def test_sum(ctx_factory):
from pytest import importorskip
importorskip("mako")
context = ctx_factory()
queue = cl.CommandQueue(context)
n = 200000
for dtype in [np.float32, np.complex64]:
a_gpu = general_clrand(queue, (n,), dtype)
a = a_gpu.get()
for slc in [
slice(None),
slice(1000, 3000),
slice(1000, -3000),
slice(1000, None),
slice(1000, None, 3),
slice(1000, 1000),
]:
sum_a = np.sum(a[slc])
if sum_a:
ref_divisor = abs(sum_a)
else:
ref_divisor = 1
if slc.step is None:
sum_a_gpu = cl_array.sum(a_gpu[slc]).get()
assert abs(sum_a_gpu - sum_a) / ref_divisor < 1e-4
sum_a_gpu_2 = cl_array.sum(a_gpu, slice=slc).get()
assert abs(sum_a_gpu_2 - sum_a) / ref_divisor < 1e-4
def test_sum(ctx_factory):
from pytest import importorskip
importorskip("mako")
context = ctx_factory()
queue = cl.CommandQueue(context)
n = 200000
for dtype in [np.float32, np.complex64]:
a_gpu = general_clrand(queue, (n,), dtype)
a = a_gpu.get()
for slc in [
slice(None),
slice(1000, 3000),
slice(1000, -3000),
slice(1000, None),
slice(1000, None, 3),
]:
sum_a = np.sum(a[slc])
if slc.step is None:
sum_a_gpu = cl_array.sum(a_gpu[slc]).get()
assert abs(sum_a_gpu - sum_a) / abs(sum_a) < 1e-4
sum_a_gpu_2 = cl_array.sum(a_gpu, slice=slc).get()
assert abs(sum_a_gpu_2 - sum_a) / abs(sum_a) < 1e-4
def _preserve_power(self, real, imag, speckles, stage):
assert stage in ('in','out'), "unrecognized _preserve_power stage %s"%stage
if stage == 'in':
# make the components into get m0 and the speckle power.
# m0 is a float2 which stores the (0,0) of re and im
# replace the (0,0) component with the average of the surrounding neighbors to prevent envelope distortion due to non-zero average magnetization
self.get_m0.execute(self.queue,(1,), real.data, imag.data, self.m0_1.data)
self.make_speckle(real,imag, speckles)
self.power_in = cla.sum(speckles).get()-(self.m0_1.get()[0])**2
self.replace_dc_component1.execute(self.queue,(1,), speckles.data, speckles.data, np.int32(self.N))
if stage == 'out':
# preserve the total amount of speckle power outside the (0,0) component
# put m0_1 back as the (0,0) component so that the average magnetization is not effected by the rescaling
self.get_m0.execute(self.queue,(1,), real.data, imag.data, self.m0_2.data)
self.make_speckle(real, imag, speckles)
self.power_out = cla.sum(speckles).get()-(self.m0_2.get()[0])**2
ratio = (np.sqrt(self.power_in/self.power_out)).astype(np.float32)
self.scalar_multiply(ratio,real)
self.scalar_multiply(ratio,imag)
self.replace_dc_component2.execute(self.queue,(1,), real.data, imag.data, self.m0_1.data)
def sum(ary, backend=None):
if backend is None:
backend = ary.backend
if backend == 'cython':
return np.sum(ary.dev)
if backend == 'opencl':
import pyopencl.array as gpuarray
return gpuarray.sum(ary.dev).get()
if backend == 'cuda':
import pycuda.gpuarray as gpuarray
return gpuarray.sum(ary.dev).get()
def pol_vor_rho(self, Y, px, py):
'''return: polarization, vorticity, density at given Y,px,py'''
self.prg.polarization_on_sf(self.queue, (self.size_sf, ), None,
self.d_pol.data, self.d_vor.data,
self.d_rho.data, self.d_smu, self.d_umu,
self.d_omegaY, self.d_etas, np.float32(Y),
np.float32(px), np.float32(py),
np.int32(self.size_sf)).wait()
polarization = cl_array.sum(self.d_pol).get()
vorticity = cl_array.sum(self.d_vor).get()
density = cl_array.sum(self.d_rho).get()
return polarization, vorticity, density
def get_divergence_error(vector):
for mu in range(3):
fft.idft(vector[mu], vector_x[mu])
derivs.divergence(queue, vector_x, div)
derivs(queue, fx=vector_x[0], pdx=pdx[0])
derivs(queue, fx=vector_x[1], pdy=pdx[1])
derivs(queue, fx=vector_x[2], pdz=pdx[2])
norm = sum([clm.fabs(pdx[mu]) for mu in range(3)])
max_err = cla.max(clm.fabs(div)) / cla.max(norm)
avg_err = cla.sum(clm.fabs(div)) / cla.sum(norm)
return max_err, avg_err
def get_total_energy_and_entropy_on_gpu(self, tau, d_ev):
NX, NY, NZ = self.cfg.NX, self.cfg.NY, self.cfg.NZ
self.kernel_bulk.total_energy_and_entropy(self.queue, (NX, NY, NZ),
None, self.a_ed.data,
self.a_entropy.data, d_ev,
self.eos_table,
np.float32(tau)).wait()
volum = tau * self.cfg.DX * self.cfg.DY * self.cfg.DZ
e_total = cl_array.sum(self.a_ed).get() * volum
s_total = cl_array.sum(self.a_entropy).get() * volum
self.energy.append(e_total)
self.entropy.append(s_total)
def __call__(self, im, nrays, nsamples, ray_step, seed_pt, cutoff, thresh):
nrays = int(nrays)
nsamples = int(nsamples)
cutoff = np.int32(cutoff)
arrays = self.setup_arrays(nrays, nsamples, cutoff)
prog = self.build_program(nrays, nsamples, ray_step)
prog.sample_rays(self.queue,
(nsamples, nrays),
None,
arrays.scratch.data,
im,
np.float32(seed_pt[0]),
np.float32(seed_pt[1]))
# take the region in the cutoff zone
cla.take(arrays.scratch,
arrays.idx,
out=arrays.pre_cutoff)
# plt.imshow(self.pre_cutoff.get())
# plt.show()
self.square_array(arrays.pre_cutoff, arrays.pre_cutoff_squared)
inside_mean = cla.sum(arrays.pre_cutoff).get() / (cutoff * nrays)
inside_sumsq = cla.sum(arrays.pre_cutoff_squared).get() / (cutoff * nrays)
inside_std = np.sqrt(inside_sumsq - inside_mean ** 2)
normed_thresh = inside_std * thresh
prog.scan_boundary(self.queue,
(nrays,),
None,
arrays.result.data,
arrays.scratch.data,
np.float32(normed_thresh))
# print normed_thresh
# plt.figure()
# plt.hold(True)
# plt.imshow(arrays.scratch.get())
# plt.plot(np.arange(0, nrays), arrays.result.get())
# plt.show()
return arrays.result.get()
def mean(t: Tensor) -> np.float32:
"""The mean of the values in a tensor."""
if t.gpu:
return clarray.sum(t._data).get().flat[0] / t._data.size
return np.mean(t._data)
def sum(t: Tensor) -> np.float32:
"""The sum of the values in a tensor."""
if t.gpu:
return clarray.sum(t._data).get().flat[0]
return np.sum(t._data)
def _calcResidual(self, step_out, tmp_results, step_in, data):
f_new = clarray.vdot(tmp_results["DADA"], tmp_results["DAd"]) + clarray.sum(
self.lambd
* clmath.log(1 + clarray.vdot(tmp_results["gradx"], tmp_results["gradx"]))
)
# TODO: calculate on GPU
f_new = np.linalg.norm(f_new.get())
grad_f = np.linalg.norm(tmp_results["gradFx"].get())
# TODO: datacosts calculate or get from outside!!!!
# datacost = 0 # self._fval_init
# TODO: calculate on GPU
datacost = 2 * np.linalg.norm(tmp_results["Ax"] - data) ** 2
# datacost = 2 * np.linalg.norm(data - b) ** 2
# self._FT.FFT(b, clarray.to_device(
# self._queue[0], (self._step_val[:, None, ...] *
# self.par["C"]))).wait()
# b = b.get()
# datacost = 2 * np.linalg.norm(data - b) ** 2
# TODO: calculate on GPU
L2Cost = np.linalg.norm(step_out["x"].get()) / (2.0 * self.delta)
regcost = self.lambd * np.sum(
np.abs(
clmath.log(
1 + clarray.vdot(tmp_results["gradx"], tmp_results["gradx"])
).get()
)
)
costs = datacost + L2Cost + regcost
return costs, f_new, grad_f
def gs_mod_gpu(idata, itera=10, osize=256):
cut = osize // 2
pl = cl.get_platforms()[0]
devices = pl.get_devices(device_type=cl.device_type.GPU)
ctx = cl.Context(devices=[devices[0]])
queue = cl.CommandQueue(ctx)
plan = Plan(idata.shape, queue=queue,
dtype=complex128) #no funciona con "complex128"
src = str(
Template(KERNEL).render(
double_support=all(has_double_support(dev) for dev in devices),
amd_double_support=all(
has_amd_double_support(dev) for dev in devices)))
prg = cl.Program(ctx, src).build()
idata_gpu = cl_array.to_device(queue,
ifftshift(idata).astype("complex128"))
fdata_gpu = cl_array.empty_like(idata_gpu)
rdata_gpu = cl_array.empty_like(idata_gpu)
plan.execute(idata_gpu.data, fdata_gpu.data)
mask = exp(2.j * pi * random(idata.shape))
mask[512 - cut:512 + cut, 512 - cut:512 + cut] = 0
idata_gpu = cl_array.to_device(
queue,
ifftshift(idata + mask).astype("complex128"))
fdata_gpu = cl_array.empty_like(idata_gpu)
rdata_gpu = cl_array.empty_like(idata_gpu)
error_gpu = cl_array.to_device(ctx, queue,
zeros(idata_gpu.shape).astype("double"))
plan.execute(idata_gpu.data, fdata_gpu.data)
e = 1000
ea = 1000
for i in range(itera):
prg.norm(queue, fdata_gpu.shape, None, fdata_gpu.data)
plan.execute(fdata_gpu.data, rdata_gpu.data, inverse=True)
#~ prg.norm1(queue, rdata_gpu.shape,None,rdata_gpu.data,idata_gpu.data,error_gpu.data, int32(cut))
norm1 = prg.norm1
norm1.set_scalar_arg_dtypes([None, None, None, int32])
norm1(queue, rdata_gpu.shape, None, rdata_gpu.data, idata_gpu.data,
error_gpu.data, int32(cut))
e = sqrt(cl_array.sum(error_gpu).get()) / (2 * cut)
#~ if e>ea:
#~
#~ break
#~ ea=e
plan.execute(rdata_gpu.data, fdata_gpu.data)
fdata = fdata_gpu.get()
fdata = ifftshift(fdata)
fdata = exp(1.j * angle(fdata))
return fdata
def gs_mod_gpu(idata,itera=10,osize=256):
cut=osize//2
pl=cl.get_platforms()[0]
devices=pl.get_devices(device_type=cl.device_type.GPU)
ctx = cl.Context(devices=[devices[0]])
queue = cl.CommandQueue(ctx)
plan = Plan(idata.shape, queue=queue,dtype=complex128) #no funciona con "complex128"
src = str(Template(KERNEL).render(
double_support=all(
has_double_support(dev) for dev in devices),
amd_double_support=all(
has_amd_double_support(dev) for dev in devices)
))
prg = cl.Program(ctx,src).build()
idata_gpu=cl_array.to_device(queue, ifftshift(idata).astype("complex128"))
fdata_gpu=cl_array.empty_like(idata_gpu)
rdata_gpu=cl_array.empty_like(idata_gpu)
plan.execute(idata_gpu.data,fdata_gpu.data)
mask=exp(2.j*pi*random(idata.shape))
mask[512-cut:512+cut,512-cut:512+cut]=0
idata_gpu=cl_array.to_device(queue, ifftshift(idata+mask).astype("complex128"))
fdata_gpu=cl_array.empty_like(idata_gpu)
rdata_gpu=cl_array.empty_like(idata_gpu)
error_gpu=cl_array.to_device(ctx, queue, zeros(idata_gpu.shape).astype("double"))
plan.execute(idata_gpu.data,fdata_gpu.data)
e=1000
ea=1000
for i in range (itera):
prg.norm(queue, fdata_gpu.shape, None,fdata_gpu.data)
plan.execute(fdata_gpu.data,rdata_gpu.data,inverse=True)
#~ prg.norm1(queue, rdata_gpu.shape,None,rdata_gpu.data,idata_gpu.data,error_gpu.data, int32(cut))
norm1=prg.norm1
norm1.set_scalar_arg_dtypes([None, None, None, int32])
norm1(queue, rdata_gpu.shape,None,rdata_gpu.data,idata_gpu.data,error_gpu.data, int32(cut))
e= sqrt(cl_array.sum(error_gpu).get())/(2*cut)
#~ if e>ea:
#~
#~ break
#~ ea=e
plan.execute(rdata_gpu.data,fdata_gpu.data)
fdata=fdata_gpu.get()
fdata=ifftshift(fdata)
fdata=exp(1.j*angle(fdata))
return fdata
def get_divergence_errors(hij):
max_errors = []
avg_errors = []
for i in range(1, 4):
for mu in range(3):
fft.idft(hij[tensor_id(i, mu + 1)], vector_x[mu])
derivs.divergence(queue, vector_x, div)
derivs(queue, fx=vector_x[0], pdx=pdx[0])
derivs(queue, fx=vector_x[1], pdy=pdx[1])
derivs(queue, fx=vector_x[2], pdz=pdx[2])
norm = sum([clm.fabs(pdx[mu]) for mu in range(3)])
max_errors.append(cla.max(clm.fabs(div)) / cla.max(norm))
avg_errors.append(cla.sum(clm.fabs(div)) / cla.sum(norm))
return np.array(max_errors), np.array(avg_errors)
def minZerrKernSHG_gpu(self):
krn = self.progs.progs["minZerrSHG"].minZerrSHG
krn.set_scalar_arg_dtypes((None, None, None, None, None, None, None, None, np.int32))
krn.set_args(
self.Esig_t_tau_p_cla.data,
self.Et_cla.data,
self.dZ_cla.data,
self.X0_cla.data,
self.X1_cla.data,
self.X2_cla.data,
self.X3_cla.data,
self.X4_cla.data,
self.N,
)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
krn = self.progs.progs["normEsig"].normEsig
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Esig_t_tau_norm_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_t_tau_p.shape, None)
ev.wait()
mx = cla.max(self.Esig_t_tau_norm_cla).get() * self.N * self.N
# Esig_t_tau = self.Esig_t_tau_p_cla.get()
# mx = ((Esig_t_tau*Esig_t_tau.conj()).real).max() * self.N*self.N
X0 = cla.sum(self.X0_cla, queue=self.q).get() / mx
X1 = cla.sum(self.X1_cla, queue=self.q).get() / mx
X2 = cla.sum(self.X2_cla, queue=self.q).get() / mx
X3 = cla.sum(self.X3_cla, queue=self.q).get() / mx
X4 = cla.sum(self.X4_cla, queue=self.q).get() / mx
root.debug("".join(("X0=", str(X0), ", type ", str(type(X0)))))
root.debug(
"".join(("Poly: ", str(X4), " x^4 + ", str(X3), " x^3 + ", str(X2), " x^2 + ", str(X1), " x + ", str(X0)))
)
# Polynomial in dZ (expansion of differential)
X = np.array([X0, X1, X2, X3, X4]).astype(np.double)
root.debug("".join(("Esig_t_tau_p norm max: ", str(mx / (self.N * self.N)))))
return X
def _cl_count_complexes(self, weight):
# Count all sampled complexes
self._cl_tot_complex += cl_array.sum(self._cl_interspace,
dtype=np.dtype(np.float32)) * weight
self._cl_kernels.set_to_i32(np.int32(0), self._cl_hist)
self._cl_kernels.histogram(self.queue, self._cl_red_interspace, self._cl_hist)
self._cl_kernels.multiply_add(self._cl_hist, weight,
self._cl_consistent_complexes)
self.queue.finish()
def _ggr_spa_error(self,spa):
# promote the available spins by value target*spa. store in the spa_buffer. bound spa_buffer.
# calculate the new total magnetization for this spa value. difference of total and desired is the error function.
self.ggr_promote_spins(self.domains,self.available,self.spa_buffer,self.target*spa)
self.bound(self.spa_buffer,self.spa_buffer)
buffer_average = (cla.sum(self.spa_buffer).get())/self.N2
e = abs(buffer_average-self.goal_m)
#print " %.6e, %.3e"%(spa,e)
return e
def sum(*args, **kwargs):
a = args[0]
if a.ndim==0 or not 'axis' in kwargs.keys():
res = clarray.sum(a, queue=queue) #np.sum(*args, **kwargs)
if not isinstance(res, myclArray):
res.__class__ = myclArray
res.reinit()
return res
else:
kwargs['prg2load'] = programs.sum
return _sum(*args, **kwargs)
def alter_sum():
ctx = cl_init()
queue = cl.CommandQueue(ctx)
n = 10**6
a_gpu = cl_array.to_device(queue, np.random.randn(n).astype(np.float32))
b_gpu = cl_array.to_device(queue, np.random.randn(n).astype(np.float32))
cl_sum = cl_array.sum(a_gpu).get()
numpy_sum = np.sum(a_gpu.get())
print cl_sum, numpy_sum
def test_sum(ctx_getter):
context = ctx_getter()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
a_gpu = clrand(context, queue, (200000,), np.float32)
a = a_gpu.get()
sum_a = np.sum(a)
sum_a_gpu = cl_array.sum(a_gpu).get()
assert abs(sum_a_gpu-sum_a)/abs(sum_a) < 1e-4
def radon_normest(queue, r_struct):
img = clarray.to_device(
queue, require(random.randn(*r_struct[1]), float32, 'F'))
sino = clarray.zeros(queue, r_struct[2], dtype=float32, order='F')
V = (radon(sino, img, r_struct, wait_for=img.events))
for i in range(10):
normsqr = float(clarray.sum(img).get())
img /= normsqr
sino.add_event(radon(sino, img, r_struct, wait_for=img.events))
img.add_event(radon_ad(img, sino, r_struct, wait_for=sino.events))
return sqrt(normsqr)
def alter_sum(): ctx = cl_init() queue = cl.CommandQueue(ctx) n = 10**6 a_gpu = cl_array.to_device( queue, np.random.randn(n).astype(np.float32)) b_gpu = cl_array.to_device( queue, np.random.randn(n).astype(np.float32)) cl_sum = cl_array.sum(a_gpu).get() numpy_sum = np.sum(a_gpu.get()) print cl_sum, numpy_sum
def test_sum(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
n = 200000
for dtype in [np.float32, np.complex64]:
a_gpu = general_clrand(queue, (n,), dtype)
a = a_gpu.get()
sum_a = np.sum(a)
sum_a_gpu = cl_array.sum(a_gpu).get()
assert abs(sum_a_gpu - sum_a) / abs(sum_a) < 1e-4
def test_outoforderqueue_reductions(ctx_factory):
context = ctx_factory()
try:
queue = cl.CommandQueue(context,
properties=cl.command_queue_properties.OUT_OF_ORDER_EXEC_MODE_ENABLE)
except Exception:
pytest.skip("out-of-order queue not available")
# 0/1 values to avoid accumulated rounding error
a = (np.random.rand(10**6) > 0.5).astype(np.dtype('float32'))
a[800000] = 10 # all<5 looks true until near the end
a_gpu = cl_array.to_device(queue, a)
b1 = cl_array.sum(a_gpu).get()
b2 = cl_array.dot(a_gpu, 3 - a_gpu).get()
b3 = (a_gpu < 5).all().get()
assert b1 == a.sum() and b2 == a.dot(3 - a) and b3 == 0
def check_convergence(self):
# calculate the difference of the previous domains (self.incoming) and the current domains (self.domains).
self.array_diff(self.incoming,self.domains,self.domain_diff)
# sum the difference array and divide by the area of the simulation as a metric of how much the two domains
# configurations differ.
self.power = (cla.sum(self.domain_diff).get())/self.N2
self.powerlist.append(self.power)
# set the convergence condition
if self.power > self.converged_at: self.converged = False
if self.power <= self.converged_at: self.converged = True
if 'converged' in self.returnables_list and self.converged: self.returnables['converged'] = self.domains.get()
def test_sum(ctx_getter):
context = ctx_getter()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
a_gpu = clrand(context, queue, (200000, ))
a = a_gpu.get()
sum_a = numpy.sum(a)
from pycuda.reduction import get_sum_kernel
sum_a_gpu = cl_array.sum(a_gpu).get()
assert abs(sum_a_gpu - sum_a) / abs(sum_a) < 1e-4
def test_sum(ctx_getter):
context = ctx_getter()
queue = cl.CommandQueue(context)
from pyopencl.clrandom import rand as clrand
a_gpu = clrand(context, queue, (200000,))
a = a_gpu.get()
sum_a = numpy.sum(a)
from pycuda.reduction import get_sum_kernel
sum_a_gpu = cl_array.sum(a_gpu).get()
assert abs(sum_a_gpu-sum_a)/abs(sum_a) < 1e-4
def sum(q, a, axis=None, out=None, keepdims=False):
if axis is None or a.ndim <= 1:
out_shape = (1, ) * a.ndim if keepdims else ()
return clarray.sum(a).reshape(out_shape)
if axis < 0:
axis += 2
if axis > 1:
raise ValueError('invalid axis')
if a.flags.c_contiguous:
m, n = a.shape
lda = a.shape[1]
transA = True if axis == 0 else False
sum_axis, out_axis = (m, n) if axis == 0 else (n, m)
else:
n, m = a.shape
lda = a.shape[0]
transA = False if axis == 0 else True
sum_axis, out_axis = (n, m) if axis == 0 else (m, n)
ones = clarray.empty(q, (sum_axis, ), a.dtype).fill(1.0)
if keepdims:
out_shape = (1, out_axis) if axis == 0 else (out_axis, 1)
else:
out_shape = (out_axis, )
if out is None:
out = clarray.zeros(q, out_shape, a.dtype)
else:
assert out.dtype == a.dtype
assert out.size >= out_axis
if a.dtype == np.float32:
gemv = clblaswrap.sgemv
elif a.dtype == np.float64:
gemv = clblaswrap.dgemv
else:
raise TypeError('Unsupported array type: %s' % str(a.dtype))
alpha = 1.0
beta = 0.0
ev = gemv(q, transA, m, n, alpha, a, lda, ones, 1, beta, out, 1)
ev.wait()
return out
def find_contacts(self, predict=True):
"""Call the find_contacts kernel.
Assumes that cell_centers, cell_dirs, cell_lens, cell_rads,
cell_sqs, cell_dcenters, cell_dlens, cell_dangs,
sorted_ids, and sq_inds are current on the device.
Calculates cell_n_cts, ct_frs, ct_tos, ct_dists, ct_pts,
ct_norms, ct_reldists, and n_cts.
"""
if predict:
centers = self.pred_cell_centers_dev
dirs = self.pred_cell_dirs_dev
lens = self.pred_cell_lens_dev
else:
centers = self.cell_centers_dev
dirs = self.cell_dirs_dev
lens = self.cell_lens_dev
self.program.find_plane_contacts(
self.queue, (self.n_cells, ), None, numpy.int32(self.max_cells),
numpy.int32(self.max_contacts), numpy.int32(self.n_planes),
self.plane_pts_dev.data, self.plane_norms_dev.data,
self.plane_coeffs_dev.data, centers.data, dirs.data, lens.data,
self.cell_rads_dev.data, self.cell_n_cts_dev.data,
self.ct_frs_dev.data, self.ct_tos_dev.data, self.ct_dists_dev.data,
self.ct_pts_dev.data, self.ct_norms_dev.data,
self.ct_reldists_dev.data, self.ct_stiff_dev.data).wait()
self.program.find_contacts(
self.queue, (self.n_cells, ), None, numpy.int32(self.max_cells),
numpy.int32(self.n_cells), numpy.int32(self.grid_x_min),
numpy.int32(self.grid_x_max), numpy.int32(self.grid_y_min),
numpy.int32(self.grid_y_max), numpy.int32(self.n_sqs),
numpy.int32(self.max_contacts), centers.data, dirs.data, lens.data,
self.cell_rads_dev.data, self.cell_sqs_dev.data,
self.sorted_ids_dev.data, self.sq_inds_dev.data,
self.cell_n_cts_dev.data, self.ct_frs_dev.data,
self.ct_tos_dev.data, self.ct_dists_dev.data, self.ct_pts_dev.data,
self.ct_norms_dev.data, self.ct_reldists_dev.data,
self.ct_stiff_dev.data, self.ct_overlap_dev.data).wait()
# set dtype to int32 so we don't overflow the int32 when summing
#self.n_cts = self.cell_n_cts_dev.get().sum(dtype=numpy.int32)
self.n_cts = cl_array.sum(self.cell_n_cts_dev[0:self.n_cells]).get()
def test_sum(ctx_factory):
from pytest import importorskip
importorskip("mako")
context = ctx_factory()
queue = cl.CommandQueue(context)
n = 200000
for dtype in [np.float32, np.complex64]:
a_gpu = general_clrand(queue, (n, ), dtype)
a = a_gpu.get()
for slc in [
slice(None),
slice(1000, 3000),
slice(1000, -3000),
slice(1000, None),
]:
sum_a = np.sum(a[slc])
sum_a_gpu = cl_array.sum(a_gpu[slc]).get()
assert abs(sum_a_gpu - sum_a) / abs(sum_a) < 1e-4
def _sum(a, axis=None, dtype=None, out=None, prg2load=programs.sum):
#Transpose first to shift target axis to the end
#do not transpose if axis already is the end
if axis==None:
res = clarray.sum(a, queue=queue)
if not isinstance(res, myclArray):
res.__class__ = myclArray
res.reinit()
return res
olddims = np.array(a.shape, dtype=np.uint32)
replaces = np.append(np.delete(np.arange(a.ndim), axis, 0), [axis], 0).astype(np.uint32)
if axis != a.ndim-1:
clolddims = cl.Buffer(ctx, mf.READ_ONLY| mf.COPY_HOST_PTR, hostbuf=olddims)
clreplaces = cl.Buffer(ctx, mf.READ_ONLY| mf.COPY_HOST_PTR, hostbuf=replaces)
cltrresult = cl.Buffer(ctx, mf.READ_WRITE, a.nbytes)
program = programs.transpose(a.dtype, a.ndim)
program.mitransp(queue, (a.size,), None, clolddims, clreplaces, a.data, cltrresult)
else:
cltrresult = a.data
program = prg2load(a.dtype, a.shape[axis])
#Sum for last axis
result = empty(tuple(olddims[replaces[:-1]]), a.dtype)
program.misum(queue, (int(a.size//a.shape[axis]),), None, cltrresult, result.data)
return result
def _gpu_search(self):
"""Method that actually performs the exhaustive search on the GPU"""
# make shortcuts
d = self.data
g = self.gpu_data
q = self.queue
k = g['k']
# initalize the total number of sampled complexes
tot_complexes = cl_array.sum(g['interspace'], dtype=np.float32)
# initialize time
time0 = _time()
# loop over all rotations
for n in xrange(g['nrot']):
# rotate the scanning chain object
k.rotate_image3d(q, g['sampler'], g['im_lsurf'], self.rotations[n],
g['lsurf'], d['im_center'])
# perform the FFTs and calculate the clashing and interaction volume
k.rfftn(q, g['lsurf'], g['ft_lsurf'])
k.c_conj_multiply(q, g['ft_lsurf'], g['ft_rcore'],
g['ft_clashvol'])
k.irfftn(q, g['ft_clashvol'], g['clashvol'])
k.c_conj_multiply(q, g['ft_lsurf'], g['ft_rsurf'],
g['ft_intervol'])
k.irfftn(q, g['ft_intervol'], g['intervol'])
# determine at every position if the conformation is a proper complex
k.touch(q, g['clashvol'], g['max_clash'], g['intervol'],
g['min_interaction'], g['interspace'])
if self.distance_restraints:
k.fill(q, g['restspace'], 0)
# determine the space that is consistent with a number of
# distance restraints
k.distance_restraint(q, g['restraints'], self.rotations[n],
g['restspace'])
# get the accessible interaction space also consistent with a
# certain number of distance restraints
k.multiply(q, g['restspace'], g['interspace'],
g['access_interspace'])
# calculate the total number of complexes, while taking into
# account orientational/rotational bias
tot_complexes += cl_array.sum(g['interspace'],
dtype=np.float32) * np.float32(
self.weights[n])
# take at every position in space the maximum number of consistent
# restraints for later visualization
cl_array.maximum(g['best_access_interspace'],
g['access_interspace'],
g['best_access_interspace'])
# calculate the number of accessable complexes consistent with
# EXACTLY N distance restraints
k.histogram(q, g['access_interspace'], g['subhists'],
self.weights[n], d['nrestraints'])
# Count the violations of each restraint for all complexes
# consistent with EXACTLY N restraints
k.count_violations(q, g['restraints'], self.rotations[n],
g['access_interspace'], g['viol_counter'],
self.weights[n])
# inform user
if _stdout.isatty():
self._print_progress(n, g['nrot'], time0)
# wait for calculations to finish
self.queue.finish()
# transfer the data from GPU to CPU
# get the number of accessible complexes and reduce the subhistograms
# to the final histogram
access_complexes = g['subhists'].get().sum(axis=0)
# account for the fact that we are counting the number of accessible
# complexes consistent with EXACTLY N restraints
access_complexes[0] = tot_complexes.get() - sum(access_complexes[1:])
d['accessible_complexes'] = access_complexes
d['accessible_interaction_space'] = g['best_access_interspace'].get()
# get the violation submatrices and reduce it to the final violation
# matrix
d['violations'] = g['viol_counter'].get().sum(axis=0)
def decode_OpenCL_belief_propagation(self, received_blocks,buffer_in=False,return_buffer=False):
# Set up OpenCL
if buffer_in:
channel_values_buffer = received_blocks
else:
channel_values_buffer = cl_array.to_device(self.queue,received_blocks.astype(np.float64))
varnode_output_buffer = cl_array.empty(self.queue, received_blocks.shape, dtype=np.float64)
self.send_prog(self.queue, received_blocks.shape, None,
channel_values_buffer.data,
self.inbox_memory_start_varnodes_buffer.data,
self.degree_varnode_nr_buffer.data,
self.target_memorycells_varnodes_buffer.data,
self.checknode_inbox_buffer.data)
self.queue.finish()
syndrome_zero = False
i_num = 1
while (i_num<self.imax) and (not syndrome_zero):
local_size = None
self.checknode_update_prog(self.queue, (self.degree_checknode_nr.shape[0], received_blocks[:,np.newaxis].shape[-1]), None,
self.checknode_inbox_buffer.data,
self.inbox_memory_start_checknodes_buffer.data,
self.degree_checknode_nr_buffer.data,
self.target_memorycells_checknodes_buffer.data,
self.varnode_inbox_buffer.data)
self.queue.finish()
self.varnode_update_prog(self.queue, received_blocks.shape , None,
channel_values_buffer.data,
self.varnode_inbox_buffer.data,
self.inbox_memory_start_varnodes_buffer.data,
self.degree_varnode_nr_buffer.data,
self.target_memorycells_varnodes_buffer.data,
self.checknode_inbox_buffer.data)
self.calc_syndrom_prog(self.queue, (self.degree_checknode_nr.shape[0], received_blocks[:,np.newaxis].shape[-1]), None,
self.checknode_inbox_buffer.data,
self.inbox_memory_start_checknodes_buffer.data,
self.degree_checknode_nr_buffer.data,
self.syndrom_buffer.data)
if cl_array.sum(self.syndrom_buffer).get() == 0:
syndrome_zero =True
i_num += 1
self.varoutput_prog(self.queue, received_blocks.shape , None,
channel_values_buffer.data,
self.varnode_inbox_buffer.data,
self.inbox_memory_start_varnodes_buffer.data,
self.degree_varnode_nr_buffer.data,
varnode_output_buffer.data)
self.queue.finish()
if return_buffer:
return varnode_output_buffer
else:
output_values = varnode_output_buffer.get()
return output_values
def sum_cl(queue, a, axis=None):
""" Sum of GPUArray elements in a given axis direction or all elements.
Parameters
----------
queue
PyOpenCL queue.
a : gpuarray
GPUArray with elements to be operated on.
axis : int
Axis direction to sum through, all if None.
Returns
-------
gpuarray
GPUArray sum.
Notes
-----
- This is temporary and not an efficient implementation.
"""
if axis is not None:
m, n = a.shape
kernel = cl.Program(queue.context, """
__kernel void sum0_cl(__global float *a, __global float *b, unsigned m, unsigned n)
{
int bid = get_group_id(0);
int tid = get_local_id(1);
int id = get_global_id(1) * n + get_global_id(0);
int stride = 0;
__local float sum[32000 / sizeof(float)];
sum[tid] = a[id];
sum[m] = 0.;
for (stride = 1; stride < m; stride *= 2)
{
barrier(CLK_LOCAL_MEM_FENCE);
if (tid % (2 * stride) == 0)
{
sum[tid] += sum[tid + stride];
}
}
b[bid] = sum[0];
}
__kernel void sum1_cl(__global float *a, __global float *b, unsigned m, unsigned n)
{
int bid = get_group_id(1);
int tid = get_local_id(0);
int id = get_global_id(1) * n + get_global_id(0);
int stride = 0;
__local float sum[32000 / sizeof(float)];
sum[tid] = a[id];
sum[n] = 0.;
for (stride = 1; stride < n; stride *= 2)
{
barrier(CLK_LOCAL_MEM_FENCE);
if (tid % (2 * stride) == 0)
{
sum[tid] += sum[tid + stride];
}
}
b[bid] = sum[0];
}
""").build()
if axis == 0:
b = cl_array.empty(queue, (1, n), dtype=float32)
kernel.sum0_cl(queue, (n, m), (1, m), a.data, b.data, uint32(m), uint32(n))
elif axis == 1:
b = cl_array.empty(queue, (m, 1), dtype=float32)
kernel.sum1_cl(queue, (n, m), (n, 1), a.data, b.data, uint32(m), uint32(n))
return b
else:
return cl_array.sum(a)
def _evaluate(self, valuation, cache):
if id(self) not in cache:
op = self.ops[0]._evaluate(valuation, cache)
cache[id(self)] = clarray.sum(
op, dtype=np.dtype('float32')) / np.float32(op.size)
return cache[id(self)]
def spec(filename, extra):
cuantas = extra[0]
OPEN_IMAGE = extra[1]
if(OPEN_IMAGE==True):
a = Image.open(filename)
Nx, Ny = a.size
else: # np array
a = filename
#Nx, Ny = a.shape
Nx, Ny = a.size
L = Nx*Ny
points = [] # number of elements in the structure
RESHAPE = extra[3]
CONVERT = extra[2]
if(CONVERT == True):
#gray = a.convert('L') # rgb 2 gray
#arr = np.array(gray.getdata()).astype(np.int32)
arr = np.array(filename.getdata()).astype(np.int32)
else:
if(RESHAPE == True): # ARGHH
arr = np.array(a).reshape(a.shape[0]*a.shape[1])
else:
arr = a
alphaIm = np.zeros((Nx,Ny), dtype=np.float32 ) # Nx rows x Ny columns
l = 4 # (maximum window size-1) / 2
temp = map(lambda i: 2*i+1, range(l))
temp = np.log(temp)
measure = np.zeros(l*Ny).astype(np.int32)
b = np.vstack((temp,np.ones((1,l)))).T
AA=coo_matrix(np.kron(np.identity(Ny), b))
prg = cl.Program(ctx, """
__kernel void measure(__global float *alphaIm, __global int *img, const int Nx,
const int Ny, const int size) {
int i = get_global_id(0);
int j = get_global_id(1);
// make histogram of region
int hist[256];
int t;
for(t = 0; t < 256; t++) hist[t] = 0;
int xi = max(i-size,0);
int yi = max(j-size,0);
int xf = min(i+size,Nx-1);
int yf = min(j+size,Ny-1);
int u , v;
for(int u = xi; u <= xf; u++)
for(int v = yi; v <= yf; v++)
hist[img[u*Ny+v]]++;
float res = 0;
int s;
float total = (yf-yi)*(xf-xi); // size of region
for(s = 0; s <= 255; s++) {
float v = hist[s]/total; // probability
res += v*log2(v+0.0001);
}
alphaIm[i*Ny+j] = res;
}
""").build()
#d = measure.shape[0]/2
#ms = measure[0:l*d]
img_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=arr)
alphaIm_buf = cl.Buffer(ctx, mf.WRITE_ONLY, alphaIm.nbytes)
sh = alphaIm.shape
size = 8 # Window size
prg.measure(queue, sh, None, alphaIm_buf, img_buf, np.int32(Nx), np.int32(Ny), np.int32(size))
cl.enqueue_read_buffer(queue,alphaIm_buf,alphaIm).wait()
maxim = np.max(alphaIm)
minim = np.min(alphaIm)
#print maxim, minim
import matplotlib
from matplotlib import pyplot as plt
# Alpha image
#plt.imshow(alphaIm, cmap=matplotlib.cm.gray)
#plt.show()
#return
paso = (maxim-minim)/cuantas
if(paso <= 0):
# the alpha image is monofractal
clases = np.array(map(lambda i: i+minim,np.zeros(cuantas))).astype(np.float32)
else:
clases = np.arange(minim,maxim,paso).astype(np.float32)
# Window
cant = int(np.floor(np.log(Nx)))
# concatenate the image A as [[A,A],[A,A]]
hs = np.hstack((alphaIm,alphaIm))
alphaIm = np.vstack((hs,hs))
prg = cl.Program(ctx, """
__kernel void krnl(__global int *flag, __global float *clases,
__global float* alphaIm,const int sizeBlocks, const int Ny,
const int numBlocks_y, const int c, const int cuantas,
float minim, float maxim) {
int i = get_global_id(0);
int j = get_global_id(1);
int xi = i*sizeBlocks;
int xf = (i+1)*sizeBlocks-1;
int yi = j*sizeBlocks;
int yf = (j+1)*sizeBlocks-1;
// calculate max and min for this subregion
float maxx;
float minn;
int w,t;
int first = 0;
for(w = xi; w < xf; w++) {
for(t = yi; t < yf; t++) {
float v = alphaIm[w*Ny*2 + t];
if (v >= clases[c] and v <= clases[c+1]) {
if(!first) { first = 1; maxx = minn = v; }
if(v > maxx) maxx = v;
if(v < minn) minn = v;
}
}
}
float totalDif = maxim - minim;
int nB = numBlocks_y; // num of subdivisions in the Z coordinate
int l = floor(((maxx-minim)/totalDif)*nB)+1;
int k = floor(((minn-minim)/totalDif)*nB)+1;
flag[i*numBlocks_y + j] = l-k+1;
}
""").build()
# Multifractal dimentions
falpha = np.zeros(cuantas).astype(np.float32)
clases_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=clases.astype(np.float32))
alphaIm_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=alphaIm.astype(np.float32))
for c in range(cuantas):
N = np.zeros(cant+1)
# window sizes
for k in range(1,cant+2):
sizeBlocks = 2*k+1
numBlocks_x = int(np.floor(Nx/sizeBlocks))
numBlocks_y = int(np.floor(Ny/sizeBlocks))
flag = np.zeros((numBlocks_x,numBlocks_y)).astype(np.int32)
flag_buf = cl.Buffer(ctx, mf.WRITE_ONLY, flag.nbytes)
sh = flag.shape
prg.krnl(queue, sh, None, flag_buf, clases_buf, alphaIm_buf, np.int32(sizeBlocks), np.int32(Ny), np.int32(numBlocks_y), np.int32(c), np.int32(cuantas), np.float32(minim), np.float32(maxim))
cl.enqueue_read_buffer(queue, flag_buf, flag).wait()
N[k-1] = cla.sum(cla.to_device(queue,flag)).get()
#print N
# Haussdorf (box) dimention of the alpha distribution
falpha[c] = -np.polyfit(map(lambda i: np.log((2*i+1)),range(1,cant+2)),np.log(map(lambda i: i+1,N)),1)[0]
s = np.hstack((clases,falpha))
return s
def one_iteration(self,iteration):
# iterate through one cycle of the simulation.
assert self.can_has_domains, "no domains set!"
assert self.can_has_envelope, "no goal envelope set!"
# first, copy the current state of the domain pattern to a holding buffer ("incoming")
self.copy(self.domains,self.incoming)
# now find the domain walls. modifications to the domain pattern due to rescaling only take place in the walls
self.findwalls.execute(self.queue,(self.N,self.N),self.domains.data,self.allwalls.data,self.poswalls.data,self.negwalls.data,np.int32(self.N))
if 'walls1' in self.returnables_list: self.returnables['walls1'] = self.allwalls.get()
if 'pos_walls1' in self.returnables_list: self.returnables['pos_walls1'] = self.poswalls.get()
if 'neg_walls1' in self.returnables_list: self.returnables['neg_walls1'] = self.negwalls.get()
# run the ising bias
self.ising(self.domains,self.alpha)
# rescale the domains. this operates on the class variables so no arguments are passed. self.domains stores the rescaled real-valued domains. the rescaled
# domains are bounded to the range +1 -1.
self._rescale_speckle()
self.bound(self.domains,self.domains)
if 'bounded' in self.returnables_list: self.returnables['bounded'] = self.domains.get()
# if making an ordering island, this is the command that enforces the border condition
#if use_boundary and n > boundary_turn_on: self.enforce_boundary(self.domains,self.boundary,self.boundary_values)
# so now we have self.incoming (old domains) and self.domains (rescaled domains). we want to use self.walls to enforce changes to the domain pattern
# from rescaling only within the walls. because updating can change wall location, also refind the walls.
if self.only_walls:
self.update_domains(self.domains,self.incoming,self.allwalls)
self.findwalls.execute(self.queue,(self.N,self.N),self.domains.data,self.allwalls.data,self.poswalls.data,self.negwalls.data,np.int32(self.N))
if iteration > self.m_turnon:
self.findwalls.execute(self.queue,(self.N,self.N),self.domains.data,self.allwalls.data,self.poswalls.data,self.negwalls.data,np.int32(self.N))
if 'walls2' in self.returnables_list: self.returnables['walls2'] = self.allwalls.get()
if 'pos_walls2' in self.returnables_list: self.returnables['pos_walls2'] = self.poswalls.get()
if 'neg_walls2' in self.returnables_list: self.returnables['neg_walls2'] = self.negwalls.get()
# now attempt to adjust the net magnetization in real space to achieve the target magnetization.
net_m = cla.sum(self.domains).get()
needed_m = self.goal_m-net_m
if needed_m > 0:
self.make_available1(self.available,self.negwalls,self.negpins,self.pospins)
sites = cla.sum(self.available).get()
spa = min([self.spa_max,needed_m/sites])
if needed_m < 0:
self.make_available1(self.available,self.poswalls,self.negpins,self.pospins)
sites = cla.sum(self.available).get()
spa = max([-1*self.spa_max,needed_m/sites])
self.promote_spins(self.domains,self.available,spa)
if 'promoted' in self.returnables_list: self.returnables['promoted'] = self.domains.get()
self.bound(self.domains,self.domains)
if 'domains' in self.returnables_list: self.returnables['domains'] = self.domains.get()
# ge = p.globalError(4,4,x)
# print ge[0]
# # a = []
# # print numpy.sum(x[0])
# # print time.clock() - start
import pyopencl as cl # Import the OpenCL GPU computing API
import pyopencl.array as pycl_array # Import PyOpenCL Array (a Numpy array plus an OpenCL buffer object)
import numpy as np # Import Numpy number tools
context = cl.create_some_context() # Initialize the Context
queue = cl.CommandQueue(context) # Instantiate a Queue
x = pycl_array.to_device(queue, np.random.rand(3920, 100).astype(np.float32))
# a = pycl_array.to_device(queue, np.random.rand(50000).astype(np.float32))
# b = pycl_array.to_device(queue, np.random.rand(50000).astype(np.float32))
y = np.random.rand(3920, 100).astype(np.float32)
# Create two random pyopencl arrays
# c = pycl_array.empty_like(a) # Create an empty pyopencl destination array
start = time.clock()
d = []
for i in range(3920):
d.append(pycl_array.sum(x[i]))
# d = numpy.sum(y[i])
print time.clock() - start
# print("x: {}".format(x))
# print("d: {}".format(d))
# Print all three arrays, to show sum() worked
def decode_OpenCL(self,
received_blocks,
buffer_in=False,
return_buffer=False):
# Set up OpenCL
if buffer_in:
channel_values_buffer = received_blocks
else:
channel_values_buffer = cl_array.to_device(
self.queue, received_blocks.astype(np.int32))
varnode_output_buffer = cl_array.empty(self.queue,
received_blocks.shape,
dtype=np.int32)
self.send_prog(self.queue, received_blocks.shape, None,
channel_values_buffer.data,
self.inbox_memory_start_varnodes_buffer.data,
self.degree_varnode_nr_buffer.data,
self.target_memorycells_varnodes_buffer.data,
self.checknode_inbox_buffer.data)
#self.queue.finish()
self.first_iter_prog(self.queue,
(self.degree_checknode_nr.shape[0],
received_blocks[:, np.newaxis].shape[-1]), None,
self.checknode_inbox_buffer.data,
self.inbox_memory_start_checknodes_buffer.data,
self.degree_checknode_nr_buffer.data,
self.target_memorycells_checknodes_buffer.data,
self.varnode_inbox_buffer.data,
self.cardinality_T_channel,
self.cardinality_T_decoder_ops,
self.Trellis_checknode_vector_a_buffer.data)
syndrome_zero = False
i_num = 1
while (i_num < self.imax) and (not syndrome_zero):
local_size = None #(1000, 1)
self.varnode_update_prog(
self.queue, received_blocks.shape, local_size,
channel_values_buffer.data, self.varnode_inbox_buffer.data,
self.inbox_memory_start_varnodes_buffer.data,
self.degree_varnode_nr_buffer.data,
self.target_memorycells_varnodes_buffer.data,
self.checknode_inbox_buffer.data, self.cardinality_T_channel,
self.cardinality_T_decoder_ops, i_num - 1,
self.Trellis_varnode_vector_a_buffer.data)
#self.queue.finish()
self.checknode_update_prog(
self.queue, (self.degree_checknode_nr.shape[0],
received_blocks[:, np.newaxis].shape[-1]), None,
self.checknode_inbox_buffer.data,
self.inbox_memory_start_checknodes_buffer.data,
self.degree_checknode_nr_buffer.data,
self.target_memorycells_checknodes_buffer.data,
self.varnode_inbox_buffer.data, self.cardinality_T_channel,
self.cardinality_T_decoder_ops, i_num - 1,
self.Trellis_checknode_vector_a_buffer.data)
#self.queue.finish()
self.calc_syndrom_prog(
self.queue, (self.degree_checknode_nr.shape[0],
received_blocks[:, np.newaxis].shape[-1]), None,
self.checknode_inbox_buffer.data,
self.inbox_memory_start_checknodes_buffer.data,
self.degree_checknode_nr_buffer.data,
self.cardinality_T_decoder_ops, self.syndrom_buffer.data)
#self.queue.finish()
if cl_array.sum(self.syndrom_buffer).get() == 0:
syndrome_zero = True
i_num += 1
self.varoutput_prog(self.queue, received_blocks.shape, None,
channel_values_buffer.data,
self.varnode_inbox_buffer.data,
self.inbox_memory_start_varnodes_buffer.data,
self.degree_varnode_nr_buffer.data,
self.cardinality_T_channel,
self.cardinality_T_decoder_ops, i_num - 1,
self.Trellis_varnode_vector_a_buffer.data,
varnode_output_buffer.data)
self.queue.finish()
if return_buffer:
return varnode_output_buffer
else:
pass
output_values = varnode_output_buffer.get()
return output_values
def _gpu_init(self):
q = self.queue
# Move arrays to GPU
self._cl_rcore = cl_array.to_device(q, self._rcore.astype(np.float32))
self._cl_rsurf = cl_array.to_device(q, self._rsurf.astype(np.float32))
self._cl_lcore = cl_array.to_device(q, self._lcore.astype(np.float32))
# Make the rotations float16 arrays
self._cl_rotations = np.zeros((self.rotations.shape[0], 16), dtype=np.float32)
self._cl_rotations[:, :9] = self.rotations.reshape(-1, 9)
# Allocate arrays
# Float32
self._cl_shape = tuple(self._shape)
arr_names = 'rot_lcore clashvol intervol tmp'.split()
for arr_name in arr_names:
setattr(self, '_cl_' + arr_name,
cl_array.zeros(q, self._cl_shape, dtype=np.float32)
)
# Int32
arr_names = 'interspace red_interspace restspace access_interspace'.split()
for arr_name in arr_names:
setattr(self, '_cl_' + arr_name,
cl_array.zeros(q, self._cl_shape, dtype=np.int32)
)
# Boolean
arr_names = 'not_clashing interacting'.split()
for arr_name in arr_names:
setattr(self, '_cl_' + arr_name,
cl_array.zeros(q, self._cl_shape, dtype=np.int32)
)
# Complex64
self._ft_shape = tuple([self._shape[0] // 2 + 1] + list(self._shape)[1:])
arr_names = 'lcore lcore_conj rcore rsurf tmp'.split()
for arr_name in arr_names:
setattr(self, '_cl_ft_' + arr_name,
cl_array.empty(q, self._ft_shape, dtype=np.complex64)
)
# Restraints arrays
self._cl_rrestraints = np.zeros((self._nrestraints, 4), dtype=np.float32)
self._cl_rrestraints[:, :3] = self._rrestraints
self._cl_rrestraints = cl_array.to_device(q, self._cl_rrestraints)
self._cl_lrestraints = np.zeros((self._nrestraints, 4), dtype=np.float32)
self._cl_lrestraints[:, :3] = self._lrestraints
self._cl_lrestraints = cl_array.to_device(q, self._cl_lrestraints)
self._cl_mindis = cl_array.to_device(q, self._mindis.astype(np.float32))
self._cl_maxdis = cl_array.to_device(q, self._maxdis.astype(np.float32))
self._cl_mindis2 = cl_array.to_device(q, self._mindis.astype(np.float32) ** 2)
self._cl_maxdis2 = cl_array.to_device(q, self._maxdis.astype(np.float32) ** 2)
self._cl_rot_lrestraints = cl_array.zeros_like(self._cl_rrestraints)
self._cl_restraints_center = cl_array.zeros_like(self._cl_rrestraints)
# kernels
self._kernel_constants = {'interaction_cutoff': 10,
'nrestraints': self._nrestraints,
'shape_x': self._shape[2],
'shape_y': self._shape[1],
'shape_z': self._shape[0],
'llength': self._llength,
'nreceptor_coor': 0,
'nligand_coor': 0,
}
# Counting arrays
self._cl_hist = cl_array.zeros(self.queue, self._nrestraints, dtype=np.int32)
self._cl_consistent_complexes = cl_array.zeros(self.queue,
self._nrestraints, dtype=np.float32)
self._cl_viol_hist = cl_array.zeros(self.queue, (self._nrestraints,
self._nrestraints), dtype=np.int32)
self._cl_violations = cl_array.zeros(self.queue, (self._nrestraints,
self._nrestraints), dtype=np.float32)
# Conversions
self._cl_grid_max_clash = np.float32(self._grid_max_clash)
self._cl_grid_min_interaction = np.float32(self._grid_min_interaction)
self._CL_ZERO = np.int32(0)
# Occupancy analysis
self._cl_occ_grid = {}
if self.occupancy_analysis:
for i in xrange(self.interaction_restraints_cutoff, self._nrestraints + 1):
self._cl_occ_grid[i] = cl_array.zeros(self.queue,
self._cl_shape, dtype=np.float32)
# Interaction analysis
if self._interaction_analysis:
shape = (self._lselect.shape[0], self._rselect.shape[0])
self._cl_interaction_hist = cl_array.zeros(self.queue, shape,
dtype=np.int32)
self._cl_interaction_matrix = {}
for i in xrange(self._nrestraints + 1 - self.interaction_restraints_cutoff):
self._cl_interaction_matrix[i] = cl_array.zeros(self.queue, shape,
dtype=np.float32)
# Coordinate arrays
self._cl_rselect = np.zeros((self._rselect.shape[0], 4), dtype=np.float32)
self._cl_rselect[:, :3] = self._rselect
self._cl_rselect = cl_array.to_device(q, self._cl_rselect)
self._cl_lselect = np.zeros((self._lselect.shape[0], 4), dtype=np.float32)
self._cl_lselect[:, :3] = self._lselect
self._cl_lselect = cl_array.to_device(q, self._cl_lselect)
self._cl_rot_lselect = cl_array.zeros_like(self._cl_lselect)
# Update kernel constants
self._kernel_constants['nreceptor_coor'] = self._cl_rselect.shape[0]
self._kernel_constants['nligand_coor'] = self._cl_lselect.shape[0]
self._cl_kernels = Kernels(q.context, self._kernel_constants)
self._cl_rfftn = pyclfft.RFFTn(q.context, self._shape)
self._cl_irfftn = pyclfft.iRFFTn(q.context, self._shape)
# Initial calculations
self._cl_rfftn(q, self._cl_rcore, self._cl_ft_rcore)
self._cl_rfftn(q, self._cl_rsurf, self._cl_ft_rsurf)
self._cl_tot_complex = cl_array.sum(self._cl_interspace, dtype=np.dtype(np.float32))
def _sum_OCL(a, dtype=None, queue=None, slice=None):
return cl_array.sum(a=a, dtype=dtype, queue=queue,
slice=slice).get(queue=queue)
def find_contacts(self, predict=True):
"""Call the find_contacts kernel.
Assumes that cell_centers, cell_dirs, cell_lens, cell_rads,
cell_sqs, cell_dcenters, cell_dlens, cell_dangs,
sorted_ids, and sq_inds are current on the device.
Calculates cell_n_cts, ct_frs, ct_tos, ct_dists, ct_pts,
ct_norms, ct_reldists, and n_cts.
"""
if predict:
centers = self.pred_cell_centers_dev
dirs = self.pred_cell_dirs_dev
lens = self.pred_cell_lens_dev
else:
centers = self.cell_centers_dev
dirs = self.cell_dirs_dev
lens = self.cell_lens_dev
self.program.find_plane_contacts(self.queue,
(self.n_cells,),
None,
numpy.int32(self.max_cells),
numpy.int32(self.max_contacts),
numpy.int32(self.n_planes),
self.plane_pts_dev.data,
self.plane_norms_dev.data,
self.plane_coeffs_dev.data,
centers.data,
dirs.data,
lens.data,
self.cell_rads_dev.data,
self.cell_n_cts_dev.data,
self.ct_frs_dev.data,
self.ct_tos_dev.data,
self.ct_dists_dev.data,
self.ct_pts_dev.data,
self.ct_norms_dev.data,
self.ct_reldists_dev.data,
self.ct_stiff_dev.data).wait()
self.program.find_contacts(self.queue,
(self.n_cells,),
None,
numpy.int32(self.max_cells),
numpy.int32(self.n_cells),
numpy.int32(self.grid_x_min),
numpy.int32(self.grid_x_max),
numpy.int32(self.grid_y_min),
numpy.int32(self.grid_y_max),
numpy.int32(self.n_sqs),
numpy.int32(self.max_contacts),
centers.data,
dirs.data,
lens.data,
self.cell_rads_dev.data,
self.cell_sqs_dev.data,
self.sorted_ids_dev.data,
self.sq_inds_dev.data,
self.cell_n_cts_dev.data,
self.ct_frs_dev.data,
self.ct_tos_dev.data,
self.ct_dists_dev.data,
self.ct_pts_dev.data,
self.ct_norms_dev.data,
self.ct_reldists_dev.data,
self.ct_stiff_dev.data).wait()
# set dtype to int32 so we don't overflow the int32 when summing
#self.n_cts = self.cell_n_cts_dev.get().sum(dtype=numpy.int32)
self.n_cts = cl_array.sum(self.cell_n_cts_dev).get()
def test_spectral_poisson(ctx_factory, grid_shape, proc_shape, h, dtype,
timing=False):
if ctx_factory:
ctx = ctx_factory()
else:
ctx = ps.choose_device_and_make_context()
queue = cl.CommandQueue(ctx)
mpi = ps.DomainDecomposition(proc_shape, h, grid_shape=grid_shape)
rank_shape, _ = mpi.get_rank_shape_start(grid_shape)
fft = ps.DFT(mpi, ctx, queue, grid_shape, dtype)
L = (3, 5, 7)
dx = tuple(Li / Ni for Li, Ni in zip(L, grid_shape))
dk = tuple(2 * np.pi / Li for Li in L)
if h == 0:
def get_evals_2(k, dx):
return - k**2
derivs = ps.SpectralCollocator(fft, dk)
else:
from pystella.derivs import SecondCenteredDifference
get_evals_2 = SecondCenteredDifference(h).get_eigenvalues
derivs = ps.FiniteDifferencer(mpi, h, dx, stream=False)
solver = ps.SpectralPoissonSolver(fft, dk, dx, get_evals_2)
pencil_shape = tuple(ni + 2*h for ni in rank_shape)
statistics = ps.FieldStatistics(mpi, 0, rank_shape=rank_shape,
grid_size=np.product(grid_shape))
fx = cla.empty(queue, pencil_shape, dtype)
rho = clr.rand(queue, rank_shape, dtype)
rho -= statistics(rho)["mean"]
lap = cla.empty(queue, rank_shape, dtype)
rho_h = rho.get()
for m_squared in (0, 1.2, 19.2):
solver(queue, fx, rho, m_squared=m_squared)
fx_h = fx.get()
if h > 0:
fx_h = fx_h[h:-h, h:-h, h:-h]
derivs(queue, fx=fx, lap=lap)
diff = np.fabs(lap.get() - rho_h - m_squared * fx_h)
max_err = np.max(diff) / cla.max(clm.fabs(rho))
avg_err = np.sum(diff) / cla.sum(clm.fabs(rho))
max_rtol = 1e-12 if dtype == np.float64 else 1e-4
avg_rtol = 1e-13 if dtype == np.float64 else 1e-5
assert max_err < max_rtol and avg_err < avg_rtol, \
f"solution inaccurate for {h=}, {grid_shape=}, {proc_shape=}"
if timing:
from common import timer
time = timer(lambda: solver(queue, fx, rho, m_squared=m_squared), ntime=10)
if mpi.rank == 0:
print(f"poisson took {time:.3f} ms for {grid_shape=}, {proc_shape=}")
def pyopencl_mean(x_gpu_in):
return cl_array.sum(x_gpu_in) / float(x_gpu_in.size)
def spec(filename, extra):
cuantas = extra[0]
OPEN_IMAGE = extra[1]
if(OPEN_IMAGE==True):
a = Image.open(filename)
Nx, Ny = a.size
else: # np array
a = filename
#Nx, Ny = a.shape
Nx, Ny = a.size
L = Nx*Ny
points = [] # number of elements in the structure
RESHAPE = extra[3]
CONVERT = extra[2]
if(CONVERT == True):
gray = a.convert('L') # rgb 2 gray
arr = np.array(gray.getdata()).astype(np.int32)
else:
if(RESHAPE == True): # ARGHH
arr = np.array(a).reshape(a.shape[0]*a.shape[1])
else:
arr = a
alphaIm = np.zeros((Nx,Ny), dtype=np.double ) # Nx rows x Ny columns
l = 4 # (maximum window size-1) / 2
temp = map(lambda i: 2*i+1, range(l))
temp = np.log(temp)
measure = np.zeros(l*Ny).astype(np.int32)
b = np.vstack((temp,np.ones((1,l)))).T
AA=coo_matrix(np.kron(np.identity(Ny), b))
# which: which measure to take
which = extra[4]
prg = cl.Program(ctx, """
int maxx(__global int *img, int x1, int y1, int x2, int y2, const int Ny) {
int i, j;
int maxim = 0;
for(i = x1; i < x2; i++)
for(j = y1; j < y2; j++)
if(img[i*Ny + j] > maxim) maxim = img[i*Ny + j];
return maxim;
}
int minn(__global int *img, int x1, int y1, int x2, int y2, const int Ny) {
int i, j;
int minim = 255;
for(i = x1; i < x2; i++)
for(j = y1; j < y2; j++)
if(img[i*Ny + j] < minim) minim = img[i*Ny + j];
return minim;
}
int summ(__global int *img, int x1, int y1, int x2, int y2, const int Ny) {
int i, j;
int summ = 0;
for(i = x1; i < x2; i++)
for(j = y1; j < y2; j++)
summ += img[i*Ny + j];
return summ;
}
int iso(__global int *img, int x1, int y1, int x2, int y2, const int Ny, const int x, const int y) {
int i, j;
int cant = 0;
for(i = x1; i < x2; i++)
for(j = y1; j < y2; j++)
if(img[i*Ny + j] == img[x*Ny + y]) cant++;
return cant;
}
__kernel void measure(__global int *dest, __global int *img, const int Nx,
const int Ny, const int l, int i, const int d, const int which) {
int j = get_global_id(0);
int jim = (int)(j/l)+d;
if(which == 0)
dest[j] = maxx(img,max(i-((j%l)+1),0),max(jim-((j%l)+1),0),
min(i+(j%l)+1,Nx-1),min(jim+(j%l)+1,Ny-1), Ny) + 1;
if(which == 1)
dest[j] = minn(img,max(i-((j%l)+1),0),max(jim-((j%l)+1),0),
min(i+(j%l)+1,Nx-1),min(jim+(j%l)+1,Ny-1), Ny) + 1;
if(which == 2)
dest[j] = summ(img,max(i-((j%l)+1),0),max(jim-((j%l)+1),0),
min(i+(j%l)+1,Nx-1),min(jim+(j%l)+1,Ny-1), Ny) + 1;
if(which == 3)
dest[j] = iso(img,max(i-((j%l)+1),0),max(jim-((j%l)+1),0),
min(i+(j%l)+1,Nx-1),min(jim+(j%l)+1,Ny-1), Ny, i, j) + 1;
}
""").build()
d = measure.shape[0]/2
ms = measure[0:l*d]
img_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=arr)
dest_buf = cl.Buffer(ctx, mf.WRITE_ONLY, ms.nbytes)
sh = ms.shape
for i in range(Nx):
prg.measure(queue, sh, None, dest_buf, img_buf, np.int32(Nx), np.int32(Ny), np.int32(l), np.int32(i), np.int32(0), np.int32(which))
cl.enqueue_read_buffer(queue, dest_buf, measure[0:l*d]).wait()
prg.measure(queue, sh, None, dest_buf, img_buf, np.int32(Nx), np.int32(Ny), np.int32(l), np.int32(i), np.int32(d), np.int32(which))
cl.enqueue_read_buffer(queue, dest_buf, measure[l*d:]).wait()
# Instead of doing polyfits, a sparse linear system is constructed and solved
bb=np.log(measure)
z = linsolve.lsqr(AA,bb)[0]
z = z.reshape(2,Ny,order = 'F')
alphaIm[i] = z[0]
maxim = np.max(alphaIm)
minim = np.min(alphaIm)
import matplotlib
from matplotlib import pyplot as plt
# Alpha image
#plt.imshow(alphaIm, cmap=matplotlib.cm.gray)
#plt.show()
#return
paso = (maxim-minim)/cuantas
if(paso <= 0):
# the alpha image is monofractal
clases = np.array(map(lambda i: i+minim,np.zeros(cuantas))).astype(np.float32)
else:
clases = np.arange(minim,maxim,paso).astype(np.float32)
# Window
cant = int(np.floor(np.log(Nx)))
# concatenate the image A as [[A,A],[A,A]]
hs = np.hstack((alphaIm,alphaIm))
alphaIm = np.vstack((hs,hs))
prg = cl.Program(ctx, """
__kernel void krnl(__global int *flag, __global float *clases,
__global float* alphaIm,const int sizeBlocks, const int Ny,
const int numBlocks_y, const int c, const int cuantas) {
int i = get_global_id(0);
int j = get_global_id(1);
int xi = i*sizeBlocks;
int xf = (i+1)*sizeBlocks-1;
int yi = j*sizeBlocks;
int yf = (j+1)*sizeBlocks-1;
if(xf == xi) xf = xf+1;
if(yf == yi) yf = yf+1;
int f = 0;
int s1 = xf-xi;
int s2 = yf-yi;
if(c != cuantas-1) {
// f = 1 if any pixel in block is between clases[c] and clases[c+1]
int w, t;
for(w = xi; w < xf; w++) {
for(t = yi; t < yf; t++) {
float b = alphaIm[w*Ny*2 + t];
if (b >= clases[c] and b < clases[c+1]) {
f = 1;
break;
}
}
if(f == 1) break;
}
}
else {
// f = 1 if any pixel in block is equal to classes[c]
int w, t;
for(w = xi; w < xf; w++) {
for(t = yi; t < yf; t++) {
float b = alphaIm[w*Ny*2 + t];
if (b == clases[c]) { // !!
f = 1;
break;
}
}
if(f == 1)
break;
}
}
flag[i*numBlocks_y + j] = f;
}
""").build()
# Multifractal dimentions
falpha = np.zeros(cuantas).astype(np.float32)
clases_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=clases.astype(np.float32))
alphaIm_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=alphaIm.astype(np.float32))
for c in range(cuantas):
N = np.zeros(cant+1)
# window sizes
for k in range(cant+1):
sizeBlocks = 2*k+1
numBlocks_x = int(np.ceil(Nx/sizeBlocks))
numBlocks_y = int(np.ceil(Ny/sizeBlocks))
flag = np.zeros((numBlocks_x,numBlocks_y)).astype(np.int32)
flag_buf = cl.Buffer(ctx, mf.WRITE_ONLY, flag.nbytes)
sh = flag.shape
prg.krnl(queue, sh, None, flag_buf, clases_buf, alphaIm_buf, np.int32(sizeBlocks), np.int32(Ny), np.int32(numBlocks_y), np.int32(c), np.int32(cuantas))
cl.enqueue_read_buffer(queue, flag_buf, flag).wait()
N[k] = cla.sum(cla.to_device(queue,flag)).get()
# Haussdorf (box) dimention of the alpha distribution
falpha[c] = -np.polyfit(map(lambda i: np.log(i*2+1),range(cant+1)),np.log(map(lambda i: i+1,N)),1)[0]
s = np.hstack((clases,falpha))
return s
def _gpu_search(self):
"""Method that actually performs the exhaustive search on the GPU"""
# make shortcuts
d = self.data
g = self.gpu_data
q = self.queue
k = g['k']
# initalize the total number of sampled complexes
tot_complexes = cl_array.sum(g['interspace'], dtype=np.float32)
# initialize time
time0 = _time()
# loop over all rotations
for n in xrange(g['nrot']):
# rotate the scanning chain object
k.rotate_image3d(q, g['sampler'], g['im_lsurf'],
self.rotations[n], g['lsurf'], d['im_center'])
# perform the FFTs and calculate the clashing and interaction volume
k.rfftn(q, g['lsurf'], g['ft_lsurf'])
k.c_conj_multiply(q, g['ft_lsurf'], g['ft_rcore'], g['ft_clashvol'])
k.irfftn(q, g['ft_clashvol'], g['clashvol'])
k.c_conj_multiply(q, g['ft_lsurf'], g['ft_rsurf'], g['ft_intervol'])
k.irfftn(q, g['ft_intervol'], g['intervol'])
# determine at every position if the conformation is a proper complex
k.touch(q, g['clashvol'], g['max_clash'],
g['intervol'], g['min_interaction'],
g['interspace'])
if self.distance_restraints:
k.fill(q, g['restspace'], 0)
# determine the space that is consistent with a number of
# distance restraints
k.distance_restraint(q, g['restraints'],
self.rotations[n], g['restspace'])
# get the accessible interaction space also consistent with a
# certain number of distance restraints
k.multiply(q, g['restspace'], g['interspace'], g['access_interspace'])
# calculate the total number of complexes, while taking into
# account orientational/rotational bias
tot_complexes += cl_array.sum(g['interspace'], dtype=np.float32)*np.float32(self.weights[n])
# take at every position in space the maximum number of consistent
# restraints for later visualization
cl_array.maximum(g['best_access_interspace'], g['access_interspace'], g['best_access_interspace'])
# calculate the number of accessable complexes consistent with
# EXACTLY N distance restraints
k.histogram(q, g['access_interspace'], g['subhists'], self.weights[n], d['nrestraints'])
# Count the violations of each restraint for all complexes
# consistent with EXACTLY N restraints
k.count_violations(q, g['restraints'], self.rotations[n],
g['access_interspace'], g['viol_counter'], self.weights[n])
# inform user
if _stdout.isatty():
self._print_progress(n, g['nrot'], time0)
# wait for calculations to finish
self.queue.finish()
# transfer the data from GPU to CPU
# get the number of accessible complexes and reduce the subhistograms
# to the final histogram
access_complexes = g['subhists'].get().sum(axis=0)
# account for the fact that we are counting the number of accessible
# complexes consistent with EXACTLY N restraints
access_complexes[0] = tot_complexes.get() - sum(access_complexes[1:])
d['accessible_complexes'] = access_complexes
d['accessible_interaction_space'] = g['best_access_interspace'].get()
# get the violation submatrices and reduce it to the final violation
# matrix
d['violations'] = g['viol_counter'].get().sum(axis=0)
def sum(self, a, dtype=None):
import pyopencl.array as cl_array
return cl_array.sum(
a, dtype=dtype, queue=self._array_context.queue).get()[()]
def ggr_iteration(self,iteration):
assert self.can_has_domains, "no domains set!"
assert self.can_has_envelope, "no goal envelope set!"
assert self.can_has_ggr, "must set ggr before running ggr_iteration"
# get the target net magnetization
net_m = cla.sum(self.domains).get()
self.goal_m = self.plan[iteration+1]
needed_m = self.goal_m*self.N2-net_m
self.target = np.sign(needed_m).astype(np.float32)
# copy the current domain pattern to self.incoming
self.copy(self.domains,self.incoming)
# find the domain walls. these get used in self.make_available. make the correct sites available for modification
self.findwalls.execute(self.queue,(self.N,self.N),self.domains.data,self.allwalls.data,self.poswalls.data,self.negwalls.data,np.int32(self.N))
self.make_available1(self.available,self.allwalls,self.negpins,self.pospins)
#if net_m > self.goal_m: self.make_available1(self.available,self.poswalls,self.negpins,self.pospins)
#if net_m < self.goal_m: self.make_available1(self.available,self.negwalls,self.negpins,self.pospins)
# run the ising bias
self.ising(self.domains,self.alpha)
# rescale the domains. this operates on the class variables so no arguments are passed. self.domains stores the rescaled real-valued domains.
# the rescaled domains are bounded to the range +1 -1.
# change in the domain pattern is allowed to happen only in the walls.
# enforce the recency condition to prevent domain splittings (basically, make it hard to revert changes from long ago)
self._rescale_speckle()
self.bound(self.domains,self.domains)
self.only_in_walls(self.domains,self.incoming,self.available)
self.recency.execute(self.queue,(self.N2,),
self.whenflipped.data,self.domains.data,self.incoming.data,
self.recency_need.data,self.target,np.int32(iteration)).wait()
# since the domains have been updated, refind the walls
self.findwalls.execute(self.queue,(self.N,self.N),self.domains.data,self.allwalls.data,self.poswalls.data,self.negwalls.data,np.int32(self.N))
# now adjust the magnetization so that it reaches the target
net_m = cla.sum(self.domains).get()
needed_m = self.goal_m*self.N2-net_m
self.target = np.sign(needed_m).astype(np.float32)
if net_m > 0: self.make_available2(self.available,self.poswalls,self.domains,self.target)
if net_m < 0: self.make_available2(self.available,self.negwalls,self.domains,self.target)
# now we need to run an optimizer to find the correct value for spa. this should result in an
# update to the class variable self.optimized_spa. optimized_spa is a class variable because spa
# changes slowly so using the old value as the starting point in the optimization gives a speed up.
opt_out = fminbound(self._ggr_spa_error,-1,1,full_output=1)
self.optimized_spa = opt_out[0]
# use the optimized spa value to actually promote the spins in self.domains
self.ggr_promote_spins(self.domains,self.available,self.domains,self.target*self.optimized_spa)
self.bound(self.domains,self.domains)
m_out = (cla.sum(self.domains).get())/self.N2
# update the whenflipped data, which records the iteration when each pixel changed sign
self.update_whenflipped.execute(self.queue,(self.N2,),self.whenflipped.data,self.domains.data,self.incoming.data,np.int32(iteration))
print "%.4d, %.3f, %.3f, %.3f"%(iteration, self.goal_m, m_out, self.optimized_spa)
self.ggr_tracker[iteration] = iteration, self.optimized_spa, m_out
# set a flag to save output. it is better to check m_out than to trust that the
# self.goal_m, asked for in self.plan, has actually been achieved
if m_out < self.next_crossing:
print "***"
self.checkpoint = True
self.crossed = self.next_crossing
try:
self.crossings = self.crossings[:-1]
self.next_crossing = self.crossings[-1]
except IndexError:
self.next_crossing = 0.01
else: self.checkpoint = False
