Adds test for solver

pdme/model/__init__.py

@@ -1,74 +1,4 @@
-import numpy
-from typing import Callable, Sequence
-from pdme.measurement import DotMeasurement
-import logging
+from pdme.model.model import Model
+from pdme.model.fixed_z_plane_model import FixedZPlaneModel
 
-
-class Model():
-	"""
-	Interface for models.
-	"""
-
-	def point_length(self) -> int:
-		raise NotImplementedError
-
-	def n(self) -> int:
-		raise NotImplementedError
-
-	def v_for_point_at_dot(self, dot: DotMeasurement, pt: numpy.ndarray) -> float:
-		raise NotImplementedError
-
-	def cost_for_dot(self, dot: DotMeasurement, pts: numpy.ndarray) -> float:
-		# creates numpy.ndarrays in groups of self.point_length().
-		# Will throw problems for irregular points, but that's okay for now.
-		pt_length = self.point_length()
-		chunked_pts = [pts[i: i + pt_length] for i in range(0, len(pts), pt_length)]
-		return sum(self.v_for_point_at_dot(dot, pt) for pt in chunked_pts) - dot.v
-
-	def costs(self, dots: Sequence[DotMeasurement]) -> Callable[[numpy.ndarray], numpy.ndarray]:
-		'''
-		Returns a function that returns the cost for the given list of DotMeasurements for a particular model-dependent phase space point.
-		Default implementation assumes a single dot cost from which to build the list.
-
-		Parameters
-		----------
-		dots: A list of dot measurements to use to find the cost functions.
-
-		Returns
-		----------
-		Returns the model's cost function.
-		'''
-		logging.debug(f"Constructing costs for dots: {dots}")
-
-		def costs_to_return(pts: numpy.ndarray) -> numpy.ndarray:
-			return numpy.array([self.cost_for_dot(dot, pts) for dot in dots])
-
-		return costs_to_return
-
-	def jac_for_point_at_dot(self, dot: DotMeasurement, pt: numpy.ndarray) -> numpy.ndarray:
-		raise NotImplementedError
-
-	def jac_for_dot(self, dot: DotMeasurement, pts: numpy.ndarray) -> numpy.ndarray:
-		# creates numpy.ndarrays in groups of self.point_length().
-		# Will throw problems for irregular points, but that's okay for now.
-		pt_length = self.point_length()
-		chunked_pts = [pts[i: i + pt_length] for i in range(0, len(pts), pt_length)]
-		return numpy.append([], [self.jac_for_point_at_dot(dot, pt) for pt in chunked_pts])
-
-	def jac(self, dots: Sequence[DotMeasurement]) -> Callable[[numpy.ndarray], numpy.ndarray]:
-		'''
-		Returns a function that returns the cost function's Jacobian for the given list of DotMeasurements for a particular model-dependent phase space point.
-		Default implementation assumes a single dot jacobian from which to build the list.
-
-		Parameters
-		----------
-		dots: A list of dot measurements to use to find the cost functions and their Jacobian.
-
-		Returns
-		----------
-		Returns the model's cost function's Jacobian.
-		'''
-		def jac_to_return(pts: numpy.ndarray) -> numpy.ndarray:
-			return numpy.array([self.jac_for_dot(dot, pts) for dot in dots])
-
-		return jac_to_return
+__all__ = ["Model", "FixedZPlaneModel"]

pdme/model/fixed_z_plane_model.py

@@ -1,5 +1,5 @@
 import numpy
-from pdme.model import Model
+from pdme.model.model import Model
 from pdme.measurement import DotMeasurement
 
 

pdme/model/model.py

@@ -0,0 +1,74 @@
+import numpy
+from typing import Callable, Sequence
+from pdme.measurement import DotMeasurement
+import logging
+
+
+class Model():
+	"""
+	Interface for models.
+	"""
+
+	def point_length(self) -> int:
+		raise NotImplementedError
+
+	def n(self) -> int:
+		raise NotImplementedError
+
+	def v_for_point_at_dot(self, dot: DotMeasurement, pt: numpy.ndarray) -> float:
+		raise NotImplementedError
+
+	def cost_for_dot(self, dot: DotMeasurement, pts: numpy.ndarray) -> float:
+		# creates numpy.ndarrays in groups of self.point_length().
+		# Will throw problems for irregular points, but that's okay for now.
+		pt_length = self.point_length()
+		chunked_pts = [pts[i: i + pt_length] for i in range(0, len(pts), pt_length)]
+		return sum(self.v_for_point_at_dot(dot, pt) for pt in chunked_pts) - dot.v
+
+	def costs(self, dots: Sequence[DotMeasurement]) -> Callable[[numpy.ndarray], numpy.ndarray]:
+		'''
+		Returns a function that returns the cost for the given list of DotMeasurements for a particular model-dependent phase space point.
+		Default implementation assumes a single dot cost from which to build the list.
+
+		Parameters
+		----------
+		dots: A list of dot measurements to use to find the cost functions.
+
+		Returns
+		----------
+		Returns the model's cost function.
+		'''
+		logging.debug(f"Constructing costs for dots: {dots}")
+
+		def costs_to_return(pts: numpy.ndarray) -> numpy.ndarray:
+			return numpy.array([self.cost_for_dot(dot, pts) for dot in dots])
+
+		return costs_to_return
+
+	def jac_for_point_at_dot(self, dot: DotMeasurement, pt: numpy.ndarray) -> numpy.ndarray:
+		raise NotImplementedError
+
+	def jac_for_dot(self, dot: DotMeasurement, pts: numpy.ndarray) -> numpy.ndarray:
+		# creates numpy.ndarrays in groups of self.point_length().
+		# Will throw problems for irregular points, but that's okay for now.
+		pt_length = self.point_length()
+		chunked_pts = [pts[i: i + pt_length] for i in range(0, len(pts), pt_length)]
+		return numpy.append([], [self.jac_for_point_at_dot(dot, pt) for pt in chunked_pts])
+
+	def jac(self, dots: Sequence[DotMeasurement]) -> Callable[[numpy.ndarray], numpy.ndarray]:
+		'''
+		Returns a function that returns the cost function's Jacobian for the given list of DotMeasurements for a particular model-dependent phase space point.
+		Default implementation assumes a single dot jacobian from which to build the list.
+
+		Parameters
+		----------
+		dots: A list of dot measurements to use to find the cost functions and their Jacobian.
+
+		Returns
+		----------
+		Returns the model's cost function's Jacobian.
+		'''
+		def jac_to_return(pts: numpy.ndarray) -> numpy.ndarray:
+			return numpy.array([self.jac_for_dot(dot, pts) for dot in dots])
+
+		return jac_to_return

pdme/solver/__init__.py

@@ -0,0 +1,3 @@
+from pdme.solver.solver import sol
+
+__all__ = ["sol"]

pdme/solver/solver.py

@@ -5,7 +5,7 @@ from pdme.measurement import DotMeasurement
 from typing import Sequence
 
 
-def sol(model: Model, dots: Sequence[DotMeasurement], initial_pt=None):
+def sol(model: Model, dots: Sequence[DotMeasurement], initial_pt=None, bounds=(-numpy.inf, numpy.inf)):
 	if initial_pt is None:
 		initial = numpy.tile(.1, model.n() * model.point_length())
 	else:
@@ -13,5 +13,5 @@ def sol(model: Model, dots: Sequence[DotMeasurement], initial_pt=None):
 			raise ValueError(f"The initial point {initial_pt} does not have the model's expected length: {model.point_length()}")
 		initial = numpy.tile(initial_pt, model.n())
 
-	result = scipy.optimize.least_squares(model.costs(dots), initial, jac=model.jac(dots), ftol=1e-15, gtol=3e-16)
+	result = scipy.optimize.least_squares(model.costs(dots), initial, jac=model.jac(dots), ftol=1e-15, gtol=3e-16, bounds=bounds)
 	return result