage():
 Generate random muscle activation data in CSV.
 
 options:
-    -f       force overwrite of existing file
-    -n N     set number of data points to N (default: {})
-    -p       plot data after generating it
-    -s WIN   smoothen the data using WIN window function
-             WIN should be one of {}
-    -i N     set minimum initial y value to N (default: {})
-    -I N     set maximum initial y value to N (default: {})
-    -x N     set minimum x value to N (default: {})
-    -X N     set maximum x value to N (default: {})
-    -y N     set minimum y value to N (default: {})
-    -Y N     set maximum y value to N (default: {})
-    -d N     set minimum y change value to N (default: {})
-    -D N     set maximum y change value to N (default: {})""".format(
+    -f    force overwrite of existing file
+    -n N  set number of data points to N (default: {})
+    -p    plot data after generating it
+    -s    smoothen the data using blackman window function
+    -i N  set minimum initial y value to N (default: {})
+    -I N  set maximum initial y value to N (default: {})
+    -x N  set minimum x value to N (default: {})
+    -X N  set maximum x value to N (default: {})
+    -y N  set minimum y value to N (default: {})
+    -Y N  set maximum y value to N (default: {})
+    -d N  set minimum y change value to N (default: {})
+    -D N  set maximum y change value to N (default: {})""".format(
         os.path.basename(sys.argv[0]), DEFAULT_N,
-        ", ".join(WINDOWS),
         DEFAULT_YMIN, DEFAULT_YMAX,
         DEFAULT_XMIN, DEFAULT_XMAX,
         DEFAULT_YMIN, DEFAULT_YMAX,
@@ -71,7 +70,7 @@ def generate_activation_data(N, xmin, xmax, ymin, ymax, ydmin, ydmax, imin, imax
     return act
 
 # based on scipy.org/Cookbook/SignalSmooth
-def smoothen(x, window='hanning', window_len=11):
+def smoothen(x, window=DEFAULT_WINDOW, window_len=WINDOW_LEN):
     """
     smoothen signal using a window with requested size
 
@@ -107,7 +106,7 @@ def smoothen(x, window='hanning', window_len=11):
 
 def main():
     try:
-        opts, args = getopt.getopt(sys.argv[1:], "fn:ps:i:I:x:X:y:Y:d:D:h")
+        opts, args = getopt.getopt(sys.argv[1:], "fn:psi:I:x:X:y:Y:d:D:h")
     except getopt.GetoptError as err:
         print(str(err))
         usage()
@@ -115,7 +114,7 @@ def main():
 
     do_overwrite = False
     do_plot = False
-    window = None
+    do_smooth = False
     N = DEFAULT_N
     imin = DEFAULT_YMIN
     imax = DEFAULT_YMAX
@@ -138,7 +137,7 @@ def main():
         elif o == '-p':
             do_plot = True
         elif o == '-s':
-            window = a
+            do_smooth = True
         elif o == '-i':
             try:
                 imin = float(a)
@@ -243,9 +242,16 @@ def main():
     for a in args:
         with open(a, 'w') as f:
             act = generate_activation_data(N, xmin, xmax, ymin, ymax, ydmin, ydmax, imin, imax)
-            if window:
-                sact = smoothen(act[:,1], window, WINDOW_LEN)
-                sact = sact[(WINDOW_LEN - 1) / 2:-(WINDOW_LEN - 1) / 2]
+            if do_smooth:
+                all_in_range = False
+                n = 0
+                while not all_in_range:
+                    print("Smooth pass " + str(n))
+                    sact = smoothen(act[:,1], DEFAULT_WINDOW, WINDOW_LEN)
+                    sact = sact[(WINDOW_LEN - 1) / 2:-(WINDOW_LEN - 1) / 2]
+                    if sact.min() >= ymin and sact.max() <= ymax:
+                        all_in_range = True
+                    n += 1
                 plt.plot(act[:,1])
                 plt.plot(sact)
                 plt.legend(['original', 'smoothened'])