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Example 1
def M(self):
"""Returns the :math:`M` matrix of integers that determine points at which the
functions are sampled in the unit cell.
Examples:
For `S = [2, 2, 1]`, the returned matrix is:
.. code-block:: python
np.ndarray([[0,0,0],
[1,0,0],
[0,1,0],
[1,1,0]], dtype=int)
"""
if self._M is None:
ms = np.arange(np.prod(self.S, dtype=int))
m1 = np.fmod(ms, self.S[0])
m2 = np.fmod(np.floor(ms/self.S[0]), self.S[1])
m3 = np.fmod(np.floor(ms/(self.S[0]*self.S[1])), self.S[2])
#Make sure we explicitly use an integer array; it's faster.
self._M = np.asarray(np.vstack((m1, m2, m3)).T, dtype=int)
return self._M Example 2
def setRotation(self, rot, smallangle=True):
'''
Rotation angle in degrees
'''
rad = np.deg2rad(rot)
if smallangle:
# bring rad close to zero.
rad = np.fmod(rad, 2.*pi)
if rad > pi:
rad -= 2.*pi
if rad < -pi:
rad += 2.*pi
self.T = [ 0., -rad, rad, 0. ]
else:
cr = np.cos(rad)
sr = np.sin(rad)
self.T = [ cr - 1, -sr, sr, cr - 1 ] Example 3
def single_spectrogram(inseq,fs,wlen,h,imag=False):
"""
imag: Return Imaginary Data of the STFT on True
"""
NFFT = int(2**(np.ceil(np.log2(wlen))))
K = np.sum(hamming(wlen, False))/wlen
raw_data = inseq.astype('float32')
raw_data = raw_data/np.amax(np.absolute(raw_data))
stft_data,_,_ = STFT(raw_data,wlen,h,NFFT,fs)
s = np.absolute(stft_data)/wlen/K;
if np.fmod(NFFT,2):
s[1:,:] *=2
else:
s[1:-2] *=2
real_data = np.transpose(20*np.log10(s + 10**-6)).astype(np.float32)
if imag:
imag_data = np.angle(stft_data).astype(np.float32)
return real_data,imag_data
return real_data Example 4
def dataistft(realdata,imgdata,fs,wlen,h):
nfft = int(2**(np.ceil(np.log2(wlen))))
K = np.sum(hamming(wlen, False))/wlen
realdata = np.power(20,realdata/20) - 1e-6
if np.fmod(nfft,2):
realdata[1:-1,:] /=2
else:
realdata[1:-2,:] /=2
realdata *= wlen*K
prewav = realdata.transpose()*np.exp(1j*imgdata)
istft_data,_ = ISTFT(prewav, h, nfft, fs)
max_dt = np.abs(istft_data).max()
istft_data /= max_dt
return istft_data Example 5
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b) Example 6
def func(func, minkind=None, maxkind=None):
@ophelper
def function(*args):
if allConstantNodes(args):
return ConstantNode(func(*[x.value for x in args]))
kind = commonKind(args)
if kind in ('int', 'long'):
# Exception for following NumPy casting rules
#FIXME: this is not always desirable. The following
# functions which return ints (for int inputs) on numpy
# but not on numexpr: copy, abs, fmod, ones_like
kind = 'double'
else:
# Apply regular casting rules
if minkind and kind_rank.index(minkind) > kind_rank.index(kind):
kind = minkind
if maxkind and kind_rank.index(maxkind) < kind_rank.index(kind):
kind = maxkind
return FuncNode(func.__name__, args, kind)
return function Example 7
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b) Example 8
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b) Example 9
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b) Example 10
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b) Example 11
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b) Example 12
def fmodulo(x1: Number = 1.0, x2: Number = 1.0) -> Float:
return np.fmod(x1, x2) Example 13
def calculate_bin_indices(
self, tstart, tsamp, data_size):
"""Calculate the bin that each time sample should be
added to
@param[in] tstart Time of the first element (s)
@param[in] tsamp Difference between the times of
consecutive elements (s)
@param[in] data_size Number of elements
@return Which bin each sample is folded into
"""
arrival_time = tstart + tsamp * np.arange(data_size)
phase = np.fmod(arrival_time, self.period)
return np.floor(phase / self.period * self.bins).astype(int) Example 14
def dihedral_angle(a, b, c, d):
"""
Calculate the dihedral angle between 4 vectors,
representing 4 connected points. The angle is in range [-180, 180].
@param a: the four points that define the dihedral angle
@type a: array
@return: angle in [-180, 180]
"""
v = b - c
m = numpy.cross((a - b), v)
m /= norm(m)
n = numpy.cross((d - c), v)
n /= norm(n)
c = numpy.dot(m, n)
s = numpy.dot(numpy.cross(n, m), v) / norm(v)
angle = math.degrees(math.atan2(s, c))
if angle > 0:
return numpy.fmod(angle + 180, 360) - 180
else:
return numpy.fmod(angle - 180, 360) + 180 Example 15
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b) Example 16
def compute_zero_padding_values(self, number):
"""During zero padding, we want to fill zeros before and after signal.
This function computes the number of zeros"""
number_of_zeros_before_signal = np.floor(number / 2)
if np.fmod(number, 2) == 1:
number_of_zeros_after_signal = number_of_zeros_before_signal + 1
else:
number_of_zeros_after_signal = number_of_zeros_before_signal
return number_of_zeros_before_signal, number_of_zeros_after_signal Example 17
def wav_to_image(filename, wlen, mindata, maxdata, save=False, name_save=None, ):
h = wlen/4
K = np.sum(hamming(wlen, False))/wlen
nfft = int(2**(np.ceil(np.log2(wlen))))
Fs, data_seq = wavfile.read(filename)
raw_data = data_seq.astype('float32')
max_dt = np.amax(np.absolute(raw_data))
raw_data = raw_data/max_dt
stft_data,_,_ = STFT(raw_data,wlen,h,nfft,Fs)
s = abs(stft_data)/wlen/K;
if np.fmod(nfft,2):
s[1:,:] *=2
else:
s[1:-2] *=2
data_temp = 20*np.log10(s + 10**-6)
outdata = data_temp.transpose()
"""Scaling"""
mindata = np.amin(outdata, axis=0, keepdims = True)
maxdata = np.amax(outdata, axis=0, keepdims = True)
outdata -=mindata
outdata /=(maxdata-mindata)
outdata *=0.8
outdata +=0.1
figmin = np.zeros((5,outdata.shape[1]))
figmax = np.ones((5,outdata.shape[1]))
outdata = np.concatenate((outdata,figmin,figmax), axis=0)
dpi = 96
a = float(outdata.shape[0])/dpi
b = float(outdata.shape[1])/dpi
f = plt.figure(figsize=(b,a), dpi=dpi)
f.figimage(outdata)
if save:
f.savefig(name_save, dpi=f.dpi)
return f Example 18
def ISTFT(data, h, nfft, fs):
# function: [x, t] = istft(stft, h, nfft, fs)
# stft - STFT matrix (only unique points, time across columns, freq across rows)
# h - hop size
# nfft - number of FFT points
# fs - sampling frequency, Hz
# x - signal in the time domain
# t - time vector, s
# estimate the length of the signal
coln = data.shape[1]
xlen = nfft + (coln-1)*h
x = np.zeros((xlen,))
# form a periodic hamming window
win = hamming(nfft, False)
# perform IFFT and weighted-OLA
if np.fmod(nfft,2):
lst_idx = -1
else:
lst_idx = -2
for b in range (0, h*(coln-1),h):
# extract FFT points
X = data[:,1+b/h]
X = np.concatenate((X, np.conjugate(X[lst_idx:0:-1])))
# IFFT
xprim = np.real(np.fft.ifft(X))
# weighted-OLA
x[b:b+nfft] = x[b:b+nfft] + np.transpose(xprim*win)
W0 = np.sum(win*win)
x *= h/W0
# calculate the time vector
actxlen = x.shape[0]
t = np.arange(0,actxlen-1,dtype=np.float32)/fs
return x, t Example 19
def constrainAngle( self, x):
x = np.fmod( x+180, 360)
if x < 0:
x+= 360
return x-180 Example 20
def constrainAngle( self, x):
x = np.fmod( x+180, 360)
if x < 0:
x+= 360
return x-180 Example 21
def constrainAngle( self, x):
x = np.fmod( x+180, 360)
if x < 0:
x+= 360
return x-180 Example 22
def normaliseAngle(value):
angle = np.fmod(value, 2 * np.pi);
if (angle <= -np.pi):
angle += np.pi * 2;
if (angle > np.pi):
angle -= 2 * np.pi;
return angle; Example 23
def wrap1(x):
return numpy.fmod(x + 1 - numpy.ceil(x), 1) Example 24
def clamp_longitude( lons ):
lons = np.asarray(lons)
lons = np.fmod(lons, 360.)
lons[np.where(lons < -180.)] += 360.
lons[np.where(lons > 180.)] -= 360.
return lons Example 25
def visiting(self, x, step, temperature):
dim = x.size
if step < dim:
# Changing all coordinates with a new visting value
visits = np.array([self.visit_fn(
temperature) for _ in range(dim)])
upper_sample = self.rs.random_sample()
lower_sample = self.rs.random_sample()
visits[visits > self.tail_limit] = self.tail_limit * upper_sample
visits[visits < -self.tail_limit] = -self.tail_limit * lower_sample
x_visit = visits + x
a = x_visit - self.lower
b = np.fmod(a, self.b_range) + self.b_range
x_visit = np.fmod(b, self.b_range) + self.lower
x_visit[np.fabs(
x_visit - self.lower) < self.min_visit_bound] += 1.e-10
else:
# Changing only one coordinate at a time based on Markov chain step
x_visit = np.copy(x)
visit = self.visit_fn(temperature)
if visit > self.tail_limit:
visit = self.tail_limit * self.rs.random_sample()
elif visit < -self.tail_limit:
visit = -self.tail_limit * self.rs.random_sample()
index = step - dim
x_visit[index] = visit + x[index]
a = x_visit[index] - self.lower[index]
b = np.fmod(a, self.b_range[index]) + self.b_range[index]
x_visit[index] = np.fmod(b, self.b_range[
index]) + self.lower[index]
if np.fabs(x_visit[index] - self.lower[
index]) < self.min_visit_bound:
x_visit[index] += self.min_visit_bound
return x_visit Example 26
def testFloat(self):
x = [0.5, 0.7, 0.3]
for dtype in [np.float32, np.double]:
# Test scalar and vector versions.
for denom in [x[0], [x[0]] * 3]:
x_np = np.array(x, dtype=dtype)
with self.test_session():
x_tf = constant_op.constant(x_np, shape=x_np.shape)
y_tf = math_ops.mod(x_tf, denom)
y_tf_np = y_tf.eval()
y_np = np.fmod(x_np, denom)
self.assertAllClose(y_tf_np, y_np, atol=1e-2) 