change list[] to List[] to allow for compatibility of type hinting py…

…thon<3.9

pastas/model.py

@@ -26,7 +26,7 @@
 from pastas.version import __version__
 
 # Type Hinting
-from pastas.typing import Type, Union, Optional, Tuple, pstTm, pstSM, pstNM, pstBS, pstAL, pstMl
+from pastas.typing import Type, Union, Optional, Tuple, List, pstTm, pstSM, pstNM, pstBS, pstAL, pstMl
 
 
 class Model:
@@ -1189,7 +1189,7 @@ def get_contribution(self, name: str, tmin: Optional[pstTm] = None, tmax: Option
 
         return contrib
 
-    def get_contributions(self, split: Optional[bool] = True, **kwargs) -> list[Type[Series]]:
+    def get_contributions(self, split: Optional[bool] = True, **kwargs) -> List[Type[Series]]:
         """Method to get contributions of all stressmodels.
 
         Parameters
@@ -1449,7 +1449,7 @@ def get_response_tmax(self, name: str, p: pstAL = None, cutoff: Optional[float]
 
     @get_stressmodel
     def get_stress(self, name: str, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None, freq: Optional[str] = None, warmup: Optional[float] = None,
-                   istress: Optional[int] = None, return_warmup: Optional[bool] = False, p: Optional[pstAL] = None) -> Union[Type[Series], list[Type[Series]]]:
+                   istress: Optional[int] = None, return_warmup: Optional[bool] = False, p: Optional[pstAL] = None) -> Union[Type[Series], List[Type[Series]]]:
         """Method to obtain the stress(es) from the stressmodel.
 
         Parameters

pastas/modelplots.py

@@ -15,7 +15,7 @@
     _table_formatter_params, _table_formatter_stderr
 
 # Type Hinting
-from pastas.typing import Type, Optional, pstAx, pstFi, pstTm, pstMl
+from pastas.typing import Type, Optional, List, pstAx, pstFi, pstTm, pstMl
 
 logger = logging.getLogger(__name__)
 
@@ -501,7 +501,7 @@ def cum_frequency(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = No
         obs = self.ml.observations(tmin=tmin, tmax=tmax)
         return cum_frequency(obs, sim, ax=ax, figsize=figsize, **kwargs)
 
-    def block_response(self, stressmodels: Optional[list[str]] = None, ax: Optional[pstAx] = None, figsize: Optional[tuple] = None,
+    def block_response(self, stressmodels: Optional[List[str]] = None, ax: Optional[pstAx] = None, figsize: Optional[tuple] = None,
                        **kwargs) -> pstAx:
         """Plot the block response for a specific stressmodels.
 
@@ -540,7 +540,7 @@ def block_response(self, stressmodels: Optional[list[str]] = None, ax: Optional[
         plt.legend(legend)
         return ax
 
-    def step_response(self, stressmodels: Optional[list[str]] = None, ax: Optional[pstAx] = None, figsize: Optional[tuple] = None,
+    def step_response(self, stressmodels: Optional[List[str]] = None, ax: Optional[pstAx] = None, figsize: Optional[tuple] = None,
                       **kwargs) -> pstAx:
         """Plot the step response for a specific stressmodels.
 
@@ -853,7 +853,7 @@ def summary_pdf(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None
         return fig
 
 
-def _get_height_ratios(ylims: list[list, tuple]) -> list[float]:
+def _get_height_ratios(ylims: List[list, tuple]) -> List[float]:
     height_ratios = []
     for ylim in ylims:
         hr = ylim[1] - ylim[0]
@@ -863,7 +863,7 @@ def _get_height_ratios(ylims: list[list, tuple]) -> list[float]:
     return height_ratios
 
 
-def _get_stress_series(ml, split: Optional[bool] = True) -> list[Type[Series]]:
+def _get_stress_series(ml, split: Optional[bool] = True) -> List[Type[Series]]:
     stresses = []
     for name in ml.stressmodels.keys():
         nstress = len(ml.stressmodels[name].stress)

pastas/modelstats.py

@@ -285,7 +285,7 @@ def aic(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None) -> flo
         return metrics.aic(res=res, nparam=nparam)
 
     @model_tmin_tmax
-    def summary(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None, stats: Optional[list[str]] = None) -> Type[DataFrame]:
+    def summary(self, tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None, stats: Optional[List[str]] = None) -> Type[DataFrame]:
         """Returns a Pandas DataFrame with goodness-of-fit metrics.
 
         Parameters

pastas/plots.py

@@ -15,7 +15,7 @@
 from .modelcompare import CompareModels
 
 # Type Hinting
-from pastas.typing import Type, Optional, pstAL, pstAx, pstFi, pstTm, pstMl
+from pastas.typing import Type, Optional, List, pstAL, pstAx, pstFi, pstTm, pstMl
 
 
 logger = logging.getLogger(__name__)
@@ -24,7 +24,7 @@
            "TrackSolve"]
 
 
-def compare(models: list[pstMl], adjust_height: Optional[bool] = True, **kwargs) -> pstAx:
+def compare(models: List[pstMl], adjust_height: Optional[bool] = True, **kwargs) -> pstAx:
     """Plot multiple Pastas models in one figure to visually compare models.
 
     Note
@@ -56,8 +56,8 @@ def compare(models: list[pstMl], adjust_height: Optional[bool] = True, **kwargs)
     return mc.axes
 
 
-def series(head: Optional[Type[Series]] = None, stresses: Optional[list[Type[Series]]] = None, hist: Optional[bool] = True, kde: Optional[bool] = False, titles: Optional[bool] = True,
-           tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None, labels: Optional[list[str]] = None, figsize: Optional[tuple] = (10, 5)) -> pstAx:
+def series(head: Optional[Type[Series]] = None, stresses: Optional[List[Type[Series]]] = None, hist: Optional[bool] = True, kde: Optional[bool] = False, titles: Optional[bool] = True,
+           tmin: Optional[pstTm] = None, tmax: Optional[pstTm] = None, labels: Optional[List[str]] = None, figsize: Optional[tuple] = (10, 5)) -> pstAx:
     """Plot all the input time series in a single plot.
 
     Parameters
@@ -785,7 +785,7 @@ def plot_track_solve(self, params: pstAL):
         plt.pause(1e-10)
         self.fig.canvas.draw()
 
-    def plot_track_solve_history(self, fig: Optional[pstFi] = None) -> list[pstAx]:
+    def plot_track_solve_history(self, fig: Optional[pstFi] = None) -> List[pstAx]:
         """Plot optimization history.
 
         Parameters

pastas/stressmodels.py

@@ -30,7 +30,7 @@
 
 
 # Type Hinting
-from pastas.typing import Type, Optional, Tuple, Union, pstAL, pstRF, pstRB, pstRV, pstTm, pstMl
+from pastas.typing import Type, Optional, Tuple, List, Union, pstAL, pstRF, pstRB, pstRV, pstTm, pstMl
 
 logger = getLogger(__name__)
 
@@ -539,7 +539,7 @@ class WellModel(StressModelBase):
     """
     _name = "WellModel"
 
-    def __init__(self, stress: list[Type[Series]], rfunc: pstRF, name: str, distances: pstAL, up: Optional[bool] = False, cutoff: Optional[float] = 0.999,
+    def __init__(self, stress: List[Type[Series]], rfunc: pstRF, name: str, distances: pstAL, up: Optional[bool] = False, cutoff: Optional[float] = 0.999,
                  settings: Optional[str] = "well", sort_wells: Optional[bool] = True):
         if not (isinstance(rfunc, HantushWellModel) or
                 issubclass(rfunc, HantushWellModel)):

pastas/typing/__init__.py

@@ -1 +1 @@
-from .typeh import Type, Union, Optional, Tuple, Any, pstAx, pstFi, pstTm, pstMl, pstTS, pstSM, pstNM, pstBS, pstRB, pstRV, pstCB, pstFu, pstRF, pstAL
+from .typeh import Type, Union, Optional, Tuple, List, Any, pstAx, pstFi, pstTm, pstMl, pstTS, pstSM, pstNM, pstBS, pstRB, pstRV, pstCB, pstFu, pstRF, pstAL

pastas/typing/typeh.py

@@ -23,7 +23,7 @@
 # Numpy
 from numpy.typing import ArrayLike
 # Typing
-from typing import Type, Union, Optional, Tuple, Any, TypeVar
+from typing import Type, Union, Optional, Tuple, List, Any, TypeVar
 
 pstAx = TypeVar("pstAx", bound=_AxesBase)  # Matplotlib Axes
 pstFi = TypeVar("pstFi", bound=FigureBase)  # Matplotlib Figure