feat!: removes alt_bayes bayes distinction, which was superfluous when only alt worked

deepdog/alt_bayes_run.py

@@ -1,307 +0,0 @@
-import pdme.inputs
-import pdme.model
-import pdme.measurement.input_types
-import pdme.measurement.oscillating_dipole
-import pdme.util.fast_v_calc
-import pdme.util.fast_nonlocal_spectrum
-from typing import Sequence, Tuple, List
-import datetime
-import csv
-import multiprocessing
-import logging
-import numpy
-
-
-# TODO: remove hardcode
-CHUNKSIZE = 50
-
-# TODO: It's garbage to have this here duplicated from pdme.
-DotInput = Tuple[numpy.typing.ArrayLike, float]
-
-
-_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
-	)
-	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
-	)
-	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_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:
-	"""
-	A single Bayes run for a given set of dots.
-
-	Parameters
-	----------
-	dot_inputs : Sequence[DotInput]
-	The dot inputs for this bayes run.
-
-	discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
-	The models to evaluate.
-
-	actual_model_discretisation : pdme.model.Discretisation
-	The discretisation for the model which is actually correct.
-
-	filename_slug : str
-	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
-		)
-		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_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]
-		self.actual_model = actual_model
-		self.model_count = len(self.discretisations)
-		self.monte_carlo_count = monte_carlo_count
-		self.monte_carlo_cycles = monte_carlo_cycles
-		self.target_success = target_success
-		self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
-		self.run_count = run_count
-		self.low_error = low_error
-		self.high_error = high_error
-		if pairs_low_error is None:
-			self.pairs_low_error = self.low_error
-		else:
-			self.pairs_low_error = pairs_low_error
-		if pairs_high_error is None:
-			self.pairs_high_error = self.high_error
-		else:
-			self.pairs_high_error = pairs_high_error
-		self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
-		self.compensate_zeros = True
-		self.chunksize = chunksize
-		for name in self.model_names:
-			self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
-
-		self.probabilities = [1 / self.model_count] * self.model_count
-
-		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-		if self.use_pairs:
-			self.filename = f"{timestamp}-{filename_slug}.altbayes.pairs.csv"
-		else:
-			self.filename = f"{timestamp}-{filename_slug}.altbayes.csv"
-		self.max_frequency = max_frequency
-
-		if end_threshold is not None:
-			if 0 < end_threshold < 1:
-				self.end_threshold: float = end_threshold
-				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}"
-				)
-
-	def go(self) -> None:
-		with open(self.filename, "a", newline="") as outfile:
-			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
-			writer.writeheader()
-
-		for run in range(1, self.run_count + 1):
-
-			rng = numpy.random.default_rng()
-			frequency = rng.uniform(1, self.max_frequency)
-
-			# 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
-			)
-			(
-				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_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}")
-
-			results = []
-			_logger.debug("Going to iterate over discretisations now")
-			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:
-					cycle_count = 0
-					cycle_success = 0
-					cycles = 0
-					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,
-								)
-							)
-						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,
-								)
-							)
-
-						cycle_success += current_success
-					results.append((cycle_count, cycle_success))
-
-			_logger.debug("Done, constructing output now")
-			row = {
-				"dipole_moment": actual_dipoles.dipoles[0].p,
-				"dipole_location": actual_dipoles.dipoles[0].s,
-				"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)
-			):
-
-				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)
-				]
-			)
-			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.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}"
-					)
-					break

deepdog/bayes_run.py

@@ -1,17 +1,19 @@
+import pdme.inputs
 import pdme.model
+import pdme.measurement.input_types
+import pdme.measurement.oscillating_dipole
+import pdme.util.fast_v_calc
+import pdme.util.fast_nonlocal_spectrum
 from typing import Sequence, Tuple, List
 import datetime
-import itertools
 import csv
+import multiprocessing
 import logging
 import numpy
-import scipy.optimize
-import multiprocessing
 
 
 # TODO: remove hardcode
-COST_THRESHOLD = 1e-10
+CHUNKSIZE = 50
-
 
 # TODO: It's garbage to have this here duplicated from pdme.
 DotInput = Tuple[numpy.typing.ArrayLike, float]
@@ -20,13 +22,43 @@ DotInput = Tuple[numpy.typing.ArrayLike, float]
 _logger = logging.getLogger(__name__)
 
 
-def get_a_result(
+def get_a_result(input) -> int:
-	discretisation, dots, index
+	discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
-) -> Tuple[Tuple[int, ...], scipy.optimize.OptimizeResult]:
+	sample_dipoles = discretisation.get_model().get_n_single_dipoles(
-	return (index, discretisation.solve_for_index(dots, index))
+		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))
 
 
-class BayesRun:
+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
+	)
+	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_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:
 	"""
 	A single Bayes run for a given set of dots.
 
@@ -50,29 +82,73 @@ class BayesRun:
 
 	def __init__(
 		self,
-		dot_inputs: Sequence[DotInput],
+		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,
+		run_count: int = 100,
-		max_frequency: float = None,
+		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 = dot_inputs
+		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_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]
 		self.actual_model = actual_model
 		self.model_count = len(self.discretisations)
+		self.monte_carlo_count = monte_carlo_count
+		self.monte_carlo_cycles = monte_carlo_cycles
+		self.target_success = target_success
+		self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
 		self.run_count = run_count
+		self.low_error = low_error
+		self.high_error = high_error
+		if pairs_low_error is None:
+			self.pairs_low_error = self.low_error
+		else:
+			self.pairs_low_error = pairs_low_error
+		if pairs_high_error is None:
+			self.pairs_high_error = self.high_error
+		else:
+			self.pairs_high_error = pairs_high_error
 		self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
 		self.compensate_zeros = True
+		self.chunksize = chunksize
 		for name in self.model_names:
 			self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
 
 		self.probabilities = [1 / self.model_count] * self.model_count
 
 		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-		self.filename = f"{timestamp}-{filename_slug}.csv"
+		if self.use_pairs:
+			self.filename = f"{timestamp}-{filename_slug}.altbayes.pairs.csv"
+		else:
+			self.filename = f"{timestamp}-{filename_slug}.altbayes.csv"
 		self.max_frequency = max_frequency
 
 		if end_threshold is not None:
@@ -91,52 +167,114 @@ class BayesRun:
 			writer.writeheader()
 
 		for run in range(1, self.run_count + 1):
-			frequency: float = run
+
-			if self.max_frequency is not None and self.max_frequency > 1:
 			rng = numpy.random.default_rng()
 			frequency = rng.uniform(1, self.max_frequency)
-			dipoles = self.actual_model.get_dipoles(frequency)
 
-			dots = dipoles.get_dot_measurements(self.dot_inputs)
+			# Generate the actual dipoles
-			_logger.info(f"Going to work on dipole at {dipoles.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
+			)
+			(
+				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_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}")
 
 			results = []
 			_logger.debug("Going to iterate over discretisations now")
 			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(
+					cycle_count = 0
-						pool.starmap(
+					cycle_success = 0
+					cycles = 0
+					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,
+								)
+							)
+						else:
+							current_success = sum(
+								pool.imap_unordered(
 									get_a_result,
-							zip(
+									[
-								itertools.repeat(discretisation),
+										(
-								itertools.repeat(dots),
+											discretisation,
-								discretisation.all_indices(),
+											self.dot_inputs_array,
-							),
+											lows,
+											highs,
+											self.monte_carlo_count,
+											self.max_frequency,
+										)
+									]
+									* self.monte_carlo_cycles,
+									self.chunksize,
 								)
 							)
 
+						cycle_success += current_success
+					results.append((cycle_count, cycle_success))
+
 			_logger.debug("Done, constructing output now")
 			row = {
-				"dipole_moment": dipoles.dipoles[0].p,
+				"dipole_moment": actual_dipoles.dipoles[0].p,
-				"dipole_location": dipoles.dipoles[0].s,
+				"dipole_location": actual_dipoles.dipoles[0].s,
-				"dipole_frequency": dipoles.dipoles[0].w,
+				"dipole_frequency": actual_dipoles.dipoles[0].w,
 			}
 			successes: List[float] = []
 			counts: List[int] = []
-			for model_index, (name, result) in enumerate(
+			for model_index, (name, (count, result)) in enumerate(
 				zip(self.model_names, results)
 			):
-				count = 0
-				success = 0
-				for idx, val in result:
-					count += 1
-					if val.success and val.cost <= COST_THRESHOLD:
-						success += 1
 
-				row[f"{name}_success"] = success
+				row[f"{name}_success"] = result
 				row[f"{name}_count"] = count
-				successes.append(max(success, 0.5))
+				successes.append(max(result, 0.5))
 				counts.append(count)
 
 			success_weight = sum(

deepdog/bayes_run_simulpairs.py

@@ -7,7 +7,7 @@ import pdme.util.fast_nonlocal_spectrum
 from typing import Sequence, Tuple, List
 import datetime
 import csv
-import multiprocessing
+import multiprocessingg
 import logging
 import numpy
 import numpy.random

deepdog/diagnostic.py

@@ -1,160 +0,0 @@
-from pdme.measurement import OscillatingDipole, OscillatingDipoleArrangement
-import pdme
-from deepdog.bayes_run import DotInput
-import datetime
-import numpy
-from dataclasses import dataclass
-import logging
-from typing import Sequence, Tuple
-import csv
-import itertools
-import multiprocessing
-
-_logger = logging.getLogger(__name__)
-
-
-def get_a_result(discretisation, dots, index):
-	return (index, discretisation.solve_for_index(dots, index))
-
-
-@dataclass
-class SingleDipoleDiagnostic:
-	model: str
-	index: Tuple
-	bounds: Tuple
-	actual_dipole: OscillatingDipole
-	result_dipole: OscillatingDipole
-	success: bool
-
-	def __post_init__(self) -> None:
-		self.p_actual_x = self.actual_dipole.p[0]
-		self.p_actual_y = self.actual_dipole.p[1]
-		self.p_actual_z = self.actual_dipole.p[2]
-		self.s_actual_x = self.actual_dipole.s[0]
-		self.s_actual_y = self.actual_dipole.s[1]
-		self.s_actual_z = self.actual_dipole.s[2]
-		self.p_result_x = self.result_dipole.p[0]
-		self.p_result_y = self.result_dipole.p[1]
-		self.p_result_z = self.result_dipole.p[2]
-		self.s_result_x = self.result_dipole.s[0]
-		self.s_result_y = self.result_dipole.s[1]
-		self.s_result_z = self.result_dipole.s[2]
-		self.w_actual = self.actual_dipole.w
-		self.w_result = self.result_dipole.w
-
-
-class Diagnostic:
-	"""
-	Represents a diagnostic for a single dipole moment given a set of discretisations.
-
-	Parameters
-	----------
-	dot_inputs : Sequence[DotInput]
-	The dot inputs for this diagnostic.
-
-	discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
-	The models to evaluate.
-
-	actual_model_discretisation : pdme.model.Discretisation
-	The discretisation for the model which is actually correct.
-
-	filename_slug : str
-	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,
-				)
-			]
-		)
-		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",
-		]
-
-		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-		self.filename = f"{timestamp}-{filename_slug}.diag.csv"
-
-	def go(self):
-		with open(self.filename, "a", newline="") as outfile:
-			# csv fields
-			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
-			writer.writeheader()
-
-		for (name, discretisation) in self.discretisations_with_names:
-			_logger.info(f"Working on discretisation {name}")
-
-			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(),
-					),
-				)
-
-			with open(self.filename, "a", newline="") as outfile:
-				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,
-					)
-					row = vars(diag_row)
-					_logger.debug(f"Writing result {row}")
-					writer.writerow(row)