Framework: Refactor complex functions in PLRSearch

Signed-off-by: Miroslav Los <miroslav.los@pantheon.tech>
Change-Id: Ie2f19a2e3b37e8d85656ab31ece59b89c76bea25

1 files changed, 24 insertions, 21 deletions

diff --git a/resources/libraries/python/PLRsearch/Integrator.py b/resources/libraries/python/PLRsearch/Integrator.py
index 82abe5f8a3..035afd848c 100644
--- a/resources/libraries/python/PLRsearch/Integrator.py
+++ b/resources/libraries/python/PLRsearch/Integrator.py
@@ -45,9 +45,24 @@ def try_estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
         raise
 
 
-# TODO: Pylint reports multiple complexity violations.
-# Refactor the code, using less (but structured) variables
-# and function calls for (relatively) loosly coupled code blocks.
+def generate_sample(averages, covariance_matrix, dimension, scale_coeff):
+    """Generate next sample for estimate_nd"""
+    covariance_matrix = copy.deepcopy(covariance_matrix)
+    for first in range(dimension):
+        for second in range(dimension):
+            covariance_matrix[first][second] *= scale_coeff
+    while 1:
+        sample_point = random.multivariate_normal(
+            averages, covariance_matrix, 1)[0].tolist()
+        # Multivariate Gauss can fall outside (-1, 1) interval
+        for first in range(dimension):
+            sample_coordinate = sample_point[first]
+            if sample_coordinate <= -1.0 or sample_coordinate >= 1.0:
+                break
+        else:
+            return sample_point
+
+
 def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
     """Use Bayesian inference from control queue, put result to result queue.
 
@@ -148,6 +163,7 @@ def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
         communication_pipe.recv())
     debug_list.append("Called with param_focus_tracker {tracker!r}"
                       .format(tracker=param_focus_tracker))
+
     def trace(name, value):
         """
         Add a variable (name and value) to trace list (if enabled).
@@ -163,6 +179,7 @@ def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
         """
         if trace_enabled:
             trace_list.append(name + " " + repr(value))
+
     value_logweight_function = dill.loads(dilled_function)
     samples = 0
     # Importance sampling produces samples of higher weight (important)
@@ -180,28 +197,14 @@ def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
     else:
         # Focus tracker has probably too high weight.
         param_focus_tracker.log_sum_weight = None
-    # TODO: Teach pylint the used version of numpy.random does have this member.
     random.seed(0)
     while not communication_pipe.poll():
         if max_samples and samples >= max_samples:
             break
-        # Generate next sample.
-        averages = param_focus_tracker.averages
-        covariance_matrix = copy.deepcopy(param_focus_tracker.covariance_matrix)
-        for first in range(dimension):
-            for second in range(dimension):
-                covariance_matrix[first][second] *= scale_coeff
-        while 1:
-            # TODO: Teach pylint that numpy.random does also have this member.
-            sample_point = random.multivariate_normal(
-                averages, covariance_matrix, 1)[0].tolist()
-            # Multivariate Gauss can fall outside (-1, 1) interval
-            for first in range(dimension):
-                sample_coordinate = sample_point[first]
-                if sample_coordinate <= -1.0 or sample_coordinate >= 1.0:
-                    break
-            else:  # These two breaks implement "level two continue".
-                break
+        sample_point = generate_sample(param_focus_tracker.averages,
+                                       param_focus_tracker.covariance_matrix,
+                                       dimension,
+                                       scale_coeff)
         trace("sample_point", sample_point)
         samples += 1
         trace("samples", samples)