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# lineshape functors
# -*- coding: iso-8859-1 -*-
#
# Copyright (C) 2002 Jochen Kpper <jochen@jochen-kuepper.de>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# 3. The name of the author may not be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED
# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
# EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
__doc__ = """Lineshape functors.
The objects defined in this module can be used to calculate numarrays containing
common lineshape-profiles.
For the *Profile classes the profile is only evaluated once for each object and
then reused for each call. If you only use a profile once and you are concerned
about memory consumption you could call the underlying functions directly.
"""
__author__ = "Jochen Kpper <jochen@jochen-kuepper.de>"
__date__ = "$Date: 2007/03/14 16:35:57 $"[7:-11]
__version__ = "$Revision: 1.1 $"[11:-2]
import numpy as num
from convolve._lineshape import *
class Profile(object):
"""An base object to provide a convolution kernel."""
def __init__(self, x, w, x0=0.0):
# call init for all superclasses
super(Profile, self).__init__(x, w, x0)
self._recalculate(x, w, x0)
def __call__(self):
return self._kernel
def _recalculate(self, x, w, x0):
self._kernel = self._profile(x, w, x0)
class GaussProfile(Profile):
"""An object for Gauss-folding."""
def __init__(self, x, w, x0=0.0):
self._profile = gauss
# call init for all superclasses
super(GaussProfile, self).__init__(x, w, x0)
class LorentzProfile(Profile):
"""An object for Lorentz-folding."""
def __init__(self, x, w, x0=0.0):
self._profile = lorentz
# call init for all superclasses
super(LorentzProfile, self).__init__(x, w, x0)
class VoigtProfile(Profile):
"""An object for Voigt-folding.
The constructor takes the following parameter:
|x| Scalar or numarray with values to calculate profile at.
|w| Tuple of Gaussian and Lorentzian linewidth contribution
|x0| Center frequency
"""
def __init__(self, x, w, x0=0.0):
self._profile = voigt
# call init for all superclasses
super(VoigtProfile, self).__init__(x, w, x0)
## Local Variables:
## mode: python
## mode: auto-fill
## fill-column: 80
## End:
|
