Reenable pytorch 4x4solver

.github/workflows/benchmark.yml

@@ -19,11 +19,12 @@ jobs:
           submodules: recursive
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: "3.12"
       - name: Install deps
         run: |
           curl -LsSf https://astral.sh/uv/install.sh | sh
           uv pip install --system -r requirements/dev-requirements.txt
+          uv pip install --system torch --index-url https://download.pytorch.org/whl/cpu
       - name: Install module
         run: |
           uv pip install --system .

.github/workflows/pytest.yml

@@ -29,6 +29,7 @@ jobs:
         run: |
           curl -LsSf https://astral.sh/uv/install.sh | sh
           uv pip install --system ".[fitting,dev]"
+          uv pip install --system torch --index-url https://download.pytorch.org/whl/cpu
       - name: Test with pytest
         run: |
           pytest --nbmake

.github/workflows/requirements.yml

@@ -21,6 +21,7 @@ jobs:
         run: |
           curl -LsSf https://astral.sh/uv/install.sh | sh
           uv pip install --system --no-deps .[fitting,dev]
+          uv pip install --system torch --index-url https://download.pytorch.org/whl/cpu
       - name: Install dev requirements
         run: |
           uv pip install --system -r requirements/dev-requirements.txt

requirements/dev-requirements.txt

@@ -6,10 +6,6 @@ appdirs==1.4.4 \
     # via
     #   -r requirements/fitting-requirements.txt
     #   pint
-appnope==0.1.4 \
-    --hash=sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee \
-    --hash=sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c
-    # via ipykernel
 asteval==0.9.33 \
     --hash=sha256:94981701f4d252c88aa5e821121b1aabef73a003da138fc6405169c9e675d24d \
     --hash=sha256:aae3a0308575a545c8cecc43a6632219e6a90963a56380c74632cf54311e43bf
@@ -187,13 +183,6 @@ distlib==0.3.8 \
     --hash=sha256:034db59a0b96f8ca18035f36290806a9a6e6bd9d1ff91e45a7f172eb17e51784 \
     --hash=sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64
     # via virtualenv
-exceptiongroup==1.2.1 \
-    --hash=sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad \
-    --hash=sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16
-    # via
-    #   -r requirements/fitting-requirements.txt
-    #   ipython
-    #   pytest
 executing==2.0.1 \
     --hash=sha256:35afe2ce3affba8ee97f2d69927fa823b08b472b7b994e36a52a964b93d16147 \
     --hash=sha256:eac49ca94516ccc753f9fb5ce82603156e590b27525a8bc32cce8ae302eb61bc
@@ -204,9 +193,9 @@ fastjsonschema==2.20.0 \
     --hash=sha256:3d48fc5300ee96f5d116f10fe6f28d938e6008f59a6a025c2649475b87f76a23 \
     --hash=sha256:5875f0b0fa7a0043a91e93a9b8f793bcbbba9691e7fd83dca95c28ba26d21f0a
     # via nbformat
-filelock==3.15.1 \
-    --hash=sha256:58a2549afdf9e02e10720eaa4d4470f56386d7a6f72edd7d0596337af8ed7ad8 \
-    --hash=sha256:71b3102950e91dfc1bb4209b64be4dc8854f40e5f534428d8684f953ac847fac
+filelock==3.15.3 \
+    --hash=sha256:0151273e5b5d6cf753a61ec83b3a9b7d8821c39ae9af9d7ecf2f9e2f17404103 \
+    --hash=sha256:e1199bf5194a2277273dacd50269f0d87d0682088a3c561c15674ea9005d8635
     # via virtualenv
 flexcache==0.3 \
     --hash=sha256:18743bd5a0621bfe2cf8d519e4c3bfdf57a269c15d1ced3fb4b64e0ff4600656 \
@@ -1253,12 +1242,6 @@ tenacity==8.4.1 \
     # via
     #   -r requirements/fitting-requirements.txt
     #   plotly
-tomli==2.0.1 \
-    --hash=sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc \
-    --hash=sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f
-    # via
-    #   coverage
-    #   pytest
 tornado==6.4.1 \
     --hash=sha256:163b0aafc8e23d8cdc3c9dfb24c5368af84a81e3364745ccb4427669bf84aec8 \
     --hash=sha256:25486eb223babe3eed4b8aecbac33b37e3dd6d776bc730ca14e1bf93888b979f \

requirements/fitting-requirements.txt

@@ -76,10 +76,6 @@ dill==0.3.8 \
     --hash=sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca \
     --hash=sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7
     # via lmfit
-exceptiongroup==1.2.1 \
-    --hash=sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad \
-    --hash=sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16
-    # via ipython
 executing==2.0.1 \
     --hash=sha256:35afe2ce3affba8ee97f2d69927fa823b08b472b7b994e36a52a964b93d16147 \
     --hash=sha256:eac49ca94516ccc753f9fb5ce82603156e590b27525a8bc32cce8ae302eb61bc

src/elli/solver4x4.py

@@ -4,6 +4,11 @@
 import numpy as np
 import numpy.typing as npt
 import scipy.constants as sc
+
+try:
+    import torch
+except ImportError:
+    ...
 from numpy.lib.scimath import sqrt
 from scipy.linalg import expm as scipy_expm
 
@@ -71,7 +76,30 @@ def calculate_propagation(
         """
         mats = 1j * thickness * np.einsum("nij,n->nij", delta, 2 * sc.pi / lbda)
 
-        propagator = np.asarray([scipy_expm(mat) for mat in mats])
+        propagator = scipy_expm(mats)
+
+        return propagator
+
+
+class PropagatorExpmTorch(Propagator):
+    """Propagator class using the matrix exponential provided by PyTorch."""
+
+    def calculate_propagation(
+        self, delta: npt.NDArray, thickness: float, lbda: npt.ArrayLike
+    ) -> npt.NDArray:
+        """Calculates propagation for a given Delta matrix and layer thickness with the Padé approximation of the matrix exponential.
+
+        Args:
+            delta (npt.NDArray): Delta Matrix
+            thickness (float): Thickness of layer (nm)
+            lbda (npt.ArrayLike): Wavelengths to evaluate (nm)
+
+        Returns:
+            npt.NDArray: Propagator for the given layer
+        """
+        mats = 1j * thickness * np.einsum("nij,n->nij", delta, 2 * sc.pi / lbda)
+
+        propagator = torch.linalg.matrix_exp(torch.from_numpy(mats)).numpy()
 
         return propagator
 

tests/benchmark_propagators_TiO2.py

@@ -1,10 +1,9 @@
 """Testing benchmark for each solver"""
 
-from pytest import fixture
-import numpy as np
-
 import elli
+import numpy as np
 from elli.fitting import ParamsHist
+from pytest import fixture
 
 
 @fixture
@@ -85,6 +84,17 @@ def test_solver4x4_expm(benchmark, structure):
     )
 
 
+def test_solver4x4_expm_pytorch(benchmark, structure):
+    """Benchmarks expm-torch propagator with solver4x4"""
+    benchmark.pedantic(
+        structure.evaluate,
+        args=(lbda, PHI),
+        kwargs={"solver": elli.Solver4x4, "propagator": elli.PropagatorExpmTorch()},
+        iterations=1,
+        rounds=10,
+    )
+
+
 def test_solver4x4_linear(benchmark, structure):
     """Benchmarks linear propagator with solver4x4"""
     benchmark.pedantic(

tests/test_TiO2.py

@@ -5,11 +5,10 @@
 import os
 from shutil import copytree, rmtree
 
-import numpy as np
-from pytest import fixture
-
 import elli
+import numpy as np
 from elli.fitting import ParamsHist
+from pytest import fixture
 
 
 @fixture
@@ -132,6 +131,21 @@ def test_solver4x4_expm(self, si_dispersion, meas_data):
 
         assert TestTiO2.chisqr(meas_data, sim_data) < 0.0456
 
+    def test_solver4x4_expm_torch(self, si_dispersion, meas_data):
+        """The solver4x4 with scipy propagator is within chi square accuracy"""
+        sim_data = (
+            elli.Structure(elli.AIR, self.Layer, si_dispersion)
+            .evaluate(
+                meas_data.index,
+                70,
+                solver=elli.Solver4x4,
+                propagator=elli.PropagatorExpmTorch(),
+            )
+            .rho
+        )
+
+        assert TestTiO2.chisqr(meas_data, sim_data) < 0.0456
+
     def test_solver4x4_eig(self, si_dispersion, meas_data):
         """The solver4x4 with eig propagator is within chi square accuracy"""
         sim_data = (