def time(self, str=None):
"""
Return current total time.
:arg string str: string to append after time. If None time printing will be suppressed.
:return: Current total time as float.
"""
if (str is not None) and (self._lo > self._l):
pio.pprint(self._tt, "s :", str)
return self._tt
def reset(self, str=None):
"""
Resets the timer. Returns the time taken up until the reset.
:arg string str: If not None will print time followed by string.
"""
_tt = self._tt + time.time() - self._ts
self._tt = 0.0
self._ts = time.time()
if (str is not None) and (self._lo > self._l):
pio.pprint(_tt, "s :", str)
return _tt
def _get_cell_distribution(global_cell_array=None, dims=None, top=None):
# blocks per cell
_bsc = [
int(math.ceil(float(global_cell_array[0]) / float(dims[0]))),
int(math.ceil(float(global_cell_array[1]) / float(dims[1]))),
int(math.ceil(float(global_cell_array[2]) / float(dims[2])))
]
# print dims
# print _bsc
# Calculate global distribution of cells
_bs = []
for ix in range(3):
_tmp = []
if (_bsc[ix] * (dims[ix] - 1)) < global_cell_array[ix]:
for iy in range(dims[ix] - 1):
_tmp.append(int(_bsc[ix]))
_tmp.append(int(global_cell_array[0] - (dims[ix] - 1) * _bsc[ix]))
else:
R = global_cell_array[ix] % dims[ix]
for iy in range(R):
_tmp.append(_bsc[ix])
for iy in range(dims[ix] - R):
_tmp.append(
(global_cell_array[ix] - R * _bsc[ix]) // (dims[ix] - R))
assert len(_tmp) == dims[ix], "DD size missmatch, dim: " + str(
ix) + " " + str(_tmp[:])
_tsum = 0
for tx in _tmp:
_tsum += tx
assert _tsum == global_cell_array[
ix], "DD failure to assign cells, dim: " + str(ix) + " " + str(
_tmp[:])
_bs.append(_tmp)
if runtime.VERBOSE > 1:
pio.pprint("Cell layout", _bs)
# Get local cell array
local_cell_array = (_bs[0][top[0]], _bs[1][top[1]], _bs[2][top[2]])
return local_cell_array, _bs
def stop(self, str=''):
"""
Stop timer and print time.
:arg string str: string to append after time. If None time printing will be suppressed.
"""
if (self._lo > self._l) and (self._running is True):
self._tt += time.time() - self._ts
if (self._lo > self._l) and str is not None:
pio.pprint(self._tt, "s :", str)
t_tmp = self._tt
self._ts = 0.0
self._tt = 0.0
self._running = False
return t_tmp
def tick(self):
"""
Method to call per iteration.
"""
if (self._timing is False) and (runtime.TIMER > 0):
self.timer.start()
self._count += 1
if (float(self._count) / self._max_it) * 100 > self._curr_p:
if runtime.TIMER > 0:
pio.pprint(self._curr_p, "%", self.timer.reset(), 's')
else:
pio.pprint(self._curr_p, "%")
self._curr_p += self._p
def __init__(self, steps=None, items=None):
self._s = collections.defaultdict(list)
if (steps is not None) and (items is not None):
assert len(steps) == len(
items
), "Schedule error, mis-match between number of steps and number of items."
for ix in zip(steps, items):
if (ix[0] > 0) and (ix[1] is not None):
assert (inspect.isfunction(ix[1]) or inspect.ismethod(ix[1])) is True, "Schedule error: Passed argument" \
" is not a function/method."
self._s[ix[0]].append(ix[1])
else:
pio.pprint(
"Schedule warning: steps<1 and None type functions will be ignored."
)
self._count = 0
def evaluate(self):
"""
Evaluate VAF using the current velocities held in the state with the velocities in V0.
:arg double t: Time within block of integration.
"""
if runtime.TIMER > 0:
start = time.time()
_t = self._state.time
_Ni = 1. / self._state.as_func('npart_local')
self._datdict['VT'] = self._state.velocities
self._loop.execute(None, self._datdict, {'Ni': ctypes.c_double(_Ni)})
self._V.append(self._VAF[0])
self._T.append(_t)
if runtime.TIMER > 0:
end = time.time()
pio.pprint("VAF time taken:", end - start, "s")
