def analysis(pointxy, pointxy1, values, methodOfAnalysis=""):
global file, singleValues, distances
t = values.split(" ")
COL = int(t[1])
ROW = int(t[0])
list = []
for r in range(ROW):
for c in range(COL):
list.append(
(r, c, pointxy[0], pointxy[1], pointxy1[0], pointxy1[1]))
shared_array_base = Array(c_double, ROW * COL)
singleValues = as_array(shared_array_base.get_obj())
singleValues = singleValues.reshape(COL, ROW)
shared_array = Array(c_double, ROW * COL * 50)
distances = as_array(shared_array.get_obj())
distances = distances.reshape(COL, ROW, 50)
with ProcessPoolExecutor() as executor:
if methodOfAnalysis is "strain":
executor.map(multiprocessing_func, list)
else:
executor.map(intensity, list)
entry.delete(0, tk.END)
def test_cgeometrylist_from_geometrylist():
"""Tests `from_geometrylist` of the `CGeometryList` class."""
x_coordinates = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=np.double)
y_coordinates = np.array([5.0, 6.0, 7.0, 8.0, 9.0], dtype=np.double)
values = np.array([10.0, 11.0, 12.0, 13.0, 14.0], dtype=np.double)
geometry_separator = 15.0
inner_outer_separator = 16.0
geometry_list = GeometryList(x_coordinates, y_coordinates)
geometry_list.values = values
geometry_list.geometry_separator = geometry_separator
geometry_list.inner_outer_separator = inner_outer_separator
c_geometry_list = CGeometryList.from_geometrylist(geometry_list)
# Get the numpy arrays from the ctypes object
c_geometry_list_x_coordinates = as_array(c_geometry_list.x_coordinates,
(5, ))
c_geometry_list_y_coordinates = as_array(c_geometry_list.y_coordinates,
(5, ))
c_geometry_list_values = as_array(c_geometry_list.values, (5, ))
assert_array_equal(c_geometry_list_x_coordinates, x_coordinates)
assert_array_equal(c_geometry_list_y_coordinates, y_coordinates)
assert_array_equal(c_geometry_list_values, values)
assert c_geometry_list.geometry_separator == geometry_separator
assert c_geometry_list.inner_outer_separator == inner_outer_separator
assert c_geometry_list.n_coordinates == x_coordinates.size
def setup_stage():
"""docstring for setup_stage
"""
import ctypes
e7xx = ctypes.windll.LoadLibrary('E7XX_GCS_DLL.dll')
try:
print "Connecting to stage"
id = e7xx.E7XX_ConnectRS232(1, 57600)
print "Initializing axes"
e7xx.E7XX_INI(id, '134')
print "initializing servos"
err = e7xx.E7XX_SVO(id, '134', ctl.as_ctypes(ones(4, dtype=int32)))
if err:
print "Servos initialized OK"
else:
import sys
sys.exit(e7xx.E7XX_GetError(id))
svo = ctl.as_ctypes(ones(4, dtype=int32))
err = e7xx.E7XX_qSVO(id, '134', svo)
if err:
print "Read servos OK"
else:
print e7xx.E7XX_GetError(id)
time.sleep(5)
while not(all(ctl.as_array(svo))):
e7xx.E7XX_qSVO(id, '134', svo)
print "Servo status: ", ctl.as_array(svo), ctl.as_array(svo).all()
time.sleep(1)
finally:
return e7xx, id
def test_cmesh1d_from_mesh1d():
r"""Tests `from_mesh1d` of the `CMesh1D` class with a simple mesh.
1 3
/ \ /
0 2
"""
node_x = np.array([0.0, 1.0, 2.0, 3.0], dtype=np.double)
node_y = np.array([0.0, 1.0, 0.0, 1.0], dtype=np.double)
edge_nodes = np.array([0, 1, 1, 2, 2, 3], dtype=np.int32)
mesh1d = Mesh1d(node_x, node_y, edge_nodes)
c_mesh1d = CMesh1d.from_mesh1d(mesh1d)
# Get the numpy arrays from the ctypes object
c_mesh1d_node_x = as_array(c_mesh1d.node_x, (4, ))
c_mesh1d_node_y = as_array(c_mesh1d.node_y, (4, ))
c_mesh1d_edge_nodes = as_array(c_mesh1d.edge_nodes, (6, ))
# Assert data is correct
assert_array_equal(c_mesh1d_node_x, node_x)
assert_array_equal(c_mesh1d_node_y, node_y)
assert_array_equal(c_mesh1d_edge_nodes, edge_nodes)
assert c_mesh1d.num_nodes == 4
assert c_mesh1d.num_edges == 3
def run(self):
print "CameraStreamer.run(): [pid: {}, OS pid: {}]".format(self.pid, os.getpid())
# * Interpret shared objects properly (NOTE this needs to happen in the child process)
self.image = ctypeslib.as_array(self.imageObj.get_obj()) # get flattened image array
self.image.shape = ctypeslib.as_array(self.imageShapeObj.get_obj()) # restore original shape
# * Open camera and set desired capture properties
self.camera = cv2.VideoCapture(0)
if self.camera.isOpened():
result_width = self.camera.set(cv.CV_CAP_PROP_FRAME_WIDTH, camera_frame_width)
result_height = self.camera.set(cv.CV_CAP_PROP_FRAME_HEIGHT, camera_frame_height)
print "CameraStreamer.run(): Camera frame size set to {width}x{height} (result: {result_width}, {result_height})".format(width=camera_frame_width, height=camera_frame_height, result_width=result_width, result_height=result_height)
else:
print "CameraStreamer.run(): Unable to open camera; aborting..."
self.stayAliveObj.value = False
return
# * Keep reading frames into shared image until stopped or read error occurs
while self.stayAliveObj.value:
try:
#print "CameraStreamer.run(): Frame # {}, stay alive? {}".format(self.frameCountObj.value, self.stayAliveObj.value) # [debug]
isOkay, frame = self.camera.read()
if not isOkay:
self.stayAliveObj.value = False
self.frameCountObj.value = self.frameCountObj.value + 1
self.image[:] = frame
except KeyboardInterrupt:
self.stayAliveObj.value = False
# * Clean-up
self.camera.release()
def analysis(pointxy, values, methodOfAnalysis):
global file, singleValues, distances, Usethisarray, mean_dist
t = values.split(" ")
COL = int(t[1])
ROW = int(t[0])
list = []
for r in range(ROW):
for c in range(COL):
list.append((r, c, pointxy[0], pointxy[1]))
shared_array_base = Array(c_double, ROW * COL)
singleValues = as_array(shared_array_base.get_obj())
singleValues = singleValues.reshape(COL, ROW)
shared_array = Array(c_double, ROW * COL * 20)
distances = as_array(shared_array.get_obj())
distances = distances.reshape(COL, ROW, 20)
with ProcessPoolExecutor() as executor:
if methodOfAnalysis is "strain":
executor.map(multiprocessing_func, list)
else:
executor.map(intensity, list)
entry.delete(0, tk.END)
count = 0
for i in singleValues:
mean_dist += i
count = count + 1
temp = 0
mean_dist = mean_dist / count
for i in range(len(singleValues)):
temp = singleValues[i]
singleValues[i] = (temp**(-1) - mean_dist**(-1)) / (mean_dist**(-1))
def test_struct_array_pointer(self):
from ctypes import c_int16, Structure, pointer
class Struct(Structure):
_fields_ = [('a', c_int16)]
Struct3 = 3 * Struct
c_array = (2 * Struct3)(
Struct3(Struct(a=1), Struct(a=2), Struct(a=3)),
Struct3(Struct(a=4), Struct(a=5), Struct(a=6))
)
expected = np.array([
[(1,), (2,), (3,)],
[(4,), (5,), (6,)],
], dtype=[('a', np.int16)])
def check(x):
assert_equal(x.dtype, expected.dtype)
assert_equal(x, expected)
# all of these should be equivalent
check(as_array(c_array))
check(as_array(pointer(c_array), shape=()))
check(as_array(pointer(c_array[0]), shape=(2,)))
check(as_array(pointer(c_array[0][0]), shape=(2, 3)))
def test_populate_z_photosphere(clib, formal_integral_model, p):
'''
Test the case where p < r[0]
That means we 'hit' all shells from inside to outside.
'''
func = clib.populate_z
func.restype = ctypes.c_int64
func.argtypes = [
ctypes.POINTER(StorageModel), # storage
c_double, # p
ndpointer(dtype=np.float64), # oz
ndpointer(dtype=np.int64) # oshell_id
]
size = formal_integral_model.no_of_shells_i
r_inner = as_array(formal_integral_model.r_inner_i, (size, ))
r_outer = as_array(formal_integral_model.r_outer_i, (size, ))
p = r_inner[0] * p
oz = np.zeros_like(r_inner)
oshell_id = np.zeros_like(oz, dtype=np.int64)
N = func(formal_integral_model, p, oz, oshell_id)
assert N == size
ntest.assert_allclose(oshell_id, np.arange(0, size, 1))
ntest.assert_allclose(oz, 1 - calculate_z(r_outer, p), atol=1e-5)
def K2y(self, X, X2, y):
res = zeros(X.shape[0])
if len(X.shape) == 1:
X = X.reshape((X.shape[0], 1))
if len(X2.shape) == 1:
X2 = X2.reshape((X2.shape[0], 1))
share_base = Array(ctypes.c_double, X.shape[0]*X.shape[1], lock=False)
share = as_array(share_base)
share = share.reshape(X.shape)
share[:, :] = X
share2_base = Array(ctypes.c_double, X2.shape[0]*X2.shape[1], lock=False)
share2 = as_array(share2_base)
share2 = share2.reshape(X2.shape)
share2[:, :] = X2
pool = Pool(self.num_proc, maxtasksperchild=50, initializer=initShared2, initargs=[share2, share])
cs = pool.imap(para_func2, ((i, X2.shape, X.shape, self.d, self.cnum) for i in xrange(self.m)), 10)
for c, c2 in cs:
for cls in unique(c):
if cls > -1:
res[c.flatten() == cls] += y[c2.flatten() == cls].sum()
res /= self.m
pool.close()
pool.join()
return res
def get_equiv_atom_map(crystal_coordinate, rotation, translation,
index_reference_atom):
"""Wrapper for get_equiv_atom_map defined in equiv_map.c
"""
libsym = ct.CDLL(os.path.dirname(__file__) + os.path.sep + 'libsymm.so',
mode=ct.RTLD_GLOBAL)
libsym.get_equiv_atom_map.argtypes = [
ct.c_int,
npct.ndpointer(dtype=float, ndim=2, flags='C_CONTIGUOUS'), ct.c_int,
npct.ndpointer(dtype=np.int32, ndim=3, flags='C_CONTIGUOUS'),
npct.ndpointer(dtype=float, ndim=2, flags='C_CONTIGUOUS')
]
libsym.get_equiv_atom_map.restype = ct.POINTER(ct.POINTER(ct.c_int))
_equiv_atom_map = libsym.get_equiv_atom_map(len(crystal_coordinate),
crystal_coordinate,
len(rotation), rotation,
translation,
index_reference_atom)
map_count = npct.as_array(_equiv_atom_map[0], (len(crystal_coordinate), ))
max_count = max(map_count)
equiv_atom_map = npct.as_array(
_equiv_atom_map[1],
(len(crystal_coordinate), len(crystal_coordinate), max_count))
return equiv_atom_map
def analysis(pointxy, values, methodOfAnalysis=""):
global file, singleValues, distances
t = values.split(" ")
COL = int(t[1])
ROW = int(t[0])
list = []
for r in range(ROW):
for c in range(COL):
list.append((r, c, pointxy[0], pointxy[1]))
shared_array_base = Array(c_double, ROW * COL)
singleValues = as_array(shared_array_base.get_obj())
singleValues = singleValues.reshape(COL, ROW)
shared_array = Array(c_double, ROW * COL * 50)
distances = as_array(shared_array.get_obj())
distances = distances.reshape(COL, ROW, 50)
with ProcessPoolExecutor() as executor:
if methodOfAnalysis is "strain":
executor.map(multiprocessing_func, list)
else:
executor.map(intensity, list)
entry.delete(0, tk.END)
f = open("Distances", "w")
w = writer(f)
for i in distances:
w.writerow(i)
f.close()
label1['text'] = label1['text'] + "File saved.\n"
entry.delete(0, tk.END)
def _get_parameters(self):
gx = POINTER(c_double)()
nx = c_int()
gy = POINTER(c_double)()
ny = c_int()
gz = POINTER(c_double)()
nz = c_int()
# Call C API to get grid parameters
_dll.openmc_rectilinear_mesh_get_grid(self._index, gx, nx, gy, ny, gz,
nz)
# Convert grid parameters to Numpy arrays
grid_x = as_array(gx, (nx.value, ))
grid_y = as_array(gy, (ny.value, ))
grid_z = as_array(gz, (nz.value, ))
# Calculate lower_left, upper_right, width, and dimension from grid
lower_left = np.array((grid_x[0], grid_y[0], grid_z[0]))
upper_right = np.array((grid_x[-1], grid_y[-1], grid_z[-1]))
dimension = np.array((nx.value - 1, ny.value - 1, nz.value - 1))
width = np.zeros(list(dimension) + [3])
for i, diff_x in enumerate(np.diff(grid_x)):
for j, diff_y in enumerate(np.diff(grid_y)):
for k, diff_z in enumerate(np.diff(grid_z)):
width[i, j, k, :] = diff_x, diff_y, diff_z
return (lower_left, upper_right, dimension, width)
def test_struct_array_pointer(self):
from ctypes import c_int16, Structure, pointer
class Struct(Structure):
_fields_ = [('a', c_int16)]
Struct3 = 3 * Struct
c_array = (2 * Struct3)(Struct3(Struct(a=1), Struct(a=2), Struct(a=3)),
Struct3(Struct(a=4), Struct(a=5), Struct(a=6)))
expected = np.array([
[(1, ), (2, ), (3, )],
[(4, ), (5, ), (6, )],
],
dtype=[('a', np.int16)])
def check(x):
assert_equal(x.dtype, expected.dtype)
assert_equal(x, expected)
# all of these should be equivalent
check(as_array(c_array))
check(as_array(pointer(c_array), shape=()))
check(as_array(pointer(c_array[0]), shape=(2, )))
check(as_array(pointer(c_array[0][0]), shape=(2, 3)))
def test_populate_z_shells(clib, formal_integral_model, p):
'''
Test the case where p > r[0]
'''
func = clib.populate_z
func.restype = ctypes.c_int64
func.argtypes = [
ctypes.POINTER(StorageModel), # storage
c_double, # p
ndpointer(dtype=np.float64), # oz
ndpointer(dtype=np.int64) # oshell_id
]
size = formal_integral_model.no_of_shells
r_inner = as_array(formal_integral_model.r_inner, (size,))
r_outer = as_array(formal_integral_model.r_outer, (size,))
p = r_inner[0] + (r_outer[-1] - r_inner[0]) * p
idx = np.searchsorted(r_outer, p, side='right')
oz = np.zeros(size * 2)
oshell_id = np.zeros_like(oz, dtype=np.int64)
offset = size - idx
expected_N = (offset) * 2
expected_oz = np.zeros_like(oz)
expected_oshell_id = np.zeros_like(oshell_id)
# Calculated way to determine which shells get hit
expected_oshell_id[:expected_N] = np.abs(
np.arange(0.5, expected_N, 1) - offset) - 0.5 + idx
expected_oz[0:offset] = 1 + calculate_z(
r_outer[np.arange(size, idx, -1) - 1],
p)
expected_oz[offset:expected_N] = 1 - calculate_z(
r_outer[np.arange(idx, size, 1)],
p)
N = func(
formal_integral_model,
p,
oz,
oshell_id
)
assert N == expected_N
ntest.assert_allclose(
oshell_id,
expected_oshell_id
)
ntest.assert_allclose(
oz,
expected_oz,
atol=1e-5
)
def test_populate_z_shells(clib, formal_integral_model, p):
'''
Test the case where p > r[0]
'''
func = clib.populate_z
func.restype = ctypes.c_int64
func.argtypes = [
ctypes.POINTER(StorageModel), # storage
c_double, # p
ndpointer(dtype=np.float64), # oz
ndpointer(dtype=np.int64) # oshell_id
]
size = formal_integral_model.no_of_shells_i
r_inner = as_array(formal_integral_model.r_inner_i, (size,))
r_outer = as_array(formal_integral_model.r_outer_i, (size,))
p = r_inner[0] + (r_outer[-1] - r_inner[0]) * p
idx = np.searchsorted(r_outer, p, side='right')
oz = np.zeros(size * 2)
oshell_id = np.zeros_like(oz, dtype=np.int64)
offset = size - idx
expected_N = (offset) * 2
expected_oz = np.zeros_like(oz)
expected_oshell_id = np.zeros_like(oshell_id)
# Calculated way to determine which shells get hit
expected_oshell_id[:expected_N] = np.abs(
np.arange(0.5, expected_N, 1) - offset) - 0.5 + idx
expected_oz[0:offset] = 1 + calculate_z(
r_outer[np.arange(size, idx, -1) - 1],
p)
expected_oz[offset:expected_N] = 1 - calculate_z(
r_outer[np.arange(idx, size, 1)],
p)
N = func(
formal_integral_model,
p,
oz,
oshell_id
)
assert N == expected_N
ntest.assert_allclose(
oshell_id,
expected_oshell_id
)
ntest.assert_allclose(
oz,
expected_oz,
atol=1e-5
)
def calc_correlation(params):
global POW_trace, POW_hypo, CORRELATION
a, b, c = params
POW_hypo = ctypeslib.as_array(POW_hypo)
POW_trace = ctypeslib.as_array(POW_trace)
CORRELATION = ctypeslib.as_array(CORRELATION)
# Use numpy corrcoef() function to calculate correlation coefficient
CORRELATION[a][b:c] = corrcoef(POW_hypo[a], POW_trace[b:c])[0][1]
def getCorrcoef(params):
global PC_a, PC_h, CC
(i, j1, j2) = params
PC_h = ctypeslib.as_array(PC_h)
PC_a = ctypeslib.as_array(PC_a)
CC = ctypeslib.as_array(CC)
cor = corrcoef(PC_h[i], PC_a[j1:j2])[0][1]
CC[i][j1:j2] = cor
def _get_parameters(self):
ll = POINTER(c_double)()
ur = POINTER(c_double)()
w = POINTER(c_double)()
n = c_int()
_dll.openmc_regular_mesh_get_params(self._index, ll, ur, w, n)
return (as_array(ll, (n.value, )), as_array(ur, (n.value, )),
as_array(w, (n.value, )))
def wrapper(n, x, new_x, m, g_ptr, user_data):
try:
x_array = as_array(x, (n, ))
g_array = as_array(g_ptr, (m, ))
return g(x_array, new_x, g_array)
except BaseException as e:
if callable(handler):
handler(e)
return 0
def wrapper(n, x, new_x, obj_value, user_data):
try:
x_array = as_array(x, (n, ))
obj_value_array = as_array(obj_value, ())
return f(x_array, new_x, obj_value_array)
except BaseException as e:
if callable(handler):
handler(e)
return 0
def callback_(mappingPtr, centerPtr):
mapping = ctl.as_array(mappingPtr, (len(points), ))
nclust = np.max(mapping) + 1
centers = ctl.as_array(centerPtr, (nclust, numdims))
if cb is not None:
r = cb(mapping, centers)
if r is None:
return 1
return r
return 1
def test_array(self):
from ctypes import c_int
pair_t = c_int * 2
a = as_array(pair_t(1, 2))
assert_equal(a.shape, (2, ))
assert_array_equal(a, np.array([1, 2]))
a = as_array((pair_t * 3)(pair_t(1, 2), pair_t(3, 4), pair_t(5, 6)))
assert_equal(a.shape, (3, 2))
assert_array_equal(a, np.array([[1, 2], [3, 4], [5, 6]]))
def test_array(self):
from ctypes import c_int
pair_t = c_int * 2
a = as_array(pair_t(1, 2))
assert_equal(a.shape, (2,))
assert_array_equal(a, np.array([1, 2]))
a = as_array((pair_t * 3)(pair_t(1, 2), pair_t(3, 4), pair_t(5, 6)))
assert_equal(a.shape, (3, 2))
assert_array_equal(a, np.array([[1, 2], [3, 4], [5, 6]]))
def wrapper(n, x, new_x, m, nele_jac, iRow, jCol, values, user_data):
try:
x_array = as_array(x, (n, )) if x else None
i_array = as_array(iRow, (nele_jac, )) if iRow else None
j_array = as_array(jCol, (nele_jac, )) if jCol else None
values_array = as_array(values, (nele_jac, )) if values else None
return jac_g(x_array, new_x, i_array, j_array, values_array)
except BaseException as e:
if callable(handler):
handler(e)
return 0
def worker_fit(id_w, num_workers, X_w, y_w, weights_w, shape, indices,
counter, start_barrier, params_w):
assert params_w.regularizer is not None
# reconstruct numpy shared array
num_samples, num_features = shape
weights_w = ctypeslib.as_array(weights_w)
weights_w.shape = (num_features, )
if not isspmatrix(X_w):
X_w = ctypeslib.as_array(X_w)
X_w.shape = (num_samples, num_features)
y_w = ctypeslib.as_array(y_w)
y_w.shape = (num_samples, )
memory = GradientMemory(take_k=params_w.take_k,
take_top=params_w.take_top,
with_memory=params_w.with_memory)
start_barrier.wait()
while True:
with counter.get_lock():
idx = counter.value
counter.value += 1
if idx >= num_samples * params_w.num_epoch:
break
sample_idx = indices[idx]
epoch = idx // num_samples
iteration = idx % num_samples
lr = self.lr(epoch, iteration, num_samples, num_features)
x = X_w[sample_idx]
if isspmatrix(x):
minus_grad = -1. * params_w.regularizer * weights_w
sparse_minus_grad = y[sample_idx] * x * sigmoid(
-y[sample_idx] * x.dot(weights_w).squeeze(0))
minus_grad[
sparse_minus_grad.indices] += sparse_minus_grad.data
else:
minus_grad = y[sample_idx] * x * sigmoid(
-y[sample_idx] * x.dot(weights_w))
minus_grad -= params_w.regularizer * weights_w
sparse = params_w.take_k and (params_w.take_k < num_features)
lr_minus_grad = memory(lr * minus_grad, sparse=sparse)
if sparse:
weights_w[lr_minus_grad[0]] += lr_minus_grad[1]
else:
weights_w += lr_minus_grad
def callback_(startA, numspotsA, countsPtr, indicesPtr, numIndices):
#print(f"num indices: {numIndices}. numspotsA: {numspotsA}")
counts = ctl.as_array(countsPtr, (numspotsA, ))
if numIndices == 0:
indices = np.zeros(0, dtype=np.int32)
else:
indices = ctl.as_array(indicesPtr, (numIndices, ))
r = callback(startA, counts, indices)
if r is None:
return 1
return r
def eval_wrapper(instance, x, g, n, step):
"""Wrapper function to handle converting to numpy data structures"""
x = npct.as_array(x, (n,))
g = npct.as_array(g, (n,))
if instance:
instance = instance.contents
else:
instance = None
fx, gret = evaluate(instance, x, n, step)
g[:] = gret
return fx
def wrapper(n, x, new_x, obj_factor, m, mult, new_mult, nele_hess, iRow,
jCol, values, user_data):
try:
x_array = as_array(x, (n, )) if x else None
mult_array = as_array(mult, (m, )) if mult else None
i_array = as_array(iRow, (nele_hess, )) if iRow else None
j_array = as_array(jCol, (nele_hess, )) if jCol else None
values_array = as_array(values, (nele_hess, )) if values else None
return h(x_array, new_x, obj_factor, mult_array, new_mult, i_array,
j_array, values_array)
except BaseException as e:
if callable(handler):
handler(e)
return 0
def dumper(nSamples,nlive,nPar, physLive,posterior,paramConstr, maxLogLike,logZ,logZerr,nullcontext): if dump_callback: # It's not clear to me what the desired PyMultiNest dumper callback # syntax is... but this should pass back the right numpy arrays, # without copies. Untested! pc = as_array(paramConstr,shape=(nPar,4)) dump_callback(nSamples,nlive,nPar, as_array(physLive,shape=(nPar+1,nlive)).T, as_array(posterior,shape=(nPar+2,nSamples)).T, (pc[:,0],pc[:,1],pc[:,2],pc[:,3]), # (mean,std,bestfit,map) maxLogLike,logZ,logZerr, 0)
def attack():
global SAMPLE_SIZE, KEY_SIZE, TRACE_NUM, POW_hypo, POW, trace, CORRELATION
key1 = ""
key2 = ""
while True:
# Initialise memory for parallel processing
hypo_arr = ctypeslib.as_array(
multiprocessing.Array(ctypes.c_float,
KEY_SIZE * SAMPLE_SIZE).get_obj())
POW_hypo = hypo_arr.reshape(KEY_SIZE, SAMPLE_SIZE)
trace_arr = ctypeslib.as_array(
multiprocessing.Array(ctypes.c_float,
SAMPLE_SIZE * TRACE_NUM).get_obj())
POW_trace = trace_arr.reshape(TRACE_NUM, SAMPLE_SIZE)
corrco_arr = ctypeslib.as_array(
multiprocessing.Array(ctypes.c_float,
KEY_SIZE * TRACE_NUM).get_obj())
CORRELATION = corrco_arr.reshape(KEY_SIZE, TRACE_NUM)
# Generate ciphertext inputs
ciphertexts = gen_ciphers()
# Acquire oracle decryptions and power consumption traces
traces, plaintexts = gen_samples(ciphertexts)
traces = next_trace_set(traces)
# Attack Key 2
print "---- KEY 2 ATTACK ----\n"
key2 = attack_key2(ciphertexts, traces)
print "KEY 2: " + key2
encrypt_tweak_vals(ciphertexts, key2)
# Attack Key 1
print "---- KEY 1 ATTACK ----\n"
key1 = attack_key1(plaintexts, traces)
print "KEY 1: " + key1
if not XTS_Validate(key1, key2):
# Double sample size
print "Invalid key recovered, attempting again with larger sample size"
SAMPLE_SIZE <<= 1
else:
break
return key1, key2
def dumper(nSamples,nlive,nPar,
physLive,posterior,paramConstr,
maxLogLike,logZ,logZerr,nullcontext):
if dump_callback:
# It's not clear what the desired MultiNest dumper callback
# syntax is... but this should pass back the right numpy arrays,
# without copies. Untested!
pc = as_array(paramConstr,shape=(nPar,4))
dump_callback(nSamples,nlive,nPar,
as_array(physLive,shape=(nPar+1,nlive)).T,
as_array(posterior,shape=(nPar+2,nSamples)).T,
(pc[0,:],pc[1,:],pc[2,:],pc[3,:]), # (mean,std,bestfit,map)
maxLogLike,logZ,logZerr)
def _initSharedMemory():
global PC_a, PC_h, CC
warnings.filterwarnings("ignore")
PC_h_base = multiprocessing.Array(ctypes.c_float, KEY_RANGE * SAMPLES)
PC_h = ctypeslib.as_array(PC_h_base.get_obj())
PC_h = PC_h.reshape(KEY_RANGE, SAMPLES)
PC_a_base = multiprocessing.Array(ctypes.c_float, SAMPLES * TRACE_NUM)
PC_a = ctypeslib.as_array(PC_a_base.get_obj())
PC_a = PC_a.reshape(TRACE_NUM, SAMPLES)
CC_base = multiprocessing.Array(ctypes.c_float, KEY_RANGE * TRACE_NUM)
CC = ctypeslib.as_array(CC_base.get_obj())
CC = CC.reshape(KEY_RANGE, TRACE_NUM)
def test_pointer(self):
from ctypes import c_int, cast, POINTER
p = cast((c_int * 10)(*range(10)), POINTER(c_int))
a = as_array(p, shape=(10,))
assert_equal(a.shape, (10,))
assert_array_equal(a, np.arange(10))
a = as_array(p, shape=(2, 5))
assert_equal(a.shape, (2, 5))
assert_array_equal(a, np.arange(10).reshape((2, 5)))
# shape argument is required
assert_raises(TypeError, as_array, p)
def test_pointer(self):
from ctypes import c_int, cast, POINTER
p = cast((c_int * 10)(*range(10)), POINTER(c_int))
a = as_array(p, shape=(10, ))
assert_equal(a.shape, (10, ))
assert_array_equal(a, np.arange(10))
a = as_array(p, shape=(2, 5))
assert_equal(a.shape, (2, 5))
assert_array_equal(a, np.arange(10).reshape((2, 5)))
# shape argument is required
assert_raises(TypeError, as_array, p)
def global_tallies():
"""Mean and standard deviation of the mean for each global tally.
Returns
-------
list of tuple
For each global tally, a tuple of (mean, standard deviation)
"""
ptr = POINTER(c_double)()
_dll.openmc_global_tallies(ptr)
array = as_array(ptr, (4, 3))
# Get sum, sum-of-squares, and number of realizations
sum_ = array[:, 1]
sum_sq = array[:, 2]
n = num_realizations()
# Determine mean
if n > 0:
mean = sum_ / n
else:
mean = sum_.copy()
# Determine standard deviation
nonzero = np.abs(mean) > 0
stdev = np.empty_like(mean)
stdev.fill(np.inf)
if n > 1:
stdev[nonzero] = np.sqrt((sum_sq[nonzero]/n - mean[nonzero]**2)/(n - 1))
return list(zip(mean, stdev))
def source_bank():
"""Return source bank as NumPy array
Returns
-------
numpy.ndarray
Source sites
"""
# Get pointer to source bank
ptr = POINTER(_SourceSite)()
n = c_int64()
_dll.openmc_source_bank(ptr, n)
try:
# Convert to numpy array with appropriate datatype
bank_dtype = np.dtype(_SourceSite)
return as_array(ptr, (n.value,)).view(bank_dtype)
except ValueError as err:
# If a known numpy error was raised (github.com/numpy/numpy/issues
# /14214), re-raise with a more helpful error message.
if len(err.args) == 0:
raise err
if err.args[0].startswith('invalid shape in fixed-type tuple'):
raise ValueError('The source bank is too large to access via '
'openmc.lib with this version of numpy. Use a different '
'version of numpy or reduce the bank size (fewer particles '
'per MPI process) so that it is smaller than 2 GB.') from err
else:
raise err
def make_numpy_item((v, shape)):
try:
v = ctypeslib.as_array(v)
v.shape = shape
except:
pass
return v
def cMatrixToNumpy(x):
"""
Convert a ctypes 2d array (or matrix) into a numpy array for python use
:param x: thing to convert
:return: numpy.ndarray
"""
return numpc.as_array(x).copy()
def global_tallies():
"""Mean and standard deviation of the mean for each global tally.
Returns
-------
list of tuple
For each global tally, a tuple of (mean, standard deviation)
"""
ptr = POINTER(c_double)()
_dll.openmc_global_tallies(ptr)
array = as_array(ptr, (4, 3))
# Get sum, sum-of-squares, and number of realizations
sum_ = array[:, 1]
sum_sq = array[:, 2]
n = num_realizations()
# Determine mean
if n > 0:
mean = sum_ / n
else:
mean = sum_.copy()
# Determine standard deviation
nonzero = np.abs(mean) > 0
stdev = np.empty_like(mean)
stdev.fill(np.inf)
if n > 1:
stdev[nonzero] = np.sqrt(
(sum_sq[nonzero] / n - mean[nonzero]**2) / (n - 1))
return list(zip(mean, stdev))
def get_dm_list(self):
ndms = self.get_dm_count()
func = lib.dedisp_get_dm_list
c_float_p = C.POINTER(C.c_float)
func.restype = c_float_p
array_pointer = C.cast(func(self.plan),c_float_p)
return as_array(array_pointer,shape=(ndms,)).copy()
def read(filename): ''' IIO: numpyarray = read(filename) ''' from numpy import array, zeros, ctypeslib from ctypes import c_int, c_float, c_void_p, POINTER, cast, byref iioread = libiio.iio_read_image_float_vec w=c_int() h=c_int() nch=c_int() iioread.restype = c_void_p # it's like this tptr = iioread(str(filename),byref(w),byref(h),byref(nch)) c_float_p = POINTER(c_float) # define a new type of pointer ptr = cast(tptr, c_float_p) #print w,h,nch #nasty read data into array using buffer copy #http://stackoverflow.com/questions/4355524/getting-data-from-ctypes-array-into-numpy #http://docs.scipy.org/doc/numpy/reference/generated/numpy.frombuffer.html # this numpy array uses the memory provided by the c library, which will be freed data_tmp = ctypeslib.as_array( ptr, (h.value,w.value,nch.value) ) # so we copy it to the definitive array before the free data = data_tmp.copy() # free the memory iiofreemem = libiio.freemem iiofreemem(ptr) return data
def read(filename):
'''
IIO: numpyarray = read(filename)
'''
from numpy import array, zeros, ctypeslib
from ctypes import c_int, c_float, c_void_p, POINTER, cast, byref
iioread = libiio.iio_read_image_float_vec
w = c_int()
h = c_int()
nch = c_int()
iioread.restype = c_void_p # it's like this
tptr = iioread(str(filename), byref(w), byref(h), byref(nch))
c_float_p = POINTER(c_float) # define a new type of pointer
ptr = cast(tptr, c_float_p)
#print w,h,nch
#nasty read data into array using buffer copy
#http://stackoverflow.com/questions/4355524/getting-data-from-ctypes-array-into-numpy
#http://docs.scipy.org/doc/numpy/reference/generated/numpy.frombuffer.html
# this numpy array uses the memory provided by the c library, which will be freed
data_tmp = ctypeslib.as_array(ptr, (h.value, w.value, nch.value))
# so we copy it to the definitive array before the free
data = data_tmp.copy()
# free the memory
iiofreemem = libiio.freemem
iiofreemem(ptr)
return data
def cMatrixToNumpy(x):
"""
Convert a ctypes 2d array (or matrix) into a numpy array for python use
:param x: thing to convert
:return: numpy.ndarray
"""
return numpc.as_array(x)
def borrow_memory(param, memory):
"""
Spawn different processes with the shared memory
of your theano model's variables.
Inputs:
-------
param TensorSharedVariable : the Theano shared variable where
shared memory should be used instead.
memory multiprocessing.sharedctypes : the memory shared across processes (e.g.
from `wrap_params`)
Outputs:
--------
None
Usage
-----
For each process in the target function run the theano_borrow_memory
method on the parameters you want to have share memory across processes.
In this example we have a model called "mymodel" with parameters stored in
a list called "params". We loop through each theano shared variable and
call `theano_borrow_memory` on it to share memory across processes.
def spawn_model(path, wrapped_params):
# prevent recompilation and arbitrary locks
theano.config.reoptimize_unpickled_function = False
theano.gof.compilelock.set_lock_status(False)
# load your model from its pickled instance (from path)
mymodel = MyModel.load(path)
# for each parameter in your model
# apply the borrow memory strategy to replace
# the internal parameter's memory with the
# across-process memory
for param, memory in zip(mymodel.params, wrapped_params):
borrow_memory(param, memory)
# acquire your dataset (either through some smart shared memory
# or by reloading it for each process)
dataset, dataset_labels = acquire_dataset()
# then run your model forward in this process
epochs = 20
for epoch in range(epochs):
model.update_fun(dataset, dataset_labels)
See `borrow_all_memories` for list usage.
"""
param_value = ctypeslib.as_array(memory)
param_value.shape = param.get_value(True,True).shape
param.set_value(param_value, borrow=True)
def mat_rowrange_mul(args):
# a little ugly, but allows running with a Pool
# which accept only 1 argument
a_row_domain, a_shape, b_shape, shared_a, shared_b, shared_c = args
# access shared memory object as numpy array, set dimensions
nd_c = ctypeslib.as_array(shared_c).reshape((a_shape[0],b_shape[1]))
nd_a = ctypeslib.as_array(shared_a).reshape(a_shape)
nd_b = ctypeslib.as_array(shared_b).reshape(b_shape)
# write answer to shared memory
# it would be better if numpy.dot could write "in-place"
nd_c[a_row_domain[0]:a_row_domain[1],:] = \
numpy.dot(nd_a[a_row_domain[0]:a_row_domain[1],:],nd_b)
return None
def K2(self, X, X2):
#if X.ndim == 0:
# X = X.reshape((1, 1))
#if X2.ndim == 0:
# X2 = X2.reshape((1, 1))
if X.ndim == 1:
X = X.reshape((X.shape[0], 1))
if X2.ndim == 1:
X2 = X2.reshape((X2.shape[0], 1))
if X.ndim == 0:
Xsh = 1
Xsh2 = 1
else:
Xsh = X.shape[0]
Xsh2 = X.shape[1]
if X2.ndim == 0:
X2sh = 1
X2sh2 = 1
else:
X2sh = X2.shape[0]
X2sh2 = X2.shape[1]
res = zeros((Xsh, X2sh))
share_base = Array(ctypes.c_double, Xsh*Xsh2, lock=False)
share = as_array(share_base)
share = share.reshape((Xsh, Xsh2))
share[:, :] = X
share2_base = Array(ctypes.c_double, X2sh*X2sh2, lock=False)
share2 = as_array(share2_base)
share2 = share2.reshape(X2.shape)
share2[:, :] = X2
pool = Pool(self.num_proc, maxtasksperchild=50, initializer=initShared2, initargs=[share2, share])
cs = pool.imap(para_func2, ((i, X2.shape, X.shape, self.d, self.cnum) for i in xrange(self.m)), 10)
for c, c2 in cs:
for i, c_v in enumerate(c):
for j, c_v2 in enumerate(c2):
if c_v == c_v2 and c_v != -1:
res[i, j] += 1.
res /= self.m
pool.close()
pool.join()
if X.ndim == 0:
res = res.flatten()
return res
def as_numpy( data ):
'''maps data content as a numpy array'''
ptr, shape, typename = data.getValueVoidPtr()
type = ctypeFromName.get(typename,None)
if not type: raise Exception("can't map data of type " + typename)
array = ctypes.cast( ctypes.c_void_p(ptr), ctypes.POINTER(type))
return ctypeslib.as_array(array, shape )
def make_numpy_item((v, shape)):
if shape is not None:
try:
v = ctypeslib.as_array(v)
v.shape = shape
log_debug('converting common array to numpy array')
except:
log_debug('NOT converting common array to numpy array')
pass
return v
def main():
ra = sharedctypes.RawArray("i", 4)
arr = ctypeslib.as_array(ra)
arr.shape = (2, 2)
p1 = Process(target=fill_arr, args=(arr[:1, :], 1))
p2 = Process(target=fill_arr, args=(arr[1:, :], 2))
p1.start()
p2.start()
p1.join()
p2.join()
print(arr)
def run(self):
print "StreamViewer.run(): [pid: {}, OS pid: {}]".format(self.pid, os.getpid())
# * Interpret shared objects properly (NOTE this needs to happen in the child process)
self.image = ctypeslib.as_array(self.imageObj.get_obj()) # get flattened image array
self.image.shape = ctypeslib.as_array(self.imageShapeObj.get_obj()) # restore original shape
print "StreamViewer.run(): Starting display loop [Esc or Q to quit]..."
while self.stayAliveObj.value:
try:
if self.frameCountObj.value != self.lastFrameCount:
cv2.imshow("Image", self.image)
self.lastFrameCount = self.frameCountObj.value
key = cv2.waitKey(frame_delay)
if key != -1:
keyCode = key & 0x00007f
keyChar = chr(keyCode)
if keyCode == 0x1b or keyChar == 'q':
self.stayAliveObj.value = False
except KeyboardInterrupt:
self.stayAliveObj.value = False
def move_and_image(mmc, e7xx, id, coords, exptime, image_queue, **kwargs):
"""
move_and_image moves the stage to the given coordinates, takes an
exposure for exptime seconds, then adds it to image_queue.
"""
DEBUG = False
for key in kwargs:
if key == "DEBUG":
DEBUG = True
else:
raise TypeError, 'Unknown argument "%s"' % key
if DEBUG:
print "Moving to ", coords
err = e7xx.E7XX_MOV(id, "14", ctl.as_ctypes(array(coords, dtype=float)))
if err:
print "Moved OK"
else:
err = e7xx.E7XX_GetError(id)
print err
time.sleep(0.03)
if DEBUG:
res = ctl.as_ctypes(empty(4, dtype=float))
e7xx.E7XX_qMOV(id, "14", res)
print "Moved to ", ctl.as_array(res)
noImage = True
while noImage:
try:
if image_queue.qsize() < 1000:
if DEBUG:
print "Snapping Image"
mmc.snapImage()
im1 = mmc.getImage()
if DEBUG:
print "Got image"
image_queue.put(im1)
if DEBUG:
print "Queueing image"
noImage = False
if DEBUG:
print "Leaving Loop"
except MemoryError:
if DEBUG:
print "Memory Error. Going to sleep"
time.sleep(1)
if DEBUG:
print "Done"
def AsDataStream(arr):
"""
copy numpy array to Ogre.MemoryDataStream that can be used in Ogre
@param arr: some numpy array
"""
size = int(np.prod(arr.shape) * arr.dtype.itemsize)
ret = Ogre.MemoryDataStream(size)
tp = ctypes.POINTER(ctypes.c_ubyte)
np_view = npc.as_array(ctypes.cast(int(ret.getPtr()), tp), (size, ))
np_view[:] = arr.ravel().view(np.ubyte)
return ret
def test_populate_z_photosphere(clib, formal_integral_model, p):
'''
Test the case where p < r[0]
That means we 'hit' all shells from inside to outside.
'''
func = clib.populate_z
func.restype = ctypes.c_int64
func.argtypes = [
ctypes.POINTER(StorageModel), # storage
c_double, # p
ndpointer(dtype=np.float64), # oz
ndpointer(dtype=np.int64) # oshell_id
]
size = formal_integral_model.no_of_shells_i
r_inner = as_array(formal_integral_model.r_inner_i, (size,))
r_outer = as_array(formal_integral_model.r_outer_i, (size,))
p = r_inner[0] * p
oz = np.zeros_like(r_inner)
oshell_id = np.zeros_like(oz, dtype=np.int64)
N = func(
formal_integral_model,
p,
oz,
oshell_id
)
assert N == size
ntest.assert_allclose(
oshell_id,
np.arange(0, size, 1)
)
ntest.assert_allclose(
oz,
1 - calculate_z(r_outer, p),
atol=1e-5
)
def __call__(self, *args):
input_data,component_memberships,loglikelihoods,num_components,num_dimensions,num_events,min_iters, max_iters,cvtype, ret_likelihood = args
#print input_data
input_data =input_data.ctypes.data_as(POINTER(c_float))
component_memberships = component_memberships.ctypes.data_as(POINTER(c_float))
loglikelihoods = loglikelihoods.ctypes.data_as(POINTER(c_float))
#return value
ret_likelihood = c_float()
ret_means = pointer(c_float())
ret_covar = pointer(c_float())
self._c_function(input_data,component_memberships,loglikelihoods,num_components,num_dimensions,num_events,min_iters, max_iters,cvtype, byref(ret_likelihood),byref(ret_means),byref(ret_covar))
return ret_likelihood.value,as_array(ret_means,shape=(num_components* num_dimensions,)),as_array(ret_covar,shape=(num_components* num_dimensions* num_dimensions,))
def kernel(A):
(arr, p) = pack_matrix(A)
kern = ffpack.kernel
kern.restype = ctypes.POINTER(ctypes.c_int)
kern.argtypes = [ndpointer(ctypes.c_int), ctypes.c_size_t,
ctypes.c_size_t, ctypes.c_int,
ctypes.POINTER(ctypes.c_size_t)]
kernel_size = ctypes.c_size_t(0)
K_p = kern(arr, A.row, A.column, p,
ctypes.byref(kernel_size))
dim = kernel_size.value
if dim == 0: return None
k_arr = as_array(K_p, (A.column, dim))
K = unpack_matrix((k_arr, p))
ffpack.free_k(K_p)
return K
def worker(id, job):
""" worker function for MP """
S = ctypeslib.as_array(S_ctypes)
S.shape = shape
for i in job:
for j in xrange(n_oxygen):
N = scan_nitrogen[i]
O = scan_oxygen[j]
param = (N, O)
hb_energies = calculate_hydrogen(param, shb)
param_energies = shb_pm6 + hb_energies
param_rmsd = rmsd(shb_lib, param_energies)
S[i, j] = param_rmsd
def worker(S, shape, i, j, A, C, conn):
S = ctypeslib.as_array(S)
S.shape = shape
S = S[:, i:j]
sys.stdout.flush()
while True:
try:
job = conn.recv()
except EOFError:
job = None
if job is None:
break
S[:] = dot(A, S)
if C is not None:
add(S, C, S)
conn.send(True)
conn.close()
def source_bank():
"""Return source bank as NumPy array
Returns
-------
numpy.ndarray
Source sites
"""
# Get pointer to source bank
ptr = POINTER(_Bank)()
n = c_int64()
_dll.openmc_source_bank(ptr, n)
# Convert to numpy array with appropriate datatype
bank_dtype = np.dtype(_Bank)
return as_array(ptr, (n.value,)).view(bank_dtype)
def get_matter_transfer_data(self):
"""
Get matter transfer function data and sigma8 for calculated results.
:return: :class:`.MatterTransferData` instance holding output arrays (copies, not pointers)
"""
if not self.Params.WantTransfer:
raise CAMBError("must have Params.WantTransfer to get matter transfers and power")
cdata = _MatterTransferData()
CAMBdata_mattertransferdata(self._key, byref(cdata))
data = MatterTransferData()
data.nq = cdata.num_q_trans
data.q = nplib.as_array(cdata.q_trans, shape=(data.nq,))
data.sigma_8 = fortran_array(cdata.sigma_8, cdata.sigma_8_size)
data.sigma2_vdelta_8 = fortran_array(cdata.sigma2_vdelta_8, cdata.sigma2_vdelta_8_size)
data.transfer_data = fortran_array(cdata.TransferData, cdata.TransferData_size, dtype=np.float32)
return data
