https://github.com/gwastro/pycbc
Tip revision: 28e5bc790b0c0e6436992d80c965b57e5e88e9f2 authored by Alexander Harvey Nitz on 16 March 2015, 16:02:57 UTC
merge Ian's fix, over the slightly slower one there for the case of fewer triggers than the requested amount ot keep in pycbc_coinc_findtrigs
merge Ian's fix, over the slightly slower one there for the case of fewer triggers than the requested amount ot keep in pycbc_coinc_findtrigs
Tip revision: 28e5bc7
array_cpu.py
# Copyright (C) 2012 Alex Nitz
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# =============================================================================
#
# Preamble
#
# =============================================================================
#
"""Numpy based CPU backend for PyCBC Array
"""
import numpy as _np
from array import common_kind, complex128, float64
import aligned as _algn
def zeros(length, dtype=_np.float64):
return _algn.zeros(length, dtype=dtype)
def empty(length, dtype=_np.float64):
return _algn.empty(length, dtype=dtype)
def ptr(self):
return self.data.ctypes.data
def dot(self, other):
return _np.dot(self._data,other)
def min(self):
return self.data.min()
def abs_max_loc(self):
tmp = abs(self.data)
ind = _np.argmax(tmp)
return tmp[ind], ind
def cumsum(self):
return self.data.cumsum()
def max(self):
return self.data.max()
def max_loc(self):
ind = _np.argmax(self.data)
return self.data[ind], ind
def take(self, indices):
return self.data.take(indices)
def weighted_inner(self, other, weight):
""" Return the inner product of the array with complex conjugation.
"""
if weight is None:
return self.inner(other)
cdtype = common_kind(self.dtype, other.dtype)
if cdtype.kind == 'c':
acum_dtype = complex128
else:
acum_dtype = float64
return _np.sum(self.data.conj() * other / weight, dtype=acum_dtype)
def inner(self, other):
""" Return the inner product of the array with complex conjugation.
"""
cdtype = common_kind(self.dtype, other.dtype)
if cdtype.kind == 'c':
acum_dtype = complex128
else:
acum_dtype = float64
return _np.sum(self.data.conj() * other, dtype=acum_dtype)
#return _np.vdot(self.data, other)
def vdot(self, other):
""" Return the inner product of the array with complex conjugation.
"""
return _np.vdot(self.data, other)
def squared_norm(self):
""" Return the elementwise squared norm of the array """
return (self.data.real**2 + self.data.imag**2)
def numpy(self):
return self._data
def _copy(self, self_ref, other_ref):
self_ref[:] = other_ref[:]
def _getvalue(self, index):
return self._data[index]
def sum(self):
if self.kind == 'real':
return _np.sum(self._data,dtype=float64)
else:
return _np.sum(self._data,dtype=complex128)
def clear(self):
self[:] = 0
def _scheme_matches_base_array(array):
# Since ArrayWithAligned is a subclass of ndarray,
# and since converting to ArrayWithAligned will
# *not* copy 'array', the following is the way to go:
if isinstance(array, _np.ndarray):
return True
else:
return False