From e2a27f288be2dbe050e6b21da0558fd85675c9fd Mon Sep 17 00:00:00 2001 From: Isaac Meyer Date: Mon, 23 Jul 2018 15:02:10 -0500 Subject: [PATCH] Restructured ResonanceCovarianceRange class to contain corresponding file2 data as an attribute --- .../nuclear-data-resonance-covariance.ipynb | 168 +++++++++--------- openmc/data/resonance_covariance.py | 121 ++++++------- 2 files changed, 141 insertions(+), 148 deletions(-) diff --git a/examples/jupyter/nuclear-data-resonance-covariance.ipynb b/examples/jupyter/nuclear-data-resonance-covariance.ipynb index ab2694922..b8c1764cf 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, @@ -219,7 +219,7 @@ "data": { "image/png": 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oOd59O7rPHlpdLshqp8eS9an66BlLQYa7sE89lnBs4bM9KocYInMU4yGckyL+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\nLAV9Fka61ivSiCJdH63seGuFtBgmHFPwZjfveNK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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -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:239: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n", + "/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", " warnings.warn(warn_str)\n" ] }, @@ -314,7 +314,7 @@ "source": [ "rm_resonance = gd157_endf.resonances.ranges[0]\n", "n_samples = 5\n", - "samples = gd157_endf.resonance_covariance.ranges[0].sample_resonance_parameters(n_samples, rm_resonance)\n", + "samples = gd157_endf.resonance_covariance.ranges[0].sample_resonance_parameters(n_samples)\n", "type(samples[0])\n" ] }, @@ -370,51 +370,51 @@ " \n", " \n", " 0\n", - " 0.029309\n", + " 0.0314\n", " 0\n", " 2.0\n", - " 0.000468\n", - " 0.110327\n", + " 0.000474\n", + " 0.1072\n", " 0.0\n", " 0.0\n", " \n", " \n", " 1\n", - " 2.827761\n", + " 2.8250\n", " 0\n", " 2.0\n", - " 0.000359\n", - " 0.094539\n", + " 0.000345\n", + " 0.0970\n", " 0.0\n", " 0.0\n", " \n", " \n", " 2\n", - " 16.208418\n", + " 16.2400\n", " 0\n", " 1.0\n", - " 0.000283\n", - " 0.046995\n", + " 0.000400\n", + " 0.0910\n", " 0.0\n", " 0.0\n", " \n", " \n", " 3\n", - " 16.762322\n", + " 16.7700\n", " 0\n", " 2.0\n", - " 0.013044\n", - " 0.078128\n", + " 0.012800\n", + " 0.0805\n", " 0.0\n", " 0.0\n", " \n", " \n", " 4\n", - " 20.557394\n", + " 20.5600\n", " 0\n", " 2.0\n", - " 0.011103\n", - " 0.086309\n", + " 0.011360\n", + " 0.0880\n", " 0.0\n", " 0.0\n", " \n", @@ -423,12 +423,12 @@ "" ], "text/plain": [ - " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.029309 0 2.0 0.000468 0.110327 0.0 0.0\n", - "1 2.827761 0 2.0 0.000359 0.094539 0.0 0.0\n", - "2 16.208418 0 1.0 0.000283 0.046995 0.0 0.0\n", - "3 16.762322 0 2.0 0.013044 0.078128 0.0 0.0\n", - "4 20.557394 0 2.0 0.011103 0.086309 0.0 0.0" + " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", + "0 0.0314 0 2.0 0.000474 0.1072 0.0 0.0\n", + "1 2.8250 0 2.0 0.000345 0.0970 0.0 0.0\n", + "2 16.2400 0 1.0 0.000400 0.0910 0.0 0.0\n", + "3 16.7700 0 2.0 0.012800 0.0805 0.0 0.0\n", + "4 20.5600 0 2.0 0.011360 0.0880 0.0 0.0" ] }, "execution_count": 8, @@ -486,51 +486,51 @@ " \n", " \n", " 0\n", - " 0.031344\n", + " 0.0314\n", " 0\n", " 2.0\n", - " 0.000473\n", - " 0.107136\n", + " 0.000474\n", + " 0.1072\n", " 0.0\n", " 0.0\n", " \n", " \n", " 1\n", - " 2.827026\n", + " 2.8250\n", " 0\n", " 2.0\n", - " 0.000321\n", - " 0.102900\n", + " 0.000345\n", + " 0.0970\n", " 0.0\n", " 0.0\n", " \n", " \n", " 2\n", - " 16.242791\n", + " 16.2400\n", " 0\n", " 1.0\n", - " 0.000479\n", - " 0.119832\n", + " 0.000400\n", + " 0.0910\n", " 0.0\n", " 0.0\n", " \n", " \n", " 3\n", - " 16.772147\n", + " 16.7700\n", " 0\n", " 2.0\n", - " 0.013393\n", - " 0.070252\n", + " 0.012800\n", + " 0.0805\n", " 0.0\n", " 0.0\n", " \n", " \n", " 4\n", - " 20.556324\n", + " 20.5600\n", " 0\n", " 2.0\n", - " 0.012220\n", - " 0.077122\n", + " 0.011360\n", + " 0.0880\n", " 0.0\n", " 0.0\n", " \n", @@ -539,12 +539,12 @@ "" ], "text/plain": [ - " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.031344 0 2.0 0.000473 0.107136 0.0 0.0\n", - "1 2.827026 0 2.0 0.000321 0.102900 0.0 0.0\n", - "2 16.242791 0 1.0 0.000479 0.119832 0.0 0.0\n", - "3 16.772147 0 2.0 0.013393 0.070252 0.0 0.0\n", - "4 20.556324 0 2.0 0.012220 0.077122 0.0 0.0" + " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", + "0 0.0314 0 2.0 0.000474 0.1072 0.0 0.0\n", + "1 2.8250 0 2.0 0.000345 0.0970 0.0 0.0\n", + "2 16.2400 0 1.0 0.000400 0.0910 0.0 0.0\n", + "3 16.7700 0 2.0 0.012800 0.0805 0.0 0.0\n", + "4 20.5600 0 2.0 0.011360 0.0880 0.0 0.0" ] }, "execution_count": 9, @@ -572,8 +572,8 @@ { "data": { "text/plain": [ - "[,\n", - " ]" + "[,\n", + " ]" ] }, "execution_count": 10, @@ -604,9 +604,9 @@ }, { "data": { - "image/png": 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liK8QmST3F1V9R0R2A/7hXljGmFyded7JOJ4QbWs6rBYhcX0Q8U1DTpBNbZs4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\nYJWqrgEQkbuBRar6U6C/5pkmoLIUYxS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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -746,8 +746,8 @@ "source": [ "lower_bound = 2; # inclusive\n", "upper_bound = 2; # inclusive\n", - "rm_resonance_sub, rm_res_cov_sub = gd157_endf.resonance_covariance.ranges[0].res_subset('J',[lower_bound,upper_bound], rm_resonance)\n", - "rm_resonance_sub.parameters[:5]" + "rm_res_cov_sub = gd157_endf.resonance_covariance.ranges[0].res_subset('J',[lower_bound,upper_bound])\n", + "rm_res_cov_sub.file2res.parameters[:5]" ] }, { @@ -808,7 +808,7 @@ "source": [ "old_n_parameters = gd157_endf.resonance_covariance.ranges[0].parameters.shape[0]\n", "old_shape = gd157_endf.resonance_covariance.ranges[0].covariance.shape\n", - "new_n_parameters = rm_resonance_sub.parameters.shape[0]\n", + "new_n_parameters = rm_res_cov_sub.file2res.parameters.shape[0]\n", "new_shape = rm_res_cov_sub.covariance.shape\n", "print('Number of parameters\\nOriginal: '+str(old_n_parameters)+'\\nSubet: '+str(new_n_parameters)+'\\nCovariance Size\\nOriginal: '+str(old_shape)+'\\nSubset: '+str(new_shape))\n" ] @@ -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:239: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n", + "/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", " warnings.warn(warn_str)\n" ] }, @@ -868,51 +868,51 @@ " \n", " \n", " 0\n", - " 0.033061\n", + " 0.0314\n", " 0\n", " 2.0\n", - " 0.000477\n", - " 0.104286\n", + " 0.000474\n", + " 0.1072\n", " 0.0\n", " 0.0\n", " \n", " \n", " 1\n", - " 2.822758\n", + " 2.8250\n", " 0\n", " 2.0\n", " 0.000345\n", - " 0.101087\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 2\n", - " 16.772084\n", - " 0\n", - " 2.0\n", - " 0.013277\n", - " 0.074735\n", + " 0.0970\n", " 0.0\n", " 0.0\n", " \n", " \n", " 3\n", - " 20.555977\n", + " 16.7700\n", " 0\n", " 2.0\n", - " 0.011417\n", - " 0.092437\n", + " 0.012800\n", + " 0.0805\n", " 0.0\n", " 0.0\n", " \n", " \n", " 4\n", - " 21.662213\n", + " 20.5600\n", " 0\n", " 2.0\n", - " 0.000380\n", - " 0.122282\n", + " 0.011360\n", + " 0.0880\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " 5\n", + " 21.6500\n", + " 0\n", + " 2.0\n", + " 0.000376\n", + " 0.1140\n", " 0.0\n", " 0.0\n", " \n", @@ -921,12 +921,12 @@ "" ], "text/plain": [ - " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.033061 0 2.0 0.000477 0.104286 0.0 0.0\n", - "1 2.822758 0 2.0 0.000345 0.101087 0.0 0.0\n", - "2 16.772084 0 2.0 0.013277 0.074735 0.0 0.0\n", - "3 20.555977 0 2.0 0.011417 0.092437 0.0 0.0\n", - "4 21.662213 0 2.0 0.000380 0.122282 0.0 0.0" + " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", + "0 0.0314 0 2.0 0.000474 0.1072 0.0 0.0\n", + "1 2.8250 0 2.0 0.000345 0.0970 0.0 0.0\n", + "3 16.7700 0 2.0 0.012800 0.0805 0.0 0.0\n", + "4 20.5600 0 2.0 0.011360 0.0880 0.0 0.0\n", + "5 21.6500 0 2.0 0.000376 0.1140 0.0 0.0" ] }, "execution_count": 15, @@ -935,7 +935,7 @@ } ], "source": [ - "samples_sub = rm_res_cov_sub.sample_resonance_parameters(n_samples, rm_resonance_sub)\n", + "samples_sub = rm_res_cov_sub.sample_resonance_parameters(n_samples)\n", "samples_sub[0].parameters[:5]" ] } diff --git a/openmc/data/resonance_covariance.py b/openmc/data/resonance_covariance.py index 69e677692..c78ce33f7 100644 --- a/openmc/data/resonance_covariance.py +++ b/openmc/data/resonance_covariance.py @@ -115,9 +115,9 @@ class ResonanceCovariances(Resonances): if unresolved_flag in (0, 1): # Resolved resonance region - file2params = resonances.ranges[j].parameters + resonance = resonances.ranges[j] erange = _FORMALISMS[formalism].from_endf(ev, file_obj, - items, file2params) + items, resonance) ranges.append(erange) elif unresolved_flag == 2: @@ -159,7 +159,7 @@ class ResonanceCovarianceRange: self.energy_min = energy_min self.energy_max = energy_max - def res_subset(self, parameter_str, bounds, resonances): + def res_subset(self, parameter_str, bounds): """Produce a subset of resonance parameters and the corresponding covariance matrix to an IncidentNeutron object. @@ -170,32 +170,25 @@ class ResonanceCovarianceRange: (i.e. 'energy', 'captureWidth', 'fissionWidthA'...) bounds : np.array [low numerical bound, high numerical bound] - resonances : openmc.data.ResonanceRange object - Corresponding resonance range with File 2 data. Returns ------- - res_range : openmc.data.ResonanceRange - ResonanceRange object that contains a subset of parameters - (maintains indexing of original) res_cov_range : openmc.data.ResonanceCovarianceRange ResonanceCovarianceRange object that contains a subset of the - covariance matrix (upper triangular) as well as parameters + covariance matrix (upper triangular) as well as a subset parameters + within self.file2params """ - # Copy the objects - res_range = copy.copy(resonances) - res_cov_range = copy.copy(self) + # Copy range and prevent change of original + res_cov_range = copy.deepcopy(self) - parameters = res_range.parameters + parameters = self.file2res.parameters cov = res_cov_range.covariance mpar = res_cov_range.mpar # Create mask mask1 = parameters[parameter_str] >= bounds[0] mask2 = parameters[parameter_str] <= bounds[1] mask = mask1 & mask2 - # Set the parameters for each object - res_range.parameters = parameters[mask] res_cov_range.parameters = parameters[mask] indices = res_cov_range.parameters.index.values # Build subset of covariance @@ -212,11 +205,16 @@ class ResonanceCovarianceRange: cov_subset = np.zeros([sub_cov_dim, sub_cov_dim]) tri_indices = np.triu_indices(sub_cov_dim) cov_subset[tri_indices] = cov_subset_vals + + 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_range, res_cov_range + return res_cov_range - def sample_resonance_parameters(self, n_samples, resonances): + def sample_resonance_parameters(self, n_samples): """Sample resonance parameters based on the covariances provided within an ENDF evaluation. @@ -224,8 +222,6 @@ class ResonanceCovarianceRange: ---------- n_samples : int The number of samples to produce - resonances : openmc.data.ResonanceRange object - Corresponding resonance range with File 2 data. Returns ------- @@ -239,6 +235,11 @@ class ResonanceCovarianceRange: warnings.warn(warn_str) parameters = self.parameters cov = self.covariance + # 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 nparams, params = parameters.shape # Symmetrizing covariance matrix @@ -273,10 +274,6 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidth', 'competitiveWidth'] sample_params = pd.DataFrame.from_records(records, columns=columns) - res_range = copy.copy(resonances) - # Set _prepared to False to ensure sampled paramaters are - # used during construction routine - res_range._prepared = False res_range.parameters = sample_params samples.append(res_range) @@ -304,11 +301,6 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidth', 'competitiveWidth'] sample_params = pd.DataFrame.from_records(records, columns=columns) - res_range = copy.copy(resonances) - # Set _prepared to False to ensure sampled paramaters are - # used during construction routine - res_range._prepared = False - res_range.parameters = sample_params samples.append(res_range) elif mpar == 5: @@ -335,11 +327,6 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidth', 'competitveWidth'] sample_params = pd.DataFrame.from_records(records, columns=columns) - res_range = copy.copy(resonances) - # Set _prepared to False to ensure sampled paramaters are - # used during construction routine - res_range._prepared = False - res_range.parameters = sample_params samples.append(res_range) # Handling RM Sampling @@ -366,11 +353,6 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidthA', 'fissionWidthB'] sample_params = pd.DataFrame.from_records(records, columns=columns) - res_range = copy.copy(resonances) - # Set _prepared to False to ensure sampled paramaters are - # used during construction routine - res_range._prepared = False - res_range.parameters = sample_params samples.append(res_range) elif mpar == 5: @@ -396,11 +378,6 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidthA', 'fissionWidthB'] sample_params = pd.DataFrame.from_records(records, columns=columns) - res_range = copy.copy(resonances) - # Set _prepared to False to ensure sampled paramaters are - # used during construction routine - res_range._prepared = False - res_range.parameters = sample_params samples.append(res_range) return samples @@ -425,24 +402,29 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): Resonance parameters covariance : numpy.array The covariance matrix contained within the ENDF evaluation - lcomp : int - Flag indicating format of the covariance matrix within the ENDF file mpar : int Number of parameters in covariance matrix for each individual resonance + lcomp : int + Flag indicating format of the covariance matrix within the ENDF file + file2res : openmc.data.ResonanceRange object + Corresponding resonance range with File 2 data. formalism : str String descriptor of formalism + """ - def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp): + def __init__(self, energy_min, energy_max, parameters, covariance, mpar, + lcomp, file2res): super().__init__(energy_min, energy_max) self.parameters = parameters self.covariance = covariance self.mpar = mpar self.lcomp = lcomp + self.file2res = copy.copy(file2res) self.formalism = 'mlbw' @classmethod - def from_endf(cls, ev, file_obj, items, file2params): + def from_endf(cls, ev, file_obj, items, resonance): """Create MLBW covariance data from an ENDF evaluation. Parameters @@ -455,9 +437,8 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): items : list Items from the CONT record at the start of the resonance range subsection - file2params : openmc.data.ResonanceRange object - Corresponding resonance range with File 2 data. Used for - reconstruction method + resonance : openmc.data.ResonanceRange object + Corresponding resonance range with File 2 data. Returns ------- @@ -520,7 +501,8 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): parameters = pd.DataFrame.from_records(records, columns=columns) # Add parameters from File 2 - parameters = _add_file2_contributions(parameters, file2params) + parameters = _add_file2_contributions(parameters, + resonance.parameters) # Compact format - Resonances and individual uncertainties followed by # compact correlations @@ -567,7 +549,8 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): mpar = int(covsize/nparams) # Add parameters from File 2 - parameters = _add_file2_contributions(parameters, file2params) + parameters = _add_file2_contributions(parameters, + resonance.parameters) elif lcomp == 0: cov = np.zeros([4, 4]) @@ -609,10 +592,12 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): mpar = int(covsize/nparams) # Add parameters from File 2 - parameters = _add_file2_contributions(parameters, file2params) + parameters = _add_file2_contributions(parameters, + resonance.parameters) # Create instance of class - mlbw = cls(energy_min, energy_max, parameters, cov, mpar, lcomp) + mlbw = cls(energy_min, energy_max, parameters, cov, mpar, lcomp, + resonance) return mlbw @@ -638,18 +623,20 @@ class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance): Resonance parameters covariance : numpy.array The covariance matrix contained within the ENDF evaluation - lcomp : int - Flag indicating format of the covariance matrix within the ENDF file mpar : int Number of parameters in covariance matrix for each individual resonance formalism : str String descriptor of formalism + lcomp : int + Flag indicating format of the covariance matrix within the ENDF file + file2res : openmc.data.ResonanceRange object + Corresponding resonance range with File 2 data. """ def __init__(self, energy_min, energy_max, parameters, covariance, mpar, - lcomp): + lcomp, file2res): super().__init__(energy_min, energy_max, parameters, covariance, mpar, - lcomp) + lcomp, file2res) self.formalism = 'slbw' @@ -680,21 +667,24 @@ class ReichMooreCovariance(ResonanceCovarianceRange): Flag indicating format of the covariance matrix within the ENDF file mpar : int Number of parameters in covariance matrix for each individual resonance + file2res : openmc.data.ResonanceRange object + Corresponding resonance range with File 2 data. formalism : str String descriptor of formalism """ def __init__(self, energy_min, energy_max, parameters, covariance, mpar, - lcomp): + lcomp, file2res): super().__init__(energy_min, energy_max) self.parameters = parameters self.covariance = covariance self.mpar = mpar self.lcomp = lcomp + self.file2res = copy.copy(file2res) self.formalism = 'rm' @classmethod - def from_endf(cls, ev, file_obj, items, file2params): + def from_endf(cls, ev, file_obj, items, resonance): """Create Reich-Moore resonance covariance data from an ENDF evaluation. Includes the resonance parameters contained separately in File 32. @@ -709,7 +699,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange): items : list Items from the CONT record at the start of the resonance range subsection - resonances : openmc.data.Resonance object + resonance : openmc.data.Resonance object openmc.data.Resonanance object generated from the same evaluation used to import values not contained in File 32 @@ -773,7 +763,8 @@ class ReichMooreCovariance(ResonanceCovarianceRange): parameters = pd.DataFrame.from_records(records, columns=columns) # Add parameters from File 2 - parameters = _add_file2_contributions(parameters, file2params) + parameters = _add_file2_contributions(parameters, + resonance.parameters) # Compact format - Resonances and individual uncertainties followed by # compact correlations @@ -813,10 +804,12 @@ class ReichMooreCovariance(ResonanceCovarianceRange): mpar = int(covsize/nparams) # Add parameters from File 2 - parameters = _add_file2_contributions(parameters, file2params) + parameters = _add_file2_contributions(parameters, + resonance.parameters) # Create instance of ReichMooreCovariance - rmc = cls(energy_min, energy_max, parameters, cov, mpar, lcomp) + rmc = cls(energy_min, energy_max, parameters, cov, mpar, lcomp, + resonance) return rmc