chore(deps): update dependency flake8 to v6

Reviewed-on: #30

CHANGELOG.md

@@ -2,6 +2,21 @@
 
 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.
 
+### [0.8.9](https://gitea.deepak.science:2222/physics/pdme/compare/0.8.8...0.8.9) (2023-07-23)
+
+
+### Features
+
+* adds a bunch of mcmc generation code for log spaced models, yay ([f280448](https://gitea.deepak.science:2222/physics/pdme/commit/f280448cfe2fcf5bdc5ac2317ee52b27523bb49d))
+* adds utility functions for dealing with markov chain monte carlo ([feb0a5f](https://gitea.deepak.science:2222/physics/pdme/commit/feb0a5f6453dcb5e71a07c7749cd579dab15171c))
+
+### [0.8.8](https://gitea.deepak.science:2222/physics/pdme/compare/0.8.7...0.8.8) (2023-04-09)
+
+
+### Features
+
+* adds fast calc that allows for variable temp ([36454d5](https://gitea.deepak.science:2222/physics/pdme/commit/36454d5044d93b6b178e016b84dd59a5ebaf15e2))
+
 ### [0.8.7](https://gitea.deepak.science:2222/physics/pdme/compare/0.8.6...0.8.7) (2022-09-17)
 
 

README.md

@@ -5,7 +5,7 @@
 [![Jenkins](https://img.shields.io/jenkins/build?jobUrl=https%3A%2F%2Fjenkins.deepak.science%2Fjob%2Fgitea-physics%2Fjob%2Fpdme%2Fjob%2Fmaster&style=flat-square)](https://jenkins.deepak.science/job/gitea-physics/job/pdme/job/master/)
 ![Jenkins tests](https://img.shields.io/jenkins/tests?compact_message&jobUrl=https%3A%2F%2Fjenkins.deepak.science%2Fjob%2Fgitea-physics%2Fjob%2Fpdme%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%2Fpdme%2Fjob%2Fmaster%2F&style=flat-square)
-![Maintenance](https://img.shields.io/maintenance/yes/2022?style=flat-square)
+![Maintenance](https://img.shields.io/maintenance/yes/2023?style=flat-square)
 
 This repo has library code for evaluating dipole models.
 

do.sh

@@ -39,6 +39,15 @@ test() {
 	fi
 }
 
+updatesnap() {
+	echo "I am ${FUNCNAME[0]}ing"
+	if [[ "${DO_NIX_CUSTOM:=0}" -eq 1 ]]; then
+		pytest --snapshot-update
+	else
+		poetry run pytest --snapshot-update
+	fi
+}
+
 htmlcov() {
 	if [[ "${DO_NIX_CUSTOM:=0}" -eq 1 ]]; then
 		pytest --cov-report=html

pdme/model/log_spaced_random_choice_fixed_orientation_model.py

@@ -5,6 +5,11 @@ from pdme.measurement import (
 	OscillatingDipole,
 	OscillatingDipoleArrangement,
 )
+import logging
+from typing import Optional
+import pdme.subspace_simulation
+
+_logger = logging.getLogger(__name__)
 
 
 class LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
@@ -138,3 +143,51 @@ class LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
 		w = 10 ** rng.uniform(self.wexp_min, self.wexp_max, shape)
 
 		return numpy.stack([px, py, pz, sx, sy, sz, w], axis=-1)
+
+	def markov_chain_monte_carlo_proposal(
+		self,
+		dipole: numpy.ndarray,
+		stdev: pdme.subspace_simulation.DipoleStandardDeviation,
+		rng_arg: Optional[numpy.random.Generator] = None,
+	) -> numpy.ndarray:
+		if rng_arg is None:
+			rng_to_use = self.rng
+		else:
+			rng_to_use = rng_arg
+
+		px = dipole[0]
+		py = dipole[1]
+		pz = dipole[2]
+		# won't change p for this model of fixed dipole moment.
+
+		rx = dipole[3]
+		ry = dipole[4]
+		rz = dipole[5]
+
+		tentative_rx = rx + stdev.rx_step * rng_to_use.uniform(-1, 1)
+		if tentative_rx < self.xmin or tentative_rx > self.xmax:
+			tentative_rx = rx
+
+		tentative_ry = ry + stdev.ry_step * rng_to_use.uniform(-1, 1)
+		if tentative_ry < self.ymin or tentative_ry > self.ymax:
+			tentative_ry = ry
+		tentative_rz = rz + stdev.rz_step * rng_to_use.uniform(-1, 1)
+		if tentative_rz < self.zmin or tentative_rz > self.zmax:
+			tentative_rz = rz
+
+		w = dipole[6]
+		tentative_w = numpy.exp(
+			numpy.log(w) + (stdev.w_log_step * rng_to_use.uniform(-1, 1))
+		)
+		tentative_dip = numpy.array(
+			[
+				px,
+				py,
+				pz,
+				tentative_rx,
+				tentative_ry,
+				tentative_rz,
+				tentative_w,
+			]
+		)
+		return tentative_dip

pdme/model/log_spaced_random_choice_model.py

@@ -5,6 +5,8 @@ from pdme.measurement import (
 	OscillatingDipole,
 	OscillatingDipoleArrangement,
 )
+import pdme.subspace_simulation
+from typing import Optional
 
 
 class LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel(DipoleModel):
@@ -125,3 +127,73 @@ class LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel(DipoleModel):
 		w = 10 ** rng.uniform(self.wexp_min, self.wexp_max, shape)
 
 		return numpy.stack([px, py, pz, sx, sy, sz, w], axis=-1)
+
+	def markov_chain_monte_carlo_proposal(
+		self,
+		dipole: numpy.ndarray,
+		stdev: pdme.subspace_simulation.DipoleStandardDeviation,
+		rng_arg: Optional[numpy.random.Generator] = None,
+	) -> numpy.ndarray:
+		if rng_arg is None:
+			rng_to_use = self.rng
+		else:
+			rng_to_use = rng_arg
+
+		px = dipole[0]
+		py = dipole[1]
+		pz = dipole[2]
+		theta = numpy.arccos(pz / self.pfixed)
+		phi = numpy.arctan2(py, px)
+
+		# need to step phi, theta, rx, ry, rz, w
+		# then p^\ast is 1/(2 phi_step) and Delta = phi_step(2 * {0, 1} - 1)
+		delta_phi = stdev.p_phi_step * rng_to_use.uniform(-1, 1)
+		tentative_phi = phi + delta_phi
+
+		# theta
+		delta_theta = stdev.p_theta_step * rng_to_use.uniform(-1, 1)
+		r = (numpy.sin(theta + delta_theta)) / (numpy.sin(theta))
+		if r > rng_to_use.uniform(0, 1):
+			tentative_theta = theta + delta_theta
+		else:
+			tentative_theta = theta
+
+		tentative_px = (
+			self.pfixed * numpy.sin(tentative_theta) * numpy.cos(tentative_phi)
+		)
+		tentative_py = (
+			self.pfixed * numpy.sin(tentative_theta) * numpy.sin(tentative_phi)
+		)
+		tentative_pz = self.pfixed * numpy.cos(tentative_theta)
+
+		rx = dipole[3]
+		ry = dipole[4]
+		rz = dipole[5]
+
+		tentative_rx = rx + stdev.rx_step * rng_to_use.uniform(-1, 1)
+		if tentative_rx < self.xmin or tentative_rx > self.xmax:
+			tentative_rx = rx
+
+		tentative_ry = ry + stdev.ry_step * rng_to_use.uniform(-1, 1)
+		if tentative_ry < self.ymin or tentative_ry > self.ymax:
+			tentative_ry = ry
+		tentative_rz = rz + stdev.rz_step * rng_to_use.uniform(-1, 1)
+		if tentative_rz < self.zmin or tentative_rz > self.zmax:
+			tentative_rz = rz
+
+		w = dipole[6]
+		tentative_w = numpy.exp(
+			numpy.log(w) + (stdev.w_log_step * rng_to_use.uniform(-1, 1))
+		)
+		tentative_dip = numpy.array(
+			[
+				tentative_px,
+				tentative_py,
+				tentative_pz,
+				tentative_rx,
+				tentative_ry,
+				tentative_rz,
+				tentative_w,
+			]
+		)
+		return tentative_dip

pdme/model/log_spaced_random_choice_xy_model.py

@@ -5,6 +5,8 @@ from pdme.measurement import (
 	OscillatingDipole,
 	OscillatingDipoleArrangement,
 )
+import pdme.subspace_simulation
+from typing import Optional
 
 
 class LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(DipoleModel):
@@ -123,3 +125,59 @@ class LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(DipoleModel):
 		w = 10 ** rng.uniform(self.wexp_min, self.wexp_max, shape)
 
 		return numpy.stack([px, py, pz, sx, sy, sz, w], axis=-1)
+
+	def markov_chain_monte_carlo_proposal(
+		self,
+		dipole: numpy.ndarray,
+		stdev: pdme.subspace_simulation.DipoleStandardDeviation,
+		rng_arg: Optional[numpy.random.Generator] = None,
+	) -> numpy.ndarray:
+		if rng_arg is None:
+			rng_to_use = self.rng
+		else:
+			rng_to_use = rng_arg
+
+		px = dipole[0]
+		py = dipole[1]
+		pz = dipole[2]
+		phi = numpy.arctan2(py, px)
+
+		# need to step phi, rx, ry, rz, w
+		# then p^\ast is 1/(2 phi_step) and Delta = phi_step(2 * {0, 1} - 1)
+		delta_phi = stdev.p_phi_step * rng_to_use.uniform(-1, 1)
+		tentative_phi = phi + delta_phi
+
+		tentative_px = self.pfixed * numpy.cos(tentative_phi)
+		tentative_py = self.pfixed * numpy.sin(tentative_phi)
+
+		rx = dipole[3]
+		ry = dipole[4]
+		rz = dipole[5]
+
+		tentative_rx = rx + stdev.rx_step * rng_to_use.uniform(-1, 1)
+		if tentative_rx < self.xmin or tentative_rx > self.xmax:
+			tentative_rx = rx
+
+		tentative_ry = ry + stdev.ry_step * rng_to_use.uniform(-1, 1)
+		if tentative_ry < self.ymin or tentative_ry > self.ymax:
+			tentative_ry = ry
+		tentative_rz = rz + stdev.rz_step * rng_to_use.uniform(-1, 1)
+		if tentative_rz < self.zmin or tentative_rz > self.zmax:
+			tentative_rz = rz
+
+		w = dipole[6]
+		tentative_w = numpy.exp(
+			numpy.log(w) + (stdev.w_log_step * rng_to_use.uniform(-1, 1))
+		)
+		tentative_dip = numpy.array(
+			[
+				tentative_px,
+				tentative_py,
+				pz,
+				tentative_rx,
+				tentative_ry,
+				tentative_rz,
+				tentative_w,
+			]
+		)
+		return tentative_dip

pdme/model/model.py

@@ -4,7 +4,8 @@ from pdme.measurement import (
 	OscillatingDipoleArrangement,
 )
 import logging
-
+import pdme.subspace_simulation
+from typing import List, Tuple, Optional
 
 _logger = logging.getLogger(__name__)
 
@@ -32,3 +33,67 @@ class DipoleModel:
 		For a given DipoleModel, gets a set of dipole collections as a monte_carlo_n x dipole_count x 7 numpy array.
 		"""
 		raise NotImplementedError
+
+	def markov_chain_monte_carlo_proposal(
+		self,
+		dipole: numpy.ndarray,
+		stdev: pdme.subspace_simulation.DipoleStandardDeviation,
+		rng_arg: Optional[numpy.random.Generator] = None,
+	) -> numpy.ndarray:
+		raise NotImplementedError
+
+	def get_mcmc_chain(
+		self,
+		seed,
+		cost_function,
+		chain_length,
+		stdevs: pdme.subspace_simulation.MCMCStandardDeviation,
+		initial_cost: Optional[float] = None,
+		rng_arg: Optional[numpy.random.Generator] = None,
+	) -> List[Tuple[float, numpy.ndarray]]:
+		"""
+		performs constrained markov chain monte carlo starting on seed parameter.
+		The cost function given is used as a constrained to condition the chain;
+		a new state is only accepted if cost_function(state) < cost_function(previous_state).
+		The stdevs passed in are the stdevs we're expected to use.
+
+		Because we're using this for subspace simulation where our proposal function is not too important, we're in good shape.
+		Note that for our adaptive stdevs to work, there's an unwritten contract that we sort each dipole in the state by frequency (increasing).
+
+		The seed is a list of dipoles, and each chain state is a list of dipoles as well.
+
+		initial_cost is a performance guy that lets you pre-populate the initial cost used to define the condition.
+		Probably premature optimisation.
+
+		Returns a chain of [ (cost: float, state: dipole_ndarray ) ] format.
+		"""
+		_logger.debug(
+			f"Starting Markov Chain Monte Carlo with seed: {seed} for chain length {chain_length} and provided stdevs {stdevs}"
+		)
+		chain: List[Tuple[float, numpy.ndarray]] = []
+		current = seed
+		if initial_cost is None:
+			cost_to_compare = cost_function(current)
+		else:
+			cost_to_compare = initial_cost
+		current_cost = cost_to_compare
+		for i in range(chain_length):
+			dips = []
+			for dipole_index, dipole in enumerate(current):
+				stdev = stdevs[dipole_index]
+				tentative_dip = self.markov_chain_monte_carlo_proposal(
+					dipole, stdev, rng_arg
+				)
+
+				dips.append(tentative_dip)
+			dips_array = pdme.subspace_simulation.sort_array_of_dipoles_by_frequency(
+				dips
+			)
+			tentative_cost = cost_function(dips_array)
+			if tentative_cost < cost_to_compare:
+				chain.append((tentative_cost, dips_array))
+				current = dips_array
+				current_cost = tentative_cost
+			else:
+				chain.append((current_cost, current))
+		return chain

pdme/subspace_simulation/__init__.py

@@ -0,0 +1,55 @@
+from dataclasses import dataclass
+from typing import Sequence
+import numpy
+from pdme.subspace_simulation.mcmc_costs import (
+	proportional_cost,
+	proportional_costs_vs_actual_measurement,
+)
+
+
+@dataclass
+class DipoleStandardDeviation:
+	"""
+	contains the dipole standard deviation to be used in porposals for markov chain monte carlo
+	"""
+
+	p_phi_step: float
+	p_theta_step: float
+	rx_step: float
+	ry_step: float
+	rz_step: float
+	w_log_step: float
+
+
+class MCMCStandardDeviation:
+	"""
+	wrapper for multiple standard deviations, allows for flexible length stuff
+	"""
+
+	def __init__(self, stdevs: Sequence[DipoleStandardDeviation]):
+		self.stdevs = stdevs
+		if len(stdevs) < 1:
+			raise ValueError(f"Got stdevs: {stdevs}, must have length > 1")
+
+	def __getitem__(self, key):
+		newkey = key % len(self.stdevs)
+		return self.stdevs[newkey]
+
+
+def sort_array_of_dipoles_by_frequency(configuration) -> numpy.ndarray:
+	"""
+	Say we have a situation of 2 dipoles, and we've created 8 samples. Then we'll have an (8, 2, 7) numpy array.
+	For each of the 8 samples, we want the 2 dipoles to be in order of frequency.
+
+	Utility function.
+	"""
+	return numpy.array(sorted(configuration, key=lambda l: l[6]))
+
+
+__all__ = [
+	"DipoleStandardDeviation",
+	"MCMCStandardDeviation",
+	"sort_array_of_dipoles_by_frequency",
+	"proportional_cost",
+	"proportional_costs_vs_actual_measurement",
+]

pdme/subspace_simulation/mcmc_costs.py

@@ -0,0 +1,20 @@
+import numpy
+import numpy.typing
+import pdme.util.fast_v_calc
+
+
+def proportional_cost(a: numpy.ndarray, b: numpy.ndarray) -> numpy.ndarray:
+	tops = numpy.max(b / a, axis=-1)
+	bottoms = numpy.max(a / b, axis=-1)
+	return numpy.maximum(tops, bottoms)
+
+
+def proportional_costs_vs_actual_measurement(
+	dot_inputs_array: numpy.ndarray,
+	actual_measurement_array: numpy.ndarray,
+	dipoles_to_test: numpy.ndarray,
+) -> numpy.ndarray:
+	vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(
+		dot_inputs_array, numpy.array([dipoles_to_test])
+	)
+	return proportional_cost(actual_measurement_array, vals)

pdme/util/fast_v_calc.py

@@ -69,6 +69,47 @@ def fast_vs_for_dipoleses(
 	return numpy.einsum("...j->...", ases * bses)
 
 
+def fast_vs_for_asymmetric_dipoleses(
+	dot_inputs: numpy.ndarray, dipoleses: numpy.ndarray, temp: numpy.ndarray
+) -> numpy.ndarray:
+	"""
+	No error correction here baby.
+	Expects dot_inputs to be numpy array of [rx, ry, rz, f] entries, so a n by 4 where n is number of measurement points.
+
+	Dipoleses are expected to be array of arrays of arrays:
+	list of sets of dipoles which are part of a single arrangement to be added together.
+	Within each dipole, the expected format is [px, py, pz, sx, sy, sz, e1, e2, w]
+	The passed in w is expected to be half the actual. This is bad, but here for historical reasons to be changed later.
+	"""
+	raw_ps = dipoleses[:, :, 0:3]
+	ss = dipoleses[:, :, 3:6]
+	e1s = dipoleses[:, :, 6]
+	e2s = dipoleses[:, :, 7]
+	raw_ws = dipoleses[:, :, 8]
+
+	rs = dot_inputs[:, 0:3]
+	fs = dot_inputs[:, 3]
+
+	diffses = rs[:, None] - ss[:, None, :]
+
+	w1s = numpy.exp(-e1s / temp) * raw_ws
+	w2s = numpy.exp(-e2s / temp) * raw_ws
+
+	mag_prefactor = 4 * ((w1s * w2s) / ((w1s + w2s) ** 2))
+	ws = w1s + w2s
+
+	# some annoying broadcast thing here?
+	ps = (raw_ps.T * mag_prefactor.T).T
+
+	norms = numpy.linalg.norm(diffses, axis=3) ** 3
+
+	ases = (numpy.einsum("abcd,acd->abc", diffses, ps) / norms) ** 2
+
+	bses = (1 / numpy.pi) * (ws[:, None, :] / (fs[:, None] ** 2 + ws[:, None, :] ** 2))
+
+	return numpy.einsum("...j->...", ases * bses)
+
+
 def between(a: numpy.ndarray, low: numpy.ndarray, high: numpy.ndarray) -> numpy.ndarray:
 	"""
 	Intended specifically for the case where a is a list of arrays, and each array must be between the single array low and high, but without error checking.

poetry.lock

@@ -20,28 +20,6 @@ six = "*"
 [package.extras]
 test = ["astroid", "pytest"]
 
-[[package]]
-name = "atomicwrites"
-version = "1.4.0"
-description = "Atomic file writes."
-category = "dev"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-
-[[package]]
-name = "attrs"
-version = "21.4.0"
-description = "Classes Without Boilerplate"
-category = "dev"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-
-[package.extras]
-dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"]
-docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
-tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
-
 [[package]]
 name = "backcall"
 version = "0.2.0"
@@ -91,9 +69,17 @@ category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
+[[package]]
+name = "colored"
+version = "1.4.4"
+description = "Simple library for color and formatting to terminal"
+category = "dev"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "coverage"
-version = "6.4.1"
+version = "7.2.7"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
@@ -113,6 +99,17 @@ category = "dev"
 optional = false
 python-versions = ">=3.5"
 
+[[package]]
+name = "exceptiongroup"
+version = "1.1.2"
+description = "Backport of PEP 654 (exception groups)"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+test = ["pytest (>=6)"]
+
 [[package]]
 name = "executing"
 version = "0.8.3"
@@ -349,14 +346,6 @@ python-versions = "*"
 [package.extras]
 tests = ["pytest"]
 
-[[package]]
-name = "py"
-version = "1.11.0"
-description = "library with cross-python path, ini-parsing, io, code, log facilities"
-category = "dev"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-
 [[package]]
 name = "pycodestyle"
 version = "2.8.0"
@@ -394,32 +383,30 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "6.2.5"
+version = "7.4.0"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
-atomicwrites = {version = ">=1.0", markers = "sys_platform == \"win32\""}
-attrs = ">=19.2.0"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
+exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
 pluggy = ">=0.12,<2.0"
-py = ">=1.8.2"
-toml = "*"
+tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]
+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"
-version = "3.0.0"
+version = "4.1.0"
 description = "Pytest plugin for measuring coverage."
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 coverage = {version = ">=5.2.1", extras = ["toml"]}
@@ -430,14 +417,19 @@ testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtuale
 
 [[package]]
 name = "scipy"
-version = "1.8.0"
-description = "SciPy: Scientific Library for Python"
+version = "1.10.0"
+description = "Fundamental algorithms for scientific computing in Python"
 category = "main"
 optional = false
-python-versions = ">=3.8,<3.11"
+python-versions = "<3.12,>=3.8"
 
 [package.dependencies]
-numpy = ">=1.17.3,<1.25.0"
+numpy = ">=1.19.5,<1.27.0"
+
+[package.extras]
+test = ["pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack", "pooch"]
+doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-design (>=0.2.0)", "matplotlib (>2)", "numpydoc"]
+dev = ["mypy", "typing-extensions", "pycodestyle", "flake8", "rich-click", "click", "doit (>=0.36.0)", "pydevtool"]
 
 [[package]]
 name = "six"
@@ -464,12 +456,16 @@ pure-eval = "*"
 tests = ["pytest", "typeguard", "pygments", "littleutils", "cython"]
 
 [[package]]
-name = "toml"
-version = "0.10.2"
-description = "Python Library for Tom's Obvious, Minimal Language"
+name = "syrupy"
+version = "4.0.8"
+description = "Pytest Snapshot Test Utility"
 category = "dev"
 optional = false
-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+python-versions = ">=3.8.1,<4"
+
+[package.dependencies]
+colored = ">=1.3.92,<2.0.0"
+pytest = ">=7.0.0,<8.0.0"
 
 [[package]]
 name = "tomli"
@@ -508,307 +504,51 @@ python-versions = "*"
 
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-content-hash = "53a256043845daf5745f0553c31904a14dd3ea6c6fc9c6754fb4a8e949bed86a"
+python-versions = ">=3.8.1,<3.10"
+content-hash = "b5275c33449e8f85acbf9c0f6dfe1ec4e7296adfa16360d782b33534e1223638"
 
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-    {file = "scipy-1.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad5be4039147c808e64f99c0e8a9641eb5d2fa079ff5894dcd8240e94e347af4"},
-    {file = "scipy-1.8.0-cp38-cp38-win32.whl", hash = "sha256:3d9dd6c8b93a22bf9a3a52d1327aca7e092b1299fb3afc4f89e8eba381be7b59"},
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-    {file = "scipy-1.8.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:de2e80ee1d925984c2504812a310841c241791c5279352be4707cdcd7c255039"},
-    {file = "scipy-1.8.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:c2bae431d127bf0b1da81fc24e4bba0a84d058e3a96b9dd6475dfcb3c5e8761e"},
-    {file = "scipy-1.8.0-cp39-cp39-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:723b9f878095ed994756fa4ee3060c450e2db0139c5ba248ee3f9628bd64e735"},
-    {file = "scipy-1.8.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:011d4386b53b933142f58a652aa0f149c9b9242abd4f900b9f4ea5fbafc86b89"},
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-    {file = "scipy-1.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3720d0124aced49f6f2198a6900304411dbbeed12f56951d7c66ebef05e3df6"},
-    {file = "scipy-1.8.0-cp39-cp39-win32.whl", hash = "sha256:3d573228c10a3a8c32b9037be982e6440e411b443a6267b067cac72f690b8d56"},
-    {file = "scipy-1.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:bb7088e89cd751acf66195d2f00cf009a1ea113f3019664032d9075b1e727b6c"},
-    {file = "scipy-1.8.0.tar.gz", hash = "sha256:31d4f2d6b724bc9a98e527b5849b8a7e589bf1ea630c33aa563eda912c9ff0bd"},
-]
-six = [
-    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
-    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
-]
-stack-data = [
-    {file = "stack_data-0.2.0-py3-none-any.whl", hash = "sha256:999762f9c3132308789affa03e9271bbbe947bf78311851f4d485d8402ed858e"},
-    {file = "stack_data-0.2.0.tar.gz", hash = "sha256:45692d41bd633a9503a5195552df22b583caf16f0b27c4e58c98d88c8b648e12"},
-]
-toml = [
-    {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
-    {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
-]
-tomli = [
-    {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
-    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
-]
-traitlets = [
-    {file = "traitlets-5.1.1-py3-none-any.whl", hash = "sha256:2d313cc50a42cd6c277e7d7dc8d4d7fedd06a2c215f78766ae7b1a66277e0033"},
-    {file = "traitlets-5.1.1.tar.gz", hash = "sha256:059f456c5a7c1c82b98c2e8c799f39c9b8128f6d0d46941ee118daace9eb70c7"},
-]
-typing-extensions = [
-    {file = "typing_extensions-4.0.1-py3-none-any.whl", hash = "sha256:7f001e5ac290a0c0401508864c7ec868be4e701886d5b573a9528ed3973d9d3b"},
-    {file = "typing_extensions-4.0.1.tar.gz", hash = "sha256:4ca091dea149f945ec56afb48dae714f21e8692ef22a395223bcd328961b6a0e"},
-]
-wcwidth = [
-    {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
-    {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
-]
+appnope = []
+asttokens = []
+backcall = []
+black = []
+click = []
+colorama = []
+colored = []
+coverage = []
+decorator = []
+exceptiongroup = []
+executing = []
+flake8 = []
+iniconfig = []
+ipython = []
+jedi = []
+matplotlib-inline = []
+mccabe = []
+mypy = []
+mypy-extensions = []
+numpy = []
+packaging = []
+parso = []
+pathspec = []
+pexpect = []
+pickleshare = []
+platformdirs = []
+pluggy = []
+prompt-toolkit = []
+ptyprocess = []
+pure-eval = []
+pycodestyle = []
+pyflakes = []
+pygments = []
+pyparsing = []
+pytest = []
+pytest-cov = []
+scipy = []
+six = []
+stack-data = []
+syrupy = []
+tomli = []
+traitlets = []
+typing-extensions = []
+wcwidth = []

pyproject.toml

@@ -1,23 +1,24 @@
 [tool.poetry]
 name = "pdme"
-version = "0.8.7"
+version = "0.8.9"
 description = "Python dipole model evaluator"
 authors = ["Deepak <dmallubhotla+github@gmail.com>"]
 license = "GPL-3.0-only"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = "^3.8,<3.10"
+python = ">=3.8.1,<3.10"
 numpy = "^1.22.3"
-scipy = "~1.8"
+scipy = "~1.10"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
 flake8 = "^6.0.0"
-pytest-cov = "^3.0.0"
+pytest-cov = "^4.1.0"
 mypy = "^0.961"
 ipython = "^8.2.0"
 black = "^22.3.0"
+syrupy = "^4.0.8"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]

tests/model/__snapshots__/test_log_spaced_fixed_orientation_fixedxy_orientation_mcmc.ambr

@@ -0,0 +1,55 @@
+# serializer version: 1
+# name: test_log_spaced_fixedxy_orientation_mcmc_basic
+  list([
+    tuple(
+      array([3984.46179656]),
+      array([[ 9.55610128,  2.94634152,  0.        ,  9.21529051, -2.46576127,
+           2.42481096,  9.19034554]]),
+    ),
+    tuple(
+      array([8583.99087872]),
+      array([[ 9.99991539,  0.04113671,  0.        ,  8.71258954, -2.26599865,
+           2.60452102,  6.37042214]]),
+    ),
+    tuple(
+      array([6215.6376616]),
+      array([[ 9.81950685, -1.89137124,  0.        ,  8.90637055, -2.48043039,
+           2.28444435,  8.84239221]]),
+    ),
+    tuple(
+      array([424.73328466]),
+      array([[ 1.00028483,  9.94984574,  0.        ,  8.53064898, -2.59230757,
+           2.33774773,  8.6714416 ]]),
+    ),
+    tuple(
+      array([300.92203808]),
+      array([[ 1.4003442 ,  9.90146636,  0.        ,  8.05557992, -2.6753126 ,
+           2.65915755, 13.02021385]]),
+    ),
+    tuple(
+      array([2400.01072771]),
+      array([[ 9.97761813,  0.66868263,  0.        ,  8.69171028, -2.73145011,
+           2.90140456, 19.94999593]]),
+    ),
+    tuple(
+      array([5001.46205113]),
+      array([[ 9.93976109, -1.09596962,  0.        ,  8.95245025, -2.59409162,
+           2.90140456,  9.75535945]]),
+    ),
+    tuple(
+      array([195.21980745]),
+      array([[ 0.20690762,  9.99785923,  0.        ,  9.59636585, -2.83240984,
+           2.90140456, 16.14771567]]),
+    ),
+    tuple(
+      array([2698.2588445]),
+      array([[-9.68130127, -2.50447712,  0.        ,  8.94823619, -2.92889659,
+           2.77065328, 13.63173263]]),
+    ),
+    tuple(
+      array([1193.69854739]),
+      array([[-6.16597091, -7.87278875,  0.        ,  9.62210721, -2.75993924,
+           2.77065328,  5.64553534]]),
+    ),
+  ])
+# ---

tests/model/__snapshots__/test_log_spaced_fixed_orientation_free_orientation_mcmc.ambr

@@ -0,0 +1,55 @@
+# serializer version: 1
+# name: test_log_spaced_free_orientation_mcmc_basic
+  list([
+    tuple(
+      array([3167.67112687]),
+      array([[ 9.60483896, -1.41627817, -2.3960853 , -4.76615152, -1.80902942,
+           2.11809123, 16.17452242]]),
+    ),
+    tuple(
+      array([3167.67112687]),
+      array([[ 9.60483896, -1.41627817, -2.3960853 , -4.76615152, -1.80902942,
+           2.11809123, 16.17452242]]),
+    ),
+    tuple(
+      array([3167.67112687]),
+      array([[ 9.60483896, -1.41627817, -2.3960853 , -4.76615152, -1.80902942,
+           2.11809123, 16.17452242]]),
+    ),
+    tuple(
+      array([736.03065271]),
+      array([[ 4.1660069 , -8.11557337,  4.0965663 , -4.35968351, -1.97945216,
+           2.43615641, 12.92143144]]),
+    ),
+    tuple(
+      array([736.03065271]),
+      array([[ 4.1660069 , -8.11557337,  4.0965663 , -4.35968351, -1.97945216,
+           2.43615641, 12.92143144]]),
+    ),
+    tuple(
+      array([736.03065271]),
+      array([[ 4.1660069 , -8.11557337,  4.0965663 , -4.35968351, -1.97945216,
+           2.43615641, 12.92143144]]),
+    ),
+    tuple(
+      array([2248.07799863]),
+      array([[-1.71755535, -5.59925137,  8.10545419, -4.03306318, -1.81098441,
+           2.77407111, 32.28020575]]),
+    ),
+    tuple(
+      array([1663.31067274]),
+      array([[-5.16785855,  2.7558756 ,  8.10545419, -3.34620897, -1.74763642,
+           2.42770463, 52.98214008]]),
+    ),
+    tuple(
+      array([1329.27041439]),
+      array([[ -1.39600464,   9.69718343,  -2.00394725,  -2.59147366,
+           -1.91246681,   2.07361175, 123.01833742]]),
+    ),
+    tuple(
+      array([355.76955919]),
+      array([[  9.76047401,   0.84696075,  -2.00394725,  -3.04310053,
+           -1.99338573,   2.1185589 , 271.35743739]]),
+    ),
+  ])
+# ---

tests/model/__snapshots__/test_log_spaced_fixed_orientation_mcmc.ambr

@@ -0,0 +1,55 @@
+# serializer version: 1
+# name: test_log_spaced_fixed_orientation_mcmc_basic
+  list([
+    tuple(
+      array([50.56831193]),
+      array([[ 0.        ,  0.        , 10.        , -2.3960853 ,  4.23246234,
+           2.26169242, 39.39900844]]),
+    ),
+    tuple(
+      array([50.56831193]),
+      array([[ 0.        ,  0.        , 10.        , -2.3960853 ,  4.23246234,
+           2.26169242, 39.39900844]]),
+    ),
+    tuple(
+      array([47.40865455]),
+      array([[ 0.        ,  0.        , 10.        , -2.03666518,  4.14084039,
+           2.21309317, 47.82371559]]),
+    ),
+    tuple(
+      array([47.40865455]),
+      array([[ 0.        ,  0.        , 10.        , -2.03666518,  4.14084039,
+           2.21309317, 47.82371559]]),
+    ),
+    tuple(
+      array([47.40865455]),
+      array([[ 0.        ,  0.        , 10.        , -2.03666518,  4.14084039,
+           2.21309317, 47.82371559]]),
+    ),
+    tuple(
+      array([47.40865455]),
+      array([[ 0.        ,  0.        , 10.        , -2.03666518,  4.14084039,
+           2.21309317, 47.82371559]]),
+    ),
+    tuple(
+      array([22.93279028]),
+      array([[ 0.        ,  0.        , 10.        , -1.63019717,  3.97041764,
+           2.53115835, 38.2051999 ]]),
+    ),
+    tuple(
+      array([28.81197733]),
+      array([[ 0.        ,  0.        , 10.        , -1.14570315,  4.07709911,
+           2.48697441, 49.58615195]]),
+    ),
+    tuple(
+      array([28.81197733]),
+      array([[ 0.        ,  0.        , 10.        , -1.14570315,  4.07709911,
+           2.48697441, 49.58615195]]),
+    ),
+    tuple(
+      array([40.97406005]),
+      array([[ 0.        ,  0.        , 10.        , -0.50178755,  3.83878089,
+           2.93560796, 82.07827571]]),
+    ),
+  ])
+# ---

tests/model/__snapshots__/test_log_spaced_random_count_multiple_dipole_fixed_orientation_fixed_magnitude_model.ambr

@@ -0,0 +1,31 @@
+# serializer version: 1
+# name: test_random_count_multiple_dipole_fixed_or_fixed_mag_model_get_n_dipoles
+  list([
+    list([
+      list([
+        10.0,
+        0.0,
+        6.123233995736766e-16,
+        -2.3960852996076447,
+        4.232462337639554,
+        2.2616924238635443,
+        39.399008444891905,
+      ]),
+    ]),
+  ])
+# ---
+# name: test_random_count_multiple_dipole_fixed_or_fixed_mag_model_get_n_dipoles.1
+  list([
+    list([
+      list([
+        10.0,
+        0.0,
+        6.123233995736766e-16,
+        -2.3960852996076447,
+        4.232462337639554,
+        2.2616924238635443,
+        39.399008444891905,
+      ]),
+    ]),
+  ])
+# ---

tests/model/test_log_spaced_fixed_orientation_fixedxy_orientation_mcmc.py

@@ -0,0 +1,90 @@
+from pdme.model import (
+	LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
+)
+import pdme.inputs
+import pdme.measurement.input_types
+import pdme.subspace_simulation
+import numpy
+
+SEED_TO_USE = 42
+
+
+def get_cost_function():
+	x_min = -10
+	x_max = 10
+	y_min = -5
+	y_max = 5
+	z_min = 2
+	z_max = 3
+	p_fixed = 10
+	max_frequency = 5
+
+	model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(
+		x_min,
+		x_max,
+		y_min,
+		y_max,
+		z_min,
+		z_max,
+		0,
+		max_frequency,
+		p_fixed,
+		1,
+		0.5,
+	)
+	model.rng = numpy.random.default_rng(SEED_TO_USE)
+
+	freqs = [0.01, 0.1, 1, 10, 100]
+	dot_positions = [[-1.5, 0, 0], [-0.5, 0, 0], [0.5, 0, 0], [1.5, 0, 0]]
+	dot_inputs = pdme.inputs.inputs_with_frequency_range(dot_positions, freqs)
+	dot_input_array = pdme.measurement.input_types.dot_inputs_to_array(dot_inputs)
+
+	actual_dipoles = model.get_dipoles(0, numpy.random.default_rng(SEED_TO_USE))
+	actual_measurements = actual_dipoles.get_dot_measurements(dot_inputs)
+	actual_measurements_array = numpy.array([m.v for m in actual_measurements])
+
+	def cost_to_use(sample_dipoles: numpy.ndarray) -> numpy.ndarray:
+		return pdme.subspace_simulation.proportional_costs_vs_actual_measurement(
+			dot_input_array, actual_measurements_array, sample_dipoles
+		)
+
+	return cost_to_use
+
+
+def test_log_spaced_fixedxy_orientation_mcmc_basic(snapshot):
+
+	x_min = -10
+	x_max = 10
+	y_min = -5
+	y_max = 5
+	z_min = 2
+	z_max = 3
+	p_fixed = 10
+	max_frequency = 5
+
+	model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(
+		x_min,
+		x_max,
+		y_min,
+		y_max,
+		z_min,
+		z_max,
+		0,
+		max_frequency,
+		p_fixed,
+		1,
+		0.5,
+	)
+	model.rng = numpy.random.default_rng(1234)
+
+	seed = model.get_monte_carlo_dipole_inputs(1, -1)[0]
+
+	cost_function = get_cost_function()
+	stdev = pdme.subspace_simulation.DipoleStandardDeviation(2, 2, 1, 0.25, 0.5, 1)
+	stdevs = pdme.subspace_simulation.MCMCStandardDeviation([stdev])
+
+	chain = model.get_mcmc_chain(
+		seed, cost_function, 10, stdevs, rng_arg=numpy.random.default_rng(1515)
+	)
+
+	assert chain == snapshot

tests/model/test_log_spaced_fixed_orientation_free_orientation_mcmc.py

@@ -0,0 +1,90 @@
+from pdme.model import (
+	LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
+)
+import pdme.inputs
+import pdme.measurement.input_types
+import pdme.subspace_simulation
+import numpy
+
+SEED_TO_USE = 42
+
+
+def get_cost_function():
+	x_min = -10
+	x_max = 10
+	y_min = -5
+	y_max = 5
+	z_min = 2
+	z_max = 3
+	p_fixed = 10
+	max_frequency = 5
+
+	model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel(
+		x_min,
+		x_max,
+		y_min,
+		y_max,
+		z_min,
+		z_max,
+		0,
+		max_frequency,
+		p_fixed,
+		1,
+		0.5,
+	)
+	model.rng = numpy.random.default_rng(SEED_TO_USE)
+
+	freqs = [0.01, 0.1, 1, 10, 100]
+	dot_positions = [[-1.5, 0, 0], [-0.5, 0, 0], [0.5, 0, 0], [1.5, 0, 0]]
+	dot_inputs = pdme.inputs.inputs_with_frequency_range(dot_positions, freqs)
+	dot_input_array = pdme.measurement.input_types.dot_inputs_to_array(dot_inputs)
+
+	actual_dipoles = model.get_dipoles(0, numpy.random.default_rng(SEED_TO_USE))
+	actual_measurements = actual_dipoles.get_dot_measurements(dot_inputs)
+	actual_measurements_array = numpy.array([m.v for m in actual_measurements])
+
+	def cost_to_use(sample_dipoles: numpy.ndarray) -> numpy.ndarray:
+		return pdme.subspace_simulation.proportional_costs_vs_actual_measurement(
+			dot_input_array, actual_measurements_array, sample_dipoles
+		)
+
+	return cost_to_use
+
+
+def test_log_spaced_free_orientation_mcmc_basic(snapshot):
+
+	x_min = -10
+	x_max = 10
+	y_min = -5
+	y_max = 5
+	z_min = 2
+	z_max = 3
+	p_fixed = 10
+	max_frequency = 5
+
+	model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel(
+		x_min,
+		x_max,
+		y_min,
+		y_max,
+		z_min,
+		z_max,
+		0,
+		max_frequency,
+		p_fixed,
+		1,
+		0.5,
+	)
+	model.rng = numpy.random.default_rng(1234)
+
+	seed = model.get_monte_carlo_dipole_inputs(1, -1)[0]
+
+	cost_function = get_cost_function()
+	stdev = pdme.subspace_simulation.DipoleStandardDeviation(2, 2, 1, 0.25, 0.5, 1)
+	stdevs = pdme.subspace_simulation.MCMCStandardDeviation([stdev])
+
+	chain = model.get_mcmc_chain(
+		seed, cost_function, 10, stdevs, rng_arg=numpy.random.default_rng(1515)
+	)
+
+	assert chain == snapshot

tests/model/test_log_spaced_fixed_orientation_mcmc.py

@@ -0,0 +1,98 @@
+from pdme.model import (
+	LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel,
+)
+import pdme.inputs
+import pdme.measurement.input_types
+import pdme.subspace_simulation
+import numpy
+
+SEED_TO_USE = 42
+
+
+def get_cost_function():
+	x_min = -10
+	x_max = 10
+	y_min = -5
+	y_max = 5
+	z_min = 2
+	z_max = 3
+	p_fixed = 10
+	theta = 0
+	phi = 0
+	max_frequency = 5
+
+	model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
+		x_min,
+		x_max,
+		y_min,
+		y_max,
+		z_min,
+		z_max,
+		0,
+		max_frequency,
+		p_fixed,
+		theta,
+		phi,
+		1,
+		0.5,
+	)
+	model.rng = numpy.random.default_rng(SEED_TO_USE)
+
+	freqs = [0.01, 0.1, 1, 10, 100]
+	dot_positions = [[-1.5, 0, 0], [-0.5, 0, 0], [0.5, 0, 0], [1.5, 0, 0]]
+	dot_inputs = pdme.inputs.inputs_with_frequency_range(dot_positions, freqs)
+	dot_input_array = pdme.measurement.input_types.dot_inputs_to_array(dot_inputs)
+
+	actual_dipoles = model.get_dipoles(0, numpy.random.default_rng(SEED_TO_USE))
+	actual_measurements = actual_dipoles.get_dot_measurements(dot_inputs)
+	actual_measurements_array = numpy.array([m.v for m in actual_measurements])
+
+	def cost_to_use(sample_dipoles: numpy.ndarray) -> numpy.ndarray:
+		return pdme.subspace_simulation.proportional_costs_vs_actual_measurement(
+			dot_input_array, actual_measurements_array, sample_dipoles
+		)
+
+	return cost_to_use
+
+
+def test_log_spaced_fixed_orientation_mcmc_basic(snapshot):
+
+	x_min = -10
+	x_max = 10
+	y_min = -5
+	y_max = 5
+	z_min = 2
+	z_max = 3
+	p_fixed = 10
+	theta = 0
+	phi = 0
+	max_frequency = 5
+
+	model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
+		x_min,
+		x_max,
+		y_min,
+		y_max,
+		z_min,
+		z_max,
+		0,
+		max_frequency,
+		p_fixed,
+		theta,
+		phi,
+		1,
+		0.5,
+	)
+	model.rng = numpy.random.default_rng(1234)
+
+	seed = model.get_monte_carlo_dipole_inputs(1, -1)[0]
+
+	cost_function = get_cost_function()
+	stdev = pdme.subspace_simulation.DipoleStandardDeviation(2, 2, 1, 0.25, 0.5, 1)
+	stdevs = pdme.subspace_simulation.MCMCStandardDeviation([stdev])
+
+	chain = model.get_mcmc_chain(
+		seed, cost_function, 10, stdevs, rng_arg=numpy.random.default_rng(1515)
+	)
+
+	assert chain == snapshot

tests/model/test_log_spaced_random_count_multiple_dipole_fixed_orientation_fixed_magnitude_model.py

@@ -115,7 +115,7 @@ def test_random_count_multiple_dipole_fixed_mag_model_get_dipoles_invariant():
 			)
 
 
-def test_random_count_multiple_dipole_fixed_or_fixed_mag_model_get_n_dipoles():
+def test_random_count_multiple_dipole_fixed_or_fixed_mag_model_get_n_dipoles(snapshot):
 	# TODO: this test is a bit garbage just calls things without testing.
 	x_min = -10
 	x_max = 10
@@ -145,7 +145,9 @@ def test_random_count_multiple_dipole_fixed_or_fixed_mag_model_get_n_dipoles():
 	)
 	model.rng = numpy.random.default_rng(1234)
 
-	model.get_monte_carlo_dipole_inputs(1, max_frequency)
-	model.get_monte_carlo_dipole_inputs(
+	actual_inputs = model.get_monte_carlo_dipole_inputs(1, max_frequency)
+	assert actual_inputs.tolist() == snapshot
+	actual_monte_carlo_inputs = model.get_monte_carlo_dipole_inputs(
 		1, max_frequency, numpy.random.default_rng(1234)
 	)
+	assert actual_monte_carlo_inputs.tolist() == snapshot

tests/subspace_simulation/__snapshots__/test_costs.ambr

@@ -0,0 +1,4 @@
+# serializer version: 1
+# name: test_proportional_costs
+  7000.0
+# ---

tests/subspace_simulation/__snapshots__/test_sort_dipoles.ambr

@@ -0,0 +1,32 @@
+# serializer version: 1
+# name: test_sort_dipoles_by_freq
+  list([
+    list([
+      100.0,
+      200.0,
+      300.0,
+      400.0,
+      500.0,
+      600.0,
+      0.07,
+    ]),
+    list([
+      1.0,
+      2.0,
+      3.0,
+      4.0,
+      5.0,
+      6.0,
+      7.0,
+    ]),
+    list([
+      10.0,
+      200.0,
+      30.0,
+      41.0,
+      315.0,
+      0.31,
+      100.0,
+    ]),
+  ])
+# ---

tests/subspace_simulation/__snapshots__/test_stdevs.ambr

@@ -0,0 +1,22 @@
+# serializer version: 1
+# name: test_return_four
+  DipoleStandardDeviation(p_phi_step=1, p_theta_step=2, rx_step=3, ry_step=4, rz_step=5, w_log_step=6)
+# ---
+# name: test_return_four.1
+  DipoleStandardDeviation(p_phi_step=10, p_theta_step=20, rx_step=30, ry_step=40, rz_step=50, w_log_step=60)
+# ---
+# name: test_return_four.2
+  DipoleStandardDeviation(p_phi_step=0.1, p_theta_step=0.2, rx_step=0.3, ry_step=0.4, rz_step=0.5, w_log_step=0.6)
+# ---
+# name: test_return_four.3
+  DipoleStandardDeviation(p_phi_step=1, p_theta_step=2, rx_step=3, ry_step=4, rz_step=5, w_log_step=6)
+# ---
+# name: test_return_four.4
+  DipoleStandardDeviation(p_phi_step=10, p_theta_step=20, rx_step=30, ry_step=40, rz_step=50, w_log_step=60)
+# ---
+# name: test_return_four.5
+  DipoleStandardDeviation(p_phi_step=0.1, p_theta_step=0.2, rx_step=0.3, ry_step=0.4, rz_step=0.5, w_log_step=0.6)
+# ---
+# name: test_return_one
+  DipoleStandardDeviation(p_phi_step=1, p_theta_step=2, rx_step=3, ry_step=4, rz_step=5, w_log_step=6)
+# ---

tests/subspace_simulation/test_costs.py

@@ -0,0 +1,10 @@
+import pdme.subspace_simulation
+import numpy
+
+
+def test_proportional_costs(snapshot):
+	a = numpy.array([2, 4, 5, 6, 7, 8, 10])
+	b = numpy.array([51, 13, 1, 31, 0.001, 3, 1])
+
+	actual_result = pdme.subspace_simulation.proportional_cost(a, b).tolist()
+	assert actual_result == snapshot

tests/subspace_simulation/test_sort_dipoles.py

@@ -0,0 +1,15 @@
+import numpy
+import pdme.subspace_simulation
+
+
+def test_sort_dipoles_by_freq(snapshot):
+	orig = numpy.array(
+		[
+			[1, 2, 3, 4, 5, 6, 7],
+			[100, 200, 300, 400, 500, 600, 0.07],
+			[10, 200, 30, 41, 315, 0.31, 100],
+		]
+	)
+
+	actual_sorted = pdme.subspace_simulation.sort_array_of_dipoles_by_frequency(orig)
+	assert actual_sorted.tolist() == snapshot

tests/subspace_simulation/test_stdevs.py

@@ -0,0 +1,38 @@
+import pytest
+import pdme.subspace_simulation
+
+
+def test_empty():
+	with pytest.raises(ValueError):
+		pdme.subspace_simulation.MCMCStandardDeviation([])
+
+
+def test_return_one(snapshot):
+	stdev = pdme.subspace_simulation.DipoleStandardDeviation(
+		1,
+		2,
+		3,
+		4,
+		5,
+		6,
+	)
+	stdevs = pdme.subspace_simulation.MCMCStandardDeviation([stdev])
+
+	assert stdevs[3] == snapshot
+	assert stdevs[3] == stdev
+
+
+def test_return_four(snapshot):
+	stdev_list = [
+		pdme.subspace_simulation.DipoleStandardDeviation(1, 2, 3, 4, 5, 6),
+		pdme.subspace_simulation.DipoleStandardDeviation(10, 20, 30, 40, 50, 60),
+		pdme.subspace_simulation.DipoleStandardDeviation(0.1, 0.2, 0.3, 0.4, 0.5, 0.6),
+	]
+	stdevs = pdme.subspace_simulation.MCMCStandardDeviation(stdev_list)
+
+	assert stdevs[0] == snapshot
+	assert stdevs[1] == snapshot
+	assert stdevs[2] == snapshot
+	assert stdevs[3] == snapshot
+	assert stdevs[4] == snapshot
+	assert stdevs[5] == snapshot

tests/util/test_fast_v_calc.py

@@ -107,3 +107,28 @@ def test_between():
 	expected = numpy.array([False, False, True])
 
 	numpy.testing.assert_array_equal(actual, expected, err_msg="Between calc wrong")
+
+
+def test_fast_v_calc_asymmetric_multidipoles_but_symmetric():
+	# expected format is [px, py, pz, sx, sy, sz, e1, e2, w]
+	d1 = [1, 2, 3, 4, 5, 6, 1, 1, 7 / 2]
+	d2 = [2, 5, 3, 4, -5, -6, 2, 2, 2 / 2]
+
+	dipoles = numpy.array([[d1, d2]])
+
+	dot_inputs = numpy.array([[-1, -1, -1, 11], [2, 3, 1, 5.5]])
+	# expected_ij is for dot i, dipole j
+	expected_11 = 0.00001421963287022476
+	expected_12 = 0.00001107180225755457
+	expected_21 = 0.000345021108583681380388722
+	expected_22 = 0.0000377061050587914705139781
+
+	expected = numpy.array([[expected_11 + expected_12, expected_21 + expected_22]])
+
+	numpy.testing.assert_allclose(
+		pdme.util.fast_v_calc.fast_vs_for_asymmetric_dipoleses(
+			dot_inputs, dipoles, 1e10
+		),
+		expected,
+		err_msg="Voltages at dot aren't as expected for multidipole calc.",
+	)