def find_echo_location(file):
"""A wrapper for find_half_height."""
data = read_ultrasound_new.read_ultrasound(file, 1)
trimmed_data = trim_leading_edge(data)
crossing_depth = find_half_height(trimmed_data['depth'],
trimmed_data['echo'][5])
return crossing_depth
def __init__(self, shot, channel_num):
'''Initialize a ChannelData object.
Note that shot is a Shot object, which must be created
before. Ideally, this function is only run from
Shot.add_channel_data().'''
if(not(shot.channels_used.__contains__(channel_num))):
raise ValueError("Shot %d does not use channel %d" % (shot.number,
channel_num))
#Get a pointer back to our parent Shot.
self.shot = shot
#Read in the data from the UDV file
if(not(os.path.exists(shot.filename))):
raise ValueError('Data file %s does not exist.' % self.filename)
data = rudv.read_ultrasound(shot.filename, channel_num,
verbose=0)
self.velocity = data['velocity']
self.echo = data['echo']
self.energy = data['energy']
self.depth = data['depth']
self.time = data['time']
self.channel = data['channel']
self.maxvelocity = data['maxvelocity']
#Now grab the shot parameters from the database.
params = sp.shot_params[shot.number]
if(params.__contains__('trouble_flag')):
print "Shot %d has trouble_flag set. Check notebook." % shot.number
#Pick out the transducer mounting parameters to correspond to this
#channel
channel_idx = params['channels'].index(channel_num)
self.A = params['As'][channel_idx]
self.B = params['Bs'][channel_idx]
self.offset = params['offsets'][channel_idx]
self.port = params['ports'][channel_idx]
self.time_points = self.time.size
self.spatial_points = self.depth.size
#Now run some routines to unwrap the aliased velocity, and to find
#the location of each measurement.
self.calculate_radius()
self.calculate_azimuth()
self.calculate_height()
self.unwrap_velocity()
def extract_spiral_modes(filename, channel, omega2, start_time, end_time, desiredcells=200, rin=udvh.r1, filter_threshold=1000, background_subtract=0, derotate=0, maxv=10000, minv=-10000, max_diff_mean=10000, levels=0, smoothradial=0):
if(rudv.read_ultrasound("960.BDD", 1)):
has_radial=1
else:
has_radial=0
#I use the novr option to generate v_t, so that it is less susceptible
#to noise on the radial transducer, and then run again to get the v_r
#data separately.
#If we don't have radial data, just use data_vt for the radial dataset,
#so v_r will just be 0.
data_vt = udvh.generate_profiles_alltime_novr(filename, channel, omega2)
if(has_radial):
data_vr = udvh.generate_profiles_alltime(filename, channel, omega2)
else:
data_vr = data_vt
phase = linspace(-pi, pi, 100)
time = data_vt['time']
vt = data_vt['vt']
vr = data_vr['vr']
r = data_vt['r']
if (derotate != 0):
derotate_angle = start_time*2.0*pi/(end_time-start_time)
derotate_angle = fmod(derotate_angle, 2.0*pi)
for i in range(0, time.size):
if (time[i] > start_time) & (time[i-1] <= start_time):
start_pos = i
elif (time[i] > end_time) & (time[i-1] <= end_time):
end_pos = i-1
#Trim the arrays
time = time[start_pos-10:end_pos+10]
vt = vt[start_pos-10:end_pos+10,:]
vr = vr[start_pos-10:end_pos+10,:]
#Now convert time to phase
time = time - start_time
measured_phase = time*2.0*pi/(end_time-start_time) - pi
resampled_vt = zeros([phase.size, r.size])
resampled_vr = zeros([phase.size, r.size])
#Now resample for each element of r
for i in range(0, r.size):
#Filter the data to remove spurious measurements
for k in range (0, 5):
if (vt[k,i] > maxv):
vt[k,i] = maxv
if (vt[k,i] < minv):
vt[k,i] = minv
for k in range (5, vt[:,i].size):
if abs(vt[k,i] - vt[k-5:k-1,i].mean()) > filter_threshold:
vt[k,i] = vt[k-5:k-1,i].mean()
if (vt[k,i] > maxv):
vt[k,i] = maxv
if (vt[k,i] < minv):
vt[k,i] = minv
#Need to offset theta to account for the angular extent of the
#the measurement chord.
#Note that there is some weirdness because of the branchcuts of asin.
vt_theta_offset = pi - udvh.tan_probe_angle*pi/180 - (pi - math.asin(udvh.r2*math.sin(udvh.tan_probe_angle*pi/180)/r[i]))
vt_temp_measured_phase = measured_phase - vt_theta_offset
#This is just set by the port plugs where the transducers are
#installed. The radial transducer leads the azimuthal transducer
#by 90 degrees
vr_theta_offset = -pi/2
vr_temp_measured_phase = measured_phase - vr_theta_offset
#Now resample. Array is reversed, since measurements in experiment
#are made from larger phase down to smaller phase
#(features flowing past transducer)
tck_vt = scipy.interpolate.splrep(vt_temp_measured_phase, vt[:,i], s=0)
resampled_vt[:,i] = (scipy.interpolate.splev(phase, tck_vt, der=0))[::-1]
tck_vr = scipy.interpolate.splrep(vr_temp_measured_phase, vr[:,i], s=0)
resampled_vr[:,i] = (scipy.interpolate.splev(phase, tck_vr, der=0))[::-1]
if(background_subtract == 1):
resampled_vt[:,i] = resampled_vt[:,i]-resampled_vt[:,i].mean()
if(smoothradial == 1):
for i in range(0, phase.size):
tck_sr = scipy.interpolate.splrep(r, resampled_vr[i,:], s=100)
resampled_vr[i,:] = (scipy.interpolate.splev(r,
tck_sr, der=0))
#r and phase contain the and theta coordinates
#vr and vt are arrays, with the first index the phase,
#and the second index r
data = {'r': r, 'phase': phase, 'vr': resampled_vr, 'vt': resampled_vt}
return data
def extract_spiral_modes_good(filename, channel, omega2, start_time, end_time, desiredcells=200, rin=udvh.r1, filter_threshold=1000, background_subtract=0, derotate=0, maxv=10000, minv=-10000, max_diff_mean=10000, levels=0, smoothradial=0, smoothingfactor=100):
oscillation_time = end_time-start_time
#I use the novr option to generate v_t, so that it is less susceptible
#to noise on the radial transducer, and then run again to get the v_r
#data separately.
r_data = rudv.read_ultrasound(filename, 1)
t_data = rudv.read_ultrasound(filename, channel)
r, v_r, v_t = udvh.reconstruct_avg_velocities_nonaxisymmetric(r_data, t_data,
100, omega2,
oscillation_time)
vt = zeros([r_data['time'].size, r.size])
vr = zeros([r_data['time'].size, r.size])
#Because of the resampling interval that I do in
#reconstruct_avg_velocities_nonaxisymmetric, I can't go all the way to the
#ends of the time interval.
boundary = int(oscillation_time/(t_data['time'][5]-t_data['time'][4]))/2 + 2
for i in range(boundary, r_data['time'].size-boundary):
r, v_r, v_t = udvh.reconstruct_avg_velocities_nonaxisymmetric(r_data, t_data,
i, omega2,
oscillation_time)
vt[i,:] = v_t
vr[i,:] = v_r
phase = linspace(-pi, pi, 100)
time = r_data['time']
if (derotate != 0):
derotate_angle = start_time*2.0*pi/(end_time-start_time)
derotate_angle = fmod(derotate_angle, 2.0*pi)
for i in range(0, time.size):
if (time[i] > start_time) & (time[i-1] <= start_time):
start_pos = i
elif (time[i] > end_time) & (time[i-1] <= end_time):
end_pos = i-1
#Trim the arrays
time = time[start_pos-10:end_pos+10]
vt = vt[start_pos-10:end_pos+10,:]
vr = vr[start_pos-10:end_pos+10,:]
#Now convert time to phase
time = time - start_time
measured_phase = time*2.0*pi/(end_time-start_time) - pi
resampled_vt = zeros([phase.size, r.size])
resampled_vr = zeros([phase.size, r.size])
#Now resample for each element of r
for i in range(0, r.size):
#Filter the data to remove spurious measurements
for k in range (0, 5):
if (vt[k,i] > maxv):
vt[k,i] = maxv
if (vt[k,i] < minv):
vt[k,i] = minv
for k in range (5, vt[:,i].size):
if abs(vt[k,i] - vt[k-5:k-1,i].mean()) > filter_threshold:
vt[k,i] = vt[k-5:k-1,i].mean()
if (vt[k,i] > maxv):
vt[k,i] = maxv
if (vt[k,i] < minv):
vt[k,i] = minv
#Now resample. Array is reversed, since measurements in experiment
#are made from larger phase down to smaller phase
#(features flowing past transducer)
tck_vt = scipy.interpolate.splrep(measured_phase, vt[:,i], s=0)
resampled_vt[:,i] = (scipy.interpolate.splev(phase, tck_vt, der=0))[::-1]
tck_vr = scipy.interpolate.splrep(measured_phase, vr[:,i], s=0)
resampled_vr[:,i] = (scipy.interpolate.splev(phase, tck_vr, der=0))[::-1]
if(background_subtract == 1):
resampled_vt[:,i] = resampled_vt[:,i]-resampled_vt[:,i].mean()
if(smoothradial == 1):
for i in range(0, phase.size):
tck_sr = scipy.interpolate.splrep(r, resampled_vr[i,:],
s=smoothingfactor)
resampled_vr[i,:] = (scipy.interpolate.splev(r,
tck_sr, der=0))
#r and phase contain the and theta coordinates
#vr and vt are arrays, with the first index the phase,
#and the second index r
data = {'r': r, 'phase': phase, 'vr': resampled_vr, 'vt': resampled_vt}
return data
