def main():
import sys
if len(sys.argv) <= 1:
print "Usage: example1.py file1.fil [file2.fil ...]"
sys.exit(-1)
filenames = sys.argv[1:]
h_filterbank = read_sigproc(filenames, gulp_nframe=16000, core=0)
h_filterbank = scrunch(h_filterbank, 16, core=0)
d_filterbank = copy(h_filterbank, space='cuda', gpu=0, core=2)
blocks.print_header(d_filterbank)
with bfp.block_scope(core=2, gpu=0):
d_filterbankT = transpose(d_filterbank,
['pol', 'freq', 'time']) #[1,2,0])
d_dispersionbankT = fdmt(d_filterbankT, max_dm=282.52)
blocks.print_header(d_dispersionbankT)
d_dispersionbank = transpose(d_dispersionbankT,
['time', 'pol', 'dispersion']) #[2,0,1])
h_dispersionbank = copy(d_dispersionbank, space='system', core=3)
write_pgm(h_dispersionbank, core=3)
pipeline = bfp.get_default_pipeline()
graph_filename = "example1.dot"
with open(graph_filename, 'w') as dotfile:
dotfile.write(str(pipeline.dot_graph()))
print "Wrote graph definition to", graph_filename
pipeline.run()
print "All done"
def test_null_accumulate(self):
"""Check that accumulating no spans leaves header intact"""
self.shape_settings = [-1, 1, 2]
with bfp.Pipeline() as pipeline:
c_data = blocks.sigproc.read_sigproc([self.fil_file], self.gulp_nframe)
g_data = blocks.copy(c_data, space='cuda')
call_data = CallbackBlock(
g_data, self.check_sequence_before, self.check_data_before)
accumulated = blocks.accumulate(g_data, 1)
call_data = CallbackBlock(
accumulated, self.check_sequence_after, self.check_data_after)
pipeline.run()
def main(args):
h_filterbank = read_sigproc(args.filename, gulp_nframe=16000, core=0)
h_filterbank = scrunch(h_filterbank, 16, core=0)
d_filterbank = copy(h_filterbank, space='cuda', gpu=0, core=2)
blocks.print_header(d_filterbank)
with bfp.block_scope(core=2, gpu=0):
d_filterbankT = transpose(d_filterbank,
['pol', 'freq', 'time']) #[1,2,0])
d_dispersionbankT = fdmt(d_filterbankT, max_dm=282.52)
blocks.print_header(d_dispersionbankT)
d_dispersionbank = transpose(d_dispersionbankT,
['time', 'pol', 'dispersion']) #[2,0,1])
h_dispersionbank = copy(d_dispersionbank, space='system', core=3)
write_pgm(h_dispersionbank, core=3)
pipeline = bfp.get_default_pipeline()
graph_filename = "example1.dot"
with open(graph_filename, 'w') as dotfile:
dotfile.write(str(pipeline.dot_graph()))
print("Wrote graph definition to", graph_filename)
pipeline.run()
print("All done")
b_gup2 = views.split_axis(b_read,
axis='time',
n=n_chunks,
label='time_chunk')
b_gup2 = blocks.transpose(
b_gup2, axes=['time', 'channel', 'time_chunk', 'fine_time', 'pol'])
b_gup2 = views.reverse_scale(b_gup2, 'time_chunk')
b_gup2 = views.merge_axes(b_gup2,
'time_chunk',
'fine_time',
label='fine_time')
blocks.print_header(b_gup2)
# Copy over to GPU and FFT
with bf.block_scope(fuse=True, core=2):
b_copy = blocks.copy(b_gup2, space='cuda')
b_fft = blocks.fft(b_copy, axes='fine_time', axis_labels='freq')
b_ffs = blocks.fftshift(b_fft, axes='freq')
#blocks.print_header(b_fft)
b_pow = blocks.detect(b_ffs, mode='stokes')
b_copp = blocks.copy(b_pow, space='system')
#blocks.print_header(b_copp)
# Flatten channel/freq axis to form output spectra and accumulate
b_copp = blocks.copy(b_copp, space='system', buffer_nframe=4, core=2)
b_flat = views.merge_axes(b_copp, 'channel', 'freq', label='freq')
b_acc = byip.accumulate(b_flat, n_int, core=2, buffer_nframe=4)
#blocks.print_header(b_acc)
b_hdf = byip.hdf_writer(b_acc,
shape=[n_t, n_chan, n_pol],
dtype='float32',
ospan.data.shape,
'i',
'j',
'k',
'l',
a=ispan.data,
b=ospan.data)
def grab_first(iring, axis=0):
return GrabFirstBlock(iring, axis)
with bfp.Pipeline() as pipeline:
raw_guppi = new_read_guppi_raw(
['blc1_guppi_57388_HIP113357_0010.0000.raw'], buffer_nframe=1)
g_guppi = blocks.copy(raw_guppi, space='cuda', buffer_nframe=1)
ffted = blocks.fft(g_guppi,
axes='fine_time',
axis_labels='freq',
buffer_nframe=1)
modulo = blocks.detect(ffted, mode='stokes', buffer_nframe=1)
# Take I
first_element = grab_first(modulo, 0)
transposed = blocks.transpose(first_element,
['channel', 'time', 'pol', 'freq'])
renamed = views.rename_axis(transposed, 'channel', 'beam')
blocks.print_header(renamed)
blocks.write_sigproc(renamed)
pipeline.run()
gulp_nframe=1,
buffer_nframe=4,
core=0)
blocks.print_header(b_read)
# Reshape array for midres FFT
with bf.block_scope(fuse=True, core=1):
b_midres = views.split_axis(b_read,
axis='fine_time',
n=n_fft_midres,
label='fft_window')
b_midres = blocks.transpose(
b_midres, axes=['time', 'fine_time', 'beam', 'fft_window', 'pol'])
b_midres = views.merge_axes(b_midres, 'time', 'fine_time')
b_midres = blocks.copy(b_midres,
space='system',
gulp_nframe=n_gulp_midres)
#blocks.print_header(b_midres)
# Do midres FFT + Detect on GPU
with bf.block_scope(fuse=True, core=3):
b_midres = blocks.copy(b_midres, space='cuda')
b_midres = blocks.FftBlock(b_midres,
axes='fft_window',
axis_labels='freq')
b_midres = blocks.fftshift(b_midres, axes='freq')
b_midres = blocks.detect(b_midres, mode='stokes')
b_midres = blocks.accumulate(b_midres, n_int_midres)
b_midres = blocks.copy(b_midres, space='system')
# Reshape array for lowres FFT
b_gup2 = blocks.transpose(
b_gup2, axes=['time', 'channel', 'time_chunk', 'fine_time', 'pol'])
b_gup2 = views.reverse_scale(b_gup2, 'time_chunk')
b_gup2 = views.merge_axes(b_gup2,
'time_chunk',
'fine_time',
label='fine_time')
blocks.print_header(b_gup2)
#blocks.print_header(b_gup2)
# blocks.print_header(b_gup2)
# Copy over to GPU and FFT
with bf.block_scope(fuse=True, core=3):
b_copy = blocks.copy(b_gup2, space='cuda')
b_fft = blocks.fft(b_copy, axes='fine_time', axis_labels='freq')
b_ffs = blocks.fftshift(b_fft, axes='freq')
#blocks.print_header(b_fft)
b_pow = blocks.detect(b_ffs, mode='stokes')
b_copp = blocks.copy(b_pow, space='system')
#blocks.print_header(b_copp)
# Flatten channel/freq axis to form output spectra and accumulate
b_copp = blocks.copy(b_copp, space='system', buffer_nframe=4)
b_flat = views.merge_axes(b_copp, 'channel', 'freq', label='freq')
b_acc = byip.accumulate(b_flat, n_int, core=4, buffer_nframe=4)
#blocks.print_header(b_acc)
b_hdf = byip.hdf_writer(b_acc,
shape=[n_t, n_chan, n_pol],
dtype='float32',
