chore: fix grep warning

.gitignore

@@ -155,3 +155,6 @@ out/
 /kalpa.toml
 /indexes.json
 /dots.json
+
+# nix
+result

CHANGELOG.md

@@ -2,6 +2,13 @@
 
 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.
 
+## [1.2.0](https://gitea.deepak.science:2222/physics/kalpa/compare/1.1.0...1.2.0) (2025-03-03)
+
+
+### Features
+
+* adds ability to specify which dots are used manually, and better handles collating and coalescing results ([9674276](https://gitea.deepak.science:2222/physics/kalpa/commit/96742769bedad928890a27153e7c0952a6fc1cdb))
+
 ## [1.1.0](https://gitea.deepak.science:2222/physics/kalpa/compare/1.0.1...1.1.0) (2025-03-02)
 
 

flake.nix

@@ -13,28 +13,66 @@
 	let
 		pkgs = nixpkgs.legacyPackages.${system};
 		poetry2nix = poetry2nixSrc.lib.mkPoetry2Nix { inherit pkgs; };
+		kalpaaApp = poetry2nix.mkPoetryApplication {
+			projectDir = self;
+			python = pkgs.python39;
+			preferWheels = true;
+		};
+		kalpaaEnv = poetry2nix.mkPoetryEnv {
+			projectDir = self;
+			python = pkgs.python39;
+			preferWheels = true;
+			overrides = poetry2nix.overrides.withDefaults (self: super: {
+			});
+		};
+		kalpaa-docker-image = pkgs.dockerTools.buildLayeredImage {
+			name = "kalpaa";
+			tag = "latest";
+
+
+			contents = [
+
+				# some stuff that dockertools provides?
+				# pkgs.dockerTools.usrBinEnv
+				# pkgs.dockerTools.binSh
+				# pkgs.dockerTools.caCertificates
+				# pkgs.dockerTools.fakeNss
+
+				pkgs.bash
+				pkgs.coreutils
+				# pkgs.cacert
+				# pkgs.gnutar
+				# pkgs.gzip
+				# pkgs.gnused
+				# pkgs.gnugrep
+				pkgs.uv
+				kalpaaApp
+
+			];
+
+			config = {
+				Cmd = [ "/bin/bash" ];
+				Env = [
+					"PATH=/bin"
+				];
+				WorkingDir = "/workspace";
+			};
+		};
 	in {
 		packages = {
-			kalpaApp = poetry2nix.mkPoetryApplication {
-				projectDir = self;
-				python = pkgs.python39;
-				preferWheels = true;
-			};
-			kalpaEnv = poetry2nix.mkPoetryEnv {
-				projectDir = self;
-				python = pkgs.python39;
-				preferWheels = true;
-				overrides = poetry2nix.overrides.withDefaults (self: super: {
-				});
-			};
-			default = self.packages.${system}.kalpaEnv;
+			inherit kalpaaEnv;
+			inherit kalpaaApp;
+			inherit kalpaa-docker-image;
+			default = self.packages.${system}.kalpaaEnv;
 		};
+
+
 		devShells.default = pkgs.mkShell {
-			inputsFrom = [ self.packages.${system}.kalpaEnv ];
+			inputsFrom = [ self.packages.${system}.kalpaaEnv ];
 			buildInputs = [
 				pkgs.poetry
-				self.packages.${system}.kalpaEnv
-				self.packages.${system}.kalpaApp
+				self.packages.${system}.kalpaaEnv
+				self.packages.${system}.kalpaaApp
 				pkgs.just
 				pkgs.nodejs
 			];
@@ -42,6 +80,7 @@
 				export DO_NIX_CUSTOM=1
 			'';
 		};
+
 	}
 	);
 }

justfile

@@ -70,3 +70,24 @@ release version="":
 
 # htmlcov:
 # 	poetry run pytest --cov-report=html
+
+# build docker image
+build-container:
+	#!/usr/bin/env bash
+	set -euxo pipefail
+	nix build .#kalpaa-docker-image
+
+# load the image into docker
+load-container:
+	#!/usr/bin/env bash
+	set -euxo pipefail
+	docker load < result
+
+# build and load in one step
+build-load-container: build-container load-container
+	echo "Image loaded successfully!"
+
+exec-container:
+	#!/usr/bin/env bash
+	set -euxo pipefail
+	docker run -it -v $(pwd)/kalpaa.toml:/workspace/kalpaa.toml -v $(pwd)/dots.json:/workspace/dots.json -v $(pwd)/indexes.json:/workspace/indexes.json kalpaa /bin/bash

kalpaa/config/config.py

@@ -154,6 +154,18 @@ class DeepdogConfig:
 	# Whether to use a log log cost function
 	use_log_noise: bool = False
 
+	# Manually specifying which dots to use
+	# Outer layer is multiple configurations, within that is which dots to combine, then the inner layer is to distinguish single dots and pairs.
+	# example:
+	# [
+	# 	[ ["dot1"]], # first one is to use just dot1
+	#   [ ["dot1"], ["dot2"] ] # second one is to use dot1 and dot2
+	#   [ ["dot1", "dot2"] ] # third one is to use dot1 and dot2 as a pair
+	# ]
+	manual_dot_seeds: typing.Optional[
+		typing.Sequence[typing.Sequence[typing.Sequence[str]]]
+	] = None
+
 
 @dataclass(frozen=True)
 class Config:

kalpaa/read_bin_csv.py

@@ -153,7 +153,7 @@ class MeasurementGroup:
 			input_array,
 			meas_array,
 			stdev_array,
-			log_noise=use_log_noise,
+			log_noise=use_log_noise and not self._using_pairs,
 			use_pair_measurement=self._using_pairs,
 		)
 

kalpaa/stages/stage02.py

@@ -83,7 +83,7 @@ class Stage02Runner:
 		)
 		_logger.info(f"Got dots {self.dots=}")
 
-	def _dots_to_include(self, current_dot: str) -> typing.Sequence[str]:
+	def _dots_to_include(self, current_dot: str) -> typing.List[str]:
 		if current_dot == "dot1":
 			return ["dot1"]
 		if current_dot == "dot2":
@@ -116,27 +116,60 @@ class Stage02Runner:
 					raise ValueError(f"Invalid completion status for {completion_name}")
 
 				for cost in self.config.deepdog_config.costs_to_try:
-					for dot in self.dots:
+					if self.config.deepdog_config.manual_dot_seeds is not None:
+						for config_i, manual_config in enumerate(
+							self.config.deepdog_config.manual_dot_seeds
+						):
 
-						seed_index += 1
+							seed_index += 1
+							# validate config
 
-						combined_dot_name = ",".join(
-							[d for d in self._dots_to_include(dot.label)]
-						)
-						trial_name = (
-							f"{dot.label}-{combined_dot_name}-{cost}-{job_index}"
-						)
+							dot_label = str(config_i) + str(manual_config).translate(
+								str.maketrans("", "", "[]\",' ")
+							)
+							dot_set = set()
+							for dot_entry in manual_config:
+								for dot_name in dot_entry:
+									dot_set.add(dot_name)
+							_logger.info(f"Dot set {dot_set=}")
+							dot_included = ",".join([d for d in sorted(dot_set)])
+							trial_name = (
+								f"{dot_label}-{dot_included}-{cost}-{job_index}"
+							)
 
-						_logger.info(f"Working on {trial_name=}")
-						_logger.debug(f"Have {seed_index=}")
-						self.single_run_in_subdir(
-							job_index,
-							cost,
-							dot.label,
-							trial_name,
-							seed_index,
-							override_name=override_key,
-						)
+							_logger.info(f"Working on {trial_name=}")
+							_logger.debug(f"Have {seed_index=}")
+							self.single_run_in_subdir(
+								job_index,
+								cost,
+								dot_label,
+								trial_name,
+								seed_index,
+								override_name=override_key,
+								dot_spec=manual_config,
+							)
+					else:
+						for dot in self.dots:
+
+							seed_index += 1
+
+							combined_dot_name = ",".join(
+								[d for d in self._dots_to_include(dot.label)]
+							)
+							trial_name = (
+								f"{dot.label}-{combined_dot_name}-{cost}-{job_index}"
+							)
+
+							_logger.info(f"Working on {trial_name=}")
+							_logger.debug(f"Have {seed_index=}")
+							self.single_run_in_subdir(
+								job_index,
+								cost,
+								dot.label,
+								trial_name,
+								seed_index,
+								override_name=override_key,
+							)
 				kalpaa.completions.set_completion_file(self.config, completion_name)
 
 	def single_run_in_subdir(
@@ -147,6 +180,7 @@ class Stage02Runner:
 		trial_name: str,
 		seed_index: int,
 		override_name: typing.Optional[str] = None,
+		dot_spec: typing.Optional[typing.Sequence[typing.Sequence[str]]] = None,
 	):
 		# _logger.info(f"Got job index {job_index}")
 		# NOTE This guy runs inside subdirs, obviously. In something like <kalpa>/out/z-10-2/dipoles
@@ -193,8 +227,26 @@ class Stage02Runner:
 				for tantri_index in range(num_tantri_configs)
 			]
 
-		dot_names = self._dots_to_include(dot_name)
-		_logger.debug(f"Got dot names {dot_names}")
+		single_dot_names: typing.List[str] = []
+		pair_dot_names: typing.List[typing.Tuple[str, str]] = []
+		if dot_spec is not None:
+			_logger.info(f"Received dot_spec {dot_spec}, validating")
+			for dot_entry in dot_spec:
+				_logger.debug(f"Working on {dot_entry=}")
+				if len(dot_entry) not in (1, 2):
+					raise ValueError(
+						f"Invalid dot spec {dot_spec}, {dot_entry} has wrong length"
+					)
+
+				if len(dot_entry) == 1:
+					_logger.debug(f"Adding {dot_entry[0]} to single_dot_names")
+					single_dot_names.append(dot_entry[0])
+				else:
+					pair_dot_names.append((dot_entry[0], dot_entry[1]))
+		else:
+			single_dot_names = self._dots_to_include(dot_name)
+			pair_dot_names = []
+		_logger.debug(f"Got dot names {single_dot_names=}, {pair_dot_names=}")
 
 		models = []
 
@@ -252,12 +304,25 @@ class Stage02Runner:
 
 		_logger.info(f"{deepdog_config=}")
 
-		stdev_cost_function_filters = [
-			b.stdev_cost_function_filter(
-				dot_names, cost, self.config.deepdog_config.use_log_noise
-			)
-			for b in binned_datas
-		]
+		stdev_cost_function_filters = []
+
+		if len(pair_dot_names):
+			pair_stdev_cost_function_filters = [
+				b.stdev_cost_function_filter(
+					pair_dot_names, cost, self.config.deepdog_config.use_log_noise
+				)
+				for b in binned_datas
+			]
+			stdev_cost_function_filters.extend(pair_stdev_cost_function_filters)
+
+		if len(single_dot_names):
+			single_stdev_cost_function_filters = [
+				b.stdev_cost_function_filter(
+					single_dot_names, cost, self.config.deepdog_config.use_log_noise
+				)
+				for b in binned_datas
+			]
+			stdev_cost_function_filters.extend(single_stdev_cost_function_filters)
 
 		_logger.debug(f"{stdev_cost_function_filters=}")
 		combining_filter = deepdog.direct_monte_carlo.compose_filter.ComposedDMCFilter(

kalpaa/stages/stage03.py

@@ -24,6 +24,7 @@ import tantri.dipoles.types
 # folder in curr dir
 import kalpaa
 import kalpaa.common
+import kalpaa.completions
 
 
 _logger = logging.getLogger(__name__)
@@ -48,17 +49,24 @@ OUT_FIELDNAMES = [
 ]
 
 
-def coalesced_filename(dot_name, target_cost) -> str:
-	return f"coalesced-{dot_name}-{target_cost}.csv"
+def coalesced_filename(subdir_name: str) -> str:
+	return f"coalesced-{subdir_name}.csv"
 
 
-def read_coalesced_csv(parent_path: pathlib.Path, dot_name: str, target_cost):
+def read_coalesced_csv(parent_path: pathlib.Path, subdir_name: str):
+
 	# csv_name = f"coalesced-{dot_name}-{target_cost}.csv"
-	csv_path = parent_path / coalesced_filename(dot_name, target_cost)
+	csv_path = parent_path / coalesced_filename(subdir_name)
 	_logger.debug(f"{csv_path=}")
 	with csv_path.open("r", newline="") as csvfile:
 		reader = csv.DictReader(csvfile)
 		out_list = []
+
+		subdir_split = subdir_name.rsplit("-", 1)
+
+		dot_name = subdir_split[0]
+		target_cost = subdir_split[1]
+		_logger.debug(f"{dot_name=}, {target_cost=} for subdir_name {subdir_name=}")
 		for row in reader:
 			row["dot_name"] = dot_name
 			row["target_cost"] = target_cost
@@ -86,12 +94,15 @@ class Stage03Runner:
 		with out_path.open("w", newline="") as outfile:
 			writer = csv.DictWriter(outfile, OUT_FIELDNAMES)
 			writer.writeheader()
-			for dot in self.dots:
-				for cost in self.config.deepdog_config.costs_to_try:
-					_logger.info(f"Reading {dot=} {cost=}")
-					rows = read_coalesced_csv(sorted_dir, dot, cost)
-					for row in rows:
-						writer.writerow(row)
+			for subdir in sorted_dir.iterdir():
+				if not subdir.is_dir():
+					_logger.info(f"That's not a dir {subdir=}")
+					continue
+				subdir_name = subdir.name
+				_logger.info(f"Reading for {subdir_name=}")
+				rows = read_coalesced_csv(sorted_dir, subdir_name)
+				for row in rows:
+					writer.writerow(row)
 
 	def run_in_subdir(self, subdir: pathlib.Path):
 		"""
@@ -101,33 +112,49 @@ class Stage03Runner:
 
 			_logger.debug(f"Running inside {subdir=}")
 
-			kalpaa.stages.stage03_1.move_all_in_dipoles(subdir / "dipoles")
+			subdir_name = subdir.name
+			completion_name = f"stage03_1.job_{subdir_name}.complete"
+			completion = kalpaa.completions.check_completion_file(
+				self.config, completion_name
+			)
+			if completion == kalpaa.completions.CompletionsStatus.COMPLETE:
+				_logger.info(f"Skipping {completion_name}")
+				# continue
+			elif completion == kalpaa.completions.CompletionsStatus.INVALID:
+				_logger.error(f"Invalid completion status for {completion_name}")
+				raise ValueError(f"Invalid completion status for {completion_name}")
+			else:
+				_logger.info(f"Moving dipoles for {subdir=}")
+				kalpaa.stages.stage03_1.move_all_in_dipoles(subdir / "dipoles")
+				kalpaa.completions.set_completion_file(self.config, completion_name)
 
 			seed_index = 0
 
 			sorted_dir = pathlib.Path(kalpaa.common.sorted_bayesruns_name())
 			_logger.info(f"{sorted_dir.resolve()}")
 
-			for cost in self.config.deepdog_config.costs_to_try:
-				for dot in self.dots:
+			for sorted_subdir in sorted_dir.iterdir():
+				if not subdir.is_dir():
+					_logger.info(f"That's not a dir {subdir=}")
+					continue
 
-					seed_index += 1
-					# TODO pull out
-					sorted_subdir = sorted_dir / f"{dot}-{cost}"
+				seed_index += 1
+				# TODO pull out
+				# sorted_subdir = sorted_dir / f"{dot}-{cost}"
 
-					# TODO need to refactor deepdog probs method so I don't have to dump into args like this
-					probs_args = argparse.Namespace()
-					probs_args.bayesrun_directory = sorted_subdir
-					probs_args.indexify_json = self.config.absify(
-						self.config.general_config.indexes_json_name
-					)
-					probs_args.coalesced_keys = ""
-					probs_args.uncoalesced_outfile = None
-					probs_args.coalesced_outfile = sorted_dir / coalesced_filename(
-						dot, cost
-					)
+				# TODO need to refactor deepdog probs method so I don't have to dump into args like this
+				probs_args = argparse.Namespace()
+				probs_args.bayesrun_directory = sorted_subdir
+				probs_args.indexify_json = self.config.absify(
+					self.config.general_config.indexes_json_name
+				)
+				probs_args.coalesced_keys = ""
+				probs_args.uncoalesced_outfile = None
+				probs_args.coalesced_outfile = sorted_dir / coalesced_filename(
+					sorted_subdir.name
+				)
 
-					deepdog.cli.probs.main.main(probs_args)
+				deepdog.cli.probs.main.main(probs_args)
 
 			self.merge_coalesceds(sorted_dir)
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "kalpaa"
-version = "1.1.0"
+version = "1.2.0"
 description = "Groups up and runs full run."
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 readme = "README.md"

scripts/release.sh

@@ -14,7 +14,7 @@ if [ -z "$(git status --porcelain)" ]; then
 	release_needed=false
 	if \
 		{ git log "$( git describe --tags --abbrev=0 )..HEAD" --format='%s' | cut -d: -f1 | sort -u | sed -e 's/([^)]*)//' | grep -q -i -E '^feat|fix|perf|refactor|revert$' ; } || \
-		{ git log "$( git describe --tags --abbrev=0 )..HEAD" --format='%s' | cut -d: -f1 | sort -u | sed -e 's/([^)]*)//' | grep -q -E '\!$' ; } || \
+		{ git log "$( git describe --tags --abbrev=0 )..HEAD" --format='%s' | cut -d: -f1 | sort -u | sed -e 's/([^)]*)//' | grep -q -E '!$' ; } || \
 		{ git log "$( git describe --tags --abbrev=0 )..HEAD" --format='%b' | grep -q -E '^BREAKING CHANGE:' ; }
 	then
 		release_needed=true

tests/config/__snapshots__/test_toml_as_dict_snaps.ambr

@@ -7,6 +7,7 @@
         2,
         0.2,
       ]),
+      'manual_dot_seeds': None,
       'max_monte_carlo_cycles_steps': 20,
       'target_success': 2000,
       'use_log_noise': True,
@@ -89,6 +90,7 @@
         0.5,
         0.2,
       ]),
+      'manual_dot_seeds': None,
       'max_monte_carlo_cycles_steps': 20,
       'target_success': 2000,
       'use_log_noise': True,
@@ -171,6 +173,7 @@
         0.5,
         0.2,
       ]),
+      'manual_dot_seeds': None,
       'max_monte_carlo_cycles_steps': 20,
       'target_success': 2000,
       'use_log_noise': True,
@@ -251,6 +254,7 @@
         1,
         0.1,
       ]),
+      'manual_dot_seeds': None,
       'max_monte_carlo_cycles_steps': 20,
       'target_success': 1000,
       'use_log_noise': False,

tests/config/__snapshots__/test_toml_reader.ambr

@@ -1,13 +1,13 @@
 # serializer version: 1
 # name: test_parse_config_all_fields_toml
-  Config(generation_config=GenerationConfig(counts=[1, 5, 10], orientations=[<Orientation.RANDOM: 'RANDOM'>, <Orientation.Z: 'Z'>, <Orientation.XY: 'XY'>], num_replicas=3, override_dipole_configs={'scenario1': [DipoleTO(p=array([3, 5, 7]), s=array([2, 4, 6]), w=10), DipoleTO(p=array([30, 50, 70]), s=array([20, 40, 60]), w=10.55)]}, override_measurement_filesets=None, tantri_configs=[TantriConfig(index_seed_starter=15151, num_seeds=5, delta_t=0.01, num_iterations=100), TantriConfig(index_seed_starter=1234, num_seeds=100, delta_t=1, num_iterations=200)], num_bin_time_series=25, bin_log_width=0.25), general_config=GeneralConfig(dots_json_name='test_dots.json', indexes_json_name='test_indexes.json', out_dir_name='out', log_pattern='%(asctime)s | %(message)s', measurement_type=<MeasurementTypeEnum.X_ELECTRIC_FIELD: 'x-electric-field'>, root_directory=PosixPath('test_root'), mega_merged_name='test_mega_merged.csv', mega_merged_inferenced_name='test_mega_merged_inferenced.csv', skip_to_stage=1, check_completions=False), deepdog_config=DeepdogConfig(costs_to_try=[20, 2, 0.2], target_success=2000, max_monte_carlo_cycles_steps=20, use_log_noise=True), default_model_param_config=DefaultModelParamConfig(x_min=-20, x_max=20, y_min=-10, y_max=10, z_min=5, z_max=6.5, w_log_min=-5, w_log_max=1))
+  Config(generation_config=GenerationConfig(counts=[1, 5, 10], orientations=[<Orientation.RANDOM: 'RANDOM'>, <Orientation.Z: 'Z'>, <Orientation.XY: 'XY'>], num_replicas=3, override_dipole_configs={'scenario1': [DipoleTO(p=array([3, 5, 7]), s=array([2, 4, 6]), w=10), DipoleTO(p=array([30, 50, 70]), s=array([20, 40, 60]), w=10.55)]}, override_measurement_filesets=None, tantri_configs=[TantriConfig(index_seed_starter=15151, num_seeds=5, delta_t=0.01, num_iterations=100), TantriConfig(index_seed_starter=1234, num_seeds=100, delta_t=1, num_iterations=200)], num_bin_time_series=25, bin_log_width=0.25), general_config=GeneralConfig(dots_json_name='test_dots.json', indexes_json_name='test_indexes.json', out_dir_name='out', log_pattern='%(asctime)s | %(message)s', measurement_type=<MeasurementTypeEnum.X_ELECTRIC_FIELD: 'x-electric-field'>, root_directory=PosixPath('test_root'), mega_merged_name='test_mega_merged.csv', mega_merged_inferenced_name='test_mega_merged_inferenced.csv', skip_to_stage=1, check_completions=False), deepdog_config=DeepdogConfig(costs_to_try=[20, 2, 0.2], target_success=2000, max_monte_carlo_cycles_steps=20, use_log_noise=True, manual_dot_seeds=None), default_model_param_config=DefaultModelParamConfig(x_min=-20, x_max=20, y_min=-10, y_max=10, z_min=5, z_max=6.5, w_log_min=-5, w_log_max=1))
 # ---
 # name: test_parse_config_few_fields_toml
-  Config(generation_config=GenerationConfig(counts=[1, 5, 10], orientations=[<Orientation.RANDOM: 'RANDOM'>, <Orientation.Z: 'Z'>, <Orientation.XY: 'XY'>], num_replicas=2, override_dipole_configs={'scenario1': [DipoleTO(p=array([3, 5, 7]), s=array([2, 4, 6]), w=10), DipoleTO(p=array([30, 50, 70]), s=array([20, 40, 60]), w=10.55)]}, override_measurement_filesets=None, tantri_configs=[TantriConfig(index_seed_starter=15151, num_seeds=5, delta_t=0.01, num_iterations=100), TantriConfig(index_seed_starter=1234, num_seeds=100, delta_t=1, num_iterations=200)], num_bin_time_series=25, bin_log_width=0.25), general_config=GeneralConfig(dots_json_name='dots.json', indexes_json_name='indexes.json', out_dir_name='out', log_pattern='%(asctime)s | %(process)d | %(levelname)-7s | %(name)s:%(lineno)d | %(message)s', measurement_type=<MeasurementTypeEnum.POTENTIAL: 'electric-potential'>, root_directory=PosixPath('test_root1'), mega_merged_name='mega_merged_coalesced.csv', mega_merged_inferenced_name='mega_merged_coalesced_inferenced.csv', skip_to_stage=None, check_completions=False), deepdog_config=DeepdogConfig(costs_to_try=[5, 2, 1, 0.5, 0.2], target_success=2000, max_monte_carlo_cycles_steps=20, use_log_noise=True), default_model_param_config=DefaultModelParamConfig(x_min=-20, x_max=20, y_min=-10, y_max=10, z_min=5, z_max=6.5, w_log_min=-5, w_log_max=1))
+  Config(generation_config=GenerationConfig(counts=[1, 5, 10], orientations=[<Orientation.RANDOM: 'RANDOM'>, <Orientation.Z: 'Z'>, <Orientation.XY: 'XY'>], num_replicas=2, override_dipole_configs={'scenario1': [DipoleTO(p=array([3, 5, 7]), s=array([2, 4, 6]), w=10), DipoleTO(p=array([30, 50, 70]), s=array([20, 40, 60]), w=10.55)]}, override_measurement_filesets=None, tantri_configs=[TantriConfig(index_seed_starter=15151, num_seeds=5, delta_t=0.01, num_iterations=100), TantriConfig(index_seed_starter=1234, num_seeds=100, delta_t=1, num_iterations=200)], num_bin_time_series=25, bin_log_width=0.25), general_config=GeneralConfig(dots_json_name='dots.json', indexes_json_name='indexes.json', out_dir_name='out', log_pattern='%(asctime)s | %(process)d | %(levelname)-7s | %(name)s:%(lineno)d | %(message)s', measurement_type=<MeasurementTypeEnum.POTENTIAL: 'electric-potential'>, root_directory=PosixPath('test_root1'), mega_merged_name='mega_merged_coalesced.csv', mega_merged_inferenced_name='mega_merged_coalesced_inferenced.csv', skip_to_stage=None, check_completions=False), deepdog_config=DeepdogConfig(costs_to_try=[5, 2, 1, 0.5, 0.2], target_success=2000, max_monte_carlo_cycles_steps=20, use_log_noise=True, manual_dot_seeds=None), default_model_param_config=DefaultModelParamConfig(x_min=-20, x_max=20, y_min=-10, y_max=10, z_min=5, z_max=6.5, w_log_min=-5, w_log_max=1))
 # ---
 # name: test_parse_config_geom_params_toml
-  Config(generation_config=GenerationConfig(counts=[1, 5, 10], orientations=[<Orientation.RANDOM: 'RANDOM'>, <Orientation.Z: 'Z'>, <Orientation.XY: 'XY'>], num_replicas=2, override_dipole_configs={'scenario1': [DipoleTO(p=array([3, 5, 7]), s=array([2, 4, 6]), w=10), DipoleTO(p=array([30, 50, 70]), s=array([20, 40, 60]), w=10.55)]}, override_measurement_filesets=None, tantri_configs=[TantriConfig(index_seed_starter=15151, num_seeds=5, delta_t=0.01, num_iterations=100), TantriConfig(index_seed_starter=1234, num_seeds=100, delta_t=1, num_iterations=200)], num_bin_time_series=25, bin_log_width=0.25), general_config=GeneralConfig(dots_json_name='dots.json', indexes_json_name='indexes.json', out_dir_name='out', log_pattern='%(asctime)s | %(process)d | %(levelname)-7s | %(name)s:%(lineno)d | %(message)s', measurement_type=<MeasurementTypeEnum.POTENTIAL: 'electric-potential'>, root_directory=PosixPath('test_root1'), mega_merged_name='mega_merged_coalesced.csv', mega_merged_inferenced_name='mega_merged_coalesced_inferenced.csv', skip_to_stage=None, check_completions=False), deepdog_config=DeepdogConfig(costs_to_try=[5, 2, 1, 0.5, 0.2], target_success=2000, max_monte_carlo_cycles_steps=20, use_log_noise=True), default_model_param_config=DefaultModelParamConfig(x_min=-20, x_max=20, y_min=-10, y_max=10, z_min=0, z_max=2, w_log_min=-3, w_log_max=1.5))
+  Config(generation_config=GenerationConfig(counts=[1, 5, 10], orientations=[<Orientation.RANDOM: 'RANDOM'>, <Orientation.Z: 'Z'>, <Orientation.XY: 'XY'>], num_replicas=2, override_dipole_configs={'scenario1': [DipoleTO(p=array([3, 5, 7]), s=array([2, 4, 6]), w=10), DipoleTO(p=array([30, 50, 70]), s=array([20, 40, 60]), w=10.55)]}, override_measurement_filesets=None, tantri_configs=[TantriConfig(index_seed_starter=15151, num_seeds=5, delta_t=0.01, num_iterations=100), TantriConfig(index_seed_starter=1234, num_seeds=100, delta_t=1, num_iterations=200)], num_bin_time_series=25, bin_log_width=0.25), general_config=GeneralConfig(dots_json_name='dots.json', indexes_json_name='indexes.json', out_dir_name='out', log_pattern='%(asctime)s | %(process)d | %(levelname)-7s | %(name)s:%(lineno)d | %(message)s', measurement_type=<MeasurementTypeEnum.POTENTIAL: 'electric-potential'>, root_directory=PosixPath('test_root1'), mega_merged_name='mega_merged_coalesced.csv', mega_merged_inferenced_name='mega_merged_coalesced_inferenced.csv', skip_to_stage=None, check_completions=False), deepdog_config=DeepdogConfig(costs_to_try=[5, 2, 1, 0.5, 0.2], target_success=2000, max_monte_carlo_cycles_steps=20, use_log_noise=True, manual_dot_seeds=None), default_model_param_config=DefaultModelParamConfig(x_min=-20, x_max=20, y_min=-10, y_max=10, z_min=0, z_max=2, w_log_min=-3, w_log_max=1.5))
 # ---
 # name: test_parse_config_toml
-  Config(generation_config=GenerationConfig(counts=[1, 10], orientations=[<Orientation.RANDOM: 'RANDOM'>, <Orientation.Z: 'Z'>, <Orientation.XY: 'XY'>], num_replicas=3, override_dipole_configs=None, override_measurement_filesets=None, tantri_configs=[TantriConfig(index_seed_starter=31415, num_seeds=100, delta_t=0.05, num_iterations=100000)], num_bin_time_series=25, bin_log_width=0.25), general_config=GeneralConfig(dots_json_name='test_dots.json', indexes_json_name='test_indexes.json', out_dir_name='test_out', log_pattern='%(asctime)s | %(process)d | %(levelname)-7s | %(name)s:%(lineno)d | %(message)s', measurement_type=<MeasurementTypeEnum.X_ELECTRIC_FIELD: 'x-electric-field'>, root_directory=PosixPath('test_root'), mega_merged_name='test_mega_merged.csv', mega_merged_inferenced_name='test_mega_merged_inferenced.csv', skip_to_stage=1, check_completions=False), deepdog_config=DeepdogConfig(costs_to_try=[10, 1, 0.1], target_success=1000, max_monte_carlo_cycles_steps=20, use_log_noise=False), default_model_param_config=DefaultModelParamConfig(x_min=-20, x_max=20, y_min=-10, y_max=10, z_min=5, z_max=6.5, w_log_min=-5, w_log_max=1))
+  Config(generation_config=GenerationConfig(counts=[1, 10], orientations=[<Orientation.RANDOM: 'RANDOM'>, <Orientation.Z: 'Z'>, <Orientation.XY: 'XY'>], num_replicas=3, override_dipole_configs=None, override_measurement_filesets=None, tantri_configs=[TantriConfig(index_seed_starter=31415, num_seeds=100, delta_t=0.05, num_iterations=100000)], num_bin_time_series=25, bin_log_width=0.25), general_config=GeneralConfig(dots_json_name='test_dots.json', indexes_json_name='test_indexes.json', out_dir_name='test_out', log_pattern='%(asctime)s | %(process)d | %(levelname)-7s | %(name)s:%(lineno)d | %(message)s', measurement_type=<MeasurementTypeEnum.X_ELECTRIC_FIELD: 'x-electric-field'>, root_directory=PosixPath('test_root'), mega_merged_name='test_mega_merged.csv', mega_merged_inferenced_name='test_mega_merged_inferenced.csv', skip_to_stage=1, check_completions=False), deepdog_config=DeepdogConfig(costs_to_try=[10, 1, 0.1], target_success=1000, max_monte_carlo_cycles_steps=20, use_log_noise=False, manual_dot_seeds=None), default_model_param_config=DefaultModelParamConfig(x_min=-20, x_max=20, y_min=-10, y_max=10, z_min=5, z_max=6.5, w_log_min=-5, w_log_max=1))
 # ---