From f58babba69e1bafcc25e65b2687896d2fe51ce06 Mon Sep 17 00:00:00 2001
From: schottenhamml <helen.schottenhamml@fau.de>
Date: Wed, 28 Aug 2024 14:11:06 +0200
Subject: [PATCH] Fix flake warnings.
---
src/lbmpy/flow_statistics.py | 17 +++++++++--------
tests/test_welford.py | 10 +++++++---
2 files changed, 16 insertions(+), 11 deletions(-)
diff --git a/src/lbmpy/flow_statistics.py b/src/lbmpy/flow_statistics.py
index cdba1656..d4c7e308 100644
--- a/src/lbmpy/flow_statistics.py
+++ b/src/lbmpy/flow_statistics.py
@@ -14,8 +14,8 @@ def welford_assignments(field, mean_field, sum_of_squares_field=None, sum_of_cub
the sum of squares / sum of cubes is given.
The mean value is directly updated in the mean vector field.
- The variance / covariance must be retrieved in a post-processing step. Let :math `M_{2,n}` denote the value of the sum of
- squares after the first :math `n` samples. According to Welford the biased sample variance
+ The variance / covariance must be retrieved in a post-processing step. Let :math `M_{2,n}` denote the value of the
+ sum of squares after the first :math `n` samples. According to Welford the biased sample variance
:math `\sigma_n^2` and the unbiased sample variance :math `s_n^2` are given by
.. math ::
@@ -78,7 +78,7 @@ def welford_assignments(field, mean_field, sum_of_squares_field=None, sum_of_cub
if welford_sum_of_cubes_field is not None:
assert welford_sum_of_squares_field is not None
- ### actual assignments
+ # actual assignments
counter = sp.Symbol('counter')
delta = sp.symbols(f"delta_:{dim}")
@@ -108,11 +108,12 @@ def welford_assignments(field, mean_field, sum_of_squares_field=None, sum_of_cub
for k in range(dim):
idx = (i * dim + j) * dim + k
main_assignments.append(ps.Assignment(
- welford_sum_of_cubes_field.at_index(idx), welford_sum_of_cubes_field.at_index(idx)
- - delta[k] / counter * welford_sum_of_squares_field(i * dim + j)
- - delta[j] / counter * welford_sum_of_squares_field(k * dim + i)
- - delta[i] / counter * welford_sum_of_squares_field(j * dim + k)
- + delta2[i] * (2 * delta[j] - delta2[j]) * delta[k]
+ welford_sum_of_cubes_field.at_index(idx),
+ welford_sum_of_cubes_field.at_index(idx)
+ - delta[k] / counter * welford_sum_of_squares_field(i * dim + j)
+ - delta[j] / counter * welford_sum_of_squares_field(k * dim + i)
+ - delta[i] / counter * welford_sum_of_squares_field(j * dim + k)
+ + delta2[i] * (2 * delta[j] - delta2[j]) * delta[k]
))
return main_assignments
diff --git a/tests/test_welford.py b/tests/test_welford.py
index acb3bbab..f374c727 100644
--- a/tests/test_welford.py
+++ b/tests/test_welford.py
@@ -40,7 +40,7 @@ def test_welford(order, dim):
# set random seed
np.random.seed(42)
n = int(1e4)
- x = np.random.normal(size=n*dim).reshape(n, dim)
+ x = np.random.normal(size=n * dim).reshape(n, dim)
analytical_mean = np.zeros(dim)
analytical_covariance = np.zeros(dim**2)
@@ -53,13 +53,17 @@ def test_welford(order, dim):
# calculate analytical covariances
for i in range(dim):
for j in range(dim):
- analytical_covariance[i * dim + j] = (np.sum((x[:, i] - analytical_mean[i]) * (x[:, j] - analytical_mean[j]))) / n
+ analytical_covariance[i * dim + j] \
+ = (np.sum((x[:, i] - analytical_mean[i]) * (x[:, j] - analytical_mean[j]))) / n
# calculate analytical third-order central moments
for i in range(dim):
for j in range(dim):
for k in range(dim):
- analytical_third_order_moments[(i * dim + j) * dim + k] = (np.sum((x[:, i] - analytical_mean[i]) * (x[:, j] - analytical_mean[j]) * (x[:, k] - analytical_mean[k]))) / n
+ analytical_third_order_moments[(i * dim + j) * dim + k] \
+ = (np.sum((x[:, i] - analytical_mean[i])
+ * (x[:, j] - analytical_mean[j])
+ * (x[:, k] - analytical_mean[k]))) / n
# Time loop
counter = 1
--
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