diff --git a/examples/jupyter/nuclear-data-resonance-covariance.ipynb b/examples/jupyter/nuclear-data-resonance-covariance.ipynb index 74f774f41..bc83d48fc 100644 --- a/examples/jupyter/nuclear-data-resonance-covariance.ipynb +++ b/examples/jupyter/nuclear-data-resonance-covariance.ipynb @@ -118,7 +118,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -129,7 +129,7 @@ "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAD8CAYAAAD6+lbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDQdJM7M2HCTNzNpwkDQza8NB0sysjf8PkgcWMPD86KAAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -257,18 +257,18 @@ "text": [ "Sample 1\n", " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.032307 0 2.0 0.000477 0.105887 0.0 0.0\n", - "1 2.827218 0 2.0 0.000349 0.094165 0.0 0.0\n", - "2 16.298283 0 1.0 0.000519 0.183407 0.0 0.0\n", - "3 16.771312 0 2.0 0.012540 0.078625 0.0 0.0\n", - "4 20.558190 0 2.0 0.011813 0.085244 0.0 0.0\n", + "0 0.032689 0 2.0 0.000477 0.105084 0.0 0.0\n", + "1 2.825536 0 2.0 0.000336 0.101927 0.0 0.0\n", + "2 16.224770 0 1.0 0.000287 0.025024 0.0 0.0\n", + "3 16.769618 0 2.0 0.012305 0.086891 0.0 0.0\n", + "4 20.554322 0 2.0 0.010908 0.090244 0.0 0.0\n", "Sample 2\n", " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.033027 0 2.0 0.000476 0.104104 0.0 0.0\n", - "1 2.825226 0 2.0 0.000344 0.098390 0.0 0.0\n", - "2 16.245609 0 1.0 0.000353 0.095063 0.0 0.0\n", - "3 16.764396 0 2.0 0.013475 0.075175 0.0 0.0\n", - "4 20.560505 0 2.0 0.011994 0.081186 0.0 0.0\n" + "0 0.027766 0 2.0 0.000465 0.113061 0.0 0.0\n", + "1 2.827059 0 2.0 0.000332 0.099581 0.0 0.0\n", + "2 16.210647 0 1.0 0.000456 0.062444 0.0 0.0\n", + "3 16.773836 0 2.0 0.013618 0.074771 0.0 0.0\n", + "4 20.558365 0 2.0 0.010902 0.088135 0.0 0.0\n" ] } ], @@ -296,8 +296,8 @@ { "data": { "text/plain": [ - "[,\n", - " ]" + "[,\n", + " ]" ] }, "execution_count": 10, @@ -326,9 +326,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEOCAYAAACTqoDjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYBtKl8mxDQAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEOCAYAAACTqoDjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTLCDAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/openmc/data/resonance.py b/openmc/data/resonance.py index 33d64e951..e71073919 100644 --- a/openmc/data/resonance.py +++ b/openmc/data/resonance.py @@ -203,7 +203,7 @@ class ResonanceRange(object): return cls(target_spin, energy_min, energy_max, {0: a}, {0: ap}) - def reconstruct(self, energies, use_sample=False, sample_parameters=None): + def reconstruct(self, energies): """Evaluate cross section at specified energies. Parameters @@ -222,8 +222,8 @@ class ResonanceRange(object): raise RuntimeError("Resonance reconstruction not available.") # Pre-calculate penetrations and shifts for resonances - if not self._prepared or use_sample: - self._prepare_resonances(use_sample, sample_parameters) + if not self._prepared: + self._prepare_resonances() if isinstance(energies, Iterable): elastic = np.zeros_like(energies) @@ -395,11 +395,8 @@ class MultiLevelBreitWigner(ResonanceRange): return mlbw - def _prepare_resonances(self, use_sample=False, sample_parameters=None): - if not use_sample: - df = self.parameters.copy() - else: - df = sample_parameters.copy() + def _prepare_resonances(self): + df = self.parameters.copy() # Penetration and shift factors p = np.zeros(len(df)) @@ -657,11 +654,8 @@ class ReichMoore(ResonanceRange): return rm - def _prepare_resonances(self, use_sample=False, sample_parameters=None): - if not use_sample: - df = self.parameters.copy() - else: - df = sample_parameters.copy() + def _prepare_resonances(self): + df = self.parameters.copy() # Penetration and shift factors p = np.zeros(len(df)) diff --git a/openmc/data/resonance_covariance.py b/openmc/data/resonance_covariance.py index 714d87234..ba62b4f4f 100644 --- a/openmc/data/resonance_covariance.py +++ b/openmc/data/resonance_covariance.py @@ -257,6 +257,9 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidth', 'competitiveWidth'] sample_params = pd.DataFrame.from_records(records, columns=columns) res_range = copy.copy(resonances) + res_range._prepared = False # Set prepared to False to ensure + # the sampled parameters are used + # in reconstruction res_range.parameters = sample_params samples.append(res_range) @@ -282,6 +285,9 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidth', 'competitiveWidth'] sample_params = pd.DataFrame.from_records(records, columns=columns) res_range = copy.copy(resonances) + res_range._prepared = False # Set prepared to False to ensure + # the sampled parameters are used + # in reconstruction res_range.parameters = sample_params samples.append(res_range) @@ -308,6 +314,9 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidth', 'competitveWidth'] sample_params = pd.DataFrame.from_records(records, columns=columns) res_range = copy.copy(resonances) + res_range._prepared = False # Set prepared to False to ensure + # the sampled parameters are used + # in reconstruction res_range.parameters = sample_params samples.append(res_range) @@ -334,6 +343,9 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidthA', 'fissionWidthB'] sample_params = pd.DataFrame.from_records(records, columns=columns) res_range = copy.copy(resonances) + res_range._prepared = False # Set prepared to False to ensure + # the sampled parameters are used + # in reconstruction res_range.parameters = sample_params samples.append(res_range) @@ -359,6 +371,9 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidthA', 'fissionWidthB'] sample_params = pd.DataFrame.from_records(records, columns=columns) res_range = copy.copy(resonances) + res_range._prepared = False # Set prepared to False to ensure + # the sampled parameters are used + # in reconstruction res_range.parameters = sample_params samples.append(res_range)