style(PLRsearch): format according to black

[csit.git] / resources / libraries / python / PLRsearch / stat_trackers.py

diff --git a/resources/libraries/python/PLRsearch/stat_trackers.py b/resources/libraries/python/PLRsearch/stat_trackers.py
index e0b21dc..d19eebe 100644 (file)
--- a/resources/libraries/python/PLRsearch/stat_trackers.py
+++ b/resources/libraries/python/PLRsearch/stat_trackers.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2021 Cisco and/or its affiliates.
+# Copyright (c) 2024 Cisco and/or its affiliates.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at:
@@ -64,8 +64,10 @@ class ScalarStatTracker:
         :returns: Expression constructing an equivalent instance.
         :rtype: str
         """
-        return f"ScalarStatTracker(log_sum_weight={self.log_sum_weight!r}," \
+        return (
+            f"ScalarStatTracker(log_sum_weight={self.log_sum_weight!r},"
             f"average={self.average!r},log_variance={self.log_variance!r})"
+        )
 
     def copy(self):
         """Return new ScalarStatTracker instance with the same state as self.
@@ -110,7 +112,8 @@ class ScalarStatTracker:
         if absolute_shift > 0.0:
             log_square_shift = 2 * math.log(absolute_shift)
             log_variance = log_plus(
-                log_variance, log_square_shift + log_sample_ratio)
+                log_variance, log_square_shift + log_sample_ratio
+            )
         if log_variance is not None:
             log_variance += old_log_sum_weight - new_log_sum_weight
         self.log_sum_weight = new_log_sum_weight
@@ -133,10 +136,17 @@ class ScalarDualStatTracker(ScalarStatTracker):
     One typical use is for Monte Carlo integrator to decide whether
     the partial sums so far are reliable enough.
     """
+
     def __init__(
-            self, log_sum_weight=None, average=0.0, log_variance=None,
-            log_sum_secondary_weight=None, secondary_average=0.0,
-            log_secondary_variance=None, max_log_weight=None):
+        self,
+        log_sum_weight=None,
+        average=0.0,
+        log_variance=None,
+        log_sum_secondary_weight=None,
+        secondary_average=0.0,
+        log_secondary_variance=None,
+        max_log_weight=None,
+    ):
         """Initialize new tracker instance, empty by default.
 
         :param log_sum_weight: Natural logarithm of sum of weights
@@ -177,12 +187,14 @@ class ScalarDualStatTracker(ScalarStatTracker):
         :rtype: str
         """
         sec = self.secondary
-        return f"ScalarDualStatTracker(log_sum_weight={self.log_sum_weight!r},"\
-            f"average={self.average!r},log_variance={self.log_variance!r}," \
-            f"log_sum_secondary_weight={sec.log_sum_weight!r}," \
-            f"secondary_average={sec.average!r}," \
-            f"log_secondary_variance={sec.log_variance!r}," \
+        return (
+            f"ScalarDualStatTracker(log_sum_weight={self.log_sum_weight!r},"
+            f"average={self.average!r},log_variance={self.log_variance!r},"
+            f"log_sum_secondary_weight={sec.log_sum_weight!r},"
+            f"secondary_average={sec.average!r},"
+            f"log_secondary_variance={sec.log_variance!r},"
             f"max_log_weight={self.max_log_weight!r})"
+        )
 
     def add(self, scalar_value, log_weight=0.0):
         """Return updated both stats after addition of another sample.
@@ -242,8 +254,12 @@ class VectorStatTracker:
     """
 
     def __init__(
-            self, dimension=2, log_sum_weight=None, averages=None,
-            covariance_matrix=None):
+        self,
+        dimension=2,
+        log_sum_weight=None,
+        averages=None,
+        covariance_matrix=None,
+    ):
         """Initialize new tracker instance, two-dimensional empty by default.
 
         If any of latter two arguments is None, it means
@@ -272,10 +288,12 @@ class VectorStatTracker:
         :returns: Expression constructing an equivalent instance.
         :rtype: str
         """
-        return f"VectorStatTracker(dimension={self.dimension!r}," \
-            f"log_sum_weight={self.log_sum_weight!r}," \
-            f"averages={self.averages!r}," \
+        return (
+            f"VectorStatTracker(dimension={self.dimension!r},"
+            f"log_sum_weight={self.log_sum_weight!r},"
+            f"averages={self.averages!r},"
             f"covariance_matrix={self.covariance_matrix!r})"
+        )
 
     def copy(self):
         """Return new instance with the same state as self.
@@ -287,8 +305,10 @@ class VectorStatTracker:
         :rtype: VectorStatTracker
         """
         return VectorStatTracker(
-            self.dimension, self.log_sum_weight, self.averages[:],
-            copy.deepcopy(self.covariance_matrix)
+            self.dimension,
+            self.log_sum_weight,
+            self.averages[:],
+            copy.deepcopy(self.covariance_matrix),
         )
 
     def reset(self):