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-rwxr-xr-xscripts/random-muscle-activation.py87
1 files changed, 75 insertions, 12 deletions
diff --git a/scripts/random-muscle-activation.py b/scripts/random-muscle-activation.py
index e168769..2af82b5 100755
--- a/scripts/random-muscle-activation.py
+++ b/scripts/random-muscle-activation.py
@@ -13,6 +13,7 @@ import os
import sys
import getopt
import numpy as np
+import matplotlib.pyplot as plt
import plotcsv
DEFAULT_N = 1000
@@ -23,24 +24,30 @@ DEFAULT_YMAX = 50.0
DEFAULT_YDMIN = -0.5
DEFAULT_YDMAX = 0.5
+WINDOWS = [ 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' ]
+WINDOW_LEN = 11
+
def usage():
print("""usage: {} [OPTION...] FILE...
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
- -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 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(
os.path.basename(sys.argv[0]), DEFAULT_N,
+ ", ".join(WINDOWS),
DEFAULT_YMIN, DEFAULT_YMAX,
DEFAULT_XMIN, DEFAULT_XMAX,
DEFAULT_YMIN, DEFAULT_YMAX,
@@ -63,9 +70,44 @@ 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):
+ """
+ smoothen signal using a window with requested size
+
+ input:
+ x: the input signal
+ window: type of window from 'flat', 'hanning', 'hamming', 'bartlett',
+ 'blackman'; the 'flat' method will apply a moving average
+ window_len: dimension of smoothing window, must be an odd integer
+
+ output:
+ smoothened signal
+ """
+
+ if x.ndim != 1:
+ raise ValueError, "only 1-dimensional arrays can be smoothened"
+ if x.size < window_len:
+ raise ValueError, "input vector needs to be bigger than window size"
+ if type(window_len) != int or window_len % 2 != 1:
+ raise ValueError, "window length must be an odd integer"
+
+ if window_len < 3:
+ return x
+ if not window in WINDOWS:
+ raise ValueError, "window type must be one of " + ", ".join([ "'" + w + "'" for w in WINDOWS ])
+
+ s = np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]
+ if window == 'flat':
+ w = np.ones(window_len, 'd')
+ else:
+ w = eval('np.' + window + '(window_len)')
+
+ return np.convolve(w / w.sum(), s, mode='valid')
+
def main():
try:
- opts, args = getopt.getopt(sys.argv[1:], "fn:pi:I:x:X:y:Y:d:D:h")
+ opts, args = getopt.getopt(sys.argv[1:], "fn:ps:i:I:x:X:y:Y:d:D:h")
except getopt.GetoptError as err:
print(str(err))
usage()
@@ -73,6 +115,7 @@ def main():
do_overwrite = False
do_plot = False
+ window = None
N = DEFAULT_N
imin = DEFAULT_YMIN
imax = DEFAULT_YMAX
@@ -94,6 +137,8 @@ def main():
sys.exit(-1)
elif o == '-p':
do_plot = True
+ elif o == '-s':
+ window = a
elif o == '-i':
try:
imin = float(a)
@@ -198,6 +243,24 @@ 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]
+ plt.plot(act[:,1])
+ plt.plot(sact)
+ plt.legend(['original', 'smoothened'])
+ plt.show()
+ act[:,1] = sact
+ # XXX: temp
+ # plt.plot(act[:,1])
+ # l = ['original']
+ # for w in WINDOWS:
+ # sact = smoothen(act[:,1], w, WINDOW_LEN)
+ # plt.plot(sact[(WINDOW_LEN-1)/2:-(WINDOW_LEN-1)/2])
+ # l.append(w)
+ # plt.legend(l)
+ # plt.show()
+
f.write('x,y\n')
for x, y in act:
f.write('{},{}\n'.format(x, y))