# Fields from "charactercurves.py" extracted from raw data to use for
# training. Currently: avg, rms, freq, periodicity
inputfields = ['rms', 'periodicity']
########## Load and calculate data
minibees, fields = read_minibees()
mbIds = sort(minibees.keys())
offsets = [find_offsets(minibees[mb]) for mb in mbIds]
ds = [split_data_on_offsets(minibees[mb], o)
for mb, o in zip(mbIds, offsets)]
curves = [calc_curves((d[VIDEO][:,4]-d[VIDEO][0,4])/30.0,
mag(d[VIDEO][:,5:8]), 0.1)
for d in ds]
tags = read_tags()
frames = curves[1]['time']*30
frametags = [tags.frame_tags(int(ds[1][1][0,3]), f) for f in frames]
def normalized(x):
return (x - min(x)) / (max(x)-min(x))
def avg_curves(curves):
end = min([len(c) for c in curves[0]])
output = [zeros(end) for x in range(len(curves))]
inputs = range(len(curves[0]))
for f, field in enumerate(curves):
def unwrap_time(time, timediff):
brks = [n for n,d in enumerate((time[1:]-time[:-1])) if d < -timediff]
idx = arange(time.size)
return sum([time] + [(idx > b)*time[b] for b in brks], axis=0)
time_range = (0,100000000000)
all_interp = {}
for m in minibees.keys():
time = unwrap_time(minibees[m][:,4]/30.0, 500)
time_range = (max(time_range[0], time[0]),
min(time_range[1], time[-1]))
for m in minibees.keys():
time = unwrap_time(minibees[m][:,4]/30.0, 500)
all_interp[m] = interp1d(time, mag(minibees[m][:,5:8]))
subsamp = 3
time = arange((time_range[1]-time_range[0])*subsamp)/subsamp+time_range[0]
hopsize = 20
length = 100
hopidx = arange(time.size)[:-length:hopsize]
hoptime = array([time[i] for i in hopidx])
all_mag = array([all_interp[m](time) for m in sort(minibees.keys())])
ccorr = [sum(corrcoef(all_mag[:,i:i+length])) for i in hopidx]
ccov = [sum(cov(all_mag[:,i:i+length])) for i in hopidx]
rc('legend',fontsize=8)
clf()
minibees, fields = read_minibees()
ion()
clf()
draw()
hopsize = 20
smoothing = 0.05
sr = 10.0
L1 = 1000
L2 = 500
final_results = {}
for m in minibees.keys():
print m
data = mag(minibees[m][:,5:8])
final_results[m] = plot_f0(data, sr, m, hopsize, L1, L2, smoothing)
savefig('workshop_autocorr_10hz_m%d.png'%m)
draw()
def unwrap_time(time, timediff):
brks = [n for n,d in enumerate((time[1:]-time[:-1])) if d < -timediff]
idx = arange(time.size)
return sum([time] + [(idx > b)*time[b] for b in brks], axis=0)
clf()
time_range = (0,100000000000)
freqs = []
frdiffs = []
