chore(release): 0.7.5

.flake8

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

CHANGELOG.md

@@ -2,6 +2,29 @@
 
 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.
 
+### [0.7.5](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.4...0.7.5) (2023-12-09)
+
+
+### Features
+
+* adds direct monte carlo package ([1741807](https://gitea.deepak.science:2222/physics/deepdog/commit/1741807be43d08fb51bc94518dd3b67585c04c20))
+* adds longchain logging if logging last generation ([b4e5f53](https://gitea.deepak.science:2222/physics/deepdog/commit/b4e5f5372682fc64c3734a96c4a899e018f127ce))
+* allows disabling timestamp in subset simulation bayes results ([9a4548d](https://gitea.deepak.science:2222/physics/deepdog/commit/9a4548def45a01f1f518135d4237c3dc09dcc342))
+
+### [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)
 
 

deepdog/bayes_run_with_ss.py

@@ -71,6 +71,9 @@ class BayesRunWithSubspaceSimulation:
 		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,
+		use_timestamp_for_output=True,
 	) -> None:
 		self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
 			dot_positions, frequency_range
@@ -108,8 +111,11 @@ class BayesRunWithSubspaceSimulation:
 
 		self.probabilities = [1 / self.model_count] * self.model_count
 
+		if use_timestamp_for_output:
 			timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
 			self.filename = f"{timestamp}-{filename_slug}.bayesrunwithss.csv"
+		else:
+			self.filename = f"{filename_slug}.bayesrunwithss.csv"
 		self.max_frequency = max_frequency
 
 		if end_threshold is not None:
@@ -135,14 +141,22 @@ class BayesRunWithSubspaceSimulation:
 		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
 
-	def go(self) -> None:
+		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 = 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
@@ -174,6 +188,7 @@ class BayesRunWithSubspaceSimulation:
 					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,
 				)
@@ -199,7 +214,11 @@ class BayesRunWithSubspaceSimulation:
 
 			for (name, result) in zip(self.model_names, results):
 				if result.over_target_likelihood is None:
-					clamped_likelihood = result.probs_list[-1][0] / CLAMPING_FACTOR
+					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}"
 					)
@@ -222,7 +241,9 @@ class BayesRunWithSubspaceSimulation:
 			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"
@@ -236,3 +257,5 @@ class BayesRunWithSubspaceSimulation:
 						f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
 					)
 					break
+
+		return return_result

deepdog/direct_monte_carlo/__init__.py

@@ -0,0 +1,6 @@
+from deepdog.direct_monte_carlo.direct_mc import (
+	DirectMonteCarloRun,
+	DirectMonteCarloConfig,
+)
+
+__all__ = ["DirectMonteCarloRun", "DirectMonteCarloConfig"]

deepdog/direct_monte_carlo/direct_mc.py

@@ -0,0 +1,157 @@
+import pdme.model
+import pdme.measurement
+import pdme.measurement.input_types
+import pdme.subspace_simulation
+from typing import Tuple, Sequence
+from dataclasses import dataclass
+import logging
+import numpy
+import numpy.random
+import pdme.util.fast_v_calc
+
+_logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DirectMonteCarloResult:
+	successes: int
+	monte_carlo_count: int
+	likelihood: float
+
+
+@dataclass
+class DirectMonteCarloConfig:
+	monte_carlo_count_per_cycle: int = 10000
+	monte_carlo_cycles: int = 10
+	target_success: int = 100
+	max_monte_carlo_cycles_steps: int = 10
+	monte_carlo_seed: int = 1234
+	write_successes_to_file: bool = False
+	tag: str = ""
+
+
+class DirectMonteCarloRun:
+	"""
+	A single model Direct Monte Carlo run, currently implemented only using single threading.
+	An encapsulation of the steps needed for a Bayes run.
+
+	Parameters
+	----------
+	model_name_pair : Sequence[Tuple(str, pdme.model.DipoleModel)]
+	The model to evaluate, with name.
+
+	measurements: Sequence[pdme.measurement.DotRangeMeasurement]
+	The measurements as dot ranges to use as the bounds for the Monte Carlo calculation.
+
+	monte_carlo_count_per_cycle: int
+	The number of Monte Carlo iterations to use in a single cycle calculation.
+
+	monte_carlo_cycles: int
+	The number of cycles to use in each step.
+	Increasing monte_carlo_count_per_cycle increases memory usage (and runtime), while this increases runtime, allowing
+	control over memory use.
+
+	target_success: int
+	The number of successes to target before exiting early.
+	Should likely be ~100 but can go higher to.
+
+	max_monte_carlo_cycles_steps: int
+	The number of steps to use. Each step consists of monte_carlo_cycles cycles, each of which has monte_carlo_count_per_cycle iterations.
+
+	monte_carlo_seed: int
+	The seed to use for the RNG.
+	"""
+
+	def __init__(
+		self,
+		model_name_pair: Tuple[str, pdme.model.DipoleModel],
+		measurements: Sequence[pdme.measurement.DotRangeMeasurement],
+		config: DirectMonteCarloConfig,
+	):
+		self.model_name, self.model = model_name_pair
+
+		self.measurements = measurements
+		self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
+
+		self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
+			self.dot_inputs
+		)
+
+		self.config = config
+		(
+			self.lows,
+			self.highs,
+		) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
+			self.measurements
+		)
+
+	def _single_run(self, seed) -> numpy.ndarray:
+		rng = numpy.random.default_rng(seed)
+
+		sample_dipoles = self.model.get_monte_carlo_dipole_inputs(
+			self.config.monte_carlo_count_per_cycle, -1, rng
+		)
+
+		current_sample = sample_dipoles
+		for di, low, high in zip(self.dot_inputs_array, self.lows, self.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 current_sample
+
+	def execute(self) -> DirectMonteCarloResult:
+		step_count = 0
+		total_success = 0
+		total_count = 0
+
+		count_per_step = (
+			self.config.monte_carlo_count_per_cycle * self.config.monte_carlo_cycles
+		)
+		seed_sequence = numpy.random.SeedSequence(self.config.monte_carlo_seed)
+		while (step_count < self.config.max_monte_carlo_cycles_steps) and (
+			total_success < self.config.target_success
+		):
+			_logger.debug(f"Executing step {step_count}")
+			for cycle_i, seed in enumerate(
+				seed_sequence.spawn(self.config.monte_carlo_cycles)
+			):
+				cycle_success_configs = self._single_run(seed)
+				cycle_success_count = len(cycle_success_configs)
+				if cycle_success_count > 0:
+					_logger.debug(
+						f"For cycle {cycle_i} received {cycle_success_count} successes"
+					)
+					_logger.debug(cycle_success_configs)
+					if self.config.write_successes_to_file:
+						sorted_by_freq = numpy.array(
+							[
+								pdme.subspace_simulation.sort_array_of_dipoles_by_frequency(
+									dipole_config
+								)
+								for dipole_config in cycle_success_configs
+							]
+						)
+						dipole_count = numpy.array(cycle_success_configs).shape[1]
+						for n in range(dipole_count):
+							numpy.savetxt(
+								f"{self.config.tag}_{step_count}_{cycle_i}_dipole_{n}.csv",
+								sorted_by_freq[:, n],
+								delimiter=",",
+							)
+				total_success += cycle_success_count
+			_logger.debug(f"At end of step {step_count} have {total_success} successes")
+			step_count += 1
+			total_count += count_per_step
+
+		return DirectMonteCarloResult(
+			successes=total_success,
+			monte_carlo_count=total_count,
+			likelihood=total_success / total_count,
+		)

deepdog/subset_simulation/subset_simulation_impl.py

@@ -40,6 +40,7 @@ class SubsetSimulation:
 		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
@@ -85,6 +86,8 @@ class SubsetSimulation:
 		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 = []
@@ -96,7 +99,20 @@ class SubsetSimulation:
 		)
 		# _logger.debug(sample_dipoles)
 		# _logger.debug(sample_dipoles.shape)
-		costs = self.cost_function_to_use(sample_dipoles)
+
+		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]
@@ -120,7 +136,7 @@ class SubsetSimulation:
 		mcmc_rng = numpy.random.default_rng(self.mcmc_seed)
 
 		for i in range(self.m_max):
-			next_seeds = all_chains[-self.n_c:]
+			next_seeds = all_chains[-self.n_c :]
 
 			if self.dump_last_generations:
 				_logger.info("writing out csv file")
@@ -133,6 +149,37 @@ class SubsetSimulation:
 						delimiter=",",
 					)
 
+				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}")
+				all_long_chains = []
+				for seed_index, (c, s) in enumerate(
+					next_seeds[:: len(next_seeds) // 20]
+				):
+					# 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}: doing long chain on the next seed")
+
+					long_chain = self.model.get_mcmc_chain(
+						s,
+						self.cost_function_to_use,
+						1000,
+						threshold_cost,
+						stdevs,
+						initial_cost=c,
+						rng_arg=mcmc_rng,
+					)
+					for _, chained in long_chain:
+						all_long_chains.append(chained)
+				all_long_chains_array = numpy.array(all_long_chains)
+				for n in range(self.model.n):
+					_logger.info(f"{all_long_chains_array[:, n].shape}")
+					numpy.savetxt(
+						f"long_chain_generation_{self.n_c}_{self.n_s}_{i}_dipole_{n}.csv",
+						all_long_chains_array[:, n],
+						delimiter=",",
+					)
+
 			if self.keep_probs_list:
 				for cost_index, cost_chain in enumerate(all_chains[: -self.n_c]):
 					probs_list.append(
@@ -148,11 +195,14 @@ class SubsetSimulation:
 
 			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 c, s in next_seeds:
+			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,
@@ -168,10 +218,11 @@ class SubsetSimulation:
 					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(
@@ -240,7 +291,7 @@ class SubsetSimulation:
 		# 	_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 + 1))
+		min_likelihood = ((1) / (self.n_c * self.n_s)) / (self.n_s ** (self.m_max))
 
 		result = SubsetSimulationResult(
 			probs_list=probs_list,

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() {

poetry.lock

@@ -92,6 +92,14 @@ 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"
@@ -633,6 +641,18 @@ 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"
@@ -730,7 +750,7 @@ 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 = "111972d04616ce3ddfc9039a0b38c7eb7c4a41f10390139b27e958aedac7e979"
+content-hash = "e1531b1493bac50ffe5e8f9a46a64d9b66198f7021f6d643c72f21cb53dc77ec"
 
 [metadata.files]
 black = []
@@ -741,6 +761,7 @@ charset-normalizer = []
 click = []
 click-log = []
 colorama = []
+colored = []
 coverage = []
 cryptography = []
 docutils = []
@@ -786,6 +807,7 @@ secretstorage = []
 semver = []
 six = []
 smmap = []
+syrupy = []
 tomli = []
 tomlkit = []
 tqdm = []

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.7.3"
+version = "0.7.5"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
@@ -17,6 +17,7 @@ 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