Minimizing the warnigns on create recipe

Author: tainagdcoleman

wfcommons/utils.py

@@ -7,13 +7,15 @@
 # it under the terms of the GNU General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
+import warnings
+warnings.filterwarnings('ignore')
 
 import json
 import logging
 import math
 import pathlib
 import scipy.stats
-import warnings
+
 import numpy as np
 import operator as op
 
@@ -23,6 +25,7 @@
 from typing import Any, Dict, Optional, List, Tuple
 
 
+
 class NoValue(Enum):
     def __repr__(self):
         return '<%s.%s>' % (self.__class__.__name__, self.name)
@@ -82,8 +85,6 @@ def best_fit_distribution(data: List[float], logger: Optional[Logger] = None) ->
     for dist_name in distribution_names:
         # Ignore warnings from data that can't be fit
         with warnings.catch_warnings():
-            warnings.filterwarnings('ignore')
-
             try:
                 distribution = getattr(scipy.stats, dist_name)
                 params = distribution.fit(y)
@@ -98,7 +99,7 @@ def best_fit_distribution(data: List[float], logger: Optional[Logger] = None) ->
                     best_params = params
                     best_sse = sse
             except Exception as e:
-                print(f"WARNING: distribution \"{dist_name}\" failed ({e})")
+                logger.warning(f"WARNING: distribution \"{dist_name}\" failed ({e})")
 
     logger.debug(f'Best distribution fit: {best_distribution}')
     return best_distribution, best_params

wfcommons/wfinstances/instance_analyzer.py

@@ -12,6 +12,7 @@
 import math
 import numpy
 import scipy.stats
+import warnings
 
 from logging import Logger
 from matplotlib import pyplot
@@ -207,14 +208,16 @@ def _best_fit_distribution_for_file(dict_obj, include_raw_data) -> None:
     :param include_raw_data:
     :type include_raw_data: bool
     """
-    for ext in dict_obj:
-        dict_obj[ext]['min'] = min(dict_obj[ext]['data'])
-        dict_obj[ext]['max'] = max(dict_obj[ext]['data'])
-        if dict_obj[ext]['min'] != dict_obj[ext]['max']:
-            dict_obj[ext]['distribution'] = _json_format_distribution_fit(
-                best_fit_distribution(dict_obj[ext]['data']))
-        if not include_raw_data:
-            del dict_obj[ext]['data']
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        for ext in dict_obj:
+            dict_obj[ext]['min'] = min(dict_obj[ext]['data'])
+            dict_obj[ext]['max'] = max(dict_obj[ext]['data'])
+            if dict_obj[ext]['min'] != dict_obj[ext]['max']:
+                dict_obj[ext]['distribution'] = _json_format_distribution_fit(
+                    best_fit_distribution(dict_obj[ext]['data']))
+            if not include_raw_data:
+                del dict_obj[ext]['data']
 
 
 def _json_format_distribution_fit(dist_tuple: Tuple) -> Dict[str, Any]: