OpEx Breakdown Function (#119)

* Update post_processor.py OpEx breakdown function * eliminate extra year * delete nan years line * make existing opex() method make the breakdown * Opex breakdown in Metrics.opex() * add CHANGELOG --------- Co-authored-by: dmulash <[email protected]>
WISDEM · Oct 26, 2023 · f1c85da · f1c85da
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -20,6 +20,7 @@
   - `plot_detailed_availability` plots a heatmap of the hourly turbine and farm operational levels to help in debugging simulations where availability may be running suspiciously low (i.e., a turbine might have shut down or a cable failure didn't get repaired within a reasonable time frame).
 - `Simulation.service_equipment` is now a dictionary to provide clear access to the servicing equipment details.
 - `Simulation.from_config()` requires both a library input and a file name input for the configuration after deprecating the `library` field from the configuration requirements due to it being cumbersome
+- `Metrics.opex()` provides the opex output by category if `by_category` is set to be `True`
 
 ### Methodology Updates
 

diff --git a/wombat/core/post_processor.py b/wombat/core/post_processor.py
@@ -1915,7 +1915,7 @@ def project_fixed_costs(self, frequency: str, resolution: str) -> pd.DataFrame:
 
         return costs
 
-    def opex(self, frequency: str) -> pd.DataFrame:
+    def opex(self, frequency: str, by_category: bool = False) -> pd.DataFrame:
         """Calculates the project's OpEx for the simulation at a project, annual, or
         monthly level.
 
@@ -1924,10 +1924,14 @@ def opex(self, frequency: str) -> pd.DataFrame:
         frequency : str
             One of project, annual, monthly, or month-year.
 
+        by_category : bool, optional
+            Indicates whether the values are with resepect to the various cost
+            categories (True) or not (False), by default False.
+
         Returns
         -------
         pd.DataFrame
-            The project's OpEx broken out at the desired time resolution.
+            The project's OpEx broken out at the desired time and category resolution.
         """
         frequency = _check_frequency(frequency, which="all")
 
@@ -1967,6 +1971,8 @@ def opex(self, frequency: str) -> pd.DataFrame:
         column = "OpEx"
         opex = pd.concat(opex_items, axis=1)
         opex.loc[:, column] = opex.sum(axis=1)
+        if by_category:
+            return opex
         return opex[[column]]
 
     def process_times(self) -> pd.DataFrame: