silence HantushWellModel info msg about r=1.0

pastas/model.py

@@ -1547,7 +1547,11 @@ def get_step_response(
 
     @get_stressmodel
     def get_response_tmax(
-        self, name: str, p: ArrayLike = None, cutoff: float = 0.999
+        self,
+        name: str,
+        p: ArrayLike = None,
+        cutoff: float = 0.999,
+        warn: bool = True,
     ) -> Union[float, None]:
         """Method to get the tmax used for the response function.
 
@@ -1581,7 +1585,11 @@ def get_response_tmax(
         else:
             if p is None:
                 p = self.get_parameters(name)
-            tmax = self.stressmodels[name].rfunc.get_tmax(p=p, cutoff=cutoff)
+            if self.stressmodels[name].rfunc._name == "HantushWellModel":
+                kwargs = {"warn": warn}
+            else:
+                kwargs = {}
+            tmax = self.stressmodels[name].rfunc.get_tmax(p=p, cutoff=cutoff, **kwargs)
             return tmax
 
     @get_stressmodel
@@ -1857,8 +1865,12 @@ def _check_response_tmax(self, cutoff: Optional[float] = None) -> DataFrame:
         check["len_oseries_calib"] = len_oseries_calib
 
         for sm_name in self.stressmodels:
+            if self.stressmodels[sm_name].rfunc._name == "HantushWellModel":
+                kwargs = {"warn": False}
+            else:
+                kwargs = {}
             check.loc[sm_name, "response_tmax"] = self.get_response_tmax(
-                sm_name, cutoff=cutoff
+                sm_name, cutoff=cutoff, **kwargs
             )
 
         check["check_ok"] = check["response_tmax"] < check["len_oseries_calib"]

pastas/modelcompare.py

@@ -557,7 +557,11 @@ def plot_response(
                     if smn not in ml.stressmodels:
                         continue
                     if response == "step":
-                        step = ml.get_step_response(smn, add_0=True)
+                        kwargs = {}
+                        if ml.stressmodels[smn].rfunc is not None:
+                            if ml.stressmodels[smn].rfunc._name == "HantushWellModel":
+                                kwargs = {"warn": False}
+                        step = ml.get_step_response(smn, add_0=True, **kwargs)
                         if step is None:
                             continue
                         if self.axes is None:
@@ -574,7 +578,11 @@ def plot_response(
                                 color=self.cmap(i),
                             )
                     elif response == "block":
-                        block = ml.get_block_response(smn)
+                        kwargs = {}
+                        if ml.stressmodels[smn].rfunc is not None:
+                            if ml.stressmodels[smn].rfunc._name == "HantushWellModel":
+                                kwargs = {"warn": False}
+                        block = ml.get_block_response(smn, **kwargs)
                         if block is None:
                             continue
                         if self.axes is None:

pastas/modelplots.py

@@ -265,8 +265,12 @@ def results(
                 i = i + 1
 
             # plot the step response
+            rkwargs = {}
+            if self.ml.stressmodels[sm_name].rfunc is not None:
+                if self.ml.stressmodels[sm_name].rfunc._name == "HantushWellModel":
+                    rkwargs = {"warn": False}
             response = self.ml._get_response(
-                block_or_step=block_or_step, name=sm_name, add_0=True
+                block_or_step=block_or_step, name=sm_name, add_0=True, **rkwargs
             )
 
             if response is not None:

pastas/rfunc.py

@@ -229,7 +229,12 @@ def impulse(t: ArrayLike, p: ArrayLike) -> ArrayLike:
         """
 
     def get_t(
-        self, p: ArrayLike, dt: float, cutoff: float, maxtmax: Optional[int] = None
+        self,
+        p: ArrayLike,
+        dt: float,
+        cutoff: float,
+        maxtmax: Optional[int] = None,
+        warn: bool = True,
     ) -> ArrayLike:
         """Internal method to determine the times at which to evaluate the step
         response, from t=0.
@@ -245,6 +250,9 @@ def get_t(
             proportion after which the step function is cut off.
         maxtmax: float, optional
             The maximum time of the response, usually set to the simulation length.
+        warn : bool, optional
+            only used for HantushWellModel, whether to warn when r is set to 1.0
+            for calculations.
 
         Returns
         -------
@@ -254,7 +262,10 @@ def get_t(
         if isinstance(dt, np.ndarray):
             return dt
         else:
-            tmax = self.get_tmax(p, cutoff)
+            if self._name == "HantushWellModel":
+                tmax = self.get_tmax(p, cutoff, warn=warn)
+            else:
+                tmax = self.get_tmax(p, cutoff)
             if maxtmax is not None:
                 tmax = min(tmax, maxtmax)
             tmax = max(tmax, 3 * dt)
@@ -569,16 +580,19 @@ def get_init_parameters(self, name: str) -> DataFrame:
         return parameters
 
     @staticmethod
-    def _get_distance_from_params(p: ArrayLike) -> float:
+    def _get_distance_from_params(p: ArrayLike, warn: bool = True) -> float:
         if len(p) == 3:
             r = 1.0
-            logger.info("No distance passed to HantushWellModel, assuming r=1.0.")
+            if warn:
+                logger.info("No distance passed to HantushWellModel, assuming r=1.0.")
         else:
             r = p[3]
         return r
 
-    def get_tmax(self, p: ArrayLike, cutoff: Optional[float] = None) -> float:
-        r = self._get_distance_from_params(p)
+    def get_tmax(
+        self, p: ArrayLike, cutoff: Optional[float] = None, warn: bool = True
+    ) -> float:
+        r = self._get_distance_from_params(p, warn=warn)
         # approximate formula for tmax
         if cutoff is None:
             cutoff = self.cutoff
@@ -658,10 +672,11 @@ def step(
         dt: float = 1.0,
         cutoff: Optional[float] = None,
         maxtmax: Optional[int] = None,
+        warn: bool = True,
     ) -> ArrayLike:
         A, a, b = p[:3]
-        r = self._get_distance_from_params(p)
-        t = self.get_t(p=p, dt=dt, cutoff=cutoff, maxtmax=maxtmax)
+        r = self._get_distance_from_params(p, warn=warn)
+        t = self.get_t(p=p, dt=dt, cutoff=cutoff, maxtmax=maxtmax, warn=warn)
 
         if self.quad:
             return self.quad_step(A, a, b, r, t)