diff --git a/.github/workflows/docs_test.yml b/.github/workflows/docs_test.yml index 57b0cfc24..1ff2c392a 100644 --- a/.github/workflows/docs_test.yml +++ b/.github/workflows/docs_test.yml @@ -9,7 +9,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: [3.9] + python-version: ["3.11"] steps: - uses: actions/checkout@v4 diff --git a/.readthedocs.yaml b/.readthedocs.yaml index b959ec9b8..09ff049ad 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -4,7 +4,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.8" + python: "3.11" # Build documentation in the docs/ directory with Sphinx sphinx: diff --git a/examples/3_Advanced/QA_reactorscale_fixed_orientations.py b/examples/3_Advanced/QA_reactorscale_fixed_orientations.py index 63c23fa35..60283ca7b 100644 --- a/examples/3_Advanced/QA_reactorscale_fixed_orientations.py +++ b/examples/3_Advanced/QA_reactorscale_fixed_orientations.py @@ -159,6 +159,7 @@ def initialize_coils_QA(TEST_DIR, s): import warnings keep_inds = [] +dists = np.zeros(len(base_curves)) for ii in range(len(base_curves)): counter = 0 for i in range(base_curves[0].gamma().shape[0]): @@ -174,9 +175,19 @@ def initialize_coils_QA(TEST_DIR, s): 'of a TF coil. Deleting these PSCs now.') counter += 1 break + dists[ii] = np.min(np.linalg.norm(base_curves[ii].gamma(), axis=-1)) if counter == 0: keep_inds.append(ii) +# Chop off the dipole coils in the tight inboard side +# since these coils often have very high forces and prevent the TF +# coils from moving around much +dists = dists[keep_inds] +argdists = np.argsort(dists) +keep_inds = np.array(keep_inds)[argdists] +for i in range(4): + keep_inds = np.delete(keep_inds, [0]) + print(keep_inds) base_curves = np.array(base_curves)[keep_inds] @@ -279,7 +290,11 @@ def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): CS_THRESHOLD = 1.5 CS_WEIGHT = 1e2 # Weight for the Coil Coil forces term -FORCE_WEIGHT = Weight(1e-21) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# FORCE_WEIGHT = Weight(1e-19) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT2 = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +FORCE_WEIGHT = Weight(1e-19) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons TORQUE_WEIGHT = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons TORQUE_WEIGHT2 = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons @@ -365,9 +380,9 @@ def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): regularization_list2 = np.zeros(len(coils_TF)) * regularization_rect(a, b) # Jforce = MixedLpCurveForce(coils, coils_TF, regularization_list, regularization_list2) # [SquaredMeanForce2(c, coils) for c in (base_coils)] # Jforce = MixedSquaredMeanForce(coils, coils_TF) -Jforce = sum([LpCurveForce(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=1e5 * 40) for i, c in enumerate(base_coils + base_coils_TF)]) +Jforce = sum([LpCurveForce(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=4e5 * 100) for i, c in enumerate(base_coils + base_coils_TF)]) Jforce2 = sum([SquaredMeanForce(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) -Jtorque = sum([LpCurveTorque(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=1e5 * 40) for i, c in enumerate(base_coils + base_coils_TF)]) +Jtorque = sum([LpCurveTorque(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=4e5 * 100) for i, c in enumerate(base_coils + base_coils_TF)]) Jtorque2 = sum([SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) # Jtorque = SquaredMeanTorque2(coils, coils_TF) # [SquaredMeanForce2(c, coils) for c in (base_coils)] @@ -541,7 +556,7 @@ def fun(dofs): for i in range(1, n_saves + 1): print('Iteration ' + str(i) + ' / ' + str(n_saves)) res = minimize(fun, dofs, jac=True, method='L-BFGS-B', - options={'maxiter': MAXITER, 'maxcor': 400}, tol=1e-15) + options={'maxiter': MAXITER, 'maxcor': 100}, tol=1e-15) dofs = res.x dipole_currents = [c.current.get_value() for c in bs.coils] diff --git a/examples/3_Advanced/QA_single_TFcoil.py b/examples/3_Advanced/QA_single_TFcoil.py index 0b301f83f..783aa5164 100644 --- a/examples/3_Advanced/QA_single_TFcoil.py +++ b/examples/3_Advanced/QA_single_TFcoil.py @@ -7,6 +7,7 @@ from pathlib import Path import time import numpy as np +import warnings from scipy.optimize import minimize from simsopt.field import BiotSavart, Current, coils_via_symmetries # from simsopt.field import CoilCoilNetForces, CoilCoilNetTorques, \ @@ -41,8 +42,8 @@ range_param = "half period" nphi = 32 ntheta = 32 -poff = 1.5 -coff = 2.0 +poff = 1.75 +coff = 2.5 s = SurfaceRZFourier.from_vmec_input(filename, range=range_param, nphi=nphi, ntheta=ntheta) s_inner = SurfaceRZFourier.from_vmec_input(filename, range=range_param, nphi=nphi * 4, ntheta=ntheta * 4) s_outer = SurfaceRZFourier.from_vmec_input(filename, range=range_param, nphi=nphi * 4, ntheta=ntheta * 4) @@ -88,7 +89,7 @@ def initialize_coils_QA(TEST_DIR, s): ncoils = 1 R0 = s.get_rc(0, 0) * 2 R1 = s.get_rc(1, 0) * 10 - order = 4 + order = 10 from simsopt.mhd.vmec import Vmec vmec_file = 'wout_LandremanPaul2021_QA_reactorScale_lowres_reference.nc' @@ -141,7 +142,7 @@ def initialize_coils_QA(TEST_DIR, s): aa = 0.05 bb = 0.05 -Nx = 5 +Nx = 6 Ny = Nx Nz = Nx # Create the initial coils: @@ -149,25 +150,98 @@ def initialize_coils_QA(TEST_DIR, s): s, s_inner, s_outer, Nx, Ny, Nz, order=order, coil_coil_flag=True, jax_flag=True, # numquadpoints=10 # Defaults is (order + 1) * 40 so this halves it ) +# ncoils = len(base_curves) +# print('Ncoils = ', ncoils) +# for i in range(len(base_curves)): +# # base_curves[i].set('x' + str(2 * order + 1), np.random.rand(1) - 0.5) +# # base_curves[i].set('x' + str(2 * order + 2), np.random.rand(1) - 0.5) +# # base_curves[i].set('x' + str(2 * order + 3), np.random.rand(1) - 0.5) +# # base_curves[i].set('x' + str(2 * order + 4), np.random.rand(1) - 0.5) + +# # Fix shape of each coil +# for j in range(2 * order + 1): +# base_curves[i].fix('x' + str(j)) +# # Fix center points of each coil +# # base_curves[i].fix('x' + str(2 * order + 5)) +# # base_curves[i].fix('x' + str(2 * order + 6)) +# # base_curves[i].fix('x' + str(2 * order + 7)) +# base_currents = [Current(1e-1) * 2e7 for i in range(ncoils)] +# # Fix currents in each coil +# # for i in range(ncoils): +# # base_currents[i].fix_all() + +# coils = coils_via_symmetries(base_curves, base_currents, s.nfp, True) +# base_coils = coils[:ncoils] + + +keep_inds = [] +for ii in range(len(base_curves)): + counter = 0 + for i in range(base_curves[0].gamma().shape[0]): + eps = 0.05 + for j in range(len(base_curves_TF)): + for k in range(base_curves_TF[j].gamma().shape[0]): + dij = np.sqrt(np.sum((base_curves[ii].gamma()[i, :] - base_curves_TF[j].gamma()[k, :]) ** 2)) + conflict_bool = (dij < (1.0 + eps) * base_curves[0].x[0]) + if conflict_bool: + print('bad indices = ', i, j, dij, base_curves[0].x[0]) + warnings.warn( + 'There is a PSC coil initialized such that it is within a radius' + 'of a TF coil. Deleting these PSCs now.') + counter += 1 + break + if counter == 0: + keep_inds.append(ii) + +print(keep_inds) +base_curves = np.array(base_curves)[keep_inds] + ncoils = len(base_curves) print('Ncoils = ', ncoils) +coil_normals = np.zeros((ncoils, 3)) +plasma_points = s.gamma().reshape(-1, 3) +plasma_unitnormals = s.unitnormal().reshape(-1, 3) +for i in range(ncoils): + point = (base_curves[i].get_dofs()[-3:]) + dists = np.sum((point - plasma_points) ** 2, axis=-1) + min_ind = np.argmin(dists) + coil_normals[i, :] = plasma_unitnormals[min_ind, :] + # coil_normals[i, :] = (plasma_points[min_ind, :] - point) +coil_normals = coil_normals / np.linalg.norm(coil_normals, axis=-1)[:, None] +# alphas = np.arctan2( +# -coil_normals[:, 1], +# np.sqrt(coil_normals[:, 0] ** 2 + coil_normals[:, 2] ** 2)) +# deltas = np.arcsin(coil_normals[:, 0] / \ +# np.sqrt(coil_normals[:, 0] ** 2 + coil_normals[:, 2] ** 2)) +alphas = np.arcsin( + -coil_normals[:, 1], + ) +deltas = np.arctan2(coil_normals[:, 0], coil_normals[:, 2]) for i in range(len(base_curves)): - # base_curves[i].set('x' + str(2 * order + 1), np.random.rand(1) - 0.5) - # base_curves[i].set('x' + str(2 * order + 2), np.random.rand(1) - 0.5) - # base_curves[i].set('x' + str(2 * order + 3), np.random.rand(1) - 0.5) - # base_curves[i].set('x' + str(2 * order + 4), np.random.rand(1) - 0.5) + alpha2 = alphas[i] / 2.0 + delta2 = deltas[i] / 2.0 + calpha2 = np.cos(alpha2) + salpha2 = np.sin(alpha2) + cdelta2 = np.cos(delta2) + sdelta2 = np.sin(delta2) + base_curves[i].set('x' + str(2 * order + 1), calpha2 * cdelta2) + base_curves[i].set('x' + str(2 * order + 2), salpha2 * cdelta2) + base_curves[i].set('x' + str(2 * order + 3), calpha2 * sdelta2) + base_curves[i].set('x' + str(2 * order + 4), -salpha2 * sdelta2) + # Fix orientations of each coil + base_curves[i].fix('x' + str(2 * order + 1)) + base_curves[i].fix('x' + str(2 * order + 2)) + base_curves[i].fix('x' + str(2 * order + 3)) + base_curves[i].fix('x' + str(2 * order + 4)) # Fix shape of each coil for j in range(2 * order + 1): base_curves[i].fix('x' + str(j)) # Fix center points of each coil - # base_curves[i].fix('x' + str(2 * order + 5)) - # base_curves[i].fix('x' + str(2 * order + 6)) - # base_curves[i].fix('x' + str(2 * order + 7)) + base_curves[i].fix('x' + str(2 * order + 5)) + base_curves[i].fix('x' + str(2 * order + 6)) + base_curves[i].fix('x' + str(2 * order + 7)) base_currents = [Current(1e-1) * 2e7 for i in range(ncoils)] -# Fix currents in each coil -# for i in range(ncoils): -# base_currents[i].fix_all() coils = coils_via_symmetries(base_curves, base_currents, s.nfp, True) base_coils = coils[:ncoils] @@ -210,24 +284,31 @@ def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): base_a_list = np.hstack((np.ones(len(base_coils)) * aa, np.ones(len(base_coils_TF)) * a)) base_b_list = np.hstack((np.ones(len(base_coils)) * bb, np.ones(len(base_coils_TF)) * b)) -LENGTH_WEIGHT = Weight(0.01) -LENGTH_TARGET = 90 # 90 works very well... can we get it down? -LINK_WEIGHT = 1e3 +LENGTH_WEIGHT = Weight(0.001) +LENGTH_TARGET = 80 # 90 works very well... can we get it down? +LINK_WEIGHT = 1e2 CC_THRESHOLD = 0.8 -CC_WEIGHT = 1e2 +CC_WEIGHT = 1e1 CS_THRESHOLD = 1.5 -CS_WEIGHT = 1e2 +CS_WEIGHT = 1e1 # Weight for the Coil Coil forces term -FORCE_WEIGHT = Weight(1e-19) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +FORCE_WEIGHT = Weight(1e-37) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons TORQUE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +TORQUE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons + +# FORCE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons # Directory for output OUT_DIR = ("./QA_singleTF_n{:d}_p{:.2e}_c{:.2e}_lw{:.2e}_lt{:.2e}_lkw{:.2e}" + \ "_cct{:.2e}_ccw{:.2e}_cst{:.2e}_csw{:.2e}_fw{:.2e}_fww{:2e}_tw{:.2e}/").format( ncoils, poff, coff, LENGTH_WEIGHT.value, LENGTH_TARGET, LINK_WEIGHT, CC_THRESHOLD, CC_WEIGHT, CS_THRESHOLD, CS_WEIGHT, FORCE_WEIGHT.value, FORCE_WEIGHT2.value, - TORQUE_WEIGHT.value) + TORQUE_WEIGHT.value, + TORQUE_WEIGHT2.value) if os.path.exists(OUT_DIR): shutil.rmtree(OUT_DIR) os.makedirs(OUT_DIR, exist_ok=True) @@ -281,7 +362,8 @@ def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): # coil-coil and coil-plasma distances should be between all coils Jccdist = CurveCurveDistance(curves + curves_TF, CC_THRESHOLD, num_basecurves=len(coils + coils_TF)) -Jcsdist = CurveSurfaceDistance(curves + curves_TF, s, CS_THRESHOLD) +Jcsdist = CurveSurfaceDistance(curves_TF, s, CS_THRESHOLD) +Jcsdist2 = CurveSurfaceDistance(curves + curves_TF, s, CS_THRESHOLD) # While the coil array is not moving around, they cannot # interlink. @@ -298,9 +380,13 @@ def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): regularization_list2 = np.zeros(len(coils_TF)) * regularization_rect(a, b) # Jforce = MixedLpCurveForce(coils, coils_TF, regularization_list, regularization_list2) # [SquaredMeanForce2(c, coils) for c in (base_coils)] # Jforce = MixedSquaredMeanForce(coils, coils_TF) -Jforce = sum([LpCurveForce(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=2e5 * 40) for i, c in enumerate(base_coils + base_coils_TF)]) + +###### NOTE JFORCE BELOW ONLY DOING THE DIPOLE COILS!!!! +Jforce = sum([LpCurveForce(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=4, threshold=1e5 * 100) for i, c in enumerate(base_coils + base_coils_TF)]) +# Jforce = sum([LpCurveForce(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=4, threshold=4e5 * 100) for i, c in enumerate(base_coils)]) Jforce2 = sum([SquaredMeanForce(c, coils + coils_TF) for i, c in enumerate(base_coils + base_coils_TF)]) -Jtorque = sum([LpCurveTorque(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i])) for i, c in enumerate(base_coils + base_coils_TF)]) +Jtorque = sum([LpCurveTorque(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=4e5 * 100) for i, c in enumerate(base_coils + base_coils_TF)]) +Jtorque2 = sum([SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) # Jtorque = SquaredMeanTorque2(coils, coils_TF) # [SquaredMeanForce2(c, coils) for c in (base_coils)] # Jtorque = [SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils + base_coils_TF)] @@ -317,17 +403,11 @@ def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): JF += FORCE_WEIGHT2.value * Jforce2 #\ if TORQUE_WEIGHT.value > 0.0: - JF += TORQUE_WEIGHT.value * Jtorque #\ - # + FORCE_WEIGHT2.value * Jforce2 - # + TORQUE_WEIGHT * Jtorque - # + TVE_WEIGHT * Jtve - # + SF_WEIGHT * Jsf - # + CURRENTS_WEIGHT * DipoleCurrentsObj - # + CURVATURE_WEIGHT * sum(Jcs_TF) \ - # + MSC_WEIGHT * sum(QuadraticPenalty(J, MSC_THRESHOLD) for J in Jmscs_TF) \ -# + MSC_WEIGHT * sum(QuadraticPenalty(J, MSC_THRESHOLD) for J in Jmscs) \ - # + CURVATURE_WEIGHT * sum(Jcs) \ + JF += TORQUE_WEIGHT * Jtorque +if TORQUE_WEIGHT2.value > 0.0: + JF += TORQUE_WEIGHT2 * Jtorque2 + # We don't have a general interface in SIMSOPT for optimisation problems that # are not in least-squares form, so we write a little wrapper function that we # pass directly to scipy.optimize.minimize @@ -359,6 +439,7 @@ def fun(dofs): forces_val = FORCE_WEIGHT.value * Jforce.J() forces_val2 = FORCE_WEIGHT2.value * Jforce2.J() torques_val = TORQUE_WEIGHT.value * Jtorque.J() + torques_val2 = TORQUE_WEIGHT2.value * Jtorque2.J() BdotN = np.mean(np.abs(np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2))) BdotN_over_B = np.mean(np.abs(np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2)) ) / np.mean(btot.AbsB()) @@ -373,10 +454,12 @@ def fun(dofs): valuestr += f", forceObj={forces_val:.2e}" valuestr += f", forceObj2={forces_val2:.2e}" valuestr += f", torqueObj={torques_val:.2e}" + valuestr += f", torqueObj2={torques_val2:.2e}" outstr += f", F={Jforce.J():.2e}" - outstr += f", Fpointwise={Jforce2.J():.2e}" + outstr += f", Fnet={Jforce2.J():.2e}" outstr += f", T={Jtorque.J():.2e}" - outstr += f", C-C-Sep={Jccdist.shortest_distance():.2f}, C-S-Sep={Jcsdist.shortest_distance():.2f}" + outstr += f", Tnet={Jtorque2.J():.2e}" + outstr += f", C-C-Sep={Jccdist.shortest_distance():.2f}, C-S-Sep={Jcsdist2.shortest_distance():.2f}" outstr += f", Link Number = {linkNum.J()}" outstr += f", ║∇J║={np.linalg.norm(grad):.1e}" print(outstr) @@ -438,33 +521,33 @@ def fun(dofs): Jlength.dJ() t2 = time.time() print('sum(Jls_TF) time = ', t2 - t1, ' s') -t1 = time.time() -Jforce.J() -t2 = time.time() -print('Jforces time = ', t2 - t1, ' s') -t1 = time.time() -Jforce.dJ() -t2 = time.time() -print('dJforces time = ', t2 - t1, ' s') -t1 = time.time() -Jforce2.J() -t2 = time.time() -print('Jforces2 time = ', t2 - t1, ' s') -t1 = time.time() -Jforce2.dJ() -t2 = time.time() -print('dJforces2 time = ', t2 - t1, ' s') -t1 = time.time() -Jtorque.J() -t2 = time.time() -print('Jtorques time = ', t2 - t1, ' s') -t1 = time.time() -Jtorque.dJ() -t2 = time.time() -print('dJtorques time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce.J() +# t2 = time.time() +# print('Jforces time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce.dJ() +# t2 = time.time() +# print('dJforces time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce2.J() +# t2 = time.time() +# print('Jforces2 time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce2.dJ() +# t2 = time.time() +# print('dJforces2 time = ', t2 - t1, ' s') +# t1 = time.time() +# Jtorque.J() +# t2 = time.time() +# print('Jtorques time = ', t2 - t1, ' s') +# t1 = time.time() +# Jtorque.dJ() +# t2 = time.time() +# print('dJtorques time = ', t2 - t1, ' s') n_saves = 1 -MAXITER = 400 +MAXITER = 200 for i in range(1, n_saves + 1): print('Iteration ' + str(i) + ' / ' + str(n_saves)) res = minimize(fun, dofs, jac=True, method='L-BFGS-B', diff --git a/examples/3_Advanced/QH_reactorscale_DA.py b/examples/3_Advanced/QH_reactorscale_DA.py index d6ef72a06..f5e057651 100644 --- a/examples/3_Advanced/QH_reactorscale_DA.py +++ b/examples/3_Advanced/QH_reactorscale_DA.py @@ -88,7 +88,7 @@ def initialize_coils_QH(TEST_DIR, s): ncoils = 2 R0 = s.get_rc(0, 0) * 1 R1 = s.get_rc(1, 0) * 4 - order = 4 + order = 8 from simsopt.mhd.vmec import Vmec vmec_file = 'wout_LandremanPaul2021_QH_reactorScale_lowres_reference.nc' @@ -142,7 +142,7 @@ def initialize_coils_QH(TEST_DIR, s): aa = 0.05 bb = 0.05 -Nx = 5 +Nx = 6 Ny = Nx Nz = Nx # Create the initial coils: @@ -207,18 +207,18 @@ def initialize_coils_QH(TEST_DIR, s): base_curves[i].set('x' + str(2 * order + 3), calpha2 * sdelta2) base_curves[i].set('x' + str(2 * order + 4), -salpha2 * sdelta2) # Fix orientations of each coil - base_curves[i].fix('x' + str(2 * order + 1)) - base_curves[i].fix('x' + str(2 * order + 2)) - base_curves[i].fix('x' + str(2 * order + 3)) - base_curves[i].fix('x' + str(2 * order + 4)) + # base_curves[i].fix('x' + str(2 * order + 1)) + # base_curves[i].fix('x' + str(2 * order + 2)) + # base_curves[i].fix('x' + str(2 * order + 3)) + # base_curves[i].fix('x' + str(2 * order + 4)) # Fix shape of each coil for j in range(2 * order + 1): base_curves[i].fix('x' + str(j)) # Fix center points of each coil - base_curves[i].fix('x' + str(2 * order + 5)) - base_curves[i].fix('x' + str(2 * order + 6)) - base_curves[i].fix('x' + str(2 * order + 7)) + # base_curves[i].fix('x' + str(2 * order + 5)) + # base_curves[i].fix('x' + str(2 * order + 6)) + # base_curves[i].fix('x' + str(2 * order + 7)) base_currents = [Current(1e-1) * 2e7 for i in range(ncoils)] coils = coils_via_symmetries(base_curves, base_currents, s.nfp, True) @@ -270,18 +270,18 @@ def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): CS_THRESHOLD = 1.5 CS_WEIGHT = 1e2 # Weight for the Coil Coil forces term -# FORCE_WEIGHT = Weight(1e-22) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# FORCE_WEIGHT = Weight(1e-34) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons # FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons -# TORQUE_WEIGHT = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons -# TORQUE_WEIGHT2 = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT2 = Weight(4e-27) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons FORCE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons TORQUE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons TORQUE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons # Directory for output -OUT_DIR = ("./QH_reactorscale_n{:d}_p{:.2e}_c{:.2e}_lw{:.2e}_lt{:.2e}_lkw{:.2e}" + \ +OUT_DIR = ("./QH_reactorscale_TForder{:d}_n{:d}_p{:.2e}_c{:.2e}_lw{:.2e}_lt{:.2e}_lkw{:.2e}" + \ "_cct{:.2e}_ccw{:.2e}_cst{:.2e}_csw{:.2e}_fw{:.2e}_fww{:2e}_tw{:.2e}_tww{:2e}/").format( - ncoils, poff, coff, LENGTH_WEIGHT.value, LENGTH_TARGET, LINK_WEIGHT, + curves_TF[0].order, ncoils, poff, coff, LENGTH_WEIGHT.value, LENGTH_TARGET, LINK_WEIGHT, CC_THRESHOLD, CC_WEIGHT, CS_THRESHOLD, CS_WEIGHT, FORCE_WEIGHT.value, FORCE_WEIGHT2.value, TORQUE_WEIGHT.value, @@ -355,14 +355,20 @@ def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): # Jforce2 = LpCurveForce2(coils, coils_TF, p=2, threshold=1e8) # Jforce = sum([SquaredMeanForce(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) -regularization_list = np.zeros(len(coils)) * regularization_rect(aa, bb) -regularization_list2 = np.zeros(len(coils_TF)) * regularization_rect(a, b) +regularization_list = np.ones(len(coils)) * regularization_rect(aa, bb) +regularization_list2 = np.ones(len(coils_TF)) * regularization_rect(a, b) # Jforce = MixedLpCurveForce(coils, coils_TF, regularization_list, regularization_list2) # [SquaredMeanForce2(c, coils) for c in (base_coils)] # Jforce = MixedSquaredMeanForce(coils, coils_TF) -Jforce = sum([LpCurveForce(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=1e5 * 40) for i, c in enumerate(base_coils + base_coils_TF)]) +Jforce = sum([LpCurveForce(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=4, threshold=4e5 * 100) for i, c in enumerate(base_coils + base_coils_TF)]) Jforce2 = sum([SquaredMeanForce(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) -Jtorque = sum([LpCurveTorque(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=1e5 * 40) for i, c in enumerate(base_coils + base_coils_TF)]) -Jtorque2 = sum([SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) +Jtorque = sum([LpCurveTorque(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=4e5 * 100) for i, c in enumerate(base_coils + base_coils_TF)]) +# Jtorque = sum([LpCurveTorque(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=1e5 * 100) for i, c in enumerate(base_coils + base_coils_TF)]) + + +Jtorque2 = sum([SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils_TF)]) + +# Jtorque2 = sum([SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) + # Jtorque = SquaredMeanTorque2(coils, coils_TF) # [SquaredMeanForce2(c, coils) for c in (base_coils)] # Jtorque = [SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils + base_coils_TF)] @@ -370,8 +376,8 @@ def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): JF = Jf \ + CC_WEIGHT * Jccdist \ + CS_WEIGHT * Jcsdist \ - + LINK_WEIGHT * linkNum \ - + LENGTH_WEIGHT * Jlength + + LENGTH_WEIGHT * Jlength \ + + LINK_WEIGHT * linkNum if FORCE_WEIGHT.value > 0.0: JF += FORCE_WEIGHT.value * Jforce #\ @@ -531,7 +537,7 @@ def fun(dofs): # print('dJtorques time = ', t2 - t1, ' s') n_saves = 1 -MAXITER = 400 +MAXITER = 200 for i in range(1, n_saves + 1): print('Iteration ' + str(i) + ' / ' + str(n_saves)) res = minimize(fun, dofs, jac=True, method='L-BFGS-B', diff --git a/examples/3_Advanced/QH_reactorscale_nodipoles.py b/examples/3_Advanced/QH_reactorscale_nodipoles.py new file mode 100644 index 000000000..53d98e2b2 --- /dev/null +++ b/examples/3_Advanced/QH_reactorscale_nodipoles.py @@ -0,0 +1,469 @@ +#!/usr/bin/env python +r""" +""" + +import os +import shutil +from pathlib import Path +import time +import numpy as np +from scipy.optimize import minimize +from simsopt.field import BiotSavart, Current, coils_via_symmetries +# from simsopt.field import CoilCoilNetForces, CoilCoilNetTorques, \ +# TotalVacuumEnergy, CoilSelfNetForces, CoilCoilNetForces12, CoilCoilNetTorques12 +from simsopt.field import regularization_rect +from simsopt.field.force import MeanSquaredForce, coil_force, coil_torque, coil_net_torques, coil_net_forces, LpCurveForce, \ + SquaredMeanForce, \ + MeanSquaredTorque, SquaredMeanTorque, LpCurveTorque, MixedSquaredMeanForce, MixedLpCurveForce +from simsopt.util import calculate_on_axis_B +from simsopt.geo import ( + CurveLength, CurveCurveDistance, + MeanSquaredCurvature, LpCurveCurvature, CurveSurfaceDistance, LinkingNumber, + SurfaceRZFourier, curves_to_vtk, create_equally_spaced_planar_curves, + create_planar_curves_between_two_toroidal_surfaces +) +from simsopt.objectives import Weight, SquaredFlux, QuadraticPenalty +from simsopt.util import in_github_actions +import cProfile +import re + +t1 = time.time() + +# Number of Fourier modes describing each Cartesian component of each coil: +order = 0 + +# File for the desired boundary magnetic surface: +TEST_DIR = (Path(__file__).parent / ".." / ".." / "tests" / "test_files").resolve() +input_name = 'input.LandremanPaul2021_QH_reactorScale_lowres' +filename = TEST_DIR / input_name + +# Initialize the boundary magnetic surface: +range_param = "half period" +nphi = 32 +ntheta = 32 +poff = 2.0 +coff = 3.0 +s = SurfaceRZFourier.from_vmec_input(filename, range=range_param, nphi=nphi, ntheta=ntheta) +s_inner = SurfaceRZFourier.from_vmec_input(filename, range=range_param, nphi=nphi * 4, ntheta=ntheta * 4) +s_outer = SurfaceRZFourier.from_vmec_input(filename, range=range_param, nphi=nphi * 4, ntheta=ntheta * 4) + +# Make the inner and outer surfaces by extending the plasma surface +s_inner.extend_via_normal(poff) +s_outer.extend_via_normal(poff + coff) + +qphi = nphi * 2 +qtheta = ntheta * 2 +quadpoints_phi = np.linspace(0, 1, qphi, endpoint=True) +quadpoints_theta = np.linspace(0, 1, qtheta, endpoint=True) + +# Make high resolution, full torus version of the plasma boundary for plotting +s_plot = SurfaceRZFourier.from_vmec_input( + filename, + quadpoints_phi=quadpoints_phi, + quadpoints_theta=quadpoints_theta +) + +### Initialize some TF coils +def initialize_coils_QH(TEST_DIR, s): + """ + Initializes coils for each of the target configurations that are + used for permanent magnet optimization. + + Args: + config_flag: String denoting the stellarator configuration + being initialized. + TEST_DIR: String denoting where to find the input files. + out_dir: Path or string for the output directory for saved files. + s: plasma boundary surface. + Returns: + base_curves: List of CurveXYZ class objects. + curves: List of Curve class objects. + coils: List of Coil class objects. + """ + from simsopt.geo import create_equally_spaced_curves + from simsopt.field import Current, Coil, coils_via_symmetries + from simsopt.geo import curves_to_vtk + + # generate planar TF coils + ncoils = 2 + R0 = s.get_rc(0, 0) * 1 + R1 = s.get_rc(1, 0) * 4 + order = 8 + + from simsopt.mhd.vmec import Vmec + vmec_file = 'wout_LandremanPaul2021_QH_reactorScale_lowres_reference.nc' + total_current = Vmec(TEST_DIR / vmec_file).external_current() / (2 * s.nfp) / 1.4 + print('Total current = ', total_current) + + # Only need Jax flag for CurvePlanarFourier class + base_curves = create_equally_spaced_curves( + ncoils, s.nfp, stellsym=True, + R0=R0, R1=R1, order=order, numquadpoints=256, + jax_flag=True, + ) + + base_currents = [(Current(total_current / ncoils * 1e-7) * 1e7) for _ in range(ncoils - 1)] + # base_currents = [(Current(total_current / ncoils * 1e-7) * 1e7) for _ in range(ncoils)] + # base_currents[0].fix_all() + + total_current = Current(total_current) + total_current.fix_all() + base_currents += [total_current - sum(base_currents)] + coils = coils_via_symmetries(base_curves, base_currents, s.nfp, True) + # for c in coils: + # c.current.fix_all() + # c.curve.fix_all() + + # Initialize the coil curves and save the data to vtk + curves = [c.curve for c in coils] + currents = [c.current.get_value() for c in coils] + return base_curves, curves, coils, base_currents + +# initialize the coils +base_curves_TF, curves_TF, coils_TF, currents_TF = initialize_coils_QH(TEST_DIR, s) +num_TF_unique_coils = len(coils_TF) // 4 +base_coils_TF = coils_TF[:num_TF_unique_coils] +currents_TF = np.array([coil.current.get_value() for coil in coils_TF]) + +# # Set up BiotSavart fields +bs_TF = BiotSavart(coils_TF) + +# # Calculate average, approximate on-axis B field strength +calculate_on_axis_B(bs_TF, s) + +# wire cross section for the TF coils is a square 20 cm x 20 cm +# Only need this if make self forces and TVE nonzero in the objective! +a = 0.2 +b = 0.2 +nturns = 100 +nturns_TF = 200 + +def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): + contig = np.ascontiguousarray + forces = np.zeros((len(coils), len(coils[0].curve.gamma()) + 1, 3)) + torques = np.zeros((len(coils), len(coils[0].curve.gamma()) + 1, 3)) + for i, c in enumerate(coils): + aprime = aprimes[i] + bprime = bprimes[i] + # print(np.shape(bs._coils), np.shape(coils)) + # B_other = BiotSavart([cc for j, cc in enumerate(bs._coils) if i != j]).set_points(c.curve.gamma()).B() + # print(np.shape(bs._coils)) + # B_other = bs.set_points(c.curve.gamma()).B() + # print(B_other) + # exit() + forces[i, :-1, :] = coil_force(c, allcoils, regularization_rect(aprime, bprime), nturns_list[i]) + # print(i, forces[i, :-1, :]) + # bs._coils = coils + torques[i, :-1, :] = coil_torque(c, allcoils, regularization_rect(aprime, bprime), nturns_list[i]) + + forces[:, -1, :] = forces[:, 0, :] + torques[:, -1, :] = torques[:, 0, :] + forces = forces.reshape(-1, 3) + torques = torques.reshape(-1, 3) + point_data = {"Pointwise_Forces": (contig(forces[:, 0]), contig(forces[:, 1]), contig(forces[:, 2])), + "Pointwise_Torques": (contig(torques[:, 0]), contig(torques[:, 1]), contig(torques[:, 2]))} + return point_data + +btot = bs_TF +calculate_on_axis_B(btot, s) +btot.set_points(s.gamma().reshape((-1, 3))) +# a_list = np.hstack((np.ones(len(coils)) * aa, np.ones(len(coils_TF)) * a)) +# b_list = np.hstack((np.ones(len(coils)) * bb, np.ones(len(coils_TF)) * b)) +# base_a_list = np.hstack((np.ones(len(base_coils)) * aa, np.ones(len(base_coils_TF)) * a)) +# base_b_list = np.hstack((np.ones(len(base_coils)) * bb, np.ones(len(base_coils_TF)) * b)) + +LENGTH_WEIGHT = Weight(0.001) +LENGTH_TARGET = 80 +LINK_WEIGHT = 1e3 +CC_THRESHOLD = 0.8 +CC_WEIGHT = 1e1 +CS_THRESHOLD = 1.5 +CS_WEIGHT = 1e2 +# Weight for the Coil Coil forces term +FORCE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT2 = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# FORCE_WEIGHT = Weight(1e-22) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT2 = Weight(1e-24) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# Directory for output +OUT_DIR = ("./QH_nodipoles/") +if os.path.exists(OUT_DIR): + shutil.rmtree(OUT_DIR) +os.makedirs(OUT_DIR, exist_ok=True) + +curves_to_vtk( + curves_TF, + OUT_DIR + "curves_TF_0", + close=True, + extra_point_data=pointData_forces_torques(coils_TF, coils_TF, np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b, np.ones(len(coils_TF)) * nturns_TF), + I=currents_TF, + NetForces=coil_net_forces(coils_TF, coils_TF, regularization_rect(np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b), np.ones(len(coils_TF)) * nturns_TF), + NetTorques=coil_net_torques(coils_TF, coils_TF, regularization_rect(np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b), np.ones(len(coils_TF)) * nturns_TF) +) +# Force and Torque calculations spawn a bunch of spurious BiotSavart child objects -- erase them! +for c in (coils_TF): + c._children = set() + +pointData = {"B_N": np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2)[:, :, None]} +s.to_vtk(OUT_DIR + "surf_init_DA", extra_data=pointData) + +btot.set_points(s_plot.gamma().reshape((-1, 3))) +pointData = {"B_N": np.sum(btot.B().reshape((qphi, qtheta, 3)) * s_plot.unitnormal(), axis=2)[:, :, None]} +s_plot.to_vtk(OUT_DIR + "surf_full_init_DA", extra_data=pointData) +btot.set_points(s.gamma().reshape((-1, 3))) + +# Repeat for whole B field +pointData = {"B_N": np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2)[:, :, None]} +s.to_vtk(OUT_DIR + "surf_init", extra_data=pointData) + +btot.set_points(s_plot.gamma().reshape((-1, 3))) +pointData = {"B_N": np.sum(btot.B().reshape((qphi, qtheta, 3)) * s_plot.unitnormal(), axis=2)[:, :, None]} +s_plot.to_vtk(OUT_DIR + "surf_full_init", extra_data=pointData) +btot.set_points(s.gamma().reshape((-1, 3))) + +# Define the individual terms objective function: +Jf = SquaredFlux(s, btot) +# Separate length penalties on the dipole coils and the TF coils +# since they have very different sizes +# Jls = [CurveLength(c) for c in base_curves] +Jls_TF = [CurveLength(c) for c in base_curves_TF] +Jlength = QuadraticPenalty(sum(Jls_TF), LENGTH_TARGET, "max") + +# coil-coil and coil-plasma distances should be between all coils + +### Jcc below removed the dipoles! +Jccdist = CurveCurveDistance(curves_TF, CC_THRESHOLD, num_basecurves=len(coils_TF)) +Jcsdist = CurveSurfaceDistance(curves_TF, s, CS_THRESHOLD) +Jcsdist2 = CurveSurfaceDistance(curves_TF, s, CS_THRESHOLD) + +# While the coil array is not moving around, they cannot +# interlink. +linkNum = LinkingNumber(curves_TF) + +##### Note need coils_TF + coils below!!!!!!! +# Jforce2 = sum([LpCurveForce(c, coils_TF, +# regularization=regularization_rect(base_a_list[i], base_b_list[i]), +# p=2, threshold=1e8) for i, c in enumerate(base_coils + base_coils_TF)]) +# Jforce2 = LpCurveForce2(coils, coils_TF, p=2, threshold=1e8) +# Jforce = sum([SquaredMeanForce(c, coils_TF) for c in (base_coils + base_coils_TF)]) + +# regularization_list = np.zeros(len(coils)) * regularization_rect(aa, bb) +# regularization_list2 = np.zeros(len(coils_TF)) * regularization_rect(a, b) +# Jforce = MixedLpCurveForce(coils, coils_TF, regularization_list, regularization_list2) # [SquaredMeanForce2(c, coils) for c in (base_coils)] +# Jforce = MixedSquaredMeanForce(coils, coils_TF) +Jforce = sum([LpCurveForce(c, coils_TF, regularization_rect(a, b), p=2, threshold=1e5 * 100) for i, c in enumerate(base_coils_TF)]) +# Jforce2 = sum([SquaredMeanForce(c, coils_TF) for c in (base_coils + base_coils_TF)]) +# Jtorque = sum([LpCurveTorque(c, coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=1e5 * 40) for i, c in enumerate(base_coils + base_coils_TF)]) +# Jtorque2 = sum([SquaredMeanTorque(c, coils_TF) for c in (base_coils + base_coils_TF)]) + +# Jtorque = SquaredMeanTorque2(coils, coils_TF) # [SquaredMeanForce2(c, coils) for c in (base_coils)] +# Jtorque = [SquaredMeanTorque(c, coils_TF) for c in (base_coils + base_coils_TF)] + +JF = Jf \ + + CC_WEIGHT * Jccdist \ + + CS_WEIGHT * Jcsdist \ + + LINK_WEIGHT * linkNum \ + + LENGTH_WEIGHT * Jlength + +if FORCE_WEIGHT.value > 0.0: + JF += FORCE_WEIGHT.value * Jforce #\ + +# if FORCE_WEIGHT2.value > 0.0: +# JF += FORCE_WEIGHT2.value * Jforce2 #\ + +# if TORQUE_WEIGHT.value > 0.0: +# JF += TORQUE_WEIGHT * Jtorque + +# if TORQUE_WEIGHT2.value > 0.0: +# JF += TORQUE_WEIGHT2 * Jtorque2 + # + TVE_WEIGHT * Jtve + # + SF_WEIGHT * Jsf + # + CURRENTS_WEIGHT * DipoleCurrentsObj + # + CURVATURE_WEIGHT * sum(Jcs_TF) \ + # + MSC_WEIGHT * sum(QuadraticPenalty(J, MSC_THRESHOLD) for J in Jmscs_TF) \ +# + MSC_WEIGHT * sum(QuadraticPenalty(J, MSC_THRESHOLD) for J in Jmscs) \ + # + CURVATURE_WEIGHT * sum(Jcs) \ + +# We don't have a general interface in SIMSOPT for optimisation problems that +# are not in least-squares form, so we write a little wrapper function that we +# pass directly to scipy.optimize.minimize + +import pstats, io +from pstats import SortKey +# print(btot.ancestors,len(btot.ancestors)) +# print(JF.ancestors,len(JF.ancestors)) + + +def fun(dofs): + JF.x = dofs + # pr = cProfile.Profile() + # pr.enable() + J = JF.J() + grad = JF.dJ() + # pr.disable() + # sio = io.StringIO() + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr, stream=sio).sort_stats(sortby) + # ps.print_stats(20) + # print(sio.getvalue()) + # exit() + jf = Jf.J() + length_val = LENGTH_WEIGHT.value * Jlength.J() + cc_val = CC_WEIGHT * Jccdist.J() + cs_val = CS_WEIGHT * Jcsdist.J() + link_val = LINK_WEIGHT * linkNum.J() + forces_val = FORCE_WEIGHT.value * Jforce.J() + # forces_val2 = FORCE_WEIGHT2.value * Jforce2.J() + # torques_val = TORQUE_WEIGHT.value * Jtorque.J() + # torques_val2 = TORQUE_WEIGHT2.value * Jtorque2.J() + BdotN = np.mean(np.abs(np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2))) + BdotN_over_B = np.mean(np.abs(np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2)) + ) / np.mean(btot.AbsB()) + outstr = f"J={J:.1e}, Jf={jf:.1e}, ⟨B·n⟩={BdotN:.1e}, ⟨B·n⟩/⟨B⟩={BdotN_over_B:.1e}" + valuestr = f"J={J:.2e}, Jf={jf:.2e}" + cl_string = ", ".join([f"{J.J():.1f}" for J in Jls_TF]) + outstr += f", Len=sum([{cl_string}])={sum(J.J() for J in Jls_TF):.2f}" + valuestr += f", LenObj={length_val:.2e}" + valuestr += f", ccObj={cc_val:.2e}" + valuestr += f", csObj={cs_val:.2e}" + valuestr += f", Lk1Obj={link_val:.2e}" + valuestr += f", forceObj={forces_val:.2e}" + # valuestr += f", forceObj2={forces_val2:.2e}" + # valuestr += f", torqueObj={torques_val:.2e}" + # valuestr += f", torqueObj2={torques_val2:.2e}" + outstr += f", F={Jforce.J():.2e}" + # outstr += f", Fnet={Jforce2.J():.2e}" + # outstr += f", T={Jtorque.J():.2e}" + # outstr += f", Tnet={Jtorque2.J():.2e}" + outstr += f", C-C-Sep={Jccdist.shortest_distance():.2f}, C-S-Sep={Jcsdist2.shortest_distance():.2f}" + outstr += f", Link Number = {linkNum.J()}" + outstr += f", ║∇J║={np.linalg.norm(grad):.1e}" + print(outstr) + print(valuestr) + return J, grad + + +print(""" +################################################################################ +### Perform a Taylor test ###################################################### +################################################################################ +""") +f = fun +dofs = JF.x +np.random.seed(1) +h = np.random.uniform(size=dofs.shape) +J0, dJ0 = f(dofs) +dJh = sum(dJ0 * h) +for eps in [1e-3, 1e-4, 1e-5, 1e-6, 1e-7]: + t1 = time.time() + J1, _ = f(dofs + eps*h) + J2, _ = f(dofs - eps*h) + t2 = time.time() + print("err", (J1-J2)/(2*eps) - dJh) + +print(""" +################################################################################ +### Run the optimisation ####################################################### +################################################################################ +""") + + +print('Timing calls: ') +t1 = time.time() +Jf.J() +t2 = time.time() +print('Jf time = ', t2 - t1, ' s') +t1 = time.time() +Jf.dJ() +t2 = time.time() +print('dJf time = ', t2 - t1, ' s') +t1 = time.time() +Jccdist.J() +Jccdist.dJ() +t2 = time.time() +print('Jcc time = ', t2 - t1, ' s') +t1 = time.time() +Jcsdist.J() +Jcsdist.dJ() +t2 = time.time() +print('Jcs time = ', t2 - t1, ' s') +t1 = time.time() +linkNum.J() +linkNum.dJ() +t2 = time.time() +print('linkNum time = ', t2 - t1, ' s') +t1 = time.time() +Jlength.J() +Jlength.dJ() +t2 = time.time() +print('sum(Jls_TF) time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce.J() +# t2 = time.time() +# print('Jforces time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce.dJ() +# t2 = time.time() +# print('dJforces time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce2.J() +# t2 = time.time() +# print('Jforces2 time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce2.dJ() +# t2 = time.time() +# print('dJforces2 time = ', t2 - t1, ' s') +# t1 = time.time() +# Jtorque.J() +# t2 = time.time() +# print('Jtorques time = ', t2 - t1, ' s') +# t1 = time.time() +# Jtorque.dJ() +# t2 = time.time() +# print('dJtorques time = ', t2 - t1, ' s') + +n_saves = 1 +MAXITER = 400 +for i in range(1, n_saves + 1): + print('Iteration ' + str(i) + ' / ' + str(n_saves)) + res = minimize(fun, dofs, jac=True, method='L-BFGS-B', + options={'maxiter': MAXITER, 'maxcor': 400}, tol=1e-15) + # dofs = res.x + + curves_to_vtk( + [c.curve for c in bs_TF.coils], + OUT_DIR + "curves_TF_{0:d}".format(i), + close=True, + extra_point_data=pointData_forces_torques(coils_TF, coils_TF, np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b, np.ones(len(coils_TF)) * nturns_TF), + I=[c.current.get_value() for c in bs_TF.coils], + NetForces=coil_net_forces(coils_TF, coils_TF, regularization_rect(np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b), np.ones(len(coils_TF)) * nturns_TF), + NetTorques=coil_net_torques(coils_TF, coils_TF, regularization_rect(np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b), np.ones(len(coils_TF)) * nturns_TF), + ) + + btot.set_points(s_plot.gamma().reshape((-1, 3))) + pointData = {"B_N": np.sum(btot.B().reshape((qphi, qtheta, 3)) * s_plot.unitnormal(), axis=2)[:, :, None]} + s_plot.to_vtk(OUT_DIR + "surf_full_{0:d}".format(i), extra_data=pointData) + + pointData = {"B_N / B": (np.sum(btot.B().reshape((qphi, qtheta, 3)) * s_plot.unitnormal(), axis=2 + ) / np.linalg.norm(btot.B().reshape(qphi, qtheta, 3), axis=-1))[:, :, None]} + s_plot.to_vtk(OUT_DIR + "surf_full_normalizedBn_{0:d}".format(i), extra_data=pointData) + + btot.set_points(s.gamma().reshape((-1, 3))) + calculate_on_axis_B(btot, s) + # LENGTH_WEIGHT *= 0.01 + # JF = Jf \ + # + CC_WEIGHT * Jccdist \ + # + CS_WEIGHT * Jcsdist \ + # + LINK_WEIGHT * linkNum \ + # + LINK_WEIGHT2 * linkNum2 \ + # + LENGTH_WEIGHT * sum(Jls_TF) + + +t2 = time.time() +print('Total time = ', t2 - t1) +btot.save(OUT_DIR + "biot_savart_optimized_QH.json") +print(OUT_DIR) + diff --git a/examples/3_Advanced/QH_reactorscale_notfixed.py b/examples/3_Advanced/QH_reactorscale_notfixed.py new file mode 100644 index 000000000..5f1952b01 --- /dev/null +++ b/examples/3_Advanced/QH_reactorscale_notfixed.py @@ -0,0 +1,648 @@ +#!/usr/bin/env python +r""" +""" + +import os +import shutil +from pathlib import Path +import time +import numpy as np +from scipy.optimize import minimize +from simsopt.field import BiotSavart, Current, coils_via_symmetries +# from simsopt.field import CoilCoilNetForces, CoilCoilNetTorques, \ +# TotalVacuumEnergy, CoilSelfNetForces, CoilCoilNetForces12, CoilCoilNetTorques12 +from simsopt.field import regularization_rect +from simsopt.field.force import MeanSquaredForce, coil_force, coil_torque, coil_net_torques, coil_net_forces, LpCurveForce, \ + SquaredMeanForce, \ + MeanSquaredTorque, SquaredMeanTorque, LpCurveTorque, MixedSquaredMeanForce, MixedLpCurveForce +from simsopt.util import calculate_on_axis_B +from simsopt.geo import ( + CurveLength, CurveCurveDistance, + MeanSquaredCurvature, LpCurveCurvature, CurveSurfaceDistance, LinkingNumber, + SurfaceRZFourier, curves_to_vtk, create_equally_spaced_planar_curves, + create_planar_curves_between_two_toroidal_surfaces +) +from simsopt.objectives import Weight, SquaredFlux, QuadraticPenalty +from simsopt.util import in_github_actions +import cProfile +import re + +t1 = time.time() + +# Number of Fourier modes describing each Cartesian component of each coil: +order = 0 + +# File for the desired boundary magnetic surface: +TEST_DIR = (Path(__file__).parent / ".." / ".." / "tests" / "test_files").resolve() +input_name = 'input.LandremanPaul2021_QH_reactorScale_lowres' +filename = TEST_DIR / input_name + +# Initialize the boundary magnetic surface: +range_param = "half period" +nphi = 32 +ntheta = 32 +poff = 2.0 +coff = 3.0 +s = SurfaceRZFourier.from_vmec_input(filename, range=range_param, nphi=nphi, ntheta=ntheta) +s_inner = SurfaceRZFourier.from_vmec_input(filename, range=range_param, nphi=nphi * 4, ntheta=ntheta * 4) +s_outer = SurfaceRZFourier.from_vmec_input(filename, range=range_param, nphi=nphi * 4, ntheta=ntheta * 4) + +# Make the inner and outer surfaces by extending the plasma surface +s_inner.extend_via_normal(poff) +s_outer.extend_via_normal(poff + coff) + +qphi = nphi * 2 +qtheta = ntheta * 2 +quadpoints_phi = np.linspace(0, 1, qphi, endpoint=True) +quadpoints_theta = np.linspace(0, 1, qtheta, endpoint=True) + +# Make high resolution, full torus version of the plasma boundary for plotting +s_plot = SurfaceRZFourier.from_vmec_input( + filename, + quadpoints_phi=quadpoints_phi, + quadpoints_theta=quadpoints_theta +) + +### Initialize some TF coils +def initialize_coils_QH(TEST_DIR, s): + """ + Initializes coils for each of the target configurations that are + used for permanent magnet optimization. + + Args: + config_flag: String denoting the stellarator configuration + being initialized. + TEST_DIR: String denoting where to find the input files. + out_dir: Path or string for the output directory for saved files. + s: plasma boundary surface. + Returns: + base_curves: List of CurveXYZ class objects. + curves: List of Curve class objects. + coils: List of Coil class objects. + """ + from simsopt.geo import create_equally_spaced_curves + from simsopt.field import Current, Coil, coils_via_symmetries + from simsopt.geo import curves_to_vtk + + # generate planar TF coils + ncoils = 2 + R0 = s.get_rc(0, 0) * 1 + R1 = s.get_rc(1, 0) * 4 + order = 8 + + from simsopt.mhd.vmec import Vmec + vmec_file = 'wout_LandremanPaul2021_QH_reactorScale_lowres_reference.nc' + total_current = Vmec(TEST_DIR / vmec_file).external_current() / (2 * s.nfp) / 1.4 + print('Total current = ', total_current) + + # Only need Jax flag for CurvePlanarFourier class + base_curves = create_equally_spaced_curves( + ncoils, s.nfp, stellsym=True, + R0=R0, R1=R1, order=order, numquadpoints=256, + jax_flag=True, + ) + + base_currents = [(Current(total_current / ncoils * 1e-7) * 1e7) for _ in range(ncoils - 1)] + # base_currents = [(Current(total_current / ncoils * 1e-7) * 1e7) for _ in range(ncoils)] + # base_currents[0].fix_all() + + total_current = Current(total_current) + total_current.fix_all() + base_currents += [total_current - sum(base_currents)] + coils = coils_via_symmetries(base_curves, base_currents, s.nfp, True) + # for c in coils: + # c.current.fix_all() + # c.curve.fix_all() + + # Initialize the coil curves and save the data to vtk + curves = [c.curve for c in coils] + currents = [c.current.get_value() for c in coils] + return base_curves, curves, coils, base_currents + +# initialize the coils +base_curves_TF, curves_TF, coils_TF, currents_TF = initialize_coils_QH(TEST_DIR, s) +num_TF_unique_coils = len(coils_TF) // 4 +base_coils_TF = coils_TF[:num_TF_unique_coils] +currents_TF = np.array([coil.current.get_value() for coil in coils_TF]) + +# # Set up BiotSavart fields +bs_TF = BiotSavart(coils_TF) + +# # Calculate average, approximate on-axis B field strength +calculate_on_axis_B(bs_TF, s) + +# wire cross section for the TF coils is a square 20 cm x 20 cm +# Only need this if make self forces and TVE nonzero in the objective! +a = 0.2 +b = 0.2 +nturns = 100 +nturns_TF = 200 + +# wire cross section for the dipole coils should be more like 5 cm x 5 cm +aa = 0.05 +bb = 0.05 + +Nx = 6 +Ny = Nx +Nz = Nx +# Create the initial coils: +base_curves, all_curves = create_planar_curves_between_two_toroidal_surfaces( + s, s_inner, s_outer, Nx, Ny, Nz, order=order, coil_coil_flag=True, jax_flag=True, + # numquadpoints=10 # Defaults is (order + 1) * 40 so this halves it +) +import warnings + +keep_inds = [] +for ii in range(len(base_curves)): + counter = 0 + for i in range(base_curves[0].gamma().shape[0]): + eps = 0.05 + for j in range(len(base_curves_TF)): + for k in range(base_curves_TF[j].gamma().shape[0]): + dij = np.sqrt(np.sum((base_curves[ii].gamma()[i, :] - base_curves_TF[j].gamma()[k, :]) ** 2)) + conflict_bool = (dij < (1.0 + eps) * base_curves[0].x[0]) + if conflict_bool: + print('bad indices = ', i, j, dij, base_curves[0].x[0]) + warnings.warn( + 'There is a PSC coil initialized such that it is within a radius' + 'of a TF coil. Deleting these PSCs now.') + counter += 1 + break + if counter == 0: + keep_inds.append(ii) + +print(keep_inds) +base_curves = np.array(base_curves)[keep_inds] + +ncoils = len(base_curves) +print('Ncoils = ', ncoils) +coil_normals = np.zeros((ncoils, 3)) +plasma_points = s.gamma().reshape(-1, 3) +plasma_unitnormals = s.unitnormal().reshape(-1, 3) +for i in range(ncoils): + point = (base_curves[i].get_dofs()[-3:]) + dists = np.sum((point - plasma_points) ** 2, axis=-1) + min_ind = np.argmin(dists) + coil_normals[i, :] = plasma_unitnormals[min_ind, :] + # coil_normals[i, :] = (plasma_points[min_ind, :] - point) +coil_normals = coil_normals / np.linalg.norm(coil_normals, axis=-1)[:, None] +# alphas = np.arctan2( +# -coil_normals[:, 1], +# np.sqrt(coil_normals[:, 0] ** 2 + coil_normals[:, 2] ** 2)) +# deltas = np.arcsin(coil_normals[:, 0] / \ +# np.sqrt(coil_normals[:, 0] ** 2 + coil_normals[:, 2] ** 2)) +alphas = np.arcsin( + -coil_normals[:, 1], + ) +deltas = np.arctan2(coil_normals[:, 0], coil_normals[:, 2]) +for i in range(len(base_curves)): + alpha2 = alphas[i] / 2.0 + delta2 = deltas[i] / 2.0 + calpha2 = np.cos(alpha2) + salpha2 = np.sin(alpha2) + cdelta2 = np.cos(delta2) + sdelta2 = np.sin(delta2) + base_curves[i].set('x' + str(2 * order + 1), calpha2 * cdelta2) + base_curves[i].set('x' + str(2 * order + 2), salpha2 * cdelta2) + base_curves[i].set('x' + str(2 * order + 3), calpha2 * sdelta2) + base_curves[i].set('x' + str(2 * order + 4), -salpha2 * sdelta2) + # Fix orientations of each coil + base_curves[i].fix('x' + str(2 * order + 1)) + base_curves[i].fix('x' + str(2 * order + 2)) + base_curves[i].fix('x' + str(2 * order + 3)) + base_curves[i].fix('x' + str(2 * order + 4)) + + # Fix shape of each coil + for j in range(2 * order + 1): + base_curves[i].fix('x' + str(j)) + # Fix center points of each coil + base_curves[i].fix('x' + str(2 * order + 5)) + base_curves[i].fix('x' + str(2 * order + 6)) + base_curves[i].fix('x' + str(2 * order + 7)) +base_currents = [Current(1e-1) * 2e7 for i in range(ncoils)] + +coils = coils_via_symmetries(base_curves, base_currents, s.nfp, True) +base_coils = coils[:ncoils] + +def pointData_forces_torques(coils, allcoils, aprimes, bprimes, nturns_list): + contig = np.ascontiguousarray + forces = np.zeros((len(coils), len(coils[0].curve.gamma()) + 1, 3)) + torques = np.zeros((len(coils), len(coils[0].curve.gamma()) + 1, 3)) + for i, c in enumerate(coils): + aprime = aprimes[i] + bprime = bprimes[i] + # print(np.shape(bs._coils), np.shape(coils)) + # B_other = BiotSavart([cc for j, cc in enumerate(bs._coils) if i != j]).set_points(c.curve.gamma()).B() + # print(np.shape(bs._coils)) + # B_other = bs.set_points(c.curve.gamma()).B() + # print(B_other) + # exit() + forces[i, :-1, :] = coil_force(c, allcoils, regularization_rect(aprime, bprime), nturns_list[i]) + # print(i, forces[i, :-1, :]) + # bs._coils = coils + torques[i, :-1, :] = coil_torque(c, allcoils, regularization_rect(aprime, bprime), nturns_list[i]) + + forces[:, -1, :] = forces[:, 0, :] + torques[:, -1, :] = torques[:, 0, :] + forces = forces.reshape(-1, 3) + torques = torques.reshape(-1, 3) + point_data = {"Pointwise_Forces": (contig(forces[:, 0]), contig(forces[:, 1]), contig(forces[:, 2])), + "Pointwise_Torques": (contig(torques[:, 0]), contig(torques[:, 1]), contig(torques[:, 2]))} + return point_data + +bs = BiotSavart(coils) # + coils_TF) +btot = bs + bs_TF +calculate_on_axis_B(btot, s) +btot.set_points(s.gamma().reshape((-1, 3))) +bs.set_points(s.gamma().reshape((-1, 3))) +curves = [c.curve for c in coils] +currents = [c.current.get_value() for c in coils] +a_list = np.hstack((np.ones(len(coils)) * aa, np.ones(len(coils_TF)) * a)) +b_list = np.hstack((np.ones(len(coils)) * bb, np.ones(len(coils_TF)) * b)) +base_a_list = np.hstack((np.ones(len(base_coils)) * aa, np.ones(len(base_coils_TF)) * a)) +base_b_list = np.hstack((np.ones(len(base_coils)) * bb, np.ones(len(base_coils_TF)) * b)) + +LENGTH_WEIGHT = Weight(0.001) +LENGTH_TARGET = 80 +LINK_WEIGHT = 1e3 +CC_THRESHOLD = 0.8 +CC_WEIGHT = 1e1 +CS_THRESHOLD = 1.5 +CS_WEIGHT = 1e2 +# Weight for the Coil Coil forces term + + + +######## Seems to be that the main leakage of weak references to child processes seems to be coming +# from the jacobian calculation of Jforce! +FORCE_WEIGHT = Weight(1e-34) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# TORQUE_WEIGHT2 = Weight(4e-27) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# FORCE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +FORCE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +TORQUE_WEIGHT = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +TORQUE_WEIGHT2 = Weight(0.0) # Forces are in Newtons, and typical values are ~10^5, 10^6 Newtons +# Directory for output +OUT_DIR = ("./QH_debug/") +if os.path.exists(OUT_DIR): + shutil.rmtree(OUT_DIR) +os.makedirs(OUT_DIR, exist_ok=True) + +# curves_to_vtk( +# curves_TF, +# OUT_DIR + "curves_TF_0", +# close=True, +# extra_point_data=pointData_forces_torques(coils_TF, coils + coils_TF, np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b, np.ones(len(coils_TF)) * nturns_TF), +# I=currents_TF, +# NetForces=coil_net_forces(coils_TF, coils + coils_TF, regularization_rect(np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b), np.ones(len(coils_TF)) * nturns_TF), +# NetTorques=coil_net_torques(coils_TF, coils + coils_TF, regularization_rect(np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b), np.ones(len(coils_TF)) * nturns_TF) +# ) +# curves_to_vtk( +# curves, +# OUT_DIR + "curves_0", +# close=True, +# extra_point_data=pointData_forces_torques(coils, coils + coils_TF, np.ones(len(coils)) * aa, np.ones(len(coils)) * bb, np.ones(len(coils)) * nturns), +# I=currents, +# NetForces=coil_net_forces(coils, coils + coils_TF, regularization_rect(np.ones(len(coils)) * aa, np.ones(len(coils)) * bb), np.ones(len(coils)) * nturns), +# NetTorques=coil_net_torques(coils, coils + coils_TF, regularization_rect(np.ones(len(coils)) * aa, np.ones(len(coils)) * bb), np.ones(len(coils)) * nturns) +# ) +# Force and Torque calculations spawn a bunch of spurious BiotSavart child objects -- erase them! +# for c in (coils + coils_TF): +# c._children = set() + +pointData = {"B_N": np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2)[:, :, None]} +s.to_vtk(OUT_DIR + "surf_init_DA", extra_data=pointData) + +btot.set_points(s_plot.gamma().reshape((-1, 3))) +pointData = {"B_N": np.sum(btot.B().reshape((qphi, qtheta, 3)) * s_plot.unitnormal(), axis=2)[:, :, None]} +s_plot.to_vtk(OUT_DIR + "surf_full_init_DA", extra_data=pointData) +btot.set_points(s.gamma().reshape((-1, 3))) + +# Repeat for whole B field +pointData = {"B_N": np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2)[:, :, None]} +s.to_vtk(OUT_DIR + "surf_init", extra_data=pointData) + +btot.set_points(s_plot.gamma().reshape((-1, 3))) +pointData = {"B_N": np.sum(btot.B().reshape((qphi, qtheta, 3)) * s_plot.unitnormal(), axis=2)[:, :, None]} +s_plot.to_vtk(OUT_DIR + "surf_full_init", extra_data=pointData) +btot.set_points(s.gamma().reshape((-1, 3))) + +# Define the individual terms objective function: +Jf = SquaredFlux(s, btot) +# Separate length penalties on the dipole coils and the TF coils +# since they have very different sizes +# Jls = [CurveLength(c) for c in base_curves] +Jls_TF = [CurveLength(c) for c in base_curves_TF] +Jlength = QuadraticPenalty(sum(Jls_TF), LENGTH_TARGET, "max") + +# coil-coil and coil-plasma distances should be between all coils + +### Jcc below removed the dipoles! +Jccdist = CurveCurveDistance(curves_TF, CC_THRESHOLD, num_basecurves=len(coils_TF)) +Jcsdist = CurveSurfaceDistance(curves_TF, s, CS_THRESHOLD) +Jcsdist2 = CurveSurfaceDistance(curves + curves_TF, s, CS_THRESHOLD) + +# While the coil array is not moving around, they cannot +# interlink. +linkNum = LinkingNumber(curves + curves_TF, downsample=2) + +##### Note need coils_TF + coils below!!!!!!! +# Jforce2 = sum([LpCurveForce(c, coils + coils_TF, +# regularization=regularization_rect(base_a_list[i], base_b_list[i]), +# p=2, threshold=1e8) for i, c in enumerate(base_coils + base_coils_TF)]) +# Jforce2 = LpCurveForce2(coils, coils_TF, p=2, threshold=1e8) +# Jforce = sum([SquaredMeanForce(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) + +regularization_list = np.ones(len(coils)) * regularization_rect(aa, bb) +regularization_list2 = np.ones(len(coils_TF)) * regularization_rect(a, b) +# Jforce = MixedLpCurveForce(coils, coils_TF, regularization_list, regularization_list2) # [SquaredMeanForce2(c, coils) for c in (base_coils)] +# Jforce = MixedSquaredMeanForce(coils, coils_TF) +# Jforce = MixedLpCurveForce(coils, coils_TF, regularization_list, regularization_list2, p=4, threshold=4e5 * 100, downsample=4) +Jforce = sum([LpCurveForce(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=4, threshold=4e5 * 100, downsample=4) for i, c in enumerate(base_coils + base_coils_TF)]) +# Jforce2 = sum([SquaredMeanForce(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) +# Jtorque = sum([LpCurveTorque(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=4e5 * 100) for i, c in enumerate(base_coils + base_coils_TF)]) +# Jtorque = sum([LpCurveTorque(c, coils + coils_TF, regularization_rect(a_list[i], b_list[i]), p=2, threshold=1e5 * 100) for i, c in enumerate(base_coils + base_coils_TF)]) + + +# Jtorque2 = sum([SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils_TF)]) + +# Jtorque2 = sum([SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils + base_coils_TF)]) + + +# Jtorque = SquaredMeanTorque2(coils, coils_TF) # [SquaredMeanForce2(c, coils) for c in (base_coils)] +# Jtorque = [SquaredMeanTorque(c, coils + coils_TF) for c in (base_coils + base_coils_TF)] + +JF = Jf \ + + CC_WEIGHT * Jccdist \ + + CS_WEIGHT * Jcsdist \ + + LENGTH_WEIGHT * Jlength \ + + LINK_WEIGHT * linkNum + +if FORCE_WEIGHT.value > 0.0: + JF += FORCE_WEIGHT.value * Jforce #\ + +# if FORCE_WEIGHT2.value > 0.0: +# JF += FORCE_WEIGHT2.value * Jforce2 #\ + +# if TORQUE_WEIGHT.value > 0.0: +# JF += TORQUE_WEIGHT * Jtorque + +# if TORQUE_WEIGHT2.value > 0.0: +# JF += TORQUE_WEIGHT2 * Jtorque2 + # + TVE_WEIGHT * Jtve + # + SF_WEIGHT * Jsf + # + CURRENTS_WEIGHT * DipoleCurrentsObj + # + CURVATURE_WEIGHT * sum(Jcs_TF) \ + # + MSC_WEIGHT * sum(QuadraticPenalty(J, MSC_THRESHOLD) for J in Jmscs_TF) \ +# + MSC_WEIGHT * sum(QuadraticPenalty(J, MSC_THRESHOLD) for J in Jmscs) \ + # + CURVATURE_WEIGHT * sum(Jcs) \ + +# We don't have a general interface in SIMSOPT for optimisation problems that +# are not in least-squares form, so we write a little wrapper function that we +# pass directly to scipy.optimize.minimize + + +print('Timing calls: ') +t1 = time.time() +Jf.J() +t2 = time.time() +print('Jf time = ', t2 - t1, ' s') +t1 = time.time() +Jf.dJ() +t2 = time.time() +print('dJf time = ', t2 - t1, ' s') +t1 = time.time() +Jccdist.J() +Jccdist.dJ() +t2 = time.time() +print('Jcc time = ', t2 - t1, ' s') +t1 = time.time() +Jcsdist.J() +Jcsdist.dJ() +t2 = time.time() +print('Jcs time = ', t2 - t1, ' s') +t1 = time.time() +linkNum.J() +linkNum.dJ() +t2 = time.time() +print('linkNum time = ', t2 - t1, ' s') +t1 = time.time() +Jlength.J() +Jlength.dJ() +t2 = time.time() +print('sum(Jls_TF) time = ', t2 - t1, ' s') +# t1 = time.time() +# print(Jforce.J()) +# print(Jforce.dJ()) +# t2 = time.time() +# print('Jforce time = ', t2 - t1, ' s') +t1 = time.time() +JF.J() +JF.dJ() +t2 = time.time() +print('JF time = ', t2 - t1, ' s') +import pstats, io +from pstats import SortKey +# print(btot.ancestors,len(btot.ancestors)) +# print(JF.ancestors,len(JF.ancestors)) + + +def fun(dofs): + pr = cProfile.Profile() + pr.enable() + JF.x = dofs + J = JF.J() + grad = JF.dJ() + jf = Jf.J() + length_val = LENGTH_WEIGHT.value * Jlength.J() + cc_val = CC_WEIGHT * Jccdist.J() + cs_val = CS_WEIGHT * Jcsdist.J() + link_val = LINK_WEIGHT * linkNum.J() + forces_val = FORCE_WEIGHT.value * Jforce.J() + # Jforce3.dJ() + # forces_val2 = FORCE_WEIGHT2.value * Jforce2.J() + # torques_val = TORQUE_WEIGHT.value * Jtorque.J() + # torques_val2 = TORQUE_WEIGHT2.value * Jtorque2.J() + BdotN = np.mean(np.abs(np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2))) + BdotN_over_B = np.mean(np.abs(np.sum(btot.B().reshape((nphi, ntheta, 3)) * s.unitnormal(), axis=2)) + ) / np.mean(btot.AbsB()) + outstr = f"J={J:.1e}, Jf={jf:.1e}, ⟨B·n⟩={BdotN:.1e}, ⟨B·n⟩/⟨B⟩={BdotN_over_B:.1e}" + valuestr = f"J={J:.2e}, Jf={jf:.2e}" + cl_string = ", ".join([f"{J.J():.1f}" for J in Jls_TF]) + outstr += f", Len=sum([{cl_string}])={sum(J.J() for J in Jls_TF):.2f}" + valuestr += f", LenObj={length_val:.2e}" + valuestr += f", ccObj={cc_val:.2e}" + valuestr += f", csObj={cs_val:.2e}" + valuestr += f", Lk1Obj={link_val:.2e}" + valuestr += f", forceObj={forces_val:.2e}" + # valuestr += f", forceObj2={forces_val2:.2e}" + # valuestr += f", torqueObj={torques_val:.2e}" + # valuestr += f", torqueObj2={torques_val2:.2e}" + outstr += f", F={Jforce.J():.2e}" + # outstr += f", Fnet={Jforce2.J():.2e}" + # outstr += f", T={Jtorque.J():.2e}" + # outstr += f", Tnet={Jtorque2.J():.2e}" + outstr += f", C-C-Sep={Jccdist.shortest_distance():.2f}, C-S-Sep={Jcsdist2.shortest_distance():.2f}" + outstr += f", Link Number = {linkNum.J()}" + outstr += f", ║∇J║={np.linalg.norm(grad):.1e}" + # print(coils[0]._children, coils_TF[0]._children, JF._children, Jforce._children) + print(outstr) + print(valuestr) + pr.disable() + sio = io.StringIO() + sortby = SortKey.CUMULATIVE + ps = pstats.Stats(pr, stream=sio).sort_stats(sortby) + ps.print_stats(20) + print(sio.getvalue()) + # exit() + return J, grad + + +print(""" +################################################################################ +### Perform a Taylor test ###################################################### +################################################################################ +""") +f = fun +dofs = JF.x +np.random.seed(1) +h = np.random.uniform(size=dofs.shape) + +print(""" +Calling f now +""") +J0, dJ0 = f(dofs) +dJh = sum(dJ0 * h) +for eps in [1e-3, 1e-4, 1e-5, 1e-6, 1e-7]: + t1 = time.time() + J1, _ = f(dofs + eps*h) + J2, _ = f(dofs - eps*h) + t2 = time.time() + print("err", (J1-J2)/(2*eps) - dJh) + +print(""" +################################################################################ +### Run the optimisation ####################################################### +################################################################################ +""") + + +print('Timing calls: ') +t1 = time.time() +Jf.J() +t2 = time.time() +print('Jf time = ', t2 - t1, ' s') +t1 = time.time() +Jf.dJ() +t2 = time.time() +print('dJf time = ', t2 - t1, ' s') +t1 = time.time() +Jccdist.J() +Jccdist.dJ() +t2 = time.time() +print('Jcc time = ', t2 - t1, ' s') +t1 = time.time() +Jcsdist.J() +Jcsdist.dJ() +t2 = time.time() +print('Jcs time = ', t2 - t1, ' s') +t1 = time.time() +linkNum.J() +linkNum.dJ() +t2 = time.time() +print('linkNum time = ', t2 - t1, ' s') +t1 = time.time() +Jlength.J() +Jlength.dJ() +t2 = time.time() +print('sum(Jls_TF) time = ', t2 - t1, ' s') +# t1 = time.time() +# print(Jforce.J()) +# print(Jforce.dJ()) +# t2 = time.time() +# print('Jforce time = ', t2 - t1, ' s') +t1 = time.time() +JF.J() +JF.dJ() +t2 = time.time() +print('JF time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce.J() +# t2 = time.time() +# print('Jforces time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce.dJ() +# t2 = time.time() +# print('dJforces time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce2.J() +# t2 = time.time() +# print('Jforces2 time = ', t2 - t1, ' s') +# t1 = time.time() +# Jforce2.dJ() +# t2 = time.time() +# print('dJforces2 time = ', t2 - t1, ' s') +# t1 = time.time() +# Jtorque.J() +# t2 = time.time() +# print('Jtorques time = ', t2 - t1, ' s') +# t1 = time.time() +# Jtorque.dJ() +# t2 = time.time() +# print('dJtorques time = ', t2 - t1, ' s') + +n_saves = 1 +MAXITER = 200 +for i in range(1, n_saves + 1): + print('Iteration ' + str(i) + ' / ' + str(n_saves)) + res = minimize(fun, dofs, jac=True, method='L-BFGS-B', + options={'maxiter': MAXITER, 'maxcor': 200}, tol=1e-15) + # dofs = res.x + + dipole_currents = [c.current.get_value() for c in bs.coils] + # curves_to_vtk( + # [c.curve for c in bs.coils], + # OUT_DIR + "curves_{0:d}".format(i), + # close=True, + # extra_point_data=pointData_forces_torques(coils, coils + coils_TF, np.ones(len(coils)) * aa, np.ones(len(coils)) * bb, np.ones(len(coils)) * nturns), + # I=dipole_currents, + # NetForces=coil_net_forces(coils, coils + coils_TF, regularization_rect(np.ones(len(coils)) * aa, np.ones(len(coils)) * bb), np.ones(len(coils)) * nturns), + # NetTorques=coil_net_torques(coils, coils + coils_TF, regularization_rect(np.ones(len(coils)) * aa, np.ones(len(coils)) * bb), np.ones(len(coils)) * nturns), + # ) + # curves_to_vtk( + # [c.curve for c in bs_TF.coils], + # OUT_DIR + "curves_TF_{0:d}".format(i), + # close=True, + # extra_point_data=pointData_forces_torques(coils_TF, coils + coils_TF, np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b, np.ones(len(coils_TF)) * nturns_TF), + # I=[c.current.get_value() for c in bs_TF.coils], + # NetForces=coil_net_forces(coils_TF, coils + coils_TF, regularization_rect(np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b), np.ones(len(coils_TF)) * nturns_TF), + # NetTorques=coil_net_torques(coils_TF, coils + coils_TF, regularization_rect(np.ones(len(coils_TF)) * a, np.ones(len(coils_TF)) * b), np.ones(len(coils_TF)) * nturns_TF), + # ) + + btot.set_points(s_plot.gamma().reshape((-1, 3))) + pointData = {"B_N": np.sum(btot.B().reshape((qphi, qtheta, 3)) * s_plot.unitnormal(), axis=2)[:, :, None]} + s_plot.to_vtk(OUT_DIR + "surf_full_{0:d}".format(i), extra_data=pointData) + + pointData = {"B_N / B": (np.sum(btot.B().reshape((qphi, qtheta, 3)) * s_plot.unitnormal(), axis=2 + ) / np.linalg.norm(btot.B().reshape(qphi, qtheta, 3), axis=-1))[:, :, None]} + s_plot.to_vtk(OUT_DIR + "surf_full_normalizedBn_{0:d}".format(i), extra_data=pointData) + + btot.set_points(s.gamma().reshape((-1, 3))) + print('Max I = ', np.max(np.abs(dipole_currents))) + print('Min I = ', np.min(np.abs(dipole_currents))) + calculate_on_axis_B(btot, s) + # LENGTH_WEIGHT *= 0.01 + # JF = Jf \ + # + CC_WEIGHT * Jccdist \ + # + CS_WEIGHT * Jcsdist \ + # + LINK_WEIGHT * linkNum \ + # + LINK_WEIGHT2 * linkNum2 \ + # + LENGTH_WEIGHT * sum(Jls_TF) + + +t2 = time.time() +print('Total time = ', t2 - t1) +btot.save(OUT_DIR + "biot_savart_optimized_QH.json") +print(OUT_DIR) + diff --git a/examples/3_Advanced/run_post_processing.py b/examples/3_Advanced/run_post_processing.py index 60562a667..958d841d9 100644 --- a/examples/3_Advanced/run_post_processing.py +++ b/examples/3_Advanced/run_post_processing.py @@ -88,10 +88,10 @@ def skip(rs, phis, zs): # set fieldline tracer parameters -nfieldlines = 16 +nfieldlines = 20 tmax_fl = 20000 -R0 = np.linspace(12.25, 13.1, nfieldlines) # np.linspace(s_plot.get_rc(0, 0) - s_plot.get_rc(1, 0) / 2.0, s_plot.get_rc(0, 0) + s_plot.get_rc(1, 0) / 2.0, nfieldlines) +R0 = np.linspace(12.25, 13.15, nfieldlines) # np.linspace(s_plot.get_rc(0, 0) - s_plot.get_rc(1, 0) / 2.0, s_plot.get_rc(0, 0) + s_plot.get_rc(1, 0) / 2.0, nfieldlines) Z0 = np.zeros(nfieldlines) phis = [(i / 4) * (2 * np.pi / s.nfp) for i in range(4)] print(rrange, zrange, phirange) @@ -112,4 +112,4 @@ def skip(rs, phis, zs): proc0_print(f"Time for fieldline tracing={t2-t1:.3f}s. Num steps={sum([len(l) for l in fieldlines_tys])//nfieldlines}", flush=True) # make the poincare plots if comm is None or comm.rank == 0: - plot_poincare_data(fieldlines_phi_hits, phis, 'poincare_fieldline.png', dpi=300, surf=s, aspect='auto') \ No newline at end of file + plot_poincare_data(fieldlines_phi_hits, phis, 'poincare_fieldline.png', dpi=300, surf=s, aspect='auto') diff --git a/src/simsopt/field/__init__.py b/src/simsopt/field/__init__.py index 7edb96596..e21c08ec1 100644 --- a/src/simsopt/field/__init__.py +++ b/src/simsopt/field/__init__.py @@ -1,6 +1,7 @@ from .biotsavart import * from .boozermagneticfield import * from .coil import * +from .coilset import * from .magneticfield import * from .magneticfieldclasses import * from .mgrid import * @@ -13,6 +14,7 @@ biotsavart.__all__ + boozermagneticfield.__all__ + coil.__all__ + + coilset.__all__ + magneticfield.__all__ + magneticfieldclasses.__all__ + mgrid.__all__ diff --git a/src/simsopt/field/biotsavart.py b/src/simsopt/field/biotsavart.py index 8cfe5571d..0cf2001ec 100644 --- a/src/simsopt/field/biotsavart.py +++ b/src/simsopt/field/biotsavart.py @@ -34,7 +34,7 @@ def __init__(self, coils): self._coils = coils sopp.BiotSavart.__init__(self, coils) MagneticField.__init__(self, depends_on=coils) - self.B_vjp_jax = jit(lambda v: self.B_vjp_pure(v)) + # self.B_vjp_jax = jit(lambda v: self.B_vjp_pure(v)) def dB_by_dcoilcurrents(self, compute_derivatives=0): points = self.get_points_cart_ref() diff --git a/src/simsopt/field/coil.py b/src/simsopt/field/coil.py index 6b5bf1ce3..800895738 100644 --- a/src/simsopt/field/coil.py +++ b/src/simsopt/field/coil.py @@ -16,7 +16,8 @@ 'Current', 'coils_via_symmetries', 'load_coils_from_makegrid_file', 'apply_symmetries_to_currents', 'apply_symmetries_to_curves', - 'coils_to_makegrid', 'coils_to_focus'] + 'coils_to_makegrid', 'coils_to_focus' + ] class Coil(sopp.Coil, Optimizable): diff --git a/src/simsopt/field/coilset.py b/src/simsopt/field/coilset.py new file mode 100644 index 000000000..d2ed74f8c --- /dev/null +++ b/src/simsopt/field/coilset.py @@ -0,0 +1,643 @@ +import numpy as np +import re + +from simsopt._core.optimizable import DOFs, Optimizable +from .biotsavart import BiotSavart +from simsopt.geo import (SurfaceRZFourier, Surface, CurveXYZFourier, CurveCurveDistance, CurveSurfaceDistance, LpCurveCurvature, + MeanSquaredCurvature, ArclengthVariation, + CurveLength) +from simsopt.geo import plot, curves_to_vtk, create_equally_spaced_curves +from simsopt._core.finite_difference import FiniteDifference +from simsopt._core import make_optimizable +from simsopt.objectives import SquaredFlux, QuadraticPenalty +from .coil import Coil, coils_via_symmetries, Current, load_coils_from_makegrid_file, coils_to_makegrid + +__all__ = ['CoilSet', 'ReducedCoilSet'] + +class CoilSet(Optimizable): + """ + A set of coils as a single optimizable object, and a surface on which + it evaluates the field. + + The surfaces will be cast as a SurfaceRZFourier, as it will evaluate a field + on the surface using the RZFourier/VMEC convention. + The surfaces range will be adapted to 'half period' or 'field period' + respectively if it is stellarator symmetric or not. + + The Coilset contains convenience methods to acces coil optimization penalties: + flux_penalty, length_penalty, cc_distance_penalty, cs_distance_penalty, + lp_curvature_penalty, meansquared_curvature_penalty, arc_length_variation_penalty + and total_length. + + These functions can then be used to define a FOCUS-like optimization problem + (note that the FOCUS algorithm is not a least-squares algorithm) that can be passed + to scipy.minimize, or the CoilSet can be used as a parent for a SPEC NormalField + object. + """ + def __init__(self, base_coils=None, coils=None, surface=None): + + #set the surface, change its's range if necessary + if surface is None: + self._surface = SurfaceRZFourier().copy(range=SurfaceRZFourier.RANGE_HALF_PERIOD) + #stellarator symmetric surfaces are more efficiently evaluated on a half-period + else: + if surface.stellsym: + if surface.deduced_range is surface.RANGE_HALF_PERIOD: + self._surface = surface + else: + newsurf = surface.to_RZFourier().copy(range=surface.RANGE_HALF_PERIOD) + self._surface = newsurf + else: + if surface.deduced_range is surface.RANGE_FIELD_PERIOD: + self._surface = surface + else: + newsurf = surface.to_RZFourier().copy(range=surface.RANGE_FIELD_PERIOD) + self._surface = newsurf + + # set the coils + if base_coils is not None: + self._base_coils = base_coils + if coils is None: + self.coils = coils_via_symmetries([coil.curve for coil in base_coils], [coil.current for coil in base_coils], nfp=self._surface.nfp, stellsym=self._surface.stellsym) + else: + self.coils = coils + else: + if coils is not None: + raise ValueError("If base_coils is None, coils must be None as well") + base_curves = self._circlecurves_around_surface(self._surface, coils_per_period=10) + base_currents = [Current(1e5) for _ in base_curves] + # fix the currents on the default coilset + for current in base_currents: + current.fix_all() + base_coils = [Coil(curv, curr) for (curv, curr) in zip(base_curves, base_currents)] + self._base_coils = base_coils + self.coils = coils_via_symmetries([coil.curve for coil in base_coils], [coil.current for coil in base_coils], nfp=self._surface.nfp, stellsym=self._surface.stellsym) + + self.bs = BiotSavart(self.coils) + self.bs.set_points(self._surface.gamma().reshape((-1, 3))) + super().__init__(depends_on=base_coils) + + @classmethod + def for_surface(cls, surf, coil_current=1e5, coils_per_period=5, nfp=None, current_constraint="fix_all", **kwargs): + """ + Create a CoilSet for a given surface. The coils are created using + :obj:`create_equally_spaced_curves` with the given parameters. + + Args: + surf: The surface for which to create the coils + total_current: the total current in the CoilSet + coils_per_period: the number of coils per field period + nfp: The number of field periods. + current_constraint: "fix_one" or "fix_all" or "free_all" + + Keyword Args (passed to the create_equally_spaced_curves function) + coils_per_period: The number of coils per field period + order: The order of the Fourier expansion + R0: major radius of a torus on which the initial coils are placed + R1: The radius of the coils + factor: if R0 or R1 are None, they are factor times + the first sine/cosine coefficient (factor > 1) + use_stellsym: Whether to use stellarator symmetry + """ + if nfp is None: + nfp = surf.nfp + base_curves = CoilSet._circlecurves_around_surface(surf, coils_per_period=coils_per_period, **kwargs) + base_currents = [Current(coil_current) for _ in base_curves] + if current_constraint == "fix_one": + base_currents[0].fix_all() + elif current_constraint == "fix_all": + [base_current.fix_all() for base_current in base_currents] + elif current_constraint == "free_all": + pass + else: + raise ValueError("current_constraint must be 'fix_one', 'fix_all' or 'free_all'") + base_coils = [Coil(curv, curr) for (curv, curr) in zip(base_curves, base_currents)] + coils = coils_via_symmetries(base_curves, base_currents, nfp, stellsym=True) + return cls(base_coils, coils, surf) + + @classmethod + def from_makegrid_file(cls, makegrid_file, surface, order=10, ppp=10): + """ + Create a CoilSet from a MAKEGRID file and a surface. + """ + coils = load_coils_from_makegrid_file(makegrid_file, order=order, ppp=ppp) + return cls(base_coils=coils, coils=coils, surface=surface) + + def to_makegrid_file(self, filename): + """ + Write the CoilSet to a MAKEGRID file + """ + coils_to_makegrid(filename, + [coil.curve for coil in self.coils], + [coil.current for coil in self.coils], + nfp=1) + + + def reduce(self, target_function, nsv='nonzero'): + """ + Return a ReducedCoilSet based on this CoilSet using the SVD of the mapping + given by target_function. + Args: + nsv: The number of singular vectors to keep, integer or 'nonzero' (for all nonzero singular values) + target_function: a function that takes a CoilSet and maps to a different space that is relevant for the optimization problem. + The SVD will be calculated on the Jacobian of f: coilDOFs -> target + For example a function that calculates the fourier coefficients of the + normal field on the surface. + """ + return ReducedCoilSet.from_function(self, target_function, nsv) + + @property + def surface(self): + return self._surface + + @surface.setter + def surface(self, surface: 'Surface'): + """ + set a new surface for the CoilSet. Change its range if necessary. + """ + # Changing surface requires changing the BiotSavart object. We also modify its range + if surface.nfp != self.surface.nfp: + raise ValueError("nfp must be equal to the current surface nfp") + if surface.stellsym: + if surface.deduced_range is not surface.RANGE_HALF_PERIOD: + newsurf = surface.to_RZFourier().copy(range=surface.RANGE_HALF_PERIOD) + surface = newsurf + else: # not stellsym + if surface.deduced_range is not surface.RANGE_FIELD_PERIOD: + newsurf = surface.to_RZFourier().copy(range=surface.RANGE_FIELD_PERIOD) + surface = newsurf + self.bs.set_points(surface.gamma().reshape((-1, 3))) + self._surface = surface + + @property + def base_coils(self): + return self._base_coils + + @base_coils.setter + def base_coils(self, base_coils): + for coilparent in self._base_coils: + self.remove_parent(coilparent) + self._base_coils = base_coils + self._coils = coils_via_symmetries([coil.curve for coil in base_coils], [coil.current for coil in base_coils], nfp=self._surface.nfp, stellsym=self._surface.stellsym) + self.bs = BiotSavart(self._coils) + self.bs.set_points(self._surface.gamma().reshape((-1, 3))) + for coilparent in self._base_coils: + self.append_parent(coilparent) + + @staticmethod + def _circlecurves_around_surface(surf, coils_per_period=4, order=6, R0=None, R1=None, use_stellsym=None, factor=2.): + """ + return a set of base curves for a surface using the surfaces properties where possible + """ + nfp = surf.nfp + if use_stellsym is None: + use_stellsym = surf.stellsym + if R0 is None: + R0 = surf.to_RZFourier().get_rc(0, 0) + if R1 is None: + # take the magnitude of the first-order poloidal Fourier coefficient + R1 = np.sqrt(surf.to_RZFourier().get_rc(1, 0)**2 + surf.to_RZFourier().get_zs(1, 0)**2) * factor + return create_equally_spaced_curves(coils_per_period, nfp, stellsym=use_stellsym, R0=R0, R1=R1, order=order) + + def get_dof_orders(self): + """ + get the Fourier order of the degrees of freedom corresponding to the Fourier + coefficients describing the coils. + Useful for reducing the trust region for the higher order coefficients. + in optimization. + + Returns: + An array with the fourier order of each degree of freedom. + """ + dof_names = self.dof_names + orders = np.zeros(self.dof_size) # dofs which are not Fourier coefficients are treated as zeroth' order. + # test if coils are CurveXYZFourier: + if type(self.coils[0].curve) is not CurveXYZFourier: + raise ValueError("Coils must be of type CurveXYZFourier") + # run through names, extract order using regular expression + for name in dof_names: + if name.startswith("Curve"): + match = re.search(r'\((\d+)\)', name) + if match: + order = int(match.group(1)) + orders[dof_names.index(name)] = order + return orders + + def flux_penalty(self, target=None): + """ + Return the penalty function for the quadratic flux penalty on + the surface + """ + target = SquaredFlux(self.surface, self.bs, target=target) + # make self parent as self's dofs will propagate to coils + for parent in target.parents: + target.remove_parent(parent) + target.append_parent(self) + return target + + def length_penalty(self, TOTAL_LENGTH, f): + """ + Return a QuadraticPenalty on the total length of the coils + if it is larger than TARGET_LENGTH (do not penalize if shorter) + Args: + TOTAL_LENGTH: The threshold length above which the penalty is applied + f: type of penalty, "min", "max" or "identity" + """ + # only calculate length of base_coils, others are equal + coil_multiplication_factor = len(self.coils) / len(self.base_coils) + # summing optimizables makes new optimizables + lenth_optimizable = sum(CurveLength(coil.curve) for coil in self.base_coils)*coil_multiplication_factor + target = QuadraticPenalty(lenth_optimizable, TOTAL_LENGTH, f) + # make self parent as self's dofs will propagate to coils + for parent in target.parents: + target.remove_parent(parent) + target.append_parent(self) + # return the penalty function + return target + + def cc_distance_penalty(self, DISTANCE_THRESHOLD): + """ + Return a penalty function for the distance between coils + Args: + DISTANCE_THRESHOLD: The threshold distance below which the penalty is applied + """ + curves = [coil.curve for coil in self.coils] + target = CurveCurveDistance(curves, DISTANCE_THRESHOLD, num_basecurves=len(self.base_coils)) + # make self parent as self's dofs will propagate to coils + for parent in target.parents: + target.remove_parent(parent) + target.append_parent(self) + # return the penalty function + return target + + def cs_distance_penalty(self, DISTANCE_THRESHOLD): + """ + Return a penalty function for the distance between coils and the surface + Args: + DISTANCE_THRESHOLD: The threshold distance below which the penalty is applied + """ + curves = [coil.curve for coil in self.coils] + return CurveSurfaceDistance(curves, self.surface, DISTANCE_THRESHOLD) + + def lp_curvature_penalty(self, CURVATURE_THRESHOLD, p=2): + """ + Return a penalty function on the curvature of the coils that is + the Lp norm of the curvature of the coils. Defaults to L2. + Args: + CURVATURE_THRESHOLD: The threshold curvature above which the penalty is applied + p: The p in the Lp norm for calculating the penalty + """ + base_curves = [coil.curve for coil in self.base_coils] + target = sum(LpCurveCurvature(curve, p, CURVATURE_THRESHOLD) for curve in base_curves) + # make self parent as self's dofs will propagate to coils + for parent in target.parents: + target.remove_parent(parent) + target.append_parent(self) + # return the penalty function + return target + + def meansquared_curvature_penalty(self): + """ + Return a penalty function on the mean squared curvature of the coils + """ + target = sum(MeanSquaredCurvature(coil.curve) for coil in self.base_coils) + for parent in target.parents: + target.remove_parent(parent) + target.append_parent(self) + # return the penalty function + return target + + def meansquared_curvature_threshold(self, CURVATURE_THRESHOLD): + """ + Return a penalty function on the mean squared curvature of the coils + """ + meansquaredcurvatures = [MeanSquaredCurvature(coil.curve) for coil in self.base_coils] + target = sum(QuadraticPenalty(msc, CURVATURE_THRESHOLD, "max") for msc in meansquaredcurvatures) + for parent in target.parents: + target.remove_parent(parent) + target.append_parent(self) + # return the penalty function + return target + + def arc_length_variation_penalty(self): + """ + Return a penalty function on the arc length variation of the coils + """ + target = sum(ArclengthVariation(coil.curve) for coil in self.base_coils) + for parent in target.parents: + target.remove_parent(parent) + target.append_parent(self) + # return the penalty function + return target + + def total_length_penalty(self): + """ + Return the total length of the coils + """ + multiplicity = len(self.coils) / len(self.base_coils) + target = sum(CurveLength(coil.curve) for coil in self.base_coils)*multiplicity + for parent in target.parents: + target.remove_parent(parent) + target.append_parent(self) + # return the penalty function + return target + + @property + def total_length(self): + """ + Return the total length of the coils + """ + multiplicity = len(self.coils) / len(self.base_coils) + lengthoptimizable = sum(CurveLength(coil.curve) for coil in self.base_coils)*multiplicity + return lengthoptimizable.J() + + def to_vtk(self, filename, add_biotsavart=True, close=False): + """ + Write the CoilSet and its surface to a vtk file + """ + curves_to_vtk([coil.curve for coil in self.coils], filename+'_coils', close=close) + if self.surface is not None: + if add_biotsavart: + pointData = {"B_N": np.sum(self.bs.B().reshape((len(self.surface.quadpoints_phi), len(self.surface.quadpoints_theta), 3)) * self.surface.unitnormal(), axis=2)[:, :, None]} + else: + pointData = None + self.surface.to_vtk(filename+'_surface', extra_data=pointData) + + def plot(self, **kwargs): + """ + Plot the coil's curve. This method is just shorthand for calling + the :obj:`~simsopt.geo.Curve/Surface.plot()` function on the + underlying Curve. All arguments are passed to + :obj:`simsopt.geo.Curve/Surface.plot()` + """ + #plot the coils, which are already a list: + plot(self.coils, **kwargs, show=False) + #generate a full torus for plotting, plot it: + self.surface.to_RZFourier().copy(range=SurfaceRZFourier.RANGE_FULL_TORUS).plot(**kwargs, show=True) + + +class ReducedCoilSet(CoilSet): + """ + An Optimizable that replaces a CoilSet and has as degrees of + freedom linear combinations of the CoilSet degrees of freedom + + Single-stage optimization is often accompanied with an explosion + in the numbers of degrees of freedom needed to accurately represent + the stellarator, as each coilneeds 6 DOFs per coil per Fourier order. + This makes solving the optimization problem harder in two ways: + 1: the computational cost of finite-difference gradient evaluation is + linear in the number of DOFs, and 2: the optimization problem can become + rank-deficient: changes to your DOFs cancel each other, and the minmum + is no longer unique. + + This class solves these problems by allowing physics-based dimensionality + reduction of the optimization space using singluar value-decomposition + of a fast-evalable map. + + A ReducedCoilSet consists of a Coilset, and the three elements of an SVD of + the Jacobian defined by a mapping from the coils' DOFs to a physically relevant, + fast-evaluating target. + """ + def __init__(self, coilset=None, nsv=None, s_diag=None, u_matrix=None, vh_matrix=None, function=None, **kwargs): + """ + create a ReducedCoilSet from a CoilSet and the three elements of an SVD of the Jacobian of a mapping. + """ + # create standards if initialized with None + if coilset is None: # need 'is' (idenity comparison), overloaded __eq__ fails here. + coilset = CoilSet() + if nsv is None: + nsv = coilset.dof_size + + # raise error if any of [s_diag, u_matrix, vh_matrix] are None but not all of them are: + if any([s_diag is None, u_matrix is None, vh_matrix is None]) \ + and not all([s_diag is None, u_matrix is None, vh_matrix is None]): + raise ValueError("If any of [s_diag, u_matrix, vh_matrix] are None, all must be None") + + if s_diag is None: + if nsv > coilset.dof_size: + raise ValueError("nsv must be equal to or smaller than the coilset's number of DOFs if initializing with None") + s_diag = np.ones(nsv) + u_matrix = np.eye(nsv) + vh_matrix = np.eye(nsv) + + self.function = function + # Reproduce CoilSet's attributes + self._coilset = coilset + self.bs = coilset.bs + self.bs.set_points(self.surface.gamma().reshape((-1, 3))) + # Add the SVD matrices + self._u_matrix = u_matrix + self._vh_matrix = vh_matrix + self._coil_x0 = np.copy(self.coilset.x) ### DID NOT COPY BEFORE, CHECK CONVERGENCE + self._s_diag = s_diag + if nsv > len(s_diag): + raise ValueError("nsv must be equal to or smaller than the number of singular values") + self._nsv = nsv + Optimizable.__init__(self, x0=np.zeros_like(self._s_diag[:nsv]), names=self._make_names(), external_dof_setter=ReducedCoilSet.set_dofs, **kwargs) + + @classmethod + def from_function(cls, coilset, target_function, nsv='nonzero'): + """ + Reduce an existing CoilSet to a ReducedCoilSet using the SVD of the mapping + given by target_function. + Args: + coilset: The CoilSet to reduce + target_function: a function that takes a CoilSet and maps to a different space that is relevant for the optimization problem. + The SVD will be calculated on the Jacobian of f: coilDOFs -> target + For example a function that calculates the fourier coefficients of the + normal field on the surface. + nsv: The number of singular values to keep, either an integer or 'nonzero'. defaults to 'nonzero'. + If an integer is given, this number of largest singular values will be kept. + If 'nonzero', all nonzero singular values are kept. + """ + optfunction = make_optimizable(target_function, coilset) + fd = FiniteDifference(optfunction.J) + jaccers = fd.jac() + u_matrix, s_diag, vh_matrix = np.linalg.svd(jaccers) + if nsv == 'nonzero': + nsv = len(s_diag) + return cls(coilset, nsv, s_diag, u_matrix, vh_matrix, target_function) + + def recalculate_reduced_basis(self, target_function=None): + """ + Recalculate the reduced basis using a new target function. + """ + thisfunction = target_function or self.function + if thisfunction is None: + raise ValueError("No target function provided") + self.function = thisfunction + optfunction = make_optimizable(thisfunction, self.coilset) + fd = FiniteDifference(optfunction.J) + jaccers = fd.jac() + u_matrix, s_diag, vh_matrix = np.linalg.svd(jaccers) + self.nsv = min(self.nsv, len(s_diag)) + self._u_matrix = u_matrix + self._s_diag = s_diag + self._vh_matrix = vh_matrix + self._coil_x0 = np.copy(self.coilset.x) + x = np.zeros(self.nsv) + self.replace_dofs(DOFs(x, self._make_names())) + + @property + def coilset(self): + return self._coilset + + @coilset.setter + def coilset(self, coilset): + raise ValueError("You cannot change the CoilSet of a ReducedCoilSet.") + + @property + def base_coils(self): + return self.coilset.base_coils + + @base_coils.setter + def base_coils(self, base_coils): + self.coilset.base_coils = base_coils + self.recalculate_reduced_basis(self.function) + + @property + def coils(self): + return self.coilset.coils + + @coils.setter + def coils(self, *args, **kwargs): + raise ValueError("You cannot change the coils of a ReducedCoilSet. Change the base_coils instead.") + + @property + def surface(self): + return self._coilset.surface + + @surface.setter + def surface(self, surface: 'Surface'): + """ + setting a new surface requires re-calculating + the SVD matrices. + """ + self._coilset.surface = surface + self.recalculate_reduced_basis() + + @property + def nsv(self): + """ + number of singular values used by the ReducedCoilSet + """ + return self._nsv + + @nsv.setter + def nsv(self, nsv: int): + """ + Set the number of singular values to use. + args: + nsv: the number of singular values to use, int or string 'nonzero' + """ + if nsv == 'nonzero': + nsv = len(self._s_diag) + if nsv > len(self._s_diag): + raise ValueError("nsv must be equal to or smaller than the number of singular values") + self._nsv = nsv + x = np.zeros(nsv) + x[:len(self.x)] = self.x[:len(x)] + self.replace_dofs(DOFs(x, self._make_names())) + + + @property + def rsv(self): + """ + right-singular vectors of the svd + """ + return [np.array(self._vh_matrix[i, :]) for i in range(self.nsv)] + + @property + def lsv(self): + """ + left-singular vectors of the svd + """ + return [np.array(self._u_matrix.T[i, :]) for i in range(self.nsv)] + + @property + def singular_values(self): + """ + singular values of the svd + """ + return self._s_diag[:self.nsv] + + + def get_dof_orders(self): + """ + Not available for a ReducedCoilSet + """ + raise ValueError("Not available for a ReducedCoilSet") + + def _make_names(self): + names = [f"sv{n}" for n in range(len(self._s_diag[:self.nsv]))] + return names + + def set_dofs(self, x): + #check if correct! + if len(x) != self.nsv: + raise ValueError("Wrong number of DOFs") + padx = np.pad(x, (0, len(self._coil_x0)-self.nsv), mode='constant', constant_values=0) # create zero-padded vector of length of coil DOFs. + self.coilset.x = self._coil_x0 + (padx) @ self._vh_matrix # translate reduced vectors amplitude to movement of coils. + + def plot_singular_vector(self, n, eps=1e-4, show_delta_B=True, engine='mayavi', show=False, **kwargs): + """ + Plot the n-th singular vector. + args: + n: the index of the singular vector + eps: the magnitude of the displacement. + show_delta_B: whether to show the change in the magnetic field + engine: the plotting engine to use. Defaults to 'mayavi' + """ + if engine == 'mayavi': + from mayavi import mlab + if n > self.nsv: + raise ValueError("n must be smaller than the number of singular values") + singular_vector = np.array(self.rsv[n]) + + plotsurf = self.surface.copy(range=SurfaceRZFourier.RANGE_FULL_TORUS) + if show_delta_B: + bs = self.bs + initial_points = self.bs.get_points_cart_ref() + bs.set_points(plotsurf.gamma().reshape((-1, 3))) + + current_x = np.copy(self.x) + startpositions = [np.copy(coil.curve.gamma()) for coil in self.coilset.coils] + plot(self.coilset.coils, close=True, engine='mayavi',tube_radius=0.02, color=(0, 0, 0), show=False, **kwargs) + if show_delta_B: + startB = np.copy(np.sum(bs.B().reshape((plotsurf.quadpoints_phi.size, plotsurf.quadpoints_theta.size, 3)) * plotsurf.unitnormal()*-1, axis=2)) + startB = np.concatenate((startB, startB[:1, :]), axis=0) + startB = np.concatenate((startB, startB[:, :1]), axis=1) + + # Perturb the coils by the singular vector + self.coilset.x = self.coilset.x + singular_vector*eps + newpositions = [np.copy(coil.curve.gamma()) for coil in self.coilset.coils] + if show_delta_B: + changedB = np.copy(np.sum(bs.B().reshape((plotsurf.quadpoints_phi.size, plotsurf.quadpoints_theta.size, 3)) * plotsurf.unitnormal()*-1, axis=2)) + # close the plot + changedB = np.concatenate((changedB, changedB[:1, :]), axis=0) + changedB = np.concatenate((changedB, changedB[:, :1]), axis=1) + # plot the displacement vectors + for newcoilpos, startcoilpos in zip(newpositions, startpositions): + diffs = (0.05/eps) * (startcoilpos - newcoilpos) + x = startcoilpos[:, 0] + y = startcoilpos[:, 1] + z = startcoilpos[:, 2] + # enlarge the difference vectors for better visibility + dx = diffs[:, 0] + dy = diffs[:, 1] + dz = diffs[:, 2] + mlab.quiver3d(x, y, z, dx, dy, dz, line_width=4, **kwargs) + + if show_delta_B: + plot([plotsurf,], engine='mayavi', wireframe=False, close=True, scalars=changedB-startB, colormap='Reds', show=False, **kwargs) + else: + plot([plotsurf,], engine='mayavi', wireframe=False, close=True, colormap='Reds', show=False, **kwargs) + # plot the original coils again + self.x = current_x + plot(self.coilset.coils, close=True, tube_radius=0.02, engine='mayavi', color=(1, 1, 1), show=show, **kwargs) + # set bs set points back + if show_delta_B: + bs.set_points(initial_points) + return changedB, startB + else: + raise ValueError("plotting a ReducedCoilSet is only implemented in Mayavi. you can access and plot the surface and coils attributes directly.") \ No newline at end of file diff --git a/src/simsopt/field/force.py b/src/simsopt/field/force.py index d46640ae9..b8bc6c286 100644 --- a/src/simsopt/field/force.py +++ b/src/simsopt/field/force.py @@ -39,9 +39,13 @@ def coil_force(coil, allcoils, regularization, nturns=1): ### Line below seems to be the issue -- all these BiotSavart objects seem to stick ### around and not to go out of scope after these calls! - mutual_field = BiotSavart(mutual_coils).set_points(coil.curve.gamma()).B() - mutualforce = np.cross(coil.current.get_value() * tangent, mutual_field) + mutual_field = BiotSavart(mutual_coils).set_points(coil.curve.gamma()) + B_mutual = mutual_field.B() + mutualforce = np.cross(coil.current.get_value() * tangent, B_mutual) selfforce = self_force(coil, regularization) + mutual_field._children = set() + for c in mutual_coils: + c._children = set() return (selfforce + mutualforce) / nturns def coil_net_forces(coils, allcoils, regularization, nturns=None): @@ -57,7 +61,8 @@ def coil_net_forces(coils, allcoils, regularization, nturns=None): def coil_torque(coil, allcoils, regularization, nturns=1): gamma = coil.curve.gamma() - return np.cross(gamma, coil_force(coil, allcoils, regularization, nturns)) + center = coil.curve.center(coil.curve.gamma(), coil.curve.gammadash()) + return np.cross(gamma - center, coil_force(coil, allcoils, regularization, nturns)) def coil_net_torques(coils, allcoils, regularization, nturns=None): net_torques = np.zeros((len(coils), 3)) @@ -95,8 +100,8 @@ def self_force_rect(coil, a, b): return self_force(coil, regularization_rect(a, b)) -@jit -def lp_force_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual, p, threshold): +# @jit +def lp_force_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual, p, threshold, downsample): r"""Pure function for minimizing the Lorentz force on a coil. The function is @@ -107,13 +112,17 @@ def lp_force_pure(gamma, gammadash, gammadashdash, quadpoints, current, regulari where :math:`\vec{F}` is the Lorentz force, :math:`F_0` is a threshold force, and :math:`\ell` is arclength along the coil. """ - + B_mutual = B_mutual[::downsample, :] + gamma = gamma[::downsample, :] + gammadash = gammadash[::downsample, :] + gammadashdash = gammadashdash[::downsample, :] + quadpoints = quadpoints[::downsample] B_self = B_regularized_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization) gammadash_norm = jnp.linalg.norm(gammadash, axis=1)[:, None] tangent = gammadash / gammadash_norm force = jnp.cross(current * tangent, B_self + B_mutual) force_norm = jnp.linalg.norm(force, axis=1)[:, None] - return (jnp.sum(jnp.maximum(force_norm - threshold, 0)**p * gammadash_norm)) * (1. / p) + return (jnp.sum(jnp.maximum(force_norm - threshold, 0)**p * gammadash_norm)) * (1. / p) / jnp.shape(gamma)[0] class LpCurveForce(Optimizable): @@ -128,81 +137,107 @@ class LpCurveForce(Optimizable): and :math:`\ell` is arclength along the coil. """ - def __init__(self, coil, allcoils, regularization, p=2.0, threshold=0.0): + def __init__(self, coil, allcoils, regularization, p=2.0, threshold=0.0, downsample=1): self.coil = coil self.allcoils = allcoils self.othercoils = [c for c in allcoils if c is not coil] - self.biotsavart = BiotSavart(self.othercoils) + # self.biotsavart = BiotSavart(self.othercoils) quadpoints = self.coil.curve.quadpoints + self.downsample = downsample + args = {"static_argnums": (5,)} self.J_jax = jit( - lambda gamma, gammadash, gammadashdash, current, B_mutual: - lp_force_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual, p, threshold) + lambda gamma, gammadash, gammadashdash, current, B_mutual, downsample: + lp_force_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual, p, threshold, downsample), + **args ) self.dJ_dgamma = jit( - lambda gamma, gammadash, gammadashdash, current, B_mutual: - grad(self.J_jax, argnums=0)(gamma, gammadash, gammadashdash, current, B_mutual) + lambda gamma, gammadash, gammadashdash, current, B_mutual, downsample: + grad(self.J_jax, argnums=0)(gamma, gammadash, gammadashdash, current, B_mutual, downsample), + **args ) self.dJ_dgammadash = jit( - lambda gamma, gammadash, gammadashdash, current, B_mutual: - grad(self.J_jax, argnums=1)(gamma, gammadash, gammadashdash, current, B_mutual) + lambda gamma, gammadash, gammadashdash, current, B_mutual, downsample: + grad(self.J_jax, argnums=1)(gamma, gammadash, gammadashdash, current, B_mutual, downsample), + **args ) self.dJ_dgammadashdash = jit( - lambda gamma, gammadash, gammadashdash, current, B_mutual: - grad(self.J_jax, argnums=2)(gamma, gammadash, gammadashdash, current, B_mutual) + lambda gamma, gammadash, gammadashdash, current, B_mutual, downsample: + grad(self.J_jax, argnums=2)(gamma, gammadash, gammadashdash, current, B_mutual, downsample), + **args ) self.dJ_dcurrent = jit( - lambda gamma, gammadash, gammadashdash, current, B_mutual: - grad(self.J_jax, argnums=3)(gamma, gammadash, gammadashdash, current, B_mutual) + lambda gamma, gammadash, gammadashdash, current, B_mutual, downsample: + grad(self.J_jax, argnums=3)(gamma, gammadash, gammadashdash, current, B_mutual, downsample), + **args ) self.dJ_dB_mutual = jit( - lambda gamma, gammadash, gammadashdash, current, B_mutual: - grad(self.J_jax, argnums=4)(gamma, gammadash, gammadashdash, current, B_mutual) + lambda gamma, gammadash, gammadashdash, current, B_mutual, downsample: + grad(self.J_jax, argnums=4)(gamma, gammadash, gammadashdash, current, B_mutual, downsample), + **args ) super().__init__(depends_on=allcoils) def J(self): - self.biotsavart.set_points(self.coil.curve.gamma()) + biotsavart = BiotSavart(self.othercoils) + biotsavart.set_points(self.coil.curve.gamma()) + # biotsavart._children = set() args = [ self.coil.curve.gamma(), self.coil.curve.gammadash(), self.coil.curve.gammadashdash(), self.coil.current.get_value(), - self.biotsavart.B() + biotsavart.B(), + self.downsample ] + # biotsavart._children = set() + for c in self.othercoils: + c._children = set() + c.curve._children = set() + c.current._children = set() return self.J_jax(*args) @derivative_dec def dJ(self): - self.biotsavart.set_points(self.coil.curve.gamma()) + biotsavart = BiotSavart(self.othercoils) + biotsavart.set_points(self.coil.curve.gamma()) + # biotsavart._children = set() args = [ self.coil.curve.gamma(), self.coil.curve.gammadash(), self.coil.curve.gammadashdash(), self.coil.current.get_value(), - self.biotsavart.B() + biotsavart.B(), + self.downsample ] dJ_dB = self.dJ_dB_mutual(*args) - dB_dX = self.biotsavart.dB_by_dX() + dB_dX = biotsavart.dB_by_dX() dJ_dX = np.einsum('ij,ikj->ik', dJ_dB, dB_dX) - return ( + dJ = ( self.coil.curve.dgamma_by_dcoeff_vjp(self.dJ_dgamma(*args) + dJ_dX) + self.coil.curve.dgammadash_by_dcoeff_vjp(self.dJ_dgammadash(*args)) + self.coil.curve.dgammadashdash_by_dcoeff_vjp(self.dJ_dgammadashdash(*args)) + self.coil.current.vjp(jnp.asarray([self.dJ_dcurrent(*args)])) - + self.biotsavart.B_vjp(dJ_dB) + + biotsavart.B_vjp(dJ_dB) ) + # biotsavart._children = set() + for c in self.othercoils: + c._children = set() + c.curve._children = set() + c.current._children = set() + + return dJ return_fn_map = {'J': J, 'dJ': dJ} @@ -542,21 +577,38 @@ def dJ(self): return_fn_map = {'J': J, 'dJ': dJ} - -@jit +# @jit def mixed_lp_force_pure(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, quadpoints, quadpoints2, - currents, currents2, regularizations, regularizations2, p, threshold): + currents, currents2, regularizations, regularizations2, p, threshold, + downsample=1, + ): r""" """ - B_self = [B_regularized_pure(gammas[i], gammadashs[i], gammadashdashs[i], quadpoints, currents[i], regularizations[i]) for i in range(jnp.shape(gammas)[0])] - B_self2 = [B_regularized_pure(gammas2[i], gammadashs2[i], gammadashdashs2[i], quadpoints2, currents2[i], regularizations2[i]) for i in range(jnp.shape(gammas2)[0])] + # print(jnp.shape(quadpoints), jnp.shape(gammas), jnp.shape(gammadashs), jnp.shape(gammadashdashs)) + # print(jnp.shape(quadpoints2), jnp.shape(gammas2), jnp.shape(gammadashs2), jnp.shape(gammadashdashs2)) + quadpoints = quadpoints[::downsample] + gammas = gammas[:, ::downsample, :] + gammadashs = gammadashs[:, ::downsample, :] + gammadashdashs = gammadashdashs[:, ::downsample, :] + + quadpoints2 = quadpoints2[::downsample] + gammas2 = gammas2[:, ::downsample, :] + gammadashs2 = gammadashs2[:, ::downsample, :] + gammadashdashs2 = gammadashdashs2[:, ::downsample, :] + # print(jnp.shape(quadpoints), jnp.shape(gammas), jnp.shape(gammadashs), jnp.shape(gammadashdashs)) + # print(jnp.shape(quadpoints2), jnp.shape(gammas2), jnp.shape(gammadashs2), jnp.shape(gammadashdashs2)) + + B_self = jnp.array([B_regularized_pure(gammas[i], gammadashs[i], gammadashdashs[i], quadpoints, + currents[i], regularizations[i]) for i in range(jnp.shape(gammas)[0])]) + B_self2 = jnp.array([B_regularized_pure(gammas2[i], gammadashs2[i], gammadashdashs2[i], quadpoints2, + currents2[i], regularizations2[i]) for i in range(jnp.shape(gammas2)[0])]) gammadash_norms = jnp.linalg.norm(gammadashs, axis=-1)[:, :, None] tangents = gammadashs / gammadash_norms gammadash_norms2 = jnp.linalg.norm(gammadashs2, axis=-1)[:, :, None] tangents2 = gammadashs2 / gammadash_norms2 - selfforce = jnp.array([jnp.cross(currents[i] * tangents[i], B_self[i]) for i in range(jnp.shape(gammas)[0])]) - selfforce2 = jnp.array([jnp.cross(currents2[i] * tangents2[i], B_self2[i]) for i in range(jnp.shape(gammas2)[0])]) + # selfforce = jnp.array([jnp.cross(currents[i] * tangents[i], B_self[i]) for i in range(jnp.shape(gammas)[0])]) + # selfforce2 = jnp.array([jnp.cross(currents2[i] * tangents2[i], B_self2[i]) for i in range(jnp.shape(gammas2)[0])]) eps = 1e-10 # small number to avoid blow up in the denominator when i = j r_ij = gammas[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices @@ -564,7 +616,8 @@ def mixed_lp_force_pure(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs ### Note that need to do dl1 x dl2 x r12 here instead of just (dl1 * dl2)r12 # because these are not equivalent expressions if we are squaring the pointwise forces # before integration over coil i! - cross_prod = jnp.cross(tangents[:, None, :, None, :], jnp.cross(gammadashs[None, :, None, :, :], r_ij)) + # cross_prod = jnp.cross(tangents[:, None, :, None, :], jnp.cross(gammadashs[None, :, None, :, :], r_ij)) + cross_prod = jnp.cross(gammadashs[None, :, None, :, :], r_ij) rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 Ii_Ij = currents[:, None] * currents[None, :] Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) @@ -572,29 +625,32 @@ def mixed_lp_force_pure(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs # repeat with gamma, gamma2 r_ij = gammas[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices - cross_prod = jnp.cross(tangents[:, None, :, None, :], jnp.cross(gammadashs2[None, :, None, :, :], r_ij)) + # cross_prod = jnp.cross(tangents[:, None, :, None, :], jnp.cross(gammadashs2[None, :, None, :, :], r_ij)) + cross_prod = jnp.cross(gammadashs2[None, :, None, :, :], r_ij) rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 Ii_Ij = currents[:, None] * currents2[None, :] F += jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas2)[1] - force_norm = jnp.linalg.norm(F * 1e-7 + selfforce, axis=-1) - summ = jnp.sum(jnp.maximum(force_norm[:, :, None] - threshold, 0) ** p * gammadash_norms) + force_norm = jnp.linalg.norm(jnp.cross(tangents, F * 1e-7 + currents[:, None, None] * B_self), axis=-1) + summ = jnp.sum(jnp.maximum(force_norm[:, :, None] - threshold, 0) ** p * gammadash_norms) / jnp.shape(gammas)[1] # repeat with gamma2, gamma r_ij = gammas2[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices - cross_prod = jnp.cross(tangents2[:, None, :, None, :], jnp.cross(gammadashs[None, :, None, :, :], r_ij)) + cross_prod = jnp.cross(gammadashs[None, :, None, :, :], r_ij) + # cross_prod = jnp.cross(tangents2[:, None, :, None, :], jnp.cross(gammadashs[None, :, None, :, :], r_ij)) rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 Ii_Ij = currents2[:, None] * currents[None, :] F = jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas)[1] # repeat with gamma2, gamma2 r_ij = gammas2[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices - cross_prod = jnp.cross(tangents2[:, None, :, None, :], jnp.cross(gammadashs2[None, :, None, :, :], r_ij)) + cross_prod = jnp.cross(gammadashs2[None, :, None, :, :], r_ij) + # cross_prod = jnp.cross(tangents2[:, None, :, None, :], jnp.cross(gammadashs2[None, :, None, :, :], r_ij)) rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 Ii_Ij = currents2[:, None] * currents2[None, :] Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) F += jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas2)[1] - force_norm2 = jnp.linalg.norm(F * 1e-7 + selfforce2, axis=-1) - summ += jnp.sum(jnp.maximum(force_norm2[:, :, None] - threshold, 0) ** p * gammadash_norms2) + force_norm2 = jnp.linalg.norm(jnp.cross(tangents2, F * 1e-7 + currents2[:, None, None] * B_self2), axis=-1) + summ += jnp.sum(jnp.maximum(force_norm2[:, :, None] - threshold, 0) ** p * gammadash_norms2) / jnp.shape(gammas2)[1] return summ * (1 / p) class MixedLpCurveForce(Optimizable): @@ -609,56 +665,66 @@ class MixedLpCurveForce(Optimizable): along the coil. """ - def __init__(self, allcoils, allcoils2, regularizations, regularizations2, p=2.0, threshold=0.0): + def __init__(self, allcoils, allcoils2, regularizations, regularizations2, p=2.0, threshold=0.0, downsample=1): self.allcoils = allcoils self.allcoils2 = allcoils2 quadpoints = self.allcoils[0].curve.quadpoints quadpoints2 = self.allcoils2[0].curve.quadpoints + self.downsample = downsample + args = {"static_argnums": (8,)} self.J_jax = jit( - lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: + lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample: mixed_lp_force_pure(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, quadpoints, quadpoints2, - currents, currents2, regularizations, regularizations2, p, threshold) + currents, currents2, regularizations, regularizations2, p, threshold, downsample), + **args ) self.dJ_dgamma = jit( - lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: - grad(self.J_jax, argnums=0)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2) + lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample: + grad(self.J_jax, argnums=0)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample), + **args ) self.dJ_dgamma2 = jit( - lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: - grad(self.J_jax, argnums=1)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2) + lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample: + grad(self.J_jax, argnums=1)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample), + **args ) self.dJ_dgammadash = jit( - lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: - grad(self.J_jax, argnums=2)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2) + lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample: + grad(self.J_jax, argnums=2)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample), + **args ) self.dJ_dgammadash2 = jit( - lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: - grad(self.J_jax, argnums=3)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2) + lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample: + grad(self.J_jax, argnums=3)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample), + **args ) self.dJ_dgammadashdash = jit( - lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: - grad(self.J_jax, argnums=4)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2) + lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample: + grad(self.J_jax, argnums=4)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample), + **args ) self.dJ_dgammadashdash2 = jit( - lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: - grad(self.J_jax, argnums=5)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2) + lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample: + grad(self.J_jax, argnums=5)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample), + **args ) self.dJ_dcurrent = jit( - lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: - grad(self.J_jax, argnums=6)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2) + lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample: + grad(self.J_jax, argnums=6)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample), + **args ) - self.dJ_dcurrent2 = jit( - lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: - grad(self.J_jax, argnums=7)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2) + lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample: + grad(self.J_jax, argnums=7)(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2, downsample), + **args ) super().__init__(depends_on=(allcoils + allcoils2)) @@ -675,6 +741,7 @@ def J(self): jnp.array([c.curve.gammadashdash() for c in self.allcoils2]), jnp.array([c.current.get_value() for c in self.allcoils]), jnp.array([c.current.get_value() for c in self.allcoils2]), + self.downsample ] return self.J_jax(*args) @@ -691,6 +758,7 @@ def dJ(self): jnp.array([c.curve.gammadashdash() for c in self.allcoils2]), jnp.array([c.current.get_value() for c in self.allcoils]), jnp.array([c.current.get_value() for c in self.allcoils2]), + self.downsample ] dJ_dgamma = self.dJ_dgamma(*args) dJ_dgammadash = self.dJ_dgammadash(*args) @@ -708,7 +776,7 @@ def dJ(self): + sum([c.current.vjp(jnp.asarray([dJ_dcurrent[i]])) for i, c in enumerate(self.allcoils)]) + sum([c.curve.dgamma_by_dcoeff_vjp(dJ_dgamma2[i]) for i, c in enumerate(self.allcoils2)]) + sum([c.curve.dgammadash_by_dcoeff_vjp(dJ_dgammadash2[i]) for i, c in enumerate(self.allcoils2)]) - + sum([c.curve.dgammadashdash_by_dcoeff_vjp(dJ_dgammadashdash[i]) for i, c in enumerate(self.allcoils2)]) + + sum([c.curve.dgammadashdash_by_dcoeff_vjp(dJ_dgammadashdash2[i]) for i, c in enumerate(self.allcoils2)]) + sum([c.current.vjp(jnp.asarray([dJ_dcurrent2[i]])) for i, c in enumerate(self.allcoils2)]) ) @@ -717,60 +785,6 @@ def dJ(self): return_fn_map = {'J': J, 'dJ': dJ} -@jit -def mixed_lp_torque_pure(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, - quadpoints, quadpoints2, - currents, currents2, regularizations, regularizations2, p, threshold): - r""" - """ - B_self = [B_regularized_pure(gammas[i], gammadashs[i], gammadashdashs[i], quadpoints, currents[i], regularizations[i]) for i in range(jnp.shape(gammas)[0])] - B_self2 = [B_regularized_pure(gammas2[i], gammadashs2[i], gammadashdashs2[i], quadpoints2, currents2[i], regularizations2[i]) for i in range(jnp.shape(gammas2)[0])] - gammadash_norms = jnp.linalg.norm(gammadashs, axis=-1)[:, :, None] - tangents = gammadashs / gammadash_norms - gammadash_norms2 = jnp.linalg.norm(gammadashs2, axis=-1)[:, :, None] - tangents2 = gammadashs2 / gammadash_norms2 - selftorque = jnp.array([jnp.cross(gammas[i], jnp.cross(currents[i] * tangents[i], B_self[i])) for i in range(jnp.shape(gammas)[0])]) - selftorque2 = jnp.array([jnp.cross(gammas2[i], jnp.cross(currents2[i] * tangents2[i], B_self2[i])) for i in range(jnp.shape(gammas2)[0])]) - - eps = 1e-10 # small number to avoid blow up in the denominator when i = j - r_ij = gammas[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices - - ### Note that need to do dl1 x dl2 x r12 here instead of just (dl1 * dl2)r12 - # because these are not equivalent expressions if we are squaring the pointwise forces - # before integration over coil i! - cross_prod = jnp.cross(gammas[:, None, :, None, :], jnp.cross(tangents[:, None, :, None, :], jnp.cross(gammadashs[None, :, None, :, :], r_ij))) - rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 - Ii_Ij = currents[:, None] * currents[None, :] - Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) - T = jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas)[1] - - # repeat with gamma, gamma2 - r_ij = gammas[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices - cross_prod = jnp.cross(gammas[:, None, :, None, :], jnp.cross(tangents[:, None, :, None, :], jnp.cross(gammadashs2[None, :, None, :, :], r_ij))) - rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 - Ii_Ij = currents[:, None] * currents2[None, :] - T += jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas2)[1] - torque_norm = jnp.linalg.norm(T * 1e-7 + selftorque, axis=-1) - summ = jnp.sum(jnp.maximum(torque_norm[:, :, None] - threshold, 0) ** p * gammadash_norms) - - # repeat with gamma2, gamma - r_ij = gammas2[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices - cross_prod = jnp.cross(gammas2[:, None, :, None, :], jnp.cross(tangents2[:, None, :, None, :], jnp.cross(gammadashs[None, :, None, :, :], r_ij))) - rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 - Ii_Ij = currents2[:, None] * currents[None, :] - T = jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas)[1] - - # repeat with gamma2, gamma2 - r_ij = gammas2[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices - cross_prod = jnp.cross(gammas2[:, None, :, None, :], jnp.cross(tangents2[:, None, :, None, :], jnp.cross(gammadashs2[None, :, None, :, :], r_ij))) - rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 - Ii_Ij = currents2[:, None] * currents2[None, :] - Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) - T += jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas2)[1] - torque_norm2 = jnp.linalg.norm(T * 1e-7 + selftorque2, axis=-1) - summ += jnp.sum(jnp.maximum(torque_norm2[:, :, None] - threshold, 0) ** p * gammadash_norms2) - return summ * (1 / p) - class MixedLpCurveTorque(Optimizable): r"""Optimizable class to minimize the net Lorentz force on a coil. @@ -783,15 +797,73 @@ class MixedLpCurveTorque(Optimizable): along the coil. """ + # @jit + def mixed_lp_torque_pure(self, gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, + quadpoints, quadpoints2, + currents, currents2, regularizations, regularizations2, p, threshold): + r""" + """ + B_self = [B_regularized_pure(gammas[i], gammadashs[i], gammadashdashs[i], quadpoints, currents[i], regularizations[i]) for i in range(jnp.shape(gammas)[0])] + B_self2 = [B_regularized_pure(gammas2[i], gammadashs2[i], gammadashdashs2[i], quadpoints2, currents2[i], regularizations2[i]) for i in range(jnp.shape(gammas2)[0])] + gammadash_norms = jnp.linalg.norm(gammadashs, axis=-1)[:, :, None] + tangents = gammadashs / gammadash_norms + gammadash_norms2 = jnp.linalg.norm(gammadashs2, axis=-1)[:, :, None] + tangents2 = gammadashs2 / gammadash_norms2 + centers = jnp.array([c.curve.center(gammas[i], gammadashs[i]) for i, c in enumerate(self.allcoils)])[:, None, :] + centers2 = jnp.array([c.curve.center(gammas2[i], gammadashs2[i]) for i, c in enumerate(self.allcoils2)])[:, None, :] + selftorque = jnp.array([jnp.cross((gammas - centers)[i], jnp.cross(currents[i] * tangents[i], B_self[i])) for i in range(jnp.shape(gammas)[0])]) + selftorque2 = jnp.array([jnp.cross((gammas2 - centers2)[i], jnp.cross(currents2[i] * tangents2[i], B_self2[i])) for i in range(jnp.shape(gammas2)[0])]) + + eps = 1e-10 # small number to avoid blow up in the denominator when i = j + r_ij = gammas[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices + + ### Note that need to do dl1 x dl2 x r12 here instead of just (dl1 * dl2)r12 + # because these are not equivalent expressions if we are squaring the pointwise forces + # before integration over coil i! + cross_prod = jnp.cross((gammas - centers)[:, None, :, None, :], jnp.cross(tangents[:, None, :, None, :], jnp.cross(gammadashs[None, :, None, :, :], r_ij))) + rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 + Ii_Ij = currents[:, None] * currents[None, :] + Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) + T = jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas)[1] + + # repeat with gamma, gamma2 + r_ij = gammas[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices + cross_prod = jnp.cross((gammas - centers)[:, None, :, None, :], jnp.cross(tangents[:, None, :, None, :], jnp.cross(gammadashs2[None, :, None, :, :], r_ij))) + rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 + Ii_Ij = currents[:, None] * currents2[None, :] + T += jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas2)[1] + torque_norm = jnp.linalg.norm(T * 1e-7 + selftorque, axis=-1) + summ = jnp.sum(jnp.maximum(torque_norm[:, :, None] - threshold, 0) ** p * gammadash_norms) + + # repeat with gamma2, gamma + r_ij = gammas2[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices + cross_prod = jnp.cross((gammas2 - centers2)[:, None, :, None, :], jnp.cross(tangents2[:, None, :, None, :], jnp.cross(gammadashs[None, :, None, :, :], r_ij))) + rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 + Ii_Ij = currents2[:, None] * currents[None, :] + T = jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas)[1] + + # repeat with gamma2, gamma2 + r_ij = gammas2[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices + cross_prod = jnp.cross((gammas2 - centers2)[:, None, :, None, :], jnp.cross(tangents2[:, None, :, None, :], jnp.cross(gammadashs2[None, :, None, :, :], r_ij))) + rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 + Ii_Ij = currents2[:, None] * currents2[None, :] + Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) + T += jnp.sum(Ii_Ij[:, :, None, None] * jnp.sum(cross_prod / rij_norm3[:, :, :, :, None], axis=3), axis=1) / jnp.shape(gammas2)[1] + torque_norm2 = jnp.linalg.norm(T * 1e-7 + selftorque2, axis=-1) + summ += jnp.sum(jnp.maximum(torque_norm2[:, :, None] - threshold, 0) ** p * gammadash_norms2) + return summ * (1 / p) + def __init__(self, allcoils, allcoils2, regularizations, regularizations2, p=2.0, threshold=0.0): self.allcoils = allcoils self.allcoils2 = allcoils2 quadpoints = self.allcoils[0].curve.quadpoints quadpoints2 = self.allcoils2[0].curve.quadpoints + centers = self.allcoils[0].curve.center + centers2 = self.allcoils2[0].curve.center self.J_jax = jit( lambda gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, currents, currents2: - mixed_lp_torque_pure(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, quadpoints, quadpoints2, + self.mixed_lp_torque_pure(gammas, gammas2, gammadashs, gammadashs2, gammadashdashs, gammadashdashs2, quadpoints, quadpoints2, currents, currents2, regularizations, regularizations2, p, threshold) ) @@ -891,56 +963,6 @@ def dJ(self): return_fn_map = {'J': J, 'dJ': dJ} - -@jit -def mixed_squared_mean_torque(gammas, gammas2, gammadashs, gammadashs2, currents, currents2): - r""" - """ - eps = 1e-10 # small number to avoid blow up in the denominator when i = j - r_ij = gammas[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices - cross1 = jnp.cross(gammadashs[None, :, None, :, :], r_ij) - cross2 = jnp.cross(gammadashs[:, None, :, None, :], cross1) - cross3 = jnp.cross(gammas[:, None, :, None, :], cross2) - rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 - Ii_Ij = currents[:, None] * currents[None, :] - Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) - T = Ii_Ij[:, :, None] * jnp.sum(jnp.sum(cross3 / rij_norm3[:, :, :, :, None], axis=3), axis=2) - net_torques = -jnp.sum(T, axis=1) / jnp.shape(gammas)[1] # ** 2 - - # repeat with gamma, gamma2 - r_ij = gammas[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices - cross1 = jnp.cross(gammadashs2[None, :, None, :, :], r_ij) - cross2 = jnp.cross(gammadashs[:, None, :, None, :], cross1) - cross3 = jnp.cross(gammas[:, None, :, None, :], cross2) - rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 - Ii_Ij = currents[:, None] * currents2[None, :] - T = Ii_Ij[:, :, None] * jnp.sum(jnp.sum(cross3 / rij_norm3[:, :, :, :, None], axis=3), axis=2) - net_torques += -jnp.sum(T, axis=1) / jnp.shape(gammas2)[1] - summ = jnp.sum(jnp.linalg.norm(net_torques, axis=-1) ** 2) - - # repeat with gamma2, gamma - r_ij = gammas2[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices - cross1 = jnp.cross(gammadashs[None, :, None, :, :], r_ij) - cross2 = jnp.cross(gammadashs2[:, None, :, None, :], cross1) - cross3 = jnp.cross(gammas2[:, None, :, None, :], cross2) - rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 - Ii_Ij = currents2[:, None] * currents[None, :] - T = Ii_Ij[:, :, None] * jnp.sum(jnp.sum(cross3 / rij_norm3[:, :, :, :, None], axis=3), axis=2) - net_torques = -jnp.sum(T, axis=1)/ jnp.shape(gammas)[1] # / jnp.shape(gammas2)[1] - - # repeat with gamma2, gamma2 - r_ij = gammas2[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices - cross1 = jnp.cross(gammadashs2[None, :, None, :, :], r_ij) - cross2 = jnp.cross(gammadashs2[:, None, :, None, :], cross1) - cross3 = jnp.cross(gammas2[:, None, :, None, :], cross2) - rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 - Ii_Ij = currents2[:, None] * currents2[None, :] - Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) - T = Ii_Ij[:, :, None] * jnp.sum(jnp.sum(cross3 / rij_norm3[:, :, :, :, None], axis=3), axis=2) - net_torques += -jnp.sum(T, axis=1)/ jnp.shape(gammas2)[1] #** 2 - summ += jnp.sum(jnp.linalg.norm(net_torques, axis=-1) ** 2) - return summ * 1e-14 - class MixedSquaredMeanTorque(Optimizable): r"""Optimizable class to minimize the net Lorentz force on a coil. @@ -953,13 +975,67 @@ class MixedSquaredMeanTorque(Optimizable): along the coil. """ + # @jit + def mixed_squared_mean_torque(self, gammas, gammas2, gammadashs, gammadashs2, currents, currents2): + r""" + """ + eps = 1e-10 # small number to avoid blow up in the denominator when i = j + r_ij = gammas[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices + centers = jnp.array([c.curve.center(gammas[i], gammadashs[i]) for i, c in enumerate(self.allcoils)])[:, None, :] + centers2 = jnp.array([c.curve.center(gammas2[i], gammadashs2[i]) for i, c in enumerate(self.allcoils2)])[:, None, :] + cross1 = jnp.cross(gammadashs[None, :, None, :, :], r_ij) + cross2 = jnp.cross(gammadashs[:, None, :, None, :], cross1) + cross3 = jnp.cross((gammas - centers)[:, None, :, None, :], cross2) + rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 + Ii_Ij = currents[:, None] * currents[None, :] + Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) + T = Ii_Ij[:, :, None] * jnp.sum(jnp.sum(cross3 / rij_norm3[:, :, :, :, None], axis=3), axis=2) + net_torques = -jnp.sum(T, axis=1) / jnp.shape(gammas)[1] # ** 2 + + # repeat with gamma, gamma2 + r_ij = gammas[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices + cross1 = jnp.cross(gammadashs2[None, :, None, :, :], r_ij) + cross2 = jnp.cross(gammadashs[:, None, :, None, :], cross1) + cross3 = jnp.cross((gammas - centers)[:, None, :, None, :], cross2) + rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 + Ii_Ij = currents[:, None] * currents2[None, :] + T = Ii_Ij[:, :, None] * jnp.sum(jnp.sum(cross3 / rij_norm3[:, :, :, :, None], axis=3), axis=2) + net_torques += -jnp.sum(T, axis=1) / jnp.shape(gammas2)[1] + summ = jnp.sum(jnp.linalg.norm(net_torques, axis=-1) ** 2) + + # repeat with gamma2, gamma + r_ij = gammas2[:, None, :, None, :] - gammas[None, :, None, :, :] # Note, do not use the i = j indices + cross1 = jnp.cross(gammadashs[None, :, None, :, :], r_ij) + cross2 = jnp.cross(gammadashs2[:, None, :, None, :], cross1) + cross3 = jnp.cross((gammas2 - centers2)[:, None, :, None, :], cross2) + rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 + Ii_Ij = currents2[:, None] * currents[None, :] + T = Ii_Ij[:, :, None] * jnp.sum(jnp.sum(cross3 / rij_norm3[:, :, :, :, None], axis=3), axis=2) + net_torques = -jnp.sum(T, axis=1)/ jnp.shape(gammas)[1] # / jnp.shape(gammas2)[1] + + # repeat with gamma2, gamma2 + r_ij = gammas2[:, None, :, None, :] - gammas2[None, :, None, :, :] # Note, do not use the i = j indices + cross1 = jnp.cross(gammadashs2[None, :, None, :, :], r_ij) + cross2 = jnp.cross(gammadashs2[:, None, :, None, :], cross1) + cross3 = jnp.cross((gammas2 - centers2)[:, None, :, None, :], cross2) + rij_norm3 = jnp.linalg.norm(r_ij + eps, axis=-1) ** 3 + Ii_Ij = currents2[:, None] * currents2[None, :] + Ii_Ij = Ii_Ij.at[:, :].add(-jnp.diag(jnp.diag(Ii_Ij))) + T = Ii_Ij[:, :, None] * jnp.sum(jnp.sum(cross3 / rij_norm3[:, :, :, :, None], axis=3), axis=2) + net_torques += -jnp.sum(T, axis=1)/ jnp.shape(gammas2)[1] #** 2 + summ += jnp.sum(jnp.linalg.norm(net_torques, axis=-1) ** 2) + return summ * 1e-14 + + def __init__(self, allcoils, allcoils2): self.allcoils = allcoils self.allcoils2 = allcoils2 + centers = self.allcoils[0].curve.center + centers2 = self.allcoils2[0].curve.center self.J_jax = jit( lambda gammas, gammas2, gammadashs, gammadashs2, currents, currents2: - mixed_squared_mean_torque(gammas, gammas2, gammadashs, gammadashs2, currents, currents2) + self.mixed_squared_mean_torque(gammas, gammas2, gammadashs, gammadashs2, currents, currents2) ) self.dJ_dgamma = jit( @@ -1129,16 +1205,6 @@ def dJ(self): return_fn_map = {'J': J, 'dJ': dJ} -@jit -def squared_mean_torque_pure(current, gamma, gammadash, B_mutual): - r""" - """ - # gammadash_norm = jnp.linalg.norm(gammadash, axis=1)[:, None] - # tangent = gammadash / gammadash_norm - # force = jnp.cross(gammadash, B_mutual) - # torque = jnp.cross(gamma, force) - return (current * jnp.linalg.norm(jnp.sum(jnp.cross(gamma, jnp.cross(gammadash, B_mutual)), axis=0))) ** 2 # / jnp.sum(gammadash_norm) # factor for the integral - class SquaredMeanTorque(Optimizable): r"""Optimizable class to minimize the net Lorentz force on a coil. @@ -1151,6 +1217,13 @@ class SquaredMeanTorque(Optimizable): along the coil. """ + # @jit + def squared_mean_torque_pure(self, current, gamma, gammadash, B_mutual): + r""" + """ + return (current * jnp.linalg.norm(jnp.sum(jnp.cross(gamma - self.coil.curve.center(gamma, gammadash), jnp.cross(gammadash, B_mutual)), axis=0))) ** 2 # / jnp.sum(gammadash_norm) # factor for the integral + + def __init__(self, coil, allcoils): self.coil = coil self.allcoils = allcoils @@ -1158,7 +1231,7 @@ def __init__(self, coil, allcoils): self.J_jax = jit( lambda current, gamma, gammadash, B_mutual: - squared_mean_torque_pure(current, gamma, gammadash, B_mutual) + self.squared_mean_torque_pure(current, gamma, gammadash, B_mutual) ) self.dJ_dcurrent = jit( @@ -1221,18 +1294,6 @@ def dJ(self): return_fn_map = {'J': J, 'dJ': dJ} -@jit -def mean_squared_torque_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual): - r""" - """ - B_self = B_regularized_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization) - gammadash_norm = jnp.linalg.norm(gammadash, axis=1)[:, None] - tangent = gammadash / gammadash_norm - force = jnp.cross(current * tangent, B_self + B_mutual) - torque = jnp.cross(gamma, force) - torque_norm = jnp.linalg.norm(torque, axis=1)[:, None] - return jnp.sum(gammadash_norm * torque_norm ** 2) / jnp.sum(gammadash_norm) - class MeanSquaredTorque(Optimizable): r"""Optimizable class to minimize the net Lorentz force on a coil. @@ -1245,16 +1306,29 @@ class MeanSquaredTorque(Optimizable): along the coil. """ + # @jit + def mean_squared_torque_pure(self, gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual): + r""" + """ + B_self = B_regularized_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization) + gammadash_norm = jnp.linalg.norm(gammadash, axis=1)[:, None] + tangent = gammadash / gammadash_norm + force = jnp.cross(current * tangent, B_self + B_mutual) + torque = jnp.cross(gamma - self.coil.curve.center(gamma, gammadash), force) + torque_norm = jnp.linalg.norm(torque, axis=1)[:, None] + return jnp.sum(gammadash_norm * torque_norm ** 2) / jnp.sum(gammadash_norm) + def __init__(self, coil, allcoils, regularization): self.coil = coil self.allcoils = allcoils self.othercoils = [c for c in allcoils if c is not coil] self.biotsavart = BiotSavart(self.othercoils) quadpoints = self.coil.curve.quadpoints + center = self.coil.curve.center self.J_jax = jit( lambda gamma, gammadash, gammadashdash, current, B_mutual: - mean_squared_torque_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual) + self.mean_squared_torque_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual) ) self.dJ_dgamma = jit( @@ -1292,7 +1366,7 @@ def J(self): self.coil.curve.gammadash(), self.coil.curve.gammadashdash(), self.coil.current.get_value(), - self.biotsavart.B() + self.biotsavart.B(), ] return self.J_jax(*args) @@ -1306,7 +1380,7 @@ def dJ(self): self.coil.curve.gammadash(), self.coil.curve.gammadashdash(), self.coil.current.get_value(), - self.biotsavart.B() + self.biotsavart.B(), ] dJ_dB = self.dJ_dB_mutual(*args) @@ -1323,26 +1397,6 @@ def dJ(self): return_fn_map = {'J': J, 'dJ': dJ} -@jit -def lp_torque_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual, p, threshold): - r"""Pure function for minimizing the Lorentz force on a coil. - - The function is - - .. math:: - J = \frac{1}{p}\left(\int \text{max}(|\vec{T}| - T_0, 0)^p d\ell\right) - - where :math:`\vec{T}` is the Lorentz torque, :math:`T_0` is a threshold torque, - and :math:`\ell` is arclength along the coil. - """ - B_self = B_regularized_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization) - gammadash_norm = jnp.linalg.norm(gammadash, axis=1)[:, None] - tangent = gammadash / gammadash_norm - force = jnp.cross(current * tangent, B_self + B_mutual) - torque = jnp.cross(gamma, force) - torque_norm = jnp.linalg.norm(torque, axis=1)[:, None] - return (jnp.sum(jnp.maximum(torque_norm - threshold, 0)**p * gammadash_norm)) * (1 / p) #/ jnp.sum(gammadash_norm) - class LpCurveTorque(Optimizable): r""" Optimizable class to minimize the Lorentz force on a coil. @@ -1356,16 +1410,37 @@ class LpCurveTorque(Optimizable): and :math:`\ell` is arclength along the coil. """ + # @jit + def lp_torque_pure(self, gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual, p, threshold): + r"""Pure function for minimizing the Lorentz force on a coil. + + The function is + + .. math:: + J = \frac{1}{p}\left(\int \text{max}(|\vec{T}| - T_0, 0)^p d\ell\right) + + where :math:`\vec{T}` is the Lorentz torque, :math:`T_0` is a threshold torque, + and :math:`\ell` is arclength along the coil. + """ + B_self = B_regularized_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization) + gammadash_norm = jnp.linalg.norm(gammadash, axis=1)[:, None] + tangent = gammadash / gammadash_norm + force = jnp.cross(current * tangent, B_self + B_mutual) + torque = jnp.cross(gamma - self.coil.curve.center(gamma, gammadash), force) + torque_norm = jnp.linalg.norm(torque, axis=1)[:, None] + return (jnp.sum(jnp.maximum(torque_norm - threshold, 0)**p * gammadash_norm)) * (1 / p) #/ jnp.sum(gammadash_norm) + def __init__(self, coil, allcoils, regularization, p=2.0, threshold=0.0): self.coil = coil self.allcoils = allcoils self.othercoils = [c for c in allcoils if c is not coil] self.biotsavart = BiotSavart(self.othercoils) quadpoints = self.coil.curve.quadpoints + center = self.coil.curve.center self.J_jax = jit( lambda gamma, gammadash, gammadashdash, current, B_mutual: - lp_torque_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual, p, threshold) + self.lp_torque_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization, B_mutual, p, threshold) ) self.dJ_dgamma = jit( @@ -1403,7 +1478,7 @@ def J(self): self.coil.curve.gammadash(), self.coil.curve.gammadashdash(), self.coil.current.get_value(), - self.biotsavart.B() + self.biotsavart.B(), ] return self.J_jax(*args) @@ -1417,7 +1492,7 @@ def dJ(self): self.coil.curve.gammadash(), self.coil.curve.gammadashdash(), self.coil.current.get_value(), - self.biotsavart.B() + self.biotsavart.B(), ] dJ_dB = self.dJ_dB_mutual(*args) diff --git a/src/simsopt/field/normal_field.py b/src/simsopt/field/normal_field.py index 68f09cd3e..57ca43e1e 100644 --- a/src/simsopt/field/normal_field.py +++ b/src/simsopt/field/normal_field.py @@ -4,6 +4,7 @@ from .._core.optimizable import DOFs, Optimizable from simsopt.geo import SurfaceRZFourier +from .coilset import CoilSet logger = logging.getLogger(__name__) @@ -13,7 +14,7 @@ py_spec = None logger.debug(str(e)) -__all__ = ['NormalField'] +__all__ = ['NormalField', 'CoilNormalField'] class NormalField(Optimizable): @@ -189,7 +190,7 @@ def get_dofs(self): dofs[ii] = self.vnc[mm, input_ntor+nn] return dofs - def get_index_in_array(self, m, n, mpol=None, ntor=None, even=False): + def get_index_in_array(self, m, n, mpol=None, ntor=None): """ Returns position of mode (m,n) in vns or vnc array @@ -212,8 +213,6 @@ def get_index_in_array(self, m, n, mpol=None, ntor=None, even=False): raise ValueError('n out of bound') if m == 0 and n < 0: raise ValueError('n has to be positive if m==0') - if not even and m == 0 and n == 0: - raise ValueError('m=n=0 not supported for odd series') return [m, n+ntor] @@ -428,7 +427,9 @@ def get_vnc_asarray(self, mpol=None, ntor=None): elif ntor > self.ntor: raise ValueError('ntor out of bound') - vnc = self.vns + vnc = self.vnc + if vnc is None: + vnc = np.zeros((mpol, 2*ntor+1)) return vnc[0:mpol, self.ntor-ntor:self.ntor+ntor+1] @@ -509,8 +510,238 @@ def get_real_space_field(self): provided field on the computational boundary. The returned array will be of size specified by the surfaces' quadpoints and located on the quadpoints. """ - vns, vnc = self.get_vns_vnc_asarray(mpol=self.mpol, ntor=self.ntor) + vns, vnc = self.get_vns_vnc_asarray() BdotN_unnormalized = self.surface.inverse_fourier_transform_scalar(vns, vnc, normalization=(2*np.pi)**2, stellsym=self.stellsym) normal_field_real_space = -1 * BdotN_unnormalized / np.linalg.norm(self.surface.normal(), axis=-1) return normal_field_real_space +class CoilNormalField(NormalField): + """ + A SPEC NormalField generated by a CoilSet. + + The CoilNormalField provides the same interface as the + NormalField, but its degrees of freedom are inherited + from its CoilSet parent. + + Args: + coilset: The CoilSet object from which to inherit the degrees of freedom + + Properties: + surface: The computational boundary of the SPEC simulation, + that is managed by the CoilSet. + vns/vnc: fourier harmonics of the normal field. + This property is cached, and recomputed only when the parents' DOFS (the + coils) change. + """ + def __init__(self, coilset: 'CoilSet' = None): + self._vns = None + self._vnc = None + + # Set coilset and boundary: if not given create standard ones. + if coilset is not None: + self._coilset = coilset + else: + self._coilset = CoilSet() + + self.nfp = self.surface.nfp + self.stellsym = self.surface.stellsym + self.mpol = self.surface.mpol + self.ntor = self.surface.ntor + Optimizable.__init__(self, depends_on=[self._coilset]) # call the Optimizable constructor, skip the NormalField constructor + + @property + def surface(self): + return self._coilset.surface + + @surface.setter + def surface(self, boundary): + self._coilset.surface = boundary + + @classmethod + def from_spec_object(cls, *args, **kwargs): + raise ValueError('Generate the coils separately and pass them to the CoilNormalField constructor') + + @classmethod + def from_spec(cls, *args, **kwargs): + raise ValueError('Generate the coils separately and pass them to the CoilNormalField constructor') + + @property + def vns(self): + if self._vns is None: + bnormal = np.sum(self.coilset.bs.B().reshape((self.surface.quadpoints_phi.size, self.surface.quadpoints_theta.size, 3)) * self.surface.normal()*-1, axis=2) + Vns, Vnc = self.surface.fourier_transform_scalar(bnormal[:, :], normalization=(2*np.pi)**2, stellsym=self.stellsym) + self._vns = Vns + self._vnc = Vnc + return self._vns + + @vns.setter + def vns(self, *args, **kwargs): + raise AttributeError('you cannot set vns, the coils do this!') + + @property + def vnc(self): + if self._vnc is None: + bnormal = np.sum(self.coilset.bs.B().reshape((self.surface.quadpoints_phi.size, self.surface.quadpoints_theta.size, 3)) * self.surface.normal()*-1, axis=2) + Vns, Vnc = self.surface.fourier_transform_scalar(bnormal[:, :], normalization=(2*np.pi)**2, stellsym=self.stellsym) + self._vns = Vns + self._vnc = Vnc + return self._vnc + + @vnc.setter + def vnc(self, *args, **kwargs): + raise AttributeError('you cannot set vnc, the coils do this!') + + @property + def coilset(self): + return self._coilset + + @coilset.setter + def coilset(self, coilset): + from simsopt.field import CoilSet, ReducedCoilSet + assert isinstance(coilset, (CoilSet, ReducedCoilSet)) + self.remove_parent(self._coilset) + self._coilset = coilset + self.append_parent(coilset) + self.recompute_bell() + + def reduce_coilset(self, nsv='nonzero'): + """ + Replace the coilset with a Re:w + ducedCoilSet keeping the first nsv singular values. + + Note: Should this be done by proc0 and broadcast? Is SVD deterministic? + """ + from simsopt.field import ReducedCoilSet + thiscoilset = self.coilset + if type(self.coilset) is ReducedCoilSet: + thiscoilset = self.coilset.coilset + + def target_function(coilset): + cnf = CoilNormalField(coilset) # dummy CoilNormalField to evaluate the vnc and vnc + output = cnf.vns.ravel()[coilset.surface.ntor+1:] #remove leading zeros + if not coilset.surface.stellsym: + output = np.append(output, cnf.vnc.ravel()[coilset.surface.ntor:]) #remove leading zeros + return np.ravel(output) + + reduced_coilset = thiscoilset.reduce(target_function, nsv=nsv) + logger.info(f'CoilNormalField replaced Coilset with ReducedCoilsSet with {reduced_coilset.nsv} singular values') + logger.debug('first right-singular vector: ') + logger.debug(reduced_coilset.rsv[0]) + logger.debug('singular values: ') + logger.debug(reduced_coilset._s_diag) + self.coilset = reduced_coilset # using setter; replaces parent + + def recompute_bell(self, parent=None): # Should parent be CoilSet? + self._vnc = None + self._vns = None + + def get_vns(self, m, n): + self.check_mn(m, n) + index = self.get_index_in_array(m, n) + return self.vns[index[0], index[1]] # calls cache'd getter + + def get_vnc(self, m, n): + self.check_mn(m, n) + index = self.get_index_in_array(m, n) + return self.vnc[index[0], index[1]] # calls cache'd getter + + def set_vns(self, *args, **kwargs): + raise AttributeError('you cannot set vns, the coils do this!') + + def set_vnc(self, *args, **kwargs): + raise AttributeError('you cannot set vnc, the coils do this!') + + def get_vns_asarray(self): + return self.vns + + def set_vns_asarray(self, *args, **kwargs): + raise AttributeError('you cannot set vns, the coils do this!') + + def get_vnc_asarray(self): + return self.vnc + + def set_vnc_asarray(self, *args, **kwargs): + raise AttributeError('you cannot set vnc, the coils do this!') + + def get_vns_vnc_asarray(self): + return self.vns, self.vnc + + def set_vns_vnc_asarray(self, *args, **kwargs): + raise AttributeError('you cannot set fourier components, the coils do this!') + + def change_resolution(self, *args, **kwargs): + raise ValueError('CoilNormalField has no resolution, change parameters in its coilset') + + def fixed_range(self, *args, **kwargs): + raise ValueError('no sense in fixing anything in a CoilNormalField') + + def get_index_in_array(self, m, n, mpol=None, ntor=None): + """ + Returns position of mode (m,n) in vns or vnc array + + Args: + - m: poloidal mode number + - n: toroidal mode number (normalized by Nfp) + - mpol: resolution of dofs array. If None (by default), use self.mpol + - ntor: resolution of dofs array. If None (by default), use self.ntor + - even: set to True to get vnc. Default is False + """ + + if mpol is None: + mpol = self.mpol + if ntor is None: + ntor = self.ntor + + if m < 0 or m > mpol: + raise ValueError('m out of bound') + if abs(n) > ntor: + raise ValueError('n out of bound') + if m == 0 and n < 0: + raise ValueError('n has to be positive if m==0') + + return [m, n+ntor] + + def get_dofs(self, *args, **kwargs): + raise ValueError('CoilNormalField does not have its own degrees of freedom') + + def get_index_in_dofs(self, *args, **kwargs): + raise ValueError('CoilNormalField does not have its own degrees of freedom') + + def optimize_coils(self, targetvns, targetvnc=None, TARGET_LENGTH=1000, MAXITER=300): + r""" + Simple convenience function to + optimize the coils to match the target vns and vnc using a FOCUS-style algorithm. + + Uses the simplest FOCUS optimization consisting of only + the quadratic flux penalty and a length penalty. + + Args: + targetvns: The target odd fourier modes of :math:`\mathbf{B}\cdot\mathbf{\vec{n}}`. 2D array of size + (mpol+1)x(2ntor+1). + targetvnc: The target even fourier modes of :math:`\mathbf{B}\cdot\mathbf{\vec{n}}`. 2D array of size + (mpol+1)x(2ntor+1). Ignored if stellsym if True. + TARGET_LENGTH: The target length of the coils. Default is 1000. + MAXITER: The maximum number of iterations. Default is 1000. + returns: + res: the result object from scipy.optimize.minimize + """ + from scipy.optimize import minimize + if targetvnc is None: + targetvnc = np.zeros_like(targetvns) + BdotN_unnormalized = self.surface.inverse_fourier_transform_scalar(targetvns, targetvnc, normalization=(2*np.pi)**2, stellsym=self.stellsym) + target = -1 * BdotN_unnormalized / np.linalg.norm(self.surface.normal(), axis=-1) + JF = self.coilset.flux_penalty(target=target)\ + + self.coilset.length_penalty(TOTAL_LENGTH=TARGET_LENGTH, f='max') + + def fun(dofs): + JF.x = dofs + return JF.J(), JF.dJ() + + dofs = JF.x + + res = minimize(fun, dofs, jac=True, method='L-BFGS-B', + options={'maxiter': MAXITER, 'maxcor': 300, 'iprint': 5}, tol=1e-15) + print(f'the maximum difference between coil Vns and target Vns is: {np.max(np.abs(self.vns-targetvns))}') + print(f'The root mean squared difference between the Vns produced by the coils and the target is: {np.sqrt(np.mean((self.vns-targetvns)**2))}') + return res + diff --git a/src/simsopt/field/selffield.py b/src/simsopt/field/selffield.py index 8dc2e17de..ab6fff0c0 100755 --- a/src/simsopt/field/selffield.py +++ b/src/simsopt/field/selffield.py @@ -7,8 +7,6 @@ from scipy import constants import numpy as np import jax.numpy as jnp -from .biotsavart import BiotSavart -from .coil import Coil Biot_savart_prefactor = constants.mu_0 / (4 * np.pi) @@ -57,8 +55,8 @@ def B_regularized_singularity_term(rc_prime, rc_prime_prime, regularization): def B_regularized_pure(gamma, gammadash, gammadashdash, quadpoints, current, regularization): # The factors of 2π in the next few lines come from the fact that simsopt # uses a curve parameter that goes up to 1 rather than 2π. - phi = quadpoints * 2 * jnp.pi - rc = gamma + phi = quadpoints * 2 * jnp.pi + rc = gamma rc_prime = gammadash / 2 / jnp.pi rc_prime_prime = gammadashdash / 4 / jnp.pi**2 n_quad = phi.shape[0] @@ -71,6 +69,11 @@ def B_regularized_pure(gamma, gammadash, gammadashdash, quadpoints, current, reg second_term = jnp.cross(rc_prime_prime, rc_prime)[:, None, :] * ( 0.5 * cos_fac / (cos_fac * jnp.sum(rc_prime * rc_prime, axis=1)[:, None] + regularization)**1.5)[:, :, None] integral_term = dphi * jnp.sum(first_term + second_term, 1) + # print(jnp.any(jnp.isnan(first_term))) + # print(jnp.any(jnp.isnan(second_term))) + # print(jnp.any(jnp.isnan(integral_term))) + # print(jnp.any(jnp.isnan(analytic_term))) + # print(jnp.max(jnp.abs(first_term))) # print(jnp.max(jnp.abs(second_term))) # print(jnp.max(jnp.abs(integral_term))) @@ -96,81 +99,3 @@ def B_regularized_circ(coil, a): def B_regularized_rect(coil, a, b): return B_regularized(coil, regularization_rect(a, b)) - - -def G(x, y): - """Auxiliary function for the calculation of the internal field of a rectangular crosssction""" - return y * jnp.arctan(x/y) + x/2*jnp.log(1 + y**2 / x**2) - - -def K(u, v, kappa_1, kappa_2, p, q, a, b): - """Auxiliary function for the calculation of the internal field of a rectangular crosssction""" - K = - 2 * u * v * (kappa_1 * q - kappa_2 * p) * jnp.log(a * u**2 / b + b * v**2 / a) + \ - (kappa_2 * q - kappa_1 * p) * (a * u**2 / b + b * v**2 / a) * jnp.log(a * u**2 / b + b * v**2 / a) + \ - 4 * a * u**2 * kappa_2 * p / b * \ - jnp.arctan(b * v / a * u) - 4 * b * v**2 * \ - kappa_1 * q / a * jnp.arctan(a * u / b * v) - return K - - -def local_field(coil, rho, theta, a=0.05): - """Calculate the variation of the field on the cross section""" - I = coil._current.current - phi = coil.curve.quadpoints - N_phi = phi.shape[0] - _, n, b = coil.curve.frenet_frame() - kappa = coil.curve.kappa() - b_loc = jnp.zeros((N_phi, 3)) - - for i in range(N_phi): - b_loc[i] = 2*rho/a * (-n[i]*jnp.sin(theta) + b[i]*jnp.cos(theta)) + kappa[i] / 2 * (-rho**2 / 2 * jnp.sin(2*theta) * n[i] + - (1.5-rho**2 + rho**2 / 2 * jnp.cos(2*theta)) * b[i]) - - b_loc *= constants.mu_0 * I / 4 / jnp.pi - return b_loc - - -def local_field_rect(coil, u, v, a, b): - """Calculate the variation of the field on a rectangular cross section""" - I = coil._current.current - phi = coil.curve.quadpoints - N_phi = phi.shape[0] - _, n, b = coil.curve.frenet_frame() - kappa = coil.curve.kappa() - - kappa_1 = kappa - kappa_2 = kappa - p = n - q = b - - b_kappa = jnp.zeros((N_phi, 3)) - b_b = jnp.zeros((N_phi, 3)) - b_0 = jnp.zeros((N_phi, 3)) - - for i in range(N_phi): - b_b.at[i].set(kappa[i] * b[i] / 2 * - (4 + 2*jnp.log(2) + jnp.log(rectangular_xsection_delta(a, b)))) - b_kappa.at[i].set(1 / 16 * (K(u - 1, v - 1, kappa_1[i], kappa_2[i], p[i], q[i], a, b) + K(u + 1, v + 1, kappa_1[i], kappa_2[i], p[i], q[i], a, b) - - K(u - 1, v + 1, kappa_1[i], kappa_2[i], p[i], q[i], a, b) - K(u + 1, v - 1, kappa_1[i], kappa_2[i], p[i], q[i], a, b))) - b_0.at[i].set(1 / (a * b) * ((G(b * (v - 1), a * (u - 1)) + G(b * (v + 1), a * (u + 1)) - G(b * (v + 1), a * (u - 1)) - G(b * (v - 1), a * (u + 1))) * q - - (G(a * (u - 1), b * (v - 1)) + G(a * (u + 1), b * (v + 1)) - G(a * (u - 1), b * (v + 1)) - G(a * (u - 1), b * (v + 1))) * p)) - - b_loc = constants.mu_0 * I / 4 / jnp.pi * (b_b + b_kappa + b_0) - - return b_loc - - -def field_from_other_coils(coil, coils): - """field on one coil from the other coils""" - gamma = coil.curve.gamma() - b_ext = BiotSavart(coils) - b_ext.set_points(gamma) - return b_ext.B() - - -def field_from_other_coils_pure(gamma, curves, currents): - """field on one coil from the other coils""" - coils = [Coil(curve, current) for curve, current in zip(curves, currents)] - b_ext = BiotSavart(coils) - b_ext.set_points(gamma) - return b_ext.B() \ No newline at end of file diff --git a/src/simsopt/geo/curve.py b/src/simsopt/geo/curve.py index 66fe67be1..9bc79341e 100644 --- a/src/simsopt/geo/curve.py +++ b/src/simsopt/geo/curve.py @@ -444,6 +444,9 @@ def __init__(self, quadpoints, gamma_pure, **kwargs): self.dgammadashdashdash_by_dcoeff_jax = jit(jacfwd(self.gammadashdashdash_jax)) self.dgammadashdashdash_by_dcoeff_vjp_jax = jit(lambda x, v: vjp(self.gammadashdashdash_jax, x)[1](v)[0]) + self.center_jax = jit(lambda x, v: self.center(x, v)) + self.dcenter_dgamma = jit(jacfwd(self.center_jax, argnums=0)) + self.dcenter_dgammadash = jit(jacfwd(self.center_jax, argnums=1)) self.kappa_pure = kappa_pure self.kappa_jax = jit(lambda x, v: kappa_pure(x, v)) self.kappa_impl_jax = jit(lambda x, v: kappa_pure(x, v)) @@ -467,6 +470,15 @@ def gamma_impl(self, gamma, quadpoints): def incremental_arclength_pure(self, dofs): gammadash = self.gammadash_jax(dofs) return jnp.linalg.norm(gammadash, axis=1) + + @property + def qps(self): + return self.quadpoints + + @qps.setter + def qps(self, new_quadpoints): + self.quadpoints = new_quadpoints + self.numquadpoints = len(new_quadpoints) def incremental_arclength(self): return self.incremental_arclength_jax(self.get_dofs()) @@ -691,6 +703,14 @@ def num_dofs(self): This function returns the number of dofs associated to the curve. """ return self.curve.num_dofs() + + def center(self, gamma, gammadash): + # Compute the centroid of the curve + quadpoints = self.quadpoints + N = len(quadpoints) + arclength = jnp.linalg.norm(gammadash, axis=-1) + barycenter = jnp.sum(gamma * arclength[:, None], axis=0) / N / np.pi + return barycenter def gamma_impl(self, gamma, quadpoints): r""" diff --git a/src/simsopt/geo/curveplanarfourier.py b/src/simsopt/geo/curveplanarfourier.py index 491ee79d8..7c5d16143 100644 --- a/src/simsopt/geo/curveplanarfourier.py +++ b/src/simsopt/geo/curveplanarfourier.py @@ -77,6 +77,14 @@ def set_dofs(self, dofs): self.local_x = dofs sopp.CurvePlanarFourier.set_dofs(self, dofs) + def center(self, gamma, gammadash): + # Compute the centroid of the curve + quadpoints = self.quadpoints + N = len(quadpoints) + arclength = jnp.linalg.norm(gammadash, axis=-1) + barycenter = jnp.sum(gamma * arclength[:, None], axis=0) / N / np.pi + return barycenter + def jaxplanarcurve_pure(dofs, quadpoints, order): coeffs = dofs[:2 * order + 1] q = dofs[2 * order + 1: 2 * order + 5] @@ -143,6 +151,16 @@ def set_dofs_impl(self, dofs): """ self.dof_list = np.array(dofs) + def center(self, gamma, gammadash): + # Compute the centroid of the curve + quadpoints = self.quadpoints + N = len(quadpoints) + arclength = jnp.linalg.norm(gammadash, axis=-1) + barycenter = jnp.sum(gamma * arclength[:, None], axis=0) / N / np.pi + return barycenter + + def set_quadpoints(self, quadpoints): + self.quadpoints = quadpoints # class JaxCurvePlanarFourier(JaxCurve): diff --git a/src/simsopt/geo/curvexyzfourier.py b/src/simsopt/geo/curvexyzfourier.py index 776c5bc47..f41142135 100644 --- a/src/simsopt/geo/curvexyzfourier.py +++ b/src/simsopt/geo/curvexyzfourier.py @@ -188,6 +188,13 @@ def load_curves_from_makegrid_file(filename: str, order: int, ppp=20): coils[ic].local_x = np.concatenate(dofs) return coils + def center(self, gamma, gammadash): + # Compute the centroid of the curve + quadpoints = self.quadpoints + N = len(quadpoints) + arclength = jnp.linalg.norm(gammadash, axis=-1) + barycenter = jnp.sum(gamma * arclength[:, None], axis=0) / N / np.pi + return barycenter def jaxfouriercurve_pure(dofs, quadpoints, order): k = jnp.shape(dofs)[0]//3 @@ -254,3 +261,11 @@ def set_dofs_impl(self, dofs): counter += 1 self.coefficients[i][2*j] = dofs[counter] counter += 1 + + def center(self, gamma, gammadash): + # Compute the centroid of the curve + quadpoints = self.quadpoints + N = len(quadpoints) + arclength = jnp.linalg.norm(gammadash, axis=-1) + barycenter = jnp.sum(gamma * arclength[:, None], axis=0) / N / np.pi + return barycenter \ No newline at end of file diff --git a/src/simsopt/geo/jit.py b/src/simsopt/geo/jit.py index 3f888f2f9..796fc150e 100644 --- a/src/simsopt/geo/jit.py +++ b/src/simsopt/geo/jit.py @@ -3,8 +3,8 @@ from .config import parameters -def jit(fun): +def jit(fun, **args): if parameters['jit']: - return jaxjit(fun) + return jaxjit(fun, **args) else: return fun diff --git a/src/simsopt/geo/surface.py b/src/simsopt/geo/surface.py index 081aa5633..11a16eaec 100644 --- a/src/simsopt/geo/surface.py +++ b/src/simsopt/geo/surface.py @@ -841,6 +841,22 @@ def interpolate_on_arclength_grid(self, function, theta_evaluate): function_interpolated[iphi, :] = f(theta_evaluate[iphi, :]) return function_interpolated + + @property + def deduced_range(self): + """ + The quadpoints of a surface can be anything, but are often set to + 'full torus', 'field period' or 'half period'. + Since this is not stored in the object, but often useful to know + this function deduces the range from the quadpoints + """ + if np.isclose(self.quadpoints_phi[-1], 1-1/len(self.quadpoints_phi), atol=1e-10): + return Surface.RANGE_FULL_TORUS + elif self.quadpoints_phi[0] == 0: + return Surface.RANGE_FIELD_PERIOD + else: + return Surface.RANGE_HALF_PERIOD + diff --git a/src/simsopt/geo/surfacerzfourier.py b/src/simsopt/geo/surfacerzfourier.py index f66a5c304..f816a9898 100644 --- a/src/simsopt/geo/surfacerzfourier.py +++ b/src/simsopt/geo/surfacerzfourier.py @@ -421,6 +421,76 @@ def from_pyQSC(cls, stel: Qsc, r: float = 0.1, ntheta=20, mpol=10, ntor=20, **kw surf.local_full_x = surf.get_dofs() return surf + + def copy(self, **kwargs): + """ + return a copy of the surfaceRZFourier object, but with the specified + attributes changed. + key-worded arguments accepted: + - ntheta: number of quadrature points in the theta direction + - nphi: number of quadrature points in the phi direction + - mpol: number of poloidal Fourier modes for the surface + - ntor: number of toroidal Fourier modes for the surface + - nfp: number of field periods + - stellsym: whether the surface is stellarator-symmetric + - quadpoints_theta: theta grid points + - quadpoints_phi: phi grid points + + + """ + otherntheta = self.quadpoints_theta.size + othernphi = self.quadpoints_phi.size + + ntheta = kwargs.pop("ntheta", otherntheta) + nphi = kwargs.pop("nphi", othernphi) + grid_range = kwargs.pop("range", None) + mpol = kwargs.pop("mpol", self.mpol) + ntor = kwargs.pop("ntor", self.ntor) + nfp = kwargs.pop("nfp", self.nfp) + stellsym = kwargs.pop("stellsym", self.stellsym) + quadpoints_theta = kwargs.pop("quadpoints_theta", None) + quadpoints_phi = kwargs.pop("quadpoints_phi", None) + + # recalculate the quadpoints if necessary (grid_range is not stored in the + # surface object, so assume that if it is given, the gridpoints should be + # recalculated to the specified size) + if quadpoints_theta is None and quadpoints_phi is None: + if ntheta is not otherntheta or nphi is not othernphi or grid_range is not None: + kwargs["quadpoints_phi"], kwargs["quadpoints_theta"] = Surface.get_quadpoints( + ntheta=ntheta, nphi=nphi, nfp=self.nfp, range=grid_range) + else: + kwargs["quadpoints_phi"] = self.quadpoints_phi + kwargs["quadpoints_theta"] = self.quadpoints_theta + else: + if quadpoints_theta is None: + if ntheta is not otherntheta or grid_range is not None: + kwargs["quadpoints_theta"] = Surface.get_theta_quadpoints(ntheta) + else: + kwargs["quadpoints_theta"] = self.quadpoints_theta + else: + kwargs["quadpoints_theta"] = quadpoints_theta + if quadpoints_phi is None: + if nphi is not othernphi or grid_range is not None: + kwargs["quadpoints_phi"] = Surface.get_phi_quadpoints(nphi, range=grid_range, nfp=nfp) + else: + kwargs["quadpoints_phi"] = self.quadpoints_phi + else: + kwargs["quadpoints_phi"] = quadpoints_phi + # create new surface in old resolution + surf = SurfaceRZFourier(mpol=self.mpol, ntor=self.ntor, nfp=nfp, stellsym=stellsym, + **kwargs) + surf.rc[:, :] = self.rc + surf.zs[:, :] = self.zs + if not self.stellsym: + surf.rs[:, :] = self.rs + surf.zc[:, :] = self.zc + # set to the requested resolution + surf.change_resolution(mpol, ntor) + surf.local_full_x = surf.get_dofs() + return surf + + + def change_resolution(self, mpol, ntor): """ @@ -705,7 +775,7 @@ def fourier_transform_scalar(self, scalar, mpol=None, ntor=None, normalization=N if not stellsym: A_mnc[0, ntor] = np.sum(scalar) / (ntheta_grid * nphi_grid) if normalization is not None: - if isinstance(normalization, float): + if not isinstance(normalization, float): raise ValueError("normalization must be a float") A_mns = A_mns / normalization A_mnc = A_mnc / normalization diff --git a/tests/field/test_coilset.py b/tests/field/test_coilset.py new file mode 100644 index 000000000..4032c3d61 --- /dev/null +++ b/tests/field/test_coilset.py @@ -0,0 +1,295 @@ +import unittest +import os +from simsopt.field.coilset import CoilSet, ReducedCoilSet +from simsopt.field.coil import Coil +from simsopt.geo import SurfaceRZFourier, CurveLength +from simsopt.configs import get_ncsx_data +import numpy as np +from monty.tempfile import ScratchDir +from simsopt import load, save + + + +class TestCoilSet(unittest.TestCase): + def setUp(self): + # Create a CoilSet object for testing + self.coilset = CoilSet() + + def test_default_properties(self): + coilset = CoilSet() + self.assertEqual(len(coilset.base_coils), 10) + self.assertEqual(len(coilset.coils), 20) + + def test_wrong_init(self): + curves, currents, _ = get_ncsx_data() + coils = [Coil(curve, current) for curve, current in zip(curves, currents)] + with self.assertRaises(ValueError): + CoilSet(coils = coils) + + def test_for_surface_classmethod(self): + s = SurfaceRZFourier(nfp=2, mpol=3, ntor=3) + coilset1 = CoilSet.for_surface(s, current_constraint='fix_all') + for coil in coilset1.base_coils: + self.assertEqual(coil.current.dof_size, 0) + coilset2 = CoilSet.for_surface(s, current_constraint='fix_one') + self.assertEqual(coilset2.base_coils[0].current.dof_size, 0) + for coil in coilset2.base_coils[1:]: + self.assertEqual(coil.current.dof_size, 1) + coilset3 = CoilSet.for_surface(s, current_constraint='free_all') + for coil in coilset3.base_coils: + self.assertEqual(coil.current.dof_size, 1) + + + + def test_to_from_mgrid(self): + order = 25 + ppp = 10 + with ScratchDir("."): + self.coilset.to_makegrid_file("coils.file_to_load") + loaded_coilset = CoilSet.from_makegrid_file("coils.file_to_load", self.coilset.surface, order=order, ppp=ppp) + + np.random.seed(1) + + points = np.asarray(17 * [[0.9, 0.4, -0.85]]) + points += 0.01 * (np.random.rand(*points.shape) - 0.5) + self.coilset.bs.set_points(points) + loaded_coilset.bs.set_points(points) + + B = self.coilset.bs.B() + loaded_B = loaded_coilset.bs.B() + np.testing.assert_allclose(B, loaded_B) + + + def test_surface(self): + # Test the surface property + surface = self.coilset.surface + self.assertIsNotNone(surface) + + def test_surface_setter_different_nfp(self): + with self.assertRaises(ValueError): + self.coilset.surface = SurfaceRZFourier(nfp=3) + + def test_surface_setter_nonstellsym(self): + # Test the surface setter method + first_surface = SurfaceRZFourier(nfp=2, stellsym=True) + coilset = CoilSet(surface=first_surface) + self.assertEqual(coilset.surface.deduced_range, SurfaceRZFourier.RANGE_HALF_PERIOD) + second_surface = SurfaceRZFourier(nfp=2, stellsym=False) + coilset.surface = second_surface + self.assertEqual(coilset.surface.deduced_range, SurfaceRZFourier.RANGE_FIELD_PERIOD) + + def test_surface_setter_stellsym(self): + # Test the surface setter method + new_surface = SurfaceRZFourier(nfp=1, stellsym=True) + self.coilset.surface = new_surface + self.assertEqual(self.coilset.surface.deduced_range, SurfaceRZFourier.RANGE_HALF_PERIOD) + + def test_surface_setter_field_period(self): + s = SurfaceRZFourier(nfp=2, stellsym=False).copy(range='half period') + coilset = CoilSet(surface=s) + self.assertEqual(coilset.surface.deduced_range, SurfaceRZFourier.RANGE_FIELD_PERIOD) + s2 = SurfaceRZFourier(nfp=2, stellsym=True).copy(range='field period') + coilset = CoilSet(surface=s2) + self.assertEqual(coilset.surface.deduced_range, SurfaceRZFourier.RANGE_HALF_PERIOD) + + def test_base_coils(self): + # Test the base_coils property + base_coils = self.coilset.base_coils + self.assertIsNotNone(base_coils) + + + def test_base_coils_setter(self): + # Test the base_coils setter method + curves, currents, _ = get_ncsx_data() + new_base_coils = [Coil(curve, current) for curve, current in zip(curves, currents)] + self.coilset.base_coils = new_base_coils + self.assertEqual(self.coilset.base_coils, new_base_coils) + + def test_reduce(self): + # Test the reduce method on simple collocation points + copysurf = self.coilset.surface.copy(quadpoints_theta=np.random.random(5), quadpoints_phi=np.random.random(5)) + gammas = copysurf.gamma().reshape(-1, 3) + normals = copysurf.normal().reshape(-1, 3) + def target_function(coilset): + coilset.bs.set_points(gammas) + return np.sum(coilset.bs.B() * normals, axis=-1) + reduced_coilset = self.coilset.reduce(target_function, nsv='nonzero') + self.assertIsNotNone(reduced_coilset) + + + def test_flux_penalty(self): + # Test the flux_penalty function + penalty = self.coilset.flux_penalty() + self.assertIsNotNone(penalty.J()) + + def test_length_penalty(self): + # Test the length_penalty function + TOTAL_LENGTH = 50 + penalty = self.coilset.length_penalty(TOTAL_LENGTH, f='identity') + self.assertIsNotNone(penalty.J()) + + def test_cc_distance_penalty(self): + # Test the coil-coil distance penalty function + DISTANCE_THRESHOLD = 0.1 + penalty = self.coilset.cc_distance_penalty(DISTANCE_THRESHOLD) + self.assertIsNotNone(penalty.J()) + + def test_cs_distance_penalty(self): + # Test the coil-surface distance penalty function + DISTANCE_THRESHOLD = 0.1 + penalty = self.coilset.cs_distance_penalty(DISTANCE_THRESHOLD) + self.assertIsNotNone(penalty.J()) + + def test_lp_curvature_penalty(self): + # Test the lp_curvature_penalty function + CURVATURE_THRESHOLD = 0.1 + penalty = self.coilset.lp_curvature_penalty(CURVATURE_THRESHOLD) + self.assertIsNotNone(penalty.J()) + + def test_meansquared_curvature_penalty(self): + # Test the meansquared_curvature_penalty function + penalty = self.coilset.meansquared_curvature_penalty() + self.assertIsNotNone(penalty.J()) + + def test_meansquared_curvature_threshold_penalty(self): + # Test the meansquared_curvature_penalty function + penalty = self.coilset.meansquared_curvature_threshold(CURVATURE_THRESHOLD=0.1) + self.assertIsNotNone(penalty.J()) + + def test_arc_length_variation_penalty(self): + # Test the arc_length_variation_penalty function + penalty = self.coilset.arc_length_variation_penalty() + self.assertIsNotNone(penalty.J()) + + def test_total_length_penalty(self): + # Test the total_length function + length = self.coilset.total_length_penalty() + self.assertIsNotNone(length.J()) + self.assertTrue(CurveLength(self.coilset.coils[0].curve).J() < length.J()) + + def test_total_length_property(self): + # Test the total_length property + length = self.coilset.total_length + self.assertIsNotNone(length) + self.assertTrue(length > 0) + + def test_coilset_to_vtk(self): + with ScratchDir("."): + self.coilset.to_vtk("test") + self.assertTrue(os.path.exists("test_coils.vtu")) + self.assertTrue(os.path.exists("test_surface.vts")) + with ScratchDir("."): + self.coilset.to_vtk("test2", add_biotsavart=False) + self.assertTrue(os.path.exists("test2_coils.vtu")) + self.assertTrue(os.path.exists("test2_surface.vts")) + + def test_save_load(self): + with ScratchDir("."): + save(self.coilset, "test.json") + loaded_coilset = load("test.json") + self.assertEqual(self.coilset.total_length, loaded_coilset.total_length) + + def test_dof_orders(self): + # Test the dof_orders property + dof_orders = self.coilset.get_dof_orders() + self.assertIsNotNone(dof_orders) + self.assertTrue(len(dof_orders) == self.coilset.dof_size) + +#### Inherit from TestCoilSet (all tests run exept those overloaded) +class TestReducedCoilSet(TestCoilSet): + def setUp(self): + # Create a ReducedCoilSet object using collocation points + self.unreduced_coilset = CoilSet() + copysurf = self.unreduced_coilset.surface.copy(quadpoints_theta=np.random.random(7), quadpoints_phi=np.random.random(7)) + gammas = copysurf.gamma().reshape(-1, 3) + normals = copysurf.normal().reshape(-1, 3) + def target_function(coilset): + coilset.bs.set_points(gammas) + return np.sum(coilset.bs.B() * normals, axis=-1) + self.test_target_function = target_function + self.coilset = ReducedCoilSet.from_function(self.unreduced_coilset, target_function, nsv='nonzero') + + + def test_partially_empty_init(self): + #also test rebasing with none target function + reduced_coilset = ReducedCoilSet() + self.assertIsNotNone(reduced_coilset) + with self.assertRaises(ValueError): + reduced_coilset.recalculate_reduced_basis() #target function not set; fail + reduced_coilset2 = ReducedCoilSet(self.unreduced_coilset) + self.assertIsNotNone(reduced_coilset2) + self.assertEqual(len(reduced_coilset2.x), len(self.unreduced_coilset.x)) + reduced_coilset3 = ReducedCoilSet(self.unreduced_coilset, nsv=10) + self.assertEqual(len(reduced_coilset3.x), 10) + with self.assertRaises(ValueError): + ReducedCoilSet(u_matrix=np.random.random((20,20))) + with self.assertRaises(ValueError): + ReducedCoilSet(self.unreduced_coilset, nsv=1000) + with self.assertRaises(TypeError): + ReducedCoilSet(self.unreduced_coilset, nsv=np.pi) + + def test_nsv_setter(self): + self.coilset.nsv = 10 + self.assertEqual(self.coilset.nsv, 10) + self.assertEqual(len(self.coilset.x), 10) + self.coilset.recalculate_reduced_basis() + self.assertEqual(len(self.coilset.x), 10) + with self.assertRaises(ValueError): + self.coilset.nsv = 1000 + with self.assertRaises(TypeError): + self.coilset.nsv = np.pi + self.coilset.nsv = 'nonzero' + + def test_surface_setter(self): + with self.assertRaises(ValueError): + self.coilset.surface = SurfaceRZFourier(nfp=3, stellsym=False) + self.coilset.surface = SurfaceRZFourier(nfp=1, stellsym=True) + + def test_recalculate_reduced_basis(self): + reduced_coilset = ReducedCoilSet() + reduced_coilset.recalculate_reduced_basis(self.test_target_function) + # test if the reduced basis has the correct length + self.assertEqual(len(reduced_coilset.x), len(self.test_target_function(self.unreduced_coilset))) + + def test_dof_orders(self): + with self.assertRaises(ValueError): + self.coilset.get_dof_orders() + + def test_taylor_test_of_value_decomposition(self): + # test that small displacements along the singular vectors give the correct change in the target function evaluation. + self.coilset.nsv = 10 + # calculate what the normal field on the collocation points is. + initial_function_value = np.copy(self.test_target_function(self.unreduced_coilset)) + initial_coilset_x = np.copy(self.unreduced_coilset.x) + epsilon = 1e-6 + for index, (rsv, lsv, singular_value) in enumerate(zip(self.coilset.rsv, self.coilset.lsv, self.coilset.singular_values)): + newx = np.zeros_like(self.coilset.x) + newx[index] = epsilon + self.coilset.x = newx + # test if coilset has been updated with rsv + np.testing.assert_allclose(self.unreduced_coilset.x, initial_coilset_x+epsilon*rsv, atol = 1e-6) + + new_function_value = self.test_target_function(self.coilset.coilset) + function_diff = new_function_value - initial_function_value + np.testing.assert_allclose(lsv, function_diff/(epsilon*singular_value), atol=1e-4) + + def test_wrong_setters(self): + with self.assertRaises(ValueError): + self.coilset.rsv = np.random.random(10) + with self.assertRaises(ValueError): + self.coilset.lsv = np.random.random(10) + with self.assertRaises(ValueError): + self.coilset.singular_values = np.random.random(10) + with self.assertRaises(ValueError): + self.coilset.coilset = self.unreduced_coilset + with self.assertRaises(ValueError): + self.coilset.coils = self.unreduced_coilset.coils + with self.assertRaises(ValueError): + self.set_dofs(np.random.random(self.coilset.nsv+1)) + + + + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/tests/field/test_jaxbiotsavart.py b/tests/field/test_jaxbiotsavart.py index 58c9f616d..a78d4fb7c 100644 --- a/tests/field/test_jaxbiotsavart.py +++ b/tests/field/test_jaxbiotsavart.py @@ -563,6 +563,7 @@ def test_net_force_and_torque_calculation_far_field(self): R2 = R1 # a = 1e-5 points = np.array([[0.0, 0.0, Z1], [0.0, 0.0, Z2]]) + print(points.shape) # curve0 = get_curve() curve0 = JaxCurvePlanarFourier(200, 0) dofs = np.zeros(8) @@ -570,6 +571,7 @@ def test_net_force_and_torque_calculation_far_field(self): dofs[1:5] = (np.random.rand(4) - 0.5) dofs[5:9] = points[0, :] curve0.set_dofs(dofs) + print('center = ', curve0.center(curve0.gamma(), curve0.gammadash())) q_norm = dofs[1:5] / np.linalg.norm(dofs[1:5]) normal_orig = np.array([0, 0, 1]).T rot_mat = np.array( @@ -590,6 +592,7 @@ def test_net_force_and_torque_calculation_far_field(self): dofs[1:5] = (np.random.rand(4) - 0.5) * 2 * np.pi dofs[5:9] = points[1, :] curve1.set_dofs(dofs) + print('center = ', curve1.center(curve1.gamma(), curve1.gammadash())) q_norm = dofs[1:5] / np.linalg.norm(dofs[1:5]) normal_orig = np.array([0, 0, 1]).T rot_mat = np.array( @@ -604,7 +607,9 @@ def test_net_force_and_torque_calculation_far_field(self): (1.0 - 2 * (q_norm[1] ** 2 + q_norm[2] ** 2))]]) normal1 = rot_mat @ normal_orig current1 = Current(I2) - bs = JaxBiotSavart([Coil(curve0, current0), Coil(curve1, current1)]) + coil1 = Coil(curve0, current0) + coil2 = Coil(curve1, current1) + bs = JaxBiotSavart([coil1, coil2]) # alphas = np.zeros(ncoils) # (np.random.rand(ncoils) - 0.5) * np.pi # deltas = np.zeros(ncoils) # (np.random.rand(ncoils) - 0.5) * 2 * np.pi diff --git a/tests/field/test_mgrid.py b/tests/field/test_mgrid.py index f5b9b1c2e..057acf50f 100644 --- a/tests/field/test_mgrid.py +++ b/tests/field/test_mgrid.py @@ -1,9 +1,8 @@ #!/usr/bin/env python import unittest -import os from pathlib import Path -import tempfile +from monty.tempfile import ScratchDir import numpy as np from scipy.io import netcdf_file @@ -58,8 +57,8 @@ def test_add_field_cylinder(self): def test_write(self): mgrid = MGrid.from_file(test_file) - with tempfile.TemporaryDirectory() as tmpdir: - filename = Path(tmpdir) / 'mgrid.test.nc' + with ScratchDir("."): + filename = 'mgrid.test.nc' mgrid.write(filename) with netcdf_file(filename, mmap=False) as f: @@ -92,10 +91,8 @@ def test_free_boundary_vmec(self): bs = BiotSavart(coils) eq = Vmec(input_file) nphi = 24 - original_directory = os.getcwd() - with tempfile.TemporaryDirectory() as tmpdir: - os.chdir(tmpdir) # Use temporary directory for vmec files - filename = Path(tmpdir) / "mgrid.bfield.nc" + with ScratchDir("."): + filename = "mgrid.bfield.nc" bs.to_mgrid( filename, nphi=nphi, @@ -127,6 +124,3 @@ def test_free_boundary_vmec(self): assert eq.wout.fsqz < ftol assert eq.wout.ier_flag == 0 - # If we do not change back to the original directory, so the current - # directory no longer exists, then later tests involving get_*_data will fail. - os.chdir(original_directory) diff --git a/tests/field/test_normal_field.py b/tests/field/test_normal_field.py index c17ee816b..d5152c283 100644 --- a/tests/field/test_normal_field.py +++ b/tests/field/test_normal_field.py @@ -1,11 +1,15 @@ import os import unittest import logging +import re import numpy as np +from monty.tempfile import ScratchDir from simsopt._core.util import DofLengthMismatchError -from simsopt.field import NormalField +from simsopt.field import NormalField, CoilNormalField, CoilSet, Coil, Current +from simsopt.mhd import Spec +from simsopt.geo import SurfaceRZFourier try: import py_spec @@ -24,12 +28,12 @@ def test_initialize_normal_field_from_spec(self): Test the initialization of an instance of NormalField using a SPEC input file """ - filename = os.path.join(TEST_DIR, 'Dommaschk.sp') + filename = os.path.join(TEST_DIR, "Dommaschk.sp") normal_field = NormalField.from_spec(filename) - self.assertAlmostEqual(normal_field.get_vns(m=3, n=-1), 1.71466651E-03) - self.assertAlmostEqual(normal_field.get_vns(m=5, n=1), -3.56991494E-05) + self.assertAlmostEqual(normal_field.get_vns(m=3, n=-1), 1.71466651e-03) + self.assertAlmostEqual(normal_field.get_vns(m=5, n=1), -3.56991494e-05) @unittest.skipIf(py_spec is None, "py_spec not found") def test_dofs(self): @@ -38,7 +42,7 @@ def test_dofs(self): """ # Init from SPEC input file - filename = os.path.join(TEST_DIR, 'Dommaschk.sp') + filename = os.path.join(TEST_DIR, "Dommaschk.sp") normal_field = NormalField.from_spec(filename) # Get dofs @@ -60,7 +64,7 @@ def test_dofs(self): normal_field.local_full_x = np.zeros((normal_field.ndof - 1,)) # ... Actually set them to zeros - normal_field.local_full_x = np.zeros((normal_field.ndof,)) + normal_field.local_full_x = np.zeros((normal_field.ndof,)) dofs = normal_field.local_full_x for el in dofs: @@ -122,22 +126,22 @@ def test_check_mn(self): @unittest.skipIf(py_spec is None, "py_spec not found") def test_make_names(self): """ - Test routine make_names. + Test routine make_names. We verify that first element is vns(0,1), and that there are the correct amount of elements """ # Init from SPEC input file - filename = os.path.join(TEST_DIR, 'Dommaschk.sp') + filename = os.path.join(TEST_DIR, "Dommaschk.sp") normal_field = NormalField.from_spec(filename) # Make names names = normal_field._make_names() # Check - self.assertTrue(names[0] == 'vns(0,1)') - self.assertTrue(names[-1] == 'vns(5,5)') + self.assertTrue(names[0] == "vns(0,1)") + self.assertTrue(names[-1] == "vns(5,5)") # Check length self.assertEqual(len(names), normal_field.ndof) @@ -157,7 +161,7 @@ def test_change_resolution(self): """ # 1. Init from SPEC input file - filename = os.path.join(TEST_DIR, 'Dommaschk.sp') + filename = os.path.join(TEST_DIR, "Dommaschk.sp") normal_field = NormalField.from_spec(filename) # 2. Save some selected non-zero harmonics @@ -196,8 +200,10 @@ def test_fixed_range(self): normal_field = NormalField(stellsym=True, mpol=6, ntor=4) # Check that dofs are free at initialization - [self.assertTrue(normal_field.is_free(ii)) - for ii in range(0, normal_field.ndof)] + [ + self.assertTrue(normal_field.is_free(ii)) + for ii in range(0, normal_field.ndof) + ] # Check fixed range normal_field.fixed_range(mmin=3, mmax=4, nmin=-2, nmax=1, fixed=True) @@ -212,30 +218,34 @@ def test_fixed_range(self): self.assertTrue(normal_field.is_fixed(ii)) else: self.assertTrue(normal_field.is_free(ii)) - + @unittest.skipIf(py_spec is None, "py_spec not found") def test_from_spec_object(self): """ test classmethod to instantiate from an existing SPEC object """ from simsopt.mhd import Spec + # Init from SPEC input file - filename = os.path.join(TEST_DIR, 'Dommaschk.sp') + filename = os.path.join(TEST_DIR, "Dommaschk.sp") spec = Spec(filename) normal_field = NormalField.from_spec_object(spec) normal_field_2 = NormalField.from_spec(filename) - self.assertAlmostEqual(normal_field.get_vns(m=3, n=-1), 1.71466651E-03) - self.assertAlmostEqual(normal_field.get_vns(m=5, n=1), -3.56991494E-05) + self.assertAlmostEqual(normal_field.get_vns(m=3, n=-1), 1.71466651e-03) + self.assertAlmostEqual(normal_field.get_vns(m=5, n=1), -3.56991494e-05) + + self.assertTrue( + np.allclose(normal_field.local_full_x, normal_field_2.local_full_x) + ) - self.assertTrue(np.allclose(normal_field.local_full_x, normal_field_2.local_full_x)) - @unittest.skipIf(py_spec is None, "py_spec not found") def test_fail_for_fixedb(self): """ test if instantiation fails if spec is not freeboundary """ from simsopt.mhd import Spec + # Init from SPEC input file spec = Spec() with self.assertRaises(ValueError): @@ -249,28 +259,14 @@ def test_wrong_index(self): with self.assertRaises(ValueError): normal_field.get_index_in_array(m=4, n=1) with self.assertRaises(ValueError): - normal_field.get_index_in_array(m=4, n=1, even=True) - with self.assertRaises(ValueError): normal_field.get_index_in_array(m=1, n=3) - with self.assertRaises(ValueError): - normal_field.get_index_in_array(m=1, n=3, even=True) with self.assertRaises(ValueError): normal_field.get_index_in_array(m=0, n=-1) - with self.assertRaises(ValueError): - normal_field.get_index_in_array(m=0, n=-1, even=True) with self.assertRaises(ValueError): normal_field.get_index_in_array(m=3, n=-3) - with self.assertRaises(ValueError): - normal_field.get_index_in_array(m=3, n=-3, even=True) with self.assertRaises(ValueError): normal_field.get_index_in_array(m=4, n=-3) - with self.assertRaises(ValueError): - normal_field.get_index_in_array(m=4, n=-3, even=True) - self.assertIsInstance(normal_field.get_index_in_array(m=0, n=0, even=True), list) - normal_field2 = NormalField(stellsym=True, mpol=3, ntor=2) - with self.assertRaises(ValueError): - normal_field2.get_index_in_array(m=0, n=0) - + def test_asarray_getter_setter_raises(self): """ test that if wrong bounds are given, an error is raised @@ -293,16 +289,17 @@ def test_asarray_getter_setter_raises(self): with self.assertRaises(ValueError): normal_field.set_vnc_asarray(np.zeros((4, 7)), mpol=2, ntor=3) with self.assertRaises(ValueError): - normal_field.set_vns_vnc_asarray(np.zeros((4, 7)), np.zeros((4, 7)), mpol=2, ntor=3) - - + normal_field.set_vns_vnc_asarray( + np.zeros((4, 7)), np.zeros((4, 7)), mpol=2, ntor=3 + ) + @unittest.skipIf(py_spec is None, "py_spec not found") def test_get_set_vns_vnc_asarray(self): """ - test the array-wise getter and setter functions for the + test the array-wise getter and setter functions for the Vns and Vnc arrays """ - filename = os.path.join(TEST_DIR, 'M16N08.sp') + filename = os.path.join(TEST_DIR, "M16N08.sp") normal_field = NormalField.from_spec(filename) vns = normal_field.get_vns_asarray() vnc = normal_field.get_vnc_asarray() @@ -330,20 +327,265 @@ def test_get_real_space_field(self): """ test the conversion to real space """ - filename = os.path.join(TEST_DIR, 'Dommaschk.sp') + filename = os.path.join(TEST_DIR, "Dommaschk.sp") normal_field = NormalField.from_spec(filename) real_space_field = normal_field.get_real_space_field() self.assertTrue(real_space_field is not None) - self.assertEqual(real_space_field.shape, (normal_field.surface.quadpoints_phi.size, normal_field.surface.quadpoints_theta.size)) - + self.assertEqual( + real_space_field.shape, + ( + normal_field.surface.quadpoints_phi.size, + normal_field.surface.quadpoints_theta.size, + ), + ) + def test_vns_vnc_setter(self): normal_field = NormalField() with self.assertRaises(AttributeError): normal_field.vns = 1 with self.assertRaises(AttributeError): normal_field.vnc = 1 + + +class CoilNormalFieldTests(unittest.TestCase): + def test_empty_init(self): + coil_normal_field = CoilNormalField() + self.assertIsNotNone(coil_normal_field) + + @unittest.skipIf(py_spec is None, "py_spec not found") + def test_spec_coil_correspondence_on_converged_output(self): + # Init from SPEC input file + with ScratchDir("."): + filespec = os.path.join(TEST_DIR, "M16N08.sp") + spec = Spec(filespec) + filecoils = os.path.join(TEST_DIR, "coils.M16N08") + coilset = CoilSet.from_makegrid_file(filecoils, spec.computational_boundary) + coil_normal_field = CoilNormalField(coilset) + for m in range(0, spec.mpol + 1): + for n in range(-spec.ntor, spec.ntor + 1): + if m == 0 and n <= 0: + continue + self.assertAlmostEqual( + coil_normal_field.get_vns(m=m, n=n), + spec.normal_field.get_vns(m=m, n=n), + msg=f"m={m}, n={n}", + places=6, + ) + self.assertAlmostEqual( + coil_normal_field.get_vnc(m=m, n=n), + spec.normal_field.get_vnc(m=m, n=n), + msg=f"m={m}, n={n}", + places=6, + ) + + def test_vns_vns_setter_raises(self): + coil_normal_field = CoilNormalField() + with self.assertRaises(AttributeError): + coil_normal_field.vns = coil_normal_field.vns + with self.assertRaises(AttributeError): + coil_normal_field.set_vns_asarray(coil_normal_field.vns) + with self.assertRaises(AttributeError): + coil_normal_field.set_vns(0,1, .1) + + with self.assertRaises(AttributeError): + coil_normal_field.vnc = coil_normal_field.vnc + with self.assertRaises(AttributeError): + coil_normal_field.set_vnc_asarray(coil_normal_field.vnc) + with self.assertRaises(AttributeError): + coil_normal_field.set_vnc(0,1, .1) + + with self.assertRaises(AttributeError): + coil_normal_field.set_vns_vnc_asarray(coil_normal_field.vns, coil_normal_field.vnc) + + @unittest.skipIf(py_spec is None, "py_spec not found") + def test_reduce_coilset(self): + """ + test if the coilset can be reduced, and + if the""" + with ScratchDir("."): + spec = Spec.default_freeboundary(copy_to_pwd=True) + spec.need_to_run_code = False + base_curves = CoilSet._circlecurves_around_surface( + spec.computational_boundary, coils_per_period=2, order=4 + ) + base_coils = [Coil(curve, Current(1e5)) for curve in base_curves] + for coil in base_coils: + coil.current.fix_all() # fix the currents + coilset = CoilSet( + surface=spec.computational_boundary, base_coils=base_coils + ) + cnf = CoilNormalField(coilset) + self.assertEqual(cnf.dof_names, coilset.dof_names) + self.assertEqual(cnf.dof_size, coilset.dof_size) + self.assertTrue(cnf.dof_names[0].startswith("CurveXYZFourier")) + spec.normal_field = cnf + # test if spec's recompute bell has rung: + self.assertTrue(spec.need_to_run_code) + spec.need_to_run_code = False + + cnf.reduce_coilset() + self.assertEqual(cnf.dof_size, spec.computational_boundary.ntor + spec.computational_boundary.mpol * (spec.computational_boundary.ntor * 2 + 1)) + self.assertTrue(cnf.dof_names[0].startswith("ReducedCoilSet")) + # test if specs recompute bell has rung: + self.assertTrue(spec.need_to_run_code) + # test if the degrees of freedom are al zeros: + np.testing.assert_equal(cnf.x, 0) + + # perform a taylor test; see if a small change in the first dof leads to the change in the fourier components predicted by the first l.s.v. + epsilon = 1e-5 + initial_vns = np.copy(cnf.vns) + dofs = np.zeros(cnf.dof_size) + dofs[0] = epsilon + cnf.x = dofs + vnsdiff = initial_vns - cnf.vns + vnsdiff_unraveled = vnsdiff.ravel()[ + cnf.coilset.surface.ntor + 1: + ] # hardcoded stellsym part + np.testing.assert_allclose( + cnf.coilset.lsv[0], + -1*vnsdiff_unraveled / (epsilon * cnf.coilset.singular_values[0]), + atol=1e-4, + ) + + def test_optimize_coils(self): + surface = SurfaceRZFourier(nfp=3, stellsym=False) + coilset = CoilSet(surface=surface) + cnf = CoilNormalField(coilset) + cnf.optimize_coils(targetvns=np.zeros_like(cnf.vns), targetvnc=np.zeros_like(cnf.vnc), TARGET_LENGTH=500, MAXITER=10) + + def test_inherited_methods_handled_correctly(self): + cnf = CoilNormalField() + with self.assertRaises(ValueError): + CoilNormalField.from_spec() + with self.assertRaises(ValueError): + CoilNormalField.from_spec_object() + with self.assertRaises(ValueError): + cnf.get_dofs() + with self.assertRaises(ValueError): + cnf.get_index_in_dofs() + with self.assertRaises(ValueError): + cnf.fixed_range() + with self.assertRaises(ValueError): + cnf.change_resolution() + + + def test_real_space_field(self): + surface = SurfaceRZFourier(nfp=3, stellsym=False, mpol=12, ntor=12) + base_curves = CoilSet._circlecurves_around_surface( + surface, coils_per_period=2, order=4 + ) + base_coils = [Coil(curve, Current(1e5)) for curve in base_curves] + coilset = CoilSet(base_coils=base_coils, surface=surface) + cnf = CoilNormalField(coilset) + real_space_field = cnf.get_real_space_field() #fourier-transform vns and vnc to real space + thetasize, phisize = cnf.surface.quadpoints_theta.size, cnf.surface.quadpoints_phi.size + directly_evaluated = np.copy(np.sum(cnf.coilset.bs.B().reshape(phisize, thetasize, 3) * cnf.surface.unitnormal(), axis=2)) #evaluate the field on the surface + np.testing.assert_allclose(real_space_field, directly_evaluated, atol=1e-3) + + def test_cache_vns(self): + cnf = CoilNormalField() + self.assertIsNone(cnf._vns) + tmp = cnf.vns + np.testing.assert_equal(cnf._vns, tmp) + self.assertIsNotNone(cnf._vnc) + + def test_cache_vnc(self): + cnf = CoilNormalField() + self.assertIsNone(cnf._vnc) + tmp = cnf.vnc + np.testing.assert_equal(cnf._vnc, tmp) + self.assertIsNotNone(cnf._vns) + + def test_nonstellsym_reduce(self): + """ + nonstellaratorsymmetric fields have both vns and vnc components on the field + and a different function is used to reduce the coilset. + This test ensures that the reduction works for non-stellaratorsymmetric fields + """ + surface = SurfaceRZFourier(nfp=2, stellsym=False, mpol=6, ntor=6) + base_curves = CoilSet._circlecurves_around_surface( + surface, coils_per_period=2, order=4 + ) + base_coils = [Coil(curve, Current(1e5)) for curve in base_curves] + coilset = CoilSet(base_coils=base_coils, surface=surface) + cnf = CoilNormalField(coilset) + cnf.reduce_coilset() + + initial_vns = np.copy(cnf.vns) + initial_vnc = np.copy(cnf.vnc) + + epsilon = 1e-5 + dofs = np.zeros(cnf.dof_size); dofs[0] = epsilon; cnf.x = dofs + + vnsdiff = initial_vns - cnf.vns + vncdiff = initial_vnc - cnf.vnc + + diff_unraveled = np.concatenate( + (vnsdiff.ravel()[ + cnf.coilset.surface.ntor + 1: + ], + vncdiff.ravel()[ + cnf.coilset.surface.ntor: + ] + ) + ) + np.testing.assert_allclose( + cnf.coilset.lsv[0], + -1*diff_unraveled / (epsilon * cnf.coilset.singular_values[0]), + atol=1e-4, + ) + def test_vns_vnc_asarray(self): + cnf = CoilNormalField() + vns = cnf.get_vns_asarray() + vnc = cnf.get_vnc_asarray() + self.assertIsNotNone(vns) + self.assertIsNotNone(vnc) + + def test_wrong_index(self): + """ + test if accessing a wrong m or n raises an error + """ + surface = SurfaceRZFourier(nfp=3, stellsym=False, mpol=3, ntor=2) + base_curves = CoilSet._circlecurves_around_surface( + surface, coils_per_period=2, order=4 + ) + base_coils = [Coil(curve, Current(1e5)) for curve in base_curves] + coilset = CoilSet(base_coils=base_coils, surface=surface) + cnf = CoilNormalField(coilset) + with self.assertRaises(ValueError): + cnf.get_index_in_array(m=4, n=1) + with self.assertRaises(ValueError): + cnf.get_index_in_array(m=1, n=3) + with self.assertRaises(ValueError): + cnf.get_index_in_array(m=0, n=-1) + with self.assertRaises(ValueError): + cnf.get_index_in_array(m=3, n=-3) + with self.assertRaises(ValueError): + cnf.get_index_in_array(m=4, n=-3) + + def test_double_reduction(self): + """ + test if reducing a coilset twice works + """ + surface = SurfaceRZFourier(nfp=2, stellsym=False, mpol=6, ntor=6) + base_curves = CoilSet._circlecurves_around_surface( + surface, coils_per_period=2, order=4 + ) + base_coils = [Coil(curve, Current(1e5)) for curve in base_curves] + coilset = CoilSet(base_coils=base_coils, surface=surface) + cnf = CoilNormalField(coilset) + cnf.reduce_coilset() + num1 = int(re.search(r'\d+', cnf.dof_names[0]).group()) # grab 'ReducedCoilSet*N* from first dof name + cnf.reduce_coilset() + num2 = int(re.search(r'\d+', cnf.dof_names[0]).group()) # grab 'ReducedCoilSet*N* from first dof name + self.assertEqual(num2-num1, 1) # Coilset sucessfully replaced + + + + + -if __name__ == '__main__': - unittest.main() \ No newline at end of file +if __name__ == "__main__": + unittest.main() diff --git a/tests/field/test_selffieldforces.py b/tests/field/test_selffieldforces.py index c71f90667..49265558e 100644 --- a/tests/field/test_selffieldforces.py +++ b/tests/field/test_selffieldforces.py @@ -211,7 +211,7 @@ def test_force_objectives(self): gammadash_norm = np.linalg.norm(coils[0].curve.gammadash(), axis=1) force_norm = np.linalg.norm(coil_force(coils[0], coils, regularization), axis=1) print("force_norm mean:", np.mean(force_norm), "max:", np.max(force_norm)) - objective_alt = (1 / p) * np.sum(np.maximum(force_norm - threshold, 0)**p * gammadash_norm) + objective_alt = (1 / p) * np.sum(np.maximum(force_norm - threshold, 0)**p * gammadash_norm) / np.shape(gammadash_norm)[0] print("objective:", objective, "objective_alt:", objective_alt, "diff:", objective - objective_alt) np.testing.assert_allclose(objective, objective_alt) @@ -260,22 +260,36 @@ def test_force_objectives(self): # # Test MixedLpCurveForce objective = 0.0 + objective2 = 0.0 objective_alt = 0.0 for i in range(len(coils)): objective += float(LpCurveForce(coils[i], coils, regularization, p=p, threshold=threshold).J()) + objective2 += float(LpCurveForce(coils[i], coils, regularization, p=p, threshold=threshold, downsample=2).J()) force_norm = np.linalg.norm(coil_force(coils[i], coils, regularization), axis=1) gammadash_norm = np.linalg.norm(coils[i].curve.gammadash(), axis=1) - objective_alt += (1 / p) * np.sum(np.maximum(force_norm - threshold, 0)**p * gammadash_norm) + objective_alt += (1 / p) * np.sum(np.maximum(force_norm - threshold, 0)**p * gammadash_norm) / np.shape(gammadash_norm)[0] regularization_list = np.ones(len(coils)) * regularization - objective_mixed = float(MixedLpCurveForce(coils[0:1], coils[1:], regularization_list[0:1], regularization_list[1:], p=p, threshold=threshold).J()) + objective_mixed = float(MixedLpCurveForce(coils[0:1], coils[1:], regularization_list[0:1], + regularization_list[1:], p=p, threshold=threshold).J()) print("objective:", objective, "objective_alt:", objective_alt, "diff:", objective - objective_alt) np.testing.assert_allclose(objective, objective_alt) + + print("objective:", objective, "objective2:", objective2, "diff:", objective - objective2) + np.testing.assert_allclose(objective, objective2, rtol=1e-2) + print("objective:", objective, "objective_mixed:", objective_mixed, "diff:", objective - objective_mixed) np.testing.assert_allclose(objective, objective_mixed) + objective_mixed = float(MixedLpCurveForce(coils[0:1], coils[1:], + regularization_list[0:1], regularization_list[1:], p=p, + threshold=threshold, downsample=2).J()) + + print("objective:", objective, "objective_mixed:", objective_mixed, "diff:", objective - objective_mixed) + np.testing.assert_allclose(objective, objective_mixed, rtol=1e-2) + # Test SquaredMeanTorque # Scramble the orientations so the torques are nonzero @@ -311,6 +325,8 @@ def test_force_objectives(self): np.testing.assert_allclose(objective, objective_mixed, rtol=1e-2) # # Test MixedLpCurveTorque + # for i in range(len(coils)): + # print(coils[i].curve.center()) objective = 0.0 objective_alt = 0.0 threshold = 0.0 @@ -457,6 +473,7 @@ def objectives_time_test(self): def test_update_points(self): """Confirm that Biot-Savart evaluation points are updated when the curve shapes change.""" + from simsopt.field import BiotSavart nfp = 4 ncoils = 3 I = 1.7e4 @@ -470,7 +487,8 @@ def test_update_points(self): objective = objective_class(coils[0], coils, regularization) old_objective_value = objective.J() - old_biot_savart_points = objective.biotsavart.get_points_cart() + biotsavart = BiotSavart(objective.othercoils) + old_biot_savart_points = biotsavart.get_points_cart() # A deterministic random shift to the coil dofs: shift = np.array([-0.06797948, -0.0808704 , -0.02680599, -0.02775893, -0.0325402 , @@ -486,7 +504,8 @@ def test_update_points(self): objective.x = objective.x + shift assert abs(objective.J() - old_objective_value) > 1e-6 - new_biot_savart_points = objective.biotsavart.get_points_cart() + biotsavart = BiotSavart(objective.othercoils) + new_biot_savart_points = biotsavart.get_points_cart() assert not np.allclose(old_biot_savart_points, new_biot_savart_points) # Objective2 is created directly at the new points after they are moved: objective2 = objective_class(coils[0], coils, regularization) diff --git a/tests/geo/test_surface_rzfourier.py b/tests/geo/test_surface_rzfourier.py index fce6fec00..53ecbf573 100755 --- a/tests/geo/test_surface_rzfourier.py +++ b/tests/geo/test_surface_rzfourier.py @@ -771,6 +771,26 @@ def test_fourier_transform_scalar(self): # Check that the result is the same as the original field: np.testing.assert_allclose(field/2*np.pi**2, field2) + def test_copy_method(self): + s = SurfaceRZFourier(mpol=4, ntor=5, nfp=3) + s2 = s.copy(quadpoints_phi=Surface.get_phi_quadpoints(nphi=100,range='field period')) + self.assertEqual(len(s2.quadpoints_phi), 100) + s3 = s.copy(quadpoints_theta=Surface.get_theta_quadpoints(ntheta=50)) + self.assertEqual(len(s3.quadpoints_theta), 50) + s4 = s.copy(ntheta = 42) + self.assertEqual(len(s4.quadpoints_theta), 42) + s5 = s.copy(nphi = 17) + self.assertEqual(len(s5.quadpoints_phi), 17) + s6 = s.copy(range='field period') + self.assertEqual(s6.deduced_range, Surface.RANGE_FIELD_PERIOD) + s7 = s.copy(nfp=10) + self.assertEqual(s7.nfp, 10) + s8 = s.copy(mpol=5, ntor=6) + self.assertEqual(s8.mpol, 5) + self.assertEqual(s8.ntor, 6) + s.copy() + s.copy(quadpoints_phi=Surface.get_phi_quadpoints(nphi=100,range='field period'), ntheta=82) + class SurfaceRZPseudospectralTests(unittest.TestCase): def test_names(self): """ diff --git a/tests/test_files/coils.M16N08 b/tests/test_files/coils.M16N08 new file mode 100644 index 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