X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Flibraries%2Fpython%2FPLRsearch%2FIntegrator.py;h=331bd8475b72a7c040a184400c664c01211b5835;hp=86181eaa562277b66de55adefde07c0ced00c096;hb=d68951ac245150eeefa6e0f4156e4c1b5c9e9325;hpb=ed0258a440cfad7023d643f717ab78ac568dc59b

diff --git a/resources/libraries/python/PLRsearch/Integrator.py b/resources/libraries/python/PLRsearch/Integrator.py
index 86181eaa56..331bd8475b 100644
--- a/resources/libraries/python/PLRsearch/Integrator.py
+++ b/resources/libraries/python/PLRsearch/Integrator.py
@@ -23,6 +23,7 @@ import copy
 import traceback
 
 import dill
+
 from numpy import random
 
 # TODO: Teach FD.io CSIT to use multiple dirs in PYTHONPATH,
@@ -58,7 +59,7 @@ def try_estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
         # so we have to catch them all.
         traceback_string = traceback.format_exc()
         communication_pipe.send(traceback_string)
-        # After sendig, re-raise, so usages other than "one process per call"
+        # After sending, re-raise, so usages other than "one process per call"
         # keep behaving correctly.
         raise
 
@@ -86,7 +87,8 @@ def generate_sample(averages, covariance_matrix, dimension, scale_coeff):
             covariance_matrix[first][second] *= scale_coeff
     while 1:
         sample_point = random.multivariate_normal(
-            averages, covariance_matrix, 1)[0].tolist()
+            averages, covariance_matrix, 1
+        )[0].tolist()
         # Multivariate Gauss can fall outside (-1, 1) interval
         for first in range(dimension):
             sample_coordinate = sample_point[first]
@@ -187,14 +189,15 @@ def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
     :raises numpy.linalg.LinAlgError: If the focus shape gets singular
         (due to rounding errors). Try changing scale_coeff.
     """
-
     debug_list = list()
     trace_list = list()
     # Block until input object appears.
     dimension, dilled_function, param_focus_tracker, max_samples = (
-        communication_pipe.recv())
-    debug_list.append("Called with param_focus_tracker {tracker!r}"
-                      .format(tracker=param_focus_tracker))
+        communication_pipe.recv()
+    )
+    debug_list.append(
+        f"Called with param_focus_tracker {param_focus_tracker!r}"
+    )
 
     def trace(name, value):
         """
@@ -210,7 +213,7 @@ def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
         :type value: object
         """
         if trace_enabled:
-            trace_list.append(name + " " + repr(value))
+            trace_list.append(f"{name} {value!r}")
 
     value_logweight_function = dill.loads(dilled_function)
     samples = 0
@@ -235,33 +238,39 @@ def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
             break
         sample_point = generate_sample(
             param_focus_tracker.averages, param_focus_tracker.covariance_matrix,
-            dimension, scale_coeff)
-        trace("sample_point", sample_point)
+            dimension, scale_coeff
+        )
+        trace(u"sample_point", sample_point)
         samples += 1
-        trace("samples", samples)
+        trace(u"samples", samples)
         value, log_weight = value_logweight_function(trace, *sample_point)
-        trace("value", value)
-        trace("log_weight", log_weight)
-        trace("focus tracker before adding", param_focus_tracker)
+        trace(u"value", value)
+        trace(u"log_weight", log_weight)
+        trace(u"focus tracker before adding", param_focus_tracker)
         # Update focus related statistics.
         param_distance = param_focus_tracker.add_without_dominance_get_distance(
-            sample_point, log_weight)
+            sample_point, log_weight
+        )
         # The code above looked at weight (not importance).
         # The code below looks at importance (not weight).
         log_rarity = param_distance / 2.0
-        trace("log_rarity", log_rarity)
+        trace(u"log_rarity", log_rarity)
         log_importance = log_weight + log_rarity
-        trace("log_importance", log_importance)
+        trace(u"log_importance", log_importance)
         value_tracker.add(value, log_importance)
         # Update sampled statistics.
         param_sampled_tracker.add_get_shift(sample_point, log_importance)
-    debug_list.append("integrator used " + str(samples) + " samples")
-    debug_list.append(" ".join([
-        "value_avg", str(value_tracker.average),
-        "param_sampled_avg", repr(param_sampled_tracker.averages),
-        "param_sampled_cov", repr(param_sampled_tracker.covariance_matrix),
-        "value_log_variance", str(value_tracker.log_variance),
-        "value_log_secondary_variance",
-        str(value_tracker.secondary.log_variance)]))
+    debug_list.append(f"integrator used {samples!s} samples")
+    debug_list.append(
+        u" ".join([
+            u"value_avg", str(value_tracker.average),
+            u"param_sampled_avg", repr(param_sampled_tracker.averages),
+            u"param_sampled_cov", repr(param_sampled_tracker.covariance_matrix),
+            u"value_log_variance", str(value_tracker.log_variance),
+            u"value_log_secondary_variance",
+            str(value_tracker.secondary.log_variance)
+        ])
+    )
     communication_pipe.send(
-        (value_tracker, param_focus_tracker, debug_list, trace_list, samples))
+        (value_tracker, param_focus_tracker, debug_list, trace_list, samples)
+    )