chore(release): 0.7.4

Reviewed-on: #32

.flake8

@@ -1,3 +1,3 @@
 [flake8]
-ignore = W191, E501, W503
+ignore = W191, E501, W503, E203
 max-line-length = 120

.gitignore

@@ -114,6 +114,10 @@ ENV/
 env.bak/
 venv.bak/
 
+# direnv
+.envrc
+.direnv
+
 # Spyder project settings
 .spyderproject
 .spyproject

CHANGELOG.md

@@ -2,6 +2,114 @@
 
 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)
+
+
+### Bug Fixes
+
+* removes bad logging in multiprocessing function ([8fd1b75](https://gitea.deepak.science:2222/physics/deepdog/commit/8fd1b75e1378301210bfa8f14dd09174bbd21414))
+
+### [0.6.5](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.4...0.6.5) (2023-04-09)
+
+
+### Features
+
+* adds temp aware guy using new pdme temp-flexible feature for bundling temp models ([de1ec3e](https://gitea.deepak.science:2222/physics/deepdog/commit/de1ec3e70062d418e0d4c89716905cc9313d2e26))
+
+### [0.6.4](https://gitea.deepak.science:2222/physics/deepdog/compare/0.6.3...0.6.4) (2022-08-13)
+
+
+### Features
+
+* Prints model names while running ([7ea1d71](https://gitea.deepak.science:2222/physics/deepdog/commit/7ea1d715f67e81c9fa841c5a62f1cc700ff7363d))
+
+### [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)
 
 

Jenkinsfile

@@ -4,7 +4,7 @@ pipeline {
 		label 'deepdog'  // all your pods will be named with this prefix, followed by a unique id
 		idleMinutes 5  // how long the pod will live after no jobs have run on it
 		yamlFile 'jenkins/ci-agent-pod.yaml'  // path to the pod definition relative to the root of our project
-		defaultContainer 'python'  // define a default container if more than a few stages use it, will default to jnlp container
+		defaultContainer 'poetry'  // define a default container if more than a few stages use it, will default to jnlp container
 	  }
 	}
 
@@ -12,36 +12,30 @@ pipeline {
 		parallelsAlwaysFailFast()
 	}
 
-	environment {
-		POETRY_HOME="/opt/poetry"
-		POETRY_VERSION="1.1.12"
-	}
-
 	stages {
 		stage('Build') {
 			steps {
 				echo 'Building...'
 				sh 'python --version'
-				sh 'curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python'
-				sh '${POETRY_HOME}/bin/poetry --version'
-				sh '${POETRY_HOME}/bin/poetry install'
+				sh 'poetry --version'
+				sh 'poetry install'
 			}
 		}
 		stage('Test') {
 			parallel{
 				stage('pytest') {
 					steps {
-						sh '${POETRY_HOME}/bin/poetry run pytest'
+						sh 'poetry run pytest'
 					}
 				}
 				stage('lint') {
 					steps {
-						sh '${POETRY_HOME}/bin/poetry run flake8 deepdog tests'
+						sh 'poetry run flake8 deepdog tests'
 					}
 				}
 				stage('mypy') {
 					steps {
-						sh '${POETRY_HOME}/bin/poetry run mypy deepdog'
+						sh 'poetry run mypy deepdog'
 					}
 				}
 			}
@@ -57,7 +51,7 @@ pipeline {
 			}
 			steps {
 				echo 'Deploying...'
-				sh '${POETRY_HOME}/bin/poetry publish -u ${PYPI_USR} -p ${PYPI_PSW} --build'
+				sh 'poetry publish -u ${PYPI_USR} -p ${PYPI_PSW} --build'
 			}
 		}
 

README.md

@@ -5,7 +5,7 @@
 [![Jenkins](https://img.shields.io/jenkins/build?jobUrl=https%3A%2F%2Fjenkins.deepak.science%2Fjob%2Fgitea-physics%2Fjob%2Fdeepdog%2Fjob%2Fmaster&style=flat-square)](https://jenkins.deepak.science/job/gitea-physics/job/deepdog/job/master/)
 ![Jenkins tests](https://img.shields.io/jenkins/tests?compact_message&jobUrl=https%3A%2F%2Fjenkins.deepak.science%2Fjob%2Fgitea-physics%2Fjob%2Fdeepdog%2Fjob%2Fmaster%2F&style=flat-square)
 ![Jenkins Coverage](https://img.shields.io/jenkins/coverage/cobertura?jobUrl=https%3A%2F%2Fjenkins.deepak.science%2Fjob%2Fgitea-physics%2Fjob%2Fdeepdog%2Fjob%2Fmaster%2F&style=flat-square)
-![Maintenance](https://img.shields.io/maintenance/yes/2022?style=flat-square)
+![Maintenance](https://img.shields.io/maintenance/yes/2023?style=flat-square)
 
 The DiPole DiaGnostic tool.
 

deepdog/__init__.py

@@ -2,6 +2,9 @@ import logging
 from deepdog.meta import __version__
 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():
@@ -12,6 +15,9 @@ __all__ = [
 	"get_version",
 	"BayesRun",
 	"BayesRunSimulPairs",
+	"RealSpectrumRun",
+	"TempAwareRealSpectrumRun",
+	"BayesRunWithSubspaceSimulation",
 ]
 
 

deepdog/bayes_run_with_ss.py

@@ -0,0 +1,257 @@
+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

deepdog/real_spectrum_run.py

@@ -0,0 +1,307 @@
+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, Optional
+import datetime
+import csv
+import multiprocessing
+import logging
+import numpy
+
+
+# TODO: remove hardcode
+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
+
+	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)
+
+
+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,
+		cap_core_count: int = 0,
+		pair_measurements: Optional[
+			Sequence[pdme.measurement.DotPairRangeMeasurement]
+		] = None,
+	) -> 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
+		)
+
+		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)
+
+		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")
+
+		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")
+			writer.writeheader()
+
+		(
+			lows,
+			highs,
+		) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
+			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}")
+			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_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,
+							)
+						)
+					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,
+							)
+						)
+
+					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)

deepdog/subset_simulation/__init__.py

@@ -0,0 +1,3 @@
+from deepdog.subset_simulation.subset_simulation_impl import SubsetSimulation
+
+__all__ = ["SubsetSimulation"]

deepdog/subset_simulation/subset_simulation_impl.py

@@ -0,0 +1,351 @@
+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

deepdog/temp_aware_real_spectrum_run.py

@@ -0,0 +1,231 @@
+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, Mapping
+import datetime
+import csv
+import multiprocessing
+import logging
+import numpy
+
+
+# TODO: remove hardcode
+CHUNKSIZE = 50
+
+
+_logger = logging.getLogger(__name__)
+
+
+def get_a_result_fast_filter(input) -> int:
+	# 								(
+	# 	model,
+	# 	self.dot_inputs_array_dict,
+	# 	low_high_dict,
+	# 	self.monte_carlo_count,
+	# 	seed,
+	# )
+	model, dot_inputs_dict, low_high_dict, 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 temp in dot_inputs_dict.keys():
+		dot_inputs = dot_inputs_dict[temp]
+		lows, highs = low_high_dict[temp]
+		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_asymmetric_dipoleses(
+				numpy.array([di]), current_sample, temp
+			)
+
+			current_sample = current_sample[
+				numpy.all((vals > low) & (vals < high), axis=1)
+			]
+	return len(current_sample)
+
+
+class TempAwareRealSpectrumRun:
+	"""
+	A bayes run given some real data, with potentially variable temperature.
+
+	Parameters
+	----------
+	measurements_dict : Dict[float, Sequence[pdme.measurement.DotRangeMeasurement]]
+	The dot inputs for this bayes run, in a dictionary indexed by temperatures
+
+	models_with_names : 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_dict: Mapping[
+			float, 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,
+		cap_core_count: int = 0,
+	) -> None:
+		self.measurements_dict = measurements_dict
+		self.dot_inputs_dict = {
+			k: [(measure.r, measure.f) for measure in measurements]
+			for k, measurements in measurements_dict.items()
+		}
+
+		self.dot_inputs_array_dict = {
+			k: pdme.measurement.input_types.dot_inputs_to_array(dot_inputs)
+			for k, dot_inputs in self.dot_inputs_dict.items()
+		}
+
+		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")
+		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")
+			writer.writeheader()
+
+		low_high_dict = {}
+		for temp, measurements in self.measurements_dict.items():
+			(
+				lows,
+				highs,
+			) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
+				measurements
+			)
+			low_high_dict[temp] = (lows, highs)
+
+		# 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}")
+			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)
+
+					result_func = get_a_result_fast_filter
+
+					current_success = sum(
+						pool.imap_unordered(
+							result_func,
+							[
+								(
+									model,
+									self.dot_inputs_array_dict,
+									low_high_dict,
+									self.monte_carlo_count,
+									seed,
+								)
+								for seed in seeds
+							],
+							self.chunksize,
+						)
+					)
+
+					cycle_success += current_success
+					_logger.debug(f"current running successes: {cycle_success}")
+				results.append((cycle_count, cycle_success))
+
+		_logger.debug("Done, constructing output now")
+		row: Dict[str, Union[int, float, str]] = {}
+
+		successes: List[float] = []
+		counts: List[int] = []
+		for model_index, (name, (count, result)) in enumerate(
+			zip(self.model_names, results)
+		):
+
+			row[f"{name}_success"] = result
+			row[f"{name}_count"] = count
+			successes.append(max(result, 0.5))
+			counts.append(count)
+
+		success_weight = sum(
+			[
+				(succ / count) * prob
+				for succ, count, prob in zip(successes, counts, self.probabilities)
+			]
+		)
+		new_probabilities = [
+			(succ / count) * old_prob / success_weight
+			for succ, count, old_prob in zip(successes, counts, self.probabilities)
+		]
+		self.probabilities = new_probabilities
+		for name, probability in zip(self.model_names, self.probabilities):
+			row[f"{name}_prob"] = probability
+		_logger.info(row)
+
+		with open(self.filename, "a", newline="") as outfile:
+			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
+			writer.writerow(row)

do.sh

@@ -18,7 +18,7 @@ test() {
 
 fmt() {
 	poetry run black .
-	find . -type f -name "*.py" -exec sed -i -e 's/    /\t/g' {} \;
+	find . -not \( -path "./.*" -type d -prune \) -type f -name "*.py" -exec sed -i -e 's/    /\t/g' {} \;
 }
 
 release() {

flake.lock

@@ -0,0 +1,95 @@
+{
+  "nodes": {
+    "flake-utils": {
+      "locked": {
+        "lastModified": 1648297722,
+        "narHash": "sha256-W+qlPsiZd8F3XkzXOzAoR+mpFqzm3ekQkJNa+PIh1BQ=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "0f8662f1319ad6abf89b3380dd2722369fc51ade",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "0f8662f1319ad6abf89b3380dd2722369fc51ade",
+        "type": "github"
+      }
+    },
+    "flake-utils_2": {
+      "locked": {
+        "lastModified": 1653893745,
+        "narHash": "sha256-0jntwV3Z8//YwuOjzhV2sgJJPt+HY6KhU7VZUL0fKZQ=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "1ed9fb1935d260de5fe1c2f7ee0ebaae17ed2fa1",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1655087213,
+        "narHash": "sha256-4R5oQ+OwGAAcXWYrxC4gFMTUSstGxaN8kN7e8hkum/8=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "37b6b161e536fddca54424cf80662bce735bdd1e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "37b6b161e536fddca54424cf80662bce735bdd1e",
+        "type": "github"
+      }
+    },
+    "nixpkgs_2": {
+      "locked": {
+        "lastModified": 1655046959,
+        "narHash": "sha256-gxqHZKq1ReLDe6ZMJSbmSZlLY95DsVq5o6jQihhzvmw=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "07bf3d25ce1da3bee6703657e6a787a4c6cdcea9",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "poetry2nix": {
+      "inputs": {
+        "flake-utils": "flake-utils_2",
+        "nixpkgs": "nixpkgs_2"
+      },
+      "locked": {
+        "lastModified": 1654921554,
+        "narHash": "sha256-hkfMdQAHSwLWlg0sBVvgrQdIiBP45U1/ktmFpY4g2Mo=",
+        "owner": "nix-community",
+        "repo": "poetry2nix",
+        "rev": "7b71679fa7df00e1678fc3f1d1d4f5f372341b63",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-community",
+        "repo": "poetry2nix",
+        "rev": "7b71679fa7df00e1678fc3f1d1d4f5f372341b63",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "flake-utils": "flake-utils",
+        "nixpkgs": "nixpkgs",
+        "poetry2nix": "poetry2nix"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}

flake.nix

@@ -0,0 +1,63 @@
+{
+  description = "Application packaged using poetry2nix";
+
+  inputs.flake-utils.url = "github:numtide/flake-utils?rev=0f8662f1319ad6abf89b3380dd2722369fc51ade";
+  inputs.nixpkgs.url = "github:NixOS/nixpkgs?rev=37b6b161e536fddca54424cf80662bce735bdd1e";
+  inputs.poetry2nix.url = "github:nix-community/poetry2nix?rev=7b71679fa7df00e1678fc3f1d1d4f5f372341b63";
+
+  outputs = { self, nixpkgs, flake-utils, poetry2nix }:
+    {
+      # Nixpkgs overlay providing the application
+      overlay = nixpkgs.lib.composeManyExtensions [
+        poetry2nix.overlay
+        (final: prev: {
+          # The application
+          deepdog = prev.poetry2nix.mkPoetryApplication {
+            overrides = final.poetry2nix.overrides.withDefaults (self: super: {
+              # …
+              # workaround https://github.com/nix-community/poetry2nix/issues/568
+              pdme = super.pdme.overridePythonAttrs (old: {
+                buildInputs = old.buildInputs or [ ] ++ [ final.python39.pkgs.poetry-core ];
+              });
+            });
+            projectDir = ./.;
+          };
+          deepdogEnv = prev.poetry2nix.mkPoetryEnv {
+            overrides = final.poetry2nix.overrides.withDefaults (self: super: {
+              # …
+              # workaround https://github.com/nix-community/poetry2nix/issues/568
+              pdme = super.pdme.overridePythonAttrs (old: {
+                buildInputs = old.buildInputs or [ ] ++ [ final.python39.pkgs.poetry-core ];
+              });
+            });
+            projectDir = ./.;
+          };
+        })
+      ];
+    } // (flake-utils.lib.eachDefaultSystem (system:
+      let
+        pkgs = import nixpkgs {
+          inherit system;
+          overlays = [ self.overlay ];
+        };
+      in
+      {
+        apps = {
+          deepdog = pkgs.deepdog;
+        };
+
+        defaultApp = pkgs.deepdog;
+        devShell = pkgs.mkShell {
+          buildInputs = [
+            pkgs.poetry
+            pkgs.deepdogEnv
+            pkgs.deepdog
+          ];
+          shellHook = ''
+            export DO_NIX_CUSTOM=1
+          '';
+          packages = [ pkgs.nodejs-16_x ];
+        };
+
+      }));
+}

jenkins/ci-agent-pod.yaml

@@ -1,9 +1,11 @@
 apiVersion: v1
 kind: Pod
 spec:
+  imagePullSecrets:
+    - name: regcreds
   containers:  # list of containers that you want present for your build, you can define a default container in the Jenkinsfile
-    - name: python
-      image: python:3.8
+    - name: poetry
+      image: ghcr.io/dmallubhotla/poetry-image:1
       command: ["tail", "-f", "/dev/null"]  # this or any command that is bascially a noop is required, this is so that you don't overwrite the entrypoint of the base container
       imagePullPolicy: Always # use cache or pull image for agent
       resources:  # limits the resources your build contaienr

pyproject.toml

@@ -1,20 +1,23 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.6.0"
+version = "0.7.4"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
-python = "^3.8,<3.10"
-pdme = "0.8.2"
+python = ">=3.8.1,<3.10"
+pdme = "^0.9.1"
+numpy = "1.22.3"
+scipy = "1.10"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
 flake8 = "^4.0.1"
-pytest-cov = "^3.0.0"
-mypy = "^0.950"
+pytest-cov = "^4.1.0"
+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"]

tests/__snapshots__/test_bayes_run_with_ss.ambr

@@ -0,0 +1,177 @@
+# 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]),
+    }),
+  ])
+# ---

tests/test_bayes_run_with_ss.py

@@ -0,0 +1,158 @@
+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