本文整理汇总了Python中scipy.fftpack.rfft方法的典型用法代码示例。如果您正苦于以下问题:Python fftpack.rfft方法的具体用法?Python fftpack.rfft怎么用?Python fftpack.rfft使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy.fftpack
的用法示例。
在下文中一共展示了fftpack.rfft方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _centered
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def _centered(arr, newsize):
# Return the center newsize portion of the array.
newsize = numpy.asarray(newsize)
currsize = numpy.array(arr.shape)
startind = (currsize - newsize) // 2
endind = startind + newsize
myslice = [slice(startind[k], endind[k]) for k in range(len(endind))]
return arr[tuple(myslice)]
#def _rfftn(a, s=None, axes=None):
# s, axes = _cook_nd_args(a, s, axes);
# a = fft.rfft(a, s[-1], axes[-1], overwrite_x = True)
# for ii in range(len(axes)-1):
# a = fft.fft(a, s[ii], axes[ii], overwrite_x = True)
# return a
#def _irfftn(a, s=None, axes=None):
# #a = asarray(a).astype('complex64')
# s, axes = _cook_nd_args(a, s, axes, invreal=1)
# for ii in range(len(axes)-1):
# a = fft.ifft(a, s[ii], axes[ii], overwrite_x = True);
# a = fft.ifft(a, s[-1], axes[-1], overwrite_x = True);
# a = a.real;
# return a
示例2: test_size_accuracy
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def test_size_accuracy(self):
# Sanity check for the accuracy for prime and non-prime sized inputs
if self.rdt == np.float32:
rtol = 1e-5
elif self.rdt == np.float64:
rtol = 1e-10
for size in LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES:
np.random.seed(1234)
x = np.random.rand(size).astype(self.rdt)
y = irfft(rfft(x))
self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
(size, self.rdt))
y = rfft(irfft(x))
self.assertTrue(np.linalg.norm(x - y) < rtol*np.linalg.norm(x),
(size, self.rdt))
# self.ndec is bogus; we should have a assert_array_approx_equal for number of
# significant digits
示例3: stft
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def stft(X, fftsize=128, step="half", mean_normalize=True, real=False,
compute_onesided=True):
"""
Compute STFT for 1D real valued input X
"""
if real:
local_fft = fftpack.rfft
cut = -1
else:
local_fft = fftpack.fft
cut = None
if compute_onesided:
cut = fftsize // 2 + 1
if mean_normalize:
X -= X.mean()
if step == "half":
X = halfoverlap(X, fftsize)
else:
X = overlap(X, fftsize, step)
size = fftsize
win = 0.54 - .46 * np.cos(2 * np.pi * np.arange(size) / (size - 1))
X = X * win[None]
X = local_fft(X)[:, :cut]
return X
示例4: run_mgc_example
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def run_mgc_example():
import matplotlib.pyplot as plt
fs, x = wavfile.read("test16k.wav")
pos = 3000
fftlen = 1024
win = np.blackman(fftlen) / np.sqrt(np.sum(np.blackman(fftlen) ** 2))
xw = x[pos:pos + fftlen] * win
sp = 20 * np.log10(np.abs(np.fft.rfft(xw)))
mgc_order = 20
mgc_alpha = 0.41
mgc_gamma = -0.35
mgc_arr = win2mgc(xw, order=mgc_order, alpha=mgc_alpha, gamma=mgc_gamma, verbose=True)
xwsp = 20 * np.log10(np.abs(np.fft.rfft(xw)))
sp = mgc2sp(mgc_arr, mgc_alpha, mgc_gamma, fftlen)
plt.plot(xwsp)
plt.plot(20. / np.log(10) * np.real(sp), "r")
plt.xlim(1, len(xwsp))
plt.show()
示例5: stft
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def stft(X, fftsize=128, step="half", mean_normalize=True, real=False,
compute_onesided=True):
"""
Compute STFT for 1D real valued input X
"""
if real:
local_fft = fftpack.rfft
cut = -1
else:
local_fft = fftpack.fft
cut = None
if compute_onesided:
cut = fftsize // 2
if mean_normalize:
X -= X.mean()
if step == "half":
X = halfoverlap(X, fftsize)
else:
X = overlap(X, fftsize, step)
size = fftsize
win = 0.54 - .46 * np.cos(2 * np.pi * np.arange(size) / (size - 1))
X = X * win[None]
X = local_fft(X)[:, :cut]
return X
示例6: bench_random
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def bench_random(self):
from numpy.fft import rfft as numpy_rfft
print()
print('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, end=' ')
sys.stdout.flush()
x = random([size]).astype(double)
print('|%8.2f' % measure('rfft(x)',repeat), end=' ')
sys.stdout.flush()
print('|%8.2f' % measure('numpy_rfft(x)',repeat), end=' ')
sys.stdout.flush()
print(' (secs for %s calls)' % (repeat))
sys.stdout.flush()
示例7: test_definition
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def test_definition(self):
for t in [[1, 2, 3, 4, 1, 2, 3, 4], [1, 2, 3, 4, 1, 2, 3, 4, 5]]:
x = np.array(t, dtype=self.rdt)
y = rfft(x)
y1 = direct_rdft(x)
assert_array_almost_equal(y,y1)
self.assertTrue(y.dtype == self.rdt,
"Output dtype is %s, expected %s" % (y.dtype, self.rdt))
示例8: test_random_real
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def test_random_real(self):
for size in [1,51,111,100,200,64,128,256,1024]:
x = random([size]).astype(self.rdt)
y1 = irfft(rfft(x))
y2 = rfft(irfft(x))
self.assertTrue(y1.dtype == self.rdt,
"Output dtype is %s, expected %s" % (y1.dtype, self.rdt))
self.assertTrue(y2.dtype == self.rdt,
"Output dtype is %s, expected %s" % (y2.dtype, self.rdt))
assert_array_almost_equal(y1, x, decimal=self.ndec,
err_msg="size=%d" % size)
assert_array_almost_equal(y2, x, decimal=self.ndec,
err_msg="size=%d" % size)
示例9: test_rfft
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def test_rfft(self):
overwritable = self.real_dtypes
for dtype in self.real_dtypes:
self._check_1d(rfft, dtype, (16,), -1, overwritable)
self._check_1d(rfft, dtype, (16, 2), 0, overwritable)
self._check_1d(rfft, dtype, (2, 16), 1, overwritable)
示例10: test_definition
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def test_definition(self):
for t in [[1, 2, 3, 4, 1, 2, 3, 4], [1, 2, 3, 4, 1, 2, 3, 4, 5]]:
x = np.array(t, dtype=self.rdt)
y = rfft(x)
y1 = direct_rdft(x)
assert_array_almost_equal(y,y1)
assert_equal(y.dtype, self.rdt)
示例11: test_invalid_sizes
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def test_invalid_sizes(self):
assert_raises(ValueError, rfft, [])
assert_raises(ValueError, rfft, [[1,1],[2,2]], -5)
# See gh-5790
示例12: test_non_ndarray_with_dtype
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def test_non_ndarray_with_dtype(self):
x = np.array([1., 2., 3., 4., 5.])
xs = _TestRFFTBase.MockSeries(x)
expected = [1, 2, 3, 4, 5]
out = rfft(xs)
# Data should not have been overwritten
assert_equal(x, expected)
assert_equal(xs.data, expected)
示例13: test_random_real
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def test_random_real(self):
for size in [1,51,111,100,200,64,128,256,1024]:
x = random([size]).astype(self.rdt)
y1 = irfft(rfft(x))
y2 = rfft(irfft(x))
assert_equal(y1.dtype, self.rdt)
assert_equal(y2.dtype, self.rdt)
assert_array_almost_equal(y1, x, decimal=self.ndec,
err_msg="size=%d" % size)
assert_array_almost_equal(y2, x, decimal=self.ndec,
err_msg="size=%d" % size)
示例14: test_size_accuracy
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def test_size_accuracy(self):
# Sanity check for the accuracy for prime and non-prime sized inputs
if self.rdt == np.float32:
rtol = 1e-5
elif self.rdt == np.float64:
rtol = 1e-10
for size in LARGE_COMPOSITE_SIZES + LARGE_PRIME_SIZES:
np.random.seed(1234)
x = np.random.rand(size).astype(self.rdt)
y = irfft(rfft(x))
_assert_close_in_norm(x, y, rtol, size, self.rdt)
y = rfft(irfft(x))
_assert_close_in_norm(x, y, rtol, size, self.rdt)
示例15: stft
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import rfft [as 别名]
def stft(X, windowsize=None, fftsize=None, step="half", mean_normalize=True, real=False,
window_type="hann", periodic=True, compute_onesided=True):
"""
Compute STFT for 1D real valued input X
"""
if real:
raise ValueError("real=True needs debug")
local_fft = fftpack.rfft
cut = None
else:
local_fft = fftpack.fft
cut = None
if fftsize == None:
assert windowsize is not None
enclosing_fftsize = int(2 ** np.ceil(np.log(windowsize) / np.log(2.0)))
fftsize = enclosing_fftsize
else:
windowsize = fftsize
if compute_onesided or real:
cut = fftsize // 2 + 1
if mean_normalize:
X -= X.mean()
if step == "half":
X = halfoverlap(X, windowsize)
else:
X = overlap(X, windowsize, step)
size = fftsize
if window_type == "hann" and periodic:
win = _raised_cosine_window(size, True, 0.5, 0.5)
else:
raise ValueError("No other windows currently supported")
#win = 0.54 - .46 * np.cos(2 * np.pi * np.arange(size) / (size - 1))
#win = 0.54 - .46 * np.cos(2 * np.pi * np.arange(size) / (size - 1))
X = X * win[None]
X = local_fft(X.astype(np.float64))[:, :cut]
return X