build: drop py3.9, fix deps, fix types, lc2st tests (#1412)

* chore: drop Python 3.9 support, update workflows and docs.

* lint: checks and linting with py3.10.

* remove uv lock, update docs action

* fix pymc x-failing tests

* fix python 3.13 induced errors

* fix torch deps and plot pyright

* Fix circular import test

* test more python versions

* add numfocus badge and credits in readme

* remove testmon temporarily

* add testmon adain

* test python version in CD

* change locally passing but ci failing test

* turn off fast fail

* remote test still failing, increas n

---------

Co-authored-by: manuelgloeckler <manu.gloeckler@hotmail.de>

Author: janfb

.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

@@ -17,6 +17,10 @@ jobs:
   cd:
     name: Continuous Deployment
     runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ['3.10', '3.11', '3.12', '3.13']
 
     steps:
     - name: Checkout
@@ -28,16 +32,15 @@ jobs:
     - name: Install uv and set the python version
       uses: astral-sh/setup-uv@v5
       with:
-        python-version: '3.9'
+        python-version: ${{ matrix.python-version }}
         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

@@ -1,4 +1,4 @@
-name: Tests
+name: Continuous Integration
 
 on: [pull_request, workflow_dispatch]
 
@@ -12,15 +12,15 @@ concurrency:
 
 jobs:
   ci:
-    name: CI
+    name: Continuous Integration
     runs-on: ubuntu-latest
     if: |
       github.event_name == 'push' ||
       (github.event_name == 'pull_request' && github.event.pull_request.draft == false)
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.9', '3.12']
+        python-version: ['3.10']
 
     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: |
@@ -56,7 +57,7 @@ jobs:
         restore-keys: |
           testmon-${{ runner.os }}-${{ matrix.python-version }}-
 
-    - name: Run the fast CPU tests with coverage
+    - name: Run fast CPU tests with coverage
       run: uv run pytest --testmon-forceselect -v -n auto -m "not slow and not gpu" --cov=sbi --cov-report=xml tests/
 
     - name: Upload coverage to Codecov

.github/workflows/lint.yml

@@ -22,13 +22,13 @@ 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
 
   pyright:
-    name: Check types
+    name: type checking.
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
@@ -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

@@ -5,6 +5,7 @@
 [![codecov](https://codecov.io/gh/sbi-dev/sbi/branch/main/graph/badge.svg)](https://codecov.io/gh/sbi-dev/sbi)
 [![GitHub license](https://img.shields.io/github/license/sbi-dev/sbi)](https://github.com/sbi-dev/sbi/blob/master/LICENSE.txt)
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.02505/status.svg)](https://doi.org/10.21105/joss.02505)
+[![NumFOCUS affiliated](https://camo.githubusercontent.com/a0f197cee66ccd8ed498cf64e9f3f384c78a072fe1e65bada8d3015356ac7599/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e756d464f4355532d616666696c696174656425323070726f6a6563742d6f72616e67652e7376673f7374796c653d666c617426636f6c6f72413d45313532334426636f6c6f72423d303037443841)]
 
 ## `sbi`: Simulation-Based Inference
 
@@ -59,14 +60,14 @@ 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
@@ -205,7 +206,7 @@ Durkan's `lfi`. `sbi` runs as a community project. See also
 `sbi` has been supported by the German Federal Ministry of Education and Research (BMBF)
 through project ADIMEM (FKZ 01IS18052 A-D), project SiMaLeSAM (FKZ 01IS21055A) and the
 Tübingen AI Center (FKZ 01IS18039A). Since 2024, `sbi` is supported by the appliedAI
-Institute for Europe.
+Institute for Europe, and by NumFOCUS.
 
 ## License
 

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"]
@@ -33,12 +33,12 @@ dependencies = [
     "joblib>=1.0.0",
     "matplotlib",
     "notebook <= 6.4.12",
-    "numpy<2.0.0",
+    "numpy",
     "pillow",
     "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

@@ -124,12 +124,18 @@ def plt_hist_2d(
     ax: Axes,
     samples_col: np.ndarray,
     samples_row: np.ndarray,
-    limits_col: torch.Tensor,
-    limits_row: torch.Tensor,
+    limits_col: Union[torch.Tensor, List],
+    limits_row: Union[torch.Tensor, List],
     offdiag_kwargs: Dict,
 ):
-    hist_kwargs = copy.deepcopy(offdiag_kwargs)
     """Plot 2D histogram."""
+    hist_kwargs = copy.deepcopy(offdiag_kwargs)
+
+    if isinstance(limits_col, torch.Tensor):
+        limits_col = limits_col.tolist()
+    if isinstance(limits_row, torch.Tensor):
+        limits_row = limits_row.tolist()
+
     if (
         "bins" not in hist_kwargs["np_hist_kwargs"]
         or hist_kwargs["np_hist_kwargs"]["bins"] is None
@@ -787,7 +793,9 @@ def pairplot(
     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 +806,9 @@ def pairplot(
     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 +819,9 @@ def pairplot(
     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 +922,9 @@ def marginal_plot(
     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 +2045,7 @@ def marginal_plot_with_probs_intensity(
         # 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)
     ])