chore(release): 0.4.0

Reviewed-on: #5

.versionrc

@@ -0,0 +1,10 @@
+{
+	"bumpFiles": [
+		{
+			"filename": "pyproject.toml",
+			"updater": "scripts/standard-version/pyproject-updater.js"
+		}
+	],
+	"sign": true,
+	"tag-prefix": ""
+}

CHANGELOG.md

@@ -0,0 +1,56 @@
+# Changelog
+
+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.
+
+## [0.4.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.5...0.4.0) (2022-04-10)
+
+
+### ⚠ BREAKING CHANGES
+
+* Adds pair calculations, with changing api format
+
+### Features
+
+* Adds dynamic cycle count increases to help reach minimum success count ([ec7b4ca](https://gitea.deepak.science:2222/physics/deepdog/commit/ec7b4cac393c15e94c513215c4f1ba32be2ae87a))
+* Adds pair calculations, with changing api format ([6463b13](https://gitea.deepak.science:2222/physics/deepdog/commit/6463b135ef2d212b565864b5ac1b655e014d2194))
+
+
+### Bug Fixes
+
+* uses bigfix from pdme for negatives ([c1c711f](https://gitea.deepak.science:2222/physics/deepdog/commit/c1c711f47b574d3a9b8a24dbcbdd7f50b9be8ea9))
+
+### [0.3.5](https://gitea.deepak.science:2222/physics/deepdog/compare/0.3.4...0.3.5) (2022-03-07)
+
+
+### Features
+
+* makes chunksize configurable ([88d9613](https://gitea.deepak.science:2222/physics/deepdog/commit/88d961313c1db0d49fd96939aa725a8706fa0412))
+
+### [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)
+
+
+### Features
+
+* Adds alt bayes solver with monte carlo sampler ([7284dbe](https://gitea.deepak.science:2222/physics/deepdog/commit/7284dbeb34ef46189d81fb719252dfa74b8e9fa8))
+* Updates to pdme version for faster bayes resolution ([d078004](https://gitea.deepak.science:2222/physics/deepdog/commit/d078004773d9d9dccd0a9a52ca96aa57690f9b7e))

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/__init__.py

@@ -1,6 +1,7 @@
 import logging
 from deepdog.meta import __version__
 from deepdog.bayes_run import BayesRun
+from deepdog.alt_bayes_run import AltBayesRun
 from deepdog.diagnostic import Diagnostic
 
 
@@ -8,7 +9,7 @@ def get_version():
 	return __version__
 
 
-__all__ = ["get_version", "BayesRun", "Diagnostic"]
+__all__ = ["get_version", "BayesRun", "AltBayesRun", "Diagnostic"]
 
 
 logging.getLogger(__name__).addHandler(logging.NullHandler())

deepdog/alt_bayes_run.py

@@ -0,0 +1,189 @@
+import pdme.inputs
+import pdme.model
+import pdme.measurement.input_types
+import pdme.measurement.oscillating_dipole
+import pdme.util.fast_v_calc
+import pdme.util.fast_nonlocal_spectrum
+from typing import Sequence, Tuple, List
+import datetime
+import csv
+import multiprocessing
+import logging
+import numpy
+
+
+# TODO: remove hardcode
+CHUNKSIZE = 50
+
+# TODO: It's garbage to have this here duplicated from pdme.
+DotInput = Tuple[numpy.typing.ArrayLike, float]
+
+
+_logger = logging.getLogger(__name__)
+
+
+def get_a_result(input) -> int:
+	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))
+
+
+def get_a_result_using_pairs(input) -> int:
+	discretisation, dot_inputs, pair_inputs, local_lows, local_highs, nonlocal_lows, nonlocal_highs, monte_carlo_count, max_frequency = input
+	sample_dipoles = discretisation.get_model().get_n_single_dipoles(monte_carlo_count, max_frequency)
+	local_vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
+	local_matches = pdme.util.fast_v_calc.between(local_vals, local_lows, local_highs)
+	nonlocal_vals = pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal(pair_inputs, sample_dipoles)
+	nonlocal_matches = pdme.util.fast_v_calc.between(nonlocal_vals, nonlocal_lows, nonlocal_highs)
+	combined_matches = numpy.logical_and(local_matches, nonlocal_matches)
+	return numpy.count_nonzero(combined_matches)
+
+
+class AltBayesRun():
+	'''
+	A single Bayes run for a given set of dots.
+
+	Parameters
+	----------
+	dot_inputs : Sequence[DotInput]
+		The dot inputs for this bayes run.
+	discretisations_with_names : Sequence[Tuple(str, pdme.model.Model)]
+		The models to evaluate.
+	actual_model_discretisation : pdme.model.Discretisation
+		The discretisation for the model which is actually correct.
+	filename_slug : str
+		The filename slug to include.
+	run_count: int
+		The number of runs to do.
+	'''
+	def __init__(self, dot_positions: Sequence[numpy.typing.ArrayLike], frequency_range: Sequence[float], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int = 100, low_error: float = 0.9, high_error: float = 1.1, pairs_high_error=None, pairs_low_error=None, monte_carlo_count: int = 10000, monte_carlo_cycles: int = 10, target_success: int = 100, max_monte_carlo_cycles_steps: int = 10, max_frequency: float = 20, end_threshold: float = None, chunksize: int = CHUNKSIZE, use_pairs: bool = False) -> None:
+		self.dot_inputs = pdme.inputs.inputs_with_frequency_range(dot_positions, frequency_range)
+		self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(self.dot_inputs)
+
+		self.use_pairs = use_pairs
+
+		self.dot_pair_inputs = pdme.inputs.input_pairs_with_frequency_range(dot_positions, frequency_range)
+		self.dot_pair_inputs_array = pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
+
+		self.discretisations = [disc for (_, disc) in discretisations_with_names]
+		self.model_names = [name for (name, _) in discretisations_with_names]
+		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.target_success = target_success
+		self.max_monte_carlo_cycles_steps = max_monte_carlo_cycles_steps
+		self.run_count = run_count
+		self.low_error = low_error
+		self.high_error = high_error
+		if pairs_low_error is None:
+			self.pairs_low_error = self.low_error
+		else:
+			self.pairs_low_error = pairs_low_error
+		if pairs_high_error is None:
+			self.pairs_high_error = self.high_error
+		else:
+			self.pairs_high_error = pairs_high_error
+		self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
+		self.compensate_zeros = True
+		self.chunksize = chunksize
+		for name in self.model_names:
+			self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
+
+		self.probabilities = [1 / self.model_count] * self.model_count
+
+		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+		if self.use_pairs:
+			self.filename = f"{timestamp}-{filename_slug}.altbayes.pairs.csv"
+		else:
+			self.filename = f"{timestamp}-{filename_slug}.altbayes.csv"
+		self.max_frequency = max_frequency
+
+		if end_threshold is not None:
+			if 0 < end_threshold < 1:
+				self.end_threshold: float = end_threshold
+				self.use_end_threshold = True
+				_logger.info(f"Will abort early, at {self.end_threshold}.")
+			else:
+				raise ValueError(f"end_threshold should be between 0 and 1, but is actually {end_threshold}")
+
+	def go(self) -> None:
+		with open(self.filename, "a", newline="") as outfile:
+			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
+			writer.writeheader()
+
+		for run in range(1, self.run_count + 1):
+
+			rng = numpy.random.default_rng()
+			frequency = rng.uniform(1, self.max_frequency)
+
+			# Generate the actual dipoles
+			actual_dipoles = self.actual_model.get_dipoles(frequency)
+
+			dots = actual_dipoles.get_percent_range_dot_measurements(self.dot_inputs, self.low_error, self.high_error)
+			lows, highs = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(dots)
+
+			pair_lows, pair_highs = (None, None)
+			if self.use_pairs:
+				pair_measurements = actual_dipoles.get_percent_range_dot_pair_measurements(self.dot_pair_inputs, self.pairs_low_error, self.pairs_high_error)
+				pair_lows, pair_highs = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(pair_measurements)
+
+			_logger.info(f"Going to work on dipole at {actual_dipoles.dipoles}")
+
+			results = []
+			_logger.debug("Going to iterate over discretisations now")
+			for disc_count, discretisation in enumerate(self.discretisations):
+				_logger.debug(f"Doing discretisation #{disc_count}")
+				with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
+					cycle_count = 0
+					cycle_success = 0
+					cycles = 0
+					while (cycles < self.max_monte_carlo_cycles_steps) and (cycle_success <= self.target_success):
+						_logger.debug(f"Starting cycle {cycles}")
+						cycles += 1
+						current_success = 0
+						cycle_count += self.monte_carlo_count * self.monte_carlo_cycles
+						if self.use_pairs:
+							current_success = sum(
+								pool.imap_unordered(get_a_result_using_pairs, [(discretisation, self.dot_inputs_array, self.dot_pair_inputs_array, lows, highs, pair_lows, pair_highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
+							)
+						else:
+							current_success = sum(
+								pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
+							)
+
+						cycle_success += current_success
+					results.append((cycle_count, cycle_success))
+
+			_logger.debug("Done, constructing output now")
+			row = {
+				"dipole_moment": actual_dipoles.dipoles[0].p,
+				"dipole_location": actual_dipoles.dipoles[0].s,
+				"dipole_frequency": actual_dipoles.dipoles[0].w
+			}
+			successes: List[float] = []
+			counts: List[int] = []
+			for model_index, (name, (count, result)) in enumerate(zip(self.model_names, results)):
+
+				row[f"{name}_success"] = result
+				row[f"{name}_count"] = count
+				successes.append(max(result, 0.5))
+				counts.append(count)
+
+			success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
+			new_probabilities = [(succ / count) * old_prob / success_weight for succ, count, old_prob in zip(successes, counts, self.probabilities)]
+			self.probabilities = new_probabilities
+			for name, probability in zip(self.model_names, self.probabilities):
+				row[f"{name}_prob"] = probability
+			_logger.info(row)
+
+			with open(self.filename, "a", newline="") as outfile:
+				writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
+				writer.writerow(row)
+
+			if self.use_end_threshold:
+				max_prob = max(self.probabilities)
+				if max_prob > self.end_threshold:
+					_logger.info(f"Aborting early, because {max_prob} is greater than {self.end_threshold}")
+					break

deepdog/bayes_run.py

@@ -41,7 +41,7 @@ class BayesRun():
 	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, max_frequency: 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, max_frequency: float = None, end_threshold: float = None) -> None:
 		self.dot_inputs = dot_inputs
 		self.discretisations = [disc for (_, disc) in discretisations_with_names]
 		self.model_names = [name for (name, _) in discretisations_with_names]
@@ -50,7 +50,6 @@ class BayesRun():
 		self.run_count = run_count
 		self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
 		self.compensate_zeros = True
-
 		for name in self.model_names:
 			self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
 
@@ -60,6 +59,14 @@ class BayesRun():
 		self.filename = f"{timestamp}-{filename_slug}.csv"
 		self.max_frequency = max_frequency
 
+		if end_threshold is not None:
+			if 0 < end_threshold < 1:
+				self.end_threshold: float = end_threshold
+				self.use_end_threshold = True
+				_logger.info(f"Will abort early, at {self.end_threshold}.")
+			else:
+				raise ValueError(f"end_threshold should be between 0 and 1, but is actually {end_threshold}")
+
 	def go(self) -> None:
 		with open(self.filename, "a", newline="") as outfile:
 			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
@@ -88,7 +95,8 @@ class BayesRun():
 				"dipole_location": dipoles.dipoles[0].s,
 				"dipole_frequency": dipoles.dipoles[0].w
 			}
-			successes: List[int] = []
+			successes: List[float] = []
+			counts: List[int] = []
 			for model_index, (name, result) in enumerate(zip(self.model_names, results)):
 				count = 0
 				success = 0
@@ -99,10 +107,11 @@ class BayesRun():
 
 				row[f"{name}_success"] = success
 				row[f"{name}_count"] = count
-				successes.append(max(success, 1))
+				successes.append(max(success, 0.5))
+				counts.append(count)
 
-			success_weight = sum([succ * prob for succ, prob in zip(successes, self.probabilities)])
-			new_probabilities = [succ * old_prob / success_weight for succ, old_prob in zip(successes, self.probabilities)]
+			success_weight = sum([(succ / count) * prob for succ, count, prob in zip(successes, counts, self.probabilities)])
+			new_probabilities = [(succ / count) * old_prob / success_weight for succ, count, old_prob in zip(successes, counts, self.probabilities)]
 			self.probabilities = new_probabilities
 			for name, probability in zip(self.model_names, self.probabilities):
 				row[f"{name}_prob"] = probability
@@ -111,3 +120,9 @@ class BayesRun():
 			with open(self.filename, "a", newline="") as outfile:
 				writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
 				writer.writerow(row)
+
+			if self.use_end_threshold:
+				max_prob = max(self.probabilities)
+				if max_prob > self.end_threshold:
+					_logger.info(f"Aborting early, because {max_prob} is greater than {self.end_threshold}")
+					break

deepdog/diagnostic.py

@@ -39,6 +39,8 @@ class SingleDipoleDiagnostic():
 		self.s_result_x = self.result_dipole.s[0]
 		self.s_result_y = self.result_dipole.s[1]
 		self.s_result_z = self.result_dipole.s[2]
+		self.w_actual = self.actual_dipole.w
+		self.w_result = self.result_dipole.w
 
 
 class Diagnostic():
@@ -65,7 +67,7 @@ class Diagnostic():
 		self.discretisations_with_names = discretisations_with_names
 		self.model_count = len(self.discretisations_with_names)
 
-		self.csv_fields = ["model", "index", "bounds", "p_actual_x", "p_actual_y", "p_actual_z", "s_actual_x", "s_actual_y", "s_actual_z", "actual_dipole_freq", "success", "p_result_x", "p_result_y", "p_result_z", "s_result_x", "s_result_y", "s_result_z"]
+		self.csv_fields = ["model", "index", "bounds", "p_actual_x", "p_actual_y", "p_actual_z", "s_actual_x", "s_actual_y", "s_actual_z", "w_actual", "success", "p_result_x", "p_result_y", "p_result_z", "s_result_x", "s_result_y", "s_result_z", "w_result"]
 
 		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
 		self.filename = f"{timestamp}-{filename_slug}.diag.csv"
@@ -84,14 +86,14 @@ class Diagnostic():
 				results = pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(self.dots), discretisation.all_indices()))
 
 			with open(self.filename, "a", newline='') as outfile:
-				writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect='unix')
+				writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect='unix', extrasaction="ignore")
 
 				for idx, result in results:
 
 					bounds = discretisation.bounds(idx)
 
 					actual_success = result.success and result.cost <= 1e-10
-					diag_row = SingleDipoleDiagnostic(name, idx, bounds, self.dipoles.dipoles[0], discretisation.model.solution_as_dipoles(result.normalised_x), actual_success)
+					diag_row = SingleDipoleDiagnostic(name, idx, bounds, self.dipoles.dipoles[0], discretisation.model.solution_as_dipoles(result.normalised_x)[0], actual_success)
 					row = vars(diag_row)
 					_logger.debug(f"Writing result {row}")
 					writer.writerow(row)

do.sh

@@ -16,6 +16,10 @@ test() {
    poetry run pytest
 }
 
+release() {
+   ./scripts/release.sh
+}
+
 htmlcov() {
 	poetry run pytest --cov-report=html
 }

pyproject.toml

@@ -1,18 +1,19 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.2.0"
+version = "0.4.0"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = "^3.8,<3.10"
-pdme = "^0.5.0"
+pdme = "^0.6.1"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
 flake8 = "^4.0.1"
 pytest-cov = "^3.0.0"
-mypy = "^0.931"
+mypy = "^0.942"
+python-semantic-release = "^7.24.0"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
@@ -32,3 +33,7 @@ module = [
 	"scipy.optimize"
 ]
 ignore_missing_imports = true
+
+[tool.semantic_release]
+version_toml = "pyproject.toml:tool.poetry.version"
+tag_format = "{version}"

renovate.json

@@ -0,0 +1,3 @@
+{
+	"$schema": "https://docs.renovatebot.com/renovate-schema.json"
+}

scripts/patch.sh

@@ -1,29 +0,0 @@
-#!/usr/bin/env bash
-set -Eeuo pipefail
-
-if [ -z "$(git status --porcelain)" ]; then 
-	# Working directory clean
-	branch_name=$(git symbolic-ref -q HEAD)
-	branch_name=${branch_name##refs/heads/}
-	branch_name=${branch_name:-HEAD}
-	
-	poetry version patch
-	version=`sed 's/version = "\([0-9]*.[0-9]*.[0-9]*\)"/\1/p' -n <pyproject.toml`
-	read -p "Create commit for version $version? " -n 1 -r
-	echo    # (optional) move to a new line
-	if [[ $REPLY =~ ^[Yy]$ ]]
-	then
-		# do dangerous stuff
-		echo "Creating a new patch"
-		git add pyproject.toml
-		git commit -m "Created version $version"
-		git tag -a "$version" -m "patch.sh created version $version"
-		git push --tags
-	else
-		echo "Surrendering, clean up by reverting pyproject.toml..."
-		exit 2
-	fi
-else 
-	echo "Can't create patch version, working tree unclean..."
-	exit 1
-fi

scripts/release.sh

@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+set -Eeuo pipefail
+
+if [ -z "$(git status --porcelain)" ]; then
+	branch_name=$(git symbolic-ref -q HEAD)
+	branch_name=${branch_name##refs/heads/}
+	branch_name=${branch_name:-HEAD}
+	if [ $branch_name != "master" ]; then
+		echo "The current branch is not master!"
+		echo "I'd feel uncomfortable releasing from here..."
+		exit 3
+	fi
+
+	release_needed=false
+	if \
+		{ git log "$( 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='%b' | grep -q -E '^BREAKING CHANGE:' ; }
+	then
+		release_needed=true
+	fi
+	
+	if ! [ "$release_needed" = true ]; then
+		echo "No release needed..."
+		exit 0
+	fi
+
+	# Working directory clean
+	echo "Doing a dry run..."
+	npx standard-version --dry-run
+	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
+		git push --follow-tags origin master
+	else
+		echo "okay, never mind then..."
+		exit 2
+	fi
+else 
+	echo "Can't create release, working tree unclean..."
+	exit 1
+fi

scripts/standard-version/pyproject-updater.js

@@ -0,0 +1,11 @@
+const pattern = /(\[tool\.poetry\]\nname = "deepdog"\nversion = ")(?<vers>\d+\.\d+\.\d)(")/mg;
+
+module.exports.readVersion = function (contents) {
+	const result = pattern.exec(contents);
+	return result.groups.vers;
+}
+
+module.exports.writeVersion = function (contents, version) {
+	const newContents = contents.replace(pattern, `$1${version}$3`);
+	return newContents;
+}