chore(deps): update dependency numpy to v2

CHANGELOG.md

@@ -2,28 +2,6 @@
 
 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.
 
-## [1.5.0](https://gitea.deepak.science:2222/physics/deepdog/compare/1.4.0...1.5.0) (2024-12-30)
-
-
-### Features
-
-* add configurable max number of dipoles to write ([a1b59cd](https://gitea.deepak.science:2222/physics/deepdog/commit/a1b59cd18b30359328a09210d9393f211aab30c2))
-* add configurable max number of dipoles to write ([53f8993](https://gitea.deepak.science:2222/physics/deepdog/commit/53f8993f2b155228fff5cbee84f10c62eb149a1f))
-
-## [1.4.0](https://gitea.deepak.science:2222/physics/deepdog/compare/1.3.0...1.4.0) (2024-09-04)
-
-
-### Features
-
-* add subset sim probs command for bayes for subset simulation results ([c881da2](https://gitea.deepak.science:2222/physics/deepdog/commit/c881da28370a1e51d062e1a7edaa62af6eb98d0a))
-* allows some betetr matching for single_dipole runs ([5425ce1](https://gitea.deepak.science:2222/physics/deepdog/commit/5425ce1362919af4cc4dbd5813df3be8d877b198))
-* indexifier now has len ([d962ecb](https://gitea.deepak.science:2222/physics/deepdog/commit/d962ecb11e929de1d9aa458b5d8e82270eff0039))
-
-
-### Bug Fixes
-
-* update log file arg names in cli scripts ([6a5c593](https://gitea.deepak.science:2222/physics/deepdog/commit/6a5c5931d4fc849d0d6a0f2b971523a0f039d559))
-
 ## [1.3.0](https://gitea.deepak.science:2222/physics/deepdog/compare/1.2.1...1.3.0) (2024-05-20)
 
 

deepdog/cli/probs/args.py

@@ -13,7 +13,7 @@ def parse_args() -> argparse.Namespace:
 		"probs", description="Calculating probability from finished bayesrun"
 	)
 	parser.add_argument(
-		"--log-file",
+		"--log_file",
 		type=str,
 		help="A filename for logging to, if not provided will only log to stderr",
 		default=None,

deepdog/cli/subset_sim_probs/args.py

@@ -14,7 +14,7 @@ def parse_args() -> argparse.Namespace:
 		description="Calculating probability from finished subset sim run",
 	)
 	parser.add_argument(
-		"--log-file",
+		"--log_file",
 		type=str,
 		help="A filename for logging to, if not provided will only log to stderr",
 		default=None,

deepdog/direct_monte_carlo/direct_mc.py

@@ -36,8 +36,8 @@ class DirectMonteCarloConfig:
 	tag: str = ""
 	cap_core_count: int = 0  # 0 means cap at num cores - 1
 	chunk_size: int = 50
-	write_bayesrun_file: bool = True
-	bayesrun_file_timestamp: bool = True
+	write_bayesrun_file = True
+	bayesrun_file_timestamp = True
 	# chunk size of some kind
 
 
@@ -145,21 +145,15 @@ class DirectMonteCarloRun:
 		single run wrapped up for multiprocessing call.
 
 		takes in a tuple of arguments corresponding to
-		(model_name_pair, seed, return_configs)
-
-		return_configs is a boolean, if true then will return tuple of (count, [matching configs])
-		if false, return (count, [])
+		(model_name_pair, seed)
 		"""
 		# here's where we do our work
 
-		model_name_pair, seed, return_configs = args
+		model_name_pair, seed = args
 		cycle_success_configs = self._single_run(model_name_pair, seed)
 		cycle_success_count = len(cycle_success_configs)
 
-		if return_configs:
-			return (cycle_success_count, cycle_success_configs)
-		else:
-			return (cycle_success_count, [])
+		return cycle_success_count
 
 	def execute_no_multiprocessing(self) -> Sequence[DirectMonteCarloResult]:
 
@@ -204,11 +198,9 @@ class DirectMonteCarloRun:
 							)
 							dipole_count = numpy.array(cycle_success_configs).shape[1]
 							for n in range(dipole_count):
-								number_dipoles_to_write = self.config.target_success * 5
-								_logger.info(f"Limiting to {number_dipoles_to_write=}")
 								numpy.savetxt(
 									f"{self.config.tag}_{step_count}_{cycle_i}_dipole_{n}.csv",
-									sorted_by_freq[:number_dipoles_to_write, n],
+									sorted_by_freq[:, n],
 									delimiter=",",
 								)
 					total_success += cycle_success_count
@@ -267,71 +259,15 @@ class DirectMonteCarloRun:
 
 					seeds = seed_sequence.spawn(self.config.monte_carlo_cycles)
 
-					raw_pool_results = list(
+					pool_results = sum(
 						pool.imap_unordered(
 							self._wrapped_single_run,
-							[
-								(
-									model_name_pair,
-									seed,
-									self.config.write_successes_to_file,
-								)
-								for seed in seeds
-							],
+							[(model_name_pair, seed) for seed in seeds],
 							self.config.chunk_size,
 						)
 					)
-
-					pool_results = sum(result[0] for result in raw_pool_results)
-
 					_logger.debug(f"Pool results: {pool_results}")
 
-					if self.config.write_successes_to_file:
-
-						_logger.info("Writing dipole results")
-
-						cycle_success_configs = numpy.concatenate(
-							[result[1] for result in raw_pool_results]
-						)
-
-						dipole_count = numpy.array(cycle_success_configs).shape[1]
-
-						max_number_dipoles_to_write = self.config.target_success * 5
-						_logger.debug(
-							f"Limiting to {max_number_dipoles_to_write=}, have {len(cycle_success_configs)}"
-						)
-
-						if len(cycle_success_configs):
-							sorted_by_freq = numpy.array(
-								[
-									pdme.subspace_simulation.sort_array_of_dipoles_by_frequency(
-										dipole_config
-									)
-									for dipole_config in cycle_success_configs[
-										:max_number_dipoles_to_write
-									]
-								]
-							)
-
-							for n in range(dipole_count):
-
-								dipole_filename = (
-									f"{self.config.tag}_{step_count}_dipole_{n}.csv"
-								)
-								_logger.debug(
-									f"Writing {min(len(cycle_success_configs), max_number_dipoles_to_write)} to {dipole_filename}"
-								)
-
-								numpy.savetxt(
-									dipole_filename,
-									sorted_by_freq[:, n],
-									delimiter=",",
-								)
-						else:
-							_logger.debug(
-								"Instructed to write results, but none obtained"
-							)
-
 					total_success += pool_results
 					total_count += count_per_step
 					_logger.debug(

deepdog/indexify/__init__.py

@@ -36,10 +36,6 @@ class Indexifier:
 	def indexify(self, n: int) -> typing.Dict[str, typing.Any]:
 		return self.product_dict[n]
 
-	def __len__(self) -> int:
-		weights = [len(v) for v in self.dict.values()]
-		return math.prod(weights)
-
 	def _indexify_indices(self, n: int) -> typing.Sequence[int]:
 		"""
 		legacy indexify from old scripts, copypast.

deepdog/results/filename.py

@@ -8,7 +8,6 @@ FILE_SLUG_REGEXES = [
 		r"(?P<tag>\w+)-(?P<job_index>\d+)",
 		r"mock_tarucha-(?P<job_index>\d+)",
 		r"(?:(?P<mock>mock)_)?tarucha(?:_(?P<tarucha_run_id>\d+))?-(?P<job_index>\d+)",
-		r"(?P<tag>\w+)-(?P<included_dots>[\w,]+)-(?P<target_cost>\d*\.?\d+)-(?P<job_index>\d+)",
 	]
 ]
 

pyproject.toml

@@ -1,13 +1,13 @@
 [tool.poetry]
 name = "deepdog"
-version = "1.5.0"
+version = "1.3.0"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<3.10"
 pdme = "^1.5.0"
-numpy = "1.22.3"
+numpy = "2.0.1"
 scipy = "1.10"
 tqdm = "^4.66.2"
 

tests/indexify/test_indexify.py

@@ -10,12 +10,3 @@ def test_indexifier():
 	_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"}
-	assert len(indexifier) == 9
-
-
-def test_indexifier_length_short():
-	weight_dict = {"key_1": [1, 2, 3], "key_2": ["b", "c"]}
-	indexifier = deepdog.indexify.Indexifier(weight_dict)
-	_logger.debug(f"setting up indexifier {indexifier}")
-
-	assert len(indexifier) == 6