feat: allows some betetr matching for single_dipole runs

deepdog/direct_monte_carlo/direct_mc.py

@@ -36,8 +36,8 @@ class DirectMonteCarloConfig:
 	tag: str = ""
 	cap_core_count: int = 0  # 0 means cap at num cores - 1
 	chunk_size: int = 50
-	write_bayesrun_file = True
+	write_bayesrun_file: bool = True
-	bayesrun_file_timestamp = True
+	bayesrun_file_timestamp: bool = True
 	# chunk size of some kind
 
 
@@ -145,15 +145,21 @@ class DirectMonteCarloRun:
 		single run wrapped up for multiprocessing call.
 
 		takes in a tuple of arguments corresponding to
-		(model_name_pair, seed)
+		(model_name_pair, seed, return_configs)
+
+		return_configs is a boolean, if true then will return tuple of (count, [matching configs])
+		if false, return (count, [])
 		"""
 		# here's where we do our work
 
-		model_name_pair, seed = args
+		model_name_pair, seed, return_configs = args
 		cycle_success_configs = self._single_run(model_name_pair, seed)
 		cycle_success_count = len(cycle_success_configs)
 
-		return cycle_success_count
+		if return_configs:
+			return (cycle_success_count, cycle_success_configs)
+		else:
+			return (cycle_success_count, [])
 
 	def execute_no_multiprocessing(self) -> Sequence[DirectMonteCarloResult]:
 
@@ -198,9 +204,11 @@ class DirectMonteCarloRun:
 							)
 							dipole_count = numpy.array(cycle_success_configs).shape[1]
 							for n in range(dipole_count):
+								number_dipoles_to_write = self.config.target_success * 5
+								_logger.info(f"Limiting to {number_dipoles_to_write=}")
 								numpy.savetxt(
 									f"{self.config.tag}_{step_count}_{cycle_i}_dipole_{n}.csv",
-									sorted_by_freq[:, n],
+									sorted_by_freq[:number_dipoles_to_write, n],
 									delimiter=",",
 								)
 					total_success += cycle_success_count
@@ -259,13 +267,45 @@ class DirectMonteCarloRun:
 
 					seeds = seed_sequence.spawn(self.config.monte_carlo_cycles)
 
-					pool_results = sum(
+					raw_pool_results = list(pool.imap_unordered(
-						pool.imap_unordered(
 						self._wrapped_single_run,
-							[(model_name_pair, seed) for seed in seeds],
+						[
+							(model_name_pair, seed, self.config.write_successes_to_file)
+							for seed in seeds
+						],
 						self.config.chunk_size,
+					))
+
+					pool_results = sum(result[0] for result in raw_pool_results)
+
+					if self.config.write_successes_to_file:
+						cycle_success_configs = numpy.concatenate(
+							[result[1] for result in raw_pool_results]
 						)
+						if len(cycle_success_configs):
+								
+							sorted_by_freq = numpy.array(
+								[
+									pdme.subspace_simulation.sort_array_of_dipoles_by_frequency(
+										dipole_config
 									)
+									for dipole_config in cycle_success_configs
+								]
+							)
+							dipole_count = numpy.array(cycle_success_configs).shape[1]
+
+							number_dipoles_to_write = self.config.target_success * 5
+							_logger.info(f"Limiting to {number_dipoles_to_write=}")
+
+							for n in range(dipole_count):
+								numpy.savetxt(
+									f"{self.config.tag}_{step_count}_dipole_{n}.csv",
+									sorted_by_freq[:: number_dipoles_to_write, n],
+									delimiter=",",
+							)
+						else:
+							_logger.debug("Instructed to write results, but none obtained")
+
 					_logger.debug(f"Pool results: {pool_results}")
 
 					total_success += pool_results

deepdog/results/filename.py

@@ -8,6 +8,7 @@ FILE_SLUG_REGEXES = [
 		r"(?P<tag>\w+)-(?P<job_index>\d+)",
 		r"mock_tarucha-(?P<job_index>\d+)",
 		r"(?:(?P<mock>mock)_)?tarucha(?:_(?P<tarucha_run_id>\d+))?-(?P<job_index>\d+)",
+		r"(?P<tag>\w+)-(?P<included_dots>[\w,]+)-(?P<target_cost>\d*\.?\d+)-(?P<job_index>\d+)",
 	]
 ]