chore(release): 1.0.1

.gitignore

@@ -143,3 +143,5 @@ dmypy.json
 cython_debug/
 
 *.csv
+
+local_scripts/

CHANGELOG.md

@@ -2,6 +2,54 @@
 
 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.
 
+### [1.0.1](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)
+
+
+### ⚠ BREAKING CHANGES
+
+* allows new seed spec instead of cli arg, removes old cli arg
+
+### Features
+
+* adds additional file slug parsing ([2105754](https://gitea.deepak.science:2222/physics/deepdog/commit/2105754911c89bde9dcbea9866462225604a3524))
+* Adds more powerful direct mc runs to sub for old real spectrum run ([f2b1a1d](https://gitea.deepak.science:2222/physics/deepdog/commit/f2b1a1dd3b3436e37d84f7843b9b2a202be4b51c))
+* allows new seed spec instead of cli arg, removes old cli arg ([7108dd0](https://gitea.deepak.science:2222/physics/deepdog/commit/7108dd0111c7dfd6ec204df1d0058530cd3dcab9))
+
+
+### Bug Fixes
+
+* no longer throws error for overlapping keys, the warning should hopefully be enough? ([f3ba4cb](https://gitea.deepak.science:2222/physics/deepdog/commit/f3ba4cbfd36a9f08cdc4d8774a7f745f8c98bac3))
+
+### [0.8.1](https://gitea.deepak.science:2222/physics/deepdog/compare/0.8.0...0.8.1) (2024-04-28)
+
+### [0.8.1](https://gitea.deepak.science:2222/physics/deepdog/compare/0.8.0...0.8.1) (2024-04-28)
+
+## [0.8.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.10...0.8.0) (2024-04-28)
+
+
+### ⚠ BREAKING CHANGES
+
+* fixes the spin qubit frequency phase shift calculation which had an index problem
+
+### Bug Fixes
+
+* fixes the spin qubit frequency phase shift calculation which had an index problem ([f9646e3](https://gitea.deepak.science:2222/physics/deepdog/commit/f9646e33868e1a0da8ab663230c0c692ac25bb74))
+
+### [0.7.10](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.9...0.7.10) (2024-04-28)
+
+
+### Features
+
+* adds cli probs ([4b2e573](https://gitea.deepak.science:2222/physics/deepdog/commit/4b2e57371546731137b011461849bb849d4d4e0f))
+* better management of cli wrapper ([b0ad4be](https://gitea.deepak.science:2222/physics/deepdog/commit/b0ad4bead0d4762eb7f848f6e557f6d9b61200b9))
+
 ### [0.7.9](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.8...0.7.9) (2024-04-21)
 
 

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/2023?style=flat-square)
+![Maintenance](https://img.shields.io/maintenance/yes/2024?style=flat-square)
 
 The DiPole DiaGnostic tool.
 
@@ -13,6 +13,13 @@ The DiPole DiaGnostic tool.
 
 `poetry install` to start locally
 
-Commit using [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/), and when commits are on master, release with `doo release`.
+Commit using [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/), and when commits are on master, release with `just release`.
 
+In general `just --list` has some of the useful stuff for figuring out what development tools there are.
 
+Poetry as an installer is good, even better is using Nix (maybe with direnv to automatically pick up the `devShell` from `flake.nix`).
+In either case `just` should handle actually calling things in a way that's agnostic to poetry as a runner or through nix.
+
+### local scripts
+`local_scripts` folder allows for scripts to be run using this code, but that probably isn't the most auditable for actual usage.
+The API is still only something I'm using so there's no guarantees yet that it will be stable; overall semantic versioning should help with API breaks.

deepdog/cli/probs/__init__.py

@@ -0,0 +1,5 @@
+from deepdog.cli.probs.main import wrapped_main
+
+__all__ = [
+	"wrapped_main",
+]

deepdog/cli/probs/args.py

@@ -0,0 +1,51 @@
+import argparse
+import os
+
+
+def parse_args() -> argparse.Namespace:
+	def dir_path(path):
+		if os.path.isdir(path):
+			return path
+		else:
+			raise argparse.ArgumentTypeError(f"readable_dir:{path} is not a valid path")
+
+	parser = argparse.ArgumentParser(
+		"probs", description="Calculating probability from finished bayesrun"
+	)
+	parser.add_argument(
+		"--log_file",
+		type=str,
+		help="A filename for logging to, if not provided will only log to stderr",
+		default=None,
+	)
+	parser.add_argument(
+		"--bayesrun-directory",
+		"-d",
+		type=dir_path,
+		help="The directory to search for bayesrun files, defaulting to cwd if not passed",
+		default=".",
+	)
+	parser.add_argument(
+		"--indexify-json",
+		help="A json file with the indexify config for parsing job indexes. Will skip if not present",
+		default="",
+	)
+	parser.add_argument(
+		"--coalesced-keys",
+		type=str,
+		help="A comma separated list of strings over which to coalesce data. By default coalesce over all fields within model names, ignore file level names",
+		default="",
+	)
+	parser.add_argument(
+		"--uncoalesced-outfile",
+		type=str,
+		help="output filename for uncoalesced data. If not provided, will not be written",
+		default=None,
+	)
+	parser.add_argument(
+		"--coalesced-outfile",
+		type=str,
+		help="output filename for coalesced data. If not provided, will not be written",
+		default=None,
+	)
+	return parser.parse_args()

deepdog/cli/probs/dicts.py

@@ -0,0 +1,178 @@
+import typing
+from deepdog.results import BayesrunOutput
+import logging
+import csv
+import tqdm
+
+_logger = logging.getLogger(__name__)
+
+
+def build_model_dict(
+	bayes_outputs: typing.Sequence[BayesrunOutput],
+) -> typing.Dict[
+	typing.Tuple, typing.Dict[typing.Tuple, typing.Dict["str", typing.Any]]
+]:
+	"""
+	Maybe someday do something smarter with the coalescing and stuff but don't want to so i won't
+	"""
+	# assume that everything is well formatted and the keys are the same across entire list and initialise list of keys.
+	# model dict will contain a model_key: {calculation_dict} where each calculation_dict represents a single calculation for that model,
+	# the uncoalesced version, keyed by the specific file keys
+	model_dict: typing.Dict[
+		typing.Tuple, typing.Dict[typing.Tuple, typing.Dict["str", typing.Any]]
+	] = {}
+
+	_logger.info("building model dict")
+	for out in tqdm.tqdm(bayes_outputs, desc="reading outputs", leave=False):
+		for model_result in out.results:
+			model_key = tuple(v for v in model_result.parsed_model_keys.values())
+			if model_key not in model_dict:
+				model_dict[model_key] = {}
+			calculation_dict = model_dict[model_key]
+			calculation_key = tuple(v for v in out.data.values())
+			if calculation_key not in calculation_dict:
+				calculation_dict[calculation_key] = {
+					"_model_key_dict": model_result.parsed_model_keys,
+					"_calculation_key_dict": out.data,
+					"success": model_result.success,
+					"count": model_result.count,
+				}
+			else:
+				raise ValueError(
+					f"Got {calculation_key} twice for model_key {model_key}"
+				)
+
+	return model_dict
+
+
+def write_uncoalesced_dict(
+	uncoalesced_output_filename: typing.Optional[str],
+	uncoalesced_model_dict: typing.Dict[
+		typing.Tuple, typing.Dict[typing.Tuple, typing.Dict["str", typing.Any]]
+	],
+):
+	if uncoalesced_output_filename is None or uncoalesced_output_filename == "":
+		_logger.warning("Not provided a uncoalesced filename, not going to try")
+		return
+
+	first_value = next(iter(next(iter(uncoalesced_model_dict.values())).values()))
+	model_field_names = set(first_value["_model_key_dict"].keys())
+	calculation_field_names = set(first_value["_calculation_key_dict"].keys())
+	if not (set(model_field_names).isdisjoint(calculation_field_names)):
+		_logger.info(f"Detected model field names {model_field_names}")
+		_logger.info(f"Detected calculation field names {calculation_field_names}")
+		_logger.warning(
+			f"model field names {model_field_names} and calculation {calculation_field_names} have an overlap, which is possibly a problem"
+		)
+	collected_fieldnames = list(model_field_names)
+	collected_fieldnames.extend(calculation_field_names)
+	collected_fieldnames.extend(["success", "count"])
+	_logger.info(f"Full uncoalesced fieldnames are {collected_fieldnames}")
+	with open(uncoalesced_output_filename, "w", newline="") as uncoalesced_output_file:
+		writer = csv.DictWriter(
+			uncoalesced_output_file, fieldnames=collected_fieldnames
+		)
+		writer.writeheader()
+
+		for model_dict in uncoalesced_model_dict.values():
+			for calculation in model_dict.values():
+				row = calculation["_model_key_dict"].copy()
+				row.update(calculation["_calculation_key_dict"].copy())
+				row.update(
+					{
+						"success": calculation["success"],
+						"count": calculation["count"],
+					}
+				)
+				writer.writerow(row)
+
+
+def coalesced_dict(
+	uncoalesced_model_dict: typing.Dict[
+		typing.Tuple, typing.Dict[typing.Tuple, typing.Dict["str", typing.Any]]
+	],
+	minimum_count: float = 0.1,
+):
+	"""
+	pass in uncoalesced dict
+	the minimum_count field is what we use to make sure our probs are never zero
+	"""
+	coalesced_dict = {}
+
+	# we are already iterating so for no reason because performance really doesn't matter let's count the keys ourselves
+	num_keys = 0
+
+	# first pass coalesce
+	for model_key, model_dict in uncoalesced_model_dict.items():
+		num_keys += 1
+		for calculation in model_dict.values():
+			if model_key not in coalesced_dict:
+				coalesced_dict[model_key] = {
+					"_model_key_dict": calculation["_model_key_dict"].copy(),
+					"calculations_coalesced": 0,
+					"count": 0,
+					"success": 0,
+				}
+			sub_dict = coalesced_dict[model_key]
+			sub_dict["calculations_coalesced"] += 1
+			sub_dict["count"] += calculation["count"]
+			sub_dict["success"] += calculation["success"]
+
+	# second pass do probability calculation
+
+	prior = 1 / num_keys
+	_logger.info(f"Got {num_keys} model keys, so our prior will be {prior}")
+
+	total_weight = 0
+	for coalesced_model_dict in coalesced_dict.values():
+		model_weight = (
+			max(minimum_count, coalesced_model_dict["success"])
+			/ coalesced_model_dict["count"]
+		) * prior
+		total_weight += model_weight
+
+	total_prob = 0
+	for coalesced_model_dict in coalesced_dict.values():
+		model_weight = (
+			max(minimum_count, coalesced_model_dict["success"])
+			/ coalesced_model_dict["count"]
+		)
+		prob = model_weight * prior / total_weight
+		coalesced_model_dict["prob"] = prob
+		total_prob += prob
+
+	_logger.debug(
+		f"Got a total probability of {total_prob}, which should be close to 1 up to float/rounding error"
+	)
+	return coalesced_dict
+
+
+def write_coalesced_dict(
+	coalesced_output_filename: typing.Optional[str],
+	coalesced_model_dict: typing.Dict[typing.Tuple, typing.Dict["str", typing.Any]],
+):
+	if coalesced_output_filename is None or coalesced_output_filename == "":
+		_logger.warning("Not provided a uncoalesced filename, not going to try")
+		return
+
+	first_value = next(iter(coalesced_model_dict.values()))
+	model_field_names = set(first_value["_model_key_dict"].keys())
+	_logger.info(f"Detected model field names {model_field_names}")
+
+	collected_fieldnames = list(model_field_names)
+	collected_fieldnames.extend(["calculations_coalesced", "success", "count", "prob"])
+	with open(coalesced_output_filename, "w", newline="") as coalesced_output_file:
+		writer = csv.DictWriter(coalesced_output_file, fieldnames=collected_fieldnames)
+		writer.writeheader()
+
+		for model_dict in coalesced_model_dict.values():
+			row = model_dict["_model_key_dict"].copy()
+			row.update(
+				{
+					"calculations_coalesced": model_dict["calculations_coalesced"],
+					"success": model_dict["success"],
+					"count": model_dict["count"],
+					"prob": model_dict["prob"],
+				}
+			)
+			writer.writerow(row)

deepdog/cli/probs/main.py

@@ -0,0 +1,99 @@
+import logging
+import argparse
+import json
+import deepdog.cli.probs.args
+import deepdog.cli.probs.dicts
+import deepdog.results
+import deepdog.indexify
+import pathlib
+import tqdm
+import tqdm.contrib.logging
+
+
+_logger = logging.getLogger(__name__)
+
+
+def set_up_logging(log_file: str):
+
+	log_pattern = "%(asctime)s | %(levelname)-7s | %(name)s:%(lineno)d | %(message)s"
+	if log_file is None:
+		handlers = [
+			logging.StreamHandler(),
+		]
+	else:
+		handlers = [logging.StreamHandler(), logging.FileHandler(log_file)]
+	logging.basicConfig(
+		level=logging.DEBUG,
+		format=log_pattern,
+		# it's okay to ignore this mypy error because who cares about logger handler types
+		handlers=handlers,  # type: ignore
+	)
+	logging.captureWarnings(True)
+
+
+def main(args: argparse.Namespace):
+	"""
+	Main function with passed in arguments and no additional logging setup in case we want to extract out later
+	"""
+
+	with tqdm.contrib.logging.logging_redirect_tqdm():
+		_logger.info(f"args: {args}")
+
+		try:
+			if args.coalesced_keys:
+				raise NotImplementedError(
+					"Currently not supporting coalesced keys, but maybe in future"
+				)
+		except AttributeError:
+			# we don't care if this is missing because we don't actually want it to be there
+			pass
+
+		indexifier = None
+		if args.indexify_json:
+			with open(args.indexify_json, "r") as indexify_json_file:
+				indexify_spec = json.load(indexify_json_file)
+				indexify_data = indexify_spec["indexes"]
+				if "seed_spec" in indexify_spec:
+					seed_spec = indexify_spec["seed_spec"]
+					indexify_data[seed_spec["field_name"]] = list(
+						range(seed_spec["num_seeds"])
+					)
+				# _logger.debug(f"Indexifier data looks like {indexify_data}")
+				indexifier = deepdog.indexify.Indexifier(indexify_data)
+
+		bayes_dir = pathlib.Path(args.bayesrun_directory)
+		out_files = [f for f in bayes_dir.iterdir() if f.name.endswith("bayesrun.csv")]
+		_logger.info(
+			f"Reading {len(out_files)} bayesrun.csv files in directory {args.bayesrun_directory}"
+		)
+		# _logger.info(out_files)
+		parsed_output_files = [
+			deepdog.results.read_output_file(f, indexifier)
+			for f in tqdm.tqdm(out_files, desc="reading files", leave=False)
+		]
+
+		_logger.info("building uncoalesced dict")
+		uncoalesced_dict = deepdog.cli.probs.dicts.build_model_dict(parsed_output_files)
+
+		if "uncoalesced_outfile" in args and args.uncoalesced_outfile:
+			deepdog.cli.probs.dicts.write_uncoalesced_dict(
+				args.uncoalesced_outfile, uncoalesced_dict
+			)
+		else:
+			_logger.info("Skipping writing uncoalesced")
+
+		_logger.info("building coalesced dict")
+		coalesced = deepdog.cli.probs.dicts.coalesced_dict(uncoalesced_dict)
+
+		if "coalesced_outfile" in args and args.coalesced_outfile:
+			deepdog.cli.probs.dicts.write_coalesced_dict(
+				args.coalesced_outfile, coalesced
+			)
+		else:
+			_logger.info("Skipping writing coalesced")
+
+
+def wrapped_main():
+	args = deepdog.cli.probs.args.parse_args()
+	set_up_logging(args.log_file)
+	main(args)

deepdog/direct_monte_carlo/direct_mc.py

@@ -1,22 +1,28 @@
+import csv
 import pdme.model
 import pdme.measurement
 import pdme.measurement.input_types
 import pdme.subspace_simulation
-from typing import Tuple, Dict, NewType, Any
+import datetime
+from typing import Tuple, Dict, NewType, Any, Sequence
 from dataclasses import dataclass
 import logging
 import numpy
 import numpy.random
 import pdme.util.fast_v_calc
+import multiprocessing
 
 _logger = logging.getLogger(__name__)
 
+ANTI_ZERO_SUCCESS_THRES = 0.1
+
 
 @dataclass
 class DirectMonteCarloResult:
 	successes: int
 	monte_carlo_count: int
 	likelihood: float
+	model_name: str
 
 
 @dataclass
@@ -28,6 +34,10 @@ class DirectMonteCarloConfig:
 	monte_carlo_seed: int = 1234
 	write_successes_to_file: bool = False
 	tag: str = ""
+	cap_core_count: int = 0  # 0 means cap at num cores - 1
+	chunk_size: int = 50
+	write_bayesrun_file = True
+	# chunk size of some kind
 
 
 # Aliasing dict as a generic data container
@@ -51,8 +61,8 @@ class DirectMonteCarloRun:
 
 	Parameters
 	----------
-	model_name_pair : Sequence[Tuple(str, pdme.model.DipoleModel)]
+	model_name_pairs : Sequence[Tuple(str, pdme.model.DipoleModel)]
-	The model to evaluate, with name.
+	The models to evaluate, with names
 
 	measurements: Sequence[pdme.measurement.DotRangeMeasurement]
 	The measurements as dot ranges to use as the bounds for the Monte Carlo calculation.
@@ -78,11 +88,11 @@ class DirectMonteCarloRun:
 
 	def __init__(
 		self,
-		model_name_pair: Tuple[str, pdme.model.DipoleModel],
+		model_name_pairs: Sequence[Tuple[str, pdme.model.DipoleModel]],
 		filter: DirectMonteCarloFilter,
 		config: DirectMonteCarloConfig,
 	):
-		self.model_name, self.model = model_name_pair
+		self.model_name_pairs = model_name_pairs
 
 		# self.measurements = measurements
 		# self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
@@ -100,10 +110,16 @@ class DirectMonteCarloRun:
 		# 	self.measurements
 		# )
 
-	def _single_run(self, seed) -> numpy.ndarray:
+	def _single_run(
+		self, model_name_pair: Tuple[str, pdme.model.DipoleModel], seed
+	) -> numpy.ndarray:
 		rng = numpy.random.default_rng(seed)
 
-		sample_dipoles = self.model.get_monte_carlo_dipole_inputs(
+		_, model = model_name_pair
+		# don't log here it's madness
+		# _logger.info(f"Executing for model {model_name}")
+
+		sample_dipoles = model.get_monte_carlo_dipole_inputs(
 			self.config.monte_carlo_count_per_cycle, -1, rng
 		)
 
@@ -123,52 +139,188 @@ class DirectMonteCarloRun:
 		# 	]
 		# return current_sample
 
-	def execute(self) -> DirectMonteCarloResult:
+	def _wrapped_single_run(self, args: Tuple):
-		step_count = 0
+		"""
-		total_success = 0
+		single run wrapped up for multiprocessing call.
-		total_count = 0
+
+		takes in a tuple of arguments corresponding to
+		(model_name_pair, seed)
+		"""
+		# here's where we do our work
+
+		model_name_pair, seed = args
+		cycle_success_configs = self._single_run(model_name_pair, seed)
+		cycle_success_count = len(cycle_success_configs)
+
+		return cycle_success_count
+
+	def execute_no_multiprocessing(self) -> Sequence[DirectMonteCarloResult]:
 
 		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(
+		# core count etc. logic here
-			successes=total_success,
+
-			monte_carlo_count=total_count,
+		results = []
-			likelihood=total_success / total_count,
+		for model_name_pair in self.model_name_pairs:
+			step_count = 0
+			total_success = 0
+			total_count = 0
+
+			_logger.info(f"Working on model {model_name_pair[0]}")
+			# This is probably where multiprocessing logic should go
+			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)
+				):
+					# here's where we do our work
+					cycle_success_configs = self._single_run(model_name_pair, 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
+
+			results.append(
+				DirectMonteCarloResult(
+					successes=total_success,
+					monte_carlo_count=total_count,
+					likelihood=total_success / total_count,
+					model_name=model_name_pair[0],
+				)
+			)
+		return results
+
+	def execute(self) -> Sequence[DirectMonteCarloResult]:
+
+		# set starting execution timestamp
+		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+
+		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)
+
+		# core count etc. logic here
+		core_count = multiprocessing.cpu_count() - 1 or 1
+		if (self.config.cap_core_count >= 1) and (
+			self.config.cap_core_count < core_count
+		):
+			core_count = self.config.cap_core_count
+		_logger.info(f"Using {core_count} cores")
+
+		results = []
+		with multiprocessing.Pool(core_count) as pool:
+
+			for model_name_pair in self.model_name_pairs:
+				_logger.info(f"Working on model {model_name_pair[0]}")
+				# This is probably where multiprocessing logic should go
+
+				step_count = 0
+				total_success = 0
+				total_count = 0
+
+				while (step_count < self.config.max_monte_carlo_cycles_steps) and (
+					total_success < self.config.target_success
+				):
+
+					step_count += 1
+
+					_logger.debug(f"Executing step {step_count}")
+
+					seeds = seed_sequence.spawn(self.config.monte_carlo_cycles)
+
+					pool_results = sum(
+						pool.imap_unordered(
+							self._wrapped_single_run,
+							[(model_name_pair, seed) for seed in seeds],
+							self.config.chunk_size,
+						)
+					)
+					_logger.debug(f"Pool results: {pool_results}")
+
+					total_success += pool_results
+					total_count += count_per_step
+					_logger.debug(
+						f"At end of step {step_count} have {total_success} successes"
+					)
+
+				results.append(
+					DirectMonteCarloResult(
+						successes=total_success,
+						monte_carlo_count=total_count,
+						likelihood=total_success / total_count,
+						model_name=model_name_pair[0],
+					)
+				)
+
+		if self.config.write_bayesrun_file:
+
+			filename = (
+				f"{timestamp}-{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 = {}
+
+			num_models = len(self.model_name_pairs)
+			success_weight = sum(
+				[
+					(
+						max(ANTI_ZERO_SUCCESS_THRES, res.successes)
+						/ res.monte_carlo_count
+					)
+					/ num_models
+					for res in results
+				]
+			)
+
+			for res in results:
+				row.update(
+					{
+						f"{res.model_name}_success": res.successes,
+						f"{res.model_name}_count": res.monte_carlo_count,
+						f"{res.model_name}_prob": (
+							max(ANTI_ZERO_SUCCESS_THRES, res.successes)
+							/ res.monte_carlo_count
+						)
+						/ (num_models * success_weight),
+					}
+				)
+			_logger.info(f"Writing row {row}")
+			fieldnames = list(row.keys())
+
+			with open(filename, "w", newline="") as outfile:
+				writer = csv.DictWriter(outfile, fieldnames=fieldnames, dialect="unix")
+				writer.writeheader()
+				writer.writerow(row)
+
+		return results

deepdog/direct_monte_carlo/dmc_filters.py

@@ -39,6 +39,135 @@ class SingleDotPotentialFilter(DirectMonteCarloFilter):
 		return current_sample
 
 
+class SingleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
+	def __init__(self, measurements: Sequence[pdme.measurement.DotRangeMeasurement]):
+		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.lows,
+			self.highs,
+		) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
+			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(
+				numpy.array([di]), current_sample
+			)
+			# _logger.info(vals)
+
+			current_sample = current_sample[
+				numpy.all((vals > low) & (vals < high), axis=1)
+			]
+		# _logger.info(f"leaving with {len(current_sample)}")
+		return current_sample
+
+
 class DoubleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
 	def __init__(
 		self,
@@ -59,59 +188,6 @@ class DoubleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
 			self.pair_phase_measurements
 		)
 
-	def fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
-		self, dot_pair_inputs: numpy.ndarray, dipoleses: numpy.ndarray
-	) -> numpy.ndarray:
-		"""
-		No error correction here baby.
-		"""
-		ps = dipoleses[:, :, 0:3]
-		ss = dipoleses[:, :, 3:6]
-		ws = dipoleses[:, :, 6]
-
-		r1s = dot_pair_inputs[:, 0, 0:3]
-		r2s = dot_pair_inputs[:, 1, 0:3]
-		f1s = dot_pair_inputs[:, 0, 3]
-		# Don't actually need this
-		# f2s = dot_pair_inputs[:, 1, 3]
-
-		diffses1 = r1s[:, None] - ss[:, None, :]
-		diffses2 = r2s[:, None] - ss[:, None, :]
-
-		norms1 = numpy.linalg.norm(diffses1, axis=3)
-		norms2 = numpy.linalg.norm(diffses2, axis=3)
-
-		alphses1 = (
-			(
-				3
-				* numpy.transpose(
-					numpy.transpose(
-						numpy.einsum("abcd,acd->abc", diffses1, ps) / (norms1**2)
-					)
-					* numpy.transpose(diffses1)
-				)[:, :, :, 0]
-			)
-			- ps[:, :, 0, numpy.newaxis]
-		) / (norms1**3)
-		alphses2 = (
-			(
-				3
-				* numpy.transpose(
-					numpy.transpose(
-						numpy.einsum("abcd,acd->abc", diffses2, ps) / (norms2**2)
-					)
-					* numpy.transpose(diffses2)
-				)[:, :, :, 0]
-			)
-			- ps[:, :, 0, numpy.newaxis]
-		) / (norms2**3)
-
-		bses = (1 / numpy.pi) * (
-			ws[:, None, :] / (f1s[:, None] ** 2 + ws[:, None, :] ** 2)
-		)
-
-		return numpy.einsum("...j->...", alphses1 * alphses2 * bses)
-
 	def filter_samples(self, samples: ndarray) -> ndarray:
 		current_sample = samples
 
@@ -121,16 +197,8 @@ class DoubleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
 			if len(current_sample) < 1:
 				break
 
-			###
-			# This should be  abstracted out, but we're going to dump it here for time pressure's sake
-			###
-			# vals = pdme.util.fast_nonlocal_spectrum.signarg(
-			# 	pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal_dipoleses(
-			# 		numpy.array([pi]), current_sample
-			# 	)
-			#
 			vals = pdme.util.fast_nonlocal_spectrum.signarg(
-				self.fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
+				pdme.util.fast_nonlocal_spectrum.fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
 					numpy.array([pi]), current_sample
 				)
 			)

deepdog/indexify/__init__.py

@@ -0,0 +1,58 @@
+"""
+Probably should just include a way to handle the indexify function I reuse so much.
+
+All about breaking an integer into a tuple of values from lists, which is useful because of how we do CHTC runs.
+"""
+import itertools
+import typing
+import logging
+import math
+
+_logger = logging.getLogger(__name__)
+
+
+# from https://stackoverflow.com/questions/5228158/cartesian-product-of-a-dictionary-of-lists
+def _dict_product(dicts):
+	"""
+	>>> list(dict_product(dict(number=[1,2], character='ab')))
+	[{'character': 'a', 'number': 1},
+	{'character': 'a', 'number': 2},
+	{'character': 'b', 'number': 1},
+	{'character': 'b', 'number': 2}]
+	"""
+	return list(dict(zip(dicts.keys(), x)) for x in itertools.product(*dicts.values()))
+
+
+class Indexifier:
+	"""
+	The order of keys is very important, but collections.OrderedDict is no longer needed in python 3.7.
+	I think it's okay to rely on that.
+	"""
+
+	def __init__(self, list_dict: typing.Dict[str, typing.Sequence]):
+		self.dict = list_dict
+
+	def indexify(self, n: int) -> typing.Dict[str, typing.Any]:
+		product_dict = _dict_product(self.dict)
+		return product_dict[n]
+
+	def _indexify_indices(self, n: int) -> typing.Sequence[int]:
+		"""
+		legacy indexify from old scripts, copypast.
+		could be used like
+		>>> ret = {}
+		>>> for k, i in zip(self.dict.keys(), self._indexify_indices):
+		>>>		ret[k] = self.dict[k][i]
+		>>> return ret
+		"""
+		weights = [len(v) for v in self.dict.values()]
+		N = math.prod(weights)
+		curr_n = n
+		curr_N = N
+		out = []
+		for w in weights[:-1]:
+			# print(f"current: {curr_N}, {curr_n}, {curr_n // w}")
+			curr_N = curr_N // w  # should be int division anyway
+			out.append(curr_n // curr_N)
+			curr_n = curr_n % curr_N
+		return out

deepdog/real_spectrum_run.py

@@ -112,59 +112,6 @@ def get_a_result_fast_filter_tarucha_spin_qubit_pair_phase_only(input) -> int:
 		seed,
 	) = input
 
-	def fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
-		dot_pair_inputs: numpy.ndarray, dipoleses: numpy.ndarray
-	) -> numpy.ndarray:
-		"""
-		No error correction here baby.
-		"""
-		ps = dipoleses[:, :, 0:3]
-		ss = dipoleses[:, :, 3:6]
-		ws = dipoleses[:, :, 6]
-
-		r1s = dot_pair_inputs[:, 0, 0:3]
-		r2s = dot_pair_inputs[:, 1, 0:3]
-		f1s = dot_pair_inputs[:, 0, 3]
-		# don't actually need, because we're assuming they're the same frequencies across the pair
-		# f2s = dot_pair_inputs[:, 1, 3]
-
-		diffses1 = r1s[:, None] - ss[:, None, :]
-		diffses2 = r2s[:, None] - ss[:, None, :]
-
-		norms1 = numpy.linalg.norm(diffses1, axis=3)
-		norms2 = numpy.linalg.norm(diffses2, axis=3)
-
-		alphses1 = (
-			(
-				3
-				* numpy.transpose(
-					numpy.transpose(
-						numpy.einsum("abcd,acd->abc", diffses1, ps) / (norms1**2)
-					)
-					* numpy.transpose(diffses1)
-				)[:, :, :, 0]
-			)
-			- ps[:, :, 0, numpy.newaxis]
-		) / (norms1**3)
-		alphses2 = (
-			(
-				3
-				* numpy.transpose(
-					numpy.transpose(
-						numpy.einsum("abcd,acd->abc", diffses2, ps) / (norms2**2)
-					)
-					* numpy.transpose(diffses2)
-				)[:, :, :, 0]
-			)
-			- ps[:, :, 0, numpy.newaxis]
-		) / (norms2**3)
-
-		bses = (1 / numpy.pi) * (
-			ws[:, None, :] / (f1s[:, None] ** 2 + ws[:, None, :] ** 2)
-		)
-
-		return numpy.einsum("...j->...", alphses1 * alphses2 * bses)
-
 	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(
@@ -186,7 +133,7 @@ def get_a_result_fast_filter_tarucha_spin_qubit_pair_phase_only(input) -> int:
 		# 	)
 		#
 		vals = pdme.util.fast_nonlocal_spectrum.signarg(
-			fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
+			pdme.util.fast_nonlocal_spectrum.fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
 				numpy.array([pi]), current_sample
 			)
 		)

deepdog/results/__init__.py

@@ -0,0 +1,170 @@
+import dataclasses
+import re
+import typing
+import logging
+import deepdog.indexify
+import pathlib
+import csv
+
+_logger = logging.getLogger(__name__)
+
+FILENAME_REGEX = 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*)"
+]
+
+FILE_SLUG_REGEXES = [
+	r"mock_tarucha-(?P<job_index>\d+)",
+	r"(?:(?P<mock>mock)_)?tarucha(?:_(?P<tarucha_run_id>\d+))?-(?P<job_index>\d+)",
+]
+
+
+@dataclasses.dataclass
+class BayesrunOutputFilename:
+	timestamp: str
+	filename_slug: str
+	path: pathlib.Path
+
+
+@dataclasses.dataclass
+class BayesrunColumnParsed:
+	"""
+	class for parsing a bayesrun while pulling certain special fields out
+	"""
+
+	def __init__(self, groupdict: typing.Dict[str, str]):
+		self.column_field = groupdict["field_name"]
+		self.model_field_dict = {
+			k: v for k, v in groupdict.items() if k != "field_name"
+		}
+
+	def __str__(self):
+		return f"BayesrunColumnParsed[{self.column_field}: {self.model_field_dict}]"
+
+
+@dataclasses.dataclass
+class BayesrunModelResult:
+	parsed_model_keys: typing.Dict[str, str]
+	success: int
+	count: int
+
+
+@dataclasses.dataclass
+class BayesrunOutput:
+	filename: BayesrunOutputFilename
+	data: typing.Dict["str", typing.Any]
+	results: typing.Sequence[BayesrunModelResult]
+
+
+def _batch_iterable_into_chunks(iterable, n=1):
+	"""
+	utility for batching bayesrun files where columns appear in threes
+	"""
+	for ndx in range(0, len(iterable), n):
+		yield iterable[ndx : min(ndx + n, len(iterable))]
+
+
+def _parse_bayesrun_column(
+	column: str,
+) -> typing.Optional[BayesrunColumnParsed]:
+	"""
+	Tries one by one all of a predefined list of regexes that I might have used in the past.
+	Returns the groupdict for the first match, or None if no match found.
+	"""
+	for pattern in MODEL_REGEXES:
+		match = re.match(pattern, column)
+		if match:
+			return BayesrunColumnParsed(match.groupdict())
+	else:
+		return None
+
+
+def _parse_bayesrun_row(
+	row: typing.Dict[str, str],
+) -> typing.Sequence[BayesrunModelResult]:
+
+	results = []
+	batched_keys = _batch_iterable_into_chunks(list(row.keys()), 3)
+	for model_keys in batched_keys:
+		parsed = [_parse_bayesrun_column(column) for column in model_keys]
+		values = [row[column] for column in model_keys]
+		if parsed[0] is None:
+			raise ValueError(f"no viable success row found for keys {model_keys}")
+		if parsed[1] is None:
+			raise ValueError(f"no viable count row found for keys {model_keys}")
+		if parsed[0].column_field != "success":
+			raise ValueError(f"The column {model_keys[0]} is not a success field")
+		if parsed[1].column_field != "count":
+			raise ValueError(f"The column {model_keys[1]} is not a count field")
+		parsed_keys = parsed[0].model_field_dict
+		success = int(values[0])
+		count = int(values[1])
+		results.append(
+			BayesrunModelResult(
+				parsed_model_keys=parsed_keys,
+				success=success,
+				count=count,
+			)
+		)
+	return results
+
+
+def _parse_output_filename(file: pathlib.Path) -> BayesrunOutputFilename:
+	filename = file.name
+	match = re.match(FILENAME_REGEX, filename)
+	if not match:
+		raise ValueError(f"{filename} was not a valid bayesrun output")
+	groups = match.groupdict()
+	return BayesrunOutputFilename(
+		timestamp=groups["timestamp"], filename_slug=groups["filename_slug"], path=file
+	)
+
+
+def _parse_file_slug(slug: str) -> typing.Optional[typing.Dict[str, str]]:
+	for pattern in FILE_SLUG_REGEXES:
+		match = re.match(pattern, slug)
+		if match:
+			return match.groupdict()
+	else:
+		return None
+
+
+def read_output_file(
+	file: pathlib.Path, indexifier: typing.Optional[deepdog.indexify.Indexifier]
+) -> BayesrunOutput:
+
+	parsed_filename = tag = _parse_output_filename(file)
+	out = BayesrunOutput(filename=parsed_filename, data={}, results=[])
+
+	out.data.update(dataclasses.asdict(tag))
+	parsed_tag = _parse_file_slug(parsed_filename.filename_slug)
+	if parsed_tag is None:
+		_logger.warning(
+			f"Could not parse {tag} against any matching regexes. Going to skip tag parsing"
+		)
+	else:
+		out.data.update(parsed_tag)
+		if indexifier is not None:
+			try:
+				job_index = parsed_tag["job_index"]
+				indexified = indexifier.indexify(int(job_index))
+				out.data.update(indexified)
+			except KeyError:
+				# This isn't really that important of an error, apart from the warning
+				_logger.warning(
+					f"Parsed tag to {parsed_tag}, and attempted to indexify but no job_index key was found. skipping and moving on"
+				)
+
+	with file.open() as input_file:
+		reader = csv.DictReader(input_file)
+		rows = [r for r in reader]
+		if len(rows) == 1:
+			row = rows[0]
+		else:
+			raise ValueError(f"Confused about having multiple rows in {file.name}")
+	results = _parse_bayesrun_row(row)
+
+	out.results = results
+
+	return out

flake.nix

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

justfile

@@ -46,9 +46,15 @@ fmt:
 	find deepdog -type f -name "*.py" -exec sed -i -e 's/    /\t/g' {} \;
 	find tests -type f -name "*.py" -exec sed -i -e 's/    /\t/g' {} \;
 
-# release the app, checking that our working tree is clean and ready for release
+# release the app, checking that our working tree is clean and ready for release, optionally takes target version
-release:
+release version="":
-	./scripts/release.sh
+	#!/usr/bin/env bash
+	set -euxo pipefail
+	if [[ -n "{{version}}" ]]; then
+		./scripts/release.sh {{version}}
+	else
+		./scripts/release.sh
+	fi
 
 htmlcov:
 	poetry run pytest --cov-report=html

poetry.lock

@@ -1,5 +1,20 @@
 # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
+[[package]]
+name = "backports-tarfile"
+version = "1.1.1"
+description = "Backport of CPython tarfile module"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "backports.tarfile-1.1.1-py3-none-any.whl", hash = "sha256:73e0179647803d3726d82e76089d01d8549ceca9bace469953fcb4d97cf2d417"},
+    {file = "backports_tarfile-1.1.1.tar.gz", hash = "sha256:9c2ef9696cb73374f7164e17fc761389393ca76777036f5aad42e8b93fcd8009"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["jaraco.test", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)"]
+
 [[package]]
 name = "black"
 version = "22.12.0"
@@ -250,63 +265,63 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.4.3"
+version = "7.5.0"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "coverage-7.4.3.tar.gz", hash = "sha256:276f6077a5c61447a48d133ed13e759c09e62aff0dc84274a68dc18660104d52"},
+    {file = "coverage-7.5.0.tar.gz", hash = "sha256:cf62d17310f34084c59c01e027259076479128d11e4661bb6c9acb38c5e19bb8"},
 ]
 
 [package.dependencies]
@@ -393,13 +408,13 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.0"
+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
+    {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
-    {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
+    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
 ]
 
 [package.extras]
@@ -437,60 +452,61 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.42"
+version = "3.1.43"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "GitPython-3.1.42-py3-none-any.whl", hash = "sha256:1bf9cd7c9e7255f77778ea54359e54ac22a72a5b51288c457c881057b7bb9ecd"},
+    {file = "GitPython-3.1.43-py3-none-any.whl", hash = "sha256:eec7ec56b92aad751f9912a73404bc02ba212a23adb2c7098ee668417051a1ff"},
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+    {file = "GitPython-3.1.43.tar.gz", hash = "sha256:35f314a9f878467f5453cc1fee295c3e18e52f1b99f10f6cf5b1682e968a9e7c"},
 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
 [package.extras]
-test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar"]
+doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"]
+test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
 
 [[package]]
 name = "idna"
-version = "3.6"
+version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
-    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
 [[package]]
 name = "importlib-metadata"
-version = "7.0.1"
+version = "7.1.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-7.0.1-py3-none-any.whl", hash = "sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e"},
+    {file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
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+    {file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.2"
+version = "6.4.0"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_resources-6.1.2-py3-none-any.whl", hash = "sha256:9a0a862501dc38b68adebc82970140c9e4209fc99601782925178f8386339938"},
+    {file = "importlib_resources-6.4.0-py3-none-any.whl", hash = "sha256:50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c"},
-    {file = "importlib_resources-6.1.2.tar.gz", hash = "sha256:308abf8474e2dba5f867d279237cd4076482c3de7104a40b41426370e891549b"},
+    {file = "importlib_resources-6.4.0.tar.gz", hash = "sha256:cdb2b453b8046ca4e3798eb1d84f3cce1446a0e8e7b5ef4efb600f19fc398145"},
 ]
 
 [package.dependencies]
@@ -498,7 +514,7 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
+testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
 [[package]]
 name = "iniconfig"
@@ -523,14 +539,50 @@ files = [
 ]
 
 [[package]]
-name = "jaraco.classes"
+name = "jaraco-classes"
-version = "3.3.1"
+version = "3.4.0"
 description = "Utility functions for Python class constructs"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jaraco.classes-3.3.1-py3-none-any.whl", hash = "sha256:86b534de565381f6b3c1c830d13f931d7be1a75f0081c57dff615578676e2206"},
+    {file = "jaraco.classes-3.4.0-py3-none-any.whl", hash = "sha256:f662826b6bed8cace05e7ff873ce0f9283b5c924470fe664fff1c2f00f581790"},
-    {file = "jaraco.classes-3.3.1.tar.gz", hash = "sha256:cb28a5ebda8bc47d8c8015307d93163464f9f2b91ab4006e09ff0ce07e8bfb30"},
+    {file = "jaraco.classes-3.4.0.tar.gz", hash = "sha256:47a024b51d0239c0dd8c8540c6c7f484be3b8fcf0b2d85c13825780d3b3f3acd"},
+]
+
+[package.dependencies]
+more-itertools = "*"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
+[[package]]
+name = "jaraco-context"
+version = "5.3.0"
+description = "Useful decorators and context managers"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jaraco.context-5.3.0-py3-none-any.whl", hash = "sha256:3e16388f7da43d384a1a7cd3452e72e14732ac9fe459678773a3608a812bf266"},
+    {file = "jaraco.context-5.3.0.tar.gz", hash = "sha256:c2f67165ce1f9be20f32f650f25d8edfc1646a8aeee48ae06fb35f90763576d2"},
+]
+
+[package.dependencies]
+"backports.tarfile" = {version = "*", markers = "python_version < \"3.12\""}
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["portend", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
+[[package]]
+name = "jaraco-functools"
+version = "4.0.1"
+description = "Functools like those found in stdlib"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jaraco.functools-4.0.1-py3-none-any.whl", hash = "sha256:3b24ccb921d6b593bdceb56ce14799204f473976e2a9d4b15b04d0f2c2326664"},
+    {file = "jaraco_functools-4.0.1.tar.gz", hash = "sha256:d33fa765374c0611b52f8b3a795f8900869aa88c84769d4d1746cd68fb28c3e8"},
 ]
 
 [package.dependencies]
@@ -538,7 +590,7 @@ more-itertools = "*"
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+testing = ["jaraco.classes", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "jeepney"
@@ -557,27 +609,29 @@ trio = ["async_generator", "trio"]
 
 [[package]]
 name = "keyring"
-version = "24.3.1"
+version = "25.2.0"
 description = "Store and access your passwords safely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "keyring-24.3.1-py3-none-any.whl", hash = "sha256:df38a4d7419a6a60fea5cef1e45a948a3e8430dd12ad88b0f423c5c143906218"},
+    {file = "keyring-25.2.0-py3-none-any.whl", hash = "sha256:19f17d40335444aab84b19a0d16a77ec0758a9c384e3446ae2ed8bd6d53b67a5"},
-    {file = "keyring-24.3.1.tar.gz", hash = "sha256:c3327b6ffafc0e8befbdb597cacdb4928ffe5c1212f7645f186e6d9957a898db"},
+    {file = "keyring-25.2.0.tar.gz", hash = "sha256:7045f367268ce42dba44745050164b431e46f6e92f99ef2937dfadaef368d8cf"},
 ]
 
 [package.dependencies]
 importlib-metadata = {version = ">=4.11.4", markers = "python_version < \"3.12\""}
 importlib-resources = {version = "*", markers = "python_version < \"3.9\""}
 "jaraco.classes" = "*"
+"jaraco.context" = "*"
+"jaraco.functools" = "*"
 jeepney = {version = ">=0.4.2", markers = "sys_platform == \"linux\""}
 pywin32-ctypes = {version = ">=0.2.0", markers = "sys_platform == \"win32\""}
 SecretStorage = {version = ">=3.2", markers = "sys_platform == \"linux\""}
 
 [package.extras]
 completion = ["shtab (>=1.1.0)"]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+testing = ["pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "mccabe"
@@ -656,27 +710,27 @@ files = [
 
 [[package]]
 name = "nh3"
-version = "0.2.15"
+version = "0.2.17"
 description = "Python bindings to the ammonia HTML sanitization library."
 optional = false
 python-versions = "*"
 files = [
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+    {file = "nh3-0.2.17-cp37-abi3-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:551672fd71d06cd828e282abdb810d1be24e1abb7ae2543a8fa36a71c1006fe9"},
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+    {file = "nh3-0.2.17-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a3f55fabe29164ba6026b5ad5c3151c314d136fd67415a17660b4aaddacf1b10"},
-    {file = "nh3-0.2.15-cp37-abi3-win32.whl", hash = "sha256:427fecbb1031db085eaac9931362adf4a796428ef0163070c484b5a768e71601"},
+    {file = "nh3-0.2.17-cp37-abi3-win32.whl", hash = "sha256:1a814dd7bba1cb0aba5bcb9bebcc88fd801b63e21e2450ae6c52d3b3336bc911"},
-    {file = "nh3-0.2.15-cp37-abi3-win_amd64.whl", hash = "sha256:bc2d086fb540d0fa52ce35afaded4ea526b8fc4d3339f783db55c95de40ef02e"},
+    {file = "nh3-0.2.17-cp37-abi3-win_amd64.whl", hash = "sha256:1aa52a7def528297f256de0844e8dd680ee279e79583c76d6fa73a978186ddfb"},
-    {file = "nh3-0.2.15.tar.gz", hash = "sha256:d1e30ff2d8d58fb2a14961f7aac1bbb1c51f9bdd7da727be35c63826060b0bf3"},
+    {file = "nh3-0.2.17.tar.gz", hash = "sha256:40d0741a19c3d645e54efba71cb0d8c475b59135c1e3c580f879ad5514cbf028"},
 ]
 
 [[package]]
@@ -710,13 +764,13 @@ files = [
 
 [[package]]
 name = "packaging"
-version = "23.2"
+version = "24.0"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
+    {file = "packaging-24.0-py3-none-any.whl", hash = "sha256:2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5"},
-    {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
+    {file = "packaging-24.0.tar.gz", hash = "sha256:eb82c5e3e56209074766e6885bb04b8c38a0c015d0a30036ebe7ece34c9989e9"},
 ]
 
 [[package]]
@@ -732,13 +786,13 @@ files = [
 
 [[package]]
 name = "pdme"
-version = "0.9.3"
+version = "1.0.0"
 description = "Python dipole model evaluator"
 optional = false
-python-versions = ">=3.8.1,<3.10"
+python-versions = "<3.10,>=3.8.1"
 files = [
-    {file = "pdme-0.9.3-py3-none-any.whl", hash = "sha256:0a11caa8ce18829695fa0457bbfc4d15210945fcb58be05ffb96eda8fdd7e9c8"},
+    {file = "pdme-1.0.0-py3-none-any.whl", hash = "sha256:8fb8d1bf3d88f73118da5731332ae00c721b98daf53b225069e422af1a1a67f2"},
-    {file = "pdme-0.9.3.tar.gz", hash = "sha256:0de948e301780e79bbe78887be8ac4165899cbca32fa845a1ace8c08e72e300b"},
+    {file = "pdme-1.0.0.tar.gz", hash = "sha256:02cabf2e6fc2ddaf0871d0b3afcf265bca16520ee7bc1c74672be62f7a8390bd"},
 ]
 
 [package.dependencies]
@@ -747,42 +801,43 @@ scipy = ">=1.10,<1.11"
 
 [[package]]
 name = "pkginfo"
-version = "1.9.6"
+version = "1.10.0"
 description = "Query metadata from sdists / bdists / installed packages."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "pkginfo-1.9.6-py3-none-any.whl", hash = "sha256:4b7a555a6d5a22169fcc9cf7bfd78d296b0361adad412a346c1226849af5e546"},
+    {file = "pkginfo-1.10.0-py3-none-any.whl", hash = "sha256:889a6da2ed7ffc58ab5b900d888ddce90bce912f2d2de1dc1c26f4cb9fe65097"},
-    {file = "pkginfo-1.9.6.tar.gz", hash = "sha256:8fd5896e8718a4372f0ea9cc9d96f6417c9b986e23a4d116dda26b62cc29d046"},
+    {file = "pkginfo-1.10.0.tar.gz", hash = "sha256:5df73835398d10db79f8eecd5cd86b1f6d29317589ea70796994d49399af6297"},
 ]
 
 [package.extras]
-testing = ["pytest", "pytest-cov"]
+testing = ["pytest", "pytest-cov", "wheel"]
 
 [[package]]
 name = "platformdirs"
-version = "4.2.0"
+version = "4.2.1"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "platformdirs-4.2.0-py3-none-any.whl", hash = "sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068"},
+    {file = "platformdirs-4.2.1-py3-none-any.whl", hash = "sha256:17d5a1161b3fd67b390023cb2d3b026bbd40abde6fdb052dfbd3a29c3ba22ee1"},
-    {file = "platformdirs-4.2.0.tar.gz", hash = "sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768"},
+    {file = "platformdirs-4.2.1.tar.gz", hash = "sha256:031cd18d4ec63ec53e82dceaac0417d218a6863f7745dfcc9efe7793b7039bdf"},
 ]
 
 [package.extras]
 docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
 test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+type = ["mypy (>=1.8)"]
 
 [[package]]
 name = "pluggy"
-version = "1.4.0"
+version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pluggy-1.4.0-py3-none-any.whl", hash = "sha256:7db9f7b503d67d1c5b95f59773ebb58a8c1c288129a88665838012cfb07b8981"},
+    {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
-    {file = "pluggy-1.4.0.tar.gz", hash = "sha256:8c85c2876142a764e5b7548e7d9a0e0ddb46f5185161049a79b7e974454223be"},
+    {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"},
 ]
 
 [package.extras]
@@ -802,13 +857,13 @@ files = [
 
 [[package]]
 name = "pycparser"
-version = "2.21"
+version = "2.22"
 description = "C parser in Python"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.8"
 files = [
-    {file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
+    {file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"},
-    {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
+    {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
 ]
 
 [[package]]
@@ -839,13 +894,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pytest"
-version = "8.0.2"
+version = "8.2.0"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.0.2-py3-none-any.whl", hash = "sha256:edfaaef32ce5172d5466b5127b42e0d6d35ebbe4453f0e3505d96afd93f6b096"},
+    {file = "pytest-8.2.0-py3-none-any.whl", hash = "sha256:1733f0620f6cda4095bbf0d9ff8022486e91892245bb9e7d5542c018f612f233"},
-    {file = "pytest-8.0.2.tar.gz", hash = "sha256:d4051d623a2e0b7e51960ba963193b09ce6daeb9759a451844a21e4ddedfc1bd"},
+    {file = "pytest-8.2.0.tar.gz", hash = "sha256:d507d4482197eac0ba2bae2e9babf0672eb333017bcedaa5fb1a3d42c1174b3f"},
 ]
 
 [package.dependencies]
@@ -853,11 +908,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.3.0,<2.0"
+pluggy = ">=1.5,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"
@@ -1162,13 +1217,13 @@ urllib3 = ">=1.26.0"
 
 [[package]]
 name = "typing-extensions"
-version = "4.10.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
+    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
-    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
 ]
 
 [[package]]
@@ -1204,20 +1259,20 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
 
 [[package]]
 name = "zipp"
-version = "3.17.0"
+version = "3.18.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<3.10"
-content-hash = "b7f33da5b5a2af6bcb2a4c95cf391d04a76047d4f7e5c105b7cc38c73563fa51"
+content-hash = "a28054e255cbd49396795127380c2b7a0cfd742b15cba2184322f3c4894ed041"

pyproject.toml

@@ -1,14 +1,15 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.7.9"
+version = "1.0.1"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<3.10"
-pdme = "^0.9.3"
+pdme = "^1.0.0"
 numpy = "1.22.3"
 scipy = "1.10"
+tqdm = "^4.66.2"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
@@ -19,6 +20,9 @@ python-semantic-release = "^7.24.0"
 black = "^22.3.0"
 syrupy = "^4.0.8"
 
+[tool.poetry.scripts]
+probs = "deepdog.cli.probs:wrapped_main"
+
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
@@ -38,6 +42,13 @@ module = [
 ]
 ignore_missing_imports = true
 
+[[tool.mypy.overrides]]
+module = [
+	"tqdm",
+	"tqdm.*"
+]
+ignore_missing_imports = true
+
 [tool.semantic_release]
 version_toml = "pyproject.toml:tool.poetry.version"
 tag_format = "{version}"

scripts/release.sh

@@ -25,15 +25,22 @@ if [ -z "$(git status --porcelain)" ]; then
 		exit 0
 	fi
 
+	std_version_args=()
+	if [[ -n "${1:-}" ]]; then
+		std_version_args+=( "--release-as" "$1" )
+		echo "Parameter $1 was supplied, so we should use release-as"
+	else
+		echo "No release-as parameter specifed."
+	fi
 	# Working directory clean
 	echo "Doing a dry run..."
-	npx standard-version --dry-run
+	npx standard-version --dry-run "${std_version_args[@]}"
 	read -p "Does that look good? [y/N] " -n 1 -r
 	echo    # (optional) move to a new line
 	if [[ $REPLY =~ ^[Yy]$ ]]
 	then
 		# do dangerous stuff
-		npx standard-version
+		npx standard-version "${std_version_args[@]}"
 		git push --follow-tags origin master
 	else
 		echo "okay, never mind then..."

scripts/standard-version/pyproject-updater.js

@@ -1,4 +1,4 @@
-const pattern = /(\[tool\.poetry\]\nname = "deepdog"\nversion = ")(?<vers>\d+\.\d+\.\d)(")/mg;
+const pattern = /(\[tool\.poetry\]\nname = "deepdog"\nversion = ")(?<vers>\d+\.\d+\.\d+)(")/mg;
 
 module.exports.readVersion = function (contents) {
 	const result = pattern.exec(contents);

tests/indexify/test_indexify.py

@@ -0,0 +1,12 @@
+import deepdog.indexify
+import logging
+
+_logger = logging.getLogger(__name__)
+
+
+def test_indexifier():
+	weight_dict = {"key_1": [1, 2, 3], "key_2": ["a", "b", "c"]}
+	indexifier = deepdog.indexify.Indexifier(weight_dict)
+	_logger.debug(f"setting up indexifier {indexifier}")
+	assert indexifier.indexify(0) == {"key_1": 1, "key_2": "a"}
+	assert indexifier.indexify(5) == {"key_1": 2, "key_2": "c"}

tests/results/test_column_results.py

@@ -0,0 +1,28 @@
+import deepdog.results
+
+
+def test_parse_groupdict():
+	example_column_name = (
+		"geom_-20_20_-10_10_0_5-orientation_free-dipole_count_100_success"
+	)
+
+	parsed = deepdog.results._parse_bayesrun_column(example_column_name)
+	expected = deepdog.results.BayesrunColumnParsed(
+		{
+			"xmin": "-20",
+			"xmax": "20",
+			"ymin": "-10",
+			"ymax": "10",
+			"zmin": "0",
+			"zmax": "5",
+			"orientation": "free",
+			"avg_filled": "100",
+			"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