Drop py3.9 fix ci

.github/workflows/build_docs.yml

@@ -5,6 +5,7 @@ on:
       - main
   release:
     types: [ published ]
+  workflow_dispatch:
 
 jobs:
   docs:
@@ -17,31 +18,24 @@ jobs:
         fetch-depth: 0
         lfs: false
 
-    - name: Set up Python
-      uses: actions/setup-python@v2
+    - name: Install uv and set the python version
+      uses: astral-sh/setup-uv@v5
       with:
         python-version: '3.10'
+        enable-cache: true
+        cache-dependency-glob: "pyproject.toml"
 
-    - name: Cache dependency
-      id: cache-dependencies
-      uses: actions/cache@v4
-      with:
-        path: ~/.cache/pip
-        key: ${{ runner.os }}-pip
-
-    - name: Install sbi and dependencies
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install .[doc]
+    - name: Install dependencies with uv
+      run: uv sync --all-extras --doc
 
     - name: strip output except plots and prints from tutorial notebooks
       run: |
-        python tests/strip_notebook_outputs.py tutorials/
+        uv run python tests/strip_notebook_outputs.py tutorials/
 
     - name: convert notebooks to markdown
       run: |
         cd docs
-        jupyter nbconvert --to markdown ../tutorials/*.ipynb --output-dir docs/tutorials/
+        uv run jupyter nbconvert --to markdown ../tutorials/*.ipynb --output-dir docs/tutorials/
 
     - name: Configure Git user for bot
       run: |
@@ -52,10 +46,10 @@ jobs:
       if: ${{ github.event_name == 'push' }}
       run: |
         cd docs
-        mike deploy dev --push
+        uv run mike deploy dev --push
 
     - name: Build and deploy the lastest documentation upon new release
       if: ${{ github.event_name == 'release' }}
       run: |
         cd docs
-        mike deploy ${{ github.event.release.name }} latest -u --push
+        uv run mike deploy ${{ github.event.release.name }} latest -u --push

.github/workflows/cd.yml

@@ -28,16 +28,15 @@ jobs:
     - name: Install uv and set the python version
       uses: astral-sh/setup-uv@v5
       with:
-        python-version: '3.9'
+        python-version: '3.10'
         enable-cache: true
+        cache-dependency-glob: "pyproject.toml"
 
     - name: Install dependencies with uv
-      run: |
-        uv pip install -e .[dev]
+      run: uv sync --all-extras --dev
 
     - name: Run the fast and the slow CPU tests with coverage
-      run: |
-        uv run pytest -v -x -n auto -m "not gpu" --cov=sbi --cov-report=xml tests/
+      run: uv run pytest -v -x -n auto -m "not gpu" --cov=sbi --cov-report=xml tests/
 
     - name: Upload coverage to Codecov
       uses: codecov/codecov-action@v4-beta

.github/workflows/ci.yml

@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.9', '3.12']
+        python-version: ['3.10', '3.13']
 
     steps:
     - name: Checkout
@@ -34,6 +34,7 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
         enable-cache: true
+        cache-dependency-glob: "pyproject.toml"
 
     - name: Install dependencies with uv
       run: |

.github/workflows/lint.yml

@@ -22,7 +22,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: '3.9'
+          python-version: '3.10'
       - uses: pre-commit/action@v3.0.1
         with:
           extra_args: --all-files --show-diff-on-failure
@@ -40,14 +40,12 @@ jobs:
       - name: Install uv and set the python version
         uses: astral-sh/setup-uv@v5
         with:
-          python-version: '3.9'
+          python-version: '3.10'
           enable-cache: true
+          cache-dependency-glob: "pyproject.toml"
 
       - name: Install dependencies with uv
-        run: |
-          uv pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
-          uv pip install -e .[dev]
+        run: uv sync --all-extras --dev
 
       - name: Check types with pyright
-        run: |
-          uv run pyright sbi
+        run: uv run pyright sbi

.github/workflows/publish.yml

@@ -15,7 +15,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: "3.9"
+        python-version: "3.10"
     - name: Install pypa/build
       run: >-
         python3 -m
@@ -25,7 +25,7 @@ jobs:
     - name: Build a binary wheel and a source tarball
       run: python3 -m build
     - name: Store the distribution packages
-      uses: actions/upload-artifact@v3
+      uses: actions/upload-artifact@v4
       with:
         name: python-package-distributions
         path: dist/
@@ -45,7 +45,7 @@ jobs:
 
     steps:
     - name: Download all the dists
-      uses: actions/download-artifact@v3
+      uses: actions/download-artifact@v4
       with:
         name: python-package-distributions
         path: dist/
@@ -66,7 +66,7 @@ jobs:
 
     steps:
     - name: Download all the dists
-      uses: actions/download-artifact@v3
+      uses: actions/download-artifact@v4
       with:
         name: python-package-distributions
         path: dist/

README.md

@@ -59,14 +59,14 @@ posterior = inference.build_posterior()
 
 ### Installation
 
-`sbi` requires Python 3.9 or higher. While a GPU isn't necessary, it can improve
+`sbi` requires Python 3.10 or higher. While a GPU isn't necessary, it can improve
 performance in some cases. We recommend using a virtual environment with
 [`conda`](https://docs.conda.io/en/latest/miniconda.html) for an easy setup.
 
 If `conda` is installed on the system, an environment for installing `sbi` can be created as follows:
 
 ```
-conda create -n sbi_env python=3.9 && conda activate sbi_env
+conda create -n sbi_env python=3.10 && conda activate sbi_env
 ```
 
 ### From PyPI

docs/docs/install.md

@@ -1,6 +1,6 @@
 # Installation
 
-`sbi` requires Python 3.9 or higher. A GPU is not required, but can lead to
+`sbi` requires Python 3.10 or higher. A GPU is not required, but can lead to
 speed-up in some cases. We recommend using a
 [`conda`](https://docs.conda.io/en/latest/miniconda.html) virtual environment
 ([Miniconda installation

pyproject.toml

@@ -24,7 +24,7 @@ classifiers = [
     "Programming Language :: Python :: 3",
     "Development Status :: 3 - Alpha",
 ]
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dynamic = ["version"]
 readme = "README.md"
 keywords = ["Bayesian inference", "simulation-based inference", "PyTorch"]
@@ -38,7 +38,7 @@ dependencies = [
     "pyknos>=0.16.0",
     "pyro-ppl>=1.3.1",
     "scikit-learn",
-    "scipy<1.13",
+    "scipy",
     "tensorboard",
     "torch>=1.13.0, <2.6.0",
     "tqdm",
@@ -140,7 +140,7 @@ xfail_strict = true
 [tool.pyright]
 include = ["sbi"]
 exclude = ["**/__pycache__", "**/__node_modules__", ".git", "docs", "tutorials", "tests"]
-python_version = "3.9"
+python_version = "3.10"
 reportUnsupportedDunderAll = false
 reportGeneralTypeIssues = false
 reportInvalidTypeForm = false

sbi/analysis/plot.py

@@ -787,7 +787,9 @@
     diag_kwargs_list = to_list_kwargs(diag_kwargs, len(samples))
     diag_func = get_diag_funcs(diag_list)
     diag_kwargs_filled = []
-    for i, (diag_i, diag_kwargs_i) in enumerate(zip(diag_list, diag_kwargs_list)):
+    for i, (diag_i, diag_kwargs_i) in enumerate(
+        zip(diag_list, diag_kwargs_list, strict=False)
+    ):
         diag_kwarg_filled_i = _get_default_diag_kwargs(diag_i, i)
         # update the defaults dictionary with user provided values
         diag_kwarg_filled_i = _update(diag_kwarg_filled_i, diag_kwargs_i)
@@ -798,7 +800,9 @@
     upper_kwargs_list = to_list_kwargs(upper_kwargs, len(samples))
     upper_func = get_offdiag_funcs(upper_list)
     upper_kwargs_filled = []
-    for i, (upper_i, upper_kwargs_i) in enumerate(zip(upper_list, upper_kwargs_list)):
+    for i, (upper_i, upper_kwargs_i) in enumerate(
+        zip(upper_list, upper_kwargs_list, strict=False)
+    ):
         upper_kwarg_filled_i = _get_default_offdiag_kwargs(upper_i, i)
         # update the defaults dictionary with user provided values
         upper_kwarg_filled_i = _update(upper_kwarg_filled_i, upper_kwargs_i)
@@ -809,7 +813,9 @@
     lower_kwargs_list = to_list_kwargs(lower_kwargs, len(samples))
     lower_func = get_offdiag_funcs(lower_list)
     lower_kwargs_filled = []
-    for i, (lower_i, lower_kwargs_i) in enumerate(zip(lower_list, lower_kwargs_list)):
+    for i, (lower_i, lower_kwargs_i) in enumerate(
+        zip(lower_list, lower_kwargs_list, strict=False)
+    ):
         lower_kwarg_filled_i = _get_default_offdiag_kwargs(lower_i, i)
         # update the defaults dictionary with user provided values
         lower_kwarg_filled_i = _update(lower_kwarg_filled_i, lower_kwargs_i)
@@ -910,7 +916,9 @@
     diag_kwargs_list = to_list_kwargs(diag_kwargs, len(samples))
     diag_func = get_diag_funcs(diag_list)
     diag_kwargs_filled = []
-    for i, (diag_i, diag_kwargs_i) in enumerate(zip(diag_list, diag_kwargs_list)):
+    for i, (diag_i, diag_kwargs_i) in enumerate(
+        zip(diag_list, diag_kwargs_list, strict=False)
+    ):
         diag_kwarg_filled_i = _get_default_diag_kwargs(diag_i, i)
         diag_kwarg_filled_i = _update(diag_kwarg_filled_i, diag_kwargs_i)
         diag_kwargs_filled.append(diag_kwarg_filled_i)
@@ -2031,7 +2039,7 @@
         # normalize color intensity
         norm = Normalize(vmin=vmin, vmax=vmax)
         # set color intensity
-        for w, p in zip(weights, patches):
+        for w, p in zip(weights, patches, strict=False):
             p.set_facecolor(cmap(w))
         if show_colorbar:
             plt.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax_, label=label)

sbi/diagnostics/sbc.py

@@ -138,7 +138,7 @@ def _run_sbc(
     ranks = torch.zeros((num_sbc_samples, len(reduce_fns)))
     # Iterate over all sbc samples and calculate ranks.
     for sbc_idx, (true_theta, x_i) in tqdm(
-        enumerate(zip(thetas, xs)),
+        enumerate(zip(thetas, xs, strict=False)),
         total=num_sbc_samples,
         disable=not show_progress_bar,
         desc=f"Calculating ranks for {num_sbc_samples} sbc samples.",
@@ -188,7 +188,7 @@ def get_nltp(thetas: Tensor, xs: Tensor, posterior: NeuralPosterior) -> Tensor:
     nltp = torch.zeros(thetas.shape[0])
     unnormalized_log_prob = not isinstance(posterior, (DirectPosterior, ScorePosterior))
 
-    for idx, (tho, xo) in enumerate(zip(thetas, xs)):
+    for idx, (tho, xo) in enumerate(zip(thetas, xs, strict=False)):
         # Log prob of true params under posterior.
         if unnormalized_log_prob:
             nltp[idx] = -posterior.potential(tho, x=xo)
@@ -266,7 +266,7 @@ def check_prior_vs_dap(prior_samples: Tensor, dap_samples: Tensor) -> Tensor:
 
     return torch.tensor([
         c2st(s1.unsqueeze(1), s2.unsqueeze(1))
-        for s1, s2 in zip(prior_samples.T, dap_samples.T)
+        for s1, s2 in zip(prior_samples.T, dap_samples.T, strict=False)
     ])
 
 

sbi/neural_nets/factory.py

@@ -112,6 +112,7 @@ def classifier_nn(
                 check_net_device(embedding_net_theta, "cpu", embedding_net_warn_msg),
                 check_net_device(embedding_net_x, "cpu", embedding_net_warn_msg),
             ),
+            strict=False,
         ),
         **kwargs,
     )
@@ -194,6 +195,7 @@ def likelihood_nn(
                 check_net_device(embedding_net, "cpu", embedding_net_warn_msg),
                 num_components,
             ),
+            strict=False,
         ),
         **kwargs,
     )
@@ -332,6 +334,7 @@ def posterior_nn(
                 check_net_device(embedding_net, "cpu", embedding_net_warn_msg),
                 num_components,
             ),
+            strict=False,
         ),
         **kwargs,
     )
@@ -434,6 +437,7 @@ def posterior_score_nn(
                 hidden_features,
                 embedding_net,
             ),
+            strict=False,
         ),
         **kwargs,
     )

sbi/samplers/mcmc/slice_numpy.py

@@ -289,7 +289,7 @@ def run(self, num_samples: int) -> np.ndarray:
         ):
             all_samples: Sequence[np.ndarray] = Parallel(n_jobs=self.num_workers)(  # pyright: ignore[reportAssignmentType]
                 delayed(self.run_fun)(num_samples, initial_params_batch, seed)
-                for initial_params_batch, seed in zip(self.x, seeds)
+                for initial_params_batch, seed in zip(self.x, seeds, strict=False)
             )
 
         samples = np.stack(all_samples).astype(np.float32)

sbi/samplers/vi/vi_divergence_optimizers.py

@@ -200,7 +200,7 @@
 
     def update_state(self) -> None:
         """This updates the current state."""
-        for state_para, para in zip(self.state_dict, self.q.parameters()):
+        for state_para, para in zip(self.state_dict, self.q.parameters(), strict=False):
             if torch.isfinite(para).all():
                 state_para.data = para.data.clone()
             else:
@@ -213,7 +213,7 @@
         Args:
             warm_up_rounds: Number of warm_up_round one should do after failure.
         """
-        for state_para, para in zip(self.state_dict, self.q.parameters()):
+        for state_para, para in zip(self.state_dict, self.q.parameters(), strict=False):
             para.data = state_para.data.clone().to(para.device)
         self._optimizer.__init__(self.q.parameters(), self.learning_rate)
         self.warm_up(warm_up_rounds)
@@ -464,7 +464,7 @@
     def update_surrogate_q(self) -> None:
         """Updates the surrogate with new parameters."""
         for param, param_surro in zip(
-            self.q.parameters(), self._surrogate_q.parameters()
+            self.q.parameters(), self._surrogate_q.parameters(), strict=False
         ):
             param_surro.data = param.data
             param_surro.requires_grad = False

sbi/simulators/simutils.py

@@ -73,7 +73,7 @@ def simulator_seeded(theta: Tensor, seed) -> Tensor:
             ) as _:
                 simulation_outputs: List[Tensor] = Parallel(n_jobs=num_workers)(  # pyright: ignore[reportAssignmentType]
                     delayed(simulator_seeded)(batch, batch_seed)
-                    for batch, batch_seed in zip(batches, batch_seeds)
+                    for batch, batch_seed in zip(batches, batch_seeds, strict=False)
                 )
         else:
             pbar = tqdm(

sbi/utils/conditional_density_utils.py

@@ -73,7 +73,7 @@ def _compute_covariance(
     # Compute E[X] * E[Y].
     expected_values_of_marginals = [
         _expected_value_f_of_x(prob.unsqueeze(0), lim.unsqueeze(0))
-        for prob, lim in zip(_calc_marginals(probs, limits), limits)
+        for prob, lim in zip(_calc_marginals(probs, limits), limits, strict=False)
     ]
 
     return expected_value_of_joint - f(*expected_values_of_marginals)
@@ -104,7 +104,7 @@ def _expected_value_f_of_x(
 
     x_values_over_which_we_integrate = [
         torch.linspace(lim[0].item(), lim[1].item(), prob.shape[0], device=probs.device)
-        for lim, prob in zip(torch.flip(limits, [0]), probs)
+        for lim, prob in zip(torch.flip(limits, [0]), probs, strict=False)
     ]  # See #403 and #404 for flip().
     grids = list(torch.meshgrid(x_values_over_which_we_integrate))
     expected_val = torch.sum(f(*grids) * probs)

sbi/utils/diagnostics_utils.py

@@ -75,7 +75,7 @@ def sample_fun(
             tqdm(
                 Parallel(return_as="generator", n_jobs=num_workers)(
                     delayed(sample_fun)(posterior, sample_shape, x=x, seed=s)
-                    for x, s in zip(xs, seeds)
+                    for x, s in zip(xs, seeds, strict=False)
                 ),
                 disable=not show_progress_bar,
                 total=len(xs),