The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def operate(self, x):
"""
Apply the separable filter to the signal vector *x*.
"""
X = NP.fft.fftn(x, s=self.k_full)
if NP.isrealobj(self.h) and NP.isrealobj(x):
y = NP.real(NP.fft.ifftn(self.H * X))
else:
y = NP.fft.ifftn(self.H * X)
if self.mode == 'full' or self.mode == 'circ':
return y
elif self.mode == 'valid':
slice_list = []
for i in range(self.ndim):
if self.m[i]-1 == 0:
slice_list.append(slice(None, None, None))
else:
slice_list.append(slice(self.m[i]-1, -(self.m[i]-1), None))
return y[slice_list]
else:
assert(False) Example 2
def correlate_periodic(a, v=None):
"""Cross-correlation of two 1-dimensional periodic sequences.
a and v must be sequences with the same length. If v is not specified, it is
assumed to be the same as a (i.e. the function computes auto-correlation).
:param a: input sequence #1
:param v: input sequence #2
:returns: discrete periodic cross-correlation of a and v
"""
a_fft = _np.fft.fft(_np.asarray(a))
if v is None:
v_cfft = a_fft.conj()
else:
v_cfft = _np.fft.fft(_np.asarray(v)).conj()
x = _np.fft.ifft(a_fft * v_cfft)
if _np.isrealobj(a) and (v is None or _np.isrealobj(v)):
x = x.real
return x Example 3
def inverse(self, encoded, duration=None):
'''Inverse static tag transformation'''
ann = jams.Annotation(namespace=self.namespace, duration=duration)
if np.isrealobj(encoded):
detected = (encoded >= 0.5)
else:
detected = encoded
for vd in self.encoder.inverse_transform(np.atleast_2d(detected))[0]:
vid = np.flatnonzero(self.encoder.transform(np.atleast_2d(vd)))
ann.append(time=0,
duration=duration,
value=vd,
confidence=encoded[vid])
return ann Example 4
def decode_events(self, encoded):
'''Decode labeled events into (time, value) pairs
Parameters
----------
encoded : np.ndarray, shape=(n_frames, m)
Frame-level annotation encodings as produced by ``encode_events``.
Real-valued inputs are thresholded at 0.5.
Returns
-------
[(time, value)] : iterable of tuples
where `time` is the event time and `value` is an
np.ndarray, shape=(m,) of the encoded value at that time
'''
if np.isrealobj(encoded):
encoded = (encoded >= 0.5)
times = frames_to_time(np.arange(encoded.shape[0]),
sr=self.sr,
hop_length=self.hop_length)
return zip(times, encoded) Example 5
def atal(x, order, num_coefs):
x = np.atleast_1d(x)
n = x.size
if x.ndim > 1:
raise ValueError("Only rank 1 input supported for now.")
if not np.isrealobj(x):
raise ValueError("Only real input supported for now.")
a, e, kk = lpc(x, order)
c = np.zeros(num_coefs)
c[0] = a[0]
for m in range(1, order+1):
c[m] = - a[m]
for k in range(1, m):
c[m] += (float(k)/float(m)-1)*a[k]*c[m-k]
for m in range(order+1, num_coefs):
for k in range(1, order+1):
c[m] += (float(k)/float(m)-1)*a[k]*c[m-k]
return c Example 6
def test_poly(self):
assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
[1, -3, -2, 6])
# From matlab docs
A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
# Should produce real output for perfect conjugates
assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
1-2j, 1.+3.5j, 1-3.5j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
assert_(np.isrealobj(np.poly([1j, -1j])))
assert_(np.isrealobj(np.poly([1, -1])))
assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
np.random.seed(42)
a = np.random.randn(100) + 1j*np.random.randn(100)
assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) Example 7
def polyval(self, chebcoeff):
"""
Compute the interpolation values at Chebyshev points.
chebcoeff: Chebyshev coefficients
"""
N = len(chebcoeff)
if N == 1:
return chebcoeff
data = even_data(chebcoeff)/2
data[0] *= 2
data[N-1] *= 2
fftdata = 2*(N-1)*fftpack.ifft(data, axis=0)
complex_values = fftdata[:N]
# convert to real if input was real
if np.isrealobj(chebcoeff):
values = np.real(complex_values)
else:
values = complex_values
return values Example 8
def dct(data):
"""
Compute DCT using FFT
"""
N = len(data)//2
fftdata = fftpack.fft(data, axis=0)[:N+1]
fftdata /= N
fftdata[0] /= 2.
fftdata[-1] /= 2.
if np.isrealobj(data):
data = np.real(fftdata)
else:
data = fftdata
return data
# ----------------------------------------------------------------
# Add overloaded operators
# ---------------------------------------------------------------- Example 9
def isreal(self):
"""Returns True if entire signal is real."""
return np.all(np.isreal(self._ydata))
# return np.isrealobj(self._ydata) Example 10
def fftconv(a, b, axes=(0,1)):
"""
Compute a multi-dimensional convolution via the Discrete Fourier Transform.
Parameters
----------
a : array_like
Input array
b : array_like
Input array
axes : sequence of ints, optional (default (0,1))
Axes on which to perform convolution
Returns
-------
ab : ndarray
Convolution of input arrays, a and b, along specified axes
"""
if np.isrealobj(a) and np.isrealobj(b):
fft = rfftn
ifft = irfftn
else:
fft = fftn
ifft = ifftn
dims = np.maximum([a.shape[i] for i in axes], [b.shape[i] for i in axes])
af = fft(a, dims, axes)
bf = fft(b, dims, axes)
return ifft(af*bf, dims, axes) Example 11
def evaluate(self, ind, **kwargs):
"""
Note that math functions used in the solutions are imported from either
utilities.fitness.math_functions or called from numpy.
:param ind: An individual to be evaluated.
:param kwargs: An optional parameter for problems with training/test
data. Specifies the distribution (i.e. training or test) upon which
evaluation is to be performed.
:return: The fitness of the evaluated individual.
"""
dist = kwargs.get('dist', 'training')
if dist == "training":
# Set training datasets.
x = self.training_in
y = self.training_exp
elif dist == "test":
# Set test datasets.
x = self.test_in
y = self.test_exp
else:
raise ValueError("Unknown dist: " + dist)
if params['OPTIMIZE_CONSTANTS']:
# if we are training, then optimize the constants by
# gradient descent and save the resulting phenotype
# string as ind.phenotype_with_c0123 (eg x[0] +
# c[0] * x[1]**c[1]) and values for constants as
# ind.opt_consts (eg (0.5, 0.7). Later, when testing,
# use the saved string and constants to evaluate.
if dist == "training":
return optimize_constants(x, y, ind)
else:
# this string has been created during training
phen = ind.phenotype_consec_consts
c = ind.opt_consts
# phen will refer to x (ie test_in), and possibly to c
yhat = eval(phen)
assert np.isrealobj(yhat)
# let's always call the error function with the
# true values first, the estimate second
return params['ERROR_METRIC'](y, yhat)
else:
# phenotype won't refer to C
yhat = eval(ind.phenotype)
assert np.isrealobj(yhat)
# let's always call the error function with the true
# values first, the estimate second
return params['ERROR_METRIC'](y, yhat) Example 12
def periodogram(x, nfft=None, fs=1):
"""Compute the periodogram of the given signal, with the given fft size.
Parameters
----------
x : array-like
input signal
nfft : int
size of the fft to compute the periodogram. If None (default), the
length of the signal is used. if nfft > n, the signal is 0 padded.
fs : float
Sampling rate. By default, is 1 (normalized frequency. e.g. 0.5 is the
Nyquist limit).
Returns
-------
pxx : array-like
The psd estimate.
fgrid : array-like
Frequency grid over which the periodogram was estimated.
Examples
--------
Generate a signal with two sinusoids, and compute its periodogram:
>>> fs = 1000
>>> x = np.sin(2 * np.pi * 0.1 * fs * np.linspace(0, 0.5, 0.5*fs))
>>> x += np.sin(2 * np.pi * 0.2 * fs * np.linspace(0, 0.5, 0.5*fs))
>>> px, fx = periodogram(x, 512, fs)
Notes
-----
Only real signals supported for now.
Returns the one-sided version of the periodogram.
Discrepency with matlab: matlab compute the psd in unit of power / radian /
sample, and we compute the psd in unit of power / sample: to get the same
result as matlab, just multiply the result from talkbox by 2pi"""
x = np.atleast_1d(x)
n = x.size
if x.ndim > 1:
raise ValueError("Only rank 1 input supported for now.")
if not np.isrealobj(x):
raise ValueError("Only real input supported for now.")
if not nfft:
nfft = n
if nfft < n:
raise ValueError("nfft < signal size not supported yet")
pxx = np.abs(fft(x, nfft)) ** 2
if nfft % 2 == 0:
pn = nfft / 2 + 1
else:
pn = (nfft + 1 )/ 2
fgrid = np.linspace(0, fs * 0.5, pn)
return pxx[:pn] / (n * fs), fgrid Example 13
def arspec(x, order, nfft=None, fs=1):
"""Compute the spectral density using an AR model.
An AR model of the signal is estimated through the Yule-Walker equations;
the estimated AR coefficient are then used to compute the spectrum, which
can be computed explicitely for AR models.
Parameters
----------
x : array-like
input signal
order : int
Order of the LPC computation.
nfft : int
size of the fft to compute the periodogram. If None (default), the
length of the signal is used. if nfft > n, the signal is 0 padded.
fs : float
Sampling rate. By default, is 1 (normalized frequency. e.g. 0.5 is the
Nyquist limit).
Returns
-------
pxx : array-like
The psd estimate.
fgrid : array-like
Frequency grid over which the periodogram was estimated.
"""
x = np.atleast_1d(x)
n = x.size
if x.ndim > 1:
raise ValueError("Only rank 1 input supported for now.")
if not np.isrealobj(x):
raise ValueError("Only real input supported for now.")
if not nfft:
nfft = n
a, e, k = lpc(x, order)
# This is not enough to deal correctly with even/odd size
if nfft % 2 == 0:
pn = nfft / 2 + 1
else:
pn = (nfft + 1 )/ 2
px = 1 / np.fft.fft(a, nfft)[:pn]
pxx = np.real(np.conj(px) * px)
pxx /= fs / e
fx = np.linspace(0, fs * 0.5, pxx.size)
return pxx, fx Example 14
def _write_raw_buffer(fid, buf, cals, fmt, inv_comp):
"""Write raw buffer
Parameters
----------
fid : file descriptor
an open raw data file.
buf : array
The buffer to write.
cals : array
Calibration factors.
fmt : str
'short', 'int', 'single', or 'double' for 16/32 bit int or 32/64 bit
float for each item. This will be doubled for complex datatypes. Note
that short and int formats cannot be used for complex data.
inv_comp : array | None
The CTF compensation matrix used to revert compensation
change when reading.
"""
if buf.shape[0] != len(cals):
raise ValueError('buffer and calibration sizes do not match')
if fmt not in ['short', 'int', 'single', 'double']:
raise ValueError('fmt must be "short", "single", or "double"')
if np.isrealobj(buf):
if fmt == 'short':
write_function = write_dau_pack16
elif fmt == 'int':
write_function = write_int
elif fmt == 'single':
write_function = write_float
else:
write_function = write_double
else:
if fmt == 'single':
write_function = write_complex64
elif fmt == 'double':
write_function = write_complex128
else:
raise ValueError('only "single" and "double" supported for '
'writing complex data')
if inv_comp is not None:
buf = np.dot(inv_comp / np.ravel(cals)[:, None], buf)
else:
buf = buf / np.ravel(cals)[:, None]
write_function(fid, FIFF.FIFF_DATA_BUFFER, buf) 