Use Black for code formatting

.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+  "[python]": {
+    "editor.defaultFormatter": "ms-python.black-formatter",
+    "editor.formatOnSave": true
+  },
+  "black-formatter.args": ["--line-length", "100"]
+}

README.md

@@ -3,6 +3,7 @@
 Python files and Jupyter notebooks for processing the Annual Technology Baseline (ATB) electricity data and determining LCOE and other metrics. All documentation and data for the ATB is available at the [ATB website](https://atb.nrel.gov).
 
 ## Installation and Requirements
+
 The pipeline requires [Python](https://www.python.org) 3.10 or newer. Dependancies can be installed using `pip`:
 
 ```
@@ -21,6 +22,7 @@ $ pytest
 Tests take about a minute and should complete without errors. The ATB pipeline uses [xlwings](https://www.xlwings.org/) for accessing the ATB data workbook and requires a copy of Microsoft Excel. Currently the full pipeline will only run on MacOS and Windows. Linux support may be added in the future.
 
 ## Running the ATB Electricity Pipeline
+
 Running the pipeline requires downloaded the most current data in `xlsx` format from the
 [ATB website](https://atb.nrel.gov). The pipeline may be ran for one or all ATB electricity technologies.
 Data may be exported in several formats. Below are several example workflows. It is assumed that all
@@ -54,6 +56,7 @@ $ python -m lcoe_calculator.process_all --help
 ```
 
 ## Debt Fraction Calculator
+
 The debt fraction calculator uses [PySAM](https://nrel-pysam.readthedocs.io/en/main/) to calculate
 debt fractions for one or all ATB technologies. To calculate debt fractions for all technologies run
 the following from the repository root directory:
@@ -81,6 +84,7 @@ $ python -m debt_fraction_calculator.debt_fraction_calc --help
 ```
 
 ## Example Jupyter Notebooks
+
 The `./example_notebooks` directory has several [Jupyter](https://jupyter.org/) notebooks showing
 how to perform various tasks. The notebooks are a good way to understand how to use the code and
 experiment with the ATB pipeline code. Jupyter must first be installed before use:
@@ -98,14 +102,20 @@ $ jupyter-notebook
 in the root repository directory.
 
 ## Notable Directories
+
 - `./lcoe_calculator` Extract technology metrics from the data workbook `xlsx` file and calculate LCOE
-using Python.
+  using Python.
 - `./debt_fraction_calculator` Given data and assumptions in the data workbook xlsx file, calculate
-debt fractions using PySAM.
+  debt fractions using PySAM.
 - `./example_notebooks` Example Jupyter notebooks showing how to perform various operations.
 - `./tests` Tests for code in this repository.
 
-## Citing this package
+## Citing this Package
+
 Mirletz, Brian, Bannister, Michael, Vimmerstedt, Laura, Stright, Dana, and Heine, Matthew.
 "ATB-calc (Annual Technology Baseline Calculators) [SWR-23-60]." Computer software. August 02, 2023.
-https://github.com/NREL/ATB-calc. https://doi.org/10.11578/dc.20230914.2.
+https://github.com/NREL/ATB-calc. https://doi.org/10.11578/dc.20230914.2.
+
+## Code Formatting
+
+This project uses the [Black](https://black.readthedocs.io/en/stable/index.html) code formatting VS Code [plugin](https://marketplace.visualstudio.com/items?itemName=ms-python.black-formatter). Please install and run the plugin before making commits or a pull request.

lcoe_calculator/abstract_extractor.py

@@ -17,8 +17,15 @@ class AbstractExtractor(ABC):
     """
 
     @abstractmethod
-    def __init__(self, data_workbook_fname: str, sheet_name: str, case: str, crp: CrpChoiceType,
-                 scenarios: List[str], base_year: int):
+    def __init__(
+        self,
+        data_workbook_fname: str,
+        sheet_name: str,
+        case: str,
+        crp: CrpChoiceType,
+        scenarios: List[str],
+        base_year: int,
+    ):
         """
         @param data_workbook_fname - file name of data workbook
         @param sheet_name - name of sheet to process
@@ -29,8 +36,9 @@ def __init__(self, data_workbook_fname: str, sheet_name: str, case: str, crp: Cr
         """
 
     @abstractmethod
-    def get_metric_values(self, metric: str, num_tds: int, split_metrics: bool = False)\
-          -> pd.DataFrame:
+    def get_metric_values(
+        self, metric: str, num_tds: int, split_metrics: bool = False
+    ) -> pd.DataFrame:
         """
         Grab metric values table.
 
@@ -42,7 +50,7 @@ def get_metric_values(self, metric: str, num_tds: int, split_metrics: bool = Fal
 
     @abstractmethod
     def get_tax_credits(self) -> pd.DataFrame:
-        """ Get tax credit """
+        """Get tax credit"""
 
     @abstractmethod
     def get_cff(self, cff_name: str, rows: int) -> pd.DataFrame:
@@ -64,7 +72,9 @@ def get_fin_assump(self) -> pd.DataFrame:
         """
 
     @abstractmethod
-    def get_wacc(self, tech_name: str | None = None) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    def get_wacc(
+        self, tech_name: str | None = None
+    ) -> Tuple[pd.DataFrame, pd.DataFrame]:
         """
         Extract values for tech and case from WACC sheet.