def test_grid():
def total_elements(dim):
return reduce(lambda x, y: x * y, dim, 1)
num_spectra = 1024 * 64
start_spectrum = 0
end_spectrum = num_spectra
spectrum_mask = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_BOOL, (num_spectra, ))
x = libsakurapy.new_uninitialized_aligned_buffer(libsakurapy.TYPE_DOUBLE,
(num_spectra, ))
y = libsakurapy.new_uninitialized_aligned_buffer(libsakurapy.TYPE_DOUBLE,
(num_spectra, ))
support = 10
sampling = 2
num_polarizations = 4
num_polarizations_for_grid = 2
num_channels = 2048
num_channels_for_grid = 1024
polarization_map = libsakurapy.new_aligned_buffer(
libsakurapy.TYPE_INT32,
map(lambda x: x % num_polarizations_for_grid,
range(num_polarizations)))
channel_map = libsakurapy.new_aligned_buffer(
libsakurapy.TYPE_INT32,
map(lambda x: x % num_channels_for_grid, range(num_channels)))
dim = (num_spectra, num_polarizations, num_channels)
print total_elements(dim)
mask = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_INT8, dim)
mask = libsakurapy.uint8_to_bool(
total_elements(dim), mask,
libsakurapy.new_uninitialized_aligned_buffer(libsakurapy.TYPE_BOOL,
dim))
value = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, dim)
weight = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, (num_spectra, num_channels))
num_convolution_table = int(
math.ceil(math.sqrt(2.) * (support + 1) * sampling))
convolution_table = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, (num_convolution_table, ))
width, height = (160, 100)
weight_sum = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_DOUBLE,
(num_polarizations_for_grid, num_channels_for_grid))
dim = (height, width, num_polarizations_for_grid, num_channels_for_grid)
weight_of_grid = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, dim)
grid = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, dim)
libsakurapy.grid_convolving(
num_spectra, start_spectrum, end_spectrum, spectrum_mask, x, y,
support, sampling, num_polarizations, polarization_map, num_channels,
channel_map, mask, value, weight, False, num_convolution_table,
convolution_table, num_polarizations_for_grid, num_channels_for_grid,
width, height, weight_sum, weight_of_grid, grid)
def test_bit():
# Test operate_bits_uint8_or
mask = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_BOOL,
[True] * 4 + [False] * 4)
data8 = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_INT8,
[0, 2, 1, 3] * 2)
ndata = libsakurapy.get_elements_of_aligned_buffer(data8)[0]
result = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_INT8, (ndata, ))
out = libsakurapy.operate_bits_uint8_or(2, ndata, data8, mask, result)
del mask, data8, result, ndata, out
def test_stats():
try:
libsakurapy.compute_statistics()
except Exception as e:
print e
num = 4
mask = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_BOOL,
(True, ) * num)
data = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, range(num))
result = libsakurapy.compute_statistics(4, data, mask)
print result
del mask
del data
def test_calibration():
ndata = 7
yon = [5.0 for i in range(ndata)]
yoff = [(on - 1.0) for on in yon]
factor = [float(i) for i in range(ndata)]
ondata = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, yon)
offdata = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, yoff)
facdata = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, factor)
result = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, (ndata, ))
out = libsakurapy.apply_position_switch_calibration(
len(factor), facdata, ndata, ondata, offdata, result)
# the result should be [0.0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5]
del yon, yoff, factor, ondata, offdata, facdata, result, out
def test_baseline():
ndata = 8
order = 1
ctxbl = libsakurapy.create_baseline_context(
libsakurapy.BASELINE_TYPE_POLYNOMIAL, order, ndata)
y = [0.5 * i for i in range(ndata)]
m = [True] * ndata
y[4] += 3.
m[4] = False
data = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, y)
mask = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_BOOL, m)
result = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, (ndata, ))
final_mask = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_BOOL, (ndata, ))
out = libsakurapy.subtract_baseline(ndata, data, mask, ctxbl, 5., 1, True,
final_mask, result)
# The result should be [0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0]
del ctxbl
def test_complement():
n = 1024
dim = (n, )
data = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_INT32, dim)
mask = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_BOOL, dim)
lower = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_INT32, (0, ))
upper = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_INT32,
(2100000000, ))
result = libsakurapy.set_true_int_in_ranges_exclusive(
n, data, 1, lower, upper, mask)
del lower, upper, data, result
data = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, dim)
result = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, dim)
libsakurapy.complement_masked_value_float(n, data, mask, result)
del data, mask, result
def test_convolve1D():
ndata = 10
width = 3
y = [0.] * ndata
y[5] = 1.0
ctx1D = libsakurapy.create_convolve1D_context(
ndata, libsakurapy.CONVOLVE1D_KERNEL_TYPE_GAUSSIAN, width, True)
data = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, y)
result = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, (ndata, ))
out = libsakurapy.convolve1D(ctx1D, ndata, data, result)
del ctx1D, data, result, out
def test_interpolate():
# interpolate in Y-axis
nchan = 4
yin = [0., 1.]
yout = [0.75]
zin = [float(6.0)] * nchan + [float(5.0)] * nchan
nbase = len(yin)
npos = len(yout)
order = 1
zindata = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, zin)
yindata = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_DOUBLE, yin)
youtdata = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_DOUBLE, yout)
zoutdata = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, (npos, nchan))
result = libsakurapy.interpolate_float_yaxis(
libsakurapy.INTERPOLATION_METHOD_LINEAR, order, nbase, yindata, nchan,
zindata, npos, youtdata, zoutdata)
# the result should be [5.25]*nchan
del yin, yout, zin, zindata, yindata, youtdata, zoutdata
# interpolate in X-axis
xin = [0., 1.]
xout = [0.25]
nbase = len(xin)
npos = len(xout)
nrow = 3
zin = [float(6.0), float(5.0)] * nrow
order = 1
zindata = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, zin)
xindata = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_DOUBLE, xin)
xoutdata = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_DOUBLE, xout)
zoutdata = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_FLOAT, (npos, nrow))
result = libsakurapy.interpolate_float_xaxis(
libsakurapy.INTERPOLATION_METHOD_LINEAR, order, nbase, xindata, nrow,
zindata, npos, xoutdata, zoutdata)
def test_range():
n = 1024 * 1024 * 16
dim = (n, )
data = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_INT32, dim)
mask = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_BOOL, dim)
lower = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_INT32,
(1, 100, 200))
upper = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_INT32,
(10, 110, 210))
result = libsakurapy.set_true_int_in_ranges_exclusive(
n, data, 3, lower, upper, mask)
del n, dim, data, mask, lower, upper, result
# Test set_true_float_in_ranges_exclusive
dataf = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT,
[0., 2., 1., 3.])
ndata = libsakurapy.get_elements_of_aligned_buffer(dataf)[0]
maskf = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_BOOL,
[True, True, True, True])
lowerf = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, [0.5])
upperf = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_FLOAT, [2.5])
result = libsakurapy.set_true_float_in_ranges_exclusive(
ndata, dataf, 1, lowerf, upperf, maskf)
del dataf, ndata, maskf, lowerf, upperf, result
def test_logical():
n = 1024 * 1024 * 16
dim = (n, )
buf = libsakurapy.new_uninitialized_aligned_buffer(libsakurapy.TYPE_FLOAT,
dim)
bl = libsakurapy.new_uninitialized_aligned_buffer(libsakurapy.TYPE_BOOL,
dim)
libsakurapy.set_false_float_if_nan_or_inf(n, buf, bl)
del buf
del bl
ui8 = libsakurapy.new_uninitialized_aligned_buffer(libsakurapy.TYPE_INT8,
dim)
bl = libsakurapy.new_uninitialized_aligned_buffer(libsakurapy.TYPE_BOOL,
dim)
libsakurapy.uint8_to_bool(n, ui8, bl)
ui32 = libsakurapy.new_uninitialized_aligned_buffer(
libsakurapy.TYPE_INT32, dim)
libsakurapy.uint32_to_bool(n, ui32, bl)
bl2 = libsakurapy.new_uninitialized_aligned_buffer(libsakurapy.TYPE_BOOL,
dim)
libsakurapy.invert_bool(n, bl, bl2)
libsakurapy.invert_bool(n, bl, bl) # in place
del ui8
del ui32
del bl
del bl2
src1 = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_BOOL,
(True, False, True, False))
src2 = libsakurapy.new_aligned_buffer(libsakurapy.TYPE_BOOL,
(True, True, False, False))
dst = libsakurapy.new_uninitialized_aligned_buffer(libsakurapy.TYPE_BOOL,
(4, ))
out = libsakurapy.logical_and(4, src1, src2, dst)