fixes by ruff formatter

examples/HH_helper_functions.py

@@ -214,15 +214,15 @@ def calculate_summary_statistics(x):
     )
 
     # concatenation of summary statistics
-    sum_stats_vec = np.concatenate(
-        (
-            np.array([spike_times_stim.shape[0]]),
-            np.array(
-                [rest_pot, rest_pot_std, np.mean(x["data"][(t > t_on) & (t < t_off)])]
-            ),
-            moments,
-        )
-    )
+    sum_stats_vec = np.concatenate((
+        np.array([spike_times_stim.shape[0]]),
+        np.array([
+            rest_pot,
+            rest_pot_std,
+            np.mean(x["data"][(t > t_on) & (t < t_off)]),
+        ]),
+        moments,
+    ))
     sum_stats_vec = sum_stats_vec[0:n_summary]
 
     return sum_stats_vec

sbi/analysis/conditional_density.py

@@ -145,24 +145,22 @@ def conditional_corrcoeff(
     correlation_matrices = []
     for cond in condition:
         correlation_matrices.append(
-            torch.stack(
-                [
-                    compute_corrcoeff(
-                        eval_conditional_density(
-                            density,
-                            cond.to(device),
-                            limits.to(device),
-                            dim1=dim1,
-                            dim2=dim2,
-                            resolution=resolution,
-                        ),
-                        limits[[dim1, dim2]].to(device),
-                    )
-                    for dim1 in subset_
-                    for dim2 in subset_
-                    if dim1 < dim2
-                ]
-            )
+            torch.stack([
+                compute_corrcoeff(
+                    eval_conditional_density(
+                        density,
+                        cond.to(device),
+                        limits.to(device),
+                        dim1=dim1,
+                        dim2=dim2,
+                        resolution=resolution,
+                    ),
+                    limits[[dim1, dim2]].to(device),
+                )
+                for dim1 in subset_
+                for dim2 in subset_
+                if dim1 < dim2
+            ])
         )
 
     average_correlations = torch.mean(torch.stack(correlation_matrices), dim=0)
@@ -294,7 +292,9 @@ def conditional_potential(
     condition = atleast_2d_float32_tensor(condition)
 
     conditioned_potential_fn = ConditionedPotential(
-        potential_fn, condition, dims_to_sample  # type: ignore
+        potential_fn,
+        condition,
+        dims_to_sample,  # type: ignore
     )
 
     restricted_prior = RestrictedPriorForConditional(prior, dims_to_sample)

sbi/analysis/plot.py

@@ -31,9 +31,9 @@ def hex2rgb(hex):
 def rgb2hex(RGB):
     # Components need to be integers for hex to make sense
     RGB = [int(x) for x in RGB]
-    return "#" + "".join(
-        ["0{0:x}".format(v) if v < 16 else "{0:x}".format(v) for v in RGB]
-    )
+    return "#" + "".join([
+        "0{0:x}".format(v) if v < 16 else "{0:x}".format(v) for v in RGB
+    ])
 
 
 def _update(d, u):
@@ -870,12 +870,10 @@ def _arrange_plots(
                 else:
                     _format_axis(ax, xhide=True, yhide=True)
             if opts["tick_labels"] is not None:
-                ax.set_xticklabels(
-                    (
-                        str(opts["tick_labels"][col][0]),
-                        str(opts["tick_labels"][col][1]),
-                    )
-                )
+                ax.set_xticklabels((
+                    str(opts["tick_labels"][col][0]),
+                    str(opts["tick_labels"][col][1]),
+                ))
 
             # Diagonals
             if current == "diag":
@@ -1135,9 +1133,9 @@ def _sbc_rank_plot(
         figsize = (num_parameters * 4, num_rows * 5) if params_in_subplots else (8, 5)
 
     if parameter_labels is None:
-        parameter_labels = [f"dim {i+1}" for i in range(num_parameters)]
+        parameter_labels = [f"dim {i + 1}" for i in range(num_parameters)]
     if ranks_labels is None:
-        ranks_labels = [f"rank set {i+1}" for i in range(num_ranks)]
+        ranks_labels = [f"rank set {i + 1}" for i in range(num_ranks)]
     if num_bins is None:
         # Recommendation from Talts et al.
         num_bins = num_sbc_runs // 20

sbi/analysis/sbc.py

@@ -293,12 +293,10 @@ def check_prior_vs_dap(prior_samples: Tensor, dap_samples: Tensor) -> Tensor:
 
     assert prior_samples.shape == dap_samples.shape
 
-    return torch.tensor(
-        [
-            c2st(s1.unsqueeze(1), s2.unsqueeze(1))
-            for s1, s2 in zip(prior_samples.T, dap_samples.T)
-        ]
-    )
+    return torch.tensor([
+        c2st(s1.unsqueeze(1), s2.unsqueeze(1))
+        for s1, s2 in zip(prior_samples.T, dap_samples.T)
+    ])
 
 
 def check_uniformity_frequentist(ranks, num_posterior_samples) -> Tensor:
@@ -344,20 +342,18 @@ def check_uniformity_c2st(
             one for each dim_parameters.
     """
 
-    c2st_scores = torch.tensor(
+    c2st_scores = torch.tensor([
         [
-            [
-                c2st(
-                    rks.unsqueeze(1),
-                    Uniform(zeros(1), num_posterior_samples * ones(1)).sample(
-                        torch.Size((ranks.shape[0],))
-                    ),
-                )
-                for rks in ranks.T
-            ]
-            for _ in range(num_repetitions)
+            c2st(
+                rks.unsqueeze(1),
+                Uniform(zeros(1), num_posterior_samples * ones(1)).sample(
+                    torch.Size((ranks.shape[0],))
+                ),
+            )
+            for rks in ranks.T
         ]
-    )
+        for _ in range(num_repetitions)
+    ])
 
     # Use variance over repetitions to estimate robustness of c2st.
     if (c2st_scores.std(0) > 0.05).any():

sbi/analysis/tensorboard_output.py

@@ -1,6 +1,7 @@
 # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
 # under the Affero General Public License v3, see <https://www.gnu.org/licenses/>.
 """Utils for processing tensorboard event data."""
+
 import inspect
 import logging
 from copy import deepcopy

sbi/inference/abc/smcabc.py

@@ -197,15 +197,13 @@ def sass_simulator(theta):
         )
         log_weights = torch.log(1 / num_particles * torch.ones(num_particles))
 
-        self.logger.info(
-            (
-                "population=%s, eps=%s, ess=%s, num_sims=%s",
-                pop_idx,
-                epsilon,
-                1.0,
-                num_initial_pop,
-            )
-        )
+        self.logger.info((
+            "population=%s, eps=%s, ess=%s, num_sims=%s",
+            pop_idx,
+            epsilon,
+            1.0,
+            num_initial_pop,
+        ))
 
         all_particles = [particles]
         all_log_weights = [log_weights]
@@ -246,14 +244,12 @@ def sass_simulator(theta):
                     particles, log_weights, ess_min, pop_idx
                 )
 
-            self.logger.info(
-                (
-                    "population=%s done: eps={epsilon:.6f}, num_sims=%s.",
-                    pop_idx,
-                    epsilon,
-                    self.simulation_counter,
-                )
-            )
+            self.logger.info((
+                "population=%s done: eps={epsilon:.6f}, num_sims=%s.",
+                pop_idx,
+                epsilon,
+                self.simulation_counter,
+            ))
 
             # collect results
             all_particles.append(particles)
@@ -479,14 +475,12 @@ def _get_next_epsilon(self, distances: Tensor, quantile: float) -> float:
         try:
             qidx = torch.where(distances_cdf >= quantile)[0][0]
         except IndexError:
-            self.logger.warning(
-                (
-                    """Accepted unique distances=%s don't match quantile=%s. Selecting
+            self.logger.warning((
+                """Accepted unique distances=%s don't match quantile=%s. Selecting
                     last distance.""",
-                    distances,
-                    quantile,
-                )
-            )
+                distances,
+                quantile,
+            ))
             qidx = -1
 
         # The new epsilon is given by that distance.

sbi/inference/posteriors/base_posterior.py

@@ -245,8 +245,9 @@ def map(
         return self._map
 
     def __repr__(self):
-        desc = f"""{self.__class__.__name__} sampler for potential_fn=<{self.
-                potential_fn.__class__.__name__}>"""
+        desc = f"""{self.__class__.__name__} sampler for potential_fn=<{
+            self.potential_fn.__class__.__name__
+        }>"""
         return desc
 
     def __str__(self):

sbi/inference/posteriors/mcmc_posterior.py

@@ -534,7 +534,8 @@ def _pyro_mcmc(
         )
         sampler.run()
         samples = next(iter(sampler.get_samples().values())).reshape(
-            -1, initial_params.shape[1]  # .shape[1] = dim of theta
+            -1,
+            initial_params.shape[1],  # .shape[1] = dim of theta
         )
 
         # Save posterior sampler.
@@ -695,9 +696,9 @@ def get_arviz_inference_data(self) -> InferenceData:
                 *samples_shape
             )
 
-            inference_data = az.convert_to_inference_data(
-                {f"{self.param_name}": samples}
-            )
+            inference_data = az.convert_to_inference_data({
+                f"{self.param_name}": samples
+            })
 
         return inference_data
 

sbi/inference/snle/mnle.py

@@ -148,8 +148,9 @@ def build_posterior(
 
         assert isinstance(
             likelihood_estimator, MixedDensityEstimator
-        ), f"""net must be of type MixedDensityEstimator but is {type
-            (likelihood_estimator)}."""
+        ), f"""net must be of type MixedDensityEstimator but is {
+            type(likelihood_estimator)
+        }."""
 
         (
             potential_fn,

sbi/inference/snpe/snpe_a.py

@@ -407,9 +407,7 @@ def __init__(
                 logits_pp,
                 m_pp,
                 prec_pp,
-            ) = proposal.posterior_estimator._posthoc_correction(
-                proposal.default_x
-            )  # type: ignore
+            ) = proposal.posterior_estimator._posthoc_correction(proposal.default_x)  # type: ignore
             self._logits_pp, self._m_pp, self._prec_pp = (
                 logits_pp.detach(),
                 m_pp.detach(),
@@ -853,10 +851,12 @@ def _logits_posterior(
 
         # Compute for proposal, density estimator, and proposal posterior:
         exponent_pp = utils.batched_mixture_vmv(
-            precisions_pp, means_pp  # m_0 in eq (26) in Appendix C of [1]
+            precisions_pp,
+            means_pp,  # m_0 in eq (26) in Appendix C of [1]
         )
         exponent_d = utils.batched_mixture_vmv(
-            precisions_d, means_d  # m_k in eq (26) in Appendix C of [1]
+            precisions_d,
+            means_d,  # m_k in eq (26) in Appendix C of [1]
         )
         exponent_post = utils.batched_mixture_vmv(
             precisions_post,

sbi/neural_nets/density_estimators/flow.py

@@ -99,16 +99,19 @@ def sample(self, sample_shape: Shape, condition: Tensor) -> Tensor:
         if len(condition.shape) == condition_dims:
             # nflows.sample() expects conditions to be batched.
             condition = condition.unsqueeze(0)
-            samples = self.net.sample(num_samples, context=condition).reshape(
-                (*sample_shape, -1)
-            )
+            samples = self.net.sample(num_samples, context=condition).reshape((
+                *sample_shape,
+                -1,
+            ))
         else:
             # For batched conditions, we need to reshape the conditions and the samples
             batch_shape = condition.shape[:-condition_dims]
             condition = condition.reshape(-1, *self._condition_shape)
-            samples = self.net.sample(num_samples, context=condition).reshape(
-                (*batch_shape, *sample_shape, -1)
-            )
+            samples = self.net.sample(num_samples, context=condition).reshape((
+                *batch_shape,
+                *sample_shape,
+                -1,
+            ))
 
         return samples
 

sbi/samplers/mcmc/slice.py

@@ -154,20 +154,16 @@ def _sample_from_conditional(self, params, dim):
         def _log_prob_d(x):
             assert self.potential_fn is not None, "Chain not initialized."
 
-            return -self.potential_fn(
-                {
-                    self._site_name: torch.cat(
-                        (
-                            params[self._site_name].view(-1)[:dim],
-                            x.reshape(1),
-                            params[self._site_name].view(-1)[dim + 1 :],
-                        )
-                    ).unsqueeze(
-                        0
-                    )  # TODO: The unsqueeze seems to give a speed up, figure out when
-                    # this is the case exactly
-                }
-            )
+            return -self.potential_fn({
+                self._site_name: torch.cat((
+                    params[self._site_name].view(-1)[:dim],
+                    x.reshape(1),
+                    params[self._site_name].view(-1)[dim + 1 :],
+                )).unsqueeze(
+                    0
+                )  # TODO: The unsqueeze seems to give a speed up, figure out when
+                # this is the case exactly
+            })
 
         assert (
             self._site_name is not None and self._width is not None