def mapblks(segfile, outputs, params, start, stride, chunk, **kwargs):
'''
Open the segmentation file segfile, output files in the list outputs,
and loop through the segmentation in strides to produce output
parameter maps. kwargs are optional arguments to be passed to the
segmentation routine.
The arguments start and stride refer to chunks rather than slices.
'''
# Open the files
seg = mio.Slicer(segfile)
sndfile, atnfile, denfile = [mio.Slicer(o) for o in outputs]
# Add the chunk size to the kwargs for convenience
kwargs['chunk'] = chunk
# Loop through the chunks to process output
for n in range(start * chunk, seg.shape[-1], stride * chunk):
print('Processing chunk', n)
snd, atn, den = segmentation.maptissueblk(seg, params, n, **kwargs)
# Figure out how many slices need to be written
oend = min(seg.shape[-1], n + snd.shape[-1])
# Write the outputs
sndfile[n:oend] = snd
atnfile[n:oend] = atn
denfile[n:oend] = den
def fuzzyblks(infile, outfile, nbr, start, stride, chunk):
'''
Open the input file infile, output file outfile (which should exist)
and loop through the input in strides to fuzzify boundarys in the input
using cutil.fuzzyimg with a neighborhood nbr.
The arguments start and stride refer to chunks rather than slices.
'''
# Open the files
inmat = mio.Slicer(infile)
outmat = mio.Slicer(outfile)
# Compute the one-sided pad depth
pad = (nbr - 1) // 2
# Loop through the chunks to process output
for n in range(start * chunk, inmat.shape[-1], stride * chunk):
print('Processing chunk', n)
# Read the chunk
start = max(0, n - pad)
finish = min(n + chunk + pad, inmat.shape[-1])
block = inmat[start:finish]
# Fuzzify the block
outblk = cutil.fuzzyimg(block, nbr)
# Figure out the proper slices of the output block
istart = n - start
iend = min(chunk + istart, istart + outmat.shape[-1] - n)
# Figure out how many slices need to be written
oend = n + iend - istart
# Write the block to output, automatically converting types
outmat[n:oend] = outblk[:, :, istart:iend]
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
progname = sys.argv[0]
# Set default options
a = b = 1.
try:
nproc = multiprocessing.cpu_count()
except NotImplementedError:
nproc = 1
optlist, args = getopt.getopt(argv, 'p:a:b:h')
# Parse the options list
for opt in optlist:
if opt[0] == '-a': a = complex(opt[1])
elif opt[0] == '-b': b = complex(opt[1])
elif opt[0] == '-p': nproc = int(opt[1])
else:
usage(progname)
return 128
# There must be at least three file names
if len(args) < 2:
usage(progname)
return 128
# If the coefficients have zero imaginary parts, cast them as real
a, b = [c if np.iscomplex(c) else c.real for c in [a, b]]
# Make sure that the shapes of all of the inputs agree
inputs = [mio.Slicer(arg) for arg in args[1:]]
if tuple(inputs[0].shape) != tuple(inputs[1].shape):
raise ValueError('Array sizes must agree')
# Determine the output type based on the greater of the two precisions
chtypes = [np.dtype(i.dtype).char for i in inputs]
# Determine if a complex value (uppercase code) exists
if True in [c.isupper() for c in chtypes]: cplx = True
elif np.iscomplex(a) or np.iscomplex(b): cplx = True
else: cplx = False
# Grab the highest precision and render it complex if necessary
otype = sorted(c.lower() for c in chtypes)[0]
if cplx: otype = otype.upper()
# Now create the output file, truncating if necessary
output = mio.Slicer(args[0], inputs[0].shape, np.dtype(otype).type, True)
nslice = output.shape[-1]
# Compute, in parallel, the slice sums
p = multiprocessing.Pool(processes=nproc)
p.map(caxpby,
([i, args[0], a, args[1], b, args[2]] for i in range(nslice)))
return 0
def filtblks(infile, outfile, stdev, pad, bgv, start, stride, chunk):
'''
Open the input file infile, output file outfile (which should exist)
and loop through the input in strides to filter with a Fourier Gaussian
of standard deviation stdev. The input is padded by twice the value pad
and is assumed to have a homogeneous background value bgv.
The arguments start and stride refer to chunks rather than slices.
'''
# Open the files
inmat = mio.Slicer(infile)
outmat = mio.Slicer(outfile)
# Loop through the chunks to process output
for n in range(start * chunk, inmat.shape[-1], stride * chunk):
print('Processing chunk', n)
# Read the chunk
start = max(0, n - pad)
finish = min(n + chunk + pad, inmat.shape[-1])
block = inmat[start:finish]
outblk = np.zeros_like(block)
# Filter the block along the three dimensions successively
gaussian_filter1d(block,
stdev,
axis=0,
output=outblk,
mode='constant',
cval=bgv)
gaussian_filter1d(outblk,
stdev,
axis=1,
output=block,
mode='constant',
cval=bgv)
gaussian_filter1d(outblk,
stdev,
axis=2,
output=outblk,
mode='constant',
cval=bgv)
# Figure out the proper slices of the output block
istart = n - start
iend = min(chunk + istart, istart + outmat.shape[-1] - n)
# Figure out how many slices need to be written
oend = n + iend - istart
# Write the block to output, automatically converting types
outmat[n:oend] = outblk[:, :, istart:iend]
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
progname = sys.argv[0]
# Default values
nrpoc = process.preferred_process_count()
chunk, stdev, pad, bgval = 8, 8, 24, 0.
optlist, args = getopt.getopt(argv, 'p:c:g:b:h')
# Parse the options list
for opt in optlist:
if opt[0] == '-p': nproc = int(opt[1])
elif opt[0] == '-c': chunk = int(opt[1])
elif opt[0] == '-b': bgval = float(opt[1])
elif opt[0] == '-g':
kstr = opt[1].split(',')
pad = int(kstr[0])
stdev = float(kstr[1])
else:
usage(progname)
return 128
# The input and output files must be specified
if len(args) < 2:
usage(progname)
return 128
# Grab the shape of the input file and the number of slices
infile = mio.Slicer(args[0])
# The output file must be created and truncated
outfile = mio.Slicer(args[1], infile.shape, infile.dtype, True)
try:
with process.ProcessPool() as pool:
for n in range(nproc):
args = (args[0], args[1], stdev, pad, bgval, n, nproc, chunk)
pool.addtask(target=filtblks, args=args)
pool.start()
pool.wait()
except:
outfile._backer.truncate(0)
raise
return 0
def caxpby(args):
'''
For an argument list args = (i, z, a, x, b, y) for integer i; matrix
file names z, x, y; and constants a, b; compute
z[i] = a * x[i] + b * y[i].
Nothing is returned.
'''
# Grab the constants from the argument list
i, a, b = args[::2]
# Open the files in the argument list as Slicer objects
z, x, y = [mio.Slicer(arg) for arg in args[1::2]]
# Compute and store the output
z[i] = a * x[i] + b * y[i]
def main (argv = None):
if argv is None:
argv = sys.argv[1:]
progname = sys.argv[0]
perslice = False
try: nproc = multiprocessing.cpu_count()
except NotImplementedError: nproc = 1
optlist, args = getopt.getopt (argv, 'p:sh')
# Parse the options list
for opt in optlist:
if opt[0] == '-s': perslice = True
elif opt[0] == '-p': nproc = int(opt[1])
else:
usage (progname)
return 128
# There must be at least two files to compare
if len(args) < 2:
usage (progname)
return 128
# Make sure that the shapes of all of the files agree
sizes = [mio.Slicer(a).shape for a in args]
for l, r in zip(sizes[:-1], sizes[1:]):
if tuple(l) != tuple(r): raise ValueError('Array sizes must agree')
# Grab the number of slices in the reference file
nslice = sizes[-1][-1]
# Compute, in parallel, the slice difference norms
p = multiprocessing.Pool(processes=nproc)
errs = np.array(p.map(slicerr, (tuple([i] + args) for i in range(nslice))))
# Normalize the slice differences from each file
errs = errs[:,:-1] / la.norm(errs[:,-1])
if perslice:
# Denominator is averaged over all slices for per-slice errors
errs *= math.sqrt(nslice)
for erow in errs: print(' '.join('%-11.6e' % ev for ev in erow))
else:
# Collapse the per-slice errors into a global RMS error
for i, ecol in enumerate(errs.T):
print('%4d %11.6e' % (i, la.norm(ecol)))
return 0
def slicerr(args): ''' For an argument list args = (i, <file1>, [...], <ref>), return the Frobenius error of the differences between slice i in each file and the reference. The last returned value is the Frobenius norm of slice i of the reference. ''' i = args[0] files = [mio.Slicer(a) for a in args[1:]] # Compute the squared magnitude sums df = [la.norm(f[i] - files[-1][i]) for f in files[:-1]] # Add the reference norm df += [la.norm(files[-1][i])] return df
elif opt[0] == '-n': n = int(opt[1])
else:
usage(execname)
sys.exit(128)
if len(args) < 2:
usage(execname)
sys.exit(128)
# Grab the number of samples of the polar angle
nt = int(args[0])
# Compute the polar samples as Gauss-Lobatto nodes or regular samples
theta = harmonic.polararray(nt, not regular)
# Build a generator of cell coordinates
hc = n / 2. + 0.5
coords = itertools.product(dc * np.mgrid[-hc + 1:hc], repeat=3)
# Build the matrix class
f = wavecl.FarMatrix(theta, dc, iord)
print("Building %d-by-%d far-field matrix" % (f.nsamp, n**3))
# Create the output file
output = mio.Slicer(args[1], [f.nsamp, n**3], np.complex64, True)
# Build the matrix row-by-row and write it to output
for i, c in enumerate(coords):
output[i] = f.fillrow(c)
def fhfft(infile, outfile, groupmap, **kwargs):
'''
For a real WaveformSet file infile, perform Hadamard decoding and then
a DFT of the temporal samples. The Hadamard decoding follows the
grouping configuration stored in groupmap, a map
(element index) -> (local Hadamard index, group number)
that defines Hadamard groups and must agree with the local group
configuration represented in the input. The resulting transformed
records will be stored in the output outfile. The nature of outfile
depends on the optional argument trmap (see below).
If trmap is not provided, all records will be written as a binary blob;
the outfile should be a single string providing the location of the
output. The output will have shape Ns x Nt x Nr, where Ns is the number
of output samples per waveform (as governed by the spectral or temporal
windows applied), Nt is the number of input transmit channels, and Nr
is the number of input receive channels.
If trmap is provided, outfile should be a one-to-one map from the keys
of trmap to output files. A WaveformMap object will be created for each
key in trmap and stored at the location indicated by the corresponding
value in outfile.
Output file(s) will be created or truncated.
Any TGC parameters in the input, accessible as wset.context['tgc'],
will be used to adjust the amplitudes of the waveforms prior to
applying Hadamard and Fourier transforms.
The kwargs contain optional values or default overrides:
* freqs (default: None): When not None, a sequence (start, end)
to be passed as slice(start, end) to bandpass filter the input after
Hadamard decoding.
* rolloff (default: None): When not None, an integer that defines the
half-width of a Hann window that rolls off the bandpass filter
specified in freqs. Ignored if freqs is not provided.
* nsamp (default: None): The length of the time window over which
waveforms are considered (and DFTs are performed), starting from
global time 0 (i.e., without consideration for input F2C). If None,
the value of nsamp in the input is used.
** NOTE: Because the time window always starts at global time 0,
a waveform with a data window (start, length) will be cropped when
(f2c + start + length) > nsamp, even if nsamp is the value encoded in
the file.
* tgcsamps (default: 16 [for integer datatypes] or 0 [else]): The
number of temporal samples to which a single TGC parameter applies.
Signals will be scaled by an appropriate section of the multiplier
mpy = (invtgc[:,np.newaxis] *
np.ones((ntgc, tgcsamps), dtype=np.float32)).ravel('C'),
where the values invtgc = 10.**(-wset.context['tgc'] / 20.) and
ntgc = len(wset.context['tgc']). The multiplier mpy is defined over a
window that starts at file sample 0 (global time wset.f2c).
Set tgcsamps to 0 (or None) to disable compensation. If the
WaveformSet includes TGC parameters and tgcsamps is a positive
integer, then len(mpy) must be at least long enough to encompass all
data windows encoded in the file.
* tgcmap (default: None): If provided, should be a two-column, rank-2
Numpy array (or compatible sequence) that relates nominal gains in
column 0 to actual gains in column 1. The rows of the array will be
used as control points in a piecewise linear interpolation (using
numpy.interp) that will map TGC parameters specified in the
WaveformSet file to actual gains. In other words, the TGC values
described above will be replaced with
tgc = np.interp(tgc, tgcmap[:,0], tgcmap[:,1])
whenever tgcmap is provided.
* tdout (default: False): Set to True to output time-domain waveforms
rather than spectral samples. Preserves input acquisition windows.
* signs (default: None): When not None, should be a sequence of length
wset.txgrps.size that specifies a 1 for any local Hadamard index
(corresponding to lines in the file) that should be negated, and 0
anywhere else. Ignored when an FHT is not performed.
* trmap (default: None): If provided, must be a map from a label
(referencing an output location in the map outfile) to a map from
receive indices to lists of transmit indices that, together, identify
transmit-receive pairs to extract from the input.
* start (default: 0) and stride (default: 1): For an input WaveformSet
wset, process receive channels in wset.rxidx[start::stride].
* lock (default: None): If not None, it should be a context manager
that is invoked to serialize writes to output.
* event (default: None): Only used then trmap is not provided. If not
None, event.set() and event.wait() are called to ensure the output
header is written to the binary-blob output before records are
appended. The value event.is_set() should be False prior to
execution.
'''
# Override acquisition window, if desired
nsamp = kwargs.pop('nsamp', None)
# Grab synchronization mechanisms
try:
lock = kwargs.pop('lock')
except KeyError:
lock = multiprocessing.Lock()
try:
event = kwargs.pop('event')
except KeyError:
event = multiprocessing.Event()
# Grab FFT and FHT switches and options
tdout = kwargs.pop('tdout', False)
freqs = kwargs.pop('freqs', None)
rolloff = kwargs.pop('rolloff', None)
dofft = (freqs is not None) or not tdout
if freqs is not None:
flo, fhi = freqs
if rolloff and not 0 < rolloff < (fhi - flo) // 2:
raise ValueError(
'Rolloff must be None or less than half bandwidth')
# Grab striding information
start = kwargs.pop('start', 0)
stride = kwargs.pop('stride', 1)
# Grab sign map information
signs = kwargs.pop('signs', None)
# Grab the number of samples per TGC value and an optional gain map
tgcsamps = kwargs.pop('tgcsamps', None)
tgcmap = kwargs.pop('tgcmap', None)
trmap = kwargs.pop('trmap', None)
if len(kwargs):
raise TypeError(f"Unrecognized keyword '{next(iter(kwargs))}'")
# Open the input and create a corresponding output
wset = WaveformSet.load(infile)
# Pull default sample count from input file
if nsamp is None: nsamp = wset.nsamp
elif wset.nsamp < nsamp: wset.nsamp = nsamp
# Handle TGC compensation if necessary
try:
tgc = np.asarray(wset.context['tgc'], dtype=np.float32)
except (KeyError, AttributeError):
tgc = np.array([], dtype=np.float32)
if tgcmap is not None:
# Make sure that the TGC map is sorted and interpolate
tgx, tgy = zip(*sorted((k, v) for k, v in tgcmap))
# TGC curves are always float32, regardless of tgcmap types
tgc = np.interp(tgc, tgx, tgy).astype(np.float32)
# Pick a suitable default value for tgcsamps
if tgcsamps is None:
tgcsamps = 16 if np.issubdtype(wset.dtype, np.integer) else 0
# Linearize, invert, and expand the TGC curves
tgc = ((10.**(-tgc[:, np.newaxis] / 20.) * np.ones(
(len(tgc), tgcsamps), dtype=np.float32))).ravel('C')
# Figure out the data type of compensated waveforms
if len(tgc): itype = np.dtype(wset.dtype.type(0) * tgc.dtype.type(0))
else: itype = wset.dtype
# Make sure that the data type is always floating-point
if not np.issubdtype(itype, np.floating): itype = np.dtype('float64')
# Create a WaveformSet object to hold the ungrouped data
ftype = _r2c_datatype(itype)
otype = ftype if not tdout else itype
# Make sure the WaveformSet has a local configuration
try:
gcount, gsize = wset.txgrps
except TypeError:
raise ValueError('A valid Tx-group configuration is required')
if gsize < 1 or (gsize & (gsize - 1)):
raise ValueError('Hadamard length must be a positive power of 2')
# Validate local portion of the group map and assign
wset.groupmap = groupmap
if signs is not None:
# Ensure signs has values 0 or 1 in the right type
signs = np.asarray([1 - 2 * s for s in signs], dtype=itype)
if signs.ndim != 1 or len(signs) != gsize:
msg = f'Sign list must have shape ({wset.txgrps[1]},)'
raise ValueError(msg)
# Identify all FHTs represented by stored transmission indices
fhts = {}
for i in wset.txidx:
g, l = i // gsize, i % gsize
try:
fhts[g].append(l)
except KeyError:
fhts[g] = [l]
# Verify that all FHTs are complete
for g, ll in fhts.items():
if len(ll) != gsize:
raise ValueError(f'FHT group {gi} is incomplete')
if any(i != j for i, j in enumerate(sorted(ll))):
raise ValueError(f'FHT group {gi} has improper local indices')
# Map each FHT group to a list of row indices for the FHT
# and each element corresponding to an FHT output to row indices
gidx = lambda l, g: g * gsize + l
fhts = {g: [wset.tx2row(gidx(l, g)) for l in range(gsize)] for g in fhts}
invgroups = {(l, g): i for i, (l, g) in wset.groupmap.items()}
el2row = {
invgroups[l, g]: wset.tx2row(gidx(l, g))
for g in fhts for l in range(gsize)
}
# Create intermediate (FHT) and output (FHFFT) arrays
# FFT axis is contiguous for FFT performance
b = pyfftw.empty_aligned((wset.ntx, nsamp), dtype=itype, order='C')
if dofft:
# Create FFT output and a plan
cdim = (wset.ntx, nsamp // 2 + 1)
c = pyfftw.empty_aligned(cdim, dtype=ftype, order='C')
fwdfft = pyfftw.FFTW(b, c, axes=(1, ), direction='FFTW_FORWARD')
# Create an inverse FFT plan for time-domain output
if tdout:
invfft = pyfftw.FFTW(c, b, axes=(1, ), direction='FFTW_BACKWARD')
# Find the spectral window of interest
fswin = specwin(cdim[1], freqs)
# Try to build bandpass tails
if rolloff: tails = np.hanning(2 * int(rolloff))
else: tails = np.array([])
if trmap:
# Identify the subset of receive channels needed
allrx = reduce(set.union, (trm.keys() for trm in trmap.values()),
set())
rxneeded = sorted(allrx.intersection(wset.rxidx))[start::stride]
else:
rxneeded = wset.rxidx[start::stride]
# In blob mode, the first write must create a header
with lock:
if not event.is_set():
# Create a sliced binary matrix output
windim = (nsamp if tdout else fswin.length, wset.ntx, wset.nrx)
mio.Slicer(outfile, dtype=otype, trunc=True, dim=windim)
event.set()
# Ensure the output header has been written
event.wait()
# Map receive channels to rows (slabs) in the output
rx2slab = dict((i, j) for (j, i) in enumerate(sorted(wset.rxidx)))
# Map transmit channels to decoded FHT rows
outrows = [r for (e, r) in sorted(el2row.items())]
outbin = mio.Slicer(outfile)
for rxc in rxneeded:
# Find the input window relative to 0 f2c
iwin = wset.getheader(rxc).win.shift(wset.f2c)
owin = (0, nsamp)
try:
# Find overlap of global input and output windows
ostart, istart, dlength = cutil.overlap(owin, iwin)
except TypeError:
# Default to 0-length windows at start of acquisition
iwin = Window(0, 0, nonneg=True)
owin = Window(0, 0, nonneg=True)
else:
# Convert input and output windows from global f2c to file f2c
iwin = Window(istart, dlength, nonneg=True)
owin = Window(ostart, dlength, nonneg=True)
# Read the data over the input window
data = wset.getrecord(rxc, window=iwin)[1]
# Clear the data array
b[:, :] = 0.
ws, we = owin.start, owin.end
if iwin.length and gsize > 1:
# Perform grouped Hadamard transforms with optional sign flips
for grp, rows in fhts.items():
# Ensure FHT axis is contiguous for performance
dblk = np.asfortranarray(data[rows, :])
b[rows, ws:we] = fwht(dblk, axes=0) / gsize
if signs is not None: b[rows, ws:we] *= signs[:, np.newaxis]
else: b[:, ws:we] = data
# Time-gain compensation, if necessary
if len(tgc) and iwin.length:
twin = (0, len(tgc))
try:
tstart, istart, dlength = cutil.overlap(twin, iwin)
if dlength != iwin.length: raise ValueError
except (TypeError, ValueError):
raise ValueError(
f'TGC curve does not encompass data for channel {rxc}')
b[:, ws:we] *= tgc[np.newaxis, tstart:tstart + dlength]
if dofft:
fwdfft()
# Suppress content out of the band
c[:, :fswin.start] = 0.
c[:, fswin.end:] = 0.
# Bandpass filter the spectral samples
if len(tails) > 0:
ltails = len(tails) // 2
c[:, fswin.start:fswin.start +
ltails] *= tails[np.newaxis, :ltails]
c[:, fswin.end - ltails:fswin.end] *= tails[np.newaxis,
-ltails:]
# Revert to time-domain representation if necessary
if tdout: invfft()
if not trmap:
# Write the binary blob for this receive channel
orow = rx2slab[rxc]
with lock:
if tdout: outbin[orow] = b[outrows, :].T
else: outbin[orow] = c[outrows, fswin.start:fswin.end].T
# Nothing more to do in blob mode
continue
# Slice desired range from output data
if tdout:
dblock = b[:, ws:we]
dstart = ws
else:
dblock = c[:, fswin.start:fswin.end]
dstart = fswin.start
for label, trm in trmap.items():
# Pull tx list for this tier and rx channel, if possible
try:
tl = trm[rxc]
except KeyError:
tl = []
if not len(tl): continue
# Collect all transmissions for this rx channel
wmap = WaveformMap()
for t in tl:
# Make sure transmission is represented in output
try:
row = el2row[t]
except KeyError:
continue
wave = Waveform(nsamp, dblock[row], dstart)
wmap[t, rxc] = wave
# Flush the waveform map to disk
with lock:
wmap.store(outfile[label], append=True)
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
progname = sys.argv[0]
# Default values
random = True
nproc = process.preferred_process_count()
chunk = 8
optlist, args = getopt.getopt(argv, 'p:nd:s:c:g:h')
# Extra arguments are added as kwargs
kwargs = {}
# Parse the options list
for opt in optlist:
if opt[0] == '-n': random = False
elif opt[0] == '-p': nproc = int(opt[1])
elif opt[0] == '-d': kwargs['scatden'] = float(opt[1])
elif opt[0] == '-s': kwargs['scatsd'] = float(opt[1])
elif opt[0] == '-c': chunk = int(opt[1])
elif opt[0] == '-g':
kstr = opt[1].split(',')
kwargs['smoothp'] = [int(kstr[0]), float(kstr[1])]
else:
usage(progname)
return 128
# The segmentation file and the parameter file must be specified
if len(args) < 5:
usage(progname)
return 128
# Read the tissue parameters
pmat = np.loadtxt(args[1])
# Split the parameters into sound speed, attenuation and density
params = [p.tolist() for p in [pmat[:, :2], pmat[:, 2:4], pmat[:, 4:6]]]
# Eliminate the standard deviation if random scatterers are not desired
if not random: params = [[[p[0], None] for p in pv] for pv in params]
# Grab the shape of the segmentation file and the number of slices
segfile = mio.Slicer(args[0])
# The output files need to be created and truncated
outputs = args[2:]
outfiles = [
mio.Slicer(o, segfile.shape, segfile.dtype, True) for o in outputs
]
try:
with process.ProcessPool() as pool:
for n in range(nproc):
args = (args[0], outputs, params, n, nproc, chunk)
pool.addtask(target=mapblks, args=args, kwargs=kwargs)
pool.start()
pool.wait()
except:
for f in outfiles:
f._backer.truncate(0)
raise
return 0
elif opt[0] == '-i': iord = int(opt[1]) elif opt[0] == '-t': tol = float(opt[1]) elif opt[0] == '-g': gpu = regular = True else: usage(execname) sys.exit(128) if len(args) < 3: usage(execname) sys.exit(128) # Grab the number of interpolated polar samples ntf = int(args[0]) # Create a generator to read the matrix column by column inmat = mio.Slicer(args[1]) # Compute the input number of samples of the polar angle ntc = int(2. + math.sqrt(4. + 0.5 * (inmat.shape[0] - 10.))) # The total number of output samples nsamp = 2 * (ntf - 2)**2 + 2 if not gpu: # Build coarse and fine polar samples thetas = [harmonic.polararray(n, not regular) for n in [ntc, ntf]] # Create the interpolation matrix if iord > 0: a = harmonic.SphericalInterpolator(thetas, iord) else: a = harmonic.HarmonicSpline(thetas, tol) else: a = clinterp.HarmonicSpline(ntc, 2 * (ntc - 2), tol)
