explicitly add dh to quadrature tests

Author: CosmoMatt

.pip_readme.rst

@@ -29,7 +29,7 @@ available resources and desired angular resolution $L$.
 
 As of version 1.0.2 `S2FFT` also provides PyTorch implementations of underlying 
 precompute transforms. In future releases this support will be extended to our 
-recursion based algorithms.
+on-the-fly algorithms.
 
 Documentation
 =============

README.md

@@ -24,7 +24,7 @@ available resources and desired angular resolution $L$.
 
 As of version 1.0.2 `S2FFT` also provides PyTorch implementations of underlying 
 precompute transforms. In future releases this support will be extended to our 
-recursion based algorithms.
+on-the-fly algorithms.
 
 ## Algorithms :zap:
 

docs/index.rst

@@ -12,7 +12,7 @@ may select depending on available resources and desired angular resolution :math
 
 As of version 1.0.2 ``S2FFT`` also provides PyTorch implementations of underlying 
 precompute transforms. In future releases this support will be extended to our 
-recursion based algorithms.
+on-the-fly algorithms.
 
 Algorithms |:zap:|
 -------------------

s2fft/utils/quadrature_torch.py

@@ -137,9 +137,9 @@ def quad_weight_dh_theta_only(theta: float, L: int) -> float:
     """
     w = 0.0
     for k in range(0, L):
-        w += torch.sin((2 * k + 1) * theta) / (2 * k + 1)
+        w += torch.sin((2 * k + 1) * torch.from_numpy(theta)) / (2 * k + 1)
 
-    w *= 2 / L * torch.sin(theta)
+    w *= 2 / L * torch.sin(torch.from_numpy(theta))
 
     return w
 

tests/test_quadrature.py

@@ -9,7 +9,7 @@
 
 
 @pytest.mark.parametrize("L", [5, 6])
-@pytest.mark.parametrize("sampling", ["mw", "mwss"])
+@pytest.mark.parametrize("sampling", ["mw", "mwss", "dh"])
 @pytest.mark.parametrize("method", ["numpy", "jax", "torch"])
 def test_quadrature_mw_weights(flm_generator, L: int, sampling: str, method: str):
     spin = 0