def check_definition(self):
x = [1, 2, 3, 4, 1, 2, 3, 4]
x1 = [1, 2 + 3j, 4 + 1j, 2 + 3j, 4, 2 - 3j, 4 - 1j, 2 - 3j]
y = irfft(x)
y1 = direct_irdft(x)
assert_array_almost_equal(y, y1)
assert_array_almost_equal(y, ifft(x1))
x = [1, 2, 3, 4, 1, 2, 3, 4, 5]
x1 = [1, 2 + 3j, 4 + 1j, 2 + 3j, 4 + 5j, 4 - 5j, 2 - 3j, 4 - 1j, 2 - 3j]
y = irfft(x)
y1 = direct_irdft(x)
assert_array_almost_equal(y, y1)
assert_array_almost_equal(y, ifft(x1))
def bench_random(self, level=5):
from numpy.fft import irfft as numpy_irfft
print
print "Inverse Fast Fourier Transform (real data)"
print "=================================="
print " size | scipy | numpy "
print "----------------------------------"
for size, repeat in [
(100, 7000),
(1000, 2000),
(256, 10000),
(512, 10000),
(1024, 1000),
(2048, 1000),
(2048 * 2, 500),
(2048 * 4, 500),
]:
print "%5s" % size,
sys.stdout.flush()
x = random([size]).astype(double)
x1 = zeros(size / 2 + 1, dtype=cdouble)
x1[0] = x[0]
for i in range(1, size / 2):
x1[i] = x[2 * i - 1] + 1j * x[2 * i]
if not size % 2:
x1[-1] = x[-1]
y = irfft(x)
print "|%8.2f" % self.measure("irfft(x)", repeat),
sys.stdout.flush()
assert_array_almost_equal(numpy_irfft(x1, size), y)
print "|%8.2f" % self.measure("numpy_irfft(x1,size)", repeat),
sys.stdout.flush()
print " (secs for %s calls)" % (repeat)
sys.stdout.flush()
def check_random_real(self):
for size in [1, 51, 111, 100, 200, 64, 128, 256, 1024]:
x = random([size]).astype(double)
assert_array_almost_equal(irfft(rfft(x)), x)
assert_array_almost_equal(rfft(irfft(x)), x)
