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 predict(image,the_net):
inputs = []
try:
tmp_input = image
tmp_input = cv2.resize(tmp_input,(SIZE,SIZE))
tmp_input = tmp_input[11:11+128,11:11+128];
tmp_input = np.subtract(tmp_input,mean)
tmp_input = tmp_input.transpose((2, 0, 1))
tmp_input = np.require(tmp_input, dtype=np.float32)
except Exception as e:
raise Exception("Image damaged or illegal file format")
return
the_net.blobs['data'].reshape(1, *tmp_input.shape)
the_net.reshape()
the_net.blobs['data'].data[...] = tmp_input
the_net.forward()
scores = the_net.blobs['prob'].data[0]
return copy.deepcopy(scores) Example 2
def predict(the_net,image):
inputs = []
if not os.path.exists(image):
raise Exception("Image path not exist")
return
try:
tmp_input = cv2.imread(image)
tmp_input = cv2.resize(tmp_input,(SIZE,SIZE))
tmp_input = tmp_input[11:11+128,11:11+128]
tmp_input = np.subtract(tmp_input,mean)
tmp_input = tmp_input.transpose((2, 0, 1))
tmp_input = np.require(tmp_input, dtype=np.float32)
except Exception as e:
#raise Exception("Image damaged or illegal file format")
return None
the_net.blobs['data'].reshape(1, *tmp_input.shape)
the_net.reshape()
the_net.blobs['data'].data[...] = tmp_input
the_net.forward()
scores = copy.deepcopy(the_net.blobs['feature'].data)
return scores Example 3
def predict(image,the_net):
inputs = []
try:
tmp_input = image
tmp_input = cv2.resize(tmp_input,(SIZE,SIZE))
tmp_input = tmp_input[13:13+224,13:13+224];
tmp_input = np.subtract(tmp_input,mean)
tmp_input = tmp_input.transpose((2, 0, 1))
tmp_input = np.require(tmp_input, dtype=np.float32)
except Exception as e:
raise Exception("Image damaged or illegal file format")
return
the_net.blobs['data'].reshape(1, *tmp_input.shape)
the_net.reshape()
the_net.blobs['data'].data[...] = tmp_input
the_net.forward()
scores = the_net.blobs['prob'].data[0]
return copy.deepcopy(scores) Example 4
def predict(image,the_net):
inputs = []
try:
tmp_input = image
tmp_input = cv2.resize(tmp_input,(SIZE,SIZE))
tmp_input = tmp_input[11:11+128,11:11+128];
tmp_input = np.subtract(tmp_input,mean)
tmp_input = tmp_input.transpose((2, 0, 1))
tmp_input = np.require(tmp_input, dtype=np.float32)
except Exception as e:
raise Exception("Image damaged or illegal file format")
return
the_net.blobs['data'].reshape(1, *tmp_input.shape)
the_net.reshape()
the_net.blobs['data'].data[...] = tmp_input
the_net.forward()
scores = the_net.blobs['prob'].data[0]
return copy.deepcopy(scores) Example 5
def predict(the_net,image):
inputs = []
if not os.path.exists(image):
raise Exception("Image path not exist")
return
try:
tmp_input = cv2.imread(image)
tmp_input = cv2.resize(tmp_input,(SIZE,SIZE))
tmp_input = tmp_input[13:13+224,13:13+224]
#tmp_input = np.subtract(tmp_input,mean)
tmp_input = tmp_input.transpose((2, 0, 1))
tmp_input = np.require(tmp_input, dtype=np.float32)
except Exception as e:
#raise Exception("Image damaged or illegal file format")
return None
the_net.blobs['data'].reshape(1, *tmp_input.shape)
the_net.reshape()
the_net.blobs['data'].data[...] = tmp_input
the_net.forward()
scores = copy.deepcopy(the_net.blobs['fc6'].data)
return scores Example 6
def predict(image,the_net):
inputs = []
try:
tmp_input = image
tmp_input = cv2.resize(tmp_input,(SIZE,SIZE))
tmp_input = tmp_input[13:13+224,13:13+224];
tmp_input = np.subtract(tmp_input,mean)
tmp_input = tmp_input.transpose((2, 0, 1))
tmp_input = np.require(tmp_input, dtype=np.float32)
except Exception as e:
raise Exception("Image damaged or illegal file format")
return
the_net.blobs['data'].reshape(1, *tmp_input.shape)
the_net.reshape()
the_net.blobs['data'].data[...] = tmp_input
the_net.forward()
scores = the_net.blobs['prob'].data[0]
return copy.deepcopy(scores) Example 7
def predict(image,the_net):
inputs = []
try:
tmp_input = image
tmp_input = cv2.resize(tmp_input,(SIZE,SIZE))
tmp_input = tmp_input[13:13+224,13:13+224];
tmp_input = np.subtract(tmp_input,mean)
tmp_input = tmp_input.transpose((2, 0, 1))
tmp_input = np.require(tmp_input, dtype=np.float32)
except Exception as e:
raise Exception("Image damaged or illegal file format")
return
the_net.blobs['data'].reshape(1, *tmp_input.shape)
the_net.reshape()
the_net.blobs['data'].data[...] = tmp_input
the_net.forward()
scores = the_net.blobs['prob'].data[0]
return copy.deepcopy(scores) Example 8
def predict(the_net,image):
inputs = []
if not os.path.exists(image):
raise Exception("Image path not exist")
return
try:
tmp_input = cv2.imread(image)
tmp_input = cv2.resize(tmp_input,(SIZE,SIZE))
tmp_input = tmp_input[13:13+224,13:13+224]
tmp_input = np.subtract(tmp_input,mean)
tmp_input = tmp_input.transpose((2, 0, 1))
tmp_input = np.require(tmp_input, dtype=np.float32)
except Exception as e:
#raise Exception("Image damaged or illegal file format")
return None
the_net.blobs['data'].reshape(1, *tmp_input.shape)
the_net.reshape()
the_net.blobs['data'].data[...] = tmp_input
the_net.forward()
scores = copy.deepcopy(the_net.blobs['fc6'].data)
return scores Example 9
def _propagate_boxes(boxes, annot_proto, frame_id):
pred_boxes = []
annots = []
for annot in annot_proto['annotations']:
for idx, box in enumerate(annot['track']):
if box['frame'] == frame_id and len(annot['track']) > idx + 1:
gt1 = box['bbox']
gt2 = annot['track'][idx+1]['bbox']
delta = bbox_transform(np.asarray([gt1]), np.asarray([gt2]))
annots.append((gt1, delta))
gt1 = [annot[0] for annot in annots]
overlaps = bbox_overlaps(np.require(boxes, dtype=np.float),
np.require(gt1, dtype=np.float))
assert len(overlaps) == len(boxes)
for gt_overlaps, box in zip(overlaps, boxes):
max_overlap = np.max(gt_overlaps)
max_gt = np.argmax(gt_overlaps)
if max_overlap < 0.5:
pred_boxes.append(box)
else:
delta = annots[max_gt][1]
pred_boxes.append(bbox_transform_inv(np.asarray([box]), delta)[0].tolist())
return pred_boxes Example 10
def epoch_to_epoch16(self, epoch):
"""
Converts a CDF EPOCH to a CDF EPOCH16 value
Parameters
==========
epoch : double
EPOCH to convert. Lists and numpy arrays are acceptable.
Returns
=======
out : (double, double)
EPOCH16 corresponding to epoch
"""
e = numpy.require(epoch, numpy.float64)
s = numpy.trunc(e / 1000.0)
#ugly numpy stuff, probably a better way....
res = numpy.hstack((s, (e - s * 1000.0) * 1e9))
if len(res) <= 2:
return res
newshape = list(res.shape[0:-2])
newshape.append(res.shape[-1] // 2)
newshape.append(2)
return numpy.rollaxis(res.reshape(newshape), -1, -2) Example 11
def epoch16_to_epoch(self, epoch16):
"""
Converts a CDF EPOCH16 to a CDF EPOCH value
Parameters
==========
epoch16 : (double, double)
EPOCH16 to convert. Lists and numpy arrays are acceptable.
LAST dimension should be 2: the two pairs of EPOCH16
Returns
=======
out : double
EPOCH corresponding to epoch16
"""
e = numpy.require(epoch16, numpy.float64)
return e[..., 0] * 1000.0 + numpy.round(e[..., 1] / 1e9) Example 12
def create_array(self):
"""Creates a numpy array to hold the data from this slice
Returns
=======
out : numpy.array
array sized, typed, and dimensioned to hold data from
this slice
"""
counts = self.counts
degen = self.degen
if self.column:
counts = self.reorder(counts)
degen = self.reorder(degen)
#TODO: Forcing C order for now, revert to using self.column later
array = numpy.empty(
[counts[i] for i in range(len(counts)) if not degen[i]],
self.zvar._np_type(), order='C')
return numpy.require(array, requirements=('C', 'A', 'W')) Example 13
def unique_rows(A, return_index=False, return_inverse=False):
"""
Similar to MATLAB's unique(A, 'rows'), this returns B, I, J
where B is the unique rows of A and I and J satisfy
A = B[J,:] and B = A[I,:]
Returns I if return_index is True
Returns J if return_inverse is True
"""
A = np.require(A, requirements='C')
assert A.ndim == 2, "array must be 2-dim'l"
B = np.unique(A.view([('', A.dtype)]*A.shape[1]),
return_index=return_index,
return_inverse=return_inverse)
if return_index or return_inverse:
return (B[0].view(A.dtype).reshape((-1, A.shape[1]), order='C'),) \
+ B[1:]
else:
return B.view(A.dtype).reshape((-1, A.shape[1]), order='C') Example 14
def test_fft(backend, batch, x, y, z):
b = backend()
N = (z, y, x, batch)
v = np.random.rand(*N) + 1j*np.random.rand(*N)
v = np.require(v, dtype=np.dtype('complex64'), requirements='F')
ax = (0,1,2)
# check forward
w_exp = np.fft.fftn(v, axes=ax)
v_d = b.copy_array(v)
u_d = b.copy_array(v)
b.fftn(u_d, v_d)
w_act = u_d.to_host()
np.testing.assert_allclose(w_act, w_exp, atol=1e-2)
# check adjoint
v_exp = np.fft.ifftn(w_act, axes=ax) * (x*y*z)
v_d = b.copy_array(w_act)
u_d = b.copy_array(w_act)
b.ifftn(u_d, v_d)
v_act = u_d.to_host()
np.testing.assert_allclose(v_act, v_exp, atol=1e-2)
# check unitary
np.testing.assert_allclose(v, v_act / (x*y*z), atol=1e-6) Example 15
def test_csr_matrix(backend, M, N, K, density):
b = backend()
c = np.dtype('complex64')
A = indigo.util.randM(M, N, density)
A_d = b.csr_matrix(b, A)
# forward
x = (np.random.rand(N,K) + 1j * np.random.rand(N,K))
x = np.require(x, dtype=c, requirements='F')
y_exp = A.astype(c) * x
x_d = b.copy_array(x)
y_d = b.zero_array(y_exp.shape, x.dtype)
A_d.forward(y_d, x_d)
y_act = y_d.to_host()
np.testing.assert_allclose(y_exp, y_act, atol=1e-5)
# adjoint
x = (np.random.rand(M,K) + 1j * np.random.rand(M,K))
x = np.require(x, dtype=c, requirements='C')
y_exp = A.H.astype(c) * x
x_d = b.copy_array(x)
y_d = b.zero_array(y_exp.shape, x.dtype)
A_d.adjoint(y_d, x_d)
y_act = y_d.to_host()
np.testing.assert_allclose(y_exp, y_act, atol=1e-5) Example 16
def test_blas_axpby(backend, n, alpha, alpha_i, beta):
b = backend()
x = (np.random.rand(n) + 1j * np.random.rand(n))
y = (np.random.rand(n) + 1j * np.random.rand(n))
x = np.require(x, dtype=np.dtype('complex64'), requirements='F')
y = np.require(y, dtype=np.dtype('complex64'), requirements='F')
x_d = b.copy_array(x)
y_d = b.copy_array(y)
alpha = alpha + 1j*alpha_i
y_exp = beta*y + alpha*x
b.axpby(beta, y_d, alpha, x_d)
y_act = y_d.to_host()
np.testing.assert_allclose(y_exp, y_act, atol=1e-6) Example 17
def write(fname, data, psz=1, origin=None, fast=False):
""" Writes a MRC file. The header will be blank except for nx,ny,nz,datatype=2 for float32.
data should be (nx,ny,nz), and will be written in Fortran order as MRC requires."""
header = np.zeros(256, dtype=np.int32) # 1024 byte header
header_f = header.view(np.float32)
header[:3] = data.shape # nx, ny, nz
header[3] = 2 # mode, 2 = float32 datatype
header[7:10] = data.shape # mx, my, mz (grid size)
header_f[10:13] = [psz * i for i in data.shape] # xlen, ylen, zlen
header_f[13:16] = 90.0 # CELLB
header[16:19] = [1, 2, 3] # axis order
if not fast:
header_f[19:22] = [data.min(), data.max(), data.mean()] # data stats
if origin is None:
header_f[49:52] = [0, 0, 0]
elif origin is "center":
header_f[49:52] = [psz * i / 2 for i in data.shape]
else:
header_f[49:52] = origin
header[52] = 542130509 # 'MAP ' chars
header[53] = 16708
with open(fname, 'wb') as f:
header.tofile(f)
np.require(np.reshape(data, (-1,), order='F'), dtype=np.float32).tofile(f) Example 18
def append(fname, data):
with open(fname, 'r+b') as f:
nx, ny, nz = np.fromfile(f, dtype=np.int32, count=3) # First 12 bytes of stack.
f.seek(36) # First byte of zlen.
zlen = np.fromfile(f, dtype=np.float32, count=1)
if data.shape[0] != nx or data.shape[1] != ny:
raise Exception
f.seek(0, os.SEEK_END)
np.require(np.reshape(data, (-1,), order='F'), dtype=np.float32).tofile(f)
# Update header after new data is written.
apix = zlen / nz
nz += data.shape[2]
zlen += apix * data.shape[2]
f.seek(8)
nz.tofile(f)
f.seek(36)
zlen.tofile(f) Example 19
def deprocess_net_image(image): image = image.copy() # use copy don't modify destructively image = image[::-1] # BGR to RGB image = image.transpose(1,2,0) # CHW to HCW image += [123,117,104] # undo mean subtraction # clamp values in [0,255] image[image < 0], image[image > 255] = 0 , 255 # round and cast form float32 to uint8 image = np.round(image) image = np.require(image,dtype=np.uint8) return image # Load the 1000 ImageNet labels from # ilsvrc12/synset_words.txt, and the 5 style labels from finetune_flickr_style/style_names.txt. # Load ImageNet labels to imagenet_labels
Example 20
def _KeenerMatrix(self, A, C, regularization, func, epsilon):
"""func is a regularization function imposed on every element of matrix.
"""
# Apply Laplace Law
B = A+A.T+2;
A = A+1
A = A/B
# Regularization
if func is not None:
h = np.frompyfunc(func, 1, 1)
A = np.require(h(A), dtype=np.float32)
# divide by contest number
C = C+C.T
c = np.sum(C, axis=1)
if regularization:
A = A/np.expand_dims(c, axis=1)
A[C==0]=0
if epsilon is not None:
A += epsilon*np.ones(A.shape, A.dtype)
return A Example 21
def __init__(self, table=None, filename=''):
"""
table: the pandas DataFrame that records rankable objects competition
record
filename: the hdf5 filename that stores the DataFrame. The DataFrame
must be indexed by 'item_pair_rate'.
"""
if table is None:
table = pd.read_hdf(filename, "item_pair_rate")
table = table[['primary','secondary','rate1','rate2','weight']]
self.table = table
# itemid to index table
idx = self._extract_list(self.table)
self.itemlist = idx
temptable = table.iloc[:,:2].values
pair = np.fromfunction(np.vectorize(lambda i, j: idx[temptable[i,j]]),
temptable.shape)
pair = np.require(pair, dtype=np.int32)
self.pair = pair Example 22
def RateDifferenceVoteMatrix(self):
"""This function outputs only Point Difference Matrix.
It can be ensured that every element of the matrix are not less than 0
"""
idx = self.itemlist
table = self.table
icnt = len(idx)
# allocate space for computing
D = np.zeros((icnt, icnt), dtype=np.float32)
pair = self.pair
fast_rate_diff_vote_matrix_build(pair,
np.require(table.iloc[:,2:].values, dtype=np.float32),
D)
return D Example 23
def SimpleDifferenceVoteMatrix(self):
"""This function outputs only Simple Difference Vote Matrix.
"""
idx = self.itemlist
table = self.table
icnt = len(idx)
# allocate space for computing
D = np.zeros((icnt, icnt), dtype=np.float32)
pair = self.pair
fast_simple_diff_vote_matrix_build(pair,
np.require(table.iloc[:,2:].values, dtype=np.float32),
D)
return D Example 24
def RateVoteMatrix(self):
"""This function outputs only Simple Difference Vote Matrix.
"""
idx = self.itemlist
table = self.table
icnt = len(idx)
# allocate space for computing
D = np.zeros((icnt, icnt), dtype=np.float32)
pair = self.pair
fast_rate_vote_matrix_build(pair,
np.require(table.iloc[:,2:].values, dtype=np.float32),
D)
return D Example 25
def MasseyVector(self):
"""This function produces X'Wy
"""
idx = self.itemlist
table = self.table
pair = self.pair
j = np.ravel(pair)
i = np.repeat(np.arange(table.shape[0], dtype=np.int32), 2, axis=0)
data = np.array([[1,-1]],dtype=np.float32)
data = np.ravel(np.repeat(data, table.shape[0], axis=0))
X = coo_matrix((data, (i, j)), shape=(table.shape[0], len(idx)))
X = X.tocsr()
W = np.require(table.iloc[:,4].values, np.float32)
y = table.iloc[:, 2].values - table.iloc[:, 3].values;
Wy=np.multiply(W, y)
return X.T*Wy Example 26
def isIntegral(solution):
return np.all(solution == np.require(solution, dtype = 'int_')) Example 27
def filter(self, filter_ind):
filter_ind = np.require(filter_ind, dtype = 'bool')
new = self.copy()
new.objvals = self.objvals[filter_ind]
new.solutions = self.solutions[filter_ind]
return new Example 28
def generate_binary_data(n_rows = 1000000, n_cols = 20):
X = np.random.randint(low=0, high=2, size=(n_rows, n_cols))
Y = np.random.randint(low=0, high=2, size=(n_rows, 1))
pos_ind = Y == 1
Y[~pos_ind] = -1
Z = X * Y
Z = np.require(Z, requirements=['F'], dtype=np.float64)
return Z Example 29
def generate_integer_model(n_cols = 20, rho_ub = 100, rho_lb = -100, sparse_pct = 0.5):
rho = np.random.randint(low=rho_lb, high=rho_ub, size=n_cols)
rho = np.require(rho, dtype=Z.dtype, requirements=['F'])
nnz_count = int(sparse_pct * np.floor(n_cols / 2))
set_to_zero = np.random.choice(range(0, n_cols), size=nnz_count, replace=False)
rho[set_to_zero] = 0.0
return rho Example 30
def is_integer(rho):
"""
checks if numpy array is an integer vector
Parameters
----------
rho
Returns
-------
"""
return np.array_equal(rho, np.require(rho, dtype=np.int_)) Example 31
def set_and_check_flag(self, flag, dtype, arr):
if dtype is None:
dtype = arr.dtype
b = np.require(arr, dtype, [flag])
assert_(b.flags[flag])
assert_(b.dtype == dtype)
# a further call to np.require ought to return the same array
# unless OWNDATA is specified.
c = np.require(b, None, [flag])
if flag[0] != 'O':
assert_(c is b)
else:
assert_(c.flags[flag]) Example 32
def test_unknown_requirement(self):
a = self.generate_all_false('f8')
assert_raises(KeyError, np.require, a, None, 'Q') Example 33
def test_non_array_input(self):
a = np.require([1, 2, 3, 4], 'i4', ['C', 'A', 'O'])
assert_(a.flags['O'])
assert_(a.flags['C'])
assert_(a.flags['A'])
assert_(a.dtype == 'i4')
assert_equal(a, [1, 2, 3, 4]) Example 34
def test_C_and_F_simul(self):
a = self.generate_all_false('f8')
assert_raises(ValueError, np.require, a, None, ['C', 'F']) Example 35
def test_ensure_array(self):
class ArraySubclass(np.ndarray):
pass
a = ArraySubclass((2, 2))
b = np.require(a, None, ['E'])
assert_(type(b) is np.ndarray) Example 36
def _gt_propagate_boxes(boxes, annot_proto, frame_id, window, overlap_thres):
pred_boxes = []
annots = []
for annot in annot_proto['annotations']:
for idx, box in enumerate(annot['track']):
if box['frame'] == frame_id:
gt1 = box['bbox']
deltas = []
deltas.append(gt1)
for offset in xrange(1, window):
try:
gt2 = annot['track'][idx+offset]['bbox']
except IndexError:
gt2 = gt1
delta = bbox_transform(np.asarray([gt1]), np.asarray([gt2]))
deltas.append(delta)
annots.append(deltas)
gt1s = [annot[0] for annot in annots]
if not gt1s:
# no grount-truth, boxes remain still
return np.tile(np.asarray(boxes)[:,np.newaxis,:], [1,window-1,1])
overlaps = bbox_overlaps(np.require(boxes, dtype=np.float),
np.require(gt1s, dtype=np.float))
assert len(overlaps) == len(boxes)
for gt_overlaps, box in zip(overlaps, boxes):
max_overlap = np.max(gt_overlaps)
max_gt = np.argmax(gt_overlaps)
sequence_box = []
if max_overlap < overlap_thres:
for offset in xrange(1, window):
sequence_box.append(box)
else:
for offset in xrange(1, window):
delta = annots[max_gt][offset]
sequence_box.append(
bbox_transform_inv(np.asarray([box]), delta)[0].tolist())
pred_boxes.append((sequence_box))
return np.asarray(pred_boxes) Example 37
def _setup_arrays(x,v,m,omega=None):
sindx= numpy.argsort(x)
# Keep track of amount of mass above and below and compute acceleration
mass_below= numpy.cumsum(m[sindx])
mass_below[-1]= 0.
mass_below= numpy.roll(mass_below,1)
mass_above= numpy.cumsum(m[sindx][::-1])[::-1]
mass_above[0]= 0.
mass_above= numpy.roll(mass_above,-1)
a= (mass_above-mass_below)[numpy.argsort(sindx)]
# Solve for all collisions, using C code for consistency
tcoll= []
for xi,vi,ai,xii,vii,aii in zip(x[sindx][:-1],v[sindx][:-1],a[sindx][:-1],
x[sindx][1:],v[sindx][1:],a[sindx][1:]):
if omega is None:
tcoll.append(_wendy_solve_coll_quad_func(xi-xii,vi-vii,(ai-aii)/2.))
else:
tcoll.append(_wendy_solve_coll_harm_func(xi-xii,vi-vii,ai-aii,omega))
tcoll= numpy.array(tcoll)
cindx= ctypes.c_int(numpy.argmin(tcoll))
next_tcoll= ctypes.c_double(tcoll[cindx])
# Prepare for C
err= ctypes.c_int(0)
#Array requirements
x= numpy.require(x,dtype=numpy.float64,requirements=['C','W'])
v= numpy.require(v,dtype=numpy.float64,requirements=['C','W'])
a= numpy.require(a,dtype=numpy.float64,requirements=['C','W'])
m= numpy.require(m,dtype=numpy.float64,requirements=['C','W'])
sindx= numpy.require(sindx,dtype=numpy.int32,requirements=['C','W'])
tcoll= numpy.require(tcoll,dtype=numpy.float64,requirements=['C','W'])
return (x,v,m,a,sindx,cindx,next_tcoll,tcoll,err) Example 38
def calculate(self, parameters_dict):
"""Calculate DKI statistics like the mean, axial and radial kurtosis.
The Mean Kurtosis (MK) is calculated by averaging the Kurtosis over orientations on the unit sphere.
The Axial Kurtosis (AK) is obtained using the principal direction of diffusion (fe; first eigenvec)
from the Tensor as its direction and then averaging the Kurtosis over +fe and -fe.
Finally, the Radial Kurtosis (RK) is calculated by averaging the Kurtosis over a circle of directions around
the first eigenvec.
Args:
parameters_dict (dict): the fitted Kurtosis parameters, this requires a dictionary with at least
the elements:
'd', 'dperp0', 'dperp1', 'theta', 'phi', 'psi', 'W_0000', 'W_1000', 'W_1100', 'W_1110',
'W_1111', 'W_2000', 'W_2100', 'W_2110', 'W_2111', 'W_2200', 'W_2210', 'W_2211',
'W_2220', 'W_2221', 'W_2222'.
Returns:
dict: maps for the Mean Kurtosis (MK), Axial Kurtosis (AK) and Radial Kurtosis (RK).
"""
if parameters_dict['d'].dtype == np.float32:
np_dtype = np.float32
mot_float_type = SimpleCLDataType.from_string('float')
double_precision = False
else:
np_dtype = np.float64
mot_float_type = SimpleCLDataType.from_string('double')
double_precision = True
param_names = ['d', 'dperp0', 'dperp1', 'theta', 'phi', 'psi', 'W_0000', 'W_1000', 'W_1100', 'W_1110',
'W_1111', 'W_2000', 'W_2100', 'W_2110', 'W_2111', 'W_2200', 'W_2210', 'W_2211',
'W_2220', 'W_2221', 'W_2222']
parameters = np.require(np.column_stack([parameters_dict[n] for n in param_names]),
np_dtype, requirements=['C', 'A', 'O'])
directions = convert_data_to_dtype(self._get_spherical_samples(), 'mot_float_type4', mot_float_type)
return self._calculate(parameters, param_names, directions, double_precision) Example 39
def required(ob):
"""Return an object that meets Shapely requirements for self-owned
C-continguous data, copying if necessary, or just return the original
object."""
if has_numpy and hasattr(ob, '__array_interface__'):
return numpy.require(ob, numpy.float64, ["C", "OWNDATA"])
else:
return ob Example 40
def set_and_check_flag(self, flag, dtype, arr):
if dtype is None:
dtype = arr.dtype
b = np.require(arr, dtype, [flag])
assert_(b.flags[flag])
assert_(b.dtype == dtype)
# a further call to np.require ought to return the same array
# unless OWNDATA is specified.
c = np.require(b, None, [flag])
if flag[0] != 'O':
assert_(c is b)
else:
assert_(c.flags[flag]) Example 41
def test_unknown_requirement(self):
a = self.generate_all_false('f8')
assert_raises(KeyError, np.require, a, None, 'Q') Example 42
def test_non_array_input(self):
a = np.require([1, 2, 3, 4], 'i4', ['C', 'A', 'O'])
assert_(a.flags['O'])
assert_(a.flags['C'])
assert_(a.flags['A'])
assert_(a.dtype == 'i4')
assert_equal(a, [1, 2, 3, 4]) Example 43
def test_C_and_F_simul(self):
a = self.generate_all_false('f8')
assert_raises(ValueError, np.require, a, None, ['C', 'F']) Example 44
def test_ensure_array(self):
class ArraySubclass(np.ndarray):
pass
a = ArraySubclass((2, 2))
b = np.require(a, None, ['E'])
assert_(type(b) is np.ndarray) Example 45
def slidingWindow(s, span, stride=None, axis=0):
"""Sliding window.
"""
#s = np.ascontiguousarray(s)
s = np.require(s, requirements=['C', 'O'])
if stride is None:
stride = span
# catch some bad values since this is a common place for
# bugs to crop up in other routines
if span > s.shape[axis]:
raise ValueError('Span of %d exceeds input length of %d.' % (span, s.shape[axis]))
if span < 0:
raise ValueError('Negative span of %d is invalid.' % span)
if stride < 1:
raise ValueError('Stride of %d is not positive.' % stride)
nWin = int(np.ceil((s.shape[axis]-span+1) / float(stride)))
shape = list(s.shape)
shape[axis] = span
shape.insert(axis, nWin)
strides = list(s.strides)
strides.insert(axis, stride*strides[axis])
return npst.as_strided(s, shape=shape, strides=strides) Example 46
def train(self, x, g, optimFunc, **kwargs):
x = util.segmat(x)
x = np.require(x, requirements=['O', 'C'])
g = util.segmat(g)
g = np.require(g, requirements=['O', 'C'])
self.trainResult = optimFunc(self, x=x, g=g, **kwargs) Example 47
def train(self, classData, optimFunc, **kwargs):
x, g = label.indicatorsFromList(classData)
x = np.require(x, requirements=['O', 'C'])
self.trainResult = optimFunc(self, x=x, g=g, **kwargs) Example 48
def train(self, classData, optimFunc, **kwargs):
x, g = label.indicatorsFromList(classData)
x = np.require(x, requirements=['O', 'C'])
self.trainResult = optimFunc(self, x=x, g=g, **kwargs)
#dv = self.discrim(x, accum='mult')
#self.normSoftmaxMean = dv.mean()
#self.normSoftmaxStd = dv.std()
#self.normSoftmaxMin = dv.min()
#self.normSoftmaxMax = (dv-self.normSoftmaxMin).max() Example 49
def _bad_tt2000(*args, **kwargs):
"""Convenience function for complaining that TT2000 not supported"""
raise NotImplementedError(
'TT2000 functions require CDF library 3.4.0 or later') Example 50
def convert_input_array(self, buffer):
"""Converts a buffer of raw data from this slice
EPOCH(16) variables always need to be converted.
CHAR need converted to Unicode if py3k
Parameters
==========
buffer : numpy.array
data as read from the CDF file
Returns
=======
out : numpy.array
converted data
"""
result = self._flip_array(buffer)
#Convert to derived types
cdftype = self.zvar.type()
if not self.zvar._raw:
if cdftype in (const.CDF_CHAR.value, const.CDF_UCHAR.value) and \
str != bytes:
dt = numpy.dtype('U{0}'.format(result.dtype.itemsize))
result = numpy.require(numpy.char.array(result).decode(),
dtype=dt)
elif cdftype == const.CDF_EPOCH.value:
result = lib.v_epoch_to_datetime(result)
elif cdftype == const.CDF_EPOCH16.value:
result = lib.v_epoch16_to_datetime(result)
elif cdftype == const.CDF_TIME_TT2000.value:
result = lib.v_tt2000_to_datetime(result)
return result 