fmt: auto format changes

deepdog/direct_monte_carlo/__init__.py

@@ -1,3 +1,6 @@
-from deepdog.direct_monte_carlo.direct_mc import DirectMonteCarloRun, DirectMonteCarloConfig
+from deepdog.direct_monte_carlo.direct_mc import (
+	DirectMonteCarloRun,
+	DirectMonteCarloConfig,
+)
 
 __all__ = ["DirectMonteCarloRun", "DirectMonteCarloConfig"]

deepdog/direct_monte_carlo/direct_mc.py

@@ -18,6 +18,7 @@ class DirectMonteCarloResult:
 	monte_carlo_count: int
 	likelihood: float
 
+
 @dataclass
 class DirectMonteCarloConfig:
 	monte_carlo_count_per_cycle: int = 10000
@@ -28,6 +29,7 @@ class DirectMonteCarloConfig:
 	write_successes_to_file: bool = False
 	tag: str = ""
 
+
 class DirectMonteCarloRun:
 	"""
 	A single model Direct Monte Carlo run, currently implemented only using single threading.
@@ -82,6 +84,7 @@ class DirectMonteCarloRun:
 		) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
 			self.measurements
 		)
+
 	def _single_run(self, seed) -> numpy.ndarray:
 		rng = numpy.random.default_rng(seed)
 
@@ -98,7 +101,9 @@ class DirectMonteCarloRun:
 				numpy.array([di]), current_sample
 			)
 
-			current_sample = current_sample[numpy.all((vals > low) & (vals < high), axis=1)]
+			current_sample = current_sample[
+				numpy.all((vals > low) & (vals < high), axis=1)
+			]
 		return current_sample
 
 	def execute(self) -> DirectMonteCarloResult:
@@ -106,23 +111,30 @@ class DirectMonteCarloRun:
 		total_success = 0
 		total_count = 0
 
-		count_per_step = self.config.monte_carlo_count_per_cycle * self.config.monte_carlo_cycles
+		count_per_step = (
+			self.config.monte_carlo_count_per_cycle * self.config.monte_carlo_cycles
+		)
 		seed_sequence = numpy.random.SeedSequence(self.config.monte_carlo_seed)
-		while (
+		while (step_count < self.config.max_monte_carlo_cycles_steps) and (
-			(step_count < self.config.max_monte_carlo_cycles_steps) and 
+			total_success < self.config.target_success
-			(total_success < self.config.target_success)
 		):
 			_logger.debug(f"Executing step {step_count}")
-			for cycle_i, seed in enumerate(seed_sequence.spawn(self.config.monte_carlo_cycles)):
+			for cycle_i, seed in enumerate(
+				seed_sequence.spawn(self.config.monte_carlo_cycles)
+			):
 				cycle_success_configs = self._single_run(seed)
 				cycle_success_count = len(cycle_success_configs)
 				if cycle_success_count > 0:
-					_logger.debug(f"For cycle {cycle_i} received {cycle_success_count} successes")
+					_logger.debug(
+						f"For cycle {cycle_i} received {cycle_success_count} successes"
+					)
 					_logger.debug(cycle_success_configs)
 					if self.config.write_successes_to_file:
 						sorted_by_freq = numpy.array(
 							[
-								pdme.subspace_simulation.sort_array_of_dipoles_by_frequency(dipole_config)
+								pdme.subspace_simulation.sort_array_of_dipoles_by_frequency(
+									dipole_config
+								)
 								for dipole_config in cycle_success_configs
 							]
 						)
@@ -138,9 +150,8 @@ class DirectMonteCarloRun:
 			step_count += 1
 			total_count += count_per_step
 
-
 		return DirectMonteCarloResult(
 			successes=total_success,
 			monte_carlo_count=total_count,
-			likelihood=total_success/total_count
+			likelihood=total_success / total_count,
 		)

deepdog/subset_simulation/subset_simulation_impl.py

@@ -101,11 +101,17 @@ class SubsetSimulation:
 		# _logger.debug(sample_dipoles.shape)
 
 		raw_costs = []
-		_logger.debug(f"Using iterated cost function thing with chunk size {self.initial_cost_chunk_size}")
+		_logger.debug(
+			f"Using iterated cost function thing with chunk size {self.initial_cost_chunk_size}"
+		)
 
 		for x in range(0, len(sample_dipoles), self.initial_cost_chunk_size):
 			_logger.debug(f"doing chunk {x}")
-			raw_costs.extend(self.cost_function_to_use(sample_dipoles[x: x + self.initial_cost_chunk_size]))
+			raw_costs.extend(
+				self.cost_function_to_use(
+					sample_dipoles[x : x + self.initial_cost_chunk_size]
+				)
+			)
 		costs = numpy.array(raw_costs)
 
 		_logger.debug(f"costs: {costs}")
@@ -147,13 +153,12 @@ class SubsetSimulation:
 				stdevs = self.get_stdevs_from_arrays(next_seeds_as_array)
 				_logger.info(f"got stdevs: {stdevs.stdevs}")
 				all_long_chains = []
-				for seed_index, (c, s) in enumerate(next_seeds[::len(next_seeds) // 20]):
+				for seed_index, (c, s) in enumerate(
+					next_seeds[:: len(next_seeds) // 20]
+				):
 					# chain = mcmc(s, threshold_cost, n_s, model, dot_inputs_array, actual_measurement_array, mcmc_rng, curr_cost=c, stdevs=stdevs)
 					# until new version gotta do
-					_logger.debug(
+					_logger.debug(f"\t{seed_index}: doing long chain on the next seed")
-						f"\t{seed_index}: doing long chain on the next seed"
-					)
-				
 
 					long_chain = self.model.get_mcmc_chain(
 						s,
@@ -175,7 +180,6 @@ class SubsetSimulation:
 						delimiter=",",
 					)
 
-
 			if self.keep_probs_list:
 				for cost_index, cost_chain in enumerate(all_chains[: -self.n_c]):
 					probs_list.append(

tests/test_bayes_run_with_ss.py

@@ -151,7 +151,7 @@ def test_bayesss_with_tighter_cost(snapshot):
 		ss_default_upper_w_log_step=4,
 		ss_dump_last_generation=False,
 		write_output_to_bayesruncsv=False,
-		ss_initial_costs_chunk_size=1
+		ss_initial_costs_chunk_size=1,
 	)
 	result = square_run.go()