chore(release): 0.6.3

Reviewed-on: #14

CHANGELOG.md

@@ -2,6 +2,97 @@
 
 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.
 
+### [0.6.3](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.2...0.6.3) (2022-06-12)
+
+
+### Features
+
+* adds fast filter variant ([2c5c122](https://gitea.deepak.science:2222/physics/deepdog/commit/2c5c1228209e51d17253f07470e2f1e6dc6872d7))
+* adds tester for fast filter real spectrum ([0a1a277](https://gitea.deepak.science:2222/physics/deepdog/commit/0a1a27759b0d4ab01da214b76ab14bf2b1fe00e3))
+
+### [0.6.2](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.1...0.6.2) (2022-05-26)
+
+
+### Features
+
+* adds better import api for real data run ([d7e0f13](https://gitea.deepak.science:2222/physics/deepdog/commit/d7e0f13ca55197b24cb534c80f321ee76b9c4a40))
+
+### [0.6.1](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.0...0.6.1) (2022-05-22)
+
+
+### Features
+
+* adds new runner for real spectra ([bd56f24](https://gitea.deepak.science:2222/physics/deepdog/commit/bd56f247748babb2ee1f2a1182d25aa968bff5a5))
+
+## [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)
 
 

deepdog/__init__.py

@@ -1,15 +1,20 @@
 import logging
 from deepdog.meta import __version__
 from deepdog.bayes_run import BayesRun
-from deepdog.alt_bayes_run import AltBayesRun
+from deepdog.bayes_run_simulpairs import BayesRunSimulPairs
-from deepdog.diagnostic import Diagnostic
+from deepdog.real_spectrum_run import RealSpectrumRun
 
 
 def get_version():
 	return __version__
 
 
-__all__ = ["get_version", "BayesRun", "AltBayesRun", "Diagnostic"]
+__all__ = [
+	"get_version",
+	"BayesRun",
+	"BayesRunSimulPairs",
+	"RealSpectrumRun",
+]
 
 
 logging.getLogger(__name__).addHandler(logging.NullHandler())

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

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]:
+def get_a_result(input) -> int:
-	return (index, discretisation.solve_for_index(dots, index))
+	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.
+	The dot inputs for this bayes run.
-	discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
+
-		The models to evaluate.
+	models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
-	actual_model_discretisation : pdme.model.Discretisation
+	The models to evaluate.
-		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.
+	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:
+
-		self.dot_inputs = dot_inputs
+	def __init__(
-		self.discretisations = [disc for (_, disc) in discretisations_with_names]
+		self,
-		self.model_names = [name for (name, _) in discretisations_with_names]
+		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)
+			# Generate the actual dipoles
-			_logger.info(f"Going to work on dipole at {dipoles.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")
+			_logger.debug("Going to iterate over models now")
-			for disc_count, discretisation in enumerate(self.discretisations):
+			for model_count, model in enumerate(self.models):
-				_logger.debug(f"Doing discretisation #{disc_count}")
+				_logger.debug(f"Doing model #{model_count}")
-				with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
+				core_count = multiprocessing.cpu_count() - 1 or 1
-					results.append(pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(dots), discretisation.all_indices())))
+				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_moment_1": actual_dipoles.dipoles[0].p,
-				"dipole_location": dipoles.dipoles[0].s,
+				"dipole_location_1": actual_dipoles.dipoles[0].s,
-				"dipole_frequency": dipoles.dipoles[0].w
+				"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)):
+			for model_index, (name, (count, result)) in enumerate(
-				count = 0
+				zip(self.model_names, results)
-				success = 0
+			):
-				for idx, val in result:
-					count += 1
-					if val.success and val.cost <= COST_THRESHOLD:
-						success += 1
 
-				row[f"{name}_success"] = success
+				row[f"{name}_success"] = result
 				row[f"{name}_count"] = count
-				successes.append(max(success, 0.5))
+				successes.append(max(result, 0.5))
 				counts.append(count)
 
-			success_weight = sum([(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/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

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)

deepdog/meta.py

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

deepdog/real_spectrum_run.py

@@ -0,0 +1,220 @@
+import pdme.inputs
+import pdme.model
+import pdme.measurement
+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, Dict, Union
+import datetime
+import csv
+import multiprocessing
+import logging
+import numpy
+
+
+# TODO: remove hardcode
+CHUNKSIZE = 50
+
+
+_logger = logging.getLogger(__name__)
+
+
+def get_a_result(input) -> int:
+	model, dot_inputs, lows, highs, monte_carlo_count, seed = input
+
+	rng = numpy.random.default_rng(seed)
+	# TODO: A long term refactor is to pull the frequency stuff out from here. The None stands for max_frequency, which is unneeded in the actually useful models.
+	sample_dipoles = model.get_monte_carlo_dipole_inputs(
+		monte_carlo_count, None, 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))
+
+
+def get_a_result_fast_filter(input) -> int:
+	model, dot_inputs, lows, highs, monte_carlo_count, seed = input
+
+	rng = numpy.random.default_rng(seed)
+	# TODO: A long term refactor is to pull the frequency stuff out from here. The None stands for max_frequency, which is unneeded in the actually useful models.
+	sample_dipoles = model.get_monte_carlo_dipole_inputs(
+		monte_carlo_count, None, rng_to_use=rng
+	)
+
+	current_sample = sample_dipoles
+	for di, low, high in zip(dot_inputs, lows, highs):
+
+		if len(current_sample) < 1:
+			break
+		vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(
+			numpy.array([di]), current_sample
+		)
+
+		current_sample = current_sample[numpy.all((vals > low) & (vals < high), axis=1)]
+	return len(current_sample)
+
+
+class RealSpectrumRun:
+	"""
+	A bayes run given some real data.
+
+	Parameters
+	----------
+	measurements : Sequence[pdme.measurement.DotRangeMeasurement]
+	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 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,
+		measurements: Sequence[pdme.measurement.DotRangeMeasurement],
+		models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
+		filename_slug: str,
+		monte_carlo_count: int = 10000,
+		monte_carlo_cycles: int = 10,
+		target_success: int = 100,
+		max_monte_carlo_cycles_steps: int = 10,
+		chunksize: int = CHUNKSIZE,
+		initial_seed: int = 12345,
+		use_fast_filter: bool = True,
+	) -> None:
+		self.measurements = measurements
+		self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
+
+		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.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.csv_fields = []
+
+		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"])
+
+		# for now initialise priors as uniform.
+		self.probabilities = [1 / self.model_count] * self.model_count
+
+		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+		self.use_fast_filter = use_fast_filter
+		ff_string = "no_fast_filter"
+		if self.use_fast_filter:
+			ff_string = "fast_filter"
+		self.filename = f"{timestamp}-{filename_slug}.realdata.{ff_string}.bayesrun.csv"
+		self.initial_seed = initial_seed
+
+	def go(self) -> None:
+		with open(self.filename, "a", newline="") as outfile:
+			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
+			writer.writeheader()
+
+		(
+			lows,
+			highs,
+		) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
+			self.measurements
+		)
+
+		# define a new seed sequence for each run
+		seed_sequence = numpy.random.SeedSequence(self.initial_seed)
+
+		results = []
+		_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)
+
+					if self.use_fast_filter:
+						result_func = get_a_result_fast_filter
+					else:
+						result_func = get_a_result
+					current_success = sum(
+						pool.imap_unordered(
+							result_func,
+							[
+								(
+									model,
+									self.dot_inputs_array,
+									lows,
+									highs,
+									self.monte_carlo_count,
+									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: Dict[str, Union[int, float, str]] = {}
+
+		successes: List[float] = []
+		counts: List[int] = []
+		for model_index, (name, (count, result)) in enumerate(
+			zip(self.model_names, results)
+		):
+
+			row[f"{name}_success"] = result
+			row[f"{name}_count"] = count
+			successes.append(max(result, 0.5))
+			counts.append(count)
+
+		success_weight = sum(
+			[
+				(succ / count) * prob
+				for succ, count, prob in zip(successes, counts, self.probabilities)
+			]
+		)
+		new_probabilities = [
+			(succ / count) * old_prob / success_weight
+			for succ, count, old_prob in zip(successes, counts, self.probabilities)
+		]
+		self.probabilities = new_probabilities
+		for name, probability in zip(self.model_names, self.probabilities):
+			row[f"{name}_prob"] = probability
+		_logger.info(row)
+
+		with open(self.filename, "a", newline="") as outfile:
+			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
+			writer.writerow(row)

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"
@@ -95,14 +117,14 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "coverage"
-version = "6.3.2"
+version = "6.4.1"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 
 [package.dependencies]
-tomli = {version = "*", optional = true, markers = "extra == \"toml\""}
+tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
 
 [package.extras]
 toml = ["tomli"]
@@ -260,7 +282,7 @@ python-versions = "*"
 
 [[package]]
 name = "mypy"
-version = "0.931"
+version = "0.961"
 description = "Optional static typing for Python"
 category = "dev"
 optional = false
@@ -268,12 +290,13 @@ 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]
 dmypy = ["psutil (>=4.0)"]
 python2 = ["typed-ast (>=1.4.0,<2)"]
+reports = ["lxml"]
 
 [[package]]
 name = "mypy-extensions"
@@ -285,7 +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
@@ -302,17 +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 = "0.8.5"
 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 = ">=1.5,<1.6"
+scipy = ">=1.8,<1.9"
 
 [[package]]
 name = "pkginfo"
@@ -325,6 +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"
@@ -532,14 +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"
@@ -697,7 +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 = "ba794e6f69d42e44e2b1abc40731fd78d2f417cdc9be12d6e752dbcfa95adaad"
+content-hash = "0196604b0dfc7b3dd0bdb19f983358ef9934d47f3c209dbbc7d07712ac5f779d"
 
 [metadata.files]
 atomicwrites = [
@@ -708,6 +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"},
+    {file = "black-22.3.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:cc1e1de68c8e5444e8f94c3670bb48a2beef0e91dddfd4fcc29595ebd90bb9ce"},
+    {file = "black-22.3.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d2fc92002d44746d3e7db7cf9313cf4452f43e9ea77a2c939defce3b10b5c82"},
+    {file = "black-22.3.0-cp36-cp36m-win_amd64.whl", hash = "sha256:a6342964b43a99dbc72f72812bf88cad8f0217ae9acb47c0d4f141a6416d2d7b"},
+    {file = "black-22.3.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:328efc0cc70ccb23429d6be184a15ce613f676bdfc85e5fe8ea2a9354b4e9015"},
+    {file = "black-22.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:06f9d8846f2340dfac80ceb20200ea5d1b3f181dd0556b47af4e8e0b24fa0a6b"},
+    {file = "black-22.3.0-cp37-cp37m-win_amd64.whl", hash = "sha256:ad4efa5fad66b903b4a5f96d91461d90b9507a812b3c5de657d544215bb7877a"},
+    {file = "black-22.3.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:e8477ec6bbfe0312c128e74644ac8a02ca06bcdb8982d4ee06f209be28cdf163"},
+    {file = "black-22.3.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:637a4014c63fbf42a692d22b55d8ad6968a946b4a6ebc385c5505d9625b6a464"},
+    {file = "black-22.3.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:863714200ada56cbc366dc9ae5291ceb936573155f8bf8e9de92aef51f3ad0f0"},
+    {file = "black-22.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10dbe6e6d2988049b4655b2b739f98785a884d4d6b85bc35133a8fb9a2233176"},
+    {file = "black-22.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:cee3e11161dde1b2a33a904b850b0899e0424cc331b7295f2a9698e79f9a69a0"},
+    {file = "black-22.3.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5891ef8abc06576985de8fa88e95ab70641de6c1fca97e2a15820a9b69e51b20"},
+    {file = "black-22.3.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:30d78ba6bf080eeaf0b7b875d924b15cd46fec5fd044ddfbad38c8ea9171043a"},
+    {file = "black-22.3.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ee8f1f7228cce7dffc2b464f07ce769f478968bfb3dd1254a4c2eeed84928aad"},
+    {file = "black-22.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6ee227b696ca60dd1c507be80a6bc849a5a6ab57ac7352aad1ffec9e8b805f21"},
+    {file = "black-22.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:9b542ced1ec0ceeff5b37d69838106a6348e60db7b8fdd245294dc1d26136265"},
+    {file = "black-22.3.0-py3-none-any.whl", hash = "sha256:bc58025940a896d7e5356952228b68f793cf5fcb342be703c3a2669a1488cb72"},
+    {file = "black-22.3.0.tar.gz", hash = "sha256:35020b8886c022ced9282b51b5a875b6d1ab0c387b31a065b84db7c33085ca79"},
+]
 bleach = [
     {file = "bleach-4.1.0-py2.py3-none-any.whl", hash = "sha256:4d2651ab93271d1129ac9cbc679f524565cc8a1b791909c4a51eac4446a15994"},
     {file = "bleach-4.1.0.tar.gz", hash = "sha256:0900d8b37eba61a802ee40ac0061f8c2b5dee29c1927dd1d233e075ebf5a71da"},
@@ -785,47 +853,47 @@ colorama = [
     {file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
 ]
 coverage = [
-    {file = "coverage-6.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9b27d894748475fa858f9597c0ee1d4829f44683f3813633aaf94b19cb5453cf"},
+    {file = "coverage-6.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f1d5aa2703e1dab4ae6cf416eb0095304f49d004c39e9db1d86f57924f43006b"},
-    {file = "coverage-6.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:37d1141ad6b2466a7b53a22e08fe76994c2d35a5b6b469590424a9953155afac"},
+    {file = "coverage-6.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4ce1b258493cbf8aec43e9b50d89982346b98e9ffdfaae8ae5793bc112fb0068"},
-    {file = "coverage-6.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f9987b0354b06d4df0f4d3e0ec1ae76d7ce7cbca9a2f98c25041eb79eec766f1"},
+    {file = "coverage-6.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:83c4e737f60c6936460c5be330d296dd5b48b3963f48634c53b3f7deb0f34ec4"},
-    {file = "coverage-6.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:26e2deacd414fc2f97dd9f7676ee3eaecd299ca751412d89f40bc01557a6b1b4"},
+    {file = "coverage-6.4.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:84e65ef149028516c6d64461b95a8dbcfce95cfd5b9eb634320596173332ea84"},
-    {file = "coverage-6.3.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4dd8bafa458b5c7d061540f1ee9f18025a68e2d8471b3e858a9dad47c8d41903"},
+    {file = "coverage-6.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f69718750eaae75efe506406c490d6fc5a6161d047206cc63ce25527e8a3adad"},
-    {file = "coverage-6.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:46191097ebc381fbf89bdce207a6c107ac4ec0890d8d20f3360345ff5976155c"},
+    {file = "coverage-6.4.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e57816f8ffe46b1df8f12e1b348f06d164fd5219beba7d9433ba79608ef011cc"},
-    {file = "coverage-6.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6f89d05e028d274ce4fa1a86887b071ae1755082ef94a6740238cd7a8178804f"},
+    {file = "coverage-6.4.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:01c5615d13f3dd3aa8543afc069e5319cfa0c7d712f6e04b920431e5c564a749"},
-    {file = "coverage-6.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:58303469e9a272b4abdb9e302a780072c0633cdcc0165db7eec0f9e32f901e05"},
+    {file = "coverage-6.4.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:75ab269400706fab15981fd4bd5080c56bd5cc07c3bccb86aab5e1d5a88dc8f4"},
-    {file = "coverage-6.3.2-cp310-cp310-win32.whl", hash = "sha256:2fea046bfb455510e05be95e879f0e768d45c10c11509e20e06d8fcaa31d9e39"},
+    {file = "coverage-6.4.1-cp310-cp310-win32.whl", hash = "sha256:a7f3049243783df2e6cc6deafc49ea123522b59f464831476d3d1448e30d72df"},
-    {file = "coverage-6.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:a2a8b8bcc399edb4347a5ca8b9b87e7524c0967b335fbb08a83c8421489ddee1"},
+    {file = "coverage-6.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:ee2ddcac99b2d2aec413e36d7a429ae9ebcadf912946b13ffa88e7d4c9b712d6"},
-    {file = "coverage-6.3.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:f1555ea6d6da108e1999b2463ea1003fe03f29213e459145e70edbaf3e004aaa"},
+    {file = "coverage-6.4.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:fb73e0011b8793c053bfa85e53129ba5f0250fdc0392c1591fd35d915ec75c46"},
-    {file = "coverage-6.3.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e5f4e1edcf57ce94e5475fe09e5afa3e3145081318e5fd1a43a6b4539a97e518"},
+    {file = "coverage-6.4.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:106c16dfe494de3193ec55cac9640dd039b66e196e4641fa8ac396181578b982"},
-    {file = "coverage-6.3.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7a15dc0a14008f1da3d1ebd44bdda3e357dbabdf5a0b5034d38fcde0b5c234b7"},
+    {file = "coverage-6.4.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:87f4f3df85aa39da00fd3ec4b5abeb7407e82b68c7c5ad181308b0e2526da5d4"},
-    {file = "coverage-6.3.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:21b7745788866028adeb1e0eca3bf1101109e2dc58456cb49d2d9b99a8c516e6"},
+    {file = "coverage-6.4.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:961e2fb0680b4f5ad63234e0bf55dfb90d302740ae9c7ed0120677a94a1590cb"},
-    {file = "coverage-6.3.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:8ce257cac556cb03be4a248d92ed36904a59a4a5ff55a994e92214cde15c5bad"},
+    {file = "coverage-6.4.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:cec3a0f75c8f1031825e19cd86ee787e87cf03e4fd2865c79c057092e69e3a3b"},
-    {file = "coverage-6.3.2-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:b0be84e5a6209858a1d3e8d1806c46214e867ce1b0fd32e4ea03f4bd8b2e3359"},
+    {file = "coverage-6.4.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:129cd05ba6f0d08a766d942a9ed4b29283aff7b2cccf5b7ce279d50796860bb3"},
-    {file = "coverage-6.3.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:acf53bc2cf7282ab9b8ba346746afe703474004d9e566ad164c91a7a59f188a4"},
+    {file = "coverage-6.4.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:bf5601c33213d3cb19d17a796f8a14a9eaa5e87629a53979a5981e3e3ae166f6"},
-    {file = "coverage-6.3.2-cp37-cp37m-win32.whl", hash = "sha256:8bdde1177f2311ee552f47ae6e5aa7750c0e3291ca6b75f71f7ffe1f1dab3dca"},
+    {file = "coverage-6.4.1-cp37-cp37m-win32.whl", hash = "sha256:269eaa2c20a13a5bf17558d4dc91a8d078c4fa1872f25303dddcbba3a813085e"},
-    {file = "coverage-6.3.2-cp37-cp37m-win_amd64.whl", hash = "sha256:b31651d018b23ec463e95cf10070d0b2c548aa950a03d0b559eaa11c7e5a6fa3"},
+    {file = "coverage-6.4.1-cp37-cp37m-win_amd64.whl", hash = "sha256:f02cbbf8119db68455b9d763f2f8737bb7db7e43720afa07d8eb1604e5c5ae28"},
-    {file = "coverage-6.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:07e6db90cd9686c767dcc593dff16c8c09f9814f5e9c51034066cad3373b914d"},
+    {file = "coverage-6.4.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:ffa9297c3a453fba4717d06df579af42ab9a28022444cae7fa605af4df612d54"},
-    {file = "coverage-6.3.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2c6dbb42f3ad25760010c45191e9757e7dce981cbfb90e42feef301d71540059"},
+    {file = "coverage-6.4.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:145f296d00441ca703a659e8f3eb48ae39fb083baba2d7ce4482fb2723e050d9"},
-    {file = "coverage-6.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c76aeef1b95aff3905fb2ae2d96e319caca5b76fa41d3470b19d4e4a3a313512"},
+    {file = "coverage-6.4.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d67d44996140af8b84284e5e7d398e589574b376fb4de8ccd28d82ad8e3bea13"},
-    {file = "coverage-6.3.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8cf5cfcb1521dc3255d845d9dca3ff204b3229401994ef8d1984b32746bb45ca"},
+    {file = "coverage-6.4.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2bd9a6fc18aab8d2e18f89b7ff91c0f34ff4d5e0ba0b33e989b3cd4194c81fd9"},
-    {file = "coverage-6.3.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8fbbdc8d55990eac1b0919ca69eb5a988a802b854488c34b8f37f3e2025fa90d"},
+    {file = "coverage-6.4.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3384f2a3652cef289e38100f2d037956194a837221edd520a7ee5b42d00cc605"},
-    {file = "coverage-6.3.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:ec6bc7fe73a938933d4178c9b23c4e0568e43e220aef9472c4f6044bfc6dd0f0"},
+    {file = "coverage-6.4.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:9b3e07152b4563722be523e8cd0b209e0d1a373022cfbde395ebb6575bf6790d"},
-    {file = "coverage-6.3.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:9baff2a45ae1f17c8078452e9e5962e518eab705e50a0aa8083733ea7d45f3a6"},
+    {file = "coverage-6.4.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1480ff858b4113db2718848d7b2d1b75bc79895a9c22e76a221b9d8d62496428"},
-    {file = "coverage-6.3.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:fd9e830e9d8d89b20ab1e5af09b32d33e1a08ef4c4e14411e559556fd788e6b2"},
+    {file = "coverage-6.4.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:865d69ae811a392f4d06bde506d531f6a28a00af36f5c8649684a9e5e4a85c83"},
-    {file = "coverage-6.3.2-cp38-cp38-win32.whl", hash = "sha256:f7331dbf301b7289013175087636bbaf5b2405e57259dd2c42fdcc9fcc47325e"},
+    {file = "coverage-6.4.1-cp38-cp38-win32.whl", hash = "sha256:664a47ce62fe4bef9e2d2c430306e1428ecea207ffd68649e3b942fa8ea83b0b"},
-    {file = "coverage-6.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:68353fe7cdf91f109fc7d474461b46e7f1f14e533e911a2a2cbb8b0fc8613cf1"},
+    {file = "coverage-6.4.1-cp38-cp38-win_amd64.whl", hash = "sha256:26dff09fb0d82693ba9e6231248641d60ba606150d02ed45110f9ec26404ed1c"},
-    {file = "coverage-6.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b78e5afb39941572209f71866aa0b206c12f0109835aa0d601e41552f9b3e620"},
+    {file = "coverage-6.4.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:d9c80df769f5ec05ad21ea34be7458d1dc51ff1fb4b2219e77fe24edf462d6df"},
-    {file = "coverage-6.3.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4e21876082ed887baed0146fe222f861b5815455ada3b33b890f4105d806128d"},
+    {file = "coverage-6.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:39ee53946bf009788108b4dd2894bf1349b4e0ca18c2016ffa7d26ce46b8f10d"},
-    {file = "coverage-6.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:34626a7eee2a3da12af0507780bb51eb52dca0e1751fd1471d0810539cefb536"},
+    {file = "coverage-6.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f5b66caa62922531059bc5ac04f836860412f7f88d38a476eda0a6f11d4724f4"},
-    {file = "coverage-6.3.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1ebf730d2381158ecf3dfd4453fbca0613e16eaa547b4170e2450c9707665ce7"},
+    {file = "coverage-6.4.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fd180ed867e289964404051a958f7cccabdeed423f91a899829264bb7974d3d3"},
-    {file = "coverage-6.3.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd6fe30bd519694b356cbfcaca9bd5c1737cddd20778c6a581ae20dc8c04def2"},
+    {file = "coverage-6.4.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:84631e81dd053e8a0d4967cedab6db94345f1c36107c71698f746cb2636c63e3"},
-    {file = "coverage-6.3.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:96f8a1cb43ca1422f36492bebe63312d396491a9165ed3b9231e778d43a7fca4"},
+    {file = "coverage-6.4.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:8c08da0bd238f2970230c2a0d28ff0e99961598cb2e810245d7fc5afcf1254e8"},
-    {file = "coverage-6.3.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:dd035edafefee4d573140a76fdc785dc38829fe5a455c4bb12bac8c20cfc3d69"},
+    {file = "coverage-6.4.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:d42c549a8f41dc103a8004b9f0c433e2086add8a719da00e246e17cbe4056f72"},
-    {file = "coverage-6.3.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5ca5aeb4344b30d0bec47481536b8ba1181d50dbe783b0e4ad03c95dc1296684"},
+    {file = "coverage-6.4.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:309ce4a522ed5fca432af4ebe0f32b21d6d7ccbb0f5fcc99290e71feba67c264"},
-    {file = "coverage-6.3.2-cp39-cp39-win32.whl", hash = "sha256:f5fa5803f47e095d7ad8443d28b01d48c0359484fec1b9d8606d0e3282084bc4"},
+    {file = "coverage-6.4.1-cp39-cp39-win32.whl", hash = "sha256:fdb6f7bd51c2d1714cea40718f6149ad9be6a2ee7d93b19e9f00934c0f2a74d9"},
-    {file = "coverage-6.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:9548f10d8be799551eb3a9c74bbf2b4934ddb330e08a73320123c07f95cc2d92"},
+    {file = "coverage-6.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:342d4aefd1c3e7f620a13f4fe563154d808b69cccef415415aece4c786665397"},
-    {file = "coverage-6.3.2-pp36.pp37.pp38-none-any.whl", hash = "sha256:18d520c6860515a771708937d2f78f63cc47ab3b80cb78e86573b0a760161faf"},
+    {file = "coverage-6.4.1-pp36.pp37.pp38-none-any.whl", hash = "sha256:4803e7ccf93230accb928f3a68f00ffa80a88213af98ed338a57ad021ef06815"},
-    {file = "coverage-6.3.2.tar.gz", hash = "sha256:03e2a7826086b91ef345ff18742ee9fc47a6839ccd517061ef8fa1976e652ce9"},
+    {file = "coverage-6.4.1.tar.gz", hash = "sha256:4321f075095a096e70aff1d002030ee612b65a205a0a0f5b815280d5dc58100c"},
 ]
 cryptography = [
     {file = "cryptography-36.0.1-cp36-abi3-macosx_10_10_universal2.whl", hash = "sha256:73bc2d3f2444bcfeac67dd130ff2ea598ea5f20b40e36d19821b4df8c9c5037b"},
@@ -898,67 +966,76 @@ mccabe = [
     {file = "mccabe-0.6.1.tar.gz", hash = "sha256:dd8d182285a0fe56bace7f45b5e7d1a6ebcbf524e8f3bd87eb0f125271b8831f"},
 ]
 mypy = [
-    {file = "mypy-0.931-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3c5b42d0815e15518b1f0990cff7a705805961613e701db60387e6fb663fe78a"},
+    {file = "mypy-0.961-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:697540876638ce349b01b6786bc6094ccdaba88af446a9abb967293ce6eaa2b0"},
-    {file = "mypy-0.931-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c89702cac5b302f0c5d33b172d2b55b5df2bede3344a2fbed99ff96bddb2cf00"},
+    {file = "mypy-0.961-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b117650592e1782819829605a193360a08aa99f1fc23d1d71e1a75a142dc7e15"},
-    {file = "mypy-0.931-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:300717a07ad09525401a508ef5d105e6b56646f7942eb92715a1c8d610149714"},
+    {file = "mypy-0.961-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:bdd5ca340beffb8c44cb9dc26697628d1b88c6bddf5c2f6eb308c46f269bb6f3"},
-    {file = "mypy-0.931-cp310-cp310-win_amd64.whl", hash = "sha256:7b3f6f557ba4afc7f2ce6d3215d5db279bcf120b3cfd0add20a5d4f4abdae5bc"},
+    {file = "mypy-0.961-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:3e09f1f983a71d0672bbc97ae33ee3709d10c779beb613febc36805a6e28bb4e"},
-    {file = "mypy-0.931-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:1bf752559797c897cdd2c65f7b60c2b6969ffe458417b8d947b8340cc9cec08d"},
+    {file = "mypy-0.961-cp310-cp310-win_amd64.whl", hash = "sha256:e999229b9f3198c0c880d5e269f9f8129c8862451ce53a011326cad38b9ccd24"},
-    {file = "mypy-0.931-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:4365c60266b95a3f216a3047f1d8e3f895da6c7402e9e1ddfab96393122cc58d"},
+    {file = "mypy-0.961-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:b24be97351084b11582fef18d79004b3e4db572219deee0212078f7cf6352723"},
-    {file = "mypy-0.931-cp36-cp36m-win_amd64.whl", hash = "sha256:1b65714dc296a7991000b6ee59a35b3f550e0073411ac9d3202f6516621ba66c"},
+    {file = "mypy-0.961-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:f4a21d01fc0ba4e31d82f0fff195682e29f9401a8bdb7173891070eb260aeb3b"},
-    {file = "mypy-0.931-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:e839191b8da5b4e5d805f940537efcaa13ea5dd98418f06dc585d2891d228cf0"},
+    {file = "mypy-0.961-cp36-cp36m-win_amd64.whl", hash = "sha256:439c726a3b3da7ca84a0199a8ab444cd8896d95012c4a6c4a0d808e3147abf5d"},
-    {file = "mypy-0.931-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:50c7346a46dc76a4ed88f3277d4959de8a2bd0a0fa47fa87a4cde36fe247ac05"},
+    {file = "mypy-0.961-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:5a0b53747f713f490affdceef835d8f0cb7285187a6a44c33821b6d1f46ed813"},
-    {file = "mypy-0.931-cp37-cp37m-win_amd64.whl", hash = "sha256:d8f1ff62f7a879c9fe5917b3f9eb93a79b78aad47b533911b853a757223f72e7"},
+    {file = "mypy-0.961-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:0e9f70df36405c25cc530a86eeda1e0867863d9471fe76d1273c783df3d35c2e"},
-    {file = "mypy-0.931-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:f9fe20d0872b26c4bba1c1be02c5340de1019530302cf2dcc85c7f9fc3252ae0"},
+    {file = "mypy-0.961-cp37-cp37m-win_amd64.whl", hash = "sha256:b88f784e9e35dcaa075519096dc947a388319cb86811b6af621e3523980f1c8a"},
-    {file = "mypy-0.931-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:1b06268df7eb53a8feea99cbfff77a6e2b205e70bf31743e786678ef87ee8069"},
+    {file = "mypy-0.961-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:d5aaf1edaa7692490f72bdb9fbd941fbf2e201713523bdb3f4038be0af8846c6"},
-    {file = "mypy-0.931-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:8c11003aaeaf7cc2d0f1bc101c1cc9454ec4cc9cb825aef3cafff8a5fdf4c799"},
+    {file = "mypy-0.961-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:9f5f5a74085d9a81a1f9c78081d60a0040c3efb3f28e5c9912b900adf59a16e6"},
-    {file = "mypy-0.931-cp38-cp38-win_amd64.whl", hash = "sha256:d9d2b84b2007cea426e327d2483238f040c49405a6bf4074f605f0156c91a47a"},
+    {file = "mypy-0.961-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:f4b794db44168a4fc886e3450201365c9526a522c46ba089b55e1f11c163750d"},
-    {file = "mypy-0.931-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:ff3bf387c14c805ab1388185dd22d6b210824e164d4bb324b195ff34e322d166"},
+    {file = "mypy-0.961-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:64759a273d590040a592e0f4186539858c948302c653c2eac840c7a3cd29e51b"},
-    {file = "mypy-0.931-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5b56154f8c09427bae082b32275a21f500b24d93c88d69a5e82f3978018a0266"},
+    {file = "mypy-0.961-cp38-cp38-win_amd64.whl", hash = "sha256:63e85a03770ebf403291ec50097954cc5caf2a9205c888ce3a61bd3f82e17569"},
-    {file = "mypy-0.931-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:8ca7f8c4b1584d63c9a0f827c37ba7a47226c19a23a753d52e5b5eddb201afcd"},
+    {file = "mypy-0.961-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5f1332964963d4832a94bebc10f13d3279be3ce8f6c64da563d6ee6e2eeda932"},
-    {file = "mypy-0.931-cp39-cp39-win_amd64.whl", hash = "sha256:74f7eccbfd436abe9c352ad9fb65872cc0f1f0a868e9d9c44db0893440f0c697"},
+    {file = "mypy-0.961-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:006be38474216b833eca29ff6b73e143386f352e10e9c2fbe76aa8549e5554f5"},
-    {file = "mypy-0.931-py3-none-any.whl", hash = "sha256:1171f2e0859cfff2d366da2c7092b06130f232c636a3f7301e3feb8b41f6377d"},
+    {file = "mypy-0.961-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9940e6916ed9371809b35b2154baf1f684acba935cd09928952310fbddaba648"},
-    {file = "mypy-0.931.tar.gz", hash = "sha256:0038b21890867793581e4cb0d810829f5fd4441aa75796b53033af3aa30430ce"},
+    {file = "mypy-0.961-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:a5ea0875a049de1b63b972456542f04643daf320d27dc592d7c3d9cd5d9bf950"},
+    {file = "mypy-0.961-cp39-cp39-win_amd64.whl", hash = "sha256:1ece702f29270ec6af25db8cf6185c04c02311c6bb21a69f423d40e527b75c56"},
+    {file = "mypy-0.961-py3-none-any.whl", hash = "sha256:03c6cc893e7563e7b2949b969e63f02c000b32502a1b4d1314cabe391aa87d66"},
+    {file = "mypy-0.961.tar.gz", hash = "sha256:f730d56cb924d371c26b8eaddeea3cc07d78ff51c521c6d04899ac6904b75492"},
 ]
 mypy-extensions = [
     {file = "mypy_extensions-0.4.3-py2.py3-none-any.whl", hash = "sha256:090fedd75945a69ae91ce1303b5824f428daf5a028d2f6ab8a299250a846f15d"},
     {file = "mypy_extensions-0.4.3.tar.gz", hash = "sha256:2d82818f5bb3e369420cb3c4060a7970edba416647068eb4c5343488a6c604a8"},
 ]
 numpy = [
-    {file = "numpy-1.22.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3d62d6b0870b53799204515145935608cdeb4cebb95a26800b6750e48884cc5b"},
+    {file = "numpy-1.22.3-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:92bfa69cfbdf7dfc3040978ad09a48091143cffb778ec3b03fa170c494118d75"},
-    {file = "numpy-1.22.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:831f2df87bd3afdfc77829bc94bd997a7c212663889d56518359c827d7113b1f"},
+    {file = "numpy-1.22.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8251ed96f38b47b4295b1ae51631de7ffa8260b5b087808ef09a39a9d66c97ab"},
-    {file = "numpy-1.22.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8d1563060e77096367952fb44fca595f2b2f477156de389ce7c0ade3aef29e21"},
+    {file = "numpy-1.22.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48a3aecd3b997bf452a2dedb11f4e79bc5bfd21a1d4cc760e703c31d57c84b3e"},
-    {file = "numpy-1.22.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69958735d5e01f7b38226a6c6e7187d72b7e4d42b6b496aca5860b611ca0c193"},
+    {file = "numpy-1.22.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a3bae1a2ed00e90b3ba5f7bd0a7c7999b55d609e0c54ceb2b076a25e345fa9f4"},
-    {file = "numpy-1.22.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:45a7dfbf9ed8d68fd39763940591db7637cf8817c5bce1a44f7b56c97cbe211e"},
+    {file = "numpy-1.22.3-cp310-cp310-win32.whl", hash = "sha256:f950f8845b480cffe522913d35567e29dd381b0dc7e4ce6a4a9f9156417d2430"},
-    {file = "numpy-1.22.1-cp310-cp310-win_amd64.whl", hash = "sha256:7e957ca8112c689b728037cea9c9567c27cf912741fabda9efc2c7d33d29dfa1"},
+    {file = "numpy-1.22.3-cp310-cp310-win_amd64.whl", hash = "sha256:08d9b008d0156c70dc392bb3ab3abb6e7a711383c3247b410b39962263576cd4"},
-    {file = "numpy-1.22.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:800dfeaffb2219d49377da1371d710d7952c9533b57f3d51b15e61c4269a1b5b"},
+    {file = "numpy-1.22.3-cp38-cp38-macosx_10_14_x86_64.whl", hash = "sha256:201b4d0552831f7250a08d3b38de0d989d6f6e4658b709a02a73c524ccc6ffce"},
-    {file = "numpy-1.22.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:65f5e257987601fdfc63f1d02fca4d1c44a2b85b802f03bd6abc2b0b14648dd2"},
+    {file = "numpy-1.22.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:f8c1f39caad2c896bc0018f699882b345b2a63708008be29b1f355ebf6f933fe"},
-    {file = "numpy-1.22.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:632e062569b0fe05654b15ef0e91a53c0a95d08ffe698b66f6ba0f927ad267c2"},
+    {file = "numpy-1.22.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:568dfd16224abddafb1cbcce2ff14f522abe037268514dd7e42c6776a1c3f8e5"},
-    {file = "numpy-1.22.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d245a2bf79188d3f361137608c3cd12ed79076badd743dc660750a9f3074f7c"},
+    {file = "numpy-1.22.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ca688e1b9b95d80250bca34b11a05e389b1420d00e87a0d12dc45f131f704a1"},
-    {file = "numpy-1.22.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:26b4018a19d2ad9606ce9089f3d52206a41b23de5dfe8dc947d2ec49ce45d015"},
+    {file = "numpy-1.22.3-cp38-cp38-win32.whl", hash = "sha256:e7927a589df200c5e23c57970bafbd0cd322459aa7b1ff73b7c2e84d6e3eae62"},
-    {file = "numpy-1.22.1-cp38-cp38-win32.whl", hash = "sha256:f8ad59e6e341f38266f1549c7c2ec70ea0e3d1effb62a44e5c3dba41c55f0187"},
+    {file = "numpy-1.22.3-cp38-cp38-win_amd64.whl", hash = "sha256:07a8c89a04997625236c5ecb7afe35a02af3896c8aa01890a849913a2309c676"},
-    {file = "numpy-1.22.1-cp38-cp38-win_amd64.whl", hash = "sha256:60f19c61b589d44fbbab8ff126640ae712e163299c2dd422bfe4edc7ec51aa9b"},
+    {file = "numpy-1.22.3-cp39-cp39-macosx_10_14_x86_64.whl", hash = "sha256:2c10a93606e0b4b95c9b04b77dc349b398fdfbda382d2a39ba5a822f669a0123"},
-    {file = "numpy-1.22.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:2db01d9838a497ba2aa9a87515aeaf458f42351d72d4e7f3b8ddbd1eba9479f2"},
+    {file = "numpy-1.22.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:fade0d4f4d292b6f39951b6836d7a3c7ef5b2347f3c420cd9820a1d90d794802"},
-    {file = "numpy-1.22.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bcd19dab43b852b03868796f533b5f5561e6c0e3048415e675bec8d2e9d286c1"},
+    {file = "numpy-1.22.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5bfb1bb598e8229c2d5d48db1860bcf4311337864ea3efdbe1171fb0c5da515d"},
-    {file = "numpy-1.22.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:78bfbdf809fc236490e7e65715bbd98377b122f329457fffde206299e163e7f3"},
+    {file = "numpy-1.22.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:97098b95aa4e418529099c26558eeb8486e66bd1e53a6b606d684d0c3616b168"},
-    {file = "numpy-1.22.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c51124df17f012c3b757380782ae46eee85213a3215e51477e559739f57d9bf6"},
+    {file = "numpy-1.22.3-cp39-cp39-win32.whl", hash = "sha256:fdf3c08bce27132395d3c3ba1503cac12e17282358cb4bddc25cc46b0aca07aa"},
-    {file = "numpy-1.22.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:88d54b7b516f0ca38a69590557814de2dd638d7d4ed04864826acaac5ebb8f01"},
+    {file = "numpy-1.22.3-cp39-cp39-win_amd64.whl", hash = "sha256:639b54cdf6aa4f82fe37ebf70401bbb74b8508fddcf4797f9fe59615b8c5813a"},
-    {file = "numpy-1.22.1-cp39-cp39-win32.whl", hash = "sha256:b5ec9a5eaf391761c61fd873363ef3560a3614e9b4ead17347e4deda4358bca4"},
+    {file = "numpy-1.22.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c34ea7e9d13a70bf2ab64a2532fe149a9aced424cd05a2c4ba662fd989e3e45f"},
-    {file = "numpy-1.22.1-cp39-cp39-win_amd64.whl", hash = "sha256:4ac4d7c9f8ea2a79d721ebfcce81705fc3cd61a10b731354f1049eb8c99521e8"},
+    {file = "numpy-1.22.3.zip", hash = "sha256:dbc7601a3b7472d559dc7b933b18b4b66f9aa7452c120e87dfb33d02008c8a18"},
-    {file = "numpy-1.22.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e60ef82c358ded965fdd3132b5738eade055f48067ac8a5a8ac75acc00cad31f"},
-    {file = "numpy-1.22.1.zip", hash = "sha256:e348ccf5bc5235fc405ab19d53bec215bb373300e5523c7b476cc0da8a5e9973"},
 ]
 packaging = [
     {file = "packaging-21.3-py3-none-any.whl", hash = "sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522"},
     {file = "packaging-21.3.tar.gz", hash = "sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb"},
 ]
+pathspec = [
+    {file = "pathspec-0.9.0-py2.py3-none-any.whl", hash = "sha256:7d15c4ddb0b5c802d161efc417ec1a2558ea2653c2e8ad9c19098201dc1c993a"},
+    {file = "pathspec-0.9.0.tar.gz", hash = "sha256:e564499435a2673d586f6b2130bb5b95f04a3ba06f81b8f895b651a3c76aabb1"},
+]
 pdme = [
-    {file = "pdme-0.5.4-py3-none-any.whl", hash = "sha256:90ba75efbec04f5a505c9c5228824538510149e218372e758c15150d5967d92b"},
+    {file = "pdme-0.8.5-py3-none-any.whl", hash = "sha256:1a248a249cc9205e6ba54653e9625b2d2596141bed77c0adc596ad9e448a07f2"},
-    {file = "pdme-0.5.4.tar.gz", hash = "sha256:82bff2ccc8f38996c23b43ab7d7dc80d87a6b340492f368861e7748105b50174"},
+    {file = "pdme-0.8.5.tar.gz", hash = "sha256:56f8864caf2c5309ca5d178ed42ee1f375c1eeea516920f7922a0ee9dfd2b969"},
 ]
 pkginfo = [
     {file = "pkginfo-1.8.2-py2.py3-none-any.whl", hash = "sha256:c24c487c6a7f72c66e816ab1796b96ac6c3d14d49338293d2141664330b55ffc"},
     {file = "pkginfo-1.8.2.tar.gz", hash = "sha256:542e0d0b6750e2e21c20179803e40ab50598d8066d51097a0e382cba9eb02bff"},
 ]
+platformdirs = [
+    {file = "platformdirs-2.5.1-py3-none-any.whl", hash = "sha256:bcae7cab893c2d310a711b70b24efb93334febe65f8de776ee320b517471e227"},
+    {file = "platformdirs-2.5.1.tar.gz", hash = "sha256:7535e70dfa32e84d4b34996ea99c5e432fa29a708d0f4e394bbcb2a8faa4f16d"},
+]
 pluggy = [
     {file = "pluggy-1.0.0-py2.py3-none-any.whl", hash = "sha256:74134bbf457f031a36d68416e1509f34bd5ccc019f0bcc952c7b909d06b37bd3"},
     {file = "pluggy-1.0.0.tar.gz", hash = "sha256:4224373bacce55f955a878bf9cfa763c1e360858e330072059e10bad68531159"},
@@ -1024,31 +1101,29 @@ rfc3986 = [
     {file = "rfc3986-2.0.0.tar.gz", hash = "sha256:97aacf9dbd4bfd829baad6e6309fa6573aaf1be3f6fa735c8ab05e46cecb261c"},
 ]
 scipy = [
-    {file = "scipy-1.5.4-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:4f12d13ffbc16e988fa40809cbbd7a8b45bc05ff6ea0ba8e3e41f6f4db3a9e47"},
+    {file = "scipy-1.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:87b01c7d5761e8a266a0fbdb9d88dcba0910d63c1c671bdb4d99d29f469e9e03"},
-    {file = "scipy-1.5.4-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:a254b98dbcc744c723a838c03b74a8a34c0558c9ac5c86d5561703362231107d"},
+    {file = "scipy-1.8.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:ae3e327da323d82e918e593460e23babdce40d7ab21490ddf9fc06dec6b91a18"},
-    {file = "scipy-1.5.4-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:368c0f69f93186309e1b4beb8e26d51dd6f5010b79264c0f1e9ca00cd92ea8c9"},
+    {file = "scipy-1.8.0-cp310-cp310-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:16e09ef68b352d73befa8bcaf3ebe25d3941fe1a58c82909d5589856e6bc8174"},
-    {file = "scipy-1.5.4-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:4598cf03136067000855d6b44d7a1f4f46994164bcd450fb2c3d481afc25dd06"},
+    {file = "scipy-1.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c17a1878d00a5dd2797ccd73623ceca9d02375328f6218ee6d921e1325e61aff"},
-    {file = "scipy-1.5.4-cp36-cp36m-win32.whl", hash = "sha256:e98d49a5717369d8241d6cf33ecb0ca72deee392414118198a8e5b4c35c56340"},
+    {file = "scipy-1.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:937d28722f13302febde29847bbe554b89073fbb924a30475e5ed7b028898b5f"},
-    {file = "scipy-1.5.4-cp36-cp36m-win_amd64.whl", hash = "sha256:65923bc3809524e46fb7eb4d6346552cbb6a1ffc41be748535aa502a2e3d3389"},
+    {file = "scipy-1.8.0-cp310-cp310-win_amd64.whl", hash = "sha256:8f4d059a97b29c91afad46b1737274cb282357a305a80bdd9e8adf3b0ca6a3f0"},
-    {file = "scipy-1.5.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9ad4fcddcbf5dc67619379782e6aeef41218a79e17979aaed01ed099876c0e62"},
+    {file = "scipy-1.8.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:38aa39b6724cb65271e469013aeb6f2ce66fd44f093e241c28a9c6bc64fd79ed"},
-    {file = "scipy-1.5.4-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:f87b39f4d69cf7d7529d7b1098cb712033b17ea7714aed831b95628f483fd012"},
+    {file = "scipy-1.8.0-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:559a8a4c03a5ba9fe3232f39ed24f86457e4f3f6c0abbeae1fb945029f092720"},
-    {file = "scipy-1.5.4-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:25b241034215247481f53355e05f9e25462682b13bd9191359075682adcd9554"},
+    {file = "scipy-1.8.0-cp38-cp38-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:f4a6d3b9f9797eb2d43938ac2c5d96d02aed17ef170c8b38f11798717523ddba"},
-    {file = "scipy-1.5.4-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:fa789583fc94a7689b45834453fec095245c7e69c58561dc159b5d5277057e4c"},
+    {file = "scipy-1.8.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:92b2c2af4183ed09afb595709a8ef5783b2baf7f41e26ece24e1329c109691a7"},
-    {file = "scipy-1.5.4-cp37-cp37m-win32.whl", hash = "sha256:d6d25c41a009e3c6b7e757338948d0076ee1dd1770d1c09ec131f11946883c54"},
+    {file = "scipy-1.8.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a279e27c7f4566ef18bab1b1e2c37d168e365080974758d107e7d237d3f0f484"},
-    {file = "scipy-1.5.4-cp37-cp37m-win_amd64.whl", hash = "sha256:2c872de0c69ed20fb1a9b9cf6f77298b04a26f0b8720a5457be08be254366c6e"},
+    {file = "scipy-1.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad5be4039147c808e64f99c0e8a9641eb5d2fa079ff5894dcd8240e94e347af4"},
-    {file = "scipy-1.5.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:e360cb2299028d0b0d0f65a5c5e51fc16a335f1603aa2357c25766c8dab56938"},
+    {file = "scipy-1.8.0-cp38-cp38-win32.whl", hash = "sha256:3d9dd6c8b93a22bf9a3a52d1327aca7e092b1299fb3afc4f89e8eba381be7b59"},
-    {file = "scipy-1.5.4-cp38-cp38-manylinux1_i686.whl", hash = "sha256:3397c129b479846d7eaa18f999369a24322d008fac0782e7828fa567358c36ce"},
+    {file = "scipy-1.8.0-cp38-cp38-win_amd64.whl", hash = "sha256:5e73343c5e0d413c1f937302b2e04fb07872f5843041bcfd50699aef6e95e399"},
-    {file = "scipy-1.5.4-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:168c45c0c32e23f613db7c9e4e780bc61982d71dcd406ead746c7c7c2f2004ce"},
+    {file = "scipy-1.8.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:de2e80ee1d925984c2504812a310841c241791c5279352be4707cdcd7c255039"},
-    {file = "scipy-1.5.4-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:213bc59191da2f479984ad4ec39406bf949a99aba70e9237b916ce7547b6ef42"},
+    {file = "scipy-1.8.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:c2bae431d127bf0b1da81fc24e4bba0a84d058e3a96b9dd6475dfcb3c5e8761e"},
-    {file = "scipy-1.5.4-cp38-cp38-win32.whl", hash = "sha256:634568a3018bc16a83cda28d4f7aed0d803dd5618facb36e977e53b2df868443"},
+    {file = "scipy-1.8.0-cp39-cp39-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:723b9f878095ed994756fa4ee3060c450e2db0139c5ba248ee3f9628bd64e735"},
-    {file = "scipy-1.5.4-cp38-cp38-win_amd64.whl", hash = "sha256:b03c4338d6d3d299e8ca494194c0ae4f611548da59e3c038813f1a43976cb437"},
+    {file = "scipy-1.8.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:011d4386b53b933142f58a652aa0f149c9b9242abd4f900b9f4ea5fbafc86b89"},
-    {file = "scipy-1.5.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3d5db5d815370c28d938cf9b0809dade4acf7aba57eaf7ef733bfedc9b2474c4"},
+    {file = "scipy-1.8.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6f0cd9c0bd374ef834ee1e0f0999678d49dcc400ea6209113d81528958f97c7"},
-    {file = "scipy-1.5.4-cp39-cp39-manylinux1_i686.whl", hash = "sha256:6b0ceb23560f46dd236a8ad4378fc40bad1783e997604ba845e131d6c680963e"},
+    {file = "scipy-1.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3720d0124aced49f6f2198a6900304411dbbeed12f56951d7c66ebef05e3df6"},
-    {file = "scipy-1.5.4-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:ed572470af2438b526ea574ff8f05e7f39b44ac37f712105e57fc4d53a6fb660"},
+    {file = "scipy-1.8.0-cp39-cp39-win32.whl", hash = "sha256:3d573228c10a3a8c32b9037be982e6440e411b443a6267b067cac72f690b8d56"},
-    {file = "scipy-1.5.4-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:8c8d6ca19c8497344b810b0b0344f8375af5f6bb9c98bd42e33f747417ab3f57"},
+    {file = "scipy-1.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:bb7088e89cd751acf66195d2f00cf009a1ea113f3019664032d9075b1e727b6c"},
-    {file = "scipy-1.5.4-cp39-cp39-win32.whl", hash = "sha256:d84cadd7d7998433334c99fa55bcba0d8b4aeff0edb123b2a1dfcface538e474"},
+    {file = "scipy-1.8.0.tar.gz", hash = "sha256:31d4f2d6b724bc9a98e527b5849b8a7e589bf1ea630c33aa563eda912c9ff0bd"},
-    {file = "scipy-1.5.4-cp39-cp39-win_amd64.whl", hash = "sha256:cc1f78ebc982cd0602c9a7615d878396bec94908db67d4ecddca864d049112f2"},
-    {file = "scipy-1.5.4.tar.gz", hash = "sha256:4a453d5e5689de62e5d38edf40af3f17560bfd63c9c5bd228c18c1f99afa155b"},
 ]
 secretstorage = [
     {file = "SecretStorage-3.3.1-py3-none-any.whl", hash = "sha256:422d82c36172d88d6a0ed5afdec956514b189ddbfb72fefab0c8a1cee4eaf71f"},

pyproject.toml

@@ -1,19 +1,20 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.3.5"
+version = "0.6.3"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = "^3.8,<3.10"
-pdme = "^0.5.4"
+pdme = "^0.8.5"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
 flake8 = "^4.0.1"
 pytest-cov = "^3.0.0"
-mypy = "^0.931"
+mypy = "^0.961"
 python-semantic-release = "^7.24.0"
+black = "^22.3.0"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]