diff options
| -rwxr-xr-x | scripts/random-muscle-activation.py | 87 |
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)) |