From 7771196fc6d2fbebdf8dc46c74e4281a2e58a792 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Oriol=20Tint=C3=B3?= <oriol.tinto@lmu.de>
Date: Wed, 14 Jun 2023 15:35:56 +0200
Subject: [PATCH] For the analysis, using the time average of any given metric.
---
.../compression/analyzer/analyzer_utils.py | 24 ++++++++++++++-----
1 file changed, 18 insertions(+), 6 deletions(-)
diff --git a/enstools/compression/analyzer/analyzer_utils.py b/enstools/compression/analyzer/analyzer_utils.py
index dc2697b..a1c7870 100644
--- a/enstools/compression/analyzer/analyzer_utils.py
+++ b/enstools/compression/analyzer/analyzer_utils.py
@@ -6,6 +6,7 @@ import logging
from typing import List, Dict
import xarray
+import numpy as np
from enstools.compression.analyzer.analysis_options import AnalysisOptions
from enstools.compression.metrics import DataArrayMetrics
@@ -23,7 +24,8 @@ def get_metrics(reference_data: xarray.DataArray, recovered_data: xarray.DataArr
:return: a dictionary with the requested metrics
"""
metrics = DataArrayMetrics(reference_data, recovered_data)
- return {metric: float(metrics[metric]) for metric in metric_names if metric != "compression_ratio"}
+ # TODO: Is the average the proper thing to use here?
+ return {metric: float(np.average(metrics[metric])) for metric in metric_names if metric != "compression_ratio"}
def check_compression_ratio(compression_ratio: float, thresholds: dict):
@@ -228,6 +230,7 @@ def discrete_bisection_method(parameters_list: list,
retry_repeated=5,
threshold=0.1,
direct_relation=True,
+ results=None,
):
"""
Apply the bisection method on a set of discrete parameters.
@@ -248,6 +251,10 @@ def discrete_bisection_method(parameters_list: list,
:raises: Exception if the maximum depth is reached.
"""
+
+ if results is None:
+ results = {}
+
middle_index = len(parameters_list) // 2
middle = parameters_list[middle_index]
@@ -260,13 +267,20 @@ def discrete_bisection_method(parameters_list: list,
middle,
float(value_at_middle))
+ results[middle] = value_at_middle
+
# If the value at the middle is positive (all thresholds are fulfilled) we can return the parameter at the middle,
# otherwise select the safer one.
parameter_to_return = middle if value_at_middle > 0.0 else parameters_list[-1]
# In case the accuracy exit condition is reached, return the parameter value at that point
- if 0.0 <= value_at_middle < threshold:
- return parameter_to_return
+ if 0.0 <= value_at_middle < threshold or depth >= max_depth or\
+ (value_at_middle == last_value and retry_repeated == 0):
+ positive_results = {k: v for k,v in results.items() if v > 0}
+ if positive_results:
+ return min(positive_results, key=positive_results.get)
+ else:
+ return middle
# If the value is the same that the last try, we can retry few times
if value_at_middle == last_value:
@@ -274,9 +288,6 @@ def discrete_bisection_method(parameters_list: list,
return parameter_to_return
retry_repeated -= 1
- # In case having reached the maximum depth, return the proper value
- if depth >= max_depth:
- return parameter_to_return
# # Otherwise, set new parameter range and call the function again
if comparison(value_at_middle, direct_relation=direct_relation):
@@ -293,4 +304,5 @@ def discrete_bisection_method(parameters_list: list,
retry_repeated=retry_repeated,
threshold=threshold,
direct_relation=direct_relation,
+ results=results,
)
--
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