def test_sdsmooth_fail04(self):
"""test_sdsmooth_fail04 --- outfile exists (overwrite=False)"""
shutil.copytree(self.infile, self.outfile)
self.result = sdsmooth(infile=self.infile,
kernel='gaussian',
outfile=self.outfile,
overwrite=False)
def test000(self):
# testing kwidth from 1 to 5.
for kwidth in range(1,6):
result = sdsmooth(infile=self.infile, outfile=self.outfile,
datacolumn=self.datacolumn, overwrite=True,
kernel='boxcar', kwidth = kwidth)
with sdutil.tbmanager(self.outfile) as tb:
for irow in range(tb.nrows()):
spec = tb.getcell(self.datacolumn.upper(), irow)
for ipol in range(len(spec)):
center = self.centers[str(irow)+str(ipol)]
self._checkResult(spec[ipol], kwidth, center)
def test000(self):
# testing kwidth from 1 to 5.
for kwidth in range(1, 6):
result = sdsmooth(infile=self.infile,
outfile=self.outfile,
datacolumn=self.datacolumn,
overwrite=True,
kernel='boxcar',
kwidth=kwidth)
with sdutil.tbmanager(self.outfile) as tb:
for irow in range(tb.nrows()):
spec = tb.getcell(self.datacolumn.upper(), irow)
for ipol in range(len(spec)):
center = self.centers[str(irow) + str(ipol)]
self._checkResult(spec[ipol], kwidth, center)
def test_sdsmooth_fail05(self):
"""test_sdsmooth_fail05 --- empty outfile"""
self.result = sdsmooth(infile=self.infile, kernel='gaussian', outfile='')
def test_sdsmooth_fail04(self):
"""test_sdsmooth_fail04 --- outfile exists (overwrite=False)"""
shutil.copytree(self.infile, self.outfile)
self.result = sdsmooth(infile=self.infile, kernel='gaussian', outfile=self.outfile, overwrite=False)
def test_sdsmooth_fail03(self):
"""test_sdsmooth_fail03 --- invalid selection (empty selection result)"""
self.result = sdsmooth(infile=self.infile, kernel='gaussian', outfile=self.outfile, spw='3')
def test_sdsmooth_fail02(self):
"""test_sdsmooth_fail02 --- invalid kernel type"""
self.result = sdsmooth(infile=self.infile, kernel='normal', outfile=self.outfile)
def test_sdsmooth_fail01(self):
"""test_sdsmooth_fail01 --- default parameters (raises an error)"""
self.result = sdsmooth()
def test_sdsmooth_fail01(self):
"""test_sdsmooth_fail01 --- default parameters (raises an error)"""
self.result = sdsmooth()
def test_sdsmooth_float_fail01(self):
"""test_sdsmooth_complex_fail01 --- non-existing data column (DATA)"""
self.result = sdsmooth(infile=self.infile,
outfile=self.outfile,
kernel='gaussian',
datacolumn='data')
def test_sdsmooth_fail06(self):
"""test_sdsmooth_fail06 --- invalid data column name"""
self.result = sdsmooth(infile=self.infile,
outfile=self.outfile,
kernel='gaussian',
datacolumn='spectra')
def test_sdsmooth_fail05(self):
"""test_sdsmooth_fail05 --- empty outfile"""
self.result = sdsmooth(infile=self.infile,
kernel='gaussian',
outfile='')
def test_sdsmooth_fail03(self):
"""test_sdsmooth_fail03 --- invalid selection (empty selection result)"""
self.result = sdsmooth(infile=self.infile,
kernel='gaussian',
outfile=self.outfile,
spw='3')
def test_sdsmooth_fail02(self):
"""test_sdsmooth_fail02 --- invalid kernel type"""
self.result = sdsmooth(infile=self.infile,
kernel='normal',
outfile=self.outfile)
def test_sdsmooth_fail06(self):
"""test_sdsmooth_fail06 --- invalid data column name"""
self.result = sdsmooth(infile=self.infile, outfile=self.outfile, kernel='gaussian', datacolumn='spectra')
def run_test(self, sel_param, datacolumn, reindex=True):
inparams = self._get_selection_string(sel_param)
inparams.update(self.common_param)
sdsmooth(datacolumn=datacolumn, reindex=reindex, **inparams)
self._test_result(inparams["outfile"], sel_param, datacolumn)
def test_sdsmooth_float_fail01(self):
"""test_sdsmooth_complex_fail01 --- non-existing data column (DATA)"""
self.result = sdsmooth(infile=self.infile, outfile=self.outfile, kernel='gaussian', datacolumn='data')
def run_test(self, *args, **kwargs):
datacol_name = self.datacolumn.upper()
weight_mode = hasattr(self, 'weight_propagation') and getattr(self, 'weight_propagation') is True
if kwargs.has_key('kwidth'):
kwidth = kwargs['kwidth']
else:
kwidth = 5
self.result = sdsmooth(infile=self.infile, outfile=self.outfile, kernel='gaussian', datacolumn=self.datacolumn, **kwargs)
# sanity check
self.assertIsNone(self.result, msg='The task must complete without error')
self.assertTrue(os.path.exists(self.outfile), msg='Output file is not properly created.')
if kwargs.has_key('spw'):
spw = kwargs['spw']
else:
spw = ''
dd_selection = None
if len(spw) == 0:
expected_nrow = 2
with sdutil.tbmanager(self.infile) as tb:
data_in = tb.getvarcol(datacol_name)
flag_in = tb.getvarcol('FLAG')
if weight_mode is True:
weight_in = tb.getvarcol('WEIGHT_SPECTRUM')
else:
myms = gentools(['ms'])[0]
a = myms.msseltoindex(self.infile, spw=spw)
spw_selection = a['spw']
dd_selection = a['dd']
expected_nrow = len(spw_selection)
with sdutil.tbmanager(self.infile) as tb:
try:
tsel = tb.query('DATA_DESC_ID IN %s'%(dd_selection.tolist()))
data_in = tsel.getvarcol(datacol_name)
flag_in = tsel.getvarcol('FLAG')
if weight_mode is True:
weight_in = tsel.getvarcol('WEIGHT_SPECTRUM')
finally:
tsel.close()
with sdutil.tbmanager(self.outfile) as tb:
nrow = tb.nrows()
data_out = tb.getvarcol(datacol_name)
flag_out = tb.getvarcol('FLAG')
if weight_mode is True:
weight_out = tb.getvarcol('WEIGHT_SPECTRUM')
# verify nrow
self.assertEqual(nrow, expected_nrow, msg='Number of rows mismatch (expected %s actual %s)'%(expected_nrow, nrow))
# verify data
eps = 1.0e-6
for key in data_out.keys():
row_in = data_in[key]
flg_in = flag_in[key]
row_in[numpy.where(flg_in == True)] = 0.0
row_out = data_out[key]
self.assertEqual(row_in.shape, row_out.shape, msg='Shape mismatch in row %s'%(key))
npol, nchan, _ = row_out.shape
kernel_array = gaussian_kernel(nchan, kwidth)
expected = numpy.convolve(row_in[0,:,0], kernel_array, mode='same')
output = row_out[0,:,0]
zero_index = numpy.where(numpy.abs(expected) <= eps)
self.assertTrue(all(numpy.abs(output[zero_index]) < eps), msg='Failed to verify zero values: row %s'%(key))
nonzero_index= numpy.where(numpy.abs(expected) > eps)
diff = numpy.abs((output[nonzero_index] - expected[nonzero_index]) / expected[nonzero_index].max())
#print diff
#print output[nonzero_index]
#print expected[nonzero_index]
self.assertTrue(all(diff < eps), msg='Failed to verify nonzero values: row %s'%(key))
#print 'row_in', row_in[0,:,0].tolist()
#print 'gaussian', kernel_array.tolist()
#print 'expected', expected.tolist()
#print 'result', row_out[0,:,0].tolist()
# weight check if this is weight test
if weight_mode is True:
#print 'Weight propagation test'
wgt_in = weight_in[key]
wgt_out = weight_out[key]
wkwidth = int(kwidth + 0.5)
wkwidth += (1 if wkwidth % 2 == 0 else 0)
half_width = wkwidth / 2
peak_chan = kernel_array.argmax()
start_chan = peak_chan - half_width
wkernel = kernel_array[start_chan:start_chan+wkwidth].copy()
wkernel /= sum(wkernel)
weight_expected = wgt_in.copy()
for ichan in xrange(half_width, nchan-half_width):
s = numpy.zeros(npol, dtype=float)
for jchan in xrange(wkwidth):
s += wkernel[jchan] * wkernel[jchan] / wgt_in[:,ichan-half_width+jchan,0]
weight_expected[:,ichan,0] = 1.0 / s
#print weight_expected[:,:10]
diff = numpy.abs((wgt_out - weight_expected) / weight_expected)
self.assertTrue(all(diff.flatten() < eps), msg='Failed to verify spectral weight: row %s'%(key))
def run_test(self, sel_param, datacolumn):
inparams = self._get_selection_string(sel_param)
inparams.update(self.common_param)
sdsmooth(datacolumn=datacolumn, **inparams)
self._test_result(inparams["outfile"], sel_param, datacolumn)
def run_test(self, *args, **kwargs):
datacol_name = self.datacolumn.upper()
weight_mode = hasattr(self, 'weight_propagation') and getattr(
self, 'weight_propagation') is True
if kwargs.has_key('kwidth'):
kwidth = kwargs['kwidth']
else:
kwidth = 5
self.result = sdsmooth(infile=self.infile,
outfile=self.outfile,
kernel='gaussian',
datacolumn=self.datacolumn,
**kwargs)
# sanity check
self.assertIsNone(self.result,
msg='The task must complete without error')
self.assertTrue(os.path.exists(self.outfile),
msg='Output file is not properly created.')
if kwargs.has_key('spw'):
spw = kwargs['spw']
else:
spw = ''
dd_selection = None
if len(spw) == 0:
expected_nrow = 2
with sdutil.tbmanager(self.infile) as tb:
data_in = tb.getvarcol(datacol_name)
flag_in = tb.getvarcol('FLAG')
if weight_mode is True:
weight_in = tb.getvarcol('WEIGHT_SPECTRUM')
else:
myms = gentools(['ms'])[0]
a = myms.msseltoindex(self.infile, spw=spw)
spw_selection = a['spw']
dd_selection = a['dd']
expected_nrow = len(spw_selection)
with sdutil.tbmanager(self.infile) as tb:
try:
tsel = tb.query('DATA_DESC_ID IN %s' %
(dd_selection.tolist()))
data_in = tsel.getvarcol(datacol_name)
flag_in = tsel.getvarcol('FLAG')
if weight_mode is True:
weight_in = tsel.getvarcol('WEIGHT_SPECTRUM')
finally:
tsel.close()
with sdutil.tbmanager(self.outfile) as tb:
nrow = tb.nrows()
data_out = tb.getvarcol(datacol_name)
flag_out = tb.getvarcol('FLAG')
if weight_mode is True:
weight_out = tb.getvarcol('WEIGHT_SPECTRUM')
# verify nrow
self.assertEqual(
nrow,
expected_nrow,
msg='Number of rows mismatch (expected %s actual %s)' %
(expected_nrow, nrow))
# verify data
eps = 1.0e-6
for key in data_out.keys():
row_in = data_in[key]
flg_in = flag_in[key]
row_in[numpy.where(flg_in == True)] = 0.0
row_out = data_out[key]
self.assertEqual(row_in.shape,
row_out.shape,
msg='Shape mismatch in row %s' % (key))
npol, nchan, _ = row_out.shape
kernel_array = gaussian_kernel(nchan, kwidth)
expected = numpy.convolve(row_in[0, :, 0],
kernel_array,
mode='same')
output = row_out[0, :, 0]
zero_index = numpy.where(numpy.abs(expected) <= eps)
self.assertTrue(all(numpy.abs(output[zero_index]) < eps),
msg='Failed to verify zero values: row %s' % (key))
nonzero_index = numpy.where(numpy.abs(expected) > eps)
diff = numpy.abs(
(output[nonzero_index] - expected[nonzero_index]) /
expected[nonzero_index].max())
#print diff
#print output[nonzero_index]
#print expected[nonzero_index]
self.assertTrue(all(diff < eps),
msg='Failed to verify nonzero values: row %s' %
(key))
#print 'row_in', row_in[0,:,0].tolist()
#print 'gaussian', kernel_array.tolist()
#print 'expected', expected.tolist()
#print 'result', row_out[0,:,0].tolist()
# weight check if this is weight test
if weight_mode is True:
#print 'Weight propagation test'
wgt_in = weight_in[key]
wgt_out = weight_out[key]
wkwidth = int(kwidth + 0.5)
wkwidth += (1 if wkwidth % 2 == 0 else 0)
half_width = wkwidth / 2
peak_chan = kernel_array.argmax()
start_chan = peak_chan - half_width
wkernel = kernel_array[start_chan:start_chan + wkwidth].copy()
wkernel /= sum(wkernel)
weight_expected = wgt_in.copy()
for ichan in xrange(half_width, nchan - half_width):
s = numpy.zeros(npol, dtype=float)
for jchan in xrange(wkwidth):
s += wkernel[jchan] * wkernel[
jchan] / wgt_in[:, ichan - half_width + jchan, 0]
weight_expected[:, ichan, 0] = 1.0 / s
#print weight_expected[:,:10]
diff = numpy.abs((wgt_out - weight_expected) / weight_expected)
self.assertTrue(
all(diff.flatten() < eps),
msg='Failed to verify spectral weight: row %s' % (key))