From 31f97da8b0c2e9c7a739e40db13174e3fc99c5eb Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Fri, 17 Oct 2025 19:56:06 +0200 Subject: [PATCH 01/33] TL: added first Dahlquist and NonLinear solvers --- qmat/solvers/dahlquist.py | 244 ++++++++++++++++++++++++++++++++++++++ qmat/solvers/generic.py | 191 +++++++++++++++++++++++++++++ 2 files changed, 435 insertions(+) create mode 100644 qmat/solvers/dahlquist.py create mode 100644 qmat/solvers/generic.py diff --git a/qmat/solvers/dahlquist.py b/qmat/solvers/dahlquist.py new file mode 100644 index 0000000..c5a516c --- /dev/null +++ b/qmat/solvers/dahlquist.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Submodule containing various solvers for the Dahlquist equation that can be used with `qmat`-generated coefficients. +""" +import numpy as np + + +class Dahlquist(): + + def __init__(self, lam, u0=1, T=1, nSteps=1): + self.u0 = u0 + self.T = T + self.nSteps = nSteps + self.dt = T/nSteps + + self.lam = np.asarray(lam) + try: + lamU = self.lam*u0 + except: + raise ValueError("error when computing lam*u0") + self.uShape = tuple(lamU.shape) + self.uDtype = lamU.dtype + + @staticmethod + def checkCoeff(Q, weights): + Q = np.asarray(Q) + nNodes = Q.shape[0] + assert Q.shape == (nNodes, nNodes), "Q is not a square matrix" + + if weights is not None: + weights = np.asarray(weights) + assert weights.ndim == 1, "weights must be a 1D vector" + assert weights.size == nNodes, "weights size is not the same as the node size" + + return nNodes, Q, weights + + + def solve(self, Q, weights): + nNodes, Q, weights = self.checkCoeff(Q, weights) + + # Collocation problem matrix + A = np.eye(nNodes) - self.lam[..., None, None]*self.dt*Q + + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum[0] = self.u0 + + for i in range(self.nSteps): + b = np.ones(nNodes)*uNum[i][..., None] + uNodes = np.linalg.solve(A, b[..., None])[..., 0] + if weights is not None: + uNum[i+1] = uNum[i] + uNum[i+1] += self.dt*np.dot(self.lamI[..., None]*uNodes, weights) + else: + uNum[i+1] = uNodes[..., -1] + + return uNum + + @staticmethod + def checkCoeffSDC(Q, weights, QDelta, nSweeps): + Q = np.asarray(Q) + nodes = Q.sum(axis=1) + nNodes = nodes.size + assert Q.shape == (nNodes, nNodes), "Q is not a square matrix" + + if weights is not None: + weights = np.asarray(weights) + assert weights.ndim == 1, "weights must be a 1D vector" + assert weights.size == nNodes, "weights size is not the same as the node size" + + QDelta = np.asarray(QDelta) + if QDelta.ndim == 3: + assert QDelta.shape == (nSweeps, nNodes, nNodes), "inconsistent shape for QDelta" + else: + assert QDelta.shape == (nNodes, nNodes), "inconsistent shape for QDelta" + QDelta = np.repeat(QDelta[None, ...], nSweeps, axis=0) + + return nNodes, Q, weights, QDelta, nSweeps + + def solveSDC(self, Q, weights, QDelta, nSweeps): + nNodes, Q, weights, QDelta, nSweeps = self.checkCoeffSDC(Q, weights, QDelta, nSweeps) + + # Preconditioner for each sweeps + P = np.eye(nNodes)[None, ...] \ + - self.lam[..., None, None, None]*self.dt*QDelta + + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum[0] = self.u0 + + for i in range(self.nSteps): + + uNodes = np.ones(nNodes)*uNum[i][..., None] + uNodes = uNodes[..., :, None] # shape [..., nNodes, 1] + + for k in range(nSweeps): + + b = uNum[i][..., None, None] \ + + self.lam[..., None, None]*self.dt*(Q - QDelta[k]) @ uNodes + + # b has shape [..., nNodes, 1] + # P[k] has shape [..., nNodes, nNodes] + # output has shape [..., nNodes, 1] + uNodes = np.linalg.solve(P[..., k, :, :], b) + + uNodes = uNodes[..., :, 0] # back to shape [..., nNodes] + + if weights is None: + uNum[i+1] = uNodes[..., -1] + else: + uNum[i+1] = uNum[i] + uNum[i+1] += self.dt*np.dot(self.lam[..., None]*uNodes, weights) + + return uNum + + +class DahlquistIMEX(): + + def __init__(self, lamI, lamE, u0=1, T=1, nSteps=1): + self.u0 = u0 + self.T = T + self.nSteps = nSteps + self.dt = T/nSteps + + self.lamI = np.asarray(lamI) + self.lamE = np.asarray(lamE) + try: + lamU = (self.lamI + self.lamE)*u0 + except: + raise ValueError("error when computing (lamI + lamE)*u0") + self.uShape = tuple(lamU.shape) + self.uDtype = lamU.dtype + + + @staticmethod + def checkCoeff(QI, wI, QE, wE): + QI, QE = np.asarray(QI), np.asarray(QE) + nodes = QI.sum(axis=1) + assert np.allclose(nodes, QE.sum(axis=1)), "QI and QE do not correspond to the same nodes" + + nNodes = QI.shape[0] + assert QI.shape == (nNodes, nNodes), "QI is not a square matrix" + assert QI.shape == QE.shape, "QI and QE do not have the same shape" + + useWeights = True + if wI is None or wE is None: + assert wE is None and wI is None, "it's either weights for everyone or no weight" + useWeights = False + + return nNodes, QI, wI, QE, wE, useWeights + + + def solve(self, QI, wI, QE, wE): + nNodes, QI, wI, QE, wE, useWeights = self.checkCoeff(QI, wI, QE, wE) + + # Collocation problem matrix + A = np.eye(nNodes) \ + - self.lamI[..., None, None]*self.dt*QI \ + - self.lamE[..., None, None]*self.dt*QE + + # Solution vector for each time-step + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum[0] = self.u0 + + # Time-stepping loop + for i in range(self.nSteps): + + b = np.ones(nNodes)*uNum[i][..., None] + uNodes = np.linalg.solve(A, b[..., None])[..., 0] + + if useWeights: + uNum[i+1] = uNum[i] + uNum[i+1] += self.dt*np.dot(self.lamI[..., None]*uNodes, wI) + uNum[i+1] += self.dt*np.dot(self.lamE[..., None]*uNodes, wE) + else: + uNum[i+1] = uNodes[..., -1] + + return uNum + + + @staticmethod + def checkCoeffSDC(Q, weights, QDeltaI, QDeltaE, nSweeps): + Q = np.asarray(Q) + nodes = Q.sum(axis=1) + nNodes = nodes.size + assert Q.shape == (nNodes, nNodes), "Q is not a square matrix" + + if weights is not None: + weights = np.asarray(weights) + assert weights.ndim == 1, "weights must be a 1D vector" + assert weights.size == nNodes, "weights size is not the same as the node size" + + QDeltaI = np.asarray(QDeltaI) + QDeltaE = np.asarray(QDeltaE) + if QDeltaI.ndim == 3: + assert QDeltaI.shape == (nSweeps, nNodes, nNodes), "inconsistent shape for QDeltaI" + else: + assert QDeltaI.shape == (nNodes, nNodes), "inconsistent shape for QDeltaE" + QDeltaI = np.repeat(QDeltaI[None, ...], nSweeps, axis=0) + if QDeltaE.ndim == 3: + assert QDeltaE.shape == (nSweeps, nNodes, nNodes), "inconsistent shape for QDeltaE" + else: + assert QDeltaE.shape == (nNodes, nNodes), "inconsistent shape for QDeltaE" + QDeltaE = np.repeat(QDeltaE[None, ...], nSweeps, axis=0) + + return nNodes, Q, weights, QDeltaI, QDeltaE, nSweeps + + + def solveSDC(self, Q, weights, QDeltaI, QDeltaE, nSweeps): + nNodes, Q, weights, QDeltaI, QDeltaE, nSweeps = self.checkCoeffSDC(Q, weights, QDeltaI, QDeltaE, nSweeps) + + # Preconditioner for each sweeps + P = np.eye(nNodes)[None, ...] \ + - self.lamI[..., None, None, None]*self.dt*QDeltaI \ + - self.lamE[..., None, None, None]*self.dt*QDeltaE + + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum[0] = self.u0 + + for i in range(self.nSteps): + + uNodes = np.ones(nNodes)*uNum[i][..., None] + uNodes = uNodes[..., :, None] # shape [..., nNodes, 1] + + for k in range(nSweeps): + + b = uNum[i][..., None, None] \ + + self.lamI[..., None, None]*self.dt*(Q - QDeltaI[k]) @ uNodes \ + + self.lamE[..., None, None]*self.dt*(Q - QDeltaE[k]) @ uNodes + + # b has shape [..., nNodes, 1] + # P[k] has shape [..., nNodes, nNodes] + # output has shape [..., nNodes, 1] + uNodes = np.linalg.solve(P[..., k, :, :], b) + + uNodes = uNodes[..., :, 0] # back to shape [..., nNodes] + + if weights is None: + uNum[i+1] = uNodes[..., -1] + else: + uNum[i+1] = uNum[i] + uNum[i+1] += self.dt*np.dot( + (self.lamI[..., None] + self.lamE[..., None])*uNodes, weights) + + return uNum diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic.py new file mode 100644 index 0000000..bc63afb --- /dev/null +++ b/qmat/solvers/generic.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Submodule containing various generic solvers that can be used with `qmat`-generated coefficients. +""" +import numpy as np +import scipy.optimize as sco +from scipy.linalg import blas + +from collections import deque + +from qmat.solvers.dahlquist import Dahlquist + + +class NonLinear(): + + DEFAULT_FSOLVE = sco.fsolve + + def __init__(self, u0, evalF, fSolve=None, T=1, nSteps=1): + self.u0 = np.asarray(u0) + if self.u0.size > 1e3: + self.DEFAULT_FSOLVE = sco.newton_krylov + self.axpy = blas.get_blas_funcs('axpy', dtype=self.uDtype) + + self.T = T + self.nSteps = nSteps + self.dt = T/nSteps + + try: + uOut = np.zeros_like(u0) + uEval = evalF(u=u0, t=0, out=uOut) + except: + raise ValueError("evalF cannot be properly evaluated into an array like u0") + assert uOut is uEval, "evalF output is not its out argument" + self.evalF = evalF + + if fSolve is not None: + self.fSolve = fSolve + try: + uEval *= -1 + uEval += u0 + uOut = np.zeros_like(u0) + uSolve = fSolve(a=1, b=uEval, uInit=u0, t=0, out=uOut) + except: + raise ValueError("fSolve cannot be properly evaluated into an array like u0") + assert uOut is uSolve, "fSolve output is not its out argument" + np.testing.assert_allclose(uSolve, u0, err_msg="fSolve does not satisfy the fixed-point problem with u0") + + + @property + def uShape(self): + return self.u0.shape + + @property + def uDtype(self): + return self.u0.dtype + + def evalF(self, u, t, out): + raise NotImplementedError("very weird error ...") + + + def fSolve(self, a, b, uInit, t, out): + """ + Solve u - a*evalF(u, t) = b using uInit as initial guess and storing u into out + """ + np.copyto(out, self.DEFAULT_FSOLVE(lambda u: u - a*self.evalF(u, t) - b, uInit)) + + + @staticmethod + def lowerTri(Q:np.ndarray): + return np.allclose(np.triu(Q, k=1), np.zeros(Q.shape)) + + + def solve(self, Q, weights, uNum=None): + nNodes, Q, weights = Dahlquist.checkCoeff(Q, weights) + + assert self.lowerTri(Q), "lower triangular matrix Q expected" + Q, weights = self.dt*Q, self.dt*weights + + if uNum is None: + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum[0] = self.u0 + + rhs = np.zeros(self.uShape, dtype=self.uDtype) + fEvals = np.zeros((nNodes, *self.uShape), dtype=self.uDtype) + + times = np.linspace(0, self.T, self.nSteps+1) + tau = Q.sum(axis=1) + + # time-stepping loop + for i in range(self.nSteps): + np.copyto(uNum[i+1], uNum[i]) + uStage = uNum[i+1] + + # stages loop + for m in range(nNodes): + tStage = times[i]+tau[m] + + # build RHS + np.copyto(rhs, uNum[i]) + for j in range(m): + self.axpy(a=Q[m, j], x=fEvals[j], y=rhs) + + # solve stage (if non-zero diagonal coefficient) + if Q[m, m] != 0: + self.fSolve(a=Q[m, m], b=rhs, uInit=uStage, t=tStage, out=uStage) + else: + np.copyto(uStage, rhs) + + # eval and store stage + self.evalF(u=uStage, t=tStage, out=fEvals[m]) + + # step update (if not, uNum[i+1] is already the last stage) + if weights is not None: + uNum[i+1] = uNum[i] + for m in range(nNodes): + self.axpy(a=weights[m], x=fEvals[m], y=uNum[i+1]) + + return uNum + + + def solveSDC(self, Q, weights, QDelta, nSweeps, uNum=None): + nNodes, Q, weights, QDelta, nSweeps = Dahlquist.checkCoeffSDC(Q, weights, QDelta, nSweeps) + + for qDelta in QDelta: + assert self.lowerTri(qDelta), "lower triangular matrices QDelta expected" + Q, QDelta, weights = self.dt*Q, self.dt*QDelta, self.dt*weights + + if uNum is None: + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum[0] = self.u0 + + rhs = np.zeros(self.uShape, dtype=self.uDtype) + fEvals = deque([np.zeros_like(rhs) for _ in range(2)]) + + times = np.linspace(0, self.T, self.nSteps+1) + tau = Q.sum(axis=1) + + # time-stepping loop + for i in range(self.nSteps): + np.copyto(uNum[i+1], uNum[i]) + uNode = uNum[i+1] + + # copy initialization + self.evalF(u=uNum[i], t=times[i], out=fK0[0]) + for m in range(1, nNodes): + np.copyto(fK0[m], fK0[0]) + + # loop on sweeps + for k in range(nSweeps): + + fK0 = fEvals[0] + fK1 = fEvals[1] + qDelta = QDelta[k] + + # loop on nodes + for m in range(nNodes): + tNode = times[i]+tau[m] + + # initialize RHS + np.copyto(rhs, uNum[i]) + + # add quadrature terms + for j in range(m): + self.axpy(a=Q[m, j], x=fK0[j], y=rhs) + + # add correction terms (from previous nodes) + for j in range(m): + self.axpy(a= qDelta[m, j], x=fK1[j], y=rhs) + self.axpy(a=-qDelta[m, j], x=fK0[j], y=rhs) + + # diagonal term (current node) + if qDelta[m, m] != 0: + self.axpy(a=-qDelta[m, m], x=fK0[m], y=rhs) + self.fSolve(a=qDelta[m, m], b=rhs, uInit=uNode, t=tNode, out=uNode) + else: + np.copyto(uNode, rhs) + + # evalF on node + self.evalF(u=uNode, t=tNode, out=fK1[m]) + + # invert fK0 and fK1 for the next sweep + fEvals.rotate() + + # step update (if not, uNum[i+1] is already the last stage) + if weights is not None: + uNum[i+1] = uNum[i] + for m in range(nNodes): + self.axpy(a=weights[m], x=fK1[m], y=uNum[i+1]) + + From c3e2415fbfab422cbf88e94174ea4378b5ab8d4b Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Fri, 17 Oct 2025 20:05:31 +0200 Subject: [PATCH 02/33] TL: adapted fSolve to generic uShape --- qmat/solvers/generic.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic.py index bc63afb..3458ec9 100644 --- a/qmat/solvers/generic.py +++ b/qmat/solvers/generic.py @@ -63,7 +63,14 @@ def fSolve(self, a, b, uInit, t, out): """ Solve u - a*evalF(u, t) = b using uInit as initial guess and storing u into out """ - np.copyto(out, self.DEFAULT_FSOLVE(lambda u: u - a*self.evalF(u, t) - b, uInit)) + np.copyto( + out, + self.DEFAULT_FSOLVE( + lambda u: + u - a*self.evalF(u.reshape(self.uShape), t).ravel() - b.ravel(), + uInit.ravel() + ).reshape(self.uShape) + ) @staticmethod From 917737f3b6e818a0dbb787eef125c4178807a922 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Fri, 17 Oct 2025 20:12:35 +0200 Subject: [PATCH 03/33] TL: minor details --- qmat/solvers/generic.py | 1 + 1 file changed, 1 insertion(+) diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic.py index 3458ec9..6561e77 100644 --- a/qmat/solvers/generic.py +++ b/qmat/solvers/generic.py @@ -71,6 +71,7 @@ def fSolve(self, a, b, uInit, t, out): uInit.ravel() ).reshape(self.uShape) ) + return out @staticmethod From 0d11b2733c8da5366fdd0dfecf3882d352a41fbf Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Sun, 19 Oct 2025 22:56:19 +0200 Subject: [PATCH 04/33] TL: still trying stuff --- qmat/solvers/generic.py | 325 ++++++++++++++++++++++++++++++---------- 1 file changed, 250 insertions(+), 75 deletions(-) diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic.py index 6561e77..b2cdbde 100644 --- a/qmat/solvers/generic.py +++ b/qmat/solvers/generic.py @@ -12,111 +12,118 @@ from qmat.solvers.dahlquist import Dahlquist -class NonLinear(): - - DEFAULT_FSOLVE = sco.fsolve - - def __init__(self, u0, evalF, fSolve=None, T=1, nSteps=1): - self.u0 = np.asarray(u0) - if self.u0.size > 1e3: - self.DEFAULT_FSOLVE = sco.newton_krylov - self.axpy = blas.get_blas_funcs('axpy', dtype=self.uDtype) - - self.T = T +class LinearMultiNode(): + + def __init__(self, u0, evalF, fSolve=None, tEnd=1, nSteps=1, t0=0): + u0 = np.asarray(u0) + if u0.size < 1e3: + self.innerSolver = sco.fsolve + else: + self.innerSolver = sco.newton_krylov + self.u0 = u0 + self.t0 = t0 + self.tEnd = tEnd self.nSteps = nSteps - self.dt = T/nSteps - + self.dt = (tEnd-t0)/nSteps + try: - uOut = np.zeros_like(u0) - uEval = evalF(u=u0, t=0, out=uOut) + uEval = np.zeros_like(u0) + evalF(u=u0, t=t0, out=uEval) except: raise ValueError("evalF cannot be properly evaluated into an array like u0") - assert uOut is uEval, "evalF output is not its out argument" self.evalF = evalF - + if fSolve is not None: self.fSolve = fSolve try: - uEval *= -1 + dt = 1e-1 + uEval *= -dt uEval += u0 - uOut = np.zeros_like(u0) - uSolve = fSolve(a=1, b=uEval, uInit=u0, t=0, out=uOut) + uSolve = np.copy(u0) + uSolve += 1e-3*np.linalg.norm(uSolve, np.inf) + self.fSolve(a=dt, rhs=uEval, t=t0, out=uSolve) except: raise ValueError("fSolve cannot be properly evaluated into an array like u0") - assert uOut is uSolve, "fSolve output is not its out argument" - np.testing.assert_allclose(uSolve, u0, err_msg="fSolve does not satisfy the fixed-point problem with u0") - + np.testing.assert_allclose( + uSolve, u0, err_msg="fSolve does not satisfy the fixed-point problem with u0", + atol=1e-15) + + self.axpy = blas.get_blas_funcs('axpy', dtype=self.dtype) @property def uShape(self): return self.u0.shape - + @property - def uDtype(self): + def dtype(self): return self.u0.dtype - def evalF(self, u, t, out): - raise NotImplementedError("very weird error ...") + def evalF(self, u:np.ndarray, t:float, out:np.ndarray): + raise NotImplementedError("evalF must be provided") - def fSolve(self, a, b, uInit, t, out): + def fSolve(self, a:float, rhs:np.ndarray, t:float, out:np.ndarray): """ - Solve u - a*evalF(u, t) = b using uInit as initial guess and storing u into out + Solve u - a*f(u, t) = rhs using out as initial guess and storing the final solution into it """ - np.copyto( - out, - self.DEFAULT_FSOLVE( - lambda u: - u - a*self.evalF(u.reshape(self.uShape), t).ravel() - b.ravel(), - uInit.ravel() - ).reshape(self.uShape) - ) - return out + + def func(u:np.ndarray): + """compute res = u - a*f(u,t) - rhs""" + u = u.reshape(self.uShape) + res = np.empty_like(u) + self.evalF(u, t, out=res) + res *= -a + res += u + res -= rhs + return res.ravel() + + sol = self.innerSolver(func, out.ravel()).reshape(self.uShape) + np.copyto(out, sol) @staticmethod def lowerTri(Q:np.ndarray): return np.allclose(np.triu(Q, k=1), np.zeros(Q.shape)) - + def solve(self, Q, weights, uNum=None): nNodes, Q, weights = Dahlquist.checkCoeff(Q, weights) - - assert self.lowerTri(Q), "lower triangular matrix Q expected" + + assert self.lowerTri(Q), "lower triangular matrix Q expected for non-linear solver" Q, weights = self.dt*Q, self.dt*weights if uNum is None: - uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) uNum[0] = self.u0 - rhs = np.zeros(self.uShape, dtype=self.uDtype) - fEvals = np.zeros((nNodes, *self.uShape), dtype=self.uDtype) + rhs = np.zeros(self.uShape, dtype=self.dtype) + fEvals = np.zeros((nNodes, *self.uShape), dtype=self.dtype) - times = np.linspace(0, self.T, self.nSteps+1) + times = np.linspace(self.t0, self.tEnd, self.nSteps+1) tau = Q.sum(axis=1) # time-stepping loop for i in range(self.nSteps): - np.copyto(uNum[i+1], uNum[i]) - uStage = uNum[i+1] + uNode = uNum[i+1] + np.copyto(uNode, uNum[i]) - # stages loop + # loop on nodes (stages) for m in range(nNodes): - tStage = times[i]+tau[m] - + tNode = times[i]+tau[m] + # build RHS np.copyto(rhs, uNum[i]) for j in range(m): self.axpy(a=Q[m, j], x=fEvals[j], y=rhs) - # solve stage (if non-zero diagonal coefficient) + # solve node (if non-zero diagonal coefficient) if Q[m, m] != 0: - self.fSolve(a=Q[m, m], b=rhs, uInit=uStage, t=tStage, out=uStage) + self.fSolve(a=Q[m, m], rhs=rhs, t=tNode, out=uNode) else: - np.copyto(uStage, rhs) + np.copyto(uNode, rhs) - # eval and store stage - self.evalF(u=uStage, t=tStage, out=fEvals[m]) + # evalF on current store stage + self.evalF(u=uNode, t=tNode, out=fEvals[m]) # step update (if not, uNum[i+1] is already the last stage) if weights is not None: @@ -125,51 +132,55 @@ def solve(self, Q, weights, uNum=None): self.axpy(a=weights[m], x=fEvals[m], y=uNum[i+1]) return uNum - + def solveSDC(self, Q, weights, QDelta, nSweeps, uNum=None): nNodes, Q, weights, QDelta, nSweeps = Dahlquist.checkCoeffSDC(Q, weights, QDelta, nSweeps) for qDelta in QDelta: - assert self.lowerTri(qDelta), "lower triangular matrices QDelta expected" - Q, QDelta, weights = self.dt*Q, self.dt*QDelta, self.dt*weights + assert self.lowerTri(qDelta), "lower triangular matrices QDelta expected for non-linear SDC solver" + Q, QDelta = self.dt*Q, self.dt*QDelta + if weights is not None: + weights = self.dt*weights if uNum is None: - uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) uNum[0] = self.u0 - rhs = np.zeros(self.uShape, dtype=self.uDtype) - fEvals = deque([np.zeros_like(rhs) for _ in range(2)]) + rhs = np.zeros(self.uShape, dtype=self.dtype) + fEvals = deque([ + np.zeros((nNodes, *self.uShape), dtype=self.dtype) + for _ in range(2)]) - times = np.linspace(0, self.T, self.nSteps+1) + times = np.linspace(self.t0, self.tEnd, self.nSteps+1) tau = Q.sum(axis=1) # time-stepping loop for i in range(self.nSteps): - np.copyto(uNum[i+1], uNum[i]) - uNode = uNum[i+1] - + # copy initialization - self.evalF(u=uNum[i], t=times[i], out=fK0[0]) - for m in range(1, nNodes): - np.copyto(fK0[m], fK0[0]) + self.evalF(u=uNum[i], t=times[i], out=fEvals[0][0]) + np.copyto(fEvals[0][1:], fEvals[0][0]) + + uNode = uNum[i+1] - # loop on sweeps + # loop on sweeps (iterations) for k in range(nSweeps): + np.copyto(uNode, uNum[i]) fK0 = fEvals[0] fK1 = fEvals[1] qDelta = QDelta[k] - # loop on nodes + # loop on nodes (stages) for m in range(nNodes): - tNode = times[i]+tau[m] + tNode = times[i] + tau[m] # initialize RHS np.copyto(rhs, uNum[i]) # add quadrature terms - for j in range(m): + for j in range(nNodes): self.axpy(a=Q[m, j], x=fK0[j], y=rhs) # add correction terms (from previous nodes) @@ -180,11 +191,11 @@ def solveSDC(self, Q, weights, QDelta, nSweeps, uNum=None): # diagonal term (current node) if qDelta[m, m] != 0: self.axpy(a=-qDelta[m, m], x=fK0[m], y=rhs) - self.fSolve(a=qDelta[m, m], b=rhs, uInit=uNode, t=tNode, out=uNode) + self.fSolve(a=qDelta[m, m], rhs=rhs, t=tNode, out=uNode) else: np.copyto(uNode, rhs) - # evalF on node + # evalF on current node self.evalF(u=uNode, t=tNode, out=fK1[m]) # invert fK0 and fK1 for the next sweep @@ -196,4 +207,168 @@ def solveSDC(self, Q, weights, QDelta, nSweeps, uNum=None): for m in range(nNodes): self.axpy(a=weights[m], x=fK1[m], y=uNum[i+1]) - + return uNum + + +class GenericMultiNode(LinearMultiNode): + + def __init__(self, u0, evalF, nodes, fSolve=None, tEnd=1, nSteps=1, t0=0): + super().__init__(u0, evalF, fSolve, tEnd, nSteps, t0) + self.nodes = np.asarray(nodes, dtype=float) + + @property + def nNodes(self): + return self.nodes.size + nStages = nNodes + + + def evalPsi(self, u, u0, fEvals, out, t0=0): + raise NotImplementedError( + "specialized Integrator must implement its evalPsi method") + + def nodeSolve(self, u0, fEvals, out, rhs=0, t0=0): + """solve u-psi(u, u0, fEvals) = rhs""" + + def func(u:np.ndarray): + u = u.reshape(self.uShape) + res = np.empty_like(u) + self.evalPsi(u, u0, fEvals, out=res, t0=t0) + res *= -1 + res += u + res -= rhs + return res.ravel() + + sol = self.innerSolver(func, out.ravel()).reshape(self.uShape) + np.copyto(out, sol) + + + def stepUpdate(self, u0, fEvals, out, t0=0): + pass + + + def solve(self, uNum=None): + if uNum is None: + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) + uNum[0] = self.u0 + + fEvals = np.zeros((self.nNodes, *self.uShape), dtype=self.dtype) + + times = np.linspace(self.t0, self.tEnd, self.nSteps+1) + tau = self.dt*self.nodes + + # time-stepping loop + for i in range(self.nSteps): + + uNode = uNum[i+1] # use next step as buffer + + # loop on nodes + for m in range(self.nNodes): + tNode = times[i] + tau[m] + self.nodeSolve(uNum[i], fEvals[:m+1], out=uNode, t0=times[i]) + self.evalF(uNode, tNode, out=fEvals[m]) + + # step update (no-op per default) + self.stepUpdate(uNum[i], fEvals, out=uNum[i+1], t0=times[i]) + + return uNum + + + def solveSDC(self, Q, weights, nSweeps, uNum=None): + + Q = self.dt*Q + + if uNum is None: + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) + uNum[0] = self.u0 + + rhs = np.zeros(self.uShape, dtype=self.dtype) + uNodes = deque([ + np.zeros((self.nNodes, *self.uShape), dtype=self.dtype) + for _ in range(2)]) + fEvals = np.zeros((self.nNodes, *self.uShape), dtype=self.dtype) + + times = np.linspace(self.t0, self.tEnd, self.nSteps+1) + tau = self.dt*self.nodes + + # time-stepping loop + for i in range(self.nSteps): + + # copy initialization + np.copyto(uNodes[0], uNum[i]) + self.evalF(uNum[i], t=times[i], out=fEvals[0]) + np.copyto(fEvals[1:], fEvals[0]) + + uTmp = uNum[i+1] + + # loop on sweeps (iterations) + for k in range(nSweeps): + + uK0 = uNodes[0] + uK1 = uNodes[1] + + # loop on nodes (stages) + for m in range(self.nNodes): + + # initialize RHS + np.copyto(rhs, uNum[i]) + + # add quadrature terms + for j in range(self.nNodes): + self.axpy(a=Q[m, j], x=fEvals[j], y=rhs) + + # substract k correction term + if k == 0: + self.axpy(a=-tau[m], x=fEvals[0], y=rhs) + rhs -= uNum[i] + else: + self.evalPsi(uNum[i], *uK0[:m+1], out=uTmp, t0=times[i]) + rhs -= uTmp + + # solve with k+1 correction + self.nodeSolve( + uNum[i], *uK1[:m], out=uK1[m], rhs=rhs, t0=times[i]) + + # compute f evals + for m in range(self.nNodes): + self.evalF(uK1[m], t=times[i]+tau[m], out=fEvals[m]) + + # invert uK0 and uK1 for next sweep + uNodes.rotate() + + # step update (copy of last node solution per default) + self.stepUpdate(*uK1, out=uNum[i+1], t0=times[i]) + + return uNum + + + +class ForwardEuler(GenericMultiNode): + + def evalPsi(self, u, u0, fEvals, out, t0=0): + m = len(fEvals) - 1 + assert m > 0 + tau = [t0] + (t0 + self.dt*self.nodes).tolist() + np.copyto(out, u0) + for i in range(m): + dTau = tau[i+1] - tau[i] + self.axpy(a=dTau, x=fEvals[i], y=out) + + + def nodeSolve(self, *uPrev, out, rhs=0, t0=0): + self.evalPsi(*uPrev, out, out=out, t0=t0) + out += rhs + + +class BackwardEuler(GenericMultiNode): + + def evalPsi(self, u, u0, fEvals, out, t0=0): + m = len(fEvals) - 1 + assert m > 0 + tau = [t0] + (t0 + self.dt*self.nodes).tolist() + # evaluate current + self.evalF(u, tau[m], out=out) + + np.copyto(out, u0) + for i in range(m): + dTau = tau[i+1] - tau[i] + self.axpy(a=dTau, x=fEvals[i], y=out) From 9c70b69ddfe463d9f3e777861e778a02db0c7965 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Mon, 20 Oct 2025 16:13:14 +0200 Subject: [PATCH 05/33] TL: small testing on generic solver --- qmat/solvers/generic.py | 128 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 128 insertions(+) diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic.py index b2cdbde..21598c5 100644 --- a/qmat/solvers/generic.py +++ b/qmat/solvers/generic.py @@ -372,3 +372,131 @@ def evalPsi(self, u, u0, fEvals, out, t0=0): for i in range(m): dTau = tau[i+1] - tau[i] self.axpy(a=dTau, x=fEvals[i], y=out) + + +if __name__ == "__main__": + import matplotlib.pyplot as plt + from time import time + + from qmat import genQCoeffs, QDELTA_GENERATORS + from qmat.qcoeff.collocation import Collocation + + pType = "Dahlquist" + + if pType == "Dahlquist": + lam = 1j + + def evalF(u, t, out): + out[0] = u[0]*lam.real - u[1]*lam.imag + out[1] = u[1]*lam.real + u[0]*lam.imag + + u0 = np.array([1, 0], dtype=float) + fSolve = None + + + elif pType == "Lorenz": + sigma = 10 + rho = 28 + beta = 8/3 + + def evalF(u, t, out): + x, y, z = u + out[0] = sigma*(y - x) + out[1] = x*(rho - z) - y + out[2] = x*y - beta*z + + u0 = np.array([5, -5, 20], dtype=float) + + newton = { + "maxIter": 99, + "tolerance": 1e-9, + } + + gemv = blas.get_blas_funcs("gemv", dtype=u0.dtype) + + def fSolve(a, rhs, t, out): + + rhsX, rhsY, rhsZ = rhs + a2 = a**2 + a3 = a**3 + + for n in range(newton["maxIter"]): + x, y, z = out + + res = np.array([ + x - a*sigma*(y - x) - rhsX, + y - a*(x*(rho - z) - y) - rhsY, + z - a*(x*y - beta*z) - rhsZ, + ]) + + resNorm = np.linalg.norm(res, np.inf) + if resNorm <= newton["tolerance"]: + break + if np.isnan(resNorm): + break + + factor = -1.0 / ( + a3*sigma*(x*(x + y) + beta*(-rho + z + 1)) + + a2*(beta*sigma + beta - rho*sigma + sigma + x**2 + sigma*z) + + a*(beta + sigma + 1) + 1 + ) + + jacInv = factor * np.array([ + [ + beta*a2 + a2*(x**2) + beta*a + a + 1, + beta*a2*sigma + a*sigma, + -a2*sigma*x, + ], + [ + beta*a2*rho - a2*x*y - beta*a2*z + a*rho - a*z, + beta*a2*sigma + beta*a + a*sigma + 1, + -(a2*sigma + a)*x, + ], + [ + a2*rho*x - a2*x*z + a2*y + a*y, + a2*sigma*x + a2*sigma*y + a*x, + -a2*rho*sigma + a2*sigma*(1 + z) + a*sigma + a + 1, + ], + ]) + + # out += jacInv @ res + gemv(alpha=1.0, a=jacInv, x=res, beta=1.0, y=out, overwrite_y=True) + + fSolve = None + + + nodes, weights, Q = genQCoeffs("FE") + + coll = Collocation(nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") + gen = QDELTA_GENERATORS["FE"](qGen=coll) + nSweeps = 2 + QDelta = gen.genCoeffs(k=[i+1 for i in range(nSweeps)]) + + + nSteps = 1 + tEnd = np.pi/10 + prob = LinearMultiNode(u0, evalF, fSolve=fSolve, tEnd=tEnd, nSteps=nSteps) + + from qmat.solvers.generic import ForwardEuler + + solver = ForwardEuler( + u0, evalF, nodes=coll.nodes, fSolve=fSolve, + tEnd=tEnd, nSteps=nSteps) + + plt.figure(1) + plt.clf() + + tBeg = time() + # uNum = prob.solve(Q, weights) + # uNum = solver.solve() + + uNum = prob.solveSDC(coll.Q, None, QDelta, nSweeps=nSweeps) + plt.plot(uNum[:, 0], uNum[:, 1], label="ref") + + uNum = solver.solveSDC(coll.Q, None, nSweeps=nSweeps) + plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") + + plt.legend() + tWall = time()-tBeg + tWall /= nSteps * np.size(u0) + print(f"tWallScaled : {tWall:1.2e}s") From ac867500e2f50e6cf276b739486107757f53c674 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Mon, 20 Oct 2025 17:37:21 +0200 Subject: [PATCH 06/33] TL: almost there ... --- qmat/solvers/generic.py | 210 +++++++++------------------------------- qmat/solvers/test.py | 134 +++++++++++++++++++++++++ 2 files changed, 179 insertions(+), 165 deletions(-) create mode 100644 qmat/solvers/test.py diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic.py index 21598c5..d49e3fe 100644 --- a/qmat/solvers/generic.py +++ b/qmat/solvers/generic.py @@ -7,8 +7,6 @@ import scipy.optimize as sco from scipy.linalg import blas -from collections import deque - from qmat.solvers.dahlquist import Dahlquist @@ -148,9 +146,8 @@ def solveSDC(self, Q, weights, QDelta, nSweeps, uNum=None): uNum[0] = self.u0 rhs = np.zeros(self.uShape, dtype=self.dtype) - fEvals = deque([ - np.zeros((nNodes, *self.uShape), dtype=self.dtype) - for _ in range(2)]) + fEvals = [np.zeros((nNodes, *self.uShape), dtype=self.dtype) + for _ in range(2)] times = np.linspace(self.t0, self.tEnd, self.nSteps+1) tau = Q.sum(axis=1) @@ -199,7 +196,7 @@ def solveSDC(self, Q, weights, QDelta, nSweeps, uNum=None): self.evalF(u=uNode, t=tNode, out=fK1[m]) # invert fK0 and fK1 for the next sweep - fEvals.rotate() + fEvals[0], fEvals[1] = fEvals[1], fEvals[0] # step update (if not, uNum[i+1] is already the last stage) if weights is not None: @@ -222,17 +219,17 @@ def nNodes(self): nStages = nNodes - def evalPsi(self, u, u0, fEvals, out, t0=0): + def evalPsi(self, uVals, fEvals, out, t0=0): raise NotImplementedError( "specialized Integrator must implement its evalPsi method") - def nodeSolve(self, u0, fEvals, out, rhs=0, t0=0): + def nodeSolve(self, uPrev, fEvals, out, rhs=0, t0=0): """solve u-psi(u, u0, fEvals) = rhs""" def func(u:np.ndarray): u = u.reshape(self.uShape) res = np.empty_like(u) - self.evalPsi(u, u0, fEvals, out=res, t0=t0) + self.evalPsi([*uPrev, u], fEvals, out=res, t0=t0) res *= -1 res += u res -= rhs @@ -242,8 +239,9 @@ def func(u:np.ndarray): np.copyto(out, sol) - def stepUpdate(self, u0, fEvals, out, t0=0): - pass + def stepUpdate(self, u0, uNodes, fEvals, out): + np.copyto(out, uNodes[-1]) + fEvals[0], fEvals[-1] = fEvals[-1], fEvals[0] def solve(self, uNum=None): @@ -251,7 +249,10 @@ def solve(self, uNum=None): uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) uNum[0] = self.u0 - fEvals = np.zeros((self.nNodes, *self.uShape), dtype=self.dtype) + uNodes = np.zeros((self.nNodes, *self.uShape), dtype=self.dtype) + fEvals = [np.zeros(self.uShape, dtype=self.dtype) + for _ in range(self.nNodes+1)] + self.evalF(uNum[0], self.t0, out=fEvals[0]) times = np.linspace(self.t0, self.tEnd, self.nSteps+1) tau = self.dt*self.nodes @@ -259,16 +260,18 @@ def solve(self, uNum=None): # time-stepping loop for i in range(self.nSteps): - uNode = uNum[i+1] # use next step as buffer + # initialize first node with starting value for step + np.copyto(uNodes[0], uNum[i]) # loop on nodes for m in range(self.nNodes): - tNode = times[i] + tau[m] - self.nodeSolve(uNum[i], fEvals[:m+1], out=uNode, t0=times[i]) - self.evalF(uNode, tNode, out=fEvals[m]) + self.nodeSolve( + [uNum[i], *uNodes[:m]], fEvals[:m+1], out=uNodes[m], t0=times[i]) + self.evalF(u=uNodes[m], t=times[i]+tau[m], out=fEvals[m+1]) + + # step update + self.stepUpdate(uNum[i], uNodes, fEvals, out=uNum[i+1]) - # step update (no-op per default) - self.stepUpdate(uNum[i], fEvals, out=uNum[i+1], t0=times[i]) return uNum @@ -282,10 +285,11 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): uNum[0] = self.u0 rhs = np.zeros(self.uShape, dtype=self.dtype) - uNodes = deque([ - np.zeros((self.nNodes, *self.uShape), dtype=self.dtype) - for _ in range(2)]) - fEvals = np.zeros((self.nNodes, *self.uShape), dtype=self.dtype) + uNodes = [np.zeros((self.nNodes, *self.uShape), dtype=self.dtype) + for _ in range(2)] + fEvals = [[np.zeros(self.uShape, dtype=self.dtype) + for _ in range(self.nNodes+1)] + for _ in range(2)] times = np.linspace(self.t0, self.tEnd, self.nSteps+1) tau = self.dt*self.nodes @@ -344,159 +348,35 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): class ForwardEuler(GenericMultiNode): - def evalPsi(self, u, u0, fEvals, out, t0=0): - m = len(fEvals) - 1 + def evalPsi(self, uVals, fEvals, out, t0=0): + m = len(uVals) - 1 assert m > 0 + assert len(fEvals) == m + tau = [t0] + (t0 + self.dt*self.nodes).tolist() - np.copyto(out, u0) + + # u0 + dt1 f0 + dt2 f1 + ... + dtm f{m-1} + np.copyto(out, uVals[0]) for i in range(m): - dTau = tau[i+1] - tau[i] - self.axpy(a=dTau, x=fEvals[i], y=out) + self.axpy(a=tau[i+1]-tau[i], x=fEvals[i], y=out) - def nodeSolve(self, *uPrev, out, rhs=0, t0=0): - self.evalPsi(*uPrev, out, out=out, t0=t0) + def nodeSolve(self, uPrev, fEvals, out, rhs=0, t0=0): + self.evalPsi([*uPrev, out], fEvals, out, t0=t0) out += rhs class BackwardEuler(GenericMultiNode): - def evalPsi(self, u, u0, fEvals, out, t0=0): - m = len(fEvals) - 1 + def evalPsi(self, uVals, fEvals, out, t0=0): + m = len(uVals) - 1 assert m > 0 - tau = [t0] + (t0 + self.dt*self.nodes).tolist() - # evaluate current - self.evalF(u, tau[m], out=out) - - np.copyto(out, u0) - for i in range(m): - dTau = tau[i+1] - tau[i] - self.axpy(a=dTau, x=fEvals[i], y=out) - - -if __name__ == "__main__": - import matplotlib.pyplot as plt - from time import time - - from qmat import genQCoeffs, QDELTA_GENERATORS - from qmat.qcoeff.collocation import Collocation - - pType = "Dahlquist" - - if pType == "Dahlquist": - lam = 1j - - def evalF(u, t, out): - out[0] = u[0]*lam.real - u[1]*lam.imag - out[1] = u[1]*lam.real + u[0]*lam.imag - - u0 = np.array([1, 0], dtype=float) - fSolve = None - - - elif pType == "Lorenz": - sigma = 10 - rho = 28 - beta = 8/3 - - def evalF(u, t, out): - x, y, z = u - out[0] = sigma*(y - x) - out[1] = x*(rho - z) - y - out[2] = x*y - beta*z - - u0 = np.array([5, -5, 20], dtype=float) - - newton = { - "maxIter": 99, - "tolerance": 1e-9, - } + assert len(fEvals) == m - gemv = blas.get_blas_funcs("gemv", dtype=u0.dtype) - - def fSolve(a, rhs, t, out): - - rhsX, rhsY, rhsZ = rhs - a2 = a**2 - a3 = a**3 - - for n in range(newton["maxIter"]): - x, y, z = out - - res = np.array([ - x - a*sigma*(y - x) - rhsX, - y - a*(x*(rho - z) - y) - rhsY, - z - a*(x*y - beta*z) - rhsZ, - ]) - - resNorm = np.linalg.norm(res, np.inf) - if resNorm <= newton["tolerance"]: - break - if np.isnan(resNorm): - break - - factor = -1.0 / ( - a3*sigma*(x*(x + y) + beta*(-rho + z + 1)) - + a2*(beta*sigma + beta - rho*sigma + sigma + x**2 + sigma*z) - + a*(beta + sigma + 1) + 1 - ) - - jacInv = factor * np.array([ - [ - beta*a2 + a2*(x**2) + beta*a + a + 1, - beta*a2*sigma + a*sigma, - -a2*sigma*x, - ], - [ - beta*a2*rho - a2*x*y - beta*a2*z + a*rho - a*z, - beta*a2*sigma + beta*a + a*sigma + 1, - -(a2*sigma + a)*x, - ], - [ - a2*rho*x - a2*x*z + a2*y + a*y, - a2*sigma*x + a2*sigma*y + a*x, - -a2*rho*sigma + a2*sigma*(1 + z) + a*sigma + a + 1, - ], - ]) - - # out += jacInv @ res - gemv(alpha=1.0, a=jacInv, x=res, beta=1.0, y=out, overwrite_y=True) - - fSolve = None - - - nodes, weights, Q = genQCoeffs("FE") - - coll = Collocation(nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") - gen = QDELTA_GENERATORS["FE"](qGen=coll) - nSweeps = 2 - QDelta = gen.genCoeffs(k=[i+1 for i in range(nSweeps)]) - - - nSteps = 1 - tEnd = np.pi/10 - prob = LinearMultiNode(u0, evalF, fSolve=fSolve, tEnd=tEnd, nSteps=nSteps) - - from qmat.solvers.generic import ForwardEuler - - solver = ForwardEuler( - u0, evalF, nodes=coll.nodes, fSolve=fSolve, - tEnd=tEnd, nSteps=nSteps) - - plt.figure(1) - plt.clf() - - tBeg = time() - # uNum = prob.solve(Q, weights) - # uNum = solver.solve() - - uNum = prob.solveSDC(coll.Q, None, QDelta, nSweeps=nSweeps) - plt.plot(uNum[:, 0], uNum[:, 1], label="ref") + tau = [t0] + (t0 + self.dt*self.nodes).tolist() - uNum = solver.solveSDC(coll.Q, None, nSweeps=nSweeps) - plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") - - plt.legend() - tWall = time()-tBeg - tWall /= nSteps * np.size(u0) - print(f"tWallScaled : {tWall:1.2e}s") + # dtm f{m} + ... + dt2 f2 + dt1 f1 + u0 + self.evalF(uVals[-1], tau[m+1], out=out) + for i in range(m-1): + self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) + out += uVals[0] diff --git a/qmat/solvers/test.py b/qmat/solvers/test.py new file mode 100644 index 0000000..aa0077a --- /dev/null +++ b/qmat/solvers/test.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Oct 20 17:26:04 2025 + +@author: cpf5546 +""" +import numpy as np +from scipy.linalg import blas + +import matplotlib.pyplot as plt +from time import time + +from qmat import genQCoeffs, QDELTA_GENERATORS +from qmat.qcoeff.collocation import Collocation +from qmat.solvers.generic import LinearMultiNode, ForwardEuler + +pType = "Lorenz" + +if pType == "Dahlquist": + lam = 1j + + def evalF(u, t, out): + out[0] = u[0]*lam.real - u[1]*lam.imag + out[1] = u[1]*lam.real + u[0]*lam.imag + + u0 = np.array([1, 0], dtype=float) + fSolve = None + + +elif pType == "Lorenz": + sigma = 10 + rho = 28 + beta = 8/3 + + def evalF(u, t, out): + x, y, z = u + out[0] = sigma*(y - x) + out[1] = x*(rho - z) - y + out[2] = x*y - beta*z + + u0 = np.array([5, -5, 20], dtype=float) + + newton = { + "maxIter": 99, + "tolerance": 1e-9, + } + + gemv = blas.get_blas_funcs("gemv", dtype=u0.dtype) + + def fSolve(a, rhs, t, out): + + rhsX, rhsY, rhsZ = rhs + a2 = a**2 + a3 = a**3 + + for n in range(newton["maxIter"]): + x, y, z = out + + res = np.array([ + x - a*sigma*(y - x) - rhsX, + y - a*(x*(rho - z) - y) - rhsY, + z - a*(x*y - beta*z) - rhsZ, + ]) + + resNorm = np.linalg.norm(res, np.inf) + if resNorm <= newton["tolerance"]: + break + if np.isnan(resNorm): + break + + factor = -1.0 / ( + a3*sigma*(x*(x + y) + beta*(-rho + z + 1)) + + a2*(beta*sigma + beta - rho*sigma + sigma + x**2 + sigma*z) + + a*(beta + sigma + 1) + 1 + ) + + jacInv = factor * np.array([ + [ + beta*a2 + a2*(x**2) + beta*a + a + 1, + beta*a2*sigma + a*sigma, + -a2*sigma*x, + ], + [ + beta*a2*rho - a2*x*y - beta*a2*z + a*rho - a*z, + beta*a2*sigma + beta*a + a*sigma + 1, + -(a2*sigma + a)*x, + ], + [ + a2*rho*x - a2*x*z + a2*y + a*y, + a2*sigma*x + a2*sigma*y + a*x, + -a2*rho*sigma + a2*sigma*(1 + z) + a*sigma + a + 1, + ], + ]) + + # out += jacInv @ res + gemv(alpha=1.0, a=jacInv, x=res, beta=1.0, y=out, overwrite_y=True) + + fSolve = None # because own implementation is cute, but still less efficient + + +nodes, weights, Q = genQCoeffs("FE") + +coll = Collocation(nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") +gen = QDELTA_GENERATORS["FE"](qGen=coll) +nSweeps = 2 +QDelta = gen.genCoeffs(k=[i+1 for i in range(nSweeps)]) + + +nSteps = 1000 +tEnd = np.pi +prob = LinearMultiNode(u0, evalF, fSolve=fSolve, tEnd=tEnd, nSteps=nSteps) + +solver = ForwardEuler( + u0, evalF, nodes=[0.25, 0.5, 0.75, 1], fSolve=fSolve, + tEnd=tEnd, nSteps=nSteps//4) + +plt.figure(1) +plt.clf() + +tBeg = time() + +uNum = prob.solve(Q, weights) +# uNum = prob.solveSDC(coll.Q, None, QDelta, nSweeps=nSweeps) +plt.plot(uNum[:, 0], uNum[:, 1], label="ref") + +uNum = solver.solve() +# uNum = solver.solveSDC(coll.Q, None, nSweeps=nSweeps) +plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") + +plt.legend() +tWall = time()-tBeg +tWall /= nSteps * np.size(u0) +print(f"tWallScaled : {tWall:1.2e}s") From 1cd2d3e192d8b5683f8b73790804a00cd049b8ce Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Mon, 20 Oct 2025 23:27:14 +0200 Subject: [PATCH 07/33] TL: first working version --- qmat/solvers/generic.py | 71 +++++++++++++++++++++++++---------------- qmat/solvers/test.py | 40 +++++++++++++---------- 2 files changed, 67 insertions(+), 44 deletions(-) diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic.py index d49e3fe..079027c 100644 --- a/qmat/solvers/generic.py +++ b/qmat/solvers/generic.py @@ -240,6 +240,8 @@ def func(u:np.ndarray): def stepUpdate(self, u0, uNodes, fEvals, out): + """Update end-step solution and ensure that fEvals[0] contains its evaluation""" + assert self.nodes[-1] == 1 np.copyto(out, uNodes[-1]) fEvals[0], fEvals[-1] = fEvals[-1], fEvals[0] @@ -272,13 +274,14 @@ def solve(self, uNum=None): # step update self.stepUpdate(uNum[i], uNodes, fEvals, out=uNum[i+1]) - return uNum def solveSDC(self, Q, weights, nSweeps, uNum=None): Q = self.dt*Q + if weights is not None: + weights = self.dt*np.asarray(weights) if uNum is None: uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) @@ -290,6 +293,7 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): fEvals = [[np.zeros(self.uShape, dtype=self.dtype) for _ in range(self.nNodes+1)] for _ in range(2)] + self.evalF(uNum[0], self.t0, out=fEvals[0][0]) times = np.linspace(self.t0, self.tEnd, self.nSteps+1) tau = self.dt*self.nodes @@ -299,16 +303,17 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): # copy initialization np.copyto(uNodes[0], uNum[i]) - self.evalF(uNum[i], t=times[i], out=fEvals[0]) - np.copyto(fEvals[1:], fEvals[0]) + np.copyto(fEvals[1][0], fEvals[0][0]) # u_0^{1} = u_0^{0} + for m in range(self.nNodes): + np.copyto(fEvals[0][m+1], fEvals[0][0]) # u_m^{k} = u_0^{0} uTmp = uNum[i+1] # loop on sweeps (iterations) - for k in range(nSweeps): + for _ in range(nSweeps): - uK0 = uNodes[0] - uK1 = uNodes[1] + uK0, uK1 = uNodes + fK0, fK1 = fEvals # loop on nodes (stages) for m in range(self.nNodes): @@ -317,30 +322,34 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): np.copyto(rhs, uNum[i]) # add quadrature terms + fK = fK0[1:] # note : ignore f(u0) term in fK0 for j in range(self.nNodes): - self.axpy(a=Q[m, j], x=fEvals[j], y=rhs) + self.axpy(a=Q[m, j], x=fK[j], y=rhs) # substract k correction term - if k == 0: - self.axpy(a=-tau[m], x=fEvals[0], y=rhs) - rhs -= uNum[i] - else: - self.evalPsi(uNum[i], *uK0[:m+1], out=uTmp, t0=times[i]) - rhs -= uTmp + self.evalPsi( + [uNum[i], *uK0[:m+1]], fK0[:m+2], out=uTmp, t0=times[i]) + rhs -= uTmp # solve with k+1 correction self.nodeSolve( - uNum[i], *uK1[:m], out=uK1[m], rhs=rhs, t0=times[i]) + [uNum[i], *uK1[:m]], fK1[:m+1], out=uK1[m], rhs=rhs, t0=times[i]) - # compute f evals - for m in range(self.nNodes): - self.evalF(uK1[m], t=times[i]+tau[m], out=fEvals[m]) + # evalF on k+1 node solution + self.evalF(uK1[m], t=times[i]+tau[m], out=fK1[m+1]) - # invert uK0 and uK1 for next sweep - uNodes.rotate() + # invert uK0/fK0 and uK1/fK1 for next sweep + fEvals[0], fEvals[1] = fEvals[1], fEvals[0] + uNodes[0], uNodes[1] = uNodes[1], uNodes[0] - # step update (copy of last node solution per default) - self.stepUpdate(*uK1, out=uNum[i+1], t0=times[i]) + # step update + if weights is not None: + uNum[i+1] = uNum[i] + fK = fEvals[0][1:] # note : ignore f(u0) term in fK0 + for m in range(self.nNodes): + self.axpy(a=weights[m], x=fK[m], y=uNum[i+1]) + else: + self.stepUpdate(uNum[i], uNodes[0], fEvals[0], out=uNum[i+1]) return uNum @@ -351,7 +360,7 @@ class ForwardEuler(GenericMultiNode): def evalPsi(self, uVals, fEvals, out, t0=0): m = len(uVals) - 1 assert m > 0 - assert len(fEvals) == m + assert len(fEvals) in [m, m+1] tau = [t0] + (t0 + self.dt*self.nodes).tolist() @@ -371,12 +380,20 @@ class BackwardEuler(GenericMultiNode): def evalPsi(self, uVals, fEvals, out, t0=0): m = len(uVals) - 1 assert m > 0 - assert len(fEvals) == m + assert len(fEvals) in [m, m+1] tau = [t0] + (t0 + self.dt*self.nodes).tolist() # dtm f{m} + ... + dt2 f2 + dt1 f1 + u0 - self.evalF(uVals[-1], tau[m+1], out=out) - for i in range(m-1): - self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) - out += uVals[0] + if len(fEvals) == m: + # f{m} not given, must evaluate and sum with the other terms + self.evalF(uVals[m], tau[m], out=out) + out *= tau[m]-tau[m-1] + for i in range(m-1): + self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) + out += uVals[0] + else: + # f{m} given, use its value + np.copyto(out, uVals[0]) + for i in range(m): + self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) diff --git a/qmat/solvers/test.py b/qmat/solvers/test.py index aa0077a..6e52fcf 100644 --- a/qmat/solvers/test.py +++ b/qmat/solvers/test.py @@ -13,7 +13,7 @@ from qmat import genQCoeffs, QDELTA_GENERATORS from qmat.qcoeff.collocation import Collocation -from qmat.solvers.generic import LinearMultiNode, ForwardEuler +from qmat.solvers.generic import LinearMultiNode, BackwardEuler pType = "Lorenz" @@ -99,36 +99,42 @@ def fSolve(a, rhs, t, out): fSolve = None # because own implementation is cute, but still less efficient -nodes, weights, Q = genQCoeffs("FE") +nodes, weights, Q = genQCoeffs("BE") coll = Collocation(nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") -gen = QDELTA_GENERATORS["FE"](qGen=coll) -nSweeps = 2 +gen = QDELTA_GENERATORS["BE"](qGen=coll) +nSweeps = 3 QDelta = gen.genCoeffs(k=[i+1 for i in range(nSweeps)]) -nSteps = 1000 -tEnd = np.pi +nSteps = 4000 +tEnd = 4*np.pi prob = LinearMultiNode(u0, evalF, fSolve=fSolve, tEnd=tEnd, nSteps=nSteps) -solver = ForwardEuler( - u0, evalF, nodes=[0.25, 0.5, 0.75, 1], fSolve=fSolve, - tEnd=tEnd, nSteps=nSteps//4) +solver = BackwardEuler( + u0, evalF, nodes=coll.nodes, fSolve=fSolve, + tEnd=tEnd, nSteps=nSteps) plt.figure(1) plt.clf() tBeg = time() -uNum = prob.solve(Q, weights) -# uNum = prob.solveSDC(coll.Q, None, QDelta, nSweeps=nSweeps) -plt.plot(uNum[:, 0], uNum[:, 1], label="ref") -uNum = solver.solve() -# uNum = solver.solveSDC(coll.Q, None, nSweeps=nSweeps) -plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") +tBeg = time() +# uNumRef = prob.solve(Q, weights) +uNumRef = prob.solveSDC(coll.Q, coll.weights, QDelta, nSweeps=nSweeps) +tWall = time()-tBeg +tWall /= nSteps * np.size(u0) +print(f"tWallScaled[linear] : {tWall:1.2e}s") +plt.plot(uNumRef[:, 0], uNumRef[:, 1], label="ref") -plt.legend() +tBeg = time() +# uNum = solver.solve() +uNum = solver.solveSDC(coll.Q, coll.weights, nSweeps=nSweeps) +plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") tWall = time()-tBeg tWall /= nSteps * np.size(u0) -print(f"tWallScaled : {tWall:1.2e}s") +print(f"tWallScaled[generic] : {tWall:1.2e}s") + +plt.legend() From fb68b49ce06fee2ac9e38a6a04ef7a830846e79f Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Tue, 21 Oct 2025 01:51:45 +0200 Subject: [PATCH 08/33] TL: finally done ! --- qmat/lagrange.py | 8 ++--- qmat/solvers/dahlquist.py | 8 +++-- qmat/solvers/generic.py | 74 +++++++++++++++++++++++---------------- qmat/solvers/test.py | 32 ++++++++++------- 4 files changed, 74 insertions(+), 48 deletions(-) diff --git a/qmat/lagrange.py b/qmat/lagrange.py index ccc618c..ab84845 100644 --- a/qmat/lagrange.py +++ b/qmat/lagrange.py @@ -298,7 +298,7 @@ def hasDuplicates(self)->bool: """Wether the points have duplicates or not""" return self.nPoints > self.nUniquePoints - def getInterpolationMatrix(self, times, duplicates=True): + def getInterpolationMatrix(self, times, duplicates=True) -> np.ndarray: r""" Compute the interpolation matrix for a given set of discrete "time" points. @@ -354,7 +354,7 @@ def getInterpolationMatrix(self, times, duplicates=True): return P - def getIntegrationMatrix(self, intervals, numQuad='FEJER', duplicates=True): + def getIntegrationMatrix(self, intervals, numQuad='FEJER', duplicates=True) -> np.ndarray: r""" Compute the integration matrix for a given set of intervals. @@ -437,7 +437,7 @@ def getIntegrationMatrix(self, intervals, numQuad='FEJER', duplicates=True): return Q - def getDerivativeMatrix(self, order=1, duplicates=True): + def getDerivativeMatrix(self, order=1, duplicates=True) -> np.ndarray: r""" Generate derivative matrix of first or second order (or both) based on the Lagrange interpolant. @@ -531,7 +531,7 @@ def getDerivativeMatrix(self, order=1, duplicates=True): else: return D1, D2 - def getDerivationMatrix(self, *args, **kwargs): + def getDerivationMatrix(self, *args, **kwargs) -> np.ndarray: import warnings warnings.warn("Function `getDerivationMatrix` is deprecated. Use `getDerivativeMatrix` instead!", DeprecationWarning) return self.getDerivativeMatrix(*args, **kwargs) diff --git a/qmat/solvers/dahlquist.py b/qmat/solvers/dahlquist.py index c5a516c..9504fda 100644 --- a/qmat/solvers/dahlquist.py +++ b/qmat/solvers/dahlquist.py @@ -32,6 +32,8 @@ def checkCoeff(Q, weights): weights = np.asarray(weights) assert weights.ndim == 1, "weights must be a 1D vector" assert weights.size == nNodes, "weights size is not the same as the node size" + else: + assert np.allclose(Q.sum(axis=1)[-1], 1), "last node must be 1 if weights are not given" return nNodes, Q, weights @@ -40,7 +42,7 @@ def solve(self, Q, weights): nNodes, Q, weights = self.checkCoeff(Q, weights) # Collocation problem matrix - A = np.eye(nNodes) - self.lam[..., None, None]*self.dt*Q + A = np.eye(nNodes) - self.lam[..., None, None]*self.dt*Q uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) uNum[0] = self.u0 @@ -67,6 +69,8 @@ def checkCoeffSDC(Q, weights, QDelta, nSweeps): weights = np.asarray(weights) assert weights.ndim == 1, "weights must be a 1D vector" assert weights.size == nNodes, "weights size is not the same as the node size" + else: + assert np.allclose(nodes[-1], 1), "last node must be 1 if weights are not given" QDelta = np.asarray(QDelta) if QDelta.ndim == 3: @@ -96,7 +100,7 @@ def solveSDC(self, Q, weights, QDelta, nSweeps): b = uNum[i][..., None, None] \ + self.lam[..., None, None]*self.dt*(Q - QDelta[k]) @ uNodes - + # b has shape [..., nNodes, 1] # P[k] has shape [..., nNodes, nNodes] # output has shape [..., nNodes, 1] diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic.py index 079027c..b790bd0 100644 --- a/qmat/solvers/generic.py +++ b/qmat/solvers/generic.py @@ -8,6 +8,7 @@ from scipy.linalg import blas from qmat.solvers.dahlquist import Dahlquist +from qmat.lagrange import LagrangeApproximation class LinearMultiNode(): @@ -125,14 +126,14 @@ def solve(self, Q, weights, uNum=None): # step update (if not, uNum[i+1] is already the last stage) if weights is not None: - uNum[i+1] = uNum[i] + np.copyto(uNum[i+1], uNum[i]) for m in range(nNodes): self.axpy(a=weights[m], x=fEvals[m], y=uNum[i+1]) return uNum - def solveSDC(self, Q, weights, QDelta, nSweeps, uNum=None): + def solveSDC(self, nSweeps, Q, weights, QDelta, uNum=None): nNodes, Q, weights, QDelta, nSweeps = Dahlquist.checkCoeffSDC(Q, weights, QDelta, nSweeps) for qDelta in QDelta: @@ -200,7 +201,7 @@ def solveSDC(self, Q, weights, QDelta, nSweeps, uNum=None): # step update (if not, uNum[i+1] is already the last stage) if weights is not None: - uNum[i+1] = uNum[i] + np.copyto(uNum[i+1], uNum[i]) for m in range(nNodes): self.axpy(a=weights[m], x=fK1[m], y=uNum[i+1]) @@ -219,7 +220,7 @@ def nNodes(self): nStages = nNodes - def evalPsi(self, uVals, fEvals, out, t0=0): + def evalPhi(self, uVals, fEvals, out, t0=0): raise NotImplementedError( "specialized Integrator must implement its evalPsi method") @@ -229,7 +230,7 @@ def nodeSolve(self, uPrev, fEvals, out, rhs=0, t0=0): def func(u:np.ndarray): u = u.reshape(self.uShape) res = np.empty_like(u) - self.evalPsi([*uPrev, u], fEvals, out=res, t0=t0) + self.evalPhi([*uPrev, u], fEvals, out=res, t0=t0) res *= -1 res += u res -= rhs @@ -268,7 +269,7 @@ def solve(self, uNum=None): # loop on nodes for m in range(self.nNodes): self.nodeSolve( - [uNum[i], *uNodes[:m]], fEvals[:m+1], out=uNodes[m], t0=times[i]) + [uNum[i], *uNodes[:m]], fEvals[:m+1], rhs=uNum[i], out=uNodes[m], t0=times[i]) self.evalF(u=uNodes[m], t=times[i]+tau[m], out=fEvals[m+1]) # step update @@ -277,11 +278,24 @@ def solve(self, uNum=None): return uNum - def solveSDC(self, Q, weights, nSweeps, uNum=None): + def solveSDC(self, nSweeps, Q=None, weights=None, uNum=None): + + if Q is None: + approx = LagrangeApproximation(self.nodes) + Q = approx.getIntegrationMatrix([(0, tau) for tau in self.nodes]) + if weights is True: + weights = approx.getIntegrationMatrix([(0, 1)]).ravel() + else: + weights = None + else: + nNodes, Q, weights = Dahlquist.checkCoeff(Q, weights) + + assert nNodes == self.nNodes, "solver and Q do not have the same number of nodes" + assert np.allclose(Q.sum(axis=1), self.nodes), "solver and Q do not have the same nodes" Q = self.dt*Q if weights is not None: - weights = self.dt*np.asarray(weights) + weights = self.dt*weights if uNum is None: uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) @@ -293,7 +307,7 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): fEvals = [[np.zeros(self.uShape, dtype=self.dtype) for _ in range(self.nNodes+1)] for _ in range(2)] - self.evalF(uNum[0], self.t0, out=fEvals[0][0]) + times = np.linspace(self.t0, self.tEnd, self.nSteps+1) tau = self.dt*self.nodes @@ -303,11 +317,12 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): # copy initialization np.copyto(uNodes[0], uNum[i]) + self.evalF(uNum[i], self.t0, out=fEvals[0][0]) np.copyto(fEvals[1][0], fEvals[0][0]) # u_0^{1} = u_0^{0} for m in range(self.nNodes): np.copyto(fEvals[0][m+1], fEvals[0][0]) # u_m^{k} = u_0^{0} - uTmp = uNum[i+1] + uTmp = uNum[i+1] # use next step as buffer for k correction term # loop on sweeps (iterations) for _ in range(nSweeps): @@ -327,7 +342,7 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): self.axpy(a=Q[m, j], x=fK[j], y=rhs) # substract k correction term - self.evalPsi( + self.evalPhi( [uNum[i], *uK0[:m+1]], fK0[:m+2], out=uTmp, t0=times[i]) rhs -= uTmp @@ -344,8 +359,8 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): # step update if weights is not None: - uNum[i+1] = uNum[i] - fK = fEvals[0][1:] # note : ignore f(u0) term in fK0 + np.copyto(uNum[i+1], uNum[i]) + fK = fK1[1:] # note : ignore f(u0) term in fK0 for m in range(self.nNodes): self.axpy(a=weights[m], x=fK[m], y=uNum[i+1]) else: @@ -357,43 +372,42 @@ def solveSDC(self, Q, weights, nSweeps, uNum=None): class ForwardEuler(GenericMultiNode): - def evalPsi(self, uVals, fEvals, out, t0=0): + def evalPhi(self, uVals, fEvals, out, t0=0): m = len(uVals) - 1 assert m > 0 assert len(fEvals) in [m, m+1] tau = [t0] + (t0 + self.dt*self.nodes).tolist() - # u0 + dt1 f0 + dt2 f1 + ... + dtm f{m-1} - np.copyto(out, uVals[0]) - for i in range(m): + # dt1 f0 + dt2 f1 + ... + dtm f{m-1} + np.copyto(out, fEvals[0]) + out *= tau[1]-tau[0] + for i in range(1, m): self.axpy(a=tau[i+1]-tau[i], x=fEvals[i], y=out) def nodeSolve(self, uPrev, fEvals, out, rhs=0, t0=0): - self.evalPsi([*uPrev, out], fEvals, out, t0=t0) + self.evalPhi([*uPrev, out], fEvals, out, t0=t0) out += rhs + class BackwardEuler(GenericMultiNode): - def evalPsi(self, uVals, fEvals, out, t0=0): + def evalPhi(self, uVals, fEvals, out, t0=0): m = len(uVals) - 1 assert m > 0 assert len(fEvals) in [m, m+1] tau = [t0] + (t0 + self.dt*self.nodes).tolist() - # dtm f{m} + ... + dt2 f2 + dt1 f1 + u0 + # dt1 f1 + dt2 f2 + ... + dtm f{m} if len(fEvals) == m: - # f{m} not given, must evaluate and sum with the other terms - self.evalF(uVals[m], tau[m], out=out) - out *= tau[m]-tau[m-1] - for i in range(m-1): - self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) - out += uVals[0] + self.evalF(uVals[m], tau[m], out=out) # f{m} not given else: - # f{m} given, use its value - np.copyto(out, uVals[0]) - for i in range(m): - self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) + np.copyto(out, fEvals[-1]) # f{m} given, use its value + out *= tau[m]-tau[m-1] + for i in range(m-1): + self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) + + # TODO : override nodeSolve for better efficiency diff --git a/qmat/solvers/test.py b/qmat/solvers/test.py index 6e52fcf..fc655b9 100644 --- a/qmat/solvers/test.py +++ b/qmat/solvers/test.py @@ -13,9 +13,9 @@ from qmat import genQCoeffs, QDELTA_GENERATORS from qmat.qcoeff.collocation import Collocation -from qmat.solvers.generic import LinearMultiNode, BackwardEuler +from qmat.solvers.generic import LinearMultiNode, BackwardEuler, ForwardEuler -pType = "Lorenz" +pType = "Dahlquist" if pType == "Dahlquist": lam = 1j @@ -99,31 +99,37 @@ def fSolve(a, rhs, t, out): fSolve = None # because own implementation is cute, but still less efficient -nodes, weights, Q = genQCoeffs("BE") +corr = "FE" + +nodes, weights, Q = genQCoeffs(corr) coll = Collocation(nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") -gen = QDELTA_GENERATORS["BE"](qGen=coll) -nSweeps = 3 +gen = QDELTA_GENERATORS[corr](qGen=coll) +nSweeps = 4 QDelta = gen.genCoeffs(k=[i+1 for i in range(nSweeps)]) +useWeights = False -nSteps = 4000 -tEnd = 4*np.pi +nPeriod = 1 +nSteps = nPeriod*10 +tEnd = nPeriod*np.pi prob = LinearMultiNode(u0, evalF, fSolve=fSolve, tEnd=tEnd, nSteps=nSteps) -solver = BackwardEuler( +regNodes = [0.25, 0.5, 0.75, 1] + +Solver = BackwardEuler if corr == "BE" else ForwardEuler + +solver = Solver( u0, evalF, nodes=coll.nodes, fSolve=fSolve, tEnd=tEnd, nSteps=nSteps) plt.figure(1) plt.clf() -tBeg = time() - tBeg = time() # uNumRef = prob.solve(Q, weights) -uNumRef = prob.solveSDC(coll.Q, coll.weights, QDelta, nSweeps=nSweeps) +uNumRef = prob.solveSDC(nSweeps, coll.Q, coll.weights if useWeights else None, QDelta) tWall = time()-tBeg tWall /= nSteps * np.size(u0) print(f"tWallScaled[linear] : {tWall:1.2e}s") @@ -131,10 +137,12 @@ def fSolve(a, rhs, t, out): tBeg = time() # uNum = solver.solve() -uNum = solver.solveSDC(coll.Q, coll.weights, nSweeps=nSweeps) +uNum = solver.solveSDC(nSweeps, weights=useWeights) plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") tWall = time()-tBeg tWall /= nSteps * np.size(u0) print(f"tWallScaled[generic] : {tWall:1.2e}s") plt.legend() + +print(uNumRef[-1] - uNum[-1]) From 9d1619c9f135ce62cdd247cd32e90f0c324c9955 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Tue, 21 Oct 2025 16:21:03 +0200 Subject: [PATCH 09/33] TL: added specialized nodeSolve for BE generic solver --- qmat/solvers/generic.py | 75 ++++++++++++++++++++++++++++++++++++++++- qmat/solvers/test.py | 18 +++++----- 2 files changed, 83 insertions(+), 10 deletions(-) diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic.py index b790bd0..e114777 100644 --- a/qmat/solvers/generic.py +++ b/qmat/solvers/generic.py @@ -11,6 +11,67 @@ from qmat.lagrange import LagrangeApproximation +class Problem(): + + def __init__(self, u0): + u0 = np.asarray(u0) + if u0.size < 1e3: + self.innerSolver = sco.fsolve + else: + self.innerSolver = sco.newton_krylov + + @property + def uShape(self): + return self.u0.shape + + @property + def dtype(self): + return self.u0.dtype + + def evalF(self, u:np.ndarray, t:float, out:np.ndarray): + raise NotImplementedError("evalF must be provided") + + def fSolve(self, a:float, rhs:np.ndarray, t:float, out:np.ndarray): + """ + Solve u - a*f(u, t) = rhs using out as initial guess and storing the final solution into it + """ + + def func(u:np.ndarray): + """compute res = u - a*f(u,t) - rhs""" + u = u.reshape(self.uShape) + res = np.empty_like(u) + self.evalF(u, t, out=res) + res *= -a + res += u + res -= rhs + return res.ravel() + + sol = self.innerSolver(func, out.ravel()).reshape(self.uShape) + np.copyto(out, sol) + + def test(self, t0=0, dt=1e-1, eps=1e-3): + u0 = self.u0 + + try: + uEval = np.zeros_like(u0) + self.evalF(u=u0, t=t0, out=uEval) + except: + raise ValueError("evalF cannot be properly evaluated into an array like u0") + + try: + dt = dt + uEval *= -dt + uEval += u0 + uSolve = np.copy(u0) + uSolve += eps*np.linalg.norm(uSolve, np.inf) + self.fSolve(a=dt, rhs=uEval, t=t0, out=uSolve) + except: + raise ValueError("fSolve cannot be properly evaluated into an array like u0") + np.testing.assert_allclose( + uSolve, u0, err_msg="fSolve does not satisfy the fixed-point problem with u0", + atol=1e-15) + + class LinearMultiNode(): def __init__(self, u0, evalF, fSolve=None, tEnd=1, nSteps=1, t0=0): @@ -20,6 +81,8 @@ def __init__(self, u0, evalF, fSolve=None, tEnd=1, nSteps=1, t0=0): else: self.innerSolver = sco.newton_krylov self.u0 = u0 + + self.t0 = t0 self.tEnd = tEnd self.nSteps = nSteps @@ -410,4 +473,14 @@ def evalPhi(self, uVals, fEvals, out, t0=0): for i in range(m-1): self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) - # TODO : override nodeSolve for better efficiency + def nodeSolve(self, uPrev, fEvals, out, rhs=0, t0=0): + assert len(uPrev) == len(fEvals) + m = len(uPrev) + assert m > 0 + tau = [t0] + (t0 + self.dt*self.nodes).tolist() + + rhs = np.zeros_like(out) + rhs + for i in range(m-1): + self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=rhs) + + self.fSolve(tau[m]-tau[m-1], rhs, tau[m], out) diff --git a/qmat/solvers/test.py b/qmat/solvers/test.py index fc655b9..f9b5b58 100644 --- a/qmat/solvers/test.py +++ b/qmat/solvers/test.py @@ -15,7 +15,7 @@ from qmat.qcoeff.collocation import Collocation from qmat.solvers.generic import LinearMultiNode, BackwardEuler, ForwardEuler -pType = "Dahlquist" +pType = "Lorenz" if pType == "Dahlquist": lam = 1j @@ -99,7 +99,7 @@ def fSolve(a, rhs, t, out): fSolve = None # because own implementation is cute, but still less efficient -corr = "FE" +corr = "BE" nodes, weights, Q = genQCoeffs(corr) @@ -111,7 +111,7 @@ def fSolve(a, rhs, t, out): nPeriod = 1 -nSteps = nPeriod*10 +nSteps = nPeriod*1000 tEnd = nPeriod*np.pi prob = LinearMultiNode(u0, evalF, fSolve=fSolve, tEnd=tEnd, nSteps=nSteps) @@ -120,24 +120,24 @@ def fSolve(a, rhs, t, out): Solver = BackwardEuler if corr == "BE" else ForwardEuler solver = Solver( - u0, evalF, nodes=coll.nodes, fSolve=fSolve, - tEnd=tEnd, nSteps=nSteps) + u0, evalF, nodes=regNodes, fSolve=fSolve, + tEnd=tEnd, nSteps=nSteps//4) plt.figure(1) plt.clf() tBeg = time() -# uNumRef = prob.solve(Q, weights) -uNumRef = prob.solveSDC(nSweeps, coll.Q, coll.weights if useWeights else None, QDelta) +uNumRef = prob.solve(Q, weights) +# uNumRef = prob.solveSDC(nSweeps, coll.Q, coll.weights if useWeights else None, QDelta) tWall = time()-tBeg tWall /= nSteps * np.size(u0) print(f"tWallScaled[linear] : {tWall:1.2e}s") plt.plot(uNumRef[:, 0], uNumRef[:, 1], label="ref") tBeg = time() -# uNum = solver.solve() -uNum = solver.solveSDC(nSweeps, weights=useWeights) +uNum = solver.solve() +# uNum = solver.solveSDC(nSweeps, weights=useWeights) plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") tWall = time()-tBeg tWall /= nSteps * np.size(u0) From 314952634739f8487dfbe591b1b6bb9cda8a95aa Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Tue, 21 Oct 2025 17:52:29 +0200 Subject: [PATCH 10/33] TL: setup new structure --- qmat/playgrounds/__init__.py | 7 + qmat/playgrounds/tibo/test.py | 76 +++++++++ qmat/solvers/__init__.py | 5 + .../{generic.py => generic/__init__.py} | 146 ++++------------- qmat/solvers/generic/diffops.py | 98 ++++++++++++ qmat/solvers/generic/integrators.py | 60 +++++++ qmat/solvers/test.py | 148 ------------------ 7 files changed, 276 insertions(+), 264 deletions(-) create mode 100644 qmat/playgrounds/__init__.py create mode 100644 qmat/playgrounds/tibo/test.py create mode 100644 qmat/solvers/__init__.py rename qmat/solvers/{generic.py => generic/__init__.py} (76%) create mode 100644 qmat/solvers/generic/diffops.py create mode 100644 qmat/solvers/generic/integrators.py delete mode 100644 qmat/solvers/test.py diff --git a/qmat/playgrounds/__init__.py b/qmat/playgrounds/__init__.py new file mode 100644 index 0000000..111c444 --- /dev/null +++ b/qmat/playgrounds/__init__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Folders containing different experiments performed with `qmat`. + + đŸ“Ŗ Codes in those folder are not tested by the CI pipeline. +""" diff --git a/qmat/playgrounds/tibo/test.py b/qmat/playgrounds/tibo/test.py new file mode 100644 index 0000000..eb66c4e --- /dev/null +++ b/qmat/playgrounds/tibo/test.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Oct 20 17:26:04 2025 + +@author: cpf5546 +""" +import numpy as np + +import matplotlib.pyplot as plt +from time import time + +from qmat import genQCoeffs, QDELTA_GENERATORS +from qmat.qcoeff.collocation import Collocation +from qmat.solvers.generic import LinearMultiNode + +from qmat.solvers.generic.diffops import Dahlquist, Lorenz +from qmat.solvers.generic.integrators import ForwardEuler, BackwardEuler + + +pType = "Lorenz" +nPeriod = 1 +nSteps = nPeriod*1000 +tEnd = nPeriod*np.pi + +corr = "FE" +useSDC = False +useWeights = False +nSweeps = 4 + + +if pType == "Dahlquist": + diffOp = Dahlquist() +elif pType == "Lorenz": + diffOp = Lorenz() +nDOF = diffOp.u0.size + +nodes, weights, Q = genQCoeffs(corr) +coll = Collocation(nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") +gen = QDELTA_GENERATORS[corr](qGen=coll) +QDelta = gen.genCoeffs(k=[i+1 for i in range(nSweeps)]) + +prob = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) +Solver = BackwardEuler if corr == "BE" else ForwardEuler +if useSDC: + solver = Solver(diffOp, nodes=coll.nodes, tEnd=tEnd, nSteps=nSteps) +else: + regNodes = [0.25, 0.5, 0.75, 1] + solver = Solver(diffOp, nodes=regNodes, tEnd=tEnd, nSteps=nSteps//4) + +if useSDC: + tBeg = time() + uNumRef = prob.solveSDC(nSweeps, coll.Q, coll.weights if useWeights else None, QDelta) +else: + tBeg = time() + uNumRef = prob.solve(Q, weights) +tWall = time()-tBeg +tWall /= nSteps * nDOF +print(f"tWallScaled[linear] : {tWall:1.2e}s") + +if useSDC: + tBeg = time() + uNum = solver.solveSDC(nSweeps, weights=useWeights) +else: + tBeg = time() + uNum = solver.solve() +tWall = time()-tBeg +tWall /= nSteps * nDOF +print(f"tWallScaled[generic] : {tWall:1.2e}s") +print(uNumRef[-1] - uNum[-1]) + +plt.figure(1) +plt.clf() +plt.plot(uNumRef[:, 0], uNumRef[:, 1], label="ref") +plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") +plt.legend() diff --git a/qmat/solvers/__init__.py b/qmat/solvers/__init__.py new file mode 100644 index 0000000..40f4ba1 --- /dev/null +++ b/qmat/solvers/__init__.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Solvers implementations that can make use of `qmat`-generated coefficients. +""" diff --git a/qmat/solvers/generic.py b/qmat/solvers/generic/__init__.py similarity index 76% rename from qmat/solvers/generic.py rename to qmat/solvers/generic/__init__.py index e114777..dd87d29 100644 --- a/qmat/solvers/generic.py +++ b/qmat/solvers/generic/__init__.py @@ -11,11 +11,14 @@ from qmat.lagrange import LagrangeApproximation -class Problem(): +class DiffOperator(): def __init__(self, u0): - u0 = np.asarray(u0) - if u0.size < 1e3: + for name in ["u0", "innerSolver"]: + assert not hasattr(self, name), \ + f"{name} attribute is reserved for the base DiffOperator class" + self.u0 = np.asarray(u0) + if self.u0.size < 1e3: self.innerSolver = sco.fsolve else: self.innerSolver = sco.newton_krylov @@ -29,11 +32,15 @@ def dtype(self): return self.u0.dtype def evalF(self, u:np.ndarray, t:float, out:np.ndarray): + """ + Evaluate f(u,t) and store the result into out + """ raise NotImplementedError("evalF must be provided") def fSolve(self, a:float, rhs:np.ndarray, t:float, out:np.ndarray): """ - Solve u - a*f(u, t) = rhs using out as initial guess and storing the final solution into it + Solve u - a*f(u, t) = rhs using out as initial guess + and store the result into out """ def func(u:np.ndarray): @@ -51,7 +58,6 @@ def func(u:np.ndarray): def test(self, t0=0, dt=1e-1, eps=1e-3): u0 = self.u0 - try: uEval = np.zeros_like(u0) self.evalF(u=u0, t=t0, out=uEval) @@ -74,73 +80,36 @@ def test(self, t0=0, dt=1e-1, eps=1e-3): class LinearMultiNode(): - def __init__(self, u0, evalF, fSolve=None, tEnd=1, nSteps=1, t0=0): - u0 = np.asarray(u0) - if u0.size < 1e3: - self.innerSolver = sco.fsolve - else: - self.innerSolver = sco.newton_krylov - self.u0 = u0 - + def __init__(self, diffOp:DiffOperator, tEnd=1, nSteps=1, t0=0, testDiffOp=True): + assert isinstance(diffOp, DiffOperator) + self.diffOp = diffOp + if testDiffOp: + self.diffOp.test() + self.axpy = blas.get_blas_funcs('axpy', dtype=self.dtype) self.t0 = t0 self.tEnd = tEnd self.nSteps = nSteps self.dt = (tEnd-t0)/nSteps - try: - uEval = np.zeros_like(u0) - evalF(u=u0, t=t0, out=uEval) - except: - raise ValueError("evalF cannot be properly evaluated into an array like u0") - self.evalF = evalF - - if fSolve is not None: - self.fSolve = fSolve - try: - dt = 1e-1 - uEval *= -dt - uEval += u0 - uSolve = np.copy(u0) - uSolve += 1e-3*np.linalg.norm(uSolve, np.inf) - self.fSolve(a=dt, rhs=uEval, t=t0, out=uSolve) - except: - raise ValueError("fSolve cannot be properly evaluated into an array like u0") - np.testing.assert_allclose( - uSolve, u0, err_msg="fSolve does not satisfy the fixed-point problem with u0", - atol=1e-15) - - self.axpy = blas.get_blas_funcs('axpy', dtype=self.dtype) + @property + def u0(self): + return self.diffOp.u0 @property def uShape(self): - return self.u0.shape + return self.diffOp.uShape @property def dtype(self): - return self.u0.dtype + return self.diffOp.dtype def evalF(self, u:np.ndarray, t:float, out:np.ndarray): - raise NotImplementedError("evalF must be provided") + self.diffOp.evalF(u, t, out) def fSolve(self, a:float, rhs:np.ndarray, t:float, out:np.ndarray): - """ - Solve u - a*f(u, t) = rhs using out as initial guess and storing the final solution into it - """ - - def func(u:np.ndarray): - """compute res = u - a*f(u,t) - rhs""" - u = u.reshape(self.uShape) - res = np.empty_like(u) - self.evalF(u, t, out=res) - res *= -a - res += u - res -= rhs - return res.ravel() - - sol = self.innerSolver(func, out.ravel()).reshape(self.uShape) - np.copyto(out, sol) + self.diffOp.fSolve(a, rhs, t, out) @staticmethod @@ -273,22 +242,21 @@ def solveSDC(self, nSweeps, Q, weights, QDelta, uNum=None): class GenericMultiNode(LinearMultiNode): - def __init__(self, u0, evalF, nodes, fSolve=None, tEnd=1, nSteps=1, t0=0): - super().__init__(u0, evalF, fSolve, tEnd, nSteps, t0) + def __init__(self, diffOp:DiffOperator, nodes, tEnd=1, nSteps=1, t0=0): + super().__init__(diffOp, tEnd, nSteps, t0) self.nodes = np.asarray(nodes, dtype=float) @property def nNodes(self): return self.nodes.size - nStages = nNodes def evalPhi(self, uVals, fEvals, out, t0=0): raise NotImplementedError( "specialized Integrator must implement its evalPsi method") - def nodeSolve(self, uPrev, fEvals, out, rhs=0, t0=0): - """solve u-psi(u, u0, fEvals) = rhs""" + def phiSolve(self, uPrev, fEvals, out, rhs=0, t0=0): + """solve u-phi(u, u0, fEvals) = rhs""" def func(u:np.ndarray): u = u.reshape(self.uShape) @@ -331,7 +299,7 @@ def solve(self, uNum=None): # loop on nodes for m in range(self.nNodes): - self.nodeSolve( + self.phiSolve( [uNum[i], *uNodes[:m]], fEvals[:m+1], rhs=uNum[i], out=uNodes[m], t0=times[i]) self.evalF(u=uNodes[m], t=times[i]+tau[m], out=fEvals[m+1]) @@ -410,7 +378,7 @@ def solveSDC(self, nSweeps, Q=None, weights=None, uNum=None): rhs -= uTmp # solve with k+1 correction - self.nodeSolve( + self.phiSolve( [uNum[i], *uK1[:m]], fK1[:m+1], out=uK1[m], rhs=rhs, t0=times[i]) # evalF on k+1 node solution @@ -430,57 +398,3 @@ def solveSDC(self, nSweeps, Q=None, weights=None, uNum=None): self.stepUpdate(uNum[i], uNodes[0], fEvals[0], out=uNum[i+1]) return uNum - - - -class ForwardEuler(GenericMultiNode): - - def evalPhi(self, uVals, fEvals, out, t0=0): - m = len(uVals) - 1 - assert m > 0 - assert len(fEvals) in [m, m+1] - - tau = [t0] + (t0 + self.dt*self.nodes).tolist() - - # dt1 f0 + dt2 f1 + ... + dtm f{m-1} - np.copyto(out, fEvals[0]) - out *= tau[1]-tau[0] - for i in range(1, m): - self.axpy(a=tau[i+1]-tau[i], x=fEvals[i], y=out) - - - def nodeSolve(self, uPrev, fEvals, out, rhs=0, t0=0): - self.evalPhi([*uPrev, out], fEvals, out, t0=t0) - out += rhs - - - -class BackwardEuler(GenericMultiNode): - - def evalPhi(self, uVals, fEvals, out, t0=0): - m = len(uVals) - 1 - assert m > 0 - assert len(fEvals) in [m, m+1] - - tau = [t0] + (t0 + self.dt*self.nodes).tolist() - - # dt1 f1 + dt2 f2 + ... + dtm f{m} - if len(fEvals) == m: - self.evalF(uVals[m], tau[m], out=out) # f{m} not given - else: - np.copyto(out, fEvals[-1]) # f{m} given, use its value - out *= tau[m]-tau[m-1] - for i in range(m-1): - self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) - - def nodeSolve(self, uPrev, fEvals, out, rhs=0, t0=0): - assert len(uPrev) == len(fEvals) - m = len(uPrev) - assert m > 0 - tau = [t0] + (t0 + self.dt*self.nodes).tolist() - - rhs = np.zeros_like(out) + rhs - for i in range(m-1): - self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=rhs) - - self.fSolve(tau[m]-tau[m-1], rhs, tau[m], out) diff --git a/qmat/solvers/generic/diffops.py b/qmat/solvers/generic/diffops.py new file mode 100644 index 0000000..bd946e8 --- /dev/null +++ b/qmat/solvers/generic/diffops.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Oct 21 17:00:11 2025 + +@author: cpf5546 +""" +import numpy as np +from scipy.linalg import blas + +from qmat.solvers.generic import DiffOperator + + +class Dahlquist(DiffOperator): + + def __init__(self, lam=1j): + self.lam = lam + u0 = np.array([1, 0], dtype=float) + super().__init__(u0) + + + def evalF(self, u, t, out): + lam = self.lam + out[0] = u[0]*lam.real - u[1]*lam.imag + out[1] = u[1]*lam.real + u[0]*lam.imag + + +class Lorenz(DiffOperator): + + def __init__(self, sigma=10, rho=28, beta=8/3, nativeFSolve=False): + self.params = [sigma, rho, beta] + self.newton = { + "maxIter": 99, + "tolerance": 1e-9, + } + u0 = np.array([5, -5, 20], dtype=float) + self.gemv = blas.get_blas_funcs("gemv", dtype=u0.dtype) + super().__init__(u0) + + if nativeFSolve: + self.fSolve = self.fSolve_NATIVE + + def evalF(self, u, t, out): + sigma, rho, beta = self.params + x, y, z = u + out[0] = sigma*(y - x) + out[1] = x*(rho - z) - y + out[2] = x*y - beta*z + + def fSolve_NATIVE(self, a, rhs, t, out): + sigma, rho, beta = self.params + newton = self.newton + + rhsX, rhsY, rhsZ = rhs + a2 = a**2 + a3 = a**3 + + for n in range(newton["maxIter"]): + x, y, z = out + + res = np.array([ + x - a*sigma*(y - x) - rhsX, + y - a*(x*(rho - z) - y) - rhsY, + z - a*(x*y - beta*z) - rhsZ, + ]) + + resNorm = np.linalg.norm(res, np.inf) + if resNorm <= newton["tolerance"]: + break + if np.isnan(resNorm): + break + + factor = -1.0 / ( + a3*sigma*(x*(x + y) + beta*(-rho + z + 1)) + + a2*(beta*sigma + beta - rho*sigma + sigma + x**2 + sigma*z) + + a*(beta + sigma + 1) + 1 + ) + + jacInv = factor * np.array([ + [ + beta*a2 + a2*(x**2) + beta*a + a + 1, + beta*a2*sigma + a*sigma, + -a2*sigma*x, + ], + [ + beta*a2*rho - a2*x*y - beta*a2*z + a*rho - a*z, + beta*a2*sigma + beta*a + a*sigma + 1, + -(a2*sigma + a)*x, + ], + [ + a2*rho*x - a2*x*z + a2*y + a*y, + a2*sigma*x + a2*sigma*y + a*x, + -a2*rho*sigma + a2*sigma*(1 + z) + a*sigma + a + 1, + ], + ]) + + # out += jacInv @ res + self.gemv(alpha=1.0, a=jacInv, x=res, beta=1.0, y=out, overwrite_y=True) diff --git a/qmat/solvers/generic/integrators.py b/qmat/solvers/generic/integrators.py new file mode 100644 index 0000000..4a43ed5 --- /dev/null +++ b/qmat/solvers/generic/integrators.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Specialized implementations of GenericMultiNode solver +""" +import numpy as np + +from qmat.solvers.generic import GenericMultiNode + +class ForwardEuler(GenericMultiNode): + + def evalPhi(self, uVals, fEvals, out, t0=0): + m = len(uVals) - 1 + assert m > 0 + assert len(fEvals) in [m, m+1] + + tau = [t0] + (t0 + self.dt*self.nodes).tolist() + + # dt1 f0 + dt2 f1 + ... + dtm f{m-1} + np.copyto(out, fEvals[0]) + out *= tau[1]-tau[0] + for i in range(1, m): + self.axpy(a=tau[i+1]-tau[i], x=fEvals[i], y=out) + + + def phiSolve(self, uPrev, fEvals, out, rhs=0, t0=0): + self.evalPhi([*uPrev, out], fEvals, out, t0=t0) + out += rhs + + + +class BackwardEuler(GenericMultiNode): + + def evalPhi(self, uVals, fEvals, out, t0=0): + m = len(uVals) - 1 + assert m > 0 + assert len(fEvals) in [m, m+1] + + tau = [t0] + (t0 + self.dt*self.nodes).tolist() + + # dt1 f1 + dt2 f2 + ... + dtm f{m} + if len(fEvals) == m: + self.evalF(uVals[m], tau[m], out=out) # f{m} not given + else: + np.copyto(out, fEvals[-1]) # f{m} given, use its value + out *= tau[m]-tau[m-1] + for i in range(m-1): + self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=out) + + def phiSolve(self, uPrev, fEvals, out, rhs=0, t0=0): + assert len(uPrev) == len(fEvals) + m = len(uPrev) + assert m > 0 + tau = [t0] + (t0 + self.dt*self.nodes).tolist() + + rhs = np.zeros_like(out) + rhs + for i in range(m-1): + self.axpy(a=tau[i+1]-tau[i], x=fEvals[i+1], y=rhs) + + self.fSolve(tau[m]-tau[m-1], rhs, tau[m], out) diff --git a/qmat/solvers/test.py b/qmat/solvers/test.py deleted file mode 100644 index f9b5b58..0000000 --- a/qmat/solvers/test.py +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Mon Oct 20 17:26:04 2025 - -@author: cpf5546 -""" -import numpy as np -from scipy.linalg import blas - -import matplotlib.pyplot as plt -from time import time - -from qmat import genQCoeffs, QDELTA_GENERATORS -from qmat.qcoeff.collocation import Collocation -from qmat.solvers.generic import LinearMultiNode, BackwardEuler, ForwardEuler - -pType = "Lorenz" - -if pType == "Dahlquist": - lam = 1j - - def evalF(u, t, out): - out[0] = u[0]*lam.real - u[1]*lam.imag - out[1] = u[1]*lam.real + u[0]*lam.imag - - u0 = np.array([1, 0], dtype=float) - fSolve = None - - -elif pType == "Lorenz": - sigma = 10 - rho = 28 - beta = 8/3 - - def evalF(u, t, out): - x, y, z = u - out[0] = sigma*(y - x) - out[1] = x*(rho - z) - y - out[2] = x*y - beta*z - - u0 = np.array([5, -5, 20], dtype=float) - - newton = { - "maxIter": 99, - "tolerance": 1e-9, - } - - gemv = blas.get_blas_funcs("gemv", dtype=u0.dtype) - - def fSolve(a, rhs, t, out): - - rhsX, rhsY, rhsZ = rhs - a2 = a**2 - a3 = a**3 - - for n in range(newton["maxIter"]): - x, y, z = out - - res = np.array([ - x - a*sigma*(y - x) - rhsX, - y - a*(x*(rho - z) - y) - rhsY, - z - a*(x*y - beta*z) - rhsZ, - ]) - - resNorm = np.linalg.norm(res, np.inf) - if resNorm <= newton["tolerance"]: - break - if np.isnan(resNorm): - break - - factor = -1.0 / ( - a3*sigma*(x*(x + y) + beta*(-rho + z + 1)) - + a2*(beta*sigma + beta - rho*sigma + sigma + x**2 + sigma*z) - + a*(beta + sigma + 1) + 1 - ) - - jacInv = factor * np.array([ - [ - beta*a2 + a2*(x**2) + beta*a + a + 1, - beta*a2*sigma + a*sigma, - -a2*sigma*x, - ], - [ - beta*a2*rho - a2*x*y - beta*a2*z + a*rho - a*z, - beta*a2*sigma + beta*a + a*sigma + 1, - -(a2*sigma + a)*x, - ], - [ - a2*rho*x - a2*x*z + a2*y + a*y, - a2*sigma*x + a2*sigma*y + a*x, - -a2*rho*sigma + a2*sigma*(1 + z) + a*sigma + a + 1, - ], - ]) - - # out += jacInv @ res - gemv(alpha=1.0, a=jacInv, x=res, beta=1.0, y=out, overwrite_y=True) - - fSolve = None # because own implementation is cute, but still less efficient - - -corr = "BE" - -nodes, weights, Q = genQCoeffs(corr) - -coll = Collocation(nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") -gen = QDELTA_GENERATORS[corr](qGen=coll) -nSweeps = 4 -QDelta = gen.genCoeffs(k=[i+1 for i in range(nSweeps)]) -useWeights = False - - -nPeriod = 1 -nSteps = nPeriod*1000 -tEnd = nPeriod*np.pi -prob = LinearMultiNode(u0, evalF, fSolve=fSolve, tEnd=tEnd, nSteps=nSteps) - -regNodes = [0.25, 0.5, 0.75, 1] - -Solver = BackwardEuler if corr == "BE" else ForwardEuler - -solver = Solver( - u0, evalF, nodes=regNodes, fSolve=fSolve, - tEnd=tEnd, nSteps=nSteps//4) - -plt.figure(1) -plt.clf() - - -tBeg = time() -uNumRef = prob.solve(Q, weights) -# uNumRef = prob.solveSDC(nSweeps, coll.Q, coll.weights if useWeights else None, QDelta) -tWall = time()-tBeg -tWall /= nSteps * np.size(u0) -print(f"tWallScaled[linear] : {tWall:1.2e}s") -plt.plot(uNumRef[:, 0], uNumRef[:, 1], label="ref") - -tBeg = time() -uNum = solver.solve() -# uNum = solver.solveSDC(nSweeps, weights=useWeights) -plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") -tWall = time()-tBeg -tWall /= nSteps * np.size(u0) -print(f"tWallScaled[generic] : {tWall:1.2e}s") - -plt.legend() - -print(uNumRef[-1] - uNum[-1]) From 4a6b51a4122b0335ca810a4a71e31effc084c5ca Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Tue, 21 Oct 2025 18:31:30 +0200 Subject: [PATCH 11/33] TL: added ProtheroRobinson DiffOperator --- qmat/playgrounds/tibo/test.py | 26 ++++--- qmat/solvers/generic/diffops.py | 106 +++++++++++++++++++++++++++- qmat/solvers/generic/integrators.py | 2 - 3 files changed, 123 insertions(+), 11 deletions(-) diff --git a/qmat/playgrounds/tibo/test.py b/qmat/playgrounds/tibo/test.py index eb66c4e..c449d92 100644 --- a/qmat/playgrounds/tibo/test.py +++ b/qmat/playgrounds/tibo/test.py @@ -14,13 +14,13 @@ from qmat.qcoeff.collocation import Collocation from qmat.solvers.generic import LinearMultiNode -from qmat.solvers.generic.diffops import Dahlquist, Lorenz +from qmat.solvers.generic.diffops import Dahlquist, Lorenz, ProtheroRobinson from qmat.solvers.generic.integrators import ForwardEuler, BackwardEuler -pType = "Lorenz" +pType = "ProtheroRobinson" nPeriod = 1 -nSteps = nPeriod*1000 +nSteps = nPeriod*10000 tEnd = nPeriod*np.pi corr = "FE" @@ -33,6 +33,8 @@ diffOp = Dahlquist() elif pType == "Lorenz": diffOp = Lorenz() +elif pType == "ProtheroRobinson": + diffOp = ProtheroRobinson(nonLinear=True) nDOF = diffOp.u0.size nodes, weights, Q = genQCoeffs(corr) @@ -50,10 +52,10 @@ if useSDC: tBeg = time() - uNumRef = prob.solveSDC(nSweeps, coll.Q, coll.weights if useWeights else None, QDelta) + uRef = prob.solveSDC(nSweeps, coll.Q, coll.weights if useWeights else None, QDelta) else: tBeg = time() - uNumRef = prob.solve(Q, weights) + uRef = prob.solve(Q, weights) tWall = time()-tBeg tWall /= nSteps * nDOF print(f"tWallScaled[linear] : {tWall:1.2e}s") @@ -67,10 +69,18 @@ tWall = time()-tBeg tWall /= nSteps * nDOF print(f"tWallScaled[generic] : {tWall:1.2e}s") -print(uNumRef[-1] - uNum[-1]) +print(uRef[-1] - uNum[-1]) plt.figure(1) plt.clf() -plt.plot(uNumRef[:, 0], uNumRef[:, 1], label="ref") -plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") +if pType == "ProtheroRobinson": + times = np.linspace(0, tEnd, nSteps+1) + plt.plot(times, uRef[:, 0], label="ref") + if useSDC: + plt.plot(times, uNum[:, 0], label="integrator") + else: + plt.plot(times[::4], uNum[:, 0], label="integrator") +else: + plt.plot(uRef[:, 0], uRef[:, 1], label="ref") + plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") plt.legend() diff --git a/qmat/solvers/generic/diffops.py b/qmat/solvers/generic/diffops.py index bd946e8..c6ca7cc 100644 --- a/qmat/solvers/generic/diffops.py +++ b/qmat/solvers/generic/diffops.py @@ -55,7 +55,7 @@ def fSolve_NATIVE(self, a, rhs, t, out): a2 = a**2 a3 = a**3 - for n in range(newton["maxIter"]): + for _ in range(newton["maxIter"]): x, y, z = out res = np.array([ @@ -96,3 +96,107 @@ def fSolve_NATIVE(self, a, rhs, t, out): # out += jacInv @ res self.gemv(alpha=1.0, a=jacInv, x=res, beta=1.0, y=out, overwrite_y=True) + + +class ProtheroRobinson(DiffOperator): + r""" + Implement the Prothero-Robinson problem: + + .. math:: + \frac{du}{dt} = -\frac{u-g(t)}{\epsilon} + \frac{dg}{dt}, \quad u(0) = g(0)., + + with :math:`\epsilon` a stiffness parameter, that makes the problem more stiff + the smaller it is (usual taken value is :math:`\epsilon=1e^{-3}`). + Exact solution is given by :math:`u(t)=g(t)`, and this implementation uses + :math:`g(t)=\cos(t)`. + + Implement also the non-linear form of this problem: + + .. math:: + \frac{du}{dt} = -\frac{u^3-g(t)^3}{\epsilon} + \frac{dg}{dt}, \quad u(0) = g(0). + + To use an other exact solution, one just have to derivate this class + and overload the `g` and `dg` methods. For instance, + to use :math:`g(t)=e^{-0.2*t}`, define and use the following class: + + >>> class MyProtheroRobinson(ProtheroRobinson): + >>> + >>> def g(self, t): + >>> return np.exp(-0.2 * t) + >>> + >>> def dg(self, t): + >>> return (-0.2) * np.exp(-0.2 * t) + + Parameters + ---------- + epsilon : float, optional + Stiffness parameter. The default is 1e-3. + nonLinear : bool, optional + Wether or not to use the non-linear form of the problem. The default is False. + nativeFSolve : bool, optional + Wether or not use the native fSolver using exact Jacobian. The default is True. + + Reference + --------- + A. Prothero and A. Robinson, On the stability and accuracy of one-step methods for solving + stiff systems of ordinary differential equations, Mathematics of Computation, 28 (1974), + pp. 145–162. + """ + + def __init__(self, epsilon=1e-3, nonLinear=False, nativeFSolve=True): + self.epsilon = epsilon + self.newton = { + "maxIter": 200, + "tolerance": 5e-15, + } + self.evalF = self.evalF_LIN if nonLinear else self.evalF_NONLIN + self.jac = self.jac_NONLIN if nonLinear else self.jac_LIN + if nativeFSolve: + self.fSolve = self.fSolve_NATIVE + super().__init__([self.g(0)]) + + # ------------------------------------------------------------------------- + # g function (analytical solution), and its first derivative + # ------------------------------------------------------------------------- + def g(self, t): + return np.cos(t) + + def dg(self, t): + return -np.sin(t) + + # ------------------------------------------------------------------------- + # f(u,t) and Jacobian functions + # ------------------------------------------------------------------------- + def evalF_LIN(self, u, t, out): + np.copyto(out, -self.epsilon**(-1) * (u - self.g(t)) + self.dg(t)) + + def evalF_NONLIN(self, u, t, out): + np.copyto(out, -self.epsilon**(-1) * (u**3 - self.g(t)**3) + self.dg(t)) + + def jac(self, u, t): + raise NotImplementedError() + + def jac_LIN(self, u, t): + return -self.epsilon**(-1) + + def jac_NONLIN(self, u, t): + return -self.epsilon**(-1) * 3*u**2 + + def fSolve_NATIVE(self, a, rhs, t, out): + newton = self.newton + u = out + + for _ in range(newton["maxIter"]): + res = np.array([0.0]) + self.evalF(u, t, out=res) + res *= -a + res += u + res -= rhs + resNorm = np.linalg.norm(res, np.inf) + if resNorm <= newton["tolerance"]: + break + if np.isnan(resNorm): + break + + jac = 1 - a * self.jac(u, t) + u -= res / jac diff --git a/qmat/solvers/generic/integrators.py b/qmat/solvers/generic/integrators.py index 4a43ed5..3f912a8 100644 --- a/qmat/solvers/generic/integrators.py +++ b/qmat/solvers/generic/integrators.py @@ -22,13 +22,11 @@ def evalPhi(self, uVals, fEvals, out, t0=0): for i in range(1, m): self.axpy(a=tau[i+1]-tau[i], x=fEvals[i], y=out) - def phiSolve(self, uPrev, fEvals, out, rhs=0, t0=0): self.evalPhi([*uPrev, out], fEvals, out, t0=t0) out += rhs - class BackwardEuler(GenericMultiNode): def evalPhi(self, uVals, fEvals, out, t0=0): From 9b5fe1a1c685444fd4d558ad75192436e5bde419 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Tue, 21 Oct 2025 18:59:26 +0200 Subject: [PATCH 12/33] TL: refactoring and some documentation --- qmat/playgrounds/tibo/README.md | 4 ++ .../playgrounds/tibo/orthogonalPolynomials.py | 46 ++++++++++++++++++ qmat/playgrounds/tibo/test.py | 7 +-- qmat/solvers/dahlquist.py | 3 +- qmat/solvers/generic/__init__.py | 14 +++--- qmat/solvers/generic/diffops.py | 47 ++++++++++++++++--- qmat/solvers/generic/integrators.py | 2 +- 7 files changed, 106 insertions(+), 17 deletions(-) create mode 100644 qmat/playgrounds/tibo/README.md create mode 100644 qmat/playgrounds/tibo/orthogonalPolynomials.py diff --git a/qmat/playgrounds/tibo/README.md b/qmat/playgrounds/tibo/README.md new file mode 100644 index 0000000..d23933a --- /dev/null +++ b/qmat/playgrounds/tibo/README.md @@ -0,0 +1,4 @@ +# Personal playground (Tibo) + +- [orthogonalPolynomials](./orthogonalPolynomials.py) : how to generate orthogonal polynomial values from any distribution with arbitrary order in a numerical stable fashion +- [test.py](./test.py) : playground to test the new generic solvers \ No newline at end of file diff --git a/qmat/playgrounds/tibo/orthogonalPolynomials.py b/qmat/playgrounds/tibo/orthogonalPolynomials.py new file mode 100644 index 0000000..4054093 --- /dev/null +++ b/qmat/playgrounds/tibo/orthogonalPolynomials.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Compute and display orthogonal polynomials of any degree using `qmat` +""" +import numpy as np +import matplotlib.pyplot as plt +from qmat.nodes import NodesGenerator + +deg = 100 +polyType = "CHEBY-1" + +gen = NodesGenerator(polyType) +n = deg + 1 +alpha, beta = gen.getOrthogPolyCoefficients(n) + +t = np.linspace(-1, 1, num=1000000) + +# Generate monic polynomials (leading coefficient is 1) +if deg == 0: + out = 0*t + 1. +else: + pi = np.array([np.zeros_like(t) for i in range(3)]) + pi[1:] += 1 + for alpha_j, beta_j in zip(alpha, beta): + pi[2] *= (t-alpha_j) + pi[0] *= beta_j + pi[2] -= pi[0] + pi[0] = pi[1] + pi[1] = pi[2] + out = np.copy(pi[2]) + +# Scaling (depends on the kind of the polynomial) +if polyType == "CHEBY-1": + out *= 2**deg + ylim = (-1.1, 1.1) +elif polyType == ["CHEBY-2", "CHEBY-3", "CHEBY-4"]: + out *= 2**(deg+(deg>0)) + ylim = (-1.6, 1.6) + +plt.plot(t, out, label=f"{polyType}, $p={deg}$") +plt.ylim(*ylim) +plt.legend() +plt.xlabel("$t$") +plt.ylabel("$p(t)$") +plt.grid(True) diff --git a/qmat/playgrounds/tibo/test.py b/qmat/playgrounds/tibo/test.py index c449d92..99562b8 100644 --- a/qmat/playgrounds/tibo/test.py +++ b/qmat/playgrounds/tibo/test.py @@ -18,9 +18,9 @@ from qmat.solvers.generic.integrators import ForwardEuler, BackwardEuler -pType = "ProtheroRobinson" +pType = "Lorenz" nPeriod = 1 -nSteps = nPeriod*10000 +nSteps = nPeriod*1000 tEnd = nPeriod*np.pi corr = "FE" @@ -34,7 +34,8 @@ elif pType == "Lorenz": diffOp = Lorenz() elif pType == "ProtheroRobinson": - diffOp = ProtheroRobinson(nonLinear=True) + nSteps *= 10 + diffOp = ProtheroRobinson(nonLinear=False) nDOF = diffOp.u0.size nodes, weights, Q = genQCoeffs(corr) diff --git a/qmat/solvers/dahlquist.py b/qmat/solvers/dahlquist.py index 9504fda..3645648 100644 --- a/qmat/solvers/dahlquist.py +++ b/qmat/solvers/dahlquist.py @@ -1,7 +1,8 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Submodule containing various solvers for the Dahlquist equation that can be used with `qmat`-generated coefficients. +Submodule containing various solvers for the Dahlquist equation +that can make use of `qmat`-generated coefficients. """ import numpy as np diff --git a/qmat/solvers/generic/__init__.py b/qmat/solvers/generic/__init__.py index dd87d29..e1e3a15 100644 --- a/qmat/solvers/generic/__init__.py +++ b/qmat/solvers/generic/__init__.py @@ -11,12 +11,14 @@ from qmat.lagrange import LagrangeApproximation -class DiffOperator(): - +class DiffOp(): + """ + Base class for Differential Operators + """ def __init__(self, u0): for name in ["u0", "innerSolver"]: assert not hasattr(self, name), \ - f"{name} attribute is reserved for the base DiffOperator class" + f"{name} attribute is reserved for the base DiffOp class" self.u0 = np.asarray(u0) if self.u0.size < 1e3: self.innerSolver = sco.fsolve @@ -80,8 +82,8 @@ def test(self, t0=0, dt=1e-1, eps=1e-3): class LinearMultiNode(): - def __init__(self, diffOp:DiffOperator, tEnd=1, nSteps=1, t0=0, testDiffOp=True): - assert isinstance(diffOp, DiffOperator) + def __init__(self, diffOp:DiffOp, tEnd=1, nSteps=1, t0=0, testDiffOp=True): + assert isinstance(diffOp, DiffOp) self.diffOp = diffOp if testDiffOp: self.diffOp.test() @@ -242,7 +244,7 @@ def solveSDC(self, nSweeps, Q, weights, QDelta, uNum=None): class GenericMultiNode(LinearMultiNode): - def __init__(self, diffOp:DiffOperator, nodes, tEnd=1, nSteps=1, t0=0): + def __init__(self, diffOp:DiffOp, nodes, tEnd=1, nSteps=1, t0=0): super().__init__(diffOp, tEnd, nSteps, t0) self.nodes = np.asarray(nodes, dtype=float) diff --git a/qmat/solvers/generic/diffops.py b/qmat/solvers/generic/diffops.py index c6ca7cc..ac98bed 100644 --- a/qmat/solvers/generic/diffops.py +++ b/qmat/solvers/generic/diffops.py @@ -8,10 +8,10 @@ import numpy as np from scipy.linalg import blas -from qmat.solvers.generic import DiffOperator +from qmat.solvers.generic import DiffOp -class Dahlquist(DiffOperator): +class Dahlquist(DiffOp): def __init__(self, lam=1j): self.lam = lam @@ -25,21 +25,52 @@ def evalF(self, u, t, out): out[1] = u[1]*lam.real + u[0]*lam.imag -class Lorenz(DiffOperator): +class Lorenz(DiffOp): + r""" + RHS of the Lorentz system, which can be written : + + .. math:: + \frac{dx}{dt} = \sigma (y-x), \; \frac{dy}{dt} = x (\rho - z) - y, + \; \frac{dz}{dt} = xy - \beta z, + + with starting initial solution :math:`u_0=(x_0,y_0,z_0)=(5, -5, 20)`. + Considering the three dimensional vector :math:`u=(x,y,z)`, the formal + expression of :math:`f` is then + + .. math:: + f(u,t) = [ \sigma (y-x), x (\rho - z) - y, xy - \beta z ] + + Parameters + ---------- + sigma: float, optional + The :math:`\sigma` parameter (default=10). + rho: float, optional + The :math:`\rho` parameter (default=28). + beta: float, optional + The :math:`\beta` parameter (default=8/3). + nativeFSolve: bool, optional + Wether or not using the native fSolve method (default is False). + """ def __init__(self, sigma=10, rho=28, beta=8/3, nativeFSolve=False): self.params = [sigma, rho, beta] + r"""list containing :math:`\sigma`, :math:`\rho` and :math:`\beta`""" + self.newton = { "maxIter": 99, "tolerance": 1e-9, } + """parameters for the Newton iteration used in native fSolve""" + u0 = np.array([5, -5, 20], dtype=float) self.gemv = blas.get_blas_funcs("gemv", dtype=u0.dtype) - super().__init__(u0) + """level-2 blas gemv function used in the native solver (just for flex, doesn't bring anything)""" + super().__init__(u0) if nativeFSolve: self.fSolve = self.fSolve_NATIVE + def evalF(self, u, t, out): sigma, rho, beta = self.params x, y, z = u @@ -98,7 +129,7 @@ def fSolve_NATIVE(self, a, rhs, t, out): self.gemv(alpha=1.0, a=jacInv, x=res, beta=1.0, y=out, overwrite_y=True) -class ProtheroRobinson(DiffOperator): +class ProtheroRobinson(DiffOp): r""" Implement the Prothero-Robinson problem: @@ -149,12 +180,16 @@ def __init__(self, epsilon=1e-3, nonLinear=False, nativeFSolve=True): "maxIter": 200, "tolerance": 5e-15, } - self.evalF = self.evalF_LIN if nonLinear else self.evalF_NONLIN + self.evalF = self.evalF_NONLIN if nonLinear else self.evalF_LIN self.jac = self.jac_NONLIN if nonLinear else self.jac_LIN if nativeFSolve: self.fSolve = self.fSolve_NATIVE super().__init__([self.g(0)]) + @property + def nonLinear(self): + return self.evalF == self.evalF_LIN + # ------------------------------------------------------------------------- # g function (analytical solution), and its first derivative # ------------------------------------------------------------------------- diff --git a/qmat/solvers/generic/integrators.py b/qmat/solvers/generic/integrators.py index 3f912a8..0606e4e 100644 --- a/qmat/solvers/generic/integrators.py +++ b/qmat/solvers/generic/integrators.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Specialized implementations of GenericMultiNode solver +Specialized implementations of GenericMultiNode solvers """ import numpy as np From 303c30949fe62806deb7db9ae58f684fd581a6de Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Tue, 21 Oct 2025 20:53:10 +0200 Subject: [PATCH 13/33] TL: implemented tests for dahlquist solvers --- docs/notebooks/04_sdc.ipynb | 16 +-- docs/notebooks/05_residuals.ipynb | 4 +- pyproject.toml | 3 + qmat/qdelta/__init__.py | 2 +- qmat/solvers/dahlquist.py | 2 +- qmat/{ => solvers}/sdc.py | 0 tests/test_qdelta/test_timestepping.py | 2 +- tests/test_solvers/test_dahlquist.py | 98 +++++++++++++++++++ .../test_sdc.py} | 12 +-- 9 files changed, 120 insertions(+), 19 deletions(-) rename qmat/{ => solvers}/sdc.py (100%) create mode 100644 tests/test_solvers/test_dahlquist.py rename tests/{test_4_sdc.py => test_solvers/test_sdc.py} (95%) diff --git a/docs/notebooks/04_sdc.ipynb b/docs/notebooks/04_sdc.ipynb index 187ab2f..8bc7bab 100644 --- a/docs/notebooks/04_sdc.ipynb +++ b/docs/notebooks/04_sdc.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ "\n", "coll = Q_GENERATORS[\"coll\"].getInstance() # use default input parameters for the Collocation class\n", "nodes, weights, Q = coll.genCoeffs()\n", - "QDelta = QDELTA_GENERATORS[\"BE\"](nodes=nodes).getQDelta() # simple Backward Euler based approximation \n", + "QDelta = QDELTA_GENERATORS[\"BE\"](nodes=nodes).getQDelta() # simple Backward Euler based approximation\n", "\n", "nSteps = 12\n", "uNum = np.zeros(nSteps+1, dtype=complex)\n", @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -204,7 +204,7 @@ " for k in range(nSweeps):\n", " # nodes solution update\n", " b = uNum[i] + lam*dt*(Q-QDelta) @ uNodes\n", - " uNodes = np.linalg.solve(P, b) \n", + " uNodes = np.linalg.solve(P, b)\n", "\n", " uNum[i+1] = uNum[i] + lam*dt*weights.dot(uNodes) # prolongation" ] @@ -218,11 +218,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from qmat.sdc import solveDahlquistSDC\n", + "from qmat.solvers.sdc import solveDahlquistSDC\n", "uNum = solveDahlquistSDC(lam, u0, T, nSteps, nSweeps, Q, QDelta, weights)" ] }, @@ -270,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -289,7 +289,7 @@ } ], "source": [ - "from qmat.sdc import errorDahlquistSDC\n", + "from qmat.solvers.sdc import errorDahlquistSDC\n", "\n", "for nSweeps in [1, 2, 3, 4, 5, 6, 8, 9]:\n", " err = errorDahlquistSDC(lam, u0, T, nSteps, nSweeps, Q, QDelta, weights)\n", diff --git a/docs/notebooks/05_residuals.ipynb b/docs/notebooks/05_residuals.ipynb index 33718f1..776210b 100644 --- a/docs/notebooks/05_residuals.ipynb +++ b/docs/notebooks/05_residuals.ipynb @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -217,7 +217,7 @@ } ], "source": [ - "from qmat.sdc import solveDahlquistSDC\n", + "from qmat.solvers.sdc import solveDahlquistSDC\n", "\n", "for nSweeps, sym in zip([10, 8, 6, 4], [\"^\", \"o\", \"s\", \">\"]):\n", "\n", diff --git a/pyproject.toml b/pyproject.toml index 63dae6f..2bb6bad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,6 +77,9 @@ pythonpath = [ relative_files = true concurrency = ['multiprocessing'] source = ['qmat'] +omit = [ + '*/qmat/playgrounds/*' +] [tool.coverage.report] skip_empty = true diff --git a/qmat/qdelta/__init__.py b/qmat/qdelta/__init__.py index ef1d299..fa0f26e 100644 --- a/qmat/qdelta/__init__.py +++ b/qmat/qdelta/__init__.py @@ -227,7 +227,7 @@ def genCoeffs(self, k=None, form="Z2N", dTau=False): return out if len(out) > 1 else out[0] -QDELTA_GENERATORS: dict[str, dict[QDeltaGenerator]] = {} +QDELTA_GENERATORS: dict[str, type[QDeltaGenerator]] = {} """Dictionary containing all specialized :class:`QDeltaGenerator` classes""" def register(cls: type[T]) -> type[T]: diff --git a/qmat/solvers/dahlquist.py b/qmat/solvers/dahlquist.py index 3645648..b1b1433 100644 --- a/qmat/solvers/dahlquist.py +++ b/qmat/solvers/dahlquist.py @@ -53,7 +53,7 @@ def solve(self, Q, weights): uNodes = np.linalg.solve(A, b[..., None])[..., 0] if weights is not None: uNum[i+1] = uNum[i] - uNum[i+1] += self.dt*np.dot(self.lamI[..., None]*uNodes, weights) + uNum[i+1] += self.dt*np.dot(self.lam[..., None]*uNodes, weights) else: uNum[i+1] = uNodes[..., -1] diff --git a/qmat/sdc.py b/qmat/solvers/sdc.py similarity index 100% rename from qmat/sdc.py rename to qmat/solvers/sdc.py diff --git a/tests/test_qdelta/test_timestepping.py b/tests/test_qdelta/test_timestepping.py index 76fb11a..d568eba 100644 --- a/tests/test_qdelta/test_timestepping.py +++ b/tests/test_qdelta/test_timestepping.py @@ -7,7 +7,7 @@ from qmat.mathutils import numericalOrder from qmat.qcoeff.collocation import Collocation from qmat.nodes import NODE_TYPES, QUAD_TYPES -from qmat.sdc import errorDahlquistSDC, getOrderSDC +from qmat.solvers.sdc import errorDahlquistSDC, getOrderSDC SCHEMES = getClasses(QDELTA_GENERATORS, module="timestepping") diff --git a/tests/test_solvers/test_dahlquist.py b/tests/test_solvers/test_dahlquist.py new file mode 100644 index 0000000..0357b70 --- /dev/null +++ b/tests/test_solvers/test_dahlquist.py @@ -0,0 +1,98 @@ +import pytest +import numpy as np + +from qmat import Q_GENERATORS, QDELTA_GENERATORS +from qmat.solvers.dahlquist import Dahlquist, DahlquistIMEX +from qmat.solvers.sdc import solveDahlquistSDC + + +@pytest.mark.parametrize("lam", [1j, -1]) +@pytest.mark.parametrize("dim", [1, 2, 3]) +@pytest.mark.parametrize("nSteps", [1, 2, 5]) +@pytest.mark.parametrize("tEnd", [1, 2, 5]) +@pytest.mark.parametrize("scheme", ["RK4", "DIRK43", "Collocation"]) +def testDahlquist(scheme, tEnd, nSteps, dim, lam): + qGen = Q_GENERATORS[scheme].getInstance() + + lamVals = lam*np.linspace(0, 1, 4**dim).reshape((4,)*dim) + ref = np.array([qGen.solveDahlquist(lam, 1, tEnd, nSteps) + for lam in lamVals.ravel()]).T.reshape((-1, *lamVals.shape)) + solver = Dahlquist(lamVals, 1, tEnd, nSteps) + + sol1 = solver.solve(qGen.Q, qGen.weights) + assert np.allclose(sol1, ref), \ + "Dahlquist solver do not give the same solution as reference solver" + + if scheme == "Collocation": + assert np.allclose(qGen.nodes[-1], 1), \ + "default instance for Collocation does have 1 as last node, but test depends on it" + sol2 = solver.solve(qGen.Q, None) + assert np.allclose(sol2, ref), \ + "Dahlquist without solver do not give the same solution as reference solver" + + +@pytest.mark.parametrize("lam", [1j, -1]) +@pytest.mark.parametrize("dim", [1, 2]) +@pytest.mark.parametrize("weights", [True, False]) +@pytest.mark.parametrize("nSweeps", [1, 4]) +@pytest.mark.parametrize("nSteps", [1, 5]) +@pytest.mark.parametrize("tEnd", [1, 5]) +@pytest.mark.parametrize("scheme", ["BE", "FE", "MIN-SR-FLEX"]) +def testDahlquistSDC(scheme, tEnd, nSteps, nSweeps, weights, dim, lam): + coll = Q_GENERATORS["Collocation"](nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") + approx = QDELTA_GENERATORS[scheme](qGen=coll) + QDelta = approx.genCoeffs(k=[i+1 for i in range(nSweeps)]) + + lamVals = lam*np.linspace(0, 1, 4**dim).reshape((4,)*dim) + ref = np.array([solveDahlquistSDC(lam, 1, tEnd, nSteps, + nSweeps, coll.Q, QDelta, coll.weights if weights else None) + for lam in lamVals.ravel()]).T.reshape((-1, *lamVals.shape)) + + solver = Dahlquist(lamVals, 1, tEnd, nSteps) + sol = solver.solveSDC(coll.Q, coll.weights if weights else None, QDelta, nSweeps) + assert np.allclose(sol, ref), \ + "Dahlquist SDC solver do not give the same solution as reference SDC solver" + + +@pytest.mark.parametrize("lam", [1j, -1]) +@pytest.mark.parametrize("dim", [1, 2]) +@pytest.mark.parametrize("nSteps", [1, 5]) +@pytest.mark.parametrize("tEnd", [1, 5]) +@pytest.mark.parametrize("scheme", ["RK4", "DIRK43", "Collocation"]) +def testDahlquistIMEX(scheme, tEnd, nSteps, dim, lam): + qGen = Q_GENERATORS[scheme].getInstance() + + lamVals = lam*np.linspace(0, 1, 4**dim).reshape((4,)*dim) + + basis = Dahlquist(lam=lamVals, u0=1, T=tEnd, nSteps=nSteps) + ref = basis.solve(Q=qGen.Q, weights=qGen.weights) + + solver = DahlquistIMEX(lamI=lamVals, lamE=[0], u0=1, T=tEnd, nSteps=nSteps) + sol = solver.solve(QI=qGen.Q, wI=qGen.weights, QE=qGen.Q, wE=qGen.weights) + assert np.allclose(sol, ref), \ + "DahlquistIMEX solver does not match Dahlquist solver with implicit part only" + + if scheme == "Collocation": + sol = solver.solve(QI=qGen.Q, wI=None, QE=qGen.Q, wE=None) + assert np.allclose(sol, ref), \ + "DahlquistIMEX solver without weights does not match Dahlquist solver with implicit part only" + + solver = DahlquistIMEX(lamI=[0], lamE=lamVals, u0=1, T=tEnd, nSteps=nSteps) + sol = solver.solve(QI=qGen.Q, wI=qGen.weights, QE=qGen.Q, wE=qGen.weights) + assert np.allclose(sol, ref), \ + "DahlquistIMEX solver does not match Dahlquist solver with explicit part only" + + if scheme == "Collocation": + sol = solver.solve(QI=qGen.Q, wI=None, QE=qGen.Q, wE=None) + assert np.allclose(sol, ref), \ + "DahlquistIMEX solver without weights does not match Dahlquist solver with explicit part only" + + for weights in [qGen.weights, None]: + basis = Dahlquist(lam=2*lamVals, u0=1, T=tEnd, nSteps=nSteps) + ref = basis.solve(Q=qGen.Q, weights=weights) + + solver = DahlquistIMEX(lamI=lamVals, lamE=lamVals, u0=1, T=tEnd, nSteps=nSteps) + sol = solver.solve(QI=qGen.Q, wI=weights, QE=qGen.Q, wE=weights) + detail = " with weights " if weights is not None else "" + assert np.allclose(sol, ref), \ + f"DahlquistIMEX solver {detail} does not produce the linear combination of IMEX sum" diff --git a/tests/test_4_sdc.py b/tests/test_solvers/test_sdc.py similarity index 95% rename from tests/test_4_sdc.py rename to tests/test_solvers/test_sdc.py index 02587b6..69b223b 100644 --- a/tests/test_4_sdc.py +++ b/tests/test_solvers/test_sdc.py @@ -1,7 +1,7 @@ import pytest import numpy as np -from qmat.sdc import solveDahlquistSDC +from qmat.solvers.sdc import solveDahlquistSDC from qmat.qcoeff.collocation import Collocation from qmat import QDELTA_GENERATORS @@ -9,13 +9,13 @@ @pytest.mark.parametrize("nNodes", [2, 3, 4]) @pytest.mark.parametrize("qDelta", ["BE", "FE"]) def testSweeps(qDelta, nNodes): - + coll = Collocation(nNodes=nNodes, nodeType="LEGENDRE", quadType="RADAU-RIGHT") gen = QDELTA_GENERATORS[qDelta](nodes=coll.nodes) runParams = dict( lam=1j, u0=1, T=np.pi, nSteps=10, nSweeps=nNodes, - Q=coll.Q, + Q=coll.Q, ) QD1 = gen.getQDelta() @@ -38,15 +38,15 @@ def testMonitors(nSweeps, nSteps, nNodes): lam=1j, u0=1, T=np.pi, nSteps=nSteps, nSweeps=nSweeps, Q=coll.Q, QDelta=gen.getQDelta(), ) - + uNum = solveDahlquistSDC(**runParams) uNum2, monitors = solveDahlquistSDC(**runParams, monitors=["errors", "residuals"]) - + assert np.allclose(uNum, uNum2), "solution with and without monitors are not the same" for key in ["errors", "residuals"]: assert key in monitors, f"'{key}' not in monitors" values = monitors[key] - + assert values.shape == (nSweeps+1, nSteps, nNodes), f"inconsistent shape for '{key}' values" assert np.all(np.abs(values[-1]) < np.abs(values[-2])), f"no decreasing {key}" From 77fca0d77823ca58a3603588a562ffbf85a83da6 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Tue, 21 Oct 2025 21:26:46 +0200 Subject: [PATCH 14/33] TL: added tests for diffops --- qmat/solvers/generic/__init__.py | 34 ++++++++++++++++++++++++------ qmat/solvers/generic/diffops.py | 27 +++++++++++++++++++++--- tests/test_solvers/test_diffops.py | 7 ++++++ 3 files changed, 58 insertions(+), 10 deletions(-) create mode 100644 tests/test_solvers/test_diffops.py diff --git a/qmat/solvers/generic/__init__.py b/qmat/solvers/generic/__init__.py index e1e3a15..ea7207e 100644 --- a/qmat/solvers/generic/__init__.py +++ b/qmat/solvers/generic/__init__.py @@ -6,9 +6,14 @@ import numpy as np import scipy.optimize as sco from scipy.linalg import blas +from typing import TypeVar from qmat.solvers.dahlquist import Dahlquist from qmat.lagrange import LagrangeApproximation +from qmat.utils import checkOverriding, storeClass + + +T = TypeVar("T") class DiffOp(): @@ -58,11 +63,18 @@ def func(u:np.ndarray): sol = self.innerSolver(func, out.ravel()).reshape(self.uShape) np.copyto(out, sol) - def test(self, t0=0, dt=1e-1, eps=1e-3): - u0 = self.u0 + @classmethod + def test(cls, t0=0, dt=1e-1, eps=1e-3, instance=None): + if instance is None: + try: + instance = cls() + except: + raise TypeError(f"{cls} cannot be instantiated with default parameters") + + u0 = instance.u0 try: uEval = np.zeros_like(u0) - self.evalF(u=u0, t=t0, out=uEval) + instance.evalF(u=u0, t=t0, out=uEval) except: raise ValueError("evalF cannot be properly evaluated into an array like u0") @@ -72,7 +84,7 @@ def test(self, t0=0, dt=1e-1, eps=1e-3): uEval += u0 uSolve = np.copy(u0) uSolve += eps*np.linalg.norm(uSolve, np.inf) - self.fSolve(a=dt, rhs=uEval, t=t0, out=uSolve) + instance.fSolve(a=dt, rhs=uEval, t=t0, out=uSolve) except: raise ValueError("fSolve cannot be properly evaluated into an array like u0") np.testing.assert_allclose( @@ -80,13 +92,21 @@ def test(self, t0=0, dt=1e-1, eps=1e-3): atol=1e-15) +DIFFOPS: dict[str, type[DiffOp]] = {} +"""Dictionary containing all specialized :class:`DiffOp` classes""" + +def registerDiffOp(cls: type[T]) -> type[T]: + """Class decorator to register a specialized :class:`DiffOp` class in `qmat`""" + checkOverriding(cls, "evalF", isProperty=False) + storeClass(cls, DIFFOPS) + return cls + + class LinearMultiNode(): - def __init__(self, diffOp:DiffOp, tEnd=1, nSteps=1, t0=0, testDiffOp=True): + def __init__(self, diffOp:DiffOp, tEnd=1, nSteps=1, t0=0): assert isinstance(diffOp, DiffOp) self.diffOp = diffOp - if testDiffOp: - self.diffOp.test() self.axpy = blas.get_blas_funcs('axpy', dtype=self.dtype) self.t0 = t0 diff --git a/qmat/solvers/generic/diffops.py b/qmat/solvers/generic/diffops.py index ac98bed..e43fe9e 100644 --- a/qmat/solvers/generic/diffops.py +++ b/qmat/solvers/generic/diffops.py @@ -8,9 +8,10 @@ import numpy as np from scipy.linalg import blas -from qmat.solvers.generic import DiffOp +from qmat.solvers.generic import DiffOp, registerDiffOp, DIFFOPS +@registerDiffOp class Dahlquist(DiffOp): def __init__(self, lam=1j): @@ -25,6 +26,7 @@ def evalF(self, u, t, out): out[1] = u[1]*lam.real + u[0]*lam.imag +@registerDiffOp class Lorenz(DiffOp): r""" RHS of the Lorentz system, which can be written : @@ -71,6 +73,12 @@ def __init__(self, sigma=10, rho=28, beta=8/3, nativeFSolve=False): self.fSolve = self.fSolve_NATIVE + @classmethod + def test(cls): + super().test(instance=cls()) + super().test(instance=cls(nativeFSolve=True)) + + def evalF(self, u, t, out): sigma, rho, beta = self.params x, y, z = u @@ -129,6 +137,7 @@ def fSolve_NATIVE(self, a, rhs, t, out): self.gemv(alpha=1.0, a=jacInv, x=res, beta=1.0, y=out, overwrite_y=True) +@registerDiffOp class ProtheroRobinson(DiffOp): r""" Implement the Prothero-Robinson problem: @@ -186,9 +195,16 @@ def __init__(self, epsilon=1e-3, nonLinear=False, nativeFSolve=True): self.fSolve = self.fSolve_NATIVE super().__init__([self.g(0)]) + @classmethod + def test(cls): + default = cls() + assert not default.nonLinear, "default ProtheroRobinson DiffOp is not linear" + super().test(instance=default) + super().test(instance=cls(nativeFSolve=True)) + @property def nonLinear(self): - return self.evalF == self.evalF_LIN + return self.evalF == self.evalF_NONLIN # ------------------------------------------------------------------------- # g function (analytical solution), and its first derivative @@ -202,6 +218,9 @@ def dg(self, t): # ------------------------------------------------------------------------- # f(u,t) and Jacobian functions # ------------------------------------------------------------------------- + def evalF(self, u, t, out): + raise NotImplementedError("evalF was not set on initialization") + def evalF_LIN(self, u, t, out): np.copyto(out, -self.epsilon**(-1) * (u - self.g(t)) + self.dg(t)) @@ -209,7 +228,7 @@ def evalF_NONLIN(self, u, t, out): np.copyto(out, -self.epsilon**(-1) * (u**3 - self.g(t)**3) + self.dg(t)) def jac(self, u, t): - raise NotImplementedError() + raise NotImplementedError("jac was not set on initialization") def jac_LIN(self, u, t): return -self.epsilon**(-1) @@ -235,3 +254,5 @@ def fSolve_NATIVE(self, a, rhs, t, out): jac = 1 - a * self.jac(u, t) u -= res / jac + +assert len(DIFFOPS) > 0, "something is wrong with DiffOp registration" diff --git a/tests/test_solvers/test_diffops.py b/tests/test_solvers/test_diffops.py new file mode 100644 index 0000000..51bae48 --- /dev/null +++ b/tests/test_solvers/test_diffops.py @@ -0,0 +1,7 @@ +import pytest + +from qmat.solvers.generic.diffops import DIFFOPS + +@pytest.mark.parametrize("name", DIFFOPS.keys()) +def testDiffOps(name): + DIFFOPS[name].test() \ No newline at end of file From d6646cbe1705d6de22f0d9e93124f5125d64d7d2 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Wed, 22 Oct 2025 21:08:22 +0200 Subject: [PATCH 15/33] TL: almost done with tests --- qmat/qdelta/timestepping.py | 4 +- qmat/solvers/generic/__init__.py | 21 +-- qmat/solvers/generic/diffops.py | 20 ++- tests/test_solvers/test_dahlquist.py | 40 ++++- tests/test_solvers/test_diffops.py | 13 +- tests/test_solvers/test_generic.py | 209 +++++++++++++++++++++++++++ 6 files changed, 283 insertions(+), 24 deletions(-) create mode 100644 tests/test_solvers/test_generic.py diff --git a/qmat/qdelta/timestepping.py b/qmat/qdelta/timestepping.py index 346feeb..970483c 100644 --- a/qmat/qdelta/timestepping.py +++ b/qmat/qdelta/timestepping.py @@ -23,7 +23,7 @@ class TimeStepping(QDeltaGenerator): - """ + r""" Base class for time-stepping based :math:`Q_\Delta` approximations Parameters @@ -52,7 +52,7 @@ def __init__(self, nodes, tLeft=0, **kwargs): @staticmethod def extractParams(qGen:QGenerator) -> dict: - """ + r""" Extract from a :math:`Q`-generator object all parameters required to instantiate the :math:`Q_\Delta`-generator """ diff --git a/qmat/solvers/generic/__init__.py b/qmat/solvers/generic/__init__.py index ea7207e..71b2e37 100644 --- a/qmat/solvers/generic/__init__.py +++ b/qmat/solvers/generic/__init__.py @@ -6,14 +6,9 @@ import numpy as np import scipy.optimize as sco from scipy.linalg import blas -from typing import TypeVar from qmat.solvers.dahlquist import Dahlquist from qmat.lagrange import LagrangeApproximation -from qmat.utils import checkOverriding, storeClass - - -T = TypeVar("T") class DiffOp(): @@ -91,15 +86,9 @@ def test(cls, t0=0, dt=1e-1, eps=1e-3, instance=None): uSolve, u0, err_msg="fSolve does not satisfy the fixed-point problem with u0", atol=1e-15) - -DIFFOPS: dict[str, type[DiffOp]] = {} -"""Dictionary containing all specialized :class:`DiffOp` classes""" - -def registerDiffOp(cls: type[T]) -> type[T]: - """Class decorator to register a specialized :class:`DiffOp` class in `qmat`""" - checkOverriding(cls, "evalF", isProperty=False) - storeClass(cls, DIFFOPS) - return cls + # check for nan acceptation + uSolve[:] = np.nan + instance.fSolve(a=dt, rhs=uEval, t=t0, out=uSolve) class LinearMultiNode(): @@ -143,7 +132,9 @@ def solve(self, Q, weights, uNum=None): nNodes, Q, weights = Dahlquist.checkCoeff(Q, weights) assert self.lowerTri(Q), "lower triangular matrix Q expected for non-linear solver" - Q, weights = self.dt*Q, self.dt*weights + Q = self.dt*Q + if weights is not None: + weights = self.dt*weights if uNum is None: uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) diff --git a/qmat/solvers/generic/diffops.py b/qmat/solvers/generic/diffops.py index e43fe9e..e58e1fe 100644 --- a/qmat/solvers/generic/diffops.py +++ b/qmat/solvers/generic/diffops.py @@ -7,8 +7,22 @@ """ import numpy as np from scipy.linalg import blas +from typing import TypeVar -from qmat.solvers.generic import DiffOp, registerDiffOp, DIFFOPS +from qmat.solvers.generic import DiffOp +from qmat.utils import checkOverriding, storeClass + + +T = TypeVar("T") + +DIFFOPS: dict[str, type[DiffOp]] = {} +"""Dictionary containing all specialized :class:`DiffOp` classes""" + +def registerDiffOp(cls: type[T]) -> type[T]: + """Class decorator to register a specialized :class:`DiffOp` class in `qmat`""" + checkOverriding(cls, "evalF", isProperty=False) + storeClass(cls, DIFFOPS) + return cls @registerDiffOp @@ -201,6 +215,8 @@ def test(cls): assert not default.nonLinear, "default ProtheroRobinson DiffOp is not linear" super().test(instance=default) super().test(instance=cls(nativeFSolve=True)) + nonLin = cls(nonLinear=True) + super().test(instance=nonLin) @property def nonLinear(self): @@ -254,5 +270,3 @@ def fSolve_NATIVE(self, a, rhs, t, out): jac = 1 - a * self.jac(u, t) u -= res / jac - -assert len(DIFFOPS) > 0, "something is wrong with DiffOp registration" diff --git a/tests/test_solvers/test_dahlquist.py b/tests/test_solvers/test_dahlquist.py index 0357b70..06cb5cb 100644 --- a/tests/test_solvers/test_dahlquist.py +++ b/tests/test_solvers/test_dahlquist.py @@ -41,7 +41,8 @@ def testDahlquist(scheme, tEnd, nSteps, dim, lam): def testDahlquistSDC(scheme, tEnd, nSteps, nSweeps, weights, dim, lam): coll = Q_GENERATORS["Collocation"](nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") approx = QDELTA_GENERATORS[scheme](qGen=coll) - QDelta = approx.genCoeffs(k=[i+1 for i in range(nSweeps)]) + nIters = [k+1 for k in range(nSweeps)] if scheme == "MIN-SR-FLEX" else nSweeps + QDelta = approx.genCoeffs(k=nIters) lamVals = lam*np.linspace(0, 1, 4**dim).reshape((4,)*dim) ref = np.array([solveDahlquistSDC(lam, 1, tEnd, nSteps, @@ -96,3 +97,40 @@ def testDahlquistIMEX(scheme, tEnd, nSteps, dim, lam): detail = " with weights " if weights is not None else "" assert np.allclose(sol, ref), \ f"DahlquistIMEX solver {detail} does not produce the linear combination of IMEX sum" + + +@pytest.mark.parametrize("lam", [1j, -1]) +@pytest.mark.parametrize("dim", [1, 2]) +@pytest.mark.parametrize("weights", [True, False]) +@pytest.mark.parametrize("nSweeps", [1, 4]) +@pytest.mark.parametrize("nSteps", [1, 5]) +@pytest.mark.parametrize("tEnd", [1, 5]) +@pytest.mark.parametrize("scheme", ["BE", "FE", "MIN-SR-FLEX"]) +def testDahlquistIMEXSDC(scheme, tEnd, nSteps, nSweeps, weights, dim, lam): + coll = Q_GENERATORS["Collocation"](nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") + approx = QDELTA_GENERATORS[scheme](qGen=coll) + nIters = [k+1 for k in range(nSweeps)] if scheme == "MIN-SR-FLEX" else nSweeps + QDelta = approx.genCoeffs(k=nIters) + + lamVals = lam*np.linspace(0, 1, 4**dim).reshape((4,)*dim) + ref = np.array([solveDahlquistSDC(lam, 1, tEnd, nSteps, + nSweeps, coll.Q, QDelta, coll.weights if weights else None) + for lam in lamVals.ravel()]).T.reshape((-1, *lamVals.shape)) + + solver = DahlquistIMEX(lamVals, [0], 1, tEnd, nSteps) + sol = solver.solveSDC(coll.Q, coll.weights if weights else None, QDelta, QDelta, nSweeps) + assert np.allclose(sol, ref), \ + "DahlquistIMEX SDC solver does not match reference solver for implicit only" + + solver = DahlquistIMEX([0], lamVals, 1, tEnd, nSteps) + sol = solver.solveSDC(coll.Q, coll.weights if weights else None, QDelta, QDelta, nSweeps) + assert np.allclose(sol, ref), \ + "DahlquistIMEX SDC solver does not match reference solver for explicit only" + + solver = DahlquistIMEX(lamVals, lamVals, 1, tEnd, nSteps) + sol = solver.solveSDC(coll.Q, coll.weights if weights else None, QDelta, QDelta, nSweeps) + ref = np.array([solveDahlquistSDC(2*lam, 1, tEnd, nSteps, + nSweeps, coll.Q, QDelta, coll.weights if weights else None) + for lam in lamVals.ravel()]).T.reshape((-1, *lamVals.shape)) + assert np.allclose(sol, ref), \ + "DahlquistIMEX SDC solver does not match reference solver with IMEX sum" diff --git a/tests/test_solvers/test_diffops.py b/tests/test_solvers/test_diffops.py index 51bae48..0ab05dc 100644 --- a/tests/test_solvers/test_diffops.py +++ b/tests/test_solvers/test_diffops.py @@ -1,7 +1,14 @@ import pytest -from qmat.solvers.generic.diffops import DIFFOPS +from qmat.solvers.generic.diffops import DIFFOPS, DiffOp + + +def testBase(): + diffOpSmall = DiffOp(10*[0.0]) + diffOpLarge = DiffOp(1000*[0.0]) + assert diffOpSmall.innerSolver != diffOpLarge.innerSolver + @pytest.mark.parametrize("name", DIFFOPS.keys()) -def testDiffOps(name): - DIFFOPS[name].test() \ No newline at end of file +def testImplementations(name): + DIFFOPS[name].test() diff --git a/tests/test_solvers/test_generic.py b/tests/test_solvers/test_generic.py new file mode 100644 index 0000000..26711b8 --- /dev/null +++ b/tests/test_solvers/test_generic.py @@ -0,0 +1,209 @@ +import pytest +import numpy as np + +from qmat import Q_GENERATORS, QDELTA_GENERATORS +from qmat.nodes import QUAD_TYPES +from qmat.mathutils import numericalOrder +from qmat.solvers.sdc import solveDahlquistSDC + +from qmat.solvers.generic import LinearMultiNode +from qmat.solvers.generic.diffops import Dahlquist, Lorenz, ProtheroRobinson + + +@pytest.mark.parametrize("lam", [1j, -0.01, 1j-0.01]) +@pytest.mark.parametrize("nSteps", [1, 5]) +@pytest.mark.parametrize("tEnd", [1, 5]) +@pytest.mark.parametrize("scheme", ["BE", "FE", "TRAP", "RK4", "DIRK43", + "ARK443ESDIRK", "ARK443ERK"]) +def testLinearMultiNodeDahlquist(scheme, tEnd, nSteps, lam): + diffOp = Dahlquist(lam=lam) + solver = LinearMultiNode(diffOp=diffOp, tEnd=tEnd, nSteps=nSteps) + + qGen = Q_GENERATORS[scheme].getInstance() + + uRef = qGen.solveDahlquist(lam, 1, T=tEnd, nSteps=nSteps) + + uNum = solver.solve(Q=qGen.Q, weights=qGen.weights) + uNum = uNum[:, 0] + 1j*uNum[:, 1] + + assert np.allclose(uNum, uRef), \ + "LinearMultiNode does not match reference solver for Dahlquist" + + if scheme.startswith("ARK443"): + uNum = solver.solve(Q=qGen.Q, weights=None) + uNum = uNum[:, 0] + 1j*uNum[:, 1] + + assert np.allclose(uNum, uRef), \ + "LinearMultiNode without weights does not match reference solver for Dahlquist" + + +@pytest.mark.parametrize("quadType", QUAD_TYPES) +@pytest.mark.parametrize("nSweeps", [1, 2]) +@pytest.mark.parametrize("nNodes", [1, 4]) +@pytest.mark.parametrize("lam", [1j, -0.01, 1j-0.01]) +@pytest.mark.parametrize("nSteps", [1, 2]) +@pytest.mark.parametrize("tEnd", [1, 5]) +@pytest.mark.parametrize("scheme", ["BE", "FE", "TRAP", "MIN-SR-FLEX"]) +def testLinearMultiNodeDahlquistSDC( + scheme, tEnd, nSteps, lam, nNodes, nSweeps, quadType): + if nNodes == 1 and quadType != "GAUSS": + return + + diffOp = Dahlquist(lam=lam) + solver = LinearMultiNode(diffOp=diffOp, tEnd=tEnd, nSteps=nSteps) + + coll = Q_GENERATORS["Collocation"]( + nNodes=nNodes, quadType=quadType, nodeType="LEGENDRE") + + lastNode = np.allclose(coll.nodes[-1], 1) + + approx = QDELTA_GENERATORS[scheme](qGen=coll) + kVals = [k+1 for k in range(nSweeps)] + QDelta = approx.genCoeffs(k=kVals) + + for weights in [coll.weights, None]: + if not lastNode: + continue + + uRef = solveDahlquistSDC( + lam, 1, T=tEnd, nSteps=nSteps, nSweeps=nSweeps, + Q=coll.Q, QDelta=QDelta, weights=weights) + + uNum = solver.solveSDC( + nSweeps=nSweeps, Q=coll.Q, weights=weights, QDelta=QDelta) + uNum = uNum[:, 0] + 1j*uNum[:, 1] + + details = " with weigths " if weights is not None else "" + assert np.allclose(uNum, uRef), \ + f"LinearMultiNode SDC {details} does not match reference solver for Dahlquist" + + +@pytest.fixture(scope="session") +def uRefLorentz(): + diffOp = Lorenz() + tEnd = 0.1 + qGenRef = Q_GENERATORS["RK4"].getInstance() + uRef = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=10000).solve( + qGenRef.Q, qGenRef.weights) + return {"tEnd": tEnd, "sol": uRef, "diffOp": diffOp} + + +@pytest.mark.parametrize("scheme", ["BE", "FE", "TRAP", "RK4", "DIRK43"]) +def testLinearMultiNodeLorenz(scheme, uRefLorentz): + diffOp = uRefLorentz["diffOp"] + uRef = uRefLorentz["sol"] + tEnd = uRefLorentz["tEnd"] + + nStepsVals = [10, 50, 100] + err = [] + qGen = Q_GENERATORS[scheme].getInstance() + for nSteps in nStepsVals: + solver = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) + uNum = solver.solve(qGen.Q, qGen.weights) + err.append(np.linalg.norm(uNum[-1] - uRef[-1])) + + expectedOrder = qGen.order + order, rmse = numericalOrder(nStepsVals, err) + assert rmse < 0.02, \ + f"rmse to high ({rmse}) for {scheme}" + assert abs(order-expectedOrder) < 0.1, \ + f"expected order {expectedOrder:.2f}, but got {order:.2f} for {scheme}" + + +@pytest.mark.parametrize("quadType", QUAD_TYPES) +@pytest.mark.parametrize("nSweeps", [1, 2]) +@pytest.mark.parametrize("nNodes", [3, 4]) +@pytest.mark.parametrize("scheme", ["BE", "FE", "LU"]) +def testLinearMultiNodeLorenzSDC(scheme, nNodes, nSweeps, quadType, uRefLorentz): + diffOp = Lorenz() + uRef = uRefLorentz["sol"] + tEnd = uRefLorentz["tEnd"] + + nStepsVals = [10, 50, 100] + + coll = Q_GENERATORS["Collocation"]( + nNodes=nNodes, nodeType="LEGENDRE", quadType=quadType) + approx = QDELTA_GENERATORS[scheme](qGen=coll) + nIters = [k+1 for k in range(nSweeps)] + QDelta = approx.genCoeffs(k=nIters) + + err = [] + for nSteps in nStepsVals: + solver = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) + uNum = solver.solveSDC(nSweeps, coll.Q, coll.weights, QDelta) + err.append(np.linalg.norm(uNum[-1] - uRef[-1])) + + expectedOrder = nSweeps+1 + order, rmse = numericalOrder(nStepsVals, err) + assert rmse < 0.02, \ + f"rmse to high ({rmse}) for {scheme}" + assert abs(order-expectedOrder) < 0.1, \ + f"expected order {expectedOrder:.2f}, but got {order:.2f} for {scheme}" + + +@pytest.fixture(scope="session") +def uRefProtheroRobinson(): + diffOp = ProtheroRobinson(epsilon=0.5) + tEnd = 0.5 + qGenRef = Q_GENERATORS["ARK4ERK"].getInstance() + uRef = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=1000).solve( + qGenRef.Q, qGenRef.weights) + return {"tEnd": tEnd, "sol": uRef, "diffOp": diffOp} + + +@pytest.mark.parametrize("scheme", ["ARK4EDIRK", "ARK343ESDIRK"]) +def testLinearMultiNodeProtheroRobinson(scheme, uRefProtheroRobinson): + diffOp = uRefProtheroRobinson["diffOp"] + uRef = uRefProtheroRobinson["sol"] + tEnd = uRefProtheroRobinson["tEnd"] + + nStepsVals = [20, 50, 100] + err = [] + qGen = Q_GENERATORS[scheme].getInstance() + for nSteps in nStepsVals: + solver = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) + uNum = solver.solve(qGen.Q, qGen.weights) + err.append(np.linalg.norm(uNum[-1] - uRef[-1])) + + expectedOrder = qGen.order + order, rmse = numericalOrder(nStepsVals, err) + + import matplotlib.pyplot as plt + plt.loglog(nStepsVals, err) + plt.loglog(nStepsVals, np.array(nStepsVals, dtype=float)**(-expectedOrder), "--") + + assert rmse < 0.02, \ + f"rmse to high ({rmse}) for {scheme}" + assert abs(order-expectedOrder) < 0.1, \ + f"expected order {expectedOrder:.2f}, but got {order:.2f} for {scheme}" + + +@pytest.mark.parametrize("quadType", QUAD_TYPES) +@pytest.mark.parametrize("nSweeps", [1, 2]) +@pytest.mark.parametrize("nNodes", [3, 4]) +@pytest.mark.parametrize("scheme", ["BE", "FE", "MIN-SR-FLEX"]) +def testLinearMultiNodeProtheroRobinsonSDC(scheme, nNodes, nSweeps, quadType, uRefProtheroRobinson): + diffOp = uRefProtheroRobinson["diffOp"] + uRef = uRefProtheroRobinson["sol"] + tEnd = uRefProtheroRobinson["tEnd"] + + nStepsVals = [10, 50, 100] + + coll = Q_GENERATORS["Collocation"]( + nNodes=nNodes, nodeType="LEGENDRE", quadType=quadType) + approx = QDELTA_GENERATORS[scheme](qGen=coll) + nIters = [k+1 for k in range(nSweeps)] + QDelta = approx.genCoeffs(k=nIters) + + err = [] + for nSteps in nStepsVals: + solver = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) + uNum = solver.solveSDC(nSweeps, coll.Q, coll.weights, QDelta) + err.append(np.linalg.norm(uNum[-1] - uRef[-1])) + + expectedOrder = nSweeps+1 + order, rmse = numericalOrder(nStepsVals, err) + assert rmse < 0.02, \ + f"rmse to high ({rmse}) for {scheme}" + assert abs(order-expectedOrder) < 0.1, \ + f"expected order {expectedOrder:.2f}, but got {order:.2f} for {scheme}" From 93ee440a17f36e400a98b8bbf3467259c393e6dd Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Thu, 23 Oct 2025 00:55:19 +0200 Subject: [PATCH 16/33] TL: finalized tests --- docs/devdoc/testing.md | 20 ++++--- docs/installation.md | 14 ++--- docs/notebooks/01_qCoeffs.ipynb | 2 +- docs/notebooks/02_rk.ipynb | 4 +- docs/notebooks/04_sdc.ipynb | 12 ++-- docs/notebooks/05_residuals.ipynb | 10 ++-- docs/notebooks/21_lagrange.ipynb | 22 ++------ qmat/playgrounds/tibo/test.py | 15 +++-- qmat/solvers/dahlquist.py | 2 +- qmat/solvers/generic/__init__.py | 35 ++++++------ qmat/solvers/generic/integrators.py | 8 +-- qmat/utils.py | 2 +- test.sh | 3 +- tests/test_solvers/test_generic.py | 36 ++++++------ tests/test_solvers/test_integrators.py | 77 ++++++++++++++++++++++++++ 15 files changed, 163 insertions(+), 99 deletions(-) create mode 100644 tests/test_solvers/test_integrators.py diff --git a/docs/devdoc/testing.md b/docs/devdoc/testing.md index e04b920..702b935 100644 --- a/docs/devdoc/testing.md +++ b/docs/devdoc/testing.md @@ -18,8 +18,8 @@ that you can activate using : source ./env/bin/activate ``` -> 🔔 In case you have the `base` `conda` environment as default on your computer, -> you should deactivate it before activating `env` by running `conda deactivate`. +> 🔔 In case you have the `base` `conda` environment as default on your computer, +> you should deactivate it before activating `env` by running `conda deactivate`. If not already done, install all the test dependencies listed in the [pyproject.toml](../../pyproject.toml) file under the `project.optional-dependencies` section. @@ -31,18 +31,19 @@ pip install .[test] # install qmat locally and all test dependencies pip uninstall qmat # remove the frozen qmat package installed locally ``` -> đŸ“Ŗ Remember that the [recommended installation approach for developer](../installation) is to use a simple modification of the `PYTHONPATH` environment variable. +> đŸ“Ŗ Remember that the [recommended installation approach for developer](../installation) +> is to install in **editable mode** using `pip install -e .[test]`. ## Test local changes -The first thing to do (from the root `qmat` repo) is to run : +The first thing to do (from the root `qmat` repo) is to run : ```bash python -c "import qmat" ``` -This will trigger the [registration mechanism](./structure) that test the code structure at import, -and ensures that all generators are correctly implemented +This will trigger the [registration mechanism](./structure) that test the code structure at import, +and ensures that all generators are correctly implemented (in particular, overriding of the correct methods, etc ...). Then run the full test series with : @@ -56,10 +57,11 @@ This will check : - the basic generation of all registered $Q$-coefficients and $Q_\Delta$ approximations (using functions or generator objects) - convergence order of all registered $Q$-coefficients - some properties of all registered $Q_\Delta$ approximations +- all the solvers and differential operators implemented in `qmat` 💡 **Hint :** -There is actually more than 3000 tests to check the package, that take around 1 minutes on a standard computer. +There is currently more than 6000 tests, that take around 35 seconds on a standard computer. So you may not want to run all of those every time you do a small modification somewhere 😅 ... Here are a few tricks you can use : @@ -73,12 +75,12 @@ pytest -v ./tests/test_1_nodes.py::testGauss[LEGENDRE] # run only one test func ## Check code coverage -Once all test pass, you may check locally coverage by running (from the root folder) : +Once all test pass, you may check locally coverage by running (from the `qmat` root folder) : ```bash ./test.sh coverage combine -python -m coverage html +coverage html ``` This generates a html coverage report in `htmlcov/index.html` that you can read using your favorite web browser. diff --git a/docs/installation.md b/docs/installation.md index 87c4971..88ee439 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -42,17 +42,15 @@ cd qmat # go into the local git repo pip install . ``` -For **developers who want to contribute**, recommended approach is to add -the code folder to your `PYTHONPATH` (if not done already by your IDE), _e.g_ : +For **developers who want to contribute**, recommended approach is to install +the package in _editable mode_ : ```bash cd qmat # go into the local git repo (if not already there) -export PYTHONPATH=$PYTHONPATH:$(pwd) +pip install -e .[test] ``` -> 🔔 Using `pip install -e .` is also possible for developments, but then you have a persistent installation that you should be aware of ... - - - - +This will link your python installation to your local `qmat` folder, +hence all your modifications will be taken into account at each new import of `qmat`. +> 🔔 Some IDEs also modify the `PYTHONPATH` to include the `qmat` root folder, which you can also do manually if you prefer. diff --git a/docs/notebooks/01_qCoeffs.ipynb b/docs/notebooks/01_qCoeffs.ipynb index b1e2169..8ce8f36 100644 --- a/docs/notebooks/01_qCoeffs.ipynb +++ b/docs/notebooks/01_qCoeffs.ipynb @@ -133,7 +133,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "TypeError: Collocation.__init__() got an unexpected keyword argument 'node_type'\n" + "TypeError: Collocation.__init__() got an unexpected keyword argument 'node_type'. Did you mean 'nodeType'?\n" ] } ], diff --git a/docs/notebooks/02_rk.ipynb b/docs/notebooks/02_rk.ipynb index 57ef6b3..a5c55fa 100644 --- a/docs/notebooks/02_rk.ipynb +++ b/docs/notebooks/02_rk.ipynb @@ -114,7 +114,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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t378ft27dwrvvvqszTiaT6YxzdnY29qYQVRuzNp1Bi3EbcPTyw8v3Dwv2hEatQldvRxPOjKoDmUyGKa+1wbHonnC0U4j1KXEn4BEZi4MZt0w4O6KqYdRzcAoKCnDgwAFERkbq1ENDQ5GcnFyudSxevBg9evSAu7u7Tv3u3btwd3dHUVERXnjhBUyePBm+vr6lriM/Px/5+fni49zckj8KWq0WWq22IptEgNgz9s4wKtLPPek38fZP+3VqHg1s8dfHgbCRW/L/BNw/H2VtAez6sjNOZd1B73kpYv3175NhZSFDyr+7oK6tvMx1sJ+GxX5WTkX6JhMEQTDWRK5cuYLnnnsOu3btQlBQkFifOnUqfvnlF5w6darM5TMzM+Hm5oaVK1eiX79+Yn337t04e/Ys2rRpg9zcXMyaNQtxcXE4dOgQmjdvrreeiRMnIjo6Wq++cuVK2NraVmILiapGbgEw/oD+65ExLxTCiUeiqJxSrsqw6rzudXJeqF+MwS2Kwbs+UE2Ql5eHgQMHIicnB/b2ZV99vUoCTnJyMgIDA8X6lClTsGzZMpw8Wfb1GmJiYjB9+nRcuXIF1tZPvm5HcXEx2rVrh06dOmH27Nl63y/tHRw3Nzdcv379qQ0ifVqtFomJiQgJCYFcXvarP3q6svpZVCxg2LKDSDp7Q6c+o28bvMw7fZeK+2fZiosFjFh9CBuPZ+vUp/5fa/T1e05vPPtpWOxn5eTm5sLBwaFcAceoh6gcHBxgaWmJrKwsnXp2djacnJzKXFYQBPz000+IiIgoM9wAgIWFBV588UWcOXOm1O8rFAooFAq9ulwu5w5WCeyfYT3ez1+SNZjw5zGdMW+0a4Rv+/Imi+XB/fPJfnjnRdy6VwD/KZtQVFzyGnfMH8cw5o9j2DiyE7weua7OA+ynYbGfz6YiPTPqScbW1tbw8/NDYmKiTj0xMVHnkFVptm/fjrNnz2LIkCFP/TmCICAtLQ0uLnxFSzXfkUs58IiM1Qk3djZWODwxFNP7tWW4IYOoV9sa56aGY82Hus/FPWfuQIepm5BXUGiimREZhtEv9Dd69GhERETA398fgYGBWLhwITIyMjB8+HAAQFRUFC5fvoylS5fqLLd48WJ06NABPj4+euuMjo5GQEAAmjdvjtzcXMyePRtpaWmYN2+esTeHyGhy/9ai89dbcDdf9w/L/z59CT7PKU00K5I6P/d60KhV+GH7OcRsKDlt4GpuPlp9tRFvdWiMCSovE8+Q6NkYPeD0798fN27cwKRJk5CZmQkfHx/ExcWJn4rKzMzUuyZOTk4O1qxZg1mzZpW6ztu3b+P9999HVlYWlEolfH19sWPHDrRv397Ym0NkcIIgYNkZC3yWons9m8mvtkZEoIdpJkVm54POTTHkJU8MXLQHezU3AQAr9mRgxZ4MvNdChnATz4+ooox6knF1lZubC6VSWa6TlEifVqtFXFwcwsPDeQy5kv5IvYyRq9N0al29GmLxoBdhYcFDUc+C+2flZeXcR0DMZr160pdd4VafnzytDO6flVORv9+8FxWRCZy7dhfdp2/Xq+8f1wMOdfRPiCeqSs5KG2jUKmw/fQ2D/rmnFQAEf7MVLV3s8cfHQVBYWZaxBiLT401qiKrQfW0ROn2zVS/cfNqqEGcmhzLcULXSuUVDnJkciu6uD+9jdSIzF17j4jEjoezrmBGZGgMOURWZ/L/j8B4fj4ybeWJtdEgLnJkcimY8h5iqsVfci3H0q+5o0rC2WJu95Sw8ImORfPa6CWdG9GQ8REVkZFtOXsV7S3Rvr9C2kRK/DQ+CtZUFL9lONYJCboktn3dB+vV76PrtNrE+8Mc9AIC9Y7vD0Y43PKbqgwGHyEiu3P4bQeotevWd/+6KRvV4oibVTJ4OtaFRq/DXoSv49NdUsd5+yma81MwBv7zXHpY8QZ6qAR6iIjIwbVEx/m/eLr1ws+gdf2jUKoYbkoSX27oiPSYc/fwbibWdZ6+j6Zg4/Lwr3YQzIyrBgENkQPO2nkXzsRuQdvG2WBsc5AGNWoWQVmXfnoSoppHJZPimT1scmRiKeo/clTz6r+PwiIzFoUd+D4iqGg9RERnA3vSb6PdDik7tubq1sGl0Z9Sy5sdpSdrsbORI/SoUx67kQDV7p1h/dd4u1JJbYveY7lDW4jVfqGox4BBVwo27+fD7epNefdPoTmjmqH/DQiIpa+2qhEatwvLdFzDuj6MAgL+1RWgbnYBX2rpi1oAXeC81qjI8REX0DIqLBQz9ZZ9euJnety00ahXDDZm1twPccX5qOHq0dBRrfx66As+oOKw9eMmEMyNzwoBDVEEr9lxAkzFx2HQiW6y98s8Jl2/4NSpjSSLzYWEhw4+DXsSBcT106qP/ewgekbE4m33HRDMjc8FDVETl9Pj5BQBKzi+I6g6lLc8vICpNgzoKaNQqvfPUeszYgUb1aiFxFM9TI+PgOzhET3HnvhYvTErQCzfrP+6IE5N7MdwQlUN7z/rQqFX4V08vsXbp1t9o+VU8Jv55zIQzI6liwCF6AkEQ8K/fDqHNxATcznt4teGvereCRq1CW7e6ppscUQ31cddmODMlDC888vuzJFkDj8hYbDp+1XQTI8nhISqiUvzv8BV8sjJVp8artBIZhtzSAn983FHvat9Dl5bc0mRXZDc8V7eWqaZHEsGAQ/SIx++z8wDvs0NkeK51a0GjVmHziasY8svD+7V1VG/B842UWPNhEOSWPNBAz4Z7DhGA+9oidJu+TS/crBzaARq1iuGGyIi6t3SCRq3Cex09xdrhSzloPnYD5mw+Y8KZUU3GgENmT73hJLzHx+P8tXtibUS3ZtCoVQhq5mDCmRGZl69eboWTk3vBvcHD+7VNTzwNj8hY7D5/w4Qzo5qIh6jIbG0/fQ2DftqrU2vpYo8/Pg6CwoofWyUyBRu5Jbb/qyvOZt9FjxnbxfqAhbsBAPvH9YBDHYWppkc1CAMOmZ2snPsIiNmsV0/6sivc6vNO30TVQTPHOtCoVViXegmjVh8S6/5fb0JXr4ZYPOhFWPCEfyoDD1GR2SgsKka/BSl64WbB2+2gUasYboiqodd8GyE9Jhz/94KrWNt66hqajInDst0XTDgzqu4YcMgs/LD9HJqN3YC9mpti7e2AxkiPCUcvHxcTzoyInkYmk2HmAF8cmhCKOoqHBx7G/3EUHpGxOHo5x4Szo+qKh6hI0g5cuIU35ifr1BztFNj6RRfUVnD3J6pJlLXkOBrdE4cv3cYrc3eJ9d5zdsLexgq7IrvBzoZXFqcSfIYnSbp1rwD+UzahqFjQqW8c2QlezrzTN1FN9nyjutCoVViyKx0T/zoOAMi9X4g2ExPwRrtG+Lbv85DJeH6OueMhKpKU4mIBw5cdgO/kRJ1w880bz0OjVjHcEEnI4I6eODc1HJ1aNBRraw5egmdUHP48dMWEM6PqgAGHJGP1vgw0GROH+GNZYi3Mxxnnp4aj34tuJpwZERmLpYUMS99rj31je+jUR/yaCo/IWJy/dtdEMyNT4yEqqvFOZuWi18wknZqVhQz7xvZAvdrWJpoVEVWlhnYKaNQqJJ+7joGL9oj1btO3w9OhNjZ8FgwbOa9vZU4YcKjGupdfiC7fbsO1O/k69TUfBsHPvZ6JZkVEphTU1AEatQrfJZ7GrH9u85B+/R68x8fjg05NEBXeUneBexlA/vWK/yCFA1C7sQFmTMbCgEM1jiAIGPvHUazck6FTHxPujfc7NTXRrIioOhkV0gIfd22G1+fvwtHLuQCAH3acxw87zuPnd19EVy/HknDzlxdQfL/iP8DCBnj5FENONcaAQzVK/NFMDF9+UKfW3rM+Vg7tACvedZiIHmFtZYH/fRqMizfzEPzNVrH+7s/7AAApHzrC5VnCDVASivKvM+BUYww4VG0UFBZjWYoGF27mwb2+LSICPWBtVRJaMm7kodN/tuotszuqO5yVvNM3ET2ZW31baNQqbDyWhQ+WHRDrgfOz4Wf7DVY3jYSVrFis/11kjamZ70KT7woPxRWMcfkZtSwLTDF1qoQqecn7/fffw9PTEzY2NvDz80NSUtITx27btg0ymUzv6+TJkzrj1qxZg1atWkGhUKBVq1ZYt26dsTeDjCgm7ji8x2/A5NgTWJpyAZNjT5Q8/t8x9Jq5Qy/cLH2vPTRqFcMNEZVbz9bO0KhVeCfQXawdyGuFZkf+xPzsNwAAwzRj0PLYGiy7+TKS7vlh2c2X0fLYGgzTjDHVtOkZGT3grF69GiNHjsTYsWORmpqK4OBghIWFISMjo8zlTp06hczMTPGrefPm4vdSUlLQv39/RERE4NChQ4iIiEC/fv2wZ8+eMtZI1VVM3HH8sCMdj12TD8UCsHinBiez7oi14Z2bQqNW6Vz3goioIia96oPjk3rC1e7hn8BpWe/C4/D/kJgbWOoyibmBDDk1jNEDzowZMzBkyBAMHToULVu2xMyZM+Hm5ob58+eXuZyjoyOcnZ3FL0vLhx/vmzlzJkJCQhAVFQVvb29ERUWhe/fumDlzppG3hgytoLAYi5LSnzquqYMtTk7uhcgw7yqYFRFJna21FZI/dEJCi48e+47sn6/HayUh5+8iXnqipjDqOTgFBQU4cOAAIiMjdeqhoaFITk5+wlIlfH19cf/+fbRq1Qrjxo1D165dxe+lpKRg1KhROuN79uz5xICTn5+P/PyHHyXOzS05o16r1UKr1VZkkwgQe2aI3i1J1ui9c1Oa/i82giWKodUWP31wDWPIfhL7aWiS7mdhIVrYZEDzfG+8efZrpOS9UMbgkpAzNfNdTG70AwBAW1gIVLAvku5nFahI34wacK5fv46ioiI4OTnp1J2cnJCVlVXqMi4uLli4cCH8/PyQn5+PZcuWoXv37ti2bRs6deoEAMjKyqrQOmNiYhAdHa1XT0hIgK2t7bNsGgFITEys9DqS0i1QnjcSk1JPwun28Ur/vOrMEP2kh9hPw5JiP5VF59Dln39byYrKtYwm31X8966dO5FjmflMP1uK/awKeXl55R5bJZ+ievymZ4IgPPFGaF5eXvDy8hIfBwYG4uLFi/j222/FgFPRdUZFRWH06NHi49zcXLi5uSE0NBT29vYV3h5zp9VqkZiYiJCQEMjllbtz79VkDZI2nH7quGBfb4QHeVTqZ1VXhuwnsZ+GJul+3koFNpX800NxBUn3/J66iIfi4T2uOr70ElDPt0I/UtL9rAIPjsCUh1EDjoODAywtLfXeWcnOztZ7B6YsAQEBWL58ufjY2dm5QutUKBRQKBR6dblczh2sEgzRv8Edm0Idf7rMw1QWspJxcitpX+eG+6NhsZ+GJcl+Wj38EzjG5Wcsu9n7n0elvVgWxHEPyK2sgGfsiST7WQUq0jOj/sWwtraGn5+f3ltxiYmJCAoKKvd6UlNT4eLiIj4ODAzUW2dCQkKF1knVg7WVBYYFe5Y5Zliwp3g9HCIiY6hlWYAQ+5R/Hj3+iqvkcYh9Cq+HU4MY/RDV6NGjERERAX9/fwQGBmLhwoXIyMjA8OHDAZQcPrp8+TKWLl0KoOQTUh4eHmjdujUKCgqwfPlyrFmzBmvWrBHX+dlnn6FTp06YNm0aXn31Vaxfvx6bNm3Czp07jb05ZARR4a0AAAt3pOs8rVjISsLNg+8TERnTIo+pGKYZU+pHxUPsU7DIY6oJZkXPyugBp3///rhx4wYmTZqEzMxM+Pj4IC4uDu7uJRdayszM1LkmTkFBAb744gtcvnwZtWrVQuvWrREbG4vw8HBxTFBQEFatWoVx48Zh/PjxaNq0KVavXo0OHToYe3PISKLCW8HDoTai1h4FAIxXtdS5kjERUVVY5DEVP2X3xqSskhfhEfX/4pWMa6gqOcn4o48+wkcfPX6tgRJLlizRefzll1/iyy+/fOo6+/Tpgz59+hhielRNWFk8DDNDgpuYcCZEZM6sLR9+ourBR8Kp5uHLYyIiMk8Kh5K7gj8LC5uS5ana4s02iYjIPNVuDLx8quSu4I9KvQdc/ufjyL0O6C8HlIQb3km8WmPAISIi81W7sX5QqX0BQMn5gKjfrsqnRIbBQ1REREQkOQw4REREJDkMOERERCQ5DDhEREQkOQw4REREJDkMOERERCQ5DDhEREQkOQw4REREJDkMOERERCQ5DDhEREQkOQw4REREJDkMOERERCQ5DDhEREQkOQw4REREJDkMOERERCQ5DDhEREQkOQw4REREJDkMOERERCQ5DDhEREQkOQw4REREJDkMOERERCQ5DDhEREQkOQw4REREJDkMOERERCQ5DDhEREQkOQw4REREJDkMOERERCQ5DDhEREQkOVUScL7//nt4enrCxsYGfn5+SEpKeuLYtWvXIiQkBA0bNoS9vT0CAwOxceNGnTFLliyBTCbT+7p//76xN4WIiIhqAKMHnNWrV2PkyJEYO3YsUlNTERwcjLCwMGRkZJQ6fseOHQgJCUFcXBwOHDiArl274uWXX0ZqaqrOOHt7e2RmZup82djYGHtziIiIqAawMvYPmDFjBoYMGYKhQ4cCAGbOnImNGzdi/vz5iImJ0Rs/c+ZMncdTp07F+vXr8ddff8HX11esy2QyODs7G3XuREREVDMZNeAUFBTgwIEDiIyM1KmHhoYiOTm5XOsoLi7GnTt3UL9+fZ363bt34e7ujqKiIrzwwguYPHmyTgB6VH5+PvLz88XHubm5AACtVgutVluRTSJA7Jmhe1dUVKT3M8yBsfpprthPwzLHfhrzucgc+2lIFembUQPO9evXUVRUBCcnJ526k5MTsrKyyrWO6dOn4969e+jXr59Y8/b2xpIlS9CmTRvk5uZi1qxZ6NixIw4dOoTmzZvrrSMmJgbR0dF69YSEBNja2lZwq+iBxMREg67vcLYMgCUAIC4uzqDrrgkM3U9zx34aljn182iW8Z+LzKmfhpSXl1fusUY/RAWUHE56lCAIerXS/Prrr5g4cSLWr18PR0dHsR4QEICAgADxcceOHdGuXTvMmTMHs2fP1ltPVFQURo8eLT7Ozc2Fm5sbQkNDYW9v/yybZNa0Wi0SExMREhICuVxusPX+ffAyVp47BgAIDw832HqrO2P101yxn4Zljv28vfcifks/AcDwz0Xm2E9DenAEpjyMGnAcHBxgaWmp925Ndna23rs6j1u9ejWGDBmC3377DT169ChzrIWFBV588UWcOXOm1O8rFAooFAq9ulwu5w5WCYbun6Wlpc66zQ33R8NiPw3LnPpZFc9F5tRPQ6pIz4z6KSpra2v4+fnpvRWXmJiIoKCgJy7366+/YvDgwVi5ciVUKtVTf44gCEhLS4OLi0ul50xEREQ1n9EPUY0ePRoRERHw9/dHYGAgFi5ciIyMDAwfPhxAyeGjy5cvY+nSpQBKws0777yDWbNmISAgQHz3p1atWlAqlQCA6OhoBAQEoHnz5sjNzcXs2bORlpaGefPmGXtziIiIqAYwesDp378/bty4gUmTJiEzMxM+Pj6Ii4uDu7s7ACAzM1Pnmjg//PADCgsL8fHHH+Pjjz8W64MGDcKSJUsAALdv38b777+PrKwsKJVK+Pr6YseOHWjfvr2xN4eIiIhqgCo5yfijjz7CRx99VOr3HoSWB7Zt2/bU9X333Xf47rvvDDAzIiIikiLei4qIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkp0oCzvfffw9PT0/Y2NjAz88PSUlJZY7fvn07/Pz8YGNjgyZNmmDBggV6Y9asWYNWrVpBoVCgVatWWLdunbGmT0RERDWM0QPO6tWrMXLkSIwdOxapqakIDg5GWFgYMjIySh2fnp6O8PBwBAcHIzU1FWPGjMGIESOwZs0acUxKSgr69++PiIgIHDp0CBEREejXrx/27Nlj7M0hIiKiGsDoAWfGjBkYMmQIhg4dipYtW2LmzJlwc3PD/PnzSx2/YMECNG7cGDNnzkTLli0xdOhQvPfee/j222/FMTNnzkRISAiioqLg7e2NqKgodO/eHTNnzjT25hAREVENYGXMlRcUFODAgQOIjIzUqYeGhiI5ObnUZVJSUhAaGqpT69mzJxYvXgytVgu5XI6UlBSMGjVKb8yTAk5+fj7y8/PFx7m5uQAArVYLrVZb0c0yew96ZujeFRUV6f0Mc2Csfpor9tOwzLGfxnwuMsd+GlJF+mbUgHP9+nUUFRXByclJp+7k5ISsrKxSl8nKyip1fGFhIa5fvw4XF5cnjnnSOmNiYhAdHa1XT0hIgK2tbUU2iR6RmJho0PUdzpYBsAQAxMXFGXTdNYGh+2nu2E/DMqd+Hs0y/nOROfXTkPLy8so91qgB5wGZTKbzWBAEvdrTxj9er8g6o6KiMHr0aPFxbm4u3NzcEBoaCnt7+/JtBIm0Wi0SExMREhICuVxusPX+ffAyVp47BgAIDw832HqrO2P101yxn4Zljv28vfcifks/AcDwz0Xm2E9DenAEpjyMGnAcHBxgaWmp985Kdna23jswDzg7O5c63srKCg0aNChzzJPWqVAooFAo9OpyuZw7WCUYun+WlpY66zY33B8Ni/00LHPqZ1U8F5lTPw2pIj0z6knG1tbW8PPz03srLjExEUFBQaUuExgYqDc+ISEB/v7+4oY9acyT1klERETmxeiHqEaPHo2IiAj4+/sjMDAQCxcuREZGBoYPHw6g5PDR5cuXsXTpUgDA8OHDMXfuXIwePRrDhg1DSkoKFi9ejF9//VVc52effYZOnTph2rRpePXVV7F+/Xps2rQJO3fuNPbmEBERUQ1g9IDTv39/3LhxA5MmTUJmZiZ8fHwQFxcHd3d3AEBmZqbONXE8PT0RFxeHUaNGYd68eXB1dcXs2bPxxhtviGOCgoKwatUqjBs3DuPHj0fTpk2xevVqdOjQwdibQ0RERDVAlZxk/NFHH+Gjjz4q9XtLlizRq3Xu3BkHDx4sc519+vRBnz59DDE9IiIikhjei4qIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkhwGHiIiIJIcBh4iIiCSHAYeIiIgkx6gB59atW4iIiIBSqYRSqURERARu3779xPFarRb//ve/0aZNG9SuXRuurq545513cOXKFZ1xXbp0gUwm0/kaMGCAMTeFiIiIahCjBpyBAwciLS0N8fHxiI+PR1paGiIiIp44Pi8vDwcPHsT48eNx8OBBrF27FqdPn8Yrr7yiN3bYsGHIzMwUv3744QdjbgoRERHVIFbGWvGJEycQHx+P3bt3o0OHDgCARYsWITAwEKdOnYKXl5feMkqlEomJiTq1OXPmoH379sjIyEDjxo3Fuq2tLZydnY01fSIiIqrBjBZwUlJSoFQqxXADAAEBAVAqlUhOTi414JQmJycHMpkMdevW1amvWLECy5cvh5OTE8LCwjBhwgTY2dmVuo78/Hzk5+eLj3NzcwGUHBLTarUV3DJ60DND966oqEjvZ5gDY/XTXLGfhmWO/TTmc5E59tOQKtI3owWcrKwsODo66tUdHR2RlZVVrnXcv38fkZGRGDhwIOzt7cX6W2+9BU9PTzg7O+Po0aOIiorCoUOH9N79eSAmJgbR0dF69YSEBNja2pZzi+hxT+r3szqcLQNgCQCIi4sz6LprAkP309yxn4ZlTv08mmX85yJz6qch5eXllXtshQPOxIkTSw0Lj9q3bx8AQCaT6X1PEIRS64/TarUYMGAAiouL8f333+t8b9iwYeK/fXx80Lx5c/j7++PgwYNo166d3rqioqIwevRo8XFubi7c3NwQGhqqE5yofLRaLRITExESEgK5XG6w9f598DJWnjsGAAgPDzfYeqs7Y/XTXLGfhmWO/by99yJ+Sz8BwPDPRebYT0N6cASmPCoccD755JOnfmLJw8MDhw8fxtWrV/W+d+3aNTg5OZW5vFarRb9+/ZCeno4tW7Y8NYS0a9cOcrkcZ86cKTXgKBQKKBQKvbpcLucOVgmG7p+lpaXOus0N90fDYj8Ny5z6WRXPRebUT0OqSM8qHHAcHBzg4ODw1HGBgYHIycnB3r170b59ewDAnj17kJOTg6CgoCcu9yDcnDlzBlu3bkWDBg2e+rOOHTsGrVYLFxeX8m8IERERSZbRPibesmVL9OrVC8OGDcPu3buxe/duDBs2DL1799Y5wdjb2xvr1q0DABQWFqJPnz7Yv38/VqxYgaKiImRlZSErKwsFBQUAgHPnzmHSpEnYv38/NBoN4uLi0LdvX/j6+qJjx47G2hwiIiKqQYx6HZwVK1agTZs2CA0NRWhoKJ5//nksW7ZMZ8ypU6eQk5MDALh06RL+/PNPXLp0CS+88AJcXFzEr+TkZACAtbU1Nm/ejJ49e8LLywsjRoxAaGgoNm3apPO2IhEREZkvo32KCgDq16+P5cuXlzlGEATx3x4eHjqPS+Pm5obt27cbZH5EREQkTbwXFREREUkOAw4RERFJDgMOERERSQ4DDhEREUkOAw4RERFJDgMOERERSQ4DDhEREUkOAw4RERFJDgMOERERSQ4DDhEREUkOAw4RERFJDgMOERERSQ4DDhEREUkOAw4RERFJDgMOERERSQ4DDhEREUkOAw4RERFJDgMOERERSQ4DDhEREUkOAw4RERFJDgMOERERSQ4DDhEREUkOAw4RERFJDgMOERERSQ4DDhEREUkOAw4RERFJDgMOERERSQ4DDhEREUkOAw4RERFJDgMOERERSQ4DDlUbhcXF4r8XJ51HQWFxGaOJiIyDz0XSYNSAc+vWLURERECpVEKpVCIiIgK3b98uc5nBgwdDJpPpfAUEBOiMyc/Px6effgoHBwfUrl0br7zyCi5dumTELSFji4k7jjFrj4qPJ8eegPf4DYiJO27CWRGRuYmJO47oPx8+7/C5qOYyasAZOHAg0tLSEB8fj/j4eKSlpSEiIuKpy/Xq1QuZmZniV1xcnM73R44ciXXr1mHVqlXYuXMn7t69i969e6OoqMhYm0JGFBN3HD/sSIfwWL1YAH7Ykc4nFiKqEnwukhYrY634xIkTiI+Px+7du9GhQwcAwKJFixAYGIhTp07By8vricsqFAo4OzuX+r2cnBwsXrwYy5YtQ48ePQAAy5cvh5ubGzZt2oSePXsafmPIaAoKi7EoKb3MMYuS0vF5qDesrXhElYiMg89F0mO0gJOSkgKlUimGGwAICAiAUqlEcnJymQFn27ZtcHR0RN26ddG5c2dMmTIFjo6OAIADBw5Aq9UiNDRUHO/q6gofHx8kJyeXGnDy8/ORn58vPs7NzQUAaLVaaLXaSm+ruXnQM0P0bkmyBsWPv1x6TLEALNl1Du8GeVT651VHhuwnsZ+GZi79rKrnInPpp7FUpG9GCzhZWVliKHmUo6MjsrKynrhcWFgY+vbtC3d3d6Snp2P8+PHo1q0bDhw4AIVCgaysLFhbW6NevXo6yzk5OT1xvTExMYiOjtarJyQkwNbWtoJbRg8kJiZWeh1J6RYoz5HSpNSTcLot7beHDdFPeoj9NCyp97Oqn4uk3k9jycvLK/fYCgeciRMnlhoWHrVv3z4AgEwm0/ueIAil1h/o37+/+G8fHx/4+/vD3d0dsbGxeP3115+4XFnrjYqKwujRo8XHubm5cHNzQ2hoKOzt7cvcFtKn1WqRmJiIkJAQyOXySq3rarIGSRtOP3VcsK83wiX8Do6h+knsp6GZSz//WH4QyLr+1HGVfS4yl34ay4MjMOVR4YDzySefYMCAAWWO8fDwwOHDh3H16lW97127dg1OTk7l/nkuLi5wd3fHmTNnAADOzs4oKCjArVu3dN7Fyc7ORlBQUKnrUCgUUCgUenW5XM4drBIM0b/BHZtCHX/6qW8Nz9p8FgM6eMDeRrr/X9wfDYv9NCyp9vPAhVt4Y35yucZayEqes+QGOAdHqv00tor0rMIBx8HBAQ4ODk8dFxgYiJycHOzduxft27cHAOzZswc5OTlPDCKluXHjBi5evAgXFxcAgJ+fH+RyORITE9GvXz8AQGZmJo4ePYpvvvmmoptDJmZtZYFhwZ74YUfZJ/fdKyjG8xMT8Hq75zC9b9sy3wUkInqaW/cK4D9lE4qe9urqEcOCPXmCcQ1itP+pli1bolevXhg2bBh2796N3bt3Y9iwYejdu7fOCcbe3t5Yt24dAODu3bv44osvkJKSAo1Gg23btuHll1+Gg4MDXnvtNQCAUqnEkCFD8Pnnn2Pz5s1ITU3F22+/jTZt2oifqqKaJSq8FT7o5AmLxzKLhazkCaVTi4Zibe3By/CMisP6tMtVPEsikoLiYgEfLNsP38mJOuHmmzeeh0ateuJz0QedPBEV3qqKZ0uVYbSTjAFgxYoVGDFihPiJp1deeQVz587VGXPq1Cnk5OQAACwtLXHkyBEsXboUt2/fhouLC7p27YrVq1fDzs5OXOa7776DlZUV+vXrh7///hvdu3fHkiVLYGlpaczNISOKCm+Fz0O9sSxFgws38+Be3xYRgR7iq6Vrd/Lx4pRN4vjPVqXhs1Vp2PJ5ZzRpWMdU0yaiGmT1vgz8e80RnVqYjzPmDWwHi39SzdOei6jmMGrAqV+/PpYvX17mGEF4mKBr1aqFjRs3PnW9NjY2mDNnDubMmVPpOVL1YW1lgSHBTUr9XkM7BTRqFZLPXsfAH/eI9W7Tt8OjgS3iR3aCjZwBl4j0nczKRa+ZSTo1KwsZ9o3tgXq1rfXGl/VcRDUHIynVKEHNHKBRq/BZ9+ZiTXMjD97j4zElVtofIyeiirmXX4gXp2zSCzdrPgzC2anhpYYbkg4GHKqRRoW0wOmvw9Da9eHH/BclpcMjMhZbT2abcGZEZGqCIGDMuiNoPWEjrt15eJHXqDBvaNQq+LnXK2NpkgqjHqIiMiZrKwvEjgjGxZt5CP5mq1h/d0nJdZiSI7vBtW4tU02PiEwg/mgmhi8/qFNr71EfK4d1gJUlX9ObEwYcqvHc6ttCo1Zh47EsfLDsgFgPUm9Bu8Z1sfqDQMj5xEYkaRk38tDpP1v16rujusNZaWOCGZGp8VmfJKNna2do1Cq8E+gu1g5m3EbzsRvw/bazJpwZERlLfmERes3coRdufnmvPTRqFcONGWPAIcmZ9KoPjk/qCddHnti+iT8Fj8hY7NPcNOHMiMiQpiecgte4eJzMuiPWPujcBBq1Cp0fuX4WmSceoiJJsrW2QnJUd5y+egeh3+0Q630XpAAADo4PQX1+goKoRtp55jreXrxHp9a0YW3Ejgjm5SJIxIBDktbCyQ4atQq/7b+If/1+WKy3m5yIkFZO+OFtP/ECX0RUvWXn3kf7qZv16lu/6AJPh9ommBFVZzxERWahr78b0mPC0ft5F7GWePwqmoyJw697M0w4MyJ6mqJiAW/9uFsv3Mx50xcatYrhhkrFgENmQyaTYe7Adkj7KkTnsutRa4/AIzIWx6/kmnB2RFSan3amo+mYOOw6e0Os9f/nBcvLbV1NODOq7niIisxOXVtrnP46DKkZt/Da98liPXx2EhrUtsb2L7uijoK/GkSmlHbxNv5v3i6dWj1bOXZ82RV2NnITzYpqEj6Lk9nybVwPGrUKPyadx9exJwAAN+4VwGfCRvT3d4P6jTaQyXh+DlFVysnTov3UTcgvLNapx454Ca1dlSaaFdVEPERFZm9ocBOcnRKGwCYNxNrq/RfhGRWH2MOZJpwZkfkQBAGfrDyItpMSdMLNlNd8oFGrGG6owvgODhEAK0sL/Pp+AK7m3keHR05k/HjlQXy8Etj+ry5wb8ATGYmMYc2BS/j8t0M6tR4tHbEwwp+fcqRnxoBD9Agnexto1CoknbmGiMV7xXrn/2xDc8c6+OvTl3idDSIDOXP1DkIeuU7VAwfG9UCDOgoTzIikhIeoiEoR3LwhNGoVPu7aVKydyb4L7/Hx+Cb+pAlnRlTz5RUUIihms164+e8HgdCoVQw3ZBAMOERl+FdPb5z6uhe8nOzE2vfbzsEjMhY7Tl8z4cyIaqaJfx5Dq6824krOfbH2r55e0KhVaO9Z34QzI6nhISqip1BYWWLjqE64cOMeOv9nm1h/56eSQ1h7xnSHkz1v6EdUlsTjVzFs6X6dmm/juvjvB4GQW/K1NhkeAw5RObk3qA2NWoW4I5n4aMVBsd5h6mZ08KyPFUM7wIpP1EQ6Lt3Kw0vTturVkyO7wbVuLRPMiMwFn42JKii8jQvSY8LxZns3sbYn/Saajd2ARTvOm3BmRNVHQWExXp6zUy/c/DTYHxq1iuGGjI4Bh+gZyGQyxLz+PI5G94TDIydETok7AY/IWBzMuGXC2RGZ1uzNZ9Bi3AYcuZwj1t7r6AmNWoVu3k4mnBmZEx6iIqqEOgor7B/XAycycxE2K0msv/59MuSWMuwb2wN1ba1NOEOiqpNy7gbeXLRbp+bewBYbR3bi5RWoyjHgEBlASxd7aNQqrNyTgTHrjgAAtEUCXpiUCFUbF8wd6MvbPpBkXb+bD/+vN+nVN43ujGaOdUwwIyIeoiIyqIEdGuP81HD0bP3wbfjYI5nwjIrDf/dfNOHMiAyvuFjA4J/36oWbmf1fgEatYrghk2LAITIwCwsZfojwx8HxIXj0TZsvfz8Mj8hYnMq6Y7rJERnI0hQNmoyJw7ZTD68H9brvc0iPCcf/+T5nwpkRleAhKiIjqV/bGukxKuzX3ESfBSlivefMHXC2t8GWLzrD1pq/glSzHL2cg95zdurU6iissCuyG5S15CaaFZE+PrsSGZm/R31o1CrM33YO0/65zUNW7n20+moj3g5ojK/CvUw8Q6Knu3Nfi85fb8Gd/EKd+p+fdMTzjeqaZlJEZeAhKqIq8mGXpjgzJQz+7vXE2vLdGWjxVSIO3eAJyFQ9CYKA5Wcs0G7KVp1wE/1Ka2jUKoYbqrYYcIiqkNzSAr9/GISUqG469Z9OW6L5+ARcvJlnopkR6VufdhktvkrEvusP/1R0atEQ56aGY1CQh+kmRlQOPERFZAIuylrQqFXYeiob7/68T6wHf7MVLV3ssf7jjrC24usPMo1z1+6i+/TtevV9Y3ugoR3v9E01A59BiUyoq5cjzkwORTeXYrF2IjMXLcZtwIzE0yacGZmj+9oidP7PVr1w80mrIpyZHMpwQzWKUQPOrVu3EBERAaVSCaVSiYiICNy+fbvMZWQyWalf//nPf8QxXbp00fv+gAEDjLkpREb1qkcxjnzVHZ4OtcXa7M1n4BEZi+Sz1004MzIXX//vOLzHx+PCjYeHST/r3hxnJoeiuVIw4cyIno1RD1ENHDgQly5dQnx8PADg/fffR0REBP76668nLpOZmanzeMOGDRgyZAjeeOMNnfqwYcMwadIk8XGtWrxxG9VsNnJLbP2iC85fu4tuj7yCHvjjHgDA3rHd4WhnY6rpkURtPZmNd5fs06m1eU6JNR8GwdrKAlqt1kQzI6ocowWcEydOID4+Hrt370aHDh0AAIsWLUJgYCBOnToFL6/SPxrr7Oys83j9+vXo2rUrmjRpolO3tbXVG0skBU0a1oFGrcL6tMv4bFWaWG8/ZTOCmztgybvtYWnBT11R5Vy5/TeC1Fv06klfdoVbfVsTzIjIsIwWcFJSUqBUKsVwAwABAQFQKpVITk5+YsB51NWrVxEbG4tffvlF73srVqzA8uXL4eTkhLCwMEyYMAF2dnalric/Px/5+fni49zcXACAVqvlq5Nn8KBn7J1hPKmf4a0dETYpBJHrjmFt6hUAQNKZ62g6Jg7jwr0wKNC9yudaE3D/LJu2qBhvLd6H1Is5OvX5A19Aj5aOJWMe6R37aVjsZ+VUpG9GCzhZWVlwdHTUqzs6OiIrK6tc6/jll19gZ2eH119/Xaf+1ltvwdPTE87Ozjh69CiioqJw6NAhJCYmlrqemJgYREdH69UTEhJga8tXKs/qSf2mZ/Okfna2ATq8CEQftEReUck7N1/HncLXcafweZtCNObtfkrF/VNf4mUZ/pehe1fvYOdi9PEsRkH6fsSll7Es+2lQ7Oezycsr/6U0KhxwJk6cWGpYeNS+fSXHc0u7e7IgCOW+q/JPP/2Et956CzY2uucdDBs2TPy3j48PmjdvDn9/fxw8eBDt2rXTW09UVBRGjx4tPs7NzYWbmxtCQ0Nhb29frrnQQ1qtFomJiQgJCYFczkuzV1Z5+/n6K8CxK7n4v/m7xdr0I1awtbZE0hedYM/L5APg/lma/Rdu4c0fdc+zcVHaIH5E0FNvF8J+Ghb7WTkPjsCUR4UDzieffPLUTyx5eHjg8OHDuHr1qt73rl27Bicnp1KW0pWUlIRTp05h9erVTx3brl07yOVynDlzptSAo1AooFDof7xRLpdzB6sE9s+wytPPF9wbQKNWYVmKBuPXHwMA5BUUwW/qVrzS1hWzBrxQ7hcQUsf9E7h5rwDtJuu/U5AwqhNaOJV+SP9J2E/DYj+fTUV6VuGA4+DgAAcHh6eOCwwMRE5ODvbu3Yv27dsDAPbs2YOcnBwEBQU9dfnFixfDz88Pbdu2ferYY8eOQavVwsXF5ekbQCQBEYEeeKuDO4Yu3Y8tJ7MBAH8euoI/D13Bd/3b4jXfRiaeIZlScbGAD5YfQOJx3ReZ3/Ztiz5+3DfIPBjtOjgtW7ZEr169MGzYMOzevRu7d+/GsGHD0Lt3b50TjL29vbFu3TqdZXNzc/Hbb79h6NCheus9d+4cJk2ahP3790Oj0SAuLg59+/aFr68vOnbsaKzNIap2LCxk+Gnwi9g/rodOfdTqQ/CIjMXZ7DsmmhmZ0so9GWgyJk4n3PR+3gXpMeEMN2RWjHodnBUrVmDEiBEIDQ0FALzyyiuYO3euzphTp04hJ0f3bP5Vq1ZBEAS8+eabeuu0trbG5s2bMWvWLNy9exdubm5QqVSYMGECLC0t9cYTSZ1DHQU0ahX2nL+B/gsfnp/TY8YOuNWvhYSRnVHLmr8bUnf8Si7CZyfp1BRWFtg7pgeUtjwUQubHqAGnfv36WL58eZljBEH/Cpnvv/8+3n///VLHu7m5Yft2/XukEJm7Dk1Kzs+Zs/kMpv9zm4eLN/9Gy6/i8W5HD0x4ubWJZ0jGcDe/EJ2+2Yqb9wp06n983BEvuNU1zaSIqgHei4pIYj7t3hynvw5D20ZKsfbzLg08ImOx+YT+if9UMwmCgH//fhg+EzbqhJtxqpbQqFUMN2T2eDdxIgmytrLA+k9ewqVbeXhp2laxPuSX/QCAXZHd8Fxd3t6kpoo9nImPVx7UqQU1bYCl77WHlSVftxIBDDhEktaoni00ahU2Hb+KoUv3i/WO6i1o20iJ3z8Mgpx/EGsMzfV76PLtNr363jHd4WjP+5QRPYrPbERmoEcrJ2jUKgwO8hBrhy7loPnYDZi75YzpJkblcl9bhB4ztuuFm+VDOkCjVjHcEJWCAYfIjEx8pTVOTOqlc3jq24TT8IiMxZ7zN0w4M3qSafEn4T0+Hmez74q1T7o2g0atwkvNn35NMiJzxUNURGamlrUldkV2w9nsO+gxY4dYf/AR8wPjeqBBHf0rf1PV2nH6Gt75aa9OzdvZDus/6QiFFT/2T/Q0DDhEZqqZox00ahXWHryE0f89JNb9vt6Ebt6O+PEdf1hY8LYPVe1q7n10mLpZr779X13g3qC2CWZEVDPxEBWRmXu9XSOkx4TjlbauYm3LyWw0GROHZbsvmHBm5qWwqBj9f0jRCzfz32oHjVrFcENUQQw4RASZTIbZb/ri0FehqCV/ePhj/B9H4REZi6OXc8pYmipr4Y5zaDZ2A/ak3xRrAzs0RnpMOMLa8B57RM+Ch6iISKS0lePE5F44dPE2Xp23S6z3nrMTylpy7Px3V9jZ8LL/hnIw4xZe/z5Zp+ZQR4Ht/+qC2go+PRNVBn+DiEhPW7e60KhV+GlnOib97zgAIOdvLdpMTMAb7Rrh277PQybj+TnP6nZeAV6csgnaIt1b1Wz4LBgtXexNNCsiaeEhKiJ6ovde8sS5qeEIfuTjyGsOXoJnVBz+PHTFhDOrmQRBwMcrDuKFSYk64Ub9ehto1CqGGyIDYsAhojJZWsiwbEgH7B3bXac+4tdUeETGIv36PRPNrGb5776L8IyKQ+yRTLHWs7UTzk8Nx4D2jU04MyJp4iEqIioXRzsbaNQq7Dp7HW/9uEesd/12GzwdamPDZ8GwkfP6LI87lXUHPWfu0KlZyID940JQv7a1iWZFJH18B4eIKqRjMwdo1CqM6NZMrKVfvwfv8fGIiTthwplVL/fyC9Fh6ia9cLPmw0Ccj1Ex3BAZGQMOET2T0aFeOPV1L53zRn7YcR4ekbHYdirbhDMzLUEQMO6PI2g9YSOu5uaL9X/38oZGrYKfe30Tzo7IfPAQFRE9M4WVJTZ8FoyLN/MQ/M1WsT74530AgN1R3eGsNJ8bQcYfzcLw5Qd0ai961MOvwwJgxbu2E1UpBhwiqjS3+rbQqFV6f+ADYjbD370eVr0v7T/wjwe8B1KiusFFWauUJYjI2KT7jENEVa6XjzPSY8LxdsDDTwXtv3ALzcZuwILt50w4M+MoKCxG2KwkvXDz87svQqNWMdwQmRADDhEZlEwmw9f/1wbHonvCyf7hXcnVG07CIzIW+zU3y1i65piReBotxm3AicxcsfZBpybQqFXo6uVowpkREcBDVERkJLUVVtgzpofex6T7LEiBTAYcHBeCejXwk0TJZ69j4CMfkweAJg1rI24EPyZPVJ0w4BCRUXk520GjVuG/+y7iyzWHAQCCAPhOTkTP1k6Y/5YfLCyq/20fsu/cR/spm/XqWz7vjCYN65hgRkRUFh6iIqIq0e9Ft5K7Y/s4i7WNx66iyZg4/Lo3w4QzK1tRsYC3f9yjF27mvOkLjVrFcENUTTHgEFGVkclkmP+2H9K+CoHc8uG7NlFrj8AjMlbnfJbq4Odd6Wg6Jg47z14Xa339GiE9Jhwvt3U14cyI6Gl4iIqIqlxdW2ucmRKOAxdu4Y35yWI9bFYSHOpYY9u/uqKOwnRPT4cu3sar83bp1JS15Nj5766ws5GbaFZEVBEMOERkMn7u9aBRq7BwxzlMjTsJALh+twA+EzbizfZumPpaG8hkVXd+Ts7fWgTGbEZeQZFO/X+fvgSf55RVNg8iqjweoiIik3u/U1OcnRKGDp4Pb2Pw696Su29veOTu28YiCAJG/JqKttEJOuFm8v/5QKNWMdwQ1UB8B4eIqgUrSwus/iAQWTn3ERDz8ITeD1ccBABs/1cXuDeobfCfu/bgJYz+7yGdWjdvR/z4jn+N+HQXEZWOAYeIqhVnpQ00ahW2n76GQT/tFeud/7MNLZzq4K9PX4LCqvLXmzmbfQc9ZuzQqx8Y1wMN6ihKWYKIahIeoiKiaqlzi4bQqFX4sEtTsXb66l14jYvHfzaefOb1/l1QhI7qLXrhZvX7AdCoVQw3RBLBgENE1dq/e3nj5OReaOb48Hoz87aeg0dkLHaeua4ztqCwGD8na/B7ugV+TtagoLBY5/vRfx1Dy6/icfn232Lt85AW0KhV6NCkgXE3hIiqlFEDzpQpUxAUFARbW1vUrVu3XMsIgoCJEyfC1dUVtWrVQpcuXXDs2DGdMfn5+fj000/h4OCA2rVr45VXXsGlS5eMsAVEVB3YyC2xaXRnbPuii0797cV74BEZi+zc+4iJOw7v8RswdcNpJGVZYOqG0/AevwExccex+cRVeETG4uddGnHZtm51cWZKGD7t3rxqN4aIqoRRA05BQQH69u2LDz/8sNzLfPPNN5gxYwbmzp2Lffv2wdnZGSEhIbhz5444ZuTIkVi3bh1WrVqFnTt34u7du+jduzeKiorKWDMR1XQeDrWhUaswd6CvTr391M34YUc6igXd8cUC8MOOdAz5Zb9OfVdkN6z/uCPklnwTm0iqjPrbHR0djVGjRqFNmzblGi8IAmbOnImxY8fi9ddfh4+PD3755Rfk5eVh5cqVAICcnBwsXrwY06dPR48ePeDr64vly5fjyJEj2LRpkzE3h4iqid7PuyI9Jhz9/d0qtNyP7/hDo1bhubq1jDQzIqouqtWnqNLT05GVlYXQ0FCxplAo0LlzZyQnJ+ODDz7AgQMHoNVqdca4urrCx8cHycnJ6Nmzp9568/PzkZ+fLz7OzS25HLxWq4VWqzXiFknTg56xd4bBfj67r19tCbe6Cny76exTx44Ja4HOzeuzzxXE/dOw2M/KqUjfqlXAycrKAgA4OTnp1J2cnHDhwgVxjLW1NerVq6c35sHyj4uJiUF0dLRePSEhAba2toaYullKTEw09RQkhf18NnvSLVCeN6OTUk/C6fZx409Iorh/Ghb7+Wzy8vLKPbbCAWfixImlhoVH7du3D/7+/hVdtejxS7MLgvDUy7WXNSYqKgqjR48WH+fm5sLNzQ2hoaGwt7d/5nmaK61Wi8TERISEhEAu5315Kov9rJyryRokbTj91HHBvt4ID/Iw/oQkhvunYbGflfPgCEx5VDjgfPLJJxgwYECZYzw8PCq6WgCAs7MzgJJ3aVxcXMR6dna2+K6Os7MzCgoKcOvWLZ13cbKzsxEUFFTqehUKBRQK/WtbyOVy7mCVwP4ZFvv5bAZ3bAp1/Gm9E4wfZSErGSe34knFz4r7p2Gxn8+mIj2r8G+7g4MDvL29y/yysbGp6GoBAJ6ennB2dtZ5666goADbt28Xw4ufnx/kcrnOmMzMTBw9evSJAYeIpMvaygLDgj3LHDMs2BPWDDdEZsWo5+BkZGTg5s2byMjIQFFREdLS0gAAzZo1Q506JRft8vb2RkxMDF577TXIZDKMHDkSU6dORfPmzdG8eXNMnToVtra2GDhwIABAqVRiyJAh+Pzzz9GgQQPUr18fX3zxBdq0aYMePXoYc3OIqJqKCm8FAFiUpPtRcQtZSbh58H0iMh9GDThfffUVfvnlF/Gxr2/JtSu2bt2KLl26AABOnTqFnJwcccyXX36Jv//+Gx999BFu3bqFDh06ICEhAXZ2duKY7777DlZWVujXrx/+/vtvdO/eHUuWLIGlZeXvT0NENVNUeCt8HuqNJbvOISn1JIJ9vTG4Y1O+c0NkpowacJYsWYIlS5aUOUYQdA+cy2QyTJw4ERMnTnziMjY2NpgzZw7mzJljgFkSkVRYW1ng3SAPON0+jvAgD55zQ2TG+NtPREREksOAQ0RERJLDgENERESSw4BDREREksOAQ0RERJLDgENERESSw4BDREREksOAQ0RERJLDgENERESSY9QrGVdXD66eXJHbrtNDWq0WeXl5yM3N5d1wDYD9NCz207DYT8NiPyvnwd/tx++CUBqzDDh37twBALi5uZl4JkRERFRRd+7cgVKpLHOMTChPDJKY4uJiXLlyBXZ2dpDJZKaeTo2Tm5sLNzc3XLx4Efb29qaeTo3HfhoW+2lY7KdhsZ+VIwgC7ty5A1dXV1hYlH2WjVm+g2NhYYFGjRqZeho1nr29PX9BDYj9NCz207DYT8NiP5/d0965eYAnGRMREZHkMOAQERGR5DDgUIUpFApMmDABCoXC1FORBPbTsNhPw2I/DYv9rDpmeZIxERERSRvfwSEiIiLJYcAhIiIiyWHAISIiIslhwCEiIiLJYcChcpkyZQqCgoJga2uLunXrlmsZQRAwceJEuLq6olatWujSpQuOHTtm3InWELdu3UJERASUSiWUSiUiIiJw+/btMpcZPHgwZDKZzldAQEDVTLia+f777+Hp6QkbGxv4+fkhKSmpzPHbt2+Hn58fbGxs0KRJEyxYsKCKZlozVKSf27Zt09sPZTIZTp48WYUzrr527NiBl19+Ga6urpDJZPjjjz+eugz3T+NgwKFyKSgoQN++ffHhhx+We5lvvvkGM2bMwNy5c7Fv3z44OzsjJCREvBeYORs4cCDS0tIQHx+P+Ph4pKWlISIi4qnL9erVC5mZmeJXXFxcFcy2elm9ejVGjhyJsWPHIjU1FcHBwQgLC0NGRkap49PT0xEeHo7g4GCkpqZizJgxGDFiBNasWVPFM6+eKtrPB06dOqWzLzZv3ryKZly93bt3D23btsXcuXPLNZ77pxEJRBXw888/C0ql8qnjiouLBWdnZ0GtVou1+/fvC0qlUliwYIERZ1j9HT9+XAAg7N69W6ylpKQIAISTJ08+cblBgwYJr776ahXMsHpr3769MHz4cJ2at7e3EBkZWer4L7/8UvD29tapffDBB0JAQIDR5liTVLSfW7duFQAIt27dqoLZ1WwAhHXr1pU5hvun8fAdHDKK9PR0ZGVlITQ0VKwpFAp07twZycnJJpyZ6aWkpECpVKJDhw5iLSAgAEql8qm92bZtGxwdHdGiRQsMGzYM2dnZxp5utVJQUIADBw7o7FcAEBoa+sTepaSk6I3v2bMn9u/fD61Wa7S51gTP0s8HfH194eLigu7du2Pr1q3GnKakcf80HgYcMoqsrCwAgJOTk07dyclJ/J65ysrKgqOjo17d0dGxzN6EhYVhxYoV2LJlC6ZPn459+/ahW7duyM/PN+Z0q5Xr16+jqKioQvtVVlZWqeMLCwtx/fp1o821JniWfrq4uGDhwoVYs2YN1q5dCy8vL3Tv3h07duyoiilLDvdP4zHLu4lTiYkTJyI6OrrMMfv27YO/v/8z/wyZTKbzWBAEvZpUlLefgH5fgKf3pn///uK/fXx84O/vD3d3d8TGxuL1119/xlnXTBXdr0obX1rdXFWkn15eXvDy8hIfBwYG4uLFi/j222/RqVMno85Tqrh/GgcDjhn75JNPMGDAgDLHeHh4PNO6nZ2dAZS8OnFxcRHr2dnZeq9WpKK8/Tx8+DCuXr2q971r165VqDcuLi5wd3fHmTNnKjzXmsrBwQGWlpZ67y6UtV85OzuXOt7KygoNGjQw2lxrgmfpZ2kCAgKwfPlyQ0/PLHD/NB4GHDPm4OAABwcHo6zb09MTzs7OSExMhK+vL4CS4/3bt2/HtGnTjPIzTa28/QwMDEROTg727t2L9u3bAwD27NmDnJwcBAUFlfvn3bhxAxcvXtQJkFJnbW0NPz8/JCYm4rXXXhPriYmJePXVV0tdJjAwEH/99ZdOLSEhAf7+/pDL5Uadb3X3LP0sTWpqqlnth4bE/dOITHmGM9UcFy5cEFJTU4Xo6GihTp06QmpqqpCamircuXNHHOPl5SWsXbtWfKxWqwWlUimsXbtWOHLkiPDmm28KLi4uQm5urik2oVrp1auX8PzzzwspKSlCSkqK0KZNG6F37946Yx7t5507d4TPP/9cSE5OFtLT04WtW7cKgYGBwnPPPWd2/Vy1apUgl8uFxYsXC8ePHxdGjhwp1K5dW9BoNIIgCEJkZKQQEREhjj9//rxga2srjBo1Sjh+/LiwePFiQS6XC7///rupNqFaqWg/v/vuO2HdunXC6dOnhaNHjwqRkZECAGHNmjWm2oRq5c6dO+LzIwBhxowZQmpqqnDhwgVBELh/ViUGHCqXQYMGCQD0vrZu3SqOASD8/PPP4uPi4mJhwoQJgrOzs6BQKIROnToJR44cqfrJV0M3btwQ3nrrLcHOzk6ws7MT3nrrLb2P3T7az7y8PCE0NFRo2LChIJfLhcaNGwuDBg0SMjIyqn7y1cC8efMEd3d3wdraWmjXrp2wfft28XuDBg0SOnfurDN+27Ztgq+vr2BtbS14eHgI8+fPr+IZV28V6ee0adOEpk2bCjY2NkK9evWEl156SYiNjTXBrKunBx+jf/xr0KBBgiBw/6xKMkH452wmIiIiIongx8SJiIhIchhwiIiISHIYcIiIiEhyGHCIiIhIchhwiIiISHIYcIiIiEhyGHCIiIhIchhwiIiISHIYcIiIiEhyGHCIiIhIchhwiIiISHIYcIiIiEhy/h9swGStPmxf4AAAAABJRU5ErkJggg==", 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" ] diff --git a/docs/notebooks/04_sdc.ipynb b/docs/notebooks/04_sdc.ipynb index 8bc7bab..6ff5521 100644 --- a/docs/notebooks/04_sdc.ipynb +++ b/docs/notebooks/04_sdc.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -240,7 +240,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -270,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { diff --git a/docs/notebooks/05_residuals.ipynb b/docs/notebooks/05_residuals.ipynb index 776210b..193a78e 100644 --- a/docs/notebooks/05_residuals.ipynb +++ b/docs/notebooks/05_residuals.ipynb @@ -165,7 +165,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -202,12 +202,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -261,7 +261,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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" ] @@ -183,7 +183,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -338,7 +338,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -547,22 +547,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" + "name": "python" } }, "nbformat": 4, diff --git a/qmat/playgrounds/tibo/test.py b/qmat/playgrounds/tibo/test.py index 99562b8..09a1dd3 100644 --- a/qmat/playgrounds/tibo/test.py +++ b/qmat/playgrounds/tibo/test.py @@ -12,7 +12,7 @@ from qmat import genQCoeffs, QDELTA_GENERATORS from qmat.qcoeff.collocation import Collocation -from qmat.solvers.generic import LinearMultiNode +from qmat.solvers.generic import CoeffSolver from qmat.solvers.generic.diffops import Dahlquist, Lorenz, ProtheroRobinson from qmat.solvers.generic.integrators import ForwardEuler, BackwardEuler @@ -23,11 +23,10 @@ nSteps = nPeriod*1000 tEnd = nPeriod*np.pi -corr = "FE" -useSDC = False -useWeights = False -nSweeps = 4 - +corr = "BE" +useSDC = True +useWeights = True +nSweeps = 1 if pType == "Dahlquist": diffOp = Dahlquist() @@ -39,11 +38,11 @@ nDOF = diffOp.u0.size nodes, weights, Q = genQCoeffs(corr) -coll = Collocation(nNodes=4, nodeType="LEGENDRE", quadType="RADAU-RIGHT") +coll = Collocation(nNodes=2, nodeType="LEGENDRE", quadType="RADAU-RIGHT") gen = QDELTA_GENERATORS[corr](qGen=coll) QDelta = gen.genCoeffs(k=[i+1 for i in range(nSweeps)]) -prob = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) +prob = CoeffSolver(diffOp, tEnd=tEnd, nSteps=nSteps) Solver = BackwardEuler if corr == "BE" else ForwardEuler if useSDC: solver = Solver(diffOp, nodes=coll.nodes, tEnd=tEnd, nSteps=nSteps) diff --git a/qmat/solvers/dahlquist.py b/qmat/solvers/dahlquist.py index b1b1433..4f24d8a 100644 --- a/qmat/solvers/dahlquist.py +++ b/qmat/solvers/dahlquist.py @@ -31,7 +31,7 @@ def checkCoeff(Q, weights): if weights is not None: weights = np.asarray(weights) - assert weights.ndim == 1, "weights must be a 1D vector" + assert weights.ndim == 1, f"weights must be a 1D vector, not {weights}" assert weights.size == nNodes, "weights size is not the same as the node size" else: assert np.allclose(Q.sum(axis=1)[-1], 1), "last node must be 1 if weights are not given" diff --git a/qmat/solvers/generic/__init__.py b/qmat/solvers/generic/__init__.py index 71b2e37..c5d0868 100644 --- a/qmat/solvers/generic/__init__.py +++ b/qmat/solvers/generic/__init__.py @@ -6,6 +6,7 @@ import numpy as np import scipy.optimize as sco from scipy.linalg import blas +import warnings from qmat.solvers.dahlquist import Dahlquist from qmat.lagrange import LagrangeApproximation @@ -87,11 +88,13 @@ def test(cls, t0=0, dt=1e-1, eps=1e-3, instance=None): atol=1e-15) # check for nan acceptation - uSolve[:] = np.nan - instance.fSolve(a=dt, rhs=uEval, t=t0, out=uSolve) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + uSolve[:] = np.nan + instance.fSolve(a=dt, rhs=uEval, t=t0, out=uSolve) -class LinearMultiNode(): +class CoeffSolver(): def __init__(self, diffOp:DiffOp, tEnd=1, nSteps=1, t0=0): assert isinstance(diffOp, DiffOp) @@ -253,7 +256,7 @@ def solveSDC(self, nSweeps, Q, weights, QDelta, uNum=None): return uNum -class GenericMultiNode(LinearMultiNode): +class PhiSolver(CoeffSolver): def __init__(self, diffOp:DiffOp, nodes, tEnd=1, nSteps=1, t0=0): super().__init__(diffOp, tEnd, nSteps, t0) @@ -280,7 +283,7 @@ def func(u:np.ndarray): res -= rhs return res.ravel() - sol = self.innerSolver(func, out.ravel()).reshape(self.uShape) + sol = self.diffOp.innerSolver(func, out.ravel()).reshape(self.uShape) np.copyto(out, sol) @@ -324,19 +327,17 @@ def solve(self, uNum=None): def solveSDC(self, nSweeps, Q=None, weights=None, uNum=None): - if Q is None: + if Q is None or weights is True: approx = LagrangeApproximation(self.nodes) + if Q is None: Q = approx.getIntegrationMatrix([(0, tau) for tau in self.nodes]) - if weights is True: - weights = approx.getIntegrationMatrix([(0, 1)]).ravel() - else: - weights = None - else: - nNodes, Q, weights = Dahlquist.checkCoeff(Q, weights) - - assert nNodes == self.nNodes, "solver and Q do not have the same number of nodes" - assert np.allclose(Q.sum(axis=1), self.nodes), "solver and Q do not have the same nodes" - + if weights is True: + weights = approx.getIntegrationMatrix([(0, 1)]).ravel() + if weights is False: + weights = None + nNodes, Q, weights = Dahlquist.checkCoeff(Q, weights) + assert nNodes == self.nNodes, "solver and Q do not have the same number of nodes" + assert np.allclose(Q.sum(axis=1), self.nodes), "solver and Q do not have the same nodes" Q = self.dt*Q if weights is not None: weights = self.dt*weights @@ -361,7 +362,7 @@ def solveSDC(self, nSweeps, Q=None, weights=None, uNum=None): # copy initialization np.copyto(uNodes[0], uNum[i]) - self.evalF(uNum[i], self.t0, out=fEvals[0][0]) + self.evalF(uNum[i], times[i], out=fEvals[0][0]) np.copyto(fEvals[1][0], fEvals[0][0]) # u_0^{1} = u_0^{0} for m in range(self.nNodes): np.copyto(fEvals[0][m+1], fEvals[0][0]) # u_m^{k} = u_0^{0} diff --git a/qmat/solvers/generic/integrators.py b/qmat/solvers/generic/integrators.py index 0606e4e..5fff89a 100644 --- a/qmat/solvers/generic/integrators.py +++ b/qmat/solvers/generic/integrators.py @@ -1,13 +1,13 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Specialized implementations of GenericMultiNode solvers +Specialized PhiSolver classes implementations """ import numpy as np -from qmat.solvers.generic import GenericMultiNode +from qmat.solvers.generic import PhiSolver -class ForwardEuler(GenericMultiNode): +class ForwardEuler(PhiSolver): def evalPhi(self, uVals, fEvals, out, t0=0): m = len(uVals) - 1 @@ -27,7 +27,7 @@ def phiSolve(self, uPrev, fEvals, out, rhs=0, t0=0): out += rhs -class BackwardEuler(GenericMultiNode): +class BackwardEuler(PhiSolver): def evalPhi(self, uVals, fEvals, out, t0=0): m = len(uVals) - 1 diff --git a/qmat/utils.py b/qmat/utils.py index ff5fa88..5d91508 100644 --- a/qmat/utils.py +++ b/qmat/utils.py @@ -70,7 +70,7 @@ def getClasses(dico, module=None): def useQGen(__init__): r""" - Wrapper to extract :math:`Q_\Delta`-generator parameters from `kwargs` argument, + Wrapper to extract :math:`Q_\Delta`-generator parameters from `kwargs` arguments, using either a :math:`Q`-generator `qGen` or separately given parameters. """ pNames = [p.name for p in inspect.signature(__init__).parameters.values() diff --git a/test.sh b/test.sh index 0ac47a3..804ffa7 100755 --- a/test.sh +++ b/test.sh @@ -1,4 +1,5 @@ #!/bin/bash echo "print('Loading sitecustomize.py...');import coverage;coverage.process_startup()" > sitecustomize.py -coverage run -m pytest --continue-on-collection-errors -v --durations=0 ./tests \ No newline at end of file +coverage run -m pytest --continue-on-collection-errors -v --durations=0 ./tests +rm -f sitecustomize.py \ No newline at end of file diff --git a/tests/test_solvers/test_generic.py b/tests/test_solvers/test_generic.py index 26711b8..deed3ff 100644 --- a/tests/test_solvers/test_generic.py +++ b/tests/test_solvers/test_generic.py @@ -6,7 +6,7 @@ from qmat.mathutils import numericalOrder from qmat.solvers.sdc import solveDahlquistSDC -from qmat.solvers.generic import LinearMultiNode +from qmat.solvers.generic import CoeffSolver from qmat.solvers.generic.diffops import Dahlquist, Lorenz, ProtheroRobinson @@ -15,9 +15,9 @@ @pytest.mark.parametrize("tEnd", [1, 5]) @pytest.mark.parametrize("scheme", ["BE", "FE", "TRAP", "RK4", "DIRK43", "ARK443ESDIRK", "ARK443ERK"]) -def testLinearMultiNodeDahlquist(scheme, tEnd, nSteps, lam): +def testLinearCoeffSolverDahlquist(scheme, tEnd, nSteps, lam): diffOp = Dahlquist(lam=lam) - solver = LinearMultiNode(diffOp=diffOp, tEnd=tEnd, nSteps=nSteps) + solver = CoeffSolver(diffOp=diffOp, tEnd=tEnd, nSteps=nSteps) qGen = Q_GENERATORS[scheme].getInstance() @@ -27,14 +27,14 @@ def testLinearMultiNodeDahlquist(scheme, tEnd, nSteps, lam): uNum = uNum[:, 0] + 1j*uNum[:, 1] assert np.allclose(uNum, uRef), \ - "LinearMultiNode does not match reference solver for Dahlquist" + "generic CoeffSolver does not match reference solver for Dahlquist" if scheme.startswith("ARK443"): uNum = solver.solve(Q=qGen.Q, weights=None) uNum = uNum[:, 0] + 1j*uNum[:, 1] assert np.allclose(uNum, uRef), \ - "LinearMultiNode without weights does not match reference solver for Dahlquist" + "generic CoeffSolver without weights does not match reference solver for Dahlquist" @pytest.mark.parametrize("quadType", QUAD_TYPES) @@ -44,13 +44,13 @@ def testLinearMultiNodeDahlquist(scheme, tEnd, nSteps, lam): @pytest.mark.parametrize("nSteps", [1, 2]) @pytest.mark.parametrize("tEnd", [1, 5]) @pytest.mark.parametrize("scheme", ["BE", "FE", "TRAP", "MIN-SR-FLEX"]) -def testLinearMultiNodeDahlquistSDC( +def testLinearCoeffSolverDahlquistSDC( scheme, tEnd, nSteps, lam, nNodes, nSweeps, quadType): if nNodes == 1 and quadType != "GAUSS": return diffOp = Dahlquist(lam=lam) - solver = LinearMultiNode(diffOp=diffOp, tEnd=tEnd, nSteps=nSteps) + solver = CoeffSolver(diffOp=diffOp, tEnd=tEnd, nSteps=nSteps) coll = Q_GENERATORS["Collocation"]( nNodes=nNodes, quadType=quadType, nodeType="LEGENDRE") @@ -75,7 +75,7 @@ def testLinearMultiNodeDahlquistSDC( details = " with weigths " if weights is not None else "" assert np.allclose(uNum, uRef), \ - f"LinearMultiNode SDC {details} does not match reference solver for Dahlquist" + f"generic CoeffSolver SDC {details} does not match reference solver for Dahlquist" @pytest.fixture(scope="session") @@ -83,13 +83,13 @@ def uRefLorentz(): diffOp = Lorenz() tEnd = 0.1 qGenRef = Q_GENERATORS["RK4"].getInstance() - uRef = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=10000).solve( + uRef = CoeffSolver(diffOp, tEnd=tEnd, nSteps=10000).solve( qGenRef.Q, qGenRef.weights) return {"tEnd": tEnd, "sol": uRef, "diffOp": diffOp} @pytest.mark.parametrize("scheme", ["BE", "FE", "TRAP", "RK4", "DIRK43"]) -def testLinearMultiNodeLorenz(scheme, uRefLorentz): +def testLinearCoeffSolverLorenz(scheme, uRefLorentz): diffOp = uRefLorentz["diffOp"] uRef = uRefLorentz["sol"] tEnd = uRefLorentz["tEnd"] @@ -98,7 +98,7 @@ def testLinearMultiNodeLorenz(scheme, uRefLorentz): err = [] qGen = Q_GENERATORS[scheme].getInstance() for nSteps in nStepsVals: - solver = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) + solver = CoeffSolver(diffOp, tEnd=tEnd, nSteps=nSteps) uNum = solver.solve(qGen.Q, qGen.weights) err.append(np.linalg.norm(uNum[-1] - uRef[-1])) @@ -114,7 +114,7 @@ def testLinearMultiNodeLorenz(scheme, uRefLorentz): @pytest.mark.parametrize("nSweeps", [1, 2]) @pytest.mark.parametrize("nNodes", [3, 4]) @pytest.mark.parametrize("scheme", ["BE", "FE", "LU"]) -def testLinearMultiNodeLorenzSDC(scheme, nNodes, nSweeps, quadType, uRefLorentz): +def testLinearCoeffSolverLorenzSDC(scheme, nNodes, nSweeps, quadType, uRefLorentz): diffOp = Lorenz() uRef = uRefLorentz["sol"] tEnd = uRefLorentz["tEnd"] @@ -129,7 +129,7 @@ def testLinearMultiNodeLorenzSDC(scheme, nNodes, nSweeps, quadType, uRefLorentz) err = [] for nSteps in nStepsVals: - solver = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) + solver = CoeffSolver(diffOp, tEnd=tEnd, nSteps=nSteps) uNum = solver.solveSDC(nSweeps, coll.Q, coll.weights, QDelta) err.append(np.linalg.norm(uNum[-1] - uRef[-1])) @@ -146,13 +146,13 @@ def uRefProtheroRobinson(): diffOp = ProtheroRobinson(epsilon=0.5) tEnd = 0.5 qGenRef = Q_GENERATORS["ARK4ERK"].getInstance() - uRef = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=1000).solve( + uRef = CoeffSolver(diffOp, tEnd=tEnd, nSteps=1000).solve( qGenRef.Q, qGenRef.weights) return {"tEnd": tEnd, "sol": uRef, "diffOp": diffOp} @pytest.mark.parametrize("scheme", ["ARK4EDIRK", "ARK343ESDIRK"]) -def testLinearMultiNodeProtheroRobinson(scheme, uRefProtheroRobinson): +def testLinearCoeffSolverProtheroRobinson(scheme, uRefProtheroRobinson): diffOp = uRefProtheroRobinson["diffOp"] uRef = uRefProtheroRobinson["sol"] tEnd = uRefProtheroRobinson["tEnd"] @@ -161,7 +161,7 @@ def testLinearMultiNodeProtheroRobinson(scheme, uRefProtheroRobinson): err = [] qGen = Q_GENERATORS[scheme].getInstance() for nSteps in nStepsVals: - solver = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) + solver = CoeffSolver(diffOp, tEnd=tEnd, nSteps=nSteps) uNum = solver.solve(qGen.Q, qGen.weights) err.append(np.linalg.norm(uNum[-1] - uRef[-1])) @@ -182,7 +182,7 @@ def testLinearMultiNodeProtheroRobinson(scheme, uRefProtheroRobinson): @pytest.mark.parametrize("nSweeps", [1, 2]) @pytest.mark.parametrize("nNodes", [3, 4]) @pytest.mark.parametrize("scheme", ["BE", "FE", "MIN-SR-FLEX"]) -def testLinearMultiNodeProtheroRobinsonSDC(scheme, nNodes, nSweeps, quadType, uRefProtheroRobinson): +def testLinearCoeffSolverProtheroRobinsonSDC(scheme, nNodes, nSweeps, quadType, uRefProtheroRobinson): diffOp = uRefProtheroRobinson["diffOp"] uRef = uRefProtheroRobinson["sol"] tEnd = uRefProtheroRobinson["tEnd"] @@ -197,7 +197,7 @@ def testLinearMultiNodeProtheroRobinsonSDC(scheme, nNodes, nSweeps, quadType, uR err = [] for nSteps in nStepsVals: - solver = LinearMultiNode(diffOp, tEnd=tEnd, nSteps=nSteps) + solver = CoeffSolver(diffOp, tEnd=tEnd, nSteps=nSteps) uNum = solver.solveSDC(nSweeps, coll.Q, coll.weights, QDelta) err.append(np.linalg.norm(uNum[-1] - uRef[-1])) diff --git a/tests/test_solvers/test_integrators.py b/tests/test_solvers/test_integrators.py new file mode 100644 index 0000000..c82fc9b --- /dev/null +++ b/tests/test_solvers/test_integrators.py @@ -0,0 +1,77 @@ +import pytest +import numpy as np + +from qmat import Q_GENERATORS, QDELTA_GENERATORS +from qmat.solvers.generic import CoeffSolver, PhiSolver +from qmat.solvers.generic.integrators import ForwardEuler, BackwardEuler +from qmat.solvers.generic.diffops import DIFFOPS + +EQUIVALENCES: dict[str, type[PhiSolver]] = { + "FE": ForwardEuler, + "BE": BackwardEuler, +} + +@pytest.mark.parametrize("nNodes", [1, 4, 10]) +@pytest.mark.parametrize("problem", ["Lorenz", "ProtheroRobinson"]) +@pytest.mark.parametrize("scheme", EQUIVALENCES.keys()) +def testPhiSolver(scheme, problem, nNodes): + diffOp = DIFFOPS[problem]() + tEnd = 0.1 + nSteps = 10*nNodes + + qGen = Q_GENERATORS[scheme].getInstance() + + refSolver = CoeffSolver(diffOp, tEnd=tEnd, nSteps=nSteps) + ref = refSolver.solve(qGen.Q, qGen.weights) + + regNodes = np.linspace(0, 1, num=nNodes+1)[1:] + + phiSolver = EQUIVALENCES[scheme](diffOp, nodes=regNodes, tEnd=tEnd, nSteps=nSteps//nNodes) + sol = phiSolver.solve() + + assert np.allclose(sol, ref[::nNodes]), \ + f"{phiSolver.__class__.__name__}-PhiSolver does not match equivalent CoeffSolver result" + + +@pytest.mark.parametrize("nSweeps", [1, 2, 4]) +@pytest.mark.parametrize("quadType", ["RADAU-RIGHT", "LOBATTO"]) +@pytest.mark.parametrize("nNodes", [2, 4, 8]) +@pytest.mark.parametrize("problem", ["Lorenz", "ProtheroRobinson"]) +@pytest.mark.parametrize("scheme", EQUIVALENCES.keys()) +def testPhiSolverSDC(scheme, problem, nNodes, quadType, nSweeps): + pParams = {} + if problem == "ProtheroRobinson": + pParams = {"epsilon": 0.01, "nonLinear": True} + + diffOp = DIFFOPS[problem](**pParams) + tEnd = 0.1 + nSteps = 10 + + coll = Q_GENERATORS["Collocation"](nNodes=nNodes, quadType=quadType, nodeType="LEGENDRE") + approx = QDELTA_GENERATORS[scheme](qGen=coll) + + refSolver = CoeffSolver(diffOp, tEnd=tEnd, nSteps=nSteps) + ref = refSolver.solveSDC(nSweeps, coll.Q, coll.weights, approx.getQDelta()) + + phiSolver = EQUIVALENCES[scheme](diffOp, nodes=coll.nodes, tEnd=tEnd, nSteps=nSteps) + + sol = phiSolver.solveSDC(nSweeps, Q=coll.Q, weights=True) + assert np.allclose(sol, ref), \ + f"{phiSolver.__class__.__name__}-PhiSolver SDC with given Q does not match equivalent CoeffSolver SDC result" + + sol = phiSolver.solveSDC(nSweeps, Q=None, weights=True) + assert np.allclose(sol, ref), \ + f"{phiSolver.__class__.__name__}-PhiSolver SDC does not match equivalent CoeffSolver SDC result" + + ref = refSolver.solveSDC(nSweeps, coll.Q, None, approx.getQDelta()) + sol = phiSolver.solveSDC(nSweeps, weights=None) + assert np.allclose(sol, ref), \ + f"{phiSolver.__class__.__name__}-PhiSolver SDC without weights does not match equivalent CoeffSolver SDC result" + + if scheme == "BE": + original = BackwardEuler.phiSolve + BackwardEuler.phiSolve = PhiSolver.phiSolve # use default phiSolve + sol = phiSolver.solveSDC(nSweeps, Q=coll.Q, weights=False) + BackwardEuler.phiSolve = original + assert np.allclose(sol, ref), \ + f"{phiSolver.__class__.__name__}-PhiSolver SDC with default phiSolve does not match equivalent CoeffSolver SDC result" From 04d7194584f064878dc1de29610f7291d5a52b61 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Thu, 23 Oct 2025 00:58:02 +0200 Subject: [PATCH 17/33] TL: python version rollout --- .github/workflows/ci_pipeline.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ci_pipeline.yml b/.github/workflows/ci_pipeline.yml index 880f50d..67c61c4 100644 --- a/.github/workflows/ci_pipeline.yml +++ b/.github/workflows/ci_pipeline.yml @@ -11,7 +11,7 @@ jobs: strategy: fail-fast: false matrix: - python: ['3.9', '3.10', '3.11', '3.12', '3.13'] + python: ['3.10', '3.11', '3.12', '3.13', '3.14'] defaults: run: shell: bash -l {0} @@ -35,7 +35,7 @@ jobs: ./test.sh - name: Upload coverage reports to Codecov uses: codecov/codecov-action@v4 - if: github.repository_owner == 'Parallel-in-Time' && matrix.python == '3.11' + if: github.repository_owner == 'Parallel-in-Time' && matrix.python == '3.13' with: flags: smart-tests verbose: true @@ -47,10 +47,10 @@ jobs: steps: - uses: actions/checkout@v3 - - name: Set up Python 3.11 + - name: Set up Python 3.13 uses: actions/setup-python@v3 with: - python-version: "3.11" + python-version: "3.13" - name: Install dependencies run: | python -m pip install --upgrade pip From 04a746baf37ca5c52c7730e4a99d9f77cfad31c5 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Thu, 23 Oct 2025 01:11:58 +0200 Subject: [PATCH 18/33] TL: started working on docs --- docs/index.rst | 2 +- pyproject.toml | 2 +- qmat/playgrounds/__init__.py | 5 +++++ qmat/playgrounds/tibo/README.md | 4 ---- qmat/playgrounds/tibo/__init__.py | 4 ++++ qmat/playgrounds/tibo/test.py | 4 +--- 6 files changed, 12 insertions(+), 9 deletions(-) delete mode 100644 qmat/playgrounds/tibo/README.md create mode 100644 qmat/playgrounds/tibo/__init__.py diff --git a/docs/index.rst b/docs/index.rst index f439d2a..dc1cc23 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -113,4 +113,4 @@ Developer ========= * `Thibaut Lunet `_ -* `Thomas Saupe (Baumann) `_ \ No newline at end of file +* `Thomas Saupe (nÊ Baumann) `_ \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 2bb6bad..c982006 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,7 @@ dependencies = [ requires-python = ">=3.9" maintainers = [ {name = "Thibaut Lunet", email = "thibaut.lunet@tuhh.de"}, - {name = "Thomas Saupe (Baumann)", email = "t.baumann@fz-juelich.de"}, + {name = "Thomas Saupe (nÊ Baumann)", email = "t.baumann@fz-juelich.de"}, ] readme = "README.md" license = {file = "LICENSE"} diff --git a/qmat/playgrounds/__init__.py b/qmat/playgrounds/__init__.py index 111c444..5a0d7be 100644 --- a/qmat/playgrounds/__init__.py +++ b/qmat/playgrounds/__init__.py @@ -4,4 +4,9 @@ Folders containing different experiments performed with `qmat`. đŸ“Ŗ Codes in those folder are not tested by the CI pipeline. + +Current playgrounds +------------------- + +- `tibo <./tibo>`_ : personal playground of `@tlunet `_ """ diff --git a/qmat/playgrounds/tibo/README.md b/qmat/playgrounds/tibo/README.md deleted file mode 100644 index d23933a..0000000 --- a/qmat/playgrounds/tibo/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Personal playground (Tibo) - -- [orthogonalPolynomials](./orthogonalPolynomials.py) : how to generate orthogonal polynomial values from any distribution with arbitrary order in a numerical stable fashion -- [test.py](./test.py) : playground to test the new generic solvers \ No newline at end of file diff --git a/qmat/playgrounds/tibo/__init__.py b/qmat/playgrounds/tibo/__init__.py new file mode 100644 index 0000000..be43215 --- /dev/null +++ b/qmat/playgrounds/tibo/__init__.py @@ -0,0 +1,4 @@ +""" +- `orthogonalPolynomials.py <./orthogonalPolynomials.py>`_ : how to generate orthogonal polynomial values from any distribution with arbitrary order in a numerically stable fashion. +- `test.py <./test.py>`_ : script to test the new generic solvers. +""" \ No newline at end of file diff --git a/qmat/playgrounds/tibo/test.py b/qmat/playgrounds/tibo/test.py index 09a1dd3..7bf21af 100644 --- a/qmat/playgrounds/tibo/test.py +++ b/qmat/playgrounds/tibo/test.py @@ -1,9 +1,7 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Created on Mon Oct 20 17:26:04 2025 - -@author: cpf5546 +Some tests ... """ import numpy as np From 22c98824faef3cb62f7716b7e7681b4734e3c4ce Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Sun, 26 Oct 2025 10:23:11 +0100 Subject: [PATCH 19/33] TL: documentation update --- docs/notebooks/02_rk.ipynb | 24 +- docs/notebooks/04_sdc.ipynb | 28 +- docs/notebooks/05_residuals.ipynb | 20 +- qmat/__init__.py | 6 +- qmat/playgrounds/__init__.py | 5 +- qmat/playgrounds/tibo/__init__.py | 7 +- qmat/playgrounds/tibo/imex.py | 87 ++++ qmat/playgrounds/tibo/lorenz.py | 45 ++ .../playgrounds/tibo/orthogonalPolynomials.py | 16 +- qmat/playgrounds/tibo/test.py | 84 ---- qmat/solvers/__init__.py | 15 +- qmat/solvers/dahlquist.py | 387 ++++++++++++++++-- qmat/solvers/generic/__init__.py | 4 +- qmat/solvers/sdc.py | 25 +- qmat/utils.py | 39 ++ tests/test_4_utils.py | 13 + tests/test_solvers/test_dahlquist.py | 10 +- tests/test_solvers/test_generic.py | 6 +- 18 files changed, 632 insertions(+), 189 deletions(-) create mode 100644 qmat/playgrounds/tibo/imex.py create mode 100644 qmat/playgrounds/tibo/lorenz.py delete mode 100644 qmat/playgrounds/tibo/test.py create mode 100644 tests/test_4_utils.py diff --git a/docs/notebooks/02_rk.ipynb b/docs/notebooks/02_rk.ipynb index a5c55fa..1dcbd09 100644 --- a/docs/notebooks/02_rk.ipynb +++ b/docs/notebooks/02_rk.ipynb @@ -37,14 +37,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "lam = 1j\n", - "T = 4*np.pi\n", + "tEnd = 4*np.pi\n", "u0 = np.exp(1j*np.pi/6)" ] }, @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ "uNum = np.zeros(nSteps+1, dtype=complex)\n", "uNum[0] = u0\n", "\n", - "dt = T/nSteps\n", + "dt = tEnd/nSteps\n", "A = np.eye(nodes.size) - lam*dt*Q # all-at-once system\n", "\n", "for i in range(nSteps):\n", @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ "plt.plot(uNum.real, uNum.imag, 'o-')\n", "plt.axis(\"equal\")\n", "\n", - "times = np.linspace(0, T, nSteps+1)\n", + "times = np.linspace(0, tEnd, nSteps+1)\n", "uExact = u0 * np.exp(lam*times)\n", "plt.plot(uExact[0].real, uExact[0].imag, 's', ms=10, c=\"orange\")\n", "plt.plot(uExact.real, uExact.imag, ':', c=\"k\")\n", @@ -169,11 +169,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# replace : \"rk = Q_GENERATORS[\"RK4\"]()\" by \n", + "# replace : \"rk = Q_GENERATORS[\"RK4\"]()\" by\n", "coll = Q_GENERATORS[\"coll\"](nNodes=4, nodeType=\"LEGENDRE\", quadType=\"RADAU-RIGHT\")" ] }, @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -208,12 +208,12 @@ } ], "source": [ - "uNum = coll.solveDahlquist(lam, u0, T, nSteps)\n", + "uNum = coll.solveDahlquist(lam, u0, tEnd, nSteps)\n", "plt.plot(uNum[0].real, uNum[0].imag, 's', ms=10, c=\"orange\")\n", "plt.plot(uNum.real, uNum.imag, 'o-')\n", "plt.axis(\"equal\")\n", "plt.grid()\n", - "print(\"L_inf error : {:1.5f}\".format(coll.errorDahlquist(lam, u0, T, nSteps, uNum=uNum)))" + "print(\"L_inf error : {:1.5f}\".format(coll.errorDahlquist(lam, u0, tEnd, nSteps, uNum=uNum)))" ] }, { @@ -262,7 +262,7 @@ "as we need to solve it ... well, _all-at-once_.\n", "\n", "> 🔍 In this case (Dahlquist), solving the _all-at-once system_ it is easy and cheap as showed above. \n", - "> But for large scale non-linear problems, this can quickly become unfeasible, as each time\n", + "> But for large scale non-linear problems, this can quickly become unfeasible, as solution at each time node\n", "> may represent thousands or millions of degrees of freedom ...\n", "\n", "For RK4 though, solving the _all-at-once system_ is much simpler : one simply needs to solve the first node solution (explicit expression for RK4 since the diagonal coefficient is 0), then use it to solve the second node solution, etc ... \n", diff --git a/docs/notebooks/04_sdc.ipynb b/docs/notebooks/04_sdc.ipynb index 6ff5521..7737577 100644 --- a/docs/notebooks/04_sdc.ipynb +++ b/docs/notebooks/04_sdc.ipynb @@ -37,14 +37,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "lam = 1j\n", - "T = 4*np.pi\n", + "tEnd = 4*np.pi\n", "u0 = np.exp(1j*np.pi/6)" ] }, @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ "uNum = np.zeros(nSteps+1, dtype=complex)\n", "uNum[0] = u0\n", "\n", - "dt = T/nSteps\n", + "dt = tEnd/nSteps\n", "A = np.eye(nodes.size) - lam*dt*Q # all-at-once system\n", "P = np.eye(nodes.size) - lam*dt*QDelta # preconditioner\n", "\n", @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -131,7 +131,7 @@ "plt.plot(uNum.real, uNum.imag, 'o-')\n", "plt.axis(\"equal\")\n", "\n", - "times = np.linspace(0, T, nSteps+1)\n", + "times = np.linspace(0, tEnd, nSteps+1)\n", "uExact = u0 * np.exp(lam*times)\n", "plt.plot(uExact.real, uExact.imag, ':', c=\"k\")\n", "print(\"L_inf error : {:1.5f}\".format(np.linalg.norm(uNum-uExact, ord=np.inf)))" @@ -218,12 +218,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from qmat.solvers.sdc import solveDahlquistSDC\n", - "uNum = solveDahlquistSDC(lam, u0, T, nSteps, nSweeps, Q, QDelta, weights)" + "uNum = solveDahlquistSDC(lam, u0, tEnd, nSteps, nSweeps, Q, QDelta, weights)" ] }, { @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -251,7 +251,7 @@ ], "source": [ "for nSweeps, sym in zip([1, 2, 3], ['o', 's', '>']):\n", - " uNum = solveDahlquistSDC(lam, u0, T, nSteps, nSweeps, Q, QDelta, weights)\n", + " uNum = solveDahlquistSDC(lam, u0, tEnd, nSteps, nSweeps, Q, QDelta, weights)\n", " plt.plot(uNum.real, uNum.imag, sym+'-', label=f\"K={nSweeps}\")\n", "plt.axis(\"equal\")\n", "plt.legend()\n", @@ -270,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -292,7 +292,7 @@ "from qmat.solvers.sdc import errorDahlquistSDC\n", "\n", "for nSweeps in [1, 2, 3, 4, 5, 6, 8, 9]:\n", - " err = errorDahlquistSDC(lam, u0, T, nSteps, nSweeps, Q, QDelta, weights)\n", + " err = errorDahlquistSDC(lam, u0, tEnd, nSteps, nSweeps, Q, QDelta, weights)\n", " print(f\"nSweeps={nSweeps}, err={err:1.5f}\")" ] }, @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -329,7 +329,7 @@ "source": [ "QDelta = QDELTA_GENERATORS[\"MIN3\"](nNodes=coll.nNodes, nodeType=coll.nodeType, quadType=coll.quadType).getQDelta()\n", "for nSweeps in [1, 2, 3, 4, 5, 6, 8, 9]:\n", - " err = errorDahlquistSDC(lam, u0, T, nSteps, nSweeps, Q, QDelta, weights)\n", + " err = errorDahlquistSDC(lam, u0, tEnd, nSteps, nSweeps, Q, QDelta, weights)\n", " print(f\"nSweeps={nSweeps}, err={err:1.5f}\")" ] }, diff --git a/docs/notebooks/05_residuals.ipynb b/docs/notebooks/05_residuals.ipynb index 193a78e..bd62f47 100644 --- a/docs/notebooks/05_residuals.ipynb +++ b/docs/notebooks/05_residuals.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -64,12 +64,12 @@ "\n", "# Problem settings\n", "lam = 1j\n", - "T = 4*np.pi\n", + "tEnd = 4*np.pi\n", "u0 = np.exp(1j*np.pi/6)\n", "\n", "nSteps = 12\n", - "dt = T/nSteps\n", - "times = np.linspace(0, T, nSteps+1)" + "dt = tEnd/nSteps\n", + "times = np.linspace(0, tEnd, nSteps+1)" ] }, { @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -222,7 +222,7 @@ "for nSweeps, sym in zip([10, 8, 6, 4], [\"^\", \"o\", \"s\", \">\"]):\n", "\n", " uNum, monitors = solveDahlquistSDC(\n", - " lam=lam, u0=u0, T=T, nSteps=nSteps, nSweeps=nSweeps,\n", + " lam=lam, u0=u0, tEnd=tEnd, nSteps=nSteps, nSweeps=nSweeps,\n", " Q=Q, QDelta=QDelta, weights=weights,\n", " monitors=[\"residuals\", \"errors\"] # list of data we want to monitor for all nodes, time-steps and sweeps\n", " )\n", @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -276,7 +276,7 @@ "for nSweeps, sym in zip([10, 8, 6, 4], [\"^\", \"o\", \"s\", \">\"]):\n", "\n", " uNum, monitors = solveDahlquistSDC(\n", - " lam=lam, u0=u0, T=T, nSteps=nSteps, nSweeps=nSweeps,\n", + " lam=lam, u0=u0, tEnd=tEnd, nSteps=nSteps, nSweeps=nSweeps,\n", " Q=Q, QDelta=QDelta, weights=weights, monitors=[\"residuals\", \"errors\"])\n", "\n", " residuals = np.max(np.linalg.norm(monitors[\"residuals\"], axis=-1, ord=np.inf), axis=-1)\n", @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -322,7 +322,7 @@ " QDelta = genQDeltaCoeffs(qDelta, qGen=coll)\n", "\n", " uNum, monitors = solveDahlquistSDC(\n", - " lam=lam, u0=u0, T=T, nSteps=nSteps, nSweeps=nSweeps,\n", + " lam=lam, u0=u0, tEnd=tEnd, nSteps=nSteps, nSweeps=nSweeps,\n", " Q=Q, QDelta=QDelta, weights=weights, monitors=[\"residuals\", \"errors\"])\n", "\n", " residuals = np.max(np.linalg.norm(monitors[\"residuals\"], axis=-1, ord=np.inf), axis=-1)\n", diff --git a/qmat/__init__.py b/qmat/__init__.py index eba857e..779a7b7 100644 --- a/qmat/__init__.py +++ b/qmat/__init__.py @@ -6,11 +6,15 @@ - :class:`qcoeff` : to generate the :math:`Q`-coefficients (Butcher tables) - :class:`qdelta` : to generate :math:`Q_\Delta` approximations for :math:`Q` matrices +**Secondary sub-packages** 🍭 + +- :class:`solvers` : implementations of time-integration solvers that make use of `qmat`-generated coefficients +- :class:`playgrounds`: **non-tested but documented** codes with experiments or small applications with `qmat` + **Utility modules** ⚙ī¸ - :class:`lagrange` : Barycentric polynomial approximations (integral, interpolation, derivative) - :class:`nodes` : generation of multiple types of quadrature nodes -- :class:`sdc` : utility function to run SDC on simple problems - :class:`mathutils` : utility functions for math operations - :class:`utils` : utility functions for the whole package diff --git a/qmat/playgrounds/__init__.py b/qmat/playgrounds/__init__.py index 5a0d7be..994c8e5 100644 --- a/qmat/playgrounds/__init__.py +++ b/qmat/playgrounds/__init__.py @@ -3,10 +3,11 @@ """ Folders containing different experiments performed with `qmat`. - đŸ“Ŗ Codes in those folder are not tested by the CI pipeline. + đŸ“Ŗ Codes in those folders are not tested by the CI pipeline, + but hopefully enough documented so you can play with it. Current playgrounds ------------------- -- `tibo <./tibo>`_ : personal playground of `@tlunet `_ +- :class:`tibo` : personal playground of `@tlunet `_ """ diff --git a/qmat/playgrounds/tibo/__init__.py b/qmat/playgrounds/tibo/__init__.py index be43215..f81a3b1 100644 --- a/qmat/playgrounds/tibo/__init__.py +++ b/qmat/playgrounds/tibo/__init__.py @@ -1,4 +1,5 @@ """ -- `orthogonalPolynomials.py <./orthogonalPolynomials.py>`_ : how to generate orthogonal polynomial values from any distribution with arbitrary order in a numerically stable fashion. -- `test.py <./test.py>`_ : script to test the new generic solvers. -""" \ No newline at end of file +- :class:`orthogonalPolynomials` : generate orthogonal polynomial values from any distribution. +- :class:`lorenz` : application example of the generic solvers to solve the Lorenz equations. +- :class:`imex` : starting development for the IMEX generic solvers. +""" diff --git a/qmat/playgrounds/tibo/imex.py b/qmat/playgrounds/tibo/imex.py new file mode 100644 index 0000000..fbe8b88 --- /dev/null +++ b/qmat/playgrounds/tibo/imex.py @@ -0,0 +1,87 @@ +import numpy as np + +from qmat.solvers.dahlquist import DahlquistIMEX +from qmat.solvers.generic import DiffOp, CoeffSolver + + +class DiffOpIMEX(DiffOp): + """ + Base class for an IMEX differential operator + """ + + def evalF2(self, u:np.ndarray, t:float, out:np.ndarray): + """ + Evaluate f_EX(u,t) and store the result into out + """ + raise NotImplementedError("evalF must be provided") + + +class CoeffSolverIMEX(CoeffSolver): + """ + Coefficient based solver class for IMEX differential operators. + """ + def __init__(self, diffOp, tEnd=1, nSteps=1, t0=0): + self.diffOp: DiffOpIMEX = None + assert isinstance(diffOp, DiffOpIMEX), \ + f"DiffOpIMEX object is required for diffOp argument, not {diffOp}" + super().__init__(diffOp, tEnd, nSteps, t0) + + + def evalF2(self, u:np.ndarray, t:float, out:np.ndarray): + self.diffOp.evalF2(u, t, out) + + def solve(self, QI, wI, QE, wE, uNum=None): + nNodes, QI, wI, QE, wE, useWeights = DahlquistIMEX.checkCoeff(QI, wI, QE, wE) + + assert self.lowerTri(QI), \ + "lower triangular matrix QI expected for non-linear IMEX solver" + assert self.lowerTri(QE, strict=True), \ + "strictly lower triangular matrix QE expected for non-linear IMEX solver" + QI, QE = self.dt*QI, self.dt*QE + if useWeights: + wI, wE = self.dt*wI, self.dt*wE + + if uNum is None: + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) + uNum[0] = self.u0 + + rhs = np.zeros(self.uShape, dtype=self.dtype) + fEvals = np.zeros((nNodes, *self.uShape), dtype=self.dtype) + fEvals2 = np.zeros((nNodes, *self.uShape), dtype=self.dtype) + + times = np.linspace(self.t0, self.tEnd, self.nSteps+1) + tau = QI.sum(axis=1) + + # time-stepping loop + for i in range(self.nSteps): + uNode = uNum[i+1] + np.copyto(uNode, uNum[i]) + + # loop on nodes (stages) + for m in range(nNodes): + tNode = times[i]+tau[m] + + # build RHS + np.copyto(rhs, uNum[i]) + for j in range(m): + self.axpy(a=QI[m, j], x=fEvals[j], y=rhs) + self.axpy(a=QE[m, j], x=fEvals2[j], y=rhs) + + # solve node (if non-zero diagonal coefficient) + if QI[m, m] != 0: + self.fSolve(a=QI[m, m], rhs=rhs, t=tNode, out=uNode) + else: + np.copyto(uNode, rhs) + + # evalF on current stage + self.evalF(u=uNode, t=tNode, out=fEvals[m]) + self.evalF2(u=uNode, t=tNode, out=fEvals2[m]) + + # step update (if not, uNum[i+1] is already the last stage) + if useWeights: + np.copyto(uNum[i+1], uNum[i]) + for m in range(nNodes): + self.axpy(a=wI[m], x=fEvals[m], y=uNum[i+1]) + self.axpy(a=wE[m], x=fEvals2[m], y=uNum[i+1]) + + return uNum \ No newline at end of file diff --git a/qmat/playgrounds/tibo/lorenz.py b/qmat/playgrounds/tibo/lorenz.py new file mode 100644 index 0000000..c53b6bf --- /dev/null +++ b/qmat/playgrounds/tibo/lorenz.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Make use of the generic :class:`CoeffSolver` of `qmat` to solve the Lorenz equations + +.. literalinclude:: /../qmat/playgrounds/tibo/lorenz.py + :language: python + :linenos: + :lines: 11- +""" +import numpy as np +import matplotlib.pyplot as plt + +from qmat import genQCoeffs, QDELTA_GENERATORS +from qmat.qcoeff.collocation import Collocation +from qmat.utils import Timer + +from qmat.solvers.generic import CoeffSolver +from qmat.solvers.generic.diffops import Lorenz + +tEnd = 10 +nSteps = 1000 +diffOp = Lorenz() +solver = CoeffSolver(diffOp, tEnd=tEnd, nSteps=nSteps) + +nodes, weights, Q = genQCoeffs("RK4") +with Timer("RK solve", scale=nSteps, descr="tWall/step"): + uRK = solver.solve(Q, weights) + +coll = Collocation(nNodes=2, nodeType="LEGENDRE", quadType="RADAU-RIGHT") +gen = QDELTA_GENERATORS["FE"](qGen=coll) +QDelta = gen.getQDelta() +with Timer("SDC solve", scale=nSteps, descr="tWall/step"): + uSDC = solver.solveSDC(4, coll.Q, coll.weights, QDelta) + +plt.figure("Solution") +times = np.linspace(0, tEnd, nSteps+1) +for i, v in enumerate(["x", "y", "z"]): + p = plt.plot(times, uRK[:, i], label=f"{v} RK") + plt.plot(times, uSDC[:, i], "--", c=p[0].get_color(), label=f"{v} SDC") +plt.legend() +plt.xlabel("time") +plt.ylabel("trajectory") +plt.gcf().set_size_inches(11, 6) +plt.tight_layout() diff --git a/qmat/playgrounds/tibo/orthogonalPolynomials.py b/qmat/playgrounds/tibo/orthogonalPolynomials.py index 4054093..c5fa439 100644 --- a/qmat/playgrounds/tibo/orthogonalPolynomials.py +++ b/qmat/playgrounds/tibo/orthogonalPolynomials.py @@ -2,19 +2,27 @@ # -*- coding: utf-8 -*- """ Compute and display orthogonal polynomials of any degree using `qmat` + +.. literalinclude:: /../qmat/playgrounds/tibo/orthogonalPolynomials.py + :language: python + :linenos: + :lines: 11- """ import numpy as np import matplotlib.pyplot as plt from qmat.nodes import NodesGenerator deg = 100 -polyType = "CHEBY-1" +"""polynomial degree""" -gen = NodesGenerator(polyType) -n = deg + 1 -alpha, beta = gen.getOrthogPolyCoefficients(n) +polyType = "CHEBY-1" +"""type of polynomial""" t = np.linspace(-1, 1, num=1000000) +"""plotting points""" + +gen = NodesGenerator(polyType) +alpha, beta = gen.getOrthogPolyCoefficients(deg+1) # Generate monic polynomials (leading coefficient is 1) if deg == 0: diff --git a/qmat/playgrounds/tibo/test.py b/qmat/playgrounds/tibo/test.py deleted file mode 100644 index 7bf21af..0000000 --- a/qmat/playgrounds/tibo/test.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Some tests ... -""" -import numpy as np - -import matplotlib.pyplot as plt -from time import time - -from qmat import genQCoeffs, QDELTA_GENERATORS -from qmat.qcoeff.collocation import Collocation -from qmat.solvers.generic import CoeffSolver - -from qmat.solvers.generic.diffops import Dahlquist, Lorenz, ProtheroRobinson -from qmat.solvers.generic.integrators import ForwardEuler, BackwardEuler - - -pType = "Lorenz" -nPeriod = 1 -nSteps = nPeriod*1000 -tEnd = nPeriod*np.pi - -corr = "BE" -useSDC = True -useWeights = True -nSweeps = 1 - -if pType == "Dahlquist": - diffOp = Dahlquist() -elif pType == "Lorenz": - diffOp = Lorenz() -elif pType == "ProtheroRobinson": - nSteps *= 10 - diffOp = ProtheroRobinson(nonLinear=False) -nDOF = diffOp.u0.size - -nodes, weights, Q = genQCoeffs(corr) -coll = Collocation(nNodes=2, nodeType="LEGENDRE", quadType="RADAU-RIGHT") -gen = QDELTA_GENERATORS[corr](qGen=coll) -QDelta = gen.genCoeffs(k=[i+1 for i in range(nSweeps)]) - -prob = CoeffSolver(diffOp, tEnd=tEnd, nSteps=nSteps) -Solver = BackwardEuler if corr == "BE" else ForwardEuler -if useSDC: - solver = Solver(diffOp, nodes=coll.nodes, tEnd=tEnd, nSteps=nSteps) -else: - regNodes = [0.25, 0.5, 0.75, 1] - solver = Solver(diffOp, nodes=regNodes, tEnd=tEnd, nSteps=nSteps//4) - -if useSDC: - tBeg = time() - uRef = prob.solveSDC(nSweeps, coll.Q, coll.weights if useWeights else None, QDelta) -else: - tBeg = time() - uRef = prob.solve(Q, weights) -tWall = time()-tBeg -tWall /= nSteps * nDOF -print(f"tWallScaled[linear] : {tWall:1.2e}s") - -if useSDC: - tBeg = time() - uNum = solver.solveSDC(nSweeps, weights=useWeights) -else: - tBeg = time() - uNum = solver.solve() -tWall = time()-tBeg -tWall /= nSteps * nDOF -print(f"tWallScaled[generic] : {tWall:1.2e}s") -print(uRef[-1] - uNum[-1]) - -plt.figure(1) -plt.clf() -if pType == "ProtheroRobinson": - times = np.linspace(0, tEnd, nSteps+1) - plt.plot(times, uRef[:, 0], label="ref") - if useSDC: - plt.plot(times, uNum[:, 0], label="integrator") - else: - plt.plot(times[::4], uNum[:, 0], label="integrator") -else: - plt.plot(uRef[:, 0], uRef[:, 1], label="ref") - plt.plot(uNum[:, 0], uNum[:, 1], label="integrator") -plt.legend() diff --git a/qmat/solvers/__init__.py b/qmat/solvers/__init__.py index 40f4ba1..7c4d1de 100644 --- a/qmat/solvers/__init__.py +++ b/qmat/solvers/__init__.py @@ -1,5 +1,18 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Solvers implementations that can make use of `qmat`-generated coefficients. +Implementations of time-integration solvers that make use of `qmat`-generated coefficients. + + 🔔 Those are not fully optimized implementations of their corresponding + time-integration scheme : these are conveniences classes allowing + some first **experiments** with your problem(s) of interest. + +**Modules** ⚙ī¸ + +- :class:`sdc` : functions to run SDC on a scalar Dahlquist problem and evaluate its numerical error or convergence order +- :class:`dahlquist` : generic coefficient-based time-integration solver for the (IMEX) vectorized Dahlquist problem + +**Sub-package** đŸ“Ļ + +- :class:`generic` : time-integration solvers for generic (systems of / non-linear) ODEs """ diff --git a/qmat/solvers/dahlquist.py b/qmat/solvers/dahlquist.py index 4f24d8a..4b1288c 100644 --- a/qmat/solvers/dahlquist.py +++ b/qmat/solvers/dahlquist.py @@ -1,51 +1,140 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -""" -Submodule containing various solvers for the Dahlquist equation -that can make use of `qmat`-generated coefficients. +r""" +Solvers for the Dahlquist equation based on :math:`Q` coefficients, +also implementing SDC sweeps with given :math:`Q_\Delta` coefficients. """ import numpy as np class Dahlquist(): - - def __init__(self, lam, u0=1, T=1, nSteps=1): + r""" + Solver for the classical Dahlquist equation + + .. math:: + + \frac{du}{dt} = \lambda u, \quad u(0)=u_0, \quad t \in [0,T]. + + It can be used to solve the equation with multiple :math:`\lambda` + values (multiple trajectories). + + Parameters + ---------- + lam : scalar or array + Value(s) used for :math:`\lambda`. + u0 : scalar or array, optional + Initial value :math:`\lambda`, must be compatible with `lam`. + The default is 1. + tEnd : float, optional + Final simulation time :math:`T`. The default is 1. + nSteps : float, optional + Number of time-step to solve. The default is 1. + """ + def __init__(self, lam, u0=1, tEnd=1, nSteps=1): self.u0 = u0 - self.T = T + """initial solution value""" + + self.tEnd = tEnd + """final simulation time""" + self.nSteps = nSteps - self.dt = T/nSteps + """number of time-steps""" + + self.dt = tEnd/nSteps + """time-step size""" self.lam = np.asarray(lam) + r"""array storing the :math:`\lambda` values""" try: lamU = self.lam*u0 except: raise ValueError("error when computing lam*u0") self.uShape = tuple(lamU.shape) - self.uDtype = lamU.dtype + """shape of the solution at a given time""" + self.dtype = lamU.dtype + """solution datatype""" + @staticmethod def checkCoeff(Q, weights): + """ + Check :math:`Q` coefficients and associated weights. + + Parameters + ---------- + Q : 2D array-like + The :math:`Q` coefficients. + weights : 1D array-like + Quadrature weights associated to the nodes. + + Returns + ------- + nNodes : int + Number of nodes (stages). + Q : np.2darray + The :math:`Q` coefficients. + weights : np.1darray + Quadrature weights associated to the nodes. + """ Q = np.asarray(Q) nNodes = Q.shape[0] assert Q.shape == (nNodes, nNodes), "Q is not a square matrix" if weights is not None: weights = np.asarray(weights) - assert weights.ndim == 1, f"weights must be a 1D vector, not {weights}" - assert weights.size == nNodes, "weights size is not the same as the node size" + assert weights.ndim == 1, \ + f"weights must be a 1D vector, not {weights}" + assert weights.size == nNodes, \ + "weights size is not the same as the node size" + assert np.allclose(weights.sum(), 1), \ + "weights sum must be equal to 1" else: - assert np.allclose(Q.sum(axis=1)[-1], 1), "last node must be 1 if weights are not given" + assert np.allclose(Q.sum(axis=1)[-1], 1), \ + "last node must be 1 if weights are not given" return nNodes, Q, weights def solve(self, Q, weights): + r""" + Solve for all :math:`\lambda` using a direct solve of the :math:`Q` + matrix, *i.e* for each step it solves : + + .. math:: + + (I - \Delta{t}\lambda Q){\bf u} = {\bf u}_0, + + where :math:`{\bf u}_0` is the vector containing the initial solution + of the time-step in each entry. + The next step solution is computed using the **step update** : + + .. math:: + + u_1 = u_0 + \Delta{t}\lambda{\bf w}^T{\bf u}, + + or simply use the last **node solution** :math:`{\bf u}[-1]` if + no weights are given (`weights=None`). + + Parameters + ---------- + Q : 2D array-like + The :math:`Q` coefficients. + weights : 1D array-like or None + Quadrature weights associated to the nodes. + If None, do not use them for the step update + (requires last node equal to 1) + + Returns + ------- + uNum : np.ndarray + The solution at each time-steps (+ initial solution). + """ nNodes, Q, weights = self.checkCoeff(Q, weights) # Collocation problem matrix A = np.eye(nNodes) - self.lam[..., None, None]*self.dt*Q - uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) uNum[0] = self.u0 for i in range(self.nSteps): @@ -59,8 +148,36 @@ def solve(self, Q, weights): return uNum + @staticmethod def checkCoeffSDC(Q, weights, QDelta, nSweeps): + r""" + Check SDC coefficients + + Parameters + ---------- + Q : 2D array-like + The :math:`Q` coefficients. + weights : 1D array-like + Quadrature weights associated to the nodes. + QDelta : 2D or 3D array-like + The :math:`Q_\Delta` coefficients (3D if changes with sweeps). + nSweeps : int + Number of sweeps. + + Returns + ------- + nNodes : int + Number of nodes. + Q : np.2darray + The :math:`Q` coefficients. + weights : np.1darray + Quadrature weights associated to the nodes. + QDelta : np.2darray + The :math:`Q_\Delta` coefficients for each sweep. + nSweeps : int + The number of sweeps. + """ Q = np.asarray(Q) nodes = Q.sum(axis=1) nNodes = nodes.size @@ -69,27 +186,74 @@ def checkCoeffSDC(Q, weights, QDelta, nSweeps): if weights is not None: weights = np.asarray(weights) assert weights.ndim == 1, "weights must be a 1D vector" - assert weights.size == nNodes, "weights size is not the same as the node size" + assert weights.size == nNodes, \ + "weights size is not the same as the node size" else: - assert np.allclose(nodes[-1], 1), "last node must be 1 if weights are not given" + assert np.allclose(nodes[-1], 1), \ + "last node must be 1 if weights are not given" QDelta = np.asarray(QDelta) if QDelta.ndim == 3: - assert QDelta.shape == (nSweeps, nNodes, nNodes), "inconsistent shape for QDelta" + assert QDelta.shape == (nSweeps, nNodes, nNodes), \ + "inconsistent shape for QDelta" else: - assert QDelta.shape == (nNodes, nNodes), "inconsistent shape for QDelta" + assert QDelta.shape == (nNodes, nNodes), \ + "inconsistent shape for QDelta" QDelta = np.repeat(QDelta[None, ...], nSweeps, axis=0) return nNodes, Q, weights, QDelta, nSweeps + def solveSDC(self, Q, weights, QDelta, nSweeps): - nNodes, Q, weights, QDelta, nSweeps = self.checkCoeffSDC(Q, weights, QDelta, nSweeps) + r""" + Solve for all :math:`\lambda` using SDC sweeps, *i.e* solve for + each sweep :math:`k` : + + .. math:: + + (I - \Delta{t}\lambda Q_\Delta){\bf u}^{k+1} + = {\bf u}_0 + \Delta{t}\lambda(Q - Q_\Delta){\bf u}^{k}, + + where :math:`{\bf u}_0` is the vector containing the initial solution + of the time-step in each entry and :math:`{\bf u}^0 = {\bf u}_0` + (copy initialization). + + The next step solution is computed using the **step update** : + + .. math:: + + u_1 = u_0 + \Delta{t}\lambda{\bf w}^T{\bf u}^{K}, + + where :math:`K` is the total number of sweeps. + If no weights are given (`weights=None`), it simply uses the last + **node solution** :math:`{\bf u}[-1]`. + + Parameters + ---------- + Q : 2D array-like + The :math:`Q` coefficients. + weights : 1D array-like or None + Quadrature weights associated to the nodes. + If None, do not use them for the step update + (requires last node equal to 1) + QDelta : 2D or 3D array-like + The :math:`Q_\Delta` coefficients (3D if changes with sweeps). + nSweeps : int + Number of sweeps. + + Returns + ------- + uNum : np.ndarray + The solution at each time-steps (+ initial solution). + """ + nNodes, Q, weights, QDelta, nSweeps = self.checkCoeffSDC( + Q, weights, QDelta, nSweeps) # Preconditioner for each sweeps P = np.eye(nNodes)[None, ...] \ - self.lam[..., None, None, None]*self.dt*QDelta - uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) uNum[0] = self.u0 for i in range(self.nSteps): @@ -119,28 +283,94 @@ def solveSDC(self, Q, weights, QDelta, nSweeps): class DahlquistIMEX(): - - def __init__(self, lamI, lamE, u0=1, T=1, nSteps=1): + r""" + Solver for the IMEX Dahlquist equation + + .. math:: + + \frac{du}{dt} = (\lambda_I + \lambda_E) u, + \quad u(0)=u_0, \quad t \in [0,T]. + + It can be used to solve the equation with multiple :math:`\lambda_I` + and / or :math:`\lambda_E` values (multiple trajectories). + + Parameters + ---------- + lamI : TYPE + Value(s) used for :math:`\lambda_I`.. + lamE : scalar or array + Value(s) used for :math:`\lambda_E`. + u0 : scalar or array, optional + Initial value :math:`\lambda`, must be compatible with `lam`. + The default is 1. + tEnd : float, optional + Final simulation time :math:`T`. The default is 1. + nSteps : float, optional + Number of time-step to solve. The default is 1. + """ + def __init__(self, lamI, lamE, u0=1, tEnd=1, nSteps=1): self.u0 = u0 - self.T = T + """initial solution value""" + + self.tEnd = tEnd + """final simulation time""" + self.nSteps = nSteps - self.dt = T/nSteps + """number of time-steps""" + + self.dt = tEnd/nSteps + """time-step size""" self.lamI = np.asarray(lamI) + r"""array storing the :math:`\lambda_I` values""" self.lamE = np.asarray(lamE) + r"""array storing the :math:`\lambda_E` values""" try: lamU = (self.lamI + self.lamE)*u0 except: raise ValueError("error when computing (lamI + lamE)*u0") self.uShape = tuple(lamU.shape) - self.uDtype = lamU.dtype + """shape of the solution at one given time""" + self.dtype = lamU.dtype + """datatype of the solution array""" @staticmethod def checkCoeff(QI, wI, QE, wE): + r""" + Check IMEX :math:`Q` coefficients and assert their consistency. + + Parameters + ---------- + QI : 2D array-like + :math:`Q` coefficients used for :math:`\lambda_I`. + wI : 1D array-like or None + Weights used for the step update on :math:`\lambda_I`. + If None, then step update is not done. + QE : 2D array-like + :math:`Q` coefficients used for :math:`\lambda_E`. + wE : 1D array-like or None + Weights used for the step update on :math:`\lambda_E`. + If None, then step update is not done. + + Returns + ------- + nNodes : int + Number of nodes. + QI : np.2darray + :math:`Q` coefficients used for :math:`\lambda_I`. + wI : np.1darray or None + Weights used for the step update on :math:`\lambda_I`. + QE : np.2darray + :math:`Q` coefficients used for :math:`\lambda_E`. + wE : np.1darray or None + Weights used for the step update on :math:`\lambda_E`. + useWeights : boll + Wether or not the step update (using weights) is done. + """ QI, QE = np.asarray(QI), np.asarray(QE) - nodes = QI.sum(axis=1) - assert np.allclose(nodes, QE.sum(axis=1)), "QI and QE do not correspond to the same nodes" + assert np.allclose(QI.sum(axis=1), QE.sum(axis=1)), \ + "QI and QE do not correspond to the same nodes" nNodes = QI.shape[0] assert QI.shape == (nNodes, nNodes), "QI is not a square matrix" @@ -148,13 +378,36 @@ def checkCoeff(QI, wI, QE, wE): useWeights = True if wI is None or wE is None: - assert wE is None and wI is None, "it's either weights for everyone or no weight" + assert wE is None and wI is None, \ + "it's either weights for everyone or no weight" useWeights = False return nNodes, QI, wI, QE, wE, useWeights def solve(self, QI, wI, QE, wE): + r""" + Solve for all :math:`\lambda_I` and :math:`\lambda_E` + using a direct solve of the :math:`Q_I` and :math:`Q_E` matrices. + + Parameters + ---------- + QI : 2D array-like + :math:`Q` coefficients used for :math:`\lambda_I`. + wI : 1D array-like or None + Weights used for the step update on :math:`\lambda_I`. + If None, then step update is not done. + QE : 2D array-like + :math:`Q` coefficients used for :math:`\lambda_E`. + wE : 1D array-like or None + Weights used for the step update on :math:`\lambda_E`. + If None, then step update is not done. + + Returns + ------- + uNum : np.ndarray + The solution at each time-steps (+ initial solution). + """ nNodes, QI, wI, QE, wE, useWeights = self.checkCoeff(QI, wI, QE, wE) # Collocation problem matrix @@ -163,7 +416,7 @@ def solve(self, QI, wI, QE, wE): - self.lamE[..., None, None]*self.dt*QE # Solution vector for each time-step - uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) uNum[0] = self.u0 # Time-stepping loop @@ -184,6 +437,42 @@ def solve(self, QI, wI, QE, wE): @staticmethod def checkCoeffSDC(Q, weights, QDeltaI, QDeltaE, nSweeps): + r""" + Check coefficients given for a IMEX SDC sweeps + + Parameters + ---------- + Q : 2D array-like + The :math:`Q` coefficients. + weights : 1D array-like or none + Quadrature weights associated to the nodes. If None, last node is + used for the step update. + QDeltaE : 2D or 3D array-like + The :math:`Q_\Delta^I` coefficients used for the :math:`\lambda_I` + term (3D if changes with sweeps). + QDeltaE : 2D or 3D array-like + The :math:`Q_\Delta^E` coefficients used for the :math:`\lambda_E` + term (3D if changes with sweeps). + nSweeps : int + Number of sweeps. + + Returns + ------- + nNodes : int + Number of nodes. + Q : np.2darray + The :math:`Q` coefficients. + weights : np.1darray + Quadrature weights associated to the nodes. + QDeltaI : np.3darray + The :math:`Q_\Delta^I` coefficients used for the :math:`\lambda_I` + term for each sweeps. + QDeltaE : np.3darray + The :math:`Q_\Delta^E` coefficients used for the :math:`\lambda_E` + term for each sweeps. + nSweeps : int + Number of SDC sweeps. + """ Q = np.asarray(Q) nodes = Q.sum(axis=1) nNodes = nodes.size @@ -192,33 +481,63 @@ def checkCoeffSDC(Q, weights, QDeltaI, QDeltaE, nSweeps): if weights is not None: weights = np.asarray(weights) assert weights.ndim == 1, "weights must be a 1D vector" - assert weights.size == nNodes, "weights size is not the same as the node size" + assert weights.size == nNodes, \ + "weights size is not the same as the node size" QDeltaI = np.asarray(QDeltaI) QDeltaE = np.asarray(QDeltaE) if QDeltaI.ndim == 3: - assert QDeltaI.shape == (nSweeps, nNodes, nNodes), "inconsistent shape for QDeltaI" + assert QDeltaI.shape == (nSweeps, nNodes, nNodes), \ + "inconsistent shape for QDeltaI" else: - assert QDeltaI.shape == (nNodes, nNodes), "inconsistent shape for QDeltaE" + assert QDeltaI.shape == (nNodes, nNodes), \ + "inconsistent shape for QDeltaE" QDeltaI = np.repeat(QDeltaI[None, ...], nSweeps, axis=0) if QDeltaE.ndim == 3: - assert QDeltaE.shape == (nSweeps, nNodes, nNodes), "inconsistent shape for QDeltaE" + assert QDeltaE.shape == (nSweeps, nNodes, nNodes), \ + "inconsistent shape for QDeltaE" else: - assert QDeltaE.shape == (nNodes, nNodes), "inconsistent shape for QDeltaE" + assert QDeltaE.shape == (nNodes, nNodes), \ + "inconsistent shape for QDeltaE" QDeltaE = np.repeat(QDeltaE[None, ...], nSweeps, axis=0) return nNodes, Q, weights, QDeltaI, QDeltaE, nSweeps def solveSDC(self, Q, weights, QDeltaI, QDeltaE, nSweeps): - nNodes, Q, weights, QDeltaI, QDeltaE, nSweeps = self.checkCoeffSDC(Q, weights, QDeltaI, QDeltaE, nSweeps) + """ + Solve for all :math:`\lambda_I` and :math:`\lambda_E` using SDC sweeps. + + Parameters + ---------- + Q : 2D array-like + The :math:`Q` coefficients. + weights : 1D array-like or none + Quadrature weights associated to the nodes. If None, last node is + used for the step update. + QDeltaE : 2D or 3D array-like + The :math:`Q_\Delta^I` coefficients used for the :math:`\lambda_I` + term (3D if changes with sweeps). + QDeltaE : 2D or 3D array-like + The :math:`Q_\Delta^E` coefficients used for the :math:`\lambda_E` + term (3D if changes with sweeps). + nSweeps : int + Number of sweeps. + + Returns + ------- + uNum : np.ndarray + The solution at each time-steps (+ initial solution). + """ + nNodes, Q, weights, QDeltaI, QDeltaE, nSweeps = self.checkCoeffSDC( + Q, weights, QDeltaI, QDeltaE, nSweeps) # Preconditioner for each sweeps P = np.eye(nNodes)[None, ...] \ - self.lamI[..., None, None, None]*self.dt*QDeltaI \ - self.lamE[..., None, None, None]*self.dt*QDeltaE - uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.uDtype) + uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) uNum[0] = self.u0 for i in range(self.nSteps): diff --git a/qmat/solvers/generic/__init__.py b/qmat/solvers/generic/__init__.py index c5d0868..99489e6 100644 --- a/qmat/solvers/generic/__init__.py +++ b/qmat/solvers/generic/__init__.py @@ -127,8 +127,8 @@ def fSolve(self, a:float, rhs:np.ndarray, t:float, out:np.ndarray): @staticmethod - def lowerTri(Q:np.ndarray): - return np.allclose(np.triu(Q, k=1), np.zeros(Q.shape)) + def lowerTri(Q:np.ndarray, strict=False): + return np.allclose(np.triu(Q, k=0 if strict else 1), np.zeros(Q.shape)) def solve(self, Q, weights, uNum=None): diff --git a/qmat/solvers/sdc.py b/qmat/solvers/sdc.py index 42f3dda..218d4f9 100644 --- a/qmat/solvers/sdc.py +++ b/qmat/solvers/sdc.py @@ -6,7 +6,7 @@ import numpy as np -def solveDahlquistSDC(lam, u0, T, nSteps:int, nSweeps:int, Q:np.ndarray, QDelta:np.ndarray, +def solveDahlquistSDC(lam, u0, tEnd, nSteps:int, nSweeps:int, Q:np.ndarray, QDelta:np.ndarray, weights=None, monitors=None): r""" Solve the Dahlquist problem with SDC. @@ -17,7 +17,7 @@ def solveDahlquistSDC(lam, u0, T, nSteps:int, nSweeps:int, Q:np.ndarray, QDelta: The :math:`\lambda` coefficient. u0 : complex or float The initial solution :math:`u_0`. - T : float + tEnd : float Final time :math:`T`. nSteps : int Number of time-step for the whole :math:`[0,T]` interval. @@ -26,7 +26,7 @@ def solveDahlquistSDC(lam, u0, T, nSteps:int, nSweeps:int, Q:np.ndarray, QDelta: Q : np.ndarray Quadrature matrix :math:`Q` used for SDC. QDelta : np.ndarray - Approximate quadrature matrix :math:`Q_\Delta` used for SDC. + Approximate quadrature matrix :math:`Q_\Delta` used for SDC. If three dimensional, use the first dimension for the sweep index. weights : np.ndarray, optional Quadrature weights to use for the prologation. @@ -39,9 +39,9 @@ def solveDahlquistSDC(lam, u0, T, nSteps:int, nSweeps:int, Q:np.ndarray, QDelta: """ nodes = Q.sum(axis=1) nNodes = Q.shape[0] - dt = T/nSteps - times = np.linspace(0, T, nSteps+1) - + dt = tEnd/nSteps + times = np.linspace(0, tEnd, nSteps+1) + QDelta = np.asarray(QDelta) if QDelta.ndim == 3: assert QDelta.shape == (nSweeps, nNodes, nNodes), "inconsistent shape for QDelta" @@ -92,11 +92,11 @@ def solveDahlquistSDC(lam, u0, T, nSteps:int, nSweeps:int, Q:np.ndarray, QDelta: if monitors: return uNum, monitors - else: + else: return uNum -def errorDahlquistSDC(lam, u0, T, nSteps, nSweeps, Q, QDelta, +def errorDahlquistSDC(lam, u0, tEnd, nSteps, nSweeps, Q, QDelta, weights=None, uNum=None): r""" Compute the time :math:`L_\infty` error of SDC. @@ -107,7 +107,7 @@ def errorDahlquistSDC(lam, u0, T, nSteps, nSweeps, Q, QDelta, The :math:`\lambda` coefficient. u0 : complex or float The initial solution :math:`u_0`. - T : float + tEnd : float Final time :math:`T`. nSteps : int Number of time-step for the whole :math:`[0,T]` interval. @@ -131,10 +131,10 @@ def errorDahlquistSDC(lam, u0, T, nSteps, nSweeps, Q, QDelta, """ if uNum is None: uNum = solveDahlquistSDC( - lam, u0, T, nSteps, nSweeps, Q, QDelta, + lam, u0, tEnd, nSteps, nSweeps, Q, QDelta, weights=weights) - times = np.linspace(0, T, nSteps+1) + times = np.linspace(0, tEnd, nSteps+1) uExact = u0 * np.exp(lam*times) return np.linalg.norm(uNum-uExact, ord=np.inf) @@ -159,8 +159,7 @@ def getOrderSDC(coll, nSweeps, qDelta, prolongation): order : int Expected order of the SDC time-integration. """ - # TODO : extend with additional results from - # https://gitlab.inria.fr/sweet/sweet/-/blob/main/mule_local/python/sdc/qmatrix.py#L596 + # TODO : extend with additional using time-dependent terms nNodes, nodeType, quadType = coll.nodes.size, coll.nodeType, coll.quadType diff --git a/qmat/utils.py b/qmat/utils.py index 5d91508..2dee9c6 100644 --- a/qmat/utils.py +++ b/qmat/utils.py @@ -6,6 +6,7 @@ import inspect import pkgutil import functools +from time import time def checkOverriding(cls, name, isProperty=True): """Check if a class overrides a method with a given name""" @@ -98,3 +99,41 @@ def wrapper(self, *args, **kwargs): __init__(self, **params) return wrapper + +class Timer(): + """ + Utility Timer class, that can be used as follow : + + >>> with Timer("stuff"): # prints "Starting stuff ... + >>> # ... do stuff + >>> # prints " -- tWall : {tWall}s + + The description at the end can be replaced using the `descr` + constructor parameter, and the final wall time can be scaled + using the `scale` parameter. Can also be used like this : + + >>> clock = Timer("stuff") + >>> clock.start() + >>> # ... do stuff + >>> clock.stop() + >>> tWall = clock.tWall + """ + def __init__(self, name, scale=1, descr="tWall"): + self.name = name + self.scale = scale + self.descr = descr + + def start(self): + print(f"Starting {self.name} ...") + self.tStart = time() + + def stop(self): + self.tWall = time() - self.tStart + self.tWall /= self.scale + print(f" -- {self.descr} : {self.tWall:1.2e}s") + + def __enter__(self): + self.start() + + def __exit__(self, exc_type, exc_value, traceback): + self.stop() \ No newline at end of file diff --git a/tests/test_4_utils.py b/tests/test_4_utils.py new file mode 100644 index 0000000..f3defde --- /dev/null +++ b/tests/test_4_utils.py @@ -0,0 +1,13 @@ +from time import sleep + +from qmat.utils import Timer + +def testTimer(): + with Timer("test1"): + pass + + clock = Timer("test2") + clock.start() + sleep(0.1) + clock.stop() + assert clock.tWall >= 0.1 diff --git a/tests/test_solvers/test_dahlquist.py b/tests/test_solvers/test_dahlquist.py index 06cb5cb..d1f34bf 100644 --- a/tests/test_solvers/test_dahlquist.py +++ b/tests/test_solvers/test_dahlquist.py @@ -65,10 +65,10 @@ def testDahlquistIMEX(scheme, tEnd, nSteps, dim, lam): lamVals = lam*np.linspace(0, 1, 4**dim).reshape((4,)*dim) - basis = Dahlquist(lam=lamVals, u0=1, T=tEnd, nSteps=nSteps) + basis = Dahlquist(lam=lamVals, u0=1, tEnd=tEnd, nSteps=nSteps) ref = basis.solve(Q=qGen.Q, weights=qGen.weights) - solver = DahlquistIMEX(lamI=lamVals, lamE=[0], u0=1, T=tEnd, nSteps=nSteps) + solver = DahlquistIMEX(lamI=lamVals, lamE=[0], u0=1, tEnd=tEnd, nSteps=nSteps) sol = solver.solve(QI=qGen.Q, wI=qGen.weights, QE=qGen.Q, wE=qGen.weights) assert np.allclose(sol, ref), \ "DahlquistIMEX solver does not match Dahlquist solver with implicit part only" @@ -78,7 +78,7 @@ def testDahlquistIMEX(scheme, tEnd, nSteps, dim, lam): assert np.allclose(sol, ref), \ "DahlquistIMEX solver without weights does not match Dahlquist solver with implicit part only" - solver = DahlquistIMEX(lamI=[0], lamE=lamVals, u0=1, T=tEnd, nSteps=nSteps) + solver = DahlquistIMEX(lamI=[0], lamE=lamVals, u0=1, tEnd=tEnd, nSteps=nSteps) sol = solver.solve(QI=qGen.Q, wI=qGen.weights, QE=qGen.Q, wE=qGen.weights) assert np.allclose(sol, ref), \ "DahlquistIMEX solver does not match Dahlquist solver with explicit part only" @@ -89,10 +89,10 @@ def testDahlquistIMEX(scheme, tEnd, nSteps, dim, lam): "DahlquistIMEX solver without weights does not match Dahlquist solver with explicit part only" for weights in [qGen.weights, None]: - basis = Dahlquist(lam=2*lamVals, u0=1, T=tEnd, nSteps=nSteps) + basis = Dahlquist(lam=2*lamVals, u0=1, tEnd=tEnd, nSteps=nSteps) ref = basis.solve(Q=qGen.Q, weights=weights) - solver = DahlquistIMEX(lamI=lamVals, lamE=lamVals, u0=1, T=tEnd, nSteps=nSteps) + solver = DahlquistIMEX(lamI=lamVals, lamE=lamVals, u0=1, tEnd=tEnd, nSteps=nSteps) sol = solver.solve(QI=qGen.Q, wI=weights, QE=qGen.Q, wE=weights) detail = " with weights " if weights is not None else "" assert np.allclose(sol, ref), \ diff --git a/tests/test_solvers/test_generic.py b/tests/test_solvers/test_generic.py index deed3ff..5b491dc 100644 --- a/tests/test_solvers/test_generic.py +++ b/tests/test_solvers/test_generic.py @@ -66,7 +66,7 @@ def testLinearCoeffSolverDahlquistSDC( continue uRef = solveDahlquistSDC( - lam, 1, T=tEnd, nSteps=nSteps, nSweeps=nSweeps, + lam, 1, tEnd=tEnd, nSteps=nSteps, nSweeps=nSweeps, Q=coll.Q, QDelta=QDelta, weights=weights) uNum = solver.solveSDC( @@ -145,9 +145,7 @@ def testLinearCoeffSolverLorenzSDC(scheme, nNodes, nSweeps, quadType, uRefLorent def uRefProtheroRobinson(): diffOp = ProtheroRobinson(epsilon=0.5) tEnd = 0.5 - qGenRef = Q_GENERATORS["ARK4ERK"].getInstance() - uRef = CoeffSolver(diffOp, tEnd=tEnd, nSteps=1000).solve( - qGenRef.Q, qGenRef.weights) + uRef = [diffOp.g(tEnd)] return {"tEnd": tEnd, "sol": uRef, "diffOp": diffOp} From 4365cbecef3158377c18259dfa49e978d515e811 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Mon, 27 Oct 2025 19:21:59 +0100 Subject: [PATCH 20/33] TL: finalized docstrings for new modules --- docs/notebooks.md | 8 +- ...tialization.ipynb => 11_nonLinearRK.ipynb} | 2 +- ...olongation.ipynb => 12_nonLinearSDC.ipynb} | 2 +- qmat/playgrounds/tibo/lorenz.py | 1 + qmat/solvers/__init__.py | 3 +- qmat/solvers/dahlquist.py | 54 +- qmat/solvers/generic/__init__.py | 570 +++++++++++++++++- qmat/solvers/generic/diffops.py | 82 ++- qmat/solvers/generic/integrators.py | 59 +- 9 files changed, 724 insertions(+), 57 deletions(-) rename docs/notebooks/{12_initialization.ipynb => 11_nonLinearRK.ipynb} (73%) rename docs/notebooks/{11_prolongation.ipynb => 12_nonLinearSDC.ipynb} (71%) diff --git a/docs/notebooks.md b/docs/notebooks.md index d7796a5..08d74f1 100644 --- a/docs/notebooks.md +++ b/docs/notebooks.md @@ -5,13 +5,13 @@ All tutorials are written in jupyter notebooks, that can be : - read using the [online documentation](https://qmat.readthedocs.io/en/latest/notebooks.html) -- downloaded from the [notebook folder](https://github.com/Parallel-in-Time/qmat/tree/main/docs/notebooks) and played with +- downloaded from the [notebook folder](https://github.com/Parallel-in-Time/qmat/tree/main/docs/notebooks) and played with -> 🛠ī¸ Basic usage tutorials are finalized and polished, the rest is still in construction ... +> 🛠ī¸ Basic usage tutorials are finalized and polished, the rest is still in construction ... Notebooks are categorized into those main sections : -1. **Basic usage** : how to generate and use basic $Q$-coefficients and $Q_\Delta$ approximations, through a step-by-step tutorial going from generic Runge-Kutta methods to SDC for simple problems. +1. **Basic usage** : how to generate and use basic $Q$-coefficients and $Q_\Delta$ approximations, through a step-by-step tutorial going from generic Runge-Kutta methods to SDC for simple problems. 2. **Extended usage** : additional features or `qmat` ($S$-matrix, `hCoeffs`, `dTau` coefficients, ...) to go deeper into SDC 3. **Components usage** : how to use the main utility modules, like `qmat.lagrange`, etc ... @@ -31,7 +31,7 @@ Base usage Extended usage ============== -📜 *Going deeper into SDC's understanding* +📜 *Going deeper into advanced time-integration topics* .. toctree:: :maxdepth: 1 diff --git a/docs/notebooks/12_initialization.ipynb b/docs/notebooks/11_nonLinearRK.ipynb similarity index 73% rename from docs/notebooks/12_initialization.ipynb rename to docs/notebooks/11_nonLinearRK.ipynb index 9b7facd..623071a 100644 --- a/docs/notebooks/12_initialization.ipynb +++ b/docs/notebooks/11_nonLinearRK.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Advanced Step 3 : generalizing the initialization of SDC-type time-steppers\n", + "# Advanced Step 2 : build a Runge-Kutta solver for non-linear ODEs \n", "\n", "🛠ī¸ In construction ..." ] diff --git a/docs/notebooks/11_prolongation.ipynb b/docs/notebooks/12_nonLinearSDC.ipynb similarity index 71% rename from docs/notebooks/11_prolongation.ipynb rename to docs/notebooks/12_nonLinearSDC.ipynb index df5d22e..c235e72 100644 --- a/docs/notebooks/11_prolongation.ipynb +++ b/docs/notebooks/12_nonLinearSDC.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Advanced Step 2 : generalizing the prolongation for RK-type and SDC-type time-steppers\n", + "# Advanced Step 3 : build a Spectral Deferred Correction solver for non-linear ODEs\n", "\n", "🛠ī¸ In construction ..." ] diff --git a/qmat/playgrounds/tibo/lorenz.py b/qmat/playgrounds/tibo/lorenz.py index c53b6bf..c5a1814 100644 --- a/qmat/playgrounds/tibo/lorenz.py +++ b/qmat/playgrounds/tibo/lorenz.py @@ -2,6 +2,7 @@ # -*- coding: utf-8 -*- """ Make use of the generic :class:`CoeffSolver` of `qmat` to solve the Lorenz equations +using a RK method or Spectral Deferred Correction. .. literalinclude:: /../qmat/playgrounds/tibo/lorenz.py :language: python diff --git a/qmat/solvers/__init__.py b/qmat/solvers/__init__.py index 7c4d1de..dcb05eb 100644 --- a/qmat/solvers/__init__.py +++ b/qmat/solvers/__init__.py @@ -4,8 +4,9 @@ Implementations of time-integration solvers that make use of `qmat`-generated coefficients. 🔔 Those are not fully optimized implementations of their corresponding - time-integration scheme : these are conveniences classes allowing + time-integration scheme, but conveniences classes allowing some first **experiments** with your problem(s) of interest. + They are mostly given **Modules** ⚙ī¸ diff --git a/qmat/solvers/dahlquist.py b/qmat/solvers/dahlquist.py index 4b1288c..e9c5f38 100644 --- a/qmat/solvers/dahlquist.py +++ b/qmat/solvers/dahlquist.py @@ -98,7 +98,7 @@ def checkCoeff(Q, weights): def solve(self, Q, weights): r""" Solve for all :math:`\lambda` using a direct solve of the :math:`Q` - matrix, *i.e* for each step it solves : + matrix, *i.e* for each time-step it solves : .. math:: @@ -206,8 +206,8 @@ def checkCoeffSDC(Q, weights, QDelta, nSweeps): def solveSDC(self, Q, weights, QDelta, nSweeps): r""" - Solve for all :math:`\lambda` using SDC sweeps, *i.e* solve for - each sweep :math:`k` : + Solve for all :math:`\lambda` using SDC sweeps, *i.e* solves for + each time-step and sweep :math:`k` : .. math:: @@ -388,17 +388,34 @@ def checkCoeff(QI, wI, QE, wE): def solve(self, QI, wI, QE, wE): r""" Solve for all :math:`\lambda_I` and :math:`\lambda_E` - using a direct solve of the :math:`Q_I` and :math:`Q_E` matrices. + using a direct solve of the :math:`Q^I` and :math:`Q^E` matrices, + *i.e* for each time-step it solves : + + .. math:: + + (I - \lambda_I Q^I - \lambda_E Q^E){\bf u} = {\bf u}_0 + + where :math:`{\bf u}_0` is the vector containing the initial solution + of the time-step in each entry. + The next step solution is computed using the IMEX **step update** : + + .. math:: + + u_1 = u_0 + \Delta{t}\lambda_I{\bf w}_I^T{\bf u} + + \Delta{t}\lambda_E{\bf w}_E^T{\bf u}, + + or simply use the last **node solution** :math:`{\bf u}[-1]` if + no weights are given (`wI=wE=None`). Parameters ---------- QI : 2D array-like - :math:`Q` coefficients used for :math:`\lambda_I`. + :math:`Q^I` coefficients used for :math:`\lambda_I`. wI : 1D array-like or None Weights used for the step update on :math:`\lambda_I`. If None, then step update is not done. QE : 2D array-like - :math:`Q` coefficients used for :math:`\lambda_E`. + :math:`Q^E` coefficients used for :math:`\lambda_E`. wE : 1D array-like or None Weights used for the step update on :math:`\lambda_E`. If None, then step update is not done. @@ -505,8 +522,29 @@ def checkCoeffSDC(Q, weights, QDeltaI, QDeltaE, nSweeps): def solveSDC(self, Q, weights, QDeltaI, QDeltaE, nSweeps): - """ - Solve for all :math:`\lambda_I` and :math:`\lambda_E` using SDC sweeps. + r""" + Solve for all :math:`\lambda_I` and :math:`\lambda_E` using SDC sweeps, + *i.e* for each time-step and sweep :math:`k` it solves : + + .. math:: + + (I - \Delta{t}\lambda_I Q_\Delta^I - \Delta{t}\lambda_E Q_\Delta^I){\bf u}^{k+1} + = {\bf u}_0 + \Delta{t}\left[ + \lambda Q - \lambda_I Q_\Delta^I - \lambda_E Q_\Delta^E\right] + {\bf u}^{k}, + + where :math:`{\bf u}_0` is the vector containing the initial solution + of the time-step in each entry and :math:`{\bf u}^0 = {\bf u}_0` + (copy initialization). + The next step solution is computed using the **step update** : + + .. math:: + + u_1 = u_0 + \Delta{t}\lambda{\bf w}^T{\bf u}^{K}, + + where :math:`K` is the total number of sweeps. + If no weights are given (`weights=None`), it simply uses the last + **node solution** :math:`{\bf u}[-1]`. Parameters ---------- diff --git a/qmat/solvers/generic/__init__.py b/qmat/solvers/generic/__init__.py index 99489e6..9b0908e 100644 --- a/qmat/solvers/generic/__init__.py +++ b/qmat/solvers/generic/__init__.py @@ -1,7 +1,65 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -""" -Submodule containing various generic solvers that can be used with `qmat`-generated coefficients. +r""" +Submodule implementing generic solvers that can be used +to solve (non-linear) ODE systems of the form : + +.. math:: + + \frac{du}{dt} = f(u,t), \quad u(0) = u_0. + +All those solvers are based on a :class:`DiffOp` base class, +implementing : + +- the :math:`f(u,t)` evaluations, +- a solver for :math:`u-\alpha f(u,t)=rhs`, considering given :math:`\alpha,t,rhs`. + +While the :math:`f(u,t)` evaluations must be implemented, +a default implementation of the solver for :math:`u-\alpha f(u,t)=rhs` +is provided in the base :class:`DiffOp` class. + + 🛠ī¸ Various specialized :class:`DiffOp` classes are implemented + in the :class:`diffops` submodule. + +The solvers implemented here discretizes +a time-step :math:`[t_0, t_0+\Delta{t}]` into **time nodes** +:math:`[t_0+\Delta{t}\tau_1, ..., t_0+\Delta{t}\tau_M]` +noted :math:`[t_1,\dots,t_M]`, +also called **stages** for RK methods, at which are defined the +**node solutions** :math:`u_m \simeq u(t_m)`. +And usually, the vector containing the node solutions +:math:`{\bf u} = [u_1,\dots,u_M]^T` satisfy a **all-at-once system** : + +.. math:: + {\bf u} - \Delta{t}Q {\bf f} = {\bf u}_0, + +where :math:`{\bf f} = [f(u_1, t_1),\dots,f(u_M,t_M)]^T` is the vector +with the evaluations of each node solutions +and :math:`{\bf u}_0` is a vector containing :math:`u_0` in each entry. +The :class:`CoeffSolver` allows to solve any ODE using this coefficient-based +approach, either directly if the :math:`Q` matrix is lower triangular, +or iteratively with SDC-based sweeps if :math:`Q` is a dense matrix. + +---- + +An alternative solver approach relates all the node solutions using a +:math:`\phi` **representation** of a time-integrator, +*i.e* each node solution :math:`u_{m+1}` satisfies +the following relation : + +.. math:: + + u_{m+1} -\phi(u_0, u_1, ..., u_{m}, u_{m+1}) = u_0, + +where :math:`\phi` is solely defined by the chosen time-integrator. +The system above can be solved node-by-node in a sequential approach, +or iteratively with a SDC-based approach. +It is implemented in the abstract :class:`PhiSolver` class, +that needs to be specialized by a child class implementing +the :math:`\phi` function. + + 🛠ī¸ Specialized :class:`PhiSolver` classes are implemented in the + :class:`integrators` submodule. """ import numpy as np import scipy.optimize as sco @@ -13,39 +71,89 @@ class DiffOp(): - """ - Base class for Differential Operators + r""" + Base class for a differential operator :math:`f(u, t)` used in a generic ODE. + + It defines the evaluation of :math:`f(u, t)` at given :math:`u` and + :math:`t` with a `evalF(u, t, out)` method, that put the result + of the evaluation in the `out` array. + + Additionally, this class defines a default `fSolve` method that solves : + + .. math:: + + u - \alpha f(u,t) = rhs + + for given :math:`\alpha`, :math:`t` and :math:`rhs`. + This default method can be overridden by a more efficient specific + method for a specific differential operator. + + Note + ---- + Solutions are stored in N-dimensional :class:`numpy.ndarray`. + + Parameters + ---------- + u0 : array-like + The initial solution associated to the differential operator, to which + is extracted the generic shape and datatype of :math:`u(t)` solutions. """ def __init__(self, u0): for name in ["u0", "innerSolver"]: assert not hasattr(self, name), \ f"{name} attribute is reserved for the base DiffOp class" self.u0 = np.asarray(u0) + """Initial solution for the differential operator.""" if self.u0.size < 1e3: self.innerSolver = sco.fsolve + """Inner solver used in the default `fSolve` method.""" else: self.innerSolver = sco.newton_krylov @property def uShape(self): + """Shape of a :math:`u` solution, stored as numpy array.""" return self.u0.shape @property def dtype(self): + """Datatype of a :math:`u` solution, stored as numpy array.""" return self.u0.dtype + def evalF(self, u:np.ndarray, t:float, out:np.ndarray): """ - Evaluate f(u,t) and store the result into out + Evaluate :math:`f(u,t)` and store the result into `out`. + + Parameters + ---------- + u : np.ndarray + Input solution for the evaluation. + t : float + Time for the evaluation. + out : np.ndarray + Output array in which is stored the evaluation. """ raise NotImplementedError("evalF must be provided") + def fSolve(self, a:float, rhs:np.ndarray, t:float, out:np.ndarray): + r""" + Solve :math:`u-\alpha f(u,t)=rhs` for given :math:`u,t,rhs`, + using `out` as initial guess and storing the final result into it. + + Parameters + ---------- + a : float + The :math:`\alpha` coefficient. + rhs : np.ndarray + The right hand side. + t : float + Time for the evaluation. + out : np.ndarray + Input-output array used as initial guess, + in which is stored the solution. """ - Solve u - a*f(u, t) = rhs using out as initial guess - and store the result into out - """ - def func(u:np.ndarray): """compute res = u - a*f(u,t) - rhs""" u = u.reshape(self.uShape) @@ -59,8 +167,25 @@ def func(u:np.ndarray): sol = self.innerSolver(func, out.ravel()).reshape(self.uShape) np.copyto(out, sol) + @classmethod def test(cls, t0=0, dt=1e-1, eps=1e-3, instance=None): + """ + Class method to test the `DiffOp` implementation. + + Parameters + ---------- + t0 : float, optional + Evaluation time to test the instance. The default is 0. + dt : float, optional + Time-step to test the `fSolve` method. The default is 1e-1. + eps : float, optional + Perturbation added in the expected solution to test the + `fSolve` method. The default is 1e-3. + instance :`DiffOp`, optional + Instance to be tested. If not provided (`None`), + an instance is created using the default constructor. + """ if instance is None: try: instance = cls() @@ -95,43 +220,166 @@ def test(cls, t0=0, dt=1e-1, eps=1e-3, instance=None): class CoeffSolver(): + r""" + Solve generic (non-linear) ODE system using :math:`Q`-coefficients with lower triangular form. + It can be used to solve generic ODE systems of the form : + + .. math:: + + \frac{du}{dt} = f(u,t), \quad u(0)=u_0. + + Parameters + ---------- + diffOp : DiffOp + Differential operator for the ODE. + tEnd : float, optional + Final simulation time. The default is 1. + nSteps : int, optional + Number of simulation time-steps. The default is 1. + t0 : float, optional + Initial simulation time. The default is 0. + """ def __init__(self, diffOp:DiffOp, tEnd=1, nSteps=1, t0=0): assert isinstance(diffOp, DiffOp) self.diffOp = diffOp + """Differential Operator implementing :math:`f(u,t)`.""" self.axpy = blas.get_blas_funcs('axpy', dtype=self.dtype) + r"""BLAS-I function executing :math:`y=\alpha x + y` for any solution vectors :math:`x,y`.""" self.t0 = t0 + """Initial simulation time.""" self.tEnd = tEnd + """Final simulation time.""" self.nSteps = nSteps + """Number of simulation time-steps""" self.dt = (tEnd-t0)/nSteps + """Time-step size for the simulation""" @property def u0(self): + """Initial solution for the problem""" return self.diffOp.u0 @property def uShape(self): + """Shape of the solution at a given time.""" return self.diffOp.uShape @property def dtype(self): + """Datatype of the solution at a given time.""" return self.diffOp.dtype def evalF(self, u:np.ndarray, t:float, out:np.ndarray): + """ + Wrapper for the `DiffOp` function evaluating :math:`f(u,t)`. + + Parameters + ---------- + u : np.ndarray + Input solution for the evaluation. + t : float + Time for the evaluation. + out : np.ndarray + Output array in which is stored the evaluation. + """ self.diffOp.evalF(u, t, out) def fSolve(self, a:float, rhs:np.ndarray, t:float, out:np.ndarray): + r""" + Wrapper for the `DiffOp` function solving :math:`u-\alpha f(u,t) = rhs`. + + Parameters + ---------- + a : float + The :math:`\alpha` coefficient. + rhs : np.ndarray + The right hand side. + t : float + Time for the evaluation. + out : np.ndarray + Input-output array used as initial guess, + in which is stored the solution. + """ self.diffOp.fSolve(a, rhs, t, out) @staticmethod def lowerTri(Q:np.ndarray, strict=False): + """ + Check if a 2D matrix is lower triangular. + + Parameters + ---------- + Q : np.ndarray + Matrix to check. + strict : bool, optional + Check for strictly lower triangular matrix. The default is False. + + Returns + ------- + bool + Is the matrix (strictly) lower triangular or not. + """ return np.allclose(np.triu(Q, k=0 if strict else 1), np.zeros(Q.shape)) - def solve(self, Q, weights, uNum=None): + def solve(self, Q, weights, uNum=None, tInit=0): + r""" + Solve the ODE considering **lower-triangular** :math:`Q` coefficients. + + This is equivalent to the classical implementation of a generic + Runge-Kutta method using its Butcher table. + For each time-step, it defines a node solution (or stage) + :math:`u_{m}` that is solved using previously computed + node solution : + + .. math:: + + u_{m} - \Delta{t}q_{m,m}f(u_m,t_m) + = u_0 + \Delta{t}\sum_{j=1}^{m-1}q_{m,j}f(u_j, t_j), + + where :math:`t_m = t_0 + \tau_m` and :math:`q_{i,j}` + are the coefficients :math:`Q`. + Finally, the **step update** is done using all computed node + solutions : + + .. math:: + u(t_0+\Delta{t}) \simeq + u_0 + \sum_{m=1}^{M} \omega_{m} f(u_m, t_m), + + where :math:`\omega_{m}` are the weights associated to the + :math:`Q`-coefficients. + If no weights are provided, then it simply uses the last + node solution for the step update : + + .. math:: + u(t_0+\Delta{t}) \simeq u_M + + Parameters + ---------- + Q : np.2darray-like + The **lower-triangular** :math:`Q`-coefficients matrix. + weights : np.1darray-like + The associated :math:\omega_{m}` weights. If not provided, + use the last node solution for the update + (requires :math:`\tau_{M} = 1`). + uNum : np.ndarray, optional + Array of shape `(nSteps+1,*uShape)`, that can be use + to store the result and avoid creating it internally. + The default is None. + tInit : float, optional + Initial time offset to be added to solver's own `t0` for + successive `solve` calls. The default is 0. + + Returns + ------- + uNum : np.ndarray + Array of shape `(nSteps+1,*uShape)` that stores the solution at + each time-step. + """ nNodes, Q, weights = Dahlquist.checkCoeff(Q, weights) assert self.lowerTri(Q), "lower triangular matrix Q expected for non-linear solver" @@ -142,11 +390,13 @@ def solve(self, Q, weights, uNum=None): if uNum is None: uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) uNum[0] = self.u0 + assert np.shape(uNum) == (self.nSteps+1, *self.uShape), \ + "user-provided uNum do not have the correct shape" rhs = np.zeros(self.uShape, dtype=self.dtype) fEvals = np.zeros((nNodes, *self.uShape), dtype=self.dtype) - times = np.linspace(self.t0, self.tEnd, self.nSteps+1) + times = np.linspace(self.t0+tInit, self.tEnd+tInit, self.nSteps+1) tau = Q.sum(axis=1) # time-stepping loop @@ -181,11 +431,70 @@ def solve(self, Q, weights, uNum=None): return uNum - def solveSDC(self, nSweeps, Q, weights, QDelta, uNum=None): + def solveSDC(self, nSweeps, Q, weights, QDelta, uNum=None, tInit=0): + r""" + Solve the ODE with dense :math:`Q` coefficients using SDC sweeps. + + Considering a **lower-triangular** approximation :math:`Q_\Delta` + of :math:`Q`, it performes for each time-step :math:`K` SDC sweeps : + + .. math:: + + \begin{align} + u_{m}^{k+1} - \Delta{t}q^\Delta_{m,m}f(u_m^{k+1},t_m) + =&~ u_0 + \Delta{t}\sum_{j=1}^{M}q_{m,j}f(u_j^k, t_j) \\ + &+ \Delta{t}\sum_{j=1}^{m-1}q^\Delta_{m,j}f(u_j^{k+1},t_j) + - \Delta{t}\sum_{j=1}^{m}q^\Delta_{m,j}f(u_j^{k},t_j), + \end{align} + + where :math:`q^\Delta_{i,j}` and :math:`q_{i,j}` are the coefficients + of :math:`Q_\Delta` and :math:`Q`, respectively. + It uses a **copy initialization**, that is :math:`u_{m}^0 = u_0`. + + Finally, the **step update** is done using all computed node + solutions : + + .. math:: + u(t_0+\Delta{t}) \simeq + u_0 + \sum_{m=1}^{M} \omega_{m} f(u_m, t_m), + + where :math:`\omega_{m}` are the weights associated to the + :math:`Q`-coefficients. + If no weights are provided, then it simply uses the last + node solution for the step update : + + .. math:: + u(t_0+\Delta{t}) \simeq u_M + + Parameters + ---------- + nSweeps : int + Number of SDC sweeps :math:`K`. + Q : 2D array-like + The dense :math:`Q` matrix. + weights : 1D array-like + The associated weights :math:`\omega_{m}` for the step update. + QDelta : 2D array-like + The lower-triangular :math:`Q_\Delta` matrix. + uNum : np.ndarray, optional + Array of shape `(nSteps+1,*uShape)`, that can be use + to store the result and avoid creating it internally. + The default is None. + tInit : float, optional + Initial time offset to be added to solver's own `t0` for + successive `solve` calls. The default is 0. + + Returns + ------- + uNum : np.ndarray + Array of shape `(nSteps+1,*uShape)` that stores the solution at + each time-step. + """ nNodes, Q, weights, QDelta, nSweeps = Dahlquist.checkCoeffSDC(Q, weights, QDelta, nSweeps) - for qDelta in QDelta: - assert self.lowerTri(qDelta), "lower triangular matrices QDelta expected for non-linear SDC solver" + assert self.lowerTri(qDelta), \ + "lower triangular matrices QDelta expected for non-linear SDC solver" + Q, QDelta = self.dt*Q, self.dt*QDelta if weights is not None: weights = self.dt*weights @@ -198,7 +507,7 @@ def solveSDC(self, nSweeps, Q, weights, QDelta, uNum=None): fEvals = [np.zeros((nNodes, *self.uShape), dtype=self.dtype) for _ in range(2)] - times = np.linspace(self.t0, self.tEnd, self.nSteps+1) + times = np.linspace(self.t0+tInit, self.tEnd+tInit, self.nSteps+1) tau = Q.sum(axis=1) # time-stepping loop @@ -257,22 +566,123 @@ def solveSDC(self, nSweeps, Q, weights, QDelta, uNum=None): class PhiSolver(CoeffSolver): - + r""" + Solve generic (non-linear) ODE system using :math:`\phi` representation of time-integration solvers. + + It consider the following ODE : + + .. math:: + \frac{du}{dt} = f(u,t), + + and compute for each step the solution on **time nodes** :math:`\tau_1, ..., \tau_M` + by soving the following system : + + .. math:: + + u_{m+1} -\phi(u_0, u_1, ..., u_{m}, u_{m+1}) = u_0. + + It uses then per default the last node solution :math:`u_{M}` as initial + solution for the next step. + + ⚙ī¸ Requires the implementation of an `evalPhi` method that evaluates + the :math:`\phi` function. + Also, a default `phiSolve` method is implemented, that solves + the system above, and can be overridden for specific time-integrator + (in particular for explicit time-integrators). + Finally, it implements a default `stepUpdate` method that setup the + next time-step using the last time-node solution. + + Parameters + ---------- + diffOp : DiffOp + Differential operator for the ODE. + nodes : 1D array-like + The time nodes :math:`\tau_1, ..., \tau_M`. + tEnd : float, optional + Final simulation time. The default is 1. + nSteps : int, optional + Number of simulation time-steps. The default is 1. + t0 : float, optional + Initial simulation time. The default is 0. + """ def __init__(self, diffOp:DiffOp, nodes, tEnd=1, nSteps=1, t0=0): super().__init__(diffOp, tEnd, nSteps, t0) self.nodes = np.asarray(nodes, dtype=float) + """Time nodes for each time-step of the time-integrator.""" @property def nNodes(self): + """Number of time-nodes""" return self.nodes.size def evalPhi(self, uVals, fEvals, out, t0=0): + r""" + Evaluate the :math:`\phi` operator on time-node up to :math:`u_{m+1}`. + + Considering :math:`u_0, u_1, \dots, u_{m+1}`, + if evaluates : + + .. math:: + + \phi(u_0, u_1, ..., u_{m}, u_{m+1}), + + and store its value into the output vector `out`. + It also takes the node evaluation + :math:`f(u_0,t_0),f(u_1,\tau_1),...,f(u_{m},\tau_{m})` + as arguments, in order to avoid any additional :math:`f(u,t)` + evaluations. + + Parameters + ---------- + uVals : list[np.ndarray] of size :math:`m+2` + The :math:`m+1` time-node solutions + the initial solution :math:`u_0`. + fEvals : list[np.ndarray] of size :math:`m+1` or :math:`m+1` + The :math:`f(u,t)` evaluations at each time nodes (+ initial solution), + up to time-node :math:`m`. + It can eventually contain a pre-computed :math:`f_{m+1}` + to spare one :math:`f(u,t)` evaluation. + out : np.ndarray + Array used to store the evaluation. + t0 : float, optional + Initial step time. The default is 0. + """ raise NotImplementedError( - "specialized Integrator must implement its evalPsi method") + "specialized PhiSolver must implement its evalPhi method") + def phiSolve(self, uPrev, fEvals, out, rhs=0, t0=0): - """solve u-phi(u, u0, fEvals) = rhs""" + r""" + Solve the node update at given time-node :math:`\tau_{m+1}`. + + Considering :math:`m+1` previous known node solutions + :math:`u_0, u_1, ..., u_{m}`, it solves the following system : + + .. math:: + + u -\phi(u_0, u_1, ..., u_{m}, u) + = rhs, + + where the value given in `out` is used as **initial guess** and + to **store the computed solution**. + It also takes as argument the :math:`f` evaluations + :math:`f_0, f_1, ..., f_{m}` to avoid supplementar re-computing those. + + Parameters + ---------- + uPrev : list[np.ndarray] of size :math:`m+1` + The previous node solutions :math:`u_0, u_1, ..., u_{m}`. + fEvals : list[np.ndarray] of size :math:`m+1` + Evaluations of previous node solutions :math:`f_0, f_1, ..., f_{m}`. + out : np.ndarray + Array with the initial guess, used to store the final solution. + rhs : np.ndarray or float, optional + Right hand side used to solve the equation above. + The default is 0. + t0 : float, optional + Initial step size. The default is 0. + """ + assert len(fEvals) == len(uPrev) def func(u:np.ndarray): u = u.reshape(self.uShape) @@ -288,13 +698,60 @@ def func(u:np.ndarray): def stepUpdate(self, u0, uNodes, fEvals, out): - """Update end-step solution and ensure that fEvals[0] contains its evaluation""" + r""" + Update end-step solution to be used as initial guess for next step. + + Note + ---- + This method has to ensures that fEvals[0] contains the :math:`f(u,t)` + evaluation of the next step initial solution. + + Parameters + ---------- + u0 : np.ndarray + Initial solution for the current step. + uNodes : list[np.ndarray] + Precomputed node solutions :math:`u_1,\dots,u_M`. + fEvals : list[np.ndarray] + Precomputed node evaluation :math:`f_1,\dots,f_M`. + out : np.ndarray + Output array to store the result. + """ assert self.nodes[-1] == 1 np.copyto(out, uNodes[-1]) fEvals[0], fEvals[-1] = fEvals[-1], fEvals[0] - def solve(self, uNum=None): + def solve(self, uNum=None, tInit=0): + """ + Solve using sequential computation of node solutions for each step, + using the relation : + + .. math:: + + u_{m+1} -\phi(u_0, u_1, ..., u_{m}, u_{m+1}, f_0, f_1, ..., f_{m}) + = u_0. + + and the step update to compute :math:`u(t_0+\Delta_t)` using all + computed node solutions. + + + Parameters + ---------- + uNum : np.ndarray, optional + Array of shape `(nSteps+1,*uShape)`, that can be use + to store the result and avoid creating it internally. + The default is None. + tInit : float, optional + Initial time offset to be added to solver's own `t0` for + successive `solve` calls. The default is 0. + + Returns + ------- + uNum : np.ndarray + Array of shape `(nSteps+1,*uShape)` that stores the solution at + each time-step. + """ if uNum is None: uNum = np.zeros((self.nSteps+1, *self.uShape), dtype=self.dtype) uNum[0] = self.u0 @@ -304,7 +761,7 @@ def solve(self, uNum=None): for _ in range(self.nNodes+1)] self.evalF(uNum[0], self.t0, out=fEvals[0]) - times = np.linspace(self.t0, self.tEnd, self.nSteps+1) + times = np.linspace(self.t0+tInit, self.tEnd+tInit, self.nSteps+1) tau = self.dt*self.nodes # time-stepping loop @@ -325,8 +782,72 @@ def solve(self, uNum=None): return uNum - def solveSDC(self, nSweeps, Q=None, weights=None, uNum=None): - + def solveSDC(self, nSweeps, Q=None, weights=None, uNum=None, tInit=0): + r""" + Solve the ODE with dense :math:`Q` coefficients using SDC sweeps. + + Considering a **lower-triangular** approximation :math:`Q_\Delta` + of :math:`Q`, it performes for each time-step :math:`K` SDC sweeps : + + .. math:: + + u_{m}^{k+1} - \phi_m^{k+1} + = u_0 + \Delta{t}\sum_{j=1}^{M}q_{m,j}f(u_j^k, t_j) + - \phi_m^k, + + where + :math:`\phi_m^k:=\phi(u_0,u_1^k,\dots,u_m^k,f_0,f_1^k,\dots,f_{m-1}^k)` + and :math:`q_{i,j}` are the coefficients of the :math:`Q` matrix. + It uses a **copy initialization**, that is :math:`u_{m}^0 = u_0`. + + 💡 If we consider that :math:`\phi_m^{k}` is like + a coarse solver applied on iteration :math:`k` and + :math:`u_0 + \Delta{t}\sum_{j=1}^{M}q_{m,j}f(u_j^k, t_j)` is like + a fine solver applied to iteration :math:`k`, + then the SDC correction above furiously resemble to + a **Parareal iteration** đŸ‘ģ đŸ‘ģ đŸ‘ģ + + Finally, the **step update** is done using all computed node + solutions : + + .. math:: + u(t_0+\Delta{t}) \simeq + u_0 + \sum_{m=1}^{M} \omega_{m} f(u_m, t_m), + + where :math:`\omega_{m}` are the weights associated to the + :math:`Q`-coefficients. + If weights are not used (`weights=False`), + then it simply uses the last node solution for the step update : + + .. math:: + u(t_0+\Delta{t}) \simeq u_M + + Parameters + ---------- + nSweeps : int + Number of SDC sweeps :math:`K`. + Q : 2D array-like, optional + The dense :math:`Q` matrix. + If not provided, automatically computed using the + :class:`LagrangeApproximation` class and the solver nodes. + weights : 1D array-like, optional + The associated weights :math:`\omega_{m}` for the step update. + If not provided, automatically computed using the + :class:`LagrangeApproximation` class and the solver nodes. + uNum : np.ndarray, optional + Array of shape `(nSteps+1,*uShape)`, that can be use + to store the result and avoid creating it internally. + The default is None. + tInit : float, optional + Initial time offset to be added to solver's own `t0` for + successive `solve` calls. The default is 0. + + Returns + ------- + uNum : np.ndarray + Array of shape `(nSteps+1,*uShape)` that stores the solution at + each time-step. + """ if Q is None or weights is True: approx = LagrangeApproximation(self.nodes) if Q is None: @@ -353,8 +874,7 @@ def solveSDC(self, nSweeps, Q=None, weights=None, uNum=None): for _ in range(self.nNodes+1)] for _ in range(2)] - - times = np.linspace(self.t0, self.tEnd, self.nSteps+1) + times = np.linspace(self.t0+tInit, self.tEnd+tInit, self.nSteps+1) tau = self.dt*self.nodes # time-stepping loop diff --git a/qmat/solvers/generic/diffops.py b/qmat/solvers/generic/diffops.py index e58e1fe..df75ade 100644 --- a/qmat/solvers/generic/diffops.py +++ b/qmat/solvers/generic/diffops.py @@ -1,9 +1,7 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Created on Tue Oct 21 17:00:11 2025 - -@author: cpf5546 +Contains various specialized implementation of :class:`DiffOp` classes. """ import numpy as np from scipy.linalg import blas @@ -12,9 +10,9 @@ from qmat.solvers.generic import DiffOp from qmat.utils import checkOverriding, storeClass - T = TypeVar("T") + DIFFOPS: dict[str, type[DiffOp]] = {} """Dictionary containing all specialized :class:`DiffOp` classes""" @@ -27,7 +25,24 @@ def registerDiffOp(cls: type[T]) -> type[T]: @registerDiffOp class Dahlquist(DiffOp): + r""" + Implements a Dahlquist differential operator + + .. math:: + + f(u,t) = \lambda u + + Note + ---- + This class is implemented for illustration and testing purposes. + For real applications, consider using the + :class:`qmat.solvers.dahlquist.Dahlquist` class instead. + Parameters + ---------- + lam : complex, optional + The :math:`\lambda` value. The default is 1j. + """ def __init__(self, lam=1j): self.lam = lam u0 = np.array([1, 0], dtype=float) @@ -67,20 +82,19 @@ class Lorenz(DiffOp): nativeFSolve: bool, optional Wether or not using the native fSolve method (default is False). """ - def __init__(self, sigma=10, rho=28, beta=8/3, nativeFSolve=False): self.params = [sigma, rho, beta] - r"""list containing :math:`\sigma`, :math:`\rho` and :math:`\beta`""" + r"""List containing :math:`\sigma`, :math:`\rho` and :math:`\beta`""" self.newton = { "maxIter": 99, "tolerance": 1e-9, } - """parameters for the Newton iteration used in native fSolve""" + """Parameters for the Newton iteration used in native fSolve""" u0 = np.array([5, -5, 20], dtype=float) self.gemv = blas.get_blas_funcs("gemv", dtype=u0.dtype) - """level-2 blas gemv function used in the native solver (just for flex, doesn't bring anything)""" + """Level-2 blas gemv function used in the native solver (just for flex, very light speedup)""" super().__init__(u0) if nativeFSolve: @@ -101,6 +115,22 @@ def evalF(self, u, t, out): out[2] = x*y - beta*z def fSolve_NATIVE(self, a, rhs, t, out): + r""" + Solve :math:`u-\alpha f(u,t)=rhs` for given :math:`u,t,rhs`, + using a Newton iteration with exact Jacobian of :math:`f(u,t)`. + + Parameters + ---------- + a : float + The :math:`\alpha` coefficient. + rhs : np.ndarray + The right hand side. + t : float + Time for the evaluation. + out : np.ndarray + Input-output array used as initial guess, + in which is stored the solution. + """ sigma, rho, beta = self.params newton = self.newton @@ -157,7 +187,7 @@ class ProtheroRobinson(DiffOp): Implement the Prothero-Robinson problem: .. math:: - \frac{du}{dt} = -\frac{u-g(t)}{\epsilon} + \frac{dg}{dt}, \quad u(0) = g(0)., + \frac{du}{dt} = -\frac{u-g(t)}{\epsilon} + \frac{dg}{dt}, \quad u(0) = g(0), with :math:`\epsilon` a stiffness parameter, that makes the problem more stiff the smaller it is (usual taken value is :math:`\epsilon=1e^{-3}`). @@ -181,6 +211,12 @@ class ProtheroRobinson(DiffOp): >>> def dg(self, t): >>> return (-0.2) * np.exp(-0.2 * t) + Reference + --------- + A. Prothero and A. Robinson, + *On the stability and accuracy of one-step methods for solving stiff systems of ordinary differential equations*, + Mathematics of Computation, **28** (1974), pp. 145–162. + Parameters ---------- epsilon : float, optional @@ -189,20 +225,15 @@ class ProtheroRobinson(DiffOp): Wether or not to use the non-linear form of the problem. The default is False. nativeFSolve : bool, optional Wether or not use the native fSolver using exact Jacobian. The default is True. - - Reference - --------- - A. Prothero and A. Robinson, On the stability and accuracy of one-step methods for solving - stiff systems of ordinary differential equations, Mathematics of Computation, 28 (1974), - pp. 145–162. """ - def __init__(self, epsilon=1e-3, nonLinear=False, nativeFSolve=True): self.epsilon = epsilon + r"""Value used for :math:`\epsilon`.""" self.newton = { "maxIter": 200, "tolerance": 5e-15, } + """Parameters used for the Newton iteration in `fSolve`.""" self.evalF = self.evalF_NONLIN if nonLinear else self.evalF_LIN self.jac = self.jac_NONLIN if nonLinear else self.jac_LIN if nativeFSolve: @@ -211,6 +242,7 @@ def __init__(self, epsilon=1e-3, nonLinear=False, nativeFSolve=True): @classmethod def test(cls): + """Test both linear and non-linear version of this differential operator.""" default = cls() assert not default.nonLinear, "default ProtheroRobinson DiffOp is not linear" super().test(instance=default) @@ -220,6 +252,7 @@ def test(cls): @property def nonLinear(self): + """Wether the current operator is non-linear""" return self.evalF == self.evalF_NONLIN # ------------------------------------------------------------------------- @@ -253,6 +286,23 @@ def jac_NONLIN(self, u, t): return -self.epsilon**(-1) * 3*u**2 def fSolve_NATIVE(self, a, rhs, t, out): + r""" + Solve :math:`u-\alpha f(u,t)=rhs` for given :math:`u,t,rhs`, + using a Newton iteration with exact Jacobian (derivative) of + :math:`f(u,t)`. + + Parameters + ---------- + a : float + The :math:`\alpha` coefficient. + rhs : np.ndarray + The right hand side. + t : float + Time for the evaluation. + out : np.ndarray + Input-output array used as initial guess, + in which is stored the solution. + """ newton = self.newton u = out diff --git a/qmat/solvers/generic/integrators.py b/qmat/solvers/generic/integrators.py index 5fff89a..810320f 100644 --- a/qmat/solvers/generic/integrators.py +++ b/qmat/solvers/generic/integrators.py @@ -1,13 +1,41 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Specialized PhiSolver classes implementations +Specialized :class:`PhiSolver` classes implementing various time-integrators. """ import numpy as np from qmat.solvers.generic import PhiSolver class ForwardEuler(PhiSolver): + r""" + :math:`\phi`-based solver doing Forward Euler update between time nodes. + + It uses the following definition : + + .. math:: + + \phi(u_0, u_1, ..., u_{m}, u_{m+1}) = + \Delta\tau_{m+1} f(u_m, t_m) + ... + \Delta\tau_1 f(u_0, t_0), + + where :math:`\Delta\tau_{m} = t_{m+1} - t_{m}`. + In particular, since it does not depends on the node solution + :math:`u_{m+1}` (explicit scheme), + its `phiSolve` method is replaced by an explicit evaluation of `evalPhi`. + + Parameters + ---------- + diffOp : DiffOp + Differential operator for the ODE. + nodes : 1D array-like + The time nodes :math:`\tau_1, ..., \tau_M`. + tEnd : float, optional + Final simulation time. The default is 1. + nSteps : int, optional + Number of simulation time-steps. The default is 1. + t0 : float, optional + Initial simulation time. The default is 0. + """ def evalPhi(self, uVals, fEvals, out, t0=0): m = len(uVals) - 1 @@ -28,6 +56,35 @@ def phiSolve(self, uPrev, fEvals, out, rhs=0, t0=0): class BackwardEuler(PhiSolver): + r""" + :math:`\phi`-based solver doing Backward Euler update between time nodes. + + It uses the following definition : + + .. math:: + + \phi(u_0, u_1, ..., u_{m}, u_{m+1}) = + \Delta\tau_{m+1} f(u_{m+1}, t_{m+1}) + ... + + \Delta\tau_1 f(u_1, t_1), + + where :math:`\Delta\tau_{m} = t_{m+1} - t_{m}`. + In particular, its `phiSolve` method is rewritten + to depend directly on the `fSolve` method of the differential operator + to avoid unecessary (re-)evaluations of :math:`f(u,t)`. + + Parameters + ---------- + diffOp : DiffOp + Differential operator for the ODE. + nodes : 1D array-like + The time nodes :math:`\tau_1, ..., \tau_M`. + tEnd : float, optional + Final simulation time. The default is 1. + nSteps : int, optional + Number of simulation time-steps. The default is 1. + t0 : float, optional + Initial simulation time. The default is 0. + """ def evalPhi(self, uVals, fEvals, out, t0=0): m = len(uVals) - 1 From c81ac982e6e049dede0552857719e710b30cab6e Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Mon, 27 Oct 2025 22:56:56 +0100 Subject: [PATCH 21/33] TL: minor debuging --- qmat/qcoeff/__init__.py | 14 +++++++------- tests/test_qcoeff/test_convergence.py | 5 ++--- tests/test_solvers/test_generic.py | 2 +- tests/test_solvers/test_sdc.py | 4 ++-- 4 files changed, 12 insertions(+), 13 deletions(-) diff --git a/qmat/qcoeff/__init__.py b/qmat/qcoeff/__init__.py index 91f279e..950a49b 100644 --- a/qmat/qcoeff/__init__.py +++ b/qmat/qcoeff/__init__.py @@ -134,7 +134,7 @@ def orderEmbedded(self)->int: """Global convergence order of the associated embedded method""" return self.order - 1 - def solveDahlquist(self, lam, u0, T, nSteps, useEmbeddedWeights=False): + def solveDahlquist(self, lam, u0, tEnd, nSteps, useEmbeddedWeights=False): r""" Solve the Dahlquist test problem @@ -148,7 +148,7 @@ def solveDahlquist(self, lam, u0, T, nSteps, useEmbeddedWeights=False): The :math:`\lambda` coefficient. u0 : complex or float The initial solution :math:`u_0`. - T : float + tEnd : float Final time :math:`T`. nSteps : int Number of time-step for the whole :math:`[0,T]` interval. @@ -168,7 +168,7 @@ def solveDahlquist(self, lam, u0, T, nSteps, useEmbeddedWeights=False): uNum = np.zeros(nSteps+1, dtype=complex) uNum[0] = u0 - dt = T/nSteps + dt = tEnd/nSteps A = np.eye(nodes.size) - lam*dt*Q for i in range(nSteps): b = np.ones(nodes.size)*uNum[i] @@ -177,7 +177,7 @@ def solveDahlquist(self, lam, u0, T, nSteps, useEmbeddedWeights=False): return uNum - def errorDahlquist(self, lam, u0, T, nSteps, uNum=None, useEmbeddedWeights=False): + def errorDahlquist(self, lam, u0, tEnd, nSteps, uNum=None, useEmbeddedWeights=False): r""" Compute :math:`L_\infty` error in time for the Dahlquist problem @@ -187,7 +187,7 @@ def errorDahlquist(self, lam, u0, T, nSteps, uNum=None, useEmbeddedWeights=False The :math:`\lambda` coefficient. u0 : complex or float The initial solution :math:`u_0`. - T : float + tEnd : float Final time :math:`T`. nSteps : int Number of time-step for the whole :math:`[0,T]` interval. @@ -203,8 +203,8 @@ def errorDahlquist(self, lam, u0, T, nSteps, uNum=None, useEmbeddedWeights=False The :math:`L_\infty` norm. """ if uNum is None: - uNum = self.solveDahlquist(lam, u0, T, nSteps, useEmbeddedWeights=useEmbeddedWeights) - times = np.linspace(0, T, nSteps+1) + uNum = self.solveDahlquist(lam, u0, tEnd, nSteps, useEmbeddedWeights=useEmbeddedWeights) + times = np.linspace(0, tEnd, nSteps+1) uExact = u0 * np.exp(lam*times) return np.linalg.norm(uNum-uExact, ord=np.inf) diff --git a/tests/test_qcoeff/test_convergence.py b/tests/test_qcoeff/test_convergence.py index 659be56..a7305a4 100644 --- a/tests/test_qcoeff/test_convergence.py +++ b/tests/test_qcoeff/test_convergence.py @@ -32,7 +32,7 @@ def nStepsForTest(scheme, useEmbeddedWeights=False): u0 = 1 lam = 1j -T = 2*np.pi +tEnd = 2*np.pi @@ -49,7 +49,7 @@ def testDahlquist(scheme, useEmbeddedWeights): expectedOrder = gen.orderEmbedded if useEmbeddedWeights else gen.order nSteps = nStepsForTest(gen, useEmbeddedWeights) - err = [gen.errorDahlquist(lam, u0, T, nS, useEmbeddedWeights=useEmbeddedWeights) for nS in nSteps] + err = [gen.errorDahlquist(lam, u0, tEnd, nS, useEmbeddedWeights=useEmbeddedWeights) for nS in nSteps] order, rmse = numericalOrder(nSteps, err) assert rmse < 0.02, f"rmse to high ({rmse}) for {scheme}" assert abs(order-expectedOrder) < 0.1, f"Expected order {expectedOrder:.2f}, but got {order:.2f} for {scheme}" @@ -67,7 +67,6 @@ def testDahlquistCollocation(nNodes, nodesType, quadType, useEmbeddedWeights=Fal return None scheme = f"Collocation({nNodes}, {nodesType}, {quadType})" nSteps = nStepsForTest(gen, useEmbeddedWeights) - tEnd = T err = [gen.errorDahlquist(lam, u0, tEnd, nS, useEmbeddedWeights=useEmbeddedWeights) for nS in nSteps] order, rmse = numericalOrder(nSteps, err) expectedOrder = gen.orderEmbedded if useEmbeddedWeights else gen.order diff --git a/tests/test_solvers/test_generic.py b/tests/test_solvers/test_generic.py index 5b491dc..ff456c7 100644 --- a/tests/test_solvers/test_generic.py +++ b/tests/test_solvers/test_generic.py @@ -21,7 +21,7 @@ def testLinearCoeffSolverDahlquist(scheme, tEnd, nSteps, lam): qGen = Q_GENERATORS[scheme].getInstance() - uRef = qGen.solveDahlquist(lam, 1, T=tEnd, nSteps=nSteps) + uRef = qGen.solveDahlquist(lam, 1, tEnd=tEnd, nSteps=nSteps) uNum = solver.solve(Q=qGen.Q, weights=qGen.weights) uNum = uNum[:, 0] + 1j*uNum[:, 1] diff --git a/tests/test_solvers/test_sdc.py b/tests/test_solvers/test_sdc.py index 69b223b..52fd3d0 100644 --- a/tests/test_solvers/test_sdc.py +++ b/tests/test_solvers/test_sdc.py @@ -14,7 +14,7 @@ def testSweeps(qDelta, nNodes): gen = QDELTA_GENERATORS[qDelta](nodes=coll.nodes) runParams = dict( - lam=1j, u0=1, T=np.pi, nSteps=10, nSweeps=nNodes, + lam=1j, u0=1, tEnd=np.pi, nSteps=10, nSweeps=nNodes, Q=coll.Q, ) @@ -35,7 +35,7 @@ def testMonitors(nSweeps, nSteps, nNodes): gen = QDELTA_GENERATORS["BE"](nodes=coll.nodes) runParams = dict( - lam=1j, u0=1, T=np.pi, nSteps=nSteps, nSweeps=nSweeps, + lam=1j, u0=1, tEnd=np.pi, nSteps=nSteps, nSweeps=nSweeps, Q=coll.Q, QDelta=gen.getQDelta(), ) From 5ad4d11e7587ac00852b8427e0c37043117e581a Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Wed, 29 Oct 2025 19:14:12 +0100 Subject: [PATCH 22/33] TL: finalized devdocs --- docs/conf.py | 6 +- docs/contributing.md | 10 +++- docs/devdoc/addDiffOp.md | 57 +++++++++++++++++++ docs/devdoc/addPhiIntegrator.md | 44 ++++++++++++++ docs/devdoc/addPlayground.md | 42 ++++++++++++++ docs/devdoc/addRK.md | 15 +++-- docs/devdoc/roadmap.md | 6 +- docs/devdoc/structure.md | 38 +++++++------ docs/devdoc/testing.md | 2 + docs/devdoc/updateDoc.md | 13 +++-- docs/devdoc/versionUpdate.md | 2 +- docs/notebooks.md | 14 ++--- docs/notebooks/11_nodeFormulation.ipynb | 4 +- ...nonLinearRK.ipynb => 12_nonLinearRK.ipynb} | 2 +- ...nLinearSDC.ipynb => 13_nonLinearSDC.ipynb} | 2 +- docs/notebooks/14_phiIntegrator.ipynb | 20 +++++++ qmat/solvers/generic/__init__.py | 2 +- 17 files changed, 227 insertions(+), 52 deletions(-) create mode 100644 docs/devdoc/addDiffOp.md create mode 100644 docs/devdoc/addPhiIntegrator.md create mode 100644 docs/devdoc/addPlayground.md rename docs/notebooks/{11_nonLinearRK.ipynb => 12_nonLinearRK.ipynb} (74%) rename docs/notebooks/{12_nonLinearSDC.ipynb => 13_nonLinearSDC.ipynb} (71%) create mode 100644 docs/notebooks/14_phiIntegrator.ipynb diff --git a/docs/conf.py b/docs/conf.py index f52f940..00fe132 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -20,7 +20,7 @@ # -- Project information ----------------------------------------------------- project = 'QMat Package' -copyright = '2024 PinT Community' +copyright = '2025 PinT Community' author = 'PinT Community' # The short X.Y version @@ -72,7 +72,7 @@ autoapi_dirs = ['../qmat'] autoapi_file_patterns = ['*.py'] autoapi_options = [ - 'members', 'undoc-members', + 'members', 'undoc-members', 'show-inheritance-diagram', 'show-module-summary', ] @@ -162,5 +162,5 @@ # -- Options for nbsphinx galleries # Using _images/ is a hack to get relocated images which have been included in the pages nbsphinx_thumbnails = { - + } \ No newline at end of file diff --git a/docs/contributing.md b/docs/contributing.md index 8610836..936db8a 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -26,7 +26,7 @@ Current coverage is at 100%, so no untested line will be accepted 😇. Chosen merge strategy is to squash commits $\Rightarrow$ you don't have to care about the number of commit included in your PR, so don't be scare of making mistakes before your PR is accepted 😉 -> 🔔 Once your PR is accepted, please delete this branch from your fork and synchronize your `main` branch. When creating a new development branch later, ensure that you start from an up-to-date `main` branch of your fork. +> 🔔 Once your PR is accepted, please delete this branch from your fork and synchronize your `main` branch. When creating a new development branch later, ensure that you start from an up-to-date `main` branch of your fork. In case you are interested in contributing but don't have any idea on what, checkout out current [development roadmap đŸŽ¯](./devdoc/roadmap.md) and [project proposals 🎓](https://github.com/Parallel-in-Time/qmat/discussions/categories/project-proposals) @@ -34,8 +34,11 @@ In case you are interested in contributing but don't have any idea on what, chec _A few base memo on how to develop this package ..._ -- [General code structure](./devdoc/structure.md) +- [Code structure](./devdoc/structure.md) - [Add a Runge-Kutta scheme](./devdoc/addRK.md) +- [Add a playground](./devdoc/addPlayground.md) +- [Add a differential operator](./devdoc/addDiffOp.md) +- [Add a $\phi$-based time-integrator](./devdoc/addPhiIntegrator.md) - [Testing your changes](./devdoc/testing.md) - [Update this documentation](./devdoc/updateDoc.md) - [Version update pipeline](./devdoc/versionUpdate.md) @@ -47,6 +50,9 @@ _A few base memo on how to develop this package ..._ devdoc/structure devdoc/addRK + devdoc/addPlayground + devdoc/addDiffOp + devdoc/addPhiIntegrator devdoc/testing devdoc/updateDoc devdoc/versionUpdate diff --git a/docs/devdoc/addDiffOp.md b/docs/devdoc/addDiffOp.md new file mode 100644 index 0000000..58bde04 --- /dev/null +++ b/docs/devdoc/addDiffOp.md @@ -0,0 +1,57 @@ +# Add a differential operator + +📜 _Solvers implemented in {py:mod}`qmat.solvers.generic` can be used_ +_with others {py:class}`DiffOp ` classes_ +_than those implemented in {py:mod}`qmat.solvers.generic.diffops`._ + +To add a new one, implement it at the end of the `diffops.py` module, +using the following template : + +```python + +@registerDiffOp +class Yoodlidoo(DiffOp): + r""" + Base description, in particular its equation : + + .. math:: + + \frac{du}{dt} = ... + + And some parameters description ... + """ + def __init__(self, params="value"): + self.params = params + u0 = np.array([1, 0], dtype=float) + super().__init__(u0) + + def evalF(self, u, t, out): + # TODO : your implementation + pass +``` + +And that's all ! The `registerDiffOp` operator will automatically +- add your class in the `DIFFOPS` dictionary to make it generically available +- check if your class override properly the `evalF` function (import error if not) +- add your class to the [CI tests](./testing.md) + +> đŸ“Ŗ Per default, all `DiffOp` classes must be instantiable with default parameters +> in order to run the tests (see the {py:func}`DiffOp.test ` +> class method). But you can change that by overriding the `test` class method and put your own +> preset parameters for the test (checkout the +> {py:func}`ProtheroRobinson ` classes for an example). + +Finally, the `DiffOp` class implements a default `fSolve` method, +but you can also implement a more efficient approach tailored to your problem like this : + +```python +@registerDiffOp +class Yoodlidoo(DiffOp): + # ... + + def fSolve(self, a:float, rhs:np.ndarray, t:float, out:np.ndarray): + # TODO : your ultra-efficient implementation that will be + # way better than a generic call of scipy.optimize.fsolve + # or scipy.optimize.newton_krylov. + pass +``` \ No newline at end of file diff --git a/docs/devdoc/addPhiIntegrator.md b/docs/devdoc/addPhiIntegrator.md new file mode 100644 index 0000000..c30e385 --- /dev/null +++ b/docs/devdoc/addPhiIntegrator.md @@ -0,0 +1,44 @@ +# Add a $\phi$-based time-integrator + +📜 _Additional time schemes can be added using the [$\phi$ formulation](../notebooks/14_phiIntegrator.ipynb)_ +_to test other variants of $Q_\Delta$-coefficients free Spectral Deferred Correction._ +_For that, you can implement a new {py:mod}`PhiSolver ` class in the {py:mod}`qmat.solvers.generic.integrators` module_. + +Add your class at the end of the `qmat.solvers.generic.integrators.py` module using the following template : + +```python +class Phidlidoo(PhiSolver): + r""" + Base description, in particular its definition : + + .. math:: + + \phi(u_0, u_1, ..., u_{m}, u_{m+1}) = + ... + + And eventual parameters description ... + """ + + def evalPhi(self, uVals, fEvals, out, t0=0): + m = len(uVals) - 1 + assert m > 0 + assert len(fEvals) in [m, m+1] + + # TODO : integrators implementation +``` + +The first lines are not mandatory, but ensure that the `evalPhi` is properly evaluated. + +> đŸ“Ŗ New `PhiSolver` classes are not automatically tested, so you'll have to write +> some dedicated test for your new class in `tests.test_solvers.test_integrators.py`. +> Checkout those already implemented for `ForwardEuler` and `BackwardEuler`. + +As for the {py:class}`DiffOp ` class, +the {py:class}`PhiSolver ` implement a generic default +`phiSolve` method, that you can override by a more efficient specialized approach. + +> 💡 Note that the model above inherits the `__init__` constructor of the `PhiSolver` class, +> so it can take any `DiffOp` class as parameter. +> If your time-integrator is specialized for some kind of differential operators +> (_e.g_ a semi-Lagrangian scheme for an advective problem), +> then you probably need to override the `__init__` method in your class too. diff --git a/docs/devdoc/addPlayground.md b/docs/devdoc/addPlayground.md new file mode 100644 index 0000000..0c49dbb --- /dev/null +++ b/docs/devdoc/addPlayground.md @@ -0,0 +1,42 @@ +# Add a playground + +📜 _To add experimental scripts or usage examples, without testing everything : simply add your own **playground** in {py:mod}`qmat.playgrounds`._ + +1. create a folder with a _short & representative_ name, _e.g_ `yoodlidoo` (can also be your name for a personal playground), +2. put your script(s) in it, and document them as much as necessary so **anyone else can understand and use your code**, +3. create a `__init__.py` file in your playground folder with a short summary of your scripts in its docstring, _e.g_ + ```python + """ + - :class:`script1` : trying some stuff. + - :class:`script2` : yet another idea. + """ + ``` +4. add the item line corresponding to your playground in `qmat.playgrounds.__init__.py`, _e.g_ + ```python + """ + ... + + Current playgrounds + ------------------- + + - ... + - :class:`yoodlidoo` : some ideas to do stuff + """ + ``` +5. open a pull request against the `main` branch of `qmat`. + +> 💡 If you don't want your playground to be integrated into the main branch of`qmat` (no proper documentation, code always evolving, ...), +> you can still add a **soft link to a playground in your fork** by modifying `qmat.playgrounds.__init__.py` : +> ```python +> """ +> ... +> +> Current playgrounds +> ------------------- +> +> - ... +> - `{name} `_ : some ideas ... +> """ +> ``` +> where `name` is your playground name, `userName` your GitHub username and `branch` the branch name on your fork you are working on +> (**do not use `main`** ⚠ī¸) \ No newline at end of file diff --git a/docs/devdoc/addRK.md b/docs/devdoc/addRK.md index 2c44fe7..1c51578 100644 --- a/docs/devdoc/addRK.md +++ b/docs/devdoc/addRK.md @@ -1,13 +1,13 @@ # Add a Runge-Kutta scheme -Current $Q$-generators based on Runge-Kutta schemes are implemented in the +Current $Q$-generators based on Runge-Kutta schemes are implemented in the [`qmat.qcoeff.butcher`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/qcoeff/butcher.py) submodule. -Those are based on Butcher tables from classical schemes available in the literature, +Those are based on Butcher tables from classical schemes available in the literature, and the selected approach is to define **one class for one scheme**. ## Standard scheme -In order to add a new RK, search first for its section in the `butcher.py` file, depending on its type +In order to add a new RK, search first for its section in the `butcher.py` file, depending on its type (explicit or implicit) and its order. Then add a new class at the bottom of this section following this template : ```python @@ -43,7 +43,7 @@ A[5, :5] = [1631.0 / 55296.0, 175.0 / 512.0, 575.0 / 13824.0, 44275.0 / 110592.0 ## Convergence testing -To test your scheme ... you don't have to do anything đŸĨŗ : all RK schemes are automatically tested +To test your scheme ... you don't have to do anything đŸĨŗ : all RK schemes are automatically tested thanks to the [registration mechanism](./structure.md), that checks (in particular) the convergence order of each scheme (global truncation error). @@ -57,14 +57,14 @@ lam = 1j # purely imaginary lambda T = 2*np.pi # one time period ``` -They use three numbers of time-steps for the convergence analysis, depending on the order of the method +They use three numbers of time-steps for the convergence analysis, depending on the order of the method (see [here ...](https://github.com/Parallel-in-Time/qmat/blob/main/tests/test_qcoeff/test_convergence.py#L10)). But this automatic time-step size selection may not be adapted for methods with high error constant that require finer time-steps to actually see the theoretical order. In that case, simply add a `CONV_TEST_NSTEPS` _class attribute_ storing a list with **higher numbers of time-steps** in increasing order, high enough so the convergence test passes. -> 📜 See [SDIRK2_2 implementation](https://github.com/Parallel-in-Time/qmat/blob/e17e2dd2aebff1b09188f4314a82338355a55582/qmat/qcoeff/butcher.py#L269) for an example ... +> 📜 See [SDIRK2_2 implementation](https://github.com/Parallel-in-Time/qmat/blob/e17e2dd2aebff1b09188f4314a82338355a55582/qmat/qcoeff/butcher.py#L269) for an usage example of `CONV_TEST_NSTEPS` ... ## Embedded scheme @@ -76,7 +76,7 @@ For that, simply define a `b2` class attribute : @registerRK class NewRK(RK): """Some new RK method from ...""" - ## previous coefficients ... + ## previous coefficients ... b2 = ... # embedded coefficients ``` @@ -92,4 +92,3 @@ class NewRK(RK): def weightsEmbedded(self): return ... # effective embedded order ``` - diff --git a/docs/devdoc/roadmap.md b/docs/devdoc/roadmap.md index 87917f6..82b56ad 100644 --- a/docs/devdoc/roadmap.md +++ b/docs/devdoc/roadmap.md @@ -2,7 +2,7 @@ 📜 _Planned steps for the package development ..._ -Detailed description of all specific versions and their associated changes is available on the [Github Releases page](https://github.com/Parallel-in-Time/qmat/releases). +Detailed description of all specific versions and their associated changes is available on the [Github Releases page](https://github.com/Parallel-in-Time/qmat/releases). **Status 3 - Alpha** : `v0.0.*` @@ -32,10 +32,10 @@ Detailed description of all specific versions and their associated changes is av - ✅ use of `qmat` for [Dedalus](https://github.com/DedalusProject/dedalus) IMEX SDC time-steppers developed within [pySDC](https://github.com/Parallel-in-Time/pySDC) - distribution to other people using former version of the core `qmat` code (_e.g_ Alex Brown from Exeter, ...) - addition of a few advanced usage tutorials : - - `qmat` for non-linear ODE + - ✅ `qmat` for non-linear ODE - multilevel SDC - PFASST **Status 6 - Mature** : `v1.*.*` -- integration of SDC-Butcher theory from J. Fregin (with associated console scripts) \ No newline at end of file +- integration of SDC-Butcher theory from J. Fregin (with associated console scripts) \ No newline at end of file diff --git a/docs/devdoc/structure.md b/docs/devdoc/structure.md index 1c13723..c3b765e 100644 --- a/docs/devdoc/structure.md +++ b/docs/devdoc/structure.md @@ -1,20 +1,20 @@ -# Generic code structure +# Code structure -📜 _Quick introduction on the code design and how to extend it ..._ +📜 _Quick introduction on how the package is designed and how to extend it ..._ ## Registration mechanism The two main features, namely the generation of $Q$-coefficients and $Q_\Delta$ approximations, are respectively implemented in the `qmat.qcoeff` and `qmat.qdelta` sub-packages. Different categories of generators are implemented in dedicated submodules of their respective sub-packages, -_e.g_ : +_e.g_ : -- `qmat.qcoeff.collocation` for Collocation-based $Q$-generators +- `qmat.qcoeff.collocation` for Collocation-based $Q$-generators - `qmat.qdelta.algebraic` for algebraic based $Q_\Delta$ approximations - ... Each sub-package contains a `__init__.py` file implementing the generic parent class for all generators. -In their submodules, generators are implemented using a **registration mechanism**, +In their submodules, generators are implemented using a **registration mechanism**, _e.g_ for the Collocation-based $Q$-generators : ```python @@ -30,7 +30,7 @@ A similar mechanism is used for $Q_\Delta$ generators. The `register` function i - checks that the implemented class properly overrides the method of its parent class (more specific details below) - stores it in a centralized dictionary allowing a quick access using the class name or one of its aliases : - - `qmat.Q_GENERATORS` for $Q$-coefficients + - `qmat.Q_GENERATORS` for $Q$-coefficients - `qmat.QDELTA_GENERATORS` for the $Q_\Delta$ approximations > 💡 Different aliases for the generator can be provided with the `aliases` class attribute, but are not mandatory (defining the class attribute is optional). @@ -62,8 +62,8 @@ class MyGenerator(QGenerator): # TODO : returns an int ``` -The `nodes`, `weights`, and `Q` properties have to be overridden -(`register` actually raises an error if not) and return +The `nodes`, `weights`, and `Q` properties have to be overridden +(`register` actually raises an error if not) and return the expected arrays in `numpy.ndarray` format : 1. `nodes` : 1D vector of size `nNodes` @@ -109,7 +109,7 @@ pytest -v ./tests/test_qcoeff This will run all consistency and convergence check tests on all generators (including yours), more details on how to run the tests are provided [here ...](./testing.md) -> 🔔 Convergence tests for new $Q$-generators are automatically done depending on its order. In some particular case, you may +> 🔔 Convergence tests for new $Q$-generators are automatically done depending on its order. In some particular case, you may > have to add a `CONV_TEST_NSTEPS` class variable to your generator class for those tests to pass > (_e.g_, if your generator has a high error constant). > See [documentation on adding RK schemes](./addRK.md#convergence-testing) for more details ... @@ -139,7 +139,7 @@ The default constructor stores the $Q$ matrix that is approximated, and the `size` property is used to determine the shape of generated $Q_\Delta$ approximation, and the `zeros` property can be used to generate the initial basis for $Q_\Delta$. -> 🔔 The default constructor is used by all the specialized generators implemented in `qmat.qdelta.algebraic`, +> 🔔 The default constructor is used by all the specialized generators implemented in `qmat.qdelta.algebraic`, > as their $Q_\Delta$ approximation is build directly from the $Q$ matrix given as parameter. @@ -158,8 +158,8 @@ class MyGenerator(QDeltaGenerator): The `computeQDelta` must simply returns the $Q_\Delta$ approximation for this generator, potentially using the `zeros` property as starting basis. -**đŸ“Ŗ Important :** even if this may not be used by your generator, the `computeQDelta` method **must always** -take a `k` optional parameter corresponding to a **sweep or iteration number** in SDC or iterated RK methods, +**đŸ“Ŗ Important :** even if this may not be used by your generator, the `computeQDelta` method **must always** +take a `k` optional parameter corresponding to a **sweep or iteration number** in SDC or iterated RK methods, starting at $k=1$ for the first sweep. The default value for this parameter must be : @@ -192,12 +192,14 @@ But then it is necessary to : 1. add the `**kwargs` arguments to your constructor, but don't use it for your generator's parameters : `**kwargs` is only used when $Q_\Delta$ matrices are generated from different types of generators using one single call 2. properly redefine the `size` property if you don't store any $Q$ matrix attribute in your constructor +## Additional sub-packages -## Additional submodules +- [`qmat.solvers`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/solvers) : implements various generic ODE making use of `qmat`-generated coefficients. Can be modified to [add new differential operators](./addDiffOp.md) or [add new $\phi$-based integrators](./addPhiIntegrator.md) +- [`qmat.playgrounds](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/playgrounds) : can be modified to [add a personal playground](./addPlayground.md) (non-tested experiments / examples) -Several "utility" modules are available in `qmat` : +## Additional submodules -- [`qmat.nodes`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/nodes.py) : implement a `NodesGenerator` class for node generation with various distributions -- [`qmat.lagrange`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/lagrange.py) : implement a `LagrangeApproximation` class used to compute weights and $Q$ matrix for collocation, interpolation coefficients, ... -- [`qmat.sdc`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/sdc.py) : basic generic SDC solvers that can be used for first experiments and tests -- [`qmat.utils`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/utils.py) : as the name of the submodule suggest ... \ No newline at end of file +- [`qmat.nodes`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/nodes.py) : can be modified to add new functionalities to the `NodesGenerator` class, or improve some existing implementations +- [`qmat.lagrange`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/lagrange.py) : can be modified to add new functionalities to the `LagrangeApproximation` class, or improve some existing implementations +- [`qmat.mathutils`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/mathutils.py) : can be modified to add additional mathematical utility functions used by some parts in `qmat` (like array operations, regression tools, etc ...) +- [`qmat.utils`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/utils.py) : can be modified to add additional (non mathematical) utility functions used by some parts in `qmat` (like timers, implementation check function, etc ...) \ No newline at end of file diff --git a/docs/devdoc/testing.md b/docs/devdoc/testing.md index 702b935..5524205 100644 --- a/docs/devdoc/testing.md +++ b/docs/devdoc/testing.md @@ -85,6 +85,8 @@ coverage html This generates a html coverage report in `htmlcov/index.html` that you can read using your favorite web browser. +> đŸ“Ŗ Remember : code coverage must **stay at 100%** for a pull request to be accepted ... and the test will be reviewed to assert that they are not simple executions of your implementation 😇 + ## Testing notebook tutorials All notebooks are located in the [notebook docs folder](../notebooks). You can first check if they can be executed properly by running : diff --git a/docs/devdoc/updateDoc.md b/docs/devdoc/updateDoc.md index aa4fe73..5c43c8f 100644 --- a/docs/devdoc/updateDoc.md +++ b/docs/devdoc/updateDoc.md @@ -2,10 +2,11 @@ 📜 _If you think it can be clearer, or you want to add more details or tutorials ..._ + ## Generating local docs First you need a few dependencies (besides those for `qmat`). For that download -the [source code](https://github.com/Parallel-in-Time/qmat) and install the package with all the +the [source code](https://github.com/Parallel-in-Time/qmat) and install the package with all the `docs` dependencies locally : ```bash @@ -15,7 +16,7 @@ pip install -e .[docs] ``` > 📜 The `-e` option ensures that your installed python package is directly linked to the sources (no copy of code), -> hence modifying any part of the source code (in particular the documentation) +> hence modifying any part of the source code (in particular the documentation) > will be taken into account when `sphinx` will parse the code docstring. Then to generate the documentation website locally, simply run : @@ -25,9 +26,10 @@ cd docs make html ``` -This builds the `sphinx` documentation automatically in a `_build` folder, +This builds the `sphinx` documentation automatically in a `_build` folder, and you can view it by opening `docs/_build/html/index.html` using your favorite browser. + ## Updating a tutorial When changing a [notebook tutorial](../notebooks), you should also regenerate it entirely, in particular if you modified parts of the code. @@ -44,12 +46,13 @@ If you modified several notebooks, and as a safety, it is also possible to regen ./run.sh --all ``` -> đŸ“Ŗ When modifying only the markdown text in the notebook, it is not necessary to regenerate the notebook(s). +> đŸ“Ŗ When modifying only the markdown text in a notebooks, it is not necessary to regenerate it. + ## Adding a tutorial Feel free to add new notebooks in the "Advanced Tutorial" section, for a specific application that is not covered by the current tutorials. Just name the notebook like this : `2{idx}_{shortName}.ipynb` when `idx` corresponds to its index in category (starts at 1), -and use the `Tuto A{idx}` prefix for the notebook title. +and use the `Tuto A{idx}` prefix for the notebook title. > 💡 Don't hesitate to look at the other notebooks to use a common and consistent formatting ... \ No newline at end of file diff --git a/docs/devdoc/versionUpdate.md b/docs/devdoc/versionUpdate.md index d3ab140..5b6c7c4 100644 --- a/docs/devdoc/versionUpdate.md +++ b/docs/devdoc/versionUpdate.md @@ -5,7 +5,7 @@ See full [development roadmap](./roadmap.md) for past and planned features corresponding to each versions. For each version update (_a.k.a_ releases) **after reaching Mature status (6)**, we use the following denomination : -- patch : from `*.*.{i}` to `*.*.{i+1}` $\Rightarrow$ minor modifications, bugfixes, code reformating, additional aliases for generators +- patch : from `*.*.{i}` to `*.*.{i+1}` $\Rightarrow$ minor modifications, bugfixes, code reformating, additional aliases for generators - minor : from `*.{i}.*` to `*.{i+1}.0` $\Rightarrow$ addition of new generators, new utility functions, new scripts, ... - major : from `{i}.*.*` to `{i+1}.0.0` $\Rightarrow$ major changes in code structure, design and API diff --git a/docs/notebooks.md b/docs/notebooks.md index 08d74f1..080b9d8 100644 --- a/docs/notebooks.md +++ b/docs/notebooks.md @@ -17,10 +17,10 @@ Notebooks are categorized into those main sections : ```{eval-rst} -Base usage -========== +Base usage tutorial +=================== -📜 *From Butcher Tables to Spectral Deferred Corrections* +📜 *From Butcher Tables to Spectral Deferred Corrections ...* .. toctree:: :maxdepth: 1 @@ -28,10 +28,10 @@ Base usage notebooks/0* -Extended usage -============== +Advanced tutorials +================== -📜 *Going deeper into advanced time-integration topics* +📜 *Going deeper into advanced time-integration topics ...* .. toctree:: :maxdepth: 1 @@ -42,7 +42,7 @@ Extended usage Components usage ================ -📜 *How to use the utility modules* +📜 *How to use the utility modules ...* .. toctree:: :maxdepth: 1 diff --git a/docs/notebooks/11_nodeFormulation.ipynb b/docs/notebooks/11_nodeFormulation.ipynb index 679d058..1fe00b0 100644 --- a/docs/notebooks/11_nodeFormulation.ipynb +++ b/docs/notebooks/11_nodeFormulation.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Advanced Step 1 : Zero-to-Nodes (Z2N) and Node-to-Node (N2N)\n", + "# Advanced Tutorial 1 : Zero-to-Nodes (Z2N) and Node-to-Node (N2N) formulations for SDC\n", "\n", "📜 _If you already know about SDC from the [original paper](https://link.springer.com/content/pdf/10.1023/A:1022338906936.pdf) of Dutt, Greengard & Rokhlin, you may notice that their description is very different from the one given [in Step 4](./04_sdc.ipynb) ..._\n", "\n", @@ -311,7 +311,7 @@ "\n", "From an algorithmic perspective, implementing SDC into N2N form for Backward / Forward Euler or the trapezoid rule\n", "is usually more efficient than the Z2N form, as it usually requires less floating point operations during sweep \n", - "(correction terms use all node solution in Z2N). However, the N2N formulation has two major inconvenient when\n", + "(correction terms use all node solution in Z2N). However, the N2N formulation has two major issues when\n", "considering generic SDC methods :\n", "\n", "1. Only a few type of $Q_\\Delta$ coefficients allows a simplified N2N formulation, while some other (like LU for instance), don't have a simplified N2N formulation, hence making the Z2N implementation more efficient\n", diff --git a/docs/notebooks/11_nonLinearRK.ipynb b/docs/notebooks/12_nonLinearRK.ipynb similarity index 74% rename from docs/notebooks/11_nonLinearRK.ipynb rename to docs/notebooks/12_nonLinearRK.ipynb index 623071a..ce1d6e7 100644 --- a/docs/notebooks/11_nonLinearRK.ipynb +++ b/docs/notebooks/12_nonLinearRK.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Advanced Step 2 : build a Runge-Kutta solver for non-linear ODEs \n", + "# Advanced Tutorial 2 : build a Runge-Kutta solver for non-linear ODEs \n", "\n", "🛠ī¸ In construction ..." ] diff --git a/docs/notebooks/12_nonLinearSDC.ipynb b/docs/notebooks/13_nonLinearSDC.ipynb similarity index 71% rename from docs/notebooks/12_nonLinearSDC.ipynb rename to docs/notebooks/13_nonLinearSDC.ipynb index c235e72..8c0d3f6 100644 --- a/docs/notebooks/12_nonLinearSDC.ipynb +++ b/docs/notebooks/13_nonLinearSDC.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Advanced Step 3 : build a Spectral Deferred Correction solver for non-linear ODEs\n", + "# Advanced Tutorial 3 : build a Spectral Deferred Correction solver for non-linear ODEs\n", "\n", "🛠ī¸ In construction ..." ] diff --git a/docs/notebooks/14_phiIntegrator.ipynb b/docs/notebooks/14_phiIntegrator.ipynb new file mode 100644 index 0000000..13597d2 --- /dev/null +++ b/docs/notebooks/14_phiIntegrator.ipynb @@ -0,0 +1,20 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced Tutorial 4 : build a Spectral Deferred Correction solver based on generic time-integrators\n", + "\n", + "🛠ī¸ In construction ..." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/qmat/solvers/generic/__init__.py b/qmat/solvers/generic/__init__.py index 9b0908e..c77efc4 100644 --- a/qmat/solvers/generic/__init__.py +++ b/qmat/solvers/generic/__init__.py @@ -723,7 +723,7 @@ def stepUpdate(self, u0, uNodes, fEvals, out): def solve(self, uNum=None, tInit=0): - """ + r""" Solve using sequential computation of node solutions for each step, using the relation : From da87f41b3c63d3d933f6f5ae71095e8e69d6f9cd Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Thu, 30 Oct 2025 23:01:49 +0100 Subject: [PATCH 23/33] TL: doc update --- README.md | 7 + docs/devdoc/addDiffOp.md | 2 +- docs/devdoc/addPlayground.md | 2 +- docs/devdoc/addRK.md | 18 +- docs/devdoc/roadmap.md | 3 +- docs/devdoc/structure.md | 18 +- docs/index.rst | 6 +- docs/notebooks.md | 4 +- docs/notebooks/02_rk.ipynb | 6 +- docs/notebooks/04_sdc.ipynb | 2 +- docs/notebooks/11_nodeFormulation.ipynb | 4 +- docs/notebooks/12_nonLinearRK.ipynb | 451 +++++++++++++++++++++++- docs/notebooks/13_nonLinearSDC.ipynb | 9 +- docs/notebooks/21_lagrange.ipynb | 2 +- docs/notebooks/22_nodes.ipynb | 252 ++++++++++++- qmat/solvers/generic/__init__.py | 13 +- qmat/solvers/generic/diffops.py | 2 +- 17 files changed, 758 insertions(+), 43 deletions(-) diff --git a/README.md b/README.md index 3adda59..d2be5e7 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,8 @@ $$ and many different **lower-triangular** approximations of the $Q$ matrix, named $Q_\Delta$, which are key elements for Spectral Deferred Correction (SDC), or more general Iterated Runge-Kutta Methods. +It also contains **generic time-integration solvers** based on $Q$ and $Q_\Delta$ coefficients, +that can be used for quick testing and experiments. [![DOI](https://zenodo.org/badge/804826743.svg)](https://zenodo.org/doi/10.5281/zenodo.11956478) @@ -78,3 +80,8 @@ and the current [Development Roadmap](https://qmat.readthedocs.io/en/latest/devd - Issues Tracker : https://github.com/Parallel-in-Time/qmat/issues - Q&A : https://github.com/Parallel-in-Time/qmat/discussions/categories/q-a - Project Proposals : https://github.com/Parallel-in-Time/qmat/discussions/categories/project-proposals + +## Developers + +- [Thibaut Lunet](https://github.com/tlunet) +- [Thomas Saupe (nÊ Baumann)](https://github.com/brownbaerchen) \ No newline at end of file diff --git a/docs/devdoc/addDiffOp.md b/docs/devdoc/addDiffOp.md index 58bde04..ce7765d 100644 --- a/docs/devdoc/addDiffOp.md +++ b/docs/devdoc/addDiffOp.md @@ -39,7 +39,7 @@ And that's all ! The `registerDiffOp` operator will automatically > in order to run the tests (see the {py:func}`DiffOp.test ` > class method). But you can change that by overriding the `test` class method and put your own > preset parameters for the test (checkout the -> {py:func}`ProtheroRobinson ` classes for an example). +> {py:func}`ProtheroRobinson ` class for an example). Finally, the `DiffOp` class implements a default `fSolve` method, but you can also implement a more efficient approach tailored to your problem like this : diff --git a/docs/devdoc/addPlayground.md b/docs/devdoc/addPlayground.md index 0c49dbb..8376157 100644 --- a/docs/devdoc/addPlayground.md +++ b/docs/devdoc/addPlayground.md @@ -39,4 +39,4 @@ > """ > ``` > where `name` is your playground name, `userName` your GitHub username and `branch` the branch name on your fork you are working on -> (**do not use `main`** ⚠ī¸) \ No newline at end of file +> (**do not use the `main` branch of your fork** ⚠ī¸) \ No newline at end of file diff --git a/docs/devdoc/addRK.md b/docs/devdoc/addRK.md index 1c51578..884339a 100644 --- a/docs/devdoc/addRK.md +++ b/docs/devdoc/addRK.md @@ -1,7 +1,7 @@ # Add a Runge-Kutta scheme -Current $Q$-generators based on Runge-Kutta schemes are implemented in the -[`qmat.qcoeff.butcher`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/qcoeff/butcher.py) submodule. +Current $Q$-generators based on Runge-Kutta schemes are implemented in +{py:mod}`qmat.qcoeff.butcher`. Those are based on Butcher tables from classical schemes available in the literature, and the selected approach is to define **one class for one scheme**. @@ -22,15 +22,15 @@ class NewRK(RK): def order(self): return ... # TODO ``` -Here the `registerRK` decorators interfaces the classical `register` decorator for `QGenerator` classes, +Here the `registerRK` decorator interfaces the classical `register` decorator for `QGenerator` classes, but also : -1. check if the dimensions of the `A`, `b` and `c` are consistent -2. register the generator in a specific category with all RK-type generators +1. checks if the dimensions of `A`, `b` and `c` are consistent +2. registers the generator in a specific category with all RK-type generators > 💡 You can use either the built-in `list` or Numpy `nd.array` to add the class attributes `A`, `b` and `c`. -**Tip** : for large Butcher table, you can also use this approach (from the `CashKarp` class) : +**Tip** : for large Butcher table, you can also use this approach (from the {py:class}`CashKarp ` class) : ```python A = np.zeros((6, 6)) @@ -52,9 +52,9 @@ order of each scheme (global truncation error). All convergence tests are done on the following Dahlquist problem : ```python -u0 = 1 # unitary initial solution -lam = 1j # purely imaginary lambda -T = 2*np.pi # one time period +u0 = 1 # unitary initial solution +lam = 1j # purely imaginary lambda +tEnd = 2*np.pi # one time period ``` They use three numbers of time-steps for the convergence analysis, depending on the order of the method diff --git a/docs/devdoc/roadmap.md b/docs/devdoc/roadmap.md index 82b56ad..ed26c04 100644 --- a/docs/devdoc/roadmap.md +++ b/docs/devdoc/roadmap.md @@ -22,7 +22,7 @@ Detailed description of all specific versions and their associated changes is av - ✅ integration of `qmat` into [pySDC](https://github.com/Parallel-in-Time/pySDC), _c.f_ [associated PR](https://github.com/Parallel-in-Time/pySDC/pull/445) - ✅ refined design for $Q_\Delta$ generators - ✅ full documentation of classes and functions -- finalization of extended usage tutorials ($S$-matrix, `dTau` coefficient for initial sweep, prolongation) +- finalization of extended usage tutorials (Node-to-Node, non-linear ODEs, ...) - ✅ full definition and documentation of the version update pipeline **Status 5 - Production/Stable** : `v1.0.*` @@ -32,7 +32,6 @@ Detailed description of all specific versions and their associated changes is av - ✅ use of `qmat` for [Dedalus](https://github.com/DedalusProject/dedalus) IMEX SDC time-steppers developed within [pySDC](https://github.com/Parallel-in-Time/pySDC) - distribution to other people using former version of the core `qmat` code (_e.g_ Alex Brown from Exeter, ...) - addition of a few advanced usage tutorials : - - ✅ `qmat` for non-linear ODE - multilevel SDC - PFASST diff --git a/docs/devdoc/structure.md b/docs/devdoc/structure.md index c3b765e..4ac2b9f 100644 --- a/docs/devdoc/structure.md +++ b/docs/devdoc/structure.md @@ -5,12 +5,12 @@ ## Registration mechanism The two main features, namely the generation of $Q$-coefficients and $Q_\Delta$ approximations, -are respectively implemented in the `qmat.qcoeff` and `qmat.qdelta` sub-packages. +are respectively implemented in the {py:mod}`qmat.qcoeff` and {py:mod}`qmat.qdelta` sub-packages. Different categories of generators are implemented in dedicated submodules of their respective sub-packages, _e.g_ : -- `qmat.qcoeff.collocation` for Collocation-based $Q$-generators -- `qmat.qdelta.algebraic` for algebraic based $Q_\Delta$ approximations +- {py:mod}`qmat.qcoeff.collocation` for Collocation-based $Q$-generators +- {py:mod}`qmat.qdelta.algebraic` for algebraic based $Q_\Delta$ approximations - ... Each sub-package contains a `__init__.py` file implementing the generic parent class for all generators. @@ -194,12 +194,12 @@ But then it is necessary to : ## Additional sub-packages -- [`qmat.solvers`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/solvers) : implements various generic ODE making use of `qmat`-generated coefficients. Can be modified to [add new differential operators](./addDiffOp.md) or [add new $\phi$-based integrators](./addPhiIntegrator.md) -- [`qmat.playgrounds](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/playgrounds) : can be modified to [add a personal playground](./addPlayground.md) (non-tested experiments / examples) +- {py:mod}`qmat.solvers` : implements various generic ODE making use of `qmat`-generated coefficients. Can be modified to [add new differential operators](./addDiffOp.md) or [add new $\phi$-based integrators](./addPhiIntegrator.md) +- {py:mod}`qmat.playgrounds` : can be modified to [add a personal playground](./addPlayground.md) (non-tested experiments / examples) ## Additional submodules -- [`qmat.nodes`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/nodes.py) : can be modified to add new functionalities to the `NodesGenerator` class, or improve some existing implementations -- [`qmat.lagrange`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/lagrange.py) : can be modified to add new functionalities to the `LagrangeApproximation` class, or improve some existing implementations -- [`qmat.mathutils`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/mathutils.py) : can be modified to add additional mathematical utility functions used by some parts in `qmat` (like array operations, regression tools, etc ...) -- [`qmat.utils`](https://github.com/Parallel-in-Time/qmat/blob/main/qmat/utils.py) : can be modified to add additional (non mathematical) utility functions used by some parts in `qmat` (like timers, implementation check function, etc ...) \ No newline at end of file +- {py:mod}`qmat.nodes` : can be modified to add new functionalities to the `NodesGenerator` class, or improve some existing implementations +- {py:mod}`qmat.lagrange` : can be modified to add new functionalities to the `LagrangeApproximation` class, or improve some existing implementations +- {py:mod}`qmat.mathutils` : can be modified to add additional mathematical utility functions used by some parts in `qmat` (like array operations, regression tools, etc ...) +- {py:mod}`qmat.utils` : can be modified to add additional (non mathematical) utility functions used by some parts in `qmat` (like timers, implementation check function, etc ...) \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index dc1cc23..d8be638 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -44,6 +44,8 @@ It allows to generate :math:`Q`-coefficients for multi-stages methods (equivalen and many different **lower-triangular** approximations of the :math:`Q` matrix, named :math:`Q_\Delta`, which are key elements for Spectral Deferred Correction (SDC), or more general Iterated Runge-Kutta Methods. +It also contains **generic time-integration solvers** based on :math:`Q` and :math:`Q_\Delta` coefficients, +that can be used for quick testing and experiments. .. raw:: html @@ -109,8 +111,8 @@ Links * Q&A : https://github.com/Parallel-in-Time/qmat/discussions/categories/q-a * Project Proposals : https://github.com/Parallel-in-Time/qmat/discussions/categories/project-proposals -Developer -========= +Developers +========== * `Thibaut Lunet `_ * `Thomas Saupe (nÊ Baumann) `_ \ No newline at end of file diff --git a/docs/notebooks.md b/docs/notebooks.md index 080b9d8..1d071f7 100644 --- a/docs/notebooks.md +++ b/docs/notebooks.md @@ -12,8 +12,8 @@ All tutorials are written in jupyter notebooks, that can be : Notebooks are categorized into those main sections : 1. **Basic usage** : how to generate and use basic $Q$-coefficients and $Q_\Delta$ approximations, through a step-by-step tutorial going from generic Runge-Kutta methods to SDC for simple problems. -2. **Extended usage** : additional features or `qmat` ($S$-matrix, `hCoeffs`, `dTau` coefficients, ...) to go deeper into SDC -3. **Components usage** : how to use the main utility modules, like `qmat.lagrange`, etc ... +2. **Extended usage** : additional features or `qmat` to go deeper into time-integration (Node-to-Node formulation, use for non-linear problems, $\phi$-SDC, ...) +3. **Components usage** : how to use the main utility modules, like {py:mod}`qmat.lagrange`, etc ... ```{eval-rst} diff --git a/docs/notebooks/02_rk.ipynb b/docs/notebooks/02_rk.ipynb index 1dcbd09..19d8e46 100644 --- a/docs/notebooks/02_rk.ipynb +++ b/docs/notebooks/02_rk.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Step 2 : build a Runge-Kutta type time-stepper\n", + "# Step 2 : build a Runge-Kutta type solver\n", "\n", "📜 _Once obtained the following_ $Q$_-coefficients :_\n", "\n", @@ -17,9 +17,9 @@ "\\end{array}\n", "$$\n", "\n", - "_we can use those to build the associated time-stepping scheme and solve time-dependent problems._\n", + "_we can use those to build the associated time-stepping scheme (solver) and for time-dependent problems._\n", "\n", - "> đŸ“Ŗ Remember, this is exactly the same as Butcher tables for a Runge-Kutta method ..." + "> đŸ“Ŗ Remember, this is exactly the same as Butcher tables for Runge-Kutta methods ..." ] }, { diff --git a/docs/notebooks/04_sdc.ipynb b/docs/notebooks/04_sdc.ipynb index 7737577..9536315 100644 --- a/docs/notebooks/04_sdc.ipynb +++ b/docs/notebooks/04_sdc.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Step 4 : build a Spectral Deferred Correction type time-stepper\n", + "# Step 4 : build a Spectral Deferred Correction solver\n", "\n", "📜 _Now that we can approximate the_ $Q$ _matrix from our time-stepping scheme :_\n", "\n", diff --git a/docs/notebooks/11_nodeFormulation.ipynb b/docs/notebooks/11_nodeFormulation.ipynb index 1fe00b0..826602f 100644 --- a/docs/notebooks/11_nodeFormulation.ipynb +++ b/docs/notebooks/11_nodeFormulation.ipynb @@ -270,7 +270,7 @@ "source": [ "## Additional notes\n", "\n", - "Other type of sweep have a simplified form in N2N formulation, e.g using the trapezoid rule (Crank-Nicholson) : " + "Other sweep types have a simplified form in N2N formulation, e.g using the trapezoid rule (Crank-Nicholson) : " ] }, { @@ -310,7 +310,7 @@ "$$\n", "\n", "From an algorithmic perspective, implementing SDC into N2N form for Backward / Forward Euler or the trapezoid rule\n", - "is usually more efficient than the Z2N form, as it usually requires less floating point operations during sweep \n", + "is usually more efficient than the Z2N form, since it requires less floating point operations during sweep \n", "(correction terms use all node solution in Z2N). However, the N2N formulation has two major issues when\n", "considering generic SDC methods :\n", "\n", diff --git a/docs/notebooks/12_nonLinearRK.ipynb b/docs/notebooks/12_nonLinearRK.ipynb index ce1d6e7..4a1c839 100644 --- a/docs/notebooks/12_nonLinearRK.ipynb +++ b/docs/notebooks/12_nonLinearRK.ipynb @@ -6,13 +6,460 @@ "source": [ "# Advanced Tutorial 2 : build a Runge-Kutta solver for non-linear ODEs \n", "\n", - "🛠ī¸ In construction ..." + "📜 _Previous base tutorial on [Runge-Kutta solver](./02_rk.ipynb) focused on the Dahlquist problem to explain how to use the_ $Q$_-coefficients._\n", + "_But we can also use those for non-linear ODEs **as long as**_ $Q$ _**is lower triangular**, which is the case for all Runge-Kutta methods._\n", + "\n", + "Consider the following (non-linear) ODE system :\n", + "\n", + "$$\n", + "\\frac{du}{dt}= f(u,t), \\quad u(t_0)=u_0,\n", + "$$\n", + "\n", + "where $t_0$ is the initial time. \n", + "Computing the solution after one time-step $u(t_0+\\Delta)$ using the $Q$-coefficients (or Butcher table) or size $M$ :\n", + "\n", + "$$\n", + "\\begin{array}\n", + " {c|c}\n", + " \\tau & Q \\\\\n", + " \\hline\n", + " & w^\\top\n", + "\\end{array}\n", + "$$\n", + "\n", + "corresponds to approximate the solution at given **time nodes** (or stages)\n", + "$[t_1, \\dots, t_M$] := [t_0+\\Delta{t}\\tau_1, \\dots, t_0+\\Delta{t}\\tau_M]$ \n", + "by solving the **all-at-once system** :\n", + "\n", + "$$\n", + "{\\bf u} - \\Delta{t}Q {\\bf f} = {\\bf u}_0\n", + "$$\n", + "\n", + "where \n", + "${\\bf u} = [u_1,\\dots,u_M]^T$ is the vector containing the node solutions (or stages),\n", + "${\\bf f} = [f(u_1, t_1),\\dots,f(u_M,t_M)]^T$ the evaluations of each node solutions and\n", + "${\\bf u}_0$ a vector with $u_0$ in each of its entries.\n", + "\n", + "Then, \n", + "$u(t_0+\\Delta{t})$ can be approximated via the **step-update** :\n", + "\n", + "$$\n", + "u(t_0+\\Delta{t}) \\simeq\n", + " u_0 + \\sum_{m=1}^{M} \\omega_{m} f(u_m, t_m)\n", + "$$\n", + " \n", + "and this process can be repeated for each successive time-step.\n", + " \n", + "> đŸ“Ŗ If we do not want to solve the all-at-once problem (which can be very expensive for large problem),\n", + "> then $Q$ **must be lower-triangular** to allow solving for $u_1$ first, then for $u_2$ using the $u_1$ solution, etc ... " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisite\n", + "\n", + "Consider for example the perturbed Lorenz attractor :\n", + "\n", + "$$\n", + "\\begin{align}\n", + "\\frac{dx}{dt} &= \\sigma(y-x) \\\\\n", + "\\frac{dy}{dy} &= x(\\rho(t)-z) - y, \\quad \\rho(t) = \\rho_0 + \\epsilon \\sin(t) \\\\\n", + "\\frac{dz}{dt} &= xy - \\beta z\n", + "\\end{align}\n", + "$$\n", + "\n", + "Before solving it, we first need to define its **differential operator** $f(u, t)$ with $u=[x,y,z]^T$\n", + "and the associated initial solution $u_0$ for our problem :" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "u0 = np.array([5, -5, 20])\n", + "sigma, rho0, beta, epsilon = 10, 28, 8/3, 5\n", + "\n", + "def f(u, t):\n", + " x, y, z = u\n", + " rho = rho0 + epsilon*np.sin(t)\n", + " return np.array([sigma*(y-x), x*(rho-z)-y, x*y-beta*z])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, if we want to use an implicit method, we need to define a `fSolve` function that solves\n", + "\n", + "$$\n", + "u - \\alpha f(u, t) = rhs\n", + "$$\n", + "\n", + "for any $\\alpha$ and $rhs$, using some `uInit` parameter as initial guess. \n", + "To simplify, we can quickly implement one using the `fsolve` function of `scipy.optimize` (interface for [MINPACK](https://en.wikipedia.org/wiki/MINPACK)) :" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import fsolve\n", + "\n", + "def fSolve(a, t, rhs, uInit):\n", + "\n", + " def res(u):\n", + " return u - a*f(u, t) - rhs\n", + "\n", + " return fsolve(res, uInit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "Let's retrieve some $Q$ coefficients from `qmat` :" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qmat import genQCoeffs\n", + "nodes, weights, Q = genQCoeffs(\"DIRK43\") # Implicit RK method of order three in 4 stages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and define some arrays to store the node solutions, the step solutions and time values :" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "uNodes = np.zeros((nodes.size, u0.size))\n", + "\n", + "tEnd = 10\n", + "nSteps = 1000\n", + "\n", + "uNum = np.zeros((nSteps+1, u0.size))\n", + "times = np.linspace(0, tEnd, nSteps+1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for each time step and time node, we have to solve\n", + "\n", + "$$\n", + "u_{m} - \\Delta{t}q_{m,m}f(u_m,t_m)\n", + " = u_0 + \\Delta{t}\\sum_{j=1}^{m-1}q_{m,j}f(u_j, t_j),\n", + "$$\n", + "\n", + "and compute the step update at the end :\n", + "\n", + "$$\n", + "u(t_0+\\Delta{t}) \\simeq\n", + " u_0 + \\sum_{m=1}^{M} \\omega_{m} f(u_m, t_m).\n", + "$$\n", + "\n", + "This can be done with the following code :" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "uNum[0] = u0\n", + "for i in range(nSteps):\n", + " dt = times[i+1] - times[i]\n", + " tNodes = times[i] + dt*nodes\n", + "\n", + " # Solve for each time nodes (stages)\n", + " for m in range(len(nodes)):\n", + " rhs = uNum[i].copy()\n", + "\n", + " for j in range(m):\n", + " rhs += dt*Q[m, j]*f(uNodes[j], tNodes[j])\n", + "\n", + " if Q[m,m] == 0:\n", + " uNodes[m] = rhs\n", + " else:\n", + " uNodes[m] = fSolve(dt*Q[m, m], tNodes[m], rhs, uInit=uNum[i])\n", + "\n", + " # Step update\n", + " uNum[i+1] = uNum[i]\n", + " for m in range(len(nodes)):\n", + " uNum[i+1] += dt*weights[m]*f(uNodes[m], tNodes[m])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And that's it đŸĨŗ ! We solved our non-linear time-dependent ODE on the given time frame, without caring about what's in our $Q$-coefficients ...\n", + "\n", + "> đŸ“Ŗ For a **strictly lower triangular** $Q$ **matrix** (`Q[m,m]=0`), there is no need for the `fSolve` function, as the solution is simply $rhs$. \n", + "\n", + "We can plot the solution with respect to time : " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"time $t$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ideally, the previous code can be written into a function to allow multiple calls :" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def solve(nSteps, scheme):\n", + "\n", + " nodes, weights, Q = genQCoeffs(scheme)\n", + " uNodes = np.zeros((nodes.size, u0.size))\n", + "\n", + " uNum = np.zeros((nSteps+1, u0.size))\n", + " times = np.linspace(0, tEnd, nSteps+1)\n", + "\n", + " uNum[0] = u0\n", + " for i in range(nSteps):\n", + " dt = times[i+1] - times[i]\n", + " tNodes = times[i] + dt*nodes\n", + "\n", + " # Solve for each time nodes (stages)\n", + " for m in range(len(nodes)):\n", + " rhs = uNum[i].copy()\n", + "\n", + " for j in range(m):\n", + " rhs += dt*Q[m, j]*f(uNodes[j], tNodes[j])\n", + "\n", + " if Q[m,m] == 0:\n", + " uNodes[m] = rhs\n", + " else:\n", + " uNodes[m] = fSolve(dt*Q[m, m], tNodes[m], rhs, uInit=uNum[i])\n", + "\n", + " # Step update\n", + " uNum[i+1] = uNum[i]\n", + " for m in range(len(nodes)):\n", + " uNum[i+1] += dt*weights[m]*f(uNodes[m], tNodes[m])\n", + "\n", + " return times, uNum\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... and can be used to try different time schemes or resolution : " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "times, uNum = solve(200, \"DIRK43\")\n", + "plt.plot(times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"time $t$\"); plt.title(\"Using DIRK43 and 200 time-steps\");" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "times, uNum = solve(1000, \"BE\")\n", + "plt.plot(times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"time $t$\"); plt.title(\"Using Backward Euler and 1000 time-steps\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using the internal solver\n", + "\n", + "Such generic Runge-Kutta solver is also implemented in `qmat`, along with some classical differential operators. \n", + "For instance, to solve the non-perturbed Lorenz system using any kind of $Q$ coefficients :" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from qmat import genQCoeffs\n", + "from qmat.solvers.generic import CoeffSolver\n", + "from qmat.solvers.generic.diffops import Lorenz\n", + "\n", + "scheme = \"RK4\"\n", + "nSteps = 1000\n", + "\n", + "solver = CoeffSolver(Lorenz(), tEnd=10, nSteps=nSteps)\n", + "\n", + "nodes, weights, Q = genQCoeffs(scheme)\n", + "uNum = solver.solve(Q, weights)\n", + "\n", + "plt.plot(solver.times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(solver.times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(solver.times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"Time $t$\"); plt.title(f\"Using {scheme} and {nSteps} time-steps\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the `Lorenz` class inherit from a `DiffOp` class (differential operator), which implements $f(u,t)$ and a solver for $u-f(u,t)=rhs$.\n", + "\n", + "💡 You can also create your own `DiffOp` class if you want to solve a specific problem, using the following template :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from qmat.solvers.generic import DiffOp\n", + "\n", + "class Yoodlidoo(DiffOp):\n", + " def __init__(self, params=\"value\"):\n", + " # use your parameters ...\n", + " u0 = ... # define your initial vector\n", + " super().__init__(u0)\n", + "\n", + " def evalF(self, u, t, out):\n", + " \"\"\"\n", + " Evaluate :math:`f(u,t)` and store the result into `out`.\n", + "\n", + " Parameters\n", + " ----------\n", + " u : np.ndarray\n", + " Input solution for the evaluation.\n", + " t : float\n", + " Time for the evaluation.\n", + " out : np.ndarray\n", + " Output array in which is stored the evaluation.\n", + " \"\"\"\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> đŸ“Ŗ Note that this Runge-Kutta solver does not work if $Q$ is a dense matrix.\n", + "> However, we can still use the Spectral Deferred Correction approach without too much additional code,\n", + "> which is the topic of the [next advanced tutorial ...](./13_nonLinearSDC.ipynb)" ] } ], "metadata": { + "kernelspec": { + "display_name": "micromamba", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" } }, "nbformat": 4, diff --git a/docs/notebooks/13_nonLinearSDC.ipynb b/docs/notebooks/13_nonLinearSDC.ipynb index 8c0d3f6..563741a 100644 --- a/docs/notebooks/13_nonLinearSDC.ipynb +++ b/docs/notebooks/13_nonLinearSDC.ipynb @@ -6,7 +6,14 @@ "source": [ "# Advanced Tutorial 3 : build a Spectral Deferred Correction solver for non-linear ODEs\n", "\n", - "🛠ī¸ In construction ..." + "📜 _Previous base tutorial on [SDC](./04_sdc.ipynb) focused on the Dahlquist problem to explain how to use the_ $Q_\\Delta$_-coefficients._\n", + "_But we can also use those for non-linear ODEs **as long as**_ $Q_\\Delta$ _**is lower triangular**._\n", + "\n", + "Back the **all-at-once system** defined for the [previous tutorial](./12_nonLinearRK.ipynb) :\n", + "\n", + "$$\n", + "{\\bf u} - \\Delta{t}Q {\\bf f} = {\\bf u}_0\n", + "$$" ] } ], diff --git a/docs/notebooks/21_lagrange.ipynb b/docs/notebooks/21_lagrange.ipynb index ce144b9..516f8e7 100644 --- a/docs/notebooks/21_lagrange.ipynb +++ b/docs/notebooks/21_lagrange.ipynb @@ -7,7 +7,7 @@ "source": [ "# Tutorial 1 : using the `qmat.lagrange` module\n", "\n", - "📜 _The_ `LagrangeApproximation` _class from the_ `qmat.lagrange` _module is a multi-purpose class to perform interpolation, integration or derivative approximation from a given set of 1D points._\n", + "📜 _The_ `LagrangeApproximation` _class from_ `qmat.lagrange` _is a multi-purpose class to perform interpolation, integration or derivative approximation from a given set of 1D points._\n", "_It is based on the Barycentric Lagrange interpolation theory, originally developed by Joseph-Louis Lagrange around 1795, and widely popularized by the paper of Jean-Paul Berrut ahd Llyod N. Trefethen: [\"Barycentric Lagrange interpolation\"](https://doi.org/10.1137/S0036144502417715)._\n", "\n", "The main concept behind this class is to precompute the barycentric weights for any provided set of points, then use them to generate value-independent matrices used later to compute approximations (interpolation, integration or derivative) from values vectors." diff --git a/docs/notebooks/22_nodes.ipynb b/docs/notebooks/22_nodes.ipynb index 14c7001..9c1ef33 100644 --- a/docs/notebooks/22_nodes.ipynb +++ b/docs/notebooks/22_nodes.ipynb @@ -7,13 +7,261 @@ "source": [ "# Tutorial 2 : using the `qmat.nodes` module\n", "\n", - "🛠ī¸ In construction ..." + "📜 _The_ `NodeGenerator` _class from_ `qmat.nodes` _allows to generate sets of quadrature nodes associated to various types of orthogonal polynomials._\n", + "_It is based on the book of W. Gautschi : [Orthogonal Polynomials: Computation and Approximation](https://doi.org/10.1093/oso/9780198506720.001.0001)._\n", + "\n", + "Gauss quadrature approximate integrals by a given quadrature rule on $M$ points :\n", + "\n", + "$$\n", + "\\int_{-1}^{1} f(t)\\omega(t)dt \\simeq \\sum_{m=1}^{M} \\omega^M_m f(\\tau^M_m)\n", + "$$\n", + "\n", + "where $f(t)$ is a function of interest, $\\omega(t)$ a weight function and $\\tau^M_m, \\omega^M_m$ are the **quadrature nodes and weights** \n", + "associated to the Gauss quadrature on $M$ points. \n", + "In particular, the quadrature nodes $\\tau^M_m$ are the roots a given polynomial belonging to \n", + "an orthogonal basis with respect to the scalar product :\n", + "\n", + "$$\n", + "\\langle p,q \\rangle = \\int_{-1}^{1} p(t)q(t) \\omega(t)dt\n", + "$$\n", + "\n", + "This polynomial basis is solely determined by the weights function $\\omega(t)$, and classical polynomial basis exist already in the literature :\n", + "\n", + "- $\\omega(t) = 1$ : Legendre polynomials\n", + "- $\\omega(t) = \\frac{1}{\\sqrt{1-t^2}}$ : Chebyshev polynomials of the 1st kind\n", + "- $\\omega(t) = \\sqrt{1-t^2}$ : Chebyshev polynomials of the 2nd kind\n", + "- $\\omega(t) = \\frac{\\sqrt{1+t^2}}{\\sqrt{1-t^2}}$ : Chebyshev polynomials of the 3rd kind\n", + "- $\\omega(t) = \\frac{\\sqrt{1-t^2}}{\\sqrt{1+t^2}}$ : Chebyshev polynomials of the 4th kind" + ] + }, + { + "cell_type": "markdown", + "id": "99c66e96", + "metadata": {}, + "source": [ + "## Node generation\n", + "\n", + "The type of polynomial defines a **node type**, and the roots of the $M^{th}$ degree polynomial of this type are then the quadrature nodes $\\tau^M_m$\n", + "and can be generated, _e.g_ for $M=4$ :" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b71b9640", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.86113631 -0.33998104 0.33998104 0.86113631]\n" + ] + } + ], + "source": [ + "from qmat.nodes import NodesGenerator\n", + "\n", + "gen = NodesGenerator(nodeType=\"LEGENDRE\", quadType=\"GAUSS\")\n", + "nodes = gen.getNodes(nNodes=4)\n", + "print(nodes)" + ] + }, + { + "cell_type": "markdown", + "id": "fd22885d", + "metadata": {}, + "source": [ + "💡 Note that those nodes symmetrically distributed, which is not necessarily the case for other types of nodes _e.g_ for the Chebyshev polynomials of the fourth kind :" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a4cd7d0a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.93969262 -0.5 0.17364818 0.76604444]\n" + ] + } + ], + "source": [ + "gen = NodesGenerator(nodeType=\"CHEBY-4\", quadType=\"GAUSS\")\n", + "nodes = gen.getNodes(nNodes=4)\n", + "print(nodes)" + ] + }, + { + "cell_type": "markdown", + "id": "644b2a74", + "metadata": {}, + "source": [ + "Different types of nodes are available, checkout `qmat.nodes.NODE_TYPES` for the current list, in particular :\n", + "\n", + "- `LEGENDRE` : for Legendre polynomials\n", + "- `CHEBY-1` : for the Chebyshev polynomials of the first kind\n", + "- `CHEBY-2` : ...\n", + "\n", + "💡 You may noticed that those nodes are always **strictly included in** $[-1,1]$, hence usually named _Gauss points_.\n", + "But four specific **quadrature types** can be considered for each node type (_i.e_ for each polynomial basis) :\n", + "\n", + "- `GAUSS` : nodes do not include $-1$ or $1$,\n", + "- `LOBATTO` : nodes include $-1$ and $1$,\n", + "- `RADAU-LEFT` : nodes include $-1$ (usually called Radau-I),\n", + "- `RADAU-RIGHT` : nodes include $1$ (usually called Radau-II).\n", + "\n", + "The quadrature type is selected when instantiating the node generator, as for the node type :" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0b92caf1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-1. -0.4472136 0.4472136 1. ]\n" + ] + } + ], + "source": [ + "gen = NodesGenerator(nodeType=\"LEGENDRE\", quadType=\"LOBATTO\") # usually called Gauss-Lobatto in the literature\n", + "nodes = gen.getNodes(nNodes=4)\n", + "print(nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0a713599", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-1. -0.42720716 0.36290645 0.92144357]\n" + ] + } + ], + "source": [ + "gen = NodesGenerator(nodeType=\"CHEBY-3\", quadType=\"RADAU-LEFT\")\n", + "nodes = gen.getNodes(nNodes=4)\n", + "print(nodes)" + ] + }, + { + "cell_type": "markdown", + "id": "c720e329", + "metadata": {}, + "source": [ + "> đŸ“Ŗ We use the naming convention `RADAU-RIGHT` / `RADAU-LEFT` as it is more explicit than the usual one in the literature, \n", + "> and also because Radau-I and Radau-II nodes are usually associated to the Legendre polynomials." + ] + }, + { + "cell_type": "markdown", + "id": "bb59a6cb", + "metadata": {}, + "source": [ + "## Orthogonal polynomials\n", + "\n", + "The node computation process relies on the **three term recurrence coefficients** associated to each polynomial basis :\n", + "\n", + "$$\n", + "\\begin{gather}\n", + "\\forall j \\in \\mathbb{N}, \\quad\n", + "\\pi_{j+1}(t) = (t-\\alpha_j)\\pi_{j}(t) - \\beta_j \\pi_{j-1}(t), \\\\\n", + "\\pi_{-1}(t) = 0, \\quad \\pi_0(t) = 1.\n", + "\\end{gather}\n", + "$$\n", + "\n", + "Those coefficients are know analytically for each polynomial basis (_i.e_ node type), \n", + "and are used to generate the tri-diagonal Jacobi matrix for the weight function $\\omega$\n", + "\n", + "$$\n", + "J_\\infty^{\\omega} = \\begin{bmatrix}\n", + "\\alpha_0 & \\sqrt{\\beta_1} & & & \\\\\n", + "\\sqrt{\\beta_1} & \\alpha_1 & \\sqrt{\\beta_2} & & \\\\\n", + " & \\sqrt{\\beta_2} & \\alpha_2 & \\sqrt{\\beta_2} & \\\\\n", + " & & \\ddots & \\ddots & \\ddots\n", + "\\end{bmatrix}.\n", + "$$\n", + "\n", + "Computing the eigenvalues of the leading principal sub-matrix of size $M$ allows to retrieve the `GAUSS` nodes,\n", + "and some small modifications of this sub-matrix allow to retrieve the `LOBATTO`, `RADAU-LEFT` and `RADAU-RIGHT` nodes.\n", + "\n", + "But one can also use the orthogonal coefficients to evaluate the orthogonal polynomial of any degree, _e.g_ for $M=5$ :" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0fe4c282", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "degree = 4\n", + "\n", + "t = np.linspace(-1, 1, num=10000)\n", + "\n", + "gen = NodesGenerator(\"CHEBY-1\")\n", + "alpha, beta = gen.getOrthogPolyCoefficients(degree+1)\n", + "\n", + "pi1, pi2 = gen.evalOrthogPoly(t, alpha, beta)\n", + "\n", + "plt.plot(t, pi1, label=r\"$\\pi_{M-1}(t)$\")\n", + "plt.plot(t, pi2, label=r\"$\\pi_{M}(t)$\")\n", + "plt.legend(); plt.xlabel(\"$t$\");" + ] + }, + { + "cell_type": "markdown", + "id": "8d07a8e5", + "metadata": {}, + "source": [ + "> đŸ“Ŗ Note that the `quadType` argument does not matter when generating orthogonal polynomials, and can simply be left to its default value." ] } ], "metadata": { + "kernelspec": { + "display_name": "micromamba", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" } }, "nbformat": 4, diff --git a/qmat/solvers/generic/__init__.py b/qmat/solvers/generic/__init__.py index c77efc4..3ced6ab 100644 --- a/qmat/solvers/generic/__init__.py +++ b/qmat/solvers/generic/__init__.py @@ -271,6 +271,11 @@ def dtype(self): """Datatype of the solution at a given time.""" return self.diffOp.dtype + @property + def times(self): + """Time values for each time-step""" + return np.linspace(self.t0, self.tEnd, self.nSteps+1) + def evalF(self, u:np.ndarray, t:float, out:np.ndarray): """ Wrapper for the `DiffOp` function evaluating :math:`f(u,t)`. @@ -396,7 +401,7 @@ def solve(self, Q, weights, uNum=None, tInit=0): rhs = np.zeros(self.uShape, dtype=self.dtype) fEvals = np.zeros((nNodes, *self.uShape), dtype=self.dtype) - times = np.linspace(self.t0+tInit, self.tEnd+tInit, self.nSteps+1) + times = self.times + tInit tau = Q.sum(axis=1) # time-stepping loop @@ -507,7 +512,7 @@ def solveSDC(self, nSweeps, Q, weights, QDelta, uNum=None, tInit=0): fEvals = [np.zeros((nNodes, *self.uShape), dtype=self.dtype) for _ in range(2)] - times = np.linspace(self.t0+tInit, self.tEnd+tInit, self.nSteps+1) + times = self.times + tInit tau = Q.sum(axis=1) # time-stepping loop @@ -761,7 +766,7 @@ def solve(self, uNum=None, tInit=0): for _ in range(self.nNodes+1)] self.evalF(uNum[0], self.t0, out=fEvals[0]) - times = np.linspace(self.t0+tInit, self.tEnd+tInit, self.nSteps+1) + times = self.times + tInit tau = self.dt*self.nodes # time-stepping loop @@ -874,7 +879,7 @@ def solveSDC(self, nSweeps, Q=None, weights=None, uNum=None, tInit=0): for _ in range(self.nNodes+1)] for _ in range(2)] - times = np.linspace(self.t0+tInit, self.tEnd+tInit, self.nSteps+1) + times = self.times + tInit tau = self.dt*self.nodes # time-stepping loop diff --git a/qmat/solvers/generic/diffops.py b/qmat/solvers/generic/diffops.py index df75ade..3a1d81b 100644 --- a/qmat/solvers/generic/diffops.py +++ b/qmat/solvers/generic/diffops.py @@ -94,7 +94,7 @@ def __init__(self, sigma=10, rho=28, beta=8/3, nativeFSolve=False): u0 = np.array([5, -5, 20], dtype=float) self.gemv = blas.get_blas_funcs("gemv", dtype=u0.dtype) - """Level-2 blas gemv function used in the native solver (just for flex, very light speedup)""" + """Level-2 blas gemv function used in the native solver (just for flex, very small speedup)""" super().__init__(u0) if nativeFSolve: From 35cfd786b73b1f434517096000f4f161225fae61 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Fri, 31 Oct 2025 18:42:56 +0100 Subject: [PATCH 24/33] TL: finalized advanced tutorials --- docs/devdoc/roadmap.md | 2 +- docs/notebooks/12_nonLinearRK.ipynb | 99 +++--- docs/notebooks/13_nonLinearSDC.ipynb | 422 ++++++++++++++++++++++- docs/notebooks/14_phiIntegrator.ipynb | 463 +++++++++++++++++++++++++- 4 files changed, 935 insertions(+), 51 deletions(-) diff --git a/docs/devdoc/roadmap.md b/docs/devdoc/roadmap.md index ed26c04..d80673c 100644 --- a/docs/devdoc/roadmap.md +++ b/docs/devdoc/roadmap.md @@ -22,7 +22,7 @@ Detailed description of all specific versions and their associated changes is av - ✅ integration of `qmat` into [pySDC](https://github.com/Parallel-in-Time/pySDC), _c.f_ [associated PR](https://github.com/Parallel-in-Time/pySDC/pull/445) - ✅ refined design for $Q_\Delta$ generators - ✅ full documentation of classes and functions -- finalization of extended usage tutorials (Node-to-Node, non-linear ODEs, ...) +- ✅ finalization of extended usage tutorials (Node-to-Node, non-linear ODEs, ...) - ✅ full definition and documentation of the version update pipeline **Status 5 - Production/Stable** : `v1.0.*` diff --git a/docs/notebooks/12_nonLinearRK.ipynb b/docs/notebooks/12_nonLinearRK.ipynb index 4a1c839..6ad5a60 100644 --- a/docs/notebooks/12_nonLinearRK.ipynb +++ b/docs/notebooks/12_nonLinearRK.ipynb @@ -218,7 +218,8 @@ "source": [ "And that's it đŸĨŗ ! We solved our non-linear time-dependent ODE on the given time frame, without caring about what's in our $Q$-coefficients ...\n", "\n", - "> đŸ“Ŗ For a **strictly lower triangular** $Q$ **matrix** (`Q[m,m]=0`), there is no need for the `fSolve` function, as the solution is simply $rhs$. \n", + "> đŸ“Ŗ For a **strictly lower triangular** $Q$ **matrix** (`Q[m,m]=0`), there is no need for the `fSolve` function, as the solution is simply $rhs$.\n", + "> That's the case for all **explicit** Runge-Kutta methods. \n", "\n", "We can plot the solution with respect to time : " ] @@ -298,7 +299,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "... and can be used to try different time schemes or resolution : " + "... which can be used to try different time schemes or resolution : " ] }, { @@ -353,10 +354,57 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using the internal solver\n", + "## Using the internal RK solver\n", "\n", - "Such generic Runge-Kutta solver is also implemented in `qmat`, along with some classical differential operators. \n", - "For instance, to solve the non-perturbed Lorenz system using any kind of $Q$ coefficients :" + "Such generic Runge-Kutta solver is also available in `qmat` in the `qmat.solvers.generic.CoeffSolver` class, \n", + "and uses a more efficient implementation than the one showed above, requiring less evaluations of $f(u,t)$. \n", + "\n", + "This implementation is based on a `DiffOp` class (differential operator) \n", + "that implements the $f(u,t)$ evaluation using the following template :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from qmat.solvers.generic import DiffOp\n", + "\n", + "class Yoodlidoo(DiffOp):\n", + " def __init__(self, params=\"value\"):\n", + " # use some initialization parameters\n", + " u0 = ... # define your initial vector\n", + " super().__init__(u0)\n", + "\n", + " def evalF(self, u, t, out:np.ndarray):\n", + " r\"\"\"\n", + " Evaluate :math:`f(u,t)` and store the result into `out`.\n", + "\n", + " Parameters\n", + " ----------\n", + " u : np.ndarray\n", + " Input solution for the evaluation.\n", + " t : float\n", + " Time for the evaluation.\n", + " out : np.ndarray\n", + " Output array in which is stored the evaluation.\n", + " \"\"\"\n", + " out[:] = ... # put the result into out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "đŸ“Ŗ Note that the `evalF` function does not return the result, \n", + "but rather put the result of the evaluation into the `out` array.\n", + "This allows a memory efficient implementation of the different solvers that avoids any implicit data copy.\n", + "\n", + "> 🔔 The `DiffOp` base class also provide a default `fSolve` method, so you don't need to implement it.\n", + "\n", + "Some differential operators are already provided in `qmat` ...\n", + "for example, to solve the non-perturbed Lorenz system using a Runge-Kutta approach :" ] }, { @@ -398,47 +446,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here the `Lorenz` class inherit from a `DiffOp` class (differential operator), which implements $f(u,t)$ and a solver for $u-f(u,t)=rhs$.\n", - "\n", - "💡 You can also create your own `DiffOp` class if you want to solve a specific problem, using the following template :" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from qmat.solvers.generic import DiffOp\n", - "\n", - "class Yoodlidoo(DiffOp):\n", - " def __init__(self, params=\"value\"):\n", - " # use your parameters ...\n", - " u0 = ... # define your initial vector\n", - " super().__init__(u0)\n", - "\n", - " def evalF(self, u, t, out):\n", - " \"\"\"\n", - " Evaluate :math:`f(u,t)` and store the result into `out`.\n", + "Eventually, you can also add your own differential operator into `qmat`, see the [short developer guide](../devdoc/addDiffOp.md) on this aspect ... \n", "\n", - " Parameters\n", - " ----------\n", - " u : np.ndarray\n", - " Input solution for the evaluation.\n", - " t : float\n", - " Time for the evaluation.\n", - " out : np.ndarray\n", - " Output array in which is stored the evaluation.\n", - " \"\"\"\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ "> đŸ“Ŗ Note that this Runge-Kutta solver does not work if $Q$ is a dense matrix.\n", - "> However, we can still use the Spectral Deferred Correction approach without too much additional code,\n", + "> In that case, we can eventually use the Spectral Deferred Correction approach without too much additional code,\n", "> which is the topic of the [next advanced tutorial ...](./13_nonLinearSDC.ipynb)" ] } diff --git a/docs/notebooks/13_nonLinearSDC.ipynb b/docs/notebooks/13_nonLinearSDC.ipynb index 563741a..972646e 100644 --- a/docs/notebooks/13_nonLinearSDC.ipynb +++ b/docs/notebooks/13_nonLinearSDC.ipynb @@ -9,17 +9,431 @@ "📜 _Previous base tutorial on [SDC](./04_sdc.ipynb) focused on the Dahlquist problem to explain how to use the_ $Q_\\Delta$_-coefficients._\n", "_But we can also use those for non-linear ODEs **as long as**_ $Q_\\Delta$ _**is lower triangular**._\n", "\n", - "Back the **all-at-once system** defined for the [previous tutorial](./12_nonLinearRK.ipynb) :\n", + "Back to the **all-at-once system** defined for the [previous tutorial](./12_nonLinearRK.ipynb) :\n", "\n", "$$\n", - "{\\bf u} - \\Delta{t}Q {\\bf f} = {\\bf u}_0\n", - "$$" + "{\\bf u} - \\Delta{t}Q {\\bf f} = {\\bf u}_0,\n", + "$$\n", + "\n", + "with $Q$ a **dense matrix**. We still want to be able to solve this system using only :\n", + "\n", + "- evaluation of $f(u,t)$\n", + "- solution of $u-\\alpha f(u,t) = rhs$ for any $\\alpha,t,rhs$\n", + "\n", + "Then, we consider the preconditioned iteration to solve the all-at-once system :\n", + "\n", + "$$\n", + "(I - \\Delta{t}Q_\\Delta F)({\\bf u}^{k+1} - {\\bf u}^{k}) = {\\bf u}_0 - ({\\bf u}^{k} - \\Delta{t}Q {\\bf f}^{k})\n", + "$$\n", + "\n", + "where \n", + "${\\bf u}^{k} = [u_1^k, \\dots, u_M^k]^T$ the vector of node solutions at the \n", + "$k^{th}$ iteration and\n", + "${\\bf f}^{k} = [f(u_1^k, t_1), \\dots, f(u_M^k, t_M)]^T$ the evaluation of each of those \n", + "node solution. We use the notation\n", + "$I,F$ for the identity operator and $f$ evaluation, respectively.\n", + "\n", + "The iteration can be rewritten and simplified into\n", + "\n", + "$$\n", + "{\\bf u}^{k+1} - \\Delta{t}Q_\\Delta {\\bf f}^{k+1}\n", + " = {\\bf u}_0 + \\Delta{t}(Q-Q_\\Delta) {\\bf f}^{k}\n", + "$$\n", + "\n", + "which can be solved node by node for each iteration, as long as $Q_\\Delta$ is **lower triangular**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisite\n", + "\n", + "We use the same as for the [previous tutorial](./12_nonLinearRK.ipynb) :" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.optimize import fsolve\n", + "\n", + "u0 = np.array([5, -5, 20])\n", + "sigma, rho0, beta, epsilon = 10, 28, 8/3, 5\n", + "\n", + "\n", + "def f(u, t):\n", + " x, y, z = u\n", + " rho = rho0 + epsilon*np.sin(t)\n", + " return np.array([sigma*(y-x), x*(rho-z)-y, x*y-beta*z])\n", + "\n", + "\n", + "def fSolve(a, t, rhs, uInit):\n", + "\n", + " def res(u):\n", + " return u - a*f(u, t) - rhs\n", + "\n", + " return fsolve(res, uInit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "Let's retrieve some $Q$ and $Q_\\Delta$ coefficients fom `qmat`, using the `Collocation` class as base " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qmat.qcoeff.collocation import Collocation\n", + "from qmat import genQDeltaCoeffs\n", + "\n", + "qGen = Collocation(nNodes=4, nodeType=\"LEGENDRE\", quadType=\"RADAU-RIGHT\")\n", + "nodes, weights, Q = qGen.genCoeffs()\n", + "QDelta = genQDeltaCoeffs(\"BE\", qGen=qGen)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> 📜 Checkout the [tutorial on nodes generation](./22_nodes.ipynb) for details about `nodeType` and `quadType`.\n", + "\n", + "Then we define some arrays to store the node solutions for each iterations (considering here $K=4$ sweeps), \n", + "the step solutions and time values : " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nSweeps = 4\n", + "uNodes = np.zeros((nSweeps+1, nodes.size, u0.size)) # k=0,...,K => (nSweeps+1) node solutions vectors\n", + "\n", + "tEnd = 10\n", + "nSteps = 1000\n", + "\n", + "uNum = np.zeros((nSteps+1, u0.size))\n", + "times = np.linspace(0, tEnd, nSteps+1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for each time step, time node and iteration, we have to solve\n", + "\n", + "$$\n", + "u_m^{k+1} - \\Delta{t} q^\\Delta_{m,m} f(u_m^{k+1},t_m)\n", + " = u_0 + \\Delta{t} \\sum_{j=1}^{M} q_{m,j}f(u_j^{k},t_j)\n", + " + \\Delta{t} \\sum_{j=1}^{m-1} q^\\Delta_{m,j}f(u_j^{k+1},t_j)\n", + " - \\Delta{t} \\sum_{j=1}^{m} q^\\Delta_{m,j}f(u_j^{k},t_j)\n", + "$$\n", + "\n", + "and compute the step update at the end :\n", + "\n", + "$$\n", + "u(t_0 + \\Delta{t}) \\simeq u_0 + \\sum_{m=1}^{M} \\omega_{m} f(u_m, t_m).\n", + "$$\n", + "\n", + "This can be done with the following code :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uNum[0] = u0\n", + "for i in range(nSteps):\n", + " dt = times[i+1] - times[i]\n", + " tNodes = times[i] + dt*nodes\n", + "\n", + " # Initialize k=0 with u0\n", + " uNodes[0][:] = uNum[i]\n", + "\n", + " # Iteration loop\n", + " for k in range(nSweeps):\n", + "\n", + " # Loop on nodes\n", + " for m in range(len(nodes)):\n", + " rhs = uNum[i].copy()\n", + "\n", + " # Quadrature terms\n", + " for j in range(len(nodes)):\n", + " rhs += dt*Q[m, j]*f(uNodes[k, j], tNodes[j])\n", + "\n", + " # Correction terms\n", + " for j in range(m):\n", + " rhs += dt*QDelta[m, j]*f(uNodes[k+1, j], tNodes[j])\n", + " for j in range(m+1):\n", + " rhs -= dt*QDelta[m, j]*f(uNodes[k, j], tNodes[j])\n", + "\n", + " if QDelta[m,m] == 0:\n", + " uNodes[k+1, m] = rhs\n", + " else:\n", + " uNodes[k+1, m] = fSolve(dt*QDelta[m, m], tNodes[m], rhs, uInit=uNodes[k, m])\n", + "\n", + " # Step update\n", + " uNum[i+1] = uNum[i]\n", + " for m in range(len(nodes)):\n", + " uNum[i+1] += dt*weights[m]*f(uNodes[-1, m], tNodes[m])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And that's it đŸĨŗ ! We solved our non-linear time-dependent ODE on the given time frame using 4 SDC sweeps, \n", + "without caring about what's in our $Q$ and $Q_\\Delta$ coefficients ...\n", + "\n", + "> đŸ“Ŗ For a **strictly lower triangular** $Q_\\Delta$ **matrix** (`QDelta[m,m]=0`), there is no need for the `fSolve` function (as for the RK methods in [previous tutorial](./12_nonLinearRK.ipynb)).\n", + "> We talk then about **explicit SDC sweep**.\n", + "\n", + "> 💡 Here the same $Q_\\Delta$ matrix is used for all sweeps, but nothing prevent to use different $Q_\\Delta$ coefficient\n", + "> for each different sweeps. We just have to generate a $Q_\\Delta$ matrix with shape `(nSweeps, nNodes, nNodes)`,\n", + "> which is allowed using the `nSweeps` optional parameter of `genQDeltaCoeffs`. \n", + "\n", + "Finally, we can plot the solution with respect to time : " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"time $t$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ideally, the previous code can be written into a function to allow multiple calls :" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def solveSDC(nSteps, nSweeps, scheme):\n", + " qGen = Collocation(nNodes=4, nodeType=\"LEGENDRE\", quadType=\"RADAU-RIGHT\")\n", + " nodes, weights, Q = qGen.genCoeffs()\n", + " QDelta = genQDeltaCoeffs(scheme, qGen=qGen)\n", + "\n", + " uNodes = np.zeros((nSweeps+1, nodes.size, u0.size))\n", + "\n", + " tEnd = 10\n", + " uNum = np.zeros((nSteps+1, u0.size))\n", + " times = np.linspace(0, tEnd, nSteps+1)\n", + "\n", + " uNum[0] = u0\n", + " for i in range(nSteps):\n", + " dt = times[i+1] - times[i]\n", + " tNodes = times[i] + dt*nodes\n", + "\n", + " # Initialize k=0 with u0\n", + " uNodes[0][:] = uNum[i]\n", + "\n", + " # Iteration loop\n", + " for k in range(nSweeps):\n", + "\n", + " # Loop on nodes\n", + " for m in range(len(nodes)):\n", + " rhs = uNum[i].copy()\n", + "\n", + " # Quadrature terms\n", + " for j in range(len(nodes)):\n", + " rhs += dt*Q[m, j]*f(uNodes[k, j], tNodes[j])\n", + "\n", + " # Correction terms\n", + " for j in range(m):\n", + " rhs += dt*QDelta[m, j]*f(uNodes[k+1, j], tNodes[j])\n", + " for j in range(m+1):\n", + " rhs -= dt*QDelta[m, j]*f(uNodes[k, j], tNodes[j])\n", + "\n", + " if QDelta[m,m] == 0:\n", + " uNodes[k+1, m] = rhs\n", + " else:\n", + " uNodes[k+1, m] = fSolve(dt*QDelta[m, m], tNodes[m], rhs, uInit=uNodes[k, m])\n", + "\n", + " # Step update\n", + " uNum[i+1] = uNum[i]\n", + " for m in range(len(nodes)):\n", + " uNum[i+1] += dt*weights[m]*f(uNodes[-1, m], tNodes[m])\n", + "\n", + " return times, uNum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... which can be used to try different SDC schemes, number of sweeps or time resolution :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "times, uNum = solveSDC(200, 2, \"BE\")\n", + "plt.plot(times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"time $t$\"); plt.title(\"Using 2 sweeps of BE-SDC and 200 time-steps\");" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "times, uNum = solveSDC(400, 4, \"FE\")\n", + "plt.plot(times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"time $t$\"); plt.title(\"Using 4 sweeps of FE-SDC and 400 time-steps\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using the internal SDC solver\n", + "\n", + "Such generic SDC solver is also available in the `qmat.solvers.generic.CoeffSolver` class,\n", + "and uses a more efficient implementation than the one showed above, requiring less evaluation of $f(u,t)$.\n", + "This implementation is based on the `DiffOp` class that implements the $f(u,t)$ evaluations,\n", + "see [previous tutorial](./12_nonLinearRK.ipynb) for more details.\n", + "\n", + "Looking at the same non-perturbed Lorenz example problem, we can solve it with SDC using those few lines :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from qmat import genQDeltaCoeffs\n", + "from qmat.qcoeff.collocation import Collocation\n", + "from qmat.solvers.generic import CoeffSolver\n", + "from qmat.solvers.generic.diffops import Lorenz\n", + "\n", + "scheme = \"BE\"\n", + "nSteps = 1000\n", + "nSweeps = 4\n", + "\n", + "qGen = Collocation(nNodes=4, nodeType=\"LEGENDRE\", quadType=\"RADAU-RIGHT\")\n", + "nodes, weights, Q = qGen.genCoeffs()\n", + "QDelta = genQDeltaCoeffs(scheme, qGen=qGen)\n", + "\n", + "solver = CoeffSolver(Lorenz(), tEnd=10, nSteps=nSteps)\n", + "uNum = solver.solveSDC(nSweeps, Q, weights, QDelta)\n", + "\n", + "plt.plot(solver.times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(solver.times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(solver.times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"Time $t$\"); plt.title(f\"Using {scheme} and {nSteps} time-steps\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Eventually, you can also add your own differential operator into `qmat`, see the [short developer guide](../devdoc/addDiffOp.md) on this aspect ...\n", + "\n", + "> 💡 This coefficient-based approach for SDC, relying on a $Q_\\Delta$ matrix, allows many different variants by just changing the $Q_\\Delta$ coefficients. However, it always rely on multi-node (or multi-stage) method to define \n", + "> the approximate time-integrator used for the SDC corrections.\n", + "> But one can also define SDC in an even more generic way, using a $\\phi$-based representation of time integrators,\n", + "> which is the topic of the [next tutorial](./14_phiIntegrator.ipynb)." ] } ], "metadata": { + "kernelspec": { + "display_name": "micromamba", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" } }, "nbformat": 4, diff --git a/docs/notebooks/14_phiIntegrator.ipynb b/docs/notebooks/14_phiIntegrator.ipynb index 13597d2..6e9cf9a 100644 --- a/docs/notebooks/14_phiIntegrator.ipynb +++ b/docs/notebooks/14_phiIntegrator.ipynb @@ -6,13 +6,472 @@ "source": [ "# Advanced Tutorial 4 : build a Spectral Deferred Correction solver based on generic time-integrators\n", "\n", - "🛠ī¸ In construction ..." + "📜 _Previous advanced tutorial on [SDC](./13_nonLinearSDC.ipynb) focused on its implementation for non-linear ODEs using_ $Q_\\Delta$_-coefficients._\n", + "_But we can also define a SDC sweep **without**_ $Q_\\Delta$ _**coefficients**, and extend this idea to many other time-integration approaches._\n", + "\n", + "> Šī¸ Credits to [Martin Schreiber](https://www.martin-schreiber.info) for the original idea behind this \n", + "> [here](https://gitlab.inria.fr/sweet/sweet/-/blob/main/doc/time_integration/spectral_deferred_correction_methods/spectral_deferred_corrections_with_less_pain_ver_2024_01_19.pdf?ref_type=heads).\n", + "\n", + "\n", + "$\\phi$**-based time-integrator** : \n", + "\n", + "Considering a sequence of nodes \n", + "$\\{\\tau_1, ..., \\tau_M\\}$ discretizing one time-step \n", + "$\\{t_0, t_0+\\Delta{t}\\}$ into\n", + "$\\{t_1, \\dots, t_M\\} = \\{t_0+\\Delta{t}\\tau_1, \\dots, t_0 + \\Delta{t}\\tau_M\\}$.\n", + "We can write one time-integrator computing the step solution through all node as a \n", + "$\\phi$ function such that :\n", + "\n", + "$$\n", + "u_{m} - \\phi(u_0, u_1, ..., u_{m}) = u_0\n", + "$$\n", + "\n", + "This allows to represent any other time-integrator, \n", + "without the restriction writing it in a $Q$-coefficient framework.\n", + "In particular, if we look at the Picard form of an ODE written \n", + "at a given time node :\n", + "\n", + "$$\n", + "u_m = u_0 + \\int_{t_0}^{t_m} f(u(s), s) ds\n", + "$$\n", + "\n", + "the $\\phi$ function simply corresponds to a given discretization\n", + "of the integral into the time nodes\n", + "$\\{t_1, \\dots, t_m\\}$, with no dependency to the next time nodes.\n", + "\n", + "**Continuous Spectral Deferred Correction**\n", + "\n", + "To retrieve the original SDC formulation of SDC, we define the error\n", + "$e^{k}(t) = u(t) - u^{k}(t)$\n", + "and put it in the Picard equation above to get :\n", + "\n", + "$$\n", + "\\begin{align}\n", + "e^{k}(t) + u^{k}(t) \n", + " &= e^{k}(t_0) + u^{k}(t_0) + \\int_{t_0}^t f\\left(e^{k}(s) - u^{k}(s), s\\right) ds \\\\\n", + " &= u_0 + \\int_{t_0}^t f\\left(e^{k}(s) + u^{k}(s), s\\right) ds.\n", + "\\end{align}\n", + "$$\n", + "\n", + "Noting \n", + "$u^{k+1}(t) := e^{k}(t) + u^{k}(t)$\n", + "and adding the difference of the two same integral terms with $u^{k}$ we get :\n", + "\n", + "$$\n", + "\\begin{align}\n", + "u^{k+1}(t) \n", + " =&~ u_0 + \\int_{t_0}^t f\\left(u^{k+1}(s), s\\right) ds \\\\\n", + " &- \\int_{t_0}^t f\\left(u^{k}(s), s\\right) ds + \\int_{t_0}^t f\\left(u^{k}(s), s\\right) ds \\\\\n", + " =&~ u_0 + \\int_{t_0}^t f\\left(u^{k+1}(s), s\\right) ds - \\int_{t_0}^t f\\left(u^{k}(s), s\\right) ds \\\\\n", + " &+ \\int_{t_0}^t f\\left(u^{k}(s), s\\right) ds\n", + "\\end{align}\n", + "$$\n", + "\n", + "$\\phi$**-based Spectral Deferred Correction** : \n", + "\n", + "We write the continuous SDC equation at a given time node $t_{m+1}$,\n", + "use a given $\\phi$ time integrator to replace the two integrals, \n", + "and write the last integral using a quadrature rule **on all time nodes** :\n", + "\n", + "$$\n", + "u^{k+1}_{m+1} = u_0 + \\phi(u_0, u^{k+1}_1, ..., u^{k+1}_{m+1}) - \\phi(u_0, u^{k}_1, ..., u^{k}_{m+1})\n", + " + \\Delta{t}\\sum_{j=0}^{M} \\omega_j f(u^k_j, t_j)\n", + "$$\n", + "\n", + "or in final form :\n", + "\n", + "$$\n", + "u^{k+1}_{m+1} - \\phi(u_0, u^{k+1}_1, ..., u^{k+1}_{m+1})\n", + " = u_0 + \\Delta{t}\\sum_{j=0}^{M} \\omega_j f(u^k_j, t_j) - \\phi(u_0, u^{k}_1, ..., u^{k}_{m+1})\n", + "$$\n", + "\n", + "✨ And there it is : no need of any $Q_\\Delta$ coefficient !\n", + "We just need to use the definition of a time-integrator that allows to \n", + "\n", + "- evaluate $\\phi(u_0, u_1, ..., u_{m+1})$ for any sequences of node solutions up to the $(m+1)^{th}$ one,\n", + "- solve $u - \\phi(u_0, u_1, ..., u_{m}, u) = rhs$ for any $rhs$ vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisite\n", + "\n", + "We use the same as for the [previous tutorial](./12_nonLinearRK.ipynb) :" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.optimize import fsolve\n", + "\n", + "u0 = np.array([5, -5, 20])\n", + "sigma, rho0, beta, epsilon = 10, 28, 8/3, 5\n", + "\n", + "\n", + "def f(u, t):\n", + " x, y, z = u\n", + " rho = rho0 + epsilon*np.sin(t)\n", + " return np.array([sigma*(y-x), x*(rho-z)-y, x*y-beta*z])\n", + "\n", + "\n", + "def fSolve(a, t, rhs, uInit):\n", + "\n", + " def res(u):\n", + " return u - a*f(u, t) - rhs\n", + "\n", + " return fsolve(res, uInit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, we define our underlying collocation problem :" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qmat.qcoeff.collocation import Collocation\n", + "\n", + "qGen = Collocation(nNodes=4, nodeType=\"LEGENDRE\", quadType=\"RADAU-RIGHT\")\n", + "nodes, weights, Q = qGen.genCoeffs()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "Consider a Backward Euler step between each node, and let us write it in $\\phi$ formulation.\n", + "Defining $\\Delta{\\tau}_m = t_{m}-t_{m-1}$, we have for each node\n", + "\n", + "$$\n", + "\\begin{align}\n", + "u_1 - \\Delta{\\tau}_1 f(u_1, t_1) &= u_0 \\\\\n", + "u_2 - \\Delta{\\tau}_2 f(u_2, t_2) &= u_1 \\\\\n", + "\\dots& \\\\\n", + "u_M - \\Delta{\\tau}_M f(u_M, t_M) &= u_{M-1}\n", + "\\end{align}\n", + "$$\n", + "\n", + "By substitution we can rearrange those into :\n", + "\n", + "$$\n", + "\\begin{align}\n", + "u_1 - \\Delta{\\tau}_1 f(u_1, t_1) &= u_0 \\\\\n", + "u_2 - \\Delta{\\tau}_2 f(u_2, t_2) - \\Delta{\\tau}_1 f(u_1, t_1) &= u_0 \\\\\n", + "\\dots& \\\\\n", + "u_M - \\Delta{\\tau}_M f(u_M, t_M) - \\dots - \\Delta{\\tau}_1 f(u_1, t_1) &= u_0\n", + "\\end{align}\n", + "$$\n", + "\n", + "so we can identify the $\\phi$ function for Backward Euler :\n", + "\n", + "$$\n", + "\\phi(u_0, u_1, ..., u_{m+1}) = \\Delta{\\tau}_{m+1} f(u_{m+1}, t_{m+1}) + \\dots + \\Delta{\\tau}_1 f(u_1, t_1)\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def phi(uNodes, t0, dt):\n", + " tau = [t0] + (t0 + dt*nodes).tolist()\n", + " out = 0\n", + " for i, u in enumerate(uNodes[1:]):\n", + " dTau = tau[i+1] - tau[i]\n", + " out = out + dTau*f(u, tau[i+1])\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can define a function to solve $u - \\phi(u_0, u_1, ..., u_{m}, u) = rhs$ for any $rhs$ vector :" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import fsolve\n", + "\n", + "def phiSolve(uNodes, rhs, uInit, t0, dt):\n", + " tau = [t0] + (t0 + dt*nodes).tolist()\n", + "\n", + " for i, u in enumerate(uNodes[1:]):\n", + " dTau = tau[i+1] - tau[i]\n", + " rhs = rhs + dTau*f(u, tau[i+1])\n", + "\n", + " m = len(uNodes) - 1\n", + " dTau = tau[m+1] - tau[m]\n", + " def res(u):\n", + " return u - dTau*f(u, tau[m+1]) - rhs\n", + "\n", + " return fsolve(res, uInit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now, we just need to implement the $\\phi$-based SDC formula as defined before :" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "nSweeps = 4\n", + "uNodes = np.zeros((nSweeps+1, nodes.size, u0.size))\n", + "\n", + "tEnd = 10\n", + "nSteps = 1000\n", + "\n", + "uNum = np.zeros((nSteps+1, u0.size))\n", + "times = np.linspace(0, tEnd, nSteps+1)\n", + "\n", + "\n", + "uNum[0] = u0\n", + "for i in range(nSteps):\n", + " dt = times[i+1] - times[i]\n", + " tNodes = times[i] + dt*nodes\n", + " u0 = uNum[i]\n", + "\n", + " # Initialize k=0 with u0\n", + " uNodes[0][:] = u0\n", + "\n", + " # Iteration loop\n", + " for k in range(nSweeps):\n", + "\n", + " # Loop on nodes\n", + " for m in range(len(nodes)):\n", + " rhs = uNum[i].copy()\n", + "\n", + " # Quadrature terms\n", + " for j in range(len(nodes)):\n", + " rhs += dt*Q[m, j]*f(uNodes[k, j], tNodes[j])\n", + "\n", + " # Phi correction term\n", + " rhs -= phi([u0, *uNodes[k, :m+1]], times[i], dt)\n", + "\n", + " # Phi solve\n", + " uNodes[k+1, m] = phiSolve([u0, *uNodes[k+1, :m]], rhs, uNodes[k, m], times[i], dt)\n", + "\n", + " # Step update\n", + " uNum[i+1] = u0\n", + " for m in range(len(nodes)):\n", + " uNum[i+1] += dt*weights[m]*f(uNodes[-1, m], tNodes[m])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And that's it đŸĨŗ ! We solved our non-linear time-dependent ODE on the given time frame using 4 SDC sweeps, \n", + "without using any $Q_\\Delta$ coefficients ...\n", + "\n", + "As before, we can plot the solution with respect to time : " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"time $t$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> 💡 Note that we retrieve exactly the same solution as the first application example given in [previous tutorial](./13_nonLinearSDC.ipynb).\n", + "\n", + "And as before, we can implement a function to reuse it with different time resolution, blablabla ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using the internal $\\phi$-SDC solver\n", + "\n", + "An optimized implementation of this $\\phi$-SDC approach is implemented in the `qmat.solvers.generic.PhiSolver`\n", + "class, along with some classical time integrator written in $\\phi$ formulation.\n", + "As for the `CoeffSolver` used in previous tutorials, they use a `DiffOp` class to evaluate $f(u,t)$.\n", + "\n", + "Looking at the non-perturbed Lorenz example problem (again), we can solve it with $\\phi$-SDC using those few lines :" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from qmat.qcoeff.collocation import Collocation\n", + "from qmat.solvers.generic.integrators import BackwardEuler\n", + "from qmat.solvers.generic.diffops import Lorenz\n", + "\n", + "scheme = \"BE\"\n", + "nSteps = 1000\n", + "nSweeps = 4\n", + "\n", + "qGen = Collocation(nNodes=4, nodeType=\"LEGENDRE\", quadType=\"RADAU-RIGHT\")\n", + "nodes, weights, Q = qGen.genCoeffs()\n", + "\n", + "solver = BackwardEuler(Lorenz(), nodes, tEnd=10, nSteps=nSteps)\n", + "uNum = solver.solveSDC(nSweeps, Q, weights)\n", + "\n", + "plt.plot(solver.times, uNum[:, 0], label=\"$x(t)$\")\n", + "plt.plot(solver.times, uNum[:, 1], label=\"$y(t)$\")\n", + "plt.plot(solver.times, uNum[:, 2], label=\"$z(t)$\")\n", + "plt.legend(); plt.xlabel(\"Time $t$\"); plt.title(f\"Using {scheme} and {nSteps} time-steps\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "đŸ“Ŗ Additional $\\phi$ integrators can be implemented, using the following template :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from qmat.solvers.generic import PhiSolver\n", + "\n", + "class Phidlidoo(PhiSolver):\n", + "\n", + " def evalPhi(self, uVals, fEvals, out, t0=0):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " uVals : list[np.ndarray] of size :math:`m+2`\n", + " The :math:`m+1` time-node solutions + the initial solution :math:`u_0`.\n", + " fEvals : list[np.ndarray] of size :math:`m+1` or :math:`m+1`\n", + " The :math:`f(u,t)` evaluations at each time nodes (+ initial solution),\n", + " up to time-node :math:`m`.\n", + " It can eventually contain a pre-computed :math:`f_{m+1}`\n", + " to spare one :math:`f(u,t)` evaluation.\n", + " out : np.ndarray\n", + " Array used to store the evaluation.\n", + " t0 : float, optional\n", + " Initial step time. The default is 0.\n", + " \"\"\"\n", + " out[:] = ... # your implementation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more details, see the [short developer guide](../devdoc/addPhiIntegrator.md) on this aspect ...\n", + "\n", + "## Fun fact\n", + "\n", + "Back to the $\\phi$-SDC formula\n", + "\n", + "$$\n", + "u^{k+1}_{m+1} - \\phi(u_0, u^{k+1}_1, ..., u^{k+1}_{m+1})\n", + " = u_0 + \\Delta{t}\\sum_{j=0}^{M} \\omega_j f(u^k_j, t_j) - \\phi(u_0, u^{k}_1, ..., u^{k}_{m+1})\n", + "$$\n", + "\n", + "we can rearrange it into :\n", + "\n", + "$$\n", + "u^{k+1}_{m+1}\n", + " = u_0 + \\Delta{t}\\sum_{j=0}^{M} \\omega_j f(u^k_j, t_j) + u_0 + \\phi(u_0, u^{k+1}_1, ..., u^{k+1}_{m+1}) - u_0 - \\phi(u_0, u^{k}_1, ..., u^{k}_{m+1}).\n", + "$$\n", + "\n", + "Now, looking back at the definition of those $\\phi$ integrators,\n", + "we can actually write each part on the right hand side as a dedicated time-integrator.\n", + "So if we note :\n", + "\n", + "- $G[t_0 \\rightarrow t_{m+1}](u^{k+1}) := u_0 + \\phi(u_0, u^{k+1}_1, ..., u^{k+1}_{m+1})$,\n", + "- $G[t_0 \\rightarrow t_{m+1}](u^{k}) := u_0 + \\phi(u_0, u^{k}_1, ..., u^{k+1}_{m})$,\n", + "- $F[t_0 \\rightarrow t_{m+1}](u^{k}) := u_0 + \\Delta{t}\\sum_{j=0}^{M} \\omega_j f(u^k_j, t_j)$,\n", + "\n", + "this produces the following formula :\n", + "\n", + "$$\n", + "u^{k+1}_{m+1} = F[t_0 \\rightarrow t_{m+1}](u^{k}) + G[t_0 \\rightarrow t_{m+1}](u^{k+1}) - G[t_0 \\rightarrow t_{m+1}](u^{k}).\n", + "$$\n", + "\n", + "This resemble furiously to a [Parareal](https://en.wikipedia.org/wiki/Parareal) formula (what a chock 😮).\n", + "However, there is some particular difference in the fact that the $F$ integrator depends on point forward in time,\n", + "which is not the case in Parareal.\n" ] } ], "metadata": { + "kernelspec": { + "display_name": "micromamba", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" } }, "nbformat": 4, From 19e5064139533d21b097fd09ad98084a0adc268e Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Fri, 31 Oct 2025 18:49:15 +0100 Subject: [PATCH 25/33] TL: additional note on tutorial --- docs/notebooks/14_phiIntegrator.ipynb | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/docs/notebooks/14_phiIntegrator.ipynb b/docs/notebooks/14_phiIntegrator.ipynb index 6e9cf9a..10bcd5c 100644 --- a/docs/notebooks/14_phiIntegrator.ipynb +++ b/docs/notebooks/14_phiIntegrator.ipynb @@ -417,8 +417,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For more details, see the [short developer guide](../devdoc/addPhiIntegrator.md) on this aspect ...\n", - "\n", + "For more details, see the [short developer guide](../devdoc/addPhiIntegrator.md) on this aspect.\n", + "This can allow to develop SDC algorithm based on any kind of time-integrator \n", + "(exponential, etc ...)\n", + "\n", + "> 💡 Per default, a specialized `PhiSolver` class can take any kind of `DiffOp` class to define the ODE problem.\n", + "> But some specific time-integrators, like a Semi-Lagrangian method for advective problemss, may be restricted some \n", + "> specific problem classes.\n", + "> In that case, you can still use the base `PhiSolver` class, but you'll have to overload its constructor to provide a specific `DiffOp` instance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "## Fun fact\n", "\n", "Back to the $\\phi$-SDC formula\n", @@ -451,7 +463,7 @@ "\n", "This resemble furiously to a [Parareal](https://en.wikipedia.org/wiki/Parareal) formula (what a chock 😮).\n", "However, there is some particular difference in the fact that the $F$ integrator depends on point forward in time,\n", - "which is not the case in Parareal.\n" + "which is not the case in Parareal." ] } ], From 27fe9790e80ab46121520b7517d204149208b50e Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Fri, 31 Oct 2025 20:30:45 +0100 Subject: [PATCH 26/33] TL: regenerated notebooks --- docs/notebooks/02_rk.ipynb | 14 +++++------ docs/notebooks/04_sdc.ipynb | 18 +++++++------- docs/notebooks/05_residuals.ipynb | 16 ++++++------- docs/notebooks/12_nonLinearRK.ipynb | 20 +++------------- docs/notebooks/13_nonLinearSDC.ipynb | 34 ++++++++------------------- docs/notebooks/14_phiIntegrator.ipynb | 30 +++++++---------------- docs/notebooks/21_lagrange.ipynb | 6 ++--- docs/notebooks/22_nodes.ipynb | 16 +------------ 8 files changed, 49 insertions(+), 105 deletions(-) diff --git a/docs/notebooks/02_rk.ipynb b/docs/notebooks/02_rk.ipynb index 19d8e46..af16a11 100644 --- a/docs/notebooks/02_rk.ipynb +++ b/docs/notebooks/02_rk.ipynb @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -109,12 +109,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -198,7 +198,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/docs/notebooks/04_sdc.ipynb b/docs/notebooks/04_sdc.ipynb index 9536315..323c785 100644 --- a/docs/notebooks/04_sdc.ipynb +++ b/docs/notebooks/04_sdc.ipynb @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -117,7 +117,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -235,12 +235,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -270,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { diff --git a/docs/notebooks/05_residuals.ipynb b/docs/notebooks/05_residuals.ipynb index bd62f47..86df969 100644 --- a/docs/notebooks/05_residuals.ipynb +++ b/docs/notebooks/05_residuals.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -202,12 +202,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -256,12 +256,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -301,12 +301,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] diff --git a/docs/notebooks/12_nonLinearRK.ipynb b/docs/notebooks/12_nonLinearRK.ipynb index 6ad5a60..7174566 100644 --- a/docs/notebooks/12_nonLinearRK.ipynb +++ b/docs/notebooks/12_nonLinearRK.ipynb @@ -365,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -455,22 +455,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "micromamba", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" + "name": "python" } }, "nbformat": 4, diff --git a/docs/notebooks/13_nonLinearSDC.ipynb b/docs/notebooks/13_nonLinearSDC.ipynb index 972646e..83f2abd 100644 --- a/docs/notebooks/13_nonLinearSDC.ipynb +++ b/docs/notebooks/13_nonLinearSDC.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -243,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -329,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -367,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -418,22 +418,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "micromamba", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" + "name": "python" } }, "nbformat": 4, diff --git a/docs/notebooks/14_phiIntegrator.ipynb b/docs/notebooks/14_phiIntegrator.ipynb index 10bcd5c..0f0cef7 100644 --- a/docs/notebooks/14_phiIntegrator.ipynb +++ b/docs/notebooks/14_phiIntegrator.ipynb @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -207,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -295,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -342,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -386,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -468,22 +468,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "micromamba", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" + "name": "python" } }, "nbformat": 4, diff --git a/docs/notebooks/21_lagrange.ipynb b/docs/notebooks/21_lagrange.ipynb index 516f8e7..719199e 100644 --- a/docs/notebooks/21_lagrange.ipynb +++ b/docs/notebooks/21_lagrange.ipynb @@ -104,7 +104,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -183,7 +183,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] diff --git a/docs/notebooks/22_nodes.ipynb b/docs/notebooks/22_nodes.ipynb index 9c1ef33..3556d9b 100644 --- a/docs/notebooks/22_nodes.ipynb +++ b/docs/notebooks/22_nodes.ipynb @@ -246,22 +246,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "micromamba", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" + "name": "python" } }, "nbformat": 4, From 383f237558691b962afd913481ad7a6a244a33b6 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Sun, 2 Nov 2025 13:39:11 +0100 Subject: [PATCH 27/33] TL: beautifying --- docs/SECURITY.md | 4 ++-- docs/contributing.md | 2 +- docs/devdoc/addDiffOp.md | 2 +- docs/devdoc/addPhiIntegrator.md | 4 ++-- docs/devdoc/addRK.md | 4 ++-- docs/devdoc/structure.md | 12 +++++----- docs/devdoc/testing.md | 10 ++++---- docs/notebooks.md | 6 ++--- docs/notebooks/01_qCoeffs.ipynb | 8 ++++--- docs/notebooks/02_rk.ipynb | 29 ++++++++++++----------- docs/notebooks/03_qDelta.ipynb | 2 +- docs/notebooks/04_sdc.ipynb | 24 +++++++++++++++---- docs/notebooks/05_residuals.ipynb | 9 ++++---- docs/notebooks/12_nonLinearRK.ipynb | 28 ++++++++++++++--------- docs/notebooks/13_nonLinearSDC.ipynb | 29 ++++++++++++++--------- docs/notebooks/14_phiIntegrator.ipynb | 33 +++++++++++++++------------ qmat/qcoeff/__init__.py | 4 ++-- qmat/qdelta/__init__.py | 16 ------------- qmat/solvers/sdc.py | 22 +++++++++--------- 19 files changed, 130 insertions(+), 118 deletions(-) diff --git a/docs/SECURITY.md b/docs/SECURITY.md index 5a16cb6..936f381 100644 --- a/docs/SECURITY.md +++ b/docs/SECURITY.md @@ -12,6 +12,6 @@ While we recommend the use of the latest version, below is the list of the curre ## Reporting a Vulnerability -If you see any vulnerability with this package, please open an +If you see any vulnerability with this package, please open an [issue on the Github interface ...](https://github.com/Parallel-in-Time/qmat/issues), -so that is can be solved as quickly as possible by the developing community. \ No newline at end of file +so that is can be solved as quickly as possible by our developing community. \ No newline at end of file diff --git a/docs/contributing.md b/docs/contributing.md index 936db8a..7e042fb 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -32,7 +32,7 @@ In case you are interested in contributing but don't have any idea on what, chec ## Base recipes -_A few base memo on how to develop this package ..._ +_Some memos on how to develop this package ..._ - [Code structure](./devdoc/structure.md) - [Add a Runge-Kutta scheme](./devdoc/addRK.md) diff --git a/docs/devdoc/addDiffOp.md b/docs/devdoc/addDiffOp.md index ce7765d..e14ffed 100644 --- a/docs/devdoc/addDiffOp.md +++ b/docs/devdoc/addDiffOp.md @@ -32,7 +32,7 @@ class Yoodlidoo(DiffOp): And that's all ! The `registerDiffOp` operator will automatically - add your class in the `DIFFOPS` dictionary to make it generically available -- check if your class override properly the `evalF` function (import error if not) +- check if your class properly overrides the `evalF` function (import error if not) - add your class to the [CI tests](./testing.md) > đŸ“Ŗ Per default, all `DiffOp` classes must be instantiable with default parameters diff --git a/docs/devdoc/addPhiIntegrator.md b/docs/devdoc/addPhiIntegrator.md index c30e385..07ea3f4 100644 --- a/docs/devdoc/addPhiIntegrator.md +++ b/docs/devdoc/addPhiIntegrator.md @@ -27,14 +27,14 @@ class Phidlidoo(PhiSolver): # TODO : integrators implementation ``` -The first lines are not mandatory, but ensure that the `evalPhi` is properly evaluated. +The first assertions are not mandatory, but ensure that the `evalPhi` is properly evaluated. > đŸ“Ŗ New `PhiSolver` classes are not automatically tested, so you'll have to write > some dedicated test for your new class in `tests.test_solvers.test_integrators.py`. > Checkout those already implemented for `ForwardEuler` and `BackwardEuler`. As for the {py:class}`DiffOp ` class, -the {py:class}`PhiSolver ` implement a generic default +the {py:class}`PhiSolver ` implements a generic default `phiSolve` method, that you can override by a more efficient specialized approach. > 💡 Note that the model above inherits the `__init__` constructor of the `PhiSolver` class, diff --git a/docs/devdoc/addRK.md b/docs/devdoc/addRK.md index 884339a..91b1d42 100644 --- a/docs/devdoc/addRK.md +++ b/docs/devdoc/addRK.md @@ -7,7 +7,7 @@ and the selected approach is to define **one class for one scheme**. ## Standard scheme -In order to add a new RK, search first for its section in the `butcher.py` file, depending on its type +To add a new RK method, search first for its section in the `butcher.py` file, depending on its type (explicit or implicit) and its order. Then add a new class at the bottom of this section following this template : ```python @@ -47,7 +47,7 @@ To test your scheme ... you don't have to do anything đŸĨŗ : all RK schemes are thanks to the [registration mechanism](./structure.md), that checks (in particular) the convergence order of each scheme (global truncation error). -> ⚠ī¸ Depending on the implemented RK scheme, convergence test may fail ... in that case no worries 😉 you'll just have to adapt your scheme to the test, as explained below : +> ⚠ī¸ Depending on the implemented RK scheme, convergence test may fail ... in that case no worries 😉 you'll just have to adapt your scheme to the test, as explained below ... All convergence tests are done on the following Dahlquist problem : diff --git a/docs/devdoc/structure.md b/docs/devdoc/structure.md index 4ac2b9f..8f2634a 100644 --- a/docs/devdoc/structure.md +++ b/docs/devdoc/structure.md @@ -96,7 +96,7 @@ class MyGenerator(QGenerator): # Implementation of nodes, weights and Q properties ``` -You can provide required parameters (like `param1`) or optional ones with default value (like `param2`). +You can provide required parameters (_e.g_ `param1`) or optional ones with default value (_e.g_ `param2`). > ⚠ī¸ For required parameters, you must provide a default value in the class attribute `DEFAULT_PARAMS`, such that the `QGenerator.getInstance()` class method works. > The later is used during testing to create a default instance of the $Q$-generator, by setting required parameters values using `DEFAULT_PARAMS`. @@ -190,16 +190,16 @@ class MyGenerator(QDeltaGenerator): But then it is necessary to : 1. add the `**kwargs` arguments to your constructor, but don't use it for your generator's parameters : `**kwargs` is only used when $Q_\Delta$ matrices are generated from different types of generators using one single call -2. properly redefine the `size` property if you don't store any $Q$ matrix attribute in your constructor +2. properly redefine the `size` property **if you don't store any** $Q$ **matrix attribute** in your constructor ## Additional sub-packages - {py:mod}`qmat.solvers` : implements various generic ODE making use of `qmat`-generated coefficients. Can be modified to [add new differential operators](./addDiffOp.md) or [add new $\phi$-based integrators](./addPhiIntegrator.md) -- {py:mod}`qmat.playgrounds` : can be modified to [add a personal playground](./addPlayground.md) (non-tested experiments / examples) +- {py:mod}`qmat.playgrounds` : can be modified to [add a playground](./addPlayground.md), _i.e_ non-tested experiments or examples script ## Additional submodules -- {py:mod}`qmat.nodes` : can be modified to add new functionalities to the `NodesGenerator` class, or improve some existing implementations -- {py:mod}`qmat.lagrange` : can be modified to add new functionalities to the `LagrangeApproximation` class, or improve some existing implementations +- {py:mod}`qmat.nodes` : can be modified to add new functionalities to the `NodesGenerator` class, or improve the current implementations +- {py:mod}`qmat.lagrange` : can be modified to add new functionalities to the `LagrangeApproximation` class, or improve the current implementations - {py:mod}`qmat.mathutils` : can be modified to add additional mathematical utility functions used by some parts in `qmat` (like array operations, regression tools, etc ...) -- {py:mod}`qmat.utils` : can be modified to add additional (non mathematical) utility functions used by some parts in `qmat` (like timers, implementation check function, etc ...) \ No newline at end of file +- {py:mod}`qmat.utils` : can be modified to add additional (non mathematical) utility functions used by some parts in `qmat` (like timers, implementation check functions, etc ...) \ No newline at end of file diff --git a/docs/devdoc/testing.md b/docs/devdoc/testing.md index 5524205..aab42de 100644 --- a/docs/devdoc/testing.md +++ b/docs/devdoc/testing.md @@ -23,16 +23,14 @@ source ./env/bin/activate If not already done, install all the test dependencies listed in the [pyproject.toml](../../pyproject.toml) file under the `project.optional-dependencies` section. -Those can be installed one by one (if not already on your system), -or use this (dirty) shortcut by running from the `qmat` root folder : +Those can be installed (if not already on your system) +by running from the `qmat` root folder : ```bash -pip install .[test] # install qmat locally and all test dependencies -pip uninstall qmat # remove the frozen qmat package installed locally +pip install -e .[test] # install qmat locally and all test dependencies ``` -> đŸ“Ŗ Remember that the [recommended installation approach for developer](../installation) -> is to install in **editable mode** using `pip install -e .[test]`. +> đŸ“Ŗ Remember that this is the [recommended installation approach for developers](../installation). ## Test local changes diff --git a/docs/notebooks.md b/docs/notebooks.md index 1d071f7..cb42b2c 100644 --- a/docs/notebooks.md +++ b/docs/notebooks.md @@ -7,12 +7,10 @@ All tutorials are written in jupyter notebooks, that can be : - read using the [online documentation](https://qmat.readthedocs.io/en/latest/notebooks.html) - downloaded from the [notebook folder](https://github.com/Parallel-in-Time/qmat/tree/main/docs/notebooks) and played with -> 🛠ī¸ Basic usage tutorials are finalized and polished, the rest is still in construction ... - -Notebooks are categorized into those main sections : +> 📋 _Table of content_ : 1. **Basic usage** : how to generate and use basic $Q$-coefficients and $Q_\Delta$ approximations, through a step-by-step tutorial going from generic Runge-Kutta methods to SDC for simple problems. -2. **Extended usage** : additional features or `qmat` to go deeper into time-integration (Node-to-Node formulation, use for non-linear problems, $\phi$-SDC, ...) +2. **Advanced tutorials** : additional features or `qmat` to go deeper into time-integration (Node-to-Node formulation, use for non-linear problems, $\phi$-SDC, ...) 3. **Components usage** : how to use the main utility modules, like {py:mod}`qmat.lagrange`, etc ... diff --git a/docs/notebooks/01_qCoeffs.ipynb b/docs/notebooks/01_qCoeffs.ipynb index 8ce8f36..fe4e7e6 100644 --- a/docs/notebooks/01_qCoeffs.ipynb +++ b/docs/notebooks/01_qCoeffs.ipynb @@ -6,7 +6,7 @@ "source": [ "# Step 1 : generate $Q$-coefficients\n", "\n", - "📜 _We denote by_ $Q$**-coefficients** (or **Butcher table**) _what fully describes a multi-stage time stepping scheme (or Runge-Kutta method) :_\n", + "📜 _We denote by_ $Q$**-coefficients** (or **Butcher table**) _what fully describes a multi-stage time stepping scheme (or Runge-Kutta method):_\n", "\n", "$$\n", "Q\\text{-coefficients : }\n", @@ -119,8 +119,10 @@ "metadata": {}, "source": [ "Depending on its first given argument, `genQCoeffs` uses the associated $Q$-generator,\n", - "potentially passing keyword arguments to instantiate it \n", - "(_e.g_ the `nNodes=4, nodeType=\"LEGENDRE\", quadType=\"RADAU-RIGHT\"` for collocation).\n", + "potentially passing keyword arguments to instantiate.\n", + "\n", + "> 📜 Checkout the [tutorial on nodes generation](./22_nodes.ipynb) for details about `nodeType` and `quadType`.\n", + "\n", "If some generator arguments are missing or wrongly given, then a descriptive error is raised, for instance :" ] }, diff --git a/docs/notebooks/02_rk.ipynb b/docs/notebooks/02_rk.ipynb index af16a11..fb76ab9 100644 --- a/docs/notebooks/02_rk.ipynb +++ b/docs/notebooks/02_rk.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ "for i in range(nSteps):\n", " b = np.ones(nodes.size)*uNum[i] # ... with its RHS\n", " uNodes = np.linalg.solve(A, b) # ... and its solution\n", - " uNum[i+1] = uNum[i] + lam*dt*weights.dot(uNodes) # prolongation\n" + " uNum[i+1] = uNum[i] + lam*dt*weights.dot(uNodes) # step update\n" ] }, { @@ -94,7 +94,7 @@ "\n", "where $u_\\tau$ stores the numerical approximation at $t_n + \\Delta{t}\\tau_m$, also called the **nodes solution** (for collocation methods) or **stage solutions** (for RK methods).\n", "\n", - "2. updating the step solution with the **prolongation** :\n", + "2. updating the step solution with the **step update** :\n", "\n", "$$\n", "u_{n+1} = u_{n} + \\lambda\\Delta{t} w^T u_\\tau\n", @@ -102,7 +102,7 @@ "\n", "... and that's it\n", "\n", - "> 💡 The code is independent from the fact that we used the RK4 scheme, or whatever else ... \n", + "> 💡 The code is independent from the fact that we used the RK4 scheme $\\Rightarrow$ we can use it for any other scheme ... \n", "\n", "We show the time solution below, starting from the initial solution (orange square), and with the exact analytic solution in dashed line :" ] @@ -257,25 +257,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "While $Q$ is **dense for the collocation method**, it is not (and actually lower triangular) for RK4. \n", - "Hence in practice, solving the **all-at-once system** with the collocation method is the most expensive,\n", - "as we need to solve it ... well, _all-at-once_.\n", + "While $Q$ is **dense for the collocation method**, it is **lower triangular** for RK4, \n", + "which allows to solve for the first node (or stage) first, then the second one, etc ...\n", + "Hence we need to solve instead $M$ equations sequentially instead of solving a system of $M$ equations **all-at-once**,\n", + "reducing thus the computation cost for one time-step.\n", + "This is one reason why RK methods have been generally favored in scientific computing against collocation methods.\n", "\n", - "> 🔍 In this case (Dahlquist), solving the _all-at-once system_ it is easy and cheap as showed above. \n", + "> 🔍 In this case (Dahlquist), solving the _all-at-once system_ is easy and cheap as showed above. \n", "> But for large scale non-linear problems, this can quickly become unfeasible, as solution at each time node\n", - "> may represent thousands or millions of degrees of freedom ...\n", - "\n", - "For RK4 though, solving the _all-at-once system_ is much simpler : one simply needs to solve the first node solution (explicit expression for RK4 since the diagonal coefficient is 0), then use it to solve the second node solution, etc ... \n", - "so no need to solve the system all-at-once !\n", - "This is one reason why RK methods have been generally favored in scientific computing against collocation methods." + "> may represent thousands or millions of degrees of freedom ..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "> 🔔 However, the high accuracy of collocation methods motivates to estimate the _all-at-once solution_ in a cheaper way than a direct solve, which is the main idea of **Spectral Deferred Correction (SDC)** and **Iterated Runge-Kutta methods**.\n", - "> Those use fixed-point preconditioned iterations to solve the all-at-once system, where the preconditoner is built using a **lower triangular** approximation of the $Q$ matrix, named the $Q_\\Delta$ **matrix**.\n", + "However, the high accuracy of collocation methods motivates to solve the _all-at-once system_ in a cheaper way, \n", + "which is the main idea of **Spectral Deferred Correction (SDC)**, a.k.a **Iterated Runge-Kutta methods**.\n", + "Those use fixed-point preconditioned iterations to solve the all-at-once system, where the preconditoner is built using a **lower triangular** approximation of the $Q$ matrix, named the $Q_\\Delta$ **matrix**.\n", "\n", "The second main feature of `qmat` is then to [generate those approximations ...](./03_qDelta.ipynb)" ] diff --git a/docs/notebooks/03_qDelta.ipynb b/docs/notebooks/03_qDelta.ipynb index 5918f26..fc11992 100644 --- a/docs/notebooks/03_qDelta.ipynb +++ b/docs/notebooks/03_qDelta.ipynb @@ -202,7 +202,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "đŸ“Ŗ To allow more generic call, it is possible to give keyword arguments that will be used only for some $Q_\\Delta$ matrices :" + "To allow more generic call, it is possible to give keyword arguments that will be used only for some $Q_\\Delta$ matrices :" ] }, { diff --git a/docs/notebooks/04_sdc.ipynb b/docs/notebooks/04_sdc.ipynb index 323c785..4e4eeae 100644 --- a/docs/notebooks/04_sdc.ipynb +++ b/docs/notebooks/04_sdc.ipynb @@ -84,7 +84,7 @@ " d = np.linalg.solve(P, r) # solve with preconditioner\n", " uNodes += d # update solution\n", "\n", - " uNum[i+1] = uNum[i] + lam*dt*weights.dot(uNodes) # prolongation" + " uNum[i+1] = uNum[i] + lam*dt*weights.dot(uNodes) # step update" ] }, { @@ -98,7 +98,7 @@ " - compute the **residuals** $r = u_n - A u^k$\n", " - solve with the preconditioner to retrieve the **defect** $d = P^{-1}r$\n", " - update the node solution with the defect $u^{k+1} = u^k + d$\n", - "3. update the step solution with the **prolongation** applied on $u^{K}$\n", + "3. update the step solution with the **step update** using $u^{K}$ values\n", "\n", "And here is the obtained numerical solution and associated $L_\\infty$ error :" ] @@ -206,7 +206,7 @@ " b = uNum[i] + lam*dt*(Q-QDelta) @ uNodes\n", " uNodes = np.linalg.solve(P, b)\n", "\n", - " uNum[i+1] = uNum[i] + lam*dt*weights.dot(uNodes) # prolongation" + " uNum[i+1] = uNum[i] + lam*dt*weights.dot(uNodes) # step update" ] }, { @@ -230,7 +230,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "and we observe of the solution evolves with each sweeps :" + "and we observe how the solution evolves with each sweeps :" ] }, { @@ -347,8 +347,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "micromamba", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" } }, "nbformat": 4, diff --git a/docs/notebooks/05_residuals.ipynb b/docs/notebooks/05_residuals.ipynb index 86df969..738c28e 100644 --- a/docs/notebooks/05_residuals.ipynb +++ b/docs/notebooks/05_residuals.ipynb @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +131,7 @@ " residuals[k+1, i] = uNum[i] + lam*dt*Q @ uNodes - uNodes\n", " error[k+1, i] = uNodes - u0*np.exp(lam*(times[i] + dt*nodes))\n", "\n", - " uNum[i+1] = uNum[i] + lam*dt*weights.dot(uNodes) # prolongation" + " uNum[i+1] = uNum[i] + lam*dt*weights.dot(uNodes) # step update" ] }, { @@ -195,7 +195,7 @@ "This is because we only did 4 SDC sweeps here, so the SDC approach is way less accurate than the underlying collocation\n", "method using 4 Legendre Radau-Right nodes (order 7).\n", "This can be observed more in details by looking at different total numbers of sweeps $K$. \n", - "For that, we can use the `monitor` parameter of the `solveDahlquistSDC` function (simplifies the code), \n", + "For that, we can use the `monitor` parameter of the `solveDahlquistSDC` function (simpler code), \n", "extract the maximum $L_\\infty$ norm over all time-steps,\n", "and plot this versus the sweeps :" ] @@ -348,8 +348,7 @@ "\n", "Also, we looked at convergence only for one $\\lambda$ value, and only considered the accuracy. \n", "While this is interesting for a first look, analyzing other numerical aspects of SDC variants is critical\n", - "for a fair comparison. \n", - "This is especially true for numerical stability, which is the subject of the next tutorial (incoming ...)" + "for a fair comparison, _e.g_ numerical stability for the problem of interest." ] } ], diff --git a/docs/notebooks/12_nonLinearRK.ipynb b/docs/notebooks/12_nonLinearRK.ipynb index 7174566..648660e 100644 --- a/docs/notebooks/12_nonLinearRK.ipynb +++ b/docs/notebooks/12_nonLinearRK.ipynb @@ -27,8 +27,8 @@ "\\end{array}\n", "$$\n", "\n", - "corresponds to approximate the solution at given **time nodes** (or stages)\n", - "$[t_1, \\dots, t_M$] := [t_0+\\Delta{t}\\tau_1, \\dots, t_0+\\Delta{t}\\tau_M]$ \n", + "corresponds to approximating the solution at **time nodes** (or stages)\n", + "$[t_1, \\dots, t_M] := [t_0+\\Delta{t}\\tau_1, \\dots, t_0+\\Delta{t}\\tau_M]$ \n", "by solving the **all-at-once system** :\n", "\n", "$$\n", @@ -36,12 +36,12 @@ "$$\n", "\n", "where \n", - "${\\bf u} = [u_1,\\dots,u_M]^T$ is the vector containing the node solutions (or stages),\n", - "${\\bf f} = [f(u_1, t_1),\\dots,f(u_M,t_M)]^T$ the evaluations of each node solutions and\n", + "${\\bf u} := [u_1,\\dots,u_M]^T$ is the vector containing the node solutions (or stages),\n", + "${\\bf f} := [f(u_1, t_1),\\dots,f(u_M,t_M)]^T$ the evaluations of each node solutions and\n", "${\\bf u}_0$ a vector with $u_0$ in each of its entries.\n", "\n", "Then, \n", - "$u(t_0+\\Delta{t})$ can be approximated via the **step-update** :\n", + "$u(t_0+\\Delta{t})$ can be approximated via the **step update** :\n", "\n", "$$\n", "u(t_0+\\Delta{t}) \\simeq\n", @@ -173,7 +173,7 @@ " = u_0 + \\Delta{t}\\sum_{j=1}^{m-1}q_{m,j}f(u_j, t_j),\n", "$$\n", "\n", - "and compute the step update at the end :\n", + "and compute the step update before next time-step :\n", "\n", "$$\n", "u(t_0+\\Delta{t}) \\simeq\n", @@ -218,7 +218,7 @@ "source": [ "And that's it đŸĨŗ ! We solved our non-linear time-dependent ODE on the given time frame, without caring about what's in our $Q$-coefficients ...\n", "\n", - "> đŸ“Ŗ For a **strictly lower triangular** $Q$ **matrix** (`Q[m,m]=0`), there is no need for the `fSolve` function, as the solution is simply $rhs$.\n", + "> 🔍 For a **strictly lower triangular** $Q$ **matrix** (`Q[m,m]=0`), there is no need for the `fSolve` function, as the solution is simply $rhs$.\n", "> That's the case for all **explicit** Runge-Kutta methods. \n", "\n", "We can plot the solution with respect to time : " @@ -397,8 +397,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "đŸ“Ŗ Note that the `evalF` function does not return the result, \n", - "but rather put the result of the evaluation into the `out` array.\n", + "Note that the `evalF` function **does not return the result**, \n", + "but rather **put the result of the evaluation into the** `out` **array**.\n", "This allows a memory efficient implementation of the different solvers that avoids any implicit data copy.\n", "\n", "> 🔔 The `DiffOp` base class also provide a default `fSolve` method, so you don't need to implement it.\n", @@ -448,15 +448,21 @@ "source": [ "Eventually, you can also add your own differential operator into `qmat`, see the [short developer guide](../devdoc/addDiffOp.md) on this aspect ... \n", "\n", - "> đŸ“Ŗ Note that this Runge-Kutta solver does not work if $Q$ is a dense matrix.\n", + "> đŸ“Ŗ This Runge-Kutta solver does not work if $Q$ is a dense matrix.\n", "> In that case, we can eventually use the Spectral Deferred Correction approach without too much additional code,\n", "> which is the topic of the [next advanced tutorial ...](./13_nonLinearSDC.ipynb)" ] } ], "metadata": { + "kernelspec": { + "display_name": "micromamba", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.13.9" } }, "nbformat": 4, diff --git a/docs/notebooks/13_nonLinearSDC.ipynb b/docs/notebooks/13_nonLinearSDC.ipynb index 83f2abd..e83665c 100644 --- a/docs/notebooks/13_nonLinearSDC.ipynb +++ b/docs/notebooks/13_nonLinearSDC.ipynb @@ -27,9 +27,9 @@ "$$\n", "\n", "where \n", - "${\\bf u}^{k} = [u_1^k, \\dots, u_M^k]^T$ the vector of node solutions at the \n", + "${\\bf u}^{k} := [u_1^k, \\dots, u_M^k]^T$ the vector of node solutions at the \n", "$k^{th}$ iteration and\n", - "${\\bf f}^{k} = [f(u_1^k, t_1), \\dots, f(u_M^k, t_M)]^T$ the evaluation of each of those \n", + "${\\bf f}^{k} := [f(u_1^k, t_1), \\dots, f(u_M^k, t_M)]^T$ the evaluation of each \n", "node solution. We use the notation\n", "$I,F$ for the identity operator and $f$ evaluation, respectively.\n", "\n", @@ -85,7 +85,7 @@ "source": [ "## Implementation\n", "\n", - "Let's retrieve some $Q$ and $Q_\\Delta$ coefficients fom `qmat`, using the `Collocation` class as base " + "Let's retrieve $Q$ and $Q_\\Delta$ coefficients fom `qmat`, using the `Collocation` class as base " ] }, { @@ -141,7 +141,7 @@ " - \\Delta{t} \\sum_{j=1}^{m} q^\\Delta_{m,j}f(u_j^{k},t_j)\n", "$$\n", "\n", - "and compute the step update at the end :\n", + "and compute the step update before next step :\n", "\n", "$$\n", "u(t_0 + \\Delta{t}) \\simeq u_0 + \\sum_{m=1}^{M} \\omega_{m} f(u_m, t_m).\n", @@ -199,8 +199,8 @@ "And that's it đŸĨŗ ! We solved our non-linear time-dependent ODE on the given time frame using 4 SDC sweeps, \n", "without caring about what's in our $Q$ and $Q_\\Delta$ coefficients ...\n", "\n", - "> đŸ“Ŗ For a **strictly lower triangular** $Q_\\Delta$ **matrix** (`QDelta[m,m]=0`), there is no need for the `fSolve` function (as for the RK methods in [previous tutorial](./12_nonLinearRK.ipynb)).\n", - "> We talk then about **explicit SDC sweep**.\n", + "> 🔍 For a **strictly lower triangular** $Q_\\Delta$ **matrix** (`QDelta[m,m]=0`), there is no need for the `fSolve` function (as for the RK methods in [previous tutorial](./12_nonLinearRK.ipynb)).\n", + "> We talk then about **explicit SDC sweeps**.\n", "\n", "> 💡 Here the same $Q_\\Delta$ matrix is used for all sweeps, but nothing prevent to use different $Q_\\Delta$ coefficient\n", "> for each different sweeps. We just have to generate a $Q_\\Delta$ matrix with shape `(nSweeps, nNodes, nNodes)`,\n", @@ -360,9 +360,9 @@ "Such generic SDC solver is also available in the `qmat.solvers.generic.CoeffSolver` class,\n", "and uses a more efficient implementation than the one showed above, requiring less evaluation of $f(u,t)$.\n", "This implementation is based on the `DiffOp` class that implements the $f(u,t)$ evaluations,\n", - "see [previous tutorial](./12_nonLinearRK.ipynb) for more details.\n", + "see the last part of the [previous tutorial](./12_nonLinearRK.ipynb) for more details.\n", "\n", - "Looking at the same non-perturbed Lorenz example problem, we can solve it with SDC using those few lines :" + "Looking at the non-perturbed Lorenz example problem, we can solve it with SDC using those few lines :" ] }, { @@ -410,16 +410,23 @@ "source": [ "Eventually, you can also add your own differential operator into `qmat`, see the [short developer guide](../devdoc/addDiffOp.md) on this aspect ...\n", "\n", - "> 💡 This coefficient-based approach for SDC, relying on a $Q_\\Delta$ matrix, allows many different variants by just changing the $Q_\\Delta$ coefficients. However, it always rely on multi-node (or multi-stage) method to define \n", - "> the approximate time-integrator used for the SDC corrections.\n", + "> 💡 This coefficient-based approach for SDC, relying on a $Q_\\Delta$ matrix, allows many different variants by just changing the $Q_\\Delta$ coefficients. However, it always relies on multi-node (or multi-stage) methods to define \n", + "> the approximate time-integrator used for the SDC sweeps.\n", + ">\n", "> But one can also define SDC in an even more generic way, using a $\\phi$-based representation of time integrators,\n", "> which is the topic of the [next tutorial](./14_phiIntegrator.ipynb)." ] } ], "metadata": { + "kernelspec": { + "display_name": "micromamba", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.13.9" } }, "nbformat": 4, diff --git a/docs/notebooks/14_phiIntegrator.ipynb b/docs/notebooks/14_phiIntegrator.ipynb index 0f0cef7..952f84b 100644 --- a/docs/notebooks/14_phiIntegrator.ipynb +++ b/docs/notebooks/14_phiIntegrator.ipynb @@ -9,8 +9,7 @@ "📜 _Previous advanced tutorial on [SDC](./13_nonLinearSDC.ipynb) focused on its implementation for non-linear ODEs using_ $Q_\\Delta$_-coefficients._\n", "_But we can also define a SDC sweep **without**_ $Q_\\Delta$ _**coefficients**, and extend this idea to many other time-integration approaches._\n", "\n", - "> Šī¸ Credits to [Martin Schreiber](https://www.martin-schreiber.info) for the original idea behind this \n", - "> [here](https://gitlab.inria.fr/sweet/sweet/-/blob/main/doc/time_integration/spectral_deferred_correction_methods/spectral_deferred_corrections_with_less_pain_ver_2024_01_19.pdf?ref_type=heads).\n", + "> Šī¸ Credits to [Martin Schreiber](https://www.martin-schreiber.info) for the [original idea](https://gitlab.inria.fr/sweet/sweet/-/blob/main/doc/time_integration/spectral_deferred_correction_methods/spectral_deferred_corrections_with_less_pain_ver_2024_01_19.pdf?ref_type=heads).\n", "\n", "\n", "$\\phi$**-based time-integrator** : \n", @@ -18,7 +17,7 @@ "Considering a sequence of nodes \n", "$\\{\\tau_1, ..., \\tau_M\\}$ discretizing one time-step \n", "$\\{t_0, t_0+\\Delta{t}\\}$ into\n", - "$\\{t_1, \\dots, t_M\\} = \\{t_0+\\Delta{t}\\tau_1, \\dots, t_0 + \\Delta{t}\\tau_M\\}$.\n", + "$\\{t_1, \\dots, t_M\\} := \\{t_0+\\Delta{t}\\tau_1, \\dots, t_0 + \\Delta{t}\\tau_M\\}$.\n", "We can write one time-integrator computing the step solution through all node as a \n", "$\\phi$ function such that :\n", "\n", @@ -26,8 +25,8 @@ "u_{m} - \\phi(u_0, u_1, ..., u_{m}) = u_0\n", "$$\n", "\n", - "This allows to represent any other time-integrator, \n", - "without the restriction writing it in a $Q$-coefficient framework.\n", + "This allows to represent any time-integrator, \n", + "without writing it in a $Q$-coefficient framework.\n", "In particular, if we look at the Picard form of an ODE written \n", "at a given time node :\n", "\n", @@ -70,8 +69,8 @@ "$\\phi$**-based Spectral Deferred Correction** : \n", "\n", "We write the continuous SDC equation at a given time node $t_{m+1}$,\n", - "use a given $\\phi$ time integrator to replace the two integrals, \n", - "and write the last integral using a quadrature rule **on all time nodes** :\n", + "use a given $\\phi$ **time integrator** to replace the **first two integrals**, \n", + "and write the **last integral using a quadrature rule on all time nodes** :\n", "\n", "$$\n", "u^{k+1}_{m+1} = u_0 + \\phi(u_0, u^{k+1}_1, ..., u^{k+1}_{m+1}) - \\phi(u_0, u^{k}_1, ..., u^{k}_{m+1})\n", @@ -422,8 +421,8 @@ "(exponential, etc ...)\n", "\n", "> 💡 Per default, a specialized `PhiSolver` class can take any kind of `DiffOp` class to define the ODE problem.\n", - "> But some specific time-integrators, like a Semi-Lagrangian method for advective problemss, may be restricted some \n", - "> specific problem classes.\n", + "> But some specific time-integrators, like a Semi-Lagrangian method for advective problems, \n", + "> may be restricted to some specific problem classes.\n", "> In that case, you can still use the base `PhiSolver` class, but you'll have to overload its constructor to provide a specific `DiffOp` instance." ] }, @@ -455,21 +454,27 @@ "- $G[t_0 \\rightarrow t_{m+1}](u^{k}) := u_0 + \\phi(u_0, u^{k}_1, ..., u^{k+1}_{m})$,\n", "- $F[t_0 \\rightarrow t_{m+1}](u^{k}) := u_0 + \\Delta{t}\\sum_{j=0}^{M} \\omega_j f(u^k_j, t_j)$,\n", "\n", - "this produces the following formula :\n", + "it produces the following formula :\n", "\n", "$$\n", "u^{k+1}_{m+1} = F[t_0 \\rightarrow t_{m+1}](u^{k}) + G[t_0 \\rightarrow t_{m+1}](u^{k+1}) - G[t_0 \\rightarrow t_{m+1}](u^{k}).\n", "$$\n", "\n", - "This resemble furiously to a [Parareal](https://en.wikipedia.org/wiki/Parareal) formula (what a chock 😮).\n", - "However, there is some particular difference in the fact that the $F$ integrator depends on point forward in time,\n", - "which is not the case in Parareal." + "... which resemble furiously to a [Parareal](https://en.wikipedia.org/wiki/Parareal) formula (what a chock 😮).\n", + "\n", + "> 🔍 There is still one main difference between $\\phi$-SDC and Parareal : here the $F$ integrator **depends on nodes forward in time**, which is not the case for Parareal." ] } ], "metadata": { + "kernelspec": { + "display_name": "micromamba", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.13.9" } }, "nbformat": 4, diff --git a/qmat/qcoeff/__init__.py b/qmat/qcoeff/__init__.py index 950a49b..c1fc477 100644 --- a/qmat/qcoeff/__init__.py +++ b/qmat/qcoeff/__init__.py @@ -153,7 +153,7 @@ def solveDahlquist(self, lam, u0, tEnd, nSteps, useEmbeddedWeights=False): nSteps : int Number of time-step for the whole :math:`[0,T]` interval. useEmbeddedWeights : bool, optional - Wether or not use the embedded weights for the prolongation. The default is False. + Wether or not use the embedded weights for the step update. The default is False. Returns ------- @@ -195,7 +195,7 @@ def errorDahlquist(self, lam, u0, tEnd, nSteps, uNum=None, useEmbeddedWeights=Fa Numerical solution, if not provided use the `solveDahlquist` method to compute the solution. The default is None. useEmbeddedWeights : bool, optional - Wether or not use the embedded weights for the prolongation. The default is False. + Wether or not use the embedded weights for the step update. The default is False. Returns ------- diff --git a/qmat/qdelta/__init__.py b/qmat/qdelta/__init__.py index fa0f26e..e38301d 100644 --- a/qmat/qdelta/__init__.py +++ b/qmat/qdelta/__init__.py @@ -34,22 +34,6 @@ Note ---- -All :math:`Q_\Delta` approximations may need different parameters to be computed (e.g `nodes` for BE or `Q` for LU). -But **you don't need a different call for each approximation** : additional keyword arguments may be given, -and ignored when the approximation don't need them ... - ->>> qGen:QGenerator = ... # any QGenerator object implemented in qmat.qcoeff.[...] ->>> ->>> # Generic call with generic function ->>> from qmat.qdelta import genQDeltaCoeffs ->>> for qdType in ["BE", "LU"]: ->>> qDelta = genQDeltaCoeffs(qdType, nodes=qGen.nodes, Q=qGen.Q) ->>> ->>> # Generic call with generic QDeltaGenerator objects import ->>> from qmat.qdelta import QDELTA_GENERATORS ->>> for qdType in ["BE", "LU"]: ->>> qDelta = QDELTA_GENERATORS[qdType](nodes=qGen.nodes, Q=qGen.Q).getQDelta() - đŸ“Ŗ If you want to **cover all available approximations** implemented in `qmat`, we highly suggest to use the `qGen` keyword argument, allowing to extract any required parameter from a `QGenerator` object, e.g : diff --git a/qmat/solvers/sdc.py b/qmat/solvers/sdc.py index 218d4f9..eb8a071 100644 --- a/qmat/solvers/sdc.py +++ b/qmat/solvers/sdc.py @@ -29,8 +29,8 @@ def solveDahlquistSDC(lam, u0, tEnd, nSteps:int, nSweeps:int, Q:np.ndarray, QDel Approximate quadrature matrix :math:`Q_\Delta` used for SDC. If three dimensional, use the first dimension for the sweep index. weights : np.ndarray, optional - Quadrature weights to use for the prologation. - If None, prolongation is not performed. The default is None. + Quadrature weights to use for the step update. + If None, step update is not performed. The default is None. Returns ------- @@ -118,8 +118,8 @@ def errorDahlquistSDC(lam, u0, tEnd, nSteps, nSweeps, Q, QDelta, QDelta : np.ndarray Approximate quadrature matrix :math:`Q_\Delta` used for SDC. weights : np.ndarray, optional - Quadrature weights to use for the prologation. - If None, prolongation is not performed. The default is None. + Quadrature weights to use for the step update. + If None, step update is not performed. The default is None. uNum : np.ndarray, optional Numerical solution, if not provided use the `solveDahlquist` method to compute the solution. The default is None. @@ -139,7 +139,7 @@ def errorDahlquistSDC(lam, u0, tEnd, nSteps, nSweeps, Q, QDelta, return np.linalg.norm(uNum-uExact, ord=np.inf) -def getOrderSDC(coll, nSweeps, qDelta, prolongation): +def getOrderSDC(coll, nSweeps, qDelta, stepUpdate): r""" Give the expected order of SDC after a fixed number of iterations. @@ -151,8 +151,8 @@ def getOrderSDC(coll, nSweeps, qDelta, prolongation): Number of sweeps for SDC. qDelta : str Type of the :math:`Q_\Delta` approximation used. - prolongation : bool - Wether or not the prolongation is done at the end. + stepUpdate : bool + Wether or not the stepUpdate is done at the end. Returns ------- @@ -176,15 +176,15 @@ def getOrderSDC(coll, nSweeps, qDelta, prolongation): order += 1 # rest of sweeps order += nSweeps-1 - # take into account prolongation - if prolongation == "QUADRATURE": + # take into account step update + if stepUpdate == "QUADRATURE": order += 1 order = min(maxOrder, order) # Edge cases with bonus order # TODO: couple with the Butcher theory from Joscha to retrieve this theoretically ... - if prolongation == "QUADRATURE": # COPY initialization + if stepUpdate == "QUADRATURE": # COPY initialization if qDelta == "TRAP": if nSweeps == 1 and nNodes == 3 and nodeType == "EQUID" and quadType == "RADAU-LEFT": order += 1 @@ -226,7 +226,7 @@ def getOrderSDC(coll, nSweeps, qDelta, prolongation): if nSweeps == 3 and nNodes == 4 and nodeType in ["CHEBY-1", "CHEBY-2", "CHEBY-3", "CHEBY-4"]: order += 1 - if prolongation == "LASTNODE": + if stepUpdate == "LASTNODE": if qDelta == "BE": if nSweeps == 4 and nNodes == 3 and nodeType == "CHEBY-4" and quadType == "RADAU-RIGHT": order += 1 From dc388d57380e1906aed6ee5993a2a84fdca6b7ed Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Sun, 2 Nov 2025 18:13:06 +0100 Subject: [PATCH 28/33] TL: regenerate notebooks --- docs/notebooks/02_rk.ipynb | 2 +- docs/notebooks/04_sdc.ipynb | 16 +--------------- docs/notebooks/05_residuals.ipynb | 2 +- docs/notebooks/12_nonLinearRK.ipynb | 8 +------- docs/notebooks/13_nonLinearSDC.ipynb | 8 +------- docs/notebooks/14_phiIntegrator.ipynb | 8 +------- 6 files changed, 6 insertions(+), 38 deletions(-) diff --git a/docs/notebooks/02_rk.ipynb b/docs/notebooks/02_rk.ipynb index fb76ab9..b87f165 100644 --- a/docs/notebooks/02_rk.ipynb +++ b/docs/notebooks/02_rk.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ diff --git a/docs/notebooks/04_sdc.ipynb b/docs/notebooks/04_sdc.ipynb index 4e4eeae..16d9838 100644 --- a/docs/notebooks/04_sdc.ipynb +++ b/docs/notebooks/04_sdc.ipynb @@ -347,22 +347,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "micromamba", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" + "name": "python" } }, "nbformat": 4, diff --git a/docs/notebooks/05_residuals.ipynb b/docs/notebooks/05_residuals.ipynb index 738c28e..9f1d87a 100644 --- a/docs/notebooks/05_residuals.ipynb +++ b/docs/notebooks/05_residuals.ipynb @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ diff --git a/docs/notebooks/12_nonLinearRK.ipynb b/docs/notebooks/12_nonLinearRK.ipynb index 648660e..7066062 100644 --- a/docs/notebooks/12_nonLinearRK.ipynb +++ b/docs/notebooks/12_nonLinearRK.ipynb @@ -455,14 +455,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "micromamba", - "language": "python", - "name": "python3" - }, "language_info": { - "name": "python", - "version": "3.13.9" + "name": "python" } }, "nbformat": 4, diff --git a/docs/notebooks/13_nonLinearSDC.ipynb b/docs/notebooks/13_nonLinearSDC.ipynb index e83665c..6077f53 100644 --- a/docs/notebooks/13_nonLinearSDC.ipynb +++ b/docs/notebooks/13_nonLinearSDC.ipynb @@ -419,14 +419,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "micromamba", - "language": "python", - "name": "python3" - }, "language_info": { - "name": "python", - "version": "3.13.9" + "name": "python" } }, "nbformat": 4, diff --git a/docs/notebooks/14_phiIntegrator.ipynb b/docs/notebooks/14_phiIntegrator.ipynb index 952f84b..c9f2661 100644 --- a/docs/notebooks/14_phiIntegrator.ipynb +++ b/docs/notebooks/14_phiIntegrator.ipynb @@ -467,14 +467,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "micromamba", - "language": "python", - "name": "python3" - }, "language_info": { - "name": "python", - "version": "3.13.9" + "name": "python" } }, "nbformat": 4, From ba6a21850522ff9077e5a37a7da5a01619199490 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Sun, 2 Nov 2025 18:30:28 +0100 Subject: [PATCH 29/33] TL: trying a small update --- docs/notebooks/05_residuals.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebooks/05_residuals.ipynb b/docs/notebooks/05_residuals.ipynb index 9f1d87a..2d3cfa0 100644 --- a/docs/notebooks/05_residuals.ipynb +++ b/docs/notebooks/05_residuals.ipynb @@ -12,7 +12,7 @@ "- _one_ $Q_\\Delta$ _approximation (preconditioner for SDC, cf. [step 3](./01_qCoeffs.ipynb)),_\n", "\n", "_we need to evaluate the quality of this new time-integration method._ \n", - "_For that, we can have a look at the **residuals**._\n", + "_For that, we use the **residuals**._\n", "\n", "\n", "Starting from the SDC sweep formula\n", From 8f95e6919a713b7ed8522cf5f28c44fef3f0a256 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Sun, 2 Nov 2025 18:53:46 +0100 Subject: [PATCH 30/33] TL: minor change --- docs/notebooks/14_phiIntegrator.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebooks/14_phiIntegrator.ipynb b/docs/notebooks/14_phiIntegrator.ipynb index c9f2661..df2e508 100644 --- a/docs/notebooks/14_phiIntegrator.ipynb +++ b/docs/notebooks/14_phiIntegrator.ipynb @@ -36,7 +36,7 @@ "\n", "the $\\phi$ function simply corresponds to a given discretization\n", "of the integral into the time nodes\n", - "$\\{t_1, \\dots, t_m\\}$, with no dependency to the next time nodes.\n", + "$\\{t_1, \\dots, t_m\\}$, with **no dependency to the next time nodes**.\n", "\n", "**Continuous Spectral Deferred Correction**\n", "\n", From 5290da14ea378f9d2a11b386559c5628941adcc6 Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Mon, 3 Nov 2025 17:00:09 +0100 Subject: [PATCH 31/33] TL: solved most of thomas's comments --- docs/devdoc/addDiffOp.md | 3 +-- docs/devdoc/addRK.md | 4 ++-- docs/devdoc/testing.md | 1 + docs/devdoc/updateDoc.md | 2 +- docs/installation.md | 2 +- docs/notebooks/12_nonLinearRK.ipynb | 10 +++++----- qmat/solvers/dahlquist.py | 8 +++++++- qmat/solvers/generic/__init__.py | 1 - qmat/solvers/generic/diffops.py | 4 ++-- tests/test_solvers/test_dahlquist.py | 6 +++--- tests/test_solvers/test_generic.py | 16 ++++++++-------- 11 files changed, 31 insertions(+), 26 deletions(-) diff --git a/docs/devdoc/addDiffOp.md b/docs/devdoc/addDiffOp.md index e14ffed..f057971 100644 --- a/docs/devdoc/addDiffOp.md +++ b/docs/devdoc/addDiffOp.md @@ -1,14 +1,13 @@ # Add a differential operator 📜 _Solvers implemented in {py:mod}`qmat.solvers.generic` can be used_ -_with others {py:class}`DiffOp ` classes_ +_with other {py:class}`DiffOp ` classes_ _than those implemented in {py:mod}`qmat.solvers.generic.diffops`._ To add a new one, implement it at the end of the `diffops.py` module, using the following template : ```python - @registerDiffOp class Yoodlidoo(DiffOp): r""" diff --git a/docs/devdoc/addRK.md b/docs/devdoc/addRK.md index 91b1d42..6ec8e71 100644 --- a/docs/devdoc/addRK.md +++ b/docs/devdoc/addRK.md @@ -82,13 +82,13 @@ class NewRK(RK): Per default, $Q$-generators define the order of the embedded method (using those additional coefficient) as **one order less than the method's order** (that is, returned by the `order` property). -If this is not the case, then you should override the `weightEmbedded` property from the base class : +If this is not the case, then you should override the `orderEmbedded` property from the base class : ```python @registerRK class NewRK(RK): # ... @property - def weightsEmbedded(self): + def orderEmbedded(self): return ... # effective embedded order ``` diff --git a/docs/devdoc/testing.md b/docs/devdoc/testing.md index aab42de..817a242 100644 --- a/docs/devdoc/testing.md +++ b/docs/devdoc/testing.md @@ -28,6 +28,7 @@ by running from the `qmat` root folder : ```bash pip install -e .[test] # install qmat locally and all test dependencies +# on MAC-OS : pip install -e ".[test]" ``` > đŸ“Ŗ Remember that this is the [recommended installation approach for developers](../installation). diff --git a/docs/devdoc/updateDoc.md b/docs/devdoc/updateDoc.md index 5c43c8f..fa6400a 100644 --- a/docs/devdoc/updateDoc.md +++ b/docs/devdoc/updateDoc.md @@ -12,7 +12,7 @@ the [source code](https://github.com/Parallel-in-Time/qmat) and install the pack ```bash git clone https://github.com/Parallel-in-Time/qmat.git cd qmat -pip install -e .[docs] +pip install -e .[docs] # on MAC-OS : pip install -e ".[docs]" ``` > 📜 The `-e` option ensures that your installed python package is directly linked to the sources (no copy of code), diff --git a/docs/installation.md b/docs/installation.md index 88ee439..e19da61 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -47,7 +47,7 @@ the package in _editable mode_ : ```bash cd qmat # go into the local git repo (if not already there) -pip install -e .[test] +pip install -e .[test] # on MAC-OS : pip install -e ".[test]" ``` This will link your python installation to your local `qmat` folder, diff --git a/docs/notebooks/12_nonLinearRK.ipynb b/docs/notebooks/12_nonLinearRK.ipynb index 7066062..09e2aa5 100644 --- a/docs/notebooks/12_nonLinearRK.ipynb +++ b/docs/notebooks/12_nonLinearRK.ipynb @@ -7,7 +7,7 @@ "# Advanced Tutorial 2 : build a Runge-Kutta solver for non-linear ODEs \n", "\n", "📜 _Previous base tutorial on [Runge-Kutta solver](./02_rk.ipynb) focused on the Dahlquist problem to explain how to use the_ $Q$_-coefficients._\n", - "_But we can also use those for non-linear ODEs **as long as**_ $Q$ _**is lower triangular**, which is the case for all Runge-Kutta methods._\n", + "_But we can also use those for non-linear ODEs **as long as**_ $Q$ _**is lower triangular**._\n", "\n", "Consider the following (non-linear) ODE system :\n", "\n", @@ -32,7 +32,7 @@ "by solving the **all-at-once system** :\n", "\n", "$$\n", - "{\\bf u} - \\Delta{t}Q {\\bf f} = {\\bf u}_0\n", + "{\\bf u} - \\Delta{t}Q {\\bf f} = {\\bf u}_0 \\quad (1)\n", "$$\n", "\n", "where \n", @@ -45,13 +45,13 @@ "\n", "$$\n", "u(t_0+\\Delta{t}) \\simeq\n", - " u_0 + \\sum_{m=1}^{M} \\omega_{m} f(u_m, t_m)\n", + " u_0 + \\sum_{m=1}^{M} \\omega_{m} f(u_m, t_m) \\quad (2)\n", "$$\n", " \n", "and this process can be repeated for each successive time-step.\n", " \n", - "> đŸ“Ŗ If we do not want to solve the all-at-once problem (which can be very expensive for large problem),\n", - "> then $Q$ **must be lower-triangular** to allow solving for $u_1$ first, then for $u_2$ using the $u_1$ solution, etc ... " + "> đŸ“Ŗ If we do not want to solve $(1)$ all-at-once (which can be very expensive for large problem),\n", + "> then $Q$ **must be lower-triangular** to allow forward substitution, _i.e_ solve for $u_1$ first, then for $u_2$ using the $u_1$ solution, etc ... " ] }, { diff --git a/qmat/solvers/dahlquist.py b/qmat/solvers/dahlquist.py index e9c5f38..60da44f 100644 --- a/qmat/solvers/dahlquist.py +++ b/qmat/solvers/dahlquist.py @@ -16,7 +16,13 @@ class Dahlquist(): \frac{du}{dt} = \lambda u, \quad u(0)=u_0, \quad t \in [0,T]. It can be used to solve the equation with multiple :math:`\lambda` - values (multiple trajectories). + values (multiple trajectories) using efficient vectorized + computation. + Furthermore, it has no restriction on the used + :math:`Q` and :math:`Q_\Delta` matrices (can be dense), + which is not the case for the generic + :class:`CoeffSolver` used with + :class:`qmat.solvers.generic.diffops.Dahlquist`. Parameters ---------- diff --git a/qmat/solvers/generic/__init__.py b/qmat/solvers/generic/__init__.py index 3ced6ab..5e23d0e 100644 --- a/qmat/solvers/generic/__init__.py +++ b/qmat/solvers/generic/__init__.py @@ -200,7 +200,6 @@ def test(cls, t0=0, dt=1e-1, eps=1e-3, instance=None): raise ValueError("evalF cannot be properly evaluated into an array like u0") try: - dt = dt uEval *= -dt uEval += u0 uSolve = np.copy(u0) diff --git a/qmat/solvers/generic/diffops.py b/qmat/solvers/generic/diffops.py index 3a1d81b..b8f2ae4 100644 --- a/qmat/solvers/generic/diffops.py +++ b/qmat/solvers/generic/diffops.py @@ -35,7 +35,7 @@ class Dahlquist(DiffOp): Note ---- This class is implemented for illustration and testing purposes. - For real applications, consider using the + To solve with many :math:`\lambda` values, consider using the :class:`qmat.solvers.dahlquist.Dahlquist` class instead. Parameters @@ -58,7 +58,7 @@ def evalF(self, u, t, out): @registerDiffOp class Lorenz(DiffOp): r""" - RHS of the Lorentz system, which can be written : + RHS of the Lorenz system, which can be written : .. math:: \frac{dx}{dt} = \sigma (y-x), \; \frac{dy}{dt} = x (\rho - z) - y, diff --git a/tests/test_solvers/test_dahlquist.py b/tests/test_solvers/test_dahlquist.py index d1f34bf..cbe4d06 100644 --- a/tests/test_solvers/test_dahlquist.py +++ b/tests/test_solvers/test_dahlquist.py @@ -25,10 +25,10 @@ def testDahlquist(scheme, tEnd, nSteps, dim, lam): if scheme == "Collocation": assert np.allclose(qGen.nodes[-1], 1), \ - "default instance for Collocation does have 1 as last node, but test depends on it" + "default instance for Collocation does not have 1 as last node, but test depends on it" sol2 = solver.solve(qGen.Q, None) assert np.allclose(sol2, ref), \ - "Dahlquist without solver do not give the same solution as reference solver" + "Dahlquist without weights do not give the same solution as reference solver" @pytest.mark.parametrize("lam", [1j, -1]) @@ -73,7 +73,7 @@ def testDahlquistIMEX(scheme, tEnd, nSteps, dim, lam): assert np.allclose(sol, ref), \ "DahlquistIMEX solver does not match Dahlquist solver with implicit part only" - if scheme == "Collocation": + if scheme in ["Collocation", "DIRK43"]: sol = solver.solve(QI=qGen.Q, wI=None, QE=qGen.Q, wE=None) assert np.allclose(sol, ref), \ "DahlquistIMEX solver without weights does not match Dahlquist solver with implicit part only" diff --git a/tests/test_solvers/test_generic.py b/tests/test_solvers/test_generic.py index ff456c7..0bd9b89 100644 --- a/tests/test_solvers/test_generic.py +++ b/tests/test_solvers/test_generic.py @@ -79,7 +79,7 @@ def testLinearCoeffSolverDahlquistSDC( @pytest.fixture(scope="session") -def uRefLorentz(): +def uRefLorenz(): diffOp = Lorenz() tEnd = 0.1 qGenRef = Q_GENERATORS["RK4"].getInstance() @@ -89,10 +89,10 @@ def uRefLorentz(): @pytest.mark.parametrize("scheme", ["BE", "FE", "TRAP", "RK4", "DIRK43"]) -def testLinearCoeffSolverLorenz(scheme, uRefLorentz): - diffOp = uRefLorentz["diffOp"] - uRef = uRefLorentz["sol"] - tEnd = uRefLorentz["tEnd"] +def testLinearCoeffSolverLorenz(scheme, uRefLorenz): + diffOp = uRefLorenz["diffOp"] + uRef = uRefLorenz["sol"] + tEnd = uRefLorenz["tEnd"] nStepsVals = [10, 50, 100] err = [] @@ -114,10 +114,10 @@ def testLinearCoeffSolverLorenz(scheme, uRefLorentz): @pytest.mark.parametrize("nSweeps", [1, 2]) @pytest.mark.parametrize("nNodes", [3, 4]) @pytest.mark.parametrize("scheme", ["BE", "FE", "LU"]) -def testLinearCoeffSolverLorenzSDC(scheme, nNodes, nSweeps, quadType, uRefLorentz): +def testLinearCoeffSolverLorenzSDC(scheme, nNodes, nSweeps, quadType, uRefLorenz): diffOp = Lorenz() - uRef = uRefLorentz["sol"] - tEnd = uRefLorentz["tEnd"] + uRef = uRefLorenz["sol"] + tEnd = uRefLorenz["tEnd"] nStepsVals = [10, 50, 100] From 4039b1e0c59d9555b053656da894bc57222b625e Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Mon, 3 Nov 2025 17:43:40 +0100 Subject: [PATCH 32/33] TL: improved devdoc on DiffOp --- docs/devdoc/addDiffOp.md | 57 +++++++++++++++++++++++++++++++++------- 1 file changed, 48 insertions(+), 9 deletions(-) diff --git a/docs/devdoc/addDiffOp.md b/docs/devdoc/addDiffOp.md index f057971..8c08329 100644 --- a/docs/devdoc/addDiffOp.md +++ b/docs/devdoc/addDiffOp.md @@ -20,13 +20,24 @@ class Yoodlidoo(DiffOp): And some parameters description ... """ def __init__(self, params="value"): - self.params = params - u0 = np.array([1, 0], dtype=float) + # use some initialization parameters + u0 = ... # define your initial vector super().__init__(u0) - def evalF(self, u, t, out): - # TODO : your implementation - pass + def evalF(self, u, t, out:np.ndarray): + r""" + Evaluate :math:`f(u,t)` and store the result into `out`. + + Parameters + ---------- + u : np.ndarray + Input solution for the evaluation. + t : float + Time for the evaluation. + out : np.ndarray + Output array in which is stored the evaluation. + """ + out[:] = ... # put the result into out ``` And that's all ! The `registerDiffOp` operator will automatically @@ -40,8 +51,16 @@ And that's all ! The `registerDiffOp` operator will automatically > preset parameters for the test (checkout the > {py:func}`ProtheroRobinson ` class for an example). -Finally, the `DiffOp` class implements a default `fSolve` method, -but you can also implement a more efficient approach tailored to your problem like this : +Finally, the `DiffOp` class implements a default `fSolve` method, that solves : + +$$ +u - \alpha f(u, t) = rhs +$$ + +for any given $\alpha, t, rhs$. +It relies on generic non-linear root-finding solvers, namely `scipy.optimize.fsolve` for small problems +and `scipy.optimize.newton_krylov` for large scale problems. +You can also implement a more efficient approach tailored to your problem like this : ```python @registerDiffOp @@ -49,8 +68,28 @@ class Yoodlidoo(DiffOp): # ... def fSolve(self, a:float, rhs:np.ndarray, t:float, out:np.ndarray): + r""" + Solve :math:`u-\alpha f(u,t)=rhs` for given :math:`u,t,rhs`, + using `out` as initial guess and storing the final result into it. + + Parameters + ---------- + a : float + The :math:`\alpha` coefficient. + rhs : np.ndarray + The right hand side. + t : float + Time for the evaluation. + out : np.ndarray + Input-output array used as initial guess, + in which is stored the solution. + """ # TODO : your ultra-efficient implementation that will be # way better than a generic call of scipy.optimize.fsolve # or scipy.optimize.newton_krylov. - pass -``` \ No newline at end of file + out[:] = ... +``` + +> 🔔 Note that `out` will be used as output for the solution, +> but its input value can also be used as initial guess for any +> iterative solver you may want to use. \ No newline at end of file From 5441d0676ee9b73113ef73943463c467b10873fc Mon Sep 17 00:00:00 2001 From: Thibaut Lunet Date: Tue, 4 Nov 2025 15:34:14 +0100 Subject: [PATCH 33/33] TL: added imex stability scripts for thomas --- qmat/playgrounds/tibo/__init__.py | 2 + qmat/playgrounds/tibo/imexStabilityRK.py | 81 ++++++++++++++++++++ qmat/playgrounds/tibo/imexStabilitySDC.py | 91 +++++++++++++++++++++++ 3 files changed, 174 insertions(+) create mode 100644 qmat/playgrounds/tibo/imexStabilityRK.py create mode 100644 qmat/playgrounds/tibo/imexStabilitySDC.py diff --git a/qmat/playgrounds/tibo/__init__.py b/qmat/playgrounds/tibo/__init__.py index f81a3b1..719a39a 100644 --- a/qmat/playgrounds/tibo/__init__.py +++ b/qmat/playgrounds/tibo/__init__.py @@ -2,4 +2,6 @@ - :class:`orthogonalPolynomials` : generate orthogonal polynomial values from any distribution. - :class:`lorenz` : application example of the generic solvers to solve the Lorenz equations. - :class:`imex` : starting development for the IMEX generic solvers. +- :class:`imexStabilityRK` : investigating IMEX stability for RK methods +- :class:`imexStabilitySDC` : investigating IMEX stability for SDC methods """ diff --git a/qmat/playgrounds/tibo/imexStabilityRK.py b/qmat/playgrounds/tibo/imexStabilityRK.py new file mode 100644 index 0000000..549a917 --- /dev/null +++ b/qmat/playgrounds/tibo/imexStabilityRK.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Script investigating IMEX stability for RK methods +""" +import numpy as np +import scipy.optimize as sco +import matplotlib.pyplot as plt + +from qmat.qcoeff.butcher import RK_SCHEMES +from qmat.solvers.dahlquist import DahlquistIMEX + + +# Script parameters +implScheme = "ARK443ESDIRK" +explScheme = "ARK443ERK" +stepUpdate = False + + +# Script execution +lamE = np.linspace(0, 6, num=501) +ratio = np.logspace(-3, 2, num=301) +zI = -ratio[None, :]*lamE[:, None] +zE = 1j*lamE[:, None] + +problem = DahlquistIMEX(zI, zE) + +schemeI = RK_SCHEMES[implScheme]() +schemeE = RK_SCHEMES[explScheme]() + +QI = schemeI.Q +wI = schemeI.weights if stepUpdate else None +QE = schemeE.Q +wE = schemeE.weights if stepUpdate else None + +uNum = problem.solve(QI, wI, QE, wE) + +u1 = uNum[-1] +stab = np.abs(u1) +stab = np.clip(stab, 0, 1.2) # clip to ignore unstable area +error = np.abs(u1 - np.exp(zI+zE)) + + +plt.figure("imex stability") +plt.clf() + +coords = (ratio, lamE) +plt.contourf(*coords, stab, levels = [0, 0.2, 0.4, 0.6, 0.8, 1, 1.2]) +plt.colorbar() +plt.contour(*coords, stab, levels=[1], colors="black") +plt.contour(*coords, error, levels=[1], colors="red", linestyles=":") +plt.contour(*coords, error, levels=[1e-1], colors="orange", linestyles="-.") +plt.contour(*coords, error, levels=[1e-2], colors="gray", linestyles="--") +plt.grid(True) +plt.xscale('log') +plt.ylabel(r"$\lambda_E \Delta t$") +plt.xlabel(r"advection $\quad\leftarrow\quad\frac{-\lambda_I}{\lambda_E}\quad\rightarrow\quad$ diffusion", fontsize=20) +plt.tight_layout() + + +def imStab(x): + return np.abs(DahlquistIMEX([0], [x*1j]).solve(QI, wI, QE, wE)[-1]) - 1 + +try: + sol = sco.bisect(imStab, 1e-1, 1e2, xtol=1e-16) + print(f"CFL max [RK] : {sol}") +except: + pass + +plt.figure("stability on imaginary axis") +plt.clf() +plt.grid(True) +x = np.linspace(0, 6, num=501) +plt.plot(x, [imStab(s)[0] for s in x], label="RK") +plt.ylim(-1, 0.5) +plt.ylabel("over-amplification") +plt.xlabel(r"$\lambda_E \Delta t$") +plt.legend() +plt.tight_layout() + +plt.show() diff --git a/qmat/playgrounds/tibo/imexStabilitySDC.py b/qmat/playgrounds/tibo/imexStabilitySDC.py new file mode 100644 index 0000000..67cf711 --- /dev/null +++ b/qmat/playgrounds/tibo/imexStabilitySDC.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Script investigating IMEX stability for SDC methods +""" +import numpy as np +import scipy.optimize as sco +import matplotlib.pyplot as plt + +from qmat.qcoeff.collocation import Collocation +from qmat.qdelta import QDELTA_GENERATORS + +from qmat.solvers.dahlquist import DahlquistIMEX + + +# Script parameters +nNodes = 4 +nSweeps = 4 +stepUpdate = False +implSweep = "MIN-SR-FLEX" +explSweep = "PIC" + + +# Script execution +lamE = np.linspace(0, 6, num=501) +ratio = np.logspace(-3, 2, num=301) +zI = -ratio[None, :]*lamE[:, None] +zE = 1j*lamE[:, None] + +problem = DahlquistIMEX(zI, zE) + +coll = Collocation(nNodes=nNodes, nodeType="LEGENDRE", quadType="RADAU-RIGHT") + +genI = QDELTA_GENERATORS[implSweep](qGen=coll) +genE = QDELTA_GENERATORS[explSweep](qGen=coll) + +sweeps = [k+1 for k in range(nSweeps)] + +uNum = problem.solveSDC( + coll.Q, coll.weights if stepUpdate else None, + genI.genCoeffs(k=sweeps), genE.genCoeffs(k=sweeps), + nSweeps=nSweeps) + +u1 = uNum[-1] +stab = np.abs(u1) +stab = np.clip(stab, 0, 1.2) # clip to ignore unstable area +error = np.abs(u1 - np.exp(zI+zE)) + + +plt.figure("imex stability") +plt.clf() + +coords = (ratio, lamE) +plt.contourf(*coords, stab, levels = [0, 0.2, 0.4, 0.6, 0.8, 1, 1.2]) +plt.colorbar() +plt.contour(*coords, stab, levels=[1], colors="black") +plt.contour(*coords, error, levels=[1], colors="red", linestyles=":") +plt.contour(*coords, error, levels=[1e-1], colors="orange", linestyles="-.") +plt.contour(*coords, error, levels=[1e-2], colors="gray", linestyles="--") +plt.grid(True) +plt.xscale('log') +plt.ylabel(r"$\lambda_E \Delta t$") +plt.xlabel(r"advection $\quad\leftarrow\quad\frac{-\lambda_I}{\lambda_E}\quad\rightarrow\quad$ diffusion", fontsize=20) +plt.tight_layout() + + +def imStab(x): + uSDC = DahlquistIMEX([0], [x*1j]).solveSDC( + coll.Q, coll.weights if stepUpdate else None, + genI.genCoeffs(k=sweeps), genE.genCoeffs(k=sweeps), + nSweeps=nSweeps) + return np.abs(uSDC[-1]) - 1 + +try: + sol = sco.bisect(imStab, 1e-1, 1e2, xtol=1e-16) + print(f"CFL max [SDC] : {sol}") +except: + pass + +plt.figure("stability on imaginary axis") +plt.clf() +plt.grid(True) +x = np.linspace(0, 6, num=501) +plt.plot(x, [imStab(s)[0] for s in x], label="RK") +plt.ylim(-1, 0.5) +plt.ylabel("over-amplification") +plt.xlabel(r"$\lambda_E \Delta t$") +plt.legend() +plt.tight_layout() + +plt.show()