def simple_statistics(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
b = af.randu(5, 5)
w = af.randu(5, 1)
display_func(af.mean(a, dim=0))
display_func(af.mean(a, weights=w, dim=0))
print_func(af.mean(a))
print_func(af.mean(a, weights=w))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
display_func(af.var(a, weights=w, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
print_func(af.var(a, weights=w))
display_func(af.stdev(a, dim=0))
print_func(af.stdev(a))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
display_func(af.median(a, dim=0))
print_func(af.median(w))
print_func(af.corrcoef(a, b))
def simple_statistics(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
b = af.randu(5, 5)
w = af.randu(5, 1)
display_func(af.mean(a, dim=0))
display_func(af.mean(a, weights=w, dim=0))
print_func(af.mean(a))
print_func(af.mean(a, weights=w))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
display_func(af.var(a, weights=w, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
print_func(af.var(a, weights=w))
display_func(af.stdev(a, dim=0))
print_func(af.stdev(a))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
display_func(af.median(a, dim=0))
print_func(af.median(w))
print_func(af.corrcoef(a, b))
def initialize_f(q1, q2, p1, p2, p3, params):
m = params.mass_particle
k = params.boltzmann_constant
rho = af.select(af.abs(q2)>0.25, q1**0, 2)
p1_bulk = af.select(af.abs(q2)>0.25,
0.5 + 0.01 * (af.randu(1, q1.shape[1], q2.shape[2],
dtype = af.Dtype.f64
) - 0.5
),
-0.5 - 0.01 * (af.randu(1, q1.shape[1], q2.shape[2],
dtype = af.Dtype.f64
) - 0.5
)
)
p2_bulk = 0.01 * (af.randu(1, q1.shape[1], q2.shape[2],
dtype = af.Dtype.f64
) - 0.5
)
T = (0.8 / rho)
f = rho * (m / (2 * np.pi * k * T))**(3 / 2) \
* af.exp(-m * (p1 - p1_bulk)**2 / (2 * k * T)) \
* af.exp(-m * (p2 - p2_bulk)**2 / (2 * k * T)) \
* af.exp(-m * p3**2 / (2 * k * T))
af.eval(f)
return (f)
def simple_data(verbose=False):
display_func = _util.display_func(verbose)
display_func(af.constant(100, 3, 3, dtype=af.Dtype.f32))
display_func(af.constant(25, 3, 3, dtype=af.Dtype.c32))
display_func(af.constant(2**50, 3, 3, dtype=af.Dtype.s64))
display_func(af.constant(2+3j, 3, 3))
display_func(af.constant(3+5j, 3, 3, dtype=af.Dtype.c32))
display_func(af.range(3, 3))
display_func(af.iota(3, 3, tile_dims=(2, 2)))
display_func(af.identity(3, 3, 1, 2, af.Dtype.b8))
display_func(af.identity(3, 3, dtype=af.Dtype.c32))
a = af.randu(3, 4)
b = af.diag(a, extract=True)
c = af.diag(a, 1, extract=True)
display_func(a)
display_func(b)
display_func(c)
display_func(af.diag(b, extract=False))
display_func(af.diag(c, 1, extract=False))
display_func(af.join(0, a, a))
display_func(af.join(1, a, a, a))
display_func(af.tile(a, 2, 2))
display_func(af.reorder(a, 1, 0))
display_func(af.shift(a, -1, 1))
display_func(af.moddims(a, 6, 2))
display_func(af.flat(a))
display_func(af.flip(a, 0))
display_func(af.flip(a, 1))
display_func(af.lower(a, False))
display_func(af.lower(a, True))
display_func(af.upper(a, False))
display_func(af.upper(a, True))
a = af.randu(5, 5)
display_func(af.transpose(a))
af.transpose_inplace(a)
display_func(a)
display_func(af.select(a > 0.3, a, -0.3))
af.replace(a, a > 0.3, -0.3)
display_func(a)
display_func(af.pad(a, (1, 1, 0, 0), (2, 2, 0, 0)))
def simple_data(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
display_func(af.constant(100, 3,3, dtype=af.Dtype.f32))
display_func(af.constant(25, 3,3, dtype=af.Dtype.c32))
display_func(af.constant(2**50, 3,3, dtype=af.Dtype.s64))
display_func(af.constant(2+3j, 3,3))
display_func(af.constant(3+5j, 3,3, dtype=af.Dtype.c32))
display_func(af.range(3, 3))
display_func(af.iota(3, 3, tile_dims=(2,2)))
display_func(af.identity(3, 3, 1, 2, af.Dtype.b8))
display_func(af.identity(3, 3, dtype=af.Dtype.c32))
a = af.randu(3, 4)
b = af.diag(a, extract=True)
c = af.diag(a, 1, extract=True)
display_func(a)
display_func(b)
display_func(c)
display_func(af.diag(b, extract = False))
display_func(af.diag(c, 1, extract = False))
display_func(af.join(0, a, a))
display_func(af.join(1, a, a, a))
display_func(af.tile(a, 2, 2))
display_func(af.reorder(a, 1, 0))
display_func(af.shift(a, -1, 1))
display_func(af.moddims(a, 6, 2))
display_func(af.flat(a))
display_func(af.flip(a, 0))
display_func(af.flip(a, 1))
display_func(af.lower(a, False))
display_func(af.lower(a, True))
display_func(af.upper(a, False))
display_func(af.upper(a, True))
a = af.randu(5,5)
display_func(af.transpose(a))
af.transpose_inplace(a)
display_func(a)
display_func(af.select(a > 0.3, a, -0.3))
af.replace(a, a > 0.3, -0.3)
display_func(a)
def simple_device(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
print_func(af.device_info())
print_func(af.get_device_count())
print_func(af.is_dbl_supported())
af.sync()
curr_dev = af.get_device()
print_func(curr_dev)
for k in range(af.get_device_count()):
af.set_device(k)
dev = af.get_device()
assert(k == dev)
print_func(af.is_dbl_supported(k))
af.device_gc()
mem_info_old = af.device_mem_info()
a = af.randu(100, 100)
af.sync(dev)
mem_info = af.device_mem_info()
assert(mem_info['alloc']['buffers'] == 1 + mem_info_old['alloc']['buffers'])
assert(mem_info[ 'lock']['buffers'] == 1 + mem_info_old[ 'lock']['buffers'])
af.set_device(curr_dev)
a = af.randu(10,10)
display_func(a)
dev_ptr = af.get_device_ptr(a)
print_func(dev_ptr)
b = af.Array(src=dev_ptr, dims=a.dims(), dtype=a.dtype(), is_device=True)
display_func(b)
c = af.randu(10,10)
af.lock_array(c)
af.unlock_array(c)
a = af.constant(1, 3, 3)
b = af.constant(2, 3, 3)
af.eval(a)
af.eval(b)
print_func(a)
print_func(b)
c = a + b
d = a - b
af.eval(c, d)
print_func(c)
print_func(d)
print_func(af.set_manual_eval_flag(True))
assert(af.get_manual_eval_flag() == True)
print_func(af.set_manual_eval_flag(False))
assert(af.get_manual_eval_flag() == False)
display_func(af.is_locked_array(a))
def calc_pi_device(samples):
# Simple, array based API
# Generate uniformly distributed random numers
x = af.randu(samples)
y = af.randu(samples)
# Supports Just In Time Compilation
# The following line generates a single kernel
within_unit_circle = (x * x + y * y) < 1
# Intuitive function names
return 4 * af.count(within_unit_circle) / samples
def setup_input(n, sparsity=7):
T = af.randu(n, n, dtype=af.Dtype.f32)
A = af.floor(T * 1000)
A = A * ((A % sparsity) == 0) / 1000
A = A.T + A + n * af.identity(n, n, dtype=af.Dtype.f32)
x0 = af.randu(n, dtype=af.Dtype.f32)
b = af.matmul(A, x0)
# printing
# nnz = af.sum((A != 0))
# print "Sparsity of A: %2.2f %%" %(100*nnz/n**2,)
return A, b, x0
def simple_blas(verbose=False):
display_func = _util.display_func(verbose)
a = af.randu(5, 5)
b = af.randu(5, 5)
display_func(af.matmul(a, b))
display_func(af.matmul(a, b, af.MATPROP.TRANS))
display_func(af.matmul(a, b, af.MATPROP.NONE, af.MATPROP.TRANS))
b = af.randu(5, 1)
display_func(af.dot(b, b))
def setup_input(n, sparsity=7):
T = af.randu(n, n, dtype=af.Dtype.f32)
A = af.floor(T*1000)
A = A * ((A % sparsity) == 0) / 1000
A = A.T + A + n*af.identity(n, n, dtype=af.Dtype.f32)
x0 = af.randu(n, dtype=af.Dtype.f32)
b = af.matmul(A, x0)
# printing
# nnz = af.sum((A != 0))
# print "Sparsity of A: %2.2f %%" %(100*nnz/n**2,)
return A, b, x0
def weight_matrix(seed, innum, outnum, type='glorot', layer=0):
"""
Returns randomly initialized weight matrix of appropriate dimensions
:param innum: number of neurons in the layer i
:param outnum: number of neurons in layer i+1
:return: weight matrix
"""
np.random.seed(seed)
if type == 'glorot': W = af.randu(outnum,innum) #np.random.uniform(low=-np.sqrt(6.0/(2*layer+1)), high=np.sqrt(6.0/(2*layer+1)), size=(outnum, innum))
if type == 'normal': W = af.randu(outnum,innum)# np.random.rand(outnum, innum)
return W
def simple_blas(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5,5)
b = af.randu(5,5)
display_func(af.matmul(a,b))
display_func(af.matmul(a,b,af.MATPROP.TRANS))
display_func(af.matmul(a,b,af.MATPROP.NONE, af.MATPROP.TRANS))
b = af.randu(5,1)
display_func(af.dot(b,b))
def simple_image(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5,5,3)
dx,dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3,2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx,dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
def simple_image(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5, 5, 3)
dx, dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3, 2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx, dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
def gamma_rand_marsaglia_and_tsang_arrayfire(alpha: float,
lambda_: float,
n: int) \
-> af.array:
random_numbers = af.constant(0, n, dtype=Dtype.f32)
# Gamma(alpha, lambda) generator using Marsaglia and Tsang method
# Algorithm 4.33
if alpha >= 1.0:
d = alpha - 1 / 3
c = 1.0 / np.sqrt(9.0 * d)
number_generated = 0
number_generated_total = 0
while number_generated < n:
number_left = n - number_generated
z = af.randn(number_left, dtype=Dtype.f32)
y = (1.0 + c * z)
v = y * y * y
accept_index_1 = ((z >= -1.0 / c) & (v > 0.0))
z_accept_1 = z[accept_index_1]
# del z
v_accept_1 = v[accept_index_1]
# del v
u_accept_1 = af.randu(v_accept_1.elements(), dtype=Dtype.f32)
# del U
accept_index_2 = \
u_accept_1 < af.exp((0.5 * z_accept_1 * z_accept_1 + d - d * v_accept_1 + d * af.log(v_accept_1)))
x_accept = d * v_accept_1[accept_index_2] / lambda_
number_accept = x_accept.elements()
random_numbers[number_generated:np.minimum(n, number_generated + number_accept)] = \
x_accept[0:np.minimum(number_left, number_accept)]
number_generated += number_accept
number_generated_total += number_left
if GPUOptions.verbose:
print(f"Acceptance ratio = {n/number_generated_total}")
else:
random_numbers = gamma_rand_marsaglia_and_tsang_arrayfire(
alpha + 1, lambda_, n)
random_numbers *= af.randu(n, dtype=Dtype.f32)**(1.0 / alpha)
return random_numbers
def simple_statistics(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
b = af.randu(5, 5)
w = af.randu(5, 1)
display_func(af.mean(a, dim=0))
display_func(af.mean(a, weights=w, dim=0))
print_func(af.mean(a))
print_func(af.mean(a, weights=w))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
display_func(af.var(a, weights=w, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
print_func(af.var(a, weights=w))
mean, var = af.meanvar(a, dim=0)
display_func(mean)
display_func(var)
mean, var = af.meanvar(a, weights=w, bias=af.VARIANCE.SAMPLE, dim=0)
display_func(mean)
display_func(var)
display_func(af.stdev(a, dim=0))
print_func(af.stdev(a))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
display_func(af.median(a, dim=0))
print_func(af.median(w))
print_func(af.corrcoef(a, b))
data = af.iota(5, 3)
k = 3
dim = 0
order = af.TOPK.DEFAULT # defaults to af.TOPK.MAX
assert (dim == 0) # topk currently supports first dim only
values, indices = af.topk(data, k, dim, order)
display_func(values)
display_func(indices)
def initialize_f(q1, q2, v1, v2, v3, params):
m = params.mass
k = params.boltzmann_constant
n = af.select(af.abs(q2)>0.25, q1**0, 2)
# Random Numbers under to seed the instability:
seeding_velocities = 0.01 * (af.randu(1, 1, q1.shape[2], q1.shape[3],
dtype = af.Dtype.f64
) - 0.5
)
v1_bulk = af.select(af.abs(q2)>0.25,
-0.5 - seeding_velocities,
+0.5 + seeding_velocities
)
v2_bulk = seeding_velocities
T = (2.5 / n)
f = n * (m / (2 * np.pi * k * T))**(3 / 2) \
* af.exp(-m * (v1 - v1_bulk)**2 / (2 * k * T)) \
* af.exp(-m * (v2 - v2_bulk)**2 / (2 * k * T)) \
* af.exp(-m * v3**2 / (2 * k * T))
af.eval(f)
return (f)
def weight_matrix(seed, innum, outnum, type='glorot', layer=0):
"""
Returns randomly initialized weight matrix of appropriate dimensions
:param innum: number of neurons in the layer i
:param outnum: number of neurons in layer i+1
:return: weight matrix
"""
np.random.seed(seed)
if type == 'glorot':
W = af.randu(
outnum, innum
) #np.random.uniform(low=-np.sqrt(6.0/(2*layer+1)), high=np.sqrt(6.0/(2*layer+1)), size=(outnum, innum))
if type == 'normal':
W = af.randu(outnum, innum) # np.random.rand(outnum, innum)
return W
def simple_device(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
print_func(af.device_info())
print_func(af.get_device_count())
print_func(af.is_dbl_supported())
af.sync()
dev = af.get_device()
print_func(dev)
for k in range(af.get_device_count()):
af.set_device(k)
dev = af.get_device()
assert (k == dev)
print_func(af.is_dbl_supported(k))
af.device_gc()
mem_info_old = af.device_mem_info()
a = af.randu(100, 100)
af.sync(dev)
mem_info = af.device_mem_info()
assert (mem_info['alloc']['buffers'] == 1 +
mem_info_old['alloc']['buffers'])
assert (mem_info['lock']['buffers'] == 1 +
mem_info_old['lock']['buffers'])
af.set_device(dev)
def simple_device(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
print_func(af.device_info())
print_func(af.get_device_count())
print_func(af.is_dbl_supported())
af.sync()
dev = af.get_device()
print_func(dev)
for k in range(af.get_device_count()):
af.set_device(k)
dev = af.get_device()
assert(k == dev)
print_func(af.is_dbl_supported(k))
af.device_gc()
mem_info_old = af.device_mem_info()
a = af.randu(100, 100)
af.sync(dev)
mem_info = af.device_mem_info()
assert(mem_info['alloc']['buffers'] == 1 + mem_info_old['alloc']['buffers'])
assert(mem_info[ 'lock']['buffers'] == 1 + mem_info_old[ 'lock']['buffers'])
af.set_device(dev)
def calc_arrayfire(n):
A = af.randu(n, n)
af.sync()
def run(iters):
for t in range(iters):
B = af.matmul(A, A)
af.sync()
return run
def bandwidth_test(n_evals):
a = af.randu(32, 32, 32**3, dtype=af.Dtype.f64)
b = af.randu(32, 32, 32**3, dtype=af.Dtype.f64)
c = a + b
af.eval(c)
af.sync()
tic = af.time()
for i in range(n_evals):
c = a + b
af.eval(c)
af.sync()
toc = af.time()
bandwidth_available = memory_bandwidth(a.elements(), 2, 1, n_evals,
toc - tic)
return (bandwidth_available)
def initialize_B(q1, q2, params):
# Seeding with random fluctuation:
B1 = params.B1 * af.randu(
q1.shape[0], q1.shape[1], q1.shape[2], q1.shape[3], dtype=af.Dtype.f64)
B2 = 0 * q1**0
B3 = 0 * q1**0
af.eval(B1, B2, B3)
return (B1, B2, B3)
def calc_arrayfire(n):
A = af.randu(n, n)
af.sync()
def run(iters):
for t in range(iters):
B = af.fft2(A)
af.sync()
return run
def __init__(self):
self.N_p1 = 2
self.N_p2 = 3
self.N_p3 = 4
self.N_q1 = 5
self.N_q2 = 6
self.N_ghost = 1
self.p1_start = self.p2_start = self.p3_start = 0
self.dp1 = 2/self.N_p1
self.dp2 = 2/self.N_p2
self.dp3 = 2/self.N_p3
self.p1, self.p2, self.p3 = self._calculate_p_center()
self.physical_system = type('obj', (object,),
{'moment_exponents': moment_exponents,
'moment_coeffs': moment_coeffs
}
)
self.f = af.randu(self.N_p1 * self.N_p2 * self.N_p3,
self.N_q1 + 2 * self.N_ghost,
self.N_q2 + 2 * self.N_ghost,
dtype = af.Dtype.f64
)
self._comm = PETSc.COMM_WORLD
self._da_dump_f = PETSc.DMDA().create([self.N_q1, self.N_q2],
dof=( self.N_p1
* self.N_p2
* self.N_p3
),
stencil_width = self.N_ghost
)
self._da_dump_moments = PETSc.DMDA().create([self.N_q1, self.N_q2],
dof=len(self.physical_system.\
moment_exponents
),
stencil_width = self.N_ghost
)
self._glob_f = self._da_dump_f.createGlobalVec()
self._glob_f_array = self._glob_f.getArray()
self._glob_moments = self._da_dump_moments.createGlobalVec()
self._glob_moments_array =self._glob_moments.getArray()
PETSc.Object.setName(self._glob_f, 'distribution_function')
PETSc.Object.setName(self._glob_moments, 'moments')
def simple_statistics(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
b = af.randu(5, 5)
w = af.randu(5, 1)
display_func(af.mean(a, dim=0))
display_func(af.mean(a, weights=w, dim=0))
print_func(af.mean(a))
print_func(af.mean(a, weights=w))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
display_func(af.var(a, weights=w, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
print_func(af.var(a, weights=w))
display_func(af.stdev(a, dim=0))
print_func(af.stdev(a))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
display_func(af.median(a, dim=0))
print_func(af.median(w))
print_func(af.corrcoef(a, b))
data = af.iota(5, 3)
k = 3
dim = 0
order = af.TOPK.DEFAULT # defaults to af.TOPK.MAX
assert(dim == 0) # topk currently supports first dim only
values,indices = af.topk(data, k, dim, order)
display_func(values)
display_func(indices)
def simple_sparse(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
dd = af.randu(5, 5)
ds = dd * (dd > 0.5)
sp = af.create_sparse_from_dense(ds)
display_func(af.sparse_get_info(sp))
display_func(af.sparse_get_values(sp))
display_func(af.sparse_get_row_idx(sp))
display_func(af.sparse_get_col_idx(sp))
print_func(af.sparse_get_nnz(sp))
print_func(af.sparse_get_storage(sp))
def simple_device(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
print_func(af.device_info())
print_func(af.get_device_count())
print_func(af.is_dbl_supported())
af.sync()
curr_dev = af.get_device()
print_func(curr_dev)
for k in range(af.get_device_count()):
af.set_device(k)
dev = af.get_device()
assert (k == dev)
print_func(af.is_dbl_supported(k))
af.device_gc()
mem_info_old = af.device_mem_info()
a = af.randu(100, 100)
af.sync(dev)
mem_info = af.device_mem_info()
assert (mem_info['alloc']['buffers'] == 1 +
mem_info_old['alloc']['buffers'])
assert (mem_info['lock']['buffers'] == 1 +
mem_info_old['lock']['buffers'])
af.set_device(curr_dev)
a = af.randu(10, 10)
display_func(a)
dev_ptr = af.get_device_ptr(a)
print_func(dev_ptr)
b = af.Array(src=dev_ptr, dims=a.dims(), dtype=a.dtype(), is_device=True)
display_func(b)
af.lock_device_ptr(b)
af.unlock_device_ptr(b)
def test_convert_to_q_expanded():
obj = test()
test_array = af.randu(
(obj.N_q1 + 2 * obj.N_ghost) * (obj.N_q2 + 2 * obj.N_ghost), obj.N_p1,
obj.N_p2, obj.N_p3)
modified = convert_to_q_expanded(obj, test_array)
expected = af.moddims(test_array, obj.N_p1 * obj.N_p2 * obj.N_p3,
(obj.N_q1 + 2 * obj.N_ghost),
(obj.N_q2 + 2 * obj.N_ghost))
assert (af.sum(modified - expected) == 0)
def checkGradient(self, delta = 1e-4):
"""
check if the numerical gradient is similar to the analytical gradient. Only works for 64 bit data type.
"""
#assert af_float_datatype == af.Dtype.f64, "This will only be accurate if 64 bit datatype is used!"
shape = self.phase_obj_3d.shape
point = (np.random.randint(shape[0]), np.random.randint(shape[1]), np.random.randint(shape[2]))
illu_idx = np.random.randint(len(self.fx_illu_list))
fx_illu = self.fx_illu_list[illu_idx]
fy_illu = self.fy_illu_list[illu_idx]
x = np.ones(shape, dtype = np_complex_datatype)
if self._crop_obj.pad:
amplitude = af.randu(shape[0]//2, shape[1]//2, dtype = af_float_datatype)
else:
amplitude = af.randu(shape[0], shape[1], dtype = af_float_datatype)
print("Computing the gradient at point: ", point)
print("fx : %5.2f, fy : %5.2f " %(fx_illu, fy_illu))
def func(x0):
fields = self._scattering_obj.forward(x0, fx_illu, fy_illu)
field_scattered = self._defocus_obj.forward(field_scattered, self.prop_distances)
field_measure = self._crop_obj.forward(field_scattered)
residual = af.abs(field_measure) - amplitude
function_value = af.sum(residual*af.conjg(residual)).real
return function_value
numerical_gradient = calculateNumericalGradient(func, x, point, delta = delta)
fields = self._scattering_obj.forward(x, fx_illu, fy_illu)
forward_scattered_field = fields["forward_scattered_field"]
forward_scattered_field = self._defocus_obj.forward(forward_scattered_field, self.prop_distances)
field_measure = self._crop_obj.forward(forward_scattered_field)
fields["forward_scattered_field"] = field_measure
analytical_gradient = self._computeGradient(fields, amplitude)[0][point]
print("numerical gradient: %5.5e + %5.5e j" %(numerical_gradient.real, numerical_gradient.imag))
print("analytical gradient: %5.5e + %5.5e j" %(analytical_gradient.real, analytical_gradient.imag))
def simple_device(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
print_func(af.device_info())
print_func(af.get_device_count())
print_func(af.is_dbl_supported())
af.sync()
curr_dev = af.get_device()
print_func(curr_dev)
for k in range(af.get_device_count()):
af.set_device(k)
dev = af.get_device()
assert(k == dev)
print_func(af.is_dbl_supported(k))
af.device_gc()
mem_info_old = af.device_mem_info()
a = af.randu(100, 100)
af.sync(dev)
mem_info = af.device_mem_info()
assert(mem_info['alloc']['buffers'] == 1 + mem_info_old['alloc']['buffers'])
assert(mem_info[ 'lock']['buffers'] == 1 + mem_info_old[ 'lock']['buffers'])
af.set_device(curr_dev)
a = af.randu(10,10)
display_func(a)
dev_ptr = af.get_device_ptr(a)
print_func(dev_ptr)
b = af.Array(src=dev_ptr, dims=a.dims(), dtype=a.dtype(), is_device=True)
display_func(b)
af.lock_device_ptr(b)
af.unlock_device_ptr(b)
def __init__(self):
self.N_q1 = 32
self.N_q2 = 32
self.N_p1 = 4
self.N_p2 = 5
self.N_p3 = 6
self.p1_start = self.p2_start = self.p3_start = 0
self.dp1 = 2 / self.N_p1
self.dp2 = 2 / self.N_p2
self.dp3 = 2 / self.N_p3
self.p1, self.p2, self.p3 = self._calculate_p_center()
# Creating an object with required attributes for the test:
self.physical_system = type('obj', (object, ), {
'moment_exponents': moment_exponents,
'moment_coeffs': moment_coeffs
})
self.single_mode_evolution = False
self.f = af.randu(self.N_q1,
self.N_q2,
self.N_p1 * self.N_p2 * self.N_p3,
dtype=af.Dtype.f64)
self.Y = 2 * af.fft2(self.f) / (self.N_q1 * self.N_q2)
self._da_dump_f = PETSc.DMDA().create(
[self.N_q1, self.N_q2],
dof=(self.N_p1 * self.N_p2 * self.N_p3),
)
self._da_dump_moments = PETSc.DMDA().create([self.N_q1, self.N_q2],
dof = len(self.physical_system.\
moment_exponents)
)
self._glob_f = self._da_dump_f.createGlobalVec()
self._glob_f_value = self._da_dump_f.getVecArray(self._glob_f)
self._glob_moments = self._da_dump_moments.createGlobalVec()
self._glob_moments_value = self._da_dump_moments.\
getVecArray(self._glob_moments)
PETSc.Object.setName(self._glob_f, 'distribution_function')
PETSc.Object.setName(self._glob_moments, 'moments')
def setup_mnist(frac, expand_labels):
root_path = os.path.dirname(os.path.abspath(__file__))
file_path = root_path + '/../../assets/examples/data/mnist/'
idims, idata = read_idx(file_path + 'images-subset')
ldims, ldata = read_idx(file_path + 'labels-subset')
idims.reverse()
numdims = len(idims)
images = af.Array(idata, tuple(idims))
R = af.randu(10000, 1)
cond = R < min(frac, 0.8)
train_indices = af.where(cond)
test_indices = af.where(~cond)
train_images = af.lookup(images, train_indices, 2) / 255
test_images = af.lookup(images, test_indices, 2) / 255
num_classes = 10
num_train = train_images.dims()[2]
num_test = test_images.dims()[2]
if expand_labels:
train_labels = af.constant(0, num_classes, num_train)
test_labels = af.constant(0, num_classes, num_test)
h_train_idx = train_indices.to_list()
h_test_idx = test_indices.to_list()
for i in range(num_train):
train_labels[ldata[h_train_idx[i]], i] = 1
for i in range(num_test):
test_labels[ldata[h_test_idx[i]], i] = 1
else:
labels = af.Array(ldata, tuple(ldims))
train_labels = labels[train_indices]
test_labels = labels[test_indices]
return (num_classes, num_train, num_test, train_images, test_images,
train_labels, test_labels)
def simple_random(verbose=False):
display_func = _util.display_func(verbose)
display_func(af.randu(3, 3, 1, 2))
display_func(af.randu(3, 3, 1, 2, af.Dtype.b8))
display_func(af.randu(3, 3, dtype=af.Dtype.c32))
display_func(af.randn(3, 3, 1, 2))
display_func(af.randn(3, 3, dtype=af.Dtype.c32))
af.set_seed(1024)
assert (af.get_seed() == 1024)
engine = af.Random_Engine(af.RANDOM_ENGINE.MERSENNE_GP11213, 100)
display_func(af.randu(3, 3, 1, 2, engine=engine))
display_func(af.randu(3, 3, 1, 2, af.Dtype.s32, engine=engine))
display_func(af.randu(3, 3, dtype=af.Dtype.c32, engine=engine))
display_func(af.randn(3, 3, engine=engine))
engine.set_seed(100)
assert (engine.get_seed() == 100)
def simple_random(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
display_func(af.randu(3, 3, 1, 2))
display_func(af.randu(3, 3, 1, 2, af.Dtype.b8))
display_func(af.randu(3, 3, dtype=af.Dtype.c32))
display_func(af.randn(3, 3, 1, 2))
display_func(af.randn(3, 3, dtype=af.Dtype.c32))
af.set_seed(1024)
assert(af.get_seed() == 1024)
engine = af.Random_Engine(af.RANDOM_ENGINE.MERSENNE_GP11213, 100)
display_func(af.randu(3, 3, 1, 2, engine=engine))
display_func(af.randu(3, 3, 1, 2, af.Dtype.s32, engine=engine))
display_func(af.randu(3, 3, dtype=af.Dtype.c32, engine=engine))
display_func(af.randn(3, 3, engine=engine))
engine.set_seed(100)
assert(engine.get_seed() == 100)
#!/usr/bin/python ####################################################### # Copyright (c) 2015, ArrayFire # All rights reserved. # # This file is distributed under 3-clause BSD license. # The complete license agreement can be obtained at: # http://arrayfire.com/licenses/BSD-3-Clause ######################################################## import arrayfire as af a = af.randu(5, 5) l, u, p = af.lu(a) af.display(l) af.display(u) af.display(p) p = af.lu_inplace(a, "full") af.display(a) af.display(p) a = af.randu(5, 3) q, r, t = af.qr(a) af.display(q) af.display(r) af.display(t)
def simple_signal(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(10, 1)
pos0 = af.randu(10) * 10
display_func(af.approx1(a, pos0))
a = af.randu(3, 3)
pos0 = af.randu(3, 3) * 10
pos1 = af.randu(3, 3) * 10
display_func(af.approx2(a, pos0, pos1))
a = af.randu(8, 1)
display_func(a)
display_func(af.fft(a))
display_func(af.dft(a))
display_func(af.real(af.ifft(af.fft(a))))
display_func(af.real(af.idft(af.dft(a))))
a = af.randu(4, 4)
display_func(a)
display_func(af.fft2(a))
display_func(af.dft(a))
display_func(af.real(af.ifft2(af.fft2(a))))
display_func(af.real(af.idft(af.dft(a))))
a = af.randu(4, 4, 2)
display_func(a)
display_func(af.fft3(a))
display_func(af.dft(a))
display_func(af.real(af.ifft3(af.fft3(a))))
display_func(af.real(af.idft(af.dft(a))))
a = af.randu(10, 1)
b = af.randu(3, 1)
display_func(af.convolve1(a, b))
display_func(af.fft_convolve1(a, b))
display_func(af.convolve(a, b))
display_func(af.fft_convolve(a, b))
a = af.randu(5, 5)
b = af.randu(3, 3)
display_func(af.convolve2(a, b))
display_func(af.fft_convolve2(a, b))
display_func(af.convolve(a, b))
display_func(af.fft_convolve(a, b))
a = af.randu(5, 5, 3)
b = af.randu(3, 3, 2)
display_func(af.convolve3(a, b))
display_func(af.fft_convolve3(a, b))
display_func(af.convolve(a, b))
display_func(af.fft_convolve(a, b))
b = af.randu(3, 1)
x = af.randu(10, 1)
a = af.randu(2, 1)
display_func(af.fir(b, x))
display_func(af.iir(b, a, x))
#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
import arrayfire as af
af.info()
print(af.device_info())
print(af.get_device_count())
print(af.is_dbl_supported())
af.sync()
print('starting the loop')
for k in range(af.get_device_count()):
af.set_device(k)
dev = af.get_device()
assert(k == dev)
print(af.is_dbl_supported(k))
a = af.randu(100, 100)
af.sync(dev)
mem_info = af.device_mem_info()
assert(mem_info['alloc']['buffers'] == 1)
assert(mem_info[ 'lock']['buffers'] == 1)
# The complete license agreement can be obtained at: # http://arrayfire.com/licenses/BSD-3-Clause ######################################################## import arrayfire as af af.display(af.constant(100, 3,3, dtype=af.f32)) af.display(af.constant(25, 3,3, dtype=af.c32)) af.display(af.constant(2**50, 3,3, dtype=af.s64)) af.display(af.constant(2+3j, 3,3)) af.display(af.constant(3+5j, 3,3, dtype=af.c32)) af.display(af.range(3, 3)) af.display(af.iota(3, 3, tile_dims=(2,2))) af.display(af.randu(3, 3, 1, 2)) af.display(af.randu(3, 3, 1, 2, af.b8)) af.display(af.randu(3, 3, dtype=af.c32)) af.display(af.randn(3, 3, 1, 2)) af.display(af.randn(3, 3, dtype=af.c32)) af.set_seed(1024) assert(af.get_seed() == 1024) af.display(af.identity(3, 3, 1, 2, af.b8)) af.display(af.identity(3, 3, dtype=af.c32)) a = af.randu(3, 4) b = af.diag(a, extract=True) c = af.diag(a, 1, extract=True)
import sys
from time import time
from arrayfire import (array, randu, matmul)
import arrayfire as af
def bench(A, iters=100):
start = time()
for t in range(iters):
B = af.fft2(A)
af.sync()
return (time() - start) / iters
if __name__ == "__main__":
if (len(sys.argv) > 1):
af.set_device(int(sys.argv[1]))
af.info()
print("Benchmark N x N 2D fft")
for M in range(7, 13):
N = 1 << M
A = af.randu(N, N)
af.sync()
t = bench(A)
gflops = (10.0 * N * N * M) / (t * 1E9)
print("Time taken for %4d x %4d: %0.4f Gflops" % (N, N, gflops))
import sys
from array import array
def af_assert(left, right, eps=1E-6):
if (af.max(af.abs(left -right)) > eps):
raise ValueError("Arrays not within dictated precision")
return
if __name__ == "__main__":
if (len(sys.argv) > 1):
af.set_device(int(sys.argv[1]))
af.info()
h_dx = array('f', (1.0/12, -8.0/12, 0, 8.0/12, 1.0/12))
h_spread = array('f', (1.0/5, 1.0/5, 1.0/5, 1.0/5, 1.0/5))
img = af.randu(640, 480)
dx = af.Array(h_dx, (5,1))
spread = af.Array(h_spread, (1, 5))
kernel = af.matmul(dx, spread)
full_res = af.convolve2(img, kernel)
sep_res = af.convolve2_separable(dx, spread, img)
af_assert(full_res, sep_res)
print("full 2D convolution time: %.5f ms" %
(1000 * af.timeit(af.convolve2, img, kernel)))
print("separable 2D convolution time: %.5f ms" %
(1000 * af.timeit(af.convolve2_separable, dx, spread, img)))
def simple_image(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5,5,3)
dx,dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3,2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx,dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
display_func(af.gaussian_kernel(3, 3))
display_func(af.gaussian_kernel(3, 3, 1, 1))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
a = af.randu(6,6)
b = af.unwrap(a, 2, 2, 2, 2)
c = af.wrap(b, 6, 6, 2, 2, 2, 2)
display_func(a)
display_func(b)
display_func(c)
display_func(af.sat(a))
a = af.randu(10,10,3)
display_func(af.rgb2ycbcr(a))
display_func(af.ycbcr2rgb(a))
a = af.randu(10, 10)
b = af.canny(a, low_threshold = 0.2, high_threshold = 0.8)
display_func(af.anisotropic_diffusion(a, 0.125, 1.0, 64, af.FLUX.QUADRATIC, af.DIFFUSION.GRAD))
def simple_algorithm(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3, 3)
print_func(af.sum(a), af.product(a), af.min(a), af.max(a),
af.count(a), af.any_true(a), af.all_true(a))
display_func(af.sum(a, 0))
display_func(af.sum(a, 1))
display_func(af.product(a, 0))
display_func(af.product(a, 1))
display_func(af.min(a, 0))
display_func(af.min(a, 1))
display_func(af.max(a, 0))
display_func(af.max(a, 1))
display_func(af.count(a, 0))
display_func(af.count(a, 1))
display_func(af.any_true(a, 0))
display_func(af.any_true(a, 1))
display_func(af.all_true(a, 0))
display_func(af.all_true(a, 1))
display_func(af.accum(a, 0))
display_func(af.accum(a, 1))
display_func(af.sort(a, is_ascending=True))
display_func(af.sort(a, is_ascending=False))
b = (a > 0.1) * a
c = (a > 0.4) * a
d = b / c
print_func(af.sum(d));
print_func(af.sum(d, nan_val=0.0));
display_func(af.sum(d, dim=0, nan_val=0.0));
val,idx = af.sort_index(a, is_ascending=True)
display_func(val)
display_func(idx)
val,idx = af.sort_index(a, is_ascending=False)
display_func(val)
display_func(idx)
b = af.randu(3,3)
keys,vals = af.sort_by_key(a, b, is_ascending=True)
display_func(keys)
display_func(vals)
keys,vals = af.sort_by_key(a, b, is_ascending=False)
display_func(keys)
display_func(vals)
c = af.randu(5,1)
d = af.randu(5,1)
cc = af.set_unique(c, is_sorted=False)
dd = af.set_unique(af.sort(d), is_sorted=True)
display_func(cc)
display_func(dd)
display_func(af.set_union(cc, dd, is_unique=True))
display_func(af.set_union(cc, dd, is_unique=False))
display_func(af.set_intersect(cc, cc, is_unique=True))
display_func(af.set_intersect(cc, cc, is_unique=False))
def simple_lapack(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5,5)
l,u,p = af.lu(a)
display_func(l)
display_func(u)
display_func(p)
p = af.lu_inplace(a, "full")
display_func(a)
display_func(p)
a = af.randu(5,3)
q,r,t = af.qr(a)
display_func(q)
display_func(r)
display_func(t)
af.qr_inplace(a)
display_func(a)
a = af.randu(5, 5)
a = af.matmulTN(a, a) + 10 * af.identity(5,5)
R,info = af.cholesky(a)
display_func(R)
print_func(info)
af.cholesky_inplace(a)
display_func(a)
a = af.randu(5,5)
ai = af.inverse(a)
display_func(a)
display_func(ai)
x0 = af.randu(5, 3)
b = af.matmul(a, x0)
x1 = af.solve(a, b)
display_func(x0)
display_func(x1)
p = af.lu_inplace(a)
x2 = af.solve_lu(a, p, b)
display_func(x2)
print_func(af.rank(a))
print_func(af.det(a))
print_func(af.norm(a, af.NORM.EUCLID))
print_func(af.norm(a, af.NORM.MATRIX_1))
print_func(af.norm(a, af.NORM.MATRIX_INF))
print_func(af.norm(a, af.NORM.MATRIX_L_PQ, 1, 1))
########################################################
import sys
from time import time
from arrayfire import (array, randu, matmul)
import arrayfire as af
def bench(A, iters = 100):
start = time()
for t in range(iters):
B = af.matmul(A, A)
af.sync()
return (time() - start) / iters
if __name__ == "__main__":
if (len(sys.argv) > 1):
af.set_device(int(sys.argv[1]))
af.info()
print("Benchmark N x N matrix multiply")
for n in range(128, 2048 + 128, 128):
A = af.randu(n, n)
af.sync()
t = bench(A)
gflops = 2.0 * (n**3) / (t * 1E9)
print("Time taken for %4d x %4d: %0.4f Gflops" % (n, n, gflops))
def simple_arith(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3,3,dtype=af.Dtype.u32)
b = af.constant(4, 3, 3, dtype=af.Dtype.u32)
display_func(a)
display_func(b)
c = a + b
d = a
d += b
display_func(c)
display_func(d)
display_func(a + 2)
display_func(3 + a)
c = a - b
d = a
d -= b
display_func(c)
display_func(d)
display_func(a - 2)
display_func(3 - a)
c = a * b
d = a
d *= b
display_func(c * 2)
display_func(3 * d)
display_func(a * 2)
display_func(3 * a)
c = a / b
d = a
d /= b
display_func(c / 2.0)
display_func(3.0 / d)
display_func(a / 2)
display_func(3 / a)
c = a % b
d = a
d %= b
display_func(c % 2.0)
display_func(3.0 % d)
display_func(a % 2)
display_func(3 % a)
c = a ** b
d = a
d **= b
display_func(c ** 2.0)
display_func(3.0 ** d)
display_func(a ** 2)
display_func(3 ** a)
display_func(a < b)
display_func(a < 0.5)
display_func(0.5 < a)
display_func(a <= b)
display_func(a <= 0.5)
display_func(0.5 <= a)
display_func(a > b)
display_func(a > 0.5)
display_func(0.5 > a)
display_func(a >= b)
display_func(a >= 0.5)
display_func(0.5 >= a)
display_func(a != b)
display_func(a != 0.5)
display_func(0.5 != a)
display_func(a == b)
display_func(a == 0.5)
display_func(0.5 == a)
display_func(a & b)
display_func(a & 2)
c = a
c &= 2
display_func(c)
display_func(a | b)
display_func(a | 2)
c = a
c |= 2
display_func(c)
display_func(a >> b)
display_func(a >> 2)
c = a
c >>= 2
display_func(c)
display_func(a << b)
display_func(a << 2)
c = a
c <<= 2
display_func(c)
display_func(-a)
display_func(+a)
display_func(~a)
display_func(a)
display_func(af.cast(a, af.Dtype.c32))
display_func(af.maxof(a,b))
display_func(af.minof(a,b))
display_func(af.rem(a,b))
a = af.randu(3,3) - 0.5
b = af.randu(3,3) - 0.5
display_func(af.abs(a))
display_func(af.arg(a))
display_func(af.sign(a))
display_func(af.round(a))
display_func(af.trunc(a))
display_func(af.floor(a))
display_func(af.ceil(a))
display_func(af.hypot(a, b))
display_func(af.sin(a))
display_func(af.cos(a))
display_func(af.tan(a))
display_func(af.asin(a))
display_func(af.acos(a))
display_func(af.atan(a))
display_func(af.atan2(a, b))
c = af.cplx(a)
d = af.cplx(a,b)
display_func(c)
display_func(d)
display_func(af.real(d))
display_func(af.imag(d))
display_func(af.conjg(d))
display_func(af.sinh(a))
display_func(af.cosh(a))
display_func(af.tanh(a))
display_func(af.asinh(a))
display_func(af.acosh(a))
display_func(af.atanh(a))
a = af.abs(a)
b = af.abs(b)
display_func(af.root(a, b))
display_func(af.pow(a, b))
display_func(af.pow2(a))
display_func(af.exp(a))
display_func(af.expm1(a))
display_func(af.erf(a))
display_func(af.erfc(a))
display_func(af.log(a))
display_func(af.log1p(a))
display_func(af.log10(a))
display_func(af.log2(a))
display_func(af.sqrt(a))
display_func(af.cbrt(a))
a = af.round(5 * af.randu(3,3) - 1)
b = af.round(5 * af.randu(3,3) - 1)
display_func(af.factorial(a))
display_func(af.tgamma(a))
display_func(af.lgamma(a))
display_func(af.iszero(a))
display_func(af.isinf(a/b))
display_func(af.isnan(a/a))
a = af.randu(5, 1)
b = af.randu(1, 5)
c = af.broadcast(lambda x,y: x+y, a, b)
display_func(a)
display_func(b)
display_func(c)
@af.broadcast
def test_add(aa, bb):
return aa + bb
display_func(test_add(a, b))
def simple_index(verbose=False):
display_func = _util.display_func(verbose)
a = af.randu(5, 5)
display_func(a)
b = af.Array(a)
display_func(b)
c = a.copy()
display_func(c)
display_func(a[0, 0])
display_func(a[0])
display_func(a[:])
display_func(a[:, :])
display_func(a[0:3, ])
display_func(a[-2:-1, -1])
display_func(a[0:5])
display_func(a[0:5:2])
idx = af.Array(host.array("i", [0, 3, 2]))
display_func(idx)
aa = a[idx]
display_func(aa)
a[0] = 1
display_func(a)
a[0] = af.randu(1, 5)
display_func(a)
a[:] = af.randu(5, 5)
display_func(a)
a[:, -1] = af.randu(5, 1)
display_func(a)
a[0:5:2] = af.randu(3, 5)
display_func(a)
a[idx, idx] = af.randu(3, 3)
display_func(a)
a = af.randu(5, 1)
b = af.randu(5, 1)
display_func(a)
display_func(b)
for ii in ParallelRange(1, 3):
a[ii] = b[ii]
display_func(a)
for ii in ParallelRange(2, 5):
b[ii] = 2
display_func(b)
a = af.randu(3, 2)
rows = af.constant(0, 1, dtype=af.Dtype.s32)
b = a[:, rows]
display_func(b)
for r in range(rows.elements()):
display_func(r)
display_func(b[:, r])
a = af.randu(3)
c = af.randu(3)
b = af.constant(1, 3, dtype=af.Dtype.b8)
display_func(a)
a[b] = c
display_func(a)
def calc_pi_device(samples):
x = af.randu(samples)
y = af.randu(samples)
return 4 * af.sum(in_circle(x, y)) / samples
#!/usr/bin/python ####################################################### # Copyright (c) 2015, ArrayFire # All rights reserved. # # This file is distributed under 3-clause BSD license. # The complete license agreement can be obtained at: # http://arrayfire.com/licenses/BSD-3-Clause ######################################################## import arrayfire as af a = af.randu(5, 5) b = af.randu(5, 5) w = af.randu(5, 1) af.display(af.mean(a, dim=0)) af.display(af.mean(a, weights=w, dim=0)) print(af.mean(a)) print(af.mean(a, weights=w)) af.display(af.var(a, dim=0)) af.display(af.var(a, isbiased=True, dim=0)) af.display(af.var(a, weights=w, dim=0)) print(af.var(a)) print(af.var(a, isbiased=True)) print(af.var(a, weights=w)) af.display(af.stdev(a, dim=0)) print(af.stdev(a))
def simple_lapack(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
l, u, p = af.lu(a)
display_func(l)
display_func(u)
display_func(p)
p = af.lu_inplace(a, "full")
display_func(a)
display_func(p)
a = af.randu(5, 3)
q, r, t = af.qr(a)
display_func(q)
display_func(r)
display_func(t)
af.qr_inplace(a)
display_func(a)
a = af.randu(5, 5)
a = af.matmulTN(a, a.copy()) + 10 * af.identity(5, 5)
R, info = af.cholesky(a)
display_func(R)
print_func(info)
af.cholesky_inplace(a)
display_func(a)
a = af.randu(5, 5)
ai = af.inverse(a)
display_func(a)
display_func(ai)
x0 = af.randu(5, 3)
b = af.matmul(a, x0)
x1 = af.solve(a, b)
display_func(x0)
display_func(x1)
p = af.lu_inplace(a)
x2 = af.solve_lu(a, p, b)
display_func(x2)
print_func(af.rank(a))
print_func(af.det(a))
print_func(af.norm(a, af.NORM.EUCLID))
print_func(af.norm(a, af.NORM.MATRIX_1))
print_func(af.norm(a, af.NORM.MATRIX_INF))
print_func(af.norm(a, af.NORM.MATRIX_L_PQ, 1, 1))
a = af.randu(10, 10)
display_func(a)
u, s, vt = af.svd(a)
display_func(af.matmul(af.matmul(u, af.diag(s, 0, False)), vt))
u, s, vt = af.svd_inplace(a)
display_func(af.matmul(af.matmul(u, af.diag(s, 0, False)), vt))
#!/usr/bin/python ####################################################### # Copyright (c) 2015, ArrayFire # All rights reserved. # # This file is distributed under 3-clause BSD license. # The complete license agreement can be obtained at: # http://arrayfire.com/licenses/BSD-3-Clause ######################################################## import arrayfire as af a = af.randu(10, 1) pos0 = af.randu(10) * 10 af.display(af.approx1(a, pos0)) a = af.randu(3, 3) pos0 = af.randu(3, 3) * 10 pos1 = af.randu(3, 3) * 10 af.display(af.approx2(a, pos0, pos1)) a = af.randu(8, 1) af.display(a) af.display(af.fft(a)) af.display(af.dft(a)) af.display(af.real(af.ifft(af.fft(a)))) af.display(af.real(af.idft(af.dft(a)))) a = af.randu(4, 4)
#!/usr/bin/python ####################################################### # Copyright (c) 2015, ArrayFire # All rights reserved. # # This file is distributed under 3-clause BSD license. # The complete license agreement can be obtained at: # http://arrayfire.com/licenses/BSD-3-Clause ######################################################## import arrayfire as af a = 10 * af.randu(6, 6) a3 = 10 * af.randu(5,5,3) dx,dy = af.gradient(a) af.display(dx) af.display(dy) af.display(af.resize(a, scale=0.5)) af.display(af.resize(a, odim0=8, odim1=8)) t = af.randu(3,2) af.display(af.transform(a, t)) af.display(af.rotate(a, 3.14)) af.display(af.translate(a, 1, 1)) af.display(af.scale(a, 1.2, 1.2, 7, 7)) af.display(af.skew(a, 0.02, 0.02)) h = af.histogram(a, 3) af.display(h) af.display(af.hist_equal(a, h))
def simple_signal(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
signal = af.randu(10)
x_new = af.randu(10)
x_orig = af.randu(10)
display_func(af.approx1(signal, x_new, xp = x_orig))
signal = af.randu(3, 3)
x_new = af.randu(3, 3)
x_orig = af.randu(3, 3)
y_new = af.randu(3, 3)
y_orig = af.randu(3, 3)
display_func(af.approx2(signal, x_new, y_new, xp = x_orig, yp = y_orig))
a = af.randu(8, 1)
display_func(a)
display_func(af.fft(a))
display_func(af.dft(a))
display_func(af.real(af.ifft(af.fft(a))))
display_func(af.real(af.idft(af.dft(a))))
b = af.fft(a)
af.ifft_inplace(b)
display_func(b)
af.fft_inplace(b)
display_func(b)
b = af.fft_r2c(a)
c = af.fft_c2r(b)
display_func(b)
display_func(c)
a = af.randu(4, 4)
display_func(a)
display_func(af.fft2(a))
display_func(af.dft(a))
display_func(af.real(af.ifft2(af.fft2(a))))
display_func(af.real(af.idft(af.dft(a))))
b = af.fft2(a)
af.ifft2_inplace(b)
display_func(b)
af.fft2_inplace(b)
display_func(b)
b = af.fft2_r2c(a)
c = af.fft2_c2r(b)
display_func(b)
display_func(c)
a = af.randu(4, 4, 2)
display_func(a)
display_func(af.fft3(a))
display_func(af.dft(a))
display_func(af.real(af.ifft3(af.fft3(a))))
display_func(af.real(af.idft(af.dft(a))))
b = af.fft3(a)
af.ifft3_inplace(b)
display_func(b)
af.fft3_inplace(b)
display_func(b)
b = af.fft3_r2c(a)
c = af.fft3_c2r(b)
display_func(b)
display_func(c)
a = af.randu(10, 1)
b = af.randu(3, 1)
display_func(af.convolve1(a, b))
display_func(af.fft_convolve1(a, b))
display_func(af.convolve(a, b))
display_func(af.fft_convolve(a, b))
a = af.randu(5, 5)
b = af.randu(3, 3)
display_func(af.convolve2(a, b))
display_func(af.fft_convolve2(a, b))
display_func(af.convolve(a, b))
display_func(af.fft_convolve(a, b))
a = af.randu(5, 5, 3)
b = af.randu(3, 3, 2)
display_func(af.convolve3(a, b))
display_func(af.fft_convolve3(a, b))
display_func(af.convolve(a, b))
display_func(af.fft_convolve(a, b))
b = af.randu(3, 1)
x = af.randu(10, 1)
a = af.randu(2, 1)
display_func(af.fir(b, x))
display_func(af.iir(b, a, x))
display_func(af.medfilt1(a))
display_func(af.medfilt2(a))
display_func(af.medfilt(a))
def calc_pi_device(samples):
x = randu(samples)
y = randu(samples)
return 4 * af.sum((x * x + y * y) < 1) / samples
#!/usr/bin/python import arrayfire as af af.print_array(af.constant(100, 3,3, dtype=af.f32)) af.print_array(af.constant(25, 3,3, dtype=af.c32)) af.print_array(af.constant(2**50, 3,3, dtype=af.s64)) af.print_array(af.constant(2+3j, 3,3)) af.print_array(af.constant(3+5j, 3,3, dtype=af.c32)) af.print_array(af.range(3, 3)) af.print_array(af.iota(3, 3, tile_dims=(2,2))) af.print_array(af.randu(3, 3, 1, 2)) af.print_array(af.randu(3, 3, 1, 2, af.b8)) af.print_array(af.randu(3, 3, dtype=af.c32)) af.print_array(af.randn(3, 3, 1, 2)) af.print_array(af.randn(3, 3, dtype=af.c32)) af.set_seed(1024) assert(af.get_seed() == 1024) af.print_array(af.identity(3, 3, 1, 2, af.b8)) af.print_array(af.identity(3, 3, dtype=af.c32)) a = af.randu(3, 4) b = af.diag(a, extract=True) c = af.diag(a, 1, extract=True) af.print_array(a) af.print_array(b)
#!/usr/bin/python ####################################################### # Copyright (c) 2015, ArrayFire # All rights reserved. # # This file is distributed under 3-clause BSD license. # The complete license agreement can be obtained at: # http://arrayfire.com/licenses/BSD-3-Clause ######################################################## import arrayfire as af a = af.randu(10, 1) pos0 = af.randu(10) * 10 af.display(af.approx1(a, pos0)) a = af.randu(3, 3) pos0 = af.randu(3, 3) * 10 pos1 = af.randu(3, 3) * 10 af.display(af.approx2(a, pos0, pos1)) a = af.randu(8, 1) af.display(a) af.display(af.fft(a)) af.display(af.dft(a)) af.display(af.real(af.ifft(af.fft(a)))) af.display(af.real(af.idft(af.dft(a)))) a = af.randu(4, 4)
print("Green : Cells that are new as a result of reproduction" )
print("Blue : Cells that have died due to over population" )
print("This examples is throttled to 30 FPS so as to be a better visualization")
simple_win = af.Window(512, 512, "Conway's Game of Life - Current State")
pretty_win = af.Window(512, 512, "Conway's Game of Life - Current State with visualization")
simple_win.set_pos(25, 15)
pretty_win.set_pos(600, 25)
frame_count = 0
# Copy kernel that specifies neighborhood conditions
kernel = af.Array(h_kernel, dims=(3,3))
# Generate the initial state with 0s and 1s
state = (af.randu(game_h, game_w) > 0.4).as_type(af.Dtype.f32)
# tile 3 times to display color
display = af.tile(state, 1, 1, 3, 1)
while (not simple_win.close()) and (not pretty_win.close()):
delay = time()
if (not simple_win.close()): simple_win.image(state)
if (not pretty_win.close()): pretty_win.image(display)
frame_count += 1
if (frame_count % reset == 0):
state = (af.randu(game_h, game_w) > 0.4).as_type(af.Dtype.f32)
neighborhood = af.convolve(state, kernel)
