chore(release): 0.5.0

Reviewed-on: #7

CHANGELOG.md

@@ -2,6 +2,28 @@
 
 All notable changes to this project will be documented in this file. See [standard-version](https://github.com/conventional-changelog/standard-version) for commit guidelines.
 
+## [0.5.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.4.0...0.5.0) (2022-04-30)
+
+
+### ⚠ BREAKING CHANGES
+
+* simulpairs now uses different rng calculator
+
+### Features
+
+* adds simulpairs run ([e9277c3](https://gitea.deepak.science:2222/physics/deepdog/commit/e9277c3da777359feb352c0b19f3bb029248ba2f))
+* has better parallelisation ([edf0ba6](https://gitea.deepak.science:2222/physics/deepdog/commit/edf0ba6532c0588fce32341709cdb70e384b83f4))
+* simulpairs now uses different rng calculator ([50dbc48](https://gitea.deepak.science:2222/physics/deepdog/commit/50dbc4835e60bace9e9b4ba37415f073a3c9e479))
+
+
+### Bug Fixes
+
+* better parallelisation hopefully ([42829c0](https://gitea.deepak.science:2222/physics/deepdog/commit/42829c0327e080e18be2fb75e746f6ac0d7c2f6d))
+* Makes altbayessimulpairs available in package ([492a5e6](https://gitea.deepak.science:2222/physics/deepdog/commit/492a5e6681c85f95840e28cfd5d4ce4ca1d54eba))
+* stronger names ([0954429](https://gitea.deepak.science:2222/physics/deepdog/commit/0954429e2d015a105ff16dfbb9e7a352bf53e5e9))
+* Uses correct filename arg for passed in rng ([349341b](https://gitea.deepak.science:2222/physics/deepdog/commit/349341b405375a43b933f1fd7db4ee9fc501def3))
+* uses correct filename for pairs guy ([4c06b39](https://gitea.deepak.science:2222/physics/deepdog/commit/4c06b3912c811c93c310b1d9e4c153f2014c4f8b))
+
 ## [0.4.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.5...0.4.0) (2022-04-10)
 
 

deepdog/__init__.py

@@ -2,6 +2,7 @@ import logging
 from deepdog.meta import __version__
 from deepdog.bayes_run import BayesRun
 from deepdog.alt_bayes_run import AltBayesRun
+from deepdog.alt_bayes_run_simulpairs import AltBayesRunSimulPairs
 from deepdog.diagnostic import Diagnostic
 
 
@@ -9,7 +10,13 @@ def get_version():
 	return __version__
 
 
-__all__ = ["get_version", "BayesRun", "AltBayesRun", "Diagnostic"]
+__all__ = [
+	"get_version",
+	"BayesRun",
+	"AltBayesRun",
+	"AltBayesRunSimulPairs",
+	"Diagnostic",
+]
 
 
 logging.getLogger(__name__).addHandler(logging.NullHandler())

deepdog/alt_bayes_run.py

@@ -24,47 +24,98 @@ _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
 	----------
 	dot_inputs : Sequence[DotInput]
-		The dot inputs for this bayes run.
+	The dot inputs for this bayes run.
+
 	discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
-		The models to evaluate.
+	The models to evaluate.
+
 	actual_model_discretisation : pdme.model.Discretisation
-		The discretisation for the model which is actually correct.
+	The discretisation for the model which is actually correct.
+
 	filename_slug : str
-		The filename slug to include.
+	The filename slug to include.
+
 	run_count: int
-		The number of runs to do.
+	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 +157,9 @@ 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 +174,31 @@ 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 +210,51 @@ 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 +264,44 @@ 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

@@ -0,0 +1,357 @@
+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
+import numpy.random
+
+
+# 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_simul_result_using_pairs(input) -> numpy.ndarray:
+	(
+		discretisation,
+		dot_inputs,
+		pair_inputs,
+		local_lows,
+		local_highs,
+		nonlocal_lows,
+		nonlocal_highs,
+		monte_carlo_count,
+		monte_carlo_cycles,
+		max_frequency,
+		seed,
+	) = input
+
+	rng = numpy.random.default_rng(seed)
+	local_total = 0
+	combined_total = 0
+
+	sample_dipoles = discretisation.get_model().get_n_single_dipoles(
+		monte_carlo_count, max_frequency, rng_to_use=rng
+	)
+	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)
+
+	local_total += numpy.count_nonzero(local_matches)
+	combined_total += numpy.count_nonzero(combined_matches)
+	return numpy.array([local_total, combined_total])
+
+
+class AltBayesRunSimulPairs:
+	"""
+	A dual pairs-nonpairs 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,
+	) -> 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.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_no_pairs = [1 / self.model_count] * self.model_count
+		self.probabilities_pairs = [1 / self.model_count] * self.model_count
+
+		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+		self.filename_pairs = f"{timestamp}-{filename_slug}.simulpairs.yespairs.csv"
+		self.filename_no_pairs = f"{timestamp}-{filename_slug}.simulpairs.noopairs.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_pairs, "a", newline="") as outfile:
+			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
+			writer.writeheader()
+		with open(self.filename_no_pairs, "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)
+			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}")
+
+			# define a new seed sequence for each run
+			seed_sequence = numpy.random.SeedSequence(run)
+
+			results_pairs = []
+			results_no_pairs = []
+			_logger.debug("Going to iterate over discretisations now")
+			for disc_count, discretisation in enumerate(self.discretisations):
+				_logger.debug(f"Doing discretisation #{disc_count}")
+
+				core_count = multiprocessing.cpu_count() - 1 or 1
+				with multiprocessing.Pool(core_count) as pool:
+					cycle_count = 0
+					cycle_success_pairs = 0
+					cycle_success_no_pairs = 0
+					cycles = 0
+					while (cycles < self.max_monte_carlo_cycles_steps) and (
+						min(cycle_success_pairs, cycle_success_no_pairs)
+						<= self.target_success
+					):
+						_logger.debug(f"Starting cycle {cycles}")
+						_logger.debug(f"(pair, no_pair) successes are {(cycle_success_pairs, cycle_success_no_pairs)}")
+						cycles += 1
+						current_success_pairs = 0
+						current_success_no_pairs = 0
+						cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
+
+						# generate a seed from the sequence for each core.
+						# note this needs to be inside the loop for monte carlo cycle steps!
+						# that way we get more stuff.
+
+						seeds = seed_sequence.spawn(self.monte_carlo_cycles)
+						_logger.debug(f"Creating {self.monte_carlo_cycles} seeds")
+						current_success_both = numpy.array(
+							sum(
+								pool.imap_unordered(
+									get_a_simul_result_using_pairs,
+									[
+										(
+											discretisation,
+											self.dot_inputs_array,
+											self.dot_pair_inputs_array,
+											lows,
+											highs,
+											pair_lows,
+											pair_highs,
+											self.monte_carlo_count,
+											self.monte_carlo_cycles,
+											self.max_frequency,
+											seed,
+										)
+										for seed in seeds
+									],
+									self.chunksize,
+								)
+							)
+						)
+						current_success_no_pairs = current_success_both[0]
+						current_success_pairs = current_success_both[1]
+
+						cycle_success_no_pairs += current_success_no_pairs
+						cycle_success_pairs += current_success_pairs
+					results_pairs.append((cycle_count, cycle_success_pairs))
+					results_no_pairs.append((cycle_count, cycle_success_no_pairs))
+
+			_logger.debug("Done, constructing output now")
+			row_pairs = {
+				"dipole_moment": actual_dipoles.dipoles[0].p,
+				"dipole_location": actual_dipoles.dipoles[0].s,
+				"dipole_frequency": actual_dipoles.dipoles[0].w,
+			}
+			row_no_pairs = {
+				"dipole_moment": actual_dipoles.dipoles[0].p,
+				"dipole_location": actual_dipoles.dipoles[0].s,
+				"dipole_frequency": actual_dipoles.dipoles[0].w,
+			}
+			successes_pairs: List[float] = []
+			successes_no_pairs: List[float] = []
+			counts: List[int] = []
+			for model_index, (
+				name,
+				(count_pair, result_pair),
+				(count_no_pair, result_no_pair),
+			) in enumerate(zip(self.model_names, results_pairs, results_no_pairs)):
+
+				row_pairs[f"{name}_success"] = result_pair
+				row_pairs[f"{name}_count"] = count_pair
+				successes_pairs.append(max(result_pair, 0.5))
+
+				row_no_pairs[f"{name}_success"] = result_no_pair
+				row_no_pairs[f"{name}_count"] = count_no_pair
+				successes_no_pairs.append(max(result_no_pair, 0.5))
+
+				counts.append(count_pair)
+
+			success_weight_pair = sum(
+				[
+					(succ / count) * prob
+					for succ, count, prob in zip(
+						successes_pairs, counts, self.probabilities_pairs
+					)
+				]
+			)
+			success_weight_no_pair = sum(
+				[
+					(succ / count) * prob
+					for succ, count, prob in zip(
+						successes_no_pairs, counts, self.probabilities_no_pairs
+					)
+				]
+			)
+			new_probabilities_pair = [
+				(succ / count) * old_prob / success_weight_pair
+				for succ, count, old_prob in zip(
+					successes_pairs, counts, self.probabilities_pairs
+				)
+			]
+			new_probabilities_no_pair = [
+				(succ / count) * old_prob / success_weight_no_pair
+				for succ, count, old_prob in zip(
+					successes_no_pairs, counts, self.probabilities_no_pairs
+				)
+			]
+			self.probabilities_pairs = new_probabilities_pair
+			self.probabilities_no_pairs = new_probabilities_no_pair
+			for name, probability_pair, probability_no_pair in zip(
+				self.model_names, self.probabilities_pairs, self.probabilities_no_pairs
+			):
+				row_pairs[f"{name}_prob"] = probability_pair
+				row_no_pairs[f"{name}_prob"] = probability_no_pair
+			_logger.debug(row_pairs)
+			_logger.debug(row_no_pairs)
+
+			with open(self.filename_pairs, "a", newline="") as outfile:
+				writer = csv.DictWriter(
+					outfile, fieldnames=self.csv_fields, dialect="unix"
+				)
+				writer.writerow(row_pairs)
+			with open(self.filename_no_pairs, "a", newline="") as outfile:
+				writer = csv.DictWriter(
+					outfile, fieldnames=self.csv_fields, dialect="unix"
+				)
+				writer.writerow(row_no_pairs)
+
+			if self.use_end_threshold:
+				max_prob = min(
+					max(self.probabilities_pairs), max(self.probabilities_no_pairs)
+				)
+				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

@@ -20,28 +20,44 @@ 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
 	----------
 	dot_inputs : Sequence[DotInput]
-		The dot inputs for this bayes run.
+	The dot inputs for this bayes run.
+
 	discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
-		The models to evaluate.
+	The models to evaluate.
+
 	actual_model_discretisation : pdme.model.Discretisation
-		The discretisation for the model which is actually correct.
+	The discretisation for the model which is actually correct.
+
 	filename_slug : str
-		The filename slug to include.
+	The filename slug to include.
+
 	run_count: int
-		The number of runs to do.
+	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 +81,9 @@ 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 +105,28 @@ 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 +139,31 @@ 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
@@ -43,31 +43,71 @@ 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
 	----------
 	dot_inputs : Sequence[DotInput]
-		The dot inputs for this diagnostic.
+	The dot inputs for this diagnostic.
+
 	discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
-		The models to evaluate.
+	The models to evaluate.
+
 	actual_model_discretisation : pdme.model.Discretisation
-		The discretisation for the model which is actually correct.
+	The discretisation for the model which is actually correct.
+
 	filename_slug : str
-		The filename slug to include.
+	The filename slug to include.
+
 	run_count: int
-		The number of runs to do.
+	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 +115,7 @@ 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 +123,38 @@ 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")

do.sh

@@ -16,6 +16,11 @@ test() {
    poetry run pytest
 }
 
+fmt() {
+	poetry run black .
+	find . -type f -name "*.py" -exec sed -i -e 's/    /\t/g' {} \;
+}
+
 release() {
    ./scripts/release.sh
 }

poetry.lock

@@ -20,6 +20,28 @@ docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
 tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
 tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
 
+[[package]]
+name = "black"
+version = "22.3.0"
+description = "The uncompromising code formatter."
+category = "dev"
+optional = false
+python-versions = ">=3.6.2"
+
+[package.dependencies]
+click = ">=8.0.0"
+mypy-extensions = ">=0.4.3"
+pathspec = ">=0.9.0"
+platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+colorama = ["colorama (>=0.4.3)"]
+d = ["aiohttp (>=3.7.4)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
 [[package]]
 name = "bleach"
 version = "4.1.0"
@@ -260,7 +282,7 @@ python-versions = "*"
 
 [[package]]
 name = "mypy"
-version = "0.942"
+version = "0.950"
 description = "Optional static typing for Python"
 category = "dev"
 optional = false
@@ -268,7 +290,7 @@ python-versions = ">=3.6"
 
 [package.dependencies]
 mypy-extensions = ">=0.4.3"
-tomli = ">=1.1.0"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 typing-extensions = ">=3.10"
 
 [package.extras]
@@ -286,7 +308,7 @@ python-versions = "*"
 
 [[package]]
 name = "numpy"
-version = "1.22.1"
+version = "1.22.3"
 description = "NumPy is the fundamental package for array computing with Python."
 category = "main"
 optional = false
@@ -303,16 +325,24 @@ python-versions = ">=3.6"
 [package.dependencies]
 pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
 
+[[package]]
+name = "pathspec"
+version = "0.9.0"
+description = "Utility library for gitignore style pattern matching of file paths."
+category = "dev"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+
 [[package]]
 name = "pdme"
-version = "0.6.1"
+version = "0.6.2"
 description = "Python dipole model evaluator"
 category = "main"
 optional = false
 python-versions = ">=3.8,<3.10"
 
 [package.dependencies]
-numpy = ">=1.21.1,<2.0.0"
+numpy = ">=1.22.3,<2.0.0"
 scipy = ">=1.8,<1.9"
 
 [[package]]
@@ -326,6 +356,18 @@ python-versions = "*"
 [package.extras]
 testing = ["coverage", "nose"]
 
+[[package]]
+name = "platformdirs"
+version = "2.5.1"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["Sphinx (>=4)", "furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx-autodoc-typehints (>=1.12)"]
+test = ["appdirs (==1.4.4)", "pytest (>=6)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)"]
+
 [[package]]
 name = "pluggy"
 version = "1.0.0"
@@ -698,7 +740,7 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8,<3.10"
-content-hash = "2911e2f292dbc9f6a87bfbb0b534695e2f0ae1e1a260aecca10955e685d6f846"
+content-hash = "98877f53c0ca996cd6eaa2c3b7391e391d29c7a4d3f1e08159fc999a3e4ad296"
 
 [metadata.files]
 atomicwrites = [
@@ -709,6 +751,31 @@ attrs = [
     {file = "attrs-21.4.0-py2.py3-none-any.whl", hash = "sha256:2d27e3784d7a565d36ab851fe94887c5eccd6a463168875832a1be79c82828b4"},
     {file = "attrs-21.4.0.tar.gz", hash = "sha256:626ba8234211db98e869df76230a137c4c40a12d72445c45d5f5b716f076e2fd"},
 ]
+black = [
+    {file = "black-22.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2497f9c2386572e28921fa8bec7be3e51de6801f7459dffd6e62492531c47e09"},
+    {file = "black-22.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5795a0375eb87bfe902e80e0c8cfaedf8af4d49694d69161e5bd3206c18618bb"},
+    {file = "black-22.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e3556168e2e5c49629f7b0f377070240bd5511e45e25a4497bb0073d9dda776a"},
+    {file = "black-22.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:67c8301ec94e3bcc8906740fe071391bce40a862b7be0b86fb5382beefecd968"},
+    {file = "black-22.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:fd57160949179ec517d32ac2ac898b5f20d68ed1a9c977346efbac9c2f1e779d"},
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pyproject.toml

@@ -1,19 +1,20 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.4.0"
+version = "0.5.0"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = "^3.8,<3.10"
-pdme = "^0.6.1"
+pdme = "0.6.2"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
 flake8 = "^4.0.1"
 pytest-cov = "^3.0.0"
-mypy = "^0.942"
+mypy = "^0.950"
 python-semantic-release = "^7.24.0"
+black = "^22.3.0"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]