def initialize(self):
# pcoord dimensionality and length. This should match your outside bin
self.pcoord_ndim = 2
self.pcoord_len = pcoordlength
self.pcoord_dtype = pcoord_dtype
# These are your "outer" bins in both dimensions
outer_mapper = RectilinearBinMapper([[0.00, numpy.inf],
[0.00, 8.00, numpy.inf]])
# These define the "inner" adaptive bins. Each one indicates an independent
# adaptive binning scheme. Copy for more. Respects the parameters at the start.
adaptive_mapper0 = FuncBinMapper(function_map,
prod(binsperdim) + numberofdim *
(2 + 2 * splitIsolated) +
activetarget,
kwargs={"multi_idx": 0}) # Bottom bin
adaptive_mapper1 = FuncBinMapper(function_map,
prod(binsperdim) + numberofdim *
(2 + 2 * splitIsolated) +
activetarget,
kwargs={"multi_idx": 1}) # Top bin
# These are the bins where you'll place each adaptive scheme.
self.bin_mapper = RecursiveBinMapper(outer_mapper)
self.bin_mapper.add_mapper(adaptive_mapper0, [5, 1]) #Bottom mapper
self.bin_mapper.add_mapper(adaptive_mapper1, [5, 10]) #Top mapper
######################### JL ##########################################
# This is the what the example looks like.
#
# inf --------------------------------------------------------
# | |
# | adaptive_mapper1 |
# | |
# 8 --------------------------------------------------------
# D2 | | |
# | TARGET | adaptive_mapper0 |
# | STATE | |
# 0 --------------------------------------------------------
# 3.5(soft bound via mincap) inf
# Dimension 1 of pcoord
#
#######################################################################
self.bin_target_counts = numpy.empty((self.bin_mapper.nbins, ),
dtype=numpy.int)
# Number of walkers per bin
self.bin_target_counts[...] = bintargetcount
def initialize(self):
self.pcoord_ndim = 2
self.pcoord_len = 2
self.pcoord_dtype = numpy.float32
minfrag = 90
outer_mapper = RectilinearBinMapper([[0, minfrag - 0.5,
float('inf')], [0,
float('inf')]])
binbounds1 = [1.0 * i - 0.5
for i in xrange(minfrag, 121)] + [float('inf')]
binbounds2 = [15.0 * i * (i - 1) / 2
for i in xrange(1, 20)] + [float('inf')]
self.bin_mapper = RecursiveBinMapper(outer_mapper)
self.bin_mapper.add_mapper(
RectilinearBinMapper([binbounds1, binbounds2]), [minfrag, 1])
self.bin_target_counts = numpy.empty((self.bin_mapper.nbins, ),
numpy.int)
self.bin_target_counts[...] = 4
#this is intended to change the bins where distance < 200 A to 16 simulations per bin
for q1 in xrange(minfrag, 121):
for q2 in [0.5 + 15.0 * i * (i - 1) / 2 for i in xrange(1, 8)]:
print(q1, q2)
bin = self.bin_mapper.assign(numpy.array([(q1, q2)])) #tuple?
self.bin_target_counts[bin] = 16
