def get_automorphisms(A):
IG = ig.Graph.Adjacency(A.tolist())
return np.transpose(np.array(IG.get_automorphisms_vf2()))
def loadFile(self, group, index, *args):
'''Load configuration from a predefined list of .mat files'''
from Coordinate import Coordinate
from Window import Window
from Medium import Medium
from Signal import Signal
from System import System
from Data import Data
from Array import Array
from Image import Image
import os
PHDCODE_ROOT = os.environ['COS_ROOT']
Ni = len(args)
if type(index) == int:
idx = copy(index)
index = [idx]
for i in index:
if Ni > 0:
origin = fileLUT(group, i, args)
else:
origin = fileLUT(group, i)
if origin.type == 'ultrasound multi file':
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
#print 'Loading ' + PHDCODE_ROOT + origin.path + origin.info_file
info_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.info_file)
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
NFrames = info_file['NFrames'][0,0]
NElements = info_file['NElements'][0,0]
angles = info_file['angles']
ranges = info_file['ranges']
tmp = mat_file['frame%d'%origin.index].shape
Xd = np.zeros((tmp[0], tmp[1], tmp[2]), dtype=np.complex64)
Xd[:,:,:] = mat_file['frame%d'%i]
s = System()
s.data = Data()
s.data.Xd = Xd
s.data.angles = angles
s.data.ranges = ranges
s.data.M = NElements
s.data.NFrames = NFrames
return s
elif origin.type == 'ultrasound_simulation':
import tables as tb
s = System()
s.data = Data()
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
file = tb.openFile( (PHDCODE_ROOT + origin.path + origin.file), 'r' )
root = file.getNode( file.root )
s.data.M = root.frame_1.N_rx.read()
s.data.angles = root.frame_1.theta.read()[0]
s.data.ranges = root.frame_1.range.read()
d = root.frame_1.datacube.read().shape
s.data.Xd = np.zeros((100,d[1],d[2],d[0]),dtype=np.complex64)
for i in range(100):
tmp = np.transpose(getattr(root, 'frame_%d'%(i+1)).datacube.read(), (1, 2, 0))
s.data.Xd[i,:,:,:] = tmp
file.close()
return s
else:
print 'WW DataInput() - No method implemented for '+origin.type
def loadFile(self, group, index, *args):
'''Load configuration from a predefined list of .mat files'''
from Coordinate import Coordinate
from Window import Window
from Medium import Medium
from Signal import Signal
from System import System
from Data import Data
from Array import Array
from Image import Image
import os
PHDCODE_ROOT = os.environ['COS_ROOT']
Ni = len(args)
if type(index) == int:
idx = copy(index)
index = [idx]
for i in index:
if Ni > 0:
origin = fileLUT(group, i, args)
else:
origin = fileLUT(group, i)
if origin.type == 'ultrasound multi file':
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
#print 'Loading ' + PHDCODE_ROOT + origin.path + origin.info_file
info_file = io.loadmat(PHDCODE_ROOT + origin.path +
origin.info_file)
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
NFrames = info_file['NFrames'][0, 0]
NElements = info_file['NElements'][0, 0]
angles = info_file['angles']
ranges = info_file['ranges']
tmp = mat_file['frame%d' % origin.index].shape
Xd = np.zeros((tmp[0], tmp[1], tmp[2]), dtype=np.complex64)
Xd[:, :, :] = mat_file['frame%d' % i]
s = System()
s.data = Data()
s.data.Xd = Xd
s.data.angles = angles
s.data.ranges = ranges
s.data.M = NElements
s.data.NFrames = NFrames
return s
elif origin.type == 'ultrasound_simulation':
import tables as tb
s = System()
s.data = Data()
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
file = tb.openFile((PHDCODE_ROOT + origin.path + origin.file), 'r')
root = file.getNode(file.root)
s.data.M = root.frame_1.N_rx.read()
s.data.angles = root.frame_1.theta.read()[0]
s.data.ranges = root.frame_1.range.read()
d = root.frame_1.datacube.read().shape
s.data.Xd = np.zeros((100, d[1], d[2], d[0]), dtype=np.complex64)
for i in range(100):
tmp = np.transpose(
getattr(root, 'frame_%d' % (i + 1)).datacube.read(),
(1, 2, 0))
s.data.Xd[i, :, :, :] = tmp
file.close()
return s
else:
print 'WW DataInput() - No method implemented for ' + origin.type
def loadFile(self, group, index, *args):
'''Load configuration from a predefined list of .mat files'''
from Coordinate import Coordinate
from Window import Window
from Medium import Medium
from Signal import Signal
from System import System
from Data import Data
from Array import Array
from Image import Image
import os
PHDCODE_ROOT = os.environ['COS_ROOT']
Ni = len(args)
if type(index) == int:
idx = copy(index)
index = [idx]
for i in index:
if Ni > 0:
origin = fileLUT(group, i, args)
else:
origin = fileLUT(group, i)
# The dataset 'type' (defined by me) selects the "import method":
if origin.type == 'hugin_delayed':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
BF = mat_file['p'][0, 0]['BF'][0, 0]
s = System()
s.data = Data()
s.data.Xd = Ndarray(mat_file['dataCube'].transpose((1,0,2)))
s.data.n = Ndarray(BF['mtaxe_re' ].T)
s.data.Nt = Ndarray(BF['Nt'].T)
s.data.fs = 40e3
s.data.fc = 100e3
s.data.M = 32
s.data.d = 0.0375
s.data.c = 1500
s.desc = 'HUGIN raw data'
return s
elif origin.type == 'focus_dump_type_A' or origin.type == 'focus_dump_type_B':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
# Load the 'Sonar' dictionary and get rid of empty dimensions:
Sonar = mat_file['Sonar'][0,0]
s = System()
s.medium = Medium()
s.medium.c = Ndarray(Sonar['c'][0,0]) # Propagation speed
# self.B = 0 # Assume narrowband
# The 'rx_x','rx_y' and 'rx_z' coordinates in the Sonar struct represents
# the TX elements' location relative to the HUGIN body. We're going to
# rotate this coordination system around the x-axis such that the
# y-coordinate is 0 for all elements (22 degrees).
rx_x = Sonar['rx_x'][:,1] # RX x-coordinates (HUGIN body)
rx_y = Sonar['rx_y'][:,1] # RX y-coordinates (HUGIN body)
rx_z = Sonar['rx_z'][:,1] # RX z-coordinates (HUGIN body)
rx_yz = np.sqrt(rx_y ** 2 + rx_z ** 2) # RX y-z distance (new z-axis)
p = Coordinate(x=rx_x, y=None, z=rx_yz)# New RX coordinates [x, axis]
p.setAxesDesc(template='hugin')
s.array = Array()
s.array.p = p
# desc='RX coordinates',
# shape_desc=('x','axis'))
s.signal = Signal()
s.signal.desc = 'upchirp'
s.signal.fc = Ndarray(Sonar['fc'][0,0])
if origin.type == 'focus_dump_type_A':
s.data = Data()
s.data.X = Ndarray(mat_file['mfdata'].T.copy())
s.data.t = Ndarray(mat_file['mtaxe' ][0])
s.image = Image()
s.image.xarr = Ndarray(mat_file['xarr'].T.copy())
s.image.yarr = Ndarray(mat_file['yarr'].T.copy())
else:
s.data = Data()
s.data.X = Ndarray(mat_file['data1'].T.copy())
s.data.t = Ndarray(Sonar['T_pri'][0,0])
s.image = Image()
# s.image.xarr = Ndarray(mat_file['xarr'].T.copy())
# s.image.yarr = Ndarray(mat_file['yarr'].T.copy())
return s
elif origin.type == 'focus_sas_dump_type_A_parameters':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
# Load the 'Sonar' dictionary and get rid of empty dimensions:
Sonar = mat_file['Sonar'][0,0]
s = System()
s.medium = Medium()
s.medium.c = Ndarray(Sonar['c'][0,0]) # Propagation speed
# self.B = 0 # Assume narrowband
# The 'rx_x','rx_y' and 'rx_z' coordinates in the Sonar struct represents
# the TX elements' location relative to the HUGIN body. We're going to
# rotate this coordination system around the x-axis such that the
# y-coordinate is 0 for all elements (22 degrees).
rx_x = Sonar['rx_x'][:,1] # RX x-coordinates (HUGIN body)
rx_y = Sonar['rx_y'][:,1] # RX y-coordinates (HUGIN body)
rx_z = Sonar['rx_z'][:,1] # RX z-coordinates (HUGIN body)
rx_yz = np.sqrt(rx_y ** 2 + rx_z ** 2) # RX y-z distance (new z-axis)
p = Coordinate(x=rx_x, y=None, z=rx_yz)# New RX coordinates [x, axis]
p.setAxesDesc(template='hugin')
s.array = Array()
s.array.p = p
# desc='RX coordinates',
# shape_desc=('x','axis'))
s.signal = Signal()
s.signal.desc = 'upchirp'
s.signal.fc = Ndarray(Sonar['fc'][0,0])
return s
elif origin.type == 'focus_sas_dump_type_A_data':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
s = System()
s.data = Data()
s.data.X = Ndarray(mat_file['mfdata'].copy())
s.data.t = Ndarray(mat_file['mtaxe' ][0])
return s
elif origin.type == 'focus_sss_dump_type_A':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
s = System()
# s.a = Data()
s.img = Ndarray(mat_file['sss_image'].copy())
s.Nping = 140
s.Ny = 4000
s.Nx = 15
s.y_lim = [60,160]
return s
elif origin.type == 'hisas_sim':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
BF = mat_file['p'][0, 0]['BF'][0, 0]
s = System()
s.medium = Medium()
s.medium.c = Ndarray(BF['c'][0, 0]) # Propagation speed
s.array = Array()
s.array.desc = 'ula'
s.array.M = Ndarray(BF['n_hydros'][0, 0]) # Number of hydrophones
s.array.d = Ndarray(BF['d' ][0, 0]) # Element distance
s.data = Data()
s.data.Xd = Ndarray(mat_file['dataCube']).transpose((1,0,2))
# Ndarray(,
# axes = ['y','x','m'],
# desc = 'Delayed and matched filtered data')
# desc='Delayed and matched filtered data',
# shape_desc = ('y','x','m'),
# shape_desc_verbose = ('Range','Azimuth','Channel'))
s.data.t = Ndarray(BF['mtaxe'].T)
# Ndarray(,
# axes = ['N'],
# desc = 'Time axis')
# desc = 'Corresponding time',
# shape_desc = ('N','1'))
s.phi_min = mat_file['p'][0,0]['phi_min'][0,0]
s.phi_max = mat_file['p'][0,0]['phi_max'][0,0]
s.R_min = mat_file['p'][0,0]['R_min'][0,0]
s.R_max = mat_file['p'][0,0]['R_max'][0,0]
s.signal = Signal()
s.signal.desc = 'upchirp'
s.signal.fc = Ndarray(BF['fc' ][0, 0]) # Carrier frequency
return s
# Some datasets simply do not fall into a general category. We'll handle these
# individually...
elif origin.type == 'barge':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
# Load the 'Sonar' dictionary and get rid of empty dimensions:
Sonar = mat_file['Sonar'][0, 0] # See http://projects.scipy.org/numpy/wiki/ZeroRankArray
s = System()
# s.medium = Medium()
# s.medium.c = Sonar['c'][0, 0] # Propagation speed
#
# # Format: Sonar[dictionary key][hydrophone coordinates, bank]
# rx_x = Sonar['rx_x'][:, 1] # RX x-coordinates bank 2 (HUGIN body)
# rx_y = Sonar['rx_y'][:, 1] # RX y-coordinates bank 2 (HUGIN body)
# rx_z = Sonar['rx_z'][:, 1] # RX z-coordinates bank 2 (HUGIN body)
# rx_yz = sqrt(rx_y ** 2 + rx_z ** 2) # RX y-z distance (new z-axis)
# p = Coordinate(x=rx_x, y=None, z=rx_yz)# New RX coordinates [x, axis]
## p = vstack((rx_x, rx_yz)).T # New RX coordinates [x, axis]
#
# s.array = Array()
# s.array.p = p
## ,
## desc='RX coordinates',
## shape_desc=('x','axis'))
s.data = Data()
s.data.X = Ndarray(mat_file['data1'])
s.signal = Signal()
s.signal.fc = Ndarray(Sonar['fc'][0,0])
s.medium = Medium()
s.medium.c = Ndarray(Sonar['c'][0,0]) # Propagation speed
# s.signal = Signal()
# s.signal.fc = Sonar['fc'][0, 0] # Carrier frequency
# s.signal.bw = Sonar['bw'][0, 0]
# s.signal.desc = 'upchirp'# Waveform
return s
elif origin.type == 'ultrasound multi file':
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
#print 'Loading ' + PHDCODE_ROOT + origin.path + origin.info_file
info_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.info_file)
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
NFrames = info_file['NFrames'][0,0]
NElements = info_file['NElements'][0,0]
angles = info_file['angles']
ranges = info_file['ranges']
tmp = mat_file['frame%d'%origin.index].shape
Xd = np.zeros((tmp[0], tmp[1], tmp[2]), dtype=np.complex64)
Xd[:,:,:] = mat_file['frame%d'%i]
s = System()
s.data = Data()
s.data.Xd = Xd
s.data.angles = angles
s.data.ranges = ranges
s.data.M = NElements
s.data.NFrames = NFrames
return s
elif origin.type == 'csound':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later < on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
if origin.index == 1:
beamformedCycle = mat_file['beamformedcycle'][0,0]
#beamformedFrames = beamformedCycle[0]
beamformedCubes = beamformedCycle[1]
NFrames = beamformedCycle[2]
NFrames = NFrames[0, 0]
NElements = beamformedCycle[3]
NElements = NElements[0, 0]
params = beamformedCycle[4]
del beamformedCycle
params = params[0, 0]
#delays = params[0]
angles = params[1]*np.pi/180
ranges = params[2]
#phaseFactor = params[3]
del params
tmp = beamformedCubes[0,0].shape
Xd = np.zeros((NFrames,tmp[0],tmp[1],tmp[2]),dtype=np.complex64)
for i in range(NFrames):
Xd[i,:,:,:] = beamformedCubes[i,0][:,:,:]
elif origin.index == 2 or origin.index == 3:
if origin.index == 2:
NFrames = 10
else:
NFrames = 60
NElements = mat_file['NElements'][0,0]
angles = mat_file['angles']
ranges = mat_file['ranges'].T
tmp = mat_file['frame%d'%1].shape
Xd = np.zeros((NFrames, tmp[0], tmp[2], tmp[1]), dtype=np.complex64)
for i in range(NFrames):
Xd[i,:,:,:] = np.transpose(mat_file['frame%d'%(i+1)], (0, 2, 1))
else:
pass
#
s = System()
s.data = Data()
s.data.Xd = Xd
s.data.angles = angles
s.data.ranges = ranges
s.data.M = NElements
return s
elif origin.type == 'ultrasound_simulation':
import tables as tb
s = System()
s.data = Data()
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
file = tb.openFile( (PHDCODE_ROOT + origin.path + origin.file), 'r' )
root = file.getNode( file.root )
# s.data.fc = root.frame_1.fc.read()
# s.data.c = root.frame_1.c.read()
s.data.M = root.frame_1.N_rx.read()
s.data.angles = root.frame_1.theta.read()[0] # Only first row makes sence to me :p
s.data.ranges = root.frame_1.range.read()
d = root.frame_1.datacube.read().shape
s.data.Xd = np.zeros((100,d[1],d[2],d[0]),dtype=np.complex64)
for i in range(100):
tmp = np.transpose(getattr(root, 'frame_%d'%(i+1)).datacube.read(), (1, 2, 0))
s.data.Xd[i,:,:,:] = tmp
file.close()
return s
else:
print 'WW DataInput() - No method implemented for '+origin.type
def loadFile(self, group, index, *args):
'''Load configuration from a predefined list of .mat files'''
from Coordinate import Coordinate
from Window import Window
from Medium import Medium
from Signal import Signal
from System import System
from Data import Data
from Array import Array
from Image import Image
import os
PHDCODE_ROOT = os.environ['COS_ROOT']
Ni = len(args)
if type(index) == int:
idx = copy(index)
index = [idx]
for i in index:
if Ni > 0:
origin = fileLUT(group, i, args)
else:
origin = fileLUT(group, i)
# The dataset 'type' (defined by me) selects the "import method":
if origin.type == 'hugin_delayed':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
BF = mat_file['p'][0, 0]['BF'][0, 0]
s = System()
s.data = Data()
s.data.Xd = Ndarray(mat_file['dataCube'].transpose((1, 0, 2)))
s.data.n = Ndarray(BF['mtaxe_re'].T)
s.data.Nt = Ndarray(BF['Nt'].T)
s.data.fs = 40e3
s.data.fc = 100e3
s.data.M = 32
s.data.d = 0.0375
s.data.c = 1500
s.desc = 'HUGIN raw data'
return s
elif origin.type == 'focus_dump_type_A' or origin.type == 'focus_dump_type_B':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
# Load the 'Sonar' dictionary and get rid of empty dimensions:
Sonar = mat_file['Sonar'][0, 0]
s = System()
s.medium = Medium()
s.medium.c = Ndarray(Sonar['c'][0, 0]) # Propagation speed
# self.B = 0 # Assume narrowband
# The 'rx_x','rx_y' and 'rx_z' coordinates in the Sonar struct represents
# the TX elements' location relative to the HUGIN body. We're going to
# rotate this coordination system around the x-axis such that the
# y-coordinate is 0 for all elements (22 degrees).
rx_x = Sonar['rx_x'][:, 1] # RX x-coordinates (HUGIN body)
rx_y = Sonar['rx_y'][:, 1] # RX y-coordinates (HUGIN body)
rx_z = Sonar['rx_z'][:, 1] # RX z-coordinates (HUGIN body)
rx_yz = np.sqrt(rx_y**2 + rx_z**2) # RX y-z distance (new z-axis)
p = Coordinate(x=rx_x, y=None,
z=rx_yz) # New RX coordinates [x, axis]
p.setAxesDesc(template='hugin')
s.array = Array()
s.array.p = p
# desc='RX coordinates',
# shape_desc=('x','axis'))
s.signal = Signal()
s.signal.desc = 'upchirp'
s.signal.fc = Ndarray(Sonar['fc'][0, 0])
if origin.type == 'focus_dump_type_A':
s.data = Data()
s.data.X = Ndarray(mat_file['mfdata'].T.copy())
s.data.t = Ndarray(mat_file['mtaxe'][0])
s.image = Image()
s.image.xarr = Ndarray(mat_file['xarr'].T.copy())
s.image.yarr = Ndarray(mat_file['yarr'].T.copy())
else:
s.data = Data()
s.data.X = Ndarray(mat_file['data1'].T.copy())
s.data.t = Ndarray(Sonar['T_pri'][0, 0])
s.image = Image()
# s.image.xarr = Ndarray(mat_file['xarr'].T.copy())
# s.image.yarr = Ndarray(mat_file['yarr'].T.copy())
return s
elif origin.type == 'focus_sas_dump_type_A_parameters':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
# Load the 'Sonar' dictionary and get rid of empty dimensions:
Sonar = mat_file['Sonar'][0, 0]
s = System()
s.medium = Medium()
s.medium.c = Ndarray(Sonar['c'][0, 0]) # Propagation speed
# self.B = 0 # Assume narrowband
# The 'rx_x','rx_y' and 'rx_z' coordinates in the Sonar struct represents
# the TX elements' location relative to the HUGIN body. We're going to
# rotate this coordination system around the x-axis such that the
# y-coordinate is 0 for all elements (22 degrees).
rx_x = Sonar['rx_x'][:, 1] # RX x-coordinates (HUGIN body)
rx_y = Sonar['rx_y'][:, 1] # RX y-coordinates (HUGIN body)
rx_z = Sonar['rx_z'][:, 1] # RX z-coordinates (HUGIN body)
rx_yz = np.sqrt(rx_y**2 + rx_z**2) # RX y-z distance (new z-axis)
p = Coordinate(x=rx_x, y=None,
z=rx_yz) # New RX coordinates [x, axis]
p.setAxesDesc(template='hugin')
s.array = Array()
s.array.p = p
# desc='RX coordinates',
# shape_desc=('x','axis'))
s.signal = Signal()
s.signal.desc = 'upchirp'
s.signal.fc = Ndarray(Sonar['fc'][0, 0])
return s
elif origin.type == 'focus_sas_dump_type_A_data':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
s = System()
s.data = Data()
s.data.X = Ndarray(mat_file['mfdata'].copy())
s.data.t = Ndarray(mat_file['mtaxe'][0])
return s
elif origin.type == 'focus_sss_dump_type_A':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
s = System()
# s.a = Data()
s.img = Ndarray(mat_file['sss_image'].copy())
s.Nping = 140
s.Ny = 4000
s.Nx = 15
s.y_lim = [60, 160]
return s
elif origin.type == 'hisas_sim':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
BF = mat_file['p'][0, 0]['BF'][0, 0]
s = System()
s.medium = Medium()
s.medium.c = Ndarray(BF['c'][0, 0]) # Propagation speed
s.array = Array()
s.array.desc = 'ula'
s.array.M = Ndarray(BF['n_hydros'][0, 0]) # Number of hydrophones
s.array.d = Ndarray(BF['d'][0, 0]) # Element distance
s.data = Data()
s.data.Xd = Ndarray(mat_file['dataCube']).transpose((1, 0, 2))
# Ndarray(,
# axes = ['y','x','m'],
# desc = 'Delayed and matched filtered data')
# desc='Delayed and matched filtered data',
# shape_desc = ('y','x','m'),
# shape_desc_verbose = ('Range','Azimuth','Channel'))
s.data.t = Ndarray(BF['mtaxe'].T)
# Ndarray(,
# axes = ['N'],
# desc = 'Time axis')
# desc = 'Corresponding time',
# shape_desc = ('N','1'))
s.phi_min = mat_file['p'][0, 0]['phi_min'][0, 0]
s.phi_max = mat_file['p'][0, 0]['phi_max'][0, 0]
s.R_min = mat_file['p'][0, 0]['R_min'][0, 0]
s.R_max = mat_file['p'][0, 0]['R_max'][0, 0]
s.signal = Signal()
s.signal.desc = 'upchirp'
s.signal.fc = Ndarray(BF['fc'][0, 0]) # Carrier frequency
return s
# Some datasets simply do not fall into a general category. We'll handle these
# individually...
elif origin.type == 'barge':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
# Load the 'Sonar' dictionary and get rid of empty dimensions:
Sonar = mat_file['Sonar'][
0, 0] # See http://projects.scipy.org/numpy/wiki/ZeroRankArray
s = System()
# s.medium = Medium()
# s.medium.c = Sonar['c'][0, 0] # Propagation speed
#
# # Format: Sonar[dictionary key][hydrophone coordinates, bank]
# rx_x = Sonar['rx_x'][:, 1] # RX x-coordinates bank 2 (HUGIN body)
# rx_y = Sonar['rx_y'][:, 1] # RX y-coordinates bank 2 (HUGIN body)
# rx_z = Sonar['rx_z'][:, 1] # RX z-coordinates bank 2 (HUGIN body)
# rx_yz = sqrt(rx_y ** 2 + rx_z ** 2) # RX y-z distance (new z-axis)
# p = Coordinate(x=rx_x, y=None, z=rx_yz)# New RX coordinates [x, axis]
## p = vstack((rx_x, rx_yz)).T # New RX coordinates [x, axis]
#
# s.array = Array()
# s.array.p = p
## ,
## desc='RX coordinates',
## shape_desc=('x','axis'))
s.data = Data()
s.data.X = Ndarray(mat_file['data1'])
s.signal = Signal()
s.signal.fc = Ndarray(Sonar['fc'][0, 0])
s.medium = Medium()
s.medium.c = Ndarray(Sonar['c'][0, 0]) # Propagation speed
# s.signal = Signal()
# s.signal.fc = Sonar['fc'][0, 0] # Carrier frequency
# s.signal.bw = Sonar['bw'][0, 0]
# s.signal.desc = 'upchirp'# Waveform
return s
elif origin.type == 'ultrasound multi file':
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
#print 'Loading ' + PHDCODE_ROOT + origin.path + origin.info_file
info_file = io.loadmat(PHDCODE_ROOT + origin.path +
origin.info_file)
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
NFrames = info_file['NFrames'][0, 0]
NElements = info_file['NElements'][0, 0]
angles = info_file['angles']
ranges = info_file['ranges']
tmp = mat_file['frame%d' % origin.index].shape
Xd = np.zeros((tmp[0], tmp[1], tmp[2]), dtype=np.complex64)
Xd[:, :, :] = mat_file['frame%d' % i]
s = System()
s.data = Data()
s.data.Xd = Xd
s.data.angles = angles
s.data.ranges = ranges
s.data.M = NElements
s.data.NFrames = NFrames
return s
elif origin.type == 'csound':
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later < on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
mat_file = io.loadmat(PHDCODE_ROOT + origin.path + origin.file)
if origin.index == 1:
beamformedCycle = mat_file['beamformedcycle'][0, 0]
#beamformedFrames = beamformedCycle[0]
beamformedCubes = beamformedCycle[1]
NFrames = beamformedCycle[2]
NFrames = NFrames[0, 0]
NElements = beamformedCycle[3]
NElements = NElements[0, 0]
params = beamformedCycle[4]
del beamformedCycle
params = params[0, 0]
#delays = params[0]
angles = params[1] * np.pi / 180
ranges = params[2]
#phaseFactor = params[3]
del params
tmp = beamformedCubes[0, 0].shape
Xd = np.zeros((NFrames, tmp[0], tmp[1], tmp[2]),
dtype=np.complex64)
for i in range(NFrames):
Xd[i, :, :, :] = beamformedCubes[i, 0][:, :, :]
elif origin.index == 2 or origin.index == 3:
if origin.index == 2:
NFrames = 10
else:
NFrames = 60
NElements = mat_file['NElements'][0, 0]
angles = mat_file['angles']
ranges = mat_file['ranges'].T
tmp = mat_file['frame%d' % 1].shape
Xd = np.zeros((NFrames, tmp[0], tmp[2], tmp[1]),
dtype=np.complex64)
for i in range(NFrames):
Xd[i, :, :, :] = np.transpose(
mat_file['frame%d' % (i + 1)], (0, 2, 1))
else:
pass
#
s = System()
s.data = Data()
s.data.Xd = Xd
s.data.angles = angles
s.data.ranges = ranges
s.data.M = NElements
return s
elif origin.type == 'ultrasound_simulation':
import tables as tb
s = System()
s.data = Data()
# Using scipy.io.loadmat() to load .mat files. The result is a nparray, where structures
# in the array are dictionaries (the data is treated later on..).
print 'Loading ' + PHDCODE_ROOT + origin.path + origin.file
file = tb.openFile((PHDCODE_ROOT + origin.path + origin.file), 'r')
root = file.getNode(file.root)
# s.data.fc = root.frame_1.fc.read()
# s.data.c = root.frame_1.c.read()
s.data.M = root.frame_1.N_rx.read()
s.data.angles = root.frame_1.theta.read()[
0] # Only first row makes sence to me :p
s.data.ranges = root.frame_1.range.read()
d = root.frame_1.datacube.read().shape
s.data.Xd = np.zeros((100, d[1], d[2], d[0]), dtype=np.complex64)
for i in range(100):
tmp = np.transpose(
getattr(root, 'frame_%d' % (i + 1)).datacube.read(),
(1, 2, 0))
s.data.Xd[i, :, :, :] = tmp
file.close()
return s
else:
print 'WW DataInput() - No method implemented for ' + origin.type