/usr/share/pyshared/brian/utils/circular.py is in python-brian 1.4.1-2.
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# Copyright ENS, INRIA, CNRS
# Contributors: Romain Brette (brette@di.ens.fr) and Dan Goodman (goodman@di.ens.fr)
#
# Brian is a computer program whose purpose is to simulate models
# of biological neural networks.
#
# This software is governed by the CeCILL license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
# ----------------------------------------------------------------------------------
#
'''
Circular arrays
Ideas for speed improvements: use put, putmask and take with mode='wrap' and out=...
'''
from numpy import *
from scipy import weave
import bisect
import os
import warnings
from ..globalprefs import get_global_preference
__all__ = ['CircularVector', 'SpikeContainer']
class CircularVector(object):
'''
A vector with circular structure.
Variables:
* X = the data (array of size n)
* cursor = current position in the array (where the 0 index is)
'''
def __init__(self, n, dtype=float, useweave=False, compiler=None): # pylint: disable-msg=W0621
'''
n is the size of the vector.
'''
self.X = zeros(n, dtype=dtype)
self.dtype = dtype
self.cursor = 0
self.n = n
self._useweave = useweave
if useweave:
self._optimisedreturnarray = zeros(n, dtype=dtype)
self._cpp_compiler = compiler
self._extra_compile_args = ['-O3']
if self._cpp_compiler == 'gcc':
self._extra_compile_args += get_global_preference('gcc_options') # ['-march=native', '-ffast-math']
else:
self._cpp_compiler = ''
def reinit(self):
self.X[:] = zeros(self.n, self.dtype)
self.cursor = 0
def advance(self, k):
self.cursor = (self.cursor + k) % self.n
def __len__(self):
return self.n
def __getitem__(self, i):
'''
V[i]
'''
return self.X[(self.cursor + i) % self.n]
def __setitem__(self, i, x):
'''
V[i]=x
'''
self.X[(self.cursor + i) % self.n] = x
def __getslice__(self, i, j):
n = self.n
i0 = (self.cursor + i) % n # pylint: disable-msg=W0621
j0 = (self.cursor + j) % n # pylint: disable-msg=W0621
if j0 >= i0:
return self.X[i0:j0]
else:
#return self.X[range(i0,n)+range(0,j0)]
return concatenate((self.X[i0:], self.X[0:j0])) # this version is MUCH faster
def get_conditional(self, i, j, min, max, offset=0):
"""
Returns only those vectors with values between min and max
This rather specialised usage of a circular vector is for the
benefit of the SpikeContainer class get_spikes method, which is
in turn used by the NeuronGroup.get_spikes method.
It returns v-offset for those elements v in self[i:j] such that min<v<max.
"""
if self._useweave:
n = self.n
i0 = int((self.cursor + i) % n) # pylint: disable-msg=W0612,W0621
j0 = int((self.cursor + j) % n) # pylint: disable-msg=W0612
X = self.X # pylint: disable-msg=W0612
ret = self._optimisedreturnarray
code = """
int numgot = 0;
for(int k=i0;k!=j0;k=(k+1)%n)
{
int Xk = X(k);
if(Xk>=min && Xk<max)
ret(numgot++)=Xk-offset;
}
return_val = numgot;
"""
numgot = weave.inline(code, ['n', 'i0', 'j0', 'X', 'ret', 'offset', 'min', 'max'],
compiler=self._cpp_compiler,
type_converters=weave.converters.blitz,
extra_compile_args=self._extra_compile_args)
return ret[0:numgot]
else:
spikes = self[i:j]
spikes = spikes[bisect.bisect_left(spikes, min):bisect.bisect_left(spikes, max)]
if offset: spikes = spikes - offset
return spikes
def __setslice__(self, i, j, W):
# NB: S[4:1] does not give a circular slice but only []
# Should we change that behaviour?
# TODO: speed improvements
if j > i:
n = self.n
i0 = (self.cursor + i) % n # pylint: disable-msg=W0621
j0 = (self.cursor + j) % n
if j0 > i0:
self.X[i0:j0] = W
elif isinstance(W, ndarray):
self.X[i0:] = W[0:n - i0]
self.X[0:j0] = W[n - i0:n - i0 + j0]
def __repr__(self):
return repr(hstack((self[0:self.n - 1], self[self.n - 1:self.n])))
def __print__(self):
return (hstack((self[0:self.n - 1], self[self.n - 1:self.n]))).__print__()
class SpikeContainer(object):
'''
An object that stores previous spikes.
S[0] is an array of the last spikes (neuron indexes).
S[1] is an array with the spikes at time t-dt, etc.
S[0:50] contains all spikes in last 50 bins.
'''
def __init__(self, m, useweave=False, compiler=None):
'''
n = maximum number of spikes stored (not used anymore)
m = maximum number of bins stored
'''
if m < 2: m = 2
self.S = CircularVector(2, dtype=int, useweave=useweave, compiler=compiler)
self.ind = CircularVector(m + 1, dtype=int, useweave=useweave, compiler=compiler) # indexes of bins
self.remaining_space = 1
self._useweave = useweave
self.m = m
def reinit(self):
self.S.reinit()
self.ind.reinit()
def push(self, spikes):
'''
Stores spikes in the array at time dt.
'''
ns = len(spikes)
self.remaining_space += (self.ind[2] - self.ind[1]) % self.S.n
while ns >= self.remaining_space:
# double size of array
S = self.S
newS = CircularVector(2 * S.n, dtype=int, useweave=S._useweave, compiler=S._cpp_compiler)
newS.X[:S.n - S.cursor] = S.X[S.cursor:]
newS.X[S.n - S.cursor:S.n] = S.X[:S.cursor]
newS.cursor = S.n
self.S = newS
self.ind.X = (self.ind.X - S.cursor) % S.n
self.ind.X[self.ind.X == 0] = S.n
self.remaining_space += S.n
self.S[0:ns] = spikes
self.S.advance(ns)
self.ind.advance(1)
self.ind[0] = self.S.cursor
self.remaining_space -= ns
def lastspikes(self):
'''
Returns S[0].
'''
return self.S[self.ind[-1] - self.S.cursor:self.S.n]
def __getitem__(self, i):
'''
S[i]: returns the spikes at time t-i*dt.
'''
# NB: this could be optimized
return self.S[self.ind[-i - 1] - self.S.cursor:self.ind[-i] - self.S.cursor + self.S.n]
# optimised version of the above, but the speed improvement is not very much, might be
# better to just wait and write a fully C/C++ version of the whole library
def get_spikes(self, delay, origin, N):
"""
Returns those spikes in self[delay] between origin and origin+N
"""
return self.S.get_conditional(self.ind[-delay - 1] - self.S.cursor, \
self.ind[-delay] - self.S.cursor + self.S.n, \
origin, origin + N, origin)
def __getslice__(self, i, j):
return self.S[self.ind[-j] - self.S.cursor:self.ind[-i] - self.S.cursor + self.S.n]
def __repr__(self):
return "Spike container."
def __print__(self):
return self.__repr__()
def __reduce__(self):
return (unpickle_SpikeContainer, (self.m, tuple(self[i].copy() for i in xrange(self.m))))
try:
import ccircular.ccircular as _ccircular
class SpikeContainer(_ccircular.SpikeContainer):
def __init__(self, m, useweave=False, compiler=None):
_ccircular.SpikeContainer.__init__(self, m)
self.m = m
def __reduce__(self):
return (unpickle_SpikeContainer, (self.m, tuple(self[i].copy() for i in xrange(self.m))))
#warnings.warn('Using C++ SpikeContainer')
except ImportError:
pass
def unpickle_SpikeContainer(m, allspikes):
newsc = SpikeContainer(m)
for spikes in allspikes[::-1]:
newsc.push(spikes)
return newsc
# I am not sure that class below is useful!
class ModInt(object):
'''
A number in Z/nZ, i.e., modulo n.
Variables:
* x = number modulo n
* n
Implemented: additions and subtraction.
N.B.: not a very useful class.
'''
def __init__(self, x, n):
self.x = x
self.n = n
def __add__(self, m):
if isinstance(m, ModInt):
assert self.n == m.n
return ModInt((self.x + m.x) % self.n, self.n)
else:
return ModInt((self.x + m) % self.n, self.n)
def __radd__(self, m):
if isinstance(m, ModInt):
assert self.n == m.n
return ModInt((self.x + m.x) % self.n, self.n)
else:
return ModInt((self.x + m) % self.n, self.n)
def __sub__(self, m):
if isinstance(m, ModInt):
assert self.n == m.n
return ModInt((self.x - m.x) % self.n, self.n)
else:
return ModInt((self.x - m) % self.n, self.n)
def __repr__(self):
return str(self.x) + ' [' + str(self.n) + ']'
def __print__(self):
return self.__repr__()
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