Type hints and other fixes

README.rst

@@ -31,7 +31,7 @@ Dispatch can be installed and used in it's own environment or installed into ano
 environment using pip. To create an environment for Dispatch, navigate to the repo
 folder in terminal and run:
 
-.. code-block:: console
+.. code-block:: bash
 
    $ mamba update mamba
    $ mamba env create --name dispatch --file environment.yml
@@ -40,7 +40,7 @@ If you get a ``CondaValueError`` that the prefix already exists, that means an
 environment with the same name already exists. You must remove the old one before
 creating the new one:
 
-.. code-block:: console
+.. code-block:: bash
 
    $ mamba update mamba
    $ mamba env remove --name dispatch

src/dispatch/model.py

@@ -4,6 +4,7 @@
 import inspect
 import json
 import logging
+from collections.abc import Callable
 from datetime import datetime
 from importlib.metadata import version
 from io import BytesIO
@@ -155,7 +156,7 @@ def __init__(
         )
         self.starts = MTDF.reindex(columns=self.dispatchable_specs.index)
 
-    def add_total_costs(self, df):
+    def add_total_costs(self, df: pd.DataFrame) -> pd.DataFrame:
         """Add columns for total FOM and total startup from respective unit costs."""
         df = (
             df.reset_index()
@@ -172,7 +173,7 @@ def add_total_costs(self, df):
         return df.drop(columns=["capacity_mw"])
 
     @classmethod
-    def from_file(cls, path: Path | str):
+    def from_file(cls, path: Path | str) -> DispatchModel:
         """Recreate an instance of :class:`.DispatchModel` from disk."""
         if not isinstance(path, Path):
             path = Path(path)
@@ -289,12 +290,12 @@ def from_fresh(
         )
 
     @property
-    def dispatch_func(self):
+    def dispatch_func(self) -> Callable:
         """Appropriate dispatch engine depending on ``jit`` setting."""
         return dispatch_engine_compiled if self._metadata["jit"] else dispatch_engine
 
     @property
-    def is_redispatch(self):
+    def is_redispatch(self) -> bool:
         """True if this is redispatch, i.e. has meaningful historical dispatch."""
         # more than 2 unique values are required because any plant that begins
         # operation during the period will have both 0 and its capacity
@@ -589,7 +590,7 @@ def storage_durations(self) -> pd.DataFrame:
             axis=1,
         )
 
-    def system_level_summary(self, freq="YS", **kwargs):
+    def system_level_summary(self, freq: str = "YS", **kwargs) -> pd.DataFrame:
         """Create system and storage summary metrics."""
         out = pd.concat(
             [
@@ -648,9 +649,9 @@ def system_level_summary(self, freq="YS", **kwargs):
     def re_summary(
         self,
         by: str | None = "technology_description",
-        freq="YS",
+        freq: str = "YS",
         **kwargs,
-    ):
+    ) -> pd.DataFrame:
         """Create granular summary of renewable plant metrics."""
         if self.re_profiles is None or self.re_plant_specs is None:
             raise AssertionError(
@@ -681,12 +682,12 @@ def re_summary(
     def storage_summary(
         self,
         by: str | None = "technology_description",
-        freq="YS",
+        freq: str = "YS",
         **kwargs,
-    ):
+    ) -> pd.DataFrame:
         """Create granular summary of storage plant metrics."""
         out = (
-            self.storage_dispatch.groupby([pd.Grouper(freq="YS")])
+            self.storage_dispatch.groupby([pd.Grouper(freq=freq)])
             .sum()
             .stack()
             .reset_index()
@@ -716,7 +717,7 @@ def storage_summary(
             return out.set_index(["plant_id_eia", "generator_id", "datetime"])
         return out.groupby([by, "datetime"]).sum()
 
-    def full_output(self, freq="YS"):
+    def full_output(self, freq: str = "YS") -> pd.DataFrame:
         """Create full operations output."""
         cols = [
             "plant_name_eia",
@@ -758,9 +759,9 @@ def full_output(self, freq="YS"):
     def dispatchable_summary(
         self,
         by: str | None = "technology_description",
-        freq="YS",
+        freq: str = "YS",
         **kwargs,
-    ):
+    ) -> pd.DataFrame:
         """Create granular summary of dispatchable plant metrics.
 
         Args:
@@ -841,7 +842,7 @@ def to_file(
         compression=ZIP_DEFLATED,
         clobber=False,
         **kwargs,
-    ):
+    ) -> None:
         """Save :class:`.DispatchModel` to disk.
 
         A very ugly process at the moment because of our goal not to use pickle