def estimate_beta_band(session,area,bw=8,epoch=None,doplot=False):
'''
return betapeak-0.5*bw,betapeak+0.5*bw
'''
print 'THIS IS NOT THE ONE YOU WANT TO USE'
print 'IT IS EXPERIMENTAL COHERENCE BASED IDENTIFICATION OF BETA'
assert 0
if epoch is None: epoch = (6,-1000,3000)
allco = []
if not area is None:
chs = get_good_channels(session,area)[:2]
for a in chs:
for b in chs:
if a==b: continue
for tr in get_good_trials(session):
x = get_filtered_lfp(session,area,a,tr,epoch,None,300)
y = get_filtered_lfp(session,area,b,tr,epoch,None,300)
co,fr = cohere(x,y,Fs=1000,NFFT=256)
allco.append(co)
else:
for area in areas:
chs = get_good_channels(session,area)[:2]
for a in chs:
for b in chs:
if a==b: continue
for tr in get_good_trials(session):
x = get_filtered_lfp(session,area,a,tr,epoch,None,300)
y = get_filtered_lfp(session,area,b,tr,epoch,None,300)
co,fr = cohere(x,y,Fs=1000,NFFT=256)
allco.append(co)
allco = array(allco)
m = mean(allco,0)
sem = std(allco,0)/sqrt(shape(allco)[0])
# temporary in lieu of multitaper
smooth = ceil(float(bw)/(diff(fr)[0]))
smoothed = convolve(m,ones(smooth)/smooth,'same')
use = (fr<=56)&(fr>=5)
betafr = (fr<=30-0.5*bw)&(fr>=15+0.5*bw)
betapeak = fr[betafr][argmax(smoothed[betafr])]
if doplot:
clf()
plot(fr[use],m[use],lw=2,color='k')
plot(fr[use],smoothed[use],lw=1,color='r')
plot(fr[use],(m+sem)[use],lw=1,color='k')
plot(fr[use],(m-sem)[use],lw=1,color='k')
positivey()
xlim(*rangeover(fr[use]))
shade([[betapeak-0.5*bw],[betapeak+0.5*bw]])
draw()
return betapeak-0.5*bw,betapeak+0.5*bw
def video_overlay(pa,ned,pos_des,output_filename):
# Generate video of the performance during loiter, this
# includes the loiter position, current estimate of
# position and raw gps position
#output_filename = 'out.mp4'
import matplotlib.animation as animation
import scipy.signal as signal
import matplotlib.pyplot as plt
from numpy import nan, arange
from numpy.lib.function_base import diff
from matplotlib.pyplot import plot, xlabel, ylabel, xlim, ylim, draw
from matplotlib.mlab import find
####### plot section
fig, ax = plt.subplots(1,sharex=True)
fig.set_size_inches([8,4])
fig.patch.set_facecolor('green')
t0_s = pa['time'][0]
t1_s = pa['time'][-1]
# nan out gaps between flights
idx = find(diff(pos_des['time']) > 5000)
pos_des['End'][idx,2] = nan
# Plot position
plot(pa['East'][:,0], pa['North'][:,0], linewidth=3, color=(0.2,0.2,0.2), label="Position")
plot(pa['East'][:,0], pa['North'][:,0], linewidth=2, color=(0.8,0.8,0.8), label="Position")
desired_marker = plot(pos_des['End'][0,1], pos_des['End'][0,0], '*k', markersize=15, label="Desired")
ned_marker = plot(pa['East'][0,0], pa['North'][0,0], '.b', markersize=8)
current_marker = plot(pa['East'][0,0], pa['North'][0,0], '.r', markersize=13)
xlabel('East (m)')
ylabel('North (m)')
xlim(min(pa['East'][:,0]-20), max(pa['East'][:,0])+20)
ylim(min(pa['North'][:,0]-10), max(pa['North'][:,0])+10)
ax.set_axis_bgcolor('green')
ax.xaxis.label.set_color('white')
ax.yaxis.label.set_color('white')
ax.spines['bottom'].set_color('white')
ax.spines['top'].set_color('green')
ax.spines['right'].set_color('green')
ax.spines['left'].set_color('white')
ax.tick_params(axis='x', colors='white')
ax.tick_params(axis='y', colors='white')
fig.set_facecolor('green')
fig.subplots_adjust(left=0.15)
fig.subplots_adjust(bottom=0.15)
draw()
def init(fig=fig):
fig.set_facecolor('green')
# Plot a segment of the path
def update_img(t, pa=pa, pos_des=pos_des, ned=ned, desired_marker=desired_marker, current_marker=current_marker, ned_marker=ned_marker, t0_s=t0_s):
import numpy as np
import matplotlib.pyplot
# convert to minutes
t = t / 60
idx = np.argmin(abs(np.double(pa['time']) / 60 - t))
x = pa['East'][idx,0]
y = pa['North'][idx,0]
current_marker[0].set_xdata(x)
current_marker[0].set_ydata(y)
idx = np.argmin(abs(np.double(ned['time']) / 60 - t))
ned_marker[0].set_xdata(ned['East'][idx])
ned_marker[0].set_ydata(ned['North'][idx])
idx = np.argmin(abs(np.double(pos_des['time']) / 60 - t))
delta = abs(np.double(pos_des['time'][idx]) / 60 - t)
if delta < (1/60.0):
desired_marker[0].set_xdata(pos_des['End'][idx,1])
desired_marker[0].set_ydata(pos_des['End'][idx,0])
fig.patch.set_facecolor('green')
fps = 30.0
dpi = 150
t = arange(t0_s, t1_s, 1/fps)
ani = animation.FuncAnimation(fig,update_img,t,init_func=init,interval=0,blit=False)
writer = animation.writers['ffmpeg'](fps=30)
ani.save(output_filename,dpi=dpi,fps=30,writer=writer,savefig_kwargs={'facecolor':'green'})
def video_overlay(pa,ned,pos_des,output_filename):
# Generate video of the performance during loiter, this
# includes the loiter position, current estimate of
# position and raw gps position
#output_filename = 'out.mp4'
import matplotlib.animation as animation
import scipy.signal as signal
import matplotlib.pyplot as plt
from numpy import nan, arange
from numpy.lib.function_base import diff
from matplotlib.pyplot import plot, xlabel, ylabel, xlim, ylim, draw
from matplotlib.mlab import find
####### plot section
fig, ax = plt.subplots(1,sharex=True)
fig.set_size_inches([8,4])
fig.patch.set_facecolor('green')
t0_s = pa['time'][0]
t1_s = pa['time'][-1]
# nan out gaps between flights
idx = find(diff(pos_des['time']) > 5000)
pos_des['End'][idx,2] = nan
# Plot position
plot(pa['East'][:,0], pa['North'][:,0], linewidth=3, color=(0.2,0.2,0.2), label="Position")
plot(pa['East'][:,0], pa['North'][:,0], linewidth=2, color=(0.8,0.8,0.8), label="Position")
desired_marker = plot(pos_des['End'][0,1], pos_des['End'][0,0], '*k', markersize=15, label="Desired")
ned_marker = plot(pa['East'][0,0], pa['North'][0,0], '.b', markersize=8)
current_marker = plot(pa['East'][0,0], pa['North'][0,0], '.r', markersize=13)
xlabel('East (m)')
ylabel('North (m)')
xlim(min(pa['East'][:,0]-20), max(pa['East'][:,0])+20)
ylim(min(pa['North'][:,0]-10), max(pa['North'][:,0])+10)
ax.set_axis_bgcolor('green')
ax.xaxis.label.set_color('white')
ax.yaxis.label.set_color('white')
ax.spines['bottom'].set_color('white')
ax.spines['top'].set_color('green')
ax.spines['right'].set_color('green')
ax.spines['left'].set_color('white')
ax.tick_params(axis='x', colors='white')
ax.tick_params(axis='y', colors='white')
fig.set_facecolor('green')
fig.subplots_adjust(left=0.15)
fig.subplots_adjust(bottom=0.15)
draw()
def init(fig=fig):
fig.set_facecolor('green')
# Plot a segment of the path
def update_img(t, pa=pa, pos_des=pos_des, ned=ned, desired_marker=desired_marker, current_marker=current_marker, ned_marker=ned_marker, t0_s=t0_s):
import numpy as np
import matplotlib.pyplot
# convert to minutes
t = t / 60
idx = np.argmin(abs(np.double(pa['time']) / 60 - t))
x = pa['East'][idx,0]
y = pa['North'][idx,0]
current_marker[0].set_xdata(x)
current_marker[0].set_ydata(y)
idx = np.argmin(abs(np.double(ned['time']) / 60 - t))
ned_marker[0].set_xdata(ned['East'][idx])
ned_marker[0].set_ydata(ned['North'][idx])
idx = np.argmin(abs(np.double(pos_des['time']) / 60 - t))
delta = abs(np.double(pos_des['time'][idx]) / 60 - t)
if delta < (1/60.0):
desired_marker[0].set_xdata(pos_des['End'][idx,1])
desired_marker[0].set_ydata(pos_des['End'][idx,0])
fig.patch.set_facecolor('green')
fps = 30.0
dpi = 150
t = arange(t0_s, t1_s, 1/fps)
ani = animation.FuncAnimation(fig,update_img,t,init_func=init,interval=0,blit=False)
writer = animation.writers['ffmpeg'](fps=30)
ani.save(output_filename,dpi=dpi,fps=30,writer=writer,savefig_kwargs={'facecolor':'green'})
def get_high_beta_events(session,area,channel,epoch,
MINLEN = 40, # ms
BOXLEN = 50, # ms
THSCALE = 1.5, # sigma (standard deviations)
lowf = 10.0, # Hz
highf = 45.0, # Hz
pad = 200, # ms
clip = True,
audit = False
):
'''
get_high_beta_events(session,area,channel,epoch) will identify periods of
elevated beta-frequency power for the given channel.
Thresholds are selected per-channel based on all available trials.
The entire trial time is used when estimating the average beta power.
To avoid recomputing, we extract beta events for all trials at once.
By default events that extend past the edge of the specified epoch will
be clipped. Passing clip=False will discard these events.
returns the event threshold, and a list of event start and stop
times relative to session time (not per-trial or epoch time)
passing audit=True will enable previewing each trial and the isolated
beta events.
>>> thr,events = get_high_beta_events('SPK120925','PMd',50,(6,-1000,0))
'''
# get LFP data
signal = get_raw_lfp_session(session,area,channel)
# esimate threshold for beta events
beta_trials = [get_filtered_lfp(session,area,channel,t,(6,-1000,0),lowf,highf) for t in get_good_trials(session)]
threshold = np.std(beta_trials)*THSCALE
print 'threshold=',threshold
N = len(signal)
event,start,stop = epoch
all_events = []
all_high_beta_times = []
for trial in get_good_trials(session):
evt = get_trial_event(session,area,trial,event)
trialstart = get_trial_event(session,area,trial,4)
epochstart = evt + start + trialstart
epochstop = evt + stop + trialstart
tstart = max(0,epochstart-pad)
tstop = min(N,epochstop +pad)
filtered = bandfilter(signal[tstart:tstop],lowf,highf)
envelope = abs(hilbert(filtered))
smoothed = convolve(envelope,ones(BOXLEN)/float(BOXLEN),'same')
E = array(get_edges(smoothed>threshold))+tstart
E = E[:,(diff(E,1,0)[0]>=MINLEN)
& (E[0,:]<epochstop )
& (E[1,:]>epochstart)]
if audit: print E
if clip:
E[0,:] = np.maximum(E[0,:],epochstart)
E[1,:] = np.minimum(E[1,:],epochstop )
else:
E = E[:,(E[1,:]<=epochstop)&(E[0,:]>=epochstart)]
if audit:
clf()
axvspan(epochstart,epochstop,color=(0,0,0,0.25))
plot(arange(tstart,tstop),filtered,lw=0.7,color='k')
plot(arange(tstart,tstop),envelope,lw=0.7,color='r')
plot(arange(tstart,tstop),smoothed,lw=0.7,color='b')
ylim(-80,80)
for a,b in E.T:
axvspan(a,b,color=(1,0,0,0.5))
axhline(threshold,color='k',lw=1.5)
xlim(tstart,tstop)
draw()
wait()
all_events.extend(E.T)
assert all(diff(E,0,1)>=MINLEN)
return threshold, all_events
