chore(release): 0.3.4

CHANGELOG.md

@@ -2,6 +2,27 @@
 
 All notable changes to this project will be documented in this file. See [standard-version](https://github.com/conventional-changelog/standard-version) for commit guidelines.
 
+### [0.3.4](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.3...0.3.4) (2022-03-06)
+
+
+### Features
+
+* Changes chunksize for multiprocessing ([0784cd5](https://gitea.deepak.science:2222/physics/deepdog/commit/0784cd53d79e00684506604f094b5d820b3994d4))
+
+### [0.3.3](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.2...0.3.3) (2022-03-06)
+
+
+### Bug Fixes
+
+* Fixes count to use cycles as well ([8617e4d](https://gitea.deepak.science:2222/physics/deepdog/commit/8617e4d2742b112cc824068150682ce3b2cdd879))
+
+### [0.3.2](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.1...0.3.2) (2022-03-06)
+
+
+### Features
+
+* Adds monte carlo cycles to trade off space and cpu ([e6d8d33](https://gitea.deepak.science:2222/physics/deepdog/commit/e6d8d33c27e7922581e91c10de4f5faff2a51f8b))
+
 ### [0.3.1](https://gitea.deepak.science:2222/physics/deepdog/compare/v0.3.0...v0.3.1) (2022-03-06)
 
 

README.md

@@ -1,3 +1,18 @@
 # deepdog
 
-The dipole diagnostic tool.
+[![Conventional Commits](https://img.shields.io/badge/Conventional%20Commits-1.0.0-green.svg?style=flat-square)](https://conventionalcommits.org)
+[![PyPI](https://img.shields.io/pypi/v/deepdog?style=flat-square)](https://pypi.org/project/deepdog/)
+[![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/2022?style=flat-square)
+
+The DiPole DiaGnostic tool.
+
+## Getting started
+
+`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`.
+
+

deepdog/alt_bayes_run.py

@@ -4,13 +4,13 @@ import pdme.util.fast_v_calc
 from typing import Sequence, Tuple, List
 import datetime
 import csv
+import multiprocessing
 import logging
 import numpy
 
 
 # TODO: remove hardcode
-COST_THRESHOLD = 1e-10
+CHUNKSIZE = 20
-
 
 # TODO: It's garbage to have this here duplicated from pdme.
 DotInput = Tuple[numpy.typing.ArrayLike, float]
@@ -19,6 +19,13 @@ DotInput = Tuple[numpy.typing.ArrayLike, float]
 _logger = logging.getLogger(__name__)
 
 
+def get_a_result(input) -> int:
+	discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
+	sample_dipoles = discretisation.get_model().get_n_single_dipoles(monte_carlo_count, max_frequency)
+	vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
+	return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
+
+
 class AltBayesRun():
 	'''
 	A single Bayes run for a given set of dots.
@@ -36,7 +43,7 @@ class AltBayesRun():
 	run_count: int
 		The number of runs to do.
 	'''
-	def __init__(self, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, low_error: float = 0.9, high_error: float = 1.1, monte_carlo_count: int = 10000, max_frequency: float = 20, end_threshold: float = None) -> None:
+	def __init__(self, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, low_error: float = 0.9, high_error: float = 1.1, monte_carlo_count: int = 10000, monte_carlo_cycles: int = 10, max_frequency: float = 20, end_threshold: float = None) -> None:
 		self.dot_inputs = dot_inputs
 		self.dot_inputs_array = pdme.measurement.oscillating_dipole.dot_inputs_to_array(dot_inputs)
 		self.discretisations = [disc for (_, disc) in discretisations_with_names]
@@ -44,6 +51,7 @@ class AltBayesRun():
 		self.actual_model = actual_model
 		self.model_count = len(self.discretisations)
 		self.monte_carlo_count = monte_carlo_count
+		self.monte_carlo_cycles = monte_carlo_cycles
 		self.run_count = run_count
 		self.low_error = low_error
 		self.high_error = high_error
@@ -87,9 +95,10 @@ class AltBayesRun():
 			_logger.debug("Going to iterate over discretisations now")
 			for disc_count, discretisation in enumerate(self.discretisations):
 				_logger.debug(f"Doing discretisation #{disc_count}")
-				sample_dipoles = discretisation.get_model().get_n_single_dipoles(self.monte_carlo_count, self.max_frequency)
+				with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
-				vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(self.dot_inputs_array, sample_dipoles)
+					results.append(sum(
-				results.append(numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs)))
+						pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, CHUNKSIZE)
+					))
 
 			_logger.debug("Done, constructing output now")
 			row = {
@@ -102,9 +111,9 @@ class AltBayesRun():
 			for model_index, (name, result) in enumerate(zip(self.model_names, results)):
 
 				row[f"{name}_success"] = result
-				row[f"{name}_count"] = self.monte_carlo_count
+				row[f"{name}_count"] = self.monte_carlo_count * self.monte_carlo_cycles
 				successes.append(max(result, 0.5))
-				counts.append(self.monte_carlo_count)
+				counts.append(self.monte_carlo_count * self.monte_carlo_cycles)
 
 			success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
 			new_probabilities = [(succ / count) * old_prob / success_weight for succ, count, old_prob in zip(successes, counts, self.probabilities)]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.3.1"
+version = "0.3.4"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]