chore(release): 1.2.1

CHANGELOG.md

@@ -2,6 +2,31 @@
 
 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.
 
+### [1.2.1](https://gitea.deepak.science:2222/physics/deepdog/compare/1.2.0...1.2.1) (2024-05-12)
+
+## [1.2.0](https://gitea.deepak.science:2222/physics/deepdog/compare/1.1.0...1.2.0) (2024-05-09)
+
+
+### Features
+
+* adds additional matching regexes ([dc1d2d4](https://gitea.deepak.science:2222/physics/deepdog/commit/dc1d2d45a3e631c5efccce80f8a24fa87c6089e0))
+* adds magnitude enabled parsing option ([f0e2fa3](https://gitea.deepak.science:2222/physics/deepdog/commit/f0e2fa3da9f5a5136908d691137a904fda4e3a9a))
+
+## [1.1.0](https://gitea.deepak.science:2222/physics/deepdog/compare/1.0.1...1.1.0) (2024-05-03)
+
+
+### Features
+
+* allows disabling timestamps in directmc bayesrun files ([fb018ab](https://gitea.deepak.science:2222/physics/deepdog/commit/fb018abeae2adf4438a030140a6c905f11bb6bc1))
+* removes legacy bayes run, technically breaking but just don't use them ([5361dad](https://gitea.deepak.science:2222/physics/deepdog/commit/5361dada8be4950b5157862f6a92254b543889c3))
+
+### [1.0.1](https://gitea.deepak.science:2222/physics/deepdog/compare/1.0.0...1.0.1) (2024-05-02)
+
+
+### Bug Fixes
+
+* fixes issue of zero division error with no successes for anything ([e25db1e](https://gitea.deepak.science:2222/physics/deepdog/commit/e25db1e0f677e8d9a657fa1631305cc8f05ff9ff))
+
 ## [1.0.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.8.1...1.0.0) (2024-05-01)
 
 

deepdog/__init__.py

@@ -1,10 +1,7 @@
 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():
@@ -13,11 +10,8 @@ def get_version():
 
 __all__ = [
 	"get_version",
-	"BayesRun",
-	"BayesRunSimulPairs",
 	"RealSpectrumRun",
 	"TempAwareRealSpectrumRun",
-	"BayesRunWithSubspaceSimulation",
 ]
 
 

deepdog/bayes_run.py

@@ -1,281 +0,0 @@
-import pdme.inputs
-import pdme.model
-import pdme.measurement.input_types
-import pdme.measurement.oscillating_dipole
-import pdme.util.fast_v_calc
-import pdme.util.fast_nonlocal_spectrum
-from typing import Sequence, Tuple, List
-import datetime
-import csv
-import multiprocessing
-import logging
-import numpy
-
-
-# TODO: remove hardcode
-CHUNKSIZE = 50
-
-# TODO: It's garbage to have this here duplicated from pdme.
-DotInput = Tuple[numpy.typing.ArrayLike, float]
-
-
-_logger = logging.getLogger(__name__)
-
-
-def get_a_result(input) -> int:
-	model, dot_inputs, lows, highs, monte_carlo_count, max_frequency, seed = input
-
-	rng = numpy.random.default_rng(seed)
-	sample_dipoles = model.get_monte_carlo_dipole_inputs(
-		monte_carlo_count, max_frequency, rng_to_use=rng
-	)
-	vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(dot_inputs, sample_dipoles)
-	return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
-
-
-def get_a_result_using_pairs(input) -> int:
-	(
-		model,
-		dot_inputs,
-		pair_inputs,
-		local_lows,
-		local_highs,
-		nonlocal_lows,
-		nonlocal_highs,
-		monte_carlo_count,
-		max_frequency,
-	) = input
-	sample_dipoles = model.get_n_single_dipoles(monte_carlo_count, max_frequency)
-	local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
-	local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
-	nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(
-		pair_inputs, sample_dipoles
-	)
-	nonlocal_matches = pdme.util.fast_v_calc.between(
-		nonlocal_vals, nonlocal_lows, nonlocal_highs
-	)
-	combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
-	return numpy.count_nonzero(combined_matches)
-
-
-class BayesRun:
-	"""
-	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,
-		run_count: int = 100,
-		low_error: float = 0.9,
-		high_error: float = 1.1,
-		monte_carlo_count: int = 10000,
-		monte_carlo_cycles: int = 10,
-		target_success: int = 100,
-		max_monte_carlo_cycles_steps: int = 10,
-		max_frequency: float = 20,
-		end_threshold: float = None,
-		chunksize: int = CHUNKSIZE,
-	) -> None:
-		self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
-			dot_positions, frequency_range
-		)
-		self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
-			self.dot_inputs
-		)
-
-		self.models = [model for (_, model) in models_with_names]
-		self.model_names = [name for (name, _) in models_with_names]
-		self.actual_model = actual_model
-
-		self.n: int
-		try:
-			self.n = self.actual_model.n  # type: ignore
-		except AttributeError:
-			self.n = 1
-
-		self.model_count = len(self.models)
-		self.monte_carlo_count = monte_carlo_count
-		self.monte_carlo_cycles = monte_carlo_cycles
-		self.target_success = target_success
-		self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
-		self.run_count = run_count
-		self.low_error = low_error
-		self.high_error = high_error
-
-		self.csv_fields = []
-		for i in range(self.n):
-			self.csv_fields.extend(
-				[
-					f"dipole_moment_{i+1}",
-					f"dipole_location_{i+1}",
-					f"dipole_frequency_{i+1}",
-				]
-			)
-		self.compensate_zeros = True
-		self.chunksize = chunksize
-		for name in self.model_names:
-			self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
-
-		self.probabilities = [1 / self.model_count] * self.model_count
-
-		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-		self.filename = f"{timestamp}-{filename_slug}.bayesrun.csv"
-		self.max_frequency = max_frequency
-
-		if end_threshold is not None:
-			if 0 < end_threshold < 1:
-				self.end_threshold: float = end_threshold
-				self.use_end_threshold = True
-				_logger.info(f"Will abort early, at {self.end_threshold}.")
-			else:
-				raise ValueError(
-					f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
-				)
-
-	def go(self) -> None:
-		with open(self.filename, "a", newline="") as outfile:
-			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
-			writer.writeheader()
-
-		for run in range(1, self.run_count + 1):
-
-			# Generate the actual dipoles
-			actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
-
-			dots = actual_dipoles.get_percent_range_dot_measurements(
-				self.dot_inputs, self.low_error, self.high_error
-			)
-			(
-				lows,
-				highs,
-			) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
-				dots
-			)
-
-			_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
-
-			# define a new seed sequence for each run
-			seed_sequence = numpy.random.SeedSequence(run)
-
-			results = []
-			_logger.debug("Going to iterate over models now")
-			for model_count, model in enumerate(self.models):
-				_logger.debug(f"Doing model #{model_count}")
-				core_count = multiprocessing.cpu_count() - 1 or 1
-				with multiprocessing.Pool(core_count) as pool:
-					cycle_count = 0
-					cycle_success = 0
-					cycles = 0
-					while (cycles < self.max_monte_carlo_cycles_steps) and (
-						cycle_success <= self.target_success
-					):
-						_logger.debug(f"Starting cycle {cycles}")
-						cycles += 1
-						current_success = 0
-						cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
-
-						# generate a seed from the sequence for each core.
-						# note this needs to be inside the loop for monte carlo cycle steps!
-						# that way we get more stuff.
-						seeds = seed_sequence.spawn(self.monte_carlo_cycles)
-
-						current_success = sum(
-							pool.imap_unordered(
-								get_a_result,
-								[
-									(
-										model,
-										self.dot_inputs_array,
-										lows,
-										highs,
-										self.monte_carlo_count,
-										self.max_frequency,
-										seed,
-									)
-									for seed in seeds
-								],
-								self.chunksize,
-							)
-						)
-
-						cycle_success += current_success
-						_logger.debug(f"current running successes: {cycle_success}")
-					results.append((cycle_count, cycle_success))
-
-			_logger.debug("Done, constructing output now")
-			row = {
-				"dipole_moment_1": actual_dipoles.dipoles[0].p,
-				"dipole_location_1": actual_dipoles.dipoles[0].s,
-				"dipole_frequency_1": actual_dipoles.dipoles[0].w,
-			}
-			for i in range(1, self.n):
-				try:
-					current_dipoles = actual_dipoles.dipoles[i]
-					row[f"dipole_moment_{i+1}"] = current_dipoles.p
-					row[f"dipole_location_{i+1}"] = current_dipoles.s
-					row[f"dipole_frequency_{i+1}"] = current_dipoles.w
-				except IndexError:
-					_logger.info(f"Not writing anymore, saw end after {i}")
-					break
-
-			successes: List[float] = []
-			counts: List[int] = []
-			for model_index, (name, (count, result)) in enumerate(
-				zip(self.model_names, results)
-			):
-
-				row[f"{name}_success"] = result
-				row[f"{name}_count"] = count
-				successes.append(max(result, 0.5))
-				counts.append(count)
-
-			success_weight = sum(
-				[
-					(succ / count) * prob
-					for succ, count, prob in zip(successes, counts, self.probabilities)
-				]
-			)
-			new_probabilities = [
-				(succ / count) * old_prob / success_weight
-				for succ, count, old_prob in zip(successes, counts, self.probabilities)
-			]
-			self.probabilities = new_probabilities
-			for name, probability in zip(self.model_names, self.probabilities):
-				row[f"{name}_prob"] = probability
-			_logger.info(row)
-
-			with open(self.filename, "a", newline="") as outfile:
-				writer = csv.DictWriter(
-					outfile, fieldnames=self.csv_fields, dialect="unix"
-				)
-				writer.writerow(row)
-
-			if self.use_end_threshold:
-				max_prob = max(self.probabilities)
-				if max_prob > self.end_threshold:
-					_logger.info(
-						f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
-					)
-					break

deepdog/bayes_run_simulpairs.py

@@ -1,382 +0,0 @@
-import pdme.inputs
-import pdme.model
-import pdme.measurement.input_types
-import pdme.measurement.oscillating_dipole
-import pdme.util.fast_v_calc
-import pdme.util.fast_nonlocal_spectrum
-from typing import Sequence, Tuple, List
-import datetime
-import csv
-import multiprocessing
-import logging
-import numpy
-import numpy.random
-
-
-# TODO: remove hardcode
-CHUNKSIZE = 50
-
-# TODO: It's garbage to have this here duplicated from pdme.
-DotInput = Tuple[numpy.typing.ArrayLike, float]
-
-
-_logger = logging.getLogger(__name__)
-
-
-def get_a_simul_result_using_pairs(input) -> numpy.ndarray:
-	(
-		model,
-		dot_inputs,
-		pair_inputs,
-		local_lows,
-		local_highs,
-		nonlocal_lows,
-		nonlocal_highs,
-		monte_carlo_count,
-		monte_carlo_cycles,
-		max_frequency,
-		seed,
-	) = input
-
-	rng = numpy.random.default_rng(seed)
-	local_total = 0
-	combined_total = 0
-
-	sample_dipoles = model.get_monte_carlo_dipole_inputs(
-		monte_carlo_count, max_frequency, rng_to_use=rng
-	)
-	local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(dot_inputs, sample_dipoles)
-	local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
-	nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal_dipoleses(
-		pair_inputs, sample_dipoles
-	)
-	nonlocal_matches = pdme.util.fast_v_calc.between(
-		nonlocal_vals, nonlocal_lows, nonlocal_highs
-	)
-	combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
-
-	local_total += numpy.count_nonzero(local_matches)
-	combined_total += numpy.count_nonzero(combined_matches)
-	return numpy.array([local_total, combined_total])
-
-
-class BayesRunSimulPairs:
-	"""
-	A dual pairs-nonpairs Bayes run for a given set of dots.
-
-	Parameters
-	----------
-	dot_inputs : Sequence[DotInput]
-	The dot inputs for this bayes run.
-
-	models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
-	The models to evaluate.
-
-	actual_model : pdme.model.DipoleModel
-	The modoel for the model which is actually correct.
-
-	filename_slug : str
-	The filename slug to include.
-
-	run_count: int
-	The number of runs to do.
-	"""
-
-	def __init__(
-		self,
-		dot_positions: Sequence[numpy.typing.ArrayLike],
-		frequency_range: Sequence[float],
-		models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
-		actual_model: pdme.model.DipoleModel,
-		filename_slug: str,
-		run_count: int = 100,
-		low_error: float = 0.9,
-		high_error: float = 1.1,
-		pairs_high_error=None,
-		pairs_low_error=None,
-		monte_carlo_count: int = 10000,
-		monte_carlo_cycles: int = 10,
-		target_success: int = 100,
-		max_monte_carlo_cycles_steps: int = 10,
-		max_frequency: float = 20,
-		end_threshold: float = None,
-		chunksize: int = CHUNKSIZE,
-	) -> None:
-		self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
-			dot_positions, frequency_range
-		)
-		self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
-			self.dot_inputs
-		)
-
-		self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(
-			dot_positions, frequency_range
-		)
-		self.dot_pair_inputs_array = (
-			pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
-		)
-
-		self.models = [mod for (_, mod) in models_with_names]
-		self.model_names = [name for (name, _) in models_with_names]
-		self.actual_model = actual_model
-
-		self.n: int
-		try:
-			self.n = self.actual_model.n  # type: ignore
-		except AttributeError:
-			self.n = 1
-
-		self.model_count = len(self.models)
-		self.monte_carlo_count = monte_carlo_count
-		self.monte_carlo_cycles = monte_carlo_cycles
-		self.target_success = target_success
-		self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
-		self.run_count = run_count
-		self.low_error = low_error
-		self.high_error = high_error
-		if pairs_low_error is None:
-			self.pairs_low_error = self.low_error
-		else:
-			self.pairs_low_error = pairs_low_error
-		if pairs_high_error is None:
-			self.pairs_high_error = self.high_error
-		else:
-			self.pairs_high_error = pairs_high_error
-
-		self.csv_fields = []
-		for i in range(self.n):
-			self.csv_fields.extend(
-				[
-					f"dipole_moment_{i+1}",
-					f"dipole_location_{i+1}",
-					f"dipole_frequency_{i+1}",
-				]
-			)
-		self.compensate_zeros = True
-		self.chunksize = chunksize
-		for name in self.model_names:
-			self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
-
-		self.probabilities_no_pairs = [1 / self.model_count] * self.model_count
-		self.probabilities_pairs = [1 / self.model_count] * self.model_count
-
-		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-		self.filename_pairs = f"{timestamp}-{filename_slug}.simulpairs.yespairs.csv"
-		self.filename_no_pairs = f"{timestamp}-{filename_slug}.simulpairs.noopairs.csv"
-
-		self.max_frequency = max_frequency
-
-		if end_threshold is not None:
-			if 0 < end_threshold < 1:
-				self.end_threshold: float = end_threshold
-				self.use_end_threshold = True
-				_logger.info(f"Will abort early, at {self.end_threshold}.")
-			else:
-				raise ValueError(
-					f"end_threshold should be between 0 and 1, but is actually {end_threshold}"
-				)
-
-	def go(self) -> None:
-		with open(self.filename_pairs, "a", newline="") as outfile:
-			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
-			writer.writeheader()
-		with open(self.filename_no_pairs, "a", newline="") as outfile:
-			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
-			writer.writeheader()
-
-		for run in range(1, self.run_count + 1):
-
-			# Generate the actual dipoles
-			actual_dipoles = self.actual_model.get_dipoles(self.max_frequency)
-
-			dots = actual_dipoles.get_percent_range_dot_measurements(
-				self.dot_inputs, self.low_error, self.high_error
-			)
-			(
-				lows,
-				highs,
-			) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
-				dots
-			)
-
-			pair_lows, pair_highs = (None, None)
-			pair_measurements = actual_dipoles.get_percent_range_dot_pair_measurements(
-				self.dot_pair_inputs, self.pairs_low_error, self.pairs_high_error
-			)
-			(
-				pair_lows,
-				pair_highs,
-			) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
-				pair_measurements
-			)
-
-			_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
-
-			# define a new seed sequence for each run
-			seed_sequence = numpy.random.SeedSequence(run)
-
-			results_pairs = []
-			results_no_pairs = []
-			_logger.debug("Going to iterate over models now")
-			for model_count, model in enumerate(self.models):
-				_logger.debug(f"Doing model #{model_count}")
-
-				core_count = multiprocessing.cpu_count() - 1 or 1
-				with multiprocessing.Pool(core_count) as pool:
-					cycle_count = 0
-					cycle_success_pairs = 0
-					cycle_success_no_pairs = 0
-					cycles = 0
-					while (cycles < self.max_monte_carlo_cycles_steps) and (
-						min(cycle_success_pairs, cycle_success_no_pairs)
-						<= self.target_success
-					):
-						_logger.debug(f"Starting cycle {cycles}")
-
-						cycles += 1
-						current_success_pairs = 0
-						current_success_no_pairs = 0
-						cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
-
-						# generate a seed from the sequence for each core.
-						# note this needs to be inside the loop for monte carlo cycle steps!
-						# that way we get more stuff.
-
-						seeds = seed_sequence.spawn(self.monte_carlo_cycles)
-						_logger.debug(f"Creating {self.monte_carlo_cycles} seeds")
-						current_success_both = numpy.array(
-							sum(
-								pool.imap_unordered(
-									get_a_simul_result_using_pairs,
-									[
-										(
-											model,
-											self.dot_inputs_array,
-											self.dot_pair_inputs_array,
-											lows,
-											highs,
-											pair_lows,
-											pair_highs,
-											self.monte_carlo_count,
-											self.monte_carlo_cycles,
-											self.max_frequency,
-											seed,
-										)
-										for seed in seeds
-									],
-									self.chunksize,
-								)
-							)
-						)
-						current_success_no_pairs = current_success_both[0]
-						current_success_pairs = current_success_both[1]
-
-						cycle_success_no_pairs += current_success_no_pairs
-						cycle_success_pairs += current_success_pairs
-						_logger.debug(
-							f"(pair, no_pair) successes are {(cycle_success_pairs, cycle_success_no_pairs)}"
-						)
-					results_pairs.append((cycle_count, cycle_success_pairs))
-					results_no_pairs.append((cycle_count, cycle_success_no_pairs))
-
-			_logger.debug("Done, constructing output now")
-			row_pairs = {
-				"dipole_moment_1": actual_dipoles.dipoles[0].p,
-				"dipole_location_1": actual_dipoles.dipoles[0].s,
-				"dipole_frequency_1": actual_dipoles.dipoles[0].w,
-			}
-			row_no_pairs = {
-				"dipole_moment_1": actual_dipoles.dipoles[0].p,
-				"dipole_location_1": actual_dipoles.dipoles[0].s,
-				"dipole_frequency_1": actual_dipoles.dipoles[0].w,
-			}
-			for i in range(1, self.n):
-				try:
-					current_dipoles = actual_dipoles.dipoles[i]
-					row_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
-					row_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
-					row_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
-					row_no_pairs[f"dipole_moment_{i+1}"] = current_dipoles.p
-					row_no_pairs[f"dipole_location_{i+1}"] = current_dipoles.s
-					row_no_pairs[f"dipole_frequency_{i+1}"] = current_dipoles.w
-				except IndexError:
-					_logger.info(f"Not writing anymore, saw end after {i}")
-					break
-
-			successes_pairs: List[float] = []
-			successes_no_pairs: List[float] = []
-			counts: List[int] = []
-			for model_index, (
-				name,
-				(count_pair, result_pair),
-				(count_no_pair, result_no_pair),
-			) in enumerate(zip(self.model_names, results_pairs, results_no_pairs)):
-
-				row_pairs[f"{name}_success"] = result_pair
-				row_pairs[f"{name}_count"] = count_pair
-				successes_pairs.append(max(result_pair, 0.5))
-
-				row_no_pairs[f"{name}_success"] = result_no_pair
-				row_no_pairs[f"{name}_count"] = count_no_pair
-				successes_no_pairs.append(max(result_no_pair, 0.5))
-
-				counts.append(count_pair)
-
-			success_weight_pair = sum(
-				[
-					(succ / count) * prob
-					for succ, count, prob in zip(
-						successes_pairs, counts, self.probabilities_pairs
-					)
-				]
-			)
-			success_weight_no_pair = sum(
-				[
-					(succ / count) * prob
-					for succ, count, prob in zip(
-						successes_no_pairs, counts, self.probabilities_no_pairs
-					)
-				]
-			)
-			new_probabilities_pair = [
-				(succ / count) * old_prob / success_weight_pair
-				for succ, count, old_prob in zip(
-					successes_pairs, counts, self.probabilities_pairs
-				)
-			]
-			new_probabilities_no_pair = [
-				(succ / count) * old_prob / success_weight_no_pair
-				for succ, count, old_prob in zip(
-					successes_no_pairs, counts, self.probabilities_no_pairs
-				)
-			]
-			self.probabilities_pairs = new_probabilities_pair
-			self.probabilities_no_pairs = new_probabilities_no_pair
-			for name, probability_pair, probability_no_pair in zip(
-				self.model_names, self.probabilities_pairs, self.probabilities_no_pairs
-			):
-				row_pairs[f"{name}_prob"] = probability_pair
-				row_no_pairs[f"{name}_prob"] = probability_no_pair
-			_logger.debug(row_pairs)
-			_logger.debug(row_no_pairs)
-
-			with open(self.filename_pairs, "a", newline="") as outfile:
-				writer = csv.DictWriter(
-					outfile, fieldnames=self.csv_fields, dialect="unix"
-				)
-				writer.writerow(row_pairs)
-			with open(self.filename_no_pairs, "a", newline="") as outfile:
-				writer = csv.DictWriter(
-					outfile, fieldnames=self.csv_fields, dialect="unix"
-				)
-				writer.writerow(row_no_pairs)
-
-			if self.use_end_threshold:
-				max_prob = min(
-					max(self.probabilities_pairs), max(self.probabilities_no_pairs)
-				)
-				if max_prob > self.end_threshold:
-					_logger.info(
-						f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
-					)
-					break

deepdog/bayes_run_with_ss.py

@@ -1,261 +0,0 @@
-import deepdog.subset_simulation
-import pdme.inputs
-import pdme.model
-import pdme.measurement.input_types
-import pdme.measurement.oscillating_dipole
-import pdme.util.fast_v_calc
-import pdme.util.fast_nonlocal_spectrum
-from typing import Sequence, Tuple, List, Optional
-import datetime
-import csv
-import logging
-import numpy
-import numpy.typing
-
-
-# TODO: remove hardcode
-CHUNKSIZE = 50
-
-# TODO: It's garbage to have this here duplicated from pdme.
-DotInput = Tuple[numpy.typing.ArrayLike, float]
-
-
-CLAMPING_FACTOR = 10
-
-_logger = logging.getLogger(__name__)
-
-
-class BayesRunWithSubspaceSimulation:
-	"""
-	A single Bayes run for a given set of dots.
-
-	Parameters
-	----------
-	dot_inputs : Sequence[DotInput]
-	The dot inputs for this bayes run.
-
-	models_with_names : Sequence[Tuple(str, pdme.model.DipoleModel)]
-	The models to evaluate.
-
-	actual_model : pdme.model.DipoleModel
-	The model which is actually correct.
-
-	filename_slug : str
-	The filename slug to include.
-
-	run_count: int
-	The number of runs to do.
-	"""
-
-	def __init__(
-		self,
-		dot_positions: Sequence[numpy.typing.ArrayLike],
-		frequency_range: Sequence[float],
-		models_with_names: Sequence[Tuple[str, pdme.model.DipoleModel]],
-		actual_model: pdme.model.DipoleModel,
-		filename_slug: str,
-		max_frequency: float = 20,
-		end_threshold: float = None,
-		run_count=100,
-		chunksize: int = CHUNKSIZE,
-		ss_n_c: int = 500,
-		ss_n_s: int = 100,
-		ss_m_max: int = 15,
-		ss_target_cost: Optional[float] = None,
-		ss_level_0_seed: int = 200,
-		ss_mcmc_seed: int = 20,
-		ss_use_adaptive_steps=True,
-		ss_default_phi_step=0.01,
-		ss_default_theta_step=0.01,
-		ss_default_r_step=0.01,
-		ss_default_w_log_step=0.01,
-		ss_default_upper_w_log_step=4,
-		ss_dump_last_generation=False,
-		ss_initial_costs_chunk_size=100,
-		write_output_to_bayesruncsv=True,
-		use_timestamp_for_output=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
-
-		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:
-			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/direct_monte_carlo/direct_mc.py

@@ -14,6 +14,8 @@ import multiprocessing
 
 _logger = logging.getLogger(__name__)
 
+ANTI_ZERO_SUCCESS_THRES = 0.1
+
 
 @dataclass
 class DirectMonteCarloResult:
@@ -35,6 +37,7 @@ class DirectMonteCarloConfig:
 	cap_core_count: int = 0  # 0 means cap at num cores - 1
 	chunk_size: int = 50
 	write_bayesrun_file = True
+	bayesrun_file_timestamp = True
 	# chunk size of some kind
 
 
@@ -282,9 +285,14 @@ class DirectMonteCarloRun:
 
 		if self.config.write_bayesrun_file:
 
+			if self.config.bayesrun_file_timestamp:
+				timestamp_str = f"{timestamp}-"
+			else:
+				timestamp_str = ""
 			filename = (
-				f"{timestamp}-{self.config.tag}.realdata.fast_filter.bayesrun.csv"
+				f"{timestamp_str}{self.config.tag}.realdata.fast_filter.bayesrun.csv"
 			)
+
 			_logger.info(f"Going to write to file [{filename}]")
 			# row: Dict[str, Union[int, float, str]] = {}
 			row = {}
@@ -292,7 +300,11 @@ class DirectMonteCarloRun:
 			num_models = len(self.model_name_pairs)
 			success_weight = sum(
 				[
-					(res.successes / res.monte_carlo_count) / num_models
+					(
+						max(ANTI_ZERO_SUCCESS_THRES, res.successes)
+						/ res.monte_carlo_count
+					)
+					/ num_models
 					for res in results
 				]
 			)
@@ -303,7 +315,8 @@ class DirectMonteCarloRun:
 						f"{res.model_name}_success": res.successes,
 						f"{res.model_name}_count": res.monte_carlo_count,
 						f"{res.model_name}_prob": (
-							res.successes / res.monte_carlo_count
+							max(ANTI_ZERO_SUCCESS_THRES, res.successes)
+							/ res.monte_carlo_count
 						)
 						/ (num_models * success_weight),
 					}

deepdog/direct_monte_carlo/dmc_filters.py

@@ -54,109 +54,13 @@ class SingleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
 			self.measurements
 		)
 
-	# oh no not this again
-	def fast_s_spin_qubit_tarucha_apsd_dipoleses(
-		self, dot_inputs: numpy.ndarray, dipoleses: numpy.ndarray
-	) -> numpy.ndarray:
-		"""
-		No error correction here baby.
-		"""
-
-		# We're going to annotate the indices on this class.
-		# Let's define some indices:
-		# A -> index of dipoleses configurations
-		# j -> within a particular configuration, indexes dipole j
-		# measurement_index -> if we have 100 frequencies for example, indexes which one of them it is
-		# If we need to use numbers, let's use A -> 2, j -> 10, measurement_index -> 9 for consistency with
-		# my other notes
-
-		# axes are [dipole_config_idx A, dipole_idx j, {px, py, pz}3]
-		ps = dipoleses[:, :, 0:3]
-		# axes are [dipole_config_idx A, dipole_idx j, {sx, sy, sz}3]
-		ss = dipoleses[:, :, 3:6]
-		# axes are [dipole_config_idx A, dipole_idx j, w], last axis is just 1
-		ws = dipoleses[:, :, 6]
-
-		# dot_index is either 0 or 1 for dot1 or dot2
-		# hopefully this adhoc grammar is making sense, with the explicit labelling of the values of the last axis in cartesian space
-		# axes are [measurement_idx, {dot_index}, {rx, ry, rz}] where the inner {dot_index} is gone
-		# [measurement_idx, cartesian3]
-		rs = dot_inputs[:, 0:3]
-		# axes are [measurement_idx]
-		fs = dot_inputs[:, 3]
-
-		# first operation!
-		# r1s has shape [measurement_idx, rxs]
-		# None inserts an extra axis so the r1s[:, None] has shape
-		# [measurement_idx, 1]([rxs]) with the last rxs hidden
-		#
-		# ss has shape [ A, j, {sx, sy, sz}3], so second term has shape [A, 1, j]([sxs])
-		# these broadcast from right to left
-		# [	 measurement_idx, 1, rxs]
-		# [A,	  1,			   j, sxs]
-		# resulting in [A, measurement_idx, j, cart3] sxs rxs are both cart3
-		diffses = rs[:, None] - ss[:, None, :]
-
-		# norms takes out axis 3, the last one, giving [A, measurement_idx, j]
-		norms = numpy.linalg.norm(diffses, axis=3)
-
-		# _logger.info(f"norms1: {norms1}")
-		# _logger.info(f"norms1 shape: {norms1.shape}")
-		#
-		# diffses1 (A, measurement_idx, j, xs)
-		# ps:  (A, j, px)
-		# result is (A, measurement_idx, j)
-		# intermediate_dot_prod = numpy.einsum("abcd,acd->abc", diffses1, ps)
-		# _logger.info(f"dot product shape: {intermediate_dot_prod.shape}")
-
-		# transpose makes it (j, measurement_idx, A)
-		# transp_intermediate_dot_prod = numpy.transpose(numpy.einsum("abcd,acd->abc", diffses1, ps) / (norms1**3))
-
-		# transpose of diffses has shape (xs, j, measurement_idx, A)
-		# numpy.transpose(diffses1)
-		# _logger.info(f"dot product shape: {transp_intermediate_dot_prod.shape}")
-
-		# inner transpose is (j, measurement_idx, A) * (xs, j, measurement_idx, A)
-		# next transpose puts it back to (A, measurement_idx, j, xs)
-		# p_dot_r_times_r_term = 3 * numpy.transpose(numpy.transpose(numpy.einsum("abcd,acd->abc", diffses1, ps) / (norms1**3)) * numpy.transpose(diffses1))
-		# _logger.info(f"p_dot_r_times_r_term: {p_dot_r_times_r_term.shape}")
-
-		# only x axis puts us at (A, measurement_idx, j)
-		# p_dot_r_times_r_term_x_only = p_dot_r_times_r_term[:, :, :, 0]
-		# _logger.info(f"p_dot_r_times_r_term_x_only.shape: {p_dot_r_times_r_term_x_only.shape}")
-
-		# now to complete the numerator we subtract the ps, which are (A, j, px):
-		# slicing off the end gives us (A, j), so we newaxis to get (A, 1, j)
-		# _logger.info(ps[:, numpy.newaxis, :, 0].shape)
-		alphses = (
-			(
-				3
-				* numpy.transpose(
-					numpy.transpose(
-						numpy.einsum("abcd,acd->abc", diffses, ps) / (norms**2)
-					)
-					* numpy.transpose(diffses)
-				)[:, :, :, 0]
-			)
-			- ps[:, numpy.newaxis, :, 0]
-		) / (norms**3)
-
-		bses = (
-			2
-			* numpy.pi
-			* ws[:, None, :]
-			/ ((2 * numpy.pi * fs[:, None]) ** 2 + 4 * ws[:, None, :] ** 2)
-		)
-
-		return numpy.einsum("...j->...", alphses * alphses * bses)
-
 	def filter_samples(self, samples: ndarray) -> ndarray:
 		current_sample = samples
 		for di, low, high in zip(self.dot_inputs_array, self.lows, self.highs):
 
 			if len(current_sample) < 1:
 				break
-			vals = self.fast_s_spin_qubit_tarucha_apsd_dipoleses(
+			vals = pdme.util.fast_v_calc.fast_efieldxs_for_dipoleses(
 				numpy.array([di]), current_sample
 			)
 			# _logger.info(vals)

deepdog/indexify/__init__.py

@@ -31,10 +31,10 @@ class Indexifier:
 
 	def __init__(self, list_dict: typing.Dict[str, typing.Sequence]):
 		self.dict = list_dict
+		self.product_dict = _dict_product(self.dict)
 
 	def indexify(self, n: int) -> typing.Dict[str, typing.Any]:
-		product_dict = _dict_product(self.dict)
+		return self.product_dict[n]
-		return product_dict[n]
 
 	def _indexify_indices(self, n: int) -> typing.Sequence[int]:
 		"""

deepdog/results/__init__.py

@@ -8,17 +8,30 @@ import csv
 
 _logger = logging.getLogger(__name__)
 
-FILENAME_REGEX = r"(?P<timestamp>\d{8}-\d{6})-(?P<filename_slug>.*)\.realdata\.fast_filter\.bayesrun\.csv"
+FILENAME_REGEX = re.compile(
+	r"(?P<timestamp>\d{8}-\d{6})-(?P<filename_slug>.*)\.realdata\.fast_filter\.bayesrun\.csv"
+)
 
 MODEL_REGEXES = [
-	r"geom_(?P<xmin>-?\d+)_(?P<xmax>-?\d+)_(?P<ymin>-?\d+)_(?P<ymax>-?\d+)_(?P<zmin>-?\d+)_(?P<zmax>-?\d+)-orientation_(?P<orientation>free|fixedxy|fixedz)-dipole_count_(?P<avg_filled>\d+)_(?P<field_name>\w*)"
+	re.compile(pattern)
+	for pattern in [
+		r"geom_(?P<xmin>-?\d+)_(?P<xmax>-?\d+)_(?P<ymin>-?\d+)_(?P<ymax>-?\d+)_(?P<zmin>-?\d+)_(?P<zmax>-?\d+)-orientation_(?P<orientation>free|fixedxy|fixedz)-dipole_count_(?P<avg_filled>\d+)_(?P<field_name>\w*)",
+		r"geom_(?P<xmin>-?\d+)_(?P<xmax>-?\d+)_(?P<ymin>-?\d+)_(?P<ymax>-?\d+)_(?P<zmin>-?\d+)_(?P<zmax>-?\d+)-magnitude_(?P<log_magnitude>\d*\.?\d+)-orientation_(?P<orientation>free|fixedxy|fixedz)-dipole_count_(?P<avg_filled>\d+)_(?P<field_name>\w*)",
+		r"geom_(?P<xmin>-?\d*\.?\d+)_(?P<xmax>-?\d*\.?\d+)_(?P<ymin>-?\d*\.?\d+)_(?P<ymax>-?\d*\.?\d+)_(?P<zmin>-?\d*\.?\d+)_(?P<zmax>-?\d*\.?\d+)-magnitude_(?P<log_magnitude>\d*\.?\d+)-orientation_(?P<orientation>free|fixedxy|fixedz)-dipole_count_(?P<avg_filled>\d+)_(?P<field_name>\w*)",
+	]
 ]
 
 FILE_SLUG_REGEXES = [
-	r"mock_tarucha-(?P<job_index>\d+)",
+	re.compile(pattern)
-	r"(?:(?P<mock>mock)_)?tarucha(?:_(?P<tarucha_run_id>\d+))?-(?P<job_index>\d+)",
+	for pattern in [
+		r"(?P<tag>\w+)-(?P<job_index>\d+)",
+		r"mock_tarucha-(?P<job_index>\d+)",
+		r"(?:(?P<mock>mock)_)?tarucha(?:_(?P<tarucha_run_id>\d+))?-(?P<job_index>\d+)",
+	]
 ]
 
+SIMPLE_TAG_REGEX = re.compile(r"\w+-\d+")
+
 
 @dataclasses.dataclass
 class BayesrunOutputFilename:
@@ -27,7 +40,6 @@ class BayesrunOutputFilename:
 	path: pathlib.Path
 
 
-@dataclasses.dataclass
 class BayesrunColumnParsed:
 	"""
 	class for parsing a bayesrun while pulling certain special fields out
@@ -38,10 +50,21 @@ class BayesrunColumnParsed:
 		self.model_field_dict = {
 			k: v for k, v in groupdict.items() if k != "field_name"
 		}
+		self._groupdict_str = repr(groupdict)
 
 	def __str__(self):
 		return f"BayesrunColumnParsed[{self.column_field}: {self.model_field_dict}]"
 
+	def __repr__(self):
+		return f"BayesrunColumnParsed({self._groupdict_str})"
+
+	def __eq__(self, other):
+		if isinstance(other, BayesrunColumnParsed):
+			return (self.column_field == other.column_field) and (
+				self.model_field_dict == other.model_field_dict
+			)
+		return NotImplemented
+
 
 @dataclasses.dataclass
 class BayesrunModelResult:
@@ -73,7 +96,7 @@ def _parse_bayesrun_column(
 	Returns the groupdict for the first match, or None if no match found.
 	"""
 	for pattern in MODEL_REGEXES:
-		match = re.match(pattern, column)
+		match = pattern.match(column)
 		if match:
 			return BayesrunColumnParsed(match.groupdict())
 	else:
@@ -112,7 +135,7 @@ def _parse_bayesrun_row(
 
 def _parse_output_filename(file: pathlib.Path) -> BayesrunOutputFilename:
 	filename = file.name
-	match = re.match(FILENAME_REGEX, filename)
+	match = FILENAME_REGEX.match(filename)
 	if not match:
 		raise ValueError(f"{filename} was not a valid bayesrun output")
 	groups = match.groupdict()
@@ -123,7 +146,7 @@ def _parse_output_filename(file: pathlib.Path) -> BayesrunOutputFilename:
 
 def _parse_file_slug(slug: str) -> typing.Optional[typing.Dict[str, str]]:
 	for pattern in FILE_SLUG_REGEXES:
-		match = re.match(pattern, slug)
+		match = pattern.match(slug)
 		if match:
 			return match.groupdict()
 	else:

flake.nix

@@ -36,6 +36,7 @@
 	    self.packages.${system}.deepdogEnv
 	    self.packages.${system}.deepdogApp
 	    pkgs.just
+	    pkgs.nodejs
 	  ];
 	  shellHook = ''
 	    export DO_NIX_CUSTOM=1

poetry.lock

@@ -786,13 +786,13 @@ files = [
 
 [[package]]
 name = "pdme"
-version = "1.0.0"
+version = "1.2.0"
 description = "Python dipole model evaluator"
 optional = false
 python-versions = "<3.10,>=3.8.1"
 files = [
-    {file = "pdme-1.0.0-py3-none-any.whl", hash = "sha256:8fb8d1bf3d88f73118da5731332ae00c721b98daf53b225069e422af1a1a67f2"},
+    {file = "pdme-1.2.0-py3-none-any.whl", hash = "sha256:602710a053f22921b4adbc03d46d284149fe2367a65455cde56608708e01c84b"},
-    {file = "pdme-1.0.0.tar.gz", hash = "sha256:02cabf2e6fc2ddaf0871d0b3afcf265bca16520ee7bc1c74672be62f7a8390bd"},
+    {file = "pdme-1.2.0.tar.gz", hash = "sha256:412806d7ae384c048515e0f2cba70252778bf153800829a1d3265a0596872263"},
 ]
 
 [package.dependencies]
@@ -1275,4 +1275,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<3.10"
-content-hash = "a28054e255cbd49396795127380c2b7a0cfd742b15cba2184322f3c4894ed041"
+content-hash = "918b6736766a9c1b6732a56e1ef2e7a53241f2e25babb884881e49c299801fc9"

pyproject.toml

@@ -1,12 +1,12 @@
 [tool.poetry]
 name = "deepdog"
-version = "1.0.0"
+version = "1.2.1"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<3.10"
-pdme = "^1.0.0"
+pdme = "^1.2.0"
 numpy = "1.22.3"
 scipy = "1.10"
 tqdm = "^4.66.2"

tests/direct_monte_carlo/test_eletric_field_x_dmc_filter.py

@@ -0,0 +1,137 @@
+import pdme.measurement
+import pdme.measurement.input_types
+from pdme.model import (
+	LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
+	LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
+	LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel,
+)
+import deepdog.direct_monte_carlo.dmc_filters
+import numpy.random
+import numpy.testing
+import logging
+
+_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_electric_field_x_dmc_filter():
+
+	dipoles_raw = [
+		[(1, 2, 3), (4, 5, 6), 1],
+		[(-1, 5, 2), (6, 5, 4), 10],
+	]
+	dipoles = [
+		pdme.measurement.OscillatingDipole(numpy.array(d[0]), numpy.array(d[1]), d[2])
+		for d in dipoles_raw
+	]
+
+	_logger.debug(f"dipoles: {dipoles}")
+	dot_inputs_raw = [
+		([-1, -1, 0], 1),
+		([-1, -1, 0], 2),
+		([-1, -1, 0], 3),
+		([-1, -1, 0], 4),
+	]
+	dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(dot_inputs_raw)
+	_logger.debug(f"dot_inputs_array: {dot_inputs_array}")
+
+	arrangement = pdme.measurement.OscillatingDipoleArrangement(dipoles)
+	measurements = []
+	for input in dot_inputs_raw:
+		ex = sum(
+			[
+				dipole.s_electric_fieldx_at_position(*input)
+				for dipole in arrangement.dipoles
+			]
+		)
+		ex_low = ex * 0.5
+		ex_high = ex * 1.5
+		meas = pdme.measurement.DotRangeMeasurement(ex_low, ex_high, input[0], input[1])
+		measurements.append(meas)
+
+	filter = deepdog.direct_monte_carlo.dmc_filters.SingleDotSpinQubitFrequencyFilter(
+		measurements
+	)
+
+	samples = numpy.array(
+		[
+			[
+				[1, 2, 3, 4, 5, 6, 1],
+				[-1, 5, 2, 6, 5, 4, 10],
+			],
+			[
+				[10, 20, 30, 40, 50, 60, 1],
+				[-1, 5, 2, 6, 5, 4, 1],
+			],
+			[
+				[1, 1, 1, 1, 1, 1, 1],
+				[2, 2, 2, 2, 2, 2, 1],
+			],
+		]
+	)
+
+	expected = samples[
+		0:1
+	]  # only expect to see the first guy, because that's what generated our thing
+	filtered = filter.filter_samples(samples)
+	assert len(filtered) != len(samples), "Should have filtered some out!"
+	numpy.testing.assert_array_equal(
+		filtered, expected, "The filter should have only returned the first one"
+	)

tests/results/test_column_results.py

@@ -7,6 +7,7 @@ def test_parse_groupdict():
 	)
 
 	parsed = deepdog.results._parse_bayesrun_column(example_column_name)
+	assert parsed is not None
 	expected = deepdog.results.BayesrunColumnParsed(
 		{
 			"xmin": "-20",
@@ -23,6 +24,30 @@ def test_parse_groupdict():
 	assert parsed == expected
 
 
+def test_parse_groupdict_with_magnitude():
+	example_column_name = (
+		"geom_-20_20_-10_10_0_5-magnitude_3.5-orientation_free-dipole_count_100_success"
+	)
+
+	parsed = deepdog.results._parse_bayesrun_column(example_column_name)
+	assert parsed is not None
+	expected = deepdog.results.BayesrunColumnParsed(
+		{
+			"xmin": "-20",
+			"xmax": "20",
+			"ymin": "-10",
+			"ymax": "10",
+			"zmin": "0",
+			"zmax": "5",
+			"orientation": "free",
+			"avg_filled": "100",
+			"log_magnitude": "3.5",
+			"field_name": "success",
+		}
+	)
+	assert parsed == expected
+
+
 # def test_parse_no_match_column_name():
 # 	parsed = deepdog.results.parse_bayesrun_column("There's nothing here")
 # 	assert parsed is None

tests/test_bayes_run_with_ss.py

@@ -1,158 +0,0 @@
-import deepdog
-import logging
-import logging.config
-
-import numpy.random
-
-from pdme.model import (
-	LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
-	LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
-	LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel,
-)
-
-
-_logger = logging.getLogger(__name__)
-
-
-def fixed_z_model_func(
-	xmin,
-	xmax,
-	ymin,
-	ymax,
-	zmin,
-	zmax,
-	wexp_min,
-	wexp_max,
-	pfixed,
-	n_max,
-	prob_occupancy,
-):
-	return LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
-		xmin,
-		xmax,
-		ymin,
-		ymax,
-		zmin,
-		zmax,
-		wexp_min,
-		wexp_max,
-		pfixed,
-		0,
-		0,
-		n_max,
-		prob_occupancy,
-	)
-
-
-def get_model(orientation):
-	model_funcs = {
-		"fixedz": fixed_z_model_func,
-		"free": LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
-		"fixedxy": LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
-	}
-	model = model_funcs[orientation](
-		-10,
-		10,
-		-17.5,
-		17.5,
-		5,
-		7.5,
-		-5,
-		6.5,
-		10**3,
-		2,
-		0.99999999,
-	)
-	model.n = 2
-	model.rng = numpy.random.default_rng(1234)
-
-	return (
-		f"connors_geom-5height-orientation_{orientation}-pfixexp_{3}-dipole_count_{2}",
-		model,
-	)
-
-
-def test_basic_analysis(snapshot):
-
-	dot_positions = [[0, 0, 0], [0, 1, 0]]
-
-	freqs = [1, 10, 100]
-	models = []
-
-	orientations = ["free", "fixedxy", "fixedz"]
-	for orientation in orientations:
-		models.append(get_model(orientation))
-
-	_logger.info(f"have {len(models)} models to look at")
-	if len(models) == 1:
-		_logger.info(f"only one model, name: {models[0][0]}")
-
-	square_run = deepdog.BayesRunWithSubspaceSimulation(
-		dot_positions,
-		freqs,
-		models,
-		models[0][1],
-		filename_slug="test",
-		end_threshold=0.9,
-		ss_n_c=5,
-		ss_n_s=2,
-		ss_m_max=10,
-		ss_target_cost=150,
-		ss_level_0_seed=200,
-		ss_mcmc_seed=20,
-		ss_use_adaptive_steps=True,
-		ss_default_phi_step=0.01,
-		ss_default_theta_step=0.01,
-		ss_default_r_step=0.01,
-		ss_default_w_log_step=0.01,
-		ss_default_upper_w_log_step=4,
-		ss_dump_last_generation=False,
-		write_output_to_bayesruncsv=False,
-		ss_initial_costs_chunk_size=1000,
-	)
-	result = square_run.go()
-
-	assert result == snapshot
-
-
-def test_bayesss_with_tighter_cost(snapshot):
-
-	dot_positions = [[0, 0, 0], [0, 1, 0]]
-
-	freqs = [1, 10, 100]
-	models = []
-
-	orientations = ["free", "fixedxy", "fixedz"]
-	for orientation in orientations:
-		models.append(get_model(orientation))
-
-	_logger.info(f"have {len(models)} models to look at")
-	if len(models) == 1:
-		_logger.info(f"only one model, name: {models[0][0]}")
-
-	square_run = deepdog.BayesRunWithSubspaceSimulation(
-		dot_positions,
-		freqs,
-		models,
-		models[0][1],
-		filename_slug="test",
-		end_threshold=0.9,
-		ss_n_c=5,
-		ss_n_s=2,
-		ss_m_max=10,
-		ss_target_cost=1.5,
-		ss_level_0_seed=200,
-		ss_mcmc_seed=20,
-		ss_use_adaptive_steps=True,
-		ss_default_phi_step=0.01,
-		ss_default_theta_step=0.01,
-		ss_default_r_step=0.01,
-		ss_default_w_log_step=0.01,
-		ss_default_upper_w_log_step=4,
-		ss_dump_last_generation=False,
-		write_output_to_bayesruncsv=False,
-		ss_initial_costs_chunk_size=1,
-	)
-	result = square_run.go()
-
-	assert result == snapshot