From ec72607008b72681f300848cbcc8dfcfc0e43d98 Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 10:36:40 +0100
Subject: [PATCH 01/12] fix setting array value with single item arrays

---
 timflow/steady/constant.py |  2 +-
 timflow/steady/linesink.py |  4 ++--
 timflow/steady/well.py     | 12 ++++++------
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/timflow/steady/constant.py b/timflow/steady/constant.py
index 4ad6db0..89d662b 100644
--- a/timflow/steady/constant.py
+++ b/timflow/steady/constant.py
@@ -170,7 +170,7 @@ def equation(self):
                 # else:
                 #    mat[0, ieq:ieq+e. nunknowns] += -1
                 else:
-                    rhs[0] -= e.potentiallayers(
+                    rhs[0:1] -= e.potentiallayers(
                         self.xc[icp], self.yc[icp], self.layers
                     ).sum(0)
         return mat, rhs
diff --git a/timflow/steady/linesink.py b/timflow/steady/linesink.py
index fea0539..c18d719 100644
--- a/timflow/steady/linesink.py
+++ b/timflow/steady/linesink.py
@@ -707,7 +707,7 @@ def equation(self):
                     mat[0, ieq : ieq + e.nunknowns] = self.dischargeinf()
                     break
                 ieq += e.nunknowns
-        rhs[0] = self.Qls
+        rhs[0:1] = self.Qls
         return mat, rhs
 
     def setparams(self, sol):
@@ -1114,7 +1114,7 @@ def equation(self):
                     mat[0, ieq : ieq + self.nunknowns] = self.dischargeinf()
                     break
                 ieq += e.nunknowns
-        rhs[0] = self.Qls
+        rhs[0:1] = self.Qls
         return mat, rhs
 
 
diff --git a/timflow/steady/well.py b/timflow/steady/well.py
index 1e3e622..827d82a 100644
--- a/timflow/steady/well.py
+++ b/timflow/steady/well.py
@@ -451,7 +451,7 @@ def equation(self):
                     mat[0, ieq : ieq + e.nunknowns] = 1.0
                     break
                 ieq += e.nunknowns
-        rhs[0] = self.Qw
+        rhs[0:1] = self.Qw
         return mat, rhs
 
     def setparams(self, sol):
@@ -618,7 +618,7 @@ def equation(self):
             rhs[i] -= rhs[0]
         # first equation is head at control point equals hcp
         mat[0] = 0.0
-        rhs[0] = self.hcp
+        rhs[0:1] = self.hcp
         aq = self.model.aq.find_aquifer_data(self.xcp, self.ycp)
         ieq = 0
         for e in self.model.elementlist:
@@ -628,7 +628,7 @@ def equation(self):
                 )
                 ieq += e.nunknowns
             else:
-                rhs[0] -= e.potentiallayers(self.xcp, self.ycp, self.lcp) / aq.T[self.lcp]
+                rhs[0:1] -= e.potentiallayers(self.xcp, self.ycp, self.lcp) / aq.T[self.lcp]
         return mat, rhs
 
     def setparams(self, sol):
@@ -1004,7 +1004,7 @@ def equation(self):
                     mat[0, ieq : ieq + self.nunknowns] = 1.0
                     break
                 ieq += e.nunknowns
-        rhs[0] = self.Qw
+        rhs[0:1] = self.Qw
         return mat, rhs
 
     def setparams(self, sol):
@@ -1166,7 +1166,7 @@ def equation(self):
 
         # first equation is head at control point equals hcp
         mat[0] = 0
-        rhs[0] = self.hcp
+        rhs[0:1] = self.hcp
         aq = self.model.aq.find_aquifer_data(self.xcp, self.ycp)
         ieq = 0
         for e in self.model.elementlist:
@@ -1184,7 +1184,7 @@ def equation(self):
                     )
                     ieq += e.nunknowns
             else:
-                rhs[0] -= e.potentiallayers(self.xcp, self.ycp, self.lcp) / aq.T[self.lcp]
+                rhs[0:1] -= e.potentiallayers(self.xcp, self.ycp, self.lcp) / aq.T[self.lcp]
         return mat, rhs
 
     def setparams(self, sol):

From 1a99f467bfecfb636855ecc0944836fc1dfde066 Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 10:38:10 +0100
Subject: [PATCH 02/12] use reshape instead of setting shape directly

---
 timflow/steady/linedoublet.py     |  6 ++----
 timflow/steady/linesink.py        | 13 ++++++-------
 timflow/steady/well.py            |  4 ++--
 timflow/transient/aquifer.py      |  6 ++++--
 timflow/transient/circareasink.py |  7 +++----
 timflow/transient/circinhom.py    | 17 ++++++++---------
 timflow/transient/linedoublet.py  | 11 +++++------
 timflow/transient/linesink.py     | 21 ++++++++++-----------
 timflow/transient/well.py         |  7 +++----
 9 files changed, 43 insertions(+), 49 deletions(-)

diff --git a/timflow/steady/linedoublet.py b/timflow/steady/linedoublet.py
index 8d3ac31..36da118 100644
--- a/timflow/steady/linedoublet.py
+++ b/timflow/steady/linedoublet.py
@@ -408,8 +408,7 @@ def potinf(self, x, y, aq=None):
         rv = np.zeros((self.Nld, self.ldlist[0].nparam, aq.naq))
         for i in range(self.Nld):
             rv[i] = self.ldlist[i].potinf(x, y, aq)
-        rv.shape = (self.nparam, aq.naq)
-        return rv
+        return rv.reshape((self.nparam, aq.naq))
 
     def disvecinf(self, x, y, aq=None):
         if aq is None:
@@ -417,8 +416,7 @@ def disvecinf(self, x, y, aq=None):
         rv = np.zeros((2, self.Nld, self.ldlist[0].nparam, aq.naq))
         for i in range(self.Nld):
             rv[:, i] = self.ldlist[i].disvecinf(x, y, aq)
-        rv.shape = (2, self.nparam, aq.naq)
-        return rv
+        return rv.reshape((2, self.nparam, aq.naq))
 
     def plot(self, ax=None, layer=None):
         if ax is None:
diff --git a/timflow/steady/linesink.py b/timflow/steady/linesink.py
index c18d719..286d697 100644
--- a/timflow/steady/linesink.py
+++ b/timflow/steady/linesink.py
@@ -597,7 +597,7 @@ def initialize(self):
         self.resfac = (
             np.tile(self.res / self.whfac, self.ncp) * self.strengthinf
         )  # this is resfach !
-        self.resfac.shape = (self.ncp, self.nlayers, self.nunknowns)
+        self.resfac = self.resfac.reshape((self.ncp, self.nlayers, self.nunknowns))
         if len(self.hls) == 1:
             self.hc = self.hls * np.ones(self.nparam)
         elif len(self.hls) == self.ncp:  # head specified at control points
@@ -801,8 +801,8 @@ def potinf(self, x, y, aq=None):
         if aq in self.aq:
             for i, ls in enumerate(self.lslist):
                 rv[i] = ls.potinf(x, y, aq)
-        rv.shape = (self.nparam, aq.naq)
-        return rv
+        return rv.reshape((self.nparam, aq.naq))
+        
 
     def disvecinf(self, x, y, aq=None):
         if aq is None:
@@ -811,8 +811,7 @@ def disvecinf(self, x, y, aq=None):
         if aq in self.aq:
             for i, ls in enumerate(self.lslist):
                 rv[:, i] = ls.disvecinf(x, y, aq)
-        rv.shape = (2, self.nparam, aq.naq)
-        return rv
+        return rv.reshape((2, self.nparam, aq.naq))
 
     def dischargeinf(self):
         rv = np.zeros((self.nls, self.lslist[0].nparam))
@@ -829,7 +828,7 @@ def discharge_per_linesink(self):
             array of shape (nlay, nlinesinks)
         """
         Qls = self.parameters[:, 0] * self.dischargeinf()
-        Qls.shape = (self.nls, self.nlayers, self.order + 1)
+        Qls = Qls.reshape((self.nls, self.nlayers, self.order + 1))
         Qls = Qls.sum(axis=2)
         rv = np.zeros((self.model.aq.naq, self.nls))
         for i, q in enumerate(Qls):
@@ -840,7 +839,7 @@ def discharge(self):
         """Discharge of the element in each layer."""
         rv = np.zeros(self.aq[0].naq)
         Qls = self.parameters[:, 0] * self.dischargeinf()
-        Qls.shape = (self.nls, self.nlayers, self.order + 1)
+        Qls = Qls.reshape((self.nls, self.nlayers, self.order + 1))
         Qls = np.sum(Qls, 2)
         for i, q in enumerate(Qls):
             rv[self.layers[i]] += q
diff --git a/timflow/steady/well.py b/timflow/steady/well.py
index 827d82a..12a54a2 100644
--- a/timflow/steady/well.py
+++ b/timflow/steady/well.py
@@ -82,7 +82,7 @@ def initialize(self):
         self.parameters[:, 0] = self.Qw
         self.resfac = self.res / (2 * np.pi * self.rw * self.aq.Haq[self.layers])
         self.resfac = self.resfac * np.identity(self.nlayers)
-        self.resfac.shape = (
+        self.resfac = self.resfac.reshape(
             self.ncp,
             self.nlayers,
             self.nlayers,  # changed to nlayers from nunknowns
@@ -846,7 +846,7 @@ def potinf(self, x, y, aq=None):
             for w in self.wlist:
                 rv[j : j + w.nparam] = w.potinf(x, y, aq)
                 j += w.nparam
-        # rv.shape = (self.nparam, aq.naq)
+        # rv = rv.reshape((self.nparam, aq.naq))
         return rv
 
     def disvecinf(self, x, y, aq=None):
diff --git a/timflow/transient/aquifer.py b/timflow/transient/aquifer.py
index 4013083..a7e5016 100644
--- a/timflow/transient/aquifer.py
+++ b/timflow/transient/aquifer.py
@@ -135,10 +135,12 @@ def initialize(self):
             self.coef[:, :, i] = np.linalg.solve(v, b).T
         self.lab = 1.0 / np.sqrt(self.eigval)
         self.lab2 = self.lab.copy()
-        self.lab2.shape = (self.naq, self.model.nint, self.model.npint)
+        self.lab2 = self.lab2.reshape((self.naq, self.model.nint, self.model.npint))
         self.lababs = np.abs(self.lab2[:, :, 0])  # used to check distances
         self.eigvec2 = self.eigvec.copy()
-        self.eigvec2.shape = (self.naq, self.naq, self.model.nint, self.model.npint)
+        self.eigvec2 = self.eigvec2.reshape(
+            (self.naq, self.naq, self.model.nint, self.model.npint)
+        )
 
     def compute_lab_eigvec(self, p, returnA=False, B=None):
         sqrtpSc = np.sqrt(p * self.Scoefll * self.c)
diff --git a/timflow/transient/circareasink.py b/timflow/transient/circareasink.py
index 1ac364a..83797c2 100644
--- a/timflow/transient/circareasink.py
+++ b/timflow/transient/circareasink.py
@@ -66,7 +66,7 @@ def initialize(self):
         self.setflowcoef()
         # Since recharge is in layer 0, and RHS is -N
         self.an = self.aq.coef[0, :] * self.flowcoef
-        self.an.shape = (self.aq.naq, self.model.nint, self.model.npint)
+        self.an = self.an.reshape((self.aq.naq, self.model.nint, self.model.npint))
         self.termin = self.aq.lab2 * self.R * self.an
         self.termin2 = self.aq.lab2**2 * self.an
         self.terminq = self.R * self.an
@@ -105,8 +105,7 @@ def potinf(self, x, y, aq=None):
                     for j in range(self.model.nint):
                         if (r - self.R) / abs(self.aq.lab2[i, j, 0]) < self.rzero:
                             rv[0, i, j, :] = self.termout[i, j, :] * self.I1RK0r(r, i, j)
-        rv.shape = (self.nparam, aq.naq, self.model.npval)
-        return rv
+        return rv.reshape((self.nparam, aq.naq, self.model.npval))
 
     def disvecinf(self, x, y, aq=None):
         """Can be called with only one x,y value."""
@@ -129,7 +128,7 @@ def disvecinf(self, x, y, aq=None):
                     for j in range(self.model.nint):
                         if (r - self.R) / abs(self.aq.lab2[i, j, 0]) < self.rzero:
                             qr[0, i, j] = self.termoutq[i, j, :] * self.I1RK1r(r, i, j)
-            qr.shape = (self.nparam, aq.naq, self.model.npval)
+            qr = qr.reshape((self.nparam, aq.naq, self.model.npval))
             qx[:] = qr * (x - self.xc) / r
             qy[:] = qr * (y - self.yc) / r
         return qx, qy
diff --git a/timflow/transient/circinhom.py b/timflow/transient/circinhom.py
index 10ab21d..9a2bf4b 100644
--- a/timflow/transient/circinhom.py
+++ b/timflow/transient/circinhom.py
@@ -274,8 +274,7 @@ def potinf(self, x, y, aq=None):
                             rv[self.aqin.Naq + i, i, j, :] = self.approx.kvratio(
                                 r, self.R, self.aqout.lab2[i, j, :]
                             )
-        rv.shape = (self.Nparam, aq.Naq, self.model.Np)
-        return rv
+        return rv.reshape((self.Nparam, aq.Naq, self.model.Np))
 
     def disinf(self, x, y, aq=None):
         """Can be called with only one x,y value."""
@@ -304,7 +303,7 @@ def disinf(self, x, y, aq=None):
                                 -self.approx.ivratiop(r, self.R, self.aqin.lab2[i, j, :])
                                 / self.aqin.lab2[i, j, :]
                             )
-            qr.shape = (self.nparam, aq.naq, self.model.np)
+            qr = qr.reshape((self.nparam, aq.naq, self.model.np))
             qx[:] = qr * (x - self.x0) / r
             qy[:] = qr * (y - self.y0) / r
         if aq == self.aqout:
@@ -326,7 +325,7 @@ def disinf(self, x, y, aq=None):
                                 self.approx.kvratiop(r, self.R, self.aqout.lab2[i, j, :])
                                 / self.aqout.lab2[i, j, :]
                             )
-            qr.shape = (self.Nparam, aq.Naq, self.model.Np)
+            qr = qr.reshape((self.Nparam, aq.Naq, self.model.Np))
             qx[:] = qr * (x - self.x0) / r
             qy[:] = qr * (y - self.y0) / r
         return qx, qy
@@ -427,7 +426,7 @@ def layout(self):
 #                             for n in range(1,self.order+1):
 #                                 rv[aq.Naq+i,2*n-1,i,j,:] = pot[n] * np.cos(n*alpha)
 #                                 rv[aq.Naq+i,2*n  ,i,j,:] = pot[n] * np.sin(n*alpha)
-#         rv.shape = (self.Nparam,aq.Naq,self.model.Np)
+#         rv = rv.reshape((self.Nparam, aq.Naq, self.model.Np))
 #         return rv
 #     def disinf(self,x,y,aq=None):
 #         '''Can be called with only one x,y value'''
@@ -462,8 +461,8 @@ def layout(self):
 #                                 qr[i,2*n  ,i,j,:] = -potp[n] / 2 / self.aqin.lab2[i,j,:] * np.sin(n*alpha)
 #                                 qt[i,2*n-1,i,j,:] =  pot[n] * np.sin(n*alpha) * n / r
 #                                 qt[i,2*n  ,i,j,:] = -pot[n] * np.cos(n*alpha) * n / r
-#             qr.shape = (self.Nparam/2,aq.Naq,self.model.Np)
-#             qt.shape = (self.Nparam/2,aq.Naq,self.model.Np)
+#             qr = qr.reshape((self.Nparam / 2, aq.Naq, self.model.Np))
+#             qt = qt.reshape((self.Nparam / 2, aq.Naq, self.model.Np))
 #             qx[:self.Nparam/2,:,:] = qr * np.cos(alpha) - qt * np.sin(alpha);
 #             qy[:self.Nparam/2,:,:] = qr * np.sin(alpha) + qt * np.cos(alpha);
 #         if aq == self.aqout:
@@ -494,8 +493,8 @@ def layout(self):
 #                                 qr[i,2*n  ,i,j,:] = -potp[n] / self.aqout.lab2[i,j,:] * np.sin(n*alpha)
 #                                 qt[i,2*n-1,i,j,:] =  pot[n] * np.sin(n*alpha) * n / r
 #                                 qt[i,2*n  ,i,j,:] = -pot[n] * np.cos(n*alpha) * n / r
-#             qr.shape = (self.Nparam/2,aq.Naq,self.model.Np)
-#             qt.shape = (self.Nparam/2,aq.Naq,self.model.Np)
+#             qr = qr.reshape((self.Nparam / 2, aq.Naq, self.model.Np))
+#             qt = qt.reshape((self.Nparam / 2, aq.Naq, self.model.Np))
 #             qx[self.Nparam/2:,:,:] = qr * np.cos(alpha) - qt * np.sin(alpha);
 #             qy[self.Nparam/2:,:,:] = qr * np.sin(alpha) + qt * np.cos(alpha);
 #         return qx,qy
diff --git a/timflow/transient/linedoublet.py b/timflow/transient/linedoublet.py
index 4bbf0e1..29c2234 100644
--- a/timflow/transient/linedoublet.py
+++ b/timflow/transient/linedoublet.py
@@ -143,8 +143,7 @@ def potinf(self, x, y, aq=None):
                         )
                         for k in range(self.nlayers):
                             rv[k :: self.nlayers, i, j, :] = self.term2[k, i, j, :] * pot
-        rv.shape = (self.nparam, aq.naq, self.model.npval)
-        return rv
+        return rv.reshape((self.nparam, aq.naq, self.model.npval))
 
     def disvecinf(self, x, y, aq=None):
         """Can be called with only one x,y value."""
@@ -182,10 +181,10 @@ def disvecinf(self, x, y, aq=None):
                             rvy[k :: self.nlayers, i, j, :] = (
                                 self.term2[k, i, j, :] * qxqy[self.order + 1 :, :]
                             )
-
-        rvx.shape = (self.nparam, aq.naq, self.model.npval)
-        rvy.shape = (self.nparam, aq.naq, self.model.npval)
-        return rvx, rvy
+        return (
+            rvx.reshape((self.nparam, aq.naq, self.model.npval)),
+            rvy.reshape((self.nparam, aq.naq, self.model.npval)),
+        )
 
     def plot(self, ax=None, layer=None):
         if ax is None:
diff --git a/timflow/transient/linesink.py b/timflow/transient/linesink.py
index 06f8c24..9a3e048 100644
--- a/timflow/transient/linesink.py
+++ b/timflow/transient/linesink.py
@@ -138,8 +138,7 @@ def potinf(self, x, y, aq=None):
                     )
                     # Divide by L as the parameter is total discharge
                     rv[:, i, j, :] = self.term2[:, i, j, :] * pot / self.L
-        rv.shape = (self.nparam, aq.naq, self.model.npval)
-        return rv
+                return rv.reshape((self.nparam, aq.naq, self.model.npval))
 
     def disvecinf(self, x, y, aq=None):
         """Can be called with only one x,y value."""
@@ -172,9 +171,10 @@ def disvecinf(self, x, y, aq=None):
                         )
                     rvx[:, i, j, :] = self.term2[:, i, j, :] * qxqy[0]
                     rvy[:, i, j, :] = self.term2[:, i, j, :] * qxqy[1]
-        rvx.shape = (self.nparam, aq.naq, self.model.npval)
-        rvy.shape = (self.nparam, aq.naq, self.model.npval)
-        return rvx, rvy
+        return (
+            rvx.reshape((self.nparam, aq.naq, self.model.npval)),
+            rvy.reshape((self.nparam, aq.naq, self.model.npval)),
+        )
 
     def headinside(self, t):
         """The head inside the line-sink.
@@ -976,9 +976,7 @@ def potinf(self, x, y, aq=None):
                         )
                     for k in range(self.nlayers):
                         rv[k :: self.nlayers, i, j, :] = self.term2[k, i, j, :] * pot
-
-        rv.shape = (self.nparam, aq.naq, self.model.npval)
-        return rv
+        return rv.reshape((self.nparam, aq.naq, self.model.npval))
 
     def disvecinf(self, x, y, aq=None):
         """Can be called with only one x,y value."""
@@ -1016,9 +1014,10 @@ def disvecinf(self, x, y, aq=None):
                         rvy[k :: self.nlayers, i, j, :] = (
                             self.term2[k, i, j, :] * qxqy[self.order + 1 :, :]
                         )
-        rvx.shape = (self.nparam, aq.naq, self.model.npval)
-        rvy.shape = (self.nparam, aq.naq, self.model.npval)
-        return rvx, rvy
+        return (
+            rvx.reshape((self.nparam, aq.naq, self.model.npval)),
+            rvy.reshape((self.nparam, aq.naq, self.model.npval)),
+        )
 
     def headinside(self, t):
         """The head inside the line-sink.
diff --git a/timflow/transient/well.py b/timflow/transient/well.py
index e819cb5..5d5a8ee 100644
--- a/timflow/transient/well.py
+++ b/timflow/transient/well.py
@@ -109,8 +109,7 @@ def potinf(self, x, y, aq=None):
                         # quicker?
                         # bessel.k0besselv( r / self.aq.lab2[i,j,:], pot )
                         rv[:, i, j, :] = self.term2[:, i, j, :] * pot
-        rv.shape = (self.nparam, aq.naq, self.model.npval)
-        return rv
+        return rv.reshape((self.nparam, aq.naq, self.model.npval))
 
     def potinfone(self, x, y, jtime, aq=None):
         """Can be called with only one x,y value for time interval jtime."""
@@ -126,7 +125,7 @@ def potinfone(self, x, y, jtime, aq=None):
                 if r / abs(self.aq.lab2[i, jtime, 0]) < self.rzero:
                     pot[:] = kv(0, r / self.aq.lab2[i, jtime, :])
                     rv[:, i, :] = self.term2[:, i, jtime, :] * pot
-        # rv.shape = (self.nparam, aq.naq, self.model.npval)
+        # rv = rv.reshape((self.nparam, aq.naq, self.model.npval))
         return rv
 
     def disvecinf(self, x, y, aq=None):
@@ -151,7 +150,7 @@ def disvecinf(self, x, y, aq=None):
                             * kv(1, r / self.aq.lab2[i, j, :])
                             / self.aq.lab2[i, j, :]
                         )
-            qr.shape = (self.nparam, aq.naq, self.model.npval)
+            qr = qr.reshape((self.nparam, aq.naq, self.model.npval))
             qx[:] = qr * (x - self.xw) / r
             qy[:] = qr * (y - self.yw) / r
         return qx, qy

From 2e7c75b1fcecb568185313e8241bd51fd452e162 Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 10:38:25 +0100
Subject: [PATCH 03/12] fix setting array value with single item arrays

---
 timflow/steady/well.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/timflow/steady/well.py b/timflow/steady/well.py
index 12a54a2..831a841 100644
--- a/timflow/steady/well.py
+++ b/timflow/steady/well.py
@@ -628,7 +628,9 @@ def equation(self):
                 )
                 ieq += e.nunknowns
             else:
-                rhs[0:1] -= e.potentiallayers(self.xcp, self.ycp, self.lcp) / aq.T[self.lcp]
+                rhs[0:1] -= (
+                    e.potentiallayers(self.xcp, self.ycp, self.lcp) / aq.T[self.lcp]
+                )
         return mat, rhs
 
     def setparams(self, sol):
@@ -1184,7 +1186,9 @@ def equation(self):
                     )
                     ieq += e.nunknowns
             else:
-                rhs[0:1] -= e.potentiallayers(self.xcp, self.ycp, self.lcp) / aq.T[self.lcp]
+                rhs[0:1] -= (
+                    e.potentiallayers(self.xcp, self.ycp, self.lcp) / aq.T[self.lcp]
+                )
         return mat, rhs
 
     def setparams(self, sol):

From 88029f46bd776f4e08d4fca67e6d74e35500a06d Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 10:38:41 +0100
Subject: [PATCH 04/12] sum on list instead of generator

---
 timflow/steady/well.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/timflow/steady/well.py b/timflow/steady/well.py
index 831a841..bb79eaf 100644
--- a/timflow/steady/well.py
+++ b/timflow/steady/well.py
@@ -909,7 +909,7 @@ def equation(self):
         # include resistance by finding position of element in coefficient matrix
         # and subtracting resfac (resistance factor).
         iself = self.model.elementlist.index(self)
-        jcol = np.sum(e.nunknowns for e in self.model.elementlist[:iself])
+        jcol = np.sum([e.nunknowns for e in self.model.elementlist[:iself]])
         irow = 0
         for w in self.wlist:
             mat[irow : irow + w.nlayers, jcol : jcol + w.nunknowns] -= w.resfac[

From 62ff35b9eafc8dc60f9b0af967582757f127d94e Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 10:40:03 +0100
Subject: [PATCH 05/12] ruff

---
 timflow/steady/linesink.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/timflow/steady/linesink.py b/timflow/steady/linesink.py
index 286d697..f43e2ed 100644
--- a/timflow/steady/linesink.py
+++ b/timflow/steady/linesink.py
@@ -802,7 +802,6 @@ def potinf(self, x, y, aq=None):
             for i, ls in enumerate(self.lslist):
                 rv[i] = ls.potinf(x, y, aq)
         return rv.reshape((self.nparam, aq.naq))
-        
 
     def disvecinf(self, x, y, aq=None):
         if aq is None:

From 8adee421c3b436b635c37392b89c08fb84d0532a Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 10:40:12 +0100
Subject: [PATCH 06/12] housekeeping

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 813ba44..4ed63e6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -97,7 +97,7 @@ addopts = [
     "--cov-report=term",
     "--cov-report=lcov:./coverage/lcov.info",
     "--ignore=docs/steady/02examples/vertical_anisotropy.ipynb",
-    "--ignore=docs/steady/04benchmarks/benchmarking_besselaes.ipynb",
+    "--ignore=docs/steady/04benchmarks/besselnumba_timing.ipynb",
 ]
 markers = [
     "notebooks: run notebooks",

From 45c1b7d9ccb767a47f1f84e44439e998e70a17fb Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 10:40:28 +0100
Subject: [PATCH 07/12] improve notebook output with equal axes

---
 docs/transient/02examples/line_sink_well_sol.ipynb | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/docs/transient/02examples/line_sink_well_sol.ipynb b/docs/transient/02examples/line_sink_well_sol.ipynb
index 2fdb650..458a924 100755
--- a/docs/transient/02examples/line_sink_well_sol.ipynb
+++ b/docs/transient/02examples/line_sink_well_sol.ipynb
@@ -105,7 +105,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(16, 4))\n",
+    "plt.figure(figsize=(12, 4))\n",
     "for ilay in [0, 1, 2]:\n",
     "    ax = plt.subplot(1, 3, ilay + 1)\n",
     "    ml.plots.contour(\n",
@@ -118,7 +118,8 @@
     "        labels=True,\n",
     "        decimals=2,\n",
     "        ax=ax,\n",
-    "    )"
+    "    )\n",
+    "    ax.set_aspect(\"equal\", adjustable=\"box\")"
    ]
   },
   {

From fa2db14202d02f443eb63432dfe85158f978945c Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 10:41:58 +0100
Subject: [PATCH 08/12] set contour lines to solid

---
 docs/steady/02examples/connected_wells.ipynb | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/docs/steady/02examples/connected_wells.ipynb b/docs/steady/02examples/connected_wells.ipynb
index 346b2a3..1f152b0 100755
--- a/docs/steady/02examples/connected_wells.ipynb
+++ b/docs/steady/02examples/connected_wells.ipynb
@@ -28,9 +28,19 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
+    "import timflow\n",
     "import timflow.steady as tfs"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timflow.show_versions()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -568,8 +578,7 @@
     "    levels=levels,\n",
     "    decimals=2,\n",
     "    layers=[ilay],\n",
-    "    newfig=False,\n",
-    "    linestyles=\"dashed\",\n",
+    "    linestyles=\"solid\",\n",
     "    color=\"C1\",\n",
     ")\n",
     "plt.plot(ws.xcp, ws.ycp, \"kx\");"
@@ -718,7 +727,7 @@
     "    decimals=2,\n",
     "    layers=[ilay],\n",
     "    newfig=False,\n",
-    "    linestyles=\"dashed\",\n",
+    "    linestyles=\"solid\",\n",
     "    color=\"C1\",\n",
     ")\n",
     "plt.plot(ws.xcp, ws.ycp, \"kx\");"

From 242c86026dd801ab771354239b0ba942046742f4 Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 11:52:58 +0100
Subject: [PATCH 09/12] more numpy fixes - ndim>0 to scalar issues - sum on
 generators

---
 timflow/steady/linesink.py    | 9 ++++++---
 timflow/steady/well.py        | 6 +++---
 timflow/transient/linesink.py | 4 ++--
 timflow/transient/model.py    | 6 +++---
 4 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/timflow/steady/linesink.py b/timflow/steady/linesink.py
index f43e2ed..9bfbf9a 100644
--- a/timflow/steady/linesink.py
+++ b/timflow/steady/linesink.py
@@ -740,14 +740,16 @@ def __init__(
         self.xy = np.atleast_2d(xy).astype("d")
         if closed:
             self.xy = np.vstack((self.xy, self.xy[0]))
-        self.x, self.y = self.xy[:, 0], self.xy[:, 1]
         self.order = order  # same for all segments in string
         self.dely = dely  # same for all segments in string
         self.lslist = []
         if self.xy.shape[1] == 2:
             self.nls = len(self.xy) - 1
+            self.x, self.y = self.xy[:, 0], self.xy[:, 1]
         elif self.xy.shape[1] == 4:
             self.nls = len(self.xy)
+            self.x = None
+            self.y = None
         if self.layers.ndim == 1:
             if len(self.layers) == self.nls:
                 self.layers = self.layers[:, np.newaxis]
@@ -945,7 +947,6 @@ def __init__(
         self.res = res
         self.wh = wh
         self.model.add_element(self)
-        # TO DO: TEST FOR DIFFERENT AQUIFERS AND LAYERS
 
     def initialize(self):
         if len(self.hls) == 1:  # one value
@@ -976,6 +977,8 @@ def initialize(self):
                 y1, y2 = self.y[i : i + 2]
             elif self.xy.shape[1] == 4:
                 x1, y1, x2, y2 = self.xy[i]
+            else:
+                raise ValueError("xy should have shape (n, 2) or (n, 4)")
             self.lslist.append(
                 River(
                     self.model,
@@ -1016,7 +1019,7 @@ def equation(self):
         # include resistance by computing position of coefficients in matrix
         # and subtracting resistance terms
         iself = self.model.elementlist.index(self)
-        jcol = np.sum(e.nunknowns for e in self.model.elementlist[:iself])
+        jcol = np.sum([e.nunknowns for e in self.model.elementlist[:iself]]).astype(int)
         irow = 0
         for ls in self.lslist:
             for icp in range(ls.ncp):
diff --git a/timflow/steady/well.py b/timflow/steady/well.py
index bb79eaf..998f2b8 100644
--- a/timflow/steady/well.py
+++ b/timflow/steady/well.py
@@ -729,7 +729,7 @@ def potinf(self, x, y, aq=None):
             if r < self.rw:
                 r = self.rw  # If at well, set to at radius
             if aq.ilap:
-                pot[0] = np.log(r / self.rw) / (2 * np.pi)
+                pot[0:1] = np.log(r / self.rw) / (2 * np.pi)
                 pot[1:] = -k0(r / aq.lab[1:]) / (2 * np.pi) / k0(self.rw / aq.lab[1:])
             else:
                 pot[:] = -k0(r / aq.lab) / (2 * np.pi) / k0(self.rw / aq.lab)
@@ -753,8 +753,8 @@ def disvecinf(self, x, y, aq=None):
                 xminxw = self.rw
                 yminyw = 0.0
             if aq.ilap:
-                qx[0] = -1 / (2 * np.pi) * xminxw / rsq
-                qy[0] = -1 / (2 * np.pi) * yminyw / rsq
+                qx[0:1] = -1 / (2 * np.pi) * xminxw / rsq
+                qy[0:1] = -1 / (2 * np.pi) * yminyw / rsq
                 kone = k1(r / aq.lab[1:]) / k0(self.rw / aq.lab[1:])
                 qx[1:] = -kone * xminxw / (r * aq.lab[1:]) / (2 * np.pi)
                 qy[1:] = -kone * yminyw / (r * aq.lab[1:]) / (2 * np.pi)
diff --git a/timflow/transient/linesink.py b/timflow/transient/linesink.py
index 9a3e048..ddb0f95 100644
--- a/timflow/transient/linesink.py
+++ b/timflow/transient/linesink.py
@@ -419,8 +419,8 @@ def initialize(self):
             self.dischargeinflayers[i * self.nlayers : (i + 1) * self.nlayers, :] = (
                 self.lslist[i].dischargeinflayers
             )
-            self.xc[i] = self.lslist[i].xc
-            self.yc[i] = self.lslist[i].yc
+            self.xc[i : i + 1] = self.lslist[i].xc
+            self.yc[i : i + 1] = self.lslist[i].yc
 
     def potinf(self, x, y, aq=None):
         """Returns array (nunknowns, Nperiods)."""
diff --git a/timflow/transient/model.py b/timflow/transient/model.py
index b56d2ae..83801a3 100644
--- a/timflow/transient/model.py
+++ b/timflow/transient/model.py
@@ -416,9 +416,9 @@ def velocomp(self, x, y, z, t, aq=None, layer_ltype=None):
                     )[:, 0]
             else:  # layer = 0, so top layer
                 if aq.naq == 1:  # only one layer
-                    h[1] = self.head(x, y, t, layers=[layer], aq=aq, neglect_steady=True)[
-                        :, 0
-                    ]
+                    h[1:2] = self.head(
+                        x, y, t, layers=[layer], aq=aq, neglect_steady=True
+                    )[:, 0]
                 else:
                     h[1:] = self.head(
                         x, y, t, layers=[layer, layer + 1], aq=aq, neglect_steady=True

From 3d3d948e04de3071568d46846691901abf454d02 Mon Sep 17 00:00:00 2001
From: dbrakenhoff <d.brakenhoff@artesia-water.nl>
Date: Tue, 24 Feb 2026 11:54:43 +0100
Subject: [PATCH 10/12] more numpy fixes - ndim>0 to scalar issues - fix for
 running quad on 2d array output

---
 docs/steady/02examples/circular_area_sink.ipynb           | 2 +-
 .../00userguide/tutorials/tutorial0_start_a_model.ipynb   | 8 +++++++-
 docs/transient/05benchmarks/well_benchmarks.ipynb         | 2 +-
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/docs/steady/02examples/circular_area_sink.ipynb b/docs/steady/02examples/circular_area_sink.ipynb
index 6d99ecc..f1be9ea 100644
--- a/docs/steady/02examples/circular_area_sink.ipynb
+++ b/docs/steady/02examples/circular_area_sink.ipynb
@@ -52,7 +52,7 @@
    "source": [
     "qx = np.zeros_like(x)\n",
     "for i in range(len(x)):\n",
-    "    qx[i], qy = ml.disvec(x[i], 1e-6)\n",
+    "    qx[i : i + 1], qy = ml.disvec(x[i], 1e-6)\n",
     "plt.plot(x, qx)\n",
     "qxb = N * np.pi * R**2 / (2 * np.pi * R)\n",
     "plt.axhline(qxb, color=\"r\", ls=\"--\")\n",
diff --git a/docs/transient/00userguide/tutorials/tutorial0_start_a_model.ipynb b/docs/transient/00userguide/tutorials/tutorial0_start_a_model.ipynb
index 82d7689..376bef9 100755
--- a/docs/transient/00userguide/tutorials/tutorial0_start_a_model.ipynb
+++ b/docs/transient/00userguide/tutorials/tutorial0_start_a_model.ipynb
@@ -35,7 +35,6 @@
     "ls = tft.RiverString(ml, xy=[(-25, -30), (-15, 10), (10, 20)], tsandh=\"fixed\", layers=0)\n",
     "w = tft.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, 1000)], layers=1)\n",
     "\n",
-    "ml.initialize()\n",
     "ml.solve(silent=True)"
    ]
   },
@@ -130,6 +129,13 @@
    "source": [
     "ax = ml.plots.head_along_line(-30, 10, 0, 0, t=10, layers=[0, 1, 2])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/transient/05benchmarks/well_benchmarks.ipynb b/docs/transient/05benchmarks/well_benchmarks.ipynb
index 5e11a36..b5de1d6 100644
--- a/docs/transient/05benchmarks/well_benchmarks.ipynb
+++ b/docs/transient/05benchmarks/well_benchmarks.ipynb
@@ -181,7 +181,7 @@
    "outputs": [],
    "source": [
     "def volume(r, t=1):\n",
-    "    return -2 * np.pi * r * ml.head(r, 0, t) * ml.aq.Scoefaq[0]\n",
+    "    return (-2 * np.pi * r * ml.head(r, 0, t) * ml.aq.Scoefaq[0]).squeeze()\n",
     "\n",
     "\n",
     "quad(volume, 1e-5, np.inf)"

From 688b2585dba0ba7e3f9c2b8e9f5051419d2eacf2 Mon Sep 17 00:00:00 2001
From: Mark Bakker <markbak@gmail.com>
Date: Tue, 24 Feb 2026 12:26:34 +0100
Subject: [PATCH 11/12] Create meandering_test.ipynb

---
 .../02examples/meandering_test.ipynb          | 345 ++++++++++++++++++
 1 file changed, 345 insertions(+)
 create mode 100644 docs/transient/02examples/meandering_test.ipynb

diff --git a/docs/transient/02examples/meandering_test.ipynb b/docs/transient/02examples/meandering_test.ipynb
new file mode 100644
index 0000000..32ded38
--- /dev/null
+++ b/docs/transient/02examples/meandering_test.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Meandering river"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import timflow.transient as tft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml = tft.Model3D(\n",
+    "    kaq=[2, 1, 5, 10, 4],\n",
+    "    z=[10, 8, 6, 4, 2, 0],\n",
+    "    Saq=[0.1, 0.0001, 0.0002, 0.0002, 0.0001],\n",
+    "    phreatictop=True,\n",
+    "    kzoverkh=0.1,\n",
+    "    topboundary=\"conf\",\n",
+    "    tmin=1,\n",
+    "    tmax=10,\n",
+    "    M=1,\n",
+    ")\n",
+    "w = tft.Well(ml, -25, 0, rw=0.3, tsandQ=[(0, 100)], layers=[2, 3])\n",
+    "\n",
+    "xy = [(-50, -30), (40, 20)]\n",
+    "\n",
+    "#ls1 = tft.RiverString(ml, xy=xy, tsandh=\"fixed\", layers=[0, 1])\n",
+    "ls1 = tft.River(ml, -50, -30, 40, 20, tsandh=\"fixed\", layers=[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(-50, -30), (40, 20)]"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self.neq  3\n",
+      "solution complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "ml.solve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[-0.48400507-2.73670650e-16j, -0.47400337+3.99311632e-02j,\n",
+       "         -0.44946707+6.87721303e-02j]]])"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ls1.parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml.plots.contour(win=[-150, 150, -50, 50], ngr=40, t=1, layers=1, labels=False);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 3, 3)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ls1.equation()[0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[[-0.01236536+0.00000000e+00j, -0.01005247+5.68773936e-03j,\n",
+       "          -0.00549757+7.95377717e-03j],\n",
+       "         [-0.01153788+0.00000000e+00j, -0.00939428+5.27498912e-03j,\n",
+       "          -0.0051783 +7.37849050e-03j],\n",
+       "         [-0.0030817 +1.74249202e-18j, -0.00289097+5.57396739e-04j,\n",
+       "          -0.00247003+8.66105395e-04j]]]),\n",
+       " array([[[0.+0.j, 0.+0.j, 0.+0.j]]]))"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ls1.equation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 3, 3)"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ls1.equation()[0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[[-0.01082435+0.00000000e+00j, -0.00942422+3.86578290e-03j,\n",
+       "          -0.00656125+5.73423935e-03j],\n",
+       "         [-0.01051733+0.00000000e+00j, -0.00917613+3.71879451e-03j,\n",
+       "          -0.0064318 +5.52675484e-03j],\n",
+       "         [-0.02194105+1.24061720e-17j, -0.02006046+6.25871524e-03j,\n",
+       "          -0.01593783+1.00113014e-02j]]]),\n",
+       " array([[[0.+0.j, 0.+0.j, 0.+0.j]]]))"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ls1.equation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 4, 3)"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ls1.equation()[0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[[-0.01236536+0.00000000e+00j, -0.01005247+5.68773936e-03j,\n",
+       "          -0.00549757+7.95377717e-03j],\n",
+       "         [-0.01153788+0.00000000e+00j, -0.00939428+5.27498912e-03j,\n",
+       "          -0.0051783 +7.37849050e-03j],\n",
+       "         [-0.0030817 +1.74249202e-18j, -0.00289097+5.57396739e-04j,\n",
+       "          -0.00247003+8.66105395e-04j],\n",
+       "         [ 0.01162891-6.57535638e-18j,  0.01078775-2.71376085e-03j,\n",
+       "           0.00894205-4.30908215e-03j]],\n",
+       " \n",
+       "        [[-0.01082435+0.00000000e+00j, -0.00942422+3.86578290e-03j,\n",
+       "          -0.00656125+5.73423935e-03j],\n",
+       "         [-0.01051733+0.00000000e+00j, -0.00917613+3.71879451e-03j,\n",
+       "          -0.0064318 +5.52675484e-03j],\n",
+       "         [ 0.00581445-3.28767819e-18j,  0.00539388-1.35688042e-03j,\n",
+       "           0.00447102-2.15454108e-03j],\n",
+       "         [-0.02194105+1.24061720e-17j, -0.02006046+6.25871524e-03j,\n",
+       "          -0.01593783+1.00113014e-02j]]]),\n",
+       " array([[[0.+0.j, 0.+0.j, 0.+0.j]],\n",
+       " \n",
+       "        [[0.+0.j, 0.+0.j, 0.+0.j]]]))"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ls1.equation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ls1.nunknowns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(3)"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ml.neq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 11e12ebcc99a0020145cc5a36e4fd454ed358c49 Mon Sep 17 00:00:00 2001
From: Mark Bakker <markbak@gmail.com>
Date: Tue, 24 Feb 2026 13:00:57 +0100
Subject: [PATCH 12/12] fix meandering river

---
 .../02examples/meandering_river.ipynb         |  16 +-
 .../02examples/meandering_test.ipynb          | 345 ------------------
 timflow/transient/linesink.py                 |   2 +-
 3 files changed, 16 insertions(+), 347 deletions(-)
 delete mode 100644 docs/transient/02examples/meandering_test.ipynb

diff --git a/docs/transient/02examples/meandering_river.ipynb b/docs/transient/02examples/meandering_river.ipynb
index 5c9f585..c5d158d 100644
--- a/docs/transient/02examples/meandering_river.ipynb
+++ b/docs/transient/02examples/meandering_river.ipynb
@@ -77,8 +77,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/transient/02examples/meandering_test.ipynb b/docs/transient/02examples/meandering_test.ipynb
deleted file mode 100644
index 32ded38..0000000
--- a/docs/transient/02examples/meandering_test.ipynb
+++ /dev/null
@@ -1,345 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Meandering river"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "import timflow.transient as tft"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml = tft.Model3D(\n",
-    "    kaq=[2, 1, 5, 10, 4],\n",
-    "    z=[10, 8, 6, 4, 2, 0],\n",
-    "    Saq=[0.1, 0.0001, 0.0002, 0.0002, 0.0001],\n",
-    "    phreatictop=True,\n",
-    "    kzoverkh=0.1,\n",
-    "    topboundary=\"conf\",\n",
-    "    tmin=1,\n",
-    "    tmax=10,\n",
-    "    M=1,\n",
-    ")\n",
-    "w = tft.Well(ml, -25, 0, rw=0.3, tsandQ=[(0, 100)], layers=[2, 3])\n",
-    "\n",
-    "xy = [(-50, -30), (40, 20)]\n",
-    "\n",
-    "#ls1 = tft.RiverString(ml, xy=xy, tsandh=\"fixed\", layers=[0, 1])\n",
-    "ls1 = tft.River(ml, -50, -30, 40, 20, tsandh=\"fixed\", layers=[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(-50, -30), (40, 20)]"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "self.neq  3\n",
-      "solution complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "ml.solve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[-0.48400507-2.73670650e-16j, -0.47400337+3.99311632e-02j,\n",
-       "         -0.44946707+6.87721303e-02j]]])"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ls1.parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ml.plots.contour(win=[-150, 150, -50, 50], ngr=40, t=1, layers=1, labels=False);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 3, 3)"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ls1.equation()[0].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([[[-0.01236536+0.00000000e+00j, -0.01005247+5.68773936e-03j,\n",
-       "          -0.00549757+7.95377717e-03j],\n",
-       "         [-0.01153788+0.00000000e+00j, -0.00939428+5.27498912e-03j,\n",
-       "          -0.0051783 +7.37849050e-03j],\n",
-       "         [-0.0030817 +1.74249202e-18j, -0.00289097+5.57396739e-04j,\n",
-       "          -0.00247003+8.66105395e-04j]]]),\n",
-       " array([[[0.+0.j, 0.+0.j, 0.+0.j]]]))"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ls1.equation()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 3, 3)"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ls1.equation()[0].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([[[-0.01082435+0.00000000e+00j, -0.00942422+3.86578290e-03j,\n",
-       "          -0.00656125+5.73423935e-03j],\n",
-       "         [-0.01051733+0.00000000e+00j, -0.00917613+3.71879451e-03j,\n",
-       "          -0.0064318 +5.52675484e-03j],\n",
-       "         [-0.02194105+1.24061720e-17j, -0.02006046+6.25871524e-03j,\n",
-       "          -0.01593783+1.00113014e-02j]]]),\n",
-       " array([[[0.+0.j, 0.+0.j, 0.+0.j]]]))"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ls1.equation()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 4, 3)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ls1.equation()[0].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([[[-0.01236536+0.00000000e+00j, -0.01005247+5.68773936e-03j,\n",
-       "          -0.00549757+7.95377717e-03j],\n",
-       "         [-0.01153788+0.00000000e+00j, -0.00939428+5.27498912e-03j,\n",
-       "          -0.0051783 +7.37849050e-03j],\n",
-       "         [-0.0030817 +1.74249202e-18j, -0.00289097+5.57396739e-04j,\n",
-       "          -0.00247003+8.66105395e-04j],\n",
-       "         [ 0.01162891-6.57535638e-18j,  0.01078775-2.71376085e-03j,\n",
-       "           0.00894205-4.30908215e-03j]],\n",
-       " \n",
-       "        [[-0.01082435+0.00000000e+00j, -0.00942422+3.86578290e-03j,\n",
-       "          -0.00656125+5.73423935e-03j],\n",
-       "         [-0.01051733+0.00000000e+00j, -0.00917613+3.71879451e-03j,\n",
-       "          -0.0064318 +5.52675484e-03j],\n",
-       "         [ 0.00581445-3.28767819e-18j,  0.00539388-1.35688042e-03j,\n",
-       "           0.00447102-2.15454108e-03j],\n",
-       "         [-0.02194105+1.24061720e-17j, -0.02006046+6.25871524e-03j,\n",
-       "          -0.01593783+1.00113014e-02j]]]),\n",
-       " array([[[0.+0.j, 0.+0.j, 0.+0.j]],\n",
-       " \n",
-       "        [[0.+0.j, 0.+0.j, 0.+0.j]]]))"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ls1.equation()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ls1.nunknowns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.int64(3)"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ml.neq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/timflow/transient/linesink.py b/timflow/transient/linesink.py
index ddb0f95..0109a6a 100644
--- a/timflow/transient/linesink.py
+++ b/timflow/transient/linesink.py
@@ -138,7 +138,7 @@ def potinf(self, x, y, aq=None):
                     )
                     # Divide by L as the parameter is total discharge
                     rv[:, i, j, :] = self.term2[:, i, j, :] * pot / self.L
-                return rv.reshape((self.nparam, aq.naq, self.model.npval))
+        return rv.reshape((self.nparam, aq.naq, self.model.npval))
 
     def disvecinf(self, x, y, aq=None):
         """Can be called with only one x,y value."""