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make the window spacing configurable

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jingang 2019-07-22 11:31:39 -04:00 • committed by Jingang Liang
parent 1a932ac55f
commit 378653d039
1 changed files with 26 additions and 9 deletions
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35
openmc/data/multipole.py
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@ -532,8 +532,8 @@ def _vectfit_nuclide(endf_file, njoy_error=5e-4, vf_error=1e-3, vf_pieces=None,
return mp_data
def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
log=False):
r"""Generate window multipole library from multipole data.
spacing=None, log=False):
r"""Generate windowed multipole library from multipole data.
Parameters
----------
@ -547,6 +547,8 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
Number of equal-in-mementum spaced energy windows
n_cf : int, optional
Number of curve fitting order
spacing : float, optional
Inner window spacing (sqrt energy space)
log : bool, optional
Whether to display log
@ -557,6 +559,9 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
format.
"""
if log:
print("Start windowing")
# unpack multipole data
name = mp_data["name"]
@ -572,14 +577,23 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
# determine window size and CF order
if n_win is None:
n_win = OPTIMIZED[name][1]
if n_cf is None:
n_cf = OPTIMIZED[name][2]
if spacing is not None:
# Ensure the windows are within the multipole energy range
n_win = int((sqrt(E_max) - sqrt(E_min)) / spacing)
E_max = (sqrt(E_min) + n_win*spacing)**2
else:
# TODO: optimize windows spacing
n_win = OPTIMIZED[name][1]
# inner window size
spacing = (sqrt(E_max) - sqrt(E_min)) / n_win
# make sure inner window size is smaller than piece size
# make sure inner window size is smaller than energy piece size
if spacing > piece_width:
raise ValueError('Windows spacing cannot larger than piece spacing.')
raise ValueError('Window spacing cannot be larger than piece spacing.')
# TODO: optimize curve fit order
if n_cf is None:
n_cf = OPTIMIZED[name][2]
# sort poles (and residues) by the real component of the pole
for ip in range(n_pieces):
@ -594,6 +608,9 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
# consecutive poles and curve fit coefficients to reproduce cross section
win_data = []
for iw in range(n_win):
if log:
print("Processing window {}/{}...".format(iw+1, n_win))
# inner window boundaries
inbegin = sqrt(E_min) + spacing * iw
inend = inbegin + spacing
@ -648,7 +665,7 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
# save data for this window
win_data.append((i_piece, lp, rp, coefs))
# mark the windowed poles are used poles
# mark the windowed poles as used poles
poles_unused[i_piece][lp:rp] = 0
# flatten and shrink: keep used poles and remove unused
@ -681,7 +698,7 @@ def _windowing(mp_data, rtol=1e-3, atol=1e-5, n_win=None, n_cf=None,
wmp.data = data
wmp.windows = np.asarray(windows)
wmp.curvefit = np.asarray(curvefit)
#TODO: currently all polynomial curvefit will be Doppler brodened
# TODO: check if Doppler brodening of the polynomial curvefit is negligible
wmp.broaden_poly = np.ones((n_win,), dtype=bool)
return wmp