update style to more modern python idioms and remove dead imports

python/ngen_cal/src/ngen/cal/__main__.py

@@ -20,7 +20,7 @@
 def _loaded_plugins(pm: PluginManager) -> str:
     from .utils import type_as_import_string
 
-    plugins: List[str] = []
+    plugins: list[str] = []
     for (name, plugin) in pm.list_name_plugin():
         if not name:
             continue
@@ -34,7 +34,7 @@ def _loaded_plugins(pm: PluginManager) -> str:
     return f"Plugins Loaded: {', '.join(plugins)}"
 
 
-def main(general: General, model_conf: "Mapping[str, Any]"):
+def main(general: General, model_conf: Mapping[str, Any]):
     #seed the random number generators if requested
     if general.random_seed is not None:
         import random
@@ -72,7 +72,7 @@ def main(general: General, model_conf: "Mapping[str, Any]"):
             print("Restart not supported for PSO search, starting at 0")
         func = pso_search
 
-    print("Starting Iteration: {}".format(start_iteration))
+    print(f"Starting Iteration: {start_iteration}")
     # print("Starting Best param: {}".format(meta.best_params))
     # print("Starting Best score: {}".format(meta.best_score))
     print("Starting calibration loop")

python/ngen_cal/src/ngen/cal/_hookspec.py

@@ -10,7 +10,6 @@
     from ngen.cal.configuration import General
     from ngen.cal.meta import JobMeta
     from pandas import Series
-    from pathlib import Path
 
 hookspec = pluggy.HookspecMarker(PROJECT_SLUG)
 

python/ngen_cal/src/ngen/cal/agent.py

@@ -1,19 +1,21 @@
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from ngen.cal.meta import JobMeta
 from ngen.cal.configuration import Model
 from ngen.cal.utils import pushd
 from pathlib import Path
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
-    from typing import Sequence, Optional, Mapping, Any
+    from typing import Sequence, Mapping, Any
     from pandas import DataFrame
     from pathlib import Path
     from ngen.cal.calibratable import Adjustable
 
 class BaseAgent(ABC):
 
     @property
-    def adjustables(self) -> 'Sequence[Adjustable]':
+    def adjustables(self) -> Sequence[Adjustable]:
         return self.model.adjustables
 
     def restart(self) -> int:
@@ -29,15 +31,15 @@ def restart(self) -> int:
 
     @property
     @abstractmethod
-    def model(self) -> 'Model':
+    def model(self) -> Model:
         pass
 
     @property
     @abstractmethod
-    def job(self) -> 'JobMeta':
+    def job(self) -> JobMeta:
         pass
 
-    def update_config(self, i: int, params: 'DataFrame', id: str):
+    def update_config(self, i: int, params: DataFrame, id: str):
         """
             For a given calibration iteration, i, update the input files/configuration to prepare for that iterations
             calibration run.
@@ -57,7 +59,7 @@ def best_params(self) -> str:
 
 class Agent(BaseAgent):
 
-    def __init__(self, model_conf, workdir: 'Path', log: bool=False, restart: bool=False, parameters: 'Optional[Mapping[str, Any]]' = {}):
+    def __init__(self, model_conf, workdir: Path, log: bool=False, restart: bool=False, parameters: Mapping[str, Any] | None = {}):
         self._workdir = workdir
         self._job = None
         if restart:
@@ -86,11 +88,11 @@ def __init__(self, model_conf, workdir: 'Path', log: bool=False, restart: bool=F
         self._params = parameters
 
     @property
-    def parameters(self) -> 'Mapping[str, Any]':
+    def parameters(self) -> Mapping[str, Any]:
         return self._params
 
     @property
-    def workdir(self) -> 'Path':
+    def workdir(self) -> Path:
         return self._workdir
 
     @property
@@ -106,9 +108,9 @@ def cmd(self) -> str:
         """
             Proxy method to build command from contained model binary and args
         """
-        return "{} {}".format(self.model.get_binary(), self.model.get_args())
+        return f"{self.model.get_binary()} {self.model.get_args()}"
 
-    def duplicate(self) -> 'Agent':
+    def duplicate(self) -> Agent:
         #serialize a copy of the model
         #FIXME ??? if you do self.model.resolve_paths() here, the duplicated agent
         #doesn't have fully qualified paths...but if you do it in constructor, it works fine...

python/ngen_cal/src/ngen/cal/calibratable.py

@@ -2,14 +2,14 @@
 
 from abc import ABC, abstractmethod
 from pandas import Series, read_parquet # type: ignore
-from typing import Optional, TYPE_CHECKING
+from typing import TYPE_CHECKING
 from pathlib import Path
 
 if TYPE_CHECKING:
     from pandas import DataFrame, Series
     from pathlib import Path
     from datetime import datetime
-    from typing import Tuple, Callable, Optional
+    from typing import Callable
     from ngen.cal.model import EvaluationOptions
     from ngen.cal.meta import JobMeta
 
@@ -18,11 +18,11 @@ class Adjustable(ABC):
         An Adjustable interface defning required properties for adjusting an object's state
     """
 
-    def __init__(self, df: Optional['DataFrame'] = None):
+    def __init__(self, df: DataFrame | None = None):
         self._df = df
 
     @property
-    def df(self) -> 'DataFrame':
+    def df(self) -> DataFrame:
         """
             A dataframe of the objects parameter values to calculate indexed relative to the variables
             being calibrated.  The columns of the dataframe will be appended to with each search iterations
@@ -46,14 +46,14 @@ def id(self) -> str:
         pass
 
     @property
-    def variables(self) -> 'Series':
+    def variables(self) -> Series:
         """
             Index series of variables
         """
         return Series(self.df.index.values)
 
     @property
-    def bounds(self) -> 'Tuple[Series, Series]':
+    def bounds(self) -> tuple[Series, Series]:
         """The bounds of each parameter that is adjustable
 
         Returns:
@@ -77,11 +77,11 @@ def update_params(self, iteration: int) -> None:
         pass
 
     @property
-    def check_point_file(self) -> 'Path':
+    def check_point_file(self) -> Path:
         """
             Filename checkpoint files are saved to
         """
-        return Path('{}_parameter_df_state.parquet'.format(self.id))
+        return Path(f'{self.id}_parameter_df_state.parquet')
 
     def check_point(self, iteration: int, info: JobMeta) -> None:
         """
@@ -90,7 +90,7 @@ def check_point(self, iteration: int, info: JobMeta) -> None:
         path = info.workdir
         self.df.to_parquet(path/self.check_point_file)
 
-    def load_df(self, path: 'Path') -> None:
+    def load_df(self, path: Path) -> None:
         """
             Load saved calibration information
         """
@@ -104,9 +104,9 @@ class Evaluatable(ABC):
         An Evaluatable interface defining required properties for a evaluating and object's state
     """
 
-    eval_params: 'EvaluationOptions'
+    eval_params: EvaluationOptions
 
-    def __init__(self, eval_params: 'EvaluationOptions', **kwargs):
+    def __init__(self, eval_params: EvaluationOptions, **kwargs):
         """
         Args:
             eval_params (EvaluationOptions): The options configuring this evaluatable
@@ -115,7 +115,7 @@ def __init__(self, eval_params: 'EvaluationOptions', **kwargs):
 
     @property
     @abstractmethod
-    def output(self) -> 'DataFrame':
+    def output(self) -> DataFrame:
         """
             The output data for the calibrated object
             Calibration re-reads the output each call, as the output for given calibration is expected to change
@@ -125,7 +125,7 @@ def output(self) -> 'DataFrame':
 
     @property
     @abstractmethod
-    def observed(self) -> 'DataFrame':
+    def observed(self) -> DataFrame:
         """
             The observed data for this calibratable.
             This should be rather static, and can be set at initialization then accessed via the property
@@ -134,15 +134,15 @@ def observed(self) -> 'DataFrame':
 
     @property
     @abstractmethod
-    def evaluation_range(self) -> 'Optional[Tuple[datetime, datetime]]':
+    def evaluation_range(self) -> tuple[datetime, datetime] | None:
         """
             The datetime range to evaluate the model results at.
             This should be a tuple in the form of (start_time, end_time).
         """
         pass
 
     @property
-    def objective(self, *args, **kwargs) -> 'Callable':
+    def objective(self, *args, **kwargs) -> Callable:
         """
             The objective function to compute cost values with.
 
@@ -191,5 +191,5 @@ class Calibratable(Adjustable, Evaluatable):
     """
         A Calibratable interface defining required properties for a calibratable object
     """
-    def __init__(self, df: Optional['DataFrame'] = None):
+    def __init__(self, df: DataFrame | None = None):
         Adjustable.__init__(self, df)

python/ngen_cal/src/ngen/cal/calibration_cathment.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from pandas import DataFrame, read_csv # type: ignore
 import shutil
 from typing import TYPE_CHECKING
@@ -7,7 +9,6 @@
     from pathlib import Path
     from geopandas import GeoSeries
     from datetime import datetime
-    from typing import Tuple, Optional
     from .model import EvaluationOptions
 
 from hypy.catchment import FormulatableCatchment # type: ignore
@@ -21,7 +22,7 @@ class AdjustableCatchment(FormulatableCatchment, Adjustable):
         parameteters used by the catchment.
     """
 
-    def __init__(self,  workdir: 'Path', id: str, nexus, params: dict = {}):
+    def __init__(self,  workdir: Path, id: str, nexus, params: dict = {}):
         """Create an adjustable catchment and initialize its parameter space
 
         Args:
@@ -34,7 +35,7 @@ def __init__(self,  workdir: 'Path', id: str, nexus, params: dict = {}):
         FormulatableCatchment.__init__(self=self, catchment_id=id, params=params, outflow=nexus)
         Adjustable.__init__(self=self, df=DataFrame(params).rename(columns={'init': '0'}))
         #FIXME paramterize
-        self._output_file = workdir/'{}.csv'.format(self.id)
+        self._output_file = workdir/f'{self.id}.csv'
         self._workdir = workdir
 
     def save_output(self, i) -> None:
@@ -43,7 +44,7 @@ def save_output(self, i) -> None:
         """
         #FIXME ensure _output_file exists
         #FIXME re-enable this once more complete
-        shutil.move(self._output_file, '{}_last'.format(self._output_file))
+        shutil.move(self._output_file, f'{self._output_file}_last')
 
     #update handled in meta, TODO remove this method???
     def update_params(self, iteration: int) -> None:
@@ -54,7 +55,7 @@ class EvaluatableCatchment(Evaluatable):
         A catchment which is "observable" which means model output can be evaluated against
         these observations for this catchment.
     """
-    def __init__(self, nexus: Nexus, start_time: str, end_time: str, fabric: "GeoSeries", output_var: str, eval_params: 'EvaluationOptions'):
+    def __init__(self, nexus: Nexus, start_time: str, end_time: str, fabric: GeoSeries, output_var: str, eval_params: EvaluationOptions):
         """Initialize the evaluatable catchment
 
         Args:
@@ -81,11 +82,11 @@ def __init__(self, nexus: Nexus, start_time: str, end_time: str, fabric: "GeoSer
         self._eval_range = self.eval_params._eval_range
 
     @property
-    def evaluation_range(self) -> 'Optional[Tuple[datetime, datetime]]':
+    def evaluation_range(self) -> tuple[datetime, datetime] | None:
         return self._eval_range
 
     @property
-    def output(self) -> 'DataFrame':
+    def output(self) -> DataFrame:
         """
             The model output hydrograph for this catchment
             This re-reads the output file each call, as the output for given calibration catchment changes
@@ -112,13 +113,13 @@ def output(self, df):
         self._output = df
 
     @property
-    def observed(self) -> 'DataFrame':
+    def observed(self) -> DataFrame:
         """
             The observed hydrograph for this catchment FIXME move output/observed to calibratable?
         """
         hydrograph = self._observed
         if hydrograph is None:
-            raise(RuntimeError("Error reading observation for {}".format(self._id)))
+            raise(RuntimeError(f"Error reading observation for {self._id}"))
         return hydrograph
 
     @observed.setter
@@ -129,7 +130,7 @@ class CalibrationCatchment(AdjustableCatchment, EvaluatableCatchment):
     """
         A Calibratable interface defining required properties for a calibratable object
     """
-    def __init__(self, workdir: str, id: str, nexus: Nexus, start_time: str, end_time: str, fabric: "GeoSeries", output_var: str, eval_params: 'EvaluationOptions', params: dict = {}):
+    def __init__(self, workdir: str, id: str, nexus: Nexus, start_time: str, end_time: str, fabric: GeoSeries, output_var: str, eval_params: EvaluationOptions, params: dict = {}):
         EvaluatableCatchment.__init__(self, nexus, start_time, end_time, fabric, output_var, eval_params)
         AdjustableCatchment.__init__(self,  workdir, id, nexus, params)