chore(release): 0.6.3

Reviewed-on: #14

CHANGELOG.md

@@ -2,6 +2,58 @@
 
 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)
 
 

deepdog/__init__.py

@@ -1,9 +1,8 @@
 import logging
 from deepdog.meta import __version__
 from deepdog.bayes_run import BayesRun
-from deepdog.alt_bayes_run import AltBayesRun
-from deepdog.alt_bayes_run_simulpairs import AltBayesRunSimulPairs
-from deepdog.diagnostic import Diagnostic
+from deepdog.bayes_run_simulpairs import BayesRunSimulPairs
+from deepdog.real_spectrum_run import RealSpectrumRun
 
 
 def get_version():
@@ -13,9 +12,8 @@ def get_version():
 __all__ = [
 	"get_version",
 	"BayesRun",
-	"AltBayesRun",
-	"AltBayesRunSimulPairs",
-	"Diagnostic",
+	"BayesRunSimulPairs",
+	"RealSpectrumRun",
 ]
 
 

deepdog/alt_bayes_run.py

@@ -1,307 +0,0 @@
-import pdme.inputs
-import pdme.model
-import pdme.measurement.input_types
-import pdme.measurement.oscillating_dipole
-import pdme.util.fast_v_calc
-import pdme.util.fast_nonlocal_spectrum
-from typing import Sequence, Tuple, List
-import datetime
-import csv
-import multiprocessing
-import logging
-import numpy
-
-
-# TODO: remove hardcode
-CHUNKSIZE = 50
-
-# TODO: It's garbage to have this here duplicated from pdme.
-DotInput = Tuple[numpy.typing.ArrayLike, float]
-
-
-_logger = logging.getLogger(__name__)
-
-
-def get_a_result(input) -> int:
-	discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
-	sample_dipoles = discretisation.get_model().get_n_single_dipoles(
-		monte_carlo_count, max_frequency
-	)
-	vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
-	return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
-
-
-def get_a_result_using_pairs(input) -> int:
-	(
-		discretisation,
-		dot_inputs,
-		pair_inputs,
-		local_lows,
-		local_highs,
-		nonlocal_lows,
-		nonlocal_highs,
-		monte_carlo_count,
-		max_frequency,
-	) = input
-	sample_dipoles = discretisation.get_model().get_n_single_dipoles(
-		monte_carlo_count, max_frequency
-	)
-	local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
-	local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
-	nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(
-		pair_inputs, sample_dipoles
-	)
-	nonlocal_matches = pdme.util.fast_v_calc.between(
-		nonlocal_vals, nonlocal_lows, nonlocal_highs
-	)
-	combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
-	return numpy.count_nonzero(combined_matches)
-
-
-class AltBayesRun:
-	"""
-	A single Bayes run for a given set of dots.
-
-	Parameters
-	----------
-	dot_inputs : Sequence[DotInput]
-	The dot inputs for this bayes run.
-
-	discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
-	The models to evaluate.
-
-	actual_model_discretisation : pdme.model.Discretisation
-	The discretisation for the model which is actually correct.
-
-	filename_slug : str
-	The filename slug to include.
-
-	run_count: int
-	The number of runs to do.
-	"""
-
-	def __init__(
-		self,
-		dot_positions: Sequence[numpy.typing.ArrayLike],
-		frequency_range: Sequence[float],
-		discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
-		actual_model: pdme.model.Model,
-		filename_slug: str,
-		run_count: int = 100,
-		low_error: float = 0.9,
-		high_error: float = 1.1,
-		pairs_high_error=None,
-		pairs_low_error=None,
-		monte_carlo_count: int = 10000,
-		monte_carlo_cycles: int = 10,
-		target_success: int = 100,
-		max_monte_carlo_cycles_steps: int = 10,
-		max_frequency: float = 20,
-		end_threshold: float = None,
-		chunksize: int = CHUNKSIZE,
-		use_pairs: bool = False,
-	) -> None:
-		self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
-			dot_positions, frequency_range
-		)
-		self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
-			self.dot_inputs
-		)
-
-		self.use_pairs = use_pairs
-
-		self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(
-			dot_positions, frequency_range
-		)
-		self.dot_pair_inputs_array = (
-			pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
-		)
-
-		self.discretisations = [disc for (_, disc) in discretisations_with_names]
-		self.model_names = [name for (name, _) in discretisations_with_names]
-		self.actual_model = actual_model
-		self.model_count = len(self.discretisations)
-		self.monte_carlo_count = monte_carlo_count
-		self.monte_carlo_cycles = monte_carlo_cycles
-		self.target_success = target_success
-		self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
-		self.run_count = run_count
-		self.low_error = low_error
-		self.high_error = high_error
-		if pairs_low_error is None:
-			self.pairs_low_error = self.low_error
-		else:
-			self.pairs_low_error = pairs_low_error
-		if pairs_high_error is None:
-			self.pairs_high_error = self.high_error
-		else:
-			self.pairs_high_error = pairs_high_error
-		self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
-		self.compensate_zeros = True
-		self.chunksize = chunksize
-		for name in self.model_names:
-			self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
-
-		self.probabilities = [1 / self.model_count] * self.model_count
-
-		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-		if self.use_pairs:
-			self.filename = f"{timestamp}-{filename_slug}.altbayes.pairs.csv"
-		else:
-			self.filename = f"{timestamp}-{filename_slug}.altbayes.csv"
-		self.max_frequency = max_frequency
-
-		if end_threshold is not None:
-			if 0 < end_threshold < 1:
-				self.end_threshold: float = end_threshold
-				self.use_end_threshold = True
-				_logger.info(f"Will abort early, at {self.end_threshold}.")
-			else:
-				raise ValueError(
-					f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
-				)
-
-	def go(self) -> None:
-		with open(self.filename, "a", newline="") as outfile:
-			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
-			writer.writeheader()
-
-		for run in range(1, self.run_count + 1):
-
-			rng = numpy.random.default_rng()
-			frequency = rng.uniform(1, self.max_frequency)
-
-			# Generate the actual dipoles
-			actual_dipoles = self.actual_model.get_dipoles(frequency)
-
-			dots = actual_dipoles.get_percent_range_dot_measurements(
-				self.dot_inputs, self.low_error, self.high_error
-			)
-			(
-				lows,
-				highs,
-			) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
-				dots
-			)
-
-			pair_lows, pair_highs = (None, None)
-			if self.use_pairs:
-				pair_measurements = (
-					actual_dipoles.get_percent_range_dot_pair_measurements(
-						self.dot_pair_inputs,
-						self.pairs_low_error,
-						self.pairs_high_error,
-					)
-				)
-				(
-					pair_lows,
-					pair_highs,
-				) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
-					pair_measurements
-				)
-
-			_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
-
-			results = []
-			_logger.debug("Going to iterate over discretisations now")
-			for disc_count, discretisation in enumerate(self.discretisations):
-				_logger.debug(f"Doing discretisation #{disc_count}")
-				with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
-					cycle_count = 0
-					cycle_success = 0
-					cycles = 0
-					while (cycles < self.max_monte_carlo_cycles_steps) and (
-						cycle_success <= self.target_success
-					):
-						_logger.debug(f"Starting cycle {cycles}")
-						cycles += 1
-						current_success = 0
-						cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
-						if self.use_pairs:
-							current_success = sum(
-								pool.imap_unordered(
-									get_a_result_using_pairs,
-									[
-										(
-											discretisation,
-											self.dot_inputs_array,
-											self.dot_pair_inputs_array,
-											lows,
-											highs,
-											pair_lows,
-											pair_highs,
-											self.monte_carlo_count,
-											self.max_frequency,
-										)
-									]
-									* self.monte_carlo_cycles,
-									self.chunksize,
-								)
-							)
-						else:
-							current_success = sum(
-								pool.imap_unordered(
-									get_a_result,
-									[
-										(
-											discretisation,
-											self.dot_inputs_array,
-											lows,
-											highs,
-											self.monte_carlo_count,
-											self.max_frequency,
-										)
-									]
-									* self.monte_carlo_cycles,
-									self.chunksize,
-								)
-							)
-
-						cycle_success += current_success
-					results.append((cycle_count, cycle_success))
-
-			_logger.debug("Done, constructing output now")
-			row = {
-				"dipole_moment": actual_dipoles.dipoles[0].p,
-				"dipole_location": actual_dipoles.dipoles[0].s,
-				"dipole_frequency": actual_dipoles.dipoles[0].w,
-			}
-			successes: List[float] = []
-			counts: List[int] = []
-			for model_index, (name, (count, result)) in enumerate(
-				zip(self.model_names, results)
-			):
-
-				row[f"{name}_success"] = result
-				row[f"{name}_count"] = count
-				successes.append(max(result, 0.5))
-				counts.append(count)
-
-			success_weight = sum(
-				[
-					(succ / count) * prob
-					for succ, count, prob in zip(successes, counts, self.probabilities)
-				]
-			)
-			new_probabilities = [
-				(succ / count) * old_prob / success_weight
-				for succ, count, old_prob in zip(successes, counts, self.probabilities)
-			]
-			self.probabilities = new_probabilities
-			for name, probability in zip(self.model_names, self.probabilities):
-				row[f"{name}_prob"] = probability
-			_logger.info(row)
-
-			with open(self.filename, "a", newline="") as outfile:
-				writer = csv.DictWriter(
-					outfile, fieldnames=self.csv_fields, dialect="unix"
-				)
-				writer.writerow(row)
-
-			if self.use_end_threshold:
-				max_prob = max(self.probabilities)
-				if max_prob > self.end_threshold:
-					_logger.info(
-						f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
-					)
-					break

deepdog/bayes_run.py

@@ -1,17 +1,19 @@
+import pdme.inputs
 import pdme.model
+import pdme.measurement.input_types
+import pdme.measurement.oscillating_dipole
+import pdme.util.fast_v_calc
+import pdme.util.fast_nonlocal_spectrum
 from typing import Sequence, Tuple, List
 import datetime
-import itertools
 import csv
+import multiprocessing
 import logging
 import numpy
-import scipy.optimize
-import multiprocessing
 
 
 # TODO: remove hardcode
-COST_THRESHOLD = 1e-10
-
+CHUNKSIZE = 50
 
 # TODO: It's garbage to have this here duplicated from pdme.
 DotInput = Tuple[numpy.typing.ArrayLike, float]
@@ -20,10 +22,40 @@ 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))
+
+
+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:
@@ -35,11 +67,11 @@ class BayesRun:
 	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.
@@ -50,29 +82,66 @@ class BayesRun:
 
 	def __init__(
 		self,
-		dot_inputs: Sequence[DotInput],
-		discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
-		actual_model: pdme.model.Model,
+		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,
-		max_frequency: float = None,
+		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 = dot_inputs
-		self.discretisations = [disc for (_, disc) in discretisations_with_names]
-		self.model_names = [name for (name, _) in discretisations_with_names]
+		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:
@@ -91,52 +160,95 @@ 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(
+			_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,
-							zip(
-								itertools.repeat(discretisation),
-								itertools.repeat(dots),
-								discretisation.all_indices(),
-							),
+								[
+									(
+										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(
+			for model_index, (name, (count, result)) in enumerate(
 				zip(self.model_names, results)
 			):
-				count = 0
-				success = 0
-				for idx, val in result:
-					count += 1
-					if val.success and val.cost <= COST_THRESHOLD:
-						success += 1
 
-				row[f"{name}_success"] = success
+				row[f"{name}_success"] = result
 				row[f"{name}_count"] = count
-				successes.append(max(success, 0.5))
+				successes.append(max(result, 0.5))
 				counts.append(count)
 
 			success_weight = sum(

deepdog/bayes_run_simulpairs.py

@@ -25,7 +25,7 @@ _logger = logging.getLogger(__name__)
 
 def get_a_simul_result_using_pairs(input) -> numpy.ndarray:
 	(
-		discretisation,
+		model,
 		dot_inputs,
 		pair_inputs,
 		local_lows,
@@ -42,16 +42,12 @@ def get_a_simul_result_using_pairs(input) -> numpy.ndarray:
 	local_total = 0
 	combined_total = 0
 
-	sample_dipoles = discretisation.get_model().get_n_single_dipoles(
+	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_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(
+	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(
@@ -64,7 +60,7 @@ def get_a_simul_result_using_pairs(input) -> numpy.ndarray:
 	return numpy.array([local_total, combined_total])
 
 
-class AltBayesRunSimulPairs:
+class BayesRunSimulPairs:
 	"""
 	A dual pairs-nonpairs Bayes run for a given set of dots.
 
@@ -73,11 +69,11 @@ class AltBayesRunSimulPairs:
 	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 modoel for the model which is actually correct.
 
 	filename_slug : str
 	The filename slug to include.
@@ -90,8 +86,8 @@ class AltBayesRunSimulPairs:
 		self,
 		dot_positions: Sequence[numpy.typing.ArrayLike],
 		frequency_range: Sequence[float],
-		discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
-		actual_model: pdme.model.Model,
+		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,
@@ -120,10 +116,17 @@ class AltBayesRunSimulPairs:
 			pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
 		)
 
-		self.discretisations = [disc for (_, disc) in discretisations_with_names]
-		self.model_names = [name for (name, _) in discretisations_with_names]
+		self.models = [mod for (_, mod) 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
@@ -139,7 +142,16 @@ class AltBayesRunSimulPairs:
 			self.pairs_high_error = self.high_error
 		else:
 			self.pairs_high_error = pairs_high_error
-		self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
+
+		self.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:
@@ -174,11 +186,8 @@ class AltBayesRunSimulPairs:
 
 		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)
+			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
@@ -208,9 +217,9 @@ class AltBayesRunSimulPairs:
 
 			results_pairs = []
 			results_no_pairs = []
-			_logger.debug("Going to iterate over discretisations now")
-			for disc_count, discretisation in enumerate(self.discretisations):
-				_logger.debug(f"Doing discretisation #{disc_count}")
+			_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:
@@ -223,7 +232,7 @@ class AltBayesRunSimulPairs:
 						<= self.target_success
 					):
 						_logger.debug(f"Starting cycle {cycles}")
-						_logger.debug(f"(pair, no_pair) successes are {(cycle_success_pairs, cycle_success_no_pairs)}")
+
 						cycles += 1
 						current_success_pairs = 0
 						current_success_no_pairs = 0
@@ -241,7 +250,7 @@ class AltBayesRunSimulPairs:
 									get_a_simul_result_using_pairs,
 									[
 										(
-											discretisation,
+											model,
 											self.dot_inputs_array,
 											self.dot_pair_inputs_array,
 											lows,
@@ -264,20 +273,36 @@ class AltBayesRunSimulPairs:
 
 						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": actual_dipoles.dipoles[0].p,
-				"dipole_location": actual_dipoles.dipoles[0].s,
-				"dipole_frequency": actual_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,
 			}
 			row_no_pairs = {
-				"dipole_moment": actual_dipoles.dipoles[0].p,
-				"dipole_location": actual_dipoles.dipoles[0].s,
-				"dipole_frequency": actual_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_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] = []

deepdog/diagnostic.py

@@ -1,160 +0,0 @@
-from pdme.measurement import OscillatingDipole, OscillatingDipoleArrangement
-import pdme
-from deepdog.bayes_run import DotInput
-import datetime
-import numpy
-from dataclasses import dataclass
-import logging
-from typing import Sequence, Tuple
-import csv
-import itertools
-import multiprocessing
-
-_logger = logging.getLogger(__name__)
-
-
-def get_a_result(discretisation, dots, index):
-	return (index, discretisation.solve_for_index(dots, index))
-
-
-@dataclass
-class SingleDipoleDiagnostic:
-	model: str
-	index: Tuple
-	bounds: Tuple
-	actual_dipole: OscillatingDipole
-	result_dipole: OscillatingDipole
-	success: bool
-
-	def __post_init__(self) -> None:
-		self.p_actual_x = self.actual_dipole.p[0]
-		self.p_actual_y = self.actual_dipole.p[1]
-		self.p_actual_z = self.actual_dipole.p[2]
-		self.s_actual_x = self.actual_dipole.s[0]
-		self.s_actual_y = self.actual_dipole.s[1]
-		self.s_actual_z = self.actual_dipole.s[2]
-		self.p_result_x = self.result_dipole.p[0]
-		self.p_result_y = self.result_dipole.p[1]
-		self.p_result_z = self.result_dipole.p[2]
-		self.s_result_x = self.result_dipole.s[0]
-		self.s_result_y = self.result_dipole.s[1]
-		self.s_result_z = self.result_dipole.s[2]
-		self.w_actual = self.actual_dipole.w
-		self.w_result = self.result_dipole.w
-
-
-class Diagnostic:
-	"""
-	Represents a diagnostic for a single dipole moment given a set of discretisations.
-
-	Parameters
-	----------
-	dot_inputs : Sequence[DotInput]
-	The dot inputs for this diagnostic.
-
-	discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
-	The models to evaluate.
-
-	actual_model_discretisation : pdme.model.Discretisation
-	The discretisation for the model which is actually correct.
-
-	filename_slug : str
-	The filename slug to include.
-
-	run_count: int
-	The number of runs to do.
-	"""
-
-	def __init__(
-		self,
-		actual_dipole_moment: numpy.ndarray,
-		actual_dipole_position: numpy.ndarray,
-		actual_dipole_frequency: float,
-		dot_inputs: Sequence[DotInput],
-		discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]],
-		filename_slug: str,
-	) -> None:
-		self.dipoles = OscillatingDipoleArrangement(
-			[
-				OscillatingDipole(
-					actual_dipole_moment,
-					actual_dipole_position,
-					actual_dipole_frequency,
-				)
-			]
-		)
-		self.dots = self.dipoles.get_dot_measurements(dot_inputs)
-
-		self.discretisations_with_names = discretisations_with_names
-		self.model_count = len(self.discretisations_with_names)
-
-		self.csv_fields = [
-			"model",
-			"index",
-			"bounds",
-			"p_actual_x",
-			"p_actual_y",
-			"p_actual_z",
-			"s_actual_x",
-			"s_actual_y",
-			"s_actual_z",
-			"w_actual",
-			"success",
-			"p_result_x",
-			"p_result_y",
-			"p_result_z",
-			"s_result_x",
-			"s_result_y",
-			"s_result_z",
-			"w_result",
-		]
-
-		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-		self.filename = f"{timestamp}-{filename_slug}.diag.csv"
-
-	def go(self):
-		with open(self.filename, "a", newline="") as outfile:
-			# csv fields
-			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
-			writer.writeheader()
-
-		for (name, discretisation) in self.discretisations_with_names:
-			_logger.info(f"Working on discretisation {name}")
-
-			results = []
-			with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
-				results = pool.starmap(
-					get_a_result,
-					zip(
-						itertools.repeat(discretisation),
-						itertools.repeat(self.dots),
-						discretisation.all_indices(),
-					),
-				)
-
-			with open(self.filename, "a", newline="") as outfile:
-				writer = csv.DictWriter(
-					outfile,
-					fieldnames=self.csv_fields,
-					dialect="unix",
-					extrasaction="ignore",
-				)
-
-				for idx, result in results:
-
-					bounds = discretisation.bounds(idx)
-
-					actual_success = result.success and result.cost <= 1e-10
-					diag_row = SingleDipoleDiagnostic(
-						name,
-						idx,
-						bounds,
-						self.dipoles.dipoles[0],
-						discretisation.model.solution_as_dipoles(result.normalised_x)[
-							0
-						],
-						actual_success,
-					)
-					row = vars(diag_row)
-					_logger.debug(f"Writing result {row}")
-					writer.writerow(row)

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)

poetry.lock

@@ -117,14 +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"]
@@ -282,7 +282,7 @@ python-versions = "*"
 
 [[package]]
 name = "mypy"
-version = "0.950"
+version = "0.961"
 description = "Optional static typing for Python"
 category = "dev"
 optional = false
@@ -335,7 +335,7 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 
 [[package]]
 name = "pdme"
-version = "0.6.2"
+version = "0.8.5"
 description = "Python dipole model evaluator"
 category = "main"
 optional = false
@@ -740,7 +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 = "98877f53c0ca996cd6eaa2c3b7391e391d29c7a4d3f1e08159fc999a3e4ad296"
+content-hash = "0196604b0dfc7b3dd0bdb19f983358ef9934d47f3c209dbbc7d07712ac5f779d"
 
 [metadata.files]
 atomicwrites = [
@@ -853,47 +853,47 @@ colorama = [
     {file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
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pyproject.toml

@@ -1,18 +1,18 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.5.0"
+version = "0.6.3"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = "^3.8,<3.10"
-pdme = "0.6.2"
+pdme = "^0.8.5"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
 flake8 = "^4.0.1"
 pytest-cov = "^3.0.0"
-mypy = "^0.950"
+mypy = "^0.961"
 python-semantic-release = "^7.24.0"
 black = "^22.3.0"