chore(deps): update dependency pytest-cov to v4

2023-04-10 01:32:07 +00:00
13 changed files with 44 additions and 1171 deletions
--- a/.flake8
+++ b/.flake8
@@ -1,3 +1,3 @@
 [flake8]
-ignore = W191, E501, W503, E203
+ignore = W191, E501, W503
 max-line-length = 120
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,71 +2,6 @@

 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.

-### [0.7.4](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.3...0.7.4) (2023-07-27)
-
-
-### Features
-
-* adds configurable chunk size for the initial mc level 0 SS stage cost calculation to reduce memory usage ([9a7a3ff](https://gitea.deepak.science:2222/physics/deepdog/commit/9a7a3ff2c7ebe81d5e10647ce39844c372ff7b07))
-* allows for deepdog bayesrun with ss to not print csv to make snapshot testing possible ([8e6ead4](https://gitea.deepak.science:2222/physics/deepdog/commit/8e6ead416c9eba56f568f648d0df44caaa510cfe))
-
-
-### Bug Fixes
-
-* fixes bug if case of clamping necessary ([161bcf4](https://gitea.deepak.science:2222/physics/deepdog/commit/161bcf42addf331661c3929073688b9f2c13502c))
-* fixes bug with clamped probabilities being underestimated ([e6defc7](https://gitea.deepak.science:2222/physics/deepdog/commit/e6defc794871a48ac331023eb477bd235b78d6d0))
-
-### [0.7.3](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.2...0.7.3) (2023-07-27)
-
-
-### Features
-
-* adds utility options and avoids memory leak ([598dad1](https://gitea.deepak.science:2222/physics/deepdog/commit/598dad1e6dc8fc0b7a5b4a90c8e17bf744e8d98c))
-
-### [0.7.2](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.1...0.7.2) (2023-07-24)
-
-
-### Features
-
-* clamps results now ([9bb8fc5](https://gitea.deepak.science:2222/physics/deepdog/commit/9bb8fc50fe1bd1a285a333c5a396bfb6ac3176cf))
-
-
-### Bug Fixes
-
-* fixes clamping format etc. ([a170a3c](https://gitea.deepak.science:2222/physics/deepdog/commit/a170a3ce01adcec356e5aaab9abcc0ec4accd64b))
-
-### [0.7.1](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.0...0.7.1) (2023-07-24)
-
-
-### Features
-
-* adds subset simulation stuff ([33cab9a](https://gitea.deepak.science:2222/physics/deepdog/commit/33cab9ab4179cec13ae9e591a8ffc32df4dda989))
-
-## [0.7.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.7...0.7.0) (2023-05-01)
-
-
-### ⚠ BREAKING CHANGES
-
-* removes fastfilter parameter because it should never be needed
-
-### Features
-
-* adds pair capability to real spectrum run hopefully ([a089951](https://gitea.deepak.science:2222/physics/deepdog/commit/a089951bbefcd8a0b2efeb49b7a8090412cbb23d))
-* removes fastfilter parameter because it should never be needed ([a015daf](https://gitea.deepak.science:2222/physics/deepdog/commit/a015daf5ff6fa5f6155c8d7e02981b588840a5b0))
-
-### [0.6.7](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.6...0.6.7) (2023-04-14)
-
-
-### Features
-
-* adds option to cap core count for real spectrum run ([bf15f4a](https://gitea.deepak.science:2222/physics/deepdog/commit/bf15f4a7b7f59504983624e7d512ed7474372032))
-* adds option to cap core count for temp aware run ([12903b2](https://gitea.deepak.science:2222/physics/deepdog/commit/12903b2540cefb040174d230bc0d04719a6dc1b7))
-
-
-### Bug Fixes
-
-* avoids redefinition of core count in loop ([1cf4454](https://gitea.deepak.science:2222/physics/deepdog/commit/1cf44541531541088198bd4599d467df3e1acbcf))
-
 ### [0.6.6](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.5...0.6.6) (2023-04-09)


--- a/deepdog/init.py
+++ b/deepdog/init.py
@@ -4,7 +4,6 @@ from deepdog.bayes_run import BayesRun
 from deepdog.bayes_run_simulpairs import BayesRunSimulPairs
 from deepdog.real_spectrum_run import RealSpectrumRun
 from deepdog.temp_aware_real_spectrum_run import TempAwareRealSpectrumRun
-from deepdog.bayes_run_with_ss import BayesRunWithSubspaceSimulation


 def get_version():
@@ -17,7 +16,6 @@ __all__ = [
 	"BayesRunSimulPairs",
 	"RealSpectrumRun",
 	"TempAwareRealSpectrumRun",
-	"BayesRunWithSubspaceSimulation",
 ]


--- a/deepdog/bayes_run_with_ss.py
+++ b/deepdog/bayes_run_with_ss.py
@@ -1,257 +0,0 @@
-import deepdog.subset_simulation
-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, Optional
-import datetime
-import csv
-import logging
-import numpy
-import numpy.typing
-
-
-# TODO: remove hardcode
-CHUNKSIZE = 50
-
-# TODO: It's garbage to have this here duplicated from pdme.
-DotInput = Tuple[numpy.typing.ArrayLike, float]
-
-
-CLAMPING_FACTOR = 10
-
-_logger = logging.getLogger(__name__)
-
-
-class BayesRunWithSubspaceSimulation:
-	"""
-	A single 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 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,
-		max_frequency: float = 20,
-		end_threshold: float = None,
-		run_count=100,
-		chunksize: int = CHUNKSIZE,
-		ss_n_c: int = 500,
-		ss_n_s: int = 100,
-		ss_m_max: int = 15,
-		ss_target_cost: Optional[float] = None,
-		ss_level_0_seed: int = 200,
-		ss_mcmc_seed: int = 20,
-		ss_use_adaptive_steps=True,
-		ss_default_phi_step=0.01,
-		ss_default_theta_step=0.01,
-		ss_default_r_step=0.01,
-		ss_default_w_log_step=0.01,
-		ss_default_upper_w_log_step=4,
-		ss_dump_last_generation=False,
-		ss_initial_costs_chunk_size=100,
-		write_output_to_bayesruncsv=True,
-	) -> 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_with_names = models_with_names
-		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.n: int
-		try:
-			self.n = self.actual_model.n  # type: ignore
-		except AttributeError:
-			self.n = 1
-
-		self.model_count = len(self.models)
-
-		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}_likelihood", 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}.bayesrunwithss.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}"
-				)
-
-		self.ss_n_c = ss_n_c
-		self.ss_n_s = ss_n_s
-		self.ss_m_max = ss_m_max
-		self.ss_target_cost = ss_target_cost
-		self.ss_level_0_seed = ss_level_0_seed
-		self.ss_mcmc_seed = ss_mcmc_seed
-		self.ss_use_adaptive_steps = ss_use_adaptive_steps
-		self.ss_default_phi_step = ss_default_phi_step
-		self.ss_default_theta_step = ss_default_theta_step
-		self.ss_default_r_step = ss_default_r_step
-		self.ss_default_w_log_step = ss_default_w_log_step
-		self.ss_default_upper_w_log_step = ss_default_upper_w_log_step
-		self.ss_dump_last_generation = ss_dump_last_generation
-		self.ss_initial_costs_chunk_size = ss_initial_costs_chunk_size
-		self.run_count = run_count
-
-		self.write_output_to_csv = write_output_to_bayesruncsv
-
-	def go(self) -> Sequence:
-
-		if self.write_output_to_csv:
-			with open(self.filename, "a", newline="") as outfile:
-				writer = csv.DictWriter(
-					outfile, fieldnames=self.csv_fields, dialect="unix"
-				)
-				writer.writeheader()
-
-		return_result = []
-
-		for run in range(1, self.run_count + 1):
-
-			# Generate the actual dipoles
-			actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
-
-			measurements = actual_dipoles.get_dot_measurements(self.dot_inputs)
-
-			_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
-
-			# define a new seed sequence for each run
-
-			results = []
-			_logger.debug("Going to iterate over models now")
-			for model_count, model in enumerate(self.models_with_names):
-				_logger.debug(f"Doing model #{model_count}, {model[0]}")
-				subset_run = deepdog.subset_simulation.SubsetSimulation(
-					model,
-					self.dot_inputs,
-					measurements,
-					self.ss_n_c,
-					self.ss_n_s,
-					self.ss_m_max,
-					self.ss_target_cost,
-					self.ss_level_0_seed,
-					self.ss_mcmc_seed,
-					self.ss_use_adaptive_steps,
-					self.ss_default_phi_step,
-					self.ss_default_theta_step,
-					self.ss_default_r_step,
-					self.ss_default_w_log_step,
-					self.ss_default_upper_w_log_step,
-					initial_cost_chunk_size=self.ss_initial_costs_chunk_size,
-					keep_probs_list=False,
-					dump_last_generation_to_file=self.ss_dump_last_generation,
-				)
-				results.append(subset_run.execute())
-
-			_logger.debug("Done, constructing output now")
-			row = {
-				"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
-
-			likelihoods: List[float] = []
-
-			for (name, result) in zip(self.model_names, results):
-				if result.over_target_likelihood is None:
-					if result.lowest_likelihood is None:
-						_logger.error(f"result {result} looks bad")
-						clamped_likelihood = 10**-15
-					else:
-						clamped_likelihood = result.lowest_likelihood / CLAMPING_FACTOR
-					_logger.warning(
-						f"got a none result, clamping to {clamped_likelihood}"
-					)
-				else:
-					clamped_likelihood = result.over_target_likelihood
-				likelihoods.append(clamped_likelihood)
-				row[f"{name}_likelihood"] = clamped_likelihood
-
-			success_weight = sum(
-				[
-					likelihood * prob
-					for likelihood, prob in zip(likelihoods, self.probabilities)
-				]
-			)
-			new_probabilities = [
-				likelihood * old_prob / success_weight
-				for likelihood, old_prob in zip(likelihoods, self.probabilities)
-			]
-			self.probabilities = new_probabilities
-			for name, probability in zip(self.model_names, self.probabilities):
-				row[f"{name}_prob"] = probability
-			_logger.info(row)
-			return_result.append(row)
-
-			if self.write_output_to_csv:
-				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
-
-		return return_result
--- a/deepdog/real_spectrum_run.py
+++ b/deepdog/real_spectrum_run.py
@@ -5,7 +5,7 @@ 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, Optional
+from typing import Sequence, Tuple, List, Dict, Union
 import datetime
 import csv
 import multiprocessing
@@ -20,50 +20,16 @@ CHUNKSIZE = 50
 _logger = logging.getLogger(__name__)


-def get_a_result_fast_filter_pairs(input) -> int:
-	(
-		model,
-		dot_inputs,
-		lows,
-		highs,
-		pair_inputs,
-		pair_lows,
-		pair_highs,
-		monte_carlo_count,
-		seed,
-	) = input
+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
 	)
-
-	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)]
-
-	for pi, plow, phigh in zip(pair_inputs, pair_lows, pair_highs):
-		if len(current_sample) < 1:
-			break
-		vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal_dipoleses(
-			numpy.array([pi]), current_sample
-		)
-
-		current_sample = current_sample[
-			numpy.all(
-				((vals > plow) & (vals < phigh)) | ((vals < plow) & (vals > phigh)),
-				axis=1,
-			)
-		]
-	return len(current_sample)
+	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:
@@ -121,10 +87,7 @@ class RealSpectrumRun:
 		max_monte_carlo_cycles_steps: int = 10,
 		chunksize: int = CHUNKSIZE,
 		initial_seed: int = 12345,
-		cap_core_count: int = 0,
-		pair_measurements: Optional[
-			Sequence[pdme.measurement.DotPairRangeMeasurement]
-		] = None,
+		use_fast_filter: bool = True,
 	) -> None:
 		self.measurements = measurements
 		self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
@@ -133,21 +96,6 @@ class RealSpectrumRun:
 			self.dot_inputs
 		)

-		if pair_measurements is not None:
-			self.pair_measurements = pair_measurements
-			self.use_pair_measurements = True
-			self.dot_pair_inputs = [
-				(measure.r1, measure.r2, measure.f)
-				for measure in self.pair_measurements
-			]
-			self.dot_pair_inputs_array = (
-				pdme.measurement.input_types.dot_pair_inputs_to_array(
-					self.dot_pair_inputs
-				)
-			)
-		else:
-			self.use_pair_measurements = False
-
 		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)
@@ -168,14 +116,13 @@ class RealSpectrumRun:
 		self.probabilities = [1 / self.model_count] * self.model_count

 		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-
-		ff_string = "fast_filter"
-
+		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

-		self.cap_core_count = cap_core_count
-
 	def go(self) -> None:
 		with open(self.filename, "a", newline="") as outfile:
 			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
@@ -188,29 +135,16 @@ class RealSpectrumRun:
 			self.measurements
 		)

-		pair_lows = None
-		pair_highs = None
-		if self.use_pair_measurements:
-			(
-				pair_lows,
-				pair_highs,
-			) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
-				self.pair_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")
-		core_count = multiprocessing.cpu_count() - 1 or 1
-		if (self.cap_core_count >= 1) and (self.cap_core_count < core_count):
-			core_count = self.cap_core_count
-		_logger.info(f"Using {core_count} cores")
 		for model_count, (model, model_name) in enumerate(
 			zip(self.models, self.model_names)
 		):
 			_logger.debug(f"Doing model #{model_count}: {model_name}")
+			core_count = multiprocessing.cpu_count() - 1 or 1
 			with multiprocessing.Pool(core_count) as pool:
 				cycle_count = 0
 				cycle_success = 0
@@ -228,46 +162,27 @@ class RealSpectrumRun:
 					# that way we get more stuff.
 					seeds = seed_sequence.spawn(self.monte_carlo_cycles)

-					if self.use_pair_measurements:
-						current_success = sum(
-							pool.imap_unordered(
-								get_a_result_fast_filter_pairs,
-								[
-									(
-										model,
-										self.dot_inputs_array,
-										lows,
-										highs,
-										self.dot_pair_inputs_array,
-										pair_lows,
-										pair_highs,
-										self.monte_carlo_count,
-										seed,
-									)
-									for seed in seeds
-								],
-								self.chunksize,
-							)
-						)
+					if self.use_fast_filter:
+						result_func = get_a_result_fast_filter
 					else:
-
-						current_success = sum(
-							pool.imap_unordered(
-								get_a_result_fast_filter,
-								[
-									(
-										model,
-										self.dot_inputs_array,
-										lows,
-										highs,
-										self.monte_carlo_count,
-										seed,
-									)
-									for seed in seeds
-								],
-								self.chunksize,
-							)
+						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}")
--- a/deepdog/subset_simulation/init.py
+++ b/deepdog/subset_simulation/init.py
@@ -1,3 +0,0 @@
-from deepdog.subset_simulation.subset_simulation_impl import SubsetSimulation
-
-__all__ = ["SubsetSimulation"]
--- a/deepdog/subset_simulation/subset_simulation_impl.py
+++ b/deepdog/subset_simulation/subset_simulation_impl.py
@@ -1,351 +0,0 @@
-import logging
-import numpy
-import pdme.measurement
-import pdme.measurement.input_types
-import pdme.subspace_simulation
-from typing import Sequence, Tuple, Optional
-
-from dataclasses import dataclass
-
-_logger = logging.getLogger(__name__)
-
-
-@dataclass
-class SubsetSimulationResult:
-	probs_list: Sequence[Tuple]
-	over_target_cost: Optional[float]
-	over_target_likelihood: Optional[float]
-	under_target_cost: Optional[float]
-	under_target_likelihood: Optional[float]
-	lowest_likelihood: Optional[float]
-
-
-class SubsetSimulation:
-	def __init__(
-		self,
-		model_name_pair,
-		dot_inputs,
-		actual_measurements: Sequence[pdme.measurement.DotMeasurement],
-		n_c: int,
-		n_s: int,
-		m_max: int,
-		target_cost: Optional[float] = None,
-		level_0_seed: int = 200,
-		mcmc_seed: int = 20,
-		use_adaptive_steps=True,
-		default_phi_step=0.01,
-		default_theta_step=0.01,
-		default_r_step=0.01,
-		default_w_log_step=0.01,
-		default_upper_w_log_step=4,
-		keep_probs_list=True,
-		dump_last_generation_to_file=False,
-		initial_cost_chunk_size=100,
-	):
-		name, model = model_name_pair
-		self.model_name = name
-		self.model = model
-		_logger.info(f"got model {self.model_name}")
-
-		self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
-			dot_inputs
-		)
-		# _logger.debug(f"actual measurements: {actual_measurements}")
-		self.actual_measurement_array = numpy.array([m.v for m in actual_measurements])
-
-		def cost_function_to_use(dipoles_to_test):
-			return pdme.subspace_simulation.proportional_costs_vs_actual_measurement(
-				self.dot_inputs_array, self.actual_measurement_array, dipoles_to_test
-			)
-
-		self.cost_function_to_use = cost_function_to_use
-
-		self.n_c = n_c
-		self.n_s = n_s
-		self.m_max = m_max
-
-		self.level_0_seed = level_0_seed
-		self.mcmc_seed = mcmc_seed
-
-		self.use_adaptive_steps = use_adaptive_steps
-		self.default_phi_step = default_phi_step
-		self.default_theta_step = default_theta_step
-		self.default_r_step = default_r_step
-		self.default_w_log_step = default_w_log_step
-		self.default_upper_w_log_step = default_upper_w_log_step
-
-		_logger.info("using params:")
-		_logger.info(f"\tn_c: {self.n_c}")
-		_logger.info(f"\tn_s: {self.n_s}")
-		_logger.info(f"\tm: {self.m_max}")
-		_logger.info("let's do level 0...")
-
-		self.target_cost = target_cost
-		_logger.info(f"will stop at target cost {target_cost}")
-
-		self.keep_probs_list = keep_probs_list
-		self.dump_last_generations = dump_last_generation_to_file
-
-		self.initial_cost_chunk_size = initial_cost_chunk_size
-
-	def execute(self) -> SubsetSimulationResult:
-
-		probs_list = []
-
-		sample_dipoles = self.model.get_monte_carlo_dipole_inputs(
-			self.n_c * self.n_s,
-			-1,
-			rng_to_use=numpy.random.default_rng(self.level_0_seed),
-		)
-		# _logger.debug(sample_dipoles)
-		# _logger.debug(sample_dipoles.shape)
-
-		raw_costs = []
-		_logger.debug(f"Using iterated cost function thing with chunk size {self.initial_cost_chunk_size}")
-
-		for x in range(0, len(sample_dipoles), self.initial_cost_chunk_size):
-			_logger.debug(f"doing chunk {x}")
-			raw_costs.extend(self.cost_function_to_use(sample_dipoles[x: x + self.initial_cost_chunk_size]))
-		costs = numpy.array(raw_costs)
-
-		_logger.debug(f"costs: {costs}")
-		sorted_indexes = costs.argsort()[::-1]
-
-		_logger.debug(costs[sorted_indexes])
-		_logger.debug(sample_dipoles[sorted_indexes])
-
-		sorted_costs = costs[sorted_indexes]
-		sorted_dipoles = sample_dipoles[sorted_indexes]
-
-		threshold_cost = sorted_costs[-self.n_c]
-
-		all_dipoles = numpy.array(
-			[
-				pdme.subspace_simulation.sort_array_of_dipoles_by_frequency(samp)
-				for samp in sorted_dipoles
-			]
-		)
-		all_chains = list(zip(sorted_costs, all_dipoles))
-
-		mcmc_rng = numpy.random.default_rng(self.mcmc_seed)
-
-		for i in range(self.m_max):
-			next_seeds = all_chains[-self.n_c :]
-
-			if self.dump_last_generations:
-				_logger.info("writing out csv file")
-				next_dipoles_seed_dipoles = numpy.array([n[1] for n in next_seeds])
-				for n in range(self.model.n):
-					_logger.info(f"{next_dipoles_seed_dipoles[:, n].shape}")
-					numpy.savetxt(
-						f"generation_{self.n_c}_{self.n_s}_{i}_dipole_{n}.csv",
-						next_dipoles_seed_dipoles[:, n],
-						delimiter=",",
-					)
-
-			if self.keep_probs_list:
-				for cost_index, cost_chain in enumerate(all_chains[: -self.n_c]):
-					probs_list.append(
-						(
-							((self.n_c * self.n_s - cost_index) / (self.n_c * self.n_s))
-							/ (self.n_s ** (i)),
-							cost_chain[0],
-							i + 1,
-						)
-					)
-
-			next_seeds_as_array = numpy.array([s for _, s in next_seeds])
-
-			stdevs = self.get_stdevs_from_arrays(next_seeds_as_array)
-			_logger.info(f"got stdevs: {stdevs.stdevs}")
-			_logger.debug("Starting the MCMC")
-			all_chains = []
-			for seed_index, (c, s) in enumerate(next_seeds):
-				# chain = mcmc(s, threshold_cost, n_s, model, dot_inputs_array, actual_measurement_array, mcmc_rng, curr_cost=c, stdevs=stdevs)
-				# until new version gotta do
-				_logger.debug(
-					f"\t{seed_index}: getting another chain from the next seed"
-				)
-				chain = self.model.get_mcmc_chain(
-					s,
-					self.cost_function_to_use,
-					self.n_s,
-					threshold_cost,
-					stdevs,
-					initial_cost=c,
-					rng_arg=mcmc_rng,
-				)
-				for cost, chained in chain:
-					try:
-						filtered_cost = cost[0]
-					except IndexError:
-						filtered_cost = cost
-					all_chains.append((filtered_cost, chained))
-			_logger.debug("finished mcmc")
-			# _logger.debug(all_chains)
-
-			all_chains.sort(key=lambda c: c[0], reverse=True)
-			_logger.debug("finished sorting all_chains")
-
-			threshold_cost = all_chains[-self.n_c][0]
-			_logger.info(
-				f"current threshold cost: {threshold_cost}, at P = (1 / {self.n_s})^{i + 1}"
-			)
-			if (self.target_cost is not None) and (threshold_cost < self.target_cost):
-				_logger.info(
-					f"got a threshold cost {threshold_cost}, less than {self.target_cost}. will leave early"
-				)
-
-				cost_list = [c[0] for c in all_chains]
-				over_index = reverse_bisect_right(cost_list, self.target_cost)
-
-				shorter_probs_list = []
-				for cost_index, cost_chain in enumerate(all_chains):
-					if self.keep_probs_list:
-						probs_list.append(
-							(
-								(
-									(self.n_c * self.n_s - cost_index)
-									/ (self.n_c * self.n_s)
-								)
-								/ (self.n_s ** (i)),
-								cost_chain[0],
-								i + 1,
-							)
-						)
-					shorter_probs_list.append(
-						(
-							cost_chain[0],
-							((self.n_c * self.n_s - cost_index) / (self.n_c * self.n_s))
-							/ (self.n_s ** (i)),
-						)
-					)
-				# _logger.info(shorter_probs_list)
-				result = SubsetSimulationResult(
-					probs_list=probs_list,
-					over_target_cost=shorter_probs_list[over_index - 1][0],
-					over_target_likelihood=shorter_probs_list[over_index - 1][1],
-					under_target_cost=shorter_probs_list[over_index][0],
-					under_target_likelihood=shorter_probs_list[over_index][1],
-					lowest_likelihood=shorter_probs_list[-1][1],
-				)
-				return result
-
-			# _logger.debug([c[0] for c in all_chains[-n_c:]])
-			_logger.info(f"doing level {i + 1}")
-
-		if self.keep_probs_list:
-			for cost_index, cost_chain in enumerate(all_chains):
-				probs_list.append(
-					(
-						((self.n_c * self.n_s - cost_index) / (self.n_c * self.n_s))
-						/ (self.n_s ** (self.m_max)),
-						cost_chain[0],
-						self.m_max + 1,
-					)
-				)
-		threshold_cost = all_chains[-self.n_c][0]
-		_logger.info(
-			f"final threshold cost: {threshold_cost}, at P = (1 / {self.n_s})^{self.m_max + 1}"
-		)
-		for a in all_chains[-10:]:
-			_logger.info(a)
-		# for prob, prob_cost in probs_list:
-		# 	_logger.info(f"\t{prob}: {prob_cost}")
-		probs_list.sort(key=lambda c: c[0], reverse=True)
-
-		min_likelihood = ((1) / (self.n_c * self.n_s)) / (self.n_s ** (self.m_max))
-
-		result = SubsetSimulationResult(
-			probs_list=probs_list,
-			over_target_cost=None,
-			over_target_likelihood=None,
-			under_target_cost=None,
-			under_target_likelihood=None,
-			lowest_likelihood=min_likelihood,
-		)
-		return result
-
-	def get_stdevs_from_arrays(
-		self, array
-	) -> pdme.subspace_simulation.MCMCStandardDeviation:
-		# stdevs = get_stdevs_from_arrays(next_seeds_as_array, model)
-		if self.use_adaptive_steps:
-
-			stdev_array = []
-			count = array.shape[1]
-			for dipole_index in range(count):
-				selected = array[:, dipole_index]
-				pxs = selected[:, 0]
-				pys = selected[:, 1]
-				pzs = selected[:, 2]
-				thetas = numpy.arccos(pzs / self.model.pfixed)
-				phis = numpy.arctan2(pys, pxs)
-
-				rstdevs = numpy.maximum(
-					numpy.std(selected, axis=0)[3:6],
-					self.default_r_step / (self.n_s * 10),
-				)
-				frequency_stdevs = numpy.minimum(
-					numpy.maximum(
-						numpy.std(numpy.log(selected[:, -1])),
-						self.default_w_log_step / (self.n_s * 10),
-					),
-					self.default_upper_w_log_step,
-				)
-				stdev_array.append(
-					pdme.subspace_simulation.DipoleStandardDeviation(
-						p_theta_step=max(
-							numpy.std(thetas), self.default_theta_step / (self.n_s * 10)
-						),
-						p_phi_step=max(
-							numpy.std(phis), self.default_phi_step / (self.n_s * 10)
-						),
-						rx_step=rstdevs[0],
-						ry_step=rstdevs[1],
-						rz_step=rstdevs[2],
-						w_log_step=frequency_stdevs,
-					)
-				)
-		else:
-			default_stdev = pdme.subspace_simulation.DipoleStandardDeviation(
-				self.default_phi_step,
-				self.default_theta_step,
-				self.default_r_step,
-				self.default_r_step,
-				self.default_r_step,
-				self.default_w_log_step,
-			)
-			stdev_array = [default_stdev]
-		stdevs = pdme.subspace_simulation.MCMCStandardDeviation(stdev_array)
-		return stdevs
-
-
-def reverse_bisect_right(a, x, lo=0, hi=None):
-	"""Return the index where to insert item x in list a, assuming a is sorted in descending order.
-
-	The return value i is such that all e in a[:i] have e >= x, and all e in
-	a[i:] have e < x.  So if x already appears in the list, a.insert(x) will
-	insert just after the rightmost x already there.
-
-	Optional args lo (default 0) and hi (default len(a)) bound the
-	slice of a to be searched.
-
-	Essentially, the function returns number of elements in a which are >= than x.
-	>>> a = [8, 6, 5, 4, 2]
-	>>> reverse_bisect_right(a, 5)
-	3
-	>>> a[:reverse_bisect_right(a, 5)]
-	[8, 6, 5]
-	"""
-	if lo < 0:
-		raise ValueError("lo must be non-negative")
-	if hi is None:
-		hi = len(a)
-	while lo < hi:
-		mid = (lo + hi) // 2
-		if x > a[mid]:
-			hi = mid
-		else:
-			lo = mid + 1
-	return lo
--- a/deepdog/temp_aware_real_spectrum_run.py
+++ b/deepdog/temp_aware_real_spectrum_run.py
@@ -90,7 +90,6 @@ class TempAwareRealSpectrumRun:
 		max_monte_carlo_cycles_steps: int = 10,
 		chunksize: int = CHUNKSIZE,
 		initial_seed: int = 12345,
-		cap_core_count: int = 0,
 	) -> None:
 		self.measurements_dict = measurements_dict
 		self.dot_inputs_dict = {
@@ -127,8 +126,6 @@ class TempAwareRealSpectrumRun:
 		self.filename = f"{timestamp}-{filename_slug}.realdata.{ff_string}.bayesrun.csv"
 		self.initial_seed = initial_seed

-		self.cap_core_count = cap_core_count
-
 	def go(self) -> None:
 		with open(self.filename, "a", newline="") as outfile:
 			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
@@ -149,14 +146,11 @@ class TempAwareRealSpectrumRun:

 		results = []
 		_logger.debug("Going to iterate over models now")
-		core_count = multiprocessing.cpu_count() - 1 or 1
-		if (self.cap_core_count >= 1) and (self.cap_core_count < core_count):
-			core_count = self.cap_core_count
-		_logger.info(f"Using {core_count} cores")
 		for model_count, (model, model_name) in enumerate(
 			zip(self.models, self.model_names)
 		):
 			_logger.debug(f"Doing model #{model_count}: {model_name}")
+			core_count = multiprocessing.cpu_count() - 1 or 1
 			with multiprocessing.Pool(core_count) as pool:
 				cycle_count = 0
 				cycle_success = 0
--- a/do.sh
+++ b/do.sh
@@ -18,7 +18,7 @@ test() {

 fmt() {
 	poetry run black .
-	find . -not \( -path "./.*" -type d -prune \) -type f -name "*.py" -exec sed -i -e 's/    /\t/g' {} \;
+	find . -type f -name "*.py" -exec sed -i -e 's/    /\t/g' {} \;
 }

 release() {
--- a/poetry.lock
+++ b/poetry.lock
@@ -92,17 +92,9 @@ category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"

-[[package]]
-name = "colored"
-version = "1.4.4"
-description = "Simple library for color and formatting to terminal"
-category = "dev"
-optional = false
-python-versions = "*"
-
 [[package]]
 name = "coverage"
-version = "7.2.7"
+version = "7.2.3"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
@@ -368,11 +360,11 @@ python-versions = ">=3.7"

 [[package]]
 name = "pdme"
-version = "0.9.1"
+version = "0.8.8"
 description = "Python dipole model evaluator"
 category = "main"
 optional = false
-python-versions = ">=3.8.1,<3.10"
+python-versions = ">=3.8,<3.10"

 [package.dependencies]
 numpy = ">=1.22.3,<2.0.0"
@@ -469,11 +461,11 @@ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "no

 [[package]]
 name = "pytest-cov"
-version = "4.1.0"
+version = "4.0.0"
 description = "Pytest plugin for measuring coverage."
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.6"

 [package.dependencies]
 coverage = {version = ">=5.2.1", extras = ["toml"]}
@@ -641,18 +633,6 @@ category = "dev"
 optional = false
 python-versions = ">=3.6"

-[[package]]
-name = "syrupy"
-version = "4.0.8"
-description = "Pytest Snapshot Test Utility"
-category = "dev"
-optional = false
-python-versions = ">=3.8.1,<4"
-
-[package.dependencies]
-colored = ">=1.3.92,<2.0.0"
-pytest = ">=7.0.0,<8.0.0"
-
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -749,8 +729,8 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-co

 [metadata]
 lock-version = "1.1"
-python-versions = ">=3.8.1,<3.10"
-content-hash = "e1531b1493bac50ffe5e8f9a46a64d9b66198f7021f6d643c72f21cb53dc77ec"
+python-versions = "^3.8,<3.10"
+content-hash = "38342cfa86d08bc198ec9337b1e5f2cdd10cd372852cd73b2b079d45dffa0c6f"

 [metadata.files]
 black = []
@@ -761,7 +741,6 @@ charset-normalizer = []
 click = []
 click-log = []
 colorama = []
-colored = []
 coverage = []
 cryptography = []
 docutils = []
@@ -807,7 +786,6 @@ secretstorage = []
 semver = []
 six = []
 smmap = []
-syrupy = []
 tomli = []
 tomlkit = []
 tqdm = []
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,23 +1,22 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.7.4"
+version = "0.6.6"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]

 [tool.poetry.dependencies]
-python = ">=3.8.1,<3.10"
-pdme = "^0.9.1"
+python = "^3.8,<3.10"
+pdme = "^0.8.6"
 numpy = "1.22.3"
 scipy = "1.10"

 [tool.poetry.dev-dependencies]
 pytest = ">=6"
 flake8 = "^4.0.1"
-pytest-cov = "^4.1.0"
+pytest-cov = "^4.0.0"
 mypy = "^0.971"
 python-semantic-release = "^7.24.0"
 black = "^22.3.0"
-syrupy = "^4.0.8"

 [build-system]
 requires = ["poetry-core>=1.0.0"]
--- a/tests/snapshots/test_bayes_run_with_ss.ambr
+++ b/tests/snapshots/test_bayes_run_with_ss.ambr
@@ -1,177 +0,0 @@
-# serializer version: 1
-# name: test_basic_analysis
-  list([
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.3333333333333333,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.3333333333333333,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.3333333333333333,
-      'dipole_frequency_1': 0.006029931414230269,
-      'dipole_frequency_2': 85436.78758379082,
-      'dipole_location_1': array([-4.76615152, -6.33160296,  5.29522808]),
-      'dipole_location_2': array([-4.72700391, -2.06478573,  6.52467702]),
-      'dipole_moment_1': array([ 860.14181416, -450.27082062, -239.60852996]),
-      'dipole_moment_2': array([ 908.18325588, -208.52681777, -362.93214244]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.45,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.3103448275862069,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.9,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.6206896551724138,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.06896551724137932,
-      'dipole_frequency_1': 102275.63477261562,
-      'dipole_frequency_2': 1755280.9783485082,
-      'dipole_location_1': array([ 4.71515397, -9.70362197,  5.43016546]),
-      'dipole_location_2': array([3.42476038, 3.88562934, 5.15034328]),
-      'dipole_moment_1': array([-502.60742674, -790.60222587,  349.7626267 ]),
-      'dipole_moment_2': array([-192.42708465, -434.81009148, -879.7226844 ]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.7,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.6631578947368421,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.18947368421052635,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.7,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.1473684210526316,
-      'dipole_frequency_1': 2896.799464036654,
-      'dipole_frequency_2': 9.980565189326681e-05,
-      'dipole_location_1': array([-4.97465789, 12.54716531,  6.06324588]),
-      'dipole_location_2': array([  9.84518459, -11.1183876 ,   7.35028226]),
-      'dipole_moment_1': array([997.67961917,  19.6376112 ,  65.19004305]),
-      'dipole_moment_2': array([305.63093655, 440.57669389, 844.08643362]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.663157894736842,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.18947368421052635,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.1473684210526316,
-      'dipole_frequency_1': 1.4522667818288244,
-      'dipole_frequency_2': 2704.9795645301197,
-      'dipole_location_1': array([ 7.38183022, 16.6745801 ,  7.10428414]),
-      'dipole_location_2': array([-8.15636906, -9.56609132,  6.34141559]),
-      'dipole_moment_1': array([-145.9924693 ,  738.74936496,  657.97839986]),
-      'dipole_moment_2': array([-960.16113239,  104.96824669, -258.98314046]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.9,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.9465776293823038,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.030050083472454105,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.1,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.02337228714524208,
-      'dipole_frequency_1': 3827.2315421318913,
-      'dipole_frequency_2': 1.9301094166184413e-05,
-      'dipole_location_1': array([ 5.02067673, -0.9783039 ,  6.1431897 ]),
-      'dipole_location_2': array([ 4.66628999, 10.80907459,  7.21771744]),
-      'dipole_moment_1': array([ 871.30659253, -299.17389491, -388.99846068]),
-      'dipole_moment_2': array([-189.87268624,  677.28285845,  710.79975568]),
-    }),
-  ])
-# ---
-# name: test_bayesss_with_tighter_cost
-  list([
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.33333333333333337,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.33333333333333337,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.33333333333333337,
-      'dipole_frequency_1': 0.006029931414230269,
-      'dipole_frequency_2': 85436.78758379082,
-      'dipole_location_1': array([-4.76615152, -6.33160296,  5.29522808]),
-      'dipole_location_2': array([-4.72700391, -2.06478573,  6.52467702]),
-      'dipole_moment_1': array([ 860.14181416, -450.27082062, -239.60852996]),
-      'dipole_moment_2': array([ 908.18325588, -208.52681777, -362.93214244]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.0109375,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.1044776119402985,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.03125,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.2985074626865672,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.0625,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.5970149253731344,
-      'dipole_frequency_1': 102275.63477261562,
-      'dipole_frequency_2': 1755280.9783485082,
-      'dipole_location_1': array([ 4.71515397, -9.70362197,  5.43016546]),
-      'dipole_location_2': array([3.42476038, 3.88562934, 5.15034328]),
-      'dipole_moment_1': array([-502.60742674, -790.60222587,  349.7626267 ]),
-      'dipole_moment_2': array([-192.42708465, -434.81009148, -879.7226844 ]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 7.291135021404688e-05,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.021875,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.4666326413699001,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.0125,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.5332944472798858,
-      'dipole_frequency_1': 2896.799464036654,
-      'dipole_frequency_2': 9.980565189326681e-05,
-      'dipole_location_1': array([-4.97465789, 12.54716531,  6.06324588]),
-      'dipole_location_2': array([  9.84518459, -11.1183876 ,   7.35028226]),
-      'dipole_moment_1': array([997.67961917,  19.6376112 ,  65.19004305]),
-      'dipole_moment_2': array([305.63093655, 440.57669389, 844.08643362]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 7.291135021404688e-05,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.4666326413699001,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.5332944472798858,
-      'dipole_frequency_1': 1.4522667818288244,
-      'dipole_frequency_2': 2704.9795645301197,
-      'dipole_location_1': array([ 7.38183022, 16.6745801 ,  7.10428414]),
-      'dipole_location_2': array([-8.15636906, -9.56609132,  6.34141559]),
-      'dipole_moment_1': array([-145.9924693 ,  738.74936496,  657.97839986]),
-      'dipole_moment_2': array([-960.16113239,  104.96824669, -258.98314046]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.175,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.00012008361740869356,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.05625,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.24702915581216964,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.15,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.7528507605704217,
-      'dipole_frequency_1': 3827.2315421318913,
-      'dipole_frequency_2': 1.9301094166184413e-05,
-      'dipole_location_1': array([ 5.02067673, -0.9783039 ,  6.1431897 ]),
-      'dipole_location_2': array([ 4.66628999, 10.80907459,  7.21771744]),
-      'dipole_moment_1': array([ 871.30659253, -299.17389491, -388.99846068]),
-      'dipole_moment_2': array([-189.87268624,  677.28285845,  710.79975568]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 4.9116305003549454e-08,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.0109375,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.11316396672817797,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.028125,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.886835984155517,
-      'dipole_frequency_1': 1.1715179359592061e-05,
-      'dipole_frequency_2': 0.0019103783276337497,
-      'dipole_location_1': array([-0.95736547,  1.09273812,  7.47158641]),
-      'dipole_location_2': array([ -3.18510322, -15.64493131,   5.81623624]),
-      'dipole_moment_1': array([-184.64961369,  956.56786553,  225.57136075]),
-      'dipole_moment_2': array([ -34.63395137,  801.17771816, -597.42342885]),
-    }),
-    dict({
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 1.977090156727901e-10,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
-      'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.00045552157211010855,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.002734375,
-      'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.9995444782301809,
-      'dipole_frequency_1': 999786.9069039805,
-      'dipole_frequency_2': 186034.67996840767,
-      'dipole_location_1': array([-5.59679125,  6.3411602 ,  5.33602522]),
-      'dipole_location_2': array([-0.03412955, -6.83522954,  5.58551513]),
-      'dipole_moment_1': array([826.38270589, 491.81526944, 274.24325726]),
-      'dipole_moment_2': array([ 202.74745884, -656.07483714, -726.95204519]),
-    }),
-  ])
-# ---
--- a/tests/test_bayes_run_with_ss.py
+++ b/tests/test_bayes_run_with_ss.py
@@ -1,158 +0,0 @@
-import deepdog
-import logging
-import logging.config
-
-import numpy.random
-
-from pdme.model import (
-	LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
-	LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
-	LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel,
-)
-
-
-_logger = logging.getLogger(__name__)
-
-
-def fixed_z_model_func(
-	xmin,
-	xmax,
-	ymin,
-	ymax,
-	zmin,
-	zmax,
-	wexp_min,
-	wexp_max,
-	pfixed,
-	n_max,
-	prob_occupancy,
-):
-	return LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
-		xmin,
-		xmax,
-		ymin,
-		ymax,
-		zmin,
-		zmax,
-		wexp_min,
-		wexp_max,
-		pfixed,
-		0,
-		0,
-		n_max,
-		prob_occupancy,
-	)
-
-
-def get_model(orientation):
-	model_funcs = {
-		"fixedz": fixed_z_model_func,
-		"free": LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
-		"fixedxy": LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
-	}
-	model = model_funcs[orientation](
-		-10,
-		10,
-		-17.5,
-		17.5,
-		5,
-		7.5,
-		-5,
-		6.5,
-		10**3,
-		2,
-		0.99999999,
-	)
-	model.n = 2
-	model.rng = numpy.random.default_rng(1234)
-
-	return (
-		f"connors_geom-5height-orientation_{orientation}-pfixexp_{3}-dipole_count_{2}",
-		model,
-	)
-
-
-def test_basic_analysis(snapshot):
-
-	dot_positions = [[0, 0, 0], [0, 1, 0]]
-
-	freqs = [1, 10, 100]
-	models = []
-
-	orientations = ["free", "fixedxy", "fixedz"]
-	for orientation in orientations:
-		models.append(get_model(orientation))
-
-	_logger.info(f"have {len(models)} models to look at")
-	if len(models) == 1:
-		_logger.info(f"only one model, name: {models[0][0]}")
-
-	square_run = deepdog.BayesRunWithSubspaceSimulation(
-		dot_positions,
-		freqs,
-		models,
-		models[0][1],
-		filename_slug="test",
-		end_threshold=0.9,
-		ss_n_c=5,
-		ss_n_s=2,
-		ss_m_max=10,
-		ss_target_cost=150,
-		ss_level_0_seed=200,
-		ss_mcmc_seed=20,
-		ss_use_adaptive_steps=True,
-		ss_default_phi_step=0.01,
-		ss_default_theta_step=0.01,
-		ss_default_r_step=0.01,
-		ss_default_w_log_step=0.01,
-		ss_default_upper_w_log_step=4,
-		ss_dump_last_generation=False,
-		write_output_to_bayesruncsv=False,
-		ss_initial_costs_chunk_size=1000,
-	)
-	result = square_run.go()
-
-	assert result == snapshot
-
-
-def test_bayesss_with_tighter_cost(snapshot):
-
-	dot_positions = [[0, 0, 0], [0, 1, 0]]
-
-	freqs = [1, 10, 100]
-	models = []
-
-	orientations = ["free", "fixedxy", "fixedz"]
-	for orientation in orientations:
-		models.append(get_model(orientation))
-
-	_logger.info(f"have {len(models)} models to look at")
-	if len(models) == 1:
-		_logger.info(f"only one model, name: {models[0][0]}")
-
-	square_run = deepdog.BayesRunWithSubspaceSimulation(
-		dot_positions,
-		freqs,
-		models,
-		models[0][1],
-		filename_slug="test",
-		end_threshold=0.9,
-		ss_n_c=5,
-		ss_n_s=2,
-		ss_m_max=10,
-		ss_target_cost=1.5,
-		ss_level_0_seed=200,
-		ss_mcmc_seed=20,
-		ss_use_adaptive_steps=True,
-		ss_default_phi_step=0.01,
-		ss_default_theta_step=0.01,
-		ss_default_r_step=0.01,
-		ss_default_w_log_step=0.01,
-		ss_default_upper_w_log_step=4,
-		ss_dump_last_generation=False,
-		write_output_to_bayesruncsv=False,
-		ss_initial_costs_chunk_size=1
-	)
-	result = square_run.go()
-
-	assert result == snapshot