From 78a411ebb7e4e87b090d8e1b29c80116d5e322fc Mon Sep 17 00:00:00 2001 From: Isaac Meyer Date: Wed, 25 Jul 2018 09:41:54 -0500 Subject: [PATCH] Fix of sampling routine, change dataframe .as_matrix to .values --- .../nuclear-data-resonance-covariance.ipynb | 162 +++++++++--------- openmc/data/resonance.py | 4 +- openmc/data/resonance_covariance.py | 69 +++++--- 3 files changed, 132 insertions(+), 103 deletions(-) diff --git a/examples/jupyter/nuclear-data-resonance-covariance.ipynb b/examples/jupyter/nuclear-data-resonance-covariance.ipynb index b8c1764cf5..876cf4daba 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, @@ -217,9 +217,9 @@ }, { "data": { - "image/png": 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TeQ39AuaGz4w5BMWHz/TDDB46WFzPPWa2lrmuWBOFZUru7YQrQmcQJRZhlzGI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\nUQAAAABJRU5ErkJggg==\n", 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\n", 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oOd59O7rPHlpdLshqp8eS9an66BlLQYa7sE89lnBs4bM9KocYInMU4yGckyL+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\nEjYpfBrSvwV2/NZvjaDFoxa1Zi/wr4AvFBGIyBjwR8CrgPO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Yg/qh4uetOxa4MoDOi0F5qcV51BfosJZ0h32KLUGkn6dtHET1ShJLXnV/WZ5C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\nukwQ1s3VGJNP01A/iI+Wz5I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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -868,51 +868,51 @@ " \n", " \n", " 0\n", - " 0.0314\n", + " 0.032823\n", " 0\n", " 2.0\n", - " 0.000474\n", - " 0.1072\n", + " 0.000477\n", + " 0.104828\n", " 0.0\n", " 0.0\n", " \n", " \n", " 1\n", - " 2.8250\n", + " 2.828013\n", " 0\n", " 2.0\n", - " 0.000345\n", - " 0.0970\n", + " 0.000350\n", + " 0.093018\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", + " 2\n", + " 16.765102\n", + " 0\n", + " 2.0\n", + " 0.013206\n", + " 0.080758\n", " 0.0\n", " 0.0\n", " \n", " \n", " 3\n", - " 16.7700\n", + " 20.557704\n", " 0\n", " 2.0\n", - " 0.012800\n", - " 0.0805\n", + " 0.011632\n", + " 0.082187\n", " 0.0\n", " 0.0\n", " \n", " \n", " 4\n", - " 20.5600\n", + " 21.655469\n", " 0\n", " 2.0\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", + " 0.000347\n", + " 0.093798\n", " 0.0\n", " 0.0\n", " \n", @@ -921,12 +921,12 @@ "" ], "text/plain": [ - " 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" + " 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" ] }, "execution_count": 15, @@ -956,7 +956,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.6" } }, "nbformat": 4, diff --git a/openmc/data/resonance.py b/openmc/data/resonance.py index e71073919b..07a34703b9 100644 --- a/openmc/data/resonance.py +++ b/openmc/data/resonance.py @@ -438,7 +438,7 @@ class MultiLevelBreitWigner(ResonanceRange): self._l_values = np.array(l_values) self._competitive = np.array(competitive) for l in l_values: - self._parameter_matrix[l] = df[df.L == l].as_matrix() + self._parameter_matrix[l] = df[df.L == l].values self._prepared = True @@ -683,7 +683,7 @@ class ReichMoore(ResonanceRange): self._l_values = np.array(l_values) for (l, J) in lj_values: self._parameter_matrix[l, J] = df[(df.L == l) & - (abs(df.J) == J)].as_matrix() + (abs(df.J) == J)].values self._prepared = True diff --git a/openmc/data/resonance_covariance.py b/openmc/data/resonance_covariance.py index c78ce33f74..7710512fa5 100644 --- a/openmc/data/resonance_covariance.py +++ b/openmc/data/resonance_covariance.py @@ -253,11 +253,11 @@ class ResonanceCovarianceRange: if formalism == 'mlbw' or formalism == 'slbw': if mpar == 3: param_list = ['energy', 'neutronWidth', 'captureWidth'] - mean_array = pd.DataFrame.as_matrix(parameters[param_list]) - spin = pd.DataFrame.as_matrix(parameters['J']) - l_value = pd.DataFrame.as_matrix(parameters['L']) - gf = pd.DataFrame.as_matrix(parameters['fissionWidth']) - gx = pd.DataFrame.as_matrix(parameters['competitiveWidth']) + mean_array = parameters[param_list].values + spin = parameters['J'].values + l_value = parameters['L'].values + gf = parameters['fissionWidth'].values + gx = parameters['competitiveWidth'].values mean = mean_array.flatten() par_samples = np.random.multivariate_normal(mean, cov, size=n_samples) @@ -274,16 +274,21 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidth', 'competitiveWidth'] 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) elif mpar == 4: param_list = ['energy', 'neutronWidth', 'captureWidth', 'fissionWidth'] - mean_array = pd.DataFrame.as_matrix(parameters[param_list]) - spin = pd.DataFrame.as_matrix(parameters['J']) - l_value = pd.DataFrame.as_matrix(parameters['L']) - gx = pd.DataFrame.as_matrix(parameters['competitiveWidth']) + mean_array = parameters[param_list].values + spin = parameters['J'].values + l_value = parameters['L'].values + gx = parameters['competitiveWidth'].values mean = mean_array.flatten() par_samples = np.random.multivariate_normal(mean, cov, size=n_samples) @@ -301,14 +306,20 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidth', 'competitiveWidth'] 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) elif mpar == 5: param_list = ['energy', 'neutronWidth', 'captureWidth', 'fissionWidth', 'competitiveWidth'] - mean_array = pd.DataFrame.as_matrix(parameters[param_list]) - spin = pd.DataFrame.as_matrix(parameters['J']) - l_value = pd.DataFrame.as_matrix(parameters['L']) + mean_array = parameters[param_list].values + spin = parameters['J'].values + l_value = parameters['L'].values mean = mean_array.flatten() par_samples = np.random.multivariate_normal(mean, cov, size=n_samples) @@ -327,17 +338,23 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidth', 'competitveWidth'] 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 if formalism == 'rm': if mpar == 3: param_list = ['energy', 'neutronWidth', 'captureWidth'] - mean_array = pd.DataFrame.as_matrix(parameters[param_list]) - spin = pd.DataFrame.as_matrix(parameters['J']) - l_value = pd.DataFrame.as_matrix(parameters['L']) - gfa = pd.DataFrame.as_matrix(parameters['fissionWidthA']) - gfb = pd.DataFrame.as_matrix(parameters['fissionWidthB']) + mean_array = parameters[param_list].values + spin = parameters['J'].values + l_value = parameters['L'].values + gfa = parameters['fissionWidthA'].values + gfb = parameters['fissionWidthB'].values mean = mean_array.flatten() par_samples = np.random.multivariate_normal(mean, cov, size=n_samples) @@ -353,14 +370,20 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidthA', 'fissionWidthB'] 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) elif mpar == 5: param_list = ['energy', 'neutronWidth', 'captureWidth', 'fissionWidthA', 'fissionWidthB'] - mean_array = pd.DataFrame.as_matrix(parameters[param_list]) - spin = pd.DataFrame.as_matrix(parameters['J']) - l_value = pd.DataFrame.as_matrix(parameters['L']) + mean_array = parameters[param_list].values + spin = parameters['J'].values + l_value = parameters['L'].values mean = mean_array.flatten() par_samples = np.random.multivariate_normal(mean, cov, size=n_samples) @@ -378,6 +401,12 @@ class ResonanceCovarianceRange: 'captureWidth', 'fissionWidthA', 'fissionWidthB'] 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) return samples