Created version 0.2.2

.editorconfig

@@ -3,5 +3,5 @@ root = true
 # Unix-style newlines with a newline ending every file
 [*]
 end_of_line = lf
-insert_final_newline = true
+# insert_final_newline = true
 indent_style = tab

.gitignore

@@ -137,3 +137,5 @@ dmypy.json
 
 # Cython debug symbols
 cython_debug/
+
+*.csv

deepdog/__init__.py

@@ -1,9 +1,14 @@
 import logging
 from deepdog.meta import __version__
+from deepdog.bayes_run import BayesRun
+from deepdog.diagnostic import Diagnostic
 
 
 def get_version():
 	return __version__
 
 
+__all__ = ["get_version", "BayesRun", "Diagnostic"]
+
+
 logging.getLogger(__name__).addHandler(logging.NullHandler())

deepdog/bayes_run.py

@@ -0,0 +1,113 @@
+import pdme.model
+from typing import Sequence, Tuple, List
+import datetime
+import itertools
+import csv
+import logging
+import numpy
+import scipy.optimize
+import multiprocessing
+
+
+# TODO: remove hardcode
+COST_THRESHOLD = 1e-10
+
+
+# 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(discretisation, dots, index) -> Tuple[Tuple[int, ...], scipy.optimize.OptimizeResult]:
+	return (index, discretisation.solve_for_index(dots, index))
+
+
+class BayesRun():
+	'''
+	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_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:
+		self.dot_inputs = dot_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.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"])
+
+		self.probabilities = [1 / self.model_count] * self.model_count
+
+		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+		self.filename = f"{timestamp}-{filename_slug}.csv"
+		self.max_frequency = max_frequency
+
+	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):
+			frequency: float = run
+			if self.max_frequency is not None and self.max_frequency > 1:
+				rng = numpy.random.default_rng()
+				frequency = rng.uniform(1, self.max_frequency)
+			dipoles = self.actual_model.get_dipoles(frequency)
+
+			dots = dipoles.get_dot_measurements(self.dot_inputs)
+			_logger.info(f"Going to work on dipole at {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:
+					results.append(pool.starmap(get_a_result, zip(itertools.repeat(discretisation), itertools.repeat(dots), discretisation.all_indices())))
+
+			_logger.debug("Done, constructing output now")
+			row = {
+				"dipole_moment": dipoles.dipoles[0].p,
+				"dipole_location": dipoles.dipoles[0].s,
+				"dipole_frequency": dipoles.dipoles[0].w
+			}
+			successes: List[int] = []
+			for model_index, (name, result) in enumerate(zip(self.model_names, results)):
+				count = 0
+				success = 0
+				for idx, val in result:
+					count += 1
+					if val.success and val.cost <= COST_THRESHOLD:
+						success += 1
+
+				row[f"{name}_success"] = success
+				row[f"{name}_count"] = count
+				successes.append(max(success, 1))
+
+			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)]
+			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)

deepdog/diagnostic.py

@@ -0,0 +1,97 @@
+from pdme.measurement import OscillatingDipole, OscillatingDipoleArrangement
+import pdme
+from deepdog.bayes_run import DotInput
+import datetime
+import numpy
+from dataclasses import dataclass
+import logging
+from typing import Sequence, Tuple
+import csv
+import itertools
+import multiprocessing
+
+_logger = logging.getLogger(__name__)
+
+
+def get_a_result(discretisation, dots, index):
+	return (index, discretisation.solve_for_index(dots, index))
+
+
+@dataclass
+class SingleDipoleDiagnostic():
+	model: str
+	index: Tuple
+	bounds: Tuple
+	actual_dipole: OscillatingDipole
+	result_dipole: OscillatingDipole
+	success: bool
+
+	def __post_init__(self) -> None:
+		self.p_actual_x = self.actual_dipole.p[0]
+		self.p_actual_y = self.actual_dipole.p[1]
+		self.p_actual_z = self.actual_dipole.p[2]
+		self.s_actual_x = self.actual_dipole.s[0]
+		self.s_actual_y = self.actual_dipole.s[1]
+		self.s_actual_z = self.actual_dipole.s[2]
+		self.p_result_x = self.result_dipole.p[0]
+		self.p_result_y = self.result_dipole.p[1]
+		self.p_result_z = self.result_dipole.p[2]
+		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]
+
+
+class Diagnostic():
+	'''
+	Represents a diagnostic for a single dipole moment given a set of discretisations.
+
+	Parameters
+	----------
+	dot_inputs : Sequence[DotInput]
+		The dot inputs for this diagnostic.
+	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, actual_dipole_moment: numpy.ndarray, actual_dipole_position: numpy.ndarray, actual_dipole_frequency: float, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], filename_slug: str) -> None:
+		self.dipoles = OscillatingDipoleArrangement([OscillatingDipole(actual_dipole_moment, actual_dipole_position, actual_dipole_frequency)])
+		self.dots = self.dipoles.get_dot_measurements(dot_inputs)
+
+		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"]
+
+		timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+		self.filename = f"{timestamp}-{filename_slug}.diag.csv"
+
+	def go(self):
+		with open(self.filename, "a", newline="") as outfile:
+			# csv fields
+			writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect='unix')
+			writer.writeheader()
+
+		for (name, discretisation) in self.discretisations_with_names:
+			_logger.info(f"Working on discretisation {name}")
+
+			results = []
+			with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
+				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', 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)[0], actual_success)
+					row = vars(diag_row)
+					_logger.debug(f"Writing result {row}")
+					writer.writerow(row)

poetry.lock

@@ -30,11 +30,11 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [[package]]
 name = "coverage"
-version = "6.2"
+version = "6.3.1"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [package.dependencies]
 tomli = {version = "*", optional = true, markers = "extra == \"toml\""}
@@ -117,7 +117,7 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
 
 [[package]]
 name = "pdme"
-version = "0.0.1"
+version = "0.5.0"
 description = "Python dipole model evaluator"
 category = "main"
 optional = false
@@ -125,6 +125,7 @@ python-versions = ">=3.8,<3.10"
 
 [package.dependencies]
 numpy = ">=1.21.1,<2.0.0"
+scipy = ">=1.5,<1.6"
 
 [[package]]
 name = "pluggy"
@@ -209,6 +210,17 @@ pytest = ">=4.6"
 [package.extras]
 testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtualenv"]
 
+[[package]]
+name = "scipy"
+version = "1.5.4"
+description = "SciPy: Scientific Library for Python"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+numpy = ">=1.14.5"
+
 [[package]]
 name = "toml"
 version = "0.10.2"
@@ -236,7 +248,7 @@ python-versions = ">=3.6"
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8,<3.10"
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+content-hash = "783e64d96cac128430eb53d6ccee9c5e5693a6be7670c9b403b3322530c9b0cb"
 
 [metadata.files]
 atomicwrites = [
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@@ -367,8 +373,8 @@ packaging = [
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@@ -398,6 +404,33 @@ pytest-cov = [
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 ]
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pyproject.toml

@@ -1,12 +1,12 @@
 [tool.poetry]
 name = "deepdog"
-version = "0.1.1"
+version = "0.2.2"
 description = ""
 authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
 
 [tool.poetry.dependencies]
 python = "^3.8,<3.10"
-pdme = "^0.0.1"
+pdme = "^0.5.0"
 
 [tool.poetry.dev-dependencies]
 pytest = ">=6"
@@ -20,5 +20,15 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
-addopts = "--junitxml pytest.xml --cov deepdog --cov-report=xml:coverage.xml --cov-fail-under=90 --cov-report=html"
+addopts = "--junitxml pytest.xml --cov deepdog --cov-report=xml:coverage.xml --cov-report=html"
 junit_family = "xunit1"
+
+[tool.mypy]
+plugins = "numpy.typing.mypy_plugin"
+
+[[tool.mypy.overrides]]
+module = [
+	"scipy",
+	"scipy.optimize"
+]
+ignore_missing_imports = true