chore(release): 0.6.0

Merge pull request 'multi' (#8 ) from multi into master
Reviewed-on: #8
2022-05-21 19:27:13 -05:00 · 2022-05-22 00:24:07 +00:00 · 2022-05-21 19:18:13 -05:00 · 2022-05-21 19:15:46 -05:00 · 2022-05-07 18:45:58 -05:00 · 2022-05-07 14:49:09 -05:00
10 changed files with 826 additions and 375 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,75 @@

 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.

+## [0.6.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.5.0...0.6.0) (2022-05-22)
+
+
+### ⚠ BREAKING CHANGES
+
+* bayes run now handles multidipoles with changes to output file format etc.
+* logs multiple dipoles better maybe
+* switches over to pdme new stuff, uses models and scraps discretisations entirely
+* removes alt_bayes bayes distinction, which was superfluous when only alt worked
+
+### Features
+
+* adds pdme 0.7.0 for multiprocessing ([874d876](https://gitea.deepak.science:2222/physics/deepdog/commit/874d876c9d774433b034d47c4cc0cdac41e6f2c7))
+* bayes run now handles multidipoles with changes to output file format etc. ([5d0a7a4](https://gitea.deepak.science:2222/physics/deepdog/commit/5d0a7a4be09c58f8f8f859384f01d7912a98b8b9))
+* logs multiple dipoles better maybe ([ae8977b](https://gitea.deepak.science:2222/physics/deepdog/commit/ae8977bb1e4d6cd71e88ea0876da8f4318e030b6))
+* removes alt_bayes bayes distinction, which was superfluous when only alt worked ([101569d](https://gitea.deepak.science:2222/physics/deepdog/commit/101569d749e4f3f1842886aa2fd3321b8132278b))
+* switches over to pdme new stuff, uses models and scraps discretisations entirely ([6e29f7a](https://gitea.deepak.science:2222/physics/deepdog/commit/6e29f7a702b578c266a42bba23ac973d155ada10))
+* Uses multidipole for bayes run, with more verbose output ([df89776](https://gitea.deepak.science:2222/physics/deepdog/commit/df8977655de977fd3c4f7383dd9571e551eb1382))
+
+
+### Bug Fixes
+
+* another bug fix for csv generation ([b7da3d6](https://gitea.deepak.science:2222/physics/deepdog/commit/b7da3d61cc5c128cba1d2fcb3770b71b7f6fc4b8))
+* fixes crash when dipole count is smaller than expected max during file write ([b5e0ecb](https://gitea.deepak.science:2222/physics/deepdog/commit/b5e0ecb52886b32d9055302eacfabb69338026b4))
+* fixes format string in csv output for headers ([9afa209](https://gitea.deepak.science:2222/physics/deepdog/commit/9afa209864cdb9255988778e987fe05952848fd4))
+* fixes random issue ([eec926a](https://gitea.deepak.science:2222/physics/deepdog/commit/eec926aaac654f78942b4c6b612e4d1cdcbf81dc))
+* moves logging successes to after they've actually happened ([0caad05](https://gitea.deepak.science:2222/physics/deepdog/commit/0caad05e3cc6a9adba8bf937c3d2f944e1b096a3))
+* now doesn't double randomise frequency ([23b202b](https://gitea.deepak.science:2222/physics/deepdog/commit/23b202beb81cb89f7f20b691e83116fa53764902))
+* whoops deleted word multiprocessing ([31070b5](https://gitea.deepak.science:2222/physics/deepdog/commit/31070b5342c265d930b4c51402f42a3ee2415066))
+
+## [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)
+
+
+### ⚠ BREAKING CHANGES
+
+* Adds pair calculations, with changing api format
+
+### Features
+
+* Adds dynamic cycle count increases to help reach minimum success count ([ec7b4ca](https://gitea.deepak.science:2222/physics/deepdog/commit/ec7b4cac393c15e94c513215c4f1ba32be2ae87a))
+* Adds pair calculations, with changing api format ([6463b13](https://gitea.deepak.science:2222/physics/deepdog/commit/6463b135ef2d212b565864b5ac1b655e014d2194))
+
+
+### Bug Fixes
+
+* uses bigfix from pdme for negatives ([c1c711f](https://gitea.deepak.science:2222/physics/deepdog/commit/c1c711f47b574d3a9b8a24dbcbdd7f50b9be8ea9))
+
 ### [0.3.5](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.4...0.3.5) (2022-03-07)


--- a/deepdog/init.py
+++ b/deepdog/init.py
@@ -1,15 +1,18 @@
 import logging
 from deepdog.meta import __version__
 from deepdog.bayes_run import BayesRun
-from deepdog.alt_bayes_run import AltBayesRun
-from deepdog.diagnostic import Diagnostic
+from deepdog.bayes_run_simulpairs import BayesRunSimulPairs


 def get_version():
 	return __version__


-__all__ = ["get_version", "BayesRun", "AltBayesRun", "Diagnostic"]
+__all__ = [
+	"get_version",
+	"BayesRun",
+	"BayesRunSimulPairs",
+]


 logging.getLogger(__name__).addHandler(logging.NullHandler())
--- a/deepdog/alt_bayes_run.py
+++ b/deepdog/alt_bayes_run.py
@@ -1,134 +0,0 @@
-import pdme.model
-import pdme.measurement.oscillating_dipole
-import pdme.util.fast_v_calc
-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))
-
-
-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_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, low_error: float = 0.9, high_error: float = 1.1, monte_carlo_count: int = 10000, monte_carlo_cycles: int = 10, max_frequency: float = 20, end_threshold: float = None, chunksize: int = CHUNKSIZE) -> None:
-		self.dot_inputs = dot_inputs
-		self.dot_inputs_array = pdme.measurement.oscillating_dipole.dot_inputs_to_array(dot_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.run_count = run_count
-		self.low_error = low_error
-		self.high_error = 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}.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.oscillating_dipole.dot_range_measurements_low_high_arrays(dots)
-			_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(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)
-					))
-
-			_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, result) in enumerate(zip(self.model_names, results)):
-
-				row[f"{name}_success"] = result
-				row[f"{name}_count"] = self.monte_carlo_count * self.monte_carlo_cycles
-				successes.append(max(result, 0.5))
-				counts.append(self.monte_carlo_count * self.monte_carlo_cycles)
-
-			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
--- a/deepdog/bayes_run.py
+++ b/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]
@@ -20,43 +22,126 @@ DotInput = Tuple[numpy.typing.ArrayLike, float]
 _logger = logging.getLogger(__name__)


-def get_a_result(discretisation, dots, index) -> Tuple[Tuple[int, ...], scipy.optimize.OptimizeResult]:
-	return (index, discretisation.solve_for_index(dots, index))
+def get_a_result(input) -> int:
+	model, dot_inputs, lows, highs, monte_carlo_count, max_frequency, seed = input
+
+	rng = numpy.random.default_rng(seed)
+	sample_dipoles = model.get_monte_carlo_dipole_inputs(
+		monte_carlo_count, max_frequency, rng_to_use=rng
+	)
+	vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(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:
+	(
+		model,
+		dot_inputs,
+		pair_inputs,
+		local_lows,
+		local_highs,
+		nonlocal_lows,
+		nonlocal_highs,
+		monte_carlo_count,
+		max_frequency,
+	) = input
+	sample_dipoles = 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 BayesRun:
+	"""
 	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)]
+
+	models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
 	The models to evaluate.
-	actual_model_discretisation : pdme.model.Discretisation
-		The discretisation for the model which is actually correct.
+
+	actual_model : pdme.model.DipoleModel
+	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_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]
+	"""
+
+	def __init__(
+		self,
+		dot_positions: Sequence[numpy.typing.ArrayLike],
+		frequency_range: Sequence[float],
+		models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
+		actual_model: pdme.model.DipoleModel,
+		filename_slug: str,
+		run_count: int = 100,
+		low_error: float = 0.9,
+		high_error: float = 1.1,
+		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.models = [model for (_, model) in models_with_names]
+		self.model_names = [name for (name, _) in models_with_names]
 		self.actual_model = actual_model
-		self.model_count = len(self.discretisations)
+
+		self.n: int
+		try:
+			self.n = self.actual_model.n  # type: ignore
+		except AttributeError:
+			self.n = 1
+
+		self.model_count = len(self.models)
+		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.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
+		self.low_error = low_error
+		self.high_error = high_error
+
+		self.csv_fields = []
+		for i in range(self.n):
+			self.csv_fields.extend(
+				[
+					f"dipole_moment_{i+1}",
+					f"dipole_location_{i+1}",
+					f"dipole_frequency_{i+1}",
+				]
+			)
 		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"
+		self.filename = f"{timestamp}-{filename_slug}.bayesrun.csv"
 		self.max_frequency = max_frequency

 		if end_threshold is not None:
@@ -65,7 +150,9 @@ 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:
@@ -73,56 +160,122 @@ 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)
-			_logger.info(f"Going to work on dipole at {dipoles.dipoles}")
+			# Generate the actual dipoles
+			actual_dipoles = self.actual_model.get_dipoles(self.max_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
+			)
+
+			_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 = []
-			_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(pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(dots), discretisation.all_indices())))
+			_logger.debug("Going to iterate over models now")
+			for model_count, model in enumerate(self.models):
+				_logger.debug(f"Doing model #{model_count}")
+				core_count = multiprocessing.cpu_count() - 1 or 1
+				with multiprocessing.Pool(core_count) 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
+
+						# 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)
+
+						current_success = sum(
+							pool.imap_unordered(
+								get_a_result,
+								[
+									(
+										model,
+										self.dot_inputs_array,
+										lows,
+										highs,
+										self.monte_carlo_count,
+										self.max_frequency,
+										seed,
+									)
+									for seed in seeds
+								],
+								self.chunksize,
+							)
+						)
+
+						cycle_success += current_success
+						_logger.debug(f"current running successes: {cycle_success}")
+					results.append((cycle_count, cycle_success))

 			_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_moment_1": actual_dipoles.dipoles[0].p,
+				"dipole_location_1": actual_dipoles.dipoles[0].s,
+				"dipole_frequency_1": actual_dipoles.dipoles[0].w,
 			}
+			for i in range(1, self.n):
+				try:
+					current_dipoles = actual_dipoles.dipoles[i]
+					row[f"dipole_moment_{i+1}"] = current_dipoles.p
+					row[f"dipole_location_{i+1}"] = current_dipoles.s
+					row[f"dipole_frequency_{i+1}"] = current_dipoles.w
+				except IndexError:
+					_logger.info(f"Not writing anymore, saw end after {i}")
+					break
+
 			successes: List[float] = []
 			counts: List[int] = []
-			for model_index, (name, 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
+			for model_index, (name, (count, result)) in enumerate(
+				zip(self.model_names, results)
+			):

-				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([(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)]
+			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 = 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
--- a/deepdog/bayes_run_simulpairs.py
+++ b/deepdog/bayes_run_simulpairs.py
@@ -0,0 +1,382 @@
+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:
+	(
+		model,
+		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 = model.get_monte_carlo_dipole_inputs(
+		monte_carlo_count, max_frequency, rng_to_use=rng
+	)
+	local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(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_dipoleses(
+		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 BayesRunSimulPairs:
+	"""
+	A dual pairs-nonpairs Bayes run for a given set of dots.
+
+	Parameters
+	----------
+	dot_inputs : Sequence[DotInput]
+	The dot inputs for this bayes run.
+
+	models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
+	The models to evaluate.
+
+	actual_model : pdme.model.DipoleModel
+	The modoel 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],
+		models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
+		actual_model: pdme.model.DipoleModel,
+		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.models = [mod for (_, mod) in models_with_names]
+		self.model_names = [name for (name, _) in models_with_names]
+		self.actual_model = actual_model
+
+		self.n: int
+		try:
+			self.n = self.actual_model.n  # type: ignore
+		except AttributeError:
+			self.n = 1
+
+		self.model_count = len(self.models)
+		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 = []
+		for i in range(self.n):
+			self.csv_fields.extend(
+				[
+					f"dipole_moment_{i+1}",
+					f"dipole_location_{i+1}",
+					f"dipole_frequency_{i+1}",
+				]
+			)
+		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):
+
+			# Generate the actual dipoles
+			actual_dipoles = self.actual_model.get_dipoles(self.max_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 models now")
+			for model_count, model in enumerate(self.models):
+				_logger.debug(f"Doing model #{model_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}")
+
+						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,
+									[
+										(
+											model,
+											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
+						_logger.debug(
+							f"(pair, no_pair) successes are {(cycle_success_pairs, cycle_success_no_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_1": actual_dipoles.dipoles[0].p,
+				"dipole_location_1": actual_dipoles.dipoles[0].s,
+				"dipole_frequency_1": actual_dipoles.dipoles[0].w,
+			}
+			row_no_pairs = {
+				"dipole_moment_1": actual_dipoles.dipoles[0].p,
+				"dipole_location_1": actual_dipoles.dipoles[0].s,
+				"dipole_frequency_1": actual_dipoles.dipoles[0].w,
+			}
+			for i in range(1, self.n):
+				try:
+					current_dipoles = actual_dipoles.dipoles[i]
+					row_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
+					row_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
+					row_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
+					row_no_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
+					row_no_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
+					row_no_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
+				except IndexError:
+					_logger.info(f"Not writing anymore, saw end after {i}")
+					break
+
+			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
--- a/deepdog/diagnostic.py
+++ b/deepdog/diagnostic.py
@@ -1,99 +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)
--- a/deepdog/meta.py
+++ b/deepdog/meta.py
@@ -1,3 +1,3 @@
 from importlib.metadata import version

-__version__ = version('deepdog')
+__version__ = version("deepdog")
--- a/do.sh
+++ b/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
 }
--- a/poetry.lock
+++ b/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 = "0.941"
+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
@@ -303,17 +325,25 @@ 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 = "0.5.4"
+version = "0.8.2"
 description = "Python dipole model evaluator"
 category = "main"
 optional = false
 python-versions = ">=3.8,<3.10"

 [package.dependencies]
-numpy = ">=1.21.1,<2.0.0"
-scipy = ">=1.5,<1.6"
+numpy = ">=1.22.3,<2.0.0"
+scipy = ">=1.8,<1.9"

 [[package]]
 name = "pkginfo"
@@ -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"
@@ -533,14 +575,14 @@ idna2008 = ["idna"]

 [[package]]
 name = "scipy"
-version = "1.5.4"
+version = "1.8.0"
 description = "SciPy: Scientific Library for Python"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8,<3.11"

 [package.dependencies]
-numpy = ">=1.14.5"
+numpy = ">=1.17.3,<1.25.0"

 [[package]]
 name = "secretstorage"
@@ -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 = "df9f6e340261abeea6f47328a703efca9251bfe29731652152db39095ac41ae0"
+content-hash = "c1b27cf9297e4777e78a72caba5f560be87a0f3b27c001b7a05b18adfdca2d1c"

 [metadata.files]
 atomicwrites = [
@@ -709,6 +751,31 @@ attrs = [
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 ]
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@@ -899,70 +966,76 @@ mccabe = [
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 secretstorage = [
    {file = "SecretStorage-3.3.1-py3-none-any.whl", hash = "sha256:422d82c36172d88d6a0ed5afdec956514b189ddbfb72fefab0c8a1cee4eaf71f"},
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,19 +1,20 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.3.5"
+version = "0.6.0"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]

 [tool.poetry.dependencies]
 python = "^3.8,<3.10"
-pdme = "^0.5.4"
+pdme = "0.8.2"

 [tool.poetry.dev-dependencies]
 pytest = ">=6"
 flake8 = "^4.0.1"
 pytest-cov = "^3.0.0"
-mypy = "^0.941"
+mypy = "^0.950"
 python-semantic-release = "^7.24.0"
+black = "^22.3.0"

 [build-system]
 requires = ["poetry-core>=1.0.0"]
Author	SHA1	Message	Date
Deepak Mallubhotla	362388363f	chore(release): 0.6.0 All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details gitea-physics/deepdog/pipeline/tag This commit looks good Details	2022-05-21 19:27:13 -05:00
Deepak Mallubhotla	252b4a4414	Merge pull request 'multi' (#8 ) from multi into master All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details Reviewed-on: #8	2022-05-22 00:24:07 +00:00
Deepak Mallubhotla	bb21355f5e	style: fmt All checks were successful gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-05-21 19:18:13 -05:00
Deepak Mallubhotla	df8977655d	feat: Uses multidipole for bayes run, with more verbose output All checks were successful gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-05-21 19:15:46 -05:00
Deepak Mallubhotla	5d0a7a4be0	feat!: bayes run now handles multidipoles with changes to output file format etc. All checks were successful gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-05-07 18:45:58 -05:00
Deepak Mallubhotla	67a9721c31	style: don't use unused exception var All checks were successful gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-05-07 14:49:09 -05:00
Deepak Mallubhotla	b5e0ecb528	fix: fixes crash when dipole count is smaller than expected max during file write	2022-05-07 14:46:56 -05:00
Deepak Mallubhotla	feeb03b27c	chore: Updates to pdme 0.8.2 All checks were successful gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-05-07 11:24:15 -05:00
Deepak Mallubhotla	b7da3d61cc	fix: another bug fix for csv generation All checks were successful gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-04-30 19:56:17 -05:00
Deepak Mallubhotla	9afa209864	fix: fixes format string in csv output for headers All checks were successful gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-04-30 19:52:46 -05:00
Deepak Mallubhotla	ae8977bb1e	feat!: logs multiple dipoles better maybe Some checks are pending gitea-physics/deepdog/pipeline/head Build started... Details gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-04-30 18:49:48 -05:00
Deepak Mallubhotla	0caad05e3c	fix: moves logging successes to after they've actually happened All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details	2022-04-30 18:14:26 -05:00
Deepak Mallubhotla	eec926aaac	fix: fixes random issue	2022-04-30 18:11:22 -05:00
Deepak Mallubhotla	23b202beb8	fix: now doesn't double randomise frequency Some checks failed gitea-physics/deepdog/pipeline/head There was a failure building this commit Details	2022-04-30 17:40:55 -05:00
Deepak Mallubhotla	6e29f7a702	feat!: switches over to pdme new stuff, uses models and scraps discretisations entirely	2022-04-30 17:31:36 -05:00
Deepak Mallubhotla	31070b5342	fix: whoops deleted word multiprocessing	2022-04-30 16:44:12 -05:00
Deepak Mallubhotla	101569d749	feat!: removes alt_bayes bayes distinction, which was superfluous when only alt worked	2022-04-30 16:43:37 -05:00
Deepak Mallubhotla	874d876c9d	feat: adds pdme 0.7.0 for multiprocessing	2022-04-30 16:41:34 -05:00
Deepak Mallubhotla	3dca288177	chore(release): 0.5.0 All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details gitea-physics/deepdog/pipeline/tag This commit looks good Details	2022-04-30 11:19:38 -05:00
Deepak Mallubhotla	bd0b375751	Merge pull request 'betterparallel' (#7 ) from betterparallel into master All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details Reviewed-on: #7	2022-04-30 16:15:01 +00:00
Deepak Mallubhotla	0fabd8f7fb	Merge branch 'master' into betterparallel All checks were successful gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-04-30 16:10:15 +00:00
Deepak Mallubhotla	3ea3d1dc56	Merge pull request 'chore(deps): update dependency mypy to ^0.950' (#6 ) from renovate/mypy-0.x into master Some checks reported errors gitea-physics/deepdog/pipeline/head Something is wrong with the build of this commit Details Reviewed-on: #6	2022-04-30 16:08:57 +00:00
Deepak Mallubhotla	edf0ba6532	feat: has better parallelisation Some checks are pending gitea-physics/deepdog/pipeline/head Build started... Details gitea-physics/deepdog/pipeline/pr-master Build queued... Details	2022-04-30 10:36:10 -05:00
Renovate Bot	a487309549	chore(deps): update dependency mypy to ^0.950 All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-04-28 01:31:10 +00:00
Deepak Mallubhotla	42829c0327	fix: better parallelisation hopefully Some checks reported errors gitea-physics/deepdog/pipeline/head Something is wrong with the build of this commit Details	2022-04-24 12:13:10 -05:00
Deepak Mallubhotla	349341b405	fix: Uses correct filename arg for passed in rng All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details	2022-04-18 16:00:19 -05:00
Deepak Mallubhotla	50dbc4835e	feat!: simulpairs now uses different rng calculator	2022-04-18 12:04:30 -05:00
Deepak Mallubhotla	0954429e2d	fix: stronger names	2022-04-16 13:11:08 -05:00
Deepak Mallubhotla	4c06b3912c	fix: uses correct filename for pairs guy All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details	2022-04-16 13:04:39 -05:00
Deepak Mallubhotla	5684af783e	fmt: Adds newlines to make fmt idempotent	2022-04-16 13:04:11 -05:00
Deepak Mallubhotla	f00b29391c	style: run doo fmt All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details	2022-04-16 12:55:52 -05:00
Deepak Mallubhotla	492a5e6681	fix: Makes altbayessimulpairs available in package	2022-04-16 12:55:29 -05:00
Deepak Mallubhotla	e9277c3da7	feat: adds simulpairs run	2022-04-16 12:54:30 -05:00
Deepak Mallubhotla	1e2657adad	chore: adds doo fmt	2022-04-16 12:51:31 -05:00
Deepak Mallubhotla	f168666045	chore(release): 0.4.0 All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details gitea-physics/deepdog/pipeline/tag This commit looks good Details	2022-04-10 10:21:47 -05:00
Deepak Mallubhotla	604916a829	Merge pull request 'pairs' (#5 ) from pairs into master All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details Reviewed-on: #5	2022-04-10 15:20:05 +00:00
Deepak Mallubhotla	941313a14c	style: whitespace fixes All checks were successful gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-04-10 10:05:15 -05:00
Deepak Mallubhotla	cb64c0b7b6	Merge branch 'master' into pairs	2022-04-10 10:03:47 -05:00
Deepak Mallubhotla	ec7b4cac39	feat: Adds dynamic cycle count increases to help reach minimum success count Some checks failed gitea-physics/deepdog/pipeline/pr-master There was a failure building this commit Details	2022-03-28 15:46:40 -05:00
Deepak Mallubhotla	31e6cfaf51	lint: lint fixes Some checks failed gitea-physics/deepdog/pipeline/head This commit looks good Details gitea-physics/deepdog/pipeline/pr-master There was a failure building this commit Details	2022-03-28 12:28:24 -05:00
Deepak Mallubhotla	c1c711f47b	fix: uses bigfix from pdme for negatives Some checks failed gitea-physics/deepdog/pipeline/head There was a failure building this commit Details	2022-03-28 10:52:28 -05:00
Deepak Mallubhotla	6463b135ef	feat!: Adds pair calculations, with changing api format Some checks reported errors gitea-physics/deepdog/pipeline/head Something is wrong with the build of this commit Details	2022-03-27 19:01:14 -05:00
Deepak Mallubhotla	a283cbd670	Merge pull request 'chore(deps): update dependency mypy to ^0.942' (#3 ) from renovate/mypy-0.x into master All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details Reviewed-on: #3	2022-03-25 13:15:07 +00:00
Renovate Bot	0b45172ca0	chore(deps): update dependency mypy to ^0.942 All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details gitea-physics/deepdog/pipeline/pr-master This commit looks good Details	2022-03-25 01:30:47 +00:00
Deepak Mallubhotla	b6383d0a47	Merge pull request 'chore(deps): update dependency mypy to ^0.941' (#2 ) from renovate/mypy-0.x into master All checks were successful gitea-physics/deepdog/pipeline/head This commit looks good Details Reviewed-on: #2	2022-03-19 00:54:39 +00:00