From 6a36b4d4b7228b0d73add7efb677af8524d54a17 Mon Sep 17 00:00:00 2001
From: asteppke <asteppke@gmail.com>
Date: Thu, 13 Feb 2025 17:00:20 +0100
Subject: [PATCH 1/2] updated regularization for scipy >=1.12

---
 .../__chartrand_tvregdiff__.py                | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/pynumdiff/total_variation_regularization/__chartrand_tvregdiff__.py b/pynumdiff/total_variation_regularization/__chartrand_tvregdiff__.py
index 8b35909..2e226a6 100644
--- a/pynumdiff/total_variation_regularization/__chartrand_tvregdiff__.py
+++ b/pynumdiff/total_variation_regularization/__chartrand_tvregdiff__.py
@@ -228,7 +228,7 @@ def AT(w): return (sum(w) * np.ones(n + 1) -
             g = AT(A(u)) + ATb + alph * L * u
 
             # Prepare to solve linear equation.
-            tol = 1e-4
+            rtol = 1e-4
             # Simple preconditioner.
             P = alph * sparse.spdiags(L.diagonal() + 1, 0, n + 1, n + 1)
 
@@ -237,8 +237,8 @@ def linop(v): return (alph * L * v + AT(A(v)))
 
             if diagflag:
                 [s, info_i] = sparse.linalg.cg(
-                    linop, g, x0=None, tol=tol, maxiter=maxit, callback=None,
-                    M=P, atol='legacy')
+                    linop, g, x0=None, rtol=rtol, maxiter=maxit, callback=None,
+                    M=P, atol=0)
                 #print('iteration {0:4d}: relative change = {1:.3e}, '
                 #      'gradient norm = {2:.3e}\n'.format(ii,
                 #                                         np.linalg.norm(
@@ -251,8 +251,8 @@ def linop(v): return (alph * L * v + AT(A(v)))
                 #    print("WARNING - illegal input or breakdown")
             else:
                 [s, info_i] = sparse.linalg.cg(
-                    linop, g, x0=None, tol=tol, maxiter=maxit, callback=None,
-                    M=P, atol='legacy')
+                    linop, g, x0=None, rtol=rtol, maxiter=maxit, callback=None,
+                    M=P, atol=0)
             # Update solution.
             u = u - s
             # Display plot.
@@ -300,7 +300,7 @@ def AT(w): return (sum(w) * np.ones(len(w)) -
             # droptol = 1.0e-2
             R = sparse.dia_matrix(np.linalg.cholesky(B.todense()))
             # Prepare to solve linear equation.
-            tol = 1.0e-4
+            rtol = 1.0e-4
 
             def linop(v): return (alph * L * v + AT(A(v)))
             linop = splin.LinearOperator((n, n), linop)
@@ -308,8 +308,8 @@ def linop(v): return (alph * L * v + AT(A(v)))
             print(maxit)
             if diagflag:
                 [s, info_i] = sparse.linalg.cg(
-                    linop, -g, x0=None, tol=tol, maxiter=maxit, callback=None,
-                    M=np.dot(R.transpose(), R), atol='legacy')
+                    linop, -g, x0=None, rtol=rtol, maxiter=maxit, callback=None,
+                    M=np.dot(R.transpose(), R), atol=0)
                 print('iteration {0:4d}: relative change = {1:.3e}, '
                       'gradient norm = {2:.3e}\n'.format(ii,
                                                          np.linalg.norm(s[0]) /
@@ -322,8 +322,8 @@ def linop(v): return (alph * L * v + AT(A(v)))
 
             else:
                 [s, info_i] = sparse.linalg.cg(
-                    linop, -g, x0=None, tol=tol, maxiter=maxit, callback=None,
-                    M=np.dot(R.transpose(), R), atol='legacy')
+                    linop, -g, x0=None, rtol=rtol, maxiter=maxit, callback=None,
+                    M=np.dot(R.transpose(), R), atol=0)
             # Update current solution
             u = u + s
             # Display plot.

From c2cbdd0ff82c099af91a0f1cd71030dafea847d1 Mon Sep 17 00:00:00 2001
From: asteppke <asteppke@gmail.com>
Date: Thu, 13 Feb 2025 17:10:00 +0100
Subject: [PATCH 2/2] fixed cumtrapz depreciation

---
 pynumdiff/linear_model/_linear_model.py | 2 +-
 pynumdiff/utils/utility.py              | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/pynumdiff/linear_model/_linear_model.py b/pynumdiff/linear_model/_linear_model.py
index 39e728e..1b35822 100644
--- a/pynumdiff/linear_model/_linear_model.py
+++ b/pynumdiff/linear_model/_linear_model.py
@@ -414,7 +414,7 @@ def __integrate_dxdt_hat_matrix__(dxdt_hat, dt):
     #assert isinstance(dxdt_hat, np.matrix)
     if len(dxdt_hat.shape) == 1:
         dxdt_hat = np.reshape(dxdt_hat, [1, len(dxdt_hat)])
-    x = np.array(scipy.integrate.cumtrapz(dxdt_hat, axis=1))
+    x = np.array(scipy.integrate.cumulative_trapezoid(dxdt_hat, axis=1))
     first_value = x[:, 0:1] - np.mean(dxdt_hat[:, 0:1], axis=1).reshape(dxdt_hat.shape[0], 1)
     x = np.hstack((first_value, x))*dt
     return x
diff --git a/pynumdiff/utils/utility.py b/pynumdiff/utils/utility.py
index bbf9249..c561e71 100644
--- a/pynumdiff/utils/utility.py
+++ b/pynumdiff/utils/utility.py
@@ -213,7 +213,7 @@ def finite_difference(x, dt):
 # Trapazoidal integration, with interpolated final point so that the lengths match.
 def integrate_dxdt_hat(dxdt_hat, dt):
     """
-    Wrapper for scipy.integrate.cumtrapz to integrate dxdt_hat that ensures the integral has the same length
+    Wrapper for scipy.integrate.cumulative_trapezoid to integrate dxdt_hat that ensures the integral has the same length
 
     :param dxdt_hat: estimate derivative of timeseries
     :type dxdt_hat: np.array
@@ -224,7 +224,7 @@ def integrate_dxdt_hat(dxdt_hat, dt):
     :return: integral of dxdt_hat
     :rtype: np.array
     """
-    x = scipy.integrate.cumtrapz(dxdt_hat)
+    x = scipy.integrate.cumulative_trapezoid(dxdt_hat)
     first_value = x[0] - np.mean(dxdt_hat[0:1])
     x = np.hstack((first_value, x))*dt
     return x