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Improve loading of cross sections from hdf5

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Fix bug where reaction thresholds were not used.  Automatically build summed
reactions.  Allow Tab1 evaluations of un-ordered datasets.
This commit is contained in:
Sterling Harper 2016-07-27 18:02:38 -05:00
parent c8a188ca26
commit be7ca28dee
3 changed files with 34 additions and 27 deletions
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48
openmc/data/function.py
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@ -80,50 +80,46 @@ class Tabulated1D(object):
# Get indices for interpolation
idx = np.searchsorted(self.x, x, side='right') - 1
# Find lowest valid index
i_low = np.searchsorted(idx, 0)
# Loop over interpolation regions
for k in range(len(self.breakpoints)):
# Determine which x values are within this interpolation range
i_high = np.searchsorted(idx, self.breakpoints[k] - 1)
# Get indices for the begining and ending of this region
i_begin = self.breakpoints[k-1] - 1 if k > 0 else 0
i_end = self.breakpoints[k] - 1
# Get x values and bounding (x,y) pairs
xk = x[i_low:i_high]
xi = self.x[idx[i_low:i_high]]
xi1 = self.x[idx[i_low:i_high] + 1]
yi = self.y[idx[i_low:i_high]]
yi1 = self.y[idx[i_low:i_high] + 1]
# Figure out which idx values lie within this region
contained = (idx >= i_begin) & (idx < i_end)
xk = x[contained] # x values in this region
xi = self.x[idx[contained]] # low edge of corresponding bins
xi1 = self.x[idx[contained] + 1] # high edge of corresponding bins
yi = self.y[idx[contained]]
yi1 = self.y[idx[contained] + 1]
if self.interpolation[k] == 1:
# Histogram
y[i_low:i_high] = yi
y[contined] = yi
elif self.interpolation[k] == 2:
# Linear-linear
y[i_low:i_high] = yi + (xk - xi)/(xi1 - xi)*(yi1 - yi)
y[contained] = yi + (xk - xi)/(xi1 - xi)*(yi1 - yi)
elif self.interpolation[k] == 3:
# Linear-log
y[i_low:i_high] = yi + np.log(xk/xi)/np.log(xi1/xi)*(yi1 - yi)
y[contained] = yi + np.log(xk/xi)/np.log(xi1/xi)*(yi1 - yi)
elif self.interpolation[k] == 4:
# Log-linear
y[i_low:i_high] = yi*np.exp((xk - xi)/(xi1 - xi)*np.log(yi1/yi))
y[contained] = yi*np.exp((xk - xi)/(xi1 - xi)*np.log(yi1/yi))
elif self.interpolation[k] == 5:
# Log-log
y[i_low:i_high] = yi*np.exp(np.log(xk/xi)/np.log(xi1/xi)*np.log(yi1/yi))
y[contained] = (yi*np.exp(np.log(xk/xi)/np.log(xi1/xi)
*np.log(yi1/yi)))
i_low = i_high
# In some cases, the first/last point of x may be less than the first
# value of self.x due only to precision, so we check if they're close
# and set them equal if so. Otherwise, the interpolated value might be
# out of range (and thus zero)
if np.isclose(x[0], self.x[0], 1e-8):
y[0] = self.y[0]
if np.isclose(x[-1], self.x[-1], 1e-8):
y[-1] = self.y[-1]
# In some cases, x values might be outside the tabulated region due only
# to precision, so we check if they're close and set them equal if so.
y[np.isclose(x, self.x[0], atol=1e-14)] = self.y[0]
y[np.isclose(x, self.x[-1], atol=1e-14)] = self.y[-1]
return y if iterable else y[0]