Release 2.3.4 (#88)

* Extend the reporting if time series tolerances are exceeded and add the option to silence them with a tolerance value.

* set default tolerance value to 1e-13

---------

Co-authored-by: julian-belina <56728940+julian-belina@users.noreply.github.com>

Author: l-kotzur

.github/workflows/daily_tests.yml

@@ -1,6 +1,11 @@
 # This tests are run daily to check incompatibilties introduced by new versions of dependencies
 name: Daily tsam tests
-on: 
+on:
+  # Enables manual start
+  workflow_dispatch:
+    inputs:
+      tags:
+        description: 'Manual run' 
   schedule:
     # * is a special character in YAML so you have to quote this string
     # Some Examples for cron syntax https://crontab.guru/examples.html
@@ -10,8 +15,8 @@ on:
     # - cron: 0 0 * * 0
 
 jobs:
-  PythonAndOsTest:
-    name: Test for Python ${{matrix.python-version}} on ${{matrix.os}}  
+  DailyPythonAndOsTest:
+    name: Daily tests for Python ${{matrix.python-version}} on ${{matrix.os}}  
     runs-on: ${{matrix.os}}
     strategy:
       fail-fast: false

Makefile

@@ -6,16 +6,10 @@ LOCAL_VENV_DIR := ${LOCAL_VENVS_DIR}/${PROJECT_NAME}
 
 
 test:
-	@( \
-		source ${LOCAL_VENV_DIR}/bin/activate; \
-		pytest
-	)
+	. ${LOCAL_VENV_DIR}/bin/activate; pytest
 
 sdist:
-	@( \
-		source ${LOCAL_VENV_DIR}/bin/activate; \
-		${PYTHON} setup.py sdist
-	)
+	. ${LOCAL_VENV_DIR}/bin/activate; ${PYTHON} setup.py sdist
 
 upload:
 	twine upload dist/*

setup.py

@@ -8,7 +8,7 @@
 
 setuptools.setup(
     name="tsam",
-    version="2.3.3",
+    version="2.3.4",
     author="Leander Kotzur, Maximilian Hoffmann",
     author_email="leander.kotzur@googlemail.com, maximilian.hoffmann@julumni.fz-juelich.de",
     description="Time series aggregation module (tsam) to create typical periods",

tsam/timeseriesaggregation.py

@@ -136,6 +136,7 @@ def __init__(
         predefClusterOrder=None,
         predefClusterCenterIndices=None,
         solver="highs",
+        numericalTolerance=1e-13,
         roundOutput=None,
         addPeakMin=None,
         addPeakMax=None,
@@ -254,6 +255,10 @@ def __init__(
         :param solver: Solver that is used for k_medoids clustering. optional (default: 'cbc' )
         :type solver: string
 
+        :param numericalTolerance: Tolerance for numerical issues. Silences the warning for exceeding upper or lower bounds
+            of the time series. optional (default: 1e-13 )
+        :type numericalTolerance: float
+
         :param roundOutput: Decimals to what the output time series get round. optional (default: None )
         :type roundOutput: integer
 
@@ -321,6 +326,8 @@ def __init__(
 
         self.solver = solver
 
+        self.numericalTolerance = numericalTolerance
+
         self.segmentation = segmentation
 
         self.roundOutput = roundOutput
@@ -1097,23 +1104,29 @@ def createTypicalPeriods(self):
         if np.array(
             self.typicalPeriods.max(axis=0) > self.timeSeries.max(axis=0)
         ).any():
-            warning_list = self.typicalPeriods.max(axis=0) < self.timeSeries.max(axis=0)
-            warnings.warn(
-                "Something went wrong... At least one maximal value of the " + 
-                "aggregated time series exceeds the maximal value " + 
-                "the input time series for: " + 
-                "{}".format(list(warning_list[warning_list>0].index))
-            )
+            warning_list = self.typicalPeriods.max(axis=0) > self.timeSeries.max(axis=0)
+            diff = self.typicalPeriods.max(axis=0) - self.timeSeries.max(axis=0)
+            if abs(diff).max() > self.numericalTolerance:
+                warnings.warn(
+                    "At least one maximal value of the " + 
+                    "aggregated time series exceeds the maximal value " + 
+                    "the input time series for: " + 
+                    "{}".format(diff[warning_list[warning_list>0].index].to_dict()) +
+                    ". To silence the warning set the 'numericalTolerance' to a higher value."
+                )
         if np.array(
             self.typicalPeriods.min(axis=0) < self.timeSeries.min(axis=0)
         ).any():
             warning_list = self.typicalPeriods.min(axis=0) < self.timeSeries.min(axis=0)
-            warnings.warn(
-                "Something went wrong... At least one minimal value of the " + 
-                "aggregated time series exceeds the minimal value " + 
-                "the input time series for: " + 
-                "{}".format(list(warning_list[warning_list>0].index))
-            )
+            diff = self.typicalPeriods.min(axis=0) - self.timeSeries.min(axis=0)
+            if abs(diff).max() > self.numericalTolerance:
+                warnings.warn(
+                    "Something went wrong... At least one minimal value of the " + 
+                    "aggregated time series exceeds the minimal value " + 
+                    "the input time series for: " + 
+                        "{}".format(diff[warning_list[warning_list>0].index].to_dict()) +
+                        ". To silence the warning set the 'numericalTolerance' to a higher value."
+                )
         return self.typicalPeriods
 
     def prepareEnersysInput(self):