From 2fe7c8f7dc5924b939770a5ec3576235e6ea39ed Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Cl=C3=A9ment?= <git@drawbu.dev>
Date: Thu, 19 Mar 2026 18:13:28 -0700
Subject: [PATCH] python3Packages.tess: fix build failure

scipy has depecrated since version 1.15.0 the function `sph_harm`, and has been
removed in 1.17.0 in favor of `sph_harm_y`

https://docs.scipy.org/doc/scipy-1.16.2/reference/generated/scipy.special.sph_harm.html
---
 .../python-modules/tess/default.nix           |  4 ++++
 .../python-modules/tess/scipy_sph_harm.patch  | 20 +++++++++++++++++++
 2 files changed, 24 insertions(+)
 create mode 100644 pkgs/development/python-modules/tess/scipy_sph_harm.patch

diff --git a/pkgs/development/python-modules/tess/default.nix b/pkgs/development/python-modules/tess/default.nix
index d28de79e9d91..703e70d3ee8e 100644
--- a/pkgs/development/python-modules/tess/default.nix
+++ b/pkgs/development/python-modules/tess/default.nix
@@ -30,6 +30,10 @@ buildPythonPackage rec {
     scipy
   ];
 
+  # scipy has depecrated since version 1.15.0 the function `sph_harm`, and has
+  # been removed in 1.17.0 in favor of `sph_harm_y`
+  patches = [ ./scipy_sph_harm.patch ];
+
   pythonImportsCheck = [ "tess" ];
 
   nativeCheckInputs = [ pytestCheckHook ];
diff --git a/pkgs/development/python-modules/tess/scipy_sph_harm.patch b/pkgs/development/python-modules/tess/scipy_sph_harm.patch
new file mode 100644
index 000000000000..0d9830cff7f3
--- /dev/null
+++ b/pkgs/development/python-modules/tess/scipy_sph_harm.patch
@@ -0,0 +1,20 @@
+diff --git a/tess/__init__.py b/tess/__init__.py
+index b7f8f80..91c518f 100644
+--- a/tess/__init__.py
++++ b/tess/__init__.py
+@@ -374,13 +374,13 @@ def orderQ(l, xyz, weights=1):
+     :math:`w_i` are the `weights`, defaulting to uniform: (:math:`\frac{1}{N_b}`)
+     """
+     import numpy as np
+-    from scipy.special import sph_harm
++    from scipy.special import sph_harm_y
+ 
+     theta, phi = cart_to_spher(xyz).T
+     weights = np.ones(xyz.shape[0]) * weights
+     weights /= np.sum(weights)
+     mmeans = np.zeros(2 * l + 1, dtype=float)
+     for m in range(-l, l + 1):
+-        sph_weighted = sph_harm(m, l, phi, theta).dot(weights)  # Average of Y_{6m}
++        sph_weighted = sph_harm_y(l, m, theta, phi).dot(weights)  # Average of Y_{6m}
+         mmeans[m] = abs(sph_weighted) ** 2
+     return np.sqrt(4 * np.pi / (2 * l + 1) * np.sum(mmeans))