diff --git a/examples/jupyter/nuclear-data-resonance-covariance.ipynb b/examples/jupyter/nuclear-data-resonance-covariance.ipynb index 876cf4daba..e60be13513 100644 --- a/examples/jupyter/nuclear-data-resonance-covariance.ipynb +++ b/examples/jupyter/nuclear-data-resonance-covariance.ipynb @@ -208,7 +208,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -246,7 +246,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -296,7 +296,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/icmeyer/miniconda3/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/data/resonance_covariance.py:235: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n", + "/home/icmeyer/openmc/openmc/data/resonance_covariance.py:231: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n", " warnings.warn(warn_str)\n" ] }, @@ -370,51 +370,51 @@ " \n", " \n", " 0\n", - " 0.031892\n", + " 0.033464\n", " 0\n", " 2.0\n", - " 0.000477\n", - " 0.106883\n", + " 0.000479\n", + " 0.103833\n", " 0.0\n", " 0.0\n", " \n", " \n", " 1\n", - " 2.825068\n", + " 2.824695\n", " 0\n", " 2.0\n", - " 0.000333\n", - " 0.101242\n", + " 0.000346\n", + " 0.090186\n", " 0.0\n", " 0.0\n", " \n", " \n", " 2\n", - " 16.255167\n", + " 16.271406\n", " 0\n", " 1.0\n", - " 0.000433\n", - " 0.102033\n", + " 0.000558\n", + " 0.170612\n", " 0.0\n", " 0.0\n", " \n", " \n", " 3\n", - " 16.768821\n", + " 16.771335\n", " 0\n", " 2.0\n", - " 0.013301\n", - " 0.079907\n", + " 0.011966\n", + " 0.080398\n", " 0.0\n", " 0.0\n", " \n", " \n", " 4\n", - " 20.559310\n", + " 20.554856\n", " 0\n", " 2.0\n", - " 0.012069\n", - " 0.075562\n", + " 0.011056\n", + " 0.090749\n", " 0.0\n", " 0.0\n", " \n", @@ -424,11 +424,11 @@ ], "text/plain": [ " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.031892 0 2.0 0.000477 0.106883 0.0 0.0\n", - "1 2.825068 0 2.0 0.000333 0.101242 0.0 0.0\n", - "2 16.255167 0 1.0 0.000433 0.102033 0.0 0.0\n", - "3 16.768821 0 2.0 0.013301 0.079907 0.0 0.0\n", - "4 20.559310 0 2.0 0.012069 0.075562 0.0 0.0" + "0 0.033464 0 2.0 0.000479 0.103833 0.0 0.0\n", + "1 2.824695 0 2.0 0.000346 0.090186 0.0 0.0\n", + "2 16.271406 0 1.0 0.000558 0.170612 0.0 0.0\n", + "3 16.771335 0 2.0 0.011966 0.080398 0.0 0.0\n", + "4 20.554856 0 2.0 0.011056 0.090749 0.0 0.0" ] }, "execution_count": 8, @@ -486,51 +486,51 @@ " \n", " \n", " 0\n", - " 0.033649\n", + " 0.029919\n", " 0\n", " 2.0\n", - " 0.000480\n", - " 0.103631\n", + " 0.000472\n", + " 0.109936\n", " 0.0\n", " 0.0\n", " \n", " \n", " 1\n", - " 2.829673\n", + " 2.823121\n", " 0\n", " 2.0\n", - " 0.000332\n", - " 0.099803\n", + " 0.000349\n", + " 0.097556\n", " 0.0\n", " 0.0\n", " \n", " \n", " 2\n", - " 16.222978\n", + " 16.236232\n", " 0\n", " 1.0\n", - " 0.000331\n", - " 0.071241\n", + " 0.000443\n", + " 0.096141\n", " 0.0\n", " 0.0\n", " \n", " \n", " 3\n", - " 16.765241\n", + " 16.770362\n", " 0\n", " 2.0\n", - " 0.012566\n", - " 0.080448\n", + " 0.012942\n", + " 0.079580\n", " 0.0\n", " 0.0\n", " \n", " \n", " 4\n", - " 20.566440\n", + " 20.560065\n", " 0\n", " 2.0\n", - " 0.011168\n", - " 0.088066\n", + " 0.011043\n", + " 0.094364\n", " 0.0\n", " 0.0\n", " \n", @@ -540,11 +540,11 @@ ], "text/plain": [ " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.033649 0 2.0 0.000480 0.103631 0.0 0.0\n", - "1 2.829673 0 2.0 0.000332 0.099803 0.0 0.0\n", - "2 16.222978 0 1.0 0.000331 0.071241 0.0 0.0\n", - "3 16.765241 0 2.0 0.012566 0.080448 0.0 0.0\n", - "4 20.566440 0 2.0 0.011168 0.088066 0.0 0.0" + "0 0.029919 0 2.0 0.000472 0.109936 0.0 0.0\n", + "1 2.823121 0 2.0 0.000349 0.097556 0.0 0.0\n", + "2 16.236232 0 1.0 0.000443 0.096141 0.0 0.0\n", + "3 16.770362 0 2.0 0.012942 0.079580 0.0 0.0\n", + "4 20.560065 0 2.0 0.011043 0.094364 0.0 0.0" ] }, "execution_count": 9, @@ -572,8 +572,8 @@ { "data": { "text/plain": [ - "[,\n", - " ]" + "[,\n", + " ]" ] }, "execution_count": 10, @@ -604,7 +604,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -831,7 +831,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/icmeyer/miniconda3/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/data/resonance_covariance.py:235: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n", + "/home/icmeyer/openmc/openmc/data/resonance_covariance.py:231: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n", " warnings.warn(warn_str)\n" ] }, @@ -868,51 +868,51 @@ " \n", " \n", " 0\n", - " 0.032823\n", + " 0.029174\n", " 0\n", " 2.0\n", - " 0.000477\n", - " 0.104828\n", + " 0.000468\n", + " 0.110557\n", " 0.0\n", " 0.0\n", " \n", " \n", " 1\n", - " 2.828013\n", + " 2.826584\n", " 0\n", " 2.0\n", - " 0.000350\n", - " 0.093018\n", + " 0.000348\n", + " 0.090882\n", " 0.0\n", " 0.0\n", " \n", " \n", " 2\n", - " 16.765102\n", + " 16.768498\n", " 0\n", " 2.0\n", - " 0.013206\n", - " 0.080758\n", + " 0.013018\n", + " 0.076285\n", " 0.0\n", " 0.0\n", " \n", " \n", " 3\n", - " 20.557704\n", + " 20.561904\n", " 0\n", " 2.0\n", - " 0.011632\n", - " 0.082187\n", + " 0.010537\n", + " 0.096260\n", " 0.0\n", " 0.0\n", " \n", " \n", " 4\n", - " 21.655469\n", + " 21.652164\n", " 0\n", " 2.0\n", - " 0.000347\n", - " 0.093798\n", + " 0.000356\n", + " 0.159153\n", " 0.0\n", " 0.0\n", " \n", @@ -922,11 +922,11 @@ ], "text/plain": [ " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.032823 0 2.0 0.000477 0.104828 0.0 0.0\n", - "1 2.828013 0 2.0 0.000350 0.093018 0.0 0.0\n", - "2 16.765102 0 2.0 0.013206 0.080758 0.0 0.0\n", - "3 20.557704 0 2.0 0.011632 0.082187 0.0 0.0\n", - "4 21.655469 0 2.0 0.000347 0.093798 0.0 0.0" + "0 0.029174 0 2.0 0.000468 0.110557 0.0 0.0\n", + "1 2.826584 0 2.0 0.000348 0.090882 0.0 0.0\n", + "2 16.768498 0 2.0 0.013018 0.076285 0.0 0.0\n", + "3 20.561904 0 2.0 0.010537 0.096260 0.0 0.0\n", + "4 21.652164 0 2.0 0.000356 0.159153 0.0 0.0" ] }, "execution_count": 15, diff --git a/openmc/data/resonance.py b/openmc/data/resonance.py index 07a34703b9..5e4bd7129e 100644 --- a/openmc/data/resonance.py +++ b/openmc/data/resonance.py @@ -91,14 +91,14 @@ class Resonances(object): # Determine whether discrete or continuous representation items = get_head_record(file_obj) - n_isotope = items[4] # Number of isotopes + n_isotope = items[4] # Number of isotopes ranges = [] for iso in range(n_isotope): items = get_cont_record(file_obj) abundance = items[1] - fission_widths = (items[3] == 1) # fission widths are given? - n_ranges = items[4] # number of resonance energy ranges + fission_widths = (items[3] == 1) # fission widths are given? + n_ranges = items[4] # number of resonance energy ranges for j in range(n_ranges): items = get_cont_record(file_obj) @@ -113,7 +113,7 @@ class Resonances(object): # unresolved resonance region erange = Unresolved.from_endf(file_obj, items, fission_widths) - #erange.material = self + # erange.material = self ranges.append(erange) return cls(ranges) @@ -163,6 +163,13 @@ class ResonanceRange(object): self._prepared = False self._parameter_matrix = {} + def __copy__(self): + cls = type(self) + new_copy = cls.__new__(cls) + new_copy.__dict__.update(self.__dict__) + new_copy._prepared = False + return new_copy + @classmethod def from_endf(cls, ev, file_obj, items): """Create resonance range from an ENDF evaluation. diff --git a/openmc/data/resonance_covariance.py b/openmc/data/resonance_covariance.py index 0a74170125..ebc375fcb8 100644 --- a/openmc/data/resonance_covariance.py +++ b/openmc/data/resonance_covariance.py @@ -208,10 +208,6 @@ class ResonanceCovarianceRange: res_cov_range.file2res.parameters = parameters[mask] res_cov_range.covariance = cov_subset - # Set _prepared to False to ensure parameter subset - # used during construction routine - res_cov_range.file2res._prepared = False - return res_cov_range def sample_resonance_parameters(self, n_samples): @@ -264,29 +260,27 @@ class ResonanceCovarianceRange: records = [] for j, E in enumerate(energy): - records.append([energy[j], l_value[j], spin[j], gt[j], gn[j], - gg[j], gf[j], gx[j]]) + records.append([energy[j], l_value[j], spin[j], gt[j], + gn[j], gg[j], gf[j], gx[j]]) columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth', 'captureWidth', 'fissionWidth', 'competitiveWidth'] - sample_params = pd.DataFrame.from_records(records, columns=columns) + sample_params = pd.DataFrame.from_records(records, + columns=columns) # Copy ResonanceRange object res_range = copy.copy(self.file2res) - # Set _prepared to False to ensure sampled parameters are - # used during construction routine - res_range._prepared = False res_range.parameters = sample_params samples.append(res_range) # Handling RM sampling elif formalism == 'rm': - params = ['energy', 'L', 'J', 'neutronWidth', 'captureWidth', + params = ['energy', 'neutronWidth', 'captureWidth', 'fissionWidthA', 'fissionWidthB'] param_list = params[:mpar] mean_array = parameters[param_list].values mean = mean_array.flatten() par_samples = np.random.multivariate_normal(mean, cov, size=n_samples) - spin = parameters['J'] + spin = parameters['J'].values l_value = parameters['L'].values for sample in par_samples: energy = sample[0::mpar] @@ -305,9 +299,6 @@ class ResonanceCovarianceRange: columns=columns) # Copy ResonanceRange object res_range = copy.copy(self.file2res) - # Set _prepared to False to ensure sampled parameters are - # used during construction routine - res_range._prepared = False res_range.parameters = sample_params samples.append(res_range)