本文整理汇总了Python中scipy.fftpack.next_fast_len方法的典型用法代码示例。如果您正苦于以下问题:Python fftpack.next_fast_len方法的具体用法?Python fftpack.next_fast_len怎么用?Python fftpack.next_fast_len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
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在下文中一共展示了fftpack.next_fast_len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: __init__
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def __init__(self, **kw):
"""
Constructor of affine, equidistant 3d mesh class
ucell : unit cell vectors (in coordinate space)
Ecut : Energy cutoff to parametrize the discretization
"""
from scipy.fftpack import next_fast_len
self.ucell = kw['ucell'] if 'ucell' in kw else 30.0*np.eye(3) # Not even unit cells vectors are required by default
self.Ecut = Ecut = kw['Ecut'] if 'Ecut' in kw else 50.0 # 50.0 Hartree by default
luc = np.sqrt(np.einsum('ix,ix->i', self.ucell, self.ucell))
self.shape = nn = np.array([next_fast_len( int(np.rint(l * np.sqrt(Ecut)/2))) for l in luc], dtype=int)
self.size = np.prod(self.shape)
gc = self.ucell/(nn) # This is probable the best for finite systems, for PBC use nn, not (nn-1)
self.dv = np.abs(np.dot(gc[0], np.cross(gc[1], gc[2] )))
rr = [np.array([gc[i]*j for j in range(nn[i])]) for i in range(3)]
self.rr = rr
self.origin = kw['origin'] if 'origin' in kw else np.zeros(3)
示例2: fast_fft_len
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def fast_fft_len(n):
"""
Returns the smallest integer greater than input such that the fft can
be computed efficiently at this size
Parameters
----------
n : `int`
minimum size
Returns
-------
N : `int`
smallest integer greater than n which permits efficient ffts.
"""
N = next_fast_len(n)
return N if N % 2 == 0 else fast_fft_len(N + 1)
示例3: _fftautocorr
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def _fftautocorr(x):
"""Compute the autocorrelation of a real array and crop the result."""
N = x.shape[-1]
use_N = fftpack.next_fast_len(2*N-1)
x_fft = np.fft.rfft(x, use_N, axis=-1)
cxy = np.fft.irfft(x_fft * x_fft.conj(), n=use_N)[:, :N]
# Or equivalently (but in most cases slower):
# cxy = np.array([np.convolve(xx, yy[::-1], mode='full')
# for xx, yy in zip(x, x)])[:, N-1:2*N-1]
return cxy
示例4: optimal_fft_size
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def optimal_fft_size(target, real = False):
"""Wrapper around scipy function next_fast_len() for calculating optimal FFT padding.
scipy.fft was only added in 1.4.0, so we fall back to scipy.fftpack
if it is not available. The main difference is that next_fast_len()
does not take a second argument in the older implementation.
Parameters
----------
target : int
Length to start searching from. Must be a positive integer.
real : bool, optional
True if the FFT involves real input or output, only available
for scipy > 1.4.0
Returns
-------
int
Optimal FFT size.
"""
try: # pragma: no cover
from scipy.fft import next_fast_len
support_real = True
except ImportError: # pragma: no cover
from scipy.fftpack import next_fast_len
support_real = False
if support_real: # pragma: no cover
return next_fast_len(target, real)
else: # pragma: no cover
return next_fast_len(target)
# Functions used in correlate_library.
示例5: autocov
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def autocov(ary, axis=-1):
"""Compute autocovariance estimates for every lag for the input array.
Parameters
----------
ary : Numpy array
An array containing MCMC samples
Returns
-------
acov: Numpy array same size as the input array
"""
axis = axis if axis > 0 else len(ary.shape) + axis
n = ary.shape[axis]
m = next_fast_len(2 * n)
ary = ary - ary.mean(axis, keepdims=True)
# added to silence tuple warning for a submodule
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ifft_ary = np.fft.rfft(ary, n=m, axis=axis)
ifft_ary *= np.conjugate(ifft_ary)
shape = tuple(
slice(None) if dim_len != axis else slice(0, n) for dim_len, _ in enumerate(ary.shape)
)
cov = np.fft.irfft(ifft_ary, n=m, axis=axis)[shape]
cov /= n
return cov
示例6: shift_data_subpixel
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def shift_data_subpixel(inputs):
''' rigid shift of X by ymax and xmax '''
''' allows subpixel shifts '''
''' ** not being used ** '''
X, ymax, xmax, pad_fft = inputs
ymax = ymax.flatten()
xmax = xmax.flatten()
if X.ndim<3:
X = X[np.newaxis,:,:]
nimg, Ly0, Lx0 = X.shape
if pad_fft:
X = fft2(X.astype('float32'), (next_fast_len(Ly0), next_fast_len(Lx0)))
else:
X = fft2(X.astype('float32'))
nimg, Ly, Lx = X.shape
Ny = fft.ifftshift(np.arange(-np.fix(Ly/2), np.ceil(Ly/2)))
Nx = fft.ifftshift(np.arange(-np.fix(Lx/2), np.ceil(Lx/2)))
[Nx,Ny] = np.meshgrid(Nx,Ny)
Nx = Nx.astype('float32') / Lx
Ny = Ny.astype('float32') / Ly
dph = Nx * np.reshape(xmax, (-1,1,1)) + Ny * np.reshape(ymax, (-1,1,1))
Y = np.real(ifft2(X * np.exp((2j * np.pi) * dph)))
# crop back to original size
if Ly0<Ly or Lx0<Lx:
Lyhalf = int(np.floor(Ly/2))
Lxhalf = int(np.floor(Lx/2))
Y = Y[np.ix_(np.arange(0,nimg,1,int),
np.arange(-np.fix(Ly0/2), np.ceil(Ly0/2),1,int) + Lyhalf,
np.arange(-np.fix(Lx0/2), np.ceil(Lx0/2),1,int) + Lxhalf)]
return Y
示例7: n_fft_samples
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def n_fft_samples(self):
if self._n_fft_samples is None:
self._n_fft_samples = next_fast_len(
self.n_time_samples_per_window)
return self._n_fft_samples
示例8: _auto_correlation
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def _auto_correlation(data, axis=-1):
n_time_samples_per_window = data.shape[axis]
n_fft_samples = next_fast_len(2 * n_time_samples_per_window - 1)
dpss_fft = fft(data, n_fft_samples, axis=axis)
power = dpss_fft * dpss_fft.conj()
return np.real(ifft(power, axis=axis))
示例9: pws
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def pws(cc_array,sampling_rate,power=2,pws_timegate=5.):
'''
Performs phase-weighted stack on array of time series.
Follows methods of Schimmel and Paulssen, 1997.
If s(t) is time series data (seismogram, or cross-correlation),
S(t) = s(t) + i*H(s(t)), where H(s(t)) is Hilbert transform of s(t)
S(t) = s(t) + i*H(s(t)) = A(t)*exp(i*phi(t)), where
A(t) is envelope of s(t) and phi(t) is phase of s(t)
Phase-weighted stack, g(t), is then:
g(t) = 1/N sum j = 1:N s_j(t) * | 1/N sum k = 1:N exp[i * phi_k(t)]|^v
where N is number of traces used, v is sharpness of phase-weighted stack
PARAMETERS:
---------------------
arr: N length array of time series data (numpy.ndarray)
sampling_rate: sampling rate of time series arr (int)
power: exponent for phase stack (int)
pws_timegate: number of seconds to smooth phase stack (float)
RETURNS:
---------------------
weighted: Phase weighted stack of time series data (numpy.ndarray)
Originally written by Tim Clements
Modified by Chengxin Jiang @Harvard
'''
if cc_array.ndim == 1:
print('2D matrix is needed for pws')
return cc_array
N,M = cc_array.shape
# construct analytical signal
analytic = hilbert(cc_array,axis=1, N=next_fast_len(M))[:,:M]
phase = np.angle(analytic)
phase_stack = np.mean(np.exp(1j*phase),axis=0)
phase_stack = np.abs(phase_stack)**(power)
# weighted is the final waveforms
weighted = np.multiply(cc_array,phase_stack)
return np.mean(weighted,axis=0)
示例10: noise_processing
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def noise_processing(fft_para,dataS):
'''
this function performs time domain and frequency domain normalization if needed. in real case, we prefer use include
the normalization in the cross-correaltion steps by selecting coherency or decon (Prieto et al, 2008, 2009; Denolle et al, 2013)
PARMAETERS:
------------------------
fft_para: dictionary containing all useful variables used for fft and cc
dataS: 2D matrix of all segmented noise data
# OUTPUT VARIABLES:
source_white: 2D matrix of data spectra
'''
# load parameters first
time_norm = fft_para['time_norm']
freq_norm = fft_para['freq_norm']
smooth_N = fft_para['smooth_N']
N = dataS.shape[0]
#------to normalize in time or not------
if time_norm != 'no':
if time_norm == 'one_bit': # sign normalization
white = np.sign(dataS)
elif time_norm == 'rma': # running mean: normalization over smoothed absolute average
white = np.zeros(shape=dataS.shape,dtype=dataS.dtype)
for kkk in range(N):
white[kkk,:] = dataS[kkk,:]/moving_ave(np.abs(dataS[kkk,:]),smooth_N)
else: # don't normalize
white = dataS
#-----to whiten or not------
if freq_norm != 'no':
source_white = whiten(white,fft_para) # whiten and return FFT
else:
Nfft = int(next_fast_len(int(dataS.shape[1])))
source_white = scipy.fftpack.fft(white, Nfft, axis=1) # return FFT
return source_white
示例11: pws
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def pws(arr,sampling_rate,power=2,pws_timegate=5.):
'''
Performs phase-weighted stack on array of time series. Modified on the noise function by Tim Climents.
Follows methods of Schimmel and Paulssen, 1997.
If s(t) is time series data (seismogram, or cross-correlation),
S(t) = s(t) + i*H(s(t)), where H(s(t)) is Hilbert transform of s(t)
S(t) = s(t) + i*H(s(t)) = A(t)*exp(i*phi(t)), where
A(t) is envelope of s(t) and phi(t) is phase of s(t)
Phase-weighted stack, g(t), is then:
g(t) = 1/N sum j = 1:N s_j(t) * | 1/N sum k = 1:N exp[i * phi_k(t)]|^v
where N is number of traces used, v is sharpness of phase-weighted stack
PARAMETERS:
---------------------
arr: N length array of time series data (numpy.ndarray)
sampling_rate: sampling rate of time series arr (int)
power: exponent for phase stack (int)
pws_timegate: number of seconds to smooth phase stack (float)
RETURNS:
---------------------
weighted: Phase weighted stack of time series data (numpy.ndarray)
'''
if arr.ndim == 1:
return arr
N,M = arr.shape
analytic = hilbert(arr,axis=1, N=next_fast_len(M))[:,:M]
phase = np.angle(analytic)
phase_stack = np.mean(np.exp(1j*phase),axis=0)
phase_stack = np.abs(phase_stack)**(power)
# smoothing
#timegate_samples = int(pws_timegate * sampling_rate)
#phase_stack = moving_ave(phase_stack,timegate_samples)
weighted = np.multiply(arr,phase_stack)
return np.mean(weighted,axis=0)
示例12: pws
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def pws(arr,sampling_rate,power=2,pws_timegate=5.):
'''
Performs phase-weighted stack on array of time series. Modified on the noise function by Tim Climents.
Follows methods of Schimmel and Paulssen, 1997.
If s(t) is time series data (seismogram, or cross-correlation),
S(t) = s(t) + i*H(s(t)), where H(s(t)) is Hilbert transform of s(t)
S(t) = s(t) + i*H(s(t)) = A(t)*exp(i*phi(t)), where
A(t) is envelope of s(t) and phi(t) is phase of s(t)
Phase-weighted stack, g(t), is then:
g(t) = 1/N sum j = 1:N s_j(t) * | 1/N sum k = 1:N exp[i * phi_k(t)]|^v
where N is number of traces used, v is sharpness of phase-weighted stack
PARAMETERS:
---------------------
arr: N length array of time series data (numpy.ndarray)
sampling_rate: sampling rate of time series arr (int)
power: exponent for phase stack (int)
pws_timegate: number of seconds to smooth phase stack (float)
RETURNS:
---------------------
weighted: Phase weighted stack of time series data (numpy.ndarray)
'''
if arr.ndim == 1:
return arr
N,M = arr.shape
analytic = hilbert(arr,axis=1, N=next_fast_len(M))[:,:M]
phase = np.angle(analytic)
phase_stack = np.mean(np.exp(1j*phase),axis=0)
phase_stack = np.abs(phase_stack)**(power)
# smoothing
#timegate_samples = int(pws_timegate * sampling_rate)
#phase_stack = moving_ave(phase_stack,timegate_samples)
weighted = np.multiply(arr,phase_stack)
return np.mean(weighted,axis=0)
示例13: noise_processing
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def noise_processing(fft_para,dataS):
'''
this function performs time domain and frequency domain normalization if needed. in real case, we prefer use include
the normalization in the cross-correaltion steps by selecting coherency or decon (Prieto et al, 2008, 2009; Denolle et al, 2013)
PARMAETERS:
------------------------
fft_para: dictionary containing all useful variables used for fft and cc
dataS: 2D matrix of all segmented noise data
# OUTPUT VARIABLES:
source_white: 2D matrix of data spectra
'''
# load parameters first
time_norm = fft_para['time_norm']
freq_norm = fft_para['freq_norm']
smooth_N = fft_para['smooth_N']
N = dataS.shape[0]
#------to normalize in time or not------
if time_norm != 'no':
if time_norm == 'one_bit': # sign normalization
white = np.sign(dataS)
elif time_norm == 'rma': # running mean: normalization over smoothed absolute average
white = np.zeros(shape=dataS.shape,dtype=dataS.dtype)
for kkk in range(N):
white[kkk,:] = dataS[kkk,:]/moving_ave(np.abs(dataS[kkk,:]),smooth_N)
else: # don't normalize
white = dataS
#-----to whiten or not------
if freq_norm != 'no':
source_white = whiten(white,fft_para) # whiten and return FFT
else:
Nfft = int(next_fast_len(int(dataS.shape[1])))
source_white = scipy.fftpack.fft(white, Nfft, axis=1) # return FFT
return source_white
示例14: pws
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def pws(arr,power=2.,sampling_rate=20.,pws_timegate = 5.):
"""
Performs phase-weighted stack on array of time series.
Follows methods of Schimmel and Paulssen, 1997.
If s(t) is time series data (seismogram, or cross-correlation),
S(t) = s(t) + i*H(s(t)), where H(s(t)) is Hilbert transform of s(t)
S(t) = s(t) + i*H(s(t)) = A(t)*exp(i*phi(t)), where
A(t) is envelope of s(t) and phi(t) is phase of s(t)
Phase-weighted stack, g(t), is then:
g(t) = 1/N sum j = 1:N s_j(t) * | 1/N sum k = 1:N exp[i * phi_k(t)]|^v
where N is number of traces used, v is sharpness of phase-weighted stack
:type arr: numpy.ndarray
:param arr: N length array of time series data
:type power: float
:param power: exponent for phase stack
:type sampling_rate: float
:param sampling_rate: sampling rate of time series
:type pws_timegate: float
:param pws_timegate: number of seconds to smooth phase stack
:Returns: Phase weighted stack of time series data
:rtype: numpy.ndarray
"""
if arr.ndim == 1:
return arr
N,M = arr.shape
analytic = arr + 1j * hilbert(arr,axis=1, N=next_fast_len(M))[:,:M]
phase = np.angle(analytic)
phase_stack = np.mean(np.exp(1j*phase),axis=0)/N
phase_stack = np.abs(phase_stack)**2
# smoothing
timegate_samples = int(pws_timegate * sampling_rate)
phase_stack = runningMean(phase_stack,timegate_samples)
weighted = np.multiply(arr,phase_stack)
return np.mean(weighted,axis=0)/N
示例15: pws
# 需要导入模块: from scipy import fftpack [as 别名]
# 或者: from scipy.fftpack import next_fast_len [as 别名]
def pws(arr,sampling_rate,power=2,pws_timegate=5.):
"""
Performs phase-weighted stack on array of time series.
Modified on the noise function by Tim Climents.
Follows methods of Schimmel and Paulssen, 1997.
If s(t) is time series data (seismogram, or cross-correlation),
S(t) = s(t) + i*H(s(t)), where H(s(t)) is Hilbert transform of s(t)
S(t) = s(t) + i*H(s(t)) = A(t)*exp(i*phi(t)), where
A(t) is envelope of s(t) and phi(t) is phase of s(t)
Phase-weighted stack, g(t), is then:
g(t) = 1/N sum j = 1:N s_j(t) * | 1/N sum k = 1:N exp[i * phi_k(t)]|^v
where N is number of traces used, v is sharpness of phase-weighted stack
:type arr: numpy.ndarray
:param arr: N length array of time series data
:type power: float
:param power: exponent for phase stack
:type sampling_rate: float
:param sampling_rate: sampling rate of time series
:type pws_timegate: float
:param pws_timegate: number of seconds to smooth phase stack
:Returns: Phase weighted stack of time series data
:rtype: numpy.ndarray
"""
if arr.ndim == 1:
return arr
N,M = arr.shape
analytic = hilbert(arr,axis=1, N=next_fast_len(M))[:,:M]
phase = np.angle(analytic)
phase_stack = np.mean(np.exp(1j*phase),axis=0)
phase_stack = np.abs(phase_stack)**(power)
# smoothing
#timegate_samples = int(pws_timegate * sampling_rate)
#phase_stack = moving_ave(phase_stack,timegate_samples)
weighted = np.multiply(arr,phase_stack)
return np.mean(weighted,axis=0)