style: run doo fmt

deepdog/__init__.py

@@ -10,7 +10,13 @@ def get_version():
 	return __version__
 
 
-__all__ = ["get_version", "BayesRun", "AltBayesRun", "AltBayesRunSimulPairs", "Diagnostic"]
+__all__ = [
+	"get_version",
+	"BayesRun",
+	"AltBayesRun",
+	"AltBayesRunSimulPairs",
+	"Diagnostic",
+]
 
 
 logging.getLogger(__name__).addHandler(logging.NullHandler())

deepdog/alt_bayes_run.py

@@ -24,24 +24,42 @@ _logger = logging.getLogger(__name__)
 
 def get_a_result(input) -> int:
 	discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
-	sample_dipoles = discretisation.get_model().get_n_single_dipoles(monte_carlo_count, max_frequency)
+	sample_dipoles = discretisation.get_model().get_n_single_dipoles(
+		monte_carlo_count, max_frequency
+	)
 	vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
 	return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
 
 
 def get_a_result_using_pairs(input) -> int:
-	discretisation, dot_inputs, pair_inputs, local_lows, local_highs, nonlocal_lows, nonlocal_highs, monte_carlo_count, max_frequency = input
+	(
-	sample_dipoles = discretisation.get_model().get_n_single_dipoles(monte_carlo_count, max_frequency)
+		discretisation,
+		dot_inputs,
+		pair_inputs,
+		local_lows,
+		local_highs,
+		nonlocal_lows,
+		nonlocal_highs,
+		monte_carlo_count,
+		max_frequency,
+	) = input
+	sample_dipoles = discretisation.get_model().get_n_single_dipoles(
+		monte_carlo_count, max_frequency
+	)
 	local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
 	local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
-	nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(pair_inputs, sample_dipoles)
+	nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(
-	nonlocal_matches = pdme.util.fast_v_calc.between(nonlocal_vals, nonlocal_lows, nonlocal_highs)
+		pair_inputs, sample_dipoles
+	)
+	nonlocal_matches = pdme.util.fast_v_calc.between(
+		nonlocal_vals, nonlocal_lows, nonlocal_highs
+	)
 	combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
 	return numpy.count_nonzero(combined_matches)
 
 
-class AltBayesRun():
+class AltBayesRun:
-	'''
+	"""
 	A single Bayes run for a given set of dots.
 
 	Parameters
@@ -56,15 +74,44 @@ class AltBayesRun():
 			The filename slug to include.
 	run_count: int
 			The number of runs to do.
-	'''
+	"""
-	def __init__(self, dot_positions: Sequence[numpy.typing.ArrayLike], frequency_range: Sequence[float], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int = 100, low_error: float = 0.9, high_error: float = 1.1, pairs_high_error=None, pairs_low_error=None, monte_carlo_count: int = 10000, monte_carlo_cycles: int = 10, target_success: int = 100, max_monte_carlo_cycles_steps: int = 10, max_frequency: float = 20, end_threshold: float = None, chunksize: int = CHUNKSIZE, use_pairs: bool = False) -> None:
+
-		self.dot_inputs = pdme.inputs.inputs_with_frequency_range(dot_positions, frequency_range)
+	def __init__(
-		self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(self.dot_inputs)
+		self,
+		dot_positions: Sequence[numpy.typing.ArrayLike],
+		frequency_range: Sequence[float],
+		discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
+		actual_model: pdme.model.Model,
+		filename_slug: str,
+		run_count: int = 100,
+		low_error: float = 0.9,
+		high_error: float = 1.1,
+		pairs_high_error=None,
+		pairs_low_error=None,
+		monte_carlo_count: int = 10000,
+		monte_carlo_cycles: int = 10,
+		target_success: int = 100,
+		max_monte_carlo_cycles_steps: int = 10,
+		max_frequency: float = 20,
+		end_threshold: float = None,
+		chunksize: int = CHUNKSIZE,
+		use_pairs: bool = False,
+	) -> None:
+		self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
+			dot_positions, frequency_range
+		)
+		self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
+			self.dot_inputs
+		)
 
 		self.use_pairs = use_pairs
 
-		self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(dot_positions, frequency_range)
+		self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(
-		self.dot_pair_inputs_array = pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
+			dot_positions, frequency_range
+		)
+		self.dot_pair_inputs_array = (
+			pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
+		)
 
 		self.discretisations = [disc for (_, disc) in discretisations_with_names]
 		self.model_names = [name for (name, _) in discretisations_with_names]
@@ -106,7 +153,9 @@ class AltBayesRun():
 				self.use_end_threshold = True
 				_logger.info(f"Will abort early, at {self.end_threshold}.")
 			else:
-				raise ValueError(f"end_threshold should be between 0 and 1, but is actually {end_threshold}")
+				raise ValueError(
+					f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
+				)
 
 	def go(self) -> None:
 		with open(self.filename, "a", newline="") as outfile:
@@ -121,13 +170,31 @@ class AltBayesRun():
 			# Generate the actual dipoles
 			actual_dipoles = self.actual_model.get_dipoles(frequency)
 
-			dots = actual_dipoles.get_percent_range_dot_measurements(self.dot_inputs, self.low_error, self.high_error)
+			dots = actual_dipoles.get_percent_range_dot_measurements(
-			lows, highs = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(dots)
+				self.dot_inputs, self.low_error, self.high_error
+			)
+			(
+				lows,
+				highs,
+			) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
+				dots
+			)
 
 			pair_lows, pair_highs = (None, None)
 			if self.use_pairs:
-				pair_measurements = actual_dipoles.get_percent_range_dot_pair_measurements(self.dot_pair_inputs, self.pairs_low_error, self.pairs_high_error)
+				pair_measurements = (
-				pair_lows, pair_highs = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(pair_measurements)
+					actual_dipoles.get_percent_range_dot_pair_measurements(
+						self.dot_pair_inputs,
+						self.pairs_low_error,
+						self.pairs_high_error,
+					)
+				)
+				(
+					pair_lows,
+					pair_highs,
+				) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
+					pair_measurements
+				)
 
 			_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
 
@@ -139,18 +206,51 @@ class AltBayesRun():
 					cycle_count = 0
 					cycle_success = 0
 					cycles = 0
-					while (cycles < self.max_monte_carlo_cycles_steps) and (cycle_success <= self.target_success):
+					while (cycles < self.max_monte_carlo_cycles_steps) and (
+						cycle_success <= self.target_success
+					):
 						_logger.debug(f"Starting cycle {cycles}")
 						cycles += 1
 						current_success = 0
 						cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
 						if self.use_pairs:
 							current_success = sum(
-								pool.imap_unordered(get_a_result_using_pairs, [(discretisation, self.dot_inputs_array, self.dot_pair_inputs_array, lows, highs, pair_lows, pair_highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
+								pool.imap_unordered(
+									get_a_result_using_pairs,
+									[
+										(
+											discretisation,
+											self.dot_inputs_array,
+											self.dot_pair_inputs_array,
+											lows,
+											highs,
+											pair_lows,
+											pair_highs,
+											self.monte_carlo_count,
+											self.max_frequency,
+										)
+									]
+									* self.monte_carlo_cycles,
+									self.chunksize,
+								)
 							)
 						else:
 							current_success = sum(
-								pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
+								pool.imap_unordered(
+									get_a_result,
+									[
+										(
+											discretisation,
+											self.dot_inputs_array,
+											lows,
+											highs,
+											self.monte_carlo_count,
+											self.max_frequency,
+										)
+									]
+									* self.monte_carlo_cycles,
+									self.chunksize,
+								)
 							)
 
 						cycle_success += current_success
@@ -160,30 +260,44 @@ class AltBayesRun():
 			row = {
 				"dipole_moment": actual_dipoles.dipoles[0].p,
 				"dipole_location": actual_dipoles.dipoles[0].s,
-				"dipole_frequency": actual_dipoles.dipoles[0].w
+				"dipole_frequency": actual_dipoles.dipoles[0].w,
 			}
 			successes: List[float] = []
 			counts: List[int] = []
-			for model_index, (name, (count, result)) in enumerate(zip(self.model_names, results)):
+			for model_index, (name, (count, result)) in enumerate(
+				zip(self.model_names, results)
+			):
 
 				row[f"{name}_success"] = result
 				row[f"{name}_count"] = count
 				successes.append(max(result, 0.5))
 				counts.append(count)
 
-			success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
+			success_weight = sum(
-			new_probabilities = [(succ / count) * old_prob / success_weight for succ, count, old_prob in zip(successes, counts, self.probabilities)]
+				[
+					(succ / count) * prob
+					for succ, count, prob in zip(successes, counts, self.probabilities)
+				]
+			)
+			new_probabilities = [
+				(succ / count) * old_prob / success_weight
+				for succ, count, old_prob in zip(successes, counts, self.probabilities)
+			]
 			self.probabilities = new_probabilities
 			for name, probability in zip(self.model_names, self.probabilities):
 				row[f"{name}_prob"] = probability
 			_logger.info(row)
 
 			with open(self.filename, "a", newline="") as outfile:
-				writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
+				writer = csv.DictWriter(
+					outfile, fieldnames=self.csv_fields, dialect="unix"
+				)
 				writer.writerow(row)
 
 			if self.use_end_threshold:
 				max_prob = max(self.probabilities)
 				if max_prob > self.end_threshold:
-					_logger.info(f"Aborting early, because {max_prob} is greater than {self.end_threshold}")
+					_logger.info(
+						f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
+					)
 					break

deepdog/alt_bayes_run_simulpairs.py

@@ -206,7 +206,8 @@ class AltBayesRunSimulPairs:
 						current_success_no_pairs = 0
 						cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
 
-						current_success_both = numpy.array(sum(
+						current_success_both = numpy.array(
+							sum(
 								pool.imap_unordered(
 									get_a_simul_result_using_pairs,
 									[
@@ -225,7 +226,8 @@ class AltBayesRunSimulPairs:
 									* self.monte_carlo_cycles,
 									self.chunksize,
 								)
-						))
+							)
+						)
 						current_success_no_pairs = current_success_both[0]
 						current_success_pairs = current_success_both[1]
 

deepdog/bayes_run.py

@@ -20,12 +20,14 @@ DotInput = Tuple[numpy.typing.ArrayLike, float]
 _logger = logging.getLogger(__name__)
 
 
-def get_a_result(discretisation, dots, index) -> Tuple[Tuple[int, ...], scipy.optimize.OptimizeResult]:
+def get_a_result(
+	discretisation, dots, index
+) -> Tuple[Tuple[int, ...], scipy.optimize.OptimizeResult]:
 	return (index, discretisation.solve_for_index(dots, index))
 
 
-class BayesRun():
+class BayesRun:
-	'''
+	"""
 	A single Bayes run for a given set of dots.
 
 	Parameters
@@ -40,8 +42,18 @@ class BayesRun():
 			The filename slug to include.
 	run_count: int
 			The number of runs to do.
-	'''
+	"""
-	def __init__(self, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, max_frequency: float = None, end_threshold: float = None) -> None:
+
+	def __init__(
+		self,
+		dot_inputs: Sequence[DotInput],
+		discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
+		actual_model: pdme.model.Model,
+		filename_slug: str,
+		run_count: int,
+		max_frequency: float = None,
+		end_threshold: float = None,
+	) -> None:
 		self.dot_inputs = dot_inputs
 		self.discretisations = [disc for (_, disc) in discretisations_with_names]
 		self.model_names = [name for (name, _) in discretisations_with_names]
@@ -65,7 +77,9 @@ class BayesRun():
 				self.use_end_threshold = True
 				_logger.info(f"Will abort early, at {self.end_threshold}.")
 			else:
-				raise ValueError(f"end_threshold should be between 0 and 1, but is actually {end_threshold}")
+				raise ValueError(
+					f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
+				)
 
 	def go(self) -> None:
 		with open(self.filename, "a", newline="") as outfile:
@@ -87,17 +101,28 @@ class BayesRun():
 			for disc_count, discretisation in enumerate(self.discretisations):
 				_logger.debug(f"Doing discretisation #{disc_count}")
 				with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
-					results.append(pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(dots), discretisation.all_indices())))
+					results.append(
+						pool.starmap(
+							get_a_result,
+							zip(
+								itertools.repeat(discretisation),
+								itertools.repeat(dots),
+								discretisation.all_indices(),
+							),
+						)
+					)
 
 			_logger.debug("Done, constructing output now")
 			row = {
 				"dipole_moment": dipoles.dipoles[0].p,
 				"dipole_location": dipoles.dipoles[0].s,
-				"dipole_frequency": dipoles.dipoles[0].w
+				"dipole_frequency": dipoles.dipoles[0].w,
 			}
 			successes: List[float] = []
 			counts: List[int] = []
-			for model_index, (name, result) in enumerate(zip(self.model_names, results)):
+			for model_index, (name, result) in enumerate(
+				zip(self.model_names, results)
+			):
 				count = 0
 				success = 0
 				for idx, val in result:
@@ -110,19 +135,31 @@ class BayesRun():
 				successes.append(max(success, 0.5))
 				counts.append(count)
 
-			success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
+			success_weight = sum(
-			new_probabilities = [(succ / count) * old_prob / success_weight for succ, count, old_prob in zip(successes, counts, self.probabilities)]
+				[
+					(succ / count) * prob
+					for succ, count, prob in zip(successes, counts, self.probabilities)
+				]
+			)
+			new_probabilities = [
+				(succ / count) * old_prob / success_weight
+				for succ, count, old_prob in zip(successes, counts, self.probabilities)
+			]
 			self.probabilities = new_probabilities
 			for name, probability in zip(self.model_names, self.probabilities):
 				row[f"{name}_prob"] = probability
 			_logger.info(row)
 
 			with open(self.filename, "a", newline="") as outfile:
-				writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
+				writer = csv.DictWriter(
+					outfile, fieldnames=self.csv_fields, dialect="unix"
+				)
 				writer.writerow(row)
 
 			if self.use_end_threshold:
 				max_prob = max(self.probabilities)
 				if max_prob > self.end_threshold:
-					_logger.info(f"Aborting early, because {max_prob} is greater than {self.end_threshold}")
+					_logger.info(
+						f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
+					)
 					break

deepdog/diagnostic.py

@@ -18,7 +18,7 @@ def get_a_result(discretisation, dots, index):
 
 
 @dataclass
-class SingleDipoleDiagnostic():
+class SingleDipoleDiagnostic:
 	model: str
 	index: Tuple
 	bounds: Tuple
@@ -43,8 +43,8 @@ class SingleDipoleDiagnostic():
 		self.w_result = self.result_dipole.w
 
 
-class Diagnostic():
+class Diagnostic:
-	'''
+	"""
 	Represents a diagnostic for a single dipole moment given a set of discretisations.
 
 	Parameters
@@ -59,15 +59,51 @@ class Diagnostic():
 			The filename slug to include.
 	run_count: int
 			The number of runs to do.
-	'''
+	"""
-	def __init__(self, actual_dipole_moment: numpy.ndarray, actual_dipole_position: numpy.ndarray, actual_dipole_frequency: float, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], filename_slug: str) -> None:
+
-		self.dipoles = OscillatingDipoleArrangement([OscillatingDipole(actual_dipole_moment, actual_dipole_position, actual_dipole_frequency)])
+	def __init__(
+		self,
+		actual_dipole_moment: numpy.ndarray,
+		actual_dipole_position: numpy.ndarray,
+		actual_dipole_frequency: float,
+		dot_inputs: Sequence[DotInput],
+		discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
+		filename_slug: str,
+	) -> None:
+		self.dipoles = OscillatingDipoleArrangement(
+			[
+				OscillatingDipole(
+					actual_dipole_moment,
+					actual_dipole_position,
+					actual_dipole_frequency,
+				)
+			]
+		)
 		self.dots = self.dipoles.get_dot_measurements(dot_inputs)
 
 		self.discretisations_with_names = discretisations_with_names
 		self.model_count = len(self.discretisations_with_names)
 
-		self.csv_fields = ["model", "index", "bounds", "p_actual_x", "p_actual_y", "p_actual_z", "s_actual_x", "s_actual_y", "s_actual_z", "w_actual", "success", "p_result_x", "p_result_y", "p_result_z", "s_result_x", "s_result_y", "s_result_z", "w_result"]
+		self.csv_fields = [
+			"model",
+			"index",
+			"bounds",
+			"p_actual_x",
+			"p_actual_y",
+			"p_actual_z",
+			"s_actual_x",
+			"s_actual_y",
+			"s_actual_z",
+			"w_actual",
+			"success",
+			"p_result_x",
+			"p_result_y",
+			"p_result_z",
+			"s_result_x",
+			"s_result_y",
+			"s_result_z",
+			"w_result",
+		]
 
 		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
 		self.filename = f"{timestamp}-{filename_slug}.diag.csv"
@@ -75,7 +111,7 @@ class Diagnostic():
 	def go(self):
 		with open(self.filename, "a", newline="") as outfile:
 			# csv fields
-			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect='unix')
+			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
 			writer.writeheader()
 
 		for (name, discretisation) in self.discretisations_with_names:
@@ -83,17 +119,38 @@ class Diagnostic():
 
 			results = []
 			with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
-				results = pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(self.dots), discretisation.all_indices()))
+				results = pool.starmap(
+					get_a_result,
+					zip(
+						itertools.repeat(discretisation),
+						itertools.repeat(self.dots),
+						discretisation.all_indices(),
+					),
+				)
 
-			with open(self.filename, "a", newline='') as outfile:
+			with open(self.filename, "a", newline="") as outfile:
-				writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect='unix', extrasaction="ignore")
+				writer = csv.DictWriter(
+					outfile,
+					fieldnames=self.csv_fields,
+					dialect="unix",
+					extrasaction="ignore",
+				)
 
 				for idx, result in results:
 
 					bounds = discretisation.bounds(idx)
 
 					actual_success = result.success and result.cost <= 1e-10
-					diag_row = SingleDipoleDiagnostic(name, idx, bounds, self.dipoles.dipoles[0], discretisation.model.solution_as_dipoles(result.normalised_x)[0], actual_success)
+					diag_row = SingleDipoleDiagnostic(
+						name,
+						idx,
+						bounds,
+						self.dipoles.dipoles[0],
+						discretisation.model.solution_as_dipoles(result.normalised_x)[
+							0
+						],
+						actual_success,
+					)
 					row = vars(diag_row)
 					_logger.debug(f"Writing result {row}")
 					writer.writerow(row)

deepdog/meta.py

@@ -1,3 +1,3 @@
 from importlib.metadata import version
 
-__version__ = version('deepdog')
+__version__ = version("deepdog")