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Take advantage of np.random.multivariate size option for multiple samples

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This commit is contained in:
Isaac Meyer 2018-07-19 14:21:46 -05:00
parent 9ad8dabb9c
commit d148c24056
1 changed files with 15 additions and 10 deletions
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25
openmc/data/resonance_covariance.py
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@ -243,8 +243,9 @@ class ResonanceCovarianceRange:
gf = pd.DataFrame.as_matrix(parameters['fissionWidth'])
gx = pd.DataFrame.as_matrix(parameters['competitiveWidth'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean, cov)
par_samples = np.random.multivariate_normal(mean, cov,
size=n_samples)
for sample in par_samples:
energy = sample[0::3]
gn = sample[1::3]
gg = sample[2::3]
@ -270,8 +271,9 @@ class ResonanceCovarianceRange:
l_value = pd.DataFrame.as_matrix(parameters['L'])
gx = pd.DataFrame.as_matrix(parameters['competitiveWidth'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean, cov)
par_samples = np.random.multivariate_normal(mean, cov,
size=n_samples)
for sample in par_samples:
energy = sample[0::4]
gn = sample[1::4]
gg = sample[2::4]
@ -298,8 +300,9 @@ class ResonanceCovarianceRange:
spin = pd.DataFrame.as_matrix(parameters['J'])
l_value = pd.DataFrame.as_matrix(parameters['L'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean, cov)
par_samples = np.random.multivariate_normal(mean, cov,
size=n_samples)
for sample in par_samples:
energy = sample[0::5]
gn = sample[1::5]
gg = sample[2::5]
@ -330,8 +333,9 @@ class ResonanceCovarianceRange:
gfa = pd.DataFrame.as_matrix(parameters['fissionWidthA'])
gfb = pd.DataFrame.as_matrix(parameters['fissionWidthB'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean, cov)
par_samples = np.random.multivariate_normal(mean, cov,
size=n_samples)
for sample in par_samples:
energy = sample[0::3]
gn = sample[1::3]
gg = sample[2::3]
@ -356,8 +360,9 @@ class ResonanceCovarianceRange:
spin = pd.DataFrame.as_matrix(parameters['J'])
l_value = pd.DataFrame.as_matrix(parameters['L'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean, cov)
par_samples = np.random.multivariate_normal(mean, cov,
size=n_samples)
for sample in par_samples:
energy = sample[0::5]
gn = sample[1::5]
gg = sample[2::5]