Python numpy 模块，roll() 实例源码
我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.roll()。
def rtask_avg_proc(threshold, trend_task, window_size, task=None):
import numpy as np
data = np.empty(window_size, dtype=float)
data.fill(0.0)
cumsum = 0.0
while True:
i, n = yield task.receive()
if n is None:
break
cumsum += (n - data[0])
avg = cumsum / window_size
if avg > threshold:
trend_task.send((i, 'high', float(avg)))
elif avg < -threshold:
trend_task.send((i, 'low', float(avg)))
data = np.roll(data, -1)
data[-1] = n
raise StopIteration(0)
# This generator function is sent to remote dispycos process to save the
# received data in a file (on the remote peer).
def rtask_avg_proc(threshold, trend_task, window_size, task=None):
import numpy as np
data = np.empty(window_size, dtype=float)
data.fill(0.0)
cumsum = 0.0
while True:
i, n = yield task.receive()
if n is None:
break
cumsum += (n - data[0])
avg = cumsum / window_size
if avg > threshold:
trend_task.send((i, 'high', float(avg)))
elif avg < -threshold:
trend_task.send((i, 'low', float(avg)))
data = np.roll(data, -1)
data[-1] = n
raise StopIteration(0)
# This generator function is sent to remote dispycos process to save the
# received data in a file (on the remote peer).
def rtask_avg_proc(threshold, trend_task, window_size, task=None):
import numpy as np
data = np.empty(window_size, dtype=float)
data.fill(0.0)
cumsum = 0.0
while True:
i, n = yield task.receive()
if n is None:
break
cumsum += (n - data[0])
avg = cumsum / window_size
if avg > threshold:
trend_task.send((i, 'high', float(avg)))
elif avg < -threshold:
trend_task.send((i, 'low', float(avg)))
data = np.roll(data, -1)
data[-1] = n
raise StopIteration(0)
# This generator function is sent to remote dispycos process to save the
# received data in a file (on the remote peer).
def numpy_groupby(values, keys):
""" Group a collection of numpy arrays by key arrays.
Yields (key_tuple, view_tuple) where key_tuple is the key grouped on and view_tuple is a tuple of views into the value arrays.
values: tuple of arrays to group
keys: tuple of sorted, numeric arrays to group by """
if len(values) == 0:
return
if len(values[0]) == 0:
return
for key_array in keys:
assert len(key_array) == len(keys[0])
for value_array in values:
assert len(value_array) == len(keys[0])
# The indices where any of the keys differ from the previous key become group boundaries
key_change_indices = np.logical_or.reduce(tuple(np.concatenate(([1], np.diff(key))) != 0 for key in keys))
group_starts = np.flatnonzero(key_change_indices)
group_ends = np.roll(group_starts, -1)
group_ends[-1] = len(keys[0])
for group_start, group_end in itertools.izip(group_starts, group_ends):
yield tuple(key[group_start] for key in keys), tuple(value[group_start:group_end] for value in values)
def play(self, nb_rounds):
img_saver = save_image()
img_saver.next()
game_cnt = it.count(1)
for i in xrange(nb_rounds):
game = self.game(width=self.width, height=self.height)
screen, _ = game.next()
img_saver.send(screen)
frame_cnt = it.count()
try:
state = np.asarray([screen] * self.nb_frames)
while True:
frame_cnt.next()
act_idx = np.argmax(
self.model.predict(state[np.newaxis]), axis=-1)[0]
screen, _ = game.send(self.actions[act_idx])
state = np.roll(state, 1, axis=0)
state[0] = screen
img_saver.send(screen)
except StopIteration:
print 'Saved %4i frames for game %3i' % (
frame_cnt.next(), game_cnt.next())
img_saver.close()
def shift_dataset(m,boundarynoise):
if boundarynoise==0:
return m
nonzero_rows=np.where(m.any(axis=1))[0]
small_m=copy.deepcopy(m)
small_m=small_m[nonzero_rows,:]
small_m=small_m[:,nonzero_rows]
print small_m
print 'roll'
small_m=np.roll(small_m,boundarynoise,axis=0)
print small_m
print 'roll2'
small_m=np.roll(small_m,boundarynoise,axis=1)
print small_m
outm=np.zeros(m.shape)
for i_idx in range(len(nonzero_rows)):
i=nonzero_rows[i_idx]
for j_idx in range(i_idx,len(nonzero_rows)):
j=nonzero_rows[j_idx]
outm[i,j]=small_m[i_idx,j_idx]
outm[j,i]=outm[i,j]
return outm
def points_and_normals(self):
"""
Returns the point/normals parametrization for planes,
including clipped zmin and zmax frustums
Note: points need to be in CCW
"""
nv1, fv1 = self._front_back_vertices
nv2 = np.roll(nv1, -1, axis=0)
fv2 = np.roll(fv1, -1, axis=0)
vx = np.vstack([fv1-nv1, nv2[0]-nv1[0], fv1[2]-fv1[1]])
vy = np.vstack([fv2-fv1, nv2[1]-nv2[0], fv1[1]-fv1[0]])
pts = np.vstack([fv1, nv1[0], fv1[1]])
# vx += 1e-12
# vy += 1e-12
vx /= np.linalg.norm(vx, axis=1).reshape(-1,1)
vy /= np.linalg.norm(vy, axis=1).reshape(-1,1)
normals = np.cross(vx, vy)
normals /= np.linalg.norm(normals, axis=1).reshape(-1,1)
return pts, normals
def correlate(self, imgfft):
#Very much related to the convolution theorem, the cross-correlation
#theorem states that the Fourier transform of the cross-correlation of
#two functions is equal to the product of the individual Fourier
#transforms, where one of them has been complex conjugated:
if self.imgfft is not 0 or imgfft.imgfft is not 0:
imgcj = np.conjugate(self.imgfft)
imgft = imgfft.imgfft
prod = deepcopy(imgcj)
for x in range(imgcj.shape[0]):
for y in range(imgcj.shape[0]):
prod[x][y] = imgcj[x][y] * imgft[x][y]
cc = Corr( np.real(fft.ifft2(fft.fftshift(prod)))) # real image of the correlation
# adjust to center
cc.data = np.roll(cc.data, int(cc.data.shape[0] / 2), axis = 0)
cc.data = np.roll(cc.data, int(cc.data.shape[1] / 2), axis = 1)
else:
raise FFTnotInit()
return cc
def get_extrema(data):
# find extrema by finding indexes where diff changes sign
data_diff = np.diff(data)
asign = np.sign(data_diff)
signchange = ((np.roll(asign, 1) - asign) != 0).astype(int)
# first and last value is always a local extrema
signchange[0] = 1
# last value is missing because the diff-array is 1 value shorter than the
# input array so we have to add it again
signchange = np.append(signchange, np.array([1]))
calc_data = data[np.where(signchange != 0)]
return calc_data
def SLdshear(inputArray, k, axis):
"""
Computes the discretized shearing operator for a given inputArray, shear
number k and axis.
This version is adapted such that the MATLAB indexing can be used here in the
Python version.
"""
axis = axis - 1
if k==0:
return inputArray
rows = np.asarray(inputArray.shape)[0]
cols = np.asarray(inputArray.shape)[1]
shearedArray = np.zeros((rows, cols), dtype=inputArray.dtype)
if axis == 0:
for col in range(cols):
shearedArray[:,col] = np.roll(inputArray[:,col], int(k * np.floor(cols/2-col)))
else:
for row in range(rows):
shearedArray[row,:] = np.roll(inputArray[row,:], int(k * np.floor(rows/2-row)))
return shearedArray
def make_step(net, step_size=1.5, end='inception_4c/output', jitter=32, clip=True, objective=objective_L2):
#function BAK def make_step(net, step_size=1.5, end='inception_4c/output', jitter=32, clip=True, objective=objective_L2):
'''Basic gradient ascent step.'''
src = net.blobs['data'] # input image is stored in Net's 'data' blob
dst = net.blobs[end]
ox, oy = np.random.randint(-jitter, jitter+1, 2)
src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) # apply jitter shift
net.forward(end=end)
objective(dst) # specify the optimization objective
net.backward(start=end)
g = src.diff[0]
# apply normalized ascent step to the input image
src.data[:] += step_size/np.abs(g).mean() * g
src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) # unshift image
if clip:
bias = net.transformer.mean['data']
src.data[:] = np.clip(src.data, -bias, 255-bias)
def make_step(net, step_size=1.5, end='inception_4c/output', jitter=32, clip=True, objective=objective_L2):
#function BAK def make_step(net, step_size=1.5, end='inception_4c/output', jitter=32, clip=True, objective=objective_L2):
'''Basic gradient ascent step.'''
src = net.blobs['data'] # input image is stored in Net's 'data' blob
dst = net.blobs[end]
ox, oy = np.random.randint(-jitter, jitter+1, 2)
src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) # apply jitter shift
net.forward(end=end)
objective(dst) # specify the optimization objective
net.backward(start=end)
g = src.diff[0]
# apply normalized ascent step to the input image
src.data[:] += step_size/np.abs(g).mean() * g
src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) # unshift image
if clip:
bias = net.transformer.mean['data']
src.data[:] = np.clip(src.data, -bias, 255-bias)
def make_step(net, step_size=1.5, end='inception_4d/output', jitter=32, clip=True, objective=objective_L2):
#function BAK def make_step(net, step_size=1.5, end='inception_4c/output', jitter=32, clip=True, objective=objective_L2):
'''Basic gradient ascent step.'''
src = net.blobs['data'] # input image is stored in Net's 'data' blob
dst = net.blobs[end]
ox, oy = np.random.randint(-jitter, jitter+1, 2)
src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) # apply jitter shift
net.forward(end=end)
objective(dst) # specify the optimization objective
net.backward(start=end)
g = src.diff[0]
# apply normalized ascent step to the input image
src.data[:] += step_size/np.abs(g).mean() * g
src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) # unshift image
if clip:
bias = net.transformer.mean['data']
src.data[:] = np.clip(src.data, -bias, 255-bias)
def make_step(net, step_size=1.5, end='inception_5a/output', jitter=32, clip=False, objective=objective_L2):
#function BAK def make_step(net, step_size=1.5, end='inception_4c/output', jitter=32, clip=True, objective=objective_L2):
'''Basic gradient ascent step.'''
src = net.blobs['data'] # input image is stored in Net's 'data' blob
dst = net.blobs[end]
ox, oy = np.random.randint(-jitter, jitter+1, 2)
src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) # apply jitter shift
net.forward(end=end)
objective(dst) # specify the optimization objective
net.backward(start=end)
g = src.diff[0]
# apply normalized ascent step to the input image
src.data[:] += step_size/np.abs(g).mean() * g
src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) # unshift image
if clip:
bias = net.transformer.mean['data']
src.data[:] = np.clip(src.data, -bias, 255-bias)
def torus_faces(x, y):
faces = np.empty((x * y, 4), dtype=np.uint32)
tmp = np.arange(0, x * y)
faces[:, 0] = tmp
faces[:, 1] = np.roll(tmp, -y)
tmp += 1
tmp.shape = (x, y)
tmp[:, y - 1] -= y
tmp.shape = -1
faces[:, 3] = tmp
faces[:, 2] = np.roll(tmp, -y)
faces.shape = -1
l_total = np.empty(x * y, dtype=np.uint32)
l_total[:] = 4
l_start = np.arange(0, (x * y) * 4, 4, dtype=np.uint32)
return SvPolygon(l_start, l_total, faces)
def fft_convolve(X,Y, inv = 0):
XF = np.fft.rfft2(X)
YF = np.fft.rfft2(Y)
# YF0 = np.copy(YF)
# YF.imag = 0
# XF.imag = 0
if inv == 1:
# plt.imshow(np.real(YF)); plt.colorbar(); plt.show()
YF = np.conj(YF)
SF = XF*YF
S = np.fft.irfft2(SF)
n1,n2 = np.shape(S)
S = np.roll(S,-n1/2+1,axis = 0)
S = np.roll(S,-n2/2+1,axis = 1)
return np.real(S)
def _wakeup(self, direction=0):
position = int((direction + 15) / 30) % 12
basis = numpy.roll(self.basis, position * 4)
for i in range(1, 25):
pixels = basis * i
self.write(pixels)
time.sleep(0.005)
pixels = numpy.roll(pixels, 4)
self.write(pixels)
time.sleep(0.1)
for i in range(2):
new_pixels = numpy.roll(pixels, 4)
self.write(new_pixels * 0.5 + pixels)
pixels = new_pixels
time.sleep(0.1)
self.write(pixels)
self.pixels = pixels
def _think(self):
pixels = self.pixels
self.next.clear()
while not self.next.is_set():
pixels = numpy.roll(pixels, 4)
self.write(pixels)
time.sleep(0.2)
t = 0.1
for i in range(0, 5):
pixels = numpy.roll(pixels, 4)
self.write(pixels * (4 - i) / 4)
time.sleep(t)
t /= 2
# time.sleep(0.5)
self.pixels = pixels
def test_rolling_window(input_seq, batch_size, seq_len, strides):
# This test checks if the rolling window works
# We check if the first two samples in each batch are strided by strides
# Truncate input sequence such that last section that doesn't fit in a batch
# is thrown away
input_seq = input_seq[:seq_len * batch_size * (len(input_seq) // seq_len // batch_size)]
data_array = {'X': input_seq,
'y': np.roll(input_seq, axis=0, shift=-1)}
time_steps = seq_len
it_array = SequentialArrayIterator(data_arrays=data_array, time_steps=time_steps,
stride=strides, batch_size=batch_size, tgt_key='y',
shuffle=False)
for idx, iter_val in enumerate(it_array):
# Start of the array needs to be time_steps * idx
assert np.array_equal(iter_val['X'][0, strides:time_steps],
iter_val['X'][1, :time_steps - strides])
assert np.array_equal(iter_val['y'][0, strides:time_steps],
iter_val['y'][1, :time_steps - strides])
def pm_roll(n, v):
'''Returns `2**k * n` number of points of dimension `n` such that
p[0] = [+-v[0], ..., +-v[k], 0, ..., 0]
p[1] = [0, +-v[0], ..., +-v[k], 0, ..., 0]
...
p[n-1] = [+-v[1], ..., +-v[k], 0, ..., 0, +-v[0]]
with all +- configurations.
'''
k = len(v)
assert k <= n
pm_v = pm_array(v)
r0 = numpy.zeros((len(pm_v), n), dtype=pm_v.dtype)
r0[:, :k] = pm_v
return numpy.concatenate([
numpy.roll(r0, i, axis=1)
for i in range(n)
])
# TODO remove
def time_lag(pha, amp, axis):
"""Introduce a time lag on phase series..
Parameters
----------
pha : array_like
Array of phases of shapes (npha, ..., npts)
amp : array_like
Array of amplitudes of shapes (namp, ..., npts)
axis : int
Location of the time axis.
Returns
-------
pha : array_like
Shiffted version of phases of shapes (npha, ..., npts)
amp : array_like
Original version of amplitudes of shapes (namp, ..., npts)
"""
npts = pha.shape[-1]
return np.roll(pha, np.random.randint(npts), axis=axis), amp
def wakeup(self, direction=0):
position = int((direction + 15) / 30) % 12
basis = numpy.roll(self.basis, position * 4)
for i in range(1, 25):
pixels = basis * i
self.show(pixels)
time.sleep(0.005)
pixels = numpy.roll(pixels, 4)
self.show(pixels)
time.sleep(0.1)
for i in range(2):
new_pixels = numpy.roll(pixels, 4)
self.show(new_pixels * 0.5 + pixels)
pixels = new_pixels
time.sleep(0.1)
self.show(pixels)
self.pixels = pixels
def think(self):
pixels = self.pixels
while not self.stop:
pixels = numpy.roll(pixels, 4)
self.show(pixels)
time.sleep(0.2)
t = 0.1
for i in range(0, 5):
pixels = numpy.roll(pixels, 4)
self.show(pixels * (4 - i) / 4)
time.sleep(t)
t /= 2
self.pixels = pixels
def calc_grad_tiled(img, t_grad, tile_size=512):
'''Compute the value of tensor t_grad over the image in a tiled way.
Random shifts are applied to the image to blur tile boundaries over
multiple iterations.'''
# Pick a subregion square size
sz = tile_size
# Get the image height and width
h, w = img.shape[:2]
# Get a random shift amount in the x and y direction
sx, sy = np.random.randint(sz, size=2)
# Randomly shift the image (roll image) in the x and y directions
img_shift = np.roll(np.roll(img, sx, 1), sy, 0)
# Initialize the while image gradient as zeros
grad = np.zeros_like(img)
# Now we loop through all the sub-tiles in the image
for y in range(0, max(h-sz//2, sz),sz):
for x in range(0, max(w-sz//2, sz),sz):
# Select the sub image tile
sub = img_shift[y:y+sz,x:x+sz]
# Calculate the gradient for the tile
g = sess.run(t_grad, {t_input:sub})
# Apply the gradient of the tile to the whole image gradient
grad[y:y+sz,x:x+sz] = g
# Return the gradient, undoing the roll operation
return np.roll(np.roll(grad, -sx, 1), -sy, 0)
def column_cycle_array(posterior, amt=None):
"""Also called 'position cycle' by Kloosterman et al.
If amt is an array of the same length as posterior, then
cycle each column by the corresponding amount in amt.
Otherwise, cycle each column by a random amount."""
out = copy.deepcopy(posterior)
rows, cols = posterior.shape
if amt is None:
for col in range(cols):
if np.isnan(np.sum(posterior[:,col])):
continue
else:
out[:,col] = np.roll(posterior[:,col], np.random.randint(1, rows))
else:
if len(amt) == cols:
for col in range(cols):
if np.isnan(np.sum(posterior[:,col])):
continue
else:
out[:,col] = np.roll(posterior[:,col], int(amt[col]))
else:
raise TypeError("amt does not seem to be the correct shape!")
return out
def _within_event_incoherent_shuffle(self, kind='train'):
"""Time cycle on BinnedSpikeTrainArray, cycling only within each epoch.
We cycle each unit independently, within each epoch.
"""
if kind == 'train':
bst = self.PBEs_train
elif kind == 'test':
bst = self.PBEs_test
else:
raise ValueError("kind '{}' not understood!".format(kind))
out = copy.deepcopy(bst) # should this be deep?
data = out._data
edges = np.insert(np.cumsum(bst.lengths),0,0)
for uu in range(bst.n_units):
for ii in range(bst.n_epochs):
segment = np.squeeze(data[uu, edges[ii]:edges[ii+1]])
segment = np.roll(segment, np.random.randint(len(segment)))
data[uu, edges[ii]:edges[ii+1]] = segment
if kind == 'train':
self.PBEs_train = out
else:
self.PBEs_test = out
def _augment_speech(mfcc):
# random frequency shift ( == speed perturbation effect on MFCC )
r = np.random.randint(-2, 2)
# shifting mfcc
mfcc = np.roll(mfcc, r, axis=0)
# zero padding
if r > 0:
mfcc[:r, :] = 0
elif r < 0:
mfcc[r:, :] = 0
return mfcc
# Speech Corpus
def edge_mask(mask):
""" Find the edges of a mask or masked image
Parameters
----------
mask : 3D array
Binary mask (or masked image) with axis orientation LPS or RPS, and the
non-brain region set to 0
Returns
-------
2D array
Outline of sagittal profile (PS orientation) of mask
"""
# Sagittal profile
brain = mask.any(axis=0)
# Simple edge detection
edgemask = 4 * brain - np.roll(brain, 1, 0) - np.roll(brain, -1, 0) - \
np.roll(brain, 1, 1) - np.roll(brain, -1, 1) != 0
return edgemask.astype('uint8')
def roll(u, shift):
"""
Apply :func:`numpy.roll` to multiple array axes.
Parameters
----------
u : array_like
Input array
shift : array_like of int
Shifts to apply to axes of input `u`
Returns
-------
v : ndarray
Output array
"""
v = u.copy()
for k in range(len(shift)):
v = np.roll(v, shift[k], axis=k)
return v
def update(self, idxs, x):
# Fetch the classes for the regression
_, y = self.dataset.train_data[idxs]
# If we are doing the regression in logspace
if self.log:
x = np.log(x)
# Train the lstm so that it can predict x given the history
self.model.train_on_batch([self.history[idxs], self._to_ids(y)], x)
# Update the history to include x
full = idxs[self.cnts[idxs] == self.history.shape[1]]
self.history[full] = np.roll(self.history[full], -1, axis=1)
self.cnts[full] -= 1
self.history[idxs, self.cnts[idxs], :1] = x
self.cnts[idxs] += 1
def update(self, idxs, x):
# Fetch the classes for the regression
_, y = self.dataset.train_data[idxs]
# If we are doing the regression in logspace
if self.log:
x = np.log(x)
# Train the lstm so that it can predict x given the history
self.model.train_on_batch([self.history[idxs], self._to_ids(y)], x)
# Update the history to include x
full = idxs[self.cnts[idxs] == self.history.shape[1]]
self.history[full] = np.roll(self.history[full], -1, axis=1)
self.cnts[full] -= 1
self.history[idxs, self.cnts[idxs], :1] = x
self.cnts[idxs] += 1
def update(self, idxs, x):
# Fetch the classes for the regression
_, y = self.dataset.train_data[idxs]
# If we are doing the regression in logspace
if self.log:
x = np.log(x)
# Train the lstm so that it can predict x given the history
self.model.train_on_batch([self.history[idxs], self._to_ids(y)], x)
# Update the history to include x
full = idxs[self.cnts[idxs] == self.history.shape[1]]
self.history[full] = np.roll(self.history[full], -1, axis=1)
self.cnts[full] -= 1
self.history[idxs, self.cnts[idxs], :1] = x
self.cnts[idxs] += 1
def update(self, idxs, x):
# Fetch the classes for the regression
_, y = self.dataset.train_data[idxs]
# If we are doing the regression in logspace
if self.log:
x = np.log(x)
# Train the lstm so that it can predict x given the history
self.model.train_on_batch([self.history[idxs], self._to_ids(y)], x)
# Update the history to include x
full = idxs[self.cnts[idxs] == self.history.shape[1]]
self.history[full] = np.roll(self.history[full], -1, axis=1)
self.cnts[full] -= 1
self.history[idxs, self.cnts[idxs], :1] = x
self.cnts[idxs] += 1
def uwt_align_h2(X, inverse=False):
"""UWT h2 coefficients aligment.
If inverse = True performs the misalignment
for a correct reconstruction.
"""
J = X.shape[0] / 2
shifts = np.asarray([2 ** j for j in range(J)])
if not inverse:
shifts *= -1
for j in range(J):
X[j] = np.roll(X[j], shifts[j])
X[j + J] = np.roll(X[j + J], shifts[j])
def uwt_align_d4(X, inverse=False):
"""UWT d4 coefficients aligment.
If inverse = True performs the misalignment
for a correct reconstruction.
"""
J = X.shape[0] / 2
w_shifts = np.asarray([(3 * 2 ** j) - 1 for j in range(J)])
v_shifts = np.asarray([1] + [(2 ** (j + 1) - 1) for j in range(1, J)])
if not inverse:
w_shifts *= -1
v_shifts *= -1
for j in range(J):
X[j] = np.roll(X[j], w_shifts[j])
X[j + J] = np.roll(X[j + J], v_shifts[j])
def finalize(self,mskp_model):
print('found %i islands'%self.nbisland)
mskp = zeros((self.nyl,self.nxl),dtype=int8)
work = zeros((self.nyl,self.nxl))
mskr = zeros((self.nyl,self.nxl))
for k in range(self.nbisland):
idx = self.data[k]['idx']
psi0 = self.data[k]['psi0']
mskr[:,:]=1.
mskp[:,:]=0
mskr[idx]=0.
celltocorner(mskr,work)
mskp[work==1]=1
mskp=1-mskp
vort = (roll(mskp,-1,axis=1)+roll(mskp,-1,axis=0)
+roll(mskp,+1,axis=1)+roll(mskp,+1,axis=0) )
z=(vort)*psi0/self.dx**2#*(1-mskp)
self.rhsp[vort>0] = z[vort>0]
self.psi[mskp==1]=psi0
# print(self.psi[:,10])
print('island are ok')
def to_intervals(X):
def _roll_rows(x):
""" Circularly shift ('roll') rows i in array by -i, recursively.
If 2d-array: circularly shift each row i to the left, i times so that
X(i, j-i) = X(i, j)
If 3d-array (or 4d, 5d..):
X(i, j-i, k-j) = X(i, j, k)
"""
if len(x.shape) > 2:
x = np.array([_roll_rows(xi) for xi in x])
elif len(x.shape) == 1:
raise ValueError('Method requires nd-array with n >= 2.')
x_rolled = np.array([np.roll(xi, -i, axis=0) for i, xi in enumerate(x)])
return x_rolled
X_rolled = _roll_rows(X)
X_inv = np.sum(X_rolled, axis=0)
return X_inv
## ------------------------- feature alignment
def lower_periodic(self, periodic, direction=0):
""" Sets the periodicity of the spline object in the given direction,
keeping the geometry unchanged.
:param int periodic: new periodicity, i.e. the basis is C^k over the start/end
:param int direction: the parametric direction of the basis to modify
:return: self
"""
direction = check_direction(direction, self.pardim)
b = self.bases[direction]
while periodic < b.periodic:
self.insert_knot(self.start(direction), direction)
self.controlpoints = np.roll(self.controlpoints, -1, direction)
b.roll(1)
b.periodic -= 1
b.knots = b.knots[:-1]
if periodic > b.periodic:
raise ValueError('Cannot raise periodicity')
return self
def sharpenOld(s, kernelFunc, dist=None, scale=None,
normalize=False, m1=False, *args, **kwargs):
s = util.colmat(s)
if dist is None:
dist = np.arange(s.shape[1])+1.0
dist = np.abs(dist[None,:]-dist[:,None])
#dist = np.insert(spsig.triang(s.shape[1]-1, sym=False), 0, 0.0)
#dist = np.vstack([np.roll(dist, i) for i in xrange(dist.size)])
if scale is None:
# minimum off-diagonal distance
scale = np.min(dist[np.asarray(1.0-np.eye(dist.shape[0]), dtype=np.bool)])
kernel = kernelFunc(dist.T/scale, *args, **kwargs)
if m1:
np.fill_diagonal(kernel, 0.0)
if normalize:
kernel = kernel/np.abs(kernel.sum(axis=0))
return s - s.dot(kernel)
def get_samples(self, sample_count):
"""
Fetch a number of samples from self.wave_cache
Args:
sample_count (int): Number of samples to fetch
Returns: ndarray
"""
if self.amplitude.value <= 0:
return None
# Build samples by rolling the period cache through the buffer
rolled_array = numpy.roll(self.wave_cache,
-1 * self.last_played_sample)
# Append remaining partial period
full_count, remainder = divmod(sample_count, self.cache_length)
final_subarray = rolled_array[:int(remainder)]
return_array = numpy.concatenate((numpy.tile(rolled_array, full_count),
final_subarray))
# Keep track of where we left off to prevent popping between chunks
self.last_played_sample = int(((self.last_played_sample + remainder) %
self.cache_length))
# Multiply output by amplitude
return return_array * (self.amplitude.value *
self.amplitude_multiplier)
def gen_blurred_diag_pxy(s):
X = 1024
Y = X
# generate pdf
from scipy.stats import multivariate_normal
pxy = np.zeros((X,Y))
rv = multivariate_normal(cov=s)
for x in range(X):
pxy[x,:] = np.roll(rv.pdf(np.linspace(-X/2,X/2,X+1)[:-1]),int(X/2+x))
pxy = pxy/np.sum(pxy)
# plot p(x,y)
import matplotlib.pyplot as plt
plt.figure()
plt.contourf(pxy)
plt.ion()
plt.title("p(x,y)")
plt.show()
return pxy
def _roll_data(self):
"""
Roll window worth of data up to position zero.
Save the effort of having to expensively roll at each iteration
"""
self.buffer.values[:, :self._window, :] = \
self.buffer.values[:, -self._window:, :]
self.date_buf[:self._window] = self.date_buf[-self._window:]
self._pos = self._window
def _roll_data(self):
"""
Roll window worth of data up to position zero.
Save the effort of having to expensively roll at each iteration
"""
self.buffer.values[:, :self._window, :] = \
self.buffer.values[:, -self._window:, :]
self.date_buf[:self._window] = self.date_buf[-self._window:]
self._pos = self._window
def setSinusoidalWaveform(self,
waveTableId,
append,
lengthInPoints,
amplitudeOfTheSineCurve,
offsetOfTheSineCurve,
wavelengthOfTheSineCurveInPoints,
startPoint,
curveCenterPoint):
'''
See description of PI_WAV_SIN_P in PI GCS 2.0 DLL doc
'''
curveCenterPoint= int(round(curveCenterPoint))
wavelengthOfTheSineCurveInPoints= \
int(round(wavelengthOfTheSineCurveInPoints))
startPoint= int(round(startPoint))
lengthInPoints= int(round(lengthInPoints))
assert append == WaveformGenerator.CLEAR, 'only CLEAR implemented'
assert startPoint >= 0
assert startPoint < lengthInPoints
assert curveCenterPoint >= 0
assert startPoint + curveCenterPoint < lengthInPoints
ccUp= 0.5* curveCenterPoint
rampUp= 0.5 * amplitudeOfTheSineCurve* (1 + np.sin(
np.arange(-ccUp, ccUp) / ccUp * np.pi / 2))
ccDown= 0.5* (wavelengthOfTheSineCurveInPoints - curveCenterPoint)
rampDown= 0.5 * amplitudeOfTheSineCurve* (1 - np.sin(
np.arange(-ccDown, ccDown) / ccDown * np.pi / 2))
waveform= np.zeros(lengthInPoints) + offsetOfTheSineCurve
waveform[0: curveCenterPoint]= offsetOfTheSineCurve + rampUp
waveform[curveCenterPoint: wavelengthOfTheSineCurveInPoints]= \
offsetOfTheSineCurve + rampDown
waveform= np.roll(waveform, startPoint)
self._waveform[waveTableId]= waveform
def publish_sensor_frame(self, channel, pose=None):
"""
Publish sensor frame in which the point clouds
are drawn with reference to. sensor_frame_msg.id is hashed
by its channel (may be collisions since its right shifted by 32)
"""
# Sensor frames msg
msg = vs.obj_collection_t()
msg.id = self.channel_uid(channel)
msg.name = 'BOTFRAME_' + channel
msg.type = vs.obj_collection_t.AXIS3D
msg.reset = True
# Send sensor pose
pose_msg = vs.obj_t()
roll, pitch, yaw, x, y, z = pose.to_rpyxyz(axes='sxyz')
pose_msg.id = 0
pose_msg.x, pose_msg.y, pose_msg.z, \
pose_msg.roll, pose_msg.pitch, pose_msg.yaw = x, y, z, roll, pitch, yaw
# Save pose
self.set_sensor_pose(channel, pose)
msg.objs = [pose_msg]
msg.nobjs = len(msg.objs)
self.lc.publish("OBJ_COLLECTION", msg.encode())
def corners_to_edges(corners):
""" Edges are represented in N x 6 form """
return np.hstack([corners, np.roll(corners, 1, axis=0)])
def interpolate(self, other, this_weight):
q0, q1 = np.roll(self.q, shift=1), np.roll(other.q, shift=1)
u = 1 - this_weight
assert(u >= 0 and u <= 1)
cos_omega = np.dot(q0, q1)
if cos_omega < 0:
result = -q0[:]
cos_omega = -cos_omega
else:
result = q0[:]
cos_omega = min(cos_omega, 1)
omega = math.acos(cos_omega)
sin_omega = math.sin(omega)
a = math.sin((1-u) * omega)/ sin_omega
b = math.sin(u * omega) / sin_omega
if abs(sin_omega) < 1e-6:
# direct linear interpolation for numerically unstable regions
result = result * this_weight + q1 * u
result /= math.sqrt(np.dot(result, result))
else:
result = result*a + q1*b
return Quaternion(np.roll(result, shift=-1))
# To conversions